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Epilepsy—the condition of recurrent , unprovoked seizures—manifests in brain voltage activity with characteristic spatiotemporal patterns . These patterns include stereotyped semi-rhythmic activity produced by aggregate neuronal populations , and organized spatiotemporal phenomena , including waves . To assess these spatiotemporal patterns , we develop a mathematical model consistent with the observed neuronal population activity and determine analytically the parameter configurations that support traveling wave solutions . We then utilize high-density local field potential data recorded in vivo from human cortex preceding seizure termination from three patients to constrain the model parameters , and propose basic mechanisms that contribute to the observed traveling waves . We conclude that a relatively simple and abstract mathematical model consisting of localized interactions between excitatory cells with slow adaptation captures the quantitative features of wave propagation observed in the human local field potential preceding seizure termination .
Epilepsy is a dynamical disease [1] that manifests in many ways , including as organized patterns of brain voltage activity during a seizure . In general , a patient’s epilepsy may be classified through established clinical and imaging procedures and , based on the classification , a treatment strategy may be developed [2] . Although pharmacological and surgical treatment of epilepsy often succeeds , the exact mechanisms that lead to different kinds of epilepsy and produce a seizure are still largely unknown; common proposed biological mechanisms include altered interactions between excitatory and inhibitory neurons [3 , 4] and hyperexcitation [5] . Although the underlying mechanisms that initiate and support the seizure may widely vary [6] , some manifestations of the seizure remain stereotyped , including clinical symptoms and voltage dynamics [2] . For human patients , one of the most common observations of brain activity during seizure consists of chronic voltage recordings . These invasive or noninvasive observations provide detailed spatiotemporal information about the in vivo voltage dynamics of spontaneous seizures . Invasive local field potential ( LFP ) recordings provide fine spatial resolution of brain voltage activity during seizure , and have recently led to new insights [7–9] . LFP recordings are thought to represent the active ionic and synaptic currents within a volume of cortical tissue; in this way , the LFP captures the aggregate activity of large neuronal populations [10–12] . In healthy and diseased brain tissue , wave-like spatiotemporal activity has been observed in the field activity of many systems including the olfactory system of invertebrates [13] and vertebrates [14 , 15] , turtle visual cortex [16–20] , rat visual cortex [21–23] , rat hippocampus [24] , rat somatosensory cortex [25] , monkey motor cortex [26] , human motor cortex [27] , and human retina [28] . Coordinated spatiotemporal activity is thought to serve a functional role in computation and communication between subsystems of the brain . For example , waves are thought to support synaptic modification during development , as observed in the visual system ( e . g . , [28 , 29] ) . Although seizure activity is characterized by stereotyped voltage rhythms [30 , 31] and coupling between rhythms across space [7 , 32] , the role of spatiotemporal patterns ( e . g . , waves [21] ) remains an active research area [33 , 34] . Moreover , the biological mechanisms that support these manifestations of seizure remain incompletely understood; further understanding these features promises improved therapies for epilepsy , in addition to a deeper understanding of organized neuronal population activity in brain function and dysfunction . In addition to clinical and experimental recordings , computational models provide an alternative , powerful approach to investigate the biological mechanisms that support observed brain voltage activity . In general , the combination of experimental data and mathematical modeling has proved useful in understanding propagation dynamics in the brain . For example , experimental observations made in a cultured one-dimensional slice agree with a theoretical framework based on an integrate-and-fire model [35 , 36] , and the compression and reflection of visually evoked cortical waves [37] has been modeled in [38] . Both animal models ( e . g . , [39] ) and computational models ( e . g . , [6 , 40] ) permit controlled , detailed observations of a given seizure process , and the ability to accurately manipulate this process . Importantly , unlike typical observations from clinical recordings , models permit a detailed accounting of the biological mechanisms that support the observed activity . However , the starting assumptions of a model oversimplify the biological processes of the in vivo brain ( e . g . , removal of a brain region from the surrounding network , or omission of some cell types ) . An exact relationship between these models and human epilepsy is often difficult to determine . Clinical observations and models therefore provide different insights into seizure activity . Clinical recordings provide accurate in vivo observations of spontaneous seizures from human patients , yet the biological mechanisms that support this activity remain predominantly unknown . Models provide detailed control and manipulations of candidate biological mechanisms , but the relationship to spontaneous seizures in humans remains unknown . Ideally , a unified procedure would exploit the advantages of each approach and mitigate the disadvantages . Implementing this type of procedure linking human clinical recordings to mechanisms in an abstract and simple mathematical model is one goal of this paper . We propose to characterize invasive clinical voltage recordings from small regions of human cortex preceding seizure termination through comparison with a mathematical model . To do so , we simulate the cooperative synaptic transmembrane current found in clinical LFP recordings using a relatively simple and abstract mathematical mean field model . Mean field neural models , or neural fields , are used to represent coarse-grained variables in space , consisting of thousands of interconnected neurons ( i . e . , spanning approximately a few hundred micrometers ) [41 , 42] . Models of neural fields have a long history in computational neuroscience [21 , 43–45] , and have been successfully employed in many areas , including the study of spatiotemporal dynamics [46–51] , with features such as periodic patterns [52] , bumps and multi-bumps [53 , 54] , and waves [37 , 44 , 55–57] . Because these models are expressed as differential-integral equations , mathematical theory exists to rigorously analyze the model behavior . Here we undertake a mathematical analysis of a mean field model consistent with the observed LFP data to obtain the exact solution for traveling wave dynamics , and deduce parameter relationships that support wave propagation . We then constrain the model solutions using features of LFP recordings of traveling wave dynamics preceding seizure termination observed in a population of human subjects during seizure . In particular , by using the observed width and speed of the LFP waves we obtain parameter estimates consistent with known biological features of cortex , namely timescales and the synaptic connectivity profile . We show that a relatively simple mathematical model consisting of a population of excitatory neurons with localized interactions and an adaptation term is sufficient to mimic the observed LFP waves preceding seizure termination . In this way , the proposed framework links clinical recordings with mathematical models to propose candidate mechanisms supporting a poorly understood aspect of seizure activity: the spatiotemporal dynamics preceding seizure termination in a small patch of human cortex .
The mechanisms that produce organized neuronal population activity are extremely complex [58] . In an effort to characterize and understand the neuronal population activity observed in the clinical recordings preceding seizure termination , we implement here a relatively simple neural field model [59] . The biophysical basis for these types of models are understood by considering the interaction of a finite number of synaptically coupled neurons . Many different formulations for neural fields exist [60] , with implications for the interpretation of the model variables and parameters . These different mathematical formulations of neural field models can be broadly separated into two categories: voltage-based formulations , and activity-based formulations [59] . In a voltage-based model , the time scale of the dynamics is related to the membrane properties of the post-synaptic cells , while in an activity-based-model , the time scale of the dynamics is related to the synaptic decay [59] . We choose the latter formulation here . In its simplest form , the activity-based model is one of the most basic models to arise in mathematical neuroscience [61] . Beyond this simple form , activity-based models have been extended to include additional features ( e . g . , absolute refractoriness [41 , 62] ) . In addition , the activity-based model is consistent with the notion that the LFP dynamics are dominated by the time scale of synaptic effects [10 , 63] , and activity-based models have been proposed as more realistic than voltage-based models [64 , 65] . We note that most mathematical analysis of neural field models utilizes the voltage-based formulation [44 , 65 , 66] . In particular , in [67] the authors performed a complete analysis of the existence and stability of traveling wave solutions in the voltage-based formulation . To the best of our knowledge , a mathematical analysis of traveling wave existence and stability in an activity-based model with adaption has not been performed . We now develop a one-dimensional model to describe important features of the neuronal population activity observed in vivo . The choice of a one-dimensional model is motivated by the observation that a majority of traveling waves observed in the LFP recordings travel in approximately one-dimension , with features as described in the previous section . To simplify the model , we consider only a single population of excitatory neurons . In doing so , we will show that—in the mathematical model—inhibitory neurons are not required to mimic features of the observed LFP data immediately preceding seizure termination . To prevent the activity from remaining in a permanent excited state , which will give rise to a front solution ( see Methods ) , we include an adaptation term that directly regulates the activity . This adaptation accounts for a natural process that will drive the population activity back to a rest state . From the mathematical point of view , adding this adaptation term permits traveling pulse solutions in the model consistent with key features of the clinical recordings . As we describe , using this relatively abstract and simple activity-based model with an adaptation term , we are able to replicate the reverberation observed in the LFP recordings . The specific neural field model we employ is ut ( x , t ) =−αu ( x , t ) +αH ( 12σ∫−∞+∞e−∣x−y∣σu ( y , t ) dy+P ( x , t ) −k ) −αβ0q ( x , t ) qt ( x , t ) =δu ( x , t ) −δq ( x , t ) , ( 1 ) where u ( x , t ) is the mean synaptic activity , q ( x , t ) is the adaptation , and P ( x , t ) is an external input , all evaluated at position x and time t . In particular , we consider that u ( x , t ) represents the activity of a cortical column with extent less than 20 μm situated at position x and time t . We interpret u ( x , t ) , a dimensionless quantity , as the deviation from a baseline of activity . Therefore , u ( x , t ) = 0 represents a resting state of activity , and negative values represent a depression of resting activity [41] . We note that “negative activity” ( i . e . , a reduction in activity below the baseline rate ) in one region reduces the input received in neighboring regions . In this formulation , we interpret the adaption term , q ( x , t ) , as representing a local homeostatic regulation mechanism that evolves on a slower timescale than u ( x , t ) and acts to maintain the activity near a target baseline . When the activity u ( x , t ) falls below the baseline value ( i . e . , u ( x , t ) < 0 ) , the adaption q ( x , t ) decreases which acts to increase u ( x , t ) . Conversely , when the activity increases above baseline ( i . e . , u ( x , t ) > 0 ) , the adaption q ( x , t ) increases and acts to decrease u ( x , t ) . We note that homeostatic regulation mechanisms act on a variety of timescales , including relatively short timescales ( on the order of seconds ) [68] . H is the Heaviside function , which becomes non-zero when the synaptic input exceeds a synaptic threshold k: H ( x − k ) = { 1 if x ≥ k 0 if x < k . We note that the adaptation term in ( 1 ) is located outside of the Heaviside function . In this phenomenological model with a simple adaptive scheme , the adaptation term acts as a local feedback mechanism to depress the synaptic drive . This model is motivated by the linear negative feedback proposed in [44] . We note that , in voltage-based models , different formulations for adaption exist; these include negative feedback both inside the threshold function [44 , 51 , 69] and outside of the threshold function [49 , 53] . We show in S1 Text of Supporting Information that the model ( 1 ) updated to include the adaption term inside of the Heaviside function does not produce damped oscillations; instead , the traveling wave solution returns monotonically to rest after excitation . This monotonic evolution is inconsistent with the reverberation observed in the LFP data of interest here ( examples in Fig . 2 ) . There are 5 parameters in the model ( 1 ) . Each possesses a biological interpretation: α is the decay rate parameter for the synaptic activity term , δ is the decay rate parameter for the adaptation term , σ is the spatial rate of decay of connectivity , k is the synaptic input threshold , and β0 accounts for the strength of the adaptation term on the synaptic dynamics . For simplicity we set β = α β0 . Both time and space units were scaled to represent milliseconds and microns , respectively ( see Methods ) . There are two additional parameters that we employ in the subsequent analysis: c is the wave speed , and w is the wave width . These parameters are not directly specified in the model , but instead are features of the traveling wave dynamics . Our goal is to identify the parameter configurations that support traveling waves in this model consistent with the observed LFP activity . In particular , we are interested in solutions that support only one extremum of high amplitude activity , so called pulses , as these have been characterized using the LFP data . To that end , we first determine under what parameter configurations traveling waves of high amplitude activity exist in the model . To do so , we rewrite the equations in a moving coordinate frame z = x-ct; this frame is moving with a constant speed c . By identifying the stationary solutions of this system , we determine solutions that move with a constant speed c , and a constant width w , without changing their shape: so called traveling waves . Depending on the model parameters , we find that the linearization of the associated system in the moving coordinate frame consists of either purely real or complex eigenvalues . The explicit traveling wave solutions for both the real and imaginary case are now considered . We state the solutions here; detailed analysis may be found in Methods . The mathematical model ( 1 ) contains five free parameters: α , δ , σ , β and k . In the previous section , we began restricting these parameters by establishing relationships between parameters that support traveling wave solutions . In particular , by fixing the time scales α and δ , together with a choice of speed c and width w deduced directly from the LFP data and hence constrained by the clinical observations , we may solve for the remaining parameters β , σ , and k . The matching conditions establish a relationship between σ and β ( example in Fig . 7 ) , and by choosing β and σ we can solve for the corresponding k , as described in the previous section . We now proceed to use the “reverberation” observed in the clinical data ( examples in Fig . 2 ) to estimate the parameter β for each wave . In doing so , we will have used the clinical data and biophysical intuition to constrain further the model parameters . Visual analysis of the in vivo LFP data shows that high amplitude pulses are followed by a reverberation , i . e . , a secondary , smaller amplitude increase in activity ( for more details , see Methods ) . Due to the nature of the traveling wave solutions , this feature is only present in the complex eigenvalue solution , i . e . , when damped oscillations follow the pulse of high amplitude activity ( example in Fig . 5b ) ; we propose that the damped oscillations following the main pulse of the traveling wave mimic the reverberations observed in the LFP recordings . Hence , we restrict the following analysis to the complex eigenvalue case . We use the reverberation times estimated from the LFP data to fix the parameter β for each wave; we label these estimates βempirical . To do so , we set the periodic portion of the complex eigenvalue solution to possess a period consistent with the observed reverberation: given a reverberation time τ ( example in Fig . 8 ) , then β e m p i r i c a l = ( δ − α ) 2 4 δ + 4 π 2 δ τ 2 ( see Methods ) . In this way we constrain the model to replicate the period of the secondary bump ( i . e . , reverberation ) present in the data ( Fig . 8 ) . Having done so , the model parameters β , σ , and k are now directly determined for each observed LFP wave . As a final illustration of the suitability of the model , we consider an example numerical simulation of the model ( 1 ) ( see Methods ) . To do so , we choose a particular wave from the LFP data of Seizure 1 , estimate c and w directly from the data , and fix α = 7 . 5/s , as for this value of α non-trivial parameters from both the real and complex eigenvalue solutions can be obtained from the matching conditions . Following an initial stimulus ( 5 ms initial input at position 0 μm ) the model produces a traveling pulse that is followed by a smaller amplitude reverberation . A comparison of a wave from the clinical recordings with the real and complex eigenvalues case is shown in Fig . 11 . We note that both simulations accurately replicate features of the observed LFP wave ( namely , the speed and width ) , but that the complex eigenvalue case solution also produces a secondary bump of activity consistent with the reverberation in the observed LFP wave . We also note that , in the model , the activity decreases below 0 between the mean crest of the traveling wave and the subsequent reverberation of activity in Fig . 11 ( c ) . A decrease in activity also appears in the in vivo data between the crest of the traveling wave and the reverberation ( example in Fig . 11 ( a ) ) ; however , this decrease is smaller in magnitude than that produced in the model . An updated model that includes inhibition helps reduce this discrepancy , as illustrated in the next subsection . The original model formulation ( 1 ) is analytically tractable and capable of reproducing important features of the observed traveling wave dynamics . However , as expected , this relatively simple model exhibits some inconsistencies with the in vivo data , for example the large negativity following the traveling wave crest . Increasing the complexity of the model through the addition of more biological features may help reduce these inconsistencies . To that end , we consider an updated model that includes an inhibitory population . In particular , we implement the following system: u t ( x , t ) = − α e u ( x , t ) + α e H ( g e e ⊗ u ( x ) − g i e ⊗ v ( x ) + P ( x , t ) − k e ) − α β 0 q ( x , t ) q t ( x , t ) = δ u ( x , t ) − δ q ( x , t ) v t ( x , t ) = − α i v ( x , t ) + α i H ( g e i ⊗ u ( x ) − g i i ⊗ v ( x ) + Q ( x , t ) − k i ) , ( 2 ) where u ( x , t ) is the mean synaptic activity of the excitatory population , v ( x , t ) is the mean synaptic activity of the inhibitory population , q ( x , t ) is the adaptation term in the excitatory population , and P ( x , t ) and Q ( x , t ) are external inputs to the excitatory and inhibitory populations , respectively . The convolutions account for the spatial extent of the synaptic connectivities , g j k ⊗ w ( x ) = g ¯ j k 1 2 σ j k ∫ − ∞ + ∞ e − ∣ x − y ∣ σ j k w ( y , t ) d y , where j = {e , i} , k = {e , i} , and g ¯ j k = { 0 , 1 } . H is the Heaviside function , which becomes non-zero when the total input exceeds the threshold kj . To characterize the behavior of this model , we perform numerical simulations . We set the parameters to match the wave speed and width used for the original model ( 1 ) in Fig . 5b , and fix αi = 2 . 5/s , ki = 1 , σei = 20 μm , σie = 20 μm , and σii = 0 . We first consider the case g ¯ e i = 0 , g ¯ i e = 0 and g ¯ i i = 0 so that the excitatory and inhibitory populations do not interact . The resulting wave profile ( Fig . 12a ) reveals a large amplitude pulse , followed by a deep depression of activity , and then a smaller amplitude reverberation , as expected for the original model formulation ( 1 ) . Then , using the same parameter settings , we activate interactions between the excitatory and inhibitory populations ( g ¯ e i = 1 , g ¯ i e = 1 , g ¯ i i = 1 ) . The resulting wave profile ( Fig . 12b , c ) exhibits qualitative differences from those in the original model; by including inhibition , the wave profile becomes smoother and thinner , and the depression of activity following the large amplitude pulse is shallower . These results suggest that a neural field model with adaptation and inhibition produces wave profiles with additional features consistent with the in vivo data , including a smoother wave profile and a shallower depression of activity following the main pulse . We conclude that the original model ( 1 ) , even in the absence of inhibition , supports wave propagation as observed in the clinical recordings . However , incorporating additional biological features in the model - such as inhibition - may improve fidelity with the clinical data .
In this paper , we considered invasive local field potential ( LFP ) recordings from a population of human patients during seizures . We showed that , in the late stages of seizures , spatiotemporal patterns of activity propagate across a small patch of cortex . These patterns can be well approximated as one-dimensional plane waves , and we characterized important features of these waves ( i . e . , the speeds and widths ) . We found traveling wave speeds of ≈ 80 380 μm/ms , consistent with the propagating velocity of a pulse when GABAergic local inhibition is blocked ( e . g . , 60–90 μm/ms in [70] , 70 μm/ms in [71] , 130–190 μm/ms in [25] , and 120–150 μm/ms in [72] ) . In addition , we examined the features of small amplitude “reverberations” in the voltage activity following each wave . To further characterize the observed LFP waves , we implemented a relatively simple neural field model consisting of an excitatory population of cells with adaptation . This abstract mathematical model is flexible enough to replicate important features of wave propagation near seizure termination for the population of patients and seizures . Moreover , the relative simplicity of the model permits analytic solutions; we showed here , for the first time , that traveling wave solutions exist and are stable in this activity-based model formulation with adaptation . In addition , the model parameters permit biophysical interpretation ( e . g . , as the extent of synaptic connectivity ) . By combining analytic model solutions with features of the observed waves - such as the speed and width - we estimated parameters in the model . The estimated parameters included the timescales of activity and adaptation , and the spatial extent of the connectivity . We find that the timescale of the model consistent with the observed LFP data is biologically reasonable: the adaption is an order of magnitude slower than the activity . Measures of synaptic connectivity in a local neighborhood of cortical tissue have been reported to range from 40 μm to 2 mm [12 , 41 , 63 , 73–75] . For the deduced range of parameters obtained in this study , we find that the extent of connectivity , σ , for Patients 1 and 2 coincides with this established range . For Patient 3 , we obtain connectivities between 60 μm to 4 mm , which is larger , but not wholly inconsistent with existing estimates . We find for all three patients that the parameter β0 , which is the strength of the adaptation , is between 2 and 4; and the parameter k , which accounts for the synaptic threshold , is between 0 . 12 and 0 . 2 . The variability in the estimates of σ , β0 and k may reflect changing biophysical features during seizure ( e . g . , progressive changes in synaptic efficacy or changes in the extracellular environment ) as well as the variability inherent in measuring a noisy biological system . We also note that for the three patients , as the timescale of the activity increases , the extent of the connectivity decreases ( Fig . 10 ) suggesting that faster activities ( large α ) require less distant connectivity . Finally , we note that the parameter estimates are consistent both within individual patients , and across the population of patients and seizures . We conclude from these results the following hypothesis: plane waves observed in vivo late in human seizure can be supported in a relatively simple mathematical model without inhibition , consistent with in vitro slice and theoretical work ( e . g . , [25 , 36 , 70–72 , 76–78] ) . However , we note that inclusion of inhibition may improve features of the model ( e . g . , may better mimic aspects of the wave profile , see Fig . 12 and S2 Text in Supporting Information for additional illustrations ) . The analysis and modeling focused on an interval preceding seizure termination , in which the data have transitioned to large amplitude spike-and-wave ( or spike-and-polywave ) oscillations . A goal of this modeling study was to simulate some of the spatiotemporal aspects of this spike-and-wave activity . Animal studies suggest the mechanisms that support this spike-and-wave activity are complex . Some studies have suggested that the “wave” component of the spike-and-wave oscillation reflects inhibitory GABAergic processes [79–81] . However , other animal studies instead propose that slow intrinsic currents ( e . g . , a calcium-activated potassium current ) support the “wave” component of the spike-and-wave oscillation [82–87] , and in vitro slice experiments indicate that features of wave propagation ( i . e . , wave velocity and wave amplitude ) during epileptiform activity do not depend on inhibition [88] . In addition , during seizures with spike-and-wave oscillations , neural populations are ( at least transiently ) highly active and thereby drive large changes in intra- and extracellular ion concentrations ( e . g . , intracellular chloride accumulation and extracellular potassium accumulation ) [89] . This may result in pathological changes in brain dynamics , for example the reversal potential of GABA-receptor-mediated inhibitory postsynaptic potentials may shift to positive values [85] , and inhibitory mechanisms may engage in the generation of the depolarizing component of spike-and-wave oscillation . Here we have implemented a mathematical model with a tight focus on one aspect of the late seizure interval: the ( approximately ) one-dimensional traveling waves that appear in spike-and-wave oscillations near seizure termination . In doing so , we have presented a modeling formulation more consistent with the proposed intrinsic current mechanisms of spike-and-wave oscillations . Nevertheless , we suspect that inhibition plays a fundamental role in seizure , for example at seizure onset [90 , 91] when fast-spiking interneurons are highly active . We expect that the addition of more biophysical features to the model ( including inhibition ) will permit a better match to the in vivo LFP data ( see Fig . 12 and S2 Text of Supporting Information ) , at the cost of increased model complexity and reduced analytic tractability . In this work we implemented a relatively simple one-dimensional neural population model , consisting of a synaptic activity variable and an adaptation variable . The simplicity of the model allows rigorous mathematical analysis , although the biophysical mechanisms remain relatively abstract . The validity of the model is based on the reproduction of wave features present near seizure termination , and parameter estimates consistent with known physiology ( i . e . , estimates of synaptic connectivity and difference in timescales ) . The purpose of this model is not to capture the detailed biophysical mechanisms of seizure , as in more realistic computational models [92 , 93] . However , we may use the mathematical model to make the following prediction: the traveling waves near seizure termination represent relatively “simple” brain phenomena . Consistent with this notion , we hypothesize that the diversity of complex components that support normal cortical function ( e . g . , the diversity of inhibitory neuronal populations [94 , 95] ) have shut down , and allowed these simple dynamics to dominate . Restoration of this diversity and complexity ( e . g . , activation of silenced inhibitory neuronal populations ) would then help disrupt these pathologically organized and simple traveling waves . To further validate the model results , in vitro experiments that reproduce important features of the human in vivo data ( e . g . , the spectrographic properties [90 , 96] ) would allow detailed pharmacological exploration of the proposed biophysical mechanism of this model . In particular , the more abstract model parameters ( like β0 , the strength of the adaptation ) may be better understood in terms of specific neuronal mechanisms through experiments in controlled biological systems . These experiments may in turn motivate future work developing more biologically detailed models to provide additional insight into the spatiotemporal dynamics of seizure activity . One important future modeling direction is the further analysis and inclusion of inhibitory populations in this activity-based formulation . Such inclusions may further illuminate the mechanisms of wave propagation , and might help to explain differences in waves seen during the initial and terminal stages of human seizure . We have focused here on the analysis of the observed LFP plane waves near seizure termination . Rich spatiotemporal patterns also emerged in the clinical LFP data throughout the seizure ( and perhaps in other functional states , such as sleep ) and will require an expanded two-dimensional model for characterization . For example , we note that near seizure onset complex spatiotemporal patterns emerge , without obvious traveling wave dynamics . The mechanisms that govern the transition from these disorganized spatiotemporal dynamics to more organized traveling waves remain unknown . The analysis of seizures from more patients may help to develop more sophisticated - and biologically detailed models - to explain these complex phenomena . The combination of quantitative data analysis and mathematical modeling of seizure activity across space remains an active research area with important implications for improved treatment of epilepsy .
All patients were enrolled after informed consent was obtained and approval was granted for these studies by local Institutional Review Boards . For each patient and seizure , we analyzed a subset of the diverse spatiotemporal patterns observed approaching seizure termination . We focus here on the analysis of one-dimensional plane waves of activity , which were the most common type of wave we observed in Patients 1 and 2 ( Seizure 1 , 36 out of 40 waves; Seizure 2 , 36 out of 41; Seizure 3 , 39 out of 59; Seizure 4 , 26 out of 33; Seizure 5 , 35 out of 52 ) . Upon visual inspection , the excluded waves exhibited different spatiotemporal patterns , including disorganized waves of high activity , and two-dimensional patterns , such as waves that initiated at the center of the microelectrode array , and spiral waves . Again , we focus here only on the one-dimensional plane waves and estimates of the model parameters from these waves . For Patient 3 , we focused on a contiguous half ( 2 mm by 4 mm ) subsection of the entire ( 4 mm by 4 mm ) microelectrode array . For this patient , we were able to detect waves moving closer to the horizontal direction ( from −45° to 45° and from 135° to 225° ) . Having selected these one-dimensional waves from the three patients , all waves were analyzed using the same set of data analysis algorithms described below . Components of these data may be made available by request to the corresponding author . The purposes of the data analysis were: i ) To obtain a time interval for the propagation of each planar wave; ii ) To obtain the direction of wave propagation; iii ) To obtain the different one-dimensional paths through the two-dimensional microelectrode array for a given direction; iv ) To obtain the speed , width , and reverberation time along each one-dimensional path; and v ) To obtain the mean speed , mean width and mean reverberation time for each wave across different paths . To determine the time interval for the propagation of each planar wave , we computed the gradient of the LFP activity at each moment in time . The gradient assigns to each spatial location a vector specifying the direction and magnitude of maximal increase in activity ( Fig . 13a ) . To compute the gradient , we analyzed voltage differences between adjacent electrodes . A histogram of the angles of the gradient at each position , weighted by the magnitude of the gradient , was then constructed for each moment in time ( Fig . 13b ) . We label t0 the time at which the LFP z-scored signal at the center of the microelectrode array exceeded a threshold of 2 . 5 . We then determined the peak of the unimodal angle distribution at time t0 , which we labeled θ0 . We considered angles between θ0-20 and θ0+20 degrees and analyzed the proportion of angles within the interval ( θ0-20 , θ0+20 ) , forward and backwards in time starting at t0 . The time tinitial denotes the first time at which the number of counts in the angular interval becomes non-zero . The time tfinal is the last time at which counts appear in the angular interval . In this way , each wave is assigned a time interval ( tinitial , tfinal ) for which angles appear in the interval ( θ0-20 , θ0+20 ) . In this time interval , the weighted histograms of the angles showed a clear organization of the gradient directions and appearance of two peaks in the histogram distributions ( Fig . 13b ) . These two peaks account for the preferred angle before the wave enters the microelectrode array and after the wave exits the microelectrode array . To determine the direction of each wave we focused on the first peak ( Fig . 13b ) . This peak typically occurs in the time interval ( tinitial , t0 ) . In addition , we visually inspected each peak and verified that the associated angle accurately described the direction of propagation for each wave . The notions of t0 , tinitial , tfinal and θ0 are illustrated in Fig . 13c . Having determined the angle at which LFP activity propagated , we then constructed one-dimensional paths spanning the microelectrode array . Each path consisted of 10 adjacent electrodes and ran parallel to the direction of the observed wave . Along each such path we determined the speed and width of the wave . For each path , we determined the time at which the activity at each electrode exceeded a threshold of one standard deviation above the mean LFP computed for the entire duration of seizure termination investigated . In this way , every electrode along a path was assigned a time of wave onset , which was used to compute the speed . We used all possible combinations of the 10 electrodes along each one-dimensional path to compute the speed , resulting in a total of 45 estimates of speed . To mitigate the impact of outliers , the speed for each one-dimensional path was then calculated as the median of the 45 speed estimates . We then estimated the speed for each wave as the mean speed among the different one-dimensional paths . Depending on the direction of the wave , from the 10 electrodes that form a one-dimensional path , there is one electrode at which the large amplitude activity of the wave reaches last , and we label this the “last electrode” ( example in Fig . 14 ) . To measure the wave width , for each one-dimensional path we computed the time at which the activity at the last electrode exceeded a threshold of 2 . 5 of the LFP z-scored signal . At that instant in time , the activity of the other electrodes along the path was also determined . The location at which the activity transitioned from above the threshold ( of 2 . 5 of the LFP z-scored signal ) to below the threshold was determined . The spatial extent from the last electrode to this transition point on the one-dimensional path defined the width of the wave . An illustration of the wave width determination is shown in Fig . 14 . We note that if no electrode along the one-dimensional path transitioned to below the threshold , then the wave covered the entire spatial extent of the path , and the width of the wave indicates a lower bound . For each wave , the width refers to the mean widths obtained from all one-dimensional paths . To obtain the reverberation time we first determined the time at which the large amplitude wave of activity fell below a threshold of 0 . 5 of the LFP z-scored signal; we consider this time as the “end” of the primary traveling wave . Starting from this time point , we then determined the time for the activity to first exceed a reverberation threshold , defined as 0 . 5 of the LFP z-scored signal , and then for the activity to decrease again below this threshold . This decrease below the reverberation threshold defined the reverberation time . For an illustration of the reverberation time , see Fig . 15 . We computed the reverberation time for each electrode along the one-dimensional path . The mean among the different one-dimensional paths gave the reverberation time of each wave . Using a t-test for small samples we computed a 90% confidence interval for the mean speed and mean width of each wave ( Fig . 3 ) , where the number of samples was given by the number of one-dimensional paths existent for each wave . In the section , we describe in detail the mathematical analysis of the model ( 1 ) . We note that the model ( 1 ) supports traveling front solutions when the adaptation term is removed . However , these front solutions are not consistent with observed LFP activity , and therefore not examined here . As mentioned in Results , we use the moving frame z = x-ct and identify stationary solutions in this frame . These solutions will be of the form u ( x , t ) = u ( x-ct , t ) = u ( z , t ) and q ( x , t ) = q ( x-ct , t ) = q ( z , t ) , such that ut ( z , t ) = 0 and qt ( z , t ) = 0 . We use the connectivity function w ( z ) = 1 2 σ e − | z | σ . By making this change of variables , we obtain the system of differential-integral equations − c u ′ ( z ) = − α u ( z ) + α H ( ∫ − ∞ ∞ w ( z ¯ − z ) u ( z ¯ ) d z ¯ − k ) − β q ( z ) − c q ′ ( z ) = δ u ( z ) − δ q ( z ) , which can be rewritten in the form ( u ′ ( z ) q ′ ( z ) ) = ( α / c β / c − δ / c δ / c ) ( u ( z ) q ( z ) ) + ( − α c H ( ∫ − ∞ ∞ w ( z ¯ − z ) u ( z ¯ ) d z ¯ − k ) 0 ) . ( 3 ) We assume c > 0 which corresponds to a rightward moving wave . An analogous consideration holds for leftward moving waves ( c < 0 ) . We note that the nonlinear part of system ( 3 ) will be either zero or nonzero depending on the Heaviside function . For that reason the system can be analyzed by considering when the Heaviside function is zero ( Case 1 ) , and when the Heaviside function is non-zero ( Case 2 ) . We consider both cases below . In order to ensure the continuity of the solutions , we look at the change points from Case 1 to Case 2 . In particular , k = 1 2 σ ∫ − ∞ + ∞ e − ∣ x − y ∣ σ u ( y , t ) d y at the points x = 0 and x = w . This assumption gives rise to the matching conditions . Once the explicit traveling wave solutions are obtained , it is possible to solve for the exact value of the threshold k given by the matching conditions . We list below the solutions for the matching conditions in the case of real eigenvalues and complex eigenvalues . The linear stability of the traveling wave solutions was analyzed in detail in [97]; here , we summarize these results . To study the linear stability of the traveling wave solutions we construct a complex-valued Evans functions whose zeros determine the eigenvalues associated with the stability of the wave [98] . By obtaining the eigenvalues it is possible to determine stability ( or instability ) of the linearized wave . Using the Evans functions , we explore the stability of wave solutions for parameter choices restricted by the LFP data . We have shown that for some parameter settings two wave solutions exist ( e . g . , Fig . 6 ) . We note that one of these wave solutions is slow and narrow , whereas the other solution is fast and wide . Moreover , the fast and wide wave has speed and width consistent with the LFP data ( as illustrated in Fig . 11 ) . Using the Evans function we find that , in the case of the fast and wide wave , the associated eigenvalues consist of eigenvalues with negative real part and the trivial zero eigenvalue ( due to the translation invariance of the wave solution ) ; this implies linear stability of the fast and wide wave . In the slow and narrow wave case , we find a positive eigenvalue ( purely real ) in addition to the zero eigenvalue , implying linear instability of the wave solution . For more details , please see S3 Text of Supporting Information . Space was discretized using 2000 points , to represent the length of a one-dimensional path . To each of these points the differential equation system ( 1 ) was associated . Numerical simulations were written to solve these systems using a Runge-Kutta method of order four with Δt = 0 . 005 ms . Convolutions integrals were approximated by assuming the activity was fixed within a Δx interval , where Δx represented a distance of 40 μm . Smaller grids were also examined of Δx = 20 μm , and Δx = 10 μm , and similar results found ( not shown ) . The waves were created by applying a 5 ms input to points in space representing 10 μm . Both time and space were rescaled in order to have units of distance x in microns and time t in milliseconds . | Nearly 50 million people worldwide suffer from epilepsy , a chronic neurological condition characterized by recurrent , unprovoked seizures . Although some clinical and biological principles of seizures are known , many aspects of spontaneous human seizures remain poorly understood . Recordings from electrodes placed directly on and within the brain provide a unique view of seizure activity , and have revealed specific brain voltage patterns associated with this pathological state . In particular , there is evidence that organized waves of activity propagate over the brain during a seizure . However , quantitatively characterizing and understanding the mechanisms that support these waves remains an open challenge . The goal of this work is to address this challenge through a combination of mathematical modeling and clinical recordings . Through this interdisciplinary approach , we seek to understand general features that support the spatiotemporal patterns of seizure termination . We propose that a relatively simple and abstract mathematical model consisting of localized interactions of closely neighboring excitatory cells with slow adaptation can support the propagation of the waves found in clinical recordings . Improved understanding of the mechanisms supporting seizure activity promises novel developments in treatment strategies tailored to the observed activity of individual patients . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Biologically Constrained, Mathematical Model of Cortical Wave Propagation Preceding Seizure Termination |
Chagas disease induced by Trypanosoma cruzi ( T . cruzi ) infection is a major cause of mortality and morbidity affecting the cardiovascular system for which presently available therapies are largely inadequate . Transforming Growth Factor beta ( TGFß ) has been involved in several regulatory steps of T . cruzi invasion and in host tissue fibrosis . GW788388 is a new TGFß type I and type II receptor kinase inhibitor that can be orally administered . In the present work , we studied its effects in vivo during the acute phase of experimental Chagas disease . Male Swiss mice were infected intraperitoneally with 104 trypomastigotes of T . cruzi ( Y strain ) and evaluated clinically . We found that this compound given once 3 days post infection ( dpi ) significantly decreased parasitemia , increased survival , improved cardiac electrical conduction as measured by PR interval in electrocardiography , and restored connexin43 expression . We could further show that cardiac fibrosis development , evaluated by collagen type I and fibronectin expression , could be inhibited by this compound . Interestingly , we further demonstrated that administration of GW788388 at the end of the acute phase ( 20 dpi ) still significantly increased survival and decreased cardiac fibrosis ( evaluated by Masson's trichrome staining and collagen type I expression ) , in a stage when parasite growth is no more central to this event . This work confirms that inhibition of TGFß signaling pathway can be considered as a potential alternative strategy for the treatment of the symptomatic cardiomyopathy found in the acute and chronic phases of Chagas disease .
Chagas disease , caused by the intracellular kinetoplastid parasite Trypanosoma cruzi , is a widely spread distributed debilitating human illness , affecting 10–12 million people in Central and South America . It is a major cause of mortality and morbidity , killing 15 , 000 persons each year [1] , [2] . Chagas disease presents an acute phase of infection that is characterized by mild clinical symptoms ( fever and malaise ) and high parasitemia , but is often unmarked . Due to a potent specific immune response which control parasitemia , patients usually attain the indeterminate stage of the infection , with low-level of parasite persistence that can last from 10 to 40 years . About one in three infected individuals develops the symptomatic chronic stage of infection , which is characterized mainly by myocardiopathy or/and intestinal megasyndrome . A century has passed since the discovery of Chagas disease and the development of an efficient drug is still a challenge . As other neglected diseases , it has not received much attention of the pharmaceutical industry and present available therapies are insufficient [3] . Nifurtimox and benznidazole , the only two trypanocide drugs available , have toxic side effects , are not effective for all parasite strains and the effect in human chronic phase is still under clinical trial [4] . Moreover , no therapeutic approach targeting Chagas disease heart fibrosis is presently available . Transforming Growth Factor ß1 ( TGFß1 ) is the prototypic member of a family of polypeptide growth and differentiation factors that play a great variety of biological roles in such diverse processes as inflammation , fibrosis , immune suppression , cell proliferation , cell differentiation , and cell death [5] , [6] . TGFß is also involved in many direct and indirect interactions between infectious agents and their hosts [7] . Several studies have demonstrated that TGFß plays a major role in the establishment and pathogenesis of T . cruzi infection ( reviewed in [8] ) . Moreover , significantly higher circulating levels of TGFß1 have been observed in patients with Chagas disease cardiomyopathy [9] and in a culture system of cardiomyocytes infected by T . cruzi [10] . In order to establish its biological functions , TGFß must be activated into a mature form mainly by proteases , allowing its interaction with a specific transmembrane receptor called TGFß receptor-II ( TßRII ) , which phosphorylates and stimulates the serine/threonine kinase activity of TßRI , also called activin receptor-like kinase 5 ( ALK5 ) . Upon activation , ALK5 phosphorylates the cytoplasmic signaling proteins Smad-2 and -3 , which then associate with Smad-4 , translocate into the nucleus as a multiprotein complex , and stimulate the transcription of TGFß-responsive genes , thereby inducing specific biological responses . We have recently described that the ALK5 inhibitor , 4- ( 5-benzo[1 , 3]dioxol-5-yl-4- pyridin-2-yl-1H-imidazol-2-yl ) -benzamide ( SB431542 ) reduces the infection of cardiomyocytes by T . cruzi in vitro [11] and we could further show that it also inhibited T . cruzi infection in vivo and prevented heart damage in a mouse model [12] . This work therefore clearly demonstrated that blocking the TGFß signaling pathway could be a new therapeutical approach in the treatment of Chagas disease heart pathology . However the limitation of this compound was the preclusion to oral administration and some toxic effects . To reinforce the prove of concept , the aim of the present work was therefore to test , in the same parasite-mouse model of experimental Chagas disease , another inhibitor of the TGFß signaling pathway , 4- ( 4-[3- ( Pyridin-2-yl ) -1H-pyrazol-4-yl] pyridin-2-yl ) -N- ( tetrahydro-2Hpyran-4-yl ) benzamide ( GW788388 ) which can be orally administered and that has an improved pharmacokinetic profile [13] , [14] . We found that GW788388 added 3-day post infection ( dpi ) decreased parasitemia , increased survival , prevented heart damage , and decreased heart fibrosis . Very importantly , we also demonstrated here for the first time that when added after the end of the intense parasite growth and consequent metabolic shock phase at 20 dpi , GW788388 could still decrease mortality and heart fibrosis .
Bloodstream trypomastigotes of the Y strain were used and harvested by heart puncture from T . cruzi-infected Swiss mice at the parasitemia peak , as described previously [15] . Mice were housed for at least one week before parasite infection at the Animal Experimentation Section at the Laboratory of Innovations in Therapies , Education and Bioproducts-IOC/FIOCRUZ under environmental factors and sanitation according to “Guide for the Care and Use of Laboratory Animals” . Animal studies adhered to the International guidelines ( National Research Council . 1996 , National Academy press , Washington , DC ) . This project was approved by the FIOCRUZ Committee of Ethics in Research ( protocol number 028/09 ) .
In the first set of experiments , the inhibitor GW788388 was orally administered to male Swiss mice infected with 104 bloodstream trypomastigotes of the Y strain ( day 0 ) , at the 3rd dpi . We first performed a dose-response study by administering different doses of GW788388 ( 0 . 3 , 3 , 6 and 15 mg/kg ) and analyzed parasitemia and survival rate . The results showed a dose-dependent inhibition of parasitemia at 8 dpi from 0 . 3 to 15 mg/kg of GW788388 ( Methods S1 and Fig . S1A ) . On the other hand , the survival rate was increased with 3 or 6 mg/kg of GW788388 but unaltered at 0 . 3 and 15 mg/kg , suggesting some toxicity of the drug at this largest dose ( Fig . S1B ) . For the subsequent studies , the dose of 3 mg/kg was chosen since it was the lowest GW788388 concentration that significantly affected parasitemia without worsening mortality . The choice for 3 mg/kg GW788388 administration was further reinforced by the assays performed by Gellibert and collaborators [13] , who showed in a model of kidney fibrosis that doses as low as 3 mg/kg/mice of GW788388 significantly inhibited collagen type I mRNA levels . The control group received the vehicle buffer in which GW788388 was diluted ( 4% DMSO , 96% [0 . 5% Hydroxypropylmethylcellulose ( HPMC ) , 5% Tween 20 , 20% HCl 1 M in NaH2PO4 0 . 1 M] ) and could be considered as the placebo group . The responses of DMSO-treated infected mice were not significantly different from those of untreated infected mice , excluding any sham or placebo effect ( data not shown ) . In our model of acute infection , as previously described [12] , parasitemia peaked at 8 dpi ( Fig . 1A ) . We found that GW788388 administration at 3 dpi significantly reduced the blood parasitemia peak ( Fig . 1A ) . Further , as previously described with the compound SB421543 [11] , we could demonstrate that in vitro administration of GW788388 on cardiomyocytes impaired T . cruzi replication in host cells ( Fig . S2 ) supporting the decreased parasitemia peak found in vivo . On the other hand , no effect of GW788388 on trypomastigote forms of T . cruzi viability could be observed after direct incubation of the drug with the parasites ( unpublished result ) . We also showed that GW788388 administration significantly increased survival rates at 30 dpi ( 65% in the treated-group versus 34% in the untreated group , Fig . 1B ) . The infection induced a loss of body weight at 14 dpi [12] , which was not modified by the administration of GW788388 ( data not shown ) . To investigate whether GW788388 treatment would also affect myocardial parasitism and infiltration of inflammatory cells , we analyzed mouse infected heart sections collected at 15 dpi using histochemical techniques . Non-infected animals showed no inflammatory infiltration in the myocardium ( data not shown ) . Myocardial sections from the T . cruzi-infected sham-treated group ( Y DMSO ) had many amastigote nests ( Fig . 1C , open arrows ) and large inflammatory foci ( Fig . 1E , filled arrows ) that were frequently associated with fibrotic areas . GW788388 treatment significantly decreased the number of amastigote nests ( Fig . 1D and 1G ) . GW788388 administration also significantly decreased the area invaded by inflammatory infiltrates ( Fig . 1F and 1H ) . A more detailed count of the number of cells per inflammatory foci showed that GW788388 treatment more particularly decreased the number of large inflammatory foci within the myocardium ( larger than 20 or 50 cells per inflammatory infiltrates ) ( Table 1 ) . T . cruzi infection induces a strong hepatitis during the acute phase of Chagas disease [17] . We therefore analyzed several parameters of the liver in sham-treated versus GW788388-treated mice . Analysis of liver sections at 15 dpi revealed the presence of large inflammatory infiltrates in DMSO-treated animals ( Fig . 2A , arrow ) . GW788388 administration significantly decreased the number of these infiltrates ( Fig . 2B and C ) . We also measured two circulating markers of hepatic function which are induced by T . cruzi infection: AST ( aspartate aminotransferase ) and ALT ( alanine aminotransferase ) . We found that GW788388 administration significantly decreased the serum levels of AST and ALT ( Fig . 2D and E ) . We also measured urea , which reflects the renal functional status . Urea level was significantly increased at 15 dpi in DMSO-treated animals while GW788388 administration significantly reduced it ( Fig . 2F ) . We next analyzed electrocardiograms ( ECG ) of the different groups of mice at 15 dpi . As expected , analysis of the ECG demonstrated an atrial ventricular block with PR interval higher than 40 ms , leading to sinus bradycardia in sham-treated T . cruzi-infected animals as compared to the non-infected control group ( 495 . 8 and 774 . 2 bpm , respectively , Figure 3 and Table 2 ) . GW788388 administration significantly limited the bpm decrease at 15 dpi , with a mean heart rate of 554 . 3 ( Fig . 3 and Table 2 ) . The other parameters analyzed demonstrated that infected mice had higher QT , PR and QRS intervals compared to non-infected mice ( Table 2 ) , and that GW788388 administration ( 3 mg/kg ) also significantly decreased the QT intervals to 25 . 3 ms as compared to 29 . 6 in the infected DMSO-treated group ( Table 2 ) . A possible cause of this worsening in heart electrical conduction after the infection could be the direct effect of TGFß in heart cells . It has been already proposed that elevated TGFß levels during T . cruzi infection disorganize gap junctions , possibly contributing to abnormal impulse conduction and arrhythmia in Chagas disease [12] . To test this hypothesis , we measured connexin 43 ( Cx43 ) expression in the different groups of mice . Heart sections from at least three mice per group at 15 dpi were immunostained for Cx43 . We observed by confocal microscopy that non-infected hearts presented a dense structure of gap junction plaques ( Fig . 4A , green staining ) . A drastic change in Cx43 expression was observed in the infected hearts of vehicle-treated mice , with an important decrease in Cx43 expression and a disruption of gap junction plaques ( Fig . 4B ) . We found that GW788388 treatment reduced Cx43 disassembly and prevented the dissolution of gap junctions , preserving organized plaque distribution ( Fig . 4C ) . The mean number of Cx43 plaques and their mean length were significantly lower in the heart of infected mice at 15 dpi as compared to the non-infected group ( Fig . 4D and E ) . GW788388 treatment protected infected-mice from this loss as the decrease in the mean number of plaques was only reduced by 30% versus 45% in non-treated mice ( Fig . 4D ) and the mean length was similar to the non-infected mice ( Fig . 4E ) . Immunoblotting analysis of Cx43 expression from heart ventricles confirmed these data ( Fig . 4F and G ) . One of the best established biological function of TGFß is the stimulation of extracellular matrix ( ECM ) protein deposition . Therefore , we checked whether GW788388 treatment would affect heart fibrosis that occurs in response to T . cruzi infection . Left ventricular heart tissues were obtained from each group and the deposition of ECM proteins was studied by immunostaining for collagen type I and fibronectin at 15 dpi . We observed an interstitial fibrous heart with high levels of both collagen type I and fibronectin deposition , as observed in red on Figure 5A and C , respectively . Interestingly , we could show that oral administration of GW788388 significantly reduced collagen type I and fibronectin levels ( Fig . 5B and D , respectively ) . These data were confirmed by immunoblotting analysis of collagen type I and fibronectin expression from heart ventricles ( Fig . 5E , F and G ) . We found that GW788388-treatment decreased the phosphorylation level of Smad2 in infected hearts , demonstrating that GW788388-treatment was related to TGFß dependent signaling in vivo ( data not shown ) . Because most of the beneficial effects that we observed here with the TGFß inhibitor ( GW788388 ) might be due to the resulting decreased parasitemia due to the inhibitory effect of TGFß signaling inhibitors in host cell invasion and intracellular proliferation [11] , [12] , we next studied the effect of GW788388 oral administration after the parasitemia peak . We chose to add GW788388 at 20 dpi as by this time , only 18% of infected mice survived and 30% of them died at 24 dpi . Interestingly , we found that GW788388 administration at 20 dpi completely protected these mice ( n = 12 ) from death until 24 dpi ( Fig . 6A , inset ) . In the inset , 100 represents the percentage of survival rate calculated from 20 dpi . GW788388 administration still decreased the number of inflammatory infiltrates within the myocardium ( Table 3 ) . To verify if GW788388 treatment presented an effect in the reversion of installed fibrosis , we performed Masson's trichrome staining on heart cross-sections of infected untreated mice at 15 dpi ( Fig . 6B ) , 20 dpi ( Fig . 6C ) and 24 dpi ( Fig . 6D ) , and of infected GW788388-treated mice at 24 dpi ( Fig . 6E ) . We observed a progressive increase in collagen deposition visualized as light blue staining , which followed fibrosis progression ( from 15 to 24 dpi , Table 4 ) . At 20 dpi , which corresponded to the day of GW788388 administration , we observed a fibrotic pattern on the heart of infected mice frequently associated to inflammatory infiltrates ( Fig . 6C ) . Interestingly , four days after GW788388 administration ( i . e . 24 dpi ) we observed a decrease in collagen deposition ( Fig . 6E ) as compared to the untreated group ( Fig . 6D , Table 4 ) . Immunoblotting assays were performed to compare the expression levels of collagen type I between each group . We observed a significant increase in collagen type I expression in the DMSO infected group as compared to the non-infected group ( Fig . 6F and G , 9 fold increase ) , while GW788388 administration to infected mice significantly decreased the expression levels of collagen type I ( Fig . 6F and G ) .
We have recently demonstrated that in vivo inhibition of the TGFß signaling pathway can decrease infection and prevent heart damage [12] , suggesting that this new class of therapeutic agents should be considered in association with trypanocidal compounds for the potential treatment of Chagas disease cardiomyopathy . In the present work , we demonstrated that a more potent inhibitor of the TGFß signaling pathway , GW788388 , which can be orally administered , significantly decreased parasitemia , increased survival and restored cardiac function as measured by ECG heart frequency ( increase in bmp ) and atrial conduction ( decrease in QT interval ) . When administered at 3 dpi , we observed that GW788388 treatment reduced parasitemia and its subsequent deleterious effects . Whether the protective effect of GW788388 results only from this sole anti-infectious effect remains to be established . However , the short half-life of GW788388 in vivo ( plasma T1/2 = 1 . 3 hours; [13] ) makes it unlikely that it is mediated by long-term effects on e . g . fibrosis or cardiac rhythm . In contrast , administration of GW788388 at 20 dpi to mice that survived the metabolic distress syndrome clearly resulted in improved survival , which correlated with decreased cardiac fibrosis and has probably no causal relationship with the anti-infectious effect of the drug . Given the recent availability of reliable mouse models for chronic chagasic cardiomyopathy [18] , the present proof that orally administrated GW788388 is feasible and efficient in the acute phase will offer in the near future the possibility of testing TGFß inhibitors in the chronic phase in pre-clinical assays . Taken together , these data further support that blocking TGFß signaling could represent a potential new therapeutic approach for Chagas disease heart fibrosis treatment . It is now well established that the involvement of the TGFß signaling pathway plays an important role in the development of Chagas disease [8] . TGFß has been shown to be involved during parasite-host cell invasion , proliferation and differentiation [19]–[22] . Moreover , significantly higher circulating levels of TGFß1 have been observed in patients with Chagas disease cardiomyopathy [9] , [16] . These data incited us to test the possibility of treating the development of Chagas disease by blocking the TGFß signaling pathway . Here , we show that oral administration of GW788388 kinase signaling inhibitor prevents parasitemia , mortality , and heart fibrosis to acutely T . cruzi-infected mice in comparison to untreated-infected experimental group of animals . In lack of demonstration of GW788388 direct killing effect upon T . cruzi , we postulate the protein kinase inhibitor used may induce intracellular parasite latency [23] , [24] , such as that involved with the Plasmodium sporozoites cell cycle inhibition of initiation factor-2alpha ( elF2alpha ) kinase ( IK2 ) ; its down-regulation by removal of PO4 from elF2alpha-P gives rise to the latency [25] , [26] . In this regard , ongoing investigations in chronically T . cruzi-infected mouse model will determine whether GW788388 beneficial effects can be explained by the drug-induced parasite latency and long lasting cryptic infections . Several approaches have been developed to abrogate TGFß signaling . Antibodies directed against TGFß have been administered in diabetic rodents and this was shown to efficiently prevent glomerulosclerosis and renal insufficiency [27] . Antisense TGFß oligonucleotides were found to reduce kidney weight in diabetic mice [28] . Recently , a soluble fusion protein of TßRII was reported to reduce albuminuria in a chemically induced model of diabetic nephropathy in rats [29] . And finally , inhibitors of the kinase activity of the TßRI ( ALK5 ) have been developed . These inhibitors interact with the ALK5 ATP-binding site , thereby preventing TGFß intracellular pathways [30] . The first ALK5 inhibitor described , SB431542 , is an ATP-competitive kinase inhibitor [31] . SB431542 significantly reduced procollagen1alpha ( I ) in rat kidneys in a model of induced nephritis . It was also described that SB431542 triggers antitumor activity in vivo [32] . Our work also demonstrated that SB431542 reduced mortality , decreased parasitemia and prevented heart damage as observed by histological and ECG analysis during the acute phase of experimental Chagas disease [12] . However , the limitations of SB431542 were the need of intraperitoneal injection and the in vivo toxic effects that have been demonstrated . Recently , GW788388 was developed as an alternative to SB431542 with better in vivo exposure . GW788388 is orally active and has a good pharmacokinetic profile [13] , [14] , [30] . GW788388 administration reduced liver and renal fibrotic response in a model of chemically induced fibrosis in rats and in the db/db mouse model of spontaneous diabetic nephropathy [13] , [14] . Treatment with GW788388 also showed efficacy for preventing the fibrotic response in a skin fibrosis model [33] and attenuated cardiac dysfunction following myocardial infarction [34] . These data prompted us to test this compound during the acute phase of experimental Chagas disease . We found that oral administration of GW788388 at 3 dpi significantly reduced peripheral parasitemia and lowered parasite load in hearts of infected mice observed 15 dpi . This effect was achieved with lower administration doses ( 3 mg/kg ) than the one we previously used for SB431542 ( 10 mg/kg ) [12] , and with a single oral administration . More importantly , oral administration of GW788388 also significantly improved mice survival ( 70% in GW788388-treated mice against 30% in non-treated infected mice at 30 dpi ) . This is probably due to the combined impairment of the second wave of T . cruzi parasitemia due the decrease of parasite burden and of the early inflammatory cytokines secretion balance . Infection with T . cruzi in the acute phase is followed by a strong mononuclear cell inflammation on target tissues such as heart and liver , which could cause tissue disruption , necrosis followed by fibrotic deposition and abnormalities in electrical impulse conduction . Our data showed less inflammation on both heart and liver tissues and , moreover , less mononuclear cells by inflammatory focus . An improved ECG profile was also observed after GW788388 administration , characterized mainly by the absence of sinus node dysfunctions and reduced sinus bradycardia . PR intervals larger than 40 ms suggested slower transmission of the electrical impulses and atrioventricular block ( AVB ) , which is characteristic of acute T . cruzi infection [35] . We observed an improvement of the QT intervals following GW788388 administration , which represent the wave of ventricular recuperation and this could be related to the decrease of sudden death [36] and to the progression to a pathological chronic phase [35] . Heart failure and sudden death are the most common causes of death in patients with chronic cardiac Chagas disease [37] and altered ECG parameters correlates with increasing myocardial scar and decreasing myocardial function in these patients [38] . This results from disorganized gap junctions that could contribute to abnormal impulse conduction and arrhythmia that characterize severe cardiopathy in Chagas disease and heart fibrosis [10] . Gap junction Cx43 molecules are responsible for electrical impulse conduction in the heart [39] and are affected by TGFß [10] , [40] . We observed that GW788388 treatment preserved a correct Cx43 plaque pattern in the heart and blocked the down-regulation of Cx43 expression commonly observed following T . cruzi infection . GW788388 treatment therefore favored a regular and correct electrical impulse transmission . TGFß is also a key factor in the generation of tissue fibrosis [41] and has been correlated to development of Chagas disease symptoms in cardiac chronic phase [8] . Our data showed that administration of GW788388 to T . cruzi-infected mice significantly prevented the increase of fibronectin and collagen type I , two important components involved in heart fibrosis . These data are consistent with previous studies showing that GW788388 reduced fibrosis markers in the kidney following chemically induced nephropathy [14] , [42] . In the human acute phase of Chagas disease , symptoms are frequently mild and not noticed and it is therefore difficult to propose correct treatments with trypanocidal drugs . Therefore , in the present study , we also treated mice with GW788388 at the end of the acute phase , when there are scarce circulating parasites . Interestingly , we found that oral administration of GW788388 at 20 dpi completely protected mice from death ( 100% survival ) . Analysis of cardiac fibrosis by Masson's trichrome staining on heart cross-sections of T . cruzi-infected mice showed a strong increase of fibrosis from 15 dpi to 24 dpi ( Fig . 5 , Table 4 ) . Interestingly , we found that mice treated with GW788388 , in a single dose scheme at 20 dpi , reversed heart fibrosis observed four days later ( 24 dpi ) as compared to untreated infected mice . The level of collagen type I was also restored in GW788388 treated mice versus untreated mice . Taken together these data demonstrated that blocking TGFß signaling could decrease an installed heart fibrosis . This important finding encourages further pre-clinical assays targeting fibrotic lesions that are always involved in the severity of the clinical picture observed in the chronic cardiac disease . The development of an efficient drug for Chagas disease is still a challenge and trypanocidal drugs such as nifurtimox and benznidazole are still the only drugs employed for specific Chagas disease treatment , although the observation of serious side effects . Treatment strategy approaching the reversion of fibrosis has been demonstrated here at the end of the acute phase of experimental Chagas disease . Still , further studies on a chronic experimental model are necessary previously to clinical assays . The inhibition of TGFß signaling pathway and its biological functions could then be considered as an alternative strategy for the treatment of the symptomatic cardiomyopathy found in the acute and chronic phases of Chagas disease , in synergy with current administered drugs , enabling lower dosages and avoiding toxic effects . | Cardiac damage and dysfunction are prominent features in patients with chronic Chagas disease , which is caused by infection with the protozoan parasite Trypanosoma cruzi ( T . cruzi ) and affects 10–12 million individuals in South and Central America . Our group previously reported that transforming growth factor beta ( TGFß ) is implicated in several regulatory aspects of T . cruzi invasion and growth and in host tissue fibrosis . In the present work , we evaluated the therapeutic action of an oral inhibitor of TGFß signaling ( GW788388 ) administered during the acute phase of experimental Chagas disease . GW788388 treatment significantly reduced mortality and decreased parasitemia . Electrocardiography showed that GW788388 treatment was effective in protecting the cardiac conduction system , preserving gap junction plaque distribution and avoiding the development of cardiac fibrosis . Inhibition of TGFß signaling in vivo appears to potently decrease T . cruzi infection and to prevent heart damage in a preclinical mouse model . This suggests that this class of molecules may represent a new therapeutic tool for acute and chronic Chagas disease that warrants further pre-clinical exploration . Administration of TGFß inhibitors during chronic infection in mouse models should be further evaluated , and future clinical trials should be envisaged . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"drugs",
"and",
"devices",
"cardiovascular"
] | 2012 | Oral Administration of GW788388, an Inhibitor of Transforming Growth Factor Beta Signaling, Prevents Heart Fibrosis in Chagas Disease |
The IL-1β and type I interferon-β ( IFN-β ) molecules are important inflammatory cytokines elicited by the eukaryotic host as innate immune responses against invading pathogens and danger signals . Recently , a predominantly nuclear gamma-interferon-inducible protein 16 ( IFI16 ) involved in transcriptional regulation has emerged as an innate DNA sensor which induced IL-1β and IFN-β production through inflammasome and STING activation , respectively . Herpesvirus ( KSHV , EBV , and HSV-1 ) episomal dsDNA genome recognition by IFI16 leads to IFI16-ASC-procaspase-1 inflammasome association , cytoplasmic translocation and IL-1β production . Independent of ASC , HSV-1 genome recognition results in IFI16 interaction with STING in the cytoplasm to induce interferon-β production . However , the mechanisms of IFI16-inflammasome formation , cytoplasmic redistribution and STING activation are not known . Our studies here demonstrate that recognition of herpesvirus genomes in the nucleus by IFI16 leads into its interaction with histone acetyltransferase p300 and IFI16 acetylation resulting in IFI16-ASC interaction , inflammasome assembly , increased interaction with Ran-GTPase , cytoplasmic redistribution , caspase-1 activation , IL-1β production , and interaction with STING which results in IRF-3 phosphorylation , nuclear pIRF-3 localization and interferon-β production . ASC and STING knockdowns did not affect IFI16 acetylation indicating that this modification is upstream of inflammasome-assembly and STING-activation . Vaccinia virus replicating in the cytoplasm did not induce nuclear IFI16 acetylation and cytoplasmic translocation . IFI16 physically associates with KSHV and HSV-1 genomes as revealed by proximity ligation microscopy and chromatin-immunoprecipitation studies which is not hampered by the inhibition of acetylation , thus suggesting that acetylation of IFI16 is not required for its innate sensing of nuclear viral genomes . Collectively , these studies identify the increased nuclear acetylation of IFI16 as a dynamic essential post-genome recognition event in the nucleus that is common to the IFI16-mediated innate responses of inflammasome induction and IFN-β production during herpesvirus ( KSHV , EBV , HSV-1 ) infections .
Kaposi’s sarcoma associated herpes virus ( KSHV ) , a γ-2 herpesvirus , is etiologically associated with Kaposi’s sarcoma ( KS ) and primary effusion lymphoma ( PEL ) [1] . The hallmark of KSHV infection is the establishment of latent infection , reactivation and reinfection , and KS and PEL lesion endothelial and B cells , respectively , carry episomal KSHV latent dsDNA genome [1] . Human PEL ( B ) cell lines BCBL-1 and BC-3 carry >80 copies of the episomal latent KSHV genome/cell and the lytic cycle can be induced by chemicals . Purified virions from the supernatants are used for in vitro infection of human dermal microvascular endothelial cells ( HMVEC-d ) and foreskin fibroblast cells ( HFF ) [2] . During infection of its target cells , KSHV must be coming in contact with the host innate immune system’s pattern recognition receptors ( PRR ) , such as Toll-like receptors ( TLRs ) , RIG-I-like receptors ( RLRs ) , NOD-like receptors ( NLRs ) and absent in melanoma 2 ( AIM2 ) -like receptors ( ALRs ) . TLRs on the plasma membranes and endosomes as well as the RLRs , NLRs and AIM2 in the cytoplasm recognize pathogen or danger-associated molecular patterns ( PAMP/DAMP ) [3 , 4 , 5] . KSHV infection of HMVEC-d cells induces inflammatory cytokines including the secretion of IL-1β into the supernatants which are similar to the microenvironments of KS and PEL lesions [6] . IL-1β , IL-18 and IL-33 are synthesized as inactive proforms , undergo proteolytic processing by activated caspase-1 generated by the cleavage of procaspase-1 via inflammasomes . Most of these molecular platforms are formed by homotypic interactions of a sensor protein recognizing the danger trigger , adaptor molecule ASC ( apoptosis-associated speck-like protein containing CARD ) , and the effector procaspase-1 . NLRs are cytoplasmic inflammasome sensors of foreign molecules , including ROS , K++ , alum , bacterial products , RNA and RNA viruses replicating in the cytoplasm , while AIM2 recognizes cytoplasmic DNA including transfected DNA and DNA of pox viruses replicating in the cytoplasm [4 , 7 , 8 , 9] . They initiate the host defenses by regulating the production of IL-1β , IL-18 , IL-33 or type I interferons ( IFN ) α/β [7 , 8 , 9 , 10] . Whether innate responses recognize and respond to the presence of foreign episomal genomes of herpesviruses as well as other DNA viruses in the infected cell nuclei leading into the induction of inflammatory responses was not known initially . Our studies revealed that in vitro KSHV infection of endothelial cells induces caspase-1 activation via the nuclear resident gamma-interferon-inducible protein-16 ( IFI16 ) also known as interferon-inducible myeloid differentiation transcriptional activator . Colocalization of IFI16 with viral genome in the infected endothelial cell nucleus , induction of IFI16-ASC inflammasomes by UV-inactivated KSHV and the absence of induction by lentivirus vectors expressing KSHV genes demonstrated that a ) KSHV genes individually do not play a role in IFI16-inflammasome activation , b ) the IFI16-inflammasome is not induced against linear integrated foreign DNA , and c ) episomal KSHV genome is required for IFI16-inflammasome activation [11] . When we analyzed the gene expression in uninfected and infected HMVEC-d cells , a significant increase in caspase-1 gene expression from 2 to 24 h post-infection ( p . i . ) , significant induction of the ASC gene only at 24 h p . i . , a slight but not significant increase in IFI16 gene expression , and no increase in NLRP-1 , NLRP3 and AIM2 genes were observed [11] . We have subsequently demonstrated that only the IFI16-inflammasome is constitutively induced in KSHV latently infected endothelial and PEL cells [12] , as well as in B-lymphoma , epithelial and lymphoblastoid cells latently infected with γ-1 Epstein-Barr virus ( EBV ) [13] . Colocalization of IFI16 with the latent KSHV and EBV genome in the nuclei suggested that continuous sensing of latent genome results in the constitutive induction of IFI16-ASC inflammasomes . In addition , our studies showed that IFI16 recognizes the α-herpes simplex virus type-1 ( HSV-1 ) genome soon after its entry into the nucleus resulting in the formation of IFI16-inflammasomes [14] . The 730 aa ( 1–2190 bp ) IFI16 protein consists of an n-terminal ASC interacting PYRIN domain ( 41–261 bp ) , 200-amino-acid HIN I ( 401–895 bp ) and HIN II ( 1043–1541 bp ) domains involved in the sequence independent DNA recognition , and 2 nuclear localizing signals ( NLS; 296–311 and 387–407 bp ) which attribute to its nuclear entry after synthesis in the cytoplasm [15] . Though IFI16 is a predominately nuclear protein , after recognizing KSHV and HSV-1 DNA during de novo infection , the IFI16-ASC complex initially colocalized in the infected cell nucleus and subsequently localized in the perinuclear areas [11 , 14] . Similarly , we observed the colocalization of IFI16 and ASC both in the nucleus and cytoplasm of cells latently infected with KSHV and EBV [12 , 13] . Western blot analysis of de novo KSHV infected HMVEC-d cells showed steady levels of ASC and procaspase-1 in the nuclear fractions . Infected cells also showed higher levels of both ASC and procaspase-1 in the cytoplasmic fractions which demonstrated that ASC and procaspase-1 undergo subcellular redistribution upon infection . Active caspase-1 ( p20 ) was detected in the nucleus of infected HMVEC-d cells at 2 and 8 h post-infection demonstrating that the inflammasome is activated upon sensing KSHV in the nucleus , and the majority of activated caspase-1 was subsequently detected in the cytoplasmic fractions at later times of infection probably to prevent caspase-1 mediated adverse activities in the nucleus . Detection of caspase-1 in the cytoplasm during de novo KSHV and HSV-1 infection as well as in latently infected cells demonstrated that after recognizing viral DNA in the nucleus , the newly formed IFI16-ASC inflammasome complex is transported to the cytoplasm [11 , 12 , 13 , 14] . However , the mechanism behind the redistribution of this complex is not known . HSV-1 infection also induced IRF-3 phosphorylation through the IFI16-STING interaction in the cytoplasm . Even though the recognition of HSV-1 genome in the nucleus via IFI16 is suggested to be the factor behind the cytoplasmic STING-IRF-3 activation and IFN-β production early during infection [16] , the mechanism of post-genome detection signaling from nucleus to cytoplasm resulting in STING activation is not known . KSHV infection induces only a moderate IFN-β response early during de novo infection which was inhibited by a variety of early lytic and latent gene products at later times of infection [17] . The role of IFI16 in IFN-β production during KSHV infection is not known . Using IFI16-EGFP constructs transfection in human osteosarcoma U2OS cells , Li et al . , [15] studies showed that the two NLS motifs of IFI16 ( aa 96–100 and aa 128–131 ) are essential for the entry of newly synthesized IFI16 in the cytoplasm to the normal cell nucleus . Using a FISH assay , they demonstrated that during HSV-1 ( strain 17+ ) infection of U2OS cells ( 5 PFU/cell ) containing transfected IFI16-EGFP construct , virion DNA colocalized only with full length IFI16-EGFP with intact NLS and not with mutated NLS-IFI16-EGFP that were localized in the cytoplasm . They also observed that as reported by us for KSHV [11 , 12] , EBV [13] and HSV-1 [14] , a subset of wild type IFI16 translocated to the cytoplasm . In addition , co-IP of HSV-1 DNA-protein complexes followed by qPCR with four HSV-1 primer sets ( UL30 , US6 , RL1 and RS1 ) demonstrated the nuclear IFI16 interaction with viral DNA in the nucleus . Using uninfected U2OS transfected with DNA , Li et al . , [15] concluded that acetylation at the NLF motifs of IFI16 results in the cytoplasmic retention of newly synthesized IFI16 by prohibiting nuclear import , and the histone acetyltransferase p300 regulated the cytoplasmic IFI16 acetylation during transfection of DNA . However , the fate of nuclear IFI16 during HSV-1 infection , whether IFI16 undergo acetylation during HSV-1 infection , the role of p300 during viral DNA recognition in the nucleus , and the mechanism behind the IFI16 redistribution into the cytoplasm during infection was not studied [15] . Here , we demonstrate that the presence of KSHV genome in the nucleus induces the p300 mediated acetylation of IFI16 and this modification is the driving force behind the nuclear to cytoplasmic redistribution of the IFI16-inflammasome which was facilitated by Ran-GTPase . IFI16 acetylation is required for its interaction with ASC , inflammasome assembly and function . In addition , cytoplasmic redistribution of acetylated IFI16 is also essential for STING-IRF-3 mediated IFN-β production in KSHV and HSV-1 infected cells . These studies for the first time demonstrate that IFI16 acetylation is a dynamic post-herpes viral genome recognition event required for the IFI16-mediated innate responses of inflammasome induction ( KSHV , EBV and HSV-1 ) and IFN-β production ( KSHV and HSV-1 ) .
KSHV enters HMVEC-d and HFF cells by a rapid endocytic process which is followed by the transport of genome-containing capsid to the nuclear pore vicinity , capsid disassembly and entry of the linear dsDNA into the nucleus within 15–30 min p . i . , followed by the establishment of a latent infection [18] . Our studies have shown that IFI16 colocalized with the KSHV genome at 2 h p . i . in the nucleus of HMVEC-d cells [11] . To determine the earliest time of interaction of IFI16 with KSHV genome , HMVEC-d cells were infected with KSHV containing BrdU-labeled genome ( BrdU-KSHV ) and immunostained with anti-BrdU antibodies ( Fig 1A; Table 1 ) . IFI16 was predominantly localized in the uninfected cell nucleus ( Fig 1A , top panel ) . By 15 min p . i . , viral particles were seen in the cytoplasm and near the nuclear periphery ( Fig 1A , red arrows , middle panel ) . In contrast , significant accumulation of viral DNA was observed at 30 min p . i . in the infected cell nuclei , and most of them colocalized with IFI16 ( Fig 1A , white arrows ) . In addition , a few IFI16 signal spots were also detected in the cytoplasm at 30 min p . i . ( Fig 1A , yellow arrow ) . These results suggested that IFI16 senses the KSHV genome soon after its entry into the nucleus during de novo infection with a concomitant redistribution to the cytoplasm . To determine the kinetics of IFI16 redistribution to the cytoplasm , the cytoplasmic and nuclear fractions from uninfected cells and cells infected with KSHV for various times were analyzed by western blots ( WB ) . Consistent with the IFA results , a very faint IFI16 band was detected at 30 min p . i . in the cytoplasm which steadily increased during the observed period of 24 h p . i . ( Fig 1B , lanes 9–12 ) with a corresponding decrease in the nuclear IFI16 levels ( Fig 1B , lanes 4–6 ) . TBP and tubulin proteins were used as markers of nuclear and cytoplasmic preparation purity and as controls for equal loading ( Fig 1B , lanes 1–12 ) . When IFA was performed to validate the biochemical data , IFI16 was predominantly in the nucleus of uninfected cells ( S1A Fig , top panel ) . In contrast , at 30 min p . i . , few IFI16 signal spots were visible in the cytoplasm which increased steadily during the observed period of 24 h p . i . ( S1A Fig , red arrows ) . These results demonstrated that KSHV infection induces IFI16 redistribution from the nucleus to the cytoplasm as early as 30 min p . i . with steady increase thereafter . IFI16 has been shown to function as a transcriptional modulator via unknown mechanisms [19] . We theorized that acetylation of IFI16 could be one of the reasons for cytoplasmic transport since acetylation of HMGB-1 ( high-mobility group protein B1 ) protein involved in transcription/ chromatin bending has been shown to result in HMGB-1’s translocation into the cytoplasm [20] . Furthermore , IFI16 acetylation within the NLS motifs during transfection of DNA in U20S cells promoted cytoplasmic retention by blocking nuclear import of newly synthesized IFI16 [15] . However , the fate of IFI16 during nuclear DNA sensing was not studied . To investigate the acetylation status of IFI16 during KSHV infection , uninfected and infected cell lysates were immunoprecipitated ( IP-ed ) with anti-acetylated lysine antibody and western blotted for IFI16 . Compared to the uninfected cells , we observed a robust increase in the acetylation of IFI16 only in the infected cells ( Fig 1C , lanes 1 and 2 ) . In contrast , equal levels of acetylated tubulin were observed in both uninfected and KSHV infected cells ( Fig 1C , lanes 1 and 2 ) . The input IFI16 and loading control tubulin were of similar levels . These results suggested that the acetylation machinery was functional in both uninfected and infected cells and KSHV infection induced increased acetylation of IFI16 . When we next investigated the kinetics of IFI16 acetylation in the nuclear and cytoplasmic fractions by co-IP experiments , as early as 30 min p . i . an appreciable level of nuclear IFI16 acetylation was observed which steadily increased during the observed 24 h p . i . ( Fig 1D , lanes 2–6 ) . Correspondingly , we detected a faint band of acetylated IFI16 in the cytoplasm at 30 min p . i . , with steady increase from 2 to 24 h p . i . ( Fig 1D , lanes 9–12 ) , which corroborated the results in Fig 1B , lanes 9–12 . The faint acetylated IFI16 band detected in the nucleus of uninfected cells probably represents the basal level ( Fig 1D , lane 1 ) . These detections were not due to nuclear contamination as shown by the absence of TBP and presence of tubulin in these fractions ( Fig 1D ) . As positive control for nuclear and cytoplasmic acetylation , the proteins were IP-ed with acetylated lysine antibody and western blotted for H3 and tubulin , respectively ( Fig 1D , lanes 1–12 ) . Total H3 level was also analyzed by western blot as input control . These results were also validated by IFA using anti-IFI16 and anti-acetylated lysine antibodies ( S1B Fig ) . In the uninfected cells , IFI16 was detected in the nucleus and acetylated lysine signals were observed both in the nucleus and in the cytoplasm ( S1B Fig , top panel ) . We also observed some basal level of IFI16 and acetylated lysine colocalization in the nucleus of uninfected cells ( S1B Fig , UI , red arrow ) . In contrast , KSHV infection significantly increased the colocalization of acetylated lysine and IFI16 in the nucleus as well as in the cytoplasm in a time dependent manner ( S1B Fig ) . Taken together , these results demonstrated that during de novo KSHV infection , IFI16 recognizes the viral genome with a concomitant increase in its acetylation in the nucleus and redistribution of acetylated IFI16 to the cytoplasm of the infected cells . The cellular transcriptional coactivator protein p300 functions as a histone acetyltransferase and has been shown to be involved in the cytoplasmic acetylation of IFI16’s NLS domains [15] . To investigate the significance of nuclear acetylation of IFI16 and its redistribution , we utilized the p300 competitive inhibitor C-646 . Based on the results in BCBL-1 and HMVEC-d cells incubated with various concentrations of C-646 for 4 and 24 h ( S2A and S2B Fig ) we selected the least toxic 1 μM concentration ( 5–6% cell death ) for all further experiments . C-646 treatment did not interfere with viral entry or nuclear delivery of viral genome , and equal levels of the characteristic KSHV latent LANA-1 protein dots were detected in the treated and untreated cells ( S2C , S2D , and S2E Fig ) . Significant increase in acetylation was observed in the KSHV infected cells which was reduced by C-646 treatment ( S2F Fig , lanes 1–4 ) . The specificity of C-646 was examined by the acetylation level of H2B , one of the target proteins of p300 . IP with acetylated lysine antibody and WB for H2B showed six fold reduction in H2B acetylation by C-646 compared to the untreated KSHV ( 24 h ) infected cells ( S2G Fig , lanes 1 and 2 ) . These results demonstrated that de novo KSHV infection induced acetylation , which is in part due to p300 , can be inhibited by C-646 . To determine the effect of C-646 on IFI16 acetylation , HMVEC-d cells were either uninfected or infected with KSHV in the presence or absence of C-646 , whole cell lysates IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 . Compared to untreated infected cells , C-646 treatment completely abolished the infection induced IFI16 acetylation ( Fig 1E , lanes 1–6 ) . Immunoprecipitation of IFI16 followed by WB for IFI16 demonstrated equal pull down; in addition , β-actin levels did not change due to treatment and showed equal loading ( Fig 1E , lanes 1–6 ) . IP of IFI16 and WB with anti-acetylation antibody also validated these results which showed decreased levels of acetylated IFI16 by C-646 treatment in infected cells ( Fig 1F , lanes 1–3 ) . To investigate the effect of C-646 on KSHV infection induced acetylation mediated cytoplasmic redistribution of IFI16 , HMVEC-d cells were infected in the absence or presence of C-646 , cytoplasmic and nuclear fractions isolated and western blotted for total IFI16 . KSHV infection induced redistribution of IFI16 into the cytoplasm was abolished in C-646 treated cells ( Fig 1G , lanes 7–12 ) . Interestingly , we also observed that the nuclear IFI16 levels decreased at later time points by C-646 ( Fig 1G , lanes 4–6 ) which suggested that acetylation may have a role in the stabilization of IFI16 . These results demonstrated that IFI16 acetylation during KSHV infection is dependent on p300 and acetylation is required for the redistribution of IFI16 from the nucleus to the cytoplasm after recognition of the KSHV genome in the nucleus . To validate these results , we performed in situ-PLA which detects endogenous levels of proteins and gives the spatial distribution and localization of a single or multiple proteins ( Fig 2A ) . PLA uses oligonucleotide-linked secondary antibodies and a fluorescence-based assay to detect closely associated proteins . If epitopes of a single protein or two protein epitopes are within 40 nm proximity , the antibody-linked oligonucleotides will ligate with adaptor oligonucleotides to form complete circles that are amplified via DNA replication and detected with fluorescent sequence-specific probes which will appear as distinct dots visible under fluorescent microscopy . HMVEC-d cells were uninfected or infected in the presence or absence of C-646 and subjected to PLA using rabbit and mouse anti-IFI16 antibodies detecting different epitopes , and the detected red dots depict IFI16 ( Fig 2A ) . IFI16 was predominantly nuclear in both untreated and C-646 treated uninfected cells ( Fig 2A , top 2 panels , yellow arrows ) . In the absence of C-646 , we observed abundant cytoplasmic IFI16 localization in KSHV infected cells at 24 h p . i . ( Fig 2A , lower panels , white arrows ) , and an uninfected cell in the same field showed predominantly nuclear IFI16 ( Fig 2A , blue arrow ) . In contrast , while IFI16 was detected in the nucleus of C-646 treated infected cells , we did not observe IFI16 redistribution in the cytoplasm ( Fig 2A , lower panels ) . These results demonstrated that inhibition of acetylation compromised the cytoplasmic redistribution of IFI16 . To further elucidate the effect of C-646 on acetylation of IFI16 , PLA was performed using anti-IFI16 and anti-acetylated lysine antibodies and the observed red dots represent the acetylated IFI16 ( Fig 2B ) . Low levels of nuclear acetylated IFI16 PLA dots were detected both in the treated and untreated uninfected cells ( Fig 2B , top panel , yellow arrows ) . In contrast , at 30 min p . i . , acetylated IFI16 dots were appreciably increased in the nucleus with few dots visible in the cytoplasm , which increased to numerous acetylated IFI16 spots in a time dependent manner ( Fig 2B , left panels , white arrows ) . In contrast , with C-646 treatment the acetylated IFI16 dots did not increase either in the cytoplasm or in the nucleus of infected cells ( Fig 2B , lower three right panels ) . These studies demonstrating the reduction in cytoplasmic IFI16 redistribution by C-646 treatment validated our findings , and confirmed that IFI16 acetylation in the nucleus during KSHV infection is required for its redistribution to the cytoplasm . We have previously shown that replication incompetent UV treated KSHV ( UV-KSHV ) enters the cells , delivers the viral DNA into the nucleus and induces the IFI16-inflammasome [11] , which demonstrated that the presence of KSHV genome is the requirement for IFI16 recognition and further consequences . When lysates from HMVEC-d cells infected with KSHV or UV-KSHV for 24 h were IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 , similar to live-KSHV infected cells , acetylation of IFI16 increased in a time dependent manner by infection with UV-KSHV ( Fig 2C , lanes 1–7 ) . These results suggested that the presence of viral genome is enough to induce the IFI16 acetylation process and viral gene expression is not required . We next determined whether acetylation of IFI16 and its cytoplasmic redistribution also occur in other cell types . Compared to uninfected cells , as in HMVEC-d cells , KSHV infected HFF cells ( 24 h p . i . ) showed increased acetylation of IFI16 which was significantly inhibited by C-646 ( S3A Fig , lanes 1–4 ) , and WB for total IFI16 showed a slight reduction in C-646 treated cells ( S3A Fig , lanes 1–4 ) . In PLA analysis , infected HFF cells in the absence of the inhibitor showed robust acetylation of IFI16 and its redistribution to the cytoplasm , which was significantly abrogated by C-646 ( S3B Fig ) . Uninfected cells showed only a basal level of acetylated IFI16 in the nucleus ( S3B Fig ) . Evaluation of the total IFI16 levels by PLA using mouse and rabbit anti-IFI16 antibodies revealed that IFI16 was solely nuclear in the uninfected cells ( S3C Fig ) , while the KSHV infected cells showed IFI16 both in the nucleus and in the cytoplasm ( S3C Fig ) . However , when the cells were treated with C-646 , IFI16 was only detected in the nucleus ( S3C Fig ) . These results demonstrated that acetylation of IFI16 is essential for its redistribution to the cytoplasm of KSHV infected HFF cells . We have shown that IFI16 recognizes the latent KSHV genome and only the IFI16-inflammasome is constitutively induced in endothelial and PEL cells carrying latent genome . Hence , we determined the acetylation status of IFI16 in these cells . Whole cell lysates from control BJAB and KSHV ( + ) BCBL-1 cells were IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 . Compared to BJAB cells , we detected increased IFI16 acetylation in BCBL-1 cells which was significantly reduced by C-646; however , total IFI16 was pulled down equally in each group ( S4A Fig , lanes 1–4 ) . Examination of total IFI16 in the cytoplasmic and nuclear fractions from untreated or C-646 treated BCBL-1 cells revealed ~6–11 fold less cytoplasmic IFI16 protein levels at 4 and 24 h of drug treatment , respectively , compared to the untreated controls ( S4B Fig , lanes 4–6 ) . These results demonstrated the acetylation dependent cytoplasmic redistribution of IFI16 in the latently infected cells . As in de novo infected cells , nuclear IFI16 protein levels also decreased in the presence of C-646 indicating that IFI16 stability in KSHV infected cells may be dependent upon its acetylation . To validate these results , PLA was performed in BJAB and BCBL-1 cells using anti-IFI16 and anti-acetylated lysine antibodies ( S4C Fig ) . Compared to the few nuclear acetylated IFI16 PLA dots in the BJAB cells ( S4C Fig , upper left panel ) , we observed a significant increase in the acetylated IFI16 in the nucleus as well as in the cytoplasm of KSHV+ BCBL-1 cells ( S4C Fig , lower left panel , yellow and white arrows , respectively ) . C-646 treatment resulted in a drastic reduction in acetylated IFI16 ( S4C Fig , right panels ) . When PLA was done to examine total IFI16 and its redistribution in the absence or presence of C-646 , we did not observe any cytoplasmic IFI16 in the BJAB cells ( S4D Fig , upper panels ) . Corroborating the biochemical data in S4B Fig , increased nuclear and cytoplasmic IFI16 were observed in untreated BCBL-1 cells whereas IFI16 was mostly nuclear in the C-646 treated cells ( S4D Fig , lower panels , yellow arrows ) . The KSHV latently infected endothelial ( TIVE-LTC ) and B ( BJAB-KSHV ) cells were also analyzed for IFI16 acetylation . IP of the whole cell lysates from control endothelial TIVE and BJAB , KSHV ( + ) TIVE-LTC and BJAB-KSHV cells with anti-acetylated antibody followed by IFI16 WB revealed significantly higher levels of acetylated IFI16 in both TIVE-LTC and BJAB-KSHV cells than in the KSHV negative control cells ( S4E Fig , lanes 1–4 ) . Equal amounts of IFI16 were detected in IP and in WB reactions ( S4E Fig , lanes 1–4 ) . By PLA for IFI16 acetylation in the presence or absence of C-646 , TIVE cells showed a minimal amount of acetylated IFI16 in both treated and untreated cells ( S4F Fig , upper panels ) . In contrast , the TIVE-LTC cells showed increased levels of acetylated IFI16 both in the nucleus and in the cytoplasm ( S4F Fig , lower left panel ) . This cytoplasmic redistribution of acetylated IFI16 was abolished by C-646 ( S4F Fig , lower right panel ) . Total IFI16 levels in C-646 treated or untreated TIVE and TIVE-LTC cells were also analyzed by PLA using mouse and rabbit anti-IFI16 antibodies . In untreated and C-646 treated TIVE cells , IFI16 was solely nuclear ( S4G Fig , upper panels ) . In contrast , TIVE-LTC cells showed robust IFI16 cytoplasmic redistribution ( S4G Fig , lower left panel ) which was significantly reduced by C-646 ( S4G Fig , lower right panel ) . Taken together , these results demonstrated that similar to de novo infected HMVEC-d cells , p300 mediated acetylation plays an important role in the cytoplasmic redistribution of IFI16 in cells latently infected with KSHV . As an IFI16-ASC inflammasome is formed during EBV infection of B cells and in latently infected cells , we performed PLA for IFI16 and acetylated lysine in primary human B cells infected with KSHV or EBV as well as in cells latently infected with EBV ( S5 Fig ) . Compared to uninfected cells , both KSHV and EBV infected primary B cells showed acetylation as well as cytoplasmic redistribution of acetylated IFI16 ( S5A Fig ) . Compared to EBV negative Ramos cells , EBV latently infected Raji ( latency I ) and LCL ( latency III ) cells showed both nuclear and cytoplasmic acetylated IFI16 ( S5B Fig ) . These results demonstrated that acetylation of IFI16 and its cytoplasmic redistribution also occur in EBV infected cells . To determine the specificities of nuclear herpesvirus genome activation of IFI16 acetylation and its cytoplasmic distribution , we next used vaccinia virus replicating its dsDNA exclusively in the cytoplasm . The acetylation of IFI16 was not induced by vaccinia virus infection of HMVEC-d cells ( S6A Fig ) . Only similar levels of a few dots representing basal level of acetylation were detected in the nucleus of both uninfected and vaccinia virus infected cells ( S6A Fig ) . When mouse and rabbit antibodies were used to perform the PLA , IFI16 was predominantly detected in the nucleus of both uninfected as well as vaccinia infected HMVEC-d cells ( S6B Fig ) . These results demonstrated that vaccinia viral DNA in the cytoplasm was not recognized by nuclear IFI16 , and hence acetylation of the nuclear IFI16 and cytoplasmic translocation were not observed . These findings clearly supported our observations that the presence of nuclear KSHV , EBV and HSV-1 genomes induced the acetylation of IFI16 in the nucleus which then relocated into the cytoplasm of infected cells . The dynamic process of exporting molecules of >50-kDa from the nucleus is initiated by exportins binding to cargo and Ran-GTP protein . The guanine-nucleotide exchange factor ( GEF ) of Ran that converts Ran-GDP to GTP form is in the nucleus and GTPase-activating proteins ( GAPs ) for Ran-GTPase are present in the cytoplasm as well as on the cytoplasmic face of the nuclear pore . To determine whether Ran is responsible for IFI16 transport from the nucleus to the cytoplasm , the lysates from uninfected or KSHV infected HMVEC-d cells ( 4 h p . i . ) in the presence or absence of C-646 were IP-ed with anti-Ran-GTPase antibodies and WB for IFI16 . Compared to the uninfected cells that showed a basal level of IFI16-RAN association ( Fig 3A , lanes 1 and 2 ) , KSHV infected cells showed robust association of IFI16 with Ran-GTPase which was inhibited by C-646 ( Fig 3A , lanes 3 and 4 ) . Comparable levels of IFI16 and Ran proteins were pulled down with their corresponding antibodies ( Fig 3A , lanes 3 and 4 ) . Higher IFI16-RanGTP association in untreated KSHV infected cells corroborated the higher cytoplasmic redistribution of IFI16 shown in the earlier figures . When PLA was performed using anti-Ran and IFI16 antibodies , consistent with the IP results , the association between these two molecules increased during KSHV infection , which was abolished by C-646 ( Fig 3B ) . These results demonstrated that acetylation enhances the association of IFI16 with Ran-GTP during infection facilitating its transport to the cytoplasm and this association is dependent upon acetylation . The nuclear resident IFI16 translocates to the nucleus after its translation in the cytoplasm via its two NLS domains and acetylation of NLS has been shown to retain IFI16 in the cytoplasm [15] . To determine whether the cytoplasmic IFI16 detected during KSHV de novo infection and latency represents newly synthesized IFI16 or redistributed from the nucleus , we used 50 nM Leptomycin B ( LPT ) to block nuclear export to the cytoplasm . This concentration of LPT was not overly toxic ( 6–8% ) to HMVEC-d cells nor did it significantly affect the establishment of KSHV infection ( S7A , S7C , and S7E Fig ) . When HMVEC-d cells infected with KSHV in the presence or absence of LPT were analyzed , infected cells showed enhanced cytoplasmic redistribution of IFI16 which was abolished by LPT treatment ( Fig 3C , top panel , lanes 5–8 ) . Compared to untreated cells , nuclear IFI16 increased in LPT treated cells probably due to blocked cytoplasmic redistribution ( Fig 3C , top panel , lanes 1–4 ) . Reduced cytoplasmic and increased nuclear cyclin-B1 in LPT treated cells confirmed the hampered nuclear to cytoplasmic protein transport ( Fig 3C , second panel , lanes 1–8 ) . Since IFI16-ASC-procaspase-1 assembly was initiated in the nucleus , we next examined the effect of LPT on the transport of the other components of IFI16-inflammasomes . Procaspase-1 was detected in the nucleus of untreated uninfected and infected cells ( Fig 3C , third panel , lanes 1 and 3 ) . The increased cytoplasmic procaspase-1 in untreated infected cells was significantly decreased by LPT with a corresponding increase in the nucleus ( Fig 3C , third panel , lanes 7 and 8 , and 3 and 4 ) . We have previously observed the presence of cleaved caspase-1 in the nucleus of infected HMVEC-d cells at 2 h and 8 h p . i . and only in the cytoplasm at 24 h p . i . [11] . Similarly , cleaved caspase-1 was detected in the infected cell cytoplasm at 24 h p . i . which was abolished by LPT treatment with a concomitant increase in the nucleus ( Fig 3C , lanes 3 , 4 , 7 and 8 ) . When cell lysates of KSHV infected HMVEC-d cells in the presence or absence of LPT were analyzed by IP with anti-acetylated antibody and IFI16 WB , IFI16 was acetylated minimally in uninfected cells and to the same extent in untreated and LPT treated infected cells; however , tubulin was acetylated in both uninfected and infected samples ( Fig 3D , top 2 panel , lanes 1–4 ) . Similarly , the IFI16 and ASC association was equal in untreated and LPT treated infected cells ( Fig 3D , third panel ) . Equal amounts of ASC and IFI16 were pulled down with their corresponding antibodies and their total protein levels demonstrated that these proteins were available in sufficient and equal amounts in each of the experimental groups ( Fig 3D , lower panels lanes 1–4 ) . To rule out the possibility that the detection of acetylated IFI16 is not due to the accumulation of newly synthesized IFI16 in the cytoplasm of KSHV infected cells , we blocked protein synthesis by using cycloheximide ( CHX ) at 200 μg/ml which was neither toxic nor affected the KSHV infection of HMVEC-d cells ( S7B , S7D , and S7F Fig ) . Cytoplasmic and nuclear proteins from CHX treated or untreated cells left uninfected or infected with KSHV for 4 h were isolated and subjected to western blot analysis . In the presence or absence of cycloheximide , we did not detect cytoplasmic IFI16 in the uninfected cells . In contrast , we observed similar levels of cytoplasmic IFI16 in the infected cells in both the presence and absence of cyloheximide ( Fig 3E , lanes 1 to 8 ) . These results coupled with the LPT results suggested that the increased level of IFI16 in the cytoplasm of infected cells during KSHV infection is due to the translocation of acetylated IFI16 from the nucleus into the cytoplasm . In PLA , nuclear IFI16 was detected in the untreated and treated uninfected cells ( Fig 3F , left panels , yellow arrows ) . In contrast , in agreement with the biochemical findings , increased cytoplasmic redistribution of IFI16 in KSHV infected HMVEC-d cells was detected ( Fig 3F , top right panel , white arrows ) which was abrogated in LPT treated cells ( Fig 3F , lower right panel ) . In addition , the IFI16-ASC complex was observed in both the cytoplasm and nucleus of infected cells which was constrained to the nucleus of LPT treated cells ( Fig 3G , right most panels ) . This redistribution of IFI16-ASC complex PLA spots corroborated with earlier IFA and WB findings which demonstrated that IFI16-ASC inflammasome activation leads to redistribution of IFI16-ASC to the cytoplasm [11] . Taken together , these results demonstrated that a ) blocking nuclear export by LPT did not interfere in the acetylation of IFI16 , formation of IFI16-ASC complex or activation of caspase-1 , b ) blocking protein synthesis by CHX did not affect the cytoplasmic distribution of IFI16 from the nucleus , and c ) the increased level of IFI16 in the cytoplasm in the infected cells was due to its redistribution from the nucleus and not due to newly translated cytoplasmic IFI16 . Since the redistribution of acetylated IFI16 and inflammasome activation showed a similar pattern in the infected cells , we sought to determine whether the acetylation of IFI16 and IFI16-inflammasome activation are linked or independent of each other . As the association of IFI16 with the adaptor ASC is the first step in inflammasome activation , we examined these interactions by PLA . As shown in Fig 4A and 4B , in the untreated and uninfected HMVEC-d cells , few IFI16-ASC interacting PLA dots were visible in the nucleus representing the basal level of association which was reduced by C-646 treatment ( Fig 4A , top panels ) . In contrast , in untreated KSHV infected HMVEC-d cells , we observed a robust interaction between IFI16 and ASC both in the nucleus and in the cytoplasm ( Fig 4A , yellow and white arrows , respectively , lower left panel ) . When the C-646 treated HMVEC-d cells were infected with KSHV , the PLA dots representing IFI16-ASC interactions in the nucleus were greatly reduced with little redistribution to the cytoplasm ( Fig 4A , lower right panels ) . Examination of IFI16 and ASC by IFA ( S8A Fig ) also revealed that IFI16 was predominantly in the nucleus of the uninfected cells , while de novo KSHV infected HMVEC-d cells showed strong IFI16-ASC colocalization in the nucleus and redistribution to the cytoplasm ( S8A Fig ) . When the cells were treated with C-646 , only minimal IFI16-ASC interaction and cytoplasmic redistribution was detected ( S8A Fig , third panel ) . Similarly , when BCBL-1 cells were examined by PLA and IFA , strong interactions between IFI16 and ASC were detected both in the nucleus and cytoplasm which were compromised by C-646 ( Fig 4B and S8B Fig ) . Control BJAB cells did not show considerable IFI16 and ASC interaction in either untreated or C-646 treated cells ( Fig 4B ) . To confirm the IFI16-ASC interactions detected by PLA , cell lysates from uninfected and 4 and 24 h de novo KSHV infected HMVEC-d cells in the presence or absence of C-646 were IP-ed with ASC and western blotted for IFI16 . ASC was associated with IFI16 at 4 and 24 h p . i . but no such strong association was seen in the uninfected cells , ( Fig 4C , lanes 1–3 ) . In contrast , C-646 treatment disrupted the association between IFI16 and ASC ( Fig 4C , lanes 4–6 ) . A similar amount of IFI16 was pulled down in each group either treated or not treated with C-646 ( Fig 4C , lanes 1–6 ) . A similar to primary infection , we observed increased interaction of IFI16 with ASC in the latently infected BCBL-1 cells which was greatly reduced in C-646 treated cells ( Fig 4D , lanes 3 and 4 ) . The inputs of IFI16 and ASC were similar in all groups . These results demonstrated that the presence of KSHV genome in the nucleus induced the IFI16-ASC interaction and inflammasome formation , which are dependent upon the acetylation of IFI16 in both de novo and latent KSHV infected cells . The IFI16-inflammasome complex is formed by the homotypic interactions between PYD domains of IFI16 and ASC and CARD domains of ASC and procaspase-1 , leading into the aggregation of IFI16 molecules [11] . To confirm that the IFI16-inflammasome complex is dependent upon the acetylation of IFI16 , proteins in the cell lysates cross-linked with glutaraldehyde for 10 min were used for WB . We observed high molecular weight IFI16 aggregates in de novo KSHV infected HMVEC-d cells ( 24 h ) and in BCBL-1 cells ( Figs 4E , lane 2 , and 6F , lane 3 ) and these were severely compromised by C-646 treatment ( Figs 4E , lane 3 , and 6F , lane 4 ) . No such aggregation was detected in the uninfected cells ( Fig 4E , lane 1 , and 4F , lanes 1 and 2 ) . These results further confirmed that acetylation of IFI16 is critical for IFI16-inflammasome formation . Formation of the IFI16-ASC-procaspase-1 inflammasome leads to the generation of functional caspase-1 via auto-cleavage which results in the cleavage of the pro-forms of IL-1β , IL-18 and IL-33 cytokines . Hence , we investigated the effect of C-646 on activation of caspase-1 and its downstream cytokines production . In untreated KSHV infected HMVEC-d cells , caspase-1 activation was detected at 4 and 24 h p . i . , whereas , the C-646 treated counterparts did not show considerable cleavage of caspase-1 ( Fig 4G , top panel , lanes 1–6 ) . Activation of IL-1β and IL-33 was also inhibited by C-646 treatment ( Fig 4G , panels 2 , 3 and 4 , lanes 1–6 ) . We also observed the inhibition of procaspase-1 and pro-IL-1β cleavages by C-646 treatment in BCBL-1 cells ( Fig 4H , lanes 3 and 4 ) , and cleavage of procaspase-1 and pro-IL-1β was not detected in BJAB cells ( Fig 4H , lanes 1 and 2 ) . The C-646 treatment did not significantly affect the viability of BJAB and BCBL-1 cells ( S8C Fig ) . Compared to uninfected cells , increased secretion of IL-1β was observed in KSHV infected HMVEC-d culture supernatants ( 18 . 5 pg/ml ) which was significantly reduced ( >5-fold ) by C-646 treatment ( 3 . 8 pg/ml ) ( Fig 4I ) . We next determined the levels of active caspase-1 in BCBL-1 cells with or without C-646 by FACS using fluorescent caspase-1 detection 660-YVAD-FMK probe ( S8D and S8E Fig ) , and the percent active caspase-1 cell populations are shown in S8F Fig . Control BJAB cells unstained or stained with FLICA-660 did not show significant caspase-1 active cells ( S8D and S8F Fig ) . In contrast , nearly 50% of the untreated BCBL-1 cells contained active caspase-1 which was reduced to ~18–19% in C-646 treated cells ( S8E and S8F Fig ) . These results confirmed that acetylation of IFI16 promotes formation of functional IFI16-ASC-procaspase-1 inflammasomes leading into active caspase-1 generation and downstream cytokine production in KSHV infected cells . The recognition of viral genome by IFI16 leads into its increased interaction with ASC and inflammasome formation ( Fig 4A–4D ) . Since reduction in IFI16 acetylation hampered IFI16-ASC association ( Fig 4A–4D ) , we determined whether ASC plays roles in the acetylation of IFI16 and whether ASC associates with IFI16 after acetylation of IFI16 . We knocked down the HMVEC-d cell ASC by Si-RNA electroporation and infected with KSHV . Knockdown efficiency confirmation by WB showed ~90–95% ASC reduction with no effect on IFI16 protein ( Fig 5A , top two panels , lanes 1–8 ) . The lysates from control and ASC knocked down cells were IP-ed with anti-acetylated lysine antibody and WB for IFI16 . We observed the acetylation of IFI16 in both control and ASC knocked down cells ( Fig 5A , third panel , lanes 1–8 ) . As expected , in the absence of ASC formation of the IFI16-inflammasome complex was abrogated as shown by the absence of IFI16 in IP-reactions with anti-caspase-1 antibody and by the absence of caspase-1 activation in comparison to the Si-control KSHV infected cells . ( Fig 5A , fourth , sixth and seventh panels , lanes 1–8 ) . Caspase-1 was pulled down in all groups including ASC knocked down cells ( Fig 5A , fifth panel , lanes 1–8 ) . These results suggested that IFI16 acetylation occur independent of ASC . We next determined whether IFI16 relocates to the cytoplasm in the absence of IFI16-ASC inflammasome formation . Western blot analysis of the cytoplasmic and nuclear fractions from Si-ASC uninfected and KSHV infected HMVEC-d cells showed efficient knockdown of ASC ( Fig 5B , top panel , lanes 1–8 ) . At 24 h p . i . in the Si-control cells , we observed the presence of IFI16 in the cytoplasm which was reduced by >2-fold in the ASC knockdown cells ( Fig5B , second panel , lanes 7 and 8 ) . No IFI16 was detected in the uninfected cell cytoplasm ( Fig 5B , second panel , lanes 5 and 6 ) . Interestingly , the nuclear IFI16 level was higher in KSHV infected ASC knockdown cells compared to the uninfected cells ( Fig 5B , second panel , lanes 1–4 ) . Since KSHV infection does not increase the IFI16 mRNA and protein levels [11] , this moderate increase may be due to reduced , or lack of cytoplasmic redistribution of IFI16 . When the cytoplasmic and nuclear fractions were IP-ed with anti-acetylated lysine antibody and western blotted for IFI16 , there was no change in the nuclear acetylated IFI16 levels in control and ASC knockdown cells ( Fig 5B , third panel , lanes 3 and 4 ) . However , similar to the total IFI16 redistribution , >3-fold reduction in the acetylated IFI16 level was observed in the cytoplasm of ASC knockdown cells ( Fig 5B , third panel , lanes 7 and 8 ) . These results clearly demonstrated that in the absence of ASC , acetylation of IFI16 still takes place which is prior to inflammasome formation . The cytoplasmic redistribution of IFI16 in ASC knockdown cells must be inflammasome independent which might be attributed to cytoplasmic export of acetylated IFI16 either alone or in complex with other proteins . However , the reduced amount of IFI16 in the cytoplasm in comparison to Si-control suggested that the IFI16-ASC inflammasome contributes to the majority of the IFI16 detected in the cytoplasm of infected cells . As a follow up to C-646 inhibition of KSHV induced p300 catalyzed acetylation of IFI16 , we determined the interaction of p300 with IFI16 . When HMVEC-d cells infected with KSHV for 24 h in the presence or absence of C-646 were IP-ed for p300 and western blotted for IFI16 , we observed increased interaction of IFI16 with p300 which was reduced to basal levels with C-646 treatment ( Fig 6A , lanes 1–8 ) . After detection of a physical association between IFI16 and p300 during KSHV infection , we evaluated the enzymatic activity of p300 and its counterpart HDAC in the cytoplasmic and nuclear fractions of HMVEC-d cells infected with KSHV in the presence or absence of their corresponding inhibitor ( 1 μM C-646 for p300 and 20 μM TSA for HDAC ) . KSHV infection ( 24 h ) significantly induced p300 activity in the nucleus but not in the cytoplasm of infected cells compared to uninfected cells , which was inhibited by C-646 ( Fig 6B ) . Similarly , HDAC activity was also induced significantly in the nucleus of infected cells which was inhibited by TSA ( Fig 6C ) . These results suggested that IFI16 acetylation is probably due to increased activity of p300 . Increased nuclear p300 activation during infection further supports that acetylation of IFI16 is probably mediated by increased p300 activity in the nucleus and not in the cytoplasm . Decreased activity of enzymes by the inhibitors further verified the specificities of these assays and the functionality of C-646 and TSA ( Fig 6B and 6C ) . Next , we knocked down p300 to validate our inhibitor studies . Efficient p300 knockdown by Si-p300 with no effect on IFI16 and ASC protein levels was observed ( Fig 6D , top three panels , lanes 1–8 ) . The co-IP studies of anti-acetylated lysine antibodies and IFI16 demonstrated the abrogation of IFI16 acetylation in Si-p300 KSHV infected cells while Si-control infected cells showed robust IFI16 acetylation ( Fig 6D , fourth panel , lanes 1–8 ) . Similarly , the caspase-1 and IFI16 association was detected in the control group but was abrogated in p300 knockdown infected cells , and caspase-1 was pulled down in all the tested groups ( Fig 6D , fourth and fifth panels , lanes 1–8 ) . These results further validated our findings with C-646 . In PLA studies with anti-IFI16 and anti-acetylated antibodies , very few acetylated IFI16 PLA dots were observed in the nucleus of control or p300 knockdown infected cells ( Fig 6E , top panels , yellow arrows ) . In Si-control infected cells ( 24 h p . i . ) , a high number of acetylated IFI16 dots were visible in the nucleus and in the cytoplasm ( Fig 6E , lower left panel ) , while only a few dots , as in uninfected cells , were detectable in the p300 knockdown KSHV infected cells ( Fig 6E , lower right panel ) . IFI16 was solely in the nucleus of uninfected cells by total IFI16 PLA ( Fig 6F , top panels , yellow arrows ) . Similar to the acetylated IFI16 , total IFI16 was found in both the nucleus and cytoplasm of Si-control infected cells , while p300 knocked down infected cells showed only nuclear IFI16 ( Fig 6F , lower panels ) . When PLA was performed using anti-IFI16 and anti-ASC antibodies , the red dots representing the IFI16 and ASC association were in both the nucleus and the cytoplasm of Si-control KSHV infected cells ( Fig 6G , lower left panel , white and yellow arrows ) . In contrast , the IFI16 and ASC association was completely abrogated in p300 knockdown infected cells ( Fig 6G , lower right panel ) . These results further strengthened the finding that acetylation is required for the cytoplasmic redistribution of IFI16 and p300 is responsible for the acetylation of IFI16 . Besides inflammasome induction in KSHV , EBV and HSV-1 infected cells , IFI16 has also been shown to be involved in the induction of IFN-β gene through its cytoplasmic activation of the STING molecule leading into phosphorylation of the transcription factor IRF-3 which subsequently translocates into the nucleus to stimulate the IFN-β gene promoter [21] . KSHV infection induces only a moderate IFN-β response early during de novo infection and early lytic and latent gene products inhibit this response at later times of infection [17] , and the role of IFI16 in IFN-β production during KSHV infection is not defined . When we analyzed the role of IFI16 and its acetylation in IFN-β production , we detected IFN-β in the supernatants of KSHV infected HMVEC-d cells at 6 h p . i . , which was significantly reduced by >4 fold by C-646 treatment ( Fig 7A ) . A significant level of phosphorylated IRF-3 detected in the nucleus at 6 h p . i . was reduced in C-646 treated cells ( Fig 7B and 7C ) . Immunoprecipitation with anti-acetylated lysine antibody followed by IFI16 WB revealed the presence of acetylated IFI16 from 30 min to 24 h p . i . in KSHV infected cells , which was abolished by C-646 treatment ( Fig 7D , top panel , lanes 1–8 ) . IP reactions with anti-STING antibodies demonstrated the increased IFI16-STING interaction from 30 min to 6 h p . i . and its decrease at 24 h p . i . , which was abolished by C-646 ( Fig 7D , second panel , lanes 1–8 ) . Similarly , the levels of pIRF-3 increased in untreated KSHV infected cells which were abolished in C-646 treated infected cells ( Fig 7D , fourth panel , lanes 1–6 ) . Next , we knocked down STING in HMVEC-d cells to determine whether IFI16 acetylation is upstream or downstream to STING activation . Efficient knockdown was achieved by electroporation using STING specific Si-RNA ( Fig 7E , top panel ) . KSHV infection was not affected under these conditions as shown by the increased IFI16 acetylation which was not affected by STING knockdown ( Fig 7E , second panel ) which suggested that IFI16 acetylation is upstream to STING activation . Control tubulin protein was acetylated in uninfected and infected cells , and an equal amount of IFI16 was pulled down in all groups ( Fig 7E , fourth panel ) . IRF-3 was phosphorylated post-KSHV infection which was hampered in STING knockdown cells; however , total IRF-3 was detected in equal amounts in all the groups and results with tubulin showed equal loading ( Fig 7E , last three panels ) . An increased level of IFN-β was observed in the supernatants of HMVEC-d cells infected with KSHV which was significantly reduced by STING knockdown ( Fig 7F ) . These studies demonstrated that acetylation during KSHV infection induced IFI16 acetylation is required for its cytoplasmic interaction with STING , pIRF-3 induction , and IFN-β production , IFI16 acetylation is upstream to STING activation and STING does not play any role in IFI16 acetylation . We and others have shown that HSV-1 infection of HFF cells also induced the IFN-β gene and secretion of IFN-β which was dependent upon IFI16 and IRF-3 [16 , 22] . We utilized C-646 to determine whether IFI16 acetylation has any role in IFN-β production during HSV-1 infection . C-646 did not show any cytotoxic effects on HFF cells nor did it affect the infectivity of HSV-1 ( S9A and S9B Fig ) . At 30 min post HSV-1 infection , 20 and 16 pg/ml of IFN-β was detected in untreated and C-646 treated supernatants , respectively ( Fig 8A ) . At 6 h p . i . , 317±16 . 5 pg/ml of IFN-β was detected in untreated cells whereas significant ( >67%; p<0 . 001 ) inhibition of IFN-β production was observed in the C-646 treated cells ( 107±19 . 4 pg/ml; Fig 8A ) . When we examined the phosphorylation of IRF-3 by IFA , compared to the uninfected cells , at 6 h p . i . , appreciable levels of phosphorylated IRF-3 were detected in the nucleus and in the cytoplasm ( Fig 8B , third panel ) . In contrast , C-646 treatment prior to infection reduced these levels especially in the nucleus ( Fig 8B , fourth panel , and 8C ) . In PLA studies , similar to KSHV infected HMVEC-d cells , we observed the cytoplasmic redistribution of acetylated IFI16 in HSV-1 infected HFF cells ( 6 h p . i . ) which was inhibited by C-646 ( Fig 8D ) . IFI16 , which was predominantly nuclear in the uninfected HFF cells , was detected in the cytoplasm of HSV-1 infected cells which was abrogated by C-646 ( Fig 8E ) . The observed reduction in the total as well as acetylated IFI16 levels is probably due to the degradation of IFI16 by HSV-1 via its ICPO protein [14] . When the whole cell lysates in the presence or absence of C-646 were IP-ed with anti-acetylated lysine antibody and WB for IFI16 and IRF-3 , acetylation of IFI16 was observed as early as 30 min p . i . , which was abolished by C-646 ( Fig 8F , top panel , lanes 1–6 ) . Acetylation of IRF-3 was not observed ( Fig 8F , second panel ) . In IP-reactions with anti-STING antibodies , increased levels of IFI16 and IRF-3 were detected at 30 min and 6 h p . i . which demonstrated that IFI16 interacts with STING and STING interacts with IRF-3 ( Fig 8F , third and fourth panels ) . These interactions were abrogated by C-646 ( Fig 8F , third and fourth panels , lanes 4–6 ) . The level of pIRF-3 increased in untreated HSV-1 infected cells , whereas it was absent in C-646 treated infected cells ( Fig 8F , sixth panel , lanes 1–6 ) . As expected , IFI16 levels decreased at 6 h p . i . , and in contrast , the IFI16 level was unchanged with C-646 which further suggested that acetylation might be facilitating the stability of IFI16 . Similar to KSHV infected cells , IFI16 acetylation was not affected by STING knockdown during HSV-1 infection ( Fig 8G , lanes 1–6 ) . Equal levels of IFI16 was pulled down in both Si-control and in STING knockdown HSV-1 infected cells , and IRF-3 was activated in Si-control HSV-1 infected cells and not in STING knockdown cells ( Fig 8G , lanes 1–6 ) . HSV-1 infection induced IFN-β production was hampered in STING knockdown cells ( Fig 8H ) . Together , these results demonstrated that as in KSHV infected cells , IFI16 acetylation and its translocation to the cytoplasm in HSV-1 infected cells is also critical for its interaction with STING in the cytoplasm , subsequent STING interaction with IRF-3 , phosphorylation of IRF-3 , and nuclear translocation of pIRF-3 leading into IFN-β production . Since recognition of KSHV , HSV-1 and EBV genome by IFI16 in the nucleus of infected cells leads to inflammasome activation [11 , 13 , 14] , we determined whether acetylation of IFI16 is required for its ability to sense the viral genome . Cells were infected with BrdU-KSHV for 6 h in the presence or absence of C-646 , IFA was performed for BrdU followed by PLA using anti-IFI16 mouse and rabbit antibodies ( Fig 9A ) . IFI16 was mostly nuclear in the uninfected cells . At 6 h p . i . , we observed the appreciable colocalization of IFI16 with KSHV genome in the nucleus of both untreated as well as C-646 treated HMVEC-d cells ( Fig 9A , enlarged panels ) . As before , we observed IFI16 redistribution in the cytoplasm which was absent in C-646 treated cells ( Fig 9A ) . Increased associations of acetylated IFI16 with BrdU-KSHV were observed in untreated cells ( Fig 9B , enlarged panels , and white arrows ) which were completely abrogated by C-646 ( Fig 9B , lower enlarged panel , and 9D ) . The IFI16-KSHV genome colocalization spots in untreated and C-646 treated cells were similar and the difference was not statistically significant ( Fig 9C ) . Interestingly , the levels of acetylated IFI16 molecules associated with BrdU-KSHV were about 50% less than that of the total IFI16 associated with viral genome ( Fig 9C and 9D ) . To confirm the direct association of IFI16 with KSHV genome , we performed PLA and chromatin immunoprecipitation ( ChIP ) assays . To detect the direct binding of IFI16 with KSHV genome , we infected HMVEC-d cells with KSHV with BrdU labeled genome and performed the PLA using anti-BrdU and anti-IFI16 antibodies as this will give signal only when KSHV genome and IFI16 interact and are at close proximity ( <40 nm ) . In the PLA reactions , we observed that the number of IFI16-KSHV genome colocalization spots were similar in both the untreated as well as C-646 treated KSHV infected cells ( Fig 9E , white arrows , and 9F ) . These results further corroborated the Fig 9A results and demonstrated that IFI16 acetylation does not play any role in viral genome recognition . Similar results were also observed in HFF cells infected with BrdU genome labeled HSV-1 ( S9C and S9D Fig ) . We carried out the ChIP assay of KSHV infected BCBL-1 cells with and without C-646 treatment by pulling down the DNA associated with IFI16 and performed qPCR using primers for two different locations of KSHV and with a control GAPDH primer ( Fig 9G ) . We did not observe any significant changes in the binding of IFI16 with KSHV genome by C-646 treatment ( Fig 9G ) . These results suggested that a ) IFI16 directly associates with KSHV and HSV-1 genomes , b ) the acetylation of IFI16 is not required for genome recognition , c ) IFI16 acetylation occurs as a dynamic post-genome recognition event , and d ) post-acetylation , IFI16 probably moves away from the genome for the formation of its complexes and eventually leading to its cytoplasmic translocation .
IFI16 , a member of the ALR family , has emerged as a critical sensor against both nuclear and cytoplasmic DNA with pivotal roles in inflammasome activation and IFN production [11 , 21] . However , how the inflammasome formed as a consequence of recognition of herpesviral genomes in the nucleus by IFI16 , followed by cytoplasmic accumulation of the IFI16-ASC complex , and how HSV-1 and KSHV genome recognition in the nucleus via IFI16 lead to STING-IRF-3 activation in the cytoplasm and subsequent IFN-β production were not known . Our comprehensive studies for the first time demonstrate that acetylation of IFI16 after recognizing the viral genome occurs as a dynamic post-genome recognition event that is common to the IFI16-mediated innate responses of inflammasome induction and IFN-β production during herpesvirus infections . Several molecular mechanistic steps of nuclear innate sensing by IFI16 are revealed here ( Fig 10 ) . The first step is the recognition of nuclear foreign herpes viral genomes by IFI16 which is independent of acetylation and IFI16 interaction with ASC or STING . This is followed by IFI16’s association with p300 which mediates the acetylation of IFI16 . This is a key molecular step common to both of the IFI16 mediated innate responses of inflammasome induction and IFN-β production as IFI16’s acetylation is essential for its interaction with ASC leading into procaspase-1 interaction and activation in the nucleus , interaction with RanGTPase , cytoplasmic translocation and IL-1β induction during KSHV , EBV and HSV-1 infection . Cytoplasmic translocation of acetylated IFI16 is also critical for the activation of STING resulting in the phosphorylation of IRF-3 and IFN-β production ( Fig 10 ) . Crystal structures of overexpressed IFI16 proteins suggest that IFI16 binds to the sugar-phosphate backbone of dsDNA in a non-sequence specific manner with more affinity to superhelix and cruciform DNA [23 , 24] . Herpesviral genomes enter the nucleus as a linear , naked dsDNA with nicks and breaks and undergo rapid circularization and chromatinization [25] . Our studies demonstrating that IFI16 recognized the KSHV genome soon after its entry into the nucleus coupled with the fact that this occurs in the absence of acetylation suggests that IFI16 has evolved for rapid recognition of incoming foreign DNA ( Fig 10 ) . Studies with overexpressed proteins suggest that DNA sensing induces filamentous clusters of IFI16 due to homotypic PYD-PYD interactions and cooperative DNA binding that might amplify signals , stabilize IFI16-dsDNA complexes and could act as danger signal [26] . Our studies show such DNA recognition by IFI16 initiates its acetylation process which is essential for the innate immune functions of both inflammasome and interferon responses executed in the cytoplasm and nucleus . Colocalization of reduced levels of acetylated IFI16 with viral genomes ( Fig 9 ) compared to the non-acetylated IFI16 levels suggest that acetylation probably changes the affinity and structure of IFI16 resulting in a dynamic post-genome recognition event of IFI16’s disassociation from the DNA to facilitate its interaction with other proteins and transport into the cytoplasm in a continuous fashion with genomes always occupied with another IFI16 molecule as shown in the KSHV and EBV latently infected cells . This scenario is also supported by observations such as the acetylation of histone prompts its structural extension and charge neutralization resulting in the weakening of DNA-histone interaction [27] , and acetylation of KSHV LANA-1 resulting in its dissociation from KSHV genome [28] . Our studies show that acetylation is also critical for IFI16’s transport and interaction with STING , and subsequent IFN-β production in both HSV-1 and KSHV infected cells . Together with our earlier studies demonstrating that ASC is not required for IFN-β production [21 , 22] and the absence of IFII6-ASC-procaspase- inflammasome formation and the translocation of acetylated IFI16 in ASC knockdown cells shown here suggested that acetylated IFI16 , either alone or in combination with other yet to be identified protein ( s ) , is also relocalized during herpesviral infection resulting in the interaction with STING . Further studies determining whether IFI16 interacts with STING alone or in association with another protein ( s ) are in progress . KSHV infection of a PMA stimulated human monocytic THP-1 cell line has been shown to result in IL-1β and IFN-β production by a pathway that is independent of IFI16 [29] . This discrepancy may be due to the fact that KSHV may be undergoing abortive infection in the PMA stimulated cells as has been shown for HSV-1 in these cells [30] and DNA released from the lysosomes is probably recognized by AIM2 , c-GAS , and others to stimulate the IL-1β and interferon responses . In contrast , during in vitro infection of permissive cells , viral DNA from the capsid enters the IFI16 rich nucleus resulting in the consequences presented by our studies . Besides its role in inflammasome and interferon induction , IFI16 is also shown to be a transcriptional modulator of normal cells and the mechanisms are poorly defined [19] . Detection of a basal level of IFI16-p300 interaction and acetylated IFI16 in uninfected cells suggest that they may have roles in other cellular functions such as cell cycle regulation and transcription modulation . Increased IFI16-p300 interaction in infected cells suggests that a dynamic process is initiated; however , why the IFI16-p300 interaction increases in the presence of herpesviral DNA and whether IFI16 recruits p300 directly or via its interaction with other proteins needs to be evaluated further . The p300 HAT assay and HDAC assay performed with nuclear and cytoplasmic fractions of KSHV infected HMVEC-d cells and treatment with their respective specific inhibitors revealed the increased p300 activity in the nucleus and not in the cytoplasm , and thus supporting our conclusion that p300 acetylates the IFI16 in the nucleus after viral genome entry into the nucleus ( Fig 3B ) . Simultaneously , the increased activity of HDAC , further demonstrates that the increase in acetylation was not due to decreased activity of HDACs but due to p300 ( Fig 3C ) . Our recent studies and others suggested that IFI16 promoted the addition of repressive heterochromatin markers and reduced the active euchromatin markers on HSV-1 gene promoters resulting in the reduced binding of transcription factors and RNA pol II [22] . Whether the regulatory functions of these genes are independent or dependent on the acetylation of IFI16 needs to be determined which is beyond the scope of the present studies . Increased IFI16 oligomerization in KSHV infected cells due to acetylation suggests that acetylation mediated structural changes in IFI16 probably favors its increased binding with multiple ASC molecules leading into inflammasome assembly . Similarly , ligand mediated NLRC4 phosphorylation has been shown to be crucial for inflammasome activation [31] . In addition , ASC phosphorylation at the CARD domain has been shown to be critical for speck like aggregation and for NLRP3 and AIM2 mediated inflammasomes activation [32] . Though IFI16 has also been shown to be phosphorylated by pUL97 of HCMV that relocalizes IFI16 to the cytoplasm [33] , its role in the context of innate immunity has not been evaluated . The Li et al . , [15] studies with total cell extracts from human CEM-T lymphoblast-like cells identified six phosphorylation and nine acetylation sites on endogenous IFI16 . Which of these sites undergo modifications during the recognition of nuclear viral genomes needs to be examined further and is beyond the scope of the present study . In addition , using uninfected U2OS transfected with DNA , Li et al . , [15] demonstrated that acetylation at the NLS motifs of IFI16 results in the cytoplasmic retention of newly synthesized IFI16 by inhibiting nuclear import , and p300 regulated the cytoplasmic IFI16 acetylation during transfection of DNA . As the NLS motif is essential for IFI16 to enter the nucleus , studies with NLS mutants were not possible in our experimental approaches since these mutants IFI16 will stay in the cytoplasm and will not detect the herpes virus genome . Further understanding of IFI16’s modifications will shed additional light on its role in host innate responses as well as its cell cycle and transcription regulatory functions . In summary , our studies demonstrate that the post-genome recognition event of IFI16’s acetylation by histone acetyltransferase p300 is required for the IFI16-mediated innate immune responses of inflammasome induction , IL-1β and interferon-β production during herpesvirus infections .
HMVEC-d and HFF cells ( Clonetics , Walkersville , MD ) , TIVE , TIVE-LTC , BCBL-1 , BJAB-KSHV , Raji , LCL , BJAB , and Ramos cells were grown as described before ( 11 , 12 , 13 , 14 ) . Cells were routinely tested for mycoplasma and only mycoplasma free cells were used for experiments . Protein A-Sepharose and Protein G-Sepharose CL-4B Fast Flow beads were from GE Healthcare Bio-Sciences Corp . , Piscataway , NJ . Cyto Nuclear extract kit was from Active Motif , Carlsbad , CA . Trichostatin A ( TSA ) , Leptomycin B ( LPT ) , and nicotinamide were from Sigma-Aldrich . The CytoTox 96 non-radioactive cytotoxicity kit was from Promega , Madison , WI . SlowFade Gold Antifade reagent with DAPI was from Life Technologies . Verikine human IFN-β ELISA kit was from PBL Assay Science , Piscataway Township , NJ . IL-1β ELISA kit was from RayBiotech , Inc . Norcross , GA . The FLICA 660 Caspase-1 Assay kit was from Immunochemistry Technologies , Bloomington , MN . P300 and HDAC activity assay kits were from BioVision Inc . , Milpitas , CA . Mouse monoclonal anti-IFI16 , rabbit polyclonal anti-p300 , mouse monoclonal anti-IL-33 and rat polyclonal anti-BrdU antibodies were from Santa Cruz Biotechnology Inc . , Santa Cruz , CA . Rabbit anti-BrdU antibody was from Rockland Inc . , Gilbertsville , PA . Mouse monoclonal anti-β-actin and tubulin antibodies plus rabbit anti-human IFI16 antibodies were from Sigma-Aldrich . Mouse monoclonal anti-human IL-1β and caspase-1 antibodies were from R&D Systems , Minneapolis , MN , and Invitrogen , Carlsbad , CA , respectively . Goat polyclonal antibody against human ASC was from RayBiotech . Mouse monoclonal antibody against ASC was from MBL International , Woburn , MA . Mouse anti-human TATA binding protein ( TBP ) , rabbit anti-human Ran and mouse anti-IRF-3 antibodies were from Abcam Inc . , Cambridge , MA . Rabbit anti-cyclin B1 , rabbit monoclonal anti-STING , -p-IRF-3 , -histone H2B and H3 antibodies were from CST , Danvers , MA . Anti-rabbit , goat and mouse antibodies linked to horseradish peroxidase , Alexa Fluor-488 , -594 and -647 were from KPL Inc . , Gaithersburg , MD , or Molecular Probes , Eugene , OR . Anti-Mouse IgG ( heavy-chain spcificity ) -HRP conjugate goat antibody was from Alpha Diagnostics Intl . Inc . San Antonio , TX . Anti-mouse tagged with IR Dye 680RD secondary antibodies were from LI COR Biotechnology , Lincoln , NE . Induction of the lytic cycle in BCBL-1 cells by phorbol ester , supernatant collection , and virus purification were described previously [11 , 18] . For generating 5-bromo-2-deoxyuridine ( BrdU ) incorporated KSHV genome , BrdU labeling reagent ( Life Technologies ) was added to the culture medium in a 1:100 ( v:v ) ratio ( from the supplied stock ) [34] . KSHV DNA was extracted , copy numbers quantitated by real-time DNA-PCR , and infection was done with 30 genome copies/cell [11] . HMVEC-d or HFF cells pre-starved for 2 h in the presence or absence of inhibitors such as C-646 , leptomycin or cycloheximide , washed , left uninfected or infected with 30 DNA copies/cell in serum free medium for different time points , washed and incubated in complete medium in the presence or absence of inhibitors for different time periods . KSHV positive BCBL-1 , BJAB-KSHV and TIVE-LTC cells and uninfected BJAB and TIVE cells were incubated with inhibitors for different time points . The KOS strain of HSV-1 was produced and titer determined by plaque assay on Vero cells as described [14] . To generate BrdU labelled genome HSV-1 , we added BrdU labeling reagent ( Life Technologies ) to the culture medium at 8 h , 24 h and 48 h post infection in a 1:100 ( v:v ) ratio ( from the supplied stock ) . HFF cells were starved for 2 h in the presence or absence of C-646 , washed and infected with HSV-1 at a multiplicity of infection ( MOI ) of 1 PFU/cell ( ~25 genome copies/cell ) in serum-free DMEM with or without C-646 for different times , washed with PBS , and incubated in DMEM supplemented with 2% FBS for different time points . A non-radioactive cytotoxicity assay was performed according to the manufacturer’s protocol ( Promega ) to evaluate the various inhibitors used in this study . Briefly , HMVEC-d , BCBL-1 , TIVE , TIVE-LTC and HFF cells cultured in 12 well plates were incubated with DMSO or varying concentrations of C646 in DMSO for 4 and 24 h in their respective complete media . 100 μl of culture medium of each group was taken carefully and treated with 10 μl of lysis solution ( Promega ) and incubated for 45–60 min in a 37°C incubator with 5% CO2 . Plates were then centrifuged at 1 , 000 RPM for 3 min and 50 ul samples were transferred carefully into separate 96 well plates with 3 positive control wells of LDH ( supplied in the kit ) . 50 μl of substrate was added to each well of the plate , incubated at RT for 15–30 min , and read at 490 nm using an ELISA reader . The positive control was considered as 100% cytotoxic . HMVEC-d were starved for 2 h with or without C-646 and either left uninfected or infected with KSHV ( 30 DNA copies/cell ) at 37°C for 2 h . These cells were washed , treated with trypsin-EDTA to remove non-internalized virus , and incubated for varying times of infection [18] . Nuclei were isolated using a Nuclei EZ Prep isolation kit ( Sigma ) according to the manufacturer’s instructions . Briefly , cells were lysed on ice for 5 min with a mild lysis buffer ( Sigma ) , and nuclei were concentrated by centrifugation at 500xg for 5 min . Cytoskeletal components loosely bound to the nuclei were removed from the nuclear pellet by a repeat of the lysis and centrifugation procedures as described previously [11] . DNA was extracted from isolated nuclei using a DNeasy kit ( Qiagen , Germantown , MD ) . Internalized nuclear KSHV DNA was quantitated by amplification of the ORF73 gene by real-time DNA PCR [2] . The KSHV ORF73 gene cloned in the pGEM-T vector ( Promega ) was used for the external standard . The CT values were used to generate the standard curve and to calculate the relative copy numbers of viral DNA in the samples . Total RNAs from KSHV infected or uninfected HMVEC-d cells in the presence or absence of inhibitors were prepared using an RNeasy kit ( Qiagen ) . To quantitate viral gene expression , total RNA was subjected to real-time RT-PCR using ORF73 gene-specific primers and TaqMan probes . A standard curve using the CT values of different dilutions of in vitro-transcribed transcripts was used to calculate relative copy numbers of the transcripts . These values were normalized to those for GAPDH ( glyceraldehyde- 3-phosphate dehydrogenase ) control reactions . To obtain p values between DMSO , C-646 , Leptomycin-B and cycloheximide treated cells , an unpaired Student’s t test was used . For de novo infection , peripheral blood mononuclear cells ( PBMCs ) were obtained from the University of Pennsylvania CFAR Immunology Core , and 1X107 PBMCs were either left uninfected or infected by KSHV or EBV as previously described [13] . Briefly , PBMCs were infected with KSHV or EBV in 1 ml of RPMI 1640 medium supplemented with 10% FBS and 5 ng/ml of polybrene ( Sigma-Aldrich ) , incubated for 4 h at 37°C ( time point 0 ) , and infected and uninfected cells were centrifuged at 1 , 200g for 5 min . Cells were washed twice with RPMI medium , resuspended and cultured in six-well plates at 37°C in fresh RPMI medium with 10% FBS . At 24 h p . i . , the cells were washed twice with 1X PBS and spotted on slides . HMVEC-d cells pre-starved for 2 h in the presence or absence of C-646 were washed , uninfected or KSHV infected for 2 h , washed , and incubated with complete medium with or without C-646 for 24 h . BJAB and BCBL-1 cells were treated with C-646 for 24 h . These cells were lysed in HEPES-lysis buffer ( 100 mM NaCl , 40 mM HEPES [pH 7 . 5] , 05% ( v/v ) glycerol , 0 . 1% ( v/v ) Nonidet P-40 [NP-40] supplemented with PIC ) . The lysates were cross-linked with 5 mM glutaraldehyde for 10 min , reaction terminated with the addition of 10 μl of 1M Tris-HCL ( pH 8 . 0 ) , samples boiled in SDS buffer , and analyzed by western blot to detect IFI16 oligomerization . All Si-RNA oligonucleotides for ASC and p300 were from Santa Cruz Biotechnology , Inc . STING Si-RNA ( smart pool: Si genome TMEM173 , cat . No . M-024333-00-0010 ) were from GE-Dharmacon ( Fisher-Scientific , Pittsburgh , PA ) Primary HMVEC-d cells were transfected with Si-RNA using a Neon transfection system ( Invitrogen ) according to the manufacturer’s instructions . Briefly , subconfluent cells were detached from the culture flasks , washed once with PBS and resuspended in buffer R ( Invitrogen ) at a density of 1X107 cells/ml . 10 μl of the cell suspension was gently mixed with control Si-RNA or 100 pmol of target specific Si-RNA and then microporated at room temperature using a single pulse of 1 , 350 V for 30 ms . After microporation , cells were distributed into pre-warmed complete medium and placed at 37°C in a humidified 5% CO2 atmosphere . At 48 h post-transfection , cells were infected with KSHV as described earlier and incubated for 24 h , whole cell lysates using NETN or cytoplasmic/nuclear extract buffers were isolated , knockdown efficiency evaluated by western blotting , and then subjected to co-IP and western blotting . KSHV infection-induced protein acetylation and other protein-protein interactions were evaluated by co-IP experiments using equal amounts of WCL as well as cytoplasmic and nuclear lysates . The lysates were first incubated for 2 h with 15 μl of Protein A/G sepharose beads and then the pre-cleared lysates incubated for 2 h with immunoprecipitating antibody ( anti-acetylated lysine , -IFI16 , -ASC , -caspase-1 , -p300 antibodies ) at 4°C . The immune complexes were captured using 15 μl of Protein A/G-Sepharose beads , washed 4 times with lysis buffer , 3 times in PBS , boiled with SDS-PAGE sample buffer , resolved by 10% SDS-PAGE , and subjected to western blotting . HMVEC-d cells grown on fibronectin-coated 8 well chamber glass slides for 48 h were serum-starved in the presence or absence of inhibitors for 2 h , washed and then either left uninfected or infected with KSHV ( 30 DNA copies/cell ) for 2 h . Cells were washed with PBS , incubated in complete medium for various time points , washed , fixed in 4% paraformaldehyde for 10 min and permeabilized with 0 . 2% Triton X-100 for 5 min . BJAB , BCBL-1 and BJAB-KSHV suspension cells treated or untreated with C-646 for 24 h were fixed and permeabilized with pre-chilled acetone . Cells were washed and blocked with Image-iT FX signal enhancer ( Invitrogen ) for 20 min at RT , and incubated with specific antibodies diluted in 2% BSA for 2 h at 37°C . After washing , cells were incubated with Alexa-Fluor conjugated appropriate secondary antibodies for 1 h at 37°C , washed , mounted in DAPI , imaged with Nikon Eclipse 80i fluorescence microscope and analyzed by Nikon Elements software . A DuoLink PLA kit from Sigma-Aldrich was used to detect protein–protein interactions as per manufacturer’s protocol . Cells were infected with KSHV ( 30 DNA copies/cell ) or HSV-1 ( 1 PFU/cell; ~25 genome copies/cell ) , fixed and permeabilized as described in the IFA section and blocked with DuoLink blocking buffer for 30 min at 37°C . These cells were incubated with target specific primary antibodies diluted in DuoLink dilution buffer . After washing , the cells were incubated for another 1 h at 37°C with species specific PLA probes ( PLUS and MINUS ) under hybridization conditions and in the presence of 2 additional oligonucleotides to facilitate hybridization of PLA probes if they were in close proximity ( <40 nm ) . A ligation mixture and ligase were then added to join the two hybridized oligonucleotides to form a closed circle . Amplification solution was added to generate a concatemeric product extending from the oligonucleotide arm of the PLA probe . Finally , a detection solution consisting of fluorescently labeled oligonucleotides was added , and the labeled oligonucleotides were hybridized to the concatemeric products . The signal was detected as a distinct fluorescent dot in the Texas red or FITC green channel and analyzed by fluorescence microscopy . Negative controls consisted of samples treated as described but with only secondary antibodies . In some experiments , BrdU staining was performed before PLA to detect viral genome in the infected cells . BJAB and BCBL-1 cells were subjected to the FLICA 660-YVAD-FMK Caspase-1 assay to detect the active caspase-1 in C-646 treated or untreated cells . The BCBL-1 cells were incubated with C-646 ( p300 inhibitor ) overnight , washed and FLICA 660-YVAD-FMK caspase-1 detection reagent was applied for 1 h to stain the cells with active caspase-1 in untreated or C-646 treated cells . The cell permeable FLICA 660-YVAD-FMK caspase-1 detection reagent efficiently diffuses into cells and irreversibly binds to activated caspase-1 enzymes . Cells without active caspase-1 have a non-fluorescent status after the wash step . The cells were fixed into the fixation media provided by the manufacturer . The cells were washed to remove the unbound FLICA 660 and subjected to flow cytometry ( LSRII , BD Biosciences ) at the Flow Cytometry Facility at Rosalind Franklin University of Medicine and Science . The whole cell protein lysates ( WCL ) from uninfected and KSHV infected cells were prepared using NETN lysis buffer ( 100 mM NaCl , 20 mM Tris-HCl [pH 8 . 0] , 0 . 5 mM EDTA , 0 . 5% ( v/v ) Nonidet P-40 [NP-40] ) supplemented with 10 μM TSA , 5 mM nicotinamide and protease inhibitor cocktail . Cells were incubated on a rocker at 4°C for 15 min and sonicated at 40 amplitude three times with pulses of 15 seconds on and 10 seconds off . Lysates were clarified by centrifugation for 15 min at 4°C at 15000 x g . The nuclear and cytoplasmic extracts were prepared following the manufacturer’s procedure ( Active Motif , Carlsbad , CA ) . Equal amounts of samples were resolved by 10–20% SDS-PAGE , subjected to western blot , immunoreactive bands developed by enhanced chemiluminescence reaction ( NEN Life Sciences Products , Boston , MA ) , and the bands scanned and quantitated using an AlphaImager ( Alpha Innotech Corporation , San Leonardo , CA ) . The bands were scanned and quantitated using FluorChemFC2 software and an AlphaImager system ( Alpha Innotech Corporation , San Leonardo , CA ) . To detect the tubulin in IP samples , secondary anti-mouse ( IRdye 680 labelled ) antibody was used and immunoblots were visualized by using the LI-COR Odyssey system The HFF or HMVEC-d cells in 6 well plates were starved and infected with HSV-1 or KSHV , respectively , for 30 min or 2 h with or without C-646 and incubated for 6 h . Culture supernatants were centrifuged and subjected to ELISA for detection of IFN-β . HMVEC-d cells were starved for 2 h and infected with KSHV-1 with or without C-646 for 2 h , washed and incubated for 24 h and culture supernatant was used to detect IL-1β by ELISA performed as per manufacturer’s instructions . Briefly , the culture supernatants and standards were incubated in the pre-coated wells for 1 h , washed with the washing buffer provided in the kit and probed with IFN-β antibody for 1 h . These wells were washed , incubated with HRP tagged antibodies for 1 h , washed , incubated with substrate ( TMB ) for 15 min and the reaction was terminated with a stop solution . After 5 min , readings were taken at 450nm and calculations done using a standard curve . The p300 histone acetyltransferase activity was measured using the p300 HAT fluorometric assay kit from Biovision ( Mountain View , CA ) as per the manufacturer’s instructions with slight modifications . Briefly , HMVEC-d cells were either left uninfected or infected with KSHV for 24 h in the presence or absence of C-646 , and cytoplasmic and nuclear fractions were isolated . From each group 5 μg protein was incubated with the p300 substrate ( H3 peptide and acetyl CoA ) at 30°C for 30 min in a 96 well plate . The reaction was stopped by adding pre-chilled isopropyl alcohol followed by addition of thiol detecting probe and incubated at room temperature for 15 min . The plate was read for fluorescence at Ex/Em = 392/482 nm in a plate reader ( BioTek , Winooski , VT ) . The HDAC activity was measured using a fluorometric assay kit from BioVision ( Mountain View , CA ) as per the manufacturer’s instructions . Protein samples were prepared as in the p300 activity experiment of HMVEC-d cells infected with KSHV for 24 h in the presence or absence of Tricostatin-A ( TSA ) . From each group 10 μg of nuclear and cytoplasmic proteins were mixed in HDAC assay buffer , the fluorometric substrate was added to each well of the 96 well plate and incubated at 37°C for 30 min . The reaction was stopped by adding Lysine developer and mixed well followed by incubation at 37°C for 30 minutes . The samples were analyzed in a fluorescence plate reader ( Ex/Em = 350–380/440–460 nm ) . To detect the direct association of IFI16 with viral DNA , Chromatin Immunoprecipitation ( ChIP ) assay was performed as per manufacturer’s instructions . Briefly , untreated BCBL-1 cells ( 3 x 107 ) or cells treated with 1μM C-646 for 24 h were fixed with 1% ( v/v ) methanol-free formaldehyde in fixing buffer for 5 min , crosslinking was quenched using quenching buffer for 5 min at RT , cells were washed twice with cold PBS then incubated in lysis buffer to break the cell membrane . Intact nuclei were collected by centrifugation at 1 , 700 x g for 5 min at 4°C , resuspended in shearing buffer containing protease inhibitors and chromatin shearing performed on an AFA ( Adaptive Focused Acoustics ) ultrasonicator ( Covaris M220 ) . Following chromatin shearing , ChIP was performed as described previously ( 22 ) . Briefly , cellular debris was cleared from the sheared chromatin by centrifugation and the supernatant was incubated overnight at 4°C with 1 . 5 μg of IFI16 antibody . Samples were incubated in ChIP grade Protein G Magnetic Beads for 2 h at 4°C to collect immune complexes and then washed successively with low salt wash buffer ( 0 . 1% SDS; 1% Triton X-100; 2 mM EDTA; 20 mM Tris , [pH 8 . 1]; 150 mM NaCl ) , then high salt wash buffer ( 0 . 1% SDS; 1% Triton X-100; 2 mM EDTA; 20 mM Tris , [pH 8 . 1]; 500 mM NaCl ) , and then LiCl wash buffer ( 0 . 25 M LiCl; 1% NP-40; 1% deoxycholate; 1 mM EDTA; 10 mM Tris , [pH 8 . 1] ) . DNA-protein complexes were eluted in 1% SDS prepared in 0 . 1 M NaHCO3 . Crosslinking was reversed by adding 1 μL RNase A and NaCl ( 0 . 3 M ) and incubating at 65°C for 5 h . Protein was removed by incubating lysate with proteinase K at 55°C for 1 h . Subsequently , DNA was purified using the Wizard SV Genomic DNA Purification System ( Promega ) and resuspended in nuclease-free water . Real-time PCR was performed with the following KSHV genome ( NCBI Reference NC_009333 . 1 ) specific primers , Primer Set 1: CAAGGTTAAAGTGGGTTTGCTG , GGTTATTGGCCGTTTCTGTTTC and Primer Set 2: GCGTAATTACTTCCGAGACTGA , TTAACTCCACTTTGCA CCAAAC . As a cellular control , human positive control primer set GAPDH-2 from Active Motif was used . ChIP results are represented as fold enrichment over IgG control . Results are expressed as means ± SD of at least three independent experiments ( n≥3 ) . The p value was calculated using a Student’s T test . In all tests , p<0 . 05 was considered statistically significant . | Herpesviruses establish a latent infection in the nucleus of specific cells and reactivation results in the nuclear viral dsDNA replication and infectious virus production . Host innate responses are initiated by the presence of viral genomes and their products , and nucleus associated IFI16 protein has recently emerged as an innate DNA sensor regulating inflammatory cytokines and type I interferon ( IFN ) production . IFI16 recognizes the herpesvirus genomes ( KSHV , EBV , and HSV-1 ) in the nucleus resulting in the formation of the IFI16-ASC-Caspase-1 inflammasome complex and IL-1β production . HSV-1 genome recognition by IFI16 in the nucleus also leads to STING activation in the cytoplasm and IFN-β production . However , how IFI16 initiates inflammasome assembly and activates STING in the cytoplasm after nuclear recognition of viral genome are not known . We show that herpesvirus genome recognition in the nucleus by IFI16 leads to interaction with histone acetyltransferase-p300 and IFI16 acetylation which is essential for inflammasome assembly in the nucleus and cytoplasmic translocation , activation of STING in the cytoplasm and IFN-β production . These studies provide insight into a common molecular mechanism for the innate inflammasome assembly and STING activation response pathways that result in IL-1β and IFN-β production , respectively . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Herpesvirus Genome Recognition Induced Acetylation of Nuclear IFI16 Is Essential for Its Cytoplasmic Translocation, Inflammasome and IFN-β Responses |
The p53 tumor suppressor protein plays a critical role in cellular stress and cancer prevention . A number of post-transcriptional regulators , termed microRNAs , are closely connected with the p53-mediated cellular networks . While the molecular interactions among p53 and microRNAs have emerged , a systems-level understanding of the regulatory mechanism and the role of microRNAs-forming feedback loops with the p53 core remains elusive . Here we have identified from literature that there exist three classes of microRNA-mediated feedback loops revolving around p53 , all with the nature of positive feedback coincidentally . To explore the relationship between the cellular performance of p53 with the microRNA feedback pathways , we developed a mathematical model of the core p53-MDM2 module coupled with three microRNA-mediated positive feedback loops involving miR-192 , miR-34a , and miR-29a . Simulations and bifurcation analysis in relationship to extrinsic noise reproduce the oscillatory behavior of p53 under DNA damage in single cells , and notably show that specific microRNA abrogation can disrupt the wild-type cellular phenotype when the ubiquitous cell-to-cell variability is taken into account . To assess these in silico results we conducted microRNA-perturbation experiments in MCF7 breast cancer cells . Time-lapse microscopy of cell-population behavior in response to DNA double-strand breaks , together with image classification of single-cell phenotypes across a population , confirmed that the cellular p53 oscillations are compromised after miR-192 perturbations , matching well with the model predictions . Our study via modeling in combination with quantitative experiments provides new evidence on the role of microRNA-mediated positive feedback loops in conferring robustness to the system performance of stress-induced response of p53 .
Cells depend on complex intracellular signaling to process and react to external stimuli . One prominent type of dynamic response is the periodic accumulation of key transcription factors in the nucleus , where they elicit temporally controlled gene activation [1–4] . The tumor suppressor protein p53 , a pivotal player involved in cancer initiation and prevention [5] , undergoes oscillations in response to extracellular stress signals . Experiments show that transient DNA lesion of double-strand breaks , induced by acute application of γ-irradiation , trigger oscillatory response of the p53 protein and its negative regulator MDM2 [6–8] . At a single-cell level , the oscillation of p53 is undamped and the mean period of the pulses are constant and independent on the damage level [7] . While the cellular function of the oscillatory dynamics of these transcription factors is unclear , investigations have started to probe the significance of the p53 oscillations in inducing downstream effect such as apoptosis . For instance , recent results demonstrate that the dynamical pattern and not the absolute level of p53 protein controls the life-or-death fate decision in response to DNA damage at cellular level , highlighting the role of p53 oscillations in cellular decision making in cancer [9 , 10] . Negative feedback has the potential to generate limit-cycle oscillations and is viewed as a necessary structure for biochemical oscillators [11 , 12] . Indeed there exists a consensus in the literature that the p53-MDM2 negative autoregulatory loop is essential for the stress-induced p53 oscillations [3 , 13] . A number of mathematical models , that typically assume an explicit time delay in the core p53-MDM2 autoregulatory loop , can reproduce the undamped p53 oscillations [14–16] . More generally , coupled negative and positive feedback loops can give rise to oscillatory phenotypes [11 , 17] . The architecture of positive feedback loops , on top of a negative feedback loop , can endow performance properties such as the tunability of frequency , entrainability to cycles , and robustness under molecular noise [17–19] . Indeed , mathematical models can predict sustained oscillations under auxiliary positive feedback loops on p53 [20] , but the general role of positive feedback loops in p53 oscillations remains largely elusive . MicroRNAs are small noncoding RNAs , approximately 22 nucleotides in length that serve as post-transcriptional regulators , and have been shown to regulate the activity of nearly 30% of all protein-coding genes . Intriguingly , a set of recent studies revealed extensive crosstalk between the p53 network and microRNAs [21 , 22] . We have identified that , with respect to the dynamical behavior of the system , several microRNAs form positive feedback loops with p53 , typically through direct or indirect inhibition of MDM2 . In this work , we investigate the role of microRNA-mediated positive feedback loops in regulating the performance of p53 stress network . We first developed a mathematical model of a microRNA-p53-MDM2 network that involves three different microRNAs that form positive feedback loops . The core p53-MDM2 model is based on our previously published work [14 , 16] . We performed simulations and studied the robustness of p53 oscillations under abrogation of microRNA-mediated positive feedback loops . Furthermore , we adopted bifurcation diagrams in order to explore the system behavior under parametric variability in relationship to cellular noise . To experimentally evaluate our in silico predictions , we introduced microRNA inhibitors in a modified breast cancer cell line MCF7 , and performed time-lapse microscopy tracking single-cell p53 dynamics under induced DNA double-stranded breaks . Our experimental quantification , in agreement with modeling analysis , reveal that the three microRNA-mediated positive feedback loops confer different level of control to the robust performance of stress-induced p53 oscillations within a population of cells .
In this work , we seek to elucidate the role of miRNAs in the regulation of the p53 oscillation elicited by the stress signal of cellular DNA damage . Among the signaling regulations of p53 induced by miRNAs , we focus on feedback pathways . Intriguingly , three groups of miRNAs that are identified to be a part of feedback regulations of p53 , form positive feedback loops with the p53 pathway . The three microRNA-mediated positive feedback networks and the associated molecular interactions are described as follows . The miR-192 family , including miR-192 , miR-194 and miR-215 [23] , is directly correlated with p53 protein upregulation [24] and overexpression of these microRNAs elicited dramatic down-regulation of MDM2 at protein and mRNA levels . These findings indicate that , on top of the core autoregulation of p53 through MDM2 , there is a microRNA-mediated autoregulatory loop of p53 , where miR-192 is activated by p53 [25] and in turn inhibits the antagonizing effect of MDM2 [24] . This autoregulatory loop is a positive feedback loop with the feature of double-negative regulation ( Fig 1a ) . The miR-34 family , including miR-34a , miR-34b , and miR-34c , is upregulated by p53 [26 , 27] . Two positive feedback loops between p53 and miR-34a have been reported ( Fig 1b ) . The first loop is through the regulation by the protein named silent information regulator ( SIRT1 ) . Specifically , miR-34a inhibits SIRT1 mRNA translation [26 , 27] . In irradiated cells the SIRT1 protein acts as an antagonist of the post-translational modification of p53 and thus repressing the transcriptional activity of the p53 protein [28] . The second feedback loop is mediated by Yin Yang 1 ( YY1 ) , a ubiquitous transcription factor that negatively regulates p53 , and is directly repressed by miR-34a [29] . The YY1 protein can enhance the degradation of p53 promoted by MDM2 [30] , thereby closing a feedback loop composed of p53 , miR-34a , YY1 and MDM2 . Both of the two regulatory loops are positive feedback with double-negative regulations ( Fig 1b ) . The miR-29 family , including miR-29a , miR-29b , and miR-29c , is upregulated by p53 [31] . The miR-29 family members in turn can enhance p53 activity . For instance , all three miR-29 family members can successfully elevate the phosphorylation level of p53 by repression of Wip1 [31] , a phosphatase of p53 [13] . In addition , miR-29 microRNAs directly suppress CDC42 [32] , a Rho family CTPase , which directly inhibits the protein activity of p53 . Intriguingly , these feedback regulatory pathways are again positive feedback loops in the form of double negative feedback ( Fig 1c ) , and they are closely interlinked with the core p53-MDM2 autoregulation in that Wip1 upregulates MDM2 via inhibiting its degradation [33] ( Fig 1c ) . We first developed a mass-action model that accounts for the core p53-MDM2 autoregulatory network [14 , 16] coupled with all the three families of microRNA-based positive feedback loops at single-cell level ( Fig 2 ) . Each microRNA is modeled by accounting for the mediating microRNA component and its associated target proteins ( i . e . SIRT1 and YY1 for miR-34a , and CDC42 and Wip1 for miR-29a ) ( Fig 3a ) . We assume that the microRNA binds quickly with its target mRNA molecule and dispose the microRNA-mRNA complex into degradation [34] . The active form of ATM , a protein kinase that detects DNA damage , is induced by transient DNA damage signal , following the mathematical formula used in Batchelor et al [35] . The assumptions on the interactions among p53 , MDM2 , microRNAs and intermediate proteins , as well as the ordinary differential equations and parameters of the deterministic single-cell model are included in the Supporting Information ( S1 Text , S1 and S2 Tables ) . Note that our model includes a second negative feedback loop formed by ATM , p53 and Wip1 ( Fig 2 and S1 Text ) , a network structure proposed in previous experimental and computational studies [35 , 36] . As shown in a recent study of NF-kB , another oscillatory transcription factor , a longer negative feedback loop in addition to the core faster negative feedback could provide further system properties , such as better tracking of duration of input signal as well as potential induction of damped oscillations [37] . These behaviors potentially allow for more sophisticated signal coding and processing patterns in cellular stress response than that could be achieved by single negative feedback structure . A simulation of the deterministic model of microRNA-p53-MDM2 network at wild-type condition shows that this system yields oscillations of p53 and MDM2 with period of approximately 5 hours under constant DNA damage stimulus ( Fig 3b ) , equivalent to single-cell response to γ-irradiation or radiomimetic drug observed in our in-house experiments and published experiments [6 , 7 , 38] . We next probed the behavior of p53 in response to DNA damage and the associated role of the microRNAs . More specifically we introduced to the mathematical model inhibitors that modulate the three microRNAs by complexation reactions . We assume that the microRNAs are repressed by ~6-fold after addition of the inhibitors . Simulations of the deterministic model show that , when miR-192 is inhibited , the stress-induced oscillation of p53 is abolished , but when miR-34a or miR-29a is inhibited the oscillation of p53 persists ( Fig 3c ) . These simulations indicate that at single-cell level the p53 oscillatory behavior is more sensitive to the down-regulation of miR-192 than miR-34a and miR-29a . In light of the deterministic simulation results and considering that a prominent feature of the dynamics of a cell population is the cell-to-cell variability , we decided to investigate further the single-cell system behavior under cellular noise . The heterogeneity in a cell population has been widely observed in the experiments of the stress response of p53 and other cellular processes [6 , 7 , 39] . Cellular noise , broadly defined as stochastic fluctuations of molecular processes within and between cells , can be divided into intrinsic and extrinsic noise [40 , 41] . Intrinsic noise refers to random deviation of the molecular processes from their average deterministic kinetics within a cell , mostly due to probabilistic biochemical reactions associated with low copy numbers . Several attempts have been made to use stochastic models to study the noisy single-cell p53 dynamics under the influence of intrinsic noise due to low copy number of reactants [42 , 43] . Nevertheless , the high molecule numbers measured in the p53 network ( 104−105 ) [14 , 44] suggest that the role played by intrinsic noise may not be critical especially in the variable induction of oscillatory and non-oscillatory phenotypes in single cells [6] , as intrinsic noise mostly just results in irregular profiles of a trajectory with high copy numbers [42] . Indeed , a previous study shows that oscillation produced by limit cycle seems to be very robust under intrinsic stochasticity , where the simulated stochastic oscillations persist when the maximum molecule numbers are in the order of hundreds [45] . On the other hand , extrinsic noise generally dominates cellular stochasticity , especially in eukaryotic systems [46–48] , and arises from global factors that impact cell-to-cell variation [49] . Therefore , in this study we focus on analyzing the impact of extrinsic noise on the sustainability of p53 oscillation at single cell level . To this end , deterministic single-cell model with varying model parameters can be used to compute the impact of extrinsic noise [50–52] . In our experimental setup , the perturbations are performed using the inherently “noisy” transient transfections of microRNA inhibitors , resulting to variable down-regulation levels between cells . To probe the effect of microRNA inhibitor copy-number variability we first performed parametric simulations for a wide range of inhibitor concentrations . As illustrated in Fig 3d , the effect of the miR-192 inhibitor is more prominent , leading to gradual collapse of the oscillating p53 behavior at high concentrations . To further investigate the effect of microRNA abrogation under cellular extrinsic noise we performed bifurcation analysis . We assayed model parameters along the microRNA-mediated positive feedback loops that directly affect the transduction of the microRNA perturbation through the network . In particular , we probed 14 parameters under wild-type and the three microRNA-repressed conditions ( S3 Table ) . First , we calculated the bifurcation diagrams of the steady-state p53 concentration versus the 5 association rates between the microRNAs and their target mRNAs for the wild-type case as well as the three perturbed cases with microRNA inhibitors ( Fig 4 ) . The paired dots represent the bounds of p53 oscillation amplitude at steady state and the solid line represents stationary steady state . According to Fig 4 , the miR-192 inhibitor either reduces the range of the oscillatory p53 response , or abolishes p53 oscillation , across different affinities of miR-192 to its target . Note that the p53 response at the nominal parameter set is still within oscillatory region when miR-34a and miR-29a are inhibited , while it falls out of the oscillatory region when miR-192 is repressed . This is consistent with the persistent oscillations in the former two cases and the stationary steady state in the latter case , as shown in Fig 3b . The bifurcation diagrams versus the rest 9 parameters are shown in S1 Fig . It is noteworthy that the experiments by Geva-Zatorsky et al showed that the amplitude of p53 in individual cells is highly variable with a variation up to ~70% . There have been attempts to implement the high variation of p53’s amplitude by theoretical modeling . For instance , Jolma et al assumed that certain rate parameters were allowed to vary randomly and rapidly within a certain range to achieve the variable p53 amplitude [53] . Such method essentially implements the extrinsic noise computationally as explained above . In our model , the variable p53 amplitude can also be induced by extrinsic noise via allowing variations in parameters , whereby the impact of varying parameters on p53 amplitude is demonstrated by the bifurcation plots ( Fig 4 and S1 Fig ) . For instance , varying the value of kon1 alone between [10 , 40] achieves ~65% variation of the p53 amplitude ( see the wild-type case in the plot with respect to kon1 in Fig 4 ) . Also , varying the value of kw alone between [1 , 3] achieves ~53% variation of the p53 amplitude ( see the wild-type case in the plot with respect to kw in S1 Fig ) . Therefore , significant variation in p53 amplitude in our model is attainable by assuming considerable extrinsic noise in the model parameters . The bifurcation diagrams with respect to each of the 14 parameters embedded in the microRNA-based positive feedback loops parameters ( S3 Table ) reveal that for 8 out of the 14 parameters the repression of miR-192 leads to the smallest regions of oscillation compared to those of miR-34a and miR-29 , while for the other 6 parameters the repression of miR-192 completely abolishes the p53 oscillation over the varying ranges ( Fig 4 and S1 Fig ) . These plots indicate that the non-oscillating phenotype of an individual cell can be yielded when certain parameter , due to extrinsic fluctuation , is pushed out of the bounds of its oscillatory regime , thus providing a plausible mechanism underlying the observed heterogeneous behavior of p53 in a cell population . Moreover , if the region of p53 oscillation significantly shrinks , it is more likely for the oscillatory behavior to be ruined by extrinsic noise , and thus the probability of observing non-oscillating single-cell phonotype in a stochastic population should increase . To further quantify the impact of different types of microRNAs on regulating the p53 network , we measured the system’s robustness performance , which is the capability to maintain the oscillatory behavior of p53 in response to DNA damage ( S2 Text ) . A large robustness index defined in S2 Text predicts that the system’s probability of sustaining stable oscillation under stochastic perturbations is relatively high . As a result , for the model under a particular condition the value of its robustness index is positively correlated with the fraction of oscillating cells in a population . The robustness indices confirm that when miR-192 is repressed the oscillatory phenotype is the least robust among the three microRNAs ( S4 Table ) . Based on the model predictions , we infer that the inhibition of miR-192-mediated positive feedback loop would lead to the highest probability of non-oscillating cells across a population due to extrinsic noise . As a summary , the theoretical modeling and analysis show that the robust performance of the p53 stress network is subject to the control of specific microRNA-feedback regulation . To experimentally probe the effect of microRNA abrogation on the p53-MDM2 oscillator we used a breast cancer cell line MCF7 [38] that contains a stably integrated fluorescent reporter Venus fused to the cDNA of p53 under the expression of the metallothionein promoter ( Fig 5a ) . To down-regulate the desired microRNAs we introduced to MCF7 cells synthesized single-stranded RNA molecules that are complementary to the mature microRNA sequence . Prior to performing the microRNA perturbation experiments , we verified the expression of the three selected microRNAs using quantitative PCR ( qPCR ) . We confirmed the expression of miR-29a , miR-192 , and miR-34a as well as efficacy of their inhibitors ( Fig 5b ) ; results from qPCR show between 70% to 80% down-regulation for each microRNA targeted . We then performed two independent time-lapse experiments to test the following five conditions: wild type ( WT ) untransfected cells , negative control with transfection using a synthetic microRNA that does not target the p53-mdm2 core , and the three selected microRNA inhibitors . For the negative control case we transfected the MCF7 cells with a synthetic microRNA ( FF4 ) [54] that does not interfere with the p53-MDM2 core . The oscillations of p53 protein after addition of the microRNA inhibitors and neocarzinostatin ( NCS ) [38] were captured using time-lapse microscopy . The details of the preparation of the wild type and microRNA-perturbed MCF7 cells and the subsequent time-lapse microscopy are described in the Materials and Methods Section . The image data acquired from the time-lapse microscopy were processed using ImageJ [55] and MATLAB . First , the fluorescence signal of the nuclear-localized p53 cells in the image stack was tracked and the average fluorescent intensity of p53 in individual cells was recorded for each cell at 10-minute intervals for 20 hours . To evaluate the impact of the microRNA perturbations on the stress-induced p53 oscillations , we analyzed one hundred single-cell trajectories of p53 fluorescence profiles , after artificially induced DNA damage . The raw trajectory data was denoised using the stationary wavelet transform ( SWT ) in MATLAB [56] ( S2 Fig ) . After smoothing out the raw signal intensities to reduce noise , we classified each denoised p53 trajectory data by extracting relevant parameters of the intensity profile to help us determine whether the observed fluorescence profile possesses qualities consistent with typical p53 oscillation or not ( Materials and Methods ) . Specifically , we located relevant peaks of the time-series data and recorded their locations to identify cells that have abnormally long or short periods . If two consecutive peaks occur within 50 minutes or at least 12 hours apart , the cell was eliminated from being classified as oscillatory . For a better look at the overall trend of the dynamic fluorescence signal , we also calculated the instantaneous slope of the fluorescence profile and the frequency of the time that it is below zero during the 20 hour span . If the slope was zero more than 70% of the 20 hour period , we exclude the cell from being classified oscillating and vice versa . The entire classification process was automated in MATLAB , and the classified cell-population results of the duplicate experiments under WT , negative control , as well as the three microRNA-inhibited conditions are illustrated in S3–S7 Figs . After sorting our time-lapse data for individual cells showing oscillation , we found that the targeted microRNA suppression seems to affect the stress-induced p53 oscillation quantitatively in terms of the number of cells with oscillating p53 expression , but have a little qualitative effect on the period or amplitude of the oscillations . Specifically , we found the mean oscillation period among the selected cells to be approximately 5 hours , and this mean period remained stable after suppression of the three targeted microRNAs . As expected , we found that there was non-negligible level of cell-to-cell variability in the oscillation period based on the coefficient of variation . More importantly , we found that the microRNA suppression seems to have little effect on this variability ( Fig 5c ) . We then calculated the percentage of oscillating cells after each microRNA abrogation . In our time-lapse experiment , fluorescence signal from 100 cells were captured every 10 minutes over a 20-hour time period in 5 different conditions , giving us over 60 , 000 data points per experiment . To present this data efficiently , we employ heatmaps composed of pixels that each represents a single data point . Each row of the graph represents the p53 signal intensity trajectory of a single-cell , and each column represents a single time point . Color at each pixel is indicative of the relative intensity of the p53 signal . To highlight the differences between the fluorescence profiles of oscillating and non-oscillating cells , we re-organize the time-series heatmaps into two populations and re-order them based on the location of the first observed peak . We found that the cells transfected with the inhibitor of miR-192 show markedly decreased number of p53 oscillating cells comparing to wild type , while the population affected by inhibitors of microRNA 29a and 34a show similar occurrence rate of oscillation as the wild type ( Fig 5d ) . Note that the results from duplicate experiments show the same trend of reduced number of oscillating cells in a population only when miR-192 is repressed , although the absolute value differs ( S5 Table ) .
Here we use an approach of theoretical modeling combined with quantitative experiments to elucidate the role of microRNAs on the cellular performance of oscillatory p53 induced by DNA double-strand breaks . Our results show that the microRNA-mediated positive feedback loops influence the robust manifestation of stress-induced p53 oscillations in stochastic cellular systems . Specifically , the repression of miR-192 led to widespread collapse of the sustained p53 oscillations across a population of variable cells while the repression of miR-34a and miR-29a mildly affected the phenotype under double-stranded DNA damage . A functional role of microRNAs has been proposed in that they confer robustness to biological processes [57] , including cellular differentiation in development or tumorigenesis [58] . Notably , the microRNA-mediated functional network motifs that previously have been discovered to bestow the function of robust maintenance of cell fate are all positive feedbacks , consisting of a transcription factor and a microRNA , either with or without intermediate signaling components [58] . Our findings add new evidence of microRNA-mediated positive feedback loops that function as a mechanism that reinforces the robustness of a system phenotype . Bifurcation analysis , a method widely used in engineering to evaluate system robustness , provides effective means to delineate the variable behavior of single cells subject to extrinsic noise in parameters . Our bifurcation diagrams of the single-cell model of microRNA-p53-MDM2 network show substantially higher reduction of oscillation regions under the inhibitions of miR-192 comparing to the other two microRNAs . Theoretical studies of the biochemical oscillators arising from a core negative feedback loop plus an additional positive feedback loops have been performed recently [59 , 60] . Specifically , a modified Goodwin model consisting of a three-component negative feedback loop interlinked with different positive feedback motifs was studied for the performance of the oscillator with regard to the benefits acquired by the auxiliary positive feedback regulations . This is analogous to our wild-type model , where the core mechanism for the p53 oscillator is the p53 protein-MDM2 mRNA-MDM2 protein negative feedback loop , and it is coupled with positive feedback loops . Besides the advantages that may be gained for the oscillator by positive feedback loops , their results show that a positive feedback loop is the most beneficial for the robust performance of the oscillator if its pathway components have the fastest dynamic , such as the fastest degradation rate . In other words , the finding predicts that a positive feedback loops with faster information transduction on top of the core negative feedback is a more favorable structure to stabilize oscillation . We can apply this finding qualitatively to interpret our model behavior . For the positive feedback regulations in our model , miR-192 directly regulates MDM2 , while miR-34a and miR-29a regulate intermediate nodes prior to reaching MDM2 , before the MDM2 information is eventually fed back to p53 protein to close the loop . Consequently the miR-192 mediated double-negative feedback loop contains shorter signal-processing path than that of the miR-34a- and miR-29a-mediated feedback loops . Although miR-34-a and miR-29a each also forms a short positive feedback loop together with a mediating protein with the same length as the miR-192-MDM2 feedback loop , the MDM2 protein is degraded at a faster rate upon DNA damage and thus the latter loop would still contribute the most to the system robustness . We therefore infer that among the three groups of microRNAs forming positive feedback loops with p53 , miR-192 and its mediated feedback pathway plausibly exert the greatest impact on maintaining the robustness of p53 oscillations in response to DNA damage . A previous modeling study has proposed the role of a different positive feedback loop in enhancing the robust stability of p53 oscillation [61] , whereby the cytosolic MDM2 protein translocates into nucleus to interact with the mRNA of p53 through direct binding and promote the translation of p53 mRNA to close the loop [62 , 63] . Such positive feedback is a relatively long and slow loop involving steps of the compartmental trafficking of MDM2 protein from cytosol into nucleus , the protein-mRNA binding between MDM2 protein and p53 mRNA , and the final translation of p53 protein induced by MDM2 protein . The microRNA-mediated positive feedback loop , on the other hand , is more efficient . For instance , the positive feedback mediated by miR-192 is only composed of fast binding of microRNA to MDM2 mRNA and the post-translational degradation of p53 protein promoted by MDM2 protein . The core negative feedback is also a fast and efficient loop containing the post-translational degradation of p53 protein promoted by MDM2 protein . Note that the same type of processes in the positive feedback loops , such as the induction of mRNA or microRNA by p53 and the translation of MDM2 protein , is not enumerated in the above comparison . In addition , we note that translational process is in general much slower than post-translational regulation . In sum , the positive feedback facilitated by MDM2-enhanced translation of p53 occurs in a much lower efficiency than the core p53-MDM2 negative feedback and the microRNA-mediated positive feedback loops . It thus is reasonable to assume that the positive feedback loop through the MDM2-enhanced translation of p53 has relatively weak impact on the p53 oscillation . This probably is the reason why the recent major theoretical studies of the p53 oscillator do not account for the positive feedback loop through the MDM2-enhanced translation of p53 [36 , 42 , 64–66] . Indeed , if we add an MDM2-dependent translation term into the ordinary differential equation of the p53 mRNA with a translational rate half that of the basal translational rate to approximate the slow processes due to MDM2 translocation and MDM2-p53mRNA interaction , the simulations of the p53 oscillation are very similar to the model without the positive feedback through the MDM2-enhanced translation of p53 , indicating that this positive feedback does not have much impact on the p53 oscillation . In conclusion , our modeling and experimental results provide new evidence on the relationship between microRNAs and p53 function [23] with implications to cancer initiation and progression . Importantly , understanding the mechanisms underlying the abnormal p53 behavior due to microRNA depletion [67] may lead to innovative microRNA-based therapeutics .
The MCF7 breast cancer cell line , consisting of the fluorescent protein Venus fused to p53 , is the same as previously described [9 , 38] , a gift from Galit Lahav , Harvard Medical School . Cells were maintained in 95% humidity at 37 degrees Celsius and cultured in RPMI ( Invitrogen ) media with 10% FBS ( Invitrogen ) , 1% PenStrep ( Invitrogen 0 . 045 units/mL Penicillin , 0 . 045 units/mL Streptomycin ) . After the first splitting following resurrection from liquid nitrogen , stably integrated MCF7 cells were maintained at 20 mL volume in petri dishes with 400ug/mL G 418 disulfate salt ( 400ng/mL , Sigma ) . In a 12 well plate ( Griener ) , 80 , 000 MCF7 p53-Venus cells were plated on the afternoon before transfection . The following morning , the cells were treated with Neocarzinostatin ( Sigma ) and transfected with 3ul JetPRIME ( Polyplus ) mix with 25nM of the microRNA inhibitors 192 , 29a and 34a ( Qiagen ) according to the manufacture’s protocol . To stimulate the activity of p53 we added the radiomimetic drug Neocarzinostatin ( NCS ) , which induces the particular lesion of DNA double-strand breaks and elicits p53 to oscillate [35 , 38] . Following the addition of 0 . 8 μl NCS per well from 0 . 5 mg/mL stock ( Sigma ) and either with or without the transfection of microRNA inhibitors we commenced a time-lapse microscopy at 37 degrees Celsius with humidified 5% CO2 . Images were collected every 10 minutes for the bright field and fluorescent intensity of p53-Venus using a Hamamatsu camera attached to the Olympus IX81 microscope at 10x magnification . The time lapse ran for 24 hours and used exposure times of 10ms for Bright Field and 500ms for YFP . We chose three positions for each well , ensuring that the imaging field did not overlap between positions . The filter used for capturing Venus fluorescence is excitation ET500/20x and emission ET535/30m ( Chroma ) . Cells were incubated within the microscope at 37 degrees Celsius with approximately 5% CO2 . The image stacks were first processed by an ImageJ plug-in CGE to measure and track the average intensity of nuclear p53 in single cells [55] . For a specific cellular condition , we tracked an average of 33–34 cells within each of the three locations , and a total number of 100 cells , for 20 hrs ( see the tracked cell trajectories in S6 Table ) . The raw time trajectories of p53 intensity for 100 cells then underwent a de-noising step implemented by the Stationary Wavelet Transform De-Noising 1-D Tool of MATLAB to remove the high-frequency noise and extract the low-frequency p53 oscillation [56] ( S2 Fig ) . Finally , each trajectory was subject to a classification algorithm for the purpose of determining its phenotype , oscillating or non-oscillating . Specifically , to obtain additional characteristics from the resulting p53 trajectories , instantaneous slope at each point was calculated using MATLAB code diff , and the peaks were detected using MATLAB code mspeaks . Instantaneous slope of each trajectory , along with the locations of its peaks , were used to determine the oscillating and non-oscillating phenotypes of individual cells . The single-cell trajectories were classified as non-oscillating ( S3–S7 Figs ) if: ( a ) less than 3 peaks were detected during the time-lapse , ( b ) there were 2 consecutive peaks that occur within 5 time units ( 100 min ) , ( c ) there were 2 consecutive peaks that occur more than 70 time units ( 1400 min ) apart , and ( d ) the slope of the trajectory was negative ( or positive ) more than 75% of the time . Otherwise , the trajectory was classified as oscillating . | DNA damage triggered activities of the tumor suppressor protein p53 could be significantly dynamical . The functional role of p53 oscillations in cellular decision making during cancer development has been appreciated . A set of recent studies have revealed extensive crosstalk between the p53 network and microRNAs , but the specifics of the participation of microRNAs in the regulation of the p53 signaling pathway remains largely elusive . Here we investigated microRNAs that form feedback regulation with p53 . We enumerated the molecular interactions among these microRNAs and the p53 core and developed a mathematical model to reproduce the DNA damage induced p53 oscillations in single cells . We performed computer simulations and system analysis in combination with experimental assessment to probe the behavior of p53 under microRNA-inhibited conditions . We show that the robust cellular performance of the stress response of p53 in a breast cancer cell line is controlled by miR-192 , which forms positive feedback loops with p53 . | [
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] | [] | 2015 | MiR-192-Mediated Positive Feedback Loop Controls the Robustness of Stress-Induced p53 Oscillations in Breast Cancer Cells |
In many bacteria , inhibition of cell wall synthesis leads to cell death and lysis . The pathways and enzymes that mediate cell lysis after exposure to cell wall-acting antibiotics ( e . g . beta lactams ) are incompletely understood , but the activities of enzymes that degrade the cell wall ( ‘autolysins’ ) are thought to be critical . Here , we report that Vibrio cholerae , the cholera pathogen , is tolerant to antibiotics targeting cell wall synthesis . In response to a wide variety of cell wall- acting antibiotics , this pathogen loses its rod shape , indicative of cell wall degradation , and becomes spherical . Genetic analyses revealed that paradoxically , V . cholerae survival via sphere formation required the activity of D , D endopeptidases , enzymes that cleave the cell wall . Other autolysins proved dispensable for this process . Our findings suggest the enzymes that mediate cell wall degradation are critical for determining bacterial cell fate - sphere formation vs . lysis – after treatment with antibiotics that target cell wall synthesis .
Nearly all bacteria are surrounded by a rigid cell wall , a structure that maintains cell shape and ensures cellular integrity in the face of potentially extreme osmotic stresses in the environment . The principal component of the cell wall is peptidoglycan ( PG ) , a complex polymer that consists of a polysaccharide web with cross linked peptide sidechains found outside of the cytoplasmic membrane . PG biosynthesis is a multi-step process that begins in the cell cytoplasm , where precursor molecules are built [1] . Once precursors are exported outside the cell membrane , they are assembled into PG by Penicillin Binding Proteins ( PBPs ) , enzymes that catalyze the polymerization of polysaccharide chains and crosslinking of peptide sidechains . Beta lactam antibiotics ( penicillins , cephalosporins and carbapenems ) , which are among the most important antibiotics in current use , covalently bind to and inactivate PBPs [2] . PG’s importance for bacterial survival becomes evident when its synthesis is inhibited by beta lactams or antibiotics that block earlier steps in cell wall synthesis—cells routinely lyse . It was initially hypothesized that beta lactam-induced lysis was caused by the mechanical force generated by increased turgor pressure that arose upon cessation of PG expansion while the cell maintained other cell growth programs . However , studies in both Gram- positive and Gram-negative organisms indicate that lysis is mediated by enzymatic activity [3 , 4] . PG cleavage mediated by cell wall hydrolases , also known as autolysins , is presumed to be excessive and/or dysregulated in the absence of ongoing PG synthesis , and the resulting breaches in the cell wall are thought to lead to lysis . Most bacteria contain multiple copies of at least 3 classes of potential autolysins—amidases , lytic transglycosylases and endopeptidases—and all 3 ordinarily play important roles in PG homeostasis [5–8] . An accumulation of degradation products from these enzymes were detected in Escherichia coli cells treated with beta lactam antibiotics [9] , consistent with the possibility that lysis after inhibition of cell wall synthesis may be associated with the activity of multiple autolysins . However , multiple autolysins are not always important for beta lactam-induced lysis; e . g . , in Streptococcus pneumoniae , deletion of a single amidase ( Atl ) renders this gram-positive pathogen completely tolerant to beta lactam-induced lysis [3] . In E . coli , beta lactam-induced lysis usually starts from the cell septum [10 , 11] , suggesting that amidases , which are recruited to and activated at the site of cell division , might initiate PG cleavage associated with lysis . Supporting this idea , deletion of multiple amidases leads to a lower rate of lysis after exposure to beta-lactam antibiotics [10 , 12] . In contrast , there is contradictory evidence regarding the role of lytic transglycosylases in the lysis process . Mutants lacking multiple lytic transglycosylases are typically more susceptible to beta lactam antibiotics [13 , 14] , suggesting that these enzymes promote , rather than impair , survival after inhibition of cell wall synthesis . However , overexpression of bifunctional PBPs containing an inactive transpeptidase active site , which mimics exposure to beta lactam antibiotics , results in E . coli lysis via a process that is largely dependent on LTGs [15] . None of the other predicted cell wall lytic enzymes in E . coli have been definitively linked to beta lactam-induced lysis . Efforts to define the full set of gene products that mediate bacterial lysis after inhibition of cell wall synthesis or the relative importance of their activities have been thwarted by the fact that the observed phenotype ( lysis ) is typically rapid , potentially masking differences between mutants , and that most lytic enzymes are highly redundant . Likely because of the prevalence of cell-wall acting antibiotics in their natural habitats [16] , bacteria employ multiple strategies to cope with the dangers associated with inhibition of cell wall synthesis . The most well-studied of these strategies is resistance e . g . by beta lactamases , which inactivate beta lactams . A more passive strategy is dormancy ( e . g . , formation of persister cells ) , which allows cells to survive exposure to any normally lethal antibiotic . Persistence is mediated by activation of multiple toxin-antitoxin modules [17 , 18] , which stop growth of a small fraction of bacterial populations and thus confer tolerance to antibiotics that are only active on growing cells [19] . Bacteria that are not replicating due to reaching high cell densities also tend to be tolerant to cell wall-acting antibiotics [20] as do bacteria exposed to factors thought to stabilize the outer membrane [11] . It is unclear what other strategies might exist to survive exposure to cell wall synthesis inhibitors . Here , we report that Vibrio cholerae , the causative agent of the diarrheal disease cholera , routinely tolerates antibiotic-induced inhibition of cell wall synthesis . Similar to most bacteria , V . cholerae loses the structural integrity of its cell wall following exposure to a wide variety of cell wall synthesis inhibitors . However , in contrast to many other bacteria , this treatment results in formation of viable ( though non-dividing ) spherical cells , rather than cell lysis . Surprisingly , genetic analyses revealed that V . cholerae sphere formation depends on the activity of M23 family endopeptidases that are required for cell elongation under conditions of normal growth; in contrast , its amidase and lytic transglycosylases are not required for formation of viable spheres . Furthermore , we found that other important pathogens , including Pseudomonas aeruginosa and Acinetobacter baumannii , also fail to respond to beta lactam exposure with lysis under certain growth conditions , suggesting that intrinsic , population-wide beta lactam tolerance may be more widespread than currently appreciated .
We observed that mid- to late exponential phase cultures of V . cholerae treated with high doses of penicillin G or ampicillin ( 100 μg/ml , 20 x MIC ) failed to divide , but did not show a decline in viable cells ( i . e . , cfu ) ( Fig 1A , S1A Fig ) . Similarly , inhibition of early steps in PG synthesis by D-cycloserine , an inhibitor of D-Ala-D-Ala ligase ( 100 μg/ml , 2 x MIC ) , or phosphomycin , an inhibitor of MurA ( 100 μg/ml , 2x MIC ) , did not appreciably affect the survival of V . cholerae . Thus , although antibiotics targeting cell wall synthesis are effective in preventing V . cholerae proliferation , they do not induce the cell death typically observed in dividing cells of other species . Due to V . cholerae’s “tolerance” of these chemotherapeutic agents , their effects are not irreversible . In liquid medium , V . cholerae lost its rod-shape and eventually assumed a spherical morphology after exposure to the previously mentioned antibiotics or to meropenem ( 10 μg/ml , 100x MIC ) ( Fig 1B and Fig 1C ) . Thus , inhibition of V . cholerae cell wall synthesis results in the loss of PG’s ‘exoskeletal’ function to maintain cell shape . This is reminiscent of so-called L-forms ( artificially-induced cell wall deficient bacteria , [21 , 22] ) ; however , while L-forms proliferate in the absence of a functional cell wall , V . cholerae spheres did not divide in the presence of antibiotics ( Fig 1A ) . Moreover , E . coli L-form generation requires the use of osmotically stabilizing media ( e . g . , containing high concentrations of sucrose ) while V . cholerae survived exposure to cell wall acting antibiotics in diverse media lacking stabilizing agents , such as LB broth and rabbit cecal fluid ( see below ) . V . cholerae sphere formation appears to be independent of which step in cell wall synthesis is inhibited , since a variety of cell wall synthesis inhibitors yielded spheres . Importantly , penicillin also induced formation of viable , spherical V . cholerae in cecal fluid that was collected from infant rabbits with cholera-like diarrhea ( S1B Fig ) , demonstrating that V . cholerae’s absence of lysis in response to inhibitors of cell wall synthesis is not due to stabilizing agents present in artificial growth medium , but instead has in vivo relevance . Sphere formation typically initiated with blebbing from the midcell ( Fig 1D ) , although we did observe rare instances ( ~ 3% of cells ) where blebbing started closer to the cell poles ( Fig 1D and 1E ) . As blebs became large , the remainder of the cell became smaller , until only the poles of the original cell structure remained . Ultimately , poles were assimilated into spheres as well , although this process occurred more slowly . We used the fluorescent D-amino acid analogue HADA [23] to visualize changes within the V . cholerae cell wall during the process of sphere formation . In V . cholerae , D-amino acids and analogs like HADA can be incorporated into the cell wall via peptide sidestem modification by penicillin-insensitive L , D transpeptidases in the periplasm [23 , 24] . Thus , at least in V . cholerae , HADA can be employed as a general cell wall label , even in the presence of cell-wall acting antibiotics . In antibiotic-free cells , HADA staining was initially distributed evenly over the cell; however , the blebs induced by antibiotics lacked a HADA signal . In contrast , HADA staining was evident in the remainder of the cell for at least 20 min after blebbing commenced , consistent with maintenance of a PG-based cell structure ( Fig 1F ) . Thus , inhibition of cell wall synthesis in V . cholerae does not result in a sudden and uniform disintegration of the cell wall; instead , it seems likely that locally confined cuts in PG allow for formation of blebs ( which are presumed not to contain cell wall material ) , followed by gradual degradation of the remaining cell wall material . The ability of PG-deficient cells to survive suggests that the inner and outer bacterial membranes may collectively be able to withstand cellular turgor pressure in the absence of support from PG . To further characterize sphere anatomy , we used fluorescently labeled proteins with previously demonstrated , distinct subcellular localization patterns [25–27] . In spheres , the periplasm was condensed into one compartment ( S2A Fig ) , consistent with loss of PG’s exoskeletal function . The cytoplasm/inner membrane filled most of each sphere but appeared to often be pushed aside by the condensed periplasmic compartment , while the outer membrane was mostly circular . However , the integrity of the outer membrane in spheres appears to be reduced; spheres were highly susceptible to the membrane-acting agents triton X-100 and polymyxin B ( S2B Fig ) , to which El Tor strains of V . cholerae are normally resistant . In E . coli , lysis following exposure to cell-wall acting antibiotics appears to be partially dependent on the redundant PG amidases AmiA , AmiB , and AmiC , whose cleavage of septal PG enables daughter-cell separation [12] . The V . cholerae genome encodes a single PG amidase ( AmiB ) , and an amiB deletion mutant forms long chains of unseparated daughter cells comparable to those of amidase-deficient E . coli [28] . Unexpectedly , V . cholerae amidase-deficient cells were more , rather than less , susceptible than wild type cells to killing by penicillin , phosphomycin and D-cycloserine ( Fig 2A ) . However , this susceptibility was not associated with the bacterial lysis seen in drug-treated E . coli . We observed no significant decrease in culture density ( OD600 ) of the V . cholerae amiB mutant in response to cell-wall acting antibiotics ( S3 Fig ) , and bacterial lysis was rarely observed using light microscopy . Instead , antibiotic treatment ultimately resulted in formation of spherical cells that were similar to those formed by wild type bacteria ( Fig 2B and 2C ) . We speculate that the ultimate decline in viability of the ΔamiB mutant under these conditions may reflect its previously noted compromised cell envelope [28] . It is important to note that interpretation of cfu and OD600 data is complicated by the fact that the mutant’s multi-cell chains disintegrate into single spheres upon beta lactam treatment ( Fig 2B and 2C; discussed below ) . This is likely the cause of the observed increase in cfu directly after addition of antibiotic in Fig 2A , which may somewhat obscure a loss of viability . The mutant’s increased lag phase compared to the wild type further complicated direct comparisons based on culture density and cfu . Therefore , to directly assess AmiB’s role in the sphere formation process , we turned to single cell analysis . Time lapse analysis of PenG-induced sphere formation in the ΔamiB mutant showed that blebs formed more slowly than in wt cells ( compare S4A Fig to Fig 1D ) , however , we cannot exclude that this is merely due to the decreased growth rate of the ΔamiB strain . Moreover , blebbing of ΔamiB cells often appeared to originate from outside the midcell ( S4A Fig ) . While the exact location of blebs relative to the septum is difficult to define in this chain-forming mutant , we also noticed blebbing from almost exclusively extraseptal locations when we treated a PBP1A-deficient mutant with the antibiotic cefsulodin , which in V . cholerae inhibits only PBP1B [26] ( S4B Fig and S4C Fig ) . Since AmiB is presumably active at the septum only [28–30] , the occurrence of blebbing outside of the septum in these cells provides additional evidence that enzymes other than AmiB can initiate sphere formation in V . cholerae . In aggregate , our data suggest that AmiB may play an initiating and facilitating role in sphere formation after inhibition of cell wall synthesis in V . cholerae , but that other enzymes can partially compensate for its absence . We also assessed the role of lytic transglycosylases ( LTGs ) in V . cholerae’s response to antibiotics that inhibit PG synthesis . The V . cholerae genome encodes 6 predicted LTGs ( mltA , mltB , mltC , mltD , mltF , and slt70 ) . We found that a strain lacking 5 of these ( ΔmltABDFΔslt70; Δ5LTG ) was viable; however , we were unable to obtain a mutant lacking all six . Exposure of Δ5LTG to penicillin resulted in a ~1 log reduction in viability that was not accompanied by lysis , whereas phosphomycin or D-cycloserine did not reduce this strain’s viability ( Fig 3A and Fig 3B ) . Similarly , deletion of certain lytic transglycosylases sensitizes other bacteria to beta lactam antibiotics [13 , 14] , and this has recently been proposed to reflect the LTGs’ role in a quality control mechanism during cell wall synthesis [31] . Penicillin treatment of Δ5LTG or a single disruption of mltC , the sixth LTG , yielded spherical cells similar in appearance to those observed after treatment of the wild type strain ( Fig 3B and S5 Fig ) , but the dynamics of sphere formation in Δ5LTG cells were altered . Unlike penicillin-treated wt cells , on agarose pads , a significant proportion ( ~ 40% ) of penicillin-treated Δ5LTG cells started blebbing from sites close to or at the cell poles ( Fig 3C and 3D ) , suggesting that in the absence of LTGs , there is less cleavage of septal PG , consistent with the proposed auxiliary role for these enzymes in cell separation in other bacteria [32 , 33] . Those cells that initiated blebbing from midcell retained long polar appendages for the entire duration of the experiment ( Fig 3C ) , indicating that the comprehensive disruption of polar and lateral PG observed after beta lactam exposure of wild type cells depends upon one or more LTGs . This was also observed in liquid medium , albeit to a lesser degree , where HADA staining revealed the presence of polar appendages 1–3 hours after exposure to penicillin in Δ5LTG but not in the wt strain ( Fig 3E ) . Additionally , HADA stained Δ5LTG cells much more intensely than wt cells before exposure to penicillin ( Fig 3E and S6 Fig ) and the mutant cells also retained more fluorescent material in the periplasm during exposure to the antibiotic . Thus , the Δ5LTG mutant appears to have reduced PG turnover , a deficiency that is accentuated by exposure to beta lactam antibiotics . Since beta lactams inhibit the transpeptidase activity of PBPs , but presumably leave their transglycosylase activity intact , it is possible that the periplasmic accumulation of HADA stain in the Δ5LTG mutant reflects the build-up of minimally cross linked ( and HADA-labeled ) PG strands that would ordinarily be degraded by lytic transglycosylases . Consistent with this possibility , we found that neither the Δ5LTG mutant nor wt cells treated with phosphomycin ( an antibiotic that affects precursor synthesis and thus should not allow any PG synthesis ) accumulated HADA-labeled material ( Fig 3E ) . In summary , lytic transglycosylases , at least MltABDF and Slt70 in combination , or MltC alone , are not required for sphere formation but appear to be involved in downstream processes of cell wall degradation . Since amidases and the 5 LTGs were not critical for sphere formation , we turned our focus towards endopeptidases , two of which ( ShyA and ShyC ) are synthetically lethal and essential for cell elongation in V . cholerae [25] . The V . cholerae genome encodes five periplasmic M23 endopeptidases and one P60 family endopeptidase ( NlpC ) . We constructed a mutant that lacks all predicted non-PBP endopeptidases and expresses inducible shyA from a neutral chromosomal locus ( ΔshyA ΔshyB ΔshyC ΔnlpC ΔtagE1 ΔtagE2 Ptac:shyA; Δendo ) . In this background , depletion of shyA slowed growth , similar to our previous observations with a ΔshyA ΔshyC Ptac:shyA strain , which is defective in cell elongation but proficient in cell division [25] ( Fig 4A ) . Importantly , untreated Δendo cells did not lyse even after extended ShyA depletion ( Fig 4C and “control” ) . Unexpectedly , however , exposure of ShyA-depleted Δendo cells to penicillin G resulted in rapid loss of viability and concomitant lysis of the majority of the population ( Fig 4A and 4B ) . Lysis could be prevented and sphere formation restored by expression of shyA ( Fig 4C ) . A similar pattern was observed with D-cycloserine and phosphomycin ( S7 Fig ) . Notably , analysis of the dynamics of cell lysis using time lapse microscopy revealed that cell disintegration did not proceed through a spherical intermediate ( Fig 4C ) . These results suggest that , paradoxically , the presence of a D , D endopeptidase ( ShyA ) , a putative ‘autolysin’ , prevents lysis and enables formation of viable spheres after exposure of V . cholerae to a beta lactam antibiotic . The lysis phenotype was also observed in a ShyA-depleted ΔshyA ΔshyB ΔshyC Ptac:shyA ( S8 Fig ) strain , and expression of ShyC but not ShyB could at least partially prevent lysis of ShyA-depleted Δendo ( S8 Fig ) , demonstrating that either one of the paralogues ShyA and ShyC must be present for beta lactam tolerance and formation of viable spheres , while the other M23 endopeptidases and NlpC are dispensable . We also observed penicillin G-induced lysis of Δendo ΔamiB cells when ShyA was depleted ( S8 Fig ) , as well as in Δendo cells with additional mutations in LTG genes ( Δendo ΔmltBΔmltD and Δendo ΔmltB Δslt70 , which were the only strains with multiple LTG disruptions that we were able to make in the Δendo background ) . These results indicate that neither amidase activity nor MltB , MltD or Slt70 activity are necessary to cause lysis of Δendo cells . Lastly , ShyA-depleted Δendo cells were not more susceptible to membrane-acting agents than ShyA-replete cells ( S9 Fig ) , suggesting that the observed lysis phenotype is not simply the consequence of a general weakness of the cell envelope . Since ShyA could prevent beta lactam-mediated lysis in V . cholerae , we tested whether it could protect a heterologous organism from lysis after inhibition of cell wall synthesis . We overproduced ShyA in the EHEC isolate EDL933 and measured its survival after exposure to meropenem . Overexpression of shyA alone did not influence EHEC growth . However , expression of this endopeptidase increased EHEC’s capacity to survive meropenem exposure by ~10-fold compared to an empty vector control , overexpression of yebA , E . coli’s ShyA homologue ( Fig 5A ) or overexpression of ShyA carrying an active site mutation ( H375A ) . Meropenem-treated EDL933 expressing ShyA formed spheres , while the control cells carrying the empty vector rapidly lysed ( Fig 5B ) . These data suggest that ShyA activity is linked to survival in the presence of beta lactam antibiotics , and that ShyA-mediated cleavage of the cell wall may differ from YebA-mediated cleavage events , although it is theoretically possible that these results only reflect differences in the expression levels for the two proteins . When EDL933 was grown in the presence of Mg2+ , which is thought to have a stabilizing effect on the outer membrane [11] , cells carrying an empty ( control ) plasmid formed spheres at a low but detectable frequency after meropenem exposure . However , in this medium , visible lysis was still markedly reduced and sphere formation concomitantly enhanced after overexpression of plasmid-encoded shyA ( Fig 5B ) . ShyA’s protective effect can thus apparently be enhanced by additional stabilization of the outer membrane , suggesting that multiple processes can contribute to beta lactam tolerance . Finally , a recent report showed that Pseudomonas aeruginosa could assume a spherical morphology in response to carbapenem antibiotics , especially in media fortified with Mg2+ and Ca2+ [34] , suggesting that the absence of cell lysis after inhibition of cell wall synthesis may not be limited to V . cholerae . P . aeruginosa turned into spheroid forms on agarose pads containing meropenem via steps that resemble V . cholerae’s transformation into spheres after exposure to this antibiotic ( Fig 5C ) , suggesting that the mechanisms underlying sphere formation may be similar in these bacteria . Notably , we observed that under the same experimental conditions , the multidrug resistant Acinetobacter baumannii clinical isolate Lac-4 also failed to lyse in response to meropenem , albeit in a process unlike that observed in V . cholerae and P . aeruginosa ( Fig 5C ) . Thus , population-wide lysis in response to inhibition of cell wall synthesis may not be the norm for many bacteria . In summary , we found that inhibition of cell wall synthesis does not inexorably lead to cell lysis and death , as occurs in the model organism E . coli . Some pathogenic bacteria survive blockade of PG synthesis , and instead form viable spheres . Surprisingly , in V . cholerae , sphere formation/survival depends on the activity of PG hydrolases ( autolysins ) , particularly the D , D endopeptidase ShyA . Thus , our findings suggest that although the pathways and enzymes that mediate PG degradation after inhibition of PG synthesis are critical for determining bacterial fate under these conditions , such fates are variable: in different organisms , enzymes that ordinarily degrade PG can either lead to lysis or promote survival when PG synthesis is blocked by antibiotics . In V . cholerae , our data suggests that there is an ordered series of steps that lead to sphere formation following interference with cell wall synthesis . In the majority of cells , the first cell wall lesion from which blebbing commences is likely generated by AmiB , perhaps with some assistance from LTGs . The latter enzymes play a more critical role in downstream processes that lead to PG resorption . The procession from blebs to spheres rather than lysis requires the presence of an endopeptidase , either ShyA or ShyC . Our findings suggest that the specificity , rate or location of PG hydrolysis mediated by such D , D endopeptidases is important for preventing cell lysis; however , the exact mechanism by which these proteins prevent lysis and death requires further investigation . It is tempting to speculate that while the end result of cell wall synthesis inhibition may differ between bacteria , the hierarchy of cell wall lytic events might be conserved . It will be interesting to explore whether PG-degrading enzymes important for cell elongation are required for sphere formation in other organisms , as in V . cholerae . Our observations suggest that the absence of lysis after treatment with beta lactam antibiotics may be more common than currently appreciated , but the determinants of such survival have not been identified . Importantly , some reports have suggested that spherical bacteria can be isolated from patients treated with beta lactam antibiotics during chronic infections ( e . g . respiratory infections caused by Haemophilus influenzae , [35] ) . Thus , similar to persister cells [19] , population-wide tolerance and sphere formation may represent another fairly widespread way by which bacteria can evade the lethal consequences of beta lactam exposure . New antibiotics that target processes critical for sphere formation ( e . g . inhibitors of ShyA ) or for sphere survival might exhibit potent synergy with beta lactams and thus provide a novel approach for improved antimicrobial therapeutics .
Gene deletions were conducted by standard techniques using suicide plasmid ( pCVD442 ) containing ~600 bp flanking regions of the gene to be deleted [25] . All knockouts are substitutions of the respective open reading frame with the linker sequence 5’-TTATCATTACTCGAGTGCGGCCGCATGAAA-3’ . Overexpression plasmids were constructed by amplifying the gene of interest including its native ribosome binding site and cloning into Sma1-digested pBAD33 using isothermal assembly [36] . Cells were grown in LB medium at 37°C . Growth curves were conducted in 200 μL volume in 200 well honeycomb plates using a Biotek growth curve machine . For time-dependent killing experiments , overnight cultures were diluted 1: 100 into 3 ml LB medium and grown shaking at 37°C until cell density reached ~ 2 x 108 cfu/ml ( ~OD600 0 . 3 ) . Antibiotics ( Sigma ) were added to either 100 μg/ml ( penicillin G , ampicillin , phosphomycin , D-cycloserine , cefsulodine ) or 10 μg/ml ( meropenem ) . At the indicated time points , cells were serially diluted and spot plated to determine cfu/ml . For post-penicillin survival assays , cells grown as described above were exposed to penicillin G for three hours; then either nothing , Triton X-100 ( 1% final concentration ) or Polymixin B ( 40 μg/ml final concentration ) were added , followed by 30 min incubation at 37°C and subsequent spot-plating for cfu/ml . For antibiotic exposure assays in cecal fluid , 200 μL of cecal fluid from infected infant rabbits ( which contains a high-density monoculture of V . cholerae , [37] ) was collected ~ 16 h post infection , transferred to eppendorf tubes and incubated standing at 37°C for 3 h after addition of antibiotic . For depletion experiments , overnight cultures of Δendo were diluted 1: 100 into 3 ml LB medium containing 25 μM IPTG ( which is the minimal growth permitting IPTG concentration ) . These cultures were then grown for 2 h , washed 2 x with fresh medium and then resuspended in LB lacking IPTG and grown for an additional 1 . 5 h . Cells were then directly applied to agarose pads ( see below ) containing penicillin G with or without IPTG ( 100 μM ) . For assessment of minimum inhibitory concentrations ( MIC ) , overnight cultures were diluted 1000fold into fresh LB medium , grown for 1 h and again diluted 1:1000 in fresh LB medium . 50 μL of this inoculum were applied to 96 well plates containing 50 μL of 2fold serial dilutions of the antibiotic to be tested . The MIC was read as the lowest antibiotic concentration at which no turbidity was visible . Time lapse experiments were conducted on 0 . 8% agarose pads with 10% LB and PBS . Images were analyzed using ImageJ software , and adjusted by removing background fluorescence ( using imageJ’s built-in function , 50 px rolling ball radius ) and adjusting brightness/contrast levels where appropriate ( i . e . in Fig 1F ) . Care was taken to use the same adjustment parameters for images that were to be compared directly with each other ( i . e . in Fig 3E , wt vs . Δ5 ) . | Inhibition of bacterial cell wall synthesis by antibiotics such as penicillin can lead to unbalanced activity of a poorly defined set of lytic enzymes , termed ‘autolysins , ’ which degrade the cell wall and typically cause cell lysis . Here , we report that in Vibrio cholerae ( the cause of cholera ) , inhibition of cell wall synthesis results in the formation of viable spheres rather than cell lysis . Paradoxically , sphere formation requires the activity of cell wall degradative enzymes . Inhibition of cell wall synthesis in additional pathogens also leads to sphere formation . These findings expand our understanding of the cellular responses to cell wall acting antibiotics , demonstrating that cell wall degradative enzymes not only function as autolysins , but can also mediate cell survival in the face of cell wall insufficiency . | [
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] | [] | 2015 | Endopeptidase-Mediated Beta Lactam Tolerance |
DNA damage recognition by the nucleotide excision repair pathway requires an initial step identifying helical distortions in the DNA and a proofreading step verifying the presence of a lesion . This proofreading step is accomplished in eukaryotes by the TFIIH complex . The critical damage recognition component of TFIIH is the XPD protein , a DNA helicase that unwinds DNA and identifies the damage . Here , we describe the crystal structure of an archaeal XPD protein with high sequence identity to the human XPD protein that reveals how the structural helicase framework is combined with additional elements for strand separation and DNA scanning . Two RecA-like helicase domains are complemented by a 4Fe4S cluster domain , which has been implicated in damage recognition , and an α-helical domain . The first helicase domain together with the helical and 4Fe4S-cluster–containing domains form a central hole with a diameter sufficient in size to allow passage of a single stranded DNA . Based on our results , we suggest a model of how DNA is bound to the XPD protein , and can rationalize several of the mutations in the human XPD gene that lead to one of three severe diseases , xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy .
Nucleotide excision repair ( NER ) is the most versatile DNA repair pathway . [1–5] . NER is well known for its ability to remove bulky DNA lesions and is unique in its ability to repair structurally and chemically different substrates , including benzo[a]pyrene-guanine adducts caused by smoking , as well as guanine-cisplatin adducts formed during chemotherapy [6] . NER is the only repair mechanism in humans that is able to remove photoproducts induced by ultraviolet light . The phenotypic consequences of defective genes involved in NER are apparent in three severe diseases: xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy [1 , 7–10] . The mechanism of the human NER system , while analogous to the well-characterized bacterial system , is less well understood . Over 30 proteins have been identified in humans that are critical for mediating the individual steps leading from damage recognition to incision and repair . However , due to the paucity of specific structural intermediates , the precise role for each protein has not been fully delineated . NER has been proposed to proceed through either a “bipartite substrate discrimination” or a “multi-partite damage recognition” model [11 , 12] . It is generally believed that NER is initiated by the combined action of XPC and RAD23B , which recognize a general disruption of Watson-Crick base-pairing created in the vicinity of the damaged nucleotide . Both proteins are required to recruit the ten-subunit transcription factor TFIIH to this site . The XPD and XPB proteins are two helicases that are present in TFIIH , and which open the DNA around the lesion in an ATP-dependent fashion . This is the first catalytic step in this reaction pathway , leading to a conformational change that allows the recruitment of additional NER factors [5 , 13 , 14] . A second , more important function of the two helicases is damage verification . Recent data suggest very different roles for XPB and XPD [15] . The helicase activity of the XPB protein seems to be dispensable; however , its ATPase activity is essential for NER . This has been interpreted to suggest a wrapping of the DNA around XPB , which leads to an opening of the double-stranded DNA ( dsDNA ) close to the lesion . This opening allows the correct binding of XPD , which then utilizes its helicase activity to verify the damage and ensures that the backbone distortion is not the result of an unusual DNA sequence . This process was termed “enzymatic proofreading” and supports the bipartite damage recognition model in which the function of XPC-RAD23B is limited to the observation of a backbone distortion , and XPD is required to verify the damage through its helicase activity [16 , 17] . Very recently , it has been shown that the XPD protein contains an FeS cluster , which is essential for its function [18] . However , it is not clear whether the cluster has a structural role or is actively involved in the damage recognition process [19] . We solved the crystal structure of the XPD protein from Thermoplasma acidophilum , which shares high sequence identity to its eukaryotic homologs , and show that it contains two RecA-like helicase domains . The XPD protein displays high structural similarity to the bacterial UvrB protein , which is also required for enzymatic proofreading in NER . Two additional domains emerge from the first helicase domain and form a hole that is sufficient to allow passage of ssDNA . Furthermore , the structure delineates how different mutations in the protein lead to the human genetic disorders xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy .
Two different XPD-related protein sequences from T . acidophilum have been deposited in the National Center for Biotechnology Information ( NCBI ) and the Swiss-Prot databases , respectively . They differ only with respect to their N-terminus , with one of them containing 19 additional amino acids . We cloned both constructs and obtained crystals of the shorter protein , which was also active with respect to both its helicase and its ATPase activity ( Figure S1 ) . The protein crystallized in space group P65 and the asymmetric unit contains one XPD molecule , indicating no higher oligomeric states , which is consistent with size-exclusion chromatography results and an analysis of the model using the PISA server [20] . The structure was solved by multiwavelength anomalous diffraction ( MAD ) using the anomalous Fe signal of the endogenous FeS cluster in the protein and was refined at 2 . 9 Å resolution to an R-factor of 0 . 209 and Rfree of 0 . 287 ( Table 1 ) . The current model contains residues 23–507 and 515–615 ( 586 out of 602 residues ) of the XPD construct with residues 20 to 22 , 508 to 514 , and 616 to 620 presumably being disordered . The structure of the protein can be divided into four distinct domains . Domain 1 is formed by residues 23–87 , 178–225 , and 366–407 , domain 2 by residues 88–177 , domain 3 by residues 226–365 , and domain 4 by residues 408–615 ( Figure 1A and 1B ) . The first three domains together with α-helix 22 from domain 4 form a donut-shaped structure containing a hole with a diameter of approximately 13 Å ( Figure 1A ) . The remainder of domain 4 is positioned in front of the ring without obstructing the hole of the donut . The overall dimensions of the protein can therefore be divided into the donut with a width and height of 65 Å and 75 Å and a thickness of 29 Å . At the location of domain 4 , the width of the ring is increased to 45 Å ( Figure 1A and 1B ) . Domains 1 and 4 represent the “classical” RecA-like fold that is present in all helicases of superfamilies 1 and 2 ( SF1 and SF2 ) [21] . Both domains share approximately 9% sequence identity and can be superimposed with a root mean square ( rms ) deviation of 2 . 4 Å using 101 Cα-atoms out of 153 from domain 1 , and 201 from domain 4 , respectively . Both domains display a similar α/β/α sandwich architecture with a central parallel seven-stranded β-sheet surrounded by seven α-helices in domain 1 and a six-stranded β-sheet surrounded by seven α-helices and two 310 helices in domain 4 . The interface between domains 1 and 4 forms the composite ATP binding site . Domain 1 contains helicase motifs I , Ia , II , and III , whereas domain 4 harbors helicase motifs IV , V , and VI [22] ( Figure 2 ) . In the context of the overall XPD structure , domain 1 can be viewed as the core domain surrounded by the other three domains . Domains 2 and 3 are insertions , which emerge from domain 1 . Domain 2 is inserted between β-strands β3 and β4 , while domain 3 is inserted between α-helices α11 and α17 . Domain 4 is situated adjacent to domain 1 within the linear protein sequence ( Figures 1 and 2 ) . Notably , the closest related homolog of the full-length XPD structure as revealed by similarity searches [23] was UvrB [24] , which has been proposed to be the prokaryotic equivalent to XPD and utilizes its helicase activity for damage verification . XPD and UvrB can be superimposed with an rms deviation of 2 . 6 Å using 254 aligned Cα atoms out of 588 and 505 residues , respectively . The match is mostly mediated via the two helicase domains , whereas the other domains have no significant structural similarity to each other ( Figure S2 ) . In addition , we compared XPD to Hel308 and NS3 , two SF2 helicases ( Figure S3 ) . The superposition shows that structural similarities are again mainly confined to the RecA domains , whereas the auxiliary domains are highly variable . Hel308 and NS3 have been structurally characterized with DNA substrates , and both represent a closed state of the helicase framework [25 , 26] . No adenosine nucleotide is bound in these structures , but they are presumed to be in a preprocessive state that only requires ATP binding to reach the processive state [25] . Using the first RecA domain ( domain 1 ) as a reference point for superposition with either Hel308 or NS3 , XPD assumes a more open state that is mainly mediated via a rotation of the second RecA domain ( domain 4 ) of about 30° or 16° , respectively , relative to domain 1 ( Figure S3C ) . The composite ATP binding site is located near the hinge region when compared to the closed state of the other two helicases . Our structure may therefore reflect a ground state of XPD prior to nucleotide and/or DNA binding that underlines the conformational flexibility necessary to translate chemical energy into motion . The first insertion into helicase domain 1 is of particular interest since it contains an FeS cluster , a unique feature among the XPD-like SF2 helicases [18] . Domain 2 displays an exclusively α-helical architecture consisting of six α-helices and one 310 helix that surround the central 4Fe4S cluster ( Figure 1A , 1C , and 1D ) . The FeS cluster is coordinated by four cysteines , consistent with the coordination typically observed in 4Fe4S clusters , and all four cysteines display continuous connectivity in the electron density maps ( Figure 1C ) . A comparison of the B-factors between the 4Fe4S cluster and the surrounding protein residues reveals similar values , indicating full occupancy of the cluster . Three of the coordinating cysteines ( Cys92 , Cys128 , and Cys164 ) are located in loops , whereas the fourth cysteine , Cys113 , is located in a central position within α-helix 5 ( Figures 1 and 2 ) . Surprisingly , it was shown that the helicase activity is not affected when Cys102 or Cys105 in Sulfolobus acidocaldarius or Ferroplasma acidarmanus XPD , respectively , were mutated to serine [18 , 19] . These two residues correspond to Cys113 in our structure . Pugh et al . [19] suggested that the aerobically purified protein most likely contained a degraded 3Fe4S cluster , which is still functional , but presumably a 4Fe4S cluster is present in vivo . When any of the remaining cysteines is mutated to serine , however , the helicase activity of the enzyme is abrogated [18 , 19] . The cluster is further stabilized predominantly by hydrophobic interactions . Residues Arg88 and Tyr166 , which shield the cluster from solvent exposure , are strictly conserved and face towards a pronounced solvent-exposed groove that is formed by α-helices 5 and 8 from domain 2 and α-helix 10 from domain 1 at the back of the protein ( Figures 1D and 3 ) . The closest structural homolog for this domain identified by a secondary structure matching search [23] revealed c-myb , a transcription factor that does not contain an FeS cluster [27] . Although c-myb superimposes with a relatively low Q-score of 0 . 15 ( Figure S2B ) , it is notable that the structural similarity is restricted to the DNA binding interface of c-myb . c-Myb superimposes well with α-helices 5 , 6 , 7 , and 8 of domain 2 , of which helices 5 and 8 coincide with the DNA binding interface of c-myb ( Figure S2B ) . In the XPD structure , these helices form part of the groove mentioned above , thus indicating a possible DNA binding site . This is further emphasized by the basic nature of this groove ( Figure 3 ) , which is composed of several highly conserved , positively charged residues . However , no significant sequence conservation can be identified between c-myb and XPD in the structurally homologous regions . Domain 3 consists mostly of extended α-helices ( α-helices 12 , 13 , 14 , 15 , and 16 ) and four additional antiparallel β-strands ( β6 , β7 , β8 , β9 ) building a “β-bridge” to domain 1 . The β-bridge is further stabilized by α22 , an α-helical extension located between β15 and α23 of domain 4 . The helices can be grouped into two α-helical hairpins that stack with each other , with one hairpin containing α12 and α13 , and the second containing α15 and α16 , which is slightly distorted by the insertion of a loop . The two helical hairpins intersect at an angle of approximately 60° and create an extensive hydrophobic core between them . Helix α14 is situated in the V-shaped opening that is formed by the tilt between the two α-helical bundles ( Figure 1A and 1B ) . Similarity searches revealed no significant hit , indicating that this fold has not been encountered previously . The ring of the donut is closed at its thinnest side via an interface between domains 2 and 3 that has a buried surface area of approximately 620 Å2 . The interface is formed by 17 residues from each domain , which display little sequence conservation apart from Phe326 , which is always an aromatic residue ( Figure 2 ) . Most of the interactions are hydrophobic in character , additionally four salt bridges can be observed between Lys323/Asp99 , Arg335/Glu103 , Arg235/Glu103 , and Glu315/Lys111 . Since the presence of the FeS cluster is essential for helicase activity on dsDNA [18 , 19] , it prompted us to investigate the only other structurally characterized DNA-binding proteins with such a feature , the base excision repair proteins , MutY and Endo III [28 , 29] , with a focus on the first because a structure of a MutY-DNA complex has been described [28] . For MutY , it was shown that its FeS cluster is required for enzymatic activity and DNA binding [30] . The XPD protein contains a loop motif in the FeS cluster domain with a high density of positively charged residues similar to the FeS cluster loop motif ( FCL ) in MutY [31] . The superposition of the XPD and MutY FeS cluster domains ( Figure 4 ) reveals a similar orientation of two conserved arginines ( Arg88 in XPD and Arg153 in MutY ) . In MutY , it was shown that a neighboring conserved arginine , Arg149 , is perfectly positioned for an interaction with the DNA backbone , and bridges the distance between Arg153 and the DNA [32] . Based on the similarity to MutY , Arg88 in XPD may fulfill a similar function . Furthermore , the position of Arg88 at the surface of a pocket where DNA recognition could take place supports the idea proposed by Lukianova et al . that the FeS cluster plays an important role in arranging the residues of the FCL motif for DNA binding [31] . For MutY , it was shown that the redox properties of the [4Fe-4S]2+ cluster are modulated by the presence of DNA [33] . DNA-binding activates the cluster and facilitates oxidation [34] . Boal et al . proposed a model for DNA-mediated charge transfer ( CT ) in DNA repair in which one electron is transferred from the cluster to the DNA . In this model , the CT acts as an initial sorting mechanism , enabling a rapid scanning of undamaged regions by several glycosylase molecules , so that they are able to relocate themselves onto sites near the damage [34] . In NER , an analogous scanning mechanism seems unlikely , but a change in oxidation state of the 4Fe4S cluster upon DNA binding and as part of the damage verification step may be required , thus suggesting a functional role for the 4Fe4S cluster and not just a structural role . This hypothesis is further supported by site-directed mutagenesis studies that demonstrate that single mutations of three of the four 4Fe4S cluster coordinating cysteines to serine lead to a loss of the 4Fe4S cluster , and abrogate helicase activity , but retain a correctly folded protein that is still able to translate along ssDNA [18 , 19] . The XPD protein is a member of the SF2 helicases . To obtain insight into the DNA binding mode of XPD , we calculated the electrostatic surface potential of the protein and searched for conserved solvent-exposed amino acids ( Figures 2 , 3 , 5 , and 6 ) . The surface potential indicates a positively charged path for dsDNA along domain 4 , leading towards a highly conserved groove along domain 4 and domain 1 , which provides sufficient space for ssDNA and directs the DNA towards the hole formed by domains 1–3 . The dsDNA requires separation into ssDNA prior to entering the groove . Recently , the structure of the SF2 helicase Hel308 was determined in complex with DNA , and a prominent β-hairpin in the second helicase domain was identified that is responsible for initial strand separation [25] . It was proposed that this β-hairpin could be a general feature of SF2 helicases . In XPD however , this “wedge” is formed more likely by an α-helical extension in domain 4 ( Figure 5 ) . Despite the difference in secondary structure , it is located between helicase motifs V and VI as demonstrated for Hel308 and proposed for NS3 [25] ( Figure S3 ) . Two α-helices in XPD , α22 and α23 , form two walls of the wedge and extend farther out towards the solvent compared to other helicases such as UvrD and PcrA [35 , 36] . We propose that the tip of the wedge composed of residues in the loop between α22 and α23 separates the two DNA strands . The last two turns of α22 and the first two helical turns of α23 contain several aromatic amino acids , which could stabilize the separated DNA strands in a fashion similar to that observed for Hel308 . On one side of this wedge , the highly conserved residues Tyr540 and Tyr545 are oriented with their side chains pointing towards the solvent where they could easily form stacking interactions with the exposed bases of ssDNA . These stacking interactions can then be continued by additional solvent-exposed aromatic residues , such as Tyr23 , leading the ssDNA along the back of the protein to a position where the two strands meet again to reform dsDNA . Although exposed aromatic residues are also present on the other side of the wedge , their degree of conservation is relatively low . In our structure , Phe538 and Tyr425 could both stack against the bases in ssDNA . However , only Tyr425 is conserved , whereas Phe538 is replaced by a leucine in eukaryotic XPDs . This substitution appears to be compensated by the occurrence of Phe651 in human XPD , which substitutes for Ser552 in T . acidophilum XPD; and due to the close spatial proximity of the two side chains , they would assume similar positions ( Figure 5 ) . Consequently , there is one phenylalanine available that would represent the required stacking partner . In addition , several highly conserved , positively charged residues , such as Lys583 and Lys424 , apparently define the path for the second strand leading into the groove described above and from there continues through the central hole ( Figures 5 and 6 ) . Despite the fact that we crystallized the protein in the absence of DNA and phosphate buffer , we identified significant peaks with heights of more than 2 . 5 times the rms deviation in difference electron density maps ( Figure 6 ) that are spaced by approximately 6 . 5 Å , as well as slightly longer distances and cannot be explained by the protein model . Since the distance between phosphates in ssDNA is approximately 6 . 4 Å , it is therefore very tempting to speculate that some DNA remains bound to the protein during purification and gives rise to these residual electron density features . Further support for this hypothesis is provided by the superposition of our structure with NS3 helicase in complex with ssDNA [26] ( Figure S3 ) . Based on this superposition , we have built a model for a ssDNA binding mode ( Figure 6 ) in which the extension of the ssDNA towards the hole positions three of the phosphates into the residual electron density peaks . The postulated DNA route passes by another highly conserved surface feature in XPD , a narrow pocket that is formed by the strictly conserved Arg88 and Tyr166 on one side and Tyr185 on the other side , and is located in the wall of the central hole , directly adjacent to the 4Fe4S cluster ( Figure 6 ) . The dimensions and shape of this pocket are ideally suited to accommodate a nonmodified purine or pyrimidine base , which would be held in place through van der Waals interactions with the residues mentioned above . Due to its location , this surface feature would allow a direct coupling between the FeS cluster and a readout of the DNA . This pocket is reminiscent of the pocket for the flipped-out base that was observed in the UvrB-DNA structure [24] . Initial DNA distortion recognition in eukaryotes is achieved through the combined action of XPC and RAD23B [37] . It was shown that with the recruitment of TFIIH to the site of damage , the helicase XPD is required for proofreading , whereas XPB fulfills a structural role [15] . In the absence of an XPD-DNA complex containing a lesion , the process of proofreading remains highly speculative . The structure of XPD clearly reveals structural homology to its prokaryotic homolog UvrB . In UvrB , it was shown that a β-hairpin , which emerges from the first helicase domain , is critical for damage recognition [38–40] . However , despite the structural similarity between the two proteins , XPD does not contain a corresponding feature . In our model of the XPD-DNA complex ( Figure 6 ) , we propose that one of the DNA strands passes through the central hole , which is formed by domains 1–3 . According to studies by Naegeli et al . [41] , this would be the translocating strand , which contains the lesion , and leads to a stalled protein-DNA complex . The dimension of this hole , with a diameter of 13 Å , however , is most likely too big to provide a trap for damaged DNA . One possible candidate for the “analysis” of each base with respect to their correct structure would be the narrow pocket in the wall of the central hole described above . The size of this pocket suggests that only nondamaged bases could be accommodated , whereas a bulky DNA substrate would be excluded . This pocket is also an attractive candidate for the damage recognition process due to its close proximity to the 4Fe4S cluster and the involvement of Arg112 of human XPD ( Arg88 in our structure ) , which has been shown to cause trichothiodystrophy when mutated to histidine . TFIIH in humans is not only required for DNA repair , but is also essential for transcription [42] . XPD represents one of the ten protein subunits of TFIIH and interacts tightly with the N-terminal 236 amino acids of p44 . This interaction results in a 10-fold increase in its helicase activity [43] . It has been shown that the helicase function of XPD is not required for transcription , but is essential for NER [44] . On the other hand , XPD is required to stabilize the interaction between the core TFIIH complex , which contains seven subunits , and the cdk-activating kinase ( CAK ) subcomplex , consisting of the remaining three subunits [45 , 46] . Mutations in XPD ( Figure 2 and 7 ) can therefore lead to three different effects . The first class of mutations affects the activity of the protein directly , whereas the second group can lead to impaired interactions with p44 , thus affecting its own activity in an indirect way . The third group of mutations may lead to a destabilization of TFIIH , thereby reducing overall transcriptional activity . Based on our structure the effects of several point mutations leading to xeroderma pigmentosum , Cockayne syndrome , or trichothiodystrophy can be explained ( Figure 7 ) . Point mutations associated with xeroderma pigmentosum , such as G47R , D234N , and R666W , are located in helicase motifs I , II , and VI , respectively , and impair the ability to bind and hydrolyze ATP , thus inactivating the enzyme; however , point mutations within other regions have quite distinct effects . Arg112 ( Arg88 in T . acidophilum ) is located in the FeS cluster domain and is in direct contact with the cluster . A mutation of this residue to histidine has been identified in several TTD patients [47] . Analysis of the equivalent residue in S . acidocaldarius XPD abolished its helicase activity [18] . Arg88 is located in close vicinity to Cys113 one of the Fe-ligands , and shields the cluster , with its long side chain , from solvent . It is the first residue in a short α-helix , α 3 , which together with the opposite side of the helix forms one wall of the hole where ssDNA most likely passes through ( Figure 6 ) . The proposed role for Arg88 in analogy to MutY as described above may be accomplished by Arg112 in the human XPD protein and a mutation to histidine , as observed in trichothiodystrophy patients , could prevent this interaction , thus reducing the affinity of the protein to the DNA . However , the exact role of the 4Fe4S cluster , whether it is involved directly in the recognition process or the translocation along the DNA , remains speculative at this point . It is interesting to note that Egly and coworkers have shown this variant in human XPD to be completely devoid of helicase activity [48] . The effects of the C259Y variant can also be readily explained . This cysteine is replaced in T . acidophilum by another small hydrophobic residue , Ala236 , in α-helix 12 , which points into the hydrophobic core within domain 3 . This core stabilizes the relative position of the four α-helices within this domain as outlined above . Replacing this small hydrophobic residue with a tyrosine leads to severe steric clashes within this core and thereby destabilizes the entire domain . The two mutants Y542C and G602D are very close to each other in the structure . Tyr458 ( Tyr542 in human XPD ) is located at the beginning of α-helix 20 in domain 4 and forms hydrophobic interactions with another strictly conserved residue , Val501 ( Val599 in human XPD ) , in a neighboring β-strand . Replacing the tyrosine with a cysteine would weaken the interactions between this helix-strand pair . Gly504 ( Gly602 in the human enzyme ) is positioned between β-strands 14 and 15 in domain 4 . If this residue were to be replaced by a larger residue , it would point towards Tyr458 ( Tyr542 in human XPD ) and would thereby interfere with this side chain . The remaining four mutations D673G , G675R , D681N , and R683W/Q , although causing different diseases , are all clustered closely together towards the C-terminal end of the human XPD protein and correspond to residues Asp574 , Gly576 , Asp582 , and Arg584 in T . acidophilum XPD , respectively . It has been speculated that residues at the C-terminal end of human XPD interfere with p44 binding , thus leading to an inability to stimulate the helicase activity of XPD [43] . Of these four mutations , only G675R was analyzed with respect to its ability to interact with p44 , and it was shown that the interaction was severely diminished [43] . All other analyzed disease mutants are located further towards the C-terminal end of human XPD where our archaeal XPD contains no corresponding residues , which is not unexpected since T . acidophilum does not contain a p44 homolog . T . acidophilum Asp574 , Gly576 , Asp582 , and Arg584 are located in domain 4 and fulfill important structural roles . Asp574 forms interactions with the strictly conserved Arg570 ( Arg669 in the human enzyme ) , which is located at the end of helix 24 , and thereby stabilizes the transition from the helix to the following β-strand 16 . Gly576 is positioned in this β-strand and points towards two hydrophobic residues , Leu568 and Ile569 ( Ala667 and Ile668 in human XPD ) in α24 . A mutation of Gly675 to an arginine would push the entire helix away from the β-strand and thereby destabilize the integrity of the domain . Asp582 is located directly behind β16 and forms tight interactions with the strictly conserved Arg584 ( Arg683 in human XPD ) , and the latter forms additional interactions with Asp426 and Phe527 ( Glu509 and Tyr625 in human XPD ) , two highly conserved residues . The point mutations at the C-terminal end of XPD thus clearly play important structural roles , and any of the four mutations would interfere with the fold of domain 4 , which could also diminish the interactions with p44 . According to our protein–DNA model , however , T . acidophilum Arg584 ( Arg683 in human XPD ) also plays an important role in DNA binding and is one of the residues that may bind to the DNA close to the double-strand/single-strand junction . Replacing this positively charged residue with either a glutamine or tryptophan may severely interfere with DNA binding and thereby lead to the disease phenotype . The crystal structure of XPD from T . acidophilum revealed that the protein contains two RecA-like helicase domains and two additional domains that emerge from the first helicase domain . Surprisingly , the first three domains form a donut-shaped structure and a protein–DNA model is proposed in which one of the ssDNA strands passes through this central hole in close spatial proximity to the 4Fe4S cluster in the second domain . The high sequence homology to eukaryotic XPDs allowed the analysis of mutations leading to one of the three severe diseases xeroderma pigmentosum , Cockayne syndrome , or trichothiodystrophy and provides the basis for a more detailed analysis to understand the combined action of the helicase and the 4Fe4S cluster to achieve damage verification within the NER repair cascade .
The genes encoding two XPDs from T . acidophilum with variable N-termini ( residues 1–622 and 23–622 ) were cloned into the pET16b vector ( Novagen ) using the NdeI and XhoI restriction sites . XPD was expressed as an N-terminally His-tagged protein in Escherichia coli BL21-CodonPlus ( DE3 ) -RIL cells ( Stratagene ) by induction with 0 . 1 mM isopropyl-β-thiogalactoside at 14 °C for 18 h . The protein was purified by metal affinity chromatography ( Ni-NTA; Invitrogen ) followed by size-exclusion chromatography ( HiLoad 26/60 Superdex 200 prep grade; GE Healthcare ) in 20 mM Tris ( pH 8 ) and 500 mM NaCl . The protein was concentrated to 5 mg/ml based on a molar absorption coefficient of 65 , 140 M−1 cm−1 . For construction of the 5′ overhang DNA substrate , a 25-mer oligonucleotide ( MDJ1 , 5′-GACTACGTACTGTTACGGCTCCATC-3′ ) was 5' end labeled and annealed to the 3' end of a 50-mer oligonucleotide ( NDB , GCAGATCTGGCCTGATTGCGGTAGAGATGGAGCCGTAACAGTACGTAGTC ) . The helicase assay was carried out as described by [18] with slight modifications . Briefly , the reactions ( 10 μl ) were incubated at room temperature in 20 mM MES ( pH 6 . 5 ) , 1 mM DTT , 0 . 1 mg/ml BSA , 5 mM MgCl2 , 10 nM 32P-labeled DNA substrate , and 500 nM XPD for 10 min . The reactions were started by the addition of 3 mM ATP and transferred to a 45 °C water bath . After the specified time , 20 μl of stop solution ( 10 mM Tris-HCl [pH8] . 5 mM EDTA , 5 μM cold competitor [MDJ1] , 0 . 5% SDS , and 1 mg/ml proteinase K ) was added and incubated for 15 min at 37 °C to allow proteinase K digestion . Samples were separated on a native 10% acrylamide:bis TBE gel for 1 h at 100 V . XPD crystals were grown by vapor diffusion in hanging drops containing equal volumes of protein in 20 mM Tris/HCl ( pH 8 . 0 ) and 500 mM NaCl at a concentration of 5 mg/ml , and a reservoir solution consisting of 200 mM MgCl2 , 100 mM Hepes ( pH 8 ) , and 5%–10% PEG 400 equilibrated against the reservoir solution . Crystals grew within 7 d at 20 °C to a maximum size of 100 × 50 × 50 μm3 . Prior to data collection , the crystals were cryocooled by sequential transfer into mother liquor containing increasing amounts of glycerol in 5% steps to a final concentration of 30% . The crystals were flash cooled in liquid nitrogen , and data collection was performed at 100 K . Data sets were collected at beamline BM14 ( European Synchrotron Radiation Facility [ESRF] ) at wavelengths of 1 . 0 Å , 1 . 7 Å , 1 . 7367 Å , and 1 . 7419 Å . All data were indexed and processed using Moslfm and Scala [49 , 50] . The crystals belong to space group P65 with unit cell dimensions of a = b = 78 . 9 Å , c = 174 . 0 Å . Structure solution was achieved utilizing the anomalous signal of the endogenous Fe belonging to the 4Fe4S cluster by MAD data collection at the Fe edge . The peak and inflection datasets were obtained from one crystal and were merged with a highly isomorphous dataset collected at the remote wavelength . The Fe sites were located using ShelxD [51] , and phase improvement was achieved with Sharp [52] . Substructure solution and refinement was carried out at 4 Å resolution , and the 4Fe4S cluster was treated as a “super” atom for phasing . The initial maps were subjected to solvent flattening and phase extension to 3 . 6 Å using the programs Solomon [53] and Pirate [54] . The solvent-flattened maps were autotraced using the low-resolution quick-build option in ARP/WARP [55] and further extended manually using the programs O and Coot [56 , 57] . After assigning the maximum number of residues and side chains possible , the model was subjected to phase-restrained simulated annealing and maximum likelihood refinement using the program phenix . refine [58] . Refinement was carried out against the highest resolution dataset up to 2 . 9 Å . The model was further improved by alternating rounds of refinement and manual model building . When the model was sufficiently complete , refinement continued with TLS and restraint maximum likelihood refinement using Refmac5 [54] . The final model contains 586 out of 602 amino acid residues , the 4Fe-4S cluster , one calcium ion , and one water molecule .
Coordinates and structure factors for the XPD structure have been deposited in the Protein Data Bank ( http://www . rcsb . org/pdb/home/home . do ) using the Autodep tool from the European Bioinformatics Institute ( http://www . ebi . ac . uk/ ) with the entry code 2VSF . The Protein Data Bank accession numbers for the proteins discussed in the paper are as follow: c-myb ( 1h89 ) , UvrB ( 2fdc ) , Hel308 ( 2p6r ) , and NS3 ( 1a1v ) . | Preserving the structural integrity of DNA , and hence the genetic information stored in this molecule , is essential for cellular survival . It is estimated that the DNA in each human cell acquires about 104 lesions per day . Consequently , efficient DNA repair mechanisms have evolved to protect the genome . One of these DNA repair mechanisms , nucleotide excision repair ( NER ) , is present in all organisms and is unique in its ability to repair a broad range of damage . In humans , NER is the major repair mechanism protecting DNA from damage induced by ultraviolet light . Defects in the genes and proteins responsible for NER can lead to one of three severe diseases: xeroderma pigmentosum , Cockayne syndrome , and trichothiodystrophy . The XPD protein is one of the key components of a ten-protein complex and is essential to initiate NER . In particular , the XPD protein verifies the presence of damage to the DNA and thereby allows DNA repair to proceed . We have solved the 3-dimensional structure of the XPD protein , and show how XPD has assembled several domains to form a donut-shaped molecule , which is able to separate two DNA strands and scan the DNA for damage . The structure also helps to explain why some of the mutations that have been identified in humans are associated with disease . | [
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"biochemistry"
] | 2008 | Crystal Structure of the FeS Cluster–Containing Nucleotide Excision Repair Helicase XPD |
Cellular microscopy images contain rich insights about biology . To extract this information , researchers use features , or measurements of the patterns of interest in the images . Here , we introduce a convolutional neural network ( CNN ) to automatically design features for fluorescence microscopy . We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data . We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image , the CNN is asked to predict the fluorescence pattern in a second different cell from the same image . We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets . Moreover , we demonstrate that our features are useful for exploratory biological analysis , by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins , and by quantifying multi-localized proteins and single-cell variability . We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images .
Feature representations of cells within microscopy images are critical for quantifying cell biology in an objective way . Classically , researchers have manually designed features that measure phenomena of interest within images: for example , a researcher studying protein subcellular localization may measure the distance of a fluorescently-tagged protein from the edge of the cell [1] , or the correlation of punctuate proteins with microtubules [2] . By extracting a range of different features , an image of a cell can be represented as a set of values: these feature representations can then be used for numerous downstream applications , such as classifying the effects of pharmaceuticals on cancer cells [3] , or exploratory analyses of protein localization [1 , 4] . The success of these applications depends highly on the quality of the features used: good features are challenging to define , as they must be sensitive to differences in biology , but robust to nuisance variation such as microscopy illumination effects or single cell variation [5] . Convolutional neural networks ( CNNs ) have achieved state-of-the-art performance in tasks such as classifying cell biology in high-content microscopy and imaging flow cytometry screens [6–9] , or segmenting single cells in images [10 , 11] . A key property driving this performance is that CNNs automatically learn features that are optimized to represent the components of an image necessary for solving a training task [12] . Donahue et al . [13] and Razavian et al . [14] demonstrate that the feature representations extracted by the internal layers of CNNs trained as classifiers achieve state-of-the-art results on even very different applications than the task the CNN was originally trained for; studies specific to bio-imaging report similar observations about the quality of CNN features [6 , 15 , 16] . The features learned by CNNs are thought to be more sensitive to relevant image content than human-designed features , offering a promising alternative for feature-based image analysis applications . However , learning high-quality features with CNNs is a current research challenge because it depends highly on the training task . For example , autoencoders , or unsupervised CNNs trained to reconstruct input images , usually do not learn features that generalize well to other tasks [17–19] . On the other hand , classification tasks result in high-quality features [13 , 14] , but they rely on large , manually-labeled datasets , which are expensive and time-consuming to generate . For example , to address the challenge of labeling microscopy images in the Human Protein Atlas , the project launched a massive crowd-sourcing initiative in collaboration with an online video game , spanning over one year and involving 322 , 006 gamers [20] . Because advances in high-content throughput microscopy are leading to routine generation of thousands of images [21] , the new cell morphologies and phenotypes discovered and need for integration of datasets [4] may require the continuous update of models . Unsupervised methods that result in the learning of high-quality features without the use of manual labels would resolve the bottleneck of collecting and updating labels: we would , in principle , be able to learn feature representations for any dataset , without the need for experts to curate , label , and maintain training images . If obtaining expert-assigned labels for microscopy images is challenging , then obtaining labels for the single cells within these images is even more difficult . Studying biological phenomena at a single-cell level is of current research interest [22]: even in genetically-identical cell cultures , single-cell variability can originate from a number of important regulatory mechanisms [23] , including the cell cycle [24] , stochastic shuttling of transcription factors between compartments [25] , or variability in the DNA damage response [26] . Thus , ideally , a method would efficiently generate feature representations of single cells for arbitrary datasets using deep learning , without the need for labelled single-cell training data . In this study , we asked if CNNs could automatically learn high-quality features for representing single cells in microscopy images , without using manual labels for training . We investigate self-supervised learning , which proposes training CNNs using labels that are automatically available from input images . Self-supervised learning aims to develop a feature representation in the CNN that is useful for other tasks: the training task is only a pretext , and the CNN may not achieve a useful level of performance at this pretext task . This differs from weakly-supervised learning , where a learnt network is used directly for an auxiliary task [27] , such as segmenting tumors in histology images using a network trained to predict disease class [28] . After training , the output of the CNN is discarded , and internal layers of the CNN are used as the features . The logic is that by learning to solve the pretext task , the CNN will develop features that are useful for other applications . The central challenge in self-supervised learning is defining a pretext task that encourages the learning of generalizable features [29] . Successful self-supervised learning strategies in the context of natural images , include CNNs trained to predict the appearance of withheld image patches based upon its context [18] , the presence and location of synthetic artifacts in images [29] , or geometric rotations applied to input images [30] . The idea is that to succeed at the pretext task , the CNN needs to develop a strong internal representation of the objects and patterns in the images . When transferred to tasks such as classification , segmentation , or detection , features developed by self-supervised methods have shown state-of-the-art results compared to other unsupervised methods , and in some cases , perform competitively with features learned by supervised CNNs [17 , 29–32] . Here , we present a novel self-supervised learning method designed for microscopy images of protein expression in single cells . Our approach leverages the typical structure of these images to define the pretext training task: in many cases , each image contains multiple genetically identical cells , growing under the same experimental condition , and these cells exhibit similar patterns of protein expression . The cells are imaged in multiple “channels” ( such as multiple fluorescence colours ) that contain very different information . By exploiting this structure , we define a pretext task that relies only upon image content , with no prior human labels or annotations incorporated . In our examples , one set of channels represents one or more structures in the cell ( e . g . the cytosol , nucleus , or cytoskeleton ) , and another channel represents proteins of interest that have been fluorescently tagged to visualize their localization ( with a different protein tagged in each image ) . Then , given both channels for one cell and the structural markers for a different cell from the same image , our CNN is trained to predict the appearance of the protein of interest in the second cell , a pretext task that we term “paired cell inpainting” ( Fig 1A ) . To solve this pretext task , we reason that the CNN must identify protein localization in the first ( or “source” ) cell and reconstruct a similar protein localization in the second ( or “target” ) cell in a way that adapts to single cell variability . In Fig 1 , the protein is localized to the nucleoli of human cells–the network must recognize the localization of the protein in the source cell , but also transfer it to the equivalent structures in the target cell , despite differences in the morphology of the nucleus between the two cells . Thus , by the design of our pretext task , our method learns representations of single cells . To demonstrate the generalizability of our method , we automatically learn feature representations for datasets of both human and yeast cells , with different morphologies , microscopes and resolutions , and fluorescent tagging schemes ( Fig 1B ) . We exemplify the quality of the features learned by our models in several use-cases . First , we show that for building classifiers , the features learned through paired cell inpainting improve in discriminating protein subcellular localization classes at a single-cell level compared to other unsupervised methods . Next , we establish that our features can be used for unsupervised exploratory analysis , by performing an unsupervised proteome-wide cluster analysis of protein analysis in human cells , capturing clusters of proteins in cellular components at a resolution challenging to annotate by human eye . Finally , we determine that our features are useful for single-cell analyses , showing that our features can distinguish phenotypes in spatially-variable single cell populations .
We would like to learn a representation for single cells in a collection of microscopy images , I . We define each image i as a collection of single cells , i = {ci , 1 …ci , n} . We note that the only constraint on i is that its single cells Ci must be considered similar to each other , so i does not need to be strictly defined as a single digital image so long as this is satisfied; in our experiments , we consider an “image” i to be all fields of view corresponding to an experimental well . We define single cells to be image patches , so c∈RH×W×Z , where Z are the channels . We split the images by channel into c = ( x , y ) , where x∈RH×W×Z1 , y∈RH×W×Z2 , and Z1 , Z2 , ⊆ Z . For this work , we assign to Z1 channels corresponding to structural markers , or fluorescent tags designed to visualize structural components of the cell , where all cells in the collection of images have been labeled with the tag . We assign to Z2 channels corresponding to proteins , or channels where the tagged biomolecule will vary from image to image . We define a source cell cs , which is associated with a target cell ct satisfying constraints that both cells are from the same image , cs ∈ is , ct ∈ it , is = it , and cs ≠ ct . Our goal is to train a neural network that will solve the prediction problem yt^=f ( xs , ys , xt ) ∀cs , ct∈I , where yt^ represents the predicted protein channels that vary between images . For this work , we train the network on the prediction problem by minimizing a standard pixel-wise mean-squared error loss between the predicted target protein yt^ and the actual target protein yt: L ( yt^ , yt ) =1h∙w∑h , w ( yt^h , w-yth , w ) 2 As with other self-supervised methods , our pretext training task is only meant to develop the internal feature representation of the CNN . After training , yt^ is discarded , and the CNN is used as a feature extractor . Importantly , while our pretext task predicts a label yt , we consider our overall method to be unsupervised , because these labels are defined automatically from image content without any human supervision . One limitation in our pretext task is that some protein localizations are not fully deterministic in respect to the structure of the cell and are therefore challenging to predict given the inputs we define . In these cases , we observe that the network produces smoothed predictions that we hypothesize are an averaged guess of the localization . We show an example in Fig 1C; while the source protein is localized to the nucleus in a punctate pattern , the target protein is predicted as a smooth distribution throughout the nucleoplasm . However , as our inpainting task is a pretext and discarded after training , we are not concerned with outputting fully realistic images . The averaging effect is likely due to our choice of a mean squared loss function; should more realistic images be desired , different loss functions , such as adversarial losses [33] , may produce better results . As the goal of our training task is to obtain a CNN that can encode single cell image patches into feature representations , we construct independent encoders for ( xs , ys ) and for xt , which we call the “source cell encoder” and the “target marker encoder” , respectively . After training with our self-supervised task , we isolate the source cell encoder , and discard all other components of our model . This architecture allows us to obtain a feature representation of any single cell image patch independently without also having to input target cell markers . To obtain single cell features , we simply input a single cell image patch , and extract the output of an intermediate convolutional layer in the source cell encoder . We show a summary of our architecture in Fig 1C . Following other work in self-supervised learning [17 , 18 , 30] , we use an AlexNet architecture for the source cell encoder , although we set all kernel sizes to 3 due to the smaller sizes of our image patches , and we add batch normalization after each convolutional layer . We use a smaller number of filters and fewer convolutional layers in the architecture of the target marker encoder; we use three convolutional layers , with 16 , 32 , and 32 filters , respectively . Finally , for the decoder , we reverse the AlexNet architecture . The goal of our training is to develop the learned features of our source cell encoder , which will later be used to extract single cell feature representations . If the network were to utilize bleed-through from the fluorescent marker channels to predict the target marker image , or overfit to the data and ‘memorize’ the target cell images , then the network would not need to learn to extract useful information from the source cell images . To rule out that our trained models were subject to these effects , we produced inpainting results from a trained model , where we paired source cells with target cells where there is a mismatch between source and target protein localization . Here , the model has seen both the source and target cells during training , but never the specific pair due to the structure of our training task , as the cells originate from different images . We qualitatively confirmed that our trained networks were capable of synthesizing realistic results agreeing with the protein localization of the source cell ( S1 Fig ) , suggesting that our models are not trivially overfitting to the target marker images . Because the training inputs to our self-supervised task consist of pairs of cells , the number of possible combinations is large , as each cell may be paired with one of many other cells . This property is analogous to data augmentation , increasing the number of unique training inputs to our network . To sample training inputs , for each epoch , we iterate through every single cell in our training dataset , set it as the source cell , and draw with uniform probability a target cell from the set of all valid possible target cells . Our pretext task relies on the assumption that protein expression in single cells from the same image is similar . This assumption is not always true: in the datasets used in this work , some proteins exhibit significant single cell variability in their protein abundance or localization [34 , 35] . These proteins may contribute noise because paired single cells will have ( unpredictably ) different protein expression patterns and the model will not learn . Although the Human Protein Atlas documents variable proteins [24] , for our experiments we do not remove these and confirm that our model still learns good features in spite of this noise . For yeast cells , we used the WT2 dataset from the CYCLoPS database [36] . This collection expresses a cytosolic red fluorescent protein ( RFP ) in all cells , and tags proteins of interest with green fluorescent protein ( GFP ) . We use the RFP channel as the structural marker , and the GFP channel as the protein . To extract single cell crops from the images in this dataset , we segmented our images using YeastSpotter on the structural marker channel [37] , and extract a 64x64 pixel crop around the identified cell centers; we discarded any single cells with an area smaller than 5% or greater than 95% of the image crop , as these are likely artifacts arising from under- or over-segmentation . We discard any images with fewer than 30 cells . We preprocessed crops by rescaling each crop to be in the range of [0 , 1] . These preprocessing operations result in a total of 1 , 165 , 713 single cell image patches grouped into 4 , 069 images ( where each image is 4 fields of view ) , with a total of 4 , 069 of 4 , 138 proteins passing this filter . We also trained a second different yeast cell model , using a NOP1pr-GFP library previously published by Weill et al . [37] This collection tags all proteins of interest with GFP at the N-terminal of proteins with a NOP1 promoter , and is also imaged in brightfield . We used the brightfield channel as a structural marker and the GFP channel as the protein . We extracted and preprocessed single cell crops using the same procedure for the CYCLoPS dataset , and discarded any images with fewer than 30 single cells . These preprocessing operations result in a total of 563 , 075 single cell image patches grouped into 3 , 067 images ( where each image is 3 fields of view ) , with a total of 3 , 067 of 3 , 916 proteins passing this filter . Finally , we trained a third yeast cell model , using a dataset previously published by Tkach et al . [38] , a Nup49-RFP GFP-ORF library . This collection expresses a nuclear pore protein ( Nup49 ) fused to RFP in all cells , and tags proteins of interest with green fluorescent protein ( GFP ) . We use the RFP channel as the structural marker , and the GFP channel as the protein . We extracted and preprocessed single cell crops using the same procedure for the CYCLoPS dataset , and discarded any images with fewer than 30 single cells . These preprocessing operations result in a total of 1 , 733 , 127 single cell image patches grouped into 4 , 085 images ( where each image is 3 fields of view ) , with a total of 4 , 085 of 4 , 149 proteins passing this filter . For human cells , we use images from version 18 of the Human Protein Atlas [24] . We were able to download jpeg images for a total of 12 , 068 proteins . Each protein may have multiple experiments , which image different cell line and antibody combinations . We consider an image to be of a protein for the same cell line and antibody combination; accordingly , we have 41 , 517 images ( where each image is usually 2 fields of view ) . We downloaded 3 channels for these images . Two visualize the nuclei and microtubules , which we use as the structural marker channels . The third channel is an antibody for the protein of interest , which we use as the protein channel . To extract single cell crops from this image , we binarize the nuclear channel with an Otsu filter and find nuclei by labeling connected components as objects using the scikit-image package [39] . We filter any objects with an area of less than 400 pixels , and extract a 512x512 pixel crop around the center of mass of remaining objects . To reduce training time , we rescale the size of each crop to 64x64 pixels . We preprocessed crops by rescaling each crop to be in the range of [0 , 1] , and clipped pixels under 0 . 05 intensity for the microtubule and nuclei channels to 0 to improve contrast . Finally , we remove any images with fewer than 5 cells , leaving a total of 638 , 640 single cell image patches grouped into 41 , 285 images , with a total of 11 , 995 of 12 , 068 proteins passing this filter . Crop sizes for our datasets ( 64x64 pixels for yeast cells and 512x512 pixels for human cells ) were chosen such that a crop fully encompasses an average cell from each of these datasets . We note that different image datasets may require different crop sizes , depending on the resolution of the images and the size of the cells . While each crop is centered around a segmentation , we did not filter crops with overlapping or clumped cells , so some crops may contain multiple cells . In general , we observed that our models did not have an issue learning to inpaint protein expression from a crop with multiple cells to a crop with a single cell , or vice versa: S1 Fig shows an example of a case where we inpaint protein expression from a crop with two cells to a crop with one cell . During training , we apply random horizontal and vertical flips to source and target cells independently as data augmentation . We trained models for 30 epochs using an Adam optimizer with an initial learning rate of 1e-4 . After training , we extract representations by maximum pooling the output of an intermediate convolutional layer , across spatial dimensions . This strategy follows previous unsupervised representation extraction from self-supervised learning methods [17 , 30] , which sample activations from feature maps . To benchmark the performance of features learned using paired cell painting , we obtained features from other commonly-used feature representation strategies . As classic computer vision baselines for our yeast cell benchmarks , we obtained features extracted using CellProfiler [40] for a classification dataset of 30 , 889 image crops of yeast cells directly from the authors [41] . These features include measurements of intensity , shape , and texture , and have been optimized for classification performance . Further details are available from [41] . We also extracted features from these yeast cell image crops using interpretable expert-designed features by Handfield et al . [1] We followed procedures previously established by the authors: we segmented cells using provided software , and calculated features from the center cell in each crop . For our transfer learning baselines in our yeast cell benchmarks , we used a VGG16 model pretrained on ImageNet , using the Keras package . We benchmarked three different input strategies: ( 1 ) we mapped channels arbitrarily to RGB channels ( RFP to red , GFP to green , blue channel left empty ) ; ( 2 ) we inputted each channel separately as a greyscale image and concatenated the representations; ( 3 ) we inputted only the GFP channel as a greyscale image and used this representation alone . In addition , we benchmarked representations from each convolutional layer of VGG16 , post-processed by maximum pooling across spatial dimensions ( as we did for our self-supervised features ) . S2 Fig shows classification performance using each layer in VGG16 with the kNN classifier described in our benchmarks , across all three strategies . In general , we observed that the 3rd input strategy resulted in superior performance , with performance peaking in the 4th convolutional block of VGG16 . We report results from the top-performing layer using the top-performing input strategy in our benchmarks . Contrary to previous work in transfer learning on microscopy images by Pawlowski et al . , we extract features from the intermediate convolutional layers of our transferred VGG16 model instead of the final fully-connected layer [15] . This modification allows us to input our images at their original size , instead of resizing them to the size of the images originally used to train the transferred model . As our work operates on single cell crops , which are much smaller than the full images benchmarked in previous work ( 64x64 pixels compared to 1280x1024 pixels ) , we found that inputting images at their original size instead of stretching them resulted in performance gains: our top-performing convolutional layer ( block4_conv1 ) with inputs at original size achieved 69 . 33% accuracy , whereas resizing the images to 224x224 and using features from the final fully-connected layer ( as-is , without max-pooling , as described in [15] ) achieves 65 . 64% accuracy . In addition , we found that extracting features from images at original resolution improves run-time: on our machine , inputting 64x64 crops and extracting features from the best-performing layer was about 16 times faster than inputting resized 224x224 images and extracting features from the final fully-connected layer . Finally , for the supervised baseline , we used the model and pretrained weights provided by Kraus et al . [6] . We inputted images as previously described . To ensure that metrics reported for these features were comparable with the other accuracies we reported , we extracted features from this model and built the same classifier used for the other feature sets . We found that features from the final fully connected layer before the classification layer performed the best , and report results from this layer . To compare the performance of various feature representations with our single yeast cell dataset , we built kNN classifiers . We preprocessed each dataset by centering and scaling features by their mean and standard deviation . We employed leave-one-out cross-validation and predicted the label of each cell based on its neighbors using Euclidean distance . S1 Table shows classification accuracy with various parameterizations of k . We observed that regardless of k , feature representations were ranked the same in their classification performance , with our paired cell inpainting features always outperforming other unsupervised feature sets . However , k = 11 produced the best results for all feature sets , so we report results for this parameterization . As classic computer vision baselines for our human cell benchmarks , we curated a set of texture , correlation , and intensity features . For each crop , we measured the sum , mean , and standard deviation of intensity from pixels in the protein channels , and the Pearson correlation between the protein channel and the microtubule and nucleus channels . We extracted Haralick texture features from the protein channel at 5 scales ( 1 , 2 , 4 , 8 , and 16 pixels ) . Finally , as the transfer learning baseline for our human cell benchmarks , we extracted features from the pretrained VGG16 model using the same input strategies and layer as established in our yeast cell benchmark . To directly measure and compare how a feature set groups together cells with similar localizations in their feature spaces , measured the average pairwise distance between cells in the feature space . We preprocess single cell features by scaling to zero mean and unit variance , to control for feature-to-feature differences in scaling within feature sets . Then , to calculate the distance between two single cells c , we use the Euclidean distance between their features f:d ( cx , cy ) =∥fcx-fcy∥2 . Given two images with the same localization term , we calculate the average distance of all cells in the first image paired with all cells in the second image and normalize these distances to an expectation of the average pairwise distance between images with different localization terms . A negative normalized average pairwise distance score indicates that the distances are smaller than expectation ( so single cells in images with the same label are on average closer in the feature space ) . For the Human Protein Atlas images , we restricted our analysis to proteins that only had a single localization term shared by at least 30 proteins , resulting in proteins with 18 distinct localization terms ( as listed in Fig 2B ) . For each localization term , we calculated average pairwise distances for 1 , 000 random protein pairs with the same localization term , relative to an expectation from 1 , 000 random protein pairs with each of the possible other localization terms ( for a total of 17 , 000 pairs sampled to control for class imbalance ) . For our experiments controlling for cell line , we also introduce the constraint that the images must be of cells of the same or different cell lines , depending on the experiment . Because some localization terms and cell line combinations are rare , we did not control for class imbalance and drew 10 , 000 random protein pairs with any different localization terms ( not necessarily each other different term ) , and compared this to 10 , 000 random protein pairs with the same localization term . Hence , the distances in the two experiments are not directly comparable . For proteins localized to two compartments , we calculated a score for each cell based upon its distance to the first compartment versus its distance to the second compartment . To do so , we averaged the feature vectors for all single-cells in images annotated to localize to each compartment alone to define the average features for the two compartments . Then , for every single-cell , we calculated the distance of the single-cell’s features relative to the two compartments’ averages and take a log ratio of the distance to the first compartment divided by the distance to the second compartment . A negative number reflects that the single-cell is closer to the first compartment in the feature space , while a positive number reflects that the single-cell is closer to the second compartment . Code and pre-trained weights for the models used in this work are available at https://github . com/alexxijielu/paired_cell_inpainting .
To assess the quality of feature learned through paired cell inpainting we trained a model for yeast fluorescent microscopy images using paired cell inpainting , on an unlabelled training set comprising of the entire WT2 screen in the CyCLOPS dataset , encompassing 1 , 165 , 713 single cells from 4 , 069 images . Good features are sensitive to differences in biology , but robust to nuisance variation [5] . As a first step to understanding if our features had these properties , we compared paired cell inpainting features with other feature sets at the task of discriminating different subcellular localization classes in yeast single cells . To do this , we made use of a test set of 30 , 889 single cell image patches manually assigned to 17 different protein subcellular localization classes by Chong et al . [41] . These single cells have been curated from a different image screen than the one we used to train our model , and thus represent an independent test dataset that was never seen by the model during training . To evaluate feature sets , we constructed a simple kNN classifier ( k = 11 ) and evaluated classification performance by comparing the predicted label of each single cell based on its neighbors to its actual label . While more elaborate classifiers could be used , the kNN classifier is simple and transparent , and is frequently employed to compare feature sets for morphological profiling [15 , 16 , 42 , 43] . Like these previous works , the goal of our experiment is to assess the relative performance of various feature sets in a controlled setting , not to present an optimal classifier . To use the feature representation from a self-supervised CNN , we must first identify which layers represent generalizable information about protein expression patterns . Different layers in self-supervised models may have different properties: the earlier layers may only extract low-level features , but the later layers may be too specific to production of the pretext task [12] . For this reason , identifying self-supervised features with a layer-by-layer evaluation of the model’s properties is standard [17–19 , 29 , 30 , 32] . Since our model has five convolutional layers , it can be interpreted as outputting five different feature sets for each input image . To determine which feature set would be best for the task of classifying yeast single cells , we constructed a classifier on our test set for the features from each layer independently , as shown in Table 1 . Overall , we observe that the third ( Conv3 ) and fourth ( Conv4 ) convolutional layers result in the best performance . Next , we sought to compare our features from paired cell painting with other unsupervised feature sets commonly used for cellular microscopy images . First , we compared our features with feature sets designed by experts: CellProfiler features [40] that were previously extracted from segmented single cells by Chong et al . to train their single cell classifier [41] , and with interpretable features measuring yeast protein localization calculated using yeast-specific segmentation and feature extraction software developed by Handfield et al . [1] . Second , we compared our features with those obtained from transfer learning , i . e . , repurposing features from previously-trained supervised CNNs in other domains [13 , 14]: we used features from a VGG16 model [44] , a previous supervised CNN trained on Imagenet , a large-scale classification dataset of natural images [45] . Finally , we transferred features from an autoencoder ( an unsupervised CNN ) trained on the same dataset as our paired cell inpainting models , using our source cell encoder and decoder architecture . Overall , the CellProfiler and VGG16 features perform at 68 . 15% and 69 . 33% accuracy respectively on our evaluation set , while the yeast-specific , interpretable features are worse at 62 . 04% . Autoencoder features achieve 42 . 50% accuracy . Our features from convolutional layer 4 ( Conv4 ) perform better than any of the other features benchmarked by a large margin , achieving 87 . 98% ( Fig 2A ) . As an estimate of the upper bound of classification performance with our kNN classifier , we evaluated the performance of features extracted by the layers of the state-of-the-art supervised CNN single-cell classifier trained with most of the test set used here [6] . The features from the best performing layer from this supervised model achieve 92 . 39% accuracy , which is comparable to the 87 . 98% accuracy achieved by the self-supervised paired cell inpainting features . Taken together , these results suggest that paired cell inpainting learns features comparable with those learned by supervised neural networks for discriminating protein subcellular localization in yeast single cells . To test if these improvements in classification performance depended on the kind of classifier used , we also benchmarked the performance of logistic regression and random forest classifiers built with all feature sets ( S2 Table ) . We observed that our paired cell inpainting features continue to outperform other unsupervised feature sets by a large margin , even with different classification models . To assess if the improvements in classification performance over other unsupervised methods was because our features form a general global representation of protein localization , as opposed to just clustering similar cells locally , we visualized features for this yeast single cell dataset using UMAP [46] ( S3 Fig ) . We observed that with our paired cell inpainting features , single cells with the same label cluster together in a continuous distribution in the UMAP-reduced space , similar to those learned by the fully-supervised model , whereas this effect is not as strong with CellProfiler features or VGG16 features . Finally , to assess if our method could learn effective representations for yeast image datasets regardless of modality or fluorescent tagging scheme , we trained two new models on two additional datasets: a yeast GFP-ORF collection imaged in brightfield [47] , and a yeast GFP-ORF collection imaged with a nuclear pore structural marker [38] . We averaged the features of all single cells for each protein for each model and visualized the protein representations using UMAP ( shown in Fig 3 , along with example images from each dataset ) , coloring points using previous manual labels on the protein level [47 , 48] . We observed distinct clusters for most protein localization labels for the representation produced by each model . Remarkably , even though each model is independent and trained on an independent dataset , we observed a substantial degree of consistency between clusters in the distances of their learned representations: for example , in all three representations , the “nucleolus” cluster was close to the “nucleus” cluster , and the “ER” and “cytoplasm and nucleus” clusters were adjacent to the “cytoplasm” cluster . To further test the generalizability of the method , we trained a model for human fluorescent microscopy images with paired cell inpainting , on the entirety of the Human Protein Atlas , encompassing 638 , 640 single cells from 41 , 285 images . While the Human Protein Atlas does contain information about the general protein localization for each image , our goal is to demonstrate that our method can work on high-content human datasets without expert labels . Hence , for training our model , we do not make use of these labels in any form . To evaluate the feature representation of single human cells , we directly analyzed the pairwise distances in the feature space . In contrast to the previous analysis ( Fig 2A ) , we do not have a labeled single cell dataset for human cells: annotations in the Human Protein Atlas are at image-level . We therefore did not analyze classification performance . Instead , since we expect features to give similar representations to cells with similar protein localization patterns ( ideally independent of cell morphologies , illumination effects , or cell lines ) , we compared the distance between cells with similar localization patterns in the feature space to cells with different localization patterns . We computed the normalized average distance between cells in images for proteins with the same gene ontology ( GO ) localization term ( see Methods ) : a more negative score indicates that cells with the same GO term are on average closer together in the feature space ( Fig 2B ) . We observed that features from both the Conv3 ( -0 . 574 overall ) and Conv4 ( -0 . 349 overall ) layers show smaller distances for proteins with the same GO localizations than classic computer vision features ( -0 . 274 overall ) , autoencoder features ( -0 . 281 overall ) , and features transferred from a pre-trained VGG16 model ( -0 . 325 overall ) . To test the robustness of the feature representation to morphological differences , we repeated the above experiment , but controlling for cell line . We reasoned that if a feature set was robust , cells with similar localization patterns would show smaller distances , even if the cells had different morphologies . We compared the normalized average pairwise distance between cells from images with the same GO localization term , when both images were constrained to be from different cell lines , compared to when both images were constrained to be from the same cell lines ( Table 2 ) . We note that we normalized the results shown in Table 2 differently from that of Fig 2B , so that the distances are not directly comparable ( see Methods ) . As expected , all feature sets have higher distances in their feature spaces for cells with similar localizations , when cells come from different cell lines . However , paired cell inpainting features have the smallest drop , and still perform the best of all feature sets , even when cells come from different cell lines with different typical cell morphologies . These results indicate that our method produces single cell features that not only place similar cells closer together in the feature space , but that they are more robust to morphological variation . Interestingly , while features from Conv4 were worse at separating different protein localizations than Conv3 , its features were the most robust to cell line . Based upon this observation , we hypothesize that the earlier layers in our model may represent sensitive information directly extracted from the images , whereas later layers assemble this information into a more robust , morphology-invariant form . We also noticed that the best overall performing layer differed from our yeast model to our human model . These results suggest that the behavior of layers may differ from dataset to dataset , and a preliminary evaluation of layer-by-layer properties is important for selecting the best layer for subsequent applications of the features . However , the middle layers in our model ( Conv3 and Conv4 ) still outperformed other unsupervised feature sets in both models , suggesting that the selection of layer to use is still robust even in the absence of this preliminary evaluation . We next sought to demonstrate the utility of the features to various applications in cell biology . First , we used the paired cell inpainting features from Conv3 of our human model in an exploratory cluster analysis of human proteins ( Fig 4 ) . Similar clustering experiments have been performed for yeast proteins , and have found clusters that not only recapitulate previously-defined subcellular localization patterns , but discover protein localization and function at a higher resolution than manual assignment [1] . For this analysis , we included every protein in the Human Protein Atlas that we obtained enough single cells for ( a total of 11995 of 12068 proteins ) . All of these proteins are shown in Fig 4 . We averaged the features of all single cells for each protein ( pooling together all single cells from different cell lines and antibodies ) , and clustered the averaged features using hierarchical agglomerative clustering . While hierarchical clustering has previously been applied to a subset of human proteins in a single cell line to identify misannotated proteins [49] , our analysis is , to our knowledge , the most comprehensive for the Human Protein Atlas . Gene ontology ( GO ) enrichments for manually identified clusters in Fig 4 . For each cluster , we report selected GO annotation terms in the component ontology for Homo sapiens , their enrichment ( adjusted with the false discovery rate ) , and the number of proteins with the enrichment versus the total number of proteins in the cluster . We used all human proteins in our clustering solution as the background for these enrichments . We used GOrilla [50] , a bioinformatics tool for identifying enriched GO terms . Full lists of the proteins in each cluster are available as part of S1 Data . We find that proteins in the same cellular components generally cluster together ( Table 3 ) . We identified clusters enriched for about 15 of the 18 localization classes with more than 30 proteins . As these are the cellular components manually annotated by the Human Protein Atlas , these results suggest that our unbiased , unsupervised clustering of proteins broadly agrees with the protein localization classes assigned by biologists . Within large clusters of more general localizations , we also identified smaller sub-clusters of protein complexes or functional protein classes , including the spliceosomal complex ( Cluster A2 ) , the ribonucleoprotein complex ( Cluster E3 ) , and the preribosome ( Cluster D2 ) . These are cellular components that are not typically identifiable by human eye , suggesting that our features are describing the localization of the proteins at a higher-resolution than human annotation . To assess the ability of our clustering to find rare localization patterns , we asked if we could identify proteins localized to the nucleolar rim . Out of 226 , 732 images in the Human Protein Atlas , Sullivan et al . only classify 401 , or 0 . 18% , as nucleolar rim [20]; based on this proportion , we estimated that ~20 proteins in our clustering solution would localize in this manner . The version of the Human Protein Atlas used in this study did not incorporate the nucleolar rim annotation , but the documentation gives one example , MKI67 [24] . We found MKI67 in Cluster D1 . Of the 32 proteins closest to MKI67 in our hierarchical clustering , we identified 13 other proteins with an unambiguous nucleolar rim localization ( we list these proteins in S1 Data and provide links to their images in the Human Protein Atlas ) , although there may also be others too ambiguous to determine by eye . We show three of these in Fig 4 , NCL ( Fig 4b ) , EN1 ( Fig 4c ) , and ETV4 ( Fig 4d ) . This supports the idea that unsupervised analysis in the feature space can identify rare patterns for which training classifiers would be difficult . We observed several clusters of nuclear speck proteins , including clusters A1 , A2 , and M2 . We decided to evaluate images for proteins in these clusters to determine if there is any distinction in their phenotype . We observed that proteins in clusters A1 and A2 tended to localize purely to nuclear specks ( Fig 4a ) , whereas proteins in cluster M2 tended to localize to both nuclear specks and the cytosol ( Fig 4e ) . Consistent with this interpretation , we found a strong enrichment for the spliceosomal complex for cluster A1 , but no enrichment for cluster M2 . To further test the possibility that the unsupervised analysis in the feature space reveals higher resolution functional information than expert annotation , we asked if our features could be used to discover higher-resolution subclasses of proteins that are difficult for experts to annotate visually . First , previous work with vesicle-localized proteins in the Human Protein Atlas has suggested that these proteins can be distinguished into many subclasses [2 , 51] . We clustered features for human proteins annotated to localize to the vesicle only , and found structure in the feature representation that corresponds to visually-distinct vesicle patterns ( Abundantly and more evenly distributed in the cytosol , concentrated closer to the nucleus or sparse puncta , S4 Fig , Clusters A , B and C respectively ) . However , we were unable to associate these with any biological functions , so it is unclear if these distinctions in the patterns of vesicle distribution are functional . Next , we used features obtained from paired cell inpainting to organize proteins labelled as “punctate” in yeast dataset of images of a NOP1pr-GFP library [47] ( see Methods ) . We find that we can distinguish Golgi , peroxisome and another cluster that contained singular or sparse foci in the cells ( S5 Fig , Cluster B , C and A respectively ) . These analyses support the idea that unsupervised analysis of our features can reveal higher biological resolution than visual inspection of images . Next , we asked if features from our models could be used for the analysis of single cells of multiply-localized proteins . While multiply-localized proteins are critical to study as they are often hubs for protein-protein interactions [52 , 53] , these proteins have posed a challenge for imaging methods . Most supervised methods remove multiply-localized proteins from their training and evaluation sets [6 , 9 , 41 , 54] , focusing on only proteins localized to a single subcellular compartment . In supervised efforts , Sullivan et al . propose training with multi-label data [20] , but curating this training data is challenging due to sparsity in some combinatorial classes and difficulty in manually annotating complex mixtures of patterns . In unsupervised work , unmixing algorithms [55] and generative models [56 , 57] have been proposed , but these methods generally rely on elaborate user-defined models of the conditional dependencies between different compartments . Thus , extending models to multiply-localizing proteins is non-trivial and an ongoing area of research . We applied our features to analyze human single cells in images annotated to localize to the cytosol-and-nucleoplasm and to the nucleus-and-nucleoli by the Human Protein Atlas . Using the single-cell features from Conv3 of our human model , we measured a simple score based on distances in our feature space ( see Methods ) . For our cytosol-and-nucleoplasm analysis , a negative score indicates that the cell is closer to the average cytosol-only cell than the average nucleoplasm-only cell in the feature space , while a positive score indicates that the cell is closer to the average nucleoplasm-only cell than the average cytosol-only cell . For our nucleoli-and-nucleus analysis , a negative score indicates that the cell is closer to the average nucleoli cell than the average nucleus cell , while a positive score indicates that the cell is closer to the average nucleus cell than the average nucleoli cell . We calculated this score for every single-cell localized to both compartments , or to either compartment alone ( Fig 5 ) . We observe that single-cells with a cytosol-only annotation have a more positive score , while single-cells with a nucleus-only annotation have a more negative score ( Fig 5A ) . In contrast , single-cells annotated to localize to both compartments have a score closer to 0 . We observed that the skew of multiply-localized single-cells towards a positive or negative score reflects the proportion of protein localized to the two compartments: for example , for single cells stained for the spatially-variable protein LYPD5 in the U-251 MG cell line , we observed that positive scores corresponded to a more dominant nuclear localization , negative scores corresponded to a more dominant cytosol localization , and scores closer to 0 corresponded to a more even mix of both localizations ( Fig 5A ) . We made similar observations for nucleoli-and-nucleus single-cells ( Fig 5B ) . However , we also noticed that while most of the single-cells with a nucleoli-only annotation had negative scores , some had more positive scores , suggesting that the nucleoli-and-nucleus images are often annotated as nucleoli-only . These results suggest that simple distances in our feature space could be used to not only identify multiply-localized proteins , but also quantitate the relative distribution of protein between its compartments . We have included the mean scores for every cytosol-and-nucleoplasm and nucleoli-and-nucleus protein by cell line as S2 Data . We observed that proteins with spatial-variability sometimes had different distributions over our scores compared to more homogeneously-localized proteins ( Fig 5 ) . This observation suggested that the standard deviation could be useful in discovering proteins with single-cell variability . We looked at the 10 images with the highest standard deviations for cytosol-and-nucleoplasm single-cells , and for nucleoli-and-nucleus single-cells . For cytosol-and-nucleoplasm single-cells , 6/10 are previously annotated by the Human Protein Atlas to have single-cell variability , and we observed unambiguous , but previously unannotated , single-cell variability in the images for the remaining 4 . For nucleus-and-nucleoli single-cells , 5/10 are previously annotated , and we observed unambiguous and previously unannotated single-cell variability for the remaining 5 ( we show one example of a previously unannotated image in Fig 5B , for MYL5 in the A-431 cell line ) . While this simple metric would be less sensitive to variable proteins where the single-cells are imbalanced in their distribution of the different phenotypes , these results suggest that simple statistics in our feature space can identify at least some kinds of single-cell variability . We include the standard deviation of scores for every cytosol-and-nucleoplasm and nucleoli-and-nucleus protein by cell line in S2 Data . To organize single-cell variability , without prior knowledge of the specific compartments the protein is localized to , we decided to analyze single-cells using clustering in the feature space . We selected three proteins with spatial variability at the single-cell level: SMPDL3A , which localizes to the nucleoli and mitochondria ( Fig 6A ) ; DECR1 , which localizes to the cytosol and mitochondria ( Fig 6B ) ; and NEK1 , which localizes to the nucleoli and nuclear membrane ( Fig 6C ) . We clustered the single cells of each protein using hierarchical agglomerative clustering . Fig 6 shows the dendograms of the clustering solution for each protein , as well as the single-cell image crops associated with representative branches of the dendogram . In general , we observe distinct clusters of single cells for spatially variable proteins , visible as distinct clades in the dendograms ( Fig 6 ) . For SMPDL3A , we observe clusters of mitochondria-localized and of nucleoli-localized single cells , and for DECR1 , we observe clusters of mitochondria-localized and cytosol-and-mitochondria-localized single cells , consistent with their labels in the Human Protein Atlas . For NEK1 , we observed distinct clusters for the nuclear membrane and nucleoli , consistent with its label , but also for single-cells of a third phenotype , which appeared as fine and diffuse clumps of protein within the nucleus , not recorded in the Human Protein Atlas [24] . We hypothesize this may be due to the challenges in manually annotating a visually complex image with a heterogenous mixture of cells . These results suggest that single-cell clustering in the feature space can organize the phenotypes in a heterogeneous image and quantify the relative proportion of each phenotype .
We present a simple , but effective self-supervised learning task for learning feature representations of single cells in fluorescent microscopy images . Features learned through paired cell inpainting easily achieve state-of-the-art performance in unsupervised benchmarks for both yeast and human cells . We believe that this effectiveness is because our pretext training task controls for nuisance variation . Because our task requires the network to predict the phenotype in a different cell , the network must ignore cell-specific variation to learn more general features about the phenotype . As we show the network the target cell structural markers during training , the network is directly given experimental effects like local illumination , meaning that it does not have to learn features for these in the source cell encoder . A key advantage to self-supervised methods is that they learn features directly from data . Our method exploits the natural structure of many imaging experiments and can be applied to learn single-cell representations for virtually any multi-channel microscopy image dataset . To demonstrate the generality of paired cell inpainting , we learned representations for four independent image datasets in this study . Two of the yeast datasets have previously only been analyzed through laborious manual inspection of thousands of images [38 , 47] . We believe this is likely due to a lack of generality in previous automated methods: both of the classic computer vision methods we applied on the CyCLOPS dataset rely on accurate single-cell segmentation [1 , 58] which is challenging on the NOP1-ORF library due to highly overlapping and clumped cells , and impossible for the Nup49-RFP GFP-ORF dataset due to a lack of any cytoplasmic indicator . Similarly , neither dataset has a labeled single-cell dataset associated with it , preventing the application of supervised methods until this laborious work is complete . These datasets demonstrate important points about the context our proposed method operates in: while we show we can achieve performance gains on well-studied datasets like the CyCLOPS dataset , many biological imaging datasets are not as amenable for automated analysis . We believe that for these kinds of datasets , our method can produce similarly discriminative representations , at least on the protein level ( Fig 3 ) , enabling the fully automated analysis of these datasets without accurate segmentation or expert image labeling . For this reason , we believe that self-supervised learning is an effective way to handle the increasing volume and diversity of image datasets as high-content imaging continues to accelerate , especially with the development of scalable tagging technologies and new libraries [47 , 59] . To address this demand , other self-supervised methods are also emerging: Caicedo et al . propose training a CNN to classify cells by their drug treatment [19] . We note that in contrast to their proposed proxy task , our task is predictive , rather than discriminative , and has some technical advantages in theory: since we do not force the CNN to discriminate between experiments that yield identical phenotypes , we expect that our method is less susceptible to overfitting on experimental variation . In practice , both methods work well on for their respective benchmarked applications . The choice of proxy task may be therefore be constrained by practical considerations: for example , our task cannot be applied on single-channel images , or images where cell centers cannot be automatically identified . While self-supervised learning methods for biological images are still emerging , in the future , a comparative analysis of the performance of different methods on different applications would be highly informative to researchers deciding which proxy task to apply to their own data . While the quality of the features we learned were generally robust to different parameters and preprocessing operations on the dataset , outperforming other unsupervised methods regardless of settings , we found several factors that we believe could improve the features . First , filtering proteins that have single-cell variability in their protein expression , for example , by using labels in the Human Protein Atlas [24] . Likely , this would reduce amount of noise caused by pairing single cells with different protein expression during training . Second , while our method does not rely on accurate single-cell segmentations [1 , 40] , it uses a crop centered around single cells in the image . We found that the method we used to detect cell centers for cropping was important , with classic segmentation techniques based on watershedding [1] resulting in poorer features than the methods used here ( see Methods ) . We hypothesize that this is because artifacts included as cells increase noise during training when a cell gets paired with an artifact . Here , we focused on the proxy task . Like most previous self-supervised work , we used a simple AlexNet architecture [17–19 , 29 , 30] . However , future optimizations to the architecture will likely improve the applicability and performance of our method . A major limitation of self-supervised methods that use the AlexNet architecture is that the layer to extract features with must be determined . For most proxy tasks , the best performing-layer is an intermediate convolutional layer in AlexNet [17–19 , 29 , 30] . However , recent work optimizing architectures for self-supervised methods suggests that architectures with skip-connections can prevent the quality of self-supervised representations from degrading in later layers [60] . Applying these insights to our method might mean that the final layer could always be chosen for transfer to new tasks , improving the practicality of the method . We note that some technical points specific to our method may also improve performance . First , some proteins localize in a pattern that cannot be predicted deterministically from the structural markers of the cell alone . The inability of the network to accurately reconstruct these proteins may limit its feature learning . Future work in loss functions that encourage more realistic outputs , such as adversarial losses , could improve this issue . Second , for our experiments , we iterated over all cells in our training dataset and sampled pairs randomly . A more sophisticated pair sampling scheme may improve the quality of the features: for example , focusing on harder pairs where the morphology of the cells differs more drastically . There are also some protein localization patterns correlated with the cell cycle , such as the mitotic spindle . Due to their low penetrance in an image ( for example , only a fraction of the cells will be undergoing mitosis ) , we do not expect our model to learn these patterns very well . Even if these cells are paired correctly , there may only be one or two cells with the specific cell cycle stage required to exhibit the pattern in the entire image . Better datasets with more fields of view , or work in finding an unsupervised method for balancing the patterns in the data , may improve this issue . In the final aspect of this work , we performed , to our knowledge , the most comprehensive unsupervised analysis of protein localization spanning the entire Human Protein Atlas to date . Despite pooling all cell lines and antibodies together for proteins , we capture enrichments for high-resolution cellular components in our cluster analysis . Moreover , we showed that we could identify rare phenotypes , and use our features to discover distinct subclasses of proteins unsupervised . These results emphasize the importance of unbiased analysis , in contrast to supervised approaches . Rare phenotypes may be excluded from classifiers due to lack of training data , or lack of knowledge of which images to mine training data from . We discovered several proteins with a nucleolar rim localization , based upon the one example given by the Human Protein Atlas . Similarly , pre-defined classes may hide functionally-important variability within classes , and may be biased by human pre-conceptions of the data . We showed that coarse human-annotated classes can be clustered into distinct subclasses , and that functional protein classes such as the preribosome or splicesomal complex are clustered in protein localization data . Overall , these results demonstrate the power of unsupervised analysis in discovering new and unexpected biology , a task that supervised methods are fundamentally unable to perform , no matter how accurate they are at annotating pre-defined human knowledge . | To understand the cell biology captured by microscopy images , researchers use features , or measurements of relevant properties of cells , such as the shape or size of cells , or the intensity of fluorescent markers . Features are the starting point of most image analysis pipelines , so their quality in representing cells is fundamental to the success of an analysis . Classically , researchers have relied on features manually defined by imaging experts . In contrast , deep learning techniques based on convolutional neural networks ( CNNs ) automatically learn features , which can outperform manually-defined features at image analysis tasks . However , most CNN methods require large manually-annotated training datasets to learn useful features , limiting their practical application . Here , we developed a new CNN method that learns high-quality features for single cells in microscopy images , without the need for any labeled training data . We show that our features surpass other comparable features in identifying protein localization from images , and that our method can generalize to diverse datasets . By exploiting our method , researchers will be able to automatically obtain high-quality features customized to their own image datasets , facilitating many downstream analyses , as we highlight by demonstrating many possible use cases of our features in this study . | [
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"psychol... | 2019 | Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting |
The axon plasma membrane consists of the membrane skeleton , which comprises ring-like actin filaments connected to each other by spectrin tetramers , and the lipid bilayer , which is tethered to the skeleton via , at least , ankyrin . Currently it is unknown whether this unique axon plasma membrane skeleton ( APMS ) sets the diffusion rules of lipids and proteins in the axon . To answer this question , we developed a coarse-grain molecular dynamics model for the axon that includes the APMS , the phospholipid bilayer , transmembrane proteins ( TMPs ) , and integral monotopic proteins ( IMPs ) in both the inner and outer lipid layers . We first showed that actin rings limit the longitudinal diffusion of TMPs and the IMPs of the inner leaflet but not of the IMPs of the outer leaflet . To reconcile the experimental observations , which show restricted diffusion of IMPs of the outer leaflet , with our simulations , we conjectured the existence of actin-anchored proteins that form a fence which restricts the longitudinal diffusion of IMPs of the outer leaflet . We also showed that spectrin filaments could modify transverse diffusion of TMPs and IMPs of the inner leaflet , depending on the strength of the association between lipids and spectrin . For instance , in areas where spectrin binds to the lipid bilayer , spectrin filaments would restrict diffusion of proteins within the skeleton corrals . In contrast , in areas where spectrin and lipids are not associated , spectrin modifies the diffusion of TMPs and IMPs of the inner leaflet from normal to confined-hop diffusion . Overall , we showed that diffusion of axon plasma membrane proteins is deeply anisotropic , as longitudinal diffusion is of different type than transverse diffusion . Finally , we investigated how accumulation of TMPs affects diffusion of TMPs and IMPs of both the inner and outer leaflets by changing the density of TMPs . We showed that the APMS structure acts as a fence that restricts the diffusion of TMPs and IMPs of the inner leaflet within the membrane skeleton corrals . Our findings provide insight into how the axon skeleton acts as diffusion barrier and maintains neuronal polarity .
A neuron is an electrically excitable and highly polarized cell that primarily functions to receive , integrate , and transmit information . A neuron is comprised of three main compartments: a soma , dendrites , and an axon [1] . A key aspect of neuronal function is the integration of arriving synaptic potentials , and generation and propagation of action potentials down a single axon [2 , 3] . Multiple studies have shown that axons are cylindrical structures consisting of the axon plasma membrane ( APM ) and cytoplasm [4] . The APM is composed of two main substructures: the phospholipid bilayer , which contains ion channels and other membrane proteins , and the membrane skeleton that tethers to the phospholipid bilayer ( Fig 1 ) . The membrane cytoskeleton is also connected to axon microtubules via ankyrin proteins [5] . Recent research revealed that the axon plasma membrane skeleton ( APMS ) has a unique long-range periodic structure that is comprised of a series of actin rings distributed along the axon ( Fig 1 ) [6] . The actin rings connect to each other via extended spectrin filaments [6–8] . The consensus in the field is that the periodic structure of the APMS contributes to the integrity and mechanical stability of the axon [5 , 9–11] . A unique section of the axon called the axon initial segment ( AIS ) is usually located 20–60 μm from the soma and is the site of action potential initiation [12 , 13] . The AIS also acts as a filter , separating membrane proteins and lipids between the axon and the somatodendritic neuronal subcompartments [14–18] . Researchers have proposed that the AIS regulates protein movement between the somatodendritic and axonal regions via a surface diffusion barrier and an intracellular traffic filter [14 , 19] . This theory was confirmed by a single particle tracking ( SPT ) technique that measures the mobility of transmembrane proteins ( TMPs ) such as axonal cell-adhesion molecule L1 [18] and vesicle-associated membrane protein VAMP2 [17] . In addition , a recent study found that the motion of glycosylphosphatidylinositol-anchored green fluorescent protein ( GPI-GFP ) molecules in the AIS is confined within repetitive stripes with boundaries coinciding with the observed actin rings [20] . Currently , it is unknown how the AIS impedes the thermal motion of lipids and membrane proteins between the axon and soma . Two leading theories explain how the mobility of lipids and surface proteins is reduced in the AIS . Based on the “fence and picket” model [21] , the reduced mobility is caused by steric hindrance between the membrane skeleton and the diffusing proteins , and is further enhanced by accumulation of membrane proteins tethered to membrane scaffolding molecules , such as ankyrin-G [15 , 22 , 23] . In a similar case , studies have shown that the membrane skeleton hinders lateral diffusion of band-3 proteins in the red blood cell membrane [24 , 25] . The more recent “actin fence” model [14 , 20] suggests that axon actin rings act as barriers and confine the motion of integral monotopic proteins ( IMPs ) on the outer lipid , within the ~190 nanometer area determined by actin rings . It is possible that the “fence and picket” model , the “actin fence” model , or a combination of the two could explain diffusion in axonal membranes . However , current attempts to analyze experimental data based on the “actin fence” model are limited [14 , 20] , and no numerical studies have implemented the “fence and picket” model . More importantly , several aspects of APM protein diffusion cannot be directly answered by either model . For instance , given that actin is located intracellularly , it is not clear how the actin rings could interfere with lipids and IMPs of the outer leaflet of the phospholipid bilayer . In addition , we do not know whether diffusion of TMPs , lipids , and IMPs of the inner leaflet is influenced by the interactions of these macromolecules with the spectrin tetramers of the APMS . As the structure of the APMS is orthotropic , with a different geometry along the longitudinal versus the transverse direction , we might expect that longitudinal diffusion differs from transverse diffusion . From a fundamental physics perspective , the type of macromolecule diffusion in a cellular membrane varies as it critically depends on the local membrane composition and the time scale . In some cases , the diffusion is normal , where the mean squared displacement ( MSD ) is proportional to time; in other cases , the diffusion is abnormal with MSD disproportional to time . Identifying different diffusion types may provide insight into membrane structure and its interactions with diffusing lipids and proteins . In the case of the APM , if membrane proteins cannot pass through the actin rings , the longitudinal MSD will eventually reach a constant value that is determined by the distance between consecutive actin rings , and the diffusion will become confined . When actin or actin-associated proteins interact with diffusing proteins or lipids via steric repulsion or transient association , steric hindrance or occasional trapping can occur . In this case , thermal motion can potentially become anomalous subdiffusion with the MSD proportional to tα , with ( α<1 ) . Depending on the time scale , diffusion may also vary from normal diffusion at small time scales , to transient anomalous diffusion at intermediate time scales , and to normal diffusion with a much lower diffusion coefficient at very large time scales [25 , 26] . At small time scales , lipids and membrane proteins have not interacted yet with the boundary of the APMS corrals and the diffusion is defined as microscopic diffusion . At intermediate time scales , the interactions between diffusing particles and spectrin filaments , or between diffusing particles and mobile or tethered-to-the-APMS proteins will likely determine the type of transverse diffusion . To discern the role of the periodic AMPS structure in lipid and protein diffusion of TMPs and IMPs of the outer and inner leaflets of the axon phospholipid bilayer , we developed a coarse-grain molecular dynamics ( CGMD ) model for the APM that includes the phospholipid bilayer , APMS , and axonal membrane proteins . Using our model , we ( i ) determined the role of actin rings in the diffusive motion of TMPs and IMPs of the inner leaflet within the areas between adjacent actin rings; ( ii ) showed that the association between spectrin filaments and lipids affects the circumferential ( transverse ) diffusion of TMPs and IMPs of the inner leaflet , but not the diffusion of the lipids and IMPs of the outer leaflet; ( iii ) determined that actin rings and spectrin filaments restrict the motion of membrane proteins along and around the circumference of the axon , respectively; and ( iv ) showed that the surface diffusion barrier in the AIS is formed as a result of both the accumulation of concentrated TMPs and the actin fence . Our findings help clarify how the APMS structure limits diffusion of proteins and lipids . Furthermore , it may provide critical insight on how the dysfunction of actin spectrin-associated proteins leads to neurological and neuropsychiatric disorders [27] .
We developed a particle-based mesoscale model of the APM of the neuronal axon that includes ( 1 ) the phospholipid bilayer as a double layer , including IMPs ( in both inner and outer leaflets ) and TMPs , and ( 2 ) the APMS . We followed an approach comparable to the one that we used to model the red blood cell ( RBC ) plasma membrane , which consists of structural elements similar to those in the neuronal APM [28–30] . Regarding the plasma membrane skeleton of the RBC and the axon , both consist of spectrin filaments connected at the actin junction , although they are arranged in a fundamentally different way . In the RBC membrane , the spectrin tetramers extend to an approximately equilibrium length and are connected at actin junctions to form a two-dimensional ( 2D ) canonical hexagonal network that corresponds to an isotropic homogeneous 2D material [31] . In the neuronal APMS , actin is arranged in circular rings oriented along the circumference of the axon and connected to each other via spectrin filaments oriented along the longitudinal direction of the axon ( Fig 1 ) . The spectrin filaments extend to almost their contour length , meaning that they are under entropic tension . Because of this arrangement , the APMS behaves as an orthotropic material with different mechanical properties along the axon and perpendicular to the axon . We included here the model of the APMS for completeness . Its detailed description can be found in Zhang et al [11] . Evidence suggests that the connection between the APMS and the phospholipid bilayer is similar between the axon and the RBC membrane [6 , 32 , 33] , where the spectrin network is tethered to the phospholipid bilayer via the ankyrin-band-3 complex and glycophorin . Specifically , in RBCs a β-spectrin dimer binds to ankyrin near its C terminus , which is located at approximately the middle area of the spectrin tetramer . Ankyrin then binds to the transmembrane ion channel band-3 . In addition , actin junctional complexes bind to the phospholipid bilayer via glycophorin [34 , 35] . In the AIS , the spectrin tetramers are most likely connected to ankyrin G , as in the RBC , and ankyrin G likely anchors the phospholipid bilayer to the APMS by binding to voltage-gated sodium ( Nav ) and potassium ( Kv ) channels . Indeed , super-resolution microscopy experiments have shown that the Nav channels exhibit a periodic ring-like distribution pattern that alternates with actin rings and co-localizes with ankyrin G [6 , 8] . Although anchoring of the phospholipid bilayer to APMS at the middle area of spectrin tetramers via ankyrin G is likely , it is still unclear whether the phospholipid bilayer is also anchored at the actin rings . One possibility is that a TMP , playing the role of glycophorin in the RBC membrane , is connected directly or indirectly via an actin-associated protein to actin rings . We discuss the association between the APMS and the phospholipid bilayer in the axon further in the following section .
In this study , we investigated the effects of the APMS and accumulation of TMPs on lipid and protein diffusion in the APM . Importantly , we considered the cylindrical shape of the axon when calculating the diffusion parameters [58] . To calculate the MSD as a function of time using numerical data , we took advantage of the fact that distances on a cylindrical surface are preserved during rolling unwrap . We first unwrapped the positions of the particles on the cylinder by rolling it on a 2D plane , and then calculated the MSD in the 2D plane ( S4 Fig ) . We were able to neglect the radial displacements dr of membrane particles caused by thermal fluctuations as they were small compared to the radius r of the axon ( < 5% of the axon radius ) . Consequently , a particle’s changed coordinates within the cylindrical coordinate system ( dr , rdθ , dz ) were transferred into a 2D plane surface as ( rdθ , dz ) . Moreover , we measured the MSD ( r2dθ2+dz2 ) on the cylindrical surface on the plane with coordinates du = rdθ and dv = dz . In several instances , we decoupled the thermal motion into longitudinal diffusion ( 1D diffusion along the axon ) and transverse diffusion ( 1D diffusion along the axon’s circumference ) as shown in S4 Fig . We reconstructed the overall 2D diffusive motion by combining the MSDs of the longitudinal and transverse components of diffusion . We first established the time scale for the diffusion simulations . We measured diffusion of lipids and compared our simulations to the experimental results of Nakada and colleagues [16] . We found that the time dependence of MSD was similar for lipids in both the inner and outer leaflets ( Fig 3A ) . This suggests that the diffusion of lipids in our model was not significantly affected by the APMS . Furthermore , as the dependence of MSD with respect to time was linear , the lipids underwent normal diffusion . We calculated the diffusion coefficient to be 1 . 14×10−2σ2/ts , where σ = 2 . 227 nm and is the time scale . By matching the diffusion coefficient of lipids Dlipid = 1 . 14×10−2σ2/ts with the experimentally measured diffusion coefficient of ~0 . 3 μm2/s [16] , we determined that ts = 1 . 82×10−7s . We note that the membrane viscosity can be found by the Stokes-Einstein equation η = KBT/6πDlipidrP , where rP is the radius of the coarse grain lipid particle . Using the determined time scale , we found that the corresponding membrane viscosity is 0 . 6N⋅s/m2 , which approximates the reported experimental data [59] . We further validated this result by calculating the diffusion coefficient of the IMPs of the outer leaflet . By fitting the MSD for the IMPs of the outer leaflet with a linear function of time ( Fig 3B ) , we determined that the diffusion coefficient was Douter = 3 . 84×10−3σ2/ts = 0 . 11 μm2/s . This result is similar to the experimentally measured diffusion coefficient value for GPI-GFP in the AIS , before the establishment of diffusion strips , and in the distal axon , which lacks diffusion strips [20] . After establishing a time scale that is consistent with experimental results , we determined the conditions in which actin rings could act as “fences” for diffusing outer leaflet , inner leaflet , and transmembrane proteins . Next , we investigated the role of spectrin filaments on the diffusion of axonal membrane proteins . Ample evidence indicates that spectrin filaments are oriented along the axon , and the N terminus of βIV spectrin is connected to the actin rings . We previously showed that the spectrin filaments are under entropic tension and the periodic actin/spectrin arrangement provides the axon with mechanical resistance and flexibility [11] . As discussed in the previous section , actin rings wrap along the axon’s circumferential direction and restrict the motion of TMPs and IMPs of the inner leaflet in the longitudinal direction . Here , we examined whether the spectrin filaments exert a similar effect on the transverse diffusion of axonal IMPs of inner and outer leaflets . In neurons that have matured past ten days in vitro ( DIV ) , the AIS acts as a diffusion barrier and completely halts diffusion while the structure of the APMS remains essentially the same . By contrast , among neurons that are no older than a week ( < DIV 6 ) , proteins within the AIS are diffusive . It is possible that the lack of diffusion in the AIS at the later developmental time points is due to accumulation of ankyrin-binding TMPs in the AIS , such as Na and potassium channels [69 , 70] . We have demonstrated that the APMS hinders , but does not completely stop , the thermal motion of axonal membrane proteins . This result is similar to an earlier experimental finding in cochlea outer hair cells that showed cytoskeletal structures alone constraint but do not completely prevent lateral mobility of membrane proteins [71] . We therefore hypothesized that accumulation of TMPs in the AIS plays an important role in ceasing diffusion of APM proteins . We considered different accumulation levels of TMPs that were anchored to the APMS with a limited motion range as is typically the case with ion channels such as voltage-gated sodium ( Nav ) and potassium ( Kv ) channels ( i . e KCNQ2 ) . We attached the TMPs particles at random points of the middle surface of the lipid bilayer via a spring with stiffness k0 = 6 . 5ε/σ2 . This resulted to an average radius of gyration Rg≃4 . 83 nm of the anchored TMPs in the APM comparable to their diameter . We then created different environments by increasing the initial surface accumulation of TMPs from three particles per rectangular corral ( ρ = 3 pprc ) to 20 , 45 , 60 , and 90 ( S10 Fig ) . These surface densities of TMPs corresponds to surface area coverages of 0 . 87% , 5 . 83% , 13 . 11% , 17 . 48% , and 26 . 22% , respectively . We first measured the MSDs of IMPs of the outer leaflet at the axon’s longitudinal and transverse directions at different TMP densities , with no attraction between spectrin filaments and lipid particles ( n = 0 in Eq 4 ) . We observed that increased accumulation of TMPs did not noticeably and for the observed time scale alter the diffusion type of IMPs of the outer leaflet , which remained normal ( Fig 7A and 7B ) . However , it caused a reduction in both the longitudinal and transverse diffusion coefficients and the total corresponding diffusion coefficients from Douter , 3 pprc = 3 . 84×10−3σ2/ts for ρ = 3 pprc to Douter , 20 pprc = 2 . 36×10−3σ2/ts for ρ = 20 pprc , Douter , 45 pprc = 7 . 43×10−4σ2/ts for ρ = 45 pprc , Douter , 60 pprc = 1 . 41×10−4σ2/ts for ρ = 60 pprc , and Douter , 90 pprc = 2 . 24×10−5σ2/ts for ρ = 90 pprc , as shown in S4 Table . From the result it is clear that at ρ = 90 pprc ( ~26% surface coverage ) the diffusive motion of IMPs of the outer leaflet almost ceased since the corresponding diffusion coefficient Douter , 90 pprc for 90 pprc is more than 500 times smaller than the diffusion coefficient Dlipid = 1 . 14 × 10−2σ2/ts of particles that represent lipids . It is noted however that this reduction in diffusivity cannot cause the formation of stripes . Similarly with the IMPs of the outer leaflet , diffusion of particles representing lipids remained normal but the diffusion coefficient reduced as the accumulation of TMPs increased and at ρ = 90 pprc diffusion practically ceased ( S11 Fig and S4 Table ) . Next , we measured the MSDs of IMPs of the inner leaflet at the axon’s longitudinal and transverse directions at different TMP densities , with no attraction between spectrin filaments and lipid particles ( n = 0 in Eq 4 ) . As discussed previously , IMPs of the inner leaflet underwent confined longitudinal diffusion . This is apparent in Fig 7C for ρ = 3 pprc and ρ = 20 pprc , where MSDs almost reached their maximum value for the observed number of time steps . For ρ = 45 pprc , ρ = 60 pprc , and ρ = 90 pprc , although the longitudinal diffusion was eventually confined , the required number of time steps for the MSDs to reach their maximum value went beyond the observed time period . For example , if we used the characteristic length L = 115 . 8nm , computed in the case of ρ = 3 pprc for IMPs of the inner leaflet , we found that when ρ = 45 pprc , approximately 8×107 more time steps were required to reach the maximum MSD value . This meant that at ρ = 45 pprc ( 13 . 11% area coverage ) , the motion of IMPs of the inner leaflet was significantly hindered and the number of time steps in our simulation was not large enough to see the effect of corrals . The effect of the accumulation of TMPs was clearly reflected on the value of the microscopic diffusion coefficient , which corresponds to the slope of the linear portion of the MSD graph at small time scale . By fitting the corresponding MSDs with the expression of confined-hop diffusion , we found that as the density ρ increased from 3 to 20 , 45 , 60 , and 90 pprc , the microscopic diffusion coefficient for the IMPs of the inner leaflet decreased from Dinner , 3pprcLongitudinal=3 . 26×10−3σ2/ts to Dinner , 20pprcLongitudinal=2 . 04×10−3σ2/ts to Dinner , 45pprcLongitudinal=1 . 65×10−4σ2/ts and Dinner , 60pprcLongitudinal=9 . 87×10−5σ2/ts to Dinner , 90pprcLongitudinal=2 . 63×10−5σ2/ts respectively ( Fig 7C and S5 Table ) . In transverse diffusion , accumulation of TMPs once again failed to change the nature of diffusion and merely reduced the micro-diffusion coefficient . As discussed above , transverse diffusion can be described as confined hop diffusion . For the values ρ = 3 , 20 , 45 , 60 , and 90 pprc the micro-diffusion coefficients for the IMPs of the inner leaflet decreased from Dinner , 3pprcTransverse=3 . 28×10−3σ2/ts to Dinner , 20pprcTransverse=2 . 13×10−3σ2/ts to Dinner , 45pprcTransverse=1 . 43×10−4σ2/ts , and Dinner , 60pprcTransverse=8 . 76×10−5σ2/ts to Dinner , 90pprcTransverse=2 . 66×10−5σ2/ts ( Fig 7D and S5 Table ) . For completeness , we also investigated how accumulation of TMPs , which are not anchored to the APMS but that can thermally diffuse , affects the diffusive motion of IMPs of the inner and outer leaflet . We previously considered the effect of APMS , when the density of TMPs between the actin rings was set to ρ = 3 pprc , which was a very low density compared to the density of membrane proteins in the axons of mature neurons ( no accumulation of TMPs; S10 Fig ) [69 , 70] . To study the effect of the accumulation of TMPs , we again created different environments by increasing the initial surface density of TMPs from ρ = 3 to 20 , 45 , 60 , and 90 pprc . We found that the TMPs behave similarly to IMPs of the inner leaflet . The longitudinal diffusion was confined and the transverse diffusion was confined hop-diffusion . We also found that the diffusion types of IMPs of the outer and inner leaflets were similar with the cases when TMPs were fixed ( Fig 7 , S12 and S13 Figs ) . The results are discussed in detail in Supporting Information ( S1 Text ) .
Our results clearly illustrate that the periodic APMS is capable of hindering the diffusion of TMPs and IMPs of the inner leaflet , but the “fence” effect of the AMPS is not sufficient to completely cease diffusion . We also showed that diffusion of lipids and IMPs of the outer leaflet is not directly affected by the APMS . Accumulated TMPs can immobilize the diffusion of all axonal membrane proteins and lipids . This is a poignant result because the periodic structure of the APMS exists in both the AIS and the distal axon , but the diffusion barrier is only observed in the AIS . We propose that accumulation of TMPs in the AIS is the primary mechanism through which axonal membrane proteins and lipids are immobilized . However , the effects of the APMS and accumulation of TMPs on APM protein diffusion are not mutually exclusive and an intact AIS structure is necessary for the existence of the diffusion barrier . Indeed , Song and colleagues experimentally demonstrated that loss of ankyrin G and the resulting disruption of the APMS leads to a vanishing of the diffusion barrier in the AIS [17] . In addition , the diffusion barrier is impaired when actin is disrupted [16 , 18] . We propose the following process for the formation of the diffusion barrier in the AIS . First , during early neuronal development , when the periodic actin/spectrin cytoskeleton emerges before the accumulation of ankyrin G , the membrane proteins in the AIS diffuse freely until encountering the corral’s boundaries . These actin and spectrin “fences” restrict the motion of membrane proteins within the corrals but do not immobilize membrane proteins . Second , during the stabilization of periodic cytoskeleton , ankyrin G proteins start accumulating in the AIS , and ankyrin G-anchored proteins ( e . g . , Nav channels , Kv channels , NF 186 , and NrCAM ) are recruited . Thus , the motion of those membrane proteins are confined or immobilized by anchoring to ankyrin G . Finally , the accumulation of ankyrin G-anchored proteins and other TMPs in the AIS of a mature neuron act as “pickets” , forming a membrane environment that immobilizes all membrane proteins and lipids . Another important conclusion of this work is that diffusion of axon plasma membrane proteins is deeply anisotropic , as longitudinal diffusion is of different type than transverse diffusion . Our model predicts that longitudinal diffusion of all diffusing axon plasma proteins ( IMPs of the inner and outer layer , and TMPs ) is confined diffusion because actin rings act as impenetrable “fences” . This is in agreement with results shown in Albrecht et al . [20] where the motion of IMPs of the outer layer is confined within periodic stripes with boundaries overlapping with actin rings . The case of transverse diffusion however is different because spectrin tetramers do not interact with IMPs of the outer layer and form a permeable barrier for IMPs of the inner layer and for TMPs . As a result , the transverse diffusion of IMPs of the outer layer is normal whereas the transverse diffusion of IMPS of the inner layer and TMPs can be described as anomalous or hop diffusion . Thus , we predict that the longitudinal diffusion of the IMPs of the outer and inner layer and of TMPs is confined whereas the transverse diffusion is characterized as normal for IMPs of the outer layer and as anomalous or hop diffusion for IMPs of the inner layer and for TMPs . Our computational model can be possibly applied not only for diffusion of axon plasma membrane proteins but also for diffusion of proteins of the outer hair cell lateral wall where a similar actin-spectrin network constraints their mobility and probably induces an anisotropic diffusion [71] . We also note that diffusion anisotropicity in the case of the axon plasma membrane results directly from the anisotropicity of the structure of the APMS and its tethering to the lipid bilayer . This is different than anisotropic diffusion caused by extension or shearing of the plasma membrane as is the case of band-3 protein diffusion in the RBC plasma membrane described in Auth et al . [24] . In this case , the membrane skeleton , which is formed by triangular equilateral corals is isotropic at equilibrium [25 , 28] . However , in the deformed RBC membrane the triangular corrals are extended along the principal direction of the deformations allowing larger diffusion in this direction . In addition , deformation causes changes in membrane thermal oscillations and in the association between spectrin tetramers and lipid bilayer resulting in further changes in band-3 directional mobility [24] . Future studies should explore whether deformation of the axon plasma membrane affects diffusion of membrane proteins as a result of changes in the geometry and biophysical behavior of the APMS and its association to the lipid bilayer . In conclusion , in mature neurons the APM of the AIS functions as a diffusion barrier that ceases the movement of proteins between the soma and axon and contributes to the maintenance of neuronal polarity . Here , we introduce a CGMD model for the APM that includes representations of the APMS , the phospholipid bilayer , TMPs , and IMPs in both the inner and outer lipid layers to investigate the diffusion of lipids and membrane proteins in the APM . We first showed that at low surface density of TMPs , lipid diffusion is not affected by the APMS or by membrane proteins . This finding parallels experimental results observed in early neuronal development . Next , we observed that actin rings limit the longitudinal diffusion of TMPs and the IMPs of the inner leaflet but not of the IMPs of the outer leaflet . To reconcile the experimental observations with our simulations , we conjectured the existence of actin-anchored proteins that form a fence to restrict the longitudinal diffusion of IMPs of the outer leaflet . We also showed that spectrin filaments can modify transverse diffusion of TMPs and IMPs of the inner leaflet , depending on the strength of the association between lipids and spectrin . In the AIS , where spectrin and lipids could associate due to the presence of pleckstrin domain in spectrin IV , spectrin filaments completely restrict diffusion of proteins within the skeleton corrals and are thus likely to contribute to the immobilization of proteins during the later stage of neural development . In the distal axon , where spectrin and lipids do not associate , spectrin affects diffusion of membrane proteins in a more subtle way via a steric effect . Specifically , spectrin modifies the diffusion of TMPs and IMPs of the inner leaflet from normal to confined-hop diffusion . Finally , we simulated the effect that accumulation of TMPs has on the diffusion of TMPs and IMPs of both the inner and outer leaflets by changing the density of TMPs . We showed that the APMS structure ( i . e . , actin rings and spectrin filaments ) acts as a fence that restricts the diffusion of TMPs and IMPs of the inner leaflet within the membrane skeleton corrals . However , these fences are not sufficient to completely cease diffusion of membrane proteins . Accumulation of TMPs in the AIS is the primary source of membrane protein immobilization . In particular , a ~30-fold increase in TMP density corresponding to only ~25% of surface coverage blocks diffusion of lipids and membrane proteins due to accumulation of TMPs . Overall , we showed that diffusion of axon plasma membrane proteins is anisotropic as longitudinal diffusion is of different type than transverse ( azimuthal ) diffusion . Our findings provide insight into how protein and lipid diffusion is controlled in the AIS to allow neurons to effectively send and receive electrical signals . | The axon plasma membrane skeleton consists of repeated periodic actin ring-like structures along its length connected via spectrin tetramers and anchored to the lipid bilayer at least via ankyrin . However , it is currently unclear whether this structure controls diffusion of lipids and proteins in the axon . Here , we developed a coarse-grain molecular dynamics computational model for the axon plasma membrane that comprises minimal representations for the APMS and the lipid bilayer . In a departure from current models , we found that actin rings limit diffusion of proteins only in the inner membrane leaflet . Then , we showed that actin anchored proteins likely act as “fences” confining diffusion of proteins in the outer leaflet . Our simulations , unexpectedly , also revealed that spectrin filaments could impede transverse diffusion in the inner leaflet of the axon and in some conditions modify diffusion from normal to abnormal . We predicted that diffusion of axon plasma membrane proteins is anisotropic as longitudinal diffusion is of different type than transverse ( azimuthal ) diffusion . We conclude that the periodic structure of the axon plays a critical role in controlling diffusion of proteins and lipids in the axon plasma membrane . | [
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"cy... | 2019 | Modeling of the axon plasma membrane structure and its effects on protein diffusion |
Merkel cell polyomavirus ( MCV or MCPyV ) appears to be a causal factor in the development of Merkel cell carcinoma , a rare but highly lethal form of skin cancer . Although recent reports indicate that MCV virions are commonly shed from apparently healthy human skin , the precise cellular tropism of the virus in healthy subjects remains unclear . To begin to explore this question , we set out to identify the cellular receptors or co-receptors required for the infectious entry of MCV . Although several previously studied polyomavirus species have been shown to bind to cell surface sialic acid residues associated with glycolipids or glycoproteins , we found that sialylated glycans are not required for initial attachment of MCV virions to cultured human cell lines . Instead , glycosaminoglycans ( GAGs ) , such as heparan sulfate ( HS ) and chondroitin sulfate ( CS ) , serve as initial attachment receptors during the MCV infectious entry process . Using cell lines deficient in GAG biosynthesis , we found that N-sulfated and/or 6-O-sulfated forms of HS mediate infectious entry of MCV reporter vectors , while CS appears to be dispensable . Intriguingly , although cell lines deficient in sialylated glycans readily bind MCV capsids , the cells are highly resistant to MCV reporter vector-mediated gene transduction . This suggests that sialylated glycans play a post-attachment role in the infectious entry process . Results observed using MCV reporter vectors were confirmed using a novel system for infectious propagation of native MCV virions . Taken together , the findings suggest a model in which MCV infectious entry occurs via initial cell binding mediated primarily by HS , followed by secondary interactions with a sialylated entry co-factor . The study should facilitate the development of inhibitors of MCV infection and help shed light on the infectious entry pathways and cellular tropism of the virus .
The viral family Polyomaviridae consists of a diverse group of non-enveloped DNA viruses that infect humans as well as a range of other vertebrates . The family name is derived from the observation that murine polyomavirus causes tumors in various tissues in experimentally infected animals . The apparently broad tissue tropism of murine polyomavirus is consistent with the widespread distribution of its primary infectious entry receptors , a group of sialic acid-bearing glycolipids known as gangliosides [1] . Other well-studied polyomaviruses , such as the human polyomavirus BKV and its close relative , simian virus-40 ( SV40 ) , also employ gangliosides for infectious entry into cells ( reviewed in [2] ) . Another BKV relative , JCV , has recently been shown to bind a specific sialylated pentasaccharide , known as LSTc , that decorates either proteins or gangliosides on a restricted range of cell types [3] . This is consistent with the much narrower cellular tropism of JCV [4] , [5] . Although it has been suggested that initial attachment to sialic acid residues may be a universal infectious entry step for all polyomaviruses , the infectious entry pathways used by most members of the family have not yet been extensively investigated . Members of other non-enveloped virus families , such as the Parvoviridae , have been found to use a wide range of cellular receptors . For example , the primary cellular attachment receptors for adeno-associated viruses ( AAVs ) of the parvovirus genus Dependovirus range from gangliosides ( bovine AAV; [6] ) , to protein-linked sialic acids ( AAV4 and 5; [7] ) or a very different type of carbohydrate side-chain , heparan sulfate ( AAV2; [8] ) ( reviewed in [9] ) . Heparan sulfate ( HS ) is a type of glycosaminoglycan ( GAG ) that rarely contains sialic acid [10] , but is instead characterized by specific patterns of N- and O-linked sulfate modifications [11] . AAV6 can bind to both sialylated polysaccharides and to HS on cells , and both interactions appear to modulate transduction into various tissues [12] , [13] . In light of the Dependovirus precedent , the hypothesis that all polyomaviruses use sialic acid residues for initial attachment to cells should be viewed with caution . Seven of the nine polyomavirus species known to infect humans were discovered within the past four years [14] , [15] , [16] , [17] , [18] , [19] . Perhaps the most intriguing of these new discoveries is a human polyomavirus species named Merkel cell polyomavirus ( MCV or MCPyV ) . MCV is believed to play a causal role in Merkel cell carcinoma ( MCC ) , a highly lethal form of skin cancer ( reviewed in [20] ) . An emerging view is that , unlike BKV and JCV , which commonly infect the urinary epithelium , MCV establishes a chronic productive infection in the skin of most adults [17] , [21] , [22] . It remains unclear which of the dozen or so different cell types that can be found in the skin are the primary source of shed MCV virions . In an initial effort to better understand the cellular tropism of MCV , we set out to determine which receptors mediate initial attachment of the virion to the cell surface . A previous report by Erickson , Garcea and Tsai showed that recombinant MCV VP1 capsid protein subunits produced in bacteria can bind sialylated components of cell extracts , including the ganglioside GT1b [23] . Erickson and colleagues' data support speculation that MCV might follow an entry pathway similar to that of BKV , which has been shown to require GT1b or related complex gangliosides for infectious entry [24] . To further investigate this hypothesis , we employed MCV- and BKV-based reporter vectors ( also known as pseudoviruses ) as models for infectious entry into cultured cell lines [25] . To confirm the reporter vector-based results , we developed a system for titering the infectivity of native MCV virions . Our results support a model in which MCV uses GAGs , likely in the form of HS , as initial attachment receptors . The initial GAG-mediated binding appears to be followed by interactions between the MCV virion and sialylated host cell factors . The use of GAGs , such as HS , as attachment receptors for MCV infectious entry is strikingly reminiscent of a different family of non-enveloped viruses , the Papillomaviridae , which are exclusively tropic for keratinocytes , a cell type that forms the epidermal layers of the skin and mucosa . The results suggest a possible example of convergent adaptation to exploitation of the epidermis as an infectious niche .
Hemagglutination assays ( HA ) are a classic method for investigating the interaction of virions with the cell surface . Hemagglutination is typically mediated by interactions between virion surface proteins and sialylated glycans displayed on red blood cells ( RBCs ) . Erickson and colleagues have previously shown that recombinant MCV VP1 capsid protein subunits produced in bacteria can hemagglutinate sheep RBCs [23] . Using purified , fully-assembled MCV capsids produced in human cells ( see below ) [25] , [26] we confirmed Erickson and colleagues' sheep RBC HA results ( Figure 1 ) . In contrast to MCV , BKV capsids did not display HA activity against sheep RBCs . Many virus types show differential abilities to hemagglutinate RBCs from different animal species . This is thought to reflect differences in the display of different forms of sialylated glycans or other binding targets on the surface of RBCs from different animals [27] . BKV HA assays typically employ human RBCs [28] . Consistent with previous results , recombinant BKV capsids induced robust HA of human RBCs . In contrast , MCV capsids showed a surprising lack of HA activity against human RBCs ( Figure 1 ) . The results show that MCV and BKV engage mutually distinct attachment factors on RBCs . When the MCV major and minor capsid proteins ( VP1 and VP2 , respectively ) are co-expressed in human embryonic kidney-derived 293TT cells , they can spontaneously co-assemble around transfected reporter plasmids [26] . This results in the formation of reporter vector particles ( also known as pseudovirions ) that physically resemble native polyomavirus virions [26] and are capable of delivering encapsidated reporter plasmids to fresh target cells [25] . Similar systems have been developed for production of reporter vectors based on other polyomaviruses [5] , [29] and for papillomaviruses [30] . Using MCV , BKV and human papillomavirus type 16 ( HPV16 ) reporter vectors , we sought to identify candidate receptor or co-receptor molecules that are used by MCV for infectious entry into cultured cells . In a comparative analysis of MCV and BKV reporter vector transduction efficiency in over sixty different cell lines from various human tumors , we determined that the human lung epithelial cell line A549 was among the most MCV-transducible lines in the panel ( unpublished results ) . A549 cells were chosen for initial experiments because they also have the convenient feature of being readily transducible with BKV and HPV16 reporter vectors , allowing comparisons to these better-studied virus types . To examine the binding of MCV or BKV capsids to cultured cells , we conjugated recombinant capsids to Alexa Fluor 488 to allow monitoring of cell binding by flow cytometry . The fluorochrome-conjugated capsids exhibited HA titers similar to unconjugated capsids ( Figure S1A ) , suggesting that the dye conjugation process did not cause dramatic alterations in the cell binding properties of the capsids . Similarly , the dye conjugation procedure did not significantly affect the transducing potential of MCV reporter vectors ( Figure S1B ) . In an initial series of experiments , we examined the binding of fluorochrome-conjugated MCV and BKV capsids to A549 cells . As shown in Figure 2 ( and Figure S2 ) , the binding of MCV to A549 cells was not significantly affected by pre-treatment of the cells with a broad-spectrum neuraminidase from Arthrobacter ureafaciens that is capable of hydrolyzing most forms of sialic acid linkage [31] . BKV capsid binding to A549 cells was , as expected , sensitive to neuraminidase . The transduction of a GFP reporter plasmid into A549 cells via MCV or BKV vectors in the presence or absence of neuraminidase mirrored the binding results ( Figure 2 and Figure S2 ) . Keratinocytes and melanocytes are the two most abundant cell types in the epidermal layer of the skin . Based on the speculative assumption that MCV might productively infect one of these cell types in vivo , we examined a variety of melanocyte and keratinocyte-derived cell lines for transducibility with MCV , BKV and HPV16 reporter vectors . The human melanoma-derived line SK-MEL-2 , as well as primary adult human epidermal keratinocytes ( HEKa cells ) were found to be readily transducible with both MCV and BKV reporter vectors . Neuraminidase treatment of both SK-MEL-2 cells and HEKa cells resulted in inhibition of BKV transduction , but had little effect on MCV transduction ( Figure S3 ) , consistent with results observed using A549 cells . It is known that some sialic acids , such as the single sialic acid residue on the ganglioside GM1 ( which serves as a receptor for SV40 ) , are resistant to digestion with neuraminidase [32] . To address the possibility that MCV attachment to cells is mediated by a sialylated glycan that is resistant to neuraminidase , we used a cell line deficient in biosynthesis of sialylated glycans . The line , known as Lec2 , is a Chinese hamster ovary ( CHO ) -based mutant that lacks a functional gene for SLC35A1 , a CMP-sialic acid transporter required for sialylation of glycoprotein and glycolipid ectodomains in the lumen of the Golgi [33] . A control line , Lec2-mslc , was engineered to stably express a wild-type SLC35A1 allele . As seen in Figure 3 , restoration of the SLC35A1 gene resulted in a 12-fold increase in BKV capsid binding , confirming that the introduced gene restored the production of sialylated glycans . In contrast to BKV , there was no effect on HPV16 and only a slight improvement in MCV binding to the Lec2-mslc line . The results indicate that MCV capsid attachment to this cell line is largely independent of sialylated glycans . All CHO-based cell lines are deficient in complex gangliosides , such as GT1b [34] , [35] . Thus , restoration of the SLC35A1 sialic acid carrier to Lec2 cells would only be expected to restore sialylation of proteoglycans and simple gangliosides . Consistent with their lack of complex gangliosides , parental CHO-K1 cells ( data not shown ) and the CHO-based Lec2 cells with or without the restored SLC35A1 gene are highly resistant to BKV transduction ( Figure 3 ) . Despite the fact that MCV capsids readily bind to Lec2 cells , the line was surprisingly resistant to transduction by MCV reporter vectors ( Figure 3 ) . Reintroduction of the functional SLC35A1 allele rendered the line permissive for MCV transduction . A simple model that could explain the results would be that , while sialylated factors are not required for the initial attachment of MCV to the cell surface , the virus appears to require sialylation of a cellular factor for an entry step that occurs after stable attachment to the cell . The ability of MCV to transduce CHO-K1 and Lec2-mslc cells suggests that complex gangliosides are not necessary for MCV transduction . Indeed , while pre-treatment of cells with exogenous GT1b rescued BKV transduction of Lec2 cells , exogenous GT1b had little or no effect on MCV transduction ( Figure S4 ) . One way to reconcile the Lec2 line transduction results with the results observed for neuraminidase-treated A549 ( Figure 2 ) would be to imagine that MCV attachment to a non-sialylated cellular factor allows the capsid to loiter on the cell surface until a hypothetical sialylated entry co-factor is regenerated after removal of the neuraminidase . Our past experience studying HPV binding and entry through interactions with HS led us to test the ability of purified protein-free GAGs , including heparin and chondroitin , to inhibit MCV infection . We found that , heparin can indeed block MCV transduction of A549 cells in a dose-dependent manner , with a 50% effective dose ( EC50 ) of 4 . 2 µg/ml ( Figure 4 ) . Interestingly , moderate doses of heparin ( ∼1 µg/ml ) appeared to increase the infectivity of MCV by up to two-fold in some experimental replicates . In contrast to heparin , chondroitin-A/C preparation was a much more effective inhibitor of MCV transduction ( EC50 = 135 ng/ml ) and did not appear to enhance MCV infectivity . Consistent with previous reports [36] , [37] , the transducivity of an HPV16 reporter vector was blocked more effectively by soluble heparin ( EC50 = 1 . 2 µg/ml ) , while chondroitin-A/C only weakly inhibited HPV transduction . Comparable results were observed for MCV using the kidney-derived neuroblastoid line 293TT [30] , [38] ( heparin EC50≈12 µg/ml , chondroitin-A/C EC50≈0 . 3 µg/ml ) . The melanoma-derived line SK-MEL-2 and HEKa also showed similar GAG inhibition profiles for MCV reporter vectors ( SK-MEL-2 heparin EC50≈3 µg/ml , chondroitin-A/C EC50≈0 . 1 µg/ml; HEKa heparin EC50≈2 µg/ml , chondroitin-A/C EC50≈0 . 05 µg/ml ) . As expected , BKV transduction of A549 cells was unaffected by either of the GAG compounds ( Figure 4 ) . Other soluble GAGs , such as dermatan sulfate and chondroitin-A , were found to be poor inhibitors of the transduction of all three reporter vectors on A549 cells ( data not shown ) . Since chondroitin-A alone lacked inhibitory efficacy , it is likely that the chondroitin-C ( chondroitin-6-sulfate ) in the chondroitin-A/C preparation used here was primarily responsible for mediating inhibition of MCV transduction . Heparin has been shown to inhibit HPV entry by preventing binding of the virus to HS on the cell surface or extracellular matrix [37] , [39] . To examine the mechanism through which heparin and chondroitin-C inhibit MCV entry into A549 cells , we measured the binding of Alexa Fluor-labeled MCV , HPV16 or BKV capsids to A549 cells in the presence of increasing concentrations of these GAGs . HPV and MCV binding to A549 cells was inhibited in a dose-dependent manner by both heparin and chondroitin ( Figure S5 ) , suggesting that these GAGs inhibit transduction , at least in part , by preventing cell attachment . As expected , heparin and chondroitin had little effect on BKV binding . Treatment of cell cultures with sodium chlorate inhibits the addition of sulfate groups to GAGs [40] , reviewed in [41] . Although chlorate treatment can be toxic to some cell lines ( for example , 293TT and HEKa cells do not appear to tolerate 50 mM chlorate ) , culture of A549 cells in 50 mM chlorate for several weeks did not appear to have noticeable effects on cell morphology or growth rate ( data not shown ) . A549 cells maintained in 50 mM chlorate were extremely resistant to MCV transduction as well as binding ( Figure 5 ) . Chlorate-treated A549 cells were likewise resistant to HPV transduction . In contrast , BKV transduction of A549 cells was enhanced by chlorate treatment , confirming that the chlorate-treated cultures were healthy enough to support expression of reporter plasmids delivered via polyomavirus-based vectors . Similar chlorate treatment results were obtained with the melanoma cell line SK-MEL-2 ( Figure S6 ) . The data show that sulfate modifications , likely in the form of GAGs , are essential targets of MCV attachment and infectious entry . To examine the specificity of MCV interaction with different GAG types and to clarify the role of various GAG forms in infectious entry , cell surface HS and/or chondroitin sulfate ( CS ) were enzymatically removed using heparinase ( HSase ) and chondroitinase ( CSase ) enzymes . Enzyme activity and specificity was verified by immunofluorescent staining and flow cytometric analysis of cell surface HS and CS following treatment of A549 cells ( Figure S7 ) . Given the superior inhibitory effects of chondroitin-A/C relative to heparin , we expected that CSase treatment would have a greater impact on MCV binding and transduction than HSase treatment . Surprisingly , CSase treatment alone had little effect on MCV binding or transduction , while HSase caused a modest decrease in MCV binding and transduction ( Figure 6 ) . Combination HSase/CSase treatments synergistically inhibited MCV binding and transduction . The response of HPV to these treatments was very similar to MCV , while BKV was unaffected . Combination HSase/CSase treatments were also necessary to effectively inhibit MCV transduction of SK-MEL-2 cells and HEKa cells , confirming the importance of cell surface GAGs for MCV entry into skin-derived cell types ( Figure S8 ) . Neither CSase nor HSase alone significantly inhibited or enhanced MCV transduction of SK-MEL-2 or HEKa cells . A traditional approach to investigation of the role of particular GAG modifications in viral entry has been to compare the infectability of cell lines carrying mutations in the genes responsible for various steps in GAG biosynthesis [11] , [42] , [43] , [44] , [45] . These cell lines range from having no GAGs to simply lacking sulfate or other modifications at specific positions . We found that pgsA-745 cells , which are deficient in both HS and CS , did not bind MCV capsids efficiently and were transduced very poorly in comparison to the parental CHO-K1 line ( Figure 7 ) . Similarly , pgsD-677 cells , which lack HS but produce more CS than the parental cells , were highly resistant to MCV transduction . This suggests that HS , and not CS , is of primary functional relevance for MCV-mediated transduction of this cell type . Two other CHO mutant cell lines are deficient in the biosynthesis of specifically sulfated types of HS . Heparan sulfate modifications occur sequentially and , as a result , disruption of early modification events inhibits downstream modifications as well [11] , [46] . Normally , the first step in HS modification involves N-deacetylation and N-sulfation of N-acetylglucosamine ( GlcNAc ) residues in the HS core chain . A subsequent modification step involves epimerization of glucuronic acid residues to iduronic acid . After these modifications , the HS sequentially becomes an appropriate substrate for 2-O- , 6-O- and 3-O-sulfotransferases . Thus , pgsE-606 cells , which lack GlcNAc N-deacetylase/N-sulfotransferase activity [42] , produce HS that is deficient in all forms of modification . Another mutant cell line , pgsF-17 , is deficient in 2-O-sulfotransferase function and thus expresses HS that carries N-sulfate and iduronic acid modifications , but lacks 2-O- and 3-O-sulfate modifications [45] . In addition to N-sulfated HS , pgsF-17 cells also produce HS carrying 6-O-sulfate modifications . MCV reporter vectors readily bound and transduced pgsF-17 cells but not pgsE-606 cells , ( Figure 7 ) indicating that HS epimerization , N-sulfation and/or 6-O-sulfation are required to support MCV-mediated transduction , while HS 2-O- and 3-O-sulfation are dispensable . In comparison to HPV16 , we found that the GAG type preferences of the two reporter vectors differ somewhat , as pgsF-17 cells show reduced HPV transduction , while the MCV reporter vector readily transduced this line . This result is consistent with previous reports indicating that 2-O-sulfate groups on HS are required for efficient transduction of cultured cells with HPV16 vectors [47] . Because many cell surface proteins display GAG-binding motifs , most cell types have substantial capacity to bind free GAGs non-covalently [48] . Non-covalently associated GAG chains , including exogenously-provided heparin , can participate in a wide variety of biological functions . For example , free heparin can serve as a functional “bridge” between vascular endothelial growth factor 164 and its co-receptor neuropilin 1 [49] , [50] . Consistent with this type of bridging effect , we found that provision of exogenous heparin increased the transducibility of GAG-deficient pgsA-745 cells in a dose-dependent manner . At an apparent optimal concentration of heparin in the media of around 20 µg/ml , pgsA-745 cells became 10-fold more transducible than untreated parental CHO-K1 cells ( Figure 8 ) . MCV-mediated transduction of CHO-K1 cells was also enhanced by exogenous heparin , but the most effective dose was lower , presumably reflecting a reduced need for exogenous heparin due to the presence of native GAGs . Transduction of pgsD-677 and pgsE-606 cells was similarly enhanced by exogenously-supplied heparin , confirming that a heparin-like GAG is the primary missing factor required for MCV-mediated transduction of these HS modification mutant CHO lines ( data not shown ) . Moderate doses of chondroitin-A/C also increased MCV transduction of GAG-deficient cells slightly , but the effect was very small in comparison to the effect of heparin ( data not shown ) . In contrast to the GAG mutant CHO cell lines , MCV transduction of Lec2 cells was not rescued by exogenously-supplied heparin , suggesting that the block to MCV transduction in Lec2 cells occurs downstream of HS binding ( data not shown ) . The rescue of MCV transduction of pgsA-745 cells by exogenous heparin correlated with an improvement in capsid binding to the cell ( Figure 8 ) . Surprisingly , neither pre-incubation of pgsA-745 cells with heparin nor pre-incubation of reporter vector stocks with heparin showed dramatic effects on MCV binding or transduction ( data not shown ) . A possible explanation for this finding might be that one or more interactions in a hypothetical termolecular complex between heparin , MCV and cell surface binding targets may be of low overall affinity and relatively transitory . To test the idea that limitation of the ability of MCV to loiter on the cell surface might curtail access to a hypothetical sialylated co-receptor , we performed MCV binding and transduction assays on pgsA-745 cells supplied with exogenous heparin and treated with or without neuraminidase . Although neuraminidase treatment again had no effect on MCV binding in these experiments , the treatment modestly suppressed MCV transduction ( Figure S9 ) . The results are consistent with the idea that limitation of the ability of MCV to loiter on the cell surface reduces the engagement of a sialylated entry co-factor that regenerates after neuraminidase treatment . The results shown above suggest that attachment to cell surface HS is a critical step in MCV vector-mediated reporter gene transduction . However , the strong inhibition of MCV entry by soluble chondroitin-A/C raises questions surrounding the precise interaction of MCV with different GAG types . In an effort to better understand the physical interaction between MCV and GAGs , an ELISA-style binding assay was developed using a commercially available GAG-rich basement membrane extract ( BME ) derived from murine Engelbreth-Holm-Swarm tumor to coat the surface of 96-well protein-binding plates . Since the binding of VP1-specific antibodies might be affected by GAG-capsid interactions , we elected to detect bound reporter vector particles using Quant-iT PicoGreen stain [51] to render encapsidated DNA carried within the particles fluorescent . Increasing concentrations of HPV16 or MCV capsids in the BME-coated wells correlated with an increase in fluorescence ( Figure 9A ) . BKV capsids bound the BME-coated wells very poorly ( data not shown ) , suggesting the BME displays few binding sites for BKV . To determine whether capsid binding to the BME was the result of interactions with GAGs , BME-coated wells were pre-treated with increasing doses of HSase or CSase . Only HSase treatment of the BME resulted in major dose-dependent decreases in binding by MCV and HPV , and the highest concentration of HSase resulted in nearly complete abrogation of binding ( Figure 9B ) , indicating that both viruses predominantly bind HS displayed on BME . The slope of the MCV capsid dose-response curve for binding to BME ( Figure 9A ) is relatively shallow , with a Hill coefficient of 0 . 64±0 . 14 . A simple explanation for the occurrence of Hill slopes of less than one is that the assay is simultaneously measuring multiple binding interactions with differing affinities . This explanation is consistent with the fact that native GAGs are heterogenous and carry complex modifications that can dramatically alter their affinity for GAG-binding proteins . To circumvent this problem , we measured the ability of the more homogenous preparations of heparin and chrondroitin-A/C to interfere with the binding of MCV to BME . Interestingly , although high doses of chondroitin-A/C were able to entirely block the binding of MCV capsids to the BME , apparently saturating doses of heparin reduced MCV binding by only about 75% ( Figure 9C ) . A model for these observations would be that the BME displays two distinct targets for MCV binding and the capsid carries two distinct glycan-binding motifs . Under this model , chondroitin-A/C is capable of blocking both of the glycan-binding motifs on the capsid surface , while heparin is capable of blocking only one binding motif . Systems for culturing MCV have not yet been developed . We have previously speculated that the relative inactivity of recombinant MCV genomes transfected into cultured cells may reflect regulation of the viral life cycle in a manner reminiscent of the extensive regulatory controls on the papillomavirus life cycle [17] , [52] . Although we have previously shown that the genomic DNA of MCV primary isolates can drive the production of low levels of native virions after transfection into 293TT cells , the yield of native virions was relatively poor [17] . We found that virion yield can be improved substantially if the cloned genome is co-transfected together with expression plasmids encoding MCV small and large T antigen cDNAs ( data not shown ) . To monitor the infectivity of native MCV virions , we generated a 293TT-based line , named 293-4T , which stably expresses the MCV small and large T antigen proteins . The stable line supports the replication of MCV genomes delivered by infection with native MCV virions , allowing monitoring of the infection using quantitative PCR ( qPCR ) . The extent of MCV replication observed over several days varied between experiments , ranging from 7 . 5 fold to 70 fold . In separate experiments , we found that purified native MCV virions can be propagated in 293-4T cells ( Figure S10 ) . Using the native virion/293-4T infection system and enzymatic removal of cell surface GAGs , we confirmed that GAGs are required for MCV infection . qPCR analysis of 293-4T cells harvested immediately after viral inoculation and washing of cells treated with or without HSase/CSase revealed a 90–93% reduction in the number of cell-bound virions in the HSase/CSase treatment condition ( Figure 10 ) . The failure of the virus to bind efficiently to the HSase/CSase treated cells was reflected by a comparable decrease ( 76–83% ) in the number of replicated viral genomes observed after 5–6 days of cell growth . To control for cell health after enzyme treatment , parallel experiments were performed to measure the number of genome copies for native BKV virions . As expected , native BKV infection was either unaffected by HSase/CSase treatment or , in one of the three replicates , modestly enhanced by the enzyme treatment . Native MCV virions were also used for a panel of additional confirmatory experiments ( data not shown ) on cell types that do not appear to support the replication of MCV genomes delivered by native virions . For these experiments we made the simplifying assumption that failure to bind the cell would result in failure to infect the cell . Native virion binding was measured using qPCR of viral genomes stably associated with cells . In an initial control experiment , we found that monoclonal antibodies specific for assembled MCV capsids [53] blocked the binding of MCV virions to A549 cells . Native MCV virions also failed to bind A549 cells in the presence of chondroitin-A/C . Treatment of A549 cells with sodium chlorate likewise prevented the binding of native MCV virions . We also found that native MCV virions readily bind both Lec2 and Lec2-mslc , confirming that native MCV does not require sialylated carbohydrates for attachment to cells . Taken together , the data show that native MCV virions exhibit binding and infectivity characteristics similar to MCV reporter vectors .
Interaction with cell surface receptors is an essential first step in the process of viral infectious entry . Here we present multiple lines of evidence demonstrating that the initial attachment of MCV to cultured cells is mediated primarily by GAGs . Like HPV16 , MCV binding and infectious entry can be antagonized by soluble GAGs and the attachment and infectivity of both viruses depends on the presence of cell surface GAGs . Although MCV capsids can bind to both CS and HS , experiments using CHO-based mutant cell lines indicate that N-sulfated and/or 6-O-sulfated forms of HS are specifically required for infectious entry . The handful of other polyomaviruses whose infectious entry pathways have been carefully studied all appear to utilize sialic acid-containing receptors for the initial cell attachment step of the infectious entry process [24] , [54] , [55] , [56] , [57] . Our studies show that sialylated glycans are not required for initial attachment of MCV to cultured cell lines . Further work is needed to determine whether MCV is unusual in this regard or rather provides an example of a common trait among the two dozen or so polyomavirus species that have not yet been subjected to extensive scrutiny . MCV appears to require a sialylated glycan for a post-attachment step in the infectious entry process . It remains uncertain whether this apparent requirement for sialylated glycans is due to indirect or direct effects . For example , failure to sialylate a cellular factor might impair a biological function or subcellular localization required to support MCV entry . In this scenario , MCV might not directly bind the sialylated glycan . A more intriguing possibility is that MCV directly interacts with a sialylated glycan during the infectious entry process . Although we found no clear evidence for direct interactions between MCV capsids and sialic acid residues on cultured human cell lines , Erickson and colleagues have previously shown that MCV VP1 capsomers can bind neuraminidase-sensitive factors in concentrated extracts of sheep RBCs [23] . Erickson and colleagues also demonstrated that MCV VP1 can bind GT1b when the ganglioside is presented at high concentrations in a cell-free flotation system . This suggests a possible scenario in which an unknown sialylated factor that resembles the glycan headgroup of GT1b serves as a co-receptor that the virion directly engages after initial attachment to the cell via HS . This would be analogous to the infectious entry of HIV , which generally requires the direct engagement of a chemokine co-receptor after initial attachment to a primary attachment receptor , CD4 . It is tempting to speculate that the hypothetical sialylated co-receptor required for MCV entry might be a ganglioside . However , the fact that CHO-based lines , which are deficient in complex gangliosides [34] , [35] , are readily transducible by MCV reporter vectors would argue against this hypothesis . Further work is needed to determine which sialylated glycans , if any , MCV binds during infectious entry into human cells . Although our results using CHO cell lines indicate that HS is a more important factor than CS for MCV infectious entry , soluble heparin proved to be a less effective inhibitor of entry than chondroitin-C on all tested cell lines , including CHO ( Figure 4 ) . The results of the competitive inhibition experiments on basement membrane extracts ( Figure 9 ) suggest a possible explanation for the apparently greater efficacy of chondroitin-C for inhibiting MCV infection . These analyses indicate that although MCV has higher affinity for heparin , chondroitin-C may be a better infection inhibitor because it blocks a secondary glycan binding site on the MCV virion surface that the highly homogenous heparin preparation cannot saturate . This model could explain the observation that chondroitin-A/C is a more effective inhibitor of MCV transduction . Although heparin doses >10 µg/ml effectively inhibited MCV transduction of several human cell lines , lower doses of heparin showed variable enhancement of MCV transduction of these lines ( Figure 4 and data not shown ) . For CHO-based lines , heparin only enhanced infectivity , even at 20 µg/ml doses ( Figure 8 ) . The variable ability of heparin to either inhibit or enhance infectivity on various cell types is reminiscent of models for antibody-dependent neutralization or enhancement of the infectivity of flaviviruses ( reviewed in [58] ) . In this model , antibodies that can neutralize flaviviruses when bound at high occupancy can also enhance infection when bound at low occupancy . It is thought that this effect reflects the ability of some antibodies to serve as a bridge between the partially occluded virion and antibody-Fc receptors expressed on the surface of some cell types . Analogously , heparin might serve as a bridge in a termolecular complex between heparin-binding proteins on the cell surface and heparin binding motifs on the surface of the MCV capsid . A similar model has recently been proposed for the infectious entry of human T-cell leukemia virus-1 ( HTLV-1 ) [59] . It is also conceivable that , rather than forming a physical bridge between the MCV capsid and cell surface GAG-binding factors , heparin might induce a reversible change in the capsid structure that , in turn , permits direct binding of the capsid to a cellular co-receptor moiety . This would be reminiscent of conformational changes that are thought to occur in HPV capsids during infectious entry ( reviewed in [60] ) . In either event , it is clear that the effectiveness of GAG inhibition of MCV reporter vectors can vary dramatically between cell lines . Resolution of this issue will require more detailed knowledge of the cellular factors that support the post-attachment steps of MCV infectious entry . Polyomaviruses have a long and complex history as suspected agents of human cancer [61] . The data implicating MCV as a cause of cancer in epidermal Merkel cells appears to be the strongest case yet described for a polyomavirus . That MCV particles can be isolated from human skin surfaces and cause tumors is reminiscent of certain aspects of HPV biology . Our data clearly show that both of these viruses require initial attachment to specific forms of HS , followed by transfer to poorly understood co-receptors for infectious entry to occur . Whether this is coincidence is difficult to determine , but it will be interesting to learn if these unrelated viruses share other aspects of their biology .
A549 cells and SK-MEL-2 cells were obtained from the Developmental Therapeutics Program ( NCI/NIH ) and maintained in RPMI medium ( Invitrogen ) supplemented with 5% FBS ( Sigma ) and Glutamax-I ( Invitrogen ) . HEKa ( human epidermal keratinocytes , adult ) were purchased from Invitrogen and maintained in Medium 254 supplemented with HKGS . CHO-K1 cells , pgsA-745 , pgsD-677 , pgsE-606 , Pro5 , and Lec2 cells were obtained from ATCC and maintained in DMEM ( Invitrogen ) with 10% FBS , Glutamax-I and MEM non-essential amino acids ( D10 medium ) . pgsF-17 cells ( a kind gift from Jeff Esko [45] ) were maintained in D10 medium . Medium for the Lec2-mslc cells was supplemented with blasticidin S ( 5 µg/ml; Invitrogen ) . 293TT cells were maintained in D10 supplemented with hygromycin ( 250 µg/ml; Roche ) and 293-4T were maintained in D10 supplemented with zeocin ( 100 µg/ml; Invitrogen ) and blasticidin S ( 5 µg/ml; Invitrogen ) . Plasmids reported in this study will be made available through Addgene . org . The pMslc plasmid used to restore expression of SLC35A1 ( accession number NM_006416 ) in Lec2 cells was created by transferring the human cDNA clone of SLC35A1 ( OriGene , restriction enzymes XbaI and NcoI ) into the expression cassette of pMONO-blasti-msc ( InvivoGen , restriction enzymes AvrII and NcoI ) . 293-4T cells were created through two stable transfection steps . In the first step , 293TT cells were transfected with pMtB , an expression plasmid carrying the small T antigen ORF of MCV isolate R17a ( GenBank accession number HM011555 , [17] ) in the expression cassette of pMONO-blasti-msc . Stable blasticidin-resistant clones were isolated by limiting dilution and analyzed for small T antigen expression by immunofluorescence microscopy and western blot using polyclonal serum raised against bacterially-produced MCV small t antigen fused to a maltose binding protein affinity tag ( unpublished data ) . Stable expression of MCV small t antigen appears to be relatively toxic to 293TT cells and few clones maintained expression of the protein . One clone that stably expressed MCV small t antigen was super-transfected with a construct named pADL* , encoding MCV Large T antigen . The construct was generated by first silently mutating the splice donor and acceptor sites for the 57 kT isoform of MCV Large T antigen in the context of expression plasmid pCDNAclt206antigen1 ( p2582 ) , which was a generous gift from the Chang/Moore lab [62] . The Large T antigen ORF was also modified to remove the V5 epitope tag and proline residue 156 was mutated to serine to match the wild-type MCV consensus at that site . The modified T antigen gene was transferred into pMONO-zeo-mcs ( InvivoGen ) by restriction enzyme-based cloning . The polyclonal pADL* population was selected with both zeocin and blasticidin and the resulting stable line was named 293-4T . Nucleotide maps of plasmids used in this work and detailed protocols are available on our laboratory website <http://home . ccr . cancer . gov/Lco/> . MCV reporter vector stocks were produced using previously reported methods [25] , [63] . Briefly , 293TT cells [30] were transfected with plasmids pwM2m [53] and ph2m [25] expressing codon-modified versions of the VP1 and VP2 genes of MCV strain 339 . HPV16 reporter vectors were produced using the L1/L2 expression plasmid p16sheLL [36] . Production of BKV reporter vectors used a mixture of four novel plasmids , pwB2b pwB3b , ph2b and ph3b , which carry codon-modified versions of the capsid proteins of BKV genotype IV isolate A-66H ( accession number AB369093 , [64] ) . The capsid protein expression plasmids were co-transfected with a mixture of two reporter plasmids , pYafw [30] and pEGFP-N1 ( Clontech ) which express GFP from recombinant EF1α or CMV immediate early promoters , respectively . Forty-eight hours after transfection , the cells were harvested and lysed in Dulbecco's phosphate buffered saline ( DPBS , Invitrogen ) supplemented with 9 . 5 mM MgCl2 , 25 mM ammonium sulfate ( starting from a 1 M stock solution adjusted to pH 9 ) , antibiotic-antimycotic ( Invitrogen ) , 0 . 5% Triton X-100 ( Pierce ) and 0 . 1% RNase A/T1 cocktail ( Ambion ) . The cell lysate was incubated at 37°C overnight with the goal of promoting capsid maturation [65] . Lysates containing mature capsids were clarified by centrifugation for 10 min at 5000×g twice . The clarified supernatant was loaded onto a 27–33–39% iodixanol ( Optiprep , Sigma ) step gradient prepared in DPBS with a total of 0 . 8 M NaCl . The gradients were ultracentrifuged 3 . 5 hours in an SW55 rotor at 50 , 000 rpm ( 234 , 000×g ) . Gradient fractions were screened for the presence of encapsidated DNA using Quant-iT Picogreen dsDNA Reagent ( Invitrogen ) . The VP1 concentration of Optiprep-purified reporter vectors was determined by comparison to bovine serum albumin standards in SYPRO Ruby ( Invitrogen ) -stained SDS-PAGE gels . The MCV reporter vector stock contained 8 . 6 ng of VP1/µl , the BKV vector stock contained 4 . 3 ng of VP1/µl , and the HPV vector stock contained 2 . 9 ng of L1/µl . In various experiments examining reporter vector-mediated transduction , 0 . 2–0 . 4 µl of MCV stock , 0 . 3–0 . 6 µl of BKV stock , and 0 . 03–0 . 15 µl HPV stock was used per 96 well plate well . These concentrations generally produced between 5 and 25% GFP positivity in cell populations at the time of flow cytometric analysis . Recombinant capsids were produced as above , except that Benzonase ( Sigma ) and Plasmid Safe ( Epicentre ) nucleases were added to the lysis buffer at 0 . 1% each , with the goal of liberating capsids carrying fragments of cellular DNA [63] . Hemagglutination and basement membrane extract experiments used unlabeled capsids , while cell-binding studies used capsids covalently conjugated to Alexa Fluor 488 using previously-reported methods [53] . For production of Alexa Fluor 488 labeled capsids , a reporter plasmid encoding Gaussia luciferase ( phGluc; [25] ) was included in the initial transfection mixture . All conjugated capsid stocks were between 150 and 275 ng/µl and binding experiments used 0 . 2–0 . 4 µl of stock per 5×104 cells suspended in a volume of 100 µl . This generally achieved 10–30 fold fluorescence over background in flow cytometric analyses . Sheep blood in sodium citrate was purchased from Lampire Biological Products . Human type O+ blood was collected by finger prick immediately prior to use . Red blood cells ( RBCs ) were washed and suspended in PBS without calcium or magnesium ( Invitrogen ) at a final concentration of 0 . 5% ( v/v ) . The suspension was chilled on ice in round-bottom 96-well plates then mixed with various doses of purified capsids and allowed to settle overnight at 4°C . A549 cells were plated at 7 , 500 cells/well in 50 µl of culture medium in a 96 well plate the day prior to infection . Stock solutions of porcine heparin ( Sigma H4784 ) , porcine dermatan sulfate ( chondroitin sulfate B , Sigma C3788 ) , bovine chondroitin sulfate-A ( Sigma C9819 ) , or shark chondroitin sulfate-A/C ( Sigma C4384 ) were dissolved at 10 mg/ml in PBS ( Invitrogen ) . The GAGs were serially diluted in media to 3× the indicated concentration and 50 µl was added to cells . Reporter vector stock was then added to the cells+GAG mixture in a volume of 50 µl . To minimize plate edge effects , the outer wells of the plate were not used for the assay and were instead filled with culture medium . Approximately 72 hrs post-infection , cells were incubated with trypsin to detach them from the plate and transferred to an untreated 96 well plate and suspended in wash medium ( WM; DPBS with 1% FBS , antibiotic-antimycotic , and 10 mM HEPES , pH 8 ) and analyzed by flow cytometery for GFP reporter gene expression in a FACS Canto II with HTS ( BD Biosciences ) . To calculate 50% effective inhibitory concentrations ( EC50 ) , Prism software ( GraphPad ) was used to fit a variable slope sigmoidal dose-response curve to values representing the percentage of GFP positive cells relative to untreated infected cells . Error bars represent the standard deviation for at least three independent experiments . Cells were dislodged using PBS supplemented with 10 mM EDTA , and then pipetted with an equal volume of WM . Fifty thousand cells were added to wells of an untreated 96 well plate and washed once with WM . Cells were then washed once with a dilution series of GAG in WM . Next , the same dilution series of heparin or chondroitin A/C in WM containing Alexa Fluor conjugated capsids was added to cells , such that each well contained about 60 ng of VP1 in the indicated concentration of GAG . These plates were incubated at 4°C for one hour , and then cells were washed 3 times in WM before measurement of their fluorescence by flow cytometry . A549 cells were cultured in D10 supplemented with 50 mM sodium chlorate ( Sigma ) for 2–6 days , then pre-plated overnight at 9 , 000 cells/well in 96 well plates . The next morning , half the plate was changed into medium without chlorate to allow regeneration of sulfate modifications . The other half of the plate was changed into fresh chlorate-containing media . Six to eight hours later , reporter virus was added in medium with or without sodium chlorate to maintain the concentration of chlorate present on the cells . Forty-eight hours later , the cells were fed by addition of 100 µl of media without chlorate . After a total of about 72 hours , cells were harvested for analysis of GFP expression by flow cytometry . For experiments examining the effects of neuraminidase treatment on reporter vector transduction , cells pre-plated in 96 well plates were washed and incubated with 50 µl of DPBS containing 70 mU of neuraminidase from Arthrobacter ureafaciens ( NorthStar Bioproducts ) per 5×105 cells for 1 hour at 37°C . The cultures were then inoculated with reporter vector stock and incubated for an additional 2 hours at 37°C . The cultures were then washed once and fed with 100 µl of culture medium . In some replicates , culture medium was added directly to the neuraminidase-containing PBS in the culture well . Removing or washing away the neuraminidase/reporter vector mixture did not appear to alter the experimental outcome . After three days , the cells were harvested and analyzed for GFP expression by flow cytometry . For binding studies , conjugated capsids were added to 5×104 neuraminidase-treated ( or mock-treated ) cells in suspension in an untreated 96 well plate and incubated for 1 hour at 37°C . The cells were then washed three times prior to analysis of fluorescence by flow cytometry . Heparinase I ( 50 units , Sigma ) and heparinase III ( 5 units , Sigma ) were solubilized in 100 µl each of resuspension buffer containing 20 mM Tris , pH 7 . 5 , 50 mM NaCl , 4 mM CaCl2 and 0 . 01% BSA . The two enzymes were then combined . Chondroitinase ABC ( 2 units , Sigma ) was solubilized in 200 µl of resuspension buffer . A549 cells plated the day prior at 7 , 500 cells/well in a 96 well plate , were washed once with digestion buffer ( 20 mM HEPES , pH 7 . 5 , 150 mM NaCl , 4 mM CaCl2 and 0 . 1% BSA ) , and then treated with 2 . 5 µl of heparinase I/III stock , 2 . 5 µl of chondroitinase stock ( or both ) in 50 µl of digestion buffer . Cells were incubated in digestion buffer with or without enzyme for 2 hours at 37°C . Various doses of reporter vector stock were then added to the wells in 50 µl of OptiMEM-I ( Invitrogen ) and incubated for an additional 1 hour at 37°C . The cells were washed twice with culture medium , and then incubated in 150 µl/well culture medium for three days . Cells were then analyzed for GFP expression by flow cytometry , as above . For binding analyses , 2×105 cells dislodged using PBS and 10 mM EDTA , were treated with 3 . 5 µl each enzyme in digestion buffer for 1 . 5 hours at 37°C . Alexa Fluor conjugated capsids diluted in Opti-MEM were then added to the cells and incubated for an additional one hour at 37°C prior to washing and flow cytometric analysis . CHO-K1 , pgsA-745 , pgsD-677 , pgsE-606 , and pgsF-17 cells were plated at a density of 10 , 000 cells/well in 50 µl culture medium in a 96 well plate . Binding and infectivity studies to analyze the effect of exogenous GAGs were performed as above , except that cells were pre-plated and infected the same day in order to avoid changes in cell number resulting from slightly differing rates of growth . Cultrex BME PathClear ( Trevigen #3432-005-02 ) , a BME preparation derived from murine Engelbreth-Holm-Swarm tumor , was aliquoted and stored according to manufacturer's instructions . Black Microfluor 2 ELISA plates ( Thermo ) were coated overnight with 1 µg of BME per well in a volume of 150 µl . Coated plates were emptied and treated with 200 µl/well of 1× Blocker BSA ( Pierce ) in PBS . The block was incubated for 2 hours , with rocking , at room temperature . The plate was then washed twice with PBS plus 0 . 05% Tween 20 ( PBS/Tween; BioRad ) . To examine direct virus binding to BME , a two-fold dilution series of HPV or MCV capsids beginning at 5 µg of VP1/well in 150 µl PBS/Tween was examined for binding to BME-coated plates . Binding reactions were conducted for two hours at room temperature , with rocking . To analyze the binding of capsids to BME for all experiments , the plate was washed three times with PBS/Tween , and then treated with 150 µl/well Quant-iT PicoGreen dsDNA Reagent ( Invitrogen ) in TE buffer supplemented with 0 . 1% Proteinase K stock ( Qiagen ) . The plate was incubated in a 65°C oven for 1 hour , and then cooled for 15 min at room temperature in the dark before measuring fluorescence in a BMG Labtech POLARstar Optima microplate reader . To analyze the effect of enzymatic cleavage of GAGs on virus binding , a three fold dilution series of heparinase or chondroitinase ( prepared as described above in the section on enzymatic removal of cell-surface sialic acids or GAGs ) beginning with 4 . 5 µl of enzyme stock per well in 150 µl of digestion buffer was added to the prepared plate and incubated for 2 hours at 37°C . The plates were then washed twice with PBS/Tween , and 100 ng/well of capsids in 150 µl of PBS/Tween was added to all treated and mock-treated control wells . To measure competitive inhibition of capsid binding with heparin and chondroitin A/C , a five fold dilution series of each GAG , beginning with 100 µg of GAG per well was mixed with 50 ng of capsids in 150 µl of PBS/Tween , and then added to the BME-coated plate . MCV virions were produced by co-transfecting 293TT cells [30] with recombinant MCV isolate R17a genomic DNA , reconstituted by intramolecular re-ligation at 4 µg of plasmid DNA per ml using T4 DNA ligase ( NEB ) . The re-ligated MCV genomic DNA was co-transfected with expression plasmids carrying the MCV Large T ( pADL* ) and small t ( pMtB ) antigen genes . Cells were expanded for five days after transfection and virions were harvested using the methods outlined above for recombinant capsid production . The virions were purified by Optiprep gradient centrifugation , as above , and fractions were screened for the presence of encapsidated DNA using Quant-iT Picogreen dsDNA Reagent ( Invitrogen ) and by western blot for MCV VP1 . The characteristics of a representative stock of native virions are shown in Figure S10 . 293-4T cells were detached with trypsin and 2×105 cells/well were added to an untreated 96 well plate . Cells were washed once with digestion buffer ( see above section on enzymatic removal of cell-surface GAGs ) , and then incubated for 45 minutes at 37°C with or without 5 µl each of heparinase and chondroitinase stock solution in 150 µl digestion buffer/well . Next , native MCV virions ( production described above ) or BKV virions ( kindly provided by Gene Major , NINDS , NIH [66] ) diluted in 50 µl OptiMEM were added and the cell suspensions were incubated at 37°C for an additional 45 minutes . Cells were then washed once with culture medium and again with either PBS or culture medium . The PBS suspension was collected and frozen immediately , with the goal of establishing the initial baseline number of bound MCV genomes derived from the virus inoculum . The culture medium suspension was plated in a 24 well plate and cultured for 5 to 6 days . The cultured population was trypsinized and harvested for modified Hirt extraction ( [67] protocol at our laboratory website ) to isolate low molecular weight DNA . Baseline samples were also subjected to modified Hirt extract . One-fiftieth of the eluted DNA sample was used in a twenty microliter reaction with DyNAmo HS SYBR Green Kit ( New England Biolabs ) according to manufacturer's instructions in a 7900HT Fast RT PCR System ( Applied Biosystems ) with ROX reference dye . The primers targeting the MCV genome are 5′-GCTTGTTAAAGGAGGAGTGG-3′ and 5′-GATCTGGAGATGATCCCTTTG-3′ . The BKV-specific primers are 5′-TGGTGCTCCTGGGGCTATTGC-3′ and 5′-GCCATGCCTGATTGCTGATAGAGG-3′ . A dilution series of known quantities of MCV and BKV genomic DNA were analyzed simultaneously and used to form a standard curve and calculate the number of genome copies present in each sample . An average of 12 million copies of MCV DNA and 29 million copies of BKV DNA were measured from mock-treated baseline samples collected 45 minutes after inoculation of native virions . An average of 465 million copies of MCV DNA and 895 million copies of BKV DNA were measured 5 or 6 days later . Net values conclusively showing viral amplification were calculated by subtracting the baseline number of bound viral genomes observed 45 minutes after inoculation from the number of viral genomes observed after 5 or 6 days . Annotated nucleotide maps of all plasmids used in this work are posted on our laboratory website < http://home . ccr . cancer . gov/Lco/plasmids . asp> . The plasmids and their sequences will also be made available via Addgene . org . Accession numbers for previously-reported sequences are: MCV-R17a ( HM011555 ) , BKV-A-66H ( AB369093 ) , SLC35A1 ( NM_006416 ) . | Strong evidence suggests that Merkel cell polyomavirus ( MCV or MCPyV ) is a causative factor in the development of a large proportion of cancers arising from epidermal Merkel cells . While Merkel cell carcinoma is rare , it appears that infection with MCV is common , and many healthy people chronically shed MCV virions from the surface of their skin . In an effort to better understand the factors controlling MCV tissue tropism , we sought to characterize the cellular receptors that mediate MCV attachment to cultured cells . Several previously-examined polyomaviruses utilize sialic acid-containing glycolipids and glycoproteins to mediate cell binding and infectious entry . Our results show that , in contrast to other polyomaviruses , MCV does not require sialic acid-bearing glycans for attachment to cells , but instead uses a different group of carbohydrates called glycosaminoglycans for the initial attachment step of the infectious entry process . Interestingly , although sialic acid-bearing glycans are dispensable for initial attachment to cells , data using cells deficient in sialylated glycans suggest that sialic acids may form an essential element of a possible co-receptor that is engaged after the initial attachment of MCV to the cell via glycosaminoglycans . | [
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] | 2011 | Glycosaminoglycans and Sialylated Glycans Sequentially Facilitate Merkel Cell Polyomavirus Infectious Entry |
Significant insights into the biology of Plasmodium vivax have been gained from the ability to successfully adapt human infections to non-human primates . P . vivax strains grown in monkeys serve as a renewable source of parasites for in vitro and ex vivo experimental studies and functional assays , or for studying in vivo the relapse characteristics , mosquito species compatibilities , drug susceptibility profiles or immune responses towards potential vaccine candidates . Despite the importance of these studies , little is known as to how adaptation to a different host species may influence the genome of P . vivax . In addition , it is unclear whether these monkey-adapted strains consist of a single clonal population of parasites or if they retain the multiclonal complexity commonly observed in field isolates . Here we compare the genome sequences of seven P . vivax strains adapted to New World monkeys with those of six human clinical isolates collected directly in the field . We show that the adaptation of P . vivax parasites to monkey hosts , and their subsequent propagation , did not result in significant modifications of their genome sequence and that these monkey-adapted strains recapitulate the genomic diversity of field isolates . Our analyses also reveal that these strains are not always genetically homogeneous and should be analyzed cautiously . Overall , our study provides a framework to better leverage this important research material and fully utilize this resource for improving our understanding of P . vivax biology .
Today approximately 2 . 5 billion people are at risk of Plasmodium vivax malaria [1] . While transmission of P . falciparum is slowly decreasing in many countries committed to malaria elimination , vivax malaria displays surprising resilience in a majority of these countries [2] . This difference , likely resulting from the important biological differences between the two parasite species ( e . g . , the existence of a dormant stage in P . vivax ) , calls for specific elimination strategies targeting P . vivax more efficiently . However , our understanding of P . vivax biology remains limited by the difficulties of culturing P . vivax in vitro . The lack of an in vitro culture system notably hampers investigations of parasite cell and developmental biology , biochemistry , and the physiology of host cell and parasite interactions by decreasing the availability of the parasite to most laboratories . Rapidly advancing genomics technologies have led to a growing number of P . vivax whole genome sequences [3–6] . In-depth characterization of multi-gene families [3] , identification of single nucleotide polymorphisms [3 , 5] , gene rearrangements [7] and previously uncharacterized genes [8] have for example , provided the molecular foundations to prompt new hypotheses and studies on this important parasite . However , testing these hypotheses in vivo remains difficult and , currently , our best opportunity to investigate P . vivax biology may be through P . vivax parasites that have been adapted for propagation in New World monkeys [9] . Monkey-adapted P . vivax strains are typically generated by direct injection of parasitized erythrocytes from patients or , after passage through mosquitoes , by the injection of sporozoites dissected from infected mosquito salivary glands into Saimiri or Aotus monkeys [10] . Once infections are stably established by serial passage , these strains can be continuously propagated in monkeys by initiating further infections using sporozoites or infected erythrocytes , which can be cryopreserved for later use . These parasites are extremely useful to obtain large amount of proteins or nucleic acids from a single strain and can be shared among researchers to investigate various aspects of the parasite biology . However , important questions regarding their biological relevance and homogeneity remain unanswered . It notably remains unclear whether the host switch , from humans to New World monkeys , induces or requires specific genomic changes . While P . vivax-like parasites have been identified in great apes , to date , genomic studies have indicated that these parasites belong to a clearly distinct sister clade , basal to the human P . vivax [11] and suggest that P . vivax are specific to humans . In addition , many attempts to adapt P . vivax to New World monkeys fail to result in detectable levels of the parasite [12] , alter the parasite life cycle [13] or are only successful in a specific monkey species or subspecies [13 , 14] . ( Note that once a strain has been successfully adapted , it can typically be more easily propagated in subsequent monkeys . ) These observations suggest that the molecular mechanisms used by P . vivax to invade and survive the metabolic environment of red blood cells ( RBCs ) and evade the host innate and adaptive immune responses have been tuned to humans by thousands of years of evolution and might be maladapted to New World monkey physiology and RBCs . Successful adaptation to the new environment of New World monkey RBCs could therefore require subtle changes throughout the genome . Interestingly , P . vivax does not seem to be able to infect Old World monkeys , although these primates are more closely related to apes than the New World monkeys . On the other hand , there are clear indications that Old World primate malaria parasites can infect humans [15–18] despite consequent differences in genome sequences [19 , 20] . Note however that these infections are not usually as robust as in the natural hosts and that these parasites’ genomes have not been examined after passage in humans . Independently of the host switch , the continuous serial blood stage propagation of adapted parasites in New World monkey may also induce genomic changes as some genes become dispensable in this setting . For example , the Vietnam IV Palo Alto strain is not able to infect mosquitoes [21] suggesting that some genes underlying infectivity to mosquitoes might have been altered during propagation in monkeys . In this regard , it is important to note that many genomic rearrangements have been documented during the propagation of P . falciparum in in vitro cultures [22 , 23] . Finally , once an isolate becomes a monkey-adapted strain it is often unknown whether it consists of a single homogeneous clonal parasite population ( i . e . , a single “genotype” , later referred to as a clone ) or a complex infection as observed in genomic analysis of field isolates [5] and numerous field studies ( see e . g . , [24] ) . In this study , we compare the genomes of seven monkey-adapted strains with the genomes of six field isolates to characterize genomic changes that potentially occur during adaptation to New World monkeys and continuous propagation . We also analyze six different samples collected during the generation of the Mauritania-I and Mauritania-II strains . These analyses provide additional insights regarding the homogeneity of monkey-adapted strains and the changes that occur during the establishment and propagation of these strains .
For our analyses , we used genome sequence data previously generated from seven monkey-adapted strains: the Salvador-I [25] , Belem [5] , Chesson [8] , Brazil-I [3] , India-VII [3] , Mauritania-I [3] , and North Korean [3] strains . We compared these sequences with data from six previously sequenced field isolates from Cambodia and Madagascar ( M08 , M15 , M19 , C08 , C15 , and C127 ) [5 , 7] . For some of the analyses , we focused on four of these field isolates ( M08 , M15 , C08 , and C127 ) that carry one single highly dominant clone and therefore allow inference of the entire haploid genome sequence ( see supplemental information in [5] for details ) . Several sequencing runs were independently produced for the samples sequenced at the Broad Institute and we used , for most of our analyses , those generated using 101 bp paired-end reads ( as these are most similar to the data we generated ) . The remaining libraries were only used to assess sequencing error hotspots and unannotated paralogous sequences ( see below ) . Detailed information on the samples and sequencing libraries used is provided in S1 Table . In addition , we analyzed sequences from DNA extracted from additional blood samples collected during the generation of the Mauritania-I and Mauritania-II P . vivax strains [26] . Three blood samples ( AI-3221 , AO-521 and WR-1714 ) were collected from Aotus nancymaae monkey infections derived directly from the original patient infection in February 1995 . The infection in WR-1714 was initiated by sporozoites collected from mosquitoes fed on blood from the patient’s initial infection . DNA of the stabilate of the Mauritania-I strain sequenced by the Broad Institute [3] came from infections of two Saimiri boliviensis boliviensis monkeys , SI-3095 and SI-3097 . We also analyzed the blood sample from the patient when a relapse occurred in October 1995 and blood from an Aotus nancymaae monkey ( AI-3218 ) infection derived from this relapse after five direct serial passages in monkeys . The AO-521 , WR1714 , AI-3218 and patient samples were collected in 1995 and cryopreserved at the Division of Parasitic Diseases of the Centers for Disease Control and Prevention ( Atlanta , GA ) . The AI-3321 specimen was collected in 2006 from a monkey infected by parasitized erythrocytes from AI-653 ( that had been cryopreserved since 1995 ) . For all samples , we extracted DNA from 200 μl of cryopreserved blood using the Qiagen DNeasy Blood and Tissue kit according to the manufacturer’s instructions . We mapped sequencing reads from all samples to the P . vivax Salvador-I [25] reference genome using bowtie2 [27] . We mapped each end of all read pairs independently and considered as correctly mapped only reads best mapped to a single genomic location . Only read pairs for which both ends fulfilled this criterion were included for further analyses . We also identified read pairs that mapped to the exact same positions and randomly discarded all but one pair to eliminate reads representing DNA molecules amplified during the library preparation . In total we examined 13 strains; seven monkey adapted isolates and six human field isolates . We screened for single nucleotide variants ( SNVs ) at all nucleotide positions covered by at least 20 reads with a base quality score greater than 30 in all analyzed samples . Regions of high DNA sequence similarity were excluded from our analysis as previously described [5] . Overall , 19 . 7 Mb or 87% of the Salvador-I reference genome sequence were analyzed . Mismatches ( i . e . , SNVs ) between reads generated from a given sample and the reference genome sequence were determined using samtools mpileup [28] and the extended base alignment quality computation . Positions were considered variable only if at least 10% of the reads from a given sample supported an allele different from the reference nucleotide . We screened each genome for DNA sequence rearrangements as described in [7] . Briefly , we analyzed all read pairs that did not map in the expected configuration ( i . e . , head-to-head within 1 kb from each other ) and might be indicative of deletions ( reads mapping head-to-head but distant by more than 1 kb ) , inversions ( reads mapping in a head-to-tail configuration ) and tandem duplications ( tail-to-tail ) ( see S1 Fig . of [7] for details ) . We then identified regions of the genome with more read pairs in unusual configurations than we would expect by chance ( as modeled by a Poisson distribution ) . To avoid including artifacts occurring during library preparation , we focused on rearrangements greater than 1 kb but smaller than 100 kb . We also searched for large deletions by scanning for chromosomal regions greater than 100 kb where the sequence coverage was less than 50% of the genome average coverage of the sample . To avoid including regions where reads systematically mapped poorly ( due to high DNA sequence divergence or high repeat content ) , we restricted our analyses to loci that displayed low sequence coverage in some , but not all , of the samples . To identify positions in the P . vivax genome prone to systematic sequencing errors , we analyzed sequence data generated by the Broad Institute: for some of the monkey-adapted strains , the sequence data came from several independent sequencing reactions generated from the same library ( S1 Table ) . We examined the reference allele frequency ( RAF ) for nucleotide positions sequenced by more than 50 reads in each of three independent sequencing runs of the Brazil-I , North Korean , and Mauritania-I strains . We then catalogued genome positions that displayed a RAF between 1–10% and 90–99% in all three runs of these three samples . We focused for this analysis on positions where less than 10% of the reads differed from the main allele ( i . e . , from all other reads ) as this corresponds to the peak of RAF observed in Mauritania-I ( see Results ) . These “consistently variable” positions may represent sequencing error hotspots or unannotated paralogous sequences and were filtered out . We considered that remaining nucleotide positions sequenced at high coverage ( >150 total reads ) and with a RAF between 1–10% and 90–99% in Mauritania-I represented positions where a previously unreported minor clone differed from the major clone . We then reconstructed the haploid genome sequence of this second ( minor ) clone using the minor allele at these positions ( i . e . , we assumed that only one minor strain was present in this sample ) . In addition to the Mauritania-I strain data generated by the Broad Institute [3] , we analyzed blood samples from four additional monkey blood samples collected during the generation of the Mauritania-I strain ( derived from the initial patient infection ) and Mauritania-II strain ( derived from a relapse of the same patient ) [26] . We also analyzed blood directly collected from the patient during the relapse . The quality of the DNAs ( frozen since 1995 for most samples ) and the lack of leukocyte depletion before freezing prevented whole genome sequencing . We therefore designed primers to amplify 38 SNVs distributed across the P . vivax genome and for which we observed two alleles in the Mauritania-I Broad Institute sequence data ( S2 Table ) . Each primer was designed to include a 5’ oligonucleotide tail for barcoding and high-throughput sequencing ( see below ) . We amplified each locus with the following conditions: 94°C for 3 min; 40 cycles of 94°C for 45 sec , 56°C for 45 sec and 72°C for 45 sec; and final extension at 72°C for 3 min . We pooled the 38 amplification products obtained from each blood sample together , purified the DNA pools with Qiagen QIAquick columns and labeled them with an individual oligonucleotide barcode ( i . e . , one barcode per blood sample ) by a second amplification ( with the same conditions as previously but with only 10 cycles ) using primers targeting the 5’ oligonucleotide tail and containing the Illumina adapter sequence and the unique barcode sequence . The barcoded samples were then pooled together at equal DNA concentrations and sequenced simultaneously on an Illumina MiSeq to generate 32 , 283 , 840 paired-end reads of 150 bp ( 4 . 4–10 . 8 million pairs per sample ) . We mapped the reads on the Salvador I reference genome sequence using bowtie2 and analyzed allelic variations at the SNVs targeted . We discarded from our analysis 11 out of the 38 targeted SNVs due to allelic dropout or insufficient read coverage ( <100 X ) .
We analyzed 19 . 7 million nucleotide positions ( 87% of the Salvador I reference genome sequence ) that have been previously sequenced at more than 20 X in seven monkey-adapted strains and six field isolates and identified 140 , 949 variable positions . We refer to these variable nucleotide positions as single nucleotide variants ( SNVs ) as they may include variants that occurred during the adaptation and propagation of the strains in New World monkeys as well as single nucleotide polymorphisms ( SNPs ) . In all monkey-adapted strains and four of the field isolates , one clone of P . vivax accounted for >80% of all P . vivax sequences enabling reconstruction of the entire haploid genome sequence for this clone . To assess whether adaptation to a new host induced systematic genomic changes , we first performed a principal component analysis of all dominant clones using all SNVs identified . Interestingly , P . vivax parasites clustered according to their geographic origin and not to the host species from which the sample was obtained ( Fig . 1 ) . Further analyses of the first ten principal components ( accounting for 94% of the variance ) did not reveal any clustering of samples according to their host . This observation indicated that , at the genome level , the host switch was not a major determinant of the genetic diversity . Even if host switch did not alter the genetic diversity of P . vivax at the genome-scale , it is possible that a few critical protein coding genes or regulatory elements were systematically modified during the parasite passage from human to monkey hosts . We therefore examined every SNV throughout the genome and tested whether its alleles segregated according to the host . Throughout 19 . 7 Mb covered by more than 20 high quality reads in all samples , we did not find a single variant ( out of 140 , 949 SNVs ) for which one allele was fixed in all monkey-adapted strains and the other allele was fixed in all human field isolates ( e . g . , a position where all monkey-adapted strains would carry an A and all human isolates a T ) . Analyses of DNA sequence insertions , deletions or inversions [7][8] also failed to reveal any DNA sequence rearrangement systematically present in all samples from one group and absent from all samples from the other . Overall , our analyses suggested that adaptation to a New World monkey host did not induce systematic genomic changes nor did it leave any consistent signature in the P . vivax genome among the strains evaluated here . Once adapted to a different host , P . vivax strains can be propagated for years through successive infections of New World monkeys . We therefore wanted to determine whether this propagation could lead to genetic changes . If monkey-adapted strains accumulate mutations during propagation , we would expect that they differed more from each other or from a set reference than field isolates . In contrary , our results showed that there were , on average , 36 , 297 nucleotide differences ( 16 , 683–47 , 597 ) between a given monkey-adapted strain and the Salvador-I reference genome sequence and 40 , 730 differences ( 38 , 520–45 , 306 ) between human isolates and the reference ( p = 0 . 4 ) . Another way to test whether long-term propagation in New World monkeys result in the accumulation of mutations is to compare genome sequences from the same strain generated from DNA isolated years apart . We have independently [5] produced sequencing data from the Salvador-I strain used for generating the reference genome sequence [25] . Out of the ~12 . 2 million bases covered by 20 reads or more in our data and after filtering out ~360 kb of repetitive or potentially paralogous regions ( see [5] for details ) , we observed 3 , 116 possible SNVs ( i . e . , positions where >10% of the reads differed from the reference allele ) between the genomes of this same strain collected at two time points . However , there were only 8 positions where >90% of the reads generated differed from the reference Salvador I sequence ( note that these figures are slightly different from those presented in [5] as we used here a better read mapping algorithm ) . It is important to note that these differences represented a combination of sequencing errors and possible genuine differences . Overall , these observations suggested that propagation in New World monkeys was unlikely to lead to accumulation of many mutations in the P . vivax genome . In three of the seven monkey-adapted strains ( Belem , Brazil-I and North Korean ) , we noticed that very few ( if any ) reads mapped to a 130 kb region at the subtelomeric end of chromosome 7 ( Fig . 2 ) . While telomeric and subtelomeric regions are enriched in repeated sequences and therefore difficult to assemble , resequence and analyze , this particular deletion extended far beyond the typical repeat- and AT-rich region and was successfully sequenced in other P . vivax strains . In addition , the GC content along this subtelomeric region gradually decreases with the most abrupt change ( from ~40% to ~28% GC ) occurring around position 1 , 411 , 000 , roughly 35 kb downstream of the deletion boundary ( Fig . 2 ) . The deleted region contains 22 annotated protein coding genes including a cytoadherence linked asexual protein ( CLAG , PVX_086930 ) , an early transcribed membrane protein ( ETRAMP , PVX_086915 ) , a Phist protein ( PVX_086910 ) , ten hypothetical proteins and nine vir genes . In the Belem and Brazil-I strains , no sequence reads could be aligned to this region suggesting that the entire end of the chromosome had been deleted . The exact demarcation of the deletion did not appear to be identical between these samples , with the deletion starting at base ~1 , 367 , 000 in the Belem strain and 6 kb later , at base ~1 , 373 , 000 , in the Brazil-I and North Korean strains ( Fig . 2 ) . This could indicate independent deletion events or continuous trimming of the telomere . Evidence of this subtelomeric deletion in the North Korean strain was supported by a significant , but not complete , reduction in coverage ( ~75% less reads ) , suggesting that , within the North Korean strain , some parasites carried the deletion while some had the entire subtelomeric sequence . Interestingly , the reference allele frequency ( RAF ) profile for the North Korean strain ( Fig . 3 , light blue ) suggested that the two clones in this sample ( with or without the deletion ) were otherwise genetically identical . This observation suggested that the subtelomeric deletion occurred recently in a clonal population of parasites and that the North Korean strain of P . vivax is not genetically homogeneous anymore . It is important to note that this telomere shortening was not exclusive to monkey-adapted strains but was also observed in one of the minor clones of a field isolate from Cambodia ( C15 , Fig . 2 ) . In a previous study we showed that the Salvador-I and Belem displayed reference allele frequency ( RAF ) distributions consistent with the presence of a single clone [5] . For these samples , all reads covering a given genome position either carried a nucleotide identical to the reference allele or all carried a same but different nucleotide ( the alternative allele ) , with minor alleles represented by less than 5% of the reads likely representing sequencing errors . This pattern was also observed in three out of four monkey-adapted strains sequenced by the Broad Institute [3] ( Fig . 3 ) . In contrast , in the Chesson sample , we detected the presence of a second clone that accounted for approximately 10% of all reads ( Fig . 3 ) . At all positions that harbored two alleles for this sample , the minor allele was always identical to the Salvador-I reference allele . In addition , we did not observe a single position with a RAF of 0% ( which occurs when both clones are identical and differ from the reference genome ) suggesting that , throughout the entire genome , the minor clone sequence never differed from the Salvador-I reference genome sequence . These observations suggested that the Chesson sample we sequenced had likely been contaminated by Salvador-I DNA . The RAF spectrum of the Mauritania-I strain ( Fig . 3 ) also clearly indicated the presence of a minor clone accounting for ~5% of the P . vivax sequences . Overall , we identified 2 , 255 nucleotide positions where the two clones present in the Mauritania-I sample differed . This number of differences was much lower than we would expect for two unrelated clones ( typically around 30 , 000 nucleotide differences ) and suggested that these clones were likely related ( Fig . 4 ) . Analysis of the spatial distribution of these genetic differences revealed that the SNVs differentiating the two clones of Mauritania-I were not randomly spread throughout the genome ( as would be expected from a unrelated clone ) but instead appeared to be clustered in distinct “blocks” ( Fig . 5 ) : 1 , 969 out of 2 , 255 differences ( 87% ) were located in 153 regions ranging from 5 kb to 165 kb ( and accounting for 3 . 78 Mb or 20% of the genome sequence ) . One possible explanation for this block pattern is that several P . vivax clones were present in the original patient infection and that they recombined during the passage through Anopheles mosquitoes in the laboratory ( Fig . 6 ) and that the Mauritania-I sample originally sequenced by the Broad Institute is a mixture of a parental and a recombinant clone . To confirm the presence of multiple clones in the Mauritania-I sample , we analyzed blood samples collected at different time points along the generation of the Mauritania-I strain ( see Fig . 6 and Methods for details ) . We selected 38 SNVs differentiating the major and minor clones present in the Mauritania-I genome sequence and located in the recombinant blocks . We genotyped these SNVs ( see Methods ) in three monkey P . vivax infection samples derived from the initial malarial episode ( AI-3221 , AO-521 and WR-1714 , which was infected by sporozoites from mosquitoes fed on the patient’s blood ) and two samples from a subsequent relapse of the same patient ( blood from the relapsing patient and , after serial passage in New World monkeys , from a later monkey-adapted stabilate , AI-3218 ) . These samples are collectively referred to as Mauritania-I ( for the samples derived from the initial infection ) and Mauritania-II ( for the samples derived from the relapse ) . At each of the 27 position successfully genotyped , the monkey samples AI-3221 and AO-521 showed 100% of the reads carrying the same allele indicating that these samples were infected by a single clone ( referred to as P1 ) . The sample from the patient relapse and the monkey sample derived from this relapse ( AI-3218 ) also showed genotypes consistent with infection by parasites with the same single genotype as one another . However , this genotype was different from the P1 genotype noted in AI-3221 and AO-521 at 19 of the 27 successfully genotyped SNVs ( Fig . 6 ) indicating that the patient’s relapse parasites and the parasites passaged through AI-3218 were a distinct clone ( referred to as P2 ) . Finally , the genotypes generated from the sample WR-1714 showed two alleles at 24 out of 27 positions indicating the presence of multiple P . vivax clones in this sample . This observation confirmed that the two clones detected in the Mauritania-I genome sequence data were genuine ( and not the result of a laboratory contamination ) since the passage lineage of the sample sequenced by the Broad Institute derives from WR-1714 ( Fig . 6 ) . WR-1714 displayed genotypes consistent with the presence of both P1 and P2 clones as well as a third clone ( P3 ) at a much lower frequency ( <5% ) . Overall , our analyses are consistent with the presence of at least three clones ( P1 , P2 , and P3 ) in the original infection , a single clone ( P2 ) in the patient’s relapse blood specimen ( and the subsequent infected monkeys ) and the presence of two clones ( a predominant P1 clone and a minor recombinant clone of P1 and P3 ) in the sample sequenced by the Broad Institute ( Fig . 6 ) . Note that , since we selected SNPs differentiating the recombinant clone from the major clone ( P1 ) from the Mauritania-I genome data , the recombinant genotype is identical to its parental genotype ( P3 ) at these markers .
The main purpose of this study was to determine whether the adaptation of the human malaria parasites P . vivax to New World monkey hosts resulted in systematic genetic or genomic changes . Overall , our analyses suggested that monkey-adapted strain genomes were not significantly altered and remained representative of the original P . vivax parasite genomes circulating in the blood of the infected patient . In particular , we did not detect any fixed nucleotide differences between field isolates and monkey-adapted strains suggesting that the host switch did not lead to systematic genetic changes . Our analyses relied on the comparison of existing monkey-adapted P . vivax genomes to those of field isolates . A more elegant and straight-forward approach would be to directly compare the genomes of the same P . vivax strain generated from DNA isolated from the original patient and from an infected New World monkey after adaptation . Unfortunately , few laboratories are able to perform such host switch and they do so irregularly , and no matched DNA pairs from previous adaptations were available for genome sequencing . We have also tested whether monkey-adapted strains accumulate mutations during continuous propagation in monkeys . The mutation rate during asexual reproduction of P . vivax remains unknown and long-term culture studies similar to those performed in P . falciparum [29] are not necessarily comparable to in vivo propagation . However , analysis of the genome of the monkey-adapted Salvador-I strain sequenced from two New World monkeys separated by at least five consecutive passages revealed a small number of putative genetic changes suggesting a low asexual mutation rate ( note that these differences could also originate from sequencing errors ) . Importantly , most of these nucleotide differences between the Salvador-I reference genome and our later sequence were only supported by a small proportion of the reads and only 8 nucleotide differences were supported by 90% or more of the reads ( out of 12 Mb sequenced at more than 20 X in Salvador I ) . This observation suggested that , despite likely population bottlenecks occurring during the propagation of the Salvador-I strain in different monkeys , few novel mutations ( if any ) have drifted to fixation and that most of the possible differences observed are only present in a subset of the otherwise clonal parasite population . Studies including multiple passages will be required to confirm these findings and provide a rigorous estimate of the mutation rate during asexual reproduction . One limitation of our analyses is that we excluded regions of the P . vivax genome where high DNA sequence homology or unannotated paralogous sequences greatly complicates unambiguous read mapping and SNP calling . While we analyzed here 87% of the P . vivax reference genome , it is possible that unidentified mutations occurred , during adaptation and propagation of these strains in monkeys , in the remaining non-unique regions of the P . vivax genome . Similarly , we did not consider short indels for technical reasons and these might represent another source of possible genetic differences unaccounted for in our study . During our analyses , we observed a large deletion at the subtelomeric end of chromosome 7 in three out of the seven monkey strains , as well as in one Cambodian field isolate . While telomeres are typically difficult to sequence and assemble ( and are partially missing in the Salvador-I reference genome sequence ) , this deletion mostly included unique DNA sequences and contained little repeated sequences . Similar subtelomeric deletions have been reported in P . falciparum , both in field isolates and in vitro cultures ( e . g . , [22 , 23] ) . Interestingly , the chromosome 7 subtelomeric deletion displayed different boundaries in different samples suggesting that i ) it resulted from independent events that occurred in the P . vivax population prior to adaptation to New World monkeys or ii ) that the telomere was slowly being eroded . In addition , in the North Korean strain we observed genetic heterogeneity for this rearrangement suggesting that a proportion of the parasites in the sequenced sample carried the deletion while the rest of them had the full-length chromosomal sequence . This observation suggested that the subtelomeric loss was recent in this strain ( i . e . , post adaptation to monkeys ) and that it remained polymorphic in this otherwise clonal parasite population . This finding also raised questions regarding the presumed genetic homogeneity of monkey-adapted strains . One technical factor may artificially influence the heterogeneity of the strains: DNA samples collected from multiple individual monkeys infected with the same strain are often pooled together to obtain enough genetic material for genome sequencing . This procedure may result in laboratory contamination with another strain , especially since these strains are not differentiable without the use of genetic markers . For example , we detected a contamination of the Chesson sample by the Salvador-I strain . Such cross-contamination could have important consequences: sequencing a particular gene may , for example , reveal two different DNA sequences and suggests that there are multiple copies of that gene in this strain . Finally , we observed in the Mauritania-I sample sequenced by the Broad Institute [3] evidence of genetic heterogeneity , with the presence of at least two genetically distinct clones . Analysis of additional Mauritania-I samples confirmed that multiple clones were present in the original patient infection and revealed that different clones became isolated ( or dominant ) in different monkeys during the propagation . This observation raises important concerns on the use of monkey-adapted P . vivax strains as different aliquots of the same monkey-adapted strain might actually contain genetically different parasites and therefore might respond differently in in vitro or in vivo assays ( e . g . , of drug resistance , infection efficiency or virulence ) . On the other hand , our study illustrates the potential advantages of applying genomic tools to studies of monkey-adapted strains . Identification of multiple clones in a sample is traditionally conducted by genotyping a small number of microsatellites ( typically between 5 and 10 ) , which does not have the sensitivity necessary to differentiate closely related clones or identify clones making up less than 10% of the parasites [30] . The resources provided by genomic data now enable genotyping of several dozen of highly informative SNPs and might help in solving phenotypic discrepancies among samples from the same monkey-adapted strain . In addition , the observation of a recombinant clone in the Mauritania-I sample sequenced by the Broad Institute illustrates how application of genomic tools could guide the generation of P . vivax genetic crosses which could lead to major advances in gene mapping in P . vivax ( but see also [31] ) . The development and maintenance of monkey-adapted P . vivax strains has and will continue to be an essential tool for the study of this important malaria parasite . While we have highlighted some of the hidden problems of monkey-adapted strains , our study also provides great prospects for studying this important resource . The extensive information generated by genome sequencing provides numerous genetic markers that can easily be genotyped in a given sample to monitor the identity , complexity and purity of a given strain and improve studies of monkey-adapted strains . | In this study we compare the genome sequences of Plasmodium vivax collected directly from patients with those of parasites propagated in laboratory monkeys . We show that the adaptation and continuous propagation of Plasmodium vivax in monkeys does not induce systematic changes in the genome and , therefore , that these parasites constitute an unbiased resource for studying this important pathogen . Our analyses also reveal that some monkey-adapted Plasmodium vivax strains are not genetically homogenous and retain multiple genetically different parasites present in the original patient infection . Overall , our study confirms the utility of monkey-adapted Plasmodium vivax strains for malaria research but also shows that this resource should be analyzed cautiously as different samples of the same strain might provide different biological material . | [
"Abstract",
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] | [] | 2015 | Comparative Analysis of Field-Isolate and Monkey-Adapted Plasmodium vivax Genomes |
Biliary vessel pathology due to alveolar echicococcosis ( AE ) results in variable combinations of stenosis , necrosis and inflammation . Modern management strategies for patients with cholestasis are desperately needed . The aim is proof of principle of serial ERC ( endoscopic retrograde cholangiography ) balloon dilation for AE biliary pathology . Retrospective case series of seven consecutive patients with AE-associated biliary pathology and ERC treatment in an interdisciplinary endoscopy unit at a University Hospital which hosts a national echinococcosis treatment center . The AE patient cohort consists of 106 patients with AE of the liver of which 13 presented with cholestasis . 6/13 received bilio-digestive anastomosis and 7/13 patients were treated by ERC and are reported here . Biliary stricture balloon dilation was performed with 18-Fr balloons at the initial and with 24-Fr balloons at subsequent interventions . If indicated 10 Fr plastic stents were placed . Six patients were treated by repeated balloon dilation and stenting , one by stenting only . After an acute phase of 6 months with repeated balloon dilation , three patients showed “sustained clinical success” and four patients “assisted therapeutic success , ” of which one has not yet reached the six month endpoint . In one patient , sustained success could not be achieved despite repeated insertion of plastic stents and balloon dilation , but with temporary insertion of a fully covered self-expanding metal stent ( FCSEMS ) . There was no loss to follow up . No major complications were observed . Serial endoscopic dilation is a standard tool in the treatment of benign biliary strictures . Serial endoscopic intervention with balloon dilation combined with benzimidazole treatment can re-establish and maintain biliary duct patency in AE associated pathology and probably contributes to avoid or postpone bilio-digestive anastomosis . This approach is in accordance with current ERC guidelines and is minimally disruptive for patients .
Alveolar echinococcosis ( AE ) is a parasitic disease characterised by liver lesions with infiltrative growth comparable to malignancies . On radiological imaging microcystic honeycomb like lesions are considered characteristic but solid tumors and necrotic cavities ( ‘pseudocysts’ ) are also frequently seen which brings solid and cystic differential diagnoses into play[1] . With continuous benzimindazole treatment the 10 year survival rate is excellent for patients with non-curatively resectable AE lesions . A subgroup of AE patients presents with lesions of the liver hilum . They eventually lead to infiltration and stenosis of major biliary vessels , encasement of vascular structures , lobar atrophy and dilatation of peripheral bile ducts[2–4] . Surgery is the mainstay of AE treatment if the entire parasitic process is resectable with safe distance and has been used as a palliative procedure ( bilio-digestive anastomosis ) to restore biliary flow[5–10] . More recently endoscopic approaches receive attention . Serial endoscopic dilation with or without placement of plastic stents is a standard tool in the treatment of benign biliary strictures . Furthermore , FCSEMS are increasingly being used in benign biliary conditions such as i . e . strictures and complex bile leaks with the advantage of large calibre and longer duration of patency as well as relative ease of removal[11] . Various approaches in conditions such as post-transplant strictures and primary biliary sclerosis have been described [12–18] . The AE-driven biliary vessel pathology results in variable combinations of stenosis , necrosis and inflammation . Consequently , occlusion of biliary plastic stents due to debris needs to be considered . The objective of this series is to communicate that serial ERC treatment with balloon dilation offers a valuable solution for AE-related biliary pathology .
The Section of Clinical Tropical Medicine at Heidelberg University Hospital runs an interdisciplinary clinic for echinococcosis in cooperation with the Department of Diagnostic and Interventional Radiology with weekly radiological conferences , the Department of Surgery and the Department of Gastroenterology since 1999 . Our unit is a national clinical reference center for echinococcosis . The AE patient cohort comprises 106 patients . 80 patients had peripheral and 26 central liver lesions on radiological imaging . Cholestasis was present in in 13/26 . Of those 7 patients with cholestasis were treated with ERC , and 6 patients had bilio-digestive anastomosis . Of this cohort all patients with biliary AE-associated pathology and ERC-D ( endoscopic retrograde cholangiography-dilation ) / stenting are reported . The diagnosis of AE was confirmed in accordance with the IWGE-WHO expert consensus criteria[2] . Once AE was diagnosed all patients were started on long-term albendazole treatment and are still under treatment at the time of writing this report . The following data were extracted from patient notes: age at diagnosis , sex , treatment previous to referral , time from diagnosis to referral , biliary pathology , date of interventions and follow-ups , pre-treatment and latest results of total bilirubin , alkaline phosphatase ( AP ) , CRP , first and maximum balloon size for dilatation , types of stents , post ERC complications cholangitis , bleeding , perforation and pancreatitis . The Ethical Board of the University of Heidelberg approved ( S039/2013 ) the retrospective analysis of patient data without additional patient consent . Endoscopic treatment: ERC was carried out using a therapeutic duodenoscope ( TJF160R , TJF160VR , TJFQ180V , Olympus Corp . , Tokyo , Japan ) . Selective cannulation of the common bile duct was performed with a guide wire ( Jagwire , 0 . 035 inch , Boston Scientific , Natick , MA , USA , Visiglide , 0 . 035 inch , Olympus Corp . , Tokyo , Japan ) or a standard catheter for cases with pre-existing sphincterotomy . All procedures were performed under conscious sedation with propofol and short-acting opiates . All patients received peri-interventional antibiotic prophylaxis . After visualization of the biliary stricture balloon dilation was performed with 18-Fr balloons at the initial intervention and 24-Fr balloons at subsequent interventions if the stricture was distal to the hilum . If indicated 10 Fr plastic stents ( Endoplus Drainage , Pflugbeil , Germany ) in appropriate length and number were inserted to bridge strictures . ERC outcomes were classified as ( 1 ) ‘sustained clinical success’: period without ERC-D or stenting for ≥ 6 months after the last endoscopic intervention; ‘assisted clinical success’: period without ERC-D or stenting for < 6 months after the last endoscopic intervention; ‘failure of endoscopic treatment’: persistent stricture and/or cholangitis .
Between 2006 and 2015 seven consecutive patients with hepatic AE and biliary infiltration with cholangitis were referred for endoscopic treatment . All patients presented with jaundice . Patient characteristics , pre-referral treatment and biliary duct pathology is summarized in Table 1 . Table 2 and Fig 1 summarize laboratory results , types and frequency of interventions , outcome and follow-up . Figs 2–4 illustrate imaging and ERC findings .
Alveolar echinococcosis ( AE ) of the biliary tree is characterised by destruction of biliary vessels due to infiltrative , malignancy like growth of AE liver lesions . In general , growth of AE can be halted with albendazole , a benzimidazole with parasitostatic effect . ERC is the treatment of choice for benign biliary strictures and general ERC treatment recommendations are being applied in AE patients [11] . Our series shows that serial endoscopic balloon dilation and stenting combined with benzimidazole treatment can re-establish and maintain biliary duct patency for many years . Thus , interventions with a more profound impact on the quality of life such as percutaneous bile drainage can be avoided or postponed . Well established ERC management for AE patients could possibly even postpone liver transplantation which is highly problematic in this entity as immunosuppression favors re-growth of non-resected or non-resectable AE components and spread with distant metastases . We have observed stent occlusion of plastic stents in two patients . Of which in one repeated stent occlusion is reflected by the high frequency of stent changes at the beginning of ERC treatment . The problem of stent occlusion is also mentioned in the current expert consensus guidelines[2] and may be increasingly so in AE patients due to inflammation and necrotic debris production . This favours the concept of balloon dilation alone with restrictive policy of stent placement . Although biologically and technically plausible the limited number of patients does not yet allow drawing final conclusions on the preferable endoscopic technique . In one patient sustained success could not be achieved despite repeated insertion of plastic stents and balloon dilation but with temporary insertion of a FCSEMS , an evolving technique in benign biliary disease [11] . Our report aims at proof of concept as a first step in further research of endoscopic treatment of AE patients [19 , 20] . Innovative management strategies for AE patients with cholestasis not eligible for curative surgery are desperately needed . There is no published evidence to support the current recommendation of percutaneous drainage as an equivalent treatment alternative to ERC management [2] . Repeated balloon dilation is in accordance with current ERC guidelines and appears a promising first line management of biliary obstruction . This approach is in accordance with current ERC guidelines[11] and in general minimally disruptive for patients . | Alveolar echinococcosis ( AE ) is a zoonosis causing infiltrative liver lesions . A subgroup of patients presents with central liver lesions and biliary obstruction . At present there is no clear concept for the treatment of biliary obstruction in AE of the liver , and data from high quality trials to base treatment decisions on evidence are missing . In rare neglected infectious diseases with very low prevalence , clinical data can mainly be generated from case series . In our study we aim at the proof of principle of serial ERC balloon dilation for biliary pathology associated to alveolar echinococcosis . This approach is in accordance with current ERC guidelines and is minimally disruptive for patients . | [
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"negl... | 2016 | Endoscopic Treatment of Biliary Stenosis in Patients with Alveolar Echinococcosis – Report of 7 Consecutive Patients with Serial ERC Approach |
Alpha herpesvirus genomes encode the capacity to establish quiescent infections ( i . e . latency ) in the peripheral nervous system for the life of their hosts . Multiple times during latency , viral genomes can reactivate to start a productive infection , enabling spread of progeny virions to other hosts . Replication of alpha herpesviruses is well studied in cultured cells and many aspects of productive replication have been identified . However , many questions remain concerning how a productive or a quiescent infection is established . While infections in vivo often result in latency , infections of dissociated neuronal cultures in vitro result in a productive infection unless lytic viral replication is suppressed by DNA polymerase inhibitors or interferon . Using primary peripheral nervous system neurons cultured in modified Campenot tri-chambers , we previously reported that reactivateable , quiescent infections by pseudorabies virus ( PRV ) can be established in the absence of any inhibitor . Such infections were established in cell bodies only when physically isolated axons were infected at a very low multiplicity of infection ( MOI ) . In this report , we developed a complementation assay in compartmented neuronal cultures to investigate host and viral factors in cell bodies that prevent establishment of quiescent infection and promote productive replication of axonally delivered genomes ( i . e . escape from silencing ) . Stimulating protein kinase A ( PKA ) signaling pathways in isolated cell bodies , or superinfecting cell bodies with either UV-inactivated PRV or viral light particles ( LP ) promoted escape from genome silencing and prevented establishment of quiescent infection but with different molecular mechanisms . Activation of PKA in cell bodies triggers a slow escape from silencing in a cJun N-terminal kinase ( JNK ) dependent manner . However , escape from silencing is induced rapidly by infection with UVPRV or LP in a PKA- and JNK-independent manner . We suggest that viral tegument proteins delivered to cell bodies engage multiple signaling pathways that block silencing of viral genomes delivered by low MOI axonal infection .
In an infected host , herpesviruses initiate a productive infection cycle in a variety of cell types , but in some cells , they can establish a quiescent or latent infection [1] . These latent infections may be reactivated resulting in a productive infection . The current effective antiviral drugs suppress productive replication , but there are no treatment modalities to block the establishment of quiescent infections . Alpha herpesviruses , including herpes simplex virus ( HSV; human herpesvirus 1 and 2 ) , varicella zoster virus ( VZV; human herpesvirus 3 ) , and pseudorabies virus ( PRV; suid herpesvirus 1 ) , establish life-long latency in the peripheral nervous system ( PNS ) of their natural hosts . PNS neurons are terminally differentiated , and the association of their cell bodies with peripheral organs is mediated by axons that extend long distances . As a result , axons reside in a different milieu than their cell bodies . This architecture indeed affects the mode of alpha herpesvirus infections , but is difficult to recapitulate in vitro . Much of the research on herpesvirus latency and reactivation involves animal models , but in vitro models have been developed to provide a more reductionist approach for analysis of the molecular biology of alpha herpesvirus latency [2–4] . These studies have revealed several important findings , including how continuous neuronal signaling is required to maintain latency , and how histone modifications trigger general transcriptional activation of silenced viral promoters . Most of these in vitro models use dissociated neuron cultures where axons and cell bodies are not physically or fluidically separated . In addition , to establish a quiescent infection , isolated PNS neurons must be pretreated with interferon or replication inhibitors ( e . g . acyclovir ) to block the initial productive infection . In this report , we used the modified Campenot tri-chambers to physically and fluidically separate axons from their cell bodies . By culturing primary PNS neurons in these chambers , we were able to establish reactivateable quiescent infections by PRV in the absence of any replication inhibitor or cytokine treatment [5] . Quiescent infections were established only when isolated axons were infected with infectious virions at a very low MOI ( MOI of 0 . 01 pfu/cell ) . Here we use this simplified experimental system to establish a quiescent infection ( i . e . latency ) and to investigate the factors regulating escape from silencing . Our assay is a complementation assay in which cell bodies are exposed to different treatments at the same time that axons are infected with a very low MOI infection of a PRV recombinant that expresses mRFP-VP26 ( PRV 180 ) to enable visualization of productive replication in cell bodies . First , we found treatments that elevated cyclic adenosine monophosphate ( cAMP ) levels ( e . g . forskolin ) in the cell bodies were sufficient to promote productive replication of PRV 180 . Such escape from silencing was mediated by protein kinase A ( PKA ) activation and required cJun N-terminal kinase ( JNK ) . We also found that applying UV inactivated PRV virions ( UVPRV ) to cell bodies very efficiently promoted escape from silencing . Infection of cell bodies with UV inactivated virus mutants that could not engage cell surface receptors ( gD null ) or could not enter cells by membrane fusion ( gB null ) did not promote escape from silencing . Importantly , activating PKA in cell bodies took longer to promote escape from silencing than did exposing cell bodies to UVPRV . We then focused on understanding how inactivated PRV particles could induce such rapid escape from silencing . An important discovery came from complementation studies with light particle ( LP ) preparations generated after infection by a UL25 null PRV mutant that produced LP but no infectious virions . Herpesvirus infected cells typically produce light particles together with infectious virions [6–8] . LP contains no capsids or viral genomes , but carries outer tegument proteins and most of the viral envelope proteins . In our complementation assay , cell body infection with LP promoted efficient PRV escape from silencing . Moreover , the kinetics were comparable to those found for infection with UVPRV . Importantly , neither UVPRV- or LP-mediated escape from silencing depended on PKA or JNK signaling . US3 is one of two serine-threonine protein kinases encoded by alpha herpesvirus genomes and carried in the virion as an inner tegument protein . The activity of HSV-1 US3 protein kinase has been reported to functionally overlap with PKA [9] . EP0 , another tegument component of PRV , is functionally homologous to HSV-1 ICP0 , a potent transcriptional activator [10] . Neither the viral PKA analog US3 or the HSV-1 ICP0 homolog EP0 were required to promote escape from PRV silencing , since both UV inactivated EP0 null and Us3 null mutant viruses were able to promote productive infection . Infection with other replication incompetent DNA viruses , such as baculovirus or adenovirus vectors that efficiently transduce neurons could not stimulate escape from silencing . These results suggest that a generalized cytoplasmic or nuclear response to DNA virus infection is not responsible for the rapid escape from silencing . In our system , efficient reversal of silencing requires the delivery of viral tegument proteins , which must activate multiple cell signaling pathways . Only when viral tegument proteins are not available in the cell bodies ( as in the case of reactivation ) , PRV escape from silencing requires activated host PKA and JNK dependent signaling .
In our previous report , we established quiescent infections by infecting axons in the axonal “N” compartment of modified Campenot tri-chambers with PRV 180 ( which expresses an mRFP-VP26 fusion protein ) at an MOI of 0 . 01 ( 100 plaque forming units per dish ) [5] . At this very low MOI , no cell bodies express any mRFP-VP26 over a period of 3 weeks . Silent genomes can be reactivated after high MOI superinfection of cell bodies with UV treated PRV . This reactivated productive infection then spreads to all neurons in the chamber [5] . These observations raised several important questions including how many cell bodies initially get infected at this very low MOI , and do these silent genomes express latency associated transcripts ( LATs ) , a hallmark of authentic latent infection . To estimate the number of neurons that harbored a quiescent PRV infection , we established quiescent infections using a trans-complemented gB null mutant , which expresses a diffusible GFP under CMV promoter ( PRV 233 ) [11] . This virus is completely deficient in cell-cell spread—only those neurons infected via their axons with PRV 233 ( at an MOI of 0 . 01 ) expressed the green fluorescent protein in the soma ( Fig 1A ) . We expected the number of cell bodies harboring silent genomes would be small because quiescence was established with approximately 100 infectious particles . We counted on average 9 . 8 [±2 . 9 standard error of the mean ( SEM ) ] GFP positive cell bodies in each S compartment after low MOI PRV 233 infection between 3 to 21 days post infection ( dpi ) . We also assessed the number of cell bodies that send axons to the N compartment by adding a lipophilic dye ( DiI ) in the N compartment . We found that 49% ( ±4 . 7% SEM ) of neurons were connected to the N compartment , and of these , 0 . 45% ( ±0 . 2% SEM ) were infected with PRV 233 ( Fig 1B ) . Next , we assayed the viral transcripts from the cell bodies either from cultures with silenced PRV 180 genomes ( no capsid expression was detected in cell bodies after 7 dpi ) or from cultures that were productively infected with PRV 180 by direct infection of cell bodies at the same low MOI ( red capsid expression was detected in all cell bodes at 7 dpi ) . After 7 days , the ratio of LAT to EP0 mRNA was 34 . 5 fold more in silenced cultures when compared to cultures with productive cell body infection ( Fig 1C ) . In productively infected neurons , the EP0 transcript was 148 . 3 ( ±15 SEM ) fold more abundant than the LAT transcript . This ratio went down to 4 . 8 ( ±0 . 65 SEM ) in quiescently infected neurons at 7 dpi . These experiments showed that PRV infection of axons at an MOI of 0 . 01 results in a silenced infection in less than 0 . 5% of connected cell bodies in S compartments . These silenced genomes do not express late genes , indicated by the lack of mRFP-VP26 capsid protein fluorescence , but they do express LAT transcripts , as expected of authentic latent genomes . We established a system to study the mechanism of how low MOI PRV 180 retrograde infection which is destined to be silenced could be redirected to a productive infection . In these assays , we do not wait 3 weeks for the full establishment of quiescence . Instead , we infect axons with PRV 180 at an MOI of 0 . 01 as before , but we simultaneously treat or infect cell bodies in S compartments with drugs , inactive/mutant viruses or virus-like particles ( Fig 1D ) . Over 7 days , we monitor mRFP-VP26 expression in the cell bodies to determine the extent of productive PRV 180 infection . In this complementation assay , we call a productive infection an ‘escape from silencing’ . This assay is fundamentally distinct from `reactivation assays`that aim to investigate the reversal of repressive genome modifications after latency is established . Because elevated cyclic adenosine monophosphate ( cAMP ) levels and consequent protein kinase A ( PKA ) activation have been shown to reactivate quiescent HSV-1 infections [12 , 13] , we first tested the effect of treatments that cause elevated cAMP in our system . Treatments with forskolin or a cell-permeable dibutyryl cyclic AMP ( dbcAMP ) in the cell body compartment resulted in increased phosphorylation of PKA substrates , and this effect was blocked by additional treatment with a PKA inhibitor , H89 ( Fig 2A ) . To quantitate escape from silencing , we measured the total fluorescence ( mRFP-VP26 capsid protein ) in chambered neuronal cultures after performing background subtraction and feature selection using Fiji/ImageJ v . 1 . 48u [14 , 15] ( Fig 2B ) . When applied to cell bodies during low-MOI axonal infection , both forskolin and dbcAMP promoted PRV 180 productive infection ( Fig 2C ) . The mean value of control dishes , where no red capsid protein expression was observed is shown as the baseline . Virus infection spread throughout the S compartment after escape from silencing in approximately 7 days in the case of dbcAMP or forskolin treatment . We further confirmed that the observed effects of forskolin were due to PKA activation by including the PKA inhibitor H89 in the S compartment . Productive infection was blocked when PKA activity was inhibited ( Fig 2C ) , indicating that forskolin-mediated escape from silencing requires PKA activation . How latent alpha herpesvirus infections reactivate when no viral proteins are produced in the host cell to activate viral transcription is a topic of much research and debate . Recently , Cliffe et al . , showed that cJun N-terminal kinase ( JNK ) activity and c-Jun phosphorylation are essential for HSV-1 reactivation [3] . It was proposed that stress or injury-related stimuli , including phosphatidylinositol 3-kinase ( PI3K ) inhibition , axotomy , and nerve growth factor ( NGF ) withdrawal converge on JNK activation and c-Jun phosphorylation for reactivation [3 , 16] . To understand whether forskolin mediated PRV 180 productive infection ( which requires PKA activity ) converges on the JNK pathway , we exposed forskolin treated cell bodies to 20 μM of the JNK inhibitor , JNKII , while simultaneously infecting their axons with low MOI PRV 180 . In this condition , inhibiting JNK activity prevented the onset of PRV 180 productive infection ( Fig 2C ) . The effect of this inhibitor was confirmed by monitoring the levels of total c-Jun ( T-cJun ) and phospho c-Jun ( P-cJun ) . JNKII substantially reduced forskolin-stimulated c-Jun accumulation and completely blocked cJun activation ( Fig 2D ) . We also checked c-Jun levels when the PKA inhibitor H89 was included in forskolin treated samples . We detected less c-Jun accumulation , but phosphorylation was not blocked . Also , there was a clear shift in the molecular weight of both total and activated forms of c-Jun . To confirm that chemical inhibitor treatment did not alter viability or virus production capacity of neurons , we monitored neuronal morphology and virus infection under conditions that did not allow PRV productive infection ( forskolin+H89 and forskolin+JNKII ) . After 6 days , forskolin+H89 or forskolin+JNKII treated neurons displayed comparable morphology to untreated neurons , and PRV 180 infection , at an MOI of 5 , proceeded comparably in all conditions ( Fig 2E ) . Thus , when no viral components are delivered , PRV 180 escape from silencing occurs via a slow , PKA-dependent activation of the JNK pathway . We showed previously that superinfection of cell bodies with UV treated PRV is sufficient to reactivate quiescent PRV genomes [5] . Using our complementation assay , we asked whether virion components delivered to cell bodies can trigger escape from silencing similar to that observed with forskolin treatment . We infected cell bodies in S compartments with a high MOI of UV inactivated PRV 959 ( mNeonGreen-VP26 , a green fusion protein; UVPRV ) [5] , while simultaneously infecting axons with PRV 180 at low MOI . We chose to use green capsid PRV to be able to monitor the effect of UV inactivation . UV inactivated virus particles , as we have previously reported , are able to enter , undergo retrograde axonal transport in neurons , and deliver their defective genomes to the nuclei [5] . UVPRV does not exclude superinfection with a second PRV genome [17] . Simultaneous co-infection of cell bodies with high MOI UVPRV enabled efficient escape from silencing by PRV 180 , as indicated by red capsid signal in the cell bodies ( Fig 3A ) . Note that there is no green capsid signal in the cell bodies , confirming that the UV inactivated virus is unable to replicate or express late viral genes . Surprisingly , PRV 180 infection spread to all neurons in the S compartment in only 3 days following addition of UVPRV to the cell bodies . This is considerably faster than the 7 day period required for PKA-induced escape from silencing . Entry of UVPRV was required because escape from silencing was not observed with UV inactivated non-complemented entry deficient mutants ( UVgBnull or UVgDnull ) ( Fig 3B ) . Interestingly , while the PKA inhibitor H89 efficiently blocked the activity of host PKA ( Fig 3C ) , H89 had no effect on UVPRV-mediated escape from silencing ( Fig 3B ) . We also confirmed that PRV 180 or UVPRV infection of cell bodies induces PKA activity early after infection ( 3 hpi ) , and this activity is also efficiently inhibited by the addition of H89 1 hpi ( Fig 3C ) . These observations suggest that , upon cell body entry , components of UV treated virions trigger an accelerated escape from silencing through PKA-independent mechanisms . We also monitored viral DNA replication and expression levels of the major capsid protein ( VP5 ) , the most abundant tegument protein ( UL47 ) [18] , and the lytic transactivator protein ( EP0 ) in neuronal cultures infected by either PRV 180 alone in N compartments ( 3 day or 10 day post infection-dpi ) , UVPRV alone in S compartments ( 3 dpi ) , or both of them simultaneously as we did in the complementation assay ( 3 dpi ) . We were able to detect the major capsid protein VP5 only when PRV 180 escaped from silencing in the complementation assay ( Fig 3D ) . Tegument protein UL47 was detectable in cell bodies infected with UVPRV but the levels were less than in the co-infected samples . We detected EP0 in UVPRV infected cell bodies , but much less than UL47 in the same samples . EP0 reached high levels during PRV 180 productive infection stimulated by UVPRV complementation ( Fig 3D ) . We quantitated viral DNA amounts in cell bodies in these three conditions . In a silent infection ( PRV 180 alone ) , DNA amounts were almost at the detection limit of our Q-PCR system ( CTmean = 33 . 76 ± 0 . 29 SEM at 3 dpi , CTmean = 34 . 91 ± 0 . 08 SEM at 10 dpi ) . UVPRV infection of cell bodies yielded approximately 8x103 fold more DNA than PRV 180 alone ( CTmean = 20 . 44 ± 0 . 12 SEM ) , and approximately 12 . 8 fold less DNA than the complemented samples ( CTmean = 16 . 76 ± 0 . 06 SEM ) ( Fig 3E ) . These results show that UV treated PRV particles deliver significant amounts of tegument proteins as well as replication deficient viral DNA . Clearly one or both of these components trigger escape from silencing in the complementation assay . We hypothesized that PRV quiescence is established after low MOI axonal infection , either due to insufficient tegument proteins delivered to the nuclei to transactivate viral gene expression , or insufficient genomes to saturate the silencing machinery . We were able to test these ideas using light particles ( LP ) . We used cell supernatants from PRV UL25 null mutant infected cells . The UL25 protein is essential for proper genome encapsidation , capsid nuclear egress , and production of infectious progeny virions [19] . This mutant produces almost no infectious virions , but produces abundant virion-like light particles ( LP ) that contain tegument but do not contain capsids or genomes . We constructed PRV 495 , a double recombinant that expresses two fluorescent fusion proteins: gM-pHluorin , a green fluorescent viral glycoprotein that is incorporated into light particles , and mRFP-VP26 ( red capsid ) [7 , 20] . The PRV 495 recombinant was prepared by infecting UL25-expressing helper PK15 cells . We prepared a capsid-free LP stock by infecting non-complementing PK15 cells with complemented PRV 495 at an MOI of 5 . Varying amounts of this LP stock were run on SDS gels , and we assessed the presence of envelope proteins ( gD , Us9 ) , outer tegument ( UL47 and EP0 ) and inner tegument proteins ( UL36 , Us3 ) ( Fig 4A ) . Inner tegument proteins UL36 and Us3 were much less than the other tegument components UL47 and EP0 . The lack of capsid proteins ( VP26 and VP5 ) was confirmed by WB and by fluorescence microscopy of LP inoculum on axons and adhered to glass coverslips ( Fig 4A and 4B ) . Many gM-pHluorin positive punctae were detected attached to axons at 1 h post-inoculation . We did not detect any dual color puncta on axons or coverslips , confirming that the PRV 495 LP preparation does not contain viral capsids . We noticed some rare single color red punctae that did not colocalize with the green gM signal , which possibly represents fluorescent debris from lysed PRV 495-infected cells ( Fig 4B ) . We then inoculated cell bodies with the LP preparation to determine if LP could promote escape from PRV 180 silencing . PRV 495 LP promoted escape from silencing as fast as the UVPRV ( Fig 4C ) . This effect was not dose dependent using the amounts we tested ( 10 , 20 and 50 μl ) . Since there were no detectable capsids in our LP preparations , we concluded that viral genomes were not necessary for escape from silencing . To be sure that the LP-mediated escape from silencing is due to LP and not due to other soluble factors released from infected cells , we filtered the LP preparation through a 0 . 1 μm filter . This filtered LP preparation did not promote escape from silencing ( Fig 4C ) . This indicates that the “escape from silencing” activity is particulate , and secreted viral/host proteins or smaller cellular exosomes are not able to promote escape from silencing . Moreover , similar to what was observed with UVPRV , the PKA inhibitor H89 was not able to block LP-induced escape from silencing ( Fig 4C ) . LP infection of cell bodies induced phosphorylation of PKA targets comparable to what we found with UVPRV , and this induction was blocked by the addition of H89 ( Fig 4D ) . These data confirmed that in the absence of capsids or genomes , viral proteins incorporated into particles are sufficient to induce an accelerated escape from silencing within 3 days , and this escape does not require PKA activity . We hypothesized that a tegument protein delivered by UVPRV or LP might trigger escape from silencing when PKA signaling is blocked . Specifically , we looked at PRV Us3 , a potential analog of PKA [9] , and PRV EP0 , a functional homolog of HSV-1 ICP0 , which contains a putative cAMP response element in its promoter [10] . To understand whether the PRV tegument components Us3 or EP0 play a major role in escape from silencing , we prepared stocks of viral mutants that do not express these proteins ( Fig 5A ) . PRV 823 [21] contains a Us3 null mutation , and PRV EPO6 contains an EP0 null mutation ( see Materials and Methods ) . We then performed the complementation assay by infecting cell bodies in the S compartment with UV inactivated EP0 null ( UVEPO6 ) or Us3null ( UV823 ) while simultaneously infecting axons with low MOI PRV180 ( Fig 5B ) . The UV inactivated EPO null mutant promoted escape from silencing with comparable kinetics to UVPRV and LP in a PKA independent manner . Unexpectedly , we also found that the Us3null mutant promoted escape from silencing with or without H89 treatment ( Fig 5B ) . Infection of neurons with either of these mutants induced PKA activity in 3 hours comparable to UVPRV and H89 blocked this activity when added 1 hour after infection with either PRV mutant ( Fig 5C ) . These data suggest that UVPRV- and LP-mediated escape from silencing involves multiple signaling pathways , which may be distinct from the forskolin- and dbcAMP-mediated PKA pathway that induces a slower escape from silencing . Alternatively , another viral kinase in the tegument , which is resistant to H89 , may be able to activate productive infection by phosphorylating PKA targets ( e . g . UL13 ) . We entertained the hypothesis that UVPRV and LP infections stimulated a general cell response to virus infection , which resulted in escape from silencing . We tested replication deficient adenovirus and baculovirus recombinants expressing diffusible GFP to determine if high MOI infection of cell bodies promoted PRV 180 escape from silencing . Although both DNA virions ( one naked , one enveloped ) efficiently transduced GFP expression in neuronal cell bodies , neither promoted PRV 180 escape from silencing ( Fig 5B ) . We conclude that escape from silencing by superinfection with UVPRV or LP is not a generalized neuronal response to DNA virus infection . Rather , it is a herpesvirus-specific induction of productive cycle viral promoters by multiple tegument-host protein interactions or induction of injury response pathways . Our findings that UVPRV and LP mediate an accelerated , PKA-independent escape from silencing support the hypothesis that the molecular mechanism ( s ) promoting quiescence or productive infection might differ from the mechanisms of de-repressing viral promoters during reactivation from latency . To test whether escape from silencing mediated by UVPRV or LP is regulated by JNK activity , we infected axons with PRV 180 , simultaneously treated cell bodies with UVPRV or LP , and subsequently added a JNK inhibitor ( JNKII ) one hour post infection . When JNK activity was inhibited , neither UVPRV- nor LP-mediated escape from silencing was blocked , but the spread of infection among the S compartment neurons was delayed ( Fig 6A and 6C ) . We observed red capsid accumulation starting at 3 dpi , but the full spread of infection took 6 days ( Fig 6C ) . We also treated UVPRV or LP infected cell bodies with the PI3K inhibitor , LY294002 . In this case , the onset and spread of PRV 180 productive infection was significantly delayed but not completely blocked ( Fig 6A and 6C ) . Interestingly , we saw “strings” of infected neurons in S compartments ( Fig 6C showing limited spread of infection ) , but full spread of infection was prevented particularly when PI3K activity was inhibited . These findings are consistent with the literature indicating a role for this kinase in herpesvirus infection ( see [22] for review ) . The activity of the inhibitors also was tested in mock , UVPRV and LP treated neurons . Interestingly , treatment of neurons with UVPRV or LP increased total and phosphorylated c-Jun levels comparable to forskolin . JNKII blocked c-Jun phosphorylation in all of these samples ( Fig 6B ) , but it interfered only with forskolin-mediated and not with UVPRV- or LP-induced escape from silencing . LY294002 effectively reduced activated AKT levels ( pAKT ) as expected , and induced c-Jun accumulation and phosphorylation in all of the conditions where the inhibitor was added ( Fig 6B ) . These results further established that UVPRV- and LP-mediated escape from silencing proceeds through a distinct molecular mechanism from cAMP/PKA and JNK-mediated activation of productive infection .
Alpha herpesviruses establish latency in peripheral nervous system ( PNS ) ganglia in vivo . However , in vitro HSV-1 or PRV infections of dissociated PNS neurons in culture ( e . g . trigeminal ganglia , dorsal root ganglia , or superior cervical ganglia ) generally result in productive infection unless viral DNA replication is suppressed using DNA synthesis inhibitors or interferon treatment [23] . Using our in vitro model system , which mimics the natural route of neuronal invasion by long distance retrograde axonal transport , we previously showed that PRV infection of axons at a low MOI ( below 0 . 1 ) results in reactivateable quiescent infections in distant cell bodies . Importantly , this was the first in vitro system for establishing PRV quiescence without the use of DNA synthesis inhibitors or cytokine treatment . These findings raised the question of why the axonally delivered viral genomes can reach the cell bodies but cannot initiate a productive infection . In the current study , we aimed to uncover cellular and viral components that prevent establishment of quiescent infection and promote productive replication of axonally delivered genomes ( i . e . escape from silencing ) after low MOI infection of axons . We developed a compartmented complementation assay where axons are infected with a PRV recombinant that expresses mRFP-VP26 ( red capsid virus ) at MOI of 0 . 01 , which produces a silenced , quiescent infection . Cell bodies in a separate compartment from axons are simultaneously treated with different reagents , UV inactivated virus or virus-like particles . Using this system , we reveal two distinct molecular mechanisms to start productive infection from quiescently destined PRV genomes: host stress-mediated ( slow ) and viral tegument-mediated ( fast ) . Host stress mediated productive infection required both PKA and JNK activity , whereas viral tegument mediated productive infection did not . When cell bodies were treated with forskolin or a cell permeable cAMP analogue , PKA was activated . This resulted in escape from silencing and productive infection spreading to all cell bodies in the S compartment in approximately 7 days . We also showed that productive infection was completely lost when PKA activity was blocked . Consistent with the literature , such escape from silencing required stress-activated protein kinase ( e . g . JNK ) activity [3 , 16] . However , when S compartment neurons were complemented with a UV inactivated PRV , the axonal PRV 180 infection ( which would otherwise be destined for quiescence ) escaped silencing rapidly and spread to all neurons in the S compartment in 3 days . Infection by entry deficient UVPRV gBnull or gDnull mutants did not induce escape from silencing showing that particles must fuse and release viral components into the neuronal cell body to stimulate a productive infection . Moreover , escape from silencing did not require tegument proteins Us3 or EP0 , and surprisingly did not depend on cellular PKA or JNK activity . From the large body of literature surrounding alpha herpesvirus latency , several hypotheses emerge to explain why these viruses stay quiescent in PNS neurons in vivo . One hypothesis is that separate long-distance axonal transport of nucleocapsids and certain tegument proteins ( e . g . VP16 ) challenges the timely arrival of tegument and viral DNA in the nucleus [24] . If these components do not reach the nucleus concurrently , VP16 ( along with cellular proteins ( HCF1 , oct1 , LSD1 ) ) may not be able to orchestrate the transactivation of immediate early genes ( e . g . ICP0 ) [25] . Furthermore , upon the nuclear delivery of the herpesvirus genome , ND10 ( nuclear domain 10 ) proteins assemble at the viral DNA and viral genomes are covered with repressive histones [26] ( see [27] for review ) . The newly made ICP0 protein removes repressive histone modifications to activate early/late gene expression [27–30] . A second idea is that productive infection can initiate only after sufficient genomes are delivered to the neuronal nuclei to saturate the silencing complexes ( e . g . ND10 , Co-REST , HDACs ) . Because only a limited number of alpha herpesvirus genomes can be expressed ( fewer than 7 ) in a given neuronal or epithelial cell even after high MOI infection ( 100 pfu/cell ) , the remaining genomes might be targeted by the silencing complexes , resulting in quiescent infection [31] . To test the hypothesis that alpha herpesvirus tegument proteins alone are sufficient to trigger escape from silencing , we complemented S compartment cell bodies with light particles . PRV light particles without capsids were produced during infection by UL25 null PRV mutant . These light particles were able to initiate productive infection of axonally delivered PRV 180 as fast as UVPRV and did so in a PKA- and JNK-independent manner . Although herpesvirus infected cells yield large amounts of capsid-less virus-like particles ( up to 81% in PRV [7] , and 84% in EBV infection [8] ) that contain viral envelope and tegument proteins and use exocytosis machinery similar to infectious virions [6–8 , 32] , their function is not well understood . Recently , Gong et al . , showed that such virus-like particles produced by Kaposi`s sarcoma associated virus ( KSHV ) induce B-cell differentiation signaling to promote lytic infection suggesting a role for light particles in productive versus latency switch [8] . Our in vitro findings support the first hypothesis: Low MOI axonal infection is destined for quiescence most likely due to a lack of tegument proteins in the neuronal cell body and nucleus , which are required to initiate viral gene transcription and productive infection . This conclusion is based on results from the PRV light particle complementation assays , which suggest that excess genomes do not play a major role in escape from silencing . However , viral tegument proteins are critical for redirecting low MOI axonal infection from quiescent to productive . We have yet to identify the specific tegument or tegument proteins responsible , but potential candidates include VP16 and UL13 kinase . It is possible that multiple components are required , and a single viral protein may not be sufficient to prevent quiescence . A remaining question concerns how PKA activation leads to JNK mediated c-Jun phosphorylation . One idea is that cAMP-mediated PKA activity leads to direct activation of the dual leucine zipper kinase DLK that is upstream of JNK . This pathway has recently been shown to mediate axonal regeneration [33] and also HSV-1 reactivation [3] . Another possibility is that elevated cAMP levels activate PKA , which in turn phosphorylates mitogen activated protein kinase 4 ( MKK4 ) . MKK4 also directly phosphorylates and activates JNK ( see [34] for review ) , and MKK4 has been shown to mediate aromatase expression in response to prostaglandin E2 in a cAMP/MKK/JNK dependent manner [35] . Our low MOI axonal infection model of silencing suggests that very few cell bodies harbor silenced genomes after axonal infection , and this raises the question of how this compares to in vivo latency models . Using a spread deficient PRV ( gBnull ) mutant , we found the number of cell bodies in the S compartment with quiescent PRV infections was 9 . 8 on average . This is approximately 0 . 5% of the neurons that send axons to the N compartment . Even from this small number of cells , we detected a significant increase in expression of the PRV latency associated transcript in cultures with silenced PRV 180 infection compared to a productive infection . Because it is a hallmark of authentic latent infection , increased LAT expression validates our in vitro model of quiescence . The in vivo ocular HSV-1 latency model shows that thousands of neurons in the trigeminal ganglia harbor latent infections , and some of them undergo productive infection before genomes are silenced . We do not see this in our system because we do not see late gene expression as monitored by mRFP-VP26 fluorescent protein expression . More interestingly , in the ocular infection model , only 0 . 04% of the latently infected neurons reactivate ( 2–4 neurons/ganglion ) and extremely low level replication was detected in these neurons [36–38] . Unlike this small percentage of reactivation in the animal models , we see extensive spread rather than low level viral replication after productive infection is initiated . This difference is most probably due to the lack of immune system control in our simplified in vitro model; as soon as productive infection starts it spreads to all neurons unless replication inhibitors or cytokines are added . A two-stage program initiated by a pre-existing epigenetic switch has been proposed for HSV-1 reactivation from latency [3 , 16] . The program includes a generalized de-repression of viral promoters ( phase I or animation ) leading to de novo synthesis of many viral proteins . In the first phase , viral gene expression does not follow the regular early-to-late gene-cascade , and is not dependent on VP16 activity . Ex vivo reactivation triggered by axotomy in combination with neurotropin deprivation follows such a disordered pattern of gene expression . The second phase of reactivation closely resembles productive infection: It follows the expected transcription cascade and chromatin remodeling that is critical for full reactivation [16] . More recently , Sawtell and Thompson proposed a three-step program including pre-initiation , initiation , and progression [39] . Interestingly , explant reactivation produces new virus much faster than reactivation through PI3K-inhibition [40] . It may be that , multiple signaling pathways were activated during dissection of the ganglia that could accelerate the proposed biphasic program . UVPRV- or LP-mediated escape from silencing shows a similar rapid pattern: If viral tegument proteins are delivered into the cell body , several signaling pathways are induced , and neither PKA nor JNK seem to be a major player . In this case , productive infection proceeds rapidly . If tegument proteins are not efficiently delivered to the cell body ( as in the case of axonal entry and similar to reactivation ) , PKA and JNK activity may help de-repression or transcription of incoming viral genomes . This process takes a longer time similar to PI3K inhibition-mediated HSV-1 reactivation . We still do not know if escape from silencing differs mechanistically from reactivation from latency , or whether forskolin-induced productive infection observed in our system is more akin to reactivation rather than escape from quiescence . Our compartmented complementation assay will help dissect these phenomena by investigating whether viral lytic promoters are associated with histone H3 tri-methylation at lysine 27 ( H3K27me3 ) as shown for the HSV-1 genome [41 , 42] . Future studies will also explore the effect of type I and II interferon , as well as stress hormones ( epinephrine and corticosteroids ) on the establishment of silent PRV infection as well as escape from genome silencing .
Porcine kidney epithelial cells ( PK15; ATCC ) were used to produce and titer PRV . These cells were maintained in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum , 1% penicillin and streptomycin . PRV Becker is a wild-type laboratory strain [43] . PRV 180 expresses an mRFP-VP26 fusion protein in a PRV-Becker background [44] . PRV 959 expresses an mNeonGreen-VP26 fusion protein in a PRV Becker background [5] . PRV 233 is a gB-null mutant of PRV-Becker expressing diffusible GFP from the gG locus under the control of a cytomegalovirus ( CMV ) promoter [11] . PRV 233 stocks were propagated , and their titers were determined in PK15 cells stably transfected with LP64e3 , a plasmid expressing PRV gB ( constructed by Lisa Pomeranz in the Enquist lab ) . PRV 357 is a gD-null recombinant expressing diffusible GFP in a PRV Becker background [45] . PRV 357 is grown G5 cells that constitutively express PRV gD [46] . To obtain entry deficient virus stocks , non-complementing PK15 cells were infected with PRV 233 and PRV 357 at high MOI , and harvested at 24 hpi to obtain gB null and gD null virions , respectively . The titers of these stocks were determined based on the genomic DNA content by quantitative RT-PCR . Mutant stock titers were normalized using volumes of each stock corresponding to the amount of DNA in 106 pfu of wt PRV . PRV 823 is Us3 null of PRV-Becker expressing mRFP-VP26 fusion protein [21] and PRV EP06 is EP0null expressing mNeonGreen-VP26 in a PRV-Becker background ( constructed by Hao Oliver Huang in the Enquist lab ) . PRV 495 is UL25-null expressing mRFP-VP26 and gM-pHluorin fusion proteins [20] . Complementing cell line expressing PRV UL25 protein was a kind gift from G . Smith , Northwestern University [47] . Superior cervical ganglia ( SCGs ) were isolated from embryonic day 17 Sprague-Dawley rat embryos ( Hilltop Labs ) . Primary neurons were cultured in tri-chamber dishes as previously described [48 , 49] . Multi-well or 35-mm plastic tissue culture dishes ( Falcon ) or optical plastic dishes ( Ibidi ) were coated with 500 μg/ml of poly-DL-ornithine ( Sigma Aldrich ) and 10 μg/ml of natural mouse laminin ( Invitrogen ) . After coating , two sets of grooves were etched on the dishes . A silicone grease-coated tri-chamber ( Tyler Research ) was placed on top of a drop of 1% methylcellulose ( in neuronal medium ) covering the groves . Ganglia were trypsinized and triturated , and approximately 2/3 of an SCG was plated in the S compartment . Neurons were maintained in complete neuronal medium: Neurobasal medium ( Gibco ) supplemented with 100 ng/ml nerve growth factor 2 . 5S ( Invitrogen ) , 2% B27 ( Gibco ) and 1% penicillin and streptomycin with 2 mM glutamine ( Invitrogen ) . 2 to 3 days after seeding , 1 mM cytosine-D-arabinofuranoside ( AraC; Sigma-Aldrich ) was added for at least 2 days to eliminate non-neuronal cells . Neurons were cultured for 14–21 days prior to experiments . Before adding the virus inoculum to N and/or S compartments in tri-chambers , 1% methylcellulose prepared in neuronal medium was added in the M ( Middle ) compartment to prevent any possible leakage from either of these compartments . Neuronal infections were done using the indicated amounts of recombinant PRV . DiI was added at 2 . 5 μg/mL ( Life Technologies ) to the N compartment . Reagents used in this study: Forskolin ( Sigma ) , dibutyryl cAMP ( Selleckchem ) , H89 ( Selleckchem ) , LY294002 ( Sigma ) and JNK inhibitor II ( Calbiochem ) . Imaging was performed on a previously described Nikon Ti-E inverted epifluorescence microscope [50] . Tiled images of the entire S compartment were captured using Nikon NIS Elements software , a Cool Snap ES2 camera ( Photometrics ) , and 4x magnification objective . To measure the total fluorescence in chambered neuronal cultures , we performed background subtraction and feature selection using Fiji/ImageJ v . 1 . 48u [14] . Because the background fluorescence intensity is highly variable across tiled images , we first subtracted the local background by calculating a 20 pixel radius median filter and subtracting this median filtered image from the original . To select fluorescent cell bodies , we next performed granulometric filtering using the GranFilter plugin in Fiji/ImageJ [15] . GranFilter plugin settings were as follows: circular structure element , 3 pixel radius , 6 pixel step size . With these settings GranFilter selects fluorescent structures that are the approximate size and shape of fluorescent neuronal cell bodies . We then manually set a region of interest to exclude image artifacts caused by reflections off the chamber walls and grooves . Finally , we measured the integrated fluorescence intensity using the Measure function in Fiji/ImageJ and exported these measurements to Microsoft Excel . High magnification imaging of fluorescent particles was done using an Andor iXon Ultra EMCCD camera and 60x magnification objective . All images and movies were assembled for publication using NIS-Elements software ( Nikon ) , Fiji/ImageJ , and Adobe Photoshop . For comparative analysis , fluorescence excitation intensity , exposure time , and other imaging parameters were consistent for all experimental conditions . Dissociated neurons or cell bodies from the S compartment were lysed in radioimmunoprecipitation assay ( RIPA ) buffer supplemented with 1 mM dithiothreitol ( DTT ) and protease inhibitor cocktail ( Sigma-Aldrich ) . Lysates were incubated on ice for 30 min , sonicated , and centrifuged at 11 , 000 rpm at 4°C . Supernatants were transferred into new tubes and mixed with 5x Laemmli buffer . Samples were heated at 90°C for 5 min before resolved by 4–12% NuPAGE BisTris gels ( Invitrogen ) . Proteins were transferred to nitrocellulose membranes ( Whatman ) using semidry transfer ( Biorad ) . For blocking , membranes were incubated in 5% non-fat dry milk in phosphate-buffered saline ( PBS ) solution for 1 h at room temperature . Immunoblots were performed using primary and secondary antibodies diluted in 1% milk PBS solution . Membranes were incubated with chemiluminescent substrates ( Supersignal West Pico or Dura , Thermo scientific ) . Protein bands were visualized by exposure on HyBlot CL ( Denville scientific ) blue X-ray films . Primary antibodies used in this study: Mouse monoclonal antibody ( mAb ) anti-β-actin ( Sigma ) , anti-Us9 mouse mAb IA8 clone [51] , anti-VP5 mouse mAb ( gift of H . J . Rziha , Federal Research Center for Viruses Diseases for Animals , Tubingen , Germany ) , anti-UL47 , anti-UL35 and anti-UL36 polyclonal rabbit sera [gifts of T . Mettenleiter , Friedrich-Loeffler Institut [52–54]] , anti-Us3 mouse mAb [21] , rabbit polyclonal anti-EP0 ( gift of H . Kida , Hokkaido University ) [55] , anti-gD polyclonal rabbit sera ( gift of K . Bienkowska-Szewczyk University of Gdansk ) , anti-cJun ( Cell Signaling ) , anti-phospho-cJun ( Ser63; Cell Signaling ) anti-AKT ( Cell Signaling ) , anti-phospho-AKT ( Ser473; Cell Signaling ) , anti-PKA substrates ( P-S/T Kinase substrate Ab Sampler , Cell Signaling ) . Horseradish peroxidase-conjugated secondary mouse or rabbit antibodies ( KPL ) were used at 1:10000 dilution . Q-RT-PCR was performed with Eppendorf Realplex Mastercycler . Reaction mixture was prepared using Kapa Syber Fast qPCR kit and samples were prepared as triplicates . Each experiment was done in duplicates . To determine genomic DNA amounts in neuronal soma , each S compartment was lysed in 10 μl of RIPA . 5 μl of this lysate was treated with proteinase K ( New England Biolabs ) for 50 min . at 55°C followed by 5 min at 95°C . To titer entry deficient PRV mutants ( gBnull and gDnull ) , 90 μl of virus stock was first digested with 100 units of DNase I ( Invitrogen ) to remove contaminating DNA before proteinase K treatment . Viral genomic DNA was quantified by using UL54 specific primers as published [56] . PRV Becker nucleocapsid DNA and PRV Becker virus stock ( 1x108pfu/ml ) were used as standards to determine the amount of viral DNA corresponding to one plaque forming unit . To determine transcript amounts , total RNA was extracted from cell bodies in the S compartment using RNeasy Plus Mini kit ( Qiagen ) . cDNA synthesis was performed using SuperScript III first-strand synthesis kit ( Life Technologies ) with Oligo-dT primers . LAT specific primers ( fw: 5`-GATGCAGTCCAGACAG-3` , rev: 5`-GTAGTGGTCCCGAGTTGC-3` ) amplifying a 141 bp fragment were used . EP0 transcript was quantitated using primers ( fw: 5`-GGGTGTGAACTATATCGACACGTC-3` , rev: 5`-TCAGAGTCAGAGTGTGCCTCG-3` ) to amplify a 150 bp region . Plotted values were calculated using the –ΔΔCt method and normalization to 28S rRNA ( fw: 5`-GGGCCGAAACGATCTCAACC-3` , rev: 5`-GCCGGGCTTCTTACCCATT-3` ) . Fold changes were calculated using the comparative CT method ( 2 ( CTX-CTR ) control- ( CTX-CTR ) test ) where CTX is the threshold cycle of the gene of interest and CTR is the threshold cycle of the reference gene ( 28S rRNA ) [57] . One-way analysis of variance ( ANOVA ) with Tukey’s posttest or Mann-Whitney test was performed using GraphPad Prism 5 . 0 . Values in the text , graphs , and figure legends throughout the manuscript are means ± standard errors of the means ( SEM ) . All animal work was performed in accordance with the Princeton Institutional Animal Care and Use Committee ( protocols 1947–16 ) . Princeton personnel are required to adhere to applicable federal , state , local and institutional laws and policies governing animal research , including the Animal Welfare Act and Regulations ( AWA ) ; the Public Health Service Policy on Humane Care and Use of Laboratory Animals; the Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research and Training; and the Health Research Extension Act of 1985 . | Alpha herpesvirus infections stay life-long in infected human and animal hosts`nervous systems in a silent state ready to reactivate upon various stress signals . Remarkably , infection of epithelial cells with these viruses results in productive infection whereas infection of peripheral nervous system neurons results in non-productive silent infection ( i . e . latency ) in the natural hosts . More interestingly , infection of dissociated peripheral neurons in culture also results in productive infection unless DNA replication inhibitors are used . To study the molecular mechanisms of escape from latency , we used primary neurons cultured in compartmented tri-chambers . By this way , we recapitulated the natural route of infection by infecting axons with low dose of virus which resulted in a silent infection in a small number of neuronal cell bodies without the use of any inhibitors . Using these cultures , we developed a new complementation assay to investigate the molecular signals leading to escape from latency and establishment of productive infection . We found two different mechanisms to escape from latency: Cellular stress-mediated slow route and viral tegument mediated-fast route . Furthermore , we showed that the stress-mediated pathway requires protein kinase A and c-Jun N-terminal kinase activity while the viral tegument-mediated fast escape does not require these host cell kinase activities . We also concluded that a general response to DNA virus infection or presence of excess herpesviral genomes in the nucleus to saturate silencing complexes is not enough to escape from latency . Induction of a productive infection requires presence of tegument proteins or activation of PKA and JNK pathway . | [
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"persist... | 2017 | Compartmented neuronal cultures reveal two distinct mechanisms for alpha herpesvirus escape from genome silencing |
Humans inhale hundreds of Aspergillus conidia without adverse consequences . Powerful protective mechanisms may ensure prompt control of the pathogen and inflammation . Here we reveal a previously unknown mechanism by which the danger molecule S100B integrates pathogen– and danger–sensing pathways to restrain inflammation . Upon forming complexes with TLR2 ligands , S100B inhibited TLR2 via RAGE , through a paracrine epithelial cells/neutrophil circuit that restrained pathogen-induced inflammation . However , upon binding to nucleic acids , S100B activated intracellular TLRs eventually resolve danger-induced inflammation via transcriptional inhibition of S100B . Thus , the spatiotemporal regulation of TLRs and RAGE by S100B provides evidence for an evolving braking circuit in infection whereby an endogenous danger protects against pathogen–induced inflammation and a pathogen–sensing mechanism resolves danger–induced inflammation .
Inflammation results from recognition of invading microorganisms through pathogen–associated molecular patterns ( PAMPs ) and from reaction to tissue damage–associated molecular patterns ( DAMPs ) [1] , [2] . It is known that the innate immune system recognizes both PAMPs and DAMPs through pattern recognition receptors , such as Toll–like receptors ( TLRs ) and other receptors [3] , [4] , [5] , [6] . Multiple positive feedback loops between DAMPs and PAMPs and their overlapping receptors temporally and spatially drive immune regulatory functions . Despite the identification of specific signaling pathways negatively regulating responses to PAMPs or DAMPs [7] , [8] , the unexpected convergence of molecular pathways responsible for recognition of PAMPs and DAMPs raised the question of whether and how the host discriminates between these two molecular patterns [9] , [10] . DAMPs such as the high mobility group box 1 protein ( HMGB1 ) and S100 proteins represent important danger signals that , although primarily intracellular , may mediate inflammatory responses through autocrine/paracrine interactions with the receptor for advanced glycation end–products ( RAGE ) , a multiligand receptor of the immunoglobulin superfamily [3] , [4] , [5] , [11] , [12] . Integral to the biology of RAGE and its ligands is their up–regulation and increased accumulation in multiple biological and disease settings . The ability to activate expression programs that encode innate immune responsive genes confers to RAGE a central role in chronic inflammatory diseases . Engagement of RAGE converts a brief pulse of cellular activation to sustained cellular dysfunction , eventually leading to inflammation [4] and tumor promotion [13] . However , because RAGE is expressed in multiple , distinct cell types , including immune cells , and both murine and human RAGE genes undergo extensive splicing with distinct splice isoforms being uniquely distributed in different tissues [14] , it is not surprising that diverse signal transduction and effector pathways may be impacted by RAGE depending on sites , ligands and time course of ligand–RAGE stimulation [15] , [16] , [17] . The complexity of the system is enhanced by the findings that the ligands of RAGE may interact with distinct TLR–binding molecules thus amplifying inflammatory and immune responses in infection [11] , [18] , [19] , [20] . Thus , although promoting pathology , RAGE signaling also contributes to beneficial , inflammatory mechanisms of repair , in certain settings [5] . Ultimately , discerning the primal versus the chronic injury–provoking roles for this ligand–receptor interaction is a challenge in delineating the functions of the ligand/RAGE axis [21] . Given that RAGE is expressed at the highest levels in the lung compared to other tissues [5] , [22] and both protects and causes lung injury [5] , the DAMP/RAGE axis likely integrates with the PAMP/TLR axis in the inflammatory responses in lung infections . We have addressed whether and how the two systems interact in a mouse model of pulmonary infection with a model fungal pathogen as well a common cause of severe infections and diseases , Aspergillus fumigatus [23] . Humans inhale hundreds of conidia per day without adverse consequences [24] , except for a small minority of persons in whom defense systems fail and a life–threatening angioinvasive form of aspergillosis can develop . Some degree of inflammation is required for protection during the transitional response occurring between the rapid innate and slower adaptive response . However , progressive inflammation worsens disease and ultimately prevents pathogen eradication , a condition in which it is an exaggerated inflammatory response that likely compromises a host's ability to eradicate infection and not an “intrinsic” susceptibility to infection that determines a state of chronic or intractable disease [25] . We disclosed the complexity of signalling integration between different innate immune biosensors by showing that the spatiotemporal regulation of TLRs and RAGE by S100B limits pathogen– as well as danger–induced inflammation and ensures protection in infection .
We assessed the expression of RAGE in the lungs of mice infected with Aspergillus conidia by immunohistochemical staining , protein and gene expression analysis . RAGE expression was observed at mRNA ( Fig . 1A and Fig . S1A ) and protein ( Fig . 1B ) levels and maximally occurred in alveolar epithelial cells , as revealed by immunofluorescence staining ( Fig . 1D ) . On assessing which putative ligands of RAGE were concomitantly expressed in infection , we found that HMGB1 was not increased either at the level of gene ( Fig . 1A and Fig . S1A ) or protein ( Fig . 1B ) expression . In contrast , S100B was promptly induced in infection , and declined thereafter to return to basal levels a week later , as revealed by gene and protein expression analysis in the lung ( Fig . 1A , B and Fig . S1A ) and protein secretion in the bronchoalveolar lavage fluid ( BAL ) ( Fig . 1C ) . S100B immunoreactivity was high in bronchiolar epithelial cells as revealed in wild–type mice ( WT ) ( immunofluorescence staining in Fig . 1D ) or in transgenic mice expressing s100b–EGFP+ ( Fig . 1E ) . Further analysis on purified lung cells from transgenic mice confirmed that epithelial cells were major sources of S100B in infection ( Fig . S1B ) while Ager was expressed on epithelial cells , macrophages , dendritic cells ( DCs ) ( Text S1 ) and polymorphonuclear neutrophils ( PMNs ) ( Fig . S1C ) , as described [5] . Presumably associated with PMNs' infiltration , s100a8 and s100a9 expressions in the lung mainly occurred at 3 days post–infection ( Fig . 1A ) . These data suggest that S100B pairs with RAGE very early in infection before its transcriptional downregulation . The prompt induction of S100B followed by its downregulation suggests that S100B may serve as a danger signal to control inflammation . To determine the role of the S100B/RAGE axis in response to the fungus , we evaluated parameters of infection , inflammation and adaptive immunity in RAGE KO mice with pulmonary aspergillosis . Despite an initial higher fungal growth in the lung and brain of KO than WT mice , the fungal growth was eventually restrained in both types of mice ( Fig . 2A ) . Inflammation and signs of parenchyma damage , in contrast , were greatly exacerbated in RAGE KO mice and failed to resolve as opposed to WT mice ( Fig . 2B , upper panels with fungi magnified in the inset ) . The number of PMNs increased and maintained elevated in the lung parenchyma and the BAL fluids ( Fig . 2B , lower panels and inset ) of RAGE KO mice . Gene expression analysis of the lung confirmed the higher and persistent inflammatory response in KO than WT mice , as revealed by the higher mRNA expression of Cxcl1 , Cxcl2 and Mpo genes as well as genes for inflammatory cytokines , such as IL–1β and IL–6 ( Fig . 2C ) . Despite the fact that an inflammatory response was not observed upon challenge with inactivated conidia ( data not shown ) , the failure to resolve inflammation was not secondary to either a deficient conidiocidal activity of lung cells , including macrophages ( Fig . 2D ) , or a defective oxidant production ( Fig . 2E ) . PMNs only from KO mice showed between 25 to 35% reduction of their conidiocidal activity as compared to WT mice , a finding pointing to a requirement for RAGE in the execution of PMNs' effector activity . These data indicate that RAGE , known to mediate PMN recruitment through interaction with beta 2 integrins [26] , is neither required for lung inflammatory cell recruitment or oxidant production in aspergillosis but unexpectedly protects from unintended inflammation . Both the subverted innate inflammatory response to the fungus [25] and the requirement for RAGE in DC and T cell functions [27] , [28] would predict altered adaptive Th responses to the fungus . This was indeed the case as shown by the results of lung DC and Th cell activation in response to the fungus . Purified DCs from RAGE KO mice responded to Aspergillus conidia or hyphae with higher expression level of mRNA for IL–1β , IL–6 , IL–23 ( p19 ) , and similar levels of IL–12 ( p35 ) or IL–10 compared to WT DCs ( Fig . S2A ) . Of interest , similar to the response to the fungus , higher levels of inflammatory cytokines were also observed in KO vs WT DCs in response to the TLR2/TLR6 ligand bacterial lipopeptide macrophage–activating lipopeptide ( MALP–2 ) but not to LPS or ODN–CpG ( Fig . S2B ) . In terms of Th cell activation , although cytokine and transcription factor mRNAs were higher in unstimulated CD4+T cells from KO than WT mice , a further increased was observed for Th2 ( Gata3/Il4 ) or Th17 b ( Rorc/Il17a ) but not for Th1 ( Tbet/Ifnγ ) or Treg ( Foxp3/Il10 ) specific transcripts ( Fig . S2C ) . Thus , RAGE deficiency is associated with deregulated innate and adaptive antifungal immunity and the inflammatory program activated in DCs in response to the fungus/TLR ligands is compatible with the impairment of antifungal Th1/Treg protective responses and upregulation of inflammatory Th2/Th17 cell responses [29] , [30] . To formally prove that RAGE pairs with S100B in infection , experiments of S100B neutralization or administration were performed . We found that S100B neutralization decreased resistance to infection in WT mice as indicated by the increased fungal growth ( Fig . 3A ) , PMN recruitment and inflammation in the lung ( Fig . 3B ) , an effect that was mimicked by treatment with antibodies neutralizing RAGE engagement ( Fig . 3A , B ) . Accordingly , exogenously administered S100B decreased the fungal growth and the inflammatory pathology , but this occurred at nanomolar doses ranging from 5 to 50 ng/kg but not at doses up to 5000 ng/kg ( Fig . 3A , B ) . Both effects were RAGE–dependent being abrogated , albeit partially for low–dose S100B , in RAGE KO mice ( Fig . 3A ) . Similar experiments done for HMGB1 ( Text S1 ) showed that the fungal burden and inflammation were both increased upon its administration and involved RAGE ( Fig . S3 ) . Because S100B itself didn't show a direct activity on fungal growth and morphology ( Text S1 and Fig . S4 ) and was ineffective if given before the infection ( data not shown ) , these data suggest that S100B pairs with RAGE for anti– and pro–inflammatory activities , a feature consistent with the unique ability of S100B to exhibit opposite effects depending on doses [12] , [17] , [31] . Mechanistically , we assessed whether S100B affected the activation of nuclear factor κB ( NF–κB ) and oxidant production , important inflammatory pathways downstream RAGE activation [3] , [4] in vivo and in vitro on purified PMNs , known to respond to S100B [12] . In vivo , 500 , but not 50 , ng/kg S100B promoted RAGE–dependent NF–κB activation in the lung ( Fig . 3C ) . In vitro , micromolar but not nanomolar S100B activated NF–κB ( Fig . 3D ) and increased oxidant production in response to the fungus ( Fig . 3E ) . Interestingly , and consistent with the dose–dependent prosurvival/prodeath effects of S100B on cells [32] , Fas expression was decreased and antiapoptotic Bcl2 expression increased in WT and KO PMNs exposed to nanomolar S100B and the opposite was true with micromolar S100B acting via RAGE ( Fig . 3F ) . These data confirm that RAGE activation by S100B is dependent on doses and also suggest that S100B may possess characteristics beyond the RAGE activating function which mediate its anti–inflammatory effects . Danger–sensing mechanisms are known to participate in the TLR responses to PAMPs [11] , [20] and to negatively regulate excessive inflammation during infection [10] . Given the ability of HMGB1 to bind and act in synergy with endogenous and exogenous TLR ligands [11] , [33] , we assessed whether S100B also binds TLR ligands in solid phase by ELISA . We found that S100B highly binds exogenous and endogenous TLR ligands , such as MALP–2 , HSP70 , class B ODN–CpG ( ODN 1982 ) , mammalian DNA , fungal RNA and , partly , DNA , in a Ca2+–and dose–dependent manner , with the maximum binding activity observed at the nanomolar dose ( Fig . 4A ) . No binding was observed to Zymosan , LPS , double–stranded RNA [polyinosinic–polycytidylic acid , Poly ( I:C ) ] , non-CpG ODN ( ODN 1982 ) or single–stranded RNA ( the imidazoquinoline resiquimod R848 ) . These data suggest that S100B may interact with TLR2 ( HSP70 ) but not with Dectin–1 ( Zymosan ) , with the heterodimer TLR2/TLR6 ( MALP–2 ) and with intracellular nucleic acid–sensing TLRs . Because TLR2 activates the inflammatory state of PMNs in infection [34] , [35] and unrestrained inflammation occurred in condition of defective S100B/RAGE axis , we hypothesized that the S100B/RAGE axis may inhibit TLR2/MyD88–driven inflammation to the fungus . We assessed therefore whether and how nanomolar or micromolar S100B would affect TLR2–mediated activation of PMNs . We found that ERK phosphorylation in response to MALP–2 was inhibited by nanomolar S100B and potentiated by micromolar S100B or by blocking serum S100B . This occurs through a p38–dependent mechanism , as shown by the ability of nanomolar S100B to induce p38–phosphorylation as well as ERK phosphorylation in the presence of the specific p38 inhibitor SB202190 . Like micromolar S100B , HMGB1 activated ERK more than p38 phosphorylation ( Fig . 4B ) . These data indicate that S100B , like HMGB1 [11] , potentiates the biological activity of TLR2 ligands upon forming complexes with them . However , they also unexpectedly revealed that forming complexes with nanomolar S100B negatively regulates their functions . That p38 is a negative regulator of TLR2 expression has already been described [36] . We assessed here whether inhibition of TLR2 occurred through physical association by performing immunoprecipitation studies with TLR2–transfected HEK cells stimulated with MALP–2 , in the presence or not of S100B . Both RAGE and TLR2 were found to associate with nanomolar or micromolar S100B upon TLR2 stimulation , but TLR2 physically associated with RAGE only in the presence of nanomolar S100B ( Fig . 4C ) . Although RAGE was found to be expressed in TLR2–transfected HEK 293 cells ( unpublished observations ) , we transiently transfected TLR2–HEK 293 cells with RAGE to visualize the RAGE/TLR2 interaction using the in situ proximity ligation assay . We confirmed that RAGE strongly interacts with TLR2 in the presence of nanomolar but not micromolar S100B . In addition , the lack of interaction observed upon S100B neutralization suggests that endogenous S100B likely mediates TLR2/RAGE physical interaction in steady-state conditions ( Fig . 4D ) . Thus , S100B interacts with RAGE and TLR2 and mediates the physical association of the two at nanomolar doses . Because S100B production mainly occurred in infection via the TLR2/MyD88 pathway ( Fig . 4E , F ) , our findings indicate the existence of an autocrine/paracrine loop by which TLR2–induced S100B binds to extracellular RAGE to inhibit TLR2 upon physical association . This scenario would suggest an increased responsiveness to TLR2–mediated inflammation of RAGE KO mice . Consistent with the high reactivity of DCs to MALP–2 ( Fig . S2B ) , the inflammatory response to intranasally delivered MALP–2 was higher in RAGE KO than WT mice as compared to other TLR agonists the sensitivity to which was not different between KO and WT mice ( Fig . 4G ) . The finding that S100B production in vivo was upregulated in the absence of TLR3 and TRIF , conditions in which we noticed a defective transcriptional downregulation of S100B ( Fig . 4E ) , led us to suppose that binding to intracellular nucleic acids is a mechanism by which S100B is down-regulated and its pro–inflammatory activity restrained in infection . We resorted to lung epithelial cells as major sources of S100B in infection . We assessed p38 phosphorylation in cells from WT and selected TLR–KO mice exposed to Aspergillus resting ( RC ) or swollen ( SC ) conidia , MALP–2 , Poly ( I:C ) or ODN–CpG and the relative contribution of endogenous S100B . We found that p38 phosphorylation occurred maximally in response to Aspergillus RC and Poly ( I:C ) , to a lesser extent in response to ODN–CpG and SC , did not occur in response to MALP–2 , and was largely TLR3/TLR9/TRIF–dependent , but MyD88–independent ( Fig . 5A ) . Both Ifnb1 and Ifna1 gene expression in response to Poly ( I:C ) were unaffected upon the addition of S100B but decreased upon neutralizing S100B by siRNA ( Fig . 5B ) , a finding indicating that S100B participates in the functional sensing of intracellular nucleic acids by TLR3 . Although similar results were obtained in response to ODN–CpG , the overall responsiveness of epithelial cells to TLR9 was lower ( data not shown ) , as already reported [37] . In terms of source of intracellular nucleic acids , consistent with the binding activity of S100B in vitro , we found that fungal RNA not only complexes with S100B in infection ( Fig . 5C ) but also activates epithelial cells in a TLR3–dependent manner , as indicated by IRF3 phosphorylation ( Fig . 5D ) . The transcriptional downregulation of s100b in infection led us to hypothesize that transcription factors downstream p38/TRIF would mediate this effect . Given the existence of specific binding sites for NF–κB family members in the promoter of both human ( GenBank: M59486 ) and murine ( GenBank: NC_000076 . 5 ) S100b , we assessed whether NF–κB transcription factors regulate s100b gene expression . For this purpose , we evaluated the activation of canonical/noncanonical NF–κB pathways downstream TLR2/MyD88 and TLR3/TLR9/TRIF and their contribution to s100b gene expression . Of the two IkB kinase complex catalytic subunits , known to have opposing roles in inflammation [38] , IKKβ more than IKKα was phosphorylated in response to SC , MALP–2 , the opposite was true in response to Poly ( I:C ) , while both pathways were activated by ODN–CpG ( Fig . 5E ) . We also quantified the activation of the NF–κB family members , using an ELISA kit specific for mouse p65 , p52 and RelB . Significant nuclear translocation occurred for p52 and RelB , but not for p65 , following 15–60 min of exposure to Poly ( I:C ) . Significant translocation of all members was observed in response to ODN–CpG , while only p65 translocation occurred in response to MALP–2 ( Fig . 5F ) . The two pathways had opposite effects on S100b gene expression , as shown by experiments in which either pathway was silenced by siRNA . S100b expression was inhibited upon blocking the canonical pathway or promoted upon blocking the noncanonical , p38-dependent , pathway ( Fig . 5G ) . These data suggest that s100b expression is transcriptionally regulated by the sequential action of downstream MyD88– and TRIF–dependent NF–κB signalling pathways . Experiments in vivo confirmed that the pro- and anti-inflammatory activity of S100B is contingent upon these TLRs . The anti–inflammatory activity of nanomolar S100B , as revealed by the fungal growth restriction and PMN recruitment , occurred independently of TLR4 but required the presence of TLR2 , TLR6 and the MyD88 adaptor . Consistent with the ability of the TLR3/TLR9/TRIF pathway to downregulate s100b , S100B became pro–inflammatory at the nanomolar dose in the relative absence of TLR9 , TLR3 and the adaptor TRIF ( Fig . 6A , B ) . These in vivo findings confirm that the spatiotemporal integration of signals from TLRs and RAGE by S100B limits pathogen– and danger–induced inflammation in murine aspergillosis ( Fig . 7 ) .
To initiate an appropriate inflammatory response , organisms have developed ways to recognize potentially life–threatening signals . Our study reveals that sequential signaling between different innate immune biosensors serves to limit pathogen– and danger– induced collateral inflammation in infection . This occurs through a previously undescribed TLR/RAGE interaction via S100B , an EF–hand calcium–binding protein , with both intra– and extracellular activities , that acts in either an autocrine or paracrine manner through RAGE [12] , [15] . RAGE is known to interact with TLR9 via HMGB1 which results in either a potentiation [19] or suppression [39] of TLR9 function . We found that , upon engagement , RAGE associated with and inhibited TLR2 . This occurred through epithelial cell–released S100B that paracrinally inhibited the TLR2–dependent activity of recruited PMNs , a finding consistent with the ability of RAGE to impair neutrophil functions [40] as well as with the down–regulated TLR2 activity in pulmonary aspergillosis [41] . PMNs' recruitment is a characteristic feature of pulmonary aspergillosis [23] and PMNs' activity is tightly regulated by TLRs [34] . RAGE was dispensable for PMNs' recruitment but potently regulated TLR2–induced MAPK kinase activation , NF–κB phosphorylation and survival , via low , but not high , doses of S100B . Notably , the ability of S100B to bind TLR2 also predicts an activity on TLR2 in a RAGE-autonomous fashion . Therefore , consistent with the biology of RAGE and its ligands [3] , [42] , their up–regulation exerted a proximal role in the inflammatory cascade . However , at least for extracellular S100B , the interaction with RAGE also served to limit pathogen–induced inflammation . Thus , S100B plays a dual role in infection , restraining the inflammatory response in the early response to pathogen , through a paracrine epithelial cells/PMNs braking circuit , but also contributing , similar to HMGB1 , to excessive inflammation through feed–forward RAGE activation [26] and likely through additional TLR interactions . The opposite effects on cells observed with low and high doses of S100B could be mechanistically explained by considering that calcium binding triggers structural changes in the S100 protein that allow interaction with target proteins as an octamer or a higher-order multimer form [16] . Trophic vs toxic effects are observed on neuronal cells in which nanomolar S100B stimulate neurite growth and promote survival , while micromolar levels result in inflammatory effects [32] , [43] . Structural and biochemical data have provided evidence that octameric S100B is highly stable and triggers RAGE activation by receptor dimerisation resulting in high–affinity binding [15] , [16] to the RAGE V and C ( 1 ) domains activating NF–κB [15] . Both domains are important for ligand binding and for intracellular signaling , respectively . In contrast , nanomolar S100B required RAGE to inhibit TLR2 for the elaboration of its anti–inflammatory activity . We have already shown that some S100B–induced cellular effects may not depend on RAGE signalling yet requiring the receptor [44] . This appears to be the case in our model in which the ability of S100B to bind endogenous and exogenous TLR2 ligands may offer a plausible molecular explanation for RAGE/TLR2 physical association . How this may prevent TLR2 signalling is not obviously clear , although signalling by TLR2 upon binding of ligands possessing fatty acyl moieties suggests a dynamic model of interaction , in which only a specific orientation of the ligand favors formation of a signal inducing ternary complex [45] . Thus , similar to HMGB1 [11] , S100B , by forming complexes with various TLR ligands , may present the partner molecule to its normal receptor in a way in which the conformation of the partner molecule is changed or in an allosteric interaction with the receptor or both . The ability of S100B to bind nucleic acids , while qualifying S100B as possible sentinel for nucleic acid–mediated immune activation [20] , also serves to explain the intracellular function of S100B in epithelial cells in infection . It is known that , upon calcium binding , the change of conformation in the C–terminal domain of S100B allows the exposure of hydrophobic residues critical for the binding to a variety of target proteins [46] , thereby affecting their activities and allowing the elaboration of a variety of intracellular functions [12] . We found that S100B was able to bind , in a calcium–dependent manner , Class B ODN–CpG , mammalian DNA and fungal RNA and DNA , resulting in the activation of a p38/TRIF–dependent signalling , downstream TLR3 and TLR9 . Thus , intracellular S100B may signal trough both TLR3 and TLR9 to scavenge pathogen– and host–derived nucleic acids . Of interest , at variance with HMGB1 [19] , S100B also discloses a TLR9–depending signalling pathway that converges on TRIF rather than on MyD88 [7] . The molecular basis for this result in epithelial cells is presently under investigation , but is consistent with the finding that modification of the structure of the DNA ligand affects its sub–cellular localization and this may impact on sorting and signaling adapters as well as the biological response to TLR9 activation in DCs [47] , [48] . That S100B may affect the intracellular compartmentalization of DNA upon binding pathogen and self DNA is , ultimately , a likely expectation for a chaperon molecule that localizes to the cytoplasm in a soluble form and in complexes with cytoskeletal and filament–associated target proteins [31] . This may also predict an inherent risk of autoimmunity associated with S100B . Incidentally , elevated levels of S100B have been observed in certain immuno–mediated diseases [12] . In addition to binding fungus DNA , whose unmethylated CpG motifs activates TLR9 [49] , S100B also bound fungal RNA , a PAMP able to activate DCs for antifungal priming [50] . That endogenous mRNA [51] and pathogen RNA [52] activate TLR3 is an established finding . We found that endogenous S100B binds fungal RNA and activation of epithelial cells by fungal RNA is TLR3–dependent . Thus , in addition to sensing tissue necrosis [53] , TLR3 , abundantly expressed on epithelial cells [37] , functions as an endogenous sensor of fungal RNA . Even more interesting is the finding that the activation of the TRIF–dependent , nucleic acid sensing pathway , mainly considered an inducer of antimicrobial innate immune responses , contributes to resolution of inflammation in infection . This occurs by downregulating s100b gene expression transcriptionally via noncanonical NF–κB signalling . Although s100b gene expression is tightly regulated in human cells [54] , little is known about mechanisms regulating its transcription . The transcriptional regulation of s100b expression by the sequential action of downstream MyD88– and TRIF–dependent NF–κB signalling pathways is thus a novel finding that not only establishes a link between pathogen– and danger–sensing signaling pathways but also confirms the inhibitory role of TLR3 on the S100B/RAGE axis [55] . In toto , we have identified a mechanism that discriminates between pathogen– and danger–induced immune responses via the spatiotemporal integration of signals from different innate immune biosensors . Conceptually , our study details an evolving braking circuit in infection whereby an endogenous danger protects the host against pathogen–induced inflammation and a nucleic acid–sensing mechanism terminates danger–induced inflammation . Thus , in addition to the notion that danger signal may terminate overactive immune responses [10] , our study reveals that a pathogen–induced signal may also terminate unnecessary danger–induced injury . This raises the intriguing possibility that the host may have developed mechanisms to ameliorate the response to DAMPs via PAMPs . The scenario is dominated by the highly adaptive S100B/RAGE axis that , in sensing danger , plays a critical and unanticipated role as a fine modulator of inflammation via the promiscuous activity of S100B at the extracellular and intracellular levels . On a translational level , our findings suggest that a defective danger sensing associated with the different isoforms of the RAGE receptor may underlie individual differences in the clinical course of invasive aspergillosis and the inherent patient's susceptibility to infection .
Experiments were performed according to the Italian Approved Animal Welfare Assurance A–3143–01 . Legislative decree 157/2008-B regarding the animal licence obtained by the Italian Ministry of Health lasting for three years ( 2008–2011 ) . Infections were performed under avertin anesthesia and all efforts were made to minimize suffering . Female C57BL6 mice , 8–10 wk old , mice were purchased from Charles River ( Calco , Italy ) . Homozygous Tlr2–/– , Tlr3–/– , Tlr4–/– , Tlr9–/– , Myd88–/– and Trif–/– mice on a C57BL6 background were bred under specific pathogen–free conditions at the Animal Facility of Perugia University , Perugia , Italy . RAGE–/– mice were obtained from Dr . Angelika Bierhaus ( Heidelberg , Germany ) . s100b–EGFP+ transgenic mice [56] were obtained from Dr . Catherine Legraverend ( Montpellier , France ) . The strain of A . fumigatus was obtained as described [25] . Viable resting , swollen Aspergillus conidia and hyphae were obtained as described [30] . For infection , mice were anesthetized by intraperitoneal ( i . p . ) injection of 2 . 5% avertin ( Sigma Chemical Co , St . Louis , MO ) before instillation of a suspension of 2×107 conidia/20 µl saline intranasally ( i . n . ) . Fungi were suspended in endotoxin–free ( Detoxi–gel; Pierce , Rockford , IL ) solutions ( <1 . 0 EU/mL , as determined by the LAL method ) . Mice were monitored for fungal growth [Colony forming units ( CFU 9/organ , mean ± SE] . BAL was performed by cannulating the trachea and washing the airways with 3 ml of PBS to collect the BAL fluid . Total and differential cell counts were done by staining BAL smears with May–Grünwald Giemsa reagents ( Sigma ) before analysis . At least 200 cells per cytospin preparation were counted and the absolute number of each cell type was calculated . Photographs were taken using a high–resolution Microscopy Olympus DP71 ( Olympus , Milan , Italy ) . Mice were treated daily i . p . for 3 consecutive days starting the day of the infection with different doses of purified S100B ( see below ) , 1 mg/kg polyclonal rabbit anti–S100B antibodies ( Swant , CH–6501 Bellinzona , Switzerland ) or 0 . 5 mg/kg anti–RAGE goat polyclonal IgG ( Santa Cruz Biotechnology , inc . DBA , Milan , Italy ) . Control received PBS or isotype controls ( Sigma–Aldrich ) . MALP–2 ( 2 . 5 µg ) , Poly ( I:C ) ( 50 µg ) , ultrapure LPS from Salmonella minnesota Re 595 ( 10 µg ) ( all from Sigma Chemical Co ) and Class B ODN–CpG ( 50 µg ) [24] were given once intranasally to mice infected as above . Control received PBS or isotype controls ( Sigma–Aldrich ) . Mice were sacrificed three days after treatment for histology ( H&E staining ) and s100b expression by real–time RT–PCR . Control received PBS . For histology , sections of paraffin–embedded tissues were stained with the periodic acid–Schiff ( PAS ) , hematoxylin and eosin ( H&E ) or Gomori's methenamine Silver procedures [25] . For detecting S100B–expressing cells , lungs in OCT or purified cells from s100b–EGFP mice were analyzed . For immunohistochemistry , lung sections were incubated overnight with polyclonal anti–S100B antibody ( 1∶100 ) or polyclonal anti–RAGE antibody ( 1∶20 ) followed by the secondary antibodies , i . e . , tetramethyl rhodamine isocyanate–conjugated goat anti–rabbit IgG ( Sigma–Aldrich ) for S100B , and AlexaFluor 594 donkey anti–goat IgG ( Invitrogen ) , for RAGE . Nuclei were counter–stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Endogenous peroxidase activity was quenched using 3% H2O2 in PBS . Immunostaining of lungs from RAGE KO mice was used as negative controls . Fluorescence and immunofluorescence microscopy was performed on a DM Rb epifluorescence microscope equipped with a digital camera ( Leica , Wetzlar , Germany ) . Purified lung CD11b+Gr–1+ PMNs ( >98% pure on FACS analysis ) were obtained as described [34] . Lung epithelial cells , at ∼99% expressing cytokeratin , on pan–cytokeratin antibody staining of cytocentrifuge preparations , and >90% viable on trypan blue exclusion assay , were isolated as described [57] . The average yield of tracheal cells was 1 . 7×105 cells/trachea [±0 . 58×105 ( SD ) ] . Alveolar macrophages were purified by plastic adherence . Total lung cells , purified alveolar macrophages and PMNs were incubated with unopsonized resting conidia at 1∶1 ratio at 37°C for conidiocidal activity [percentage of colony forming units inhibition ( mean ± SE ) at 60 min] or oxidant production [oxidation of dihydrorhodamine 123 ( DHR ) , Molecular Probes ( Invitrogen S . R . L . San Giuliano Milanese , Milan , Italy , measured by fluorimetry with the multifunctional microplate reader Tecan Infinite 200 , Tecan Austria GmbH , Salzurg , Austria ) at different time points . PMNs or epithelial cells were exposed to nanomolar or micromolar S100B as described [58] , 20 µg/ml anti–S100B antibody ( SWant ) , 300 nM HMGB1 , 5 µg/ml MALP–2 , 10 µg/ml Poly ( I:C ) , 10 µg/ml ultrapure LPS from Salmonella minnesota Re 595 and 10 µg/ml ODN–CpG . In vitro experiments were done in the presence of 2% FBS . Control cells were treated with PBS , DMSO or control antibody . S100B binding to TLR ligands was assessed in solid phase by ELISA . Briefly , plates were coated overnight at 4°C with 10 µg/ml ( based on preliminary experiments ) of MALP–2 , Zymosan ( Sigma Aldrich ) , HSP70 ( StressMarq Biosciences Inc , Victoria Canada ) , Poly ( I:C ) or LPS in carbonate buffer ( pH 9 . 55 ) or total fungal DNA or RNA , human DNA , Class B ODN–CpG ( 2006 ) , non-CpG ODN ( ODN 1982 ) [24] , resiquimod ( R–848 , Invivogen , Labogen S . r . l . Rho , Italy ) in Reacti–BindTM DNA Coating Solution ( Pierce ) . Fungal DNA and RNA were obtained as described [50] . Total fungal RNA was routinely pretreated with RNase–free DNase I ( 50 units of DNase I/100 mg RNA ) ( Sigma Aldrich ) at 25°C for 2 h . Nanomolar or micromolar S100B was added in blocking buffer ( TBS 1%BSA ) for 2 h at room temperature followed by the addition of rabbit anti–S100B antibodies ( 1∶1000 ) and HRP–conjugated rabbit secondary antibody ( R&D Systems , Space Import–Export srl Milano , Italy ) . EGTA was used at 1 mM . The plates were developed using TMB Microwell Peroxidase Substrate system ( BioFX Laboratories , Inc MD , U/SA ) . ODs were read at 450 nm . Data indicate the mean ±SE of triplicates from three independent experiments . To detect S100B co-localization with fungal RNA , TLR2–transfected HEK293 cells were pulsed with fungal RNA by means of N-[1- ( 2 , 3-dioleoyloxypropyl]-N , N , N , -trimethylammonium methylsulfate ( DOTAP; Roche ) , as described [50] . After pulsing cells were fixed in 3 . 7% formaldehyde , Triton–X100 permeabilized and incubated with Syto17 red fluorescent universal nucleic acid stain ( Molecular Probe; 2 . 5 µM , 5 min ) and anti-S100B antibody ( 1∶20 dilution ) followed by FITC-conjugated goat anti–rabbit IgG ( Vector Laboratories ) . Mock-pulsed ( control ) and pulsed cells were analyzed on confocal microscope Nikon Eclipse TE-2000U ( Tokyo , Japan ) . Blots of cells lysates were incubated with monoclonal rabbit monoclonal anti–S100B IgG ( clone EP1576Y , Epitomics , CA ) , goat polyclonal anti–RAGE IgG ( Santa Cruz Biotechnology , Inc . ) , rabbit anti–HMGB1 IgG2a ( Calbiochem , Milan , Italy ) , mouse monoclonal anti–TLR2 IgG2a , Santa Cruz Biotechnology , Inc . ) , rabbit polyclonal Abs recognizing the unphosphorylated form of ERK and p38 followed by horseradish peroxidase–conjugated anti–goat , mouse or rabbit IgG ( Cell Signaling Technology ) or biotin–conjugated ( Vectastain Elite ABC system; Vector Laboratories , Burlingame , CA , USA ) secondary antibodies . Blots were developed with the Enhanced Chemiluminescence detection kit ( Amersham Pharmacia Biotech , Milan , Italy ) and SuperSignal West Pico ( Pierce ) . Scanning densitometry was done on a Scion Image apparatus . The pixel density of bands was normalized against total proteins or tubulin . The inhibitor p38 ( 5 µM , SB202190 ) was purchased from Calbiochem ( San Diego , CA ) and dissolved at 1000× the final concentration in DMSO ( Sigma ) . Control experiments included staining without the primary antibody . The human HEK293 embryonic kidney cell lines stably transfected with human TLR2 were maintained as described [35] . Cells were stimulated with MALP–2 for 30 min with and without 4 nM or µM S100B or 20 µg/ml anti–S100B antibodies ( SWant ) . Cell lysates were subjected to immunoprecipitation after overnight incubation with 2 µg/ml polyclonal anti–S100B ( SWant ) or anti–RAGE ( Santa Cruz Biotechnology , Inc ) antibody . Immunoprecipitates were probed with antibodies to the corresponding antigens . Control experiments included western blottings on immunoprecipated with an irrelevant antibody . We resorted to PLA [59] to directly visualize the RAGE interaction with TLR2 in an S100B–dependent manner . TLR2–transfected HEK293 cells were transiently transfected with a RAGE expression vector ( pcDNA3/RAGE ) or empty vector ( pcDNA3 ) and stimulated with MALP–2 for 30 min with or without 4 nM or µM S100B or 20 µg/ml anti–S100B antibody ( SWant ) . Cells were then fixed in cold methanol and treated with a rabbit anti–RAGE ( H300 , Santa Cruz Biotechnology , Inc ) and a goat anti–TLR2 antibody , and subjected to PLA ( OLINK Bioscience , Uppsala ) according to the manufacturer's instructions . Cells were visualized on the DM Rb epifluorescence microscope . To detect NF–κB ( p65 ) nuclear translocation , purified PMNs were fixed in cold methanol , permeabilized with Triton-X100 0 . 1% in PBS , incubated with blocking solution ( PBS containing 3% BSA and 1% glycine ) , and incubated overnight at 4°C with rabbit anti-p65 ( C-20 ) antibody ( sc-372 , Santa Cruz Biotechnology; 1∶50 dilution ) followed by tetramethyl rhodamine isocyanate-conjugated goat anti-rabbit IgG ( Sigma-Aldrich; 1∶50 dilution ) as secondary antibody . Nuclei were counter-stained with DAPI . Cells were visualized on the epifluorescence microscope . We used an ELISAbased TransAM Flexi NFkB Family Kit ( Active Motif ) to monitor activity of NF–κB family members . Anti–phospho–IKKα ( Ser180 ) /IKKβ ( Ser181 ) rabbit Abs ( Cell Signaling Technology ) were used for western blotting of phospho IKKα and IKKβ . Western blotting with specific polyclonal antibodies ( Santa Cruz Biotechnology ) was done to assess level of p65 . Epithelial cells were exposed to fungal RNA ( 25 µg/ml ) [50] for 8 h before determination of levels of IRF3 phosphorylation by immunoblotting with rabbit polyclonal anti–IRF3 antibodies and anti–rabbit–horseradish peroxidase ( Santa Cruz Biotechnology Inc . ) . Data are presented as immunoblots of cell lysates and fold increases ( pixel density ) in the phosphorylated to total protein ratios . Recombinant bovine S100B , 97% identical to mouse S100B , was expressed and purified as reported [31] , [32] . Purified S100B was passed through END–X B15 Endotoxin Affinity Resin column to remove contaminating bacterial endotoxin . The S100B concentration was calculated using the Mr of the S100B dimer ( 21 kDa ) . SiRNA to target IKKα , IKKβ and S100B were done as described [30] . The siRNA specific sequences were selected , synthesized and annealed by the manufacturer , and were used in combination with nontargeted control siRNA ( Ambion , Applied Biosystem International , Monza Italy ) . Transfections of siRNA ( at 1 nM/well ) were performed by using the INTERFERinTMTransfection reagent , as per manufacturer's instructions ( PEQLAB Biotechnologie GmbH , Erlangen , Germany ) . Cells were stimulated 48 h after transfection at 37°C . Expression of IKKα , IKKβ and S100B transcripts in transfected cells was evaluated by RT–PCR or western blotting . Real–time RT–PCR was performed using the iCycler iQ detection system ( Bio–Rad ) and SYBR Green chemistry ( Finnzymes Oy , Espoo , Finland ) . Cells were lysed and total RNA was extracted using RNeasy Mini Kit ( QIAGEN , Milan , Italy ) and was reverse transcribed with Sensiscript Reverse Transcriptase ( QIAGEN ) according to the manufacturer's directions . The sense/antisense primers were as follows: Ager sense 5′–GCCCTCATTGATGTCTTCCACC–3′; antisense ( 5′–GAACTCATGGCAGGCCGTGGTC–3′ ) ; s100b sense 5′–GCCCTCATTGATGTCTTCCACC–3′; antisense 5′–GAACTCATGGCAGGCCGTGGTC–3′; s100a8 sense 5′–TCGTGACAATGCCGTCTGAACTG–3′; antisense 5′–TGCTACTCCTTGTGGCTGTCTTTG–3′; s100a9 , sense 5′– CGCAGCATAACCACCATCATC–3′; antisense 5′–GCCATCAGCATCATACACTCC–3′; Hmgb1 sense , 5′–GGCTGACAAGGCTCGTTATG–3′; antisense 5′–GCAACATCACCAATGGATAAGC–3′;Fas sense 5′–CTACTGCGATTCTCCTGGCTGTG–3′; antisense 5′–AGTTTGTATTGCTGGTTGCCTGTGC–3′; Bcl2 sense 5′–ACGAGTGGGATGCTGGAGATG–3′; antisense 5′–TCAGGCTGGAAGGAGAGATGC–3′ . Other primers were as described [30] Amplification efficiencies were validated and normalized against Gapdh . The thermal profile for SYBR Green real time PCR was at 95°C for 3 min , followed by 40 cycles of denaturation for 30 s at 95°C and an annealing/extension step of 30 sec at 60°C . Each data point was examined for integrity by analysis of the amplification plot . The mRNA–normalized data were expressed as relative cytokine mRNA in stimulated cells compared to that of mock–infected cells . Data were analyzed by GraphPad Prism 4 . 03 program ( GraphPad Software , San Diego , CA ) . Student's t test or analysis of variance ( ANOVA ) and Bonferroni's test were used to determine the statistical significance ( P ) of differences in organ clearance and in vitro assays . The data reported are either from one representative experiment out of three to five independent experiments ( western blotting and RT–PCR ) or pooled from three to five experiments , otherwise . The in vivo groups consisted of 6–8 mice/group . | Inflammation results from recognition of invading microorganisms through pathogen–associated molecular patterns ( PAMPs ) and from reaction to tissue damage–associated molecular patterns ( DAMPs ) . Despite the identification of specific signaling pathways negatively regulating responses to PAMPs or DAMPs , the unexpected convergence of molecular pathways responsible for recognition of either one raised the question of whether and how the host discriminates between the two distinct molecular patterns . Here we reveal a previously unknown mechanism by which the danger molecule S100B integrates pathogen– and danger–sensing pathways to restrain inflammation in Aspergillus fumigatus infection . By disclosing protective mechanisms that ensure prompt control of the pathogen and inflammation , our results may help to explain why humans inhale hundreds of Aspergillus conidia without adverse consequences . | [
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] | 2011 | The Danger Signal S100B Integrates Pathogen– and Danger–Sensing Pathways to Restrain Inflammation |
Insecticide-treated nets ( ITNs ) are one of the main interventions used for malaria control . However , these nets may also be effective against other vector borne diseases ( VBDs ) . We conducted a systematic review and meta-analysis to estimate the efficacy of ITNs , insecticide-treated curtains ( ITCs ) and insecticide-treated house screening ( ITS ) against Chagas disease , cutaneous and visceral leishmaniasis , dengue , human African trypanosomiasis , Japanese encephalitis , lymphatic filariasis and onchocerciasis . MEDLINE , EMBASE , LILACS and Tropical Disease Bulletin databases were searched using intervention , vector- and disease-specific search terms . Cluster or individually randomised controlled trials , non-randomised trials with pre- and post-intervention data and rotational design studies were included . Analysis assessed the efficacy of ITNs , ITCs or ITS versus no intervention . Meta-analysis of clinical data was performed and percentage reduction in vector density calculated . Twenty-one studies were identified which met the inclusion criteria . Meta-analysis of clinical data could only be performed for four cutaneous leishmaniasis studies which together showed a protective efficacy of ITNs of 77% ( 95%CI: 39%–91% ) . Studies of ITC and ITS against cutaneous leishmaniasis also reported significant reductions in disease incidence . Single studies reported a high protective efficacy of ITS against dengue and ITNs against Japanese encephalitis . No studies of Chagas disease , human African trypanosomiasis or onchocerciasis were identified . There are likely to be considerable collateral benefits of ITN roll out on cutaneous leishmaniasis where this disease is co-endemic with malaria . Due to the low number of studies identified , issues with reporting of entomological outcomes , and few studies reporting clinical outcomes , it is difficult to make strong conclusions on the effect of ITNs , ITCs or ITS on other VBDs and therefore further studies be conducted . Nonetheless , it is clear that insecticide-treated materials such as ITNs have the potential to reduce pathogen transmission and morbidity from VBDs where vectors enter houses .
The World Health Organisation ( WHO ) promotes the use of Integrated Vector Management ( IVM ) to control vector borne diseases ( VBDs ) [1] . Briefly , IVM involves the use of a range of proven vector control tools used either alone or in combination selected based on knowledge of the local vector ecology and epidemiological situation . IVM can involve use of multiple vector control tools against a single disease or alternatively a single tool against multiple diseases . This is particularly the case where vector control interventions are active against more than one disease and VBDs overlap in their distribution . IVM is a WHO policy for effective , cost effective and sustainable vector control . In order to exploit synergies between VBDs and make vector control more cost effective , IVM advocates for the use of shared interventions across diseases . However , in order to be able to do this it is important to first know whether interventions are effective against multiple diseases . This was the rationale for conducting this review . We considered insecticide-treated bednets ( ITNs ) since this intervention has been rolled out already on a large scale for malaria vector control . ITNs form the mainstay of malaria vector control in many malaria endemic areas [2] . ITNs are estimated to reduce all-cause child mortality by 17% and uncomplicated Plasmodium falciparum episodes in areas of stable transmission by 50% , compared to no nets [3] . ITNs have been rolled out in malaria-endemic regions on a large scale , particularly in sub-Saharan Africa ( SSA ) . Between 2004 and 2010 , the number of ITNs delivered by manufacturers to malaria endemic countries in SSA increased from 6 million to 145 million [2] . The percentage of households owning at least one ITN in SSA is estimated to have risen from 3% in 2000 to 56% in 2012 , but declined slightly to 54% in 2013 . More work is needed to reach ITN coverage targets set by Roll Back Malaria of 80% use of ITNs by individuals in populations at risk [4] . Outside Africa , 60 million ITNs were distributed during 2009–2012 , with 10 countries accounting for 75% of the total ( India 9 . 2 million , Indonesia 6 . 1 million , Myanmar 5 . 4 million , Bangladesh 4 . 7 million , Afghanistan 4 . 3 million , Cambodia 3 . 6 million , Papua New Guinea 3 . 2 million , Haiti 3 . 0 million and Philippines 3 . 0 million ) [2] . More recently conventional ITNs have been replaced by long lasting insecticide-treated nets ( LLINs ) that maintain effective levels of insecticide for at least three years meaning that re-treatment with insecticide is not necessary . Since 2007 the WHO recommends only use of LLINs and not conventional ITNs [5] . For the purpose of this review we refer to ITNs without distinguishing between conventional ITNs or LLINs . ITNs are likely to be effective against multiple vectors and VBDs since a substantial proportion of transmission occurs indoors , but this has not been systematically assessed . As such there may be unknown collateral benefits of ITN roll-out on VBDs in addition to malaria . ITNs as well as insecticide-treated curtains ( ITC ) and insecticide-treated screening are likely to function in the same way . Disease vectors are attracted to host odours emanating either from people sleeping under ITNs or from people within houses in the case of ITCs and ITS . Vectors then coming into contact with these materials are deterred or killed and thus it can be said that the ITN and house are acting as ‘baited traps’ . ITC and ITS may also be working to some extent to prevent vectors from entering houses ( household level protection ) rather than personal protection in the case of ITNs . We conducted a systematic review to assess the efficacy of ITNs , ITCs or ITS against eight VBDs prioritised by the WHO in the Handbook for IVM [6]: Chagas disease , cutaneous and visceral leishmaniasis , dengue , human African trypanosomiasis , Japanese encephalitis , lymphatic filariasis and onchocerciasis . In this study we assessed the effect of ITNs , ITCs and ITS on clinical and entomological outcomes .
The review was carried out according to a protocol and analytical plan that was prepared in advance . A systematic search of published literature was performed in April 2013 and repeated in June 2014 using intervention-specific search terms ( for example ITN/LLIN/bednet/curtain/pyrethrins ) , as well as vector and disease specific search terms . MeSH and DeCS terms were used where appropriate . More detail on the search terms used is given in Supporting Information S1 . MEDLINE ( 1950 - ) , EMBASE ( 1980 - ) and LILACS ( 1982 - ) databases were searched and no language restrictions were applied . In April 2013 we also searched the Tropical Disease Bulletin ( 1912 - ) database . In addition , we reviewed the reference lists of key review articles and consulted with experts to identify further studies . The search was conducted as part of a larger systematic review on all types of vector control interventions against eight different VBDs [6]: Chagas disease , cutaneous and visceral leishmaniasis , dengue , human African trypanosomiasis , Japanese encephalitis , lymphatic filariasis and onchocerciasis . AW screened the search results for potentially relevant studies and full text documents were obtained for those publications deemed to be relevant . Foreign language studies were evaluated by a native speaker in consultation with AW . The articles were scrutinised to ensure that multiple publications from the same study were included only once . Studies were assessed against inclusion and exclusion criteria by AW and SL independently . Studies were included if they compared the efficacy of ITNs , ITCs or ITS versus no intervention ( control group ) in disease endemic areas . Excluded studies and reasons for their exclusion are detailed in Supporting Information S2 . We sought to compare the efficacy of ITNs , ITCs and ITS versus no intervention , rather than assess the efficacy of untreated bednets , curtains or screening or compare these untreated materials to ITNs , ITCs or ITS . We took this decision because bednets being rolled out for malaria control are insecticide-treated . Studies using hand-impregnated nets or factory manufactured LLINs were included . Studies assessed the effect of the intervention on either i ) clinical outcomes ( incidence or prevalence of disease or infection – whether this was confirmed by the patient , clinical diagnosis or diagnostically differed by study ) and/or ii ) entomological outcomes ( including human biting rate , adult vector density and Stegomyia indices , pupal/demographic indices , oviposition rates or ovitrap positivity for dengue vectors ) . Adult vector density was measured using a number of techniques including Centers for Disease Control ( CDC ) light traps , sticky traps , pyrethrum spray catches and resting catches using aspirators . Larval indices extracted for dengue were house index ( percentage of houses infested with larvae and/or pupae ) , container index ( percentage of water containers infested with active immatures ) and Breteau index ( number of positive containers per 100 houses ) . We also extracted data on pupae per person ( number of pupae collected over the total number of inhabitants of the households inspected ) , oviposition rates ( mean number of Aedes aegypti eggs per trap ) and ovitrap positivity ( percentage of traps positive for Aedes eggs ) . In terms of study designs , we included i ) randomised controlled trials ( cluster level or individual randomisation ) , ii ) non-randomised trials with pre- and post-intervention data ( for both control and intervention areas ) and iii ) rotational studies ( provided there was baseline data or allocation was random or interventions/collectors were rotated appropriately e . g . each house received each intervention ) . A rotational design is when an intervention ( s ) is moved between sampling sites for set time periods or , in the case of human landing catches , collectors are rotated between interventions . Studies performed under laboratory or semi-field conditions ( for example , experimental huts ) were excluded . We also excluded non-randomised trials without baseline data ( for both control and intervention areas ) , non-controlled programme evaluations and observational studies in which clusters or individuals were not purposely allocated to intervention and control groups . AW ( or a third party contractor ) extracted data from the publications into a pre-designed data extraction form in Microsoft Word ( Supporting Information S3 ) , along with data tables and graphs . Graphs were digitised using Engauge Digitizer software ( version 5 . 1 , http://digitizer . sourceforge . net/ ) . Preliminary analysis of data tables was conducted in Microsoft Excel . Analysis assessed the efficacy of ITNs , ITCs or ITS compared to no intervention . We used un-adjusted measures ( clinical and entomological ) throughout . This was for consistency because different studies adjust for different covariates . However , adjusted values , where available are reported for comparison . Clinical outcomes were reported as either risks or rates of disease or infection in the published papers . Meta-analysis of clinical data ( unadjusted risk of disease or infection ) was performed in Stata 13 using the metan command ( StataCorp , Texas , U . S . A . ) . Pre-intervention risk ratios were plotted on forest plots alongside post-intervention risk ratios to show comparability of groups at baseline . Statistical heterogeneity was assessed using a χ2 test . Due to the small number of studies in each comparison , we deemed there to be heterogeneity if the χ2 test p value was less than 0 . 1 [7] . If heterogeneity was found , a summary effect measure was calculated using random effect meta-analysis rather than fixed effect meta-analysis . Protective efficacy ( PE ) was calculated as PE = 1− ( risk ratio of clinical disease or infection during the intervention period ) ×100% . PE ( or relative risk reduction ) can be interpreted as the percentage reduction in risk of clinical disease or infection associated with the intervention . Standard formulas were used to calculate 95% confidence intervals for risk or rate ratios [8] . Entomological outcomes are reported as means with 95% confidence intervals , where these are reported in the published paper or could be calculated . If there were zero events then we estimated the upper 95% confidence interval as 100 x ( 3 . 7/N ) where N is the sample size [9] . For entomological outcomes , where data were available for multiple intervention and control sites , we took the average values of the outcome measure , applying equal weight to all sites . A similar approach was taken if data were available for multiple timepoints within a year or transmission season , either pre- or post- intervention . We could not use meta-analysis to analyse the entomological data due to inadequate reporting in the published manuscripts . In almost all the studies the standard error for mean vector density was not reported and could not be calculated from the data presented in the papers . For studies with baseline/post intervention data for control and intervention sites we calculated the percentage reduction in vector density using a difference in differences approach . We estimated the effect of the intervention ( J ) using the formula J = ( q1/q0 ) / ( p1/p0 ) , where q1 and q0 are , respectively , the entomological indicators ( mean density , or biting rate ) observed in the intervention and control areas post-intervention respectively and p1 and p0 are the corresponding baseline estimates of these entomological indicators [10] . We calculated the percentage reduction in entomological indicators as 100 x ( 1 - J ) . For studies in which only post intervention data were available we calculated the percent reduction in the outcome in the treatment group compared to the control group using the formula 100 x ( 1- ( q1/q0 ) [10] . We were not able to calculate confidence intervals around percentage reductions due to heterogeneity in study designs; e . g . different follow up periods pre- and post-intervention and the way in which the data was reported e . g . the total vector count was reported rather than individual observations . We followed recommendations made by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) group where possible [11] , [12] ( Supporting Information S4: PRISMA checklist ) . AW and SL assessed independently the risk of bias in the included studies using a risk of bias assessment form . This form was developed for the purposes of this review to assess entomological studies and was adapted from the Effective Practice and Organisation of Care ( EPOC ) risk of bias assessment form [13] ( Supporting Information S5 ) . A judgement of high , low or unclear risk of bias was given for a number of parameters . An overall bias assessment ( high/medium/low ) was made based on the modal bias risk . We developed a tool for assessing study quality which primarily concerns the study design and downgrades the score given to the study depending on whether sample size calculations were performed ( overall and for entomological sampling ) , the length of the follow up period and risk of bias ( Supporting Information S6 ) . This was loosely based on the Grading of Recommendations Assessment , Development and Evaluation ( GRADE ) system of rating quality of evidence [14] , but adapted for entomological studies . For the purposes of the quality assessment , we deemed a trial to be a randomised controlled trial if the published paper stated that groups were randomised to intervention or control , even if the process of sequence generation was not described in the paper .
The initial systematic literature search identified 19 , 113 unique records ( Figure 1 ) . 18 , 617 records were excluded based on review of the title and abstract . 496 full text records were reviewed and of these 310 studies met the inclusion/exclusion criteria across all types of vector control intervention . The update of the search in June 2014 identified 1 , 991 unique records , of which 125 full-text records were reviewed and 2 studies met the inclusion/exclusion criteria . In total , 21 studies assessed the efficacy of ITNs , ITCs or ITS versus no intervention and the split of these by disease was nine cutaneous leishmaniasis , five dengue , one Japanese encephalitis , three lymphatic filariasis and three visceral leishmaniasis . Summary tables of the studies identified are given in Supporting Information S7 . Only nine of the 21 studies included reported the level of insecticide resistance in the study area or conducted an insecticide bioassay . Of the 21 studies identified , fifteen were deemed to be at low risk of bias , three at medium risk and three at high risk of bias [15] ( Supporting Information S8 ) . Twelve studies were deemed to be of high quality , three medium quality and six low quality ( Supporting Information S9 ) . No studies that met the inclusion and exclusion criteria were found assessing the efficacy of ITNs , ITCs or ITS against Chagas disease , human African trypanosomiasis or onchocerciasis . A total of six studies assessing the efficacy of ITNs against cutaneous leishmaniasis were identified [15]–[20] . Of these three reported clinical data only , one reported entomological data only , and two reported both clinical and entomological data . Of the studies reporting clinical data , this was generally either a symptom questionnaire administered to participants or examination of lesions . Two studies utilised either a leishmanin skin test [20] or microscopic examination of skin scrapings from an active lesion [21] . Random effects meta-analysis of the efficacy of ITNs was conducted on data from four studies conducted in Iran ( 2 studies ) , Afghanistan and Colombia [17]–[20] ( Figure 2 , Table 1 ) . Pre-intervention incidence of cutaneous leishmaniasis was comparable in intervention and control groups in the three studies that reported this data , with 95% confidence intervals for the risk ratio crossing the null value . Random effect meta-analysis indicated a PE of ITNs against cutaneous leishmaniasis of 77% ( 95% CI: 39%–91% , P = 0 . 003 ) . Clinical data from one study in Turkey [15] was not suitable for meta-analysis because this study did not report numbers of cases or population at risk . Alten et al . reported a significant reduction in incidence of cutaneous leishmaniasis in ITN clusters , while incidence in control areas either stayed the same or increased . However , this study was deemed to be at high risk of bias and low quality . Studies assessing the efficacy of ITNs reported mixed results in terms of effect on sandfly density ranging from a relative increase of 49% to a relative reduction of 96% ( Table 2 ) . Although Emami et al . reported a highly significant PE against cutaneous leishmaniasis in Iran , no effect on the mean number of Phlebotomus sergenti captured per month was detected in this study [17] . Similarly , Alten et al . reported a beneficial effect of ITNs on clinical disease in Turkey and a percentage increase in vector density relative to the control group was documented [15] . Three studies conducted in Colombia , Venezuela and Burkina Faso assessed the efficacy of ITCs against cutaneous leishmaniasis [16] , [22] , [23] . Two studies reported entomological data while one reported both clinical and entomological data . Kroeger et al . demonstrated a high PE against cutaneous leishmaniasis of 93% ( 95% CI: −16%–100% , p = 0 . 06 ) in Venezuela ( Table 1 ) [22] . Studies that measured the entomological effect of ITCs demonstrated a high percentage reduction in vector density of 54% , 87% and 98% ( Table 2 ) . However , the 98% reduction was observed in a study that was deemed to be of low quality due to the study design employed ( non-randomised pre-post design ) , few sampling sites for entomological data and short period of follow up . A study which assessed the efficacy of ITCs and ITS against cutaneous leishmaniasis in Iran reported a PE of 16% ( 95% CI: 2%–28% , p = 0 . 03 ) [21] . This study was deemed to be of low quality due to the study design ( non-randomised pre-post design ) and high risk of bias . Three studies assessing the efficacy of ITNs on visceral leishmaniasis were identified [24]–[27] . Two studies reported only entomological data and one reported both clinical and entomological data . The Picado et al . study [27] did not show a significant effect on incident Leishmania donovani infections ( PE: 0 . 3% , 95%CI: −15%–14% , p = 0 . 97 ) or incident cases of visceral leishmaniasis ( PE: 4% , 95%CI: −81%–48% , p = 0 . 9 ) in India and Nepal ( Table 1 ) . The same study , however , did appear to show an effect on vector density with a relative reduction in the mean number of P . argentipes females per light trap night of 57% [26] ( Table 3 ) . Two studies conducted in Sudan [24] and Bangladesh , India and Nepal [25] demonstrated a 100% and 35% ( 95% CI: −56% to 75% ) reduction in vector density , respectively ( Table 3 ) . No studies were identified which assessed the efficacy of ITCs or ITS against visceral leishmaniasis . Two studies assessing the efficacy of ITNs against lymphatic filariasis were identified , both of which collected entomological data only [28] , [29] . ITNs generally were associated with a high level of protection against Anopheles species , with approximately a 98% reduction in vector density in the two studies conducted in Kenya and Papua New Guinea ( Table 4 ) . Bøgh et al . reported a lower percentage reduction in Culex quinquefasciatus density of 16% [28] . One study conducted in India assessing the efficacy of ITCs hung in eaves and doorways against lymphatic filariasis vectors was identified [30] . Poopathi et al . detected an 82% reduction in man biting density and a 79% reduction in indoor resting density of Cx . quinquefasciatus [30] ( Table 3 ) . However , this study was deemed to be of low quality mainly due to the study design employed ( non-randomised pre-post design ) , few sampling sites for entomological data and short period of follow up . One study conducted in Haiti assessed the efficacy of ITNs against dengue [31] . Based on the five month post-intervention survey this study showed that ITN use was associated with a 36% reduction in pupae per person and 77% reduction in indoor ovitrap positivity . However , the study reported that ITNs were associated with a 56% increase in house index , 143% increase in container index , 60% increase in Breteau index and 20% increase in outdoor ovitrap positivity . The bioassay results on new nets from this study site indicated only 30% mortality of A . aegypti suggesting that insecticide resistance may have been a problem . Three studies were identified that assessed the efficacy of ITCs against dengue vectors [32]–[34] . Kroeger et al . demonstrated in Mexico a beneficial effect of ITCs on house index ( 25% reduction ) and pupae per person ( 39% reduction ) , but reported a relative increase in Breteau index of 10% based on the 12 month follow up survey [32] . The authors , however , reported a community-level effect of the ITCs which meant that benefits in terms of reductions in mosquito populations spilt over into control areas . They postulate that this is why there is no significant difference between intervention and control arms . Breteau and house indices from an external control area closely follow seasonal rainfall patterns and do not show similar reductions as in the study intervention and control areas . In Thailand Lenhart et al . did not detect a beneficial effect of ITCs on house index , container index , Breteau index or pupae per person , with relative increases of 15% , 20% , 3% and 37% , respectively at the nine-month time point [33] . ITCs did , however , show a beneficial effect on indoor and outdoor oviposition rates with reductions of 44% and 49% in mean numbers of eggs per trap , respectively at the six month time point , although no significant difference between control and ITC arms was reported at three or nine months . Another study in Thailand where houses generally had a more closed design reported a 56% reduction in house index , 67% reduction in Breteau index and 63% reduction in pupae per person index six months after the start of the intervention [34] . At the 6-month follow up survey 71% of households had at least one ITC . However , at the 18-month follow up survey when ITC coverage had fallen to only 33% a much lower effect on entomological parameters was observed ( 26% reduction in house index , 8% reduction in Breteau index and 111% increase in pupae per person index ) . A study of ITS reported a beneficial effect on both house index and density index ( adult Ae . aegypti ) in Vietnam . In the intervention arm both house and density index were reduced to zero one month after installation of the screening and remained at zero for the duration of the epidemic season ( eight months post intervention ) , compared to the control arm in which seasonal peaks in both indices were observed [35] , [36] . The same study also reported a PE of ITS against IgM seropositivity of 80% ( 95% CI: 53–92% , p<0 . 001 ) compared to the control group ( Table 1 ) . This study used a non-randomised pre-post design and was deemed to be of low quality . A single study by Dutta et al . assessed the efficacy of ITNs against Japanese encephalitis vectors and seroconversion in India [37] . This study was deemed to be of low quality due to the study design employed ( non-randomised pre-post design ) and low number of sampling sites for entomological data . No effect of ITNs on mean density of adults of the Cx . vishnui group was observed ( reduction of −3 . 5% ) . The risk of seroconversion against Japanese encephalitis virus was comparable across groups at baseline , but the risk was significantly lower in the ITN group compared to the control during the two year post intervention period ( PE: 67% , 95%CI: 44–80% , p<0 . 001 ) ( Table 1 ) .
Our review shows the potential for ITNs , ITCs and ITS to reduce vector borne diseases . Of particular note is the evidence on high protective efficacy of ITNs against cutaneous leishmaniasis , which suggests that there may be considerable collateral benefits of ITN roll out where cutaneous leishmaniasis and malaria are co-endemic . There is also good evidence of the efficacy of ITC and ITS against cutaneous leishmaniasis . Weaker evidence exists for the effect of ITS on dengue and ITNs on Japanese encephalitis , but these interventions look promising . Further studies should be conducted to confirm these findings . The potential of ITNs , ITCs and ITS against Chagas disease , human African trypanosomiasis and onchocerciasis remains untested . In several studies the pattern of reduction in disease incidence was not matched by reductions in entomological parameters . This is not unsurprising given the complicated relationship between vector density and risk of human infection , particularly when vector infection rate is not taken into account . Meta-analysis showed that ITNs were able to reduce the incidence of cutaneous leishmaniasis by 77% . This finding provides support for WHO's recommendation that ITNs should be used as a vector control method against this disease [38] . This level of protective efficacy compares favourably with the 50% protective efficacy of ITNs against P . falciparum malaria shown by Lengeler [3] . Based on maps of cutaneous leishmaniasis [39] and P . falciparum endemicity [40] there are large areas , particularly in South America , where these diseases are likely to be co-endemic . Non-malaria endemic countries where cutaneous leishmaniasis is prevalent should consider rolling out ITNs as part of control efforts . Similar reductions in vector density were not observed which may be due to the ecology of the vector species or differences in collection techniques . For example studies by Alten et al . and Emami et al . sampled both endophilic and exophilic species [15] , [17] . Studies by Kroeger et al . [22] and Noazin et al . [21] reported significant effects of ITC and ITC/ITS on clinical outcomes . Clinical evidence from one study suggested that ITNs were not effective against visceral leishmaniasis [27] . However , in this study Picado et al . suggested that L . donovani transmission may have been occurring outside the home where ITNs would have little impact on preventing sandfly-human contact . In Africa observational studies led to mixed results – several studies have shown treated bednets to be protective against visceral leishmaniasis [41] , [42] , while others have shown no effect of ITNs on L . donovani infection rate in P . orientalis , although the number of infected P . orientalis identified was small in all villages [43] . In south Asia , several observational studies have shown use of ( untreated ) bednets to be protective against visceral leishmaniasis [44] , [45] . The efficacy of ITNs in preventing leishmaniasis transmission is dependent on a number of key variables related to vector biology , type of nets and human behaviour . Studies have shown protection is dependent on mesh size of the nets – nets designed to be cooler which have large holes are more likely to let sandflies though , even if they are insecticide treated [46] . ITNs are likely to be more effective where sandflies bite indoors at night and where people use ITNs consistently [47] , [48] . ITCs and ITS may be advantageous over ITNs because these interventions are in place all the time and since there is no need to set them up at night compliance is less of an issue [21] . In general , where transmission is occurring inside the home or where vectors rest indoors , we would expect ITNs , ITCs or ITS to have a beneficial effect , irrespective of whether the vectors are transmitting cutaneous or visceral leishmaniasis . It is important to have a sound grasp of sandfly biology and human behaviour in a particular setting in order to understand where transmission is occurring or where vectors rest before planning specific intervention strategies . There were no studies that met the selection criteria , which reported the efficacy of ITNs against lymphatic filariasis infection . In much of SSA and parts of the western Pacific , Anopheles mosquitoes transmit both lymphatic filariasis and malaria and so theoretically ITNs should have a beneficial effect on both diseases [49] . Observational studies have shown a beneficial effect of ITNs on lymphatic filariasis transmission where the disease is transmitted by Anopheles mosquitoes [50]–[53] and ITNs may be particularly useful in areas co-endemic for lymphatic filariasis and Loa Loa where mass drug administration of ivermectin is contraindicated due to serious adverse events [54] . However , to our knowledge no randomised controlled trials have been performed in these settings . Such a study would need to be of long duration to show a reduction in microfilaraemia given that adult worms have lifespans of between four and 10 years [55] , [56] . Alternatively , a study could use incidence of new infections in young children as an outcome [57] . The efficacy of ITNs , ITCs and ITS against Culex vectors of lymphatic filariasis , which are predominant in urban areas [58] , needs further assessment . Bøgh et al . reported a 16% reduction in indoor resting density of Cx . quinquefasciatus compared to a 98% reduction in Anopheles species [28] , presumably because Culex are less susceptible than Anopheles to pyrethroids [59]–[61] . Another explanation may be that transient reductions in vector density are masked because Culex populations are massive and the population can rapidly replace itself or immigrate . Poopathi et al . assessed the effect of insecticide-treated eave and door curtains and reported an 82% reduction in human biting density of Cx . quinquefasciatus [30] . It may be the door curtain component of this intervention which is of greatest importance given the findings of a study by Njie et al . who reported that culicines enter houses via the door rather than the eaves [62] . There is an increasing focus on intradomicile vector control for dengue [63] because Ae . aegypti rest , feed , mate and reproduce inside houses [64] . Targeting adult Ae . aegypti shifts the age structure of the vector population to younger mosquitoes , which is likely to have a large effect on human infections due to the relatively long extrinsic incubation period of the dengue virus in the mosquito [65] . However , since transmission of dengue occurs mostly during the daytime the use of bednets has rarely been considered as an intra-domiciliary control strategy . Studies identified in this review reporting an effect of ITCs and ITS on Ae . aegypti infestation levels [32] , [34]–[36] suggest that vectors are coming into contact with these interventions indoors . The likelihood of the vector coming into contact with the ITN , ITC or ITS will depend on a number of factors including the size of the home and construction . For example , Lenhart et al . state that the open construction of the homes in their study conducted in Thailand may explain why ITCs did not show any effect [33] . It is generally recognised that greater coverage of the intervention will result in mass killing , reduced vector survival and greater reductions in transmission; i . e . a community level effect . This was apparent in two of the dengue studies included in this review . In one study use of ITCs in intervention areas led to a community level effect whereby larval indices were reduced in neighbouring control areas [32] . A study by Vanlerberghe reported that a reduction in ITC coverage over time led to a reduced effect on entomological parameters [34] . A similar pattern of coverage dependent effects of ITCs on Ae . aegypti larval and pupal/demographic indices was reported in another study in Venezuela , which suggested that at least 50% coverage of ITCs was necessary to reduce Ae . aegypti infestation levels by 50% [66] . Entomological data from studies on the efficacy of ITCs and ITNs against the dengue vector Ae . aegypti were inconsistent across the different indices measured . Focks and others have questioned the reliability and sensitivity of traditional immature aegypti indices ( the house , container , and Breteau indices ) and there is growing consensus that these indices are of little value in predicting risk of human infection [67] . Ovitraps are also not recommended for assessing vector abundance because measures are often biased by competition from natural oviposition sites [63] . Instead pupal/demographic indices ( for example pupae per person ) are a better proxy for adult vector abundance or measurement of adult vector density itself [67] , [68] and are more appropriate for assessing transmission risk and directing control operations [69] , [70] . The ideal would be to have a measure similar to the entomological inoculation rate for malaria transmission ( incorporating both adult density and infection rate ) . However , adult Ae . aegypti are difficult to catch in appreciable numbers ( though this is likely to improve with development of new adult monitoring tools ) and only small proportion of adults are infected so it is difficult to detect infection [71] . The absence of studies of the two human trypanosome vectors and black flies is noteworthy . For black flies and tsetse flies , the predominantly outdoor exposure may be the main underlying reason . For triatomines , the absence of intensive bednet campaigns in Chagas disease endemic areas ( which are often non-malarious , especially for the main vector Triatoma infestans ) , and the general lack of attention to improved housing may be among the principal underlying factors for the lack of studies . Our review has several limitations that should be noted . We focused on a number of important neglected tropical diseases . This group of diseases is well-named because few studies were identified , despite conducting a comprehensive database search and contacting disease experts . We also relaxed the inclusion criteria somewhat in terms of study designs to include non-randomised studies with pre- and post-intervention data . We did not , however , do a full search of the grey literature which may mean that publication bias was introduced resulting in over-reporting of studies demonstrating that ITNs , ITCs and ITS were protective . We did not request further information from authors if reporting of methods or results was unclear in the published paper . Due to the few studies identified , summary estimates could only be generated using meta-analysis for cutaneous leishmaniasis . Studies were generally at low risk of bias but were of mixed quality . The main problems identified were with study design; e . g . short periods of follow up and incomplete reporting in the published papers; e . g . the method of sequence generation for randomisation was not reported . We took a cautious approach and did not calculate confidence intervals for entomological outcomes . This was due to i ) heterogeneity in study designs e . g . differences in follow up periods pre- and post-intervention and between studies , studies involving single houses and entomological parameters measured once versus studies with multiple clusters and measurements over an extended time period and ii ) incomplete reporting in the published papers e . g . confidence intervals and standard deviations omitted . Without knowing the uncertainty around percentage reductions it was not possible to make any conclusions regarding the entomological effect of interventions . Improved reporting of entomological data in studies and standardisation of study design and conduct should be a priority . Entomological data should always be assessed in combination with a clinical outcome where possible , and clinical outcomes with standardised diagnostic techniques and case definitions should remain the gold standard outcome for assessing the efficacy of vector control interventions . Less than half of the studies we considered reported the results of bioassays for efficacy of the insecticide used . In one of the studies conducted in Haiti there was some indication of resistance [31] . However , many of the studies were conducted prior to the early 2000s before the advent of pyrethroid resistance [72] , including those against cutaneous leishmaniasis that show a high PE . It is not possible , therefore , to say whether this level of efficacy would be observed today . Currently pyrethroids are the only class of insecticides suitable for use on LLINs and increasing coverage of pyrethroid treated materials to control multiple VBD is likely to increase selection pressure for development of resistance . Indeed , pyrethroid resistance has been detected in a number of non-malaria vectors including Cx . quinquefasciatus [73]–[75] , sand flies [76] , Ae . aegypti and Ae . albopictus [77] . Even if pyrethroid resistance increases it is likely that ITNs , ITC and ITS will still afford some level of protection against vectors due to a barrier effect . However , it would be sound to use insecticide treated materials as part of an IVM strategy including other vector control tools that do not rely on insecticide such as larval source management or make sure that different insecticide classes are used for IRS/fogging etc ( if appropriate ) . In the meantime , new types of insecticide treated materials , for example LLINs impregnated with insecticides with two different modes of action , are being developed which are showing promise against insecticide resistant malaria vectors [78] , [79] . In terms of collateral benefits there may also be beneficial effects of ITNs , curtains and screening on preventing household pests such as bedbugs , headlice , cockroaches and rodents which although not systematically assessed in this review are important benefits which increase acceptability and encourage compliance with interventions [80]–[82] . In conclusion , ITNs , ITCs and ITS have great potential to reduce VBDs . The biological insight that follows from this conclusion is that a substantial proportion of the vector population must be resting or feeding indoors . Evidence on efficacy of ITNs , ITC and ITS against multiple VBDs should be paired with maps of disease co-endemicity in order to prioritise and focus resources to areas of greatest disease burden . The use of interventions against multiple diseases has the potential to reduce costs and make better use of financial and human resources . This requires functional coordination between disease-specific programmes on planning , implementation and monitoring and evaluation with sharing of existing infrastructure and competencies . Beneficial effects on multiple VBDs will serve to increase the cost effectiveness of insecticide-treated materials and this may help to bolster the case for vector control funding . This review demonstrates some promising results , but highlights the urgent need for further well conducted studies . The efficacy of ITNs , ITCs and ITS against VBDs needs to be rigorously tested in randomised controlled trials with standardised clinical outcomes . | Malaria is a deadly disease caused by a parasite which is transmitted by anopheline mosquitoes . Bednets treated with insecticide are one of the key tools used to prevent malaria and they have been distributed on a large scale in many countries , particularly in Africa . It may be possible to control other diseases transmitted by insects using insecticide-treated bednets because many of these insects also enter houses . We did a review of studies looking at the effectiveness of insecticide-treated bednets , curtains and house screening against nine major diseases transmitted by insects . We assessed the effect these tools had on reducing numbers of the insects and disease in humans . Insecticide-treated bednets were found to be effective in preventing cutaneous leishmaniasis—a disease transmitted by sandflies—and insecticide-treated curtains and screening showed potential in preventing other insect borne diseases . Although further studies are required , it is clear that insecticide-treated bednets , curtains and screening have the potential to prevent transmission of insect-transmitted diseases . | [
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... | 2014 | Benefit of Insecticide-Treated Nets, Curtains and Screening on Vector Borne Diseases, Excluding Malaria: A Systematic Review and Meta-analysis |
Genetic factors play an important role in the etiology of breast cancer . We carried out a multi-stage genome-wide association ( GWA ) study in over 28 , 000 cases and controls recruited from 12 studies conducted in Asian and European American women to identify genetic susceptibility loci for breast cancer . After analyzing 684 , 457 SNPs in 2 , 073 cases and 2 , 084 controls in Chinese women , we evaluated 53 SNPs for fast-track replication in an independent set of 4 , 425 cases and 1 , 915 controls of Chinese origin . Four replicated SNPs were further investigated in an independent set of 6 , 173 cases and 6 , 340 controls from seven other studies conducted in Asian women . SNP rs4784227 was consistently associated with breast cancer risk across all studies with adjusted odds ratios ( 95% confidence intervals ) of 1 . 25 ( 1 . 20−1 . 31 ) per allele ( P = 3 . 2×10−25 ) in the pooled analysis of samples from all Asian samples . This SNP was also associated with breast cancer risk among European Americans ( per allele OR = 1 . 19 , 95% CI = 1 . 09−1 . 31 , P = 1 . 3×10−4 , 2 , 797 cases and 2 , 662 controls ) . SNP rs4784227 is located at 16q12 . 1 , a region identified previously for breast cancer risk among Europeans . The association of this SNP with breast cancer risk remained highly statistically significant in Asians after adjusting for previously-reported SNPs in this region . In vitro experiments using both luciferase reporter and electrophoretic mobility shift assays demonstrated functional significance of this SNP . These results provide strong evidence implicating rs4784227 as a functional causal variant for breast cancer in the locus 16q12 . 1 and demonstrate the utility of conducting genetic association studies in populations with different genetic architectures .
Breast cancer is the most common malignancy among women in the United States and many other parts of the world . Genetic factors play an important role in the etiology of breast cancer . Only a very small fraction of cases in the general population , however , can be explained by high-penetrance breast cancer susceptibility genes , such as BRCA1 and BRCA2 . Recent genome-wide association ( GWA ) studies [1]–[8] , including our own study among Chinese women in Shanghai [6] , have identified multiple common genetic susceptibility loci for breast cancer . Each of the common genetic factors identified thus far confer only a small to moderate risk for breast cancer . With the exception of our study , all other reported GWA studies have been conducted among women of European ancestry . GWA studies conducted in other populations could identify not only additional novel genetic variants for breast cancer but also help to fine map causal variants for regions reported from previous GWA studies . In early 2009 , we reported a novel genetic susceptibility locus at 6q25 . 1 for breast cancer risk in a fast-track replication of promising SNPs selected from a GWA scan of 1 , 505 cases and 1 , 522 controls recruited in the Shanghai Breast Cancer Study ( SBCS ) [6] . We have since increased the sample size for the initial GWA scan to 2 , 073 cases and 2 , 084 controls to increase the statistical power to identify novel genetic risk variants for breast cancer . We have recently completed the second fast-track replication using data and biological samples collected from 13 , 395 cases and 10 , 917 controls recruited in 12 studies of Asian and European ancestry . SNP rs4784227 , located at 16q12 . 1 , a region identified from a previous GWA study conducted in Europeans [1] , [3] , was found to be a risk variant for breast cancer in Asian women independent of SNPs reported from the previous study [1] , [3] . In vitro experimental results provide strong support for the functional significance of this SNP and suggest that this SNP may explain the association observed for breast cancer in this locus . Herein , we report findings from this large genetic study of breast cancer .
Approval was granted from relevant review boards in all study sites; all included subjects gave informed consent . Included in this consortium project were 15 , 468 cases and 13 , 001 controls from 12 studies ( Table 1 ) . Detailed descriptions of these participating studies and demographic characteristics of study participants are provided in the supplement Text S1 and Table S1 . Briefly , the consortium included 19 , 796 Chinese women from seven studies conducted in Shanghai [6] , [9] ( three studies , n = 10 , 497 ) , Tianjin [10] ( n = 3 , 115 ) , Nanjing [11] , [12] ( n = 2 , 885 ) , Taiwan [13] ( n = 2 , 131 ) , and Hong Kong [14] ( n = 1 , 168 ) ; 3 , 214 Japanese women from three studies conducted in Hawaii [15] ( n = 1 , 120 ) , Nagoya [16] ( n = 1 , 288 ) , and Nagano ( n = 806 ) [17]; and 5 , 459 European Americans from the Nashville Breast Health Study ( NBHS , n = 3 , 172 ) and the Nurses' Health Study ( NHS , n = 2 , 287 , included as part of the Cancer Genetic Markers of Susceptibility Project - CGEMS ) . All cases and controls recruited in the Shanghai studies were included in Stages I and II , and subjects from the remaining Asian studies were included in Stage III . Data from CGEMS were used for help to select SNPs for Stage II . Cases and controls recruited in NBHS and the NHS ( CGEMS ) were included in the final stage to evaluate the generalizability of the findings . Genotyping for Stage I has been described previously [6] . Briefly , the initial 300 subjects were genotyped using the Affymetrix GeneChip Mapping 500 K Array Set and the remaining 3 , 918 subjects were genotyped using the Affymetrix Genome-Wide Human SNP Array 6 . 0 . We included one negative control and three positive quality control ( QC ) samples from the Coriell Cell Repositories ( http://ccr . coriell . org/ ) in each of the 96-well plates for Affymetrix SNP Array 6 . 0 genotyping . A total of 127 positive QC samples were successfully genotyped and the average concordance rate was 99 . 9% with a median value of 100% . The sex of all study samples was confirmed to be female . The identity-by-descent analysis based on identity-by-state was conducted to detect first-degree cryptic relationships using PLINK , version 1 . 06 . All samples with a call rate <95% were excluded . The SNPs were excluded if: ( i ) minor allele frequency ( MAF ) <1% , ( ii ) call rate <95% , or ( iii ) genotyping concordance rate <95% in quality control samples . The final dataset included 2 , 073 cases and 2 , 084 controls for 684 , 457 markers . Genotyping for Stage II was completed using the iPLEX Sequenom MassArray platform . Included in each 96-well plate as QC samples were two negative controls ( water ) , two blinded duplicates , and two samples from the HapMap project . To compare the consistency between the Affymetrix and Sequenom platforms , we also included 124 samples from Stage I that were genotyped by Affymetrix SNP 6 . 0 . The mean concordance rate was 99 . 7% for the blind duplicates , 98 . 8% for HapMap samples , and 98 . 6% between Sequenom and Affymetrix 6 . 0 genotyping . Genotyping for Stage III and NBHS was performed using TaqMan at five different centers . The genotyping assay protocol was developed and validated at the Vanderbilt Molecular Epidemiology Laboratory , and TaqMan genotyping assay reagents were provided to investigators from the Tianjin study ( Tianjin Cancer Institute and Hospital ) , Nanjing study ( Nanjing Medical University ) , Multiethnic Cohort Study ( MEC , University of Southern California ) , and Nagano Breast Cancer study ( Japan National Cancer Center ) , who conducted the genotyping assays at their own laboratories . Samples from the four other studies ( Hong Kong , Taiwan , Nagoya , and Nashville ) were genotyped at the Vanderbilt Molecular Epidemiology Laboratory . During the genotyping , two negative controls were included in each 96-well plate , along with 30 unrelated European and 45 Chinese samples from the HapMap project genotyped together with each study for QC purposes . The consistency rate was 100 . 0% for the HapMap samples comparing genotyping data obtained from the current study with data obtained in the HapMap project . Each of the non-Vanderbilt laboratories was asked to genotype a trial plate containing DNA from 70 Chinese samples before genotyping study samples . The consistency rate across all centers for these trial samples was 100% compared with genotypes previously determined at Vanderbilt . In addition , replicate samples comparing 3–7% of all study samples were dispersed among the genotyping plates at all centers . A 3 . 0 kb DNA fragment containing major allele ( C ) of rs4784227 was PCR amplified by using forward primer 5′-GATCAGCTAGCCATAGTGTGGTAGCTAGTTG-3′ and backward primer 5′-GATCA CTCGAGCTGCTGGGCTTAGCTACAAG-3′ . This fragment was subcloned into luciferase reporter vector , pGL3 basic , pGL3 promoter , and pGL3 enhancer ( Promega , WI ) between Nhe1 and Xho1 restriction sites , respectively . The minor allele ( T ) sequence was generated by using QuickChange Site-Directed Mutagenesis Kit ( Strategene , La Jolla , CA ) with the following pair of oligonucleotides , 5′- GAGTATTTACATCACAATAATCAGCAAACACTACAAATTGGGAC-3′ and 5′- GTCCCAATTTGTAGTGTTTGCTGATTATTGTGATGTAAATACTC-3′ . All DNA constructs were verified by sequencing analyses . Transfection was performed with the use of FuGene 6 Transfection Reagent ( Roche Diagnostics , Indianapolis , IN ) in triplicate for each of the constructs . Briefly , 1−2×105 cells of HEK 293 , MCF-7 , MCF10A , and MDA-231 cells were seeded in 24-well plates and co-transfected with pGL4 . 73 , a Renilla expressing vector which served as a reference for transfection efficiency . Thirty-six to 48 hours later the cells were lysed with Passive Lysis Buffer , and luminescence ( relative light units ) was measured using the Dual-Luciferase Assay System ( Promega , WI ) . The rs4784227 regulatory activity was measured as a ratio of firefly luciferase activity to renilla luciferase activity , and the mean from four independent experiments are presented . Biotin-labeled , double stranded oligonucleotide probes 5′-ATTTGTAGTGTTTGCCGATTATTGTGATGT-3′ and 5′-ACATCACAATAATCGGCAAACACTACAAAT-3′ , and 5′-ATTTGTAGTGTTTGCTGATTATTGTGATGT-3′and 5′- ACATCACAATAATCAGCAAACACTACAAAT-3′ containing the major and minor allele sequence of rs4784227 were synthesized . The probes were incubated with nuclear protein extracts from MCF10A , MCF7 , and MDA-MB-231 cells , in the presence or absence of competitors , i . e . unlabelled probes . Protein-DNA complexes were resolved by polyacrylamide gel electrophoresis and detected using a LightShift Chemiluminescent EMSA kit ( Pierce Biotechnology , Rockford , IL ) . In Stage I , PLINK version 1 . 06 was used to analyze genome-wide data . Population structure was investigated by using the principal component analysis implemented in EIGENSTRAT [18] ( http://genepath . med . harvard . edu/~reich/Software . htm ) . A set of 12 , 533 SNPs with MAF≥5% in Chinese and a distance ≥25 kb between two adjacent SNPs was selected to evaluate the population structure . The first two principal components were included in logistic regression models for adjustment of population structures . Odds ratios ( OR ) and 95% confidence intervals ( CIs ) were estimated by logistic regression analysis . ORs were also estimated for the variant allele based on a log-additive model . Age was adjusted for in the analyses of Stages I and II data . In Stage III , individual data were obtained from each study for a pooled analysis . ORs from multiple studies were adjusted for age and study site . Heterogeneity across studies and between ethnicities was assessed with likelihood ratio tests . Stratified analyses by ethnicity , menopausal status , and estrogen receptor ( ER ) status were carried out . P-values based on 2-tailed tests are presented . Individual genotyping data from the Cancer Genetic Markers of Susceptibility ( CGEMS , http://cgems . cancer . gov/data/ ) study were obtained through an approved data request application in order to perform meta-analyses of GWA scan data from both the Shanghai studies and the CGEMS project . Program MACH ( http://www . sph . umich . edu/csg/abecasis/MACH/ ) was used for genotype imputation that determines the probability distribution of missing genotypes conditional on a set of known haplotypes while simultaneously estimating the fine-scale recombination map . For the Shanghai studies , imputation was based on 660 , 118 autosomal SNPs genotyped in Stage I that had a MAF>1% and passed the QC procedure , using the phased Asian data from HapMap Phase II ( release 22 ) as the reference . A total of 2 , 272 , 352 SNPs showed an imputation quality score ≥0 . 90 . The CGEMS GWA scan data were genotyped using Illumina HumanHap550 for 1 , 142 breast cancer cases and 1 , 145 controls nested within the Nurses' Health Study cohort . For CGEMS , genotypes were imputed based on 513 , 602 autosomal SNPs with MAF>1% , using phased CEU data from HapMap Phase II ( release 22 ) as the reference . A total of 2 , 168 , 847 SNPs showed an imputation quality score ≥0 . 90 . To evaluate associations between imputed SNP data and breast cancer risk , logistic regression ( additive model ) was used , in which SNPs were represented by the expected allele count , an approach that takes into account the degree of uncertainty in the genotype imputation . Meta-analyses of GWA scan data for SBCS and CGEMS were conducted for 1 , 968 , 549 SNPs with a MAF ≥1% in both populations and imputation quality scores ≥0 . 90 . Meta-analyses were performed using a weighted z-statistics method , where weights were proportional to the square root of the number of individuals in each sample and standardized such that the weights added up to one . The z-statistic summarizes the magnitude and direction of the effect relative to the reference allele . An overall z-statistic and p value were then calculated from the weighted average of the individual statistics . Calculations were implemented in the METAL package ( http://www . sph . umich . edu/csg/abecasis/Metal ) .
Of the 53 successfully genotyped SNPs in Stage II ( Table S2 ) , highly significant associations with breast cancer risk were found for rs4784227 ( 16q12 . 1 ) with OR ( 95% CI ) of 1 . 23 ( 1 . 16–1 . 31 ) per T allele ( P for trend , 1 . 3×10−8 ) ( Table 2 ) . Three other SNPs also showed a significant or marginally significant association with breast cancer risk . These four SNPs were selected for further validation in Stage III , which included 6 , 173 cases and 6 , 340 controls of Asian ancestry from seven studies in the Asia Breast Cancer Consortium ( Methods; Table S1 ) . SNP rs4784227 was consistently associated with breast cancer risk in all studies ( Figure 1 ) , with an OR of 1 . 25 ( 95% CI: 1 . 20–1 . 31 , P = 3 . 2×10−25 ) in the pooled analysis of Asian samples from all three stages . No heterogeneity in the association of this SNP with breast cancer was observed across the studies included in the consortium . The association of rs4784227 with breast cancer risk was observed in both pre-menopausal ( OR: 1 . 24 ( 1 . 17–1 . 32 ) and P = 6 . 5×10−12 ) , and post-menopausal women ( OR: 1 . 27 ( 1 . 19–1 . 35 ) and P = 3 . 0×10−14 ) ( data not shown in tables ) . The positive association was stronger in ER ( + ) breast cancer ( per allele OR = 1 . 29 , 95% CI = 1 . 23–1 . 36 , P = 3 . 0×10−23 ) than in ER ( − ) breast cancer ( per allele OR = 1 . 19 , 95% CI = 1 . 12–1 . 26 , P = 1 . 3×10−8 ) . In case-only analyses , when compared with cases with ER ( − ) cancer , ORs associated with ER ( + ) breast cancer were found to be 1 . 09 ( 95% CI: 1 . 03–1 . 16; P for trend , 5 . 8×10−3 ) . None of the other three SNPs that showed a significant association in Stage II , however , were replicated in Stage III ( Table S3 ) . SNP rs4784227 is located in 16q12 . 1 , a region where three genetic risk variants for breast cancer ( rs8051542 , rs12443621 , and rs3803662 ) were reported previously in a study conducted among women of European ancestry [1] . Of these three previously reported SNPs , the closest ( rs3803662 ) is approximately 12 . 8 Kb away from rs4784227 . The linkage disequilibrium ( LD ) pattern of this region in Asians is very different from the pattern found in European descendents ( Figure 2 and Table 3 ) . In Stage I and II samples , SNP rs4784227 is in low LD with previously-reported SNPs , with r2 being 0 . 07 , 0 . 14 , and 0 . 37 for rs12443621 , rs3803662 , and rs8051542 , respectively ( Table 3 ) . In European Americans included in the HapMap project , however , SNP rs4784227 is in strong LD with SNP rs3803662 ( r2 = 0 . 86 ) but weakly correlates with the other two SNPs . SNPs rs8051542 and rs3803662 each showed a significant association with breast cancer risk ( P = 2 . 0×10−3 and P = 1 . 7×10−4 ) in a combined analysis of data from Stages I and II ( Table 4 ) . However , after adjusting for rs4784227 the association with rs8051542 disappeared . Although the positive association with rs3803662 remained , it was of only borderline significance ( P = 0 . 12 ) . SNP rs12443621 , however , showed no association with breast cancer risk , which is consistent with results reported by the initial study of Asian women [1] . The association of rs4784227 with breast cancer risk remained highly significant after adjusting for these three previously-reported SNPs , individually or in combination ( Table 4 ) . Haplotype analyses of these four SNPs showed that all haplotypes containing the T allele of rs4784227 were associated with an increased risk of breast cancer , although not all point estimates were statistically significant at P<0 . 05 due to a small sample size for several haplotypes ( Table S4 ) . In studies conducted in European Americans , SNP rs4784227 also showed a significant association with a per allele OR ( 95% CI ) of 1 . 17 ( 1 . 03–1 . 34 ) in CGEMS and 1 . 21 ( 1 . 07–1 . 37 ) in NBHS ( Table 5 ) . A significant ( NBHS ) or marginally significant ( CGEMS ) association was observed for rs3803662 , a previously reported SNP , but not for two other previously-reported SNPs . After adjusting for rs4784227 , no association with rs3803662 was seen . On the other hand , the positive association with rs4784227 remained after adjusting for rs3803662 or the other two SNPs , although the association was no longer statistically significant at P<0 . 05 . To evaluate whether SNP rs4784227 has any intrinsic regulatory function , we conducted an in vitro luciferase assay in four cell lines including metastatic breast cancer cell MDA231 , non-metastatic breast cancer cell MCF-7 , breast epithelial cell MCF10A , and HEK293 . Luciferase reporter constructs containing a 3 kb DNA fragment with the reference allele C and the risk allele T of rs4784227 , respectively , were generated and transiently transfected into these cells . By comparing to the respective empty vectors , no luciferase activity change was observed in pGL3 basic and pGL3 enhancer vectors that harbor rs4784227 fragments , which indicate that rs4784227 fragments do not have intrinsic promoter activity ( data not shown ) . In contrast , in the pGL3 promoter vector , fragments containing rs4784227 reduced luciferase activity , and the reduction was more apparent in fragments containing risk allele T than the reference allele C ( Figure 3A ) . With the exception of the MCF7 cells , the difference between the T and C allele was statistically significant at P≤0 . 05 . To investigate whether the DNA sequence containing rs4784227 may interact with nuclear proteins and if so , whether a single nucleotide change in the rs4784227 site may alter the protein-DNA interactions , we performed electrophoretic mobility shift assays . In these assays , oligonucleotide probes corresponding to the reference allele C or the risk allele T were incubated with nuclear protein extracts from MCF10A , MCF-7 , and MDA-231 cells . Compared with reference allele C , risk allele T in rs4784227 resulted in an altered DNA-protein complex intensity in these cells ( B and II ) ( Figure 3B ) . In contrast , risk allele T did not alter the intensity of the nonspecific DNA-protein complex band ( I ) . These results were unaffected by the presence of large amounts of unlabeled competitors .
In this multi-stage GWA study of over 15 , 486 cases and 13 , 001 controls , we identified SNP rs4784227 as highly significantly associated with breast cancer in both Asians ( per allele OR = 1 . 25 , 95% CI = 1 . 20–1 . 31 , P = 3 . 2×10−25 ) and European Americans ( per allele OR = 1 . 19 , 95% CI = 1 . 09–1 . 31 , P = 1 . 3×10−4 ) . SNP rs4784227 is located at 16q12 . 1 , a region reported previously to harbor breast cancer genetic risk variants among European descendents [1] , [3] . In Asians , however , this SNP is either not in LD or only in weak LD with any of the three previously-reported SNPs in these regions , and adjusting for these SNPs did not alter the association of breast cancer with this newly-identified SNP . Although in European Americans rs4784227 is in strong LD with one of the previously-reported SNPs , rs3803602 , the positive association of rs4784227 with breast cancer remained after adjusting for previously-reported SNPs . In vitro experiments showed that risk allele T reduced luciferase activity and altered DNA-protein binding patterns . These results implicate rs4784227 as a functional genetic risk variant for breast cancer , and this SNP may explain , at least partially , the association of breast cancer with other SNPs identified in 16q12 . 1 . SNP rs4784227 is located 18 . 4 kb upstream of the TOX3 gene and in the evolutionarily-conserved region of intron of the LOC643714 gene . Several transcription factors are predicted to bind to this SNP ( http://www . cbrc . jp/research/db/TFSEARCH . html ) . This SNP , however , has not been shown to be in the coding region for any non-coding RNA or miRNA/snoRNA/scaRNA based on UCSC Genome Browser . Our luciferase reporter assays showed that the intronic region harboring rs4784227 may have intrinsic repressor activities , suggesting that rs4784227 may affect its underlying gene LOC643714 or its neighborhood gene expression and thus affect breast cancer risk . The rs4784227-associated repressing activity could be the result of differential binding affinity of transcription machinery to the rs4784227-containing DNA sequences . We examined this hypothesis by conducting electrophoretic mobility shift assays and confirmed that the risk T allele of rs4784227 significantly alter DNA-nuclear protein ( s ) interactions . Thus , it is possible that inhibitory nuclear protein ( s ) selectively bind to the risk allele T to repress transcription . A database search ( http://www . cbrc . jp/research/db/TFSEARCH . html ) for transcription factor binding sites showed that the sequence at the rs4784227 site has a high degree of similarity with several consensus elements recognized by transcription factors , of which HNF-3b and C/EBP prefer to bind DNA fragments with the risk T allele of this SNP site . However , these putative transcription factors or their associated proteins have not been confirmed to be involved in the regulation of LOC643714 or its nearby genes . In summary , through a GWA study we have identified and confirmed rs4784227 as a genetic risk variant for breast cancer . In vitro experiments showed a functional significance of this SNP that may explain the association of breast cancer with other SNPs identified at locus 16q12 . 1 . This study demonstrates the importance of conducting genetic association studies in populations with different LD structures to identify causal genetic variants for breast cancer and other complex diseases . | Breast cancer is one of the most common malignancies among women worldwide . Genetic factors play an important role in the etiology of breast cancer . To identify genetic susceptibility loci for breast cancer , we performed a genome-wide association study in 15 , 468 breast cancer cases and 13 , 001 controls . A single nucleotide polymorphism ( SNP ) rs4784227 located on chromosome 16q12 . 1 , a previously-reported region for breast cancer risk , was found to be associated with breast cancer risk . The association of this SNP with breast cancer risk remained highly significant in Asians after adjusting all previously-reported SNPs in this region . In vitro biochemical experiments using both luciferase reporter and electrophoretic mobility shift assays confirmed the functional importance of this SNP . Our results demonstrate the importance of conducting genetic association studies in populations with different genetic backgrounds to identify functional variants . | [
"Abstract",
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] | 2010 | Identification of a Functional Genetic Variant at 16q12.1 for Breast Cancer Risk: Results from the Asia Breast Cancer Consortium |
Pathogens rely on a complex virulence gene repertoire to successfully attack their hosts . We were therefore surprised to find that a single fimbrial gene reconstitution can return the virulence-attenuated commensal strain Escherichia coli 83972 to virulence , defined by a disease phenotype in human hosts . E . coli 83972pap stably reprogrammed host gene expression , by activating an acute pyelonephritis-associated , IRF7-dependent gene network . The PapG protein was internalized by human kidney cells and served as a transcriptional agonist of IRF-7 , IFN-β and MYC , suggesting direct involvement of the fimbrial adhesin in this process . IRF-7 was further identified as a potent upstream regulator ( -log ( p-value ) = 61 ) , consistent with the effects in inoculated patients . In contrast , E . coli 83972fim transiently attenuated overall gene expression in human hosts , enhancing the effects of E . coli 83972 . The inhibition of RNA processing and ribosomal assembly indicated a homeostatic rather than a pathogenic end-point . In parallel , the expression of specific ion channels and neuropeptide gene networks was transiently enhanced , in a FimH-dependent manner . The studies were performed to establish protective asymptomatic bacteriuria in human hosts and the reconstituted E . coli 83972 variants were developed to improve bacterial fitness for the human urinary tract . Unexpectedly , P fimbriae were able to drive a disease response , suggesting that like oncogene addiction in cancer , pathogens may be addicted to single super-virulence factors .
Mucosal surfaces provide ideal living conditions for the normal flora but paradoxically , they also serve as attack sites for numerous bacterial pathogens that cause extensive morbidity and mortality . Understanding this dichotomy is critical for efforts to selectively target and remove pathogens without disturbing the commensal flora or its protective effects . The complex nature of disease predicts that virulence is multifaceted and that pathogens need multiple virulence factors to initiate tissue attack , disrupt immune homeostasis and create symptoms and pathology [1–8] . It is also well established that commensals fail to cause disease , due to a lack of critical virulence genes [9 , 10] . About 50% of asymptomatic bacteriuria ( ABU ) isolates have a smaller genome size than acute pyelonephritis strains due , in part , to inactivating virulence gene deletions or point mutations [11–13] . These strains continue to accumulate loss of function mutations in vivo , supporting the notion of a virtually irreversible reductive evolution process , where spontaneous recovery of a virulent phenotype is not likely to occur . Surprisingly , these loss-of-function mutations also affect fimbrial subunits and adhesin genes [13] , which are thought to be essential for bacterial persistence at mucosal sites [14–21] . Adhesive ligands arm bacteria with molecular tools to identify preferred tissue sites and attachment plays a decisive role in colonization and long-term adaptation . Certain fimbrial adhesins are ubiquitously expressed by commensals and pathogens alike , suggesting a homeostatic role . Others , in contrast , show a strong disease association in epidemiologic studies [14] , suggesting more direct effects on pathobiology [22] . The urinary tract supports ABU; a commensal-like state [23] , which has been shown to prevent super-infection with more virulent strains [24–27] . To reproduce this protective effect , we have established a protocol to create ABU , by inoculating patients with the ABU strain E . coli 83972 [28 , 29] . The therapeutic efficacy and safety of this procedure has been documented in placebo-controlled studies in patients with incomplete bladder voiding [30] . Genome sequencing of E . coli 83972 has revealed a general “loss of virulence” phenotype , which includes fimbrial genes [13 , 31–33] . E . coli 83972 lacks functional P or type 1 fimbriae , due to attenuating point mutations in the papG adhesin gene and a large , inactivating deletion in the fim gene cluster [13] . Both fimbrial types have been proposed to enhance bacterial persistence in the urinary tract .
The aim of this study was to increase the efficiency of E . coli 83972 inoculation and extend its use to include UTI-prone patients with complete bladder voiding . To achieve this goal we equipped E . coli 83972 with functional adhesins , previously shown to enhance bacterial persistence in the murine or human urinary tract [34 , 35] . This approach also made it possible to address how fimbriae affect clinical outcome in inoculated human hosts . The chromosomal pap and fim operons were reconstituted using lambda Red-mediated recombination ( Fig 1A and 1B ) . Briefly , papGX was deleted ( ABU 83972ΔpapGX ) and replaced by functional papGX genes from uropathogenic E . coli ( UPEC ) strain E . coli CFT073 , via homologous recombination , using pKD3 ( E . coli 83972pap , Fig 1A ) The fim operon was reconstituted by replacing an internal 4 , 253-bp fim deletion , comprising the fimEAIC genes and truncated fimB and fimD genes , with the entire fim operon from pPKL4 [36] ( E . coli 83972fim , Fig 1B ) . E . coli 83972pap expressed functional P fimbriae as shown by P blood group specific agglutination of human erythrocytes ( S1A Fig ) and attachment to human kidney cells ( S1B Fig ) . E . coli 83972fim expressed functional type 1 fimbriae , as shown by α-D-methyl-mannose reversible agglutination of human and guinea pig erythrocytes and adherence to human kidney cells ( S1A–S1C Fig ) . The in vitro growth rates of E . coli 83972pap and E . coli 83972fim were unchanged , compared to E . coli 83972 ( S1D Fig ) . In this longitudinal study , five patients ( P I to P V ) were sequentially inoculated; first with the ABU strain E . coli 83972 and then with the fimbriated variants of this strain ( Fig 1C ) . Each patient contributed a pre-inoculation sample as well as samples from five time points following each inoculation , resulting in 18 samples for patients undergoing three- and 12 samples for patients undergoing two inoculations . As a result of the study design , the response to inoculation was defined relative to the pre-inoculation sample in each patient and inoculation , and changes over time were evaluated intra-individually . Significant changes were evaluated intra-individually as well as between patient groups . E . coli 83972 and the fimbriated strains established significant bacteriuria within 48 hours of inoculation and persisted for a period of at least 4 weeks or until the patients were treated to remove the strain ( S2 Fig ) . Patients , who carried E . coli 83972 or E . coli 83972fim remained asymptomatic and P III and P IV carried E . coli 83972pap asymptomatically during the entire study period . Two patients , who carried E . coli 83972pap , developed symptoms , however ( Fig 1C ) . In P V , symptoms were recorded 17 days after inoculation ( fever , general malaise and loin pain , S2A Fig ) . The patient recovered fully after antibiotic treatment , with a drop in C-reactive protein levels from 245 μg/ml to 3 . 4 μg/ml after 7 days , normal kidney function on follow up and no evidence of focal tissue damage by intravenous excretory contrast tomography . P II reported a transient febrile reaction and local symptoms from the urinary tract on day 9 after E . coli 83972pap inoculation . Inoculations with E . coli 83972pap were therefore discontinued and the study outcome is evaluated here for P I–P V . Bacterial fimA expression increased immediately after inoculation with E . coli 83972fim , followed by a rapid decline . PapA expression increased gradually , from 3 hours post inoculation with E . coli 83972pap until the time of symptoms in P V ( Fig 1D , S3 Fig ) . This difference in activation kinetics may reflect the location of the fim and pap gene clusters in the bacterial chromosome , as the fim gene cluster is part of the core chromosome but pap resides in a chromosomal island that also includes other virulence genes , such as hlyA [13] . The urine IL-6 and IL-8 responses paralleled fimbrial expression , with an earlier peak for E . coli 83972fim and a later peak for E . coli 83972pap ( Fig 1E ) . Genome-wide microarray analysis was used to address how the patients respond to bacterial inoculation . RNA was harvested from peripheral blood leucocytes immediately before inoculation and at defined times post-inoculation and significantly regulated genes were identified relative to the pre-inoculation sample in each patient ( cutoff FC ≥ 2 . 0 ) . Fimbriae-related effects on transcription were further defined by intra-individual analysis , comparing the response to the E . coli 83972 and E . coli 83972pap or E . coli 83972fim inoculations in each individual . E . coli 83972pap activated a rapid and sustained change in gene expression , which was detected after 3 hours ( S4 Fig ) and reached a maximum in P V during the symptomatic episode ( 1 , 020 regulated genes , S5 and S6A Figs ) . A peak response in P II was also observed in connection with the symptomatic episode ( 2 weeks , S6B Fig ) but not in P III and P IV , who remained asymptomatic ( S6C and S6D Fig ) . Intra-individual comparisons of E . coli 83972pap and E . coli 83972 inoculations detected little overlap , suggesting that E . coli 83972pap activates a novel , strain background independent repertoire of host genes ( P V , S4 and S5B Figs ) . A similar discrepancy was observed between E . coli 83972pap in P V and E . coli 83972fim in P I ( S4B Fig ) . By Gene set enrichment analysis ( GSEA ) and canonical pathway analysis ( Fig 2A , S7 Fig ) , type I interferon ( IFN ) signaling was identified as the top-scoring canonical pathway in P V at the onset of symptoms ( Fig 2A and 2B ) . Genes in this pathway were activated in at least one sample from each patient inoculated with E . coli 83972pap ( S8A Fig ) . Significantly regulated genes included STAT1 , IFIT1 , IFIT3 , MX1 and PSMB8 . The IFN pathway genes were not regulated in response to E . coli 83972 or E . coli 83972fim , except in P IV between 3 and 24 hours ( Fig 2C , S8B Fig ) . Furthermore , the pattern recognition receptor ( PRR ) pathway was activated in all patients inoculated with E . coli 83972pap , including TLRs 2 , 4 , 5 , 7 and 8 ( Fig 2D , S8C and S9 Figs ) [7] . In P V , regulated genes also included IRF7 , OAS3 , 1 , 2 , complement components , IFIH1 ( MDA-5 ) , DDX58 ( RIG-1 ) , IL1B , NLRC4 ( IPAF ) , TNF , RIPK2 ( RIP2 ) , IL6 and NOD2 . The wild type strain and E . coli 83972fim , in contrast , suppressed the PRR signaling pathway ( Fig 2E , S8D Fig ) . The results suggest that P fimbriae “high-jack” the transcriptional machinery of the host , creating a fimbriae-specific gene expression profile . To address to what extent pap reconstitution in an ABU strain creates a disease-like response [7 , 8 , 37–39] , we compared the repertoire of regulated genes in P V at the time of symptoms to the in vitro response of human kidney cells to the genetically closely related UPEC strain CFT073 ( both phylogroup B2 and same sequence type ) [13 , 40] . A total of 115 genes were commonly regulated , including IRF7 and genes involved in interferon- and pattern recognition signaling ( S10 Fig ) , suggesting that E . coli 83972pap activates similar facets of the innate immune response as a virulent strain . IRF-7 controls inflammation and renal tissue damage in the murine acute pyelonephritis model , through a network of pathology-associated genes [8] . A potent IRF7 response was detected in P V at the time of symptoms and a more restricted response in P II , five days after the transient symptoms ( Fig 3A and 3B ) . Furthermore , the IRF7 response was exclusive for the time of symptoms and was suppressed or not activated in the patients , who did not develop symptoms ( Fig 3C–3E ) . Downstream of IRF-7 , type I interferon genes and RIG-I pathway genes , cytokines and transcription factors were specifically activated during the symptomatic episode as well as cell surface receptors involved in innate immunity ( Fig 3E and S9 Fig ) . Consistent with these effects , E . coli 83972pap infection also stimulated a strong IRF-7 response in human kidney cells . Cytoplasmic and nuclear IRF-7 protein levels were increased in E . coli 83972pap-infected cells ( Fig 4A and 4B ) and internalization of the PapG adhesin [41] was detected , suggesting a P fimbriae-specific effect ( Fig 4C ) . This was confirmed by exposing the cells to purified PapG II adhesin protein or the PapDG II protein complex ( 5 and 25 μg/ml , Fig 4C , S11A and S11B Fig ) . IRF-7 expression was activated ( Fig 4D ) as shown by an increase in IRF-7 protein- and IRF7 , IFNB1 , MYC and IFIT3 mRNA levels ( Fig 4E ) , suggesting that P fimbriae may act as IRF7 agonists , in a PapG adhesin dependent manner . Effects of PapG on the assembly of the IRF7 promoter complex were examined in an electrophoretic mobility shift assay ( EMSA ) , using IRF7 promoter DNA as a probe ( 1563bp , -1308 to +255 , Fig 4F and 4G ) . Band shifts were detected when the probe was mixed with protein extracts from uninfected cells ( band 1 , Fig 4G , S11C Fig ) or E . coli 83972pap infected cells ( band 2 , Fig 4G ) . Anti-PapG antibodies created a further super-shift ( bands 3 and 4 ) and bands 1 and 2 were strongly attenuated by anti-IRF3- , anti-IFNβ and/or anti-MYC antibodies , consistent with the presence of these proteins in the promoter complex ( Fig 4G and 4H ) . IFN-β and MYC are known to regulate IRF7 expression by binding to the IRF7 promoter and IRF-3 forms heterodimers with IRF-7 , after activation by phosphorylation [42 , 43] . The results suggest that the PapG adhesin affects the assembly of IRF7 promoter complexes in infected cells , together with IRF-3 , IFN-β and/or MYC . Direct binding of purified PapG or PapDG to promoter DNA was not detected , however , suggesting that the other promoter constituents may be required for PapG to bind to the IRF7 promoter complex . Type 1 fimbriae are ubiquitously expressed among gram-negative bacteria , suggesting a homeostatic role . This study provided a unique opportunity to identify such effects , in inoculated human hosts . We found no evidence that type 1 fimbriae create symptoms or pathology , when expressed in the background of E . coli 83972 . Instead a rapid and profound inhibitory effect was identified in patients inoculated with E . coli 83972fim , by GSEA and analyzed using the gene ontology database ( Fig 5A ) . Genes involved in RNA processing and post-transcriptional regulation were inhibited , suggesting effects on the post-transcriptional environment in infected host cells , including predicted inhibition of 5’ RNA capping and 3’ poly ( A ) tail elongation and translation as well as intron removal , exon splicing and ribosome biogenesis ( Fig 5B , S2 Table ) . Pro-inflammatory gene sets were not significantly regulated and by Ingenuity pathway analysis ( IPA , S12 Fig ) , a limited number of weakly regulated pathways were identified , including Natural Killer ( NK ) cell signaling , which was inhibited ( S12B–S12D Fig ) and Integrin signaling , which was moderately activated ( S12E–S12G Fig ) . In addition , a number of gene sets were moderately activated by E . coli 83972fim in inoculated hosts ( P I–P IV , 3 hours , Fig 5C ) , including ion channels and genes involved neuropeptide signaling , sensory perception of pain and other stimuli . Gene network analysis further revealed strong similarities between E . coli 83972 and E . coli 83972fim ( S13 and S14 Figs ) . By aligning these responses , we detected a subset of overlapping genes that was inhibited by both strains , with effects on RNA processing and ribosome biogenesis ( S13A–S13C Fig ) . Kinetic analysis suggested that the effects of E . coli 83972 were accelerated by E . coli 83972fim , suggesting that type 1 fimbriae act , in part , by enhancing the inhibitory effects of E . coli 83972 ( S13B–S13D Fig ) . In contrast , the transcriptional response to E . coli 83972fim and E . coli 83972pap showed no similarity . A few genes were inversely regulated ( n = 33 ) ; activated by E . coli 83972pap but inhibited by E . coli 83972fim ( P I , S14 Fig ) . In depth analysis of the regulated gene sets revealed that potassium channels , ion anti-porters , voltage gated cation channels and substrate specific ion channels were up-regulated by E . coli 83972fim ( Fig 6A ) . By kinetic analysis of consecutive samples from P I–P IV , we detected an increase in Ca2+ and K+ channel expression after 3 hours , and K+ channel expression was sustained , with a maximum after 48 hours , especially in P IV ( Fig 6B ) . In addition , a moderate increase in Na+ and Cl- channel expression was recorded . These gene sets were not unique for E . coli 83972fim but were regulated also by E . coli 83972 after 24 hours , further suggesting that type 1 fimbriae enhance the effects of E . coli 83972 ( S15 Fig ) . The activation of ion channel expression was confirmed in vitro in human bladder epithelial cells , representing the site of infection in human hosts . E . coli 83972fim and E . coli 83972 infection generated an increase in K+ channel protein levels , compared to uninfected control cells ( TWIK , TRAAK and KCNJ11 ) , with E . coli 83972fim showing the most pronounced effects ( Fig 6C ) . In addition , we detected an increase in cation channel protein levels ( TRPC1 , TRPV6 ) , exclusively in E . coli 83972fim infected cells ( Fig 6D and 6E ) . The soluble receptor analogue α-D-methyl-mannopyranoside ( α-D-man . , 2 . 5% ) effectively blocked the TRPC1 and TRPV6 response , suggesting that the effects are type 1 fimbriae specific ( Fig 6F ) . This hypothesis was confirmed by treating human bladder epithelial cells with purified FimCH protein complexes ( 1 . 25–5 μg/ml ) , [44–47] . A dose-dependent increase in TRPC1 expression was detected ( Fig 6G and 6H ) and the FimCH complex was shown to activate rapid Ca2+ , K+ and Zn2+ fluxes , which were inhibited by α-D-methyl-mannopyranoside ( Fig 6I ) , suggesting that type 1 fimbriae stimulate ion fluxes , in an adhesin-dependent manner . As ion fluxes regulate a variety of cellular responses , we suggest that these findings identify a general mechanism by which type 1 fimbriae may affect tissue homeostasis at different mucosal sites . Further analysis of patients inoculated with E . coli 83972fim identified 22 significantly activated gene sets ( nominal p-value ≤ 0 . 01 ) , involved in neuronal sensing , neurotransmitter receptor activity and nervous system development ( Fig 7A and 7B ) . Genes within these categories were regulated in all inoculated individuals but the time and amplitude of the maximum response varied between the patients ( Fig 7B ) . The response was reproduced in human bladder epithelial cells , where NK1R and SP protein levels were increased in vitro after E . coli 83972fim infection [48] ( Fig 7C ) . Furthermore , the purified FimCH complex stimulated NK1R and SP expression in human bladder epithelial cells ( Fig 7D ) , suggesting that type 1 fimbriae contribute to the activation of neuropeptides and neuropeptide receptors in an adhesin-dependent manner . The results identify pronounced early effects of E . coli 83972fim on the host environment , with inhibition of RNA processing and translation and activation of ion channel- and neuropeptide responses . E . coli 83972fim enhanced both the inhibitory and activating effects of E . coli 83972 , in a FimH adhesin-dependent manner , but did not significantly alter the profile of expressed genes , compared to E . coli 83972 . In contrast , E . coli 83972pap activated host gene expression and changed the gene expression repertoire . Last , to provide a molecular context to these divergent effects , we identified upstream regulators of the responses to E . coli 83972pap or E . coli 83972fim , respectively . This analysis predicts key transcriptional regulators of the response , in this case to E . coli 83972pap or E . coli 83972fim ( Fig 8 ) . We selected the time of maximal response of each patient and fimbrial type and included all regulated genes in the respective data sets . IRF-7 was identified as a potent upstream regulator of the response to E . coli 83972pap , consistent with the effects in patients and animal models of acute pyelonephritis ( -log ( p-value ) = 61 , Fig 8A and 8B ) . Other identified transcriptional nodes included IRF-3 , which has been shown to balance the IRF-7 response by forming heterodimeric complexes and STAT1 , regulating the expression of interferon stimulated genes . E . coli 83972 , in contrast , was predicted to inhibit IRF-7 and E . coli 83972fim had no predicted effect . A weak , more pleiotropic pattern was detected for E . coli 83972fim , suggesting , that host gene expression is inhibited more broadly , by mechanisms unrelated to specific transcription factors . MYC was identified as a transcriptional regulator in P I , after 3 and 24 hours and was predicted to be inhibited , consistent with the overall inhibition of gene expression by E . coli 83972fim ( -log ( p-value ) = 6 . 4 and 8 , respectively , Fig 8C–8E ) . MYC was also predicted to be inhibited in P I by E . coli 83972 after 24 hours ( -log ( p-value ) = 4 ) but was not regulated by E . coli 83972pap .
Bacterial pathogens have evolved sophisticated molecular strategies to colonize the appropriate host niche and adherence is an essential first step to enhance their virulence [39] . Like pathogens , commensals have evolved adhesive surface ligands to enhance their fitness , but the outcome is very different , suggesting that the quality of the adhesive interactions may distinguish commensals from pathogens ( Fig 9 ) . Here , we address this question by comparing P fimbriae , which are expressed by uropathogenic E . coli strains [49] to type 1 fimbriae , which are expressed among Gram-negative bacteria , with no apparent disease association . By reconstituting the pap or fim gene clusters in the non-virulent E . coli strain 83972 and inoculating human hosts with the fimbriated variants of this strain , we have had the unique opportunity to study fimbrial function and define molecular effects in human hosts . We made the unexpected observation that the acquisition of functional P fimbriae made the ABU strain virulent in two susceptible hosts . This is explained mechanistically by bacterial reprogramming of host gene expression , including the activation of IRF-7; a transcription factor that defines tissue pathology in the murine pyelonephritis model [8] . The PapG adhesin is identified as a transcriptional IRF7 agonist , in the context of IRF-3 , IFN-β and MYC . We contrast this effect against type 1 fimbriae , which transiently inhibited genes involved in RNA processing and activated the expression of ion-channels and neuro-transmitters , with no evidence of symptoms in the host . Rather than reprogramming host gene expression , type 1 fimbriae broadly enhanced the inhibitory effects of the non-fimbriated wild type strain , suggesting a more homeostatic function . This does not exclude , however , a virulence-enhancing effect of the fimbriae , when expressed in the background of a fully virulent strain [50 , 51] . The findings illustrate the remarkably divergent effects of fimbriae in the infected host . The rationale for this study was clinical , as protective effects of E . coli 83972 inoculation have been documented , in placebo-controlled studies [29] . Fimbriae were introduced in an attempt to increase the fitness of E . coli 83972 for the urinary tract and extend the use of human inoculation therapy . There was no indication from earlier human studies that P fimbriae alone would cause a disease-like response in inoculated hosts [49 , 52] and unlike fully virulent strains , E . coli 83972pap did not activate a disease response in the murine UTI model . UPEC-associated virulence genes are attenuated in E . coli 83972 and even after prolonged carriage further attenuation of virulence has been shown to occur , suggesting that the strains evolve towards commensalism [13] . It is important to emphasize that E . coli 83972pap is sensitive to antibiotics and that antibiotic therapy resulted in rapid resolution of symptoms and infection , without sequels . These observations suggest , for the first time , that type 1 fimbriae may have potent inhibitory effects on the post-transcriptional machinery of the host , as E . coli 83972fim inhibited genes involved in RNA processing and translation . In addition we observed a rapid activation of K+ channels and solute carriers , as well as neuropeptides and their receptors , providing novel mechanistic insights into potential homeostatic effects and mechanism to broadly regulate cellular functions . Consistent with previous studies , NK cell function and integrin signaling was moderately affected by E . coli 83972fim ( Fig 9B ) . A lack of distinct upstream transcriptional regulators suggested an entirely different level of control of the host response compared to P fimbriae , mainly executed at the post-transcriptional level . While type 1 fimbriae have been shown to increase mucosal inflammation in the murine UTI model and promote the formation of intracellular communities , type 1 fimbriae alone did not act as virulence factors in this study , when expressed in the background of a non-virulent strain . The dual role of P fimbriae as bacterial sensors and transcriptional regulators is fascinating and challenges the dogma that virulence must rely on a complex set of virulence genes in every case . The difference between P and type 1 fimbriae further suggests that the repertoire of regulated host genes may distinguish disease-generating adhesins like P fimbriae from adhesive ligands that are involved in more homeostatic tissue functions . Based on these findings , we propose that bacteria may suffer from “virulence gene addiction” , in analogy with the “oncogene addiction” of cancer cells [53 , 54] . While pathogens generally rely on multiple genes to survive in the host [55] this study suggests that a single , potent virulence determinant may be sufficient to enhance or attenuate virulence . It follows that a loss of P fimbria would represent a first step towards virulence attenuation and adaptation to long-term persistence in the urinary tract . This is supported by a high frequency of inactivating papG mutations in ABU isolates [13] . The findings raise the question whether therapeutic efforts should be focused on “super-virulence” gene attenuation rather than on functions that help the normal flora to maintain homeostasis in the host .
Five patients with recurrent lower UTI and incomplete bladder emptying were included . The patients had experienced a minimum of four symptomatic episodes/year prior to enrolment and conventional treatment , including clean intermittent catheterization ( CIC ) , had been tried but failed ( S1 Table ) . The patients had anatomically normal urinary tracts as defined by cystoscopy and CT scanning . Renal function tests were normal . All patients had incomplete voiding ( residual urine between 50–300 ml; if > 100 ml treated with CIC ) , and had recurrent UTI . Inoculations were performed during a four-year period ( October 2007—June 2011 ) . Time between inoculations ranged between 4–13 months ( median 6 months ) . E . coli 83972 ( OR:K5:H– ) [24] is a widely used ABU prototype strain . The genome sequence was solved in 2010 , demonstrating virulence gene attenuation [13] . The fim gene cluster is dysfunctional due to a deletion of fimB-fimD and the PapG adhesin is inactivated by multiple point mutations in the papG coding sequence . Fimbrial expression by E . coli 83972 was re-established by cloning the intact pap and fim gene clusters from E . coli CFT073 ( see details in Supplementary Material and Methods ) . E . coli 83972 reisolates from urine were identified by PCR amplification of a DNA fragment covering the deletion in the fim gene cluster and a fragment of the 1 , 565 bp cryptic plasmid specific for E . coli 83972 . Urine samples were stored at -80°C . Growth characteristics of the wild type and P- or type 1 fimbriated strains were compared in LB ( 37°C ) for 8 hours of growth . In regular 30-minute intervals , the optical density at 600 nm wavelength of the bacterial cultures was measured . For in vitro experiments , bacteria were cultured on tryptic soy agar plates ( TSA , 16 h , 37°C ) , harvested in phosphate buffered saline ( PBS , pH 7 . 2 ) and diluted to appropriate concentration ( 105 cfu/ml , MOI 0 . 5–1 ) for infection . The A498 human kidney carcinoma cell line from a female ( A498 , American Type Culture Collection #HTB-44 ) and the 5637 human bladder grade II carcinoma cells ( 5637 , ATCC# HTB-9 ) are established models to study UTI pathogenesis [37] . Cells were cultured in RPMI-1640 supplemented with 1 mM sodium pyruvate , 1 mM non-essential amino acids , and 10% heat-inactivated fetal bovine serum ( FBS ) ( PAA ) at 37°C , 90% humidity and 5% CO2 . For experiments , epithelial cells were cultured the previous day in six-well plates ( 4-6x105 cells/well for Western blots and RNA extraction ) , or eight-well chamber slides ( 4-6x104 cells/well for confocal imaging ) , ( Thermo Fisher Scientific ) . Cells were washed and exposed to bacteria in fresh , serum-free supplemented RPMI . Cells were infected with appropriately diluted bacteria in PBS and incubated for 4 hours at 37°C with 5% CO2 . To investigate FimH specificity , cells were treated with 2 . 5% α-D-methyl-mannopyranoside for 30 minutes prior to bacterial infection . The protocol for therapeutic bladder inoculation with E . coli 83972 has been described [30 , 52] . In the present study , the protocol was modified to include only one inoculation , and this was enough for the patients to establish bacteriuria . Prior to inoculation , patients were treated with antibiotics to sterilize their urine . E . coli 83972 wild type or the fimbriated derivatives were cultured overnight ( 16 h ) in lysogeny broth ( LB ) , cells were harvested by centrifugation ( 10 min , 4 , 000 rpm ) and re-suspended in PBS to a concentration of 105 cfu/ml . Patients were inoculated with 30 ml of the solution through a catheter , which was then removed . Each patient was closely monitored . Blood and urine samples were collected prior to inoculation , three , 24 and 48 hours and at one , two , four and seven weeks after inoculation . Patients had access to a direct telephone number to the study physician at all times and were prescribed antibiotics to be used immediately in case of symptoms and upon instruction by the physician . After seven weeks the patients received antibiotic treatment , which eradicated bacteriuria in all cases . To examine the effects of fimbriae on the establishment of bacteriuria and on the host response , intra-individual comparisons were performed . Three patients were first inoculated with E . coli 83972 , subsequently with E . coli 83972fim and finally with E . coli 83972pap ( S1 Table ) . Following inoculation , the establishment of bacteriuria was followed with repeated urine cultures and the host response was monitored by urine neutrophil counts quantified in uncentrifuged urine using a hemocytometer chamber . Interleukin-6 ( IL-6 ) and IL-8 concentrations were quantified by Immulite ( Siemens ) in urine and blood . Urine samples obtained before inoculation and at each subsequent sampling point were diluted in PBS and semi-quantitatively cultured on TSA plates overnight ( 37°C ) . Prior to the inoculation the urine was sterile and neutrophil numbers , IL-6 and IL-8 concentrations were below reference values for infection . Bacteria were harvested from urine samples immediately after delivery of urine samples by the patients , briefly centrifuged and resuspended immediately in RNAprotect Bacteria ( Qiagen ) . Total RNA was extracted using the RNeasy mini kit ( Qiagen ) and reversely transcribed ( SuperScript III , Invitrogen ) in a two-step process with random hexamer primer . Prior to qPCR , the optimal annealing temperature and primer efficiency were determined . The fimA and papA transcripts were amplified using primers listed in S3 Table . Gene expression was quantified relative to frr ( ribosome-recycling factor ) . For details , see Supplementary methods . The expression of P or type 1 fimbriae was quantified by hemagglutination . Briefly , erythrocytes were harvested from heparinized human A1P1 blood , resuspended in PBS or 2 . 5% α-D-methyl-mannopyranoside in PBS and mixed with bacteria on microscopy slides . Agglutination was recorded as +++ , ++ , + or - . Bacterial adherence to the A498 kidney epithelial cell line was assessed as previously described[9] and evaluated by differential interference contrast ( DIC ) microscopy ( Carl Zeiss ) . For details , see Supplementary methods . RNA was extracted from 1 ml of heparinized peripheral whole blood collected from the participants before inoculation and at seven time points after inoculation ( 3 , 24 and 48 hours , and one , two , four and seven weeks ) . After purification with the QIAamp RNA Blood Mini Kit ( Qiagen ) , 100 ng of RNA was amplified using GeneChip 3´IVT Express Kit , after fragmentation and labeling , aRNA was hybridized onto Human Genome U219 arrays ( all Affymetrix ) for 16 hours at 45°C , either by Aros Applied Biotechnology or in-house using the GeneAtlas system ( Affymetrix ) . Transcriptomic data was normalized using Robust Multi Average ( RMA ) implemented in the Partek Express software . Fold change was calculated by comparing each sample to the pre-inoculation samples in each individual . Genes with absolute fold change >2 . 0 were considered differentially expressed . Heat-maps were constructed using the Gitools software . Differentially expressed genes and regulated pathways were analyzed using the Gene Set Enrichment Analysis ( GSEA , Broad Institute ) and the Ingenuity Pathway Analysis ( IPA , Qiagen Bioinformatics ) softwares . Fimbriae-specific effects on transcription were distinguished by comparing the response to E . coli 83972 inoculation at each time point and in each patient . PapDGII complexes [56] and PapGII truncated [57] were purified as previously described [41] . The PapDG protein complex was dissolved at 0 . 35 mg/ml in 20 mM Tris pH8 . 0 , 100 mM NaCl . The PapGII truncate was dissolved at 0 . 5 mg/ml in PBS . FimCH complexes were purified as previously described [41 , 58] and eluted in 65 mM NaCl . For details , see Supplementary methods . Polyclonal rabbit anti-PapGII antibody was made from native PapGII truncated protein at Sigma Biogenysis using standard protocol ( Rabbit #127 ) . An overnight culture of E . coli 83972 complemented with the plasmid pDD3 containing all pap genes from UPEC J96 except papG [31] was resuspended in PBS . 2 ml of the bacterial cells was lysed using an ultrasound sonicator ( 30 min at 4°C ) . After centrifugation , the pellet was resuspended in PBS and mixed with 1:100 of anti-PapG serum . The lysate-antibody mix was incubated for 2 h at room temperature and centrifuged . The resulting supernatant was used for experiments . After infection , cells were lysed with NP-40 lysis buffer , supplemented with protease and phosphatase inhibitors ( both from Roche Diagnostics ) . Total cellular proteins were run on SDS–polyacrylamide gel electrophoresis ( 4 to 12% bis-tris gels; Invitrogen ) , blotted onto poly-vinylidene difluoride membranes ( GE Healthcare ) , blocked with 5% non-fat dry milk ( NFDM ) , and incubated with rabbit anti–IRF-7 ( 1:300 , ab62505 , Abcam , Cambridge , United Kingdom ) rabbit anti-TRPC1 ( 1:500 , #ACC-010 , Alomone Labs ) and rabbit anti-TRPV6 ( 1:500 , #orb158655 , Biorbyt ) antibodies . The blots were washed with PBS Tween 0 . 1% ( PBST ) and incubated with HRP-linked secondary antibodies in 5% NFDM ( 1:4 , 000 , goat anti-rabbit- horseradish peroxidase ( HRP ) , #7074 , Cell Signaling ) . The anti-β-actin ( 1:4 , 000 in 5% NFDM , #A1978 , Sigma-Aldrich ) followed by rabbit anti-mouse Immunoglobulins HRP-linked ( 1:4 , 000 in 5% NFDM , P0260 , Dako ) was used as loading control . The blots were washed with PBST and developed with ECL Plus detection reagent ( GE Healthcare ) . Blots were imaged using the Bio-Rad ChemiDoc System ( Bio-Rad ) and quantification of densitometry of bands was done using the ImageJ software ( NIH ) . After infection , cells were fixed for 15 min with 3 . 7% formaldehyde , permeabilized with Triton X-100 ( 0 . 25% in 5% FBS/PBS ) for 10 minutes and blocked with 5% FBS/PBS for 1 hour at room temperature . Primary rabbit antibodies: anti–IRF-7 antibody ( 1:200 , ab62505 , Abcam ) , anti-PapG pre-absorbed serum ( 1:1 , 000 ) , anti-TWIK-1 ( 1:50 sc-28630 , Santa Cruz biotechnologies ) , anti-TRAAK ( 1:50 , sc-50413 , Santa Cruz biotechnologies ) , anti-KCNJ2 ( 1:100 , 3305–1 , Epitomics ) , anti-KCNJ11 ( 1:100 APC-202 , Alomone Labs ) , anti-TRPC1 ( 1:100 , ACC-010 , Alomone Labs ) , anti-TRPV6 ( 1:250 orb158655 , Biorbyt ) and secondary goat anti-rabbit Alexa Fluor 488–conjugated antibody ( 1:200 , A-11034 , Thermo Fisher Scientific ) were used . Nuclei were stained with DRAQ-5 ( ab108410 , Abcam ) . Slides were mounted using Fluoromount and examined in a LSM 510 META laser-scanning confocal microscope ( Carl Zeiss ) . Fluorescence was quantified using the ImageJ software . Total RNA was extracted from cells using the RNeasy Mini Kit ( Qiagen ) . Complementary DNA was reverse-transcribed using SuperScript III Reverse Transcriptase ( Invitrogen ) and oligo ( dT ) 20 primers ( Invitrogen ) . Transcripts were quantified using primer pairs against IRF7 , IRF3 , OAS1 , IFIT3 , MYC , IFNB1 and IFNA1 ( all QuantiTect Primer Assay , Qiagen ) . Samples were run in technical and biological duplicates and GAPDH was used as housekeeping gene . For details , see Supplementary methods . IRF7 promoter fragment [8] was amplified from human genomic DNA ( for primers see S3 Table ) and used as probe . Each reaction contained 3–5 μL of DNA probe and 2–5 μg of cell extract from E . coli 83972pap infected or uninfected A498 cells in binding buffer . For the band shift/competition assay , 1–2 μg of anti–IRF-3 , anti–MYC , anti-IFNβ , anti-PapGII or IgG2A control were used . Binding reactions were incubated at 15°C for 30 min and loaded onto a 6% nondenaturing , nonreducing polyacrylamide gel . Alternatively , samples were loaded on a 2% agarose gel . Gels were imaged using the Bio-Rad ChemiDoc System . For details , see Supplementary methods . Intracellular calcium was measured by Fluo4 NW ( Molecular Probes ) , intracellular potassium was measured by FluxOR ( Molecular Probes ) according to manufacturer’s instructions in human bladder epithelial cells grown in 96-well plates ( 60 , 000 cells/well ) after exposure to FimCH ( 5 μg ) . Extracellular Zn2+ was measured by FluoZin-3 ( Thermo Fischer Scientific ) by addition of 1 μg/ml of indicator salt for 60 minutes prior to FimCH treatment . Fluorescent intensity was measured by Infinite F200 ( Tecan ) microplate reader at 20 seconds intervals for indicated times . Data was examined in Prism version 6 . 02 ( GraphPad ) . Normalized cytokine concentrations were compared using two-way ANOVA and Sidak’s multiple comparisons tests . Changes in pathway gene expression ( fold change ) after inoculation were compared intra-individually using paired t-test ( two tailed p values ) . Staining quantifications was analyzed using unpaired t-test ( two tailed p-values ) and qRT-PCR data using multiple unpaired two-tailed Student’s t-test for homoscedastic variances . Results are presented as mean + s . e . m . and are representative of at least two independent experiments . Significance was accepted at P < 0 . 05 ( * ) , P < 0 . 01 ( ** ) or P < 0 . 001 ( *** ) . The analysis was not blinded to condition . The study was approved by the Human Ethics Committee of the Medical Faculty , Lund University , Sweden ( Dnr 298/2006; 463/2010 ) and informed consent forms were signed by all patients . | Urinary tract infections affect millions of individuals annually , and many patients suffer from recurring infections several times a year . Antibiotic resistance is increasing rapidly and new strategies are needed to treat even these common bacterial infections . One approach is to use the protective power of asymptomatic bacterial carriage , which has been shown to protect the host against symptomatic urinary tract infection . Instilling “nice” bacteria in the urinary bladder is therefore a promising alternative approach to antibiotic therapy . In an effort to increase the therapeutic use of asymptomatic bacteriuria , we reintroduced bacterial adhesion molecules into the therapeutic Escherichia coli strain 83972 and inoculated patients who are in need of alternative therapy . To our great surprise , the P fimbriated variant caused symptoms , despite lacking other virulence factors commonly thought to be necessary to cause disease . In contrast , type 1 fimbriae , did not provoke symptoms but enhanced the beneficial properties of the wild-type strain . This is explained by a divergent effect of these fimbrial types on host gene expression , where P fimbriae activate the IRF-7 transcription factor that regulates pathology in infected kidneys , suggesting that a single , potent virulence gene may be sufficient to create virulence in human hosts . | [
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"g... | 2019 | Fimbriae reprogram host gene expression – Divergent effects of P and type 1 fimbriae |
Disruption of the centromere protein J gene , CENPJ ( CPAP , MCPH6 , SCKL4 ) , which is a highly conserved and ubiquitiously expressed centrosomal protein , has been associated with primary microcephaly and the microcephalic primordial dwarfism disorder Seckel syndrome . The mechanism by which disruption of CENPJ causes the proportionate , primordial growth failure that is characteristic of Seckel syndrome is unknown . By generating a hypomorphic allele of Cenpj , we have developed a mouse ( Cenpjtm/tm ) that recapitulates many of the clinical features of Seckel syndrome , including intrauterine dwarfism , microcephaly with memory impairment , ossification defects , and ocular and skeletal abnormalities , thus providing clear confirmation that specific mutations of CENPJ can cause Seckel syndrome . Immunohistochemistry revealed increased levels of DNA damage and apoptosis throughout Cenpjtm/tm embryos and adult mice showed an elevated frequency of micronucleus induction , suggesting that Cenpj-deficiency results in genomic instability . Notably , however , genomic instability was not the result of defective ATR-dependent DNA damage signaling , as is the case for the majority of genes associated with Seckel syndrome . Instead , Cenpjtm/tm embryonic fibroblasts exhibited irregular centriole and centrosome numbers and mono- and multipolar spindles , and many were near-tetraploid with numerical and structural chromosomal abnormalities when compared to passage-matched wild-type cells . Increased cell death due to mitotic failure during embryonic development is likely to contribute to the proportionate dwarfism that is associated with CENPJ-Seckel syndrome .
Seckel syndrome is a clinically and genetically heterogeneous primordial dwarfism disorder that is characterised by intrauterine growth retardation , postnatal dwarfism , severe microcephaly , mental retardation , a prominent curved nose and receding chin , together with other clinical abnormalities [1] , [2] , [3] . Mutations in five loci have been linked with Seckel syndrome: SCKL1 and SCKL2 are due to mutation of the genes for the DNA damage response proteins ATR and CtIP ( RBBP8 ) , respectively; SCKL4 and SCKL5 are due to mutation of the genes for the centrosomal proteins CENPJ ( Centromere protein J , or centrosomal P4 . 1-associated protein , CPAP; Figure 1A ) and CEP152; while the gene responsible for SCKL3 is currently unknown [4] , [5] , [6] , [7] . Mutations in PCNT ( pericentrin ) , another centrosomal protein , have been associated with both Seckel syndrome and the overlapping dwarfism disorder , microcephalic osteodysplastic primordial dwarfism type II ( MOPDII ) [8] , [9] , [10] . Interestingly , mutations in the centrosomal proteins CEP152 ( MCPH4 ) and CENPJ ( MCPH6 ) , which are thought to interact with each other during centriole biogenesis [11] , [12] , have also been associated with primary autosomal recessive microcephaly , a genetically heterogeneous condition caused by mutation of one of eight loci ( MCPH1–8; [13] , [14] , [15] ) result in clinically indistinguishable features that include mental retardation and a severely reduced brain size of greater than two standard deviations below the average . Primary autosomal recessive microcephaly presents at birth or becomes apparent within the first few years of life [16] . Primary microcephaly is thought to be caused by a reduction in neurogenesis while the proportionate dwarfism of Seckel syndrome is thought to be the result of premature death of proliferating cells; it is not clear why different mutations in centrosomal proteins cause Seckel syndrome , MOPDII or primary microcephaly . CENPJ is a conserved , ubiquitously expressed centrosomal protein with a key role in centriole biogenesis [17] , [18] , [19] , [20] , [21] . The centrosome is a major microtubule organizing centre in somatic cells that undergoes a duplication cycle that is tightly coupled with DNA replication ( reviewed by [22] and [23] ) . Briefly , in G1 the centrosome is composed of a pair of loosely connected centrioles that are embedded in a proteinaceous matrix . In concert with DNA replication , a single procentriole forms next to each parental centriole during S-phase . The procentrioles continue to elongate and by the onset of mitosis two centrosomes are present , each comprising an older and younger centriole . The two centrosomes aid the formation of the poles of the bipolar spindle , the molecular machinery responsible for correct segregation of sister chromatids into daughter cells . Centrosome attachment to the poles also ensures that each daughter cell inherits a single centrosome , thus tightly regulating ploidy and centrosome numbers . Impaired centrosome duplication cycles or a failure of centrosome segregation result in abnormal centrosome numbers that in turn perturb bipolar spindle assembly and chromosome segregation [24] . CENPJ contains 17 exons and encodes a 1338 amino acid residue protein with a chromosomal segregation ATPase domain and a T-complex protein 10 ( TCP10 ) -like C-terminal domain . Seckel-syndrome of a consanguineous Saudi Arabian family has been associated with a homozygous splice acceptor mutation in the last nucleotide of CENPJ intron 11 ( Figure 1A ) that results in the production of three transcripts lacking either exon 12 , exons 11 and 12 or exons 11 , 12 and 13 [4] . Three CENPJ-microcephaly mutations in three consanguineous Pakistani families have been reported to date and all are predicted to cause a truncating stop codon ( Figure 1A; [14] , [15] ) . ATR , RBBP and CEP152 have been shown to play a role in maintaining genomic stability through regulation of the DNA damage response [5] , [6] , [25] , however such a role has not yet been defined for CENPJ . We set out to develop a mouse model of CENPJ-Seckel syndrome in order to establish the mechanism by which mutation of CENPJ results in this subtype of primordial dwarfism . We show that the Cenpj hypomorphic mouse that we created recapitulates many key features of Seckel syndrome , including microcephaly with memory impairment , dwarfism from birth , and skeletal abnormalities . We further establish that wide-scale genomic instability is the likely cause of cell death within Cenpjtm/tm embryos and suggest that this contributes to the developmental phenotypes observed in CENPJ-Seckel patients .
Knockout mice carrying the Cenpjtm1a ( EUCOMM ) Wtsi allele ( Figure 1A and Figure S1A ) were generated on a C57BL/6NTac; C57BL/6-Tyrc-Brd background by the Sanger Mouse Genetics Project as part of the European Conditional Mouse Mutagenesis Program ( EUCOMM; [26] ) . Correct gene targeting in founder mice was determined by a combination of standard and quantitative PCR ( Figure S1 ) . LacZ staining was detected in the brain and kidneys , while strong staining was present in the testes of mice heterozygous for the Cenpjtm1a ( EUCOMM ) Wtsi allele ( Figure S2A ) . The tm1a ( EUCOMM ) Wtsi gene-trap cassette that was introduced into the Cenpj locus is designed to truncate mRNA expression and to generate out-of-frame products following the deletion of a critical exon . Previous studies have indicated that mRNAs of certain microcephaly-associated genes are very stable [27] prompting us to perform a detailed analysis of expression and splicing at the Cenpjtm1a ( EUCOMM ) Wtsi locus . We generated Cenpjtm1a ( EUCOMM ) Wtsi/tm1a ( EUCOMM ) Wtsi ( Cenpjtm/tm ) mouse embryonic fibroblasts ( MEFs; 13 . 5 d . p . c . ) and performed SYBR Green qPCR on cDNA using primers spanning the boundaries between different exons ( Figure 1A ) . We observed a low but detectable amount of splicing over the gene-trap cassette in Cenpjtm/tm MEFs ( 2 . 1±0 . 5% of wildtype exon 4–5 levels ) and immunoblotting ( Figure 1B ) confirmed the production of low levels of apparently full-length Cenpj protein [27] . Splicing from exons 3 to 6 and 4 to 6 was detected in both Cenpjtm/tm and wildtype MEFs ( Figure S2B ) . Between exons 3 and 6 the level of splicing detected in Cenpjtm/tm MEFs was increased relative to the levels in control MEFs ( 444±95% ) , while decreased levels of splicing were observed between exons 4 and 6 ( 2 . 1±0 . 5% ) . Using the web-based ExPASy translation tool ( http://web . expasy . org/translate/ ) we predict that mRNAs that are spliced between exons 3–6 and exons 4–6 lead to the production of proteins truncated in exon 6 ( Figure S2C ) . Upstream of the tm1a ( EUCOMM ) Wtsi cassette ( exons 1–2 ) Cenpj mRNA levels were 68±19% of wildtype levels . Downstream ( from exon 6 to 17 ) of the tm1a ( EUCOMM ) Wtsi cassette Cenpj mRNA levels were approximately 20% of the levels observed in MEFs from wildtype littermates ( mean±SEM , n = 3 ) . In summary , Cenpjtm/tm MEFs are able to produce small amounts of full-length Cenpj protein due to splicing over the tm1a ( EUCOMM ) Wtsi gene-trap cassette ( exons 4–5 ) and we predict that small amounts of truncated , N-terminal Cenpj protein ( corresponding to exons 1 to 3 or 1 to 4 ) will also be produced . Phenotyping of mice was performed at the Wellcome Trust Sanger Institute ( http://www . sanger . ac . uk/mouseportal/search ? query=cenpj ) . Cenpjtm1a ( EUCOMM ) Wtsi/+ intercrosses gave close to the expected Mendelian frequency ( 25% ) of homozygote embryos at E14 . 5 ( 23 . 5% ) ; however , by E18 . 5 this had reduced to 11 . 6% , suggesting that disruption of Cenpj causes partial embryonic lethality between E14 . 5 and E18 . 5 ( χ2 test , P = 0 . 02; Figure 1C ) . The majority of runted pups identified between P0 and P21 were Cenpjtm/tm ( 22% of Cenpjtm/tm offspring vs . 4 . 7% for Cenpj+/tm , and 2 . 5% for Cenpj+/+ ) . Stunted growth was unlikely to be the result of a major feeding problem , since milk spots were observed in the stomachs of pups of all genotypes at P0 . At P14 , the frequency of Cenpjtm/tm mice was not significantly different to that found at E18 . 5 ( P14: 9 . 2% vs . E18 . 5: 11 . 6%; Figure 1C ) , suggesting that although dwarfed , Cenpjtm/tm mice are not postnatally sub-viable . One of the defining characteristics of primordial dwarfism disorders , such as Seckel syndrome , is a fetus that is small for its gestational age with postnatal growth retardation [8] , [28] , [29]; specifically , the Saudi Arabian CENPJ-Seckel kindred all have anthropometric values at least seven standard deviations below the mean [4] . Cenpjtm/tm mice showed intrauterine growth retardation ( Figure 1D; mean ± SEM bodyweight at E18 . 5 , Cenpj+/+ 1 . 12±0 . 03 g , Cenpjtm/tm 0 . 8±0 . 04 g; P = 0 . 0001 , t-test; crown-rump length at E18 . 5 , Cenpj+/+ 23 . 5±0 . 26 mm , Cenpjtm/tm 20 . 7±0 . 54 mm; P = 0 . 0001 , t-test ) . From 3–16 weeks , Cenpjtm/tm mice were significantly smaller than wild-type controls ( P = 2 . 2×10−16 , Mann-Whitney-Wilcoxon test; Figure 1E ) . The body weight of adult Cenpjtm/tm animals was 64% of wild-type controls ( mean ± SEM bodyweight at 16 weeks , Cenpj+/+ 39 . 1±1 . 35 g , Cenpjtm/tm 25 . 4±1 . 34 g; P = 9 . 3×10−11 , t-test; Figure 1E ) and length was 76% of controls ( mean ± SEM nose-to-tail base length at 14 weeks , Cenpj+/+10 . 4±0 . 05 cm , Cenpjtm/tm 7 . 9±0 . 07; P = 2 . 2×10−16 , t-test; Figure S2D ) . The skeletal abnormalities of Seckel syndrome associated with an intron 11 mutation in CENPJ include a receding chin , high forehead and prominent nasal spine [4] . Although the facial features of the CENPJ-Seckel kindred ( two siblings and three cousins ) were strikingly similar , the skeletal survey of sibling one was largely normal and that of sibling two revealed 11 ribs instead of 12 and a steep acetabular roof [4] . These findings highlight the fact that the same mutation results in clinical heterogeneity and prompted us to perform a thorough skeletal analysis of Cenpjtm/tm embryos and adult mice; Cenpjtm/tm embryos had significantly smaller skulls ( Figure 1D and 1H; mean ± SEM at E18 . 5: skull length , Cenpj+/+ 9 . 8±0 . 12 mm , Cenpjtm/tm 9 . 3±0 . 17 mm , P = 0 . 0379; inner canthal distance Cenpj+/+ 3 . 22±0 . 05 mm , Cenpjtm/tm 2 . 96±0 . 09 mm , P = 0 . 0198 ) and adult mice presented with a flatter , sloping forehead and mild elevation of the parietal bone compared to controls ( Figure 1H; 16 weeks of age , n = 8 males and n = 7 females ) . Although the Saudi Arabian CENPJ-Seckel kindred do not have clinodactyly ( curvature of the fifth finger ) , it is a frequently reported characteristic of Seckel patients [1] , [4] , [29] , [30] . We did not observe clinodactyly in Cenpjtm/tm mice , however we noted polysyndactylism of the first digit of the left hind paw in 2/9 Cenpjtm/tm embryos ( Figure 1D , inset ) , which has also been reported in mutant Pcnt ( pericentrin ) mice [31] . Furthermore , retarded ossification and decreased bone age is reported in the majority of cases of Seckel syndrome , although this clinical abnormality was not specifically addressed for the CENPJ-Seckel kindred [1] . A higher proportion of Cenpjtm/tm embryos showed incomplete or irregular ossification of the parietal and occipital bones when compared to controls ( Figure 1F; 3/5 Cenpjtm/tm , 1/38 Cenpj+/tm , 1/13 Cenpj+/+ ) . In addition , a subset of Seckel patients , including a CENPJ-Seckel patient , have 11 ribs instead of the usual 12 [4] , [30] , [32] . Although all ribs were present in Cenpjtm/tm embryos , we noted that the attachment of the ribs to the sternum followed an irregular pattern that closely corresponded to the asymmetrical distribution of ossification centers along the sternum ( Figure 1G; 3/5 Cenpjtm/tm ) . Adult Cenpjtm/tm mice displayed an irregular ribcage , with crowding of the ribs ( Figure 1H; 9/15 ) . Moreover , a subset of Seckel patients have been reported to have bilateral dislocation of the hips and elbows , with a decreased range of motion at the elbows [1] . While we did not find any evidence of dislocation we noted that the humeri of adult Cenpjtm/tm mice were anatomically disproportionate when compared with those of wild-type mice; the deltoid tuberosities were closer to the greater tubercle when normalized to humeri length ( mean±SEM . : right humeri , Cenpj+/+ 49 . 6±0 . 28% , Cenpjtm/tm 47 . 1±0 . 93%; P = 0 . 02 , t-test; Figure 1H ) , and humeri were sometimes bowed ( 6/15 ) with a very prominent medial epicondyle ( 11/15; Figure 1H ) . Furthermore , all adult Cenpjtm/tm mice ( 15/15 ) displayed an abnormal pelvis that was wider at the iliac crests ( Figure 1H , iliac crest normalised to ischiac , mean±S . E . M . : Cenpj+/+ 69 . 7±1 . 18% , Cenpjtm/tm 60 . 8±1 . 72%; P = 0 . 0003 ) and sometimes asymmetrical . Finally , we observed that all Cenpjtm/tm mice had a reduced intervertebral joint space in the lumbar and caudal regions ( Figure 1H ) . In general , lumbar and sacral vertebrae were shorter and Cenpjtm/tm mice had one to two extra sacrocaudal transitional vertebrae as a result . In 13/15 Cenpjtm/tm mice , Caudal 2/3 – Caudal 7/8 were abnormal in morphology and fused ( Figure 1H ) . Microcephaly is one of the defining characteristics of Seckel syndrome [1] . Microcephaly has been clinically defined as a head circumference of at least two standard deviations below the normal range; and in the case of Seckel syndrome associated with mutations in intron 11 of CENPJ , head circumference is seven standard deviations below the mean [4] , [33] . The average Cenpjtm/tm mouse brain weight was two standard deviations below that of control mice ( Figure 2A; P = 0 . 0002 , t-test ) . Although the two and four year-old siblings with CENPJ-Seckel syndrome described to date had relatively normal magnetic resonance imaging ( MRI ) , cranial MRI of adult patients with Seckel syndrome has revealed several neuroanatomical abnormalities aside from a reduction in brain volume [4] , [32] , [34] , [35] . We therefore assessed the area of brain regions and the thickness of the neuronal layers of the adult mouse brain ( 16 weeks; Figure S3 ) . Although the patterning of the hippocampal layers appeared normal , the length of the dentate gyrus was significantly reduced in Cenpjtm/tm when compared to Cenpj+/+ control mice ( mean±SEM . : Cenpj+/+ 4380±64 µm , Cenpjtm/tm 3797±181 µm , P = 0 . 01 , t-test; Figure 2B ) . The average thickness of the cortex , which is often reduced with mutation of microcephaly genes in mice [36] , [37] , and of the molecular , striatum radiatum and oriens layers of the hippocampus were not significantly different to wild-type controls . Similarly , the total areas of the hippocampus , corpus callosum and dorsal third ventricle were unchanged as was the total internal length of the pyramidal cell layer . History was suggestive of normal cognitive and motor development for four of the five cases within the CENPJ-Seckel kindred while one patient clearly had intellectual impairment ( IQ 60; MRI not performed [4] ) . Since the hippocampus is involved in learning and memory formation and since Seckel patients generally display learning impairments [3] , [34] , we performed a social recognition test with Cenpjtm/tm and control animals [38] , [39] , [40]; Figure 2C and 2D ) . Thus , on day one , mice were tested for habituation-dishabituation: male mice were presented with a novel , anaesthetized stimulus mouse and the time of investigation was recorded . Mice were then given a 10 minute resting period before this was repeated a further three times with the same stimulus mouse . On the fifth trial , mice were presented with an unfamiliar stimulus mouse ( Figure 2C ) . Both Cenpjtm/tm ( n = 7 ) and Cenpj+/+ ( n = 7 ) mice recognized and habituated to the novel stimulus mouse , as there was a decline in investigation time over the first four trials that was recovered on trial five ( Figure 2C , two-way ANOVA , repeated measures for trial F4 , 48 , = P<0 . 001 , effect for genotype F1 , 48 = 0 . 5482 , P = 0 . 433 , interaction F4 , 48 = 0 . 09258 , P = 0 . 9844 ) , when they were exposed to a novel mouse ( Figure 2C . Trial four vs . trial five , P = 0 . 0033 and P = 0 . 0074 , post-hoc t-test ) . These data suggest that olfaction in Cenpjtm/tm mice is not markedly affected . Twenty-four hours after the habituation-dishabituation test , a discrimination-based olfactory memory test was performed . When given a choice between the familiar ( same stimulus animal used for trials one to four ) and a new unfamiliar mouse , Cenpj+/+ animals spent less time investigating the familiar mouse than the unfamiliar one ( Figure 2D . P = 0 . 0326 , t-test ) . However , Cenpjtm/tm mice were less able to recognize the familiar from the unfamiliar animal as shown by the similar investigation time for both stimulus animals ( Figure 2D . P = 0 . 957 , t-test; Normalized discrimination Cenpj+/+ vs Cenpjtm/tm , P = 0 . 0417 ) . In summary , short-term memory and olfaction appear to be unaffected in Cenpjtm/tm mice , however long-term memory was significantly impaired . All other tests of neurological function were normal , including open field , grip strength , modified SHIRPA ( the SmithKline Beecham , Harwell , Imperial College , Royal London Hospital , phenotype assessment is a set of behavioural tests designed to test muscle , cerebellar , sensory and neuropsychiatric function ) , auditory brainstem response and hot plate assessment . All of the genes associated with Seckel syndrome have so far been shown to result in defective DNA damage responses and a lowered apoptotic threshold [5] , [6] , [7] , [25] , [41] . To test whether Cenpj-deficiency is associated with elevated levels of DNA strand breaks and/or apoptosis , we performed immunohistochemical staining of E14 . 5 embryos . Phosphorylation of histone H2AX on serine 139 ( γH2AX ) by the ATR , DNA-PK or ATM kinases occurs at sites flanking DNA strand breaks and enhances the recruitment of DNA repair proteins to sites of damage [42]; if the damage is irreparable then the cell death cascade is normally activated [43] . Compared to controls , there was a general increase in the number of γH2AX-positive cells throughout Cenpjtm/tm embryos ( Figure S4 ) and this was most pronounced in the developing telencephalon ( Figure 2E; mean±SEM . γH2AX-positive cells as a percentage of total: striatum Cenpj+/+ 0 . 47±0 . 2% , Cenpjtm/tm 2 . 3±0 . 3% , P = 0 . 004; cortex Cenpj+/+ 0 . 0% , Cenpjtm/tm 1 . 3±0 . 3% , P = 0 . 01; pro-hippocampus Cenpj+/+ 0 . 0% , Cenpjtm/tm 0 . 9±0 . 3% , P = 0 . 03 . Mann-Whitney with continuity correction ) . Similarly , there was an increase in the number of cleaved caspase-3-positive cells throughout Cenpjtm/tm embryos ( Figure S4 ) , although this was most pronounced in areas of active neurogenesis ( as determined by Ki67 staining; Figure S4 ) within the telencephalon , where Cenpj was most highly expressed ( Figure 2E; mean±S . E . M . cleaved caspase-3-positive cells as a percentage of total cells: striatum Cenpj+/+ 0 . 0% , Cenpjtm/tm 1 . 9±0 . 2% , P = 0 . 001; cortex Cenpj+/+ 0 . 0% , Cenpjtm/tm 0 . 6±0 . 2% , P = 0 . 05; pro-hippocampus Cenpj+/+ 0 . 0% , Cenpjtm/tm 0 . 5±0 . 3% , P = 0 . 14 , t-test ) . There was no detectable difference in the patterns of cellular proliferation between Cenpjtm/tm and Cenpj+/+ embryos when examined using the marker Ki67 ( Figure S4 ) . Consistent with increased levels of apoptosis in the developing telencephalon of Cenpjtm/tm embryos , reports of fetal stage Seckel syndrome ( loci responsible unknown ) have shown reduced neuron density and disorganization of cortical layers at 30 weeks gestation [28] , [29] . We therefore quantified neuron densities in areas of active neurogenesis within the telencephalon ( areas of Ki67-positive staining; Figure S4 ) and in the mid-striatum of embryos during mid-neurogenesis ( E14 . 5 ) and found that , in general , the number of neurons were decreased in Cenpjtm/tm embryos and the reduction was significant for the striatum ( Figure 2F; mean±SEM . for n = 3 ( average of two different 75 µm2 areas ) in striatum: Cenpj+/+ 104 . 9±3 . 1 , Cenpjtm/tm 94 . 7±3 . 8 , P = 0 . 0008 , t-test ) . Several clinical reports of patients with Seckel syndrome have described precocious puberty or premature thelarche [44] , [45] . It is not yet known whether the sexual development of patients with Seckel syndrome associated with CENPJ mutations is normal since the cases described so far report the phenotype of infants [4] . A thorough histopathological analysis of adult male and female Cenpjtm/tm and Cenpj+/tm mice ( n = 3 of each gender and genotype at 16 wks ) revealed several anomalies , including corticomedullary pigmentation in the adrenals of female Cenpjtm/tm mice ( Figure 3A ) . Corticomedullary pigmentation is associated with ‘X-zone’ degeneration in female mice , a sex hormone-dependent change that occurs during puberty in virgin females and is often complete by 16 weeks in C57BL/6 mice , or earlier in pregnant females [46] . By using cleaved-caspase 3 as a marker of apoptosis , we found that the adrenals of virgin Cenpjtm/tm female mice have pronounced and ongoing X-zone degeneration at 16 weeks when compared to virgin Cenpj+/+ females ( Figure 3A ) . Wild-type C57BL/6 female mice reach sexual maturity at around 6–7 weeks of age . These findings suggest that , in contrast to Seckel syndrome patients , puberty is delayed in Cenpjtm/tm female mice . In support of this , breeding records of females set up with Cenpjtm/tm males at 6–7 weeks of age showed that Cenpjtm/tm females produce their first litter around four weeks later than Cenpj+/+ females ( P = 0 . 012 , t-test; Figure 3B ) . There were no morphological differences in the reproductive tract of male or female Cenpjtm/tm animals when examined at 16 weeks of age ( data not shown ) . Although not reported for the CENPJ-Seckel kindred [4] , several individuals that have been clinically diagnosed with Seckel syndrome have ocular defects , such as spontaneous lens dislocation , myopia , astigmatism , and retinal degeneration [47] [48] . A higher proportion of Cenpjtm/tm embryos had secondary anophthalmia ( E18 . 5 , 0/13 Cenpj+/+ , 1/38 Cenpj+/tm and 1/5 Cenpjtm/tm ) and a higher proportion of Cenpjtm/tm pups still had their eyes closed at P14 ( 5/28 Cenpjtm/tm vs . 1/46 wild-type ) . At 16 weeks of age , histological analysis of the eyes from Cenpjtm/tm mice showed various structural abnormalities . In the anterior segment , the corneal endothelium and Descemet's membrane was occasionally broken ( Figure 3C ) . The anterior chamber was of normal depth but the angle was anteriorly displaced in some cases ( Figure 3C ) . Significant cataracts were not observed in Cenpjtm/tm mice but the iris showed adhesions to the lens and its base was anteriorly shifted in relation to the ciliary body ( Figure 3C ) . Also , the ciliary body processes were spaced far apart or blunted in Cenpjtm/tm animals , and in some cases , ciliary process morphology was abnormal ( Figure 3C ) . In the retina , the photoreceptor nuclei were variably reduced in number and columns were loosely packed or disorganized in Cenpjtm/tm animals ( Figure 3C ) . Other cell layers , including the retinal ganglion , inner nuclear , and retinal pigment epithelium appeared normal , and the optic nerve did not show thinning . At E14 . 5 , Cenpj was highly expressed in the retina neuroblast layer , where cells are rapidly differentiating and proliferating , but not in the inner retinal ganglion cell progenitor layer ( Figure 3D ) . During routine phenotyping , mice were subject to an intra-peritoneal glucose tolerance test , in which mice were fasted for 16 hours , a bolus of glucose was administered intraperitoneally and blood glucose concentration was monitored for 2 hours . Fifteen minutes after administration of glucose , 4/4 female Cenpjtm/tm mice ( Cenpj+/+ 20 . 5±0 . 7 mmol/l , Cenpjtm/tm 30 . 7±1 . 60 mmol/l , P = 2×10−5 , t-test ) and 2/5 male Cenpjtm/tm had blood glucose levels greater than or equal to the 97 . 5th centile of baseline controls ( n = 670 females , n = 669 males ) , although this had returned to normal by 30 minutes ( Figure 3E ) . Despite being one of the less frequently reported characteristics of Seckel syndrome , there are numerous case-reports of severe cardiac anomalies in Seckel syndrome patients [49] , [50] , [51] , [52] , [53] . Strikingly , the majority of 16-week old Cenpjtm/tm mice ( 5/6 ) and only 1/6 Cenpj+/tm and 0/4 wildtype mice showed disorganization of cardiomyocytes with an increased incidence of karyomegaly and multinucleate cells , predominantly within the interventricular septum , papillary muscle and inner myocardium ( Figure S2E ) . Cardiomyocyte karyomegaly has previously been observed in wild-type mice [54] where it may be associated with reparative processes [55] and may represent polyploidy [56] . Although the incidence and extent of karyomegaly was noticeably increased in hearts from Cenpjtm/tm mice compared to wildtype animals in this study , there was no evidence of fibrosis ( consistent with previous cardiac damage ) based on trichrome staining or alterations in apoptosis or proliferation ( cleaved caspase-3 and Ki67 , respectively; data not shown ) . Interestingly , the preponderance of karyomegaly in cardiomyocytes , hepatocytes and cells of the Harderian glands was increased in aged Cenpjtm/tm mice ( 13-month old ) when compared to age-matched control ( Figure S2E ) . Clinical chemistry was performed on animals at 16 weeks of age . Albumin levels were generally decreased in Cenpjtm/tm mice of both genders compared to controls , and this was statistically significant for males ( mean±S . E . M , Cenpj+/+ 25 . 1±0 . 31 , Cenpjtm/tm 21 . 9±0 . 52 , P = 4 . 9×10−5 , t-test; Figure 3F ) . Flow cytometric analysis of peripheral blood leukocytes at 16 weeks of age revealed a marked increase in the frequency of the CD8 T cell subset ( CD3+ CD8+ ) in both genders of Cenpjtm/tm mice compared to wild-type controls ( mean±S . E . M Males: CD8+CD3+ gated on PI− CD45+: Cenpj+/+ 4 . 3%±0 . 18 , Cenpjtm/tm 7 . 5%±0 . 55 , P = 0 . 0002 Mann-Whitney-Wilcoxon test; Figure 3G ) . Furthermore , the increase in the proportion of the peripheral blood CD8 T cell population was reflected in a concomitant increase in the frequency of total T cells in Cenpjtm/tm mice ( mean±S . E . M , CD3+ gated on PI− CD45+: Cenpj+/+ 9%±0 . 39 , Cenpjtm/tm 14%±0 . 41 , P = 2 . 9×10−5 , Mann-Whitney-Wilcoxon test; Figure 3H ) . The frequency of CD4 T cells in the peripheral blood was unaffected ( data not shown ) . CENPJ depletion in cultured cells has been reported to impair centriole assembly , disrupt centrosome integrity and lead to the formation of monopolar and multipolar spindles instead of bipolar spindles [19] , [20] , [21] , [24] , [57] . To assess the cellular phenotype of Cenpjtm/tm mice , MEFs were derived from Cenpj+/+ , Cenpj+/tm and Cenpjtm/tm littermates . For all experiments , MEFs were of early passage ( P<5 ) and were passage-matched . Cenpj protein levels were reduced in the centrosomes of Cenpjtm/tm MEFs , but residual protein remained detectable in most cells ( Figure 4A ) . Consistent with previous findings , frequencies of both monopolar and multipolar spindles were elevated in two independently derived Cenpjtm/tm MEF lines ( Figure 4B; Cenpj+/+: 2 . 1% monopolar and 8% multipolar; Cenpjtm/tm ( 1 ) : 10 . 6% monopolar and 20 . 8% multipolar; Cenpjtm/tm ( 2 ) : 8 . 9% monopolar and 18 . 9% multipolar ) . Distribution and intensities of the centrosomal proteins γ-tubulin ( Figure 4A ) , CDK5RAP2 ( Figure 4B ) , and pericentrin ( data not shown ) were unaffected in the mutant . We next asked whether spindle abnormalities are accompanied by aberrant centrosome and centriole numbers in Cenpjtm/tm MEFs . A normal mitotic cell contains two centrosomes , each containing a pair of centrioles . Supernumerary centrioles were visible in mitotic Cenpjtm/tm cells ( Figure S5 ) . To facilitate counting of centrioles , cells were arrested in mitosis using monastrol , a microtubule motor poison that prevents separation of spindle poles and thereby generates monopoles [58] . Cells with three or four centrioles were considered normal , since it is not always possible to resolve centrioles within a pair . We observed an increase in cells containing both too few ( ≤2 ) and too many centrioles ( ≥5 ) in the mutant ( Figure 4C ) . To survive , cells with supernumerary centrosomes must either inactivate these or cluster active centrosomes into two poles , a process that ensures bipolar division [23] , [59] . Clustered centrosomes were indeed observed in Cenpjtm/tm MEFs ( Figure 4B ) . Centrosome clustering however does not prevent unequal partitioning of centrosomes into daughter cells ( Figure S5 ) , which ultimately causes a disassociation between centrosome numbers and DNA ploidy . Cell-cycle analysis of Cenpjtm/tm MEFs revealed a significant increase in the number of 4C and elevated levels of >4C cells ( Figure 5A ) , indicative of polyploidy . We examined the ploidy of fifty metaphase spreads of Cenpjtm/tm and Cenpj+/+ MEFs and found that a remarkably high percentage of Cenpjtm/tm cells were near tetraploid ( Cenpj+/+ 11% vs . Cenpjtm/tm 41% ) . Twenty metaphase spreads with good fluorescent in situ hybridization ( FISH ) signals were selected for multiplex-FISH karyotyping which confirmed that many cells were near tetraploid and revealed additional defects such as aneuploidy , centromere loss , centric fusions ( Figure 5B ) and translocations ( Figure 5C; for a breakdown of anomalies see Figure S2F ) . Consistently , we found evidence of lagging chromosomes in anaphase Cenpjtm/tm MEFs ( Figure S5 ) . Furthermore we show that adult Cenpjtm/tm mice have an increased prevalence of micronucleated normochromatic erythrocytes ( P = 0 . 000004 , t-test; Figure 5D ) , thus confirming that these mutants have spontaneous genomic instability . Seckel syndrome belongs to a group of genome instability disorders collectively referred to as DNA-damage response and repair-defective syndromes [60] . So far , all cells derived from Seckel patients have been found to be impaired in signaling mediated by the DNA-damage responsive protein kinase ATR , and therefore display reduced phosphorylation of downstream ATR substrates such as the checkpoint kinase Chk1 , and have impaired G2/M cell-cycle checkpoint arrest upon treatment with DNA-damaging agents [60] , [61] . We therefore treated fibroblasts from Cenpjtm/tm embryos ( 13 . 5 d . p . c ) with the DNA damaging agent camptothecin , a DNA topoisomerase I inhibitor that causes DNA double-strand breaks specifically in S-phase [62] . Analyses revealed that Cenpjtm/tm fibroblasts were proficient for ATR-dependent and ATM-dependent phosphorylation of Chk1 ( pS345 ) and KAP1 ( pS824 ) , respectively , and showed normal activation of γH2AX ( Figure 5E ) . We found no evidence of an impaired G2/M DNA damage checkpoint as determined by the percentage of MPM2-positive cells following irradiation ( Figure S2G ) . Furthermore , CtIP-Seckel cells show defective phosphorylation of replication protein A ( RPA ) after camptothecin treatment , a phenotype associated with impaired DNA-end resection and homologous recombination [6] . However , we found no evidence of this in Cenpjtm/tm MEFs ( Figure 5E ) .
Neuroepithelial cells have apical-basal polarity , and the switch from proliferative , symmetric to neurogenic , asymmetric division is controlled by the orientation of the spindle pole during mitotic division [65] . Primary microcephaly is caused by mutations of centrosomal proteins and is thought to arise from an increase in asymmetric divisions that reduces the size of the neural progenitor pool available for future brain growth , hence the growth deficit is restricted to the brain [66] . Seckel syndrome is characterized by microcephaly and a small body size ( proportionate dwarfism ) . Interestingly , different mutations in the centrosomal proteins CENPJ or CEP152 can cause microcephaly or Seckel syndrome [4] , [14] , [15] . The CENPJ-microcephaly mutations reported to date affect exons 2 ( 17delC ) , 11 ( 3243–3246delTCAG ) and 16 ( A3704T ) [14] , [15]; these mutations are predicted , but not proven , to cause defects in spindle pole orientation and proliferation of neural progenitors in a similar manner to other microcephaly genes . CENPJ-Seckel syndrome has been associated with a homozygous splice acceptor mutation in the last nucleotide of CENPJ intron 11 that results in the skipping of either exon 12 , exons 12 and 13 or exons 11 , 12 and 13 during transcription [4] . This may represent a cellular attempt to salvage this important protein since the latter two transcripts are predicted to result in in-frame deletion and preservation of the C-terminus [4] . Notably , we found that insertion of a cassette between exons 4 and 5 of Cenpj resulted in splicing over the cassette and cryptic splicing , such that three different Cenpj mRNAs were expressed at very low levels: full length , one lacking exons 4 and 5 and one lacking exon 5 . Skipping of exons 4 and 5 or exon 5 is predicted to result in a premature stop codon and protein products that are truncated after translation of exon 3 or 4 , respectively . Immunoblotting and immunofluorescence revealed higher than expected levels of apparently full-length Cenpj protein were present in Cenpjtm/tm MEFs , which may be the result of post-transcriptional or translational regulation since the level of full-length Cenpj mRNA in Cenpjtm/tm MEFs was only 2% of wild-type levels . Furthermore , mRNA levels varied greatly between Cenpjtm/tm mouse embryonic fibroblasts , which may be due to genetic modifiers . Together with studies of CENPJ-Seckel cells , which have shown that mutations in CENPJ may result in exon skipping and the generation of multiple transcripts that may generate in-frame protein products [4] , these data suggest that expression of this critical protein may be rescued to some extent by cryptic splicing over deleterious mutations . These produce alternatively spliced mRNAs and thus the same mutation might not result in the same mRNA or protein levels in each individual . Moreover , cryptic splicing may also differ between tissues . Without a complete examination of the effects of different CENPJ-mutations on mRNA levels and splicing , and CENPJ protein levels , it is difficult to say why CENPJ mutations can either result in primary microcephaly or Seckel syndrome , or why the Cenpjtm1a ( EUCOMM ) Wtsi allele results in a mouse with a Seckel syndrome-like phenotype . However , we propose that Cenpjtm/tm mice display a Seckel syndrome-like phenotype , rather than primary microcephaly , due to a major reduction in full length Cenpj protein and therefore a lack of the protein domain ( s ) encoded by exons 11 , 12 and/or 13 . The microcephaly of Cenpjtm/tm mice ( brain weight two standard deviations below the mean ) was not as severe as CENPJ-Seckel syndrome patients , who display anthropometric values that are all at least seven standard deviations below the mean [4] . The evolutionary lineage leading to humans is marked by a dramatic increase in brain size , suggesting that disruption of genes involved in neurogenesis will have a less profound effect in mice than in humans [67] . However , this is confounded by the fact that there are several mouse models of microcephaly , such as the humanized ATR-Seckel mouse and the Cdk5rap2 mutant mouse , which display severe reductions in brain size [25] , [37] . The discrepancy between microcephaly of Cenpjtm/tm mice and CENPJ-Seckel patients may instead be due to the hypomorphic nature of the Cenpjtm1a ( EUCOMM ) Wtsi allele or by the rapid evolution of the Cenpj gene between mice ( 80% sequence identity ) and humans; the human CENPJ protein may be more efficient at regulating neurogenesis than that of the mouse [67] . The dwarfism and microcephaly of Cenpjtm/tm mice appeared to be the result of widespread DNA damage and apoptosis in embryos , rather than a reduction in cell proliferation . The level of cell death within the forebrain of the Cenpj-hypomorph embryonic mouse brain was comparable with that of the humanized ATR-Seckel mouse ( approximately 1 . 5%; [25] ) . Similarly , ATR- , CtIP- , CEP152- and PCNT-Seckel cells have increased levels of DNA damage and a lowered apoptotic threshold with no change in the rate of proliferation [5] , [6] , [25] . In contrast to cells from ATR- , CtIP- , CEP152- and PCNT-Seckel syndrome patients , we have shown that MEFs from Cenpj-deficient mice are not impaired in ATR-dependent DNA damage signaling but instead show an elevated frequency of extra centrioles , multipolar spindles , and near tetraploid karyotypes . We suspect that the embryonic fibroblast line showing 41% near tetraploid cells could come from an embryo that would not have survived to term , indicating that genomic instability may also explain the sub-Mendelian birth ratio of Cenpjtm/tm mice . We also found evidence of chromosome missegregation , chromosomal translocations and centric fusions in Cenpjtm/tm MEFs . Increased levels of pan-nuclear γH2AX in embryos may be the result of chromosome breakage , micronucleus formation or missegregation [68] , however it is possible that this reflects phosphorylation of H2AX during apoptosis-driven fragmentation of DNA [69] . The neuropathological features of Cenpjtm/tm E14 . 5 embryos were remarkably similar to fetal stage Seckel syndrome . At E14 . 5 , we found there was a reduction in neuron density within the developing telencephalon of Cenpjtm/tm mice . There are only two neuropathological reports of fetal stage Seckel syndrome ( 30 weeks gestation ) , although both showed that the cortical layers of the telencephalon were thin and that neuronal populations were less dense and less organized than age- or length-matched controls [28] , [29] . As with Cenpjtm/tm mice , the hippocampal formation was short in one fetus , but displayed normal cytoarchitectural progression [28] , [29] . Both reports indicated that the major nuclear groups of the basal ganglia , thalamus , cerebellum and brainstem showed no abnormalities in fetal stage Seckel syndrome [28] , [29] . Interestingly , we saw a >50% reduction in the number of Cenpjtm/tm embryos between mid neurogenesis ( E14 . 5 ) and the completion of neurogenesis ( E18 . 5 ) , when Cenpj is strongly expressed in the ventricular layers of the diencephalon , telencephalon , midbrain and cerebellum ( www . emouseatlas . org , www . eurexpress . org ) , suggesting that Cenpj-deficiency during this critical period of neurogenesis causes partial lethality . The majority of patients with Seckel syndrome are reported to have an IQ of <50 and are delayed in speech and reaching motor milestones , as well as displaying pyramidal signs , hyperactivity and an attention deficit [3] , [34] . Cranial MRI of adult patients with Seckel syndrome has shown a reduction in brain volume , especially the cerebral cortex , a simplified gyral pattern ( number of gyri reduced and shallow sulci ) , poorly developed frontal lobes , agenesis of the corpus callosum , reduction of white matter , brainstem and cerebellar hypoplasia , and dysmorphic or enlarged lateral ventricles [32] , [34] , [35] . A relatively normal MRI was reported for two siblings ( aged two and four years-old ) of the CENPJ-Seckel kindred and together with two cousins ( aged five and six years-old ) , all had a history of normal cognitive and motor development [4] . The third cousin ( MRI not performed , aged 16 years-old ) had an IQ<60 . Similarly , the brain regions of adult Cenpjtm/tm mice appeared anatomically proportionate , although these mice had a significantly shorter dentate gyrus than controls and this was accompanied by cognitive impairments reminiscent of Seckel syndrome patients . dSas-4 is the Drosophila homologue of CENPJ . Unlike dSas-4-depleted cells or dSas-4 mutant flies that progressively lose centrioles , Cenpjtm/tm MEFs contain centrioles even after several passages [70] , [71] . While the increase in Cenpjtm/tm cells with two or fewer centrioles is consistent with an impairment of centriole assembly , this effect is relatively mild , and therefore suggests that the mutant expresses residual , functional Cenpj protein . Ciliogenesis requires centriole biogenesis and therefore dSas-4 mutants lack both primary and motile cilia [70] . The role of CENPJ in ciliogenesis has not been extensively explored in mammals , but depletion of CENPJ in cultured cells is reported to impair primary cilium formation [72] . Cenpjtm/tm mice ( 16 weeks old ) did not display phenotypes normally associated with ciliopathies such as situs inversus or renal cystic disease , suggesting that sufficient amounts of Cenpj are available in the mutant for cilia formation in the majority of cells . However , the abnormalities in ciliary processes and photoreceptor nuclei within the eye may be attributed to ciliary defects . Moreover , unlike dSas-4 mutant males that display loss of flagella and sperm motility , Cenpjtm/tm male mice are fertile [70] , which could again be due to residual expression of Cenpj . While Cenpjtm/tm MEFs displayed irregular centriole numbers and mono- and multipolar spindles , they also showed extensive polyploidy and aneuploidy . Thus , we cannot conclude whether abnormal centriole and centrosome numbers are the cause or consequence of aberrant ploidy . Figure S6A shows the possible sequence of events that may lead to the abnormal ploidy of CENPJ-Seckel cells . Aberrant centrosome numbers are known to cause mitotic spindle abnormalities , culminating in mitotic delay , chromosome missegegration , cytokinetic failure and polyploidy . Prolonged mitotic delay can cause DNA damage , cell cycle arrest and apoptosis [73] , [74] . Chromosome missegregation can also damage chromosomes , hence triggering activation of DNA damage checkpoints [68] , [75] . Chromosome instability could therefore explain the increase in γH2AX levels and potentially , the increase in apoptosis in the mutant embryonic brain . Of all chromosome aberrations detected in the mutant MEFs , tetraploidy was the most prominent . A common cause of tetraploidy is an abortive mitotic cell cycle whereby cells enter but fail to complete mitosis [76] . Mitotic spindle abnormalities in Cenpjtm/tm cells could trigger extended mitotic arrest followed by mitotic slippage producing a tetraploid cell ( Figure S6A ) . Tetraploid Cenpjtm/tm MEFs seem to be able to proliferate , since they represented almost 40% of the metaphase cells obtained for karyotyping . Interestingly , dSas-4 mutant flies show only a small increase in the proportion of aneuploid cells ( 1% in wild-type vs . 3% in mutants ) and no polyploidy [70] , whereas the proportion of near tetraploid Cenpjtm/tm embryonic fibroblasts was surprisingly high ( ∼10% in wildtype vs ∼40% in Cenpjtm/tm MEFs ) . We suspect that Cenpj-deficiency exacerbates tetraploidy in MEFs , which are particularly susceptible to tetraploidy with passaging [77] . Nonetheless , adult Cenpjtm/tm mice show increased micronucleus induction , which is likely the result of lagging chromosomes and chromosome breakage . Cenpjtm/tm mice of both genders showed an increased incidence of hypertrophic , disorganized cardiomyoctes with karyomegaly in the endocarium and interventricular septum when compared to wildtype mice . The areas showed no evidence of degeneration or repair , however since a high proportion of Cenpjtm/tm MEFs are polyploid , this is likely to be the cause of the karyomegaly . Although one of the less frequently reported characteristics of Seckel syndrome , there are numerous case-reports of severe cardiac anomalies in Seckel syndrome patients , including atrial and ventricular septal defects , pulmonary atresia , patent ductus arteriosus and congenital heart disease [49] , [50] , [51] , [52] , [53] . It will be interesting to see whether CENPJ-Seckel patients develop cardiac defects as they age . At 16 weeks of age Cenpjtm/tm mice showed hypoalbuminemia , which is associated with chronic liver and kidney diseases , although histopathological analysis of their livers and kidneys did not reveal any abnormalities . However , the preponderance of karyomegaly in the liver and Harderian glands was increased in aged Cenpjtm/tm mice . Cenpj-deficiency may exacerbate this phenomenon in the cells of both of these tissues , which are prone to karyomegaly [78] . Familial syndromes associated with genomic instability often predispose to cancer formation since DNA damage is the source of mutations that drive malignant transformation . However only a few cancers have been reported for Seckel syndrome patients , possibly due to the shorter life-expectancy of patients with primordial dwarfism . Furthermore , since mutation of each of the five known Seckel genes , ATR , PCNT , CENPJ , CEP152 and RBBP8 ( CtIP ) , cause genomic instability that is associated with apoptosis , it is possible that Seckel cells may not have the opportunity to accumulate cancer-causing mutations . The chromosomal instability that is associated with Cenpj-deficiency could result in aneuploidy or translocations that cause loss of tumour suppressors or the formation of oncogenic fusion proteins , respectively [79] . We are currently ageing a cohort of Cenpjtm/tm mice ( currently 7–14 months old ) to determine whether Cenpj-deficiency alters the frequency of malignancy or shortens life-expectancy . We noted a small number of abnormalities in Cenpjtm/tm mice that have not been previously reported for Seckel syndrome patients or mouse models . Seckel syndrome is associated with ocular defects in humans , including spontaneous lens dislocation , myopia , astigmatism , and retinal degeneration . Ocular examination of CENPJ-Seckel patients has not yet been reported [47] , [48] , however Cenpj was highly expressed in the rapidly proliferating retinal neuroblast layer in the 14 . 5 d . p . c . mouse embryo and Cenpj-deficient mice presented with a number of ocular abnormalities . Furthermore , a small number of reports suggest that Seckel-like syndromes are associated with precocious puberty or premature thelarche [44] , [45] . In contrast , female Cenpjtm/tm mice showed signs of delayed puberty , although the reproductive tract appeared normal at 16 weeks . We built a protein-protein interaction network using all known Seckel Syndrome associated genes as query ( Figure S6B ) . By using gene ontology enrichment analysis we showed that 265 biological processes ( level 3 classification ) are significantly over-represented in the Seckel syndrome network ( Table S1 ) . As expected , many of the processes were involved in the regulation of cell cycle , cell growth and cell death . Interestingly , the network was also enriched for genes involved in the ‘response to hormone stimulus’ ‘ovulation cycle process’ and ‘sex differentiation’ ( Table S1 ) . Transient insulin resistance during puberty is a well documented phenomenon [80] . Cenpjtm/tm mice of both genders had a delayed response to glucose challenge although this was more marked in 16 week-old female mice , which may be explained by delayed puberty in female Cenpjtm/tm mice . While there are no reports of an association between abnormal glucose homeostasis and Seckel syndrome , interestingly , most individuals with MOPDII , including PCNT-MOPDII , develop insulin resistance and diabetes during childhood [81] , [82] . Aside from centrosome-mediated regulation of the cell-cycle , PCNT is thought to regulate insulin secretory vesicle docking in mouse pancreatic β-cells [83] . Whether CENPJ plays a role in glucose homeostasis remains to be determined . Finally , the proportion of CD8+CD3+ T cells were elevated in Cenpjtm/tm mice , although this was more pronounced in males . The Seckel syndrome protein-protein interaction network that we generated was significantly enriched for genes involved in ‘leukocyte mediated immunity’ , ‘leukocyte mediated cytotoxicity’ , ‘leukocyte activation’ and ‘interleukin-2 production’ ( Table S1 ) . While we are uncertain of the biological basis for these relationships , it will be interesting to see whether there are clinical correlates for these abnormalities and whether other mouse models of Seckel syndrome or primordial dwarfism share these anomalies . Mouse models of Seckel syndrome may go some way towards the molecular genetic delineation of this heterogeneous condition . The generalized activation of apoptosis as a result of genomic instability in ATR-Seckel and Cenpjtm/tm mouse embryos provides one explanation for the proportionate dwarfism of Seckel syndrome patients . In agreement with the intron 11 CENPJ-Seckel mutation , which results in the formation of three transcripts , we showed that Cenpj expression is rescued to some extent by cryptic splicing over the cassette to produce a variety of truncated mRNAs , and that there is a moderate degree of individual variation in the ability of an organism to perform this rescue . These data highlight the need for detailed mRNA expression , splicing studies , and protein analysis to establish how individual mutations affect the normal and cryptic splicing of CENPJ mRNAs for each patient directly , and not with prediction analysis tools , so as to understand why some CENPJ mutations cause microcephaly and others Seckel syndrome and how the same CENPJ mutation can cause clinical heterogeneity [4] .
Mutant mice carrying the Cenpjtm1a ( EUCOMM ) Wtsi allele were generated on a C57BL/6NTac; C57BL/6-Tyrc-Brd background ( clone EPD0028_7_G05 ) by the Sanger Mouse Genetics Project as part of the European Conditional Mouse Mutagenesis Program ( EUCOMM; [26] ) . Correct gene targeting in founder mice was determined by a combination of standard PCR and quantitative PCR ( qPCR; see Figure S1 for more details ) . Following confirmation of correct targeting , mice were genotyped for the Cenpjtm1a ( EUCOMM ) Wtsi allele by PCR using primers specific to the wildtype and mutant Cenpj alleles and to LacZ ( Figure S1D ) . In individual experiments , all mice were matched for age and gender . A cumulative baseline was generated from data arising from controls from the same genetic background , age and gender . The care and use of all mice in this study was carried out in accordance with UK Home Office regulations , UK Animals ( Scientific Procedures ) Act of 1986 . Mice were maintained in a specific pathogen free unit on a 12 hr light: 12 hr dark cycle with lights off at 7:30pm and no twilight period . The ambient temperature was 21±2°C and the humidity was 55±10% . Mice were housed using a stocking density of 3–5 mice per cage ( overall dimensions of caging: ( L×W×H ) 365×207×140 mm , floor area 530 cm2 ) in individually ventilated caging ( Tecniplast Seal Safe1284L ) receiving 60 air changes per hour . In addition to Aspen bedding substrate , standard environmental enrichment of two nestlets , a cardboard Fun Tunnel and three wooden chew blocks was provided . Mice were given water and diet ad libitum . At 4 weeks of age , mice were transferred from Mouse Breeders Diet ( Lab Diets , 5021–3 ) to a high fat ( 21 . 4% fat by crude content ) dietary challenge ( Special Diet Services , Western RD 829100 ) . To test for expression of Cenpj and cryptic splicing , total RNA was isolated from mouse embryonic fibroblasts ( 13 . 5 days post coitum ( d . p . c . ) , n = 3 ) using the RNeasy minikit ( Qiagen ) . RT-PCR was performed using the BD Sprint kit containing random hexamers ( BD Clontech , CA , USA ) . Primers were designed to exon boundaries and sequences are available on request . cDNA was quantified using SYBR Green on an ABI7900HT ( ABI , CA , USA ) . Gene expression was normalised to Gapdh and to wild-type control . Protein extracts were prepared from mouse embryonic fibroblasts ( 13 . 5 d . p . c . ) by directly harvesting cells in Laemmli buffer . Proteins were separated by SDS-PAGE , and membranes were incubated with primary antibodies to CENPJ ( Stratech , Newmarket , UK . Rabbit 1∶500 ) , KAP1 ( Abcam , Cambridge , UK . Rabbit 1∶10 000 ) , KAP1 phospho-Ser-824 ( Bethyl . Texas , US . Rabbit 1∶1000 ) , Chk1 phospho-Ser-345 ( Cell Signaling , Boston , US . Rabbit 1∶5000 ) , RPA phospho-Ser-4/8 ( Bethyl , Texas , US . Rabbit 1∶10 000 ) , H2AX phospho-Ser-139 ( γH2AX; Millipore , Billerica , US . Mouse 1∶1000 ) . Camptothecin was from Sigma ( Poole , UK ) . LacZ staining was performed on tissues perfused , removed and fixed ( 30 min ) with 4% paraformaldehyde ( pH8 ) . Tissues were then washed three times in PBS . Tissues were then incubated at 4°C for 48 h in staining buffer ( 5 mM K3Fe ( CN ) 6 , 5 mM K4Fe ( CN ) 6 , 0 . 02% IGEPAL CA-630 , 0 . 01% deoxycholate , 2 mM MgCl2 , 1 mg/ml bromo-chloro-indolyl-galactopyranoside in the dark . Tissues were fixed again with 4% paraformaldehyde overnight at 4°C before being transferred through increasing glycerol concentrations and archiving in 70% glycerol+0 . 01% sodium azide . At 18 . 5 d . p . c . litters were harvested and euthanized . The crown-rump length was measured and measurements of skulls were adapted from published methods used to assess adult mouse skulls [84] . Embryos were scalded at 67°C for 30 seconds to facilitate removal of skin , muscle and fat . Dissected embryos were then fixed in 100% ethanol for 48 h and transferred to acetone for 48 h to further de-fat the skeletons . Skeletal embryos were then stained with 0 . 015% Alcian Blue ( Sigma-Aldrich , UK; in Ethanol/Glacial Acetic Acid ) for 24 h , washed five times in 100% ethanol and transferred to 0 . 1% potassium hydroxide overnight . Skeletons were then stained with 0 . 005% Alizarin Red ( Merck , UK; in 1% KOH ) for 3 h , washed with 1% KOH three times and transferred to 20% Glycerol/1% KOH for clearing for ∼3 days . Skeletons were then transferred through increasing glycerol concentrations and archived in 70% glycerol +0 . 01% sodium azide . X-ray imaging was performed at 14 weeks of age using a Faxitron MX-20 cabinet ( Faxitron Bioptics , IL , USA ) . Mice were anesthetized with a preparation of 100 mg/kg Ketamine/10 mg/kg Xylazine . Weight and body length were measured prior to the scan . Five images were acquired: dorsoventral and lateral images of the whole body at ×1 magnification , dorsoventral and lateral images of the head at ×4 magnification and a dorsoventral image of the left forepaw at ×5 magnification . Images were acquired using an energy of 23 kV for 10 seconds per image . Images were studied visually to assess abnormalities within 41 standard parameters . To show that humeri were anatomically disproportionate the right greater tubercle - deltoid tuberosity length ( mm ) was normalized to the right greater tubercle – trochlea length ( mm ) and data were represented as a percentage . Adult tissue samples were fixed in 10% neutral buffered formalin and E14 . 5 embryos were fixed in 4% PFA . Samples were dehydrated , paraffin embedded and 4 µm sections were cut before hematoxylin and eosin staining . Neuron densities of E14 . 5 embryo brains were determined by counting the number of nuclei in three different areas that were 75 µm2 for the densely populated ventricular zones and 150 µm2 for the less densely populated mid-striatum . Adult neuroanatomical measurements ( Figure S3 ) were carried out on 40 µm thick Nissl stained sections using ImageJ freeware ( NIH ) . Heart fibrosis was assessed by Masson's Trichrome and X-zone pigmentation of the adrenals was confirmed by Periodic Acid-Schiff's staining using standard methods . Whole mouse eyes were enucleated , fixed , and sectioned for histological studies as previously described [85] . Sections of paraffin embedded tissues were dewaxed and antigen retrieval was performed in boiling 10 mM citrate buffer pH6 . Endogenous peroxidases were quenched in 3% hydrogen peroxide ( Sigma , UK ) before blocking in normal serum ( VectorLabs , UK ) . Sections were incubated in primary antibodies to Cenpj ( Stratech ) , cleaved caspase-3 ( Cell Signaling Technologies , CA , USA ) , Ki67 ( DAKO Ltd , UK ) and phospho-Ser 139 H2AX ( γH2AX , Cell Signaling Technologies , CA , USA ) . Vectorstain ABC kit and DAB ( VectorLabs , UK ) were used according to the manufacturer's instructions . Sections were counterstained with haematoxylin before clearing and mounting . The number of cells positive for cleaved-caspase 3 or γH2AX were counted in two areas of 75 µm2 the striatum , cortex and pro-hippocampus and data shown are the percentage of total cells in the area . Counts were performed independently by two individuals to confirm reproducibility . At nine weeks of age , open field , grip strength and modified SHIRPA were performed as described previously [86] , [87] . Hot plate assessment was performed at 10 weeks of age using standard techniques . Tests for social recognition and olfaction ( Cenpjtm/tm n = 9 and Cenpj+/+ n = 8 ) were performed on male mice at 3–6 months of age . Mice were habituated to a test arena identical to their home cage for 10 min . For social recognition , a stimulus mouse was placed into the test arena for 1 min , repeated four times at 10 min intervals . In the fifth trial , a second stimulus mouse was presented . 24 h later the test animals were presented with the familiar animal from trials 1–4 and a new unfamiliar animal , for 2 min . Trials were performed under red light and recorded with an overhead camera and the videos scored blind of genotype . The amount of time the test animal spent investigating , by oronasal contact or approaching within 1–2 cm , was recorded . Stimulus animals , 2–4 months old , were weight-matched to Cenpjtm/tm mice and sedated with ketamine/xylazine ( i . p . 1 g/0 . 1 g per kg of body weight ) . C57BL/6NTac mice were used for trials 1–4 and for the 24 h discrimination test , 129P2/OlaHsd mice were used for trial 5 . Three animals ( two Cenpjtm/tm and one Cenpj+/+ ) were taken out of the analysis because of their low investigation times ( less than 10 s on trial one or during the discrimination test ) . We assessed glucose tolerance in mice fed on a high-fat diet ( Western RD , 829100 , Special Diets Services ) from 4 weeks of age until 13 weeks of age . At 13 weeks , mice were fasted overnight before a blood sample was taken and glucose was measured using an Accu-Chek Aviva ( Roche ) . To perform an intra-peritoneal glucose tolerance test ( IP-GTT ) , mice were fasted for 16 h , a bolus of glucose was administered intraperitoneally and blood glucose concentration from the tail vein was measured using Accu-Chek Aviva ( Roche ) after 15 , 30 , 60 and 120 min . We performed clinical chemistry on 16-week-old mice . Mice were terminally anaesthetized and blood was collected from the retro-orbital sinus into lithium-heparin tubes . The plasma was immediately analyzed on an Olympus AU400 Analyzer . At 16 weeks of age blood samples were collected into heparin-coated tubes . The main leukocyte populations in peripheral blood were characterized by 8-colour flow cytometry with an LSRII ( BD Bioscience , UK Biosciences , UK ) and associated software . Briefly , samples were centrifuged at 5000 g for 10 min at 8°C to remove the plasma layer . Red blood cells were lysed ( Pharmalyse , BD Bioscience , UK Biosciences , UK ) and samples were centrifuged at 400 g for 3 min to pellet the white cells . White cells were resuspended in buffer ( PBS pH 7 . 45 containing 0 . 5% BSA ) , transferred to 96-well plate and washed in buffer several times before incubation in 50 µl 10 µg/ml Mouse FcBlock ( BD Bioscience , UK Biosciences , UK ) for 15 min on ice . Cells were washed in buffer and incubated with 50 µl antibody mix from two staining panels ( Tables S2 and S3 ) for 15 min on ice . Propidium iodide ( 2 . 5 mg/ml; Sigma , UK ) was added to each well and cells were incubated for a further 5 min . Cells were washed in buffer several times before 30 000 propidium iodide-negative , CD45-positive events were collected . Data were interpreted using FlowJo ( v7 . 6 , Tree Star , Inc . , OR , USA ) . The frequency of micronucleated normochromatic erythrocytes was determined by flow cytometry ( FC500 , Beckman Coulter , USA ) as described previously [88] and data were interpreted using FlowJo ( v9 . 3 . 1 , Tree Star , Inc . , OR , USA ) . Cell cycle analysis was performed on mouse embryonic fibroblasts ( 13 . 5 d . p . c . ) . Briefly , cells were fixed in 70% ethanol overnight and stained with PI solution before analyzing on a flow cytometer ( LSR Fortessa , BD , USA ) . A total of 10 , 000 events were acquired per sample . The cells were gated on PI fluorescence area versus PI fluorescence width to discriminate any doublets and clumps . The gated events were displayed on a histogram plot of PI fluorescence area . Data were analyzed using FlowJo ( v9 . 3 . 1 , Tree Star , Inc . , OR , USA ) . Mouse embryonic fibroblasts ( 13 . 5 d . p . c . ) were collected and cultured using standard methods . Primary antibodies used in this study were CDK5RAP2 ( Bethyl ) , centrin-3 ( Abnova ) , TPX2 ( Abnova ) , gamma-tubulin ( GTU88; Sigma-Aldrich ) , alpha-tubulin ( DM1A; Sigma-Aldrich ) . Secondary antibodies conjugated to Alexa Fluor 488 and 555 ( Invitrogen ) were used . DNA was stained with Hoescht ( Sigma-Aldrich ) . To detect centrosomal markers , cells were fixed in −20°C methanol for 5 min . After fixation , cells were processed for immunofluorescence and microscopy as described in [89] . For centriole counts in Figure 4C cells were treated with 100 µM monastrol for 16 h before fixation . Cells were irradiated with 3 Gy ionizing radiation using a Faxitron cabinet X-ray system and left recover for 8 h in the presence of 1 µg/ml of nocodazole ( Sigma , Poole , UK ) to trap mitotic cells . Cells were trypsinized and fixed with 4% paraformaldehyde , permeabilized with 1× phosphate buffered saline containing 0 . 2% Triton X-100 for 30 min on ice , and incubated in the presence of MPM2 primary antibodies ( Millipore , Billerica , US . Mouse 1∶100 ) and then Alexa-Fluor-488 secondary antibodies ( Life Technologies , Paisley , Scotland . Goat anti-mouse 1∶200 ) to detect mitotic cells . Metaphase spreads from MEFs ( Cenpjtm/tm and Cenpj+/+ littermates ) were prepared as for metaphase FISH and multiplex-FISH was carried out as previously described [90] . A network of Protein-Protein interactions for Seckel syndrome genes was generated using the Cytoscape 2 . 8 . 1 [91] plug-in BisoGenet 1 . 41 . 00 [92] . The Entrez Gene symbols of the Seckel syndrome associated genes ( CENPJ , PCNT , CEP152 , ATR , RBBP8 ( CtIP ) , SCKL3 , Entrez Gene IDs = 55835 , 5116 , 22995 , 545 , 5932 and 386616 respectively ) [4] , [5] , [6] , [41] were used as query to build a network of experimentally validated Protein-Protein interactions , by adding neighbours to the input nodes up to a distance of one . All the interactions are downloaded by BisoGenet from its own database SysBiomics and have been validated by one or different experimental methodologies such as X-ray crystallography , surface plasmon resonance , two hybrid systems , three hybrid systems and Western blot . The network's characteristic path length was calculated using Cytoscape's built-in plug-in NetworkAnalyzer [93] . The GOs over-representation analysis was performed using the Over-representation analysis tool of the Consensus Path Database website [94] . Statistical significance of the different GOs represented in our network was calculated by the website's tool using a Hypergeometric test . After calculating the p-value , the tool corrects it for the false discovery rate generating the corrected q-value . A q-value <0 . 01 was used as threshold for all significant results . A Shapiro-Wilk normality test was performed to assess whether data were normally distributed followed by an F-Test to assess whether equality of variances could be assumed . The significance of the difference between the means of both data sets was tested by applying a two-sided T-Test , assuming variance equality whenever the F-Test was positive ( P>0 . 05 ) . For all cases where the Shapiro-Wilk normality test was negative ( P>0 . 05 ) a Mann-Whitney non-parametric test was applied to assess the significance . Statistical analyses were performed using R-2 . 13 . 0 [95] . | Mutation of the gene CENPJ has been found to cause primary microcephaly , an inherited disorder that is characterised by severely reduced brain size . More recently , mutation of CENPJ has been associated with Seckel syndrome , a disorder that is characterised by a severe reduction in both brain and body size that is apparent at birth , mental retardation , and skeletal abnormalities , in addition to a number of other clinical manifestations . Here , we have generated a mouse that expresses only low levels of mouse Cenpj protein and find that it recapitulates many of the key features of Seckel syndrome . Moreover , we find that errors during the proliferation of Cenpjtm/tm cells frequently lead to abnormal numbers of chromosomes or damaged chromosomes , which is likely to be the cause of increased cell death during embryonic development and to contribute to the proportionate dwarfism that is characteristic of Seckel syndrome . | [
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] | 2012 | Disruption of Mouse Cenpj, a Regulator of Centriole Biogenesis, Phenocopies Seckel Syndrome |
In pathogenic HIV and SIV infections of humans and rhesus macaques ( RMs ) , preferential depletion of CD4+ Th17 cells correlates with mucosal immune dysfunction and disease progression . Interleukin ( IL ) -21 promotes differentiation of Th17 cells , long-term maintenance of functional CD8+ T cells , and differentiation of memory B cells and antibody-secreting plasma cells . We hypothesized that administration of IL-21 will improve mucosal function in the context of pathogenic HIV/SIV infections . To test this hypothesis , we infected 12 RMs with SIVmac239 and at day 14 post-infection treated six of them with rhesus rIL-21-IgFc . IL-21-treatment was safe and did not increase plasma viral load or systemic immune activation . Compared to untreated animals , IL-21-treated RMs showed ( i ) higher expression of perforin and granzyme B in total and SIV-specific CD8+ T cells and ( ii ) higher levels of intestinal Th17 cells . Remarkably , increased levels of Th17 cells were associated with reduced levels of intestinal T cell proliferation , microbial translocation and systemic activation/inflammation in the chronic infection . In conclusion , IL-21-treatment in SIV-infected RMs improved mucosal immune function through enhanced preservation of Th17 cells . Further preclinical studies of IL-21 may be warranted to test its potential use during chronic infection in conjunction with antiretroviral therapy .
Pathogenic Human Immunodeficiency Virus ( HIV ) and Simian Immunodeficiency Virus ( SIV ) infections in humans and RMs , respectively , are characterized by the establishment of a state of persistent and aberrant activation of the immune system [1] , [2] . Two key findings highlight the importance of immune activation to disease progression in HIV/SIV infections . First , the level of chronic immune activation is a strong independent predictor of disease progression in the natural history of HIV infection , and associates with impaired immune reconstitution in HIV-infected individuals on antiretroviral therapy ( ART ) [3] , [4] , [5] . Second , the absence of chronic immune activation is a central feature of nonpathogenic SIV infections in natural hosts such as sooty mangabeys ( SMs ) , African green monkeys ( AGMs ) and Mandrills [6] , [7] . While the causes of immune activation during chronic HIV/SIV infections are complex and not fully defined , several studies indicated that mucosal immune dysfunction and associated loss of mucosal barrier integrity are key contributors to this process . In particular , the HIV/SIV-associated mucosal immune dysfunction appears to favor the translocation of microbial products from the intestinal lumen into the systemic circulation , where these products activate various innate immune pathways and exert a sustained pro-inflammatory effect [8] , [9] , [10] , [11] . Chronic immune activation related to loss of mucosal barrier integrity and microbial translocation has been implicated in other pathological conditions including graft versus host disease , inflammatory bowel disease , and pancreatitis ( reviewed in [12] ) . Enterocyte apoptosis [9] , [13] , massive loss of mucosal CD4+ T cells [14] , [15] , [16] and/or preferential loss of intestinal Th17 cells [17] have all been proposed as factors contributing to the breakdown of epithelial integrity during chronic HIV/SIV infections . Several lines of evidence indicate that Th17 cells , and their signature cytokines IL-17 and IL-22 , play a key role in the maintenance of structural and immunological integrity of mucosal sites [18] , [19] . Effects of IL-17 and IL-22 include ( i ) stimulation of epithelial cells to express cytokines , chemokines and metalloproteinases involved in the recruitment , activation and migration of neutrophils to areas of bacterial infection ( reviewed in [20] ) ; ( ii ) production of antimicrobial molecules , such as defensins , by various cell types [21] , [22]; and ( iii ) preservation of the integrity of the epithelial barrier by stimulating the proliferation of enterocytes and the transcription of tight-junction proteins , such as claudins [10] , [23] . Consistent with these biological activities are the findings that Th17 cells confer protection against various extracellular pathogens , and that Th17 cell-mediated proinflammatory activity , when not properly regulated , may result in tissue damage and development of autoimmunity [24] , [25] . Intestinal Th17 cells are preferentially depleted during pathogenic HIV and SIV infection of humans and RMs , with the severity of their depletion being correlated with the levels of systemic immune activation and disease progression [17] , [26] , [27] , [28] , [29] , [30] , [31] . Of note , despite a significant depletion of bulk CD4+ T cells from the GI tract , Th17 cells are maintained at a healthy frequency during nonprogressive SIV infections of SMs and AGMs , which are characterized by preservation of mucosal immune function and lack of microbial translocation [8] , [26] , [28] , [32] . Collectively , these published data suggest that lower levels of Th17 cells contribute to the mucosal immune dysfunction and chronic immune activation that distinguish pathogenic from nonpathogenic HIV/SIV infections , and underscore the potential value of novel therapeutic approaches aimed at promoting Th17 cells . Interleukin ( IL ) -21 is the most recently identified member of the common γ-chain cytokine family that includes IL-2 , IL-4 , IL-7 , IL-9 , and IL-15 [33] , [34] . IL-21 is a multifunctional and pleiotropic cytokine mainly produced by activated CD4+ T cells , including follicular helper T cells , and NKT cells [33] , [34] , [35] . IL-21 exerts numerous immune-enhancing and immune-regulatory functions , including ( i ) favoring the long-term maintenance of functional CD8+ T-cells [36] , [37] , [38] , [39] , ( ii ) promoting the differentiation of memory B cells and antibody-secreting plasma cells [40] , [41] , [42] , [43] , and ( iii ) maintaining an adequate pool of mature , fully functional Th17 cells [44] , [45] , [46] . Reduced plasma levels of IL-21 have been found in HIV-infected individuals [47] , [48] , and we recently showed that pathogenic SIV-infection of RMs , but not nonpathogenic SIV infection of sooty mangabeys is associated with a significant loss of IL-21-producing CD4+ T cells [49] . Since pathogenic HIV/SIV infections are associated with both depletion of Th17 cells and overall reduced availability of IL-21 , we hypothesized that in vivo administration of IL-21 may result in improved immune function in HIV-infected humans and SIV-infected RM . To test this hypothesis , in this study we infected twelve RMs with SIVmac239 and at day 14 post-infection ( p . i . ) treated six of them with rhesus rIL-21-IgFc ( IL-21 throughout the manuscript ) . Our results indicate that in vivo administration of IL-21 during acute SIV infection beneficially impacts mucosal immune function by elevating levels of intestinal Th17 cells compared to controls during the treatment period without undesirable effects on viral load . Remarkably , higher levels of intestinal Th17 cells were associated with reduced levels of intestinal immune activation , microbial translocation and systemic inflammation in the chronic phase of infection .
Twelve RMs were infected intravenously ( i . v . ) with 300 TCID50 SIVmac239 ( day 0 ) . Starting from day 14 post-infection ( p . i . ) , six animals were treated with five weekly doses of 50 µg/kg IL-21 ( s . c . ) , which represents a dosage similar or lower to those tested in phase I and II clinical trials in humans ( IL-21-treated group; orange in graphs ) [49] . Specifically , the animals received IL-21 at days 14 , 21 , 28 , 35 and 42 p . i . ( Figure 1 ) . The other six SIV-infected RMs remained untreated and were used as controls ( control group; black in graphs ) . The rationale to start therapy in the early phase of infection is that the loss of intestinal Th17 cells in SIV-infected RMs is present within the first few weeks of the initial infection [27] . At numerous experimental points pre- and post-infection , as well as pre- and post-IL-21 treatment , peripheral blood ( PB ) , rectal biopsies ( RB ) , and lymph nodes ( LN ) were collected from all 12 RMs . All sample collections were performed prior to IL-21 administration at day 14 p . i . ; therefore , this experimental point represents the pre-treatment baseline . To study the effect of IL-21 administration on the immune system , samples were collected 3 days after the IL-21 doses . RRq7 , in the IL-21-treated group , died at day 75 p . i . Thus , in all graphs the arithmetic mean for IL-21-treated animals includes six RMs up to day 75 and five at later time points . We first examined the effects of IL-21 on the kinetics of SIV plasma viremia . As shown in Figure 2A ( individual animals ) and 2B ( mean±SEM for the two experimental groups ) , following experimental infection with SIVmac239 both groups of RMs experienced a rapid , exponential increase in virus replication that peaked before initiation of IL-21 treatment ( at approximately day 14 p . i . ) in four out of six IL-21-treated and three out of six control monkeys . Peak viremia occurred on d21 p . i . in two IL-21-treated animals ( RMq8 and RFj7 ) and in three controls ( RLz8 , RUj7 and RRk8 ) . In the IL-21 treatment group , two animals ( RIp10 and RMq8 ) showed lower post-peak viremia , while a third animal ( RRq7 ) , showed viral load remarkably higher than the other RMs already at day 14 p . i . , i . e . , before IL-21 treatment . This animal did not seroconvert ( data not shown ) and died at day 75 p . i . , thus resembling the phenotype of a rapid progressor [50] . Although the viral load set point was very heterogeneous within each group , there was no significant difference between the two groups at any single experiment time point ( Figure 2A , B ) . Of note , this study was not designed to assess how IL-21 affects survival in SIV-infected RMs , since all animals were started on antiretroviral therapy at wk26 p . i . These limitations notwithstanding , we did not find any significant difference in survival rates , with three out of six animals in both the IL-21 treated group ( including the rapid progressor ) and the controls group succumbing to AIDS before or immediately after initiation of ART ( data not shown ) . We next assessed the effects of IL-21 on the levels of T cells by determining the relative frequencies ( in PB , LN , and RB ) and absolute numbers ( in PB ) of CD4+ and CD8+ T cells in the sampled anatomical sites . The kinetics of the percentages ( Figure 3A ) and absolute number ( expressed as cells per µl , Figure 3B ) of circulating CD4+ T cells were overall very similar in IL-21-treated and control RMs , with no significant differences among the two groups at any single experimental points . Similar to CD4+ T cells , the percentages of circulating CD8+ T cells were also not affected by IL-21 treatment ( Figure 3C ) , although their numbers were significantly higher in IL-21-treated RMs than controls at wks 10 ( P = 0 . 04 ) and 14 ( P = 0 . 03 ) p . i . , when a mixed linear-effects model was used to assess the longitudinal significance of differences between groups ( Figure 3D ) . Similarly , IL-21 treatment did not have a significant impact on T cells in the LN , with the overall percentages of CD4+ and CD8+ T cells remaining very similar between IL-21-treated and controls animals at all tested experimental points ( data not shown ) . We next determined the effects of IL-21 treatment on intestinal T cells by measuring the levels of CD4+ and CD8+ T cells in RBs . This part of the study is important since mucosal CD4+ T cells are severely depleted during the acute phase of HIV and SIV infections , and the severity of this depletion correlates with disease progression [14] , [15] , [16] , [51] . Consistent with previous findings , SIV infection in RMs was associated with a rapid and severe depletion of CD4+ T cells in the RB ( Figure 3E ) . Interestingly , the percentages of intestinal CD4+ T cells increased between wk6 and wk23 p . i . ( 7 . 90±3 . 00 vs . 13 . 89±6 . 51 ) in IL-21-treated RMs , but decreased ( 8 . 35±0 . 97 vs . 5 . 20±1 . 58 ) during the same time period in control animals ( Figure 3E ) . However , percentages of CD4+ T cells were not statistically significant at any single experimental time point , and the two animals that better control viral load were those with higher percentages of CD4+ T cells post-infection ( Figure S1A ) . The difference in the levels of intestinal CD4+ T cells between the two groups is more evident when the results are represented as fold change relative to the percentages of CD4+ T cells at wk2 , thus accounting for the variability of the intestinal CD4+ T cell depletion during the first two weeks of infection ( Figure 3F and Figure S1B ) . This analysis showed that at wk23 p . i . IL-21-treated RMs maintained a significant fraction of the total CD4+ T cells that were present at wk2 p . i . ( fold change: 0 . 54±0 . 12; Figure 3F ) , while in control animals the levels of CD4+ T cells at wk23 dropped to 0 . 12±0 . 03 of those present at wk2 p . i . ( fold change ) , with this difference being statistically significant ( Figure 3F; P = 0 . 0018 ) . Of note , this effect of IL-21 was specific for CD4+ T cells , since no significant variations were found in the percentages of intestinal CD8+ T cells ( data not shown ) . Together with those of Figure 2 , these data indicate that IL-21 treatment in the acute phase of SIV infection of RMs has a limited impact on plasma viremia and on the levels of circulating T cells , but appears to be associated with a better preservation of intestinal CD4+ T cells . Previous studies showed that IL-21 is needed for long-term maintenance of functional CD8+ T cells in LCMV-infected mice [37] , [52] , [53] , [54] and stimulates production of cytotoxic molecules in human CD8+ T cells in vitro [55] . Based on these data , we sought to determine by flow cytometry the intracellular levels of perforin and granzyme B ( GrB ) in total and SIV-specific ( gag-tetramer+ ) CD8+ T cells , as well as their maturation subsets . Perforin and GrB analysis was performed at wks-2 ( pre-infection ) , 2 ( pre-IL-21 administration ) , 4 and 6 ( during IL-21 treatment ) , 10 and 23 p . i . in PBMC; at wks-2 , 2 , 4 , 6 and 23 in RB and at wks-2 , 2 , 6 and 23 in LN . As shown in Figure 4 , no significant differences were noted for baseline levels of perforin or GrB between IL-21-treated and control RMs in all studied anatomical sites . In comparison to control animals , IL-21-treated RMs manifested significant increases in the frequency of perforin positive CD8+ T cells in PBMC ( P = 0 . 0038 ) , LN ( P = 0 . 033 ) and RB ( P = 0 . 001 ) at wk6 p . i . , i . e . , after the final IL-21 dose ( Figure 4A ) . Interestingly , the percentage of CD8+perforin+ T cells in PBMC was still significantly higher in IL-21-treated than control RMs at wk10 p . i . , i . e . approximately one month after the last dose of IL-21 ( Figure 4A; P = 0 . 014 ) . Analysis of GrB expression showed a pattern very similar to that found for perforin , with the percentage of CD8+GrB+ T cells being significantly higher in IL-21-treated RMs at wks6 and 10 in PBMC ( P = 0 . 038 and 0 . 024 , respectively ) and at wks4 and 6 in RB ( P = 0 . 022 and 0 . 0044 ) , despite very similar levels before starting treatment ( Figure 4B ) . Differently from perforin , no differences were found in the percentages of CD8+GrB+ T cells in LN . The increase in perforin and GrB expression was specific for both central ( CD28+CD95+CCR7+ ) and effector memory ( CD28−CD95+CCR7− ) CD8+ T cell subsets of PBMC , LN and RB ( data not shown ) . We then investigated the ability of IL-21 to induce perforin and GrB in virus specific ( SIV gag tetramer+ ) CD8+ T cells in the six Mamu-A*01 animals ( three in each group ) included in the study . IL-21 did not change the frequencies of CD8+tet+ cells , which increased similarly in both groups following infection ( data not shown ) . However , similar to the observations in total CD8+ T cells , IL-21 significantly increased the percentages of SIV-specific CD8+perforin+ T cells at wk4 p . i . in RB ( P = 0 . 015 ) , at wk6 in PBMC ( P = 0 . 03 ) , LN ( P = 0 . 041 ) , and RB ( P = 0 . 028 ) , and at wk10 in PBMC ( P = 0 . 006 ) compared to control animals ( Figure 4C ) . Importantly , only in IL-21-treated RMs were the frequencies of SIV-specific CD8+perforin+ T cells in PBMC ( P = 0 . 0134 ) , LN ( P = 0 . 036 ) and RB ( P = 0 . 042 ) significantly higher at wk6 than wk2 p . i . , thus confirming the ability of IL-21 to specifically induce perforin production in virus specific CD8+T cells from different anatomical sites ( Figure 4C ) . Similar results were found for GrB , with the frequencies of SIV-specific CD8+GrB+ T cells being significantly higher in IL-21-treated animals than controls at wk6 in PBMC , LN and RB , and at wk10 in PBMC ( Figure 4D ) . Finally , we investigated the effects of IL-21 administration on perforin and GrB production by CD4+ T cells . The effects on perforin induction were limited , with the percentages of CD4+perforin+ T cells in PBMC , LN and RB remaining very similar in IL-21-treated and controls animals throughout the study ( Figure S2A ) . In contrast to perforin expression , the frequencies of CD4+GrB+ T cells were significantly increased in IL-21-treated RMs at wk6 in PBMC , LN and RB , and sustained through wk10 in PBMC ( Figure S2B ) . These data underscore the ability of IL-21 to enhance and/or maintain the expression of the T cell cytotoxic granules perforin and GrB in total and virus specific CD8+ T cells in various anatomical sites of SIV-infected RMs . Among its immunologic functions , IL-21 has been shown to promote differentiation of antigen-stimulated B cells into memory B cells and antibody-secreting plasma cells [40] , [41] , [42] , [43] . For this reason , we then sought to investigate if administration of IL-21 to SIV-infected RMs could impact the levels of the different B cell maturation subsets , which quantification was performed using a combination of antibodies for CD3 , CD20 , CD21 , CD27 , and IgD [56] , [57] , [58] . Consistent with previous reports [56] , [58] , animals in both groups showed a significant decrease in the blood frequencies of memory B cells ( CD3−CD20+CD21hiCD27+ ) at wk2 p . i . ( Figure S3A ) . While in the percentages of memory B cells further decline at wk6 and wk10 p . i . in control animals , their levels stabilize or even increase in IL-21-treated RMs . Indeed , the percentages of circulating memory B cells were significantly higher in IL-21-treated than control animals at wk6 and wk10 p . i . ( P = 0 . 004 for both time points ) . At the same experimental points , IL-21-treated RMs also showed higher frequencies of switch memory B cell ( CD3−CD20+CD21hiCD27+IgD− ) when compared to controls ( Figure S3B; wk6 P = 0 . 016; wk10 P = 0 . 042 ) . Finally , we investigated the effects of IL-21 treatment on the levels of anti-SIV antibodies . Total levels and kinetics of anti-SIV antibodies were virtually indiscernible between IL-21 treated and control RMs ( Figure S3C ) . All together , these findings suggest IL-21 treatment impacts the homeostasis of the different B cell maturation subsets by increasing the frequency of memory B cells while reducing their levels of activation . IL-21 is a key Th17-inducing cytokine , as it is able to promote both development and survival of these cells [44] , [45] , [46] , [59] . Based on these findings , we next assessed the effects of IL-21 treatment on the levels of intestinal Th17 cells , identified by flow cytometry as CD4+IL-17+ T cells after brief in vitro stimulation with PMA and ionomycin . IL-21-treated SIV-infected RMs showed levels of Th17 cells consistently higher compared to those found in controls throughout the entire treatment period ( Figure 5A ) . Remarkably , after an initial depletion from day 0 to day 14 p . i . , the percentage of Th17 cells stabilized or even increased in IL-21-treated animals up to wk6 p . i . , in marked contrast to the rapid and severe depletion of Th17 cells observed in controls ( Figure 5A; P = 0 . 004 at wk6 ) . The efficacy of IL-21 in preserving intestinal Th17 cells is particularly clear when the levels of these cells are expressed as fold change compared to pre-treatment ( i . e . , wk2 p . i . ) . As shown in Figure 5B , the fold change in the percentages of Th17 cells between wk6 ( end of treatment ) and wk2 was significantly higher for IL-21-treated than controls RMs ( 1 . 73±0 . 11 vs . 0 . 69±0 . 15; P = 0 . 0002 ) . Furthermore , better preservation of Th17 cells was noted in all six SIV-infected RMs , independent of their viral load or severity of intestinal CD4+ T cell depletion ( Figure 5B ) . Despite this beneficial effect of IL-21 treatment observed at wk6 , the levels of intestinal Th17 cells in IL-21-treated RMs declined to levels comparable to those found in control animals by wk23 p . i . , i . e . , 4 months after the last administration of IL-21 ( Figure 5A ) . These data suggest that the higher levels of intestinal Th17 cells were specifically related to IL-21 treatment and that its beneficial effects on Th17 cells were transient . Of note , the levels of Th17 cells were overall similar in blood ( Figure S4 ) and LN ( data not shown ) of treated and control RMs . Moreover , we found that IL-21 acted selectively on Th17 cells , since blood and intestinal CD4+IFN-γ+ or CD4+IL-2+ T cells were depleted in a very similar way in IL-21-treated and untreated RMs ( Figure S4 ) . A representative co-staining of IL-17 and IFN-γ within intestinal CD4+ T cells is shown in one IL-21-treated and one control RM at wks-2 ( pre-infection ) , 4 ( during the IL-21 treatment ) and 23 p . i . ( Figure 5C ) . Consistent with an effect of IL-21 on Th17 cells , plasma levels of IL-22 were higher in IL-21-treated than control animals at wk6 post infection ( fold change wk6 vs . wk2: 5 . 6±1 . 82 vs . 1 . 02±0 . 39; P = 0 . 056; Figure S5 ) . Finally , in order to assess the extent of the loss of integrity of the epithelial barrier of the GI tract , we performed immunohistochemistry analysis for the polymorphonuclear neutrophil ( PMN ) infiltration in rectal biopsy tissues of IL-21-treated and control animals at wk6 post-infection ( Figure 5D ) . PMN migration along the intestinal epithelia is the hallmark of intestinal inflammation and has been correlated with the degree of intestinal epithelial barrier dysfunction in SIV-infected RMs [60] . PMN activation leads to accumulation of myeloperoxidase ( MPO ) + PMNs adjacent to epithelial lesions , reflecting a tissue response to loss of epithelial integrity . Interestingly , levels of MPO+ PMNs in the lamina propria were significantly lower in IL-21-treated than control animals ( P = 0 . 0129; Figure 5E ) , thus indicating better maintenance of gut integrity following IL-21 treatment . Together , these findings indicate that administration of IL-21 during early SIV-infection of RMs significantly improved the preservation of intestinal Th17 cells and the integrity of the mucosal barrier . Several studies have suggested that a preferential loss of intestinal Th17 cells is a key mechanism responsible for the mucosal immune dysfunction and associated chronic immune activation that are typical of pathogenic HIV/SIV infections [17] , [26] , [27] , [28] . Indeed , in both HIV-infected humans and SIV-infected RMs , the levels of Th17 cells and those of proliferating CD4+ T cells are strictly associated [17] , [28] , [29] . To investigate if the increased levels of intestinal Th17 cells induced by IL-21 are associated with limited intestinal T cell activation and/or proliferation we compared the percentages of CD4+ or CD8+ T cells expressing the activation markers HLA-DR and CD69 or the proliferation marker Ki-67 in the RBs of IL-21-treated and control RMs . While no significant differences were found at any experimental time point in the levels of CD4+ or CD8+ T cells expressing HLA-DR or CD69 ( data not shown ) , the percentages of CD4+Ki-67+ T cells were significantly lower in IL-21-treated as compared to control RMs at wk23 p . i . ( Figure 6A; 12 . 14±0 . 80 vs . 20 . 57±2 . 37; P = 0 . 0003 ) . Remarkably , limited proliferation of intestinal CD4+ T cells was consistently found at wk23 in all IL-21-treated RMs ( Figure S6A for individual values ) . IL-21 treatment was also effective in limiting proliferation of intestinal CD8+ T cells , with the percentage of CD8+Ki-67+ T cells at wk23 p . i . being significantly lower in IL-21-treated than in controls ( Figure 6B; 8 . 61±1 . 33 vs . 12 . 1±0 . 81; P = 0 . 0080; Figure S6B for individual values ) . To further investigate the effect of IL-21 on the integrity of the intestinal mucosa , we measured the plasma levels of LPS and sCD14 , which are commonly used as surrogate markers of microbial translocation from the intestinal lumen to systemic circulation . As previously described for SIV-infected RMs , in the six control animals microbial translocation increased progressively during the transition from acute to chronic infection , with the plasma levels of LPS ( P = 0 . 0012 ) and sCD14 ( P<0 . 0001 ) at wk23 being significantly higher than those at wk2 p . i . ( Figure 6C–D ) . In contrast , in IL-21-treated RMs , plasma levels of LPS and sCD14 throughout the study remained very similar to those present before initiation of IL-21 treatment , with plasma levels of LPS and sCD14 at wk23 significantly lower than those of control animals ( Figure 6C–D; LPS: 121±13 . 7 vs . 197 . 4±14 . 2; P<0 . 0001; sCD14: 1 , 342 , 000±145 , 029 vs . 2 , 071 , 000±171 , 117; P = 0 . 0007; Figure S6C–D for individual values ) . To analyze the magnitude of the effects of IL-21 treatment on sCD14 and LPS levels , we then compared the levels of these two markers at baseline ( before infection ) versus at wk23 post-infection . At wk23 , in IL-21-treated animals , sCD14 levels were similar to those seen at baseline , while LPS levels were still higher than baseline , although significantly lower than in controls and remarkably stable ( in contrast to a progressive increase in controls ) from wk4 up to the end of the study ( Figure 6C–D ) . Finally , we sought to investigate if these reduced levels of microbial translocation observed in IL-21-treated SIV-infected RMs were associated with lower levels of systemic T cell activation and/or proliferation . Using the same markers described for the GI tract , we did not find any significant difference between IL-21-treated and control RMs in the percentages of circulating CD4+ or CD8+ T cells expressing HLA-DR , CD69 , Ki-67 or PD-1 at any of the studied experimental points ( data not shown and Figure S7 ) . In addition to cellular markers of immune activation , we measured the fold change variation compared to pre-treatment ( wk2 p . i . ) in several soluble markers of inflammation . As shown in Figure 6E–H , the fold change in plasma levels of sTNF-RII ( wk6: P = 0 . 028; wk23: P = 0 . 039 ) , IP-10 ( wk23: P = 0 . 011 ) , and neopterine ( wk23: P = 0 . 038 ) was significantly lower for IL-21-treated than controls RMs . D-dimer levels were also lower in IL-21 treated than control animals , although this difference was not statistically significant ( Figure 6H ) . Together with those of Figure 5 , these data indicate that in IL-21-treated RMs a better preservation of intestinal Th17 cells in early infection is associated with reduced levels of intestinal T cell proliferation , microbial translocation and systemic inflammation in the chronic stage of infection . To further investigate the association between Th17 cells , mucosal immune activation , and microbial translocation , we next investigated the relationship between the levels of intestinal Th17 cells , those of proliferating T cells , and the markers of microbial translocation , LPS and sCD14 . Consistent with the known role of Th17 cells in promoting mucosal immunological and physical integrity , we found that the percentages of intestinal Th17 cells at wk6 negatively correlated with the percentages of intestinal CD4+Ki-67+ T cells ( r = −0 . 6364; P = 0 . 0402 ) and the plasma levels of LPS ( r = −0 . 6909; P = 0 . 0226 ) at wk23 p . i . ( Figure 7A ) . Furthermore , when expressed as fold change compared to wk2 , the levels of intestinal Th17 cells at week 23 p . i . were inversely correlated to the percentage of intestinal CD8+Ki-67+ T cells ( r = −0 . 6308; P = 0 . 0374 ) and the plasma levels of LPS ( r = −0 . 7345; P = 0 . 0100 ) and sCD14 ( r = −0 . 6486; P = 0 . 0309 ) ( Figure 7B ) . Of note , no significant correlations were found between the levels of intestinal Th17 cells at wk6 and those of viral load at wk6 or at wk23 ( data not shown ) . Collectively , these data indicate a strong association between IL-21-dependent preservation of intestinal Th17 cells and limited levels of intestinal T cell proliferation and microbial translocation in IL-21-treated SIV-infected RMs .
In this study , we tested the hypothesis that exogenous in vivo administration of IL-21 , a cytokine that possesses numerous unique immunological properties , is beneficial in the context of pathogenic lentiviral infections . To test this hypothesis , we used the nonhuman primate model of SIV infection in rhesus macaques ( RMs ) and treated the animals with IL-21 during the acute phase of infection ( i . e . , starting at day 14 ) . The rationale for this study was based on several findings . First , a large body of evidence has shown that IL-21 is a key factor in regulating central processes that are defective and/or compromised in HIV/SIV infected humans and RMs . These processes include differentiation and homeostatic expansion of Th17 cells [44] , [45] , [46] , [59] , long-term maintenance of functional CD8+ T cells [36] , [37] , [38] , [39] , and differentiation of memory B cells and Ab-secreting plasma cells [40] , [41] , [42] , [43] . Second , the overall availability of IL-21 is reduced in both HIV-infected humans [47] , [48] and SIV-infected RMs [49] , while high level of HIV-specific CD4+IL-21+ and CD8+IL-21+ T cells are present in the rare subset of HIV-infected individuals who are able to spontaneously control HIV replication without treatment [38] , [61] . Third , we recently described in a cross-sectional study of SIV-infected RMs a strong association between the levels of IL-21-producing CD4+ T cells and those of Th17 cells , with a significant portion of the variability in Th17 cells being dictated by variability in CD4+IL-21+ T cells [49] . Fourth , we previously showed that IL-21 induces the expression of the cytotoxic molecules perforin and granzyme B in CD8+ T cells and NK cells in vitro in chronically HIV-infected individuals [55] , [62] and in vivo in a pilot study where IL-21 was administered to late-stage disease SIV-infected RMs [57] . Lastly , IL-21 is currently used in phase I and II clinical trials in renal cell carcinoma , melanoma and non-Hodgkin's lymphoma with an overall good safety profile and encouraging single agent activity [49] , [63] , [64] , [65] . Specifically , immunological analysis of these phase I studies showed that IL-21 treatment increased the mRNA levels for IFN-γ , perforin , granzyme B and granzyme A in CD8+ T cells and NK cells and the expression of ICOS and chemokine receptors ( CCR5 , CXCR6 and CXCR3 ) in both CD4+ and CD8+ T cells . According to these studies , the immunologic mechanisms of the antitumor action of IL-21 involve augmented NK cell and CD8+ T cell cytotoxicity and increased migratory properties of these cells to sites of inflammation [63] , [64] , [65] . The key findings of the current study are the following: ( i ) IL-21 treatment was safe and its administration during the acute phase of infection did not increase plasma viral load , systemic T cell activation or levels of the T cell inhibitory molecule PD-1; ( ii ) IL-21 significantly increased the expression of Perforin and GrB in total and virus specific CD8+ T cells in blood , LN , and rectal mucosa; ( iii ) IL-21 treatment increased the frequencies of memory B cells; ( iv ) IL-21 increased the levels of intestinal Th17 cells and the integrity of the mucosal barrier in the early infection; and ( v ) preservation of intestinal Th17 cells was associated with increased preservation of intestinal CD4+ T cells , reduced proliferation of intestinal CD4+ and CD8+ T cells , limited microbial translocation and systemic inflammation in the chronic phase of infection . Table S1 summarizes the experimental time points in which specific parameters were significantly different between IL-21-treated and control animals . As mentioned above , IL-21 has been implicated as a key factor regulating the differentiation and maintenance of Th17 cells , a CD4+ T cell subset crucial for mucosal immunity [44] , [45] , [46] , [59] . In HIV-infected humans and SIV-infected RMs , depletion of mucosal CD4+ T cells preferentially involves Th17 cells [17] , [26] , [27] , [28] . It is believed that Th17 cell depletion favors a breakdown of the physical and/or biological mucosal barrier with translocation of bioactive microbial products from the intestinal lumen to the systemic circulation , thus contributing to the establishment of the high levels of chronic immune activation [2] , [19] . The evidence that mucosal Th17 cells are preserved in SIV-infected sooty mangabeys , which maintain mucosal integrity and avoid microbial translocation and chronic immune activation , is consistent with a key role of these cells in controlling mucosal integrity and immune activation . The current study shows that IL-21 treatment results in better preservation of intestinal Th17 cells in SIV-infected RMs . Intriguingly , after the initial depletion of Th17 cells in the first 2 weeks p . i . , IL-21-treated RMs did not show any further loss of Th17 cells , with the percentage of these cells remaining stable until wk6 p . i . . These kinetics were remarkably different from the severe and progressive depletion of Th17 cells experienced by control RMs in the same time frame . Of note , the beneficial effects of IL-21 were specific for intestinal Th17 cells , since CD4+IFN-γ+ and CD4+IL-2+ T cells were not affected by the treatment . When this study was started , IL-22 antibodies cross-reactive for RMs were not commercially available , and therefore we could not determine if the Th17 cells induced by IL-21 treatment were able to produce IL-22 in addition to IL-17 . However , we could measure the plasma levels of IL-22 and found a five-fold increase ( p = 0 . 056 ) in the plasma levels of IL-22 at wk6 in IL-21-treated animals compared to controls . To the best of our knowledge , this is the first study showing an intervention able to selectively preserve Th17 cells in vivo in SIV-infected RMs . Unfortunately , the effects of IL-21 on Th17 cells were temporary , as four months post-treatment the depletion of Th17 cells was comparable in IL-21-treated RMs and control animals . Overall these data underscore the need for a more prolonged treatment with IL-21 in vivo and for further studies aimed at optimizing the regimen for IL-21 administrations . Depletion of intestinal Th17 cells contributes to the mucosal immune dysfunction and associated immune activation in pathogenic HIV/SIV infections with a reported association between the levels of Th17 cells and those of proliferating CD4+ T cells [17] , [28] , [29] . Consistent with a mechanistic link between mucosal Th17 cells and immune activation , in IL-21-treated RMs a better preservation of intestinal Th17 cells was associated with significantly lower levels of ( i ) proliferating intestinal CD4+ and CD8+ T cells; ( ii ) microbial translocation , as assessed by plasma levels of LPS and sCD14; and ( iii ) inflammation , as assessed by quantification of several plasma markers . In addition , the lower levels of neutrophil infiltration , commonly used as surrogate for the gut barrier integrity [60] , indicate a better preserved intestinal epithelial barrier in IL-21-treated animals than controls . These data are of interest considering that impaired mucosal integrity is a key contributor to HIV-associated chronic immune activation [2] , [9] , [10] . Surprisingly , in IL-21-treated SIV-infected RMs , limited microbial translocation and reduced levels of soluble markers of inflammation did not translate into lower levels of T cell activation or proliferation in blood . These data suggest that , at least in this study , plasma levels of LPS and sCD14 are more associated with systemic inflammation than with peripheral T cell activation/proliferation . It is also possible , however , that follow up was not long enough to observe effects of IL-21 on systemic immune activation , since animals started antiretroviral therapy at seven months p . i . IL-21 may regulate two other immunological functions that are compromised in pathogenic HIV/SIV infections , i . e . , the cytolytic potential of CD8+ T cells [36] , [37] , [38] , [39] and the differentiation of Ag-stimulated B cells into memory B cells and antibody ( Ab ) -secreting plasma cells [40] , [41] , [42] , [43] . Our results indicate that , in comparison to control animals , IL-21-treated SIV-infected RMs experience a significant increase in the frequency of perforin and granzyme positive CD8+ T cells for up to one month after IL-21 treatment . Of note , the increase in perforin and GrB expression was confirmed also for SIV-specific CD8+ T cells , and in total and virus-specific CD8+ T cells of various anatomical locations . In our examination of B cells , we found that IL-21 had an impact on the levels of the different B cell maturation subsets , with treated animals showing higher frequencies of CD20+CD21hiCD27+ memory B cells and CD20+CD21hiCD27+IgD− switch memory B cells as compared to control animals up to one month after the last IL-21 administration . Our current data on the in vivo effects of IL-21 administration on the cytotoxic potential of T cells and expansion of memory B cells is consistent with the findings that we previously reported during chronic SIV infection in RMs [57] . The originality of the data generated in the current study on the cytotoxic potential of T cells and expansion of memory B cells includes ( i ) the usage of a different form of rIL-21 as well as a different dose and route of administration , ( ii ) IL-21 treatment being performed during acute SIVmac239 infection , ( iii ) expression of cytotoxic molecules in SIV-specific CD8+ T cells , ( iv ) cytotoxic potential of T cells and expansion of memory B cells determined in various anatomical sites , including rectal mucosa . While IL-21 treatment had a beneficial impact on CD8+ T cell functions , viral loads were not significantly different between IL-21 treated and control RMs . Reduction of viral load may be very difficult to achieve with any single cytokines in particular during the acute phase of infection . It is also possible that IL-21 had diverse , multiple effects on viral load that counterbalance each other , thus resulting in a lack of variation in viral load . This finding led us to conclude that better preservation of intestinal Th17 cells , rather than changes in CD8+ T cells and B cells , is the main mechanism by which IL-21 impacts mucosal immune functions . Consistent with this interpretation , and despite the limited number of samples , higher levels of intestinal Th17 cells at wk6 were associated with lower levels of intestinal T cell proliferation and microbial translocation at wk23 p . i . A precise and extensive in situ quantitation of Th17 cells as numbers/unit of tissue by immunohistochemistry was not possible in the relatively small amount of tissues that can be collected during a longitudinal study in RMs . Furthermore , a mechanistic investigation on how IL-21 increases levels of Th17 cells is beyond the scope of the current body of work . Indeed , additional studies are needed to determine which stage of the Th17 cell differentiation pathway is mainly affected by IL-21 , and why the treatment was more effective for intestinal Th17 cells than blood or LN-resident Th17 cells . In particular , these studies should investigate several non-mutually exclusive mechanisms , including ( i ) IL-21-mediated increase in the homing of blood and LN-resident Th17 cells to mucosal sites; ( ii ) IL-21-mediated increase in the levels of Th17 cell precursors; ( iii ) IL-21-mediated differentiation of Th17 cell precursors in fully mature Th17 cells; ( iv ) IL-21-mediated increase in the proliferation and/or survival of fully mature Th17 cells; and ( v ) IL-21-mediated decrease in HIV/SIV susceptibility by Th17 cells . Finally , it will be important to expand our investigation in an experimental setting that is more directly relevant to HIV infection in humans , i . e . IL-21 treatment performed in the chronic phase of SIV infection and in association with ART . In conclusion , IL-21 treatment in SIV-infected RMs increases the levels of intestinal Th17 cells , reduces virus-associated mucosal immune dysfunction and chronic immune activation , and limits microbial translocation and systemic inflammation . These findings provide rationale for further preclinical studies aimed at exploring IL-21 as a potential immune-based intervention to be used in the context of ART during the chronic phase of infection .
All animal experimentations were conducted following guidelines established by the Animal Welfare Act and the NIH for housing and care of laboratory animals and performed in accordance with Institutional regulations after review and approval by the Institutional Animal Care and Usage Committees ( IACUC; Permit Number: 2001973 ) at the Yerkes National Primate Research Center ( YNPRC ) . All efforts were made to minimize suffering . Twelve female RMs , all housed at the Yerkes National Primate Research Center , Atlanta , GA , were included in the study . All animals were Mamu-B*08 and B*17 negative , while six of them were Mamu-A*01 positive ( RIp10 , RFj7 , RZp11 , RUj7 , RUw10 , RKt9 ) . The 12 RM were randomized in two groups ( Group 1: IL-21-treated; Group 2: controls ) of six animals based on age ( Group . 1: 8±2 . 45; Group . 2: 8±1 . 42 ) , weight ( 7 . 85±2 . 19 vs . 7 . 58±1 . 08 ) and Mamu-A*01 status ( three in each group ) . All 12 animals were infected intravenously ( i . v . ) with 300 TCID50 SIVmac239 ( day 0 ) . Starting from day 14 p . i . , the six animals of group 1 were treated with five weekly doses ( subcutaneous ) of 50 µg/kg rhesus rIL-21-IgFc ( IL-21 ) . The six RMs of group 2 remained untreated and used as controls ( Figure 1 ) . Peripheral blood ( PB ) , rectal mucosa ( RB ) and lymph node ( LN ) biopsies were collected at numerous experimental points prior- ( day -14 ) and post-infection , before ( day 14 p . i . ) , during ( day 14–42 ) and post IL-21 treatment ( Figure 1 ) . Rhesus rIL-21-IgFc fusion protein was produced in the Drosophila S2 system by the Resource for Nonhuman Primate Immune Reagents with rmamuIL-21 fused to a macaque IgG2 Fc mutated to prevent binding to complement or Fc receptors similar to a PD-1-IgFc reported before [66] . IL-21-Fc was purified by affinity chromatography to Protein-G sepharose to >95% , dyalized against PBS and tested for sterility and the potential presence of residual endotoxin . Content and bioactivity of the cytokine batches were verified by EIA ( B–D antibody pairs J148-1134 and I76–539 ) and its capacity to upregulate the expression of perforin and granzyme B in PBMC and LN cells of healthy and SIV-infected Rhesus macaques in vitro and by administration to healthy RMs [57] . The rationale for the dose utilized was based on a series of studies by our laboratories on the administration and biological effect of gamma chain stimulating cytokines IL-2 , IL-7 and IL-15 [67] , [68] , [69] and on our previous dose escalation study using E coli produced recombinant mamuIL-21 in which maximal upregulation of perforin was obtained above 10 µg/kg [57] . The rationale for using rIL-21-IgFc fusion protein and not rIL-21 as in our previous study [57] was based on our data indicating that ( i ) the biological activity based on protein content was comparable between the two molecules , and ( ii ) differently from E . coli produced rIL-21 , S2 produced IL21-Ig-Fc was not immunogenic upon repeated doses . Indeed , None of the animals developed an anti-fusion protein antibody response ( data not shown ) . Collections and processing of blood , RB and LN were performed as previously described [26] , [70] , [71] . Briefly , blood samples have been used for a complete blood count and routine chemical analysis , and plasma separated by centrifugation within 1 h of phlebotomy . Peripheral blood mononuclear cells were prepared by density gradient centrifugation . For rectal biopsies , an anoscope has been placed a short distance into the rectum and up to 20 pinch biopsies obtained with biopsy forceps . RB-derived lymphocytes have been isolated by digestion with 1 mg/ml collagenase for 1 h at 37°C , and then passed through a 70-µm cell strainer to remove residual tissue fragments . For lymph node biopsies , the skin over the axillary or inguinal region have been clipped and surgically prepared . An incision has been made over the LN , which has been exposed by blunt dissection and excised over clamps . Biopsies have been homogenized and passed through a 70-µm cell strainer to mechanically isolate lymphocytes . All samples were processed , fixed ( 1% paraformaldehyde ) , and analyzed within 24 hours of collection . Fourteen-parameter flow cytometric analysis was performed on whole blood , PBMC , LN and RB derived cells according to standard procedures using a panel of monoclonal antibodies that we and others have shown to be cross-reactive with RM [71] , [72] . Predetermined optimal concentrations were used of the following antibodies: anti-CD3-Alexa700 ( clone SP34-2 ) , anti-CD3-APC-Cy7 ( clone SP34-2 ) , anti-CD4-PE ( clone L200 ) , anti-CD8-PacBlue ( clone RPA-T8 ) , anti-CD95-PE-Cy5 ( clone DX2 ) , anti-CCR5-APC ( clone 3A9 ) , anti-Ki-67-Alexa700 ( clone B56 ) , anti-CD62L-FITC ( clone SK11 ) , anti-IL-21-Alexa Fluor647 ( clone 3A3-N2 . 1 ) , anti-IFN-γ-PE-Cy7 ( clone B27 ) , anti-CD27-Alexa-700 ( Clone M-T271 ) , anti-CD21-PE-Cy5 ( Clone B-ly4 ) , ( all from BD Pharmingen ) ; anti β7-PE ( clone FIB504 ) , anti-IL-17-Alexa Fluor488 ( clone eBio64DEC17 ) , anti-CCR7-APC ( clone 3D12 ) ( all from eBioscience ) ; anti-CD4-PacBlue ( clone OKT4 ) , anti-HLA-DR-APC-Cy7 ( clone L243 ) , anti-IL-2-Alexa700 ( clone MQ1-17H12 ) , anti-CD20-PerCPCy5 . 5 ( clone 2H7 ) ( all from Biolegend ) ; anti-CD28-ECD ( clone CD28 . 2 ) and anti-CD69-ECD ( clone TP1 . 55 . 3 ) ( Beckman Coulter ) ; anti-CD8-Qdot705 ( clone 3B5 ) , anti-Granzyme B-ECD ( Clone GB11 ) and Aqua Live/Dead amine dye-AmCyan ( Invitrogen ) ; anti-Perforin-FITC ( clone Pf344 , Mabtech ) ; antiIgD FITC ( catalog no . 2030-02 , SouthernBiotech ) . A*01CM9-Gag181–189 tetramer-APC and PE antibodies were generously provided by Dr . David Watkins ( Wisconsin National Primate Research Center , University of Wisconsin ) . Perforin and Granzyme B staining in total T cells was performed as described previously [57] . Flow cytometric acquisition was performed on at least 100 , 000 CD3+ T cells on an LSRII cytometer driven by the FACS DiVa software . Analysis of the acquired data was performed using FlowJo software ( TreeStar ) . Levels of Th17 cells were determined as the percentage of CD4+ T cells that produce IL-17 following in vitro stimulation with PMA & Ionomycin . PBMC and RB derived cells , isolated as described above , were resuspended to 1×106 cells/ml in complete RPMI 1640 medium . Cells were then incubated for 4 h at 37°C in medium containing PMA , A23187 , and Golgi Stop . Following incubation , the cells were washed and stained with surface markers for 30 min in the dark at 4°C followed by fixation and permeabilization . After permeabilization , cells were washed and stained intracellularly with the antibody for the cytokines of interest for 1 h in the dark at 4°C . Following staining , cells were washed , fixed in PBS containing 1% paraformaldehyde , and acquired on an LSRII cytometer . LPS levels were measured in plasma ( with EDTA as anticoagulant ) by the limulus amebocyte lysate ( LAL ) chromogenic endpoint assay ( Lonza Group Ltd , Allendale , NJ ) , as previously described [8] . Ten microliters of plasma was diluted 1∶10 in endotoxin-free water , and heat-inactivated at 85°C for 15 min to inactivate inhibitory plasma proteins . Results were calculated in relation to an Escherichia coli endotoxin standard provided with the assay , after background subtraction , and expressed in pg/mL . Plasma levels of sCD14 were quantified by using Human sCD14 Immunoassay kit ( R&D systems , Minneapolis , MN ) . Ten microliters of plasma was diluted 400 fold by adding 3990 µl of calibrator diluent and assayed in duplicate as per manufacturers recommendations . Results of sCD14 were expressed in pg/ml . Levels of soluble tumor necrosis factor receptors II ( sTNF-RII ) , CXCL10/IP10 , D-Dimer and neopterine were measured in plasma using commercially available rhesus cross reacting human ELISA kits as per manufacturer's instructions . Levels of sTNFRII and IP-10 were quantified using human sTNFR II and IP-10 Quantikine ELISA kit ( R&D systems , Minneapolis , MN ) and expressed as pg/ml . D-Dimer was measured by IMUCLONE D-Dimer ELISA kit ( American Diagnostica , Stamford , CT ) and expressed as ng/ml . Neopterine levels in plasma were measured by a competitive ELISA ( IBL International , Hamburg , Germany ) and expressed as nmol/L . IL-22 levels were measured in plasma using monkey specific IL-22 ELISA kit ( BlueGene , Life Sciences Advanced Technologies Inc , FL , USA ) , and expressed as pg/ml . Immunohistochemistry was performed using a biotin-free polymer approach ( Golden Bridge International , Inc . ) on 5-µm tissue sections mounted on glass slides , which were dewaxed and rehydrated with double-distilled H2O . Heat induced epitope retrieval ( HIER ) was performed by heating sections in 10 mM Citrate ( pH 6 . 0 ) in a pressure cooker set at 122°C for 30 s . After HIER , slides were rinsed in ddH2O and then loaded on an IntelliPATH autostainer ( Biocare Medical ) and stained with optimal conditions determined empirically that consisted of a blocking step using blocking buffer ( TBS with 0 . 05% Tween-20 and 0 . 5% casein ) for 10 min and an endogenous peroxidase block using 1 . 5% ( v/v ) H2O2 in TBS ( pH 7 . 4 ) for 10 min . Rabbit polyclonal anti-myeloperoxidase ( 1∶1 , 000; Dako ) was diluted in blocking buffer and incubated for 1 h at room temperature . Tissue sections were washed , and detected using the Rabbit Polink-1 HRP staining system ( Golden Bridge International , Inc ) according to manufacturer's recommendations . Sections were developed with Impact DAB ( Vector Laboratories ) . All slides were washed in ddH2O , counterstained with hematoxylin , mounted in Permount ( Fisher Scientific ) , and scanned at high magnification ( ×200 ) using the ScanScope CS System ( Aperio Technologies ) yielding high-resolution data from the entire tissue section . Representative regions of interest ( ROIs; 500 mm2 ) were identified and high-resolution images extracted from these whole-tissue scans . The percent area of the lamina propria that stained for myeloperoxidase+ neutrophils were quantified using Photoshop CS5 and Fovea tools . Serum samples from IL-21-treated and control RMs were analyzed for their reactivity to viral antigens in a concanavalin A ( ConA ) ELISA [73] . Briefly , 96-well plates were coated with 5 µg of ConA for 1 h , washed and incubated with the SIV viral supernatant , which is first incubated with triton detergent . After blocking , sera at different dilution were added to the wells and incubated for 1 h at room temperature . The plates were washed and anti-SIV IgG Ab was detected after incubating 20 min with anti-human IgG biotin/streptavidin HRP . Repeated-measures analyses for each outcome ( CD4+ T cells; CD4+Ki-67+ T cells; Th17 cells; CD8+Ki-67+; LPS; sCD14 , etc ) were performed with a means model with SAS Proc Mixed ( version 9 ) providing separate estimates of the means by weeks post-infection and treatment group . A compound-symmetic variance-covariance form in repeated measurements was assumed for each outcome and robust estimates of the standard errors of parameters were used to perform statistical tests and construct 95% confidence intervals [74] . The model-based means are unbiased with unbalanced and missing data , so long as the missing data are non-informative ( missing at random ) . T-tests were used to compare the differences between the model-based treatment means ( least-squares means ) at each time point within the framework of the mixed effects linear model . A Bonferroni adjustment ( . 05/5 , P = 0 . 01 ) was used for the five treatment comparisons performed at wk −2 , 2 , 4 , 6 , and 23 post-infection . Statistical tests were 2-sided . Pearson product-moment correlation coefficients were utilized to estimate linear associations for normally distributed data ( Figure 7B ) and Spearman rank correlation coefficients ( Figure 7A ) were used for skewed and other non-normal distributions . A P value≤0 . 05 was considered statistically significant for the correlation analyses . The mean ± SEM were used as descriptive statistics for each continuous outcome . | In the gastrointestinal tract , preferential depletion of CD4+ Th17 cells occurs during the early stage of pathogenic HIV/SIV infections and correlates with loss of mucosal integrity , microbial translocation , immune activation and disease progression . As such , therapeutic intervention aimed at preserving intestinal Th17 cells may be of critical importance . IL-21 plays an important role in promoting the differentiation and survival of Th17 cells , as well as in stimulating CD8+ T cell cytolytic function . Here , we treated SIV-infected rhesus macaques with IL-21-IgFc in the early stage of infection . Consistent with the main functions of IL-21 , we found that IL-21 treated animals had higher expression of perforin and granzyme B in total and SIV-specific CD8+ T cells and higher frequencies of intestinal Th17 cells as compared to untreated controls . Remarkably , the increased proportions of Th17 cells during early infection was associated with significantly lower levels of intestinal T cell proliferation , microbial translocation and systemic activation/inflammation during chronic infection . Thus , our results suggest that IL-21 treatment in SIV-infected RMs is effective in preserving intestinal Th17 cells and results in an improvement of mucosal immune function . Further preclinical studies may be warranted to test IL-21 during chronic infection and in conjunction with antiretroviral therapy . | [
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"response"... | 2013 | Maintenance of Intestinal Th17 Cells and Reduced Microbial Translocation in SIV-infected Rhesus Macaques Treated with Interleukin (IL)-21 |
The release of Ca from intracellular stores is key to cardiac muscle function; however , the molecular control of intracellular Ca release remains unclear . Depletion of the intracellular Ca store ( sarcoplasmic reticulum , SR ) may play an important role , but the ability to measure local SR Ca with fluorescent Ca indicators is limited by the microscope optical resolution and properties of the indicator . This leads to an uncertain degree of spatio-temporal blurring , which is not easily corrected ( by deconvolution methods ) due to the low signal-to-noise ratio of the recorded signals . In this study , a 3D computer model was constructed to calculate local Ca fluxes and consequent dye signals , which were then blurred by a measured microscope point spread function . Parameter fitting was employed to adjust a release basis function until the model output fitted recorded ( 2D ) Ca spark data . This ‘forward method’ allowed us to obtain estimates of the time-course of Ca release flux and depletion within the sub-microscopic local SR associated with a number of Ca sparks . While variability in focal position relative to Ca spark sites causes more out-of-focus events to have smaller calculated fluxes ( and less SR depletion ) , the average SR depletion was to 20±10% ( s . d . ) of the resting level . This focus problem implies that the actual SR depletion is likely to be larger and the five largest depletions analyzed were to 8±6% of the resting level . This profound depletion limits SR release flux during a Ca spark , which peaked at 8±3 pA and declined with a half time of 7±2 ms . By comparison , RyR open probability declined more slowly , suggesting release termination is dominated by neither SR Ca depletion nor intrinsic RyR gating , but results from an interaction of these processes .
During cardiac excitation-contraction coupling , calcium ( Ca ) is released from the sarcoplasmic reticulum ( SR ) through ryanodine receptors ( RyRs ) , which are concentrated in the junctional regions of the SR ( jSR ) . Ca release occurs due to ‘calcium-induced calcium release’ ( CICR ) [1] , wherein Ca efflux from the jSR produces a rapid , local increase in Ca in the cytoplasm , which can be observed with fluorescent Ca indicators as a ‘Ca spark’ [2] . The corresponding Ca depletion in the junctional and wider SR has been detected as a ‘Ca blink’ [3] . The SR Ca signal is made possible by a protocol that favors indicator loading into the SR [4] and have shown that during a Ca spark , a ∼40% decrease in local SR [Ca] appears to occur [3] , [5] . The fundamental insight provided by these and other biophysical approaches have led to local control theories [6] for the regulation of SR Ca release , however , detailed understanding of CICR has been elusive due to uncertainties in the amplitude and time-course of RyR release flux and the associated changes in local Ca concentrations near the RyRs . In particular , identification of the mechanism ( s ) responsible for termination of the inherently regenerative CICR mechanism has been especially problematic [7] . Nevertheless , integrating facets of known Ca handling systems has provided useful insight into the interplay of Ca metabolism with excitability ( e . g . [8]–[12] ) . Consideration of the sub-microscopic volume of the jSR immediately suggests that the Fluo-5N signal must under-estimate true jSR Ca depletion [3] , [13] . This problem arises from size of the confocal point spread function ( PSF ) , which encompasses both elements of the jSR and adjacent network SR . In the latter , [Ca] is high and is likely to change with a different time-course to that in the jSR . It should be noted that this situation is different during Ca spark recordings because the cytoplasmic signal is larger than the jSR volume and the confocal PSF integrates signal from regions that have low [Ca] . The potential seriousness of the blurring problem has led us to analyze the problem by using computational methods , combined with measurements of actual microscope blurring to extract the likely depth SR Ca depletion , which is central to understanding the termination of SR Ca release during Ca sparks . In principle , the problem of microscopic blurring might be reduced by deconvolution of the recorded signal with the microscope PSF . However , Ca blink signals are extremely noisy which renders this approach ( essentially ) unusable ( a problem frequently observed in inverse solutions ) . Our approach is to create a computer model that captures the general geometry underlying local SR Ca release , blur the simulated fluorescent Ca signals , then refine the underlying flux parameters by iteration until experimental records are reproduced . This ‘forward method’ for analyzing SR Ca release was introduced by Soeller and Cannell [10] . We show that local jSR depletion is likely to be heavily under-estimated by Fluo-5N signals . With estimates of jSR depletion and release flux , we were also able to obtain the first estimates of the time-course of RyR gating at the junction ( from recorded data ) and show that the decline of release flux is not simply due RyR closure . This result should have important implications for models of CICR termination .
This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol used ( R649 ) was approved by the University of Auckland Animal Ethics Committee . All surgery was performed under sodium pentobarbital anesthesia ( 140 mg/kg , i . p . ) , and every effort was made to minimize suffering . Single cardiac ventricular myocytes were obtained by enzymatic dissociation of Langendorff-perfused hearts from male Wistar rats ( ∼250 g ) , as previously described [14] . Cells were incubated with 5 µM Fluo-4 acetoxymethyl ester ( Invitrogen , California , U . S . A . ) for 25 min by adding 2 . 5 µL of a 2 mM stock ( 2 . 5% Pluronic® F-127 in dimethyl sulfoxide , Invitrogen and Sigma-Aldrich , respectively ) to 1 mL of cell suspension . Following incubation , the dye loading solution was replaced with perfusion buffer , which was a modified Tyrode's solution ( pH = 7 . 4 , in mM ) : NaCl 140 , KCl 4 , MgCl2 1 , HEPES 10 , D-glucose 10 and CaCl2 1 . Experiments were carried out at room temperature . Imaging was performed with a LSM 710 ( Zeiss , Oberkochen , Germany ) system . Resolution of the line-scan images was higher than usually performed ( e . g . [2] , [15] ) at ∼0 . 35 ms/line and 0 . 083 µm/pixel , with the scan line placed along the cell . The actual microscope PSF was measured by imaging 100 nm yellow-green Fluorospheres ( Invitrogen ) . Beads resting on the glass cover-slip of the perfusion bath and on top of live myocytes were imaged and analyzed to determine the ideal microscope PSF and the maximum PSF distortion by spherical aberration ( note that the cell does not have the same refractive index as the immersion medium ) . This process was performed for both a water- ( 40× , 1 . 1 numerical aperture ) and an oil- ( 63× , 1 . 4 numerical aperture ) immersion objective . The latter is typical of objectives used in most other Ca spark and Ca blink studies ( e . g . [2] , [3] , [5] , [15] ) . The computer model equations were solved numerically by FACSIMILE ( Flow and Chemistry Simulator , U . K . Atomic Energy Authority , 1987 ) . FACSIMILE solutions were then analyzed by custom programs written in Interactive Data Language ( IDL v6 . 3 , ITT Visual Information Solutions , Colorado , U . S . A . ) . Solutions were fit to experimental data by adjusting the peak and time-course of RyR permeability using a non-linear least-squares method [16] . The accuracy of this fitting method was tested using synthetic datasets with Poisson noise and reliable convergence was found in all cases . The computer model consisted of both cytosolic and SR compartments and was based on spherical geometry . The computational volume had a radius of 4 µm , which was divided into 40 elements ( 1≤i≤39 ) , where the center-most element ( i = 0 ) contained the jSR with reflective boundary conditions at the edge of the computational space . The diffusional volumes of the cytosol and SR were 60% and 3 . 2% of total cell volume [17] respectively , except for the first element , where SR volume was set to 8 aL to represent the jSR [3] . The equation to be solved is ( 1 ) where DX is the diffusion coefficient for diffusible species X , JX the reactive flux for all buffers B and JS the source ( and sinks ) of that species . The Laplacian in one dimension for spherical geometry was solved by a conventional finite difference scheme . Reactive fluxes were described by the general ODE: ( 2 ) where B and CaB are the ligand-free and Ca-bound forms of the buffer , respectively . All rate constants , concentrations and diffusion coefficients of buffers are given in Table 1 . FACSIMILE automatically scaled the calculated flux across compartment boundaries to take account of the different compartment volumes . The general approach has been described previously [11] . SR Ca release flux ( µM/ms ) was determined by the [Ca] gradient between the jSR and junctional space and a flexible basis release function , modified from [10]: ( 3 ) where the constants kon and koff controlled the rising and decay phases , respectively , S a scaling factor for magnitude and t0 , 1 were time shifts . Only these five parameters were adjusted by least squares minimization of model output to recorded Ca sparks , all other parameters were as given in Table 1 . The sensitivity of the model to chosen parameters is shown in the Supporting Information . The calculated release flux ( Ispark ) was used to calculate n·PO of the RyRs by: ( 4 ) where the maximum unitary RyR channel current ( imax ) and half maximal conductance ( K1/2 ) were set to 2 pA and 2 mM [Ca]jSR , respectively [18] . These constants give a single channel current ( iRyR ) of ∼0 . 6 pA when [Ca]jSR is 1 mM . V0 was the volume of the first cytosolic element ( 4 . 18 aL ) , z is the valence of Ca and F is Faraday's constant . As shown in Fig . 1B , the SR contained Fluo-5F , while only the jSR compartment contained calsequestrin ( CSQ ) and was able to release Ca ( “R” ) , which entered the first cytosolic element . The rate of SR uptake ( “U” ) were set so that when [Ca]i was transiently increased to 10 µM , return to rest occurred in a half-time of ∼160 ms: ( 5 ) where Vmax was 300 µM/s and Km was 0 . 3 µM [17] . The leak flux ( “L” ) was set so that [Ca]i at rest was 100 nM [19] . Cytosolic buffers included ATP , calmodulin ( CaM ) , Fluo-4 , troponin-C ( both high and low affinity sites , TnCI and TnCII , respectively ) and SR membrane binding sites ( SRm ) . The junctional space also included sarcolemmal membrane binding sites ( SLm ) and excluded Fluo-4 [10] . [Mg] was set to 1 mM in all compartments [20] . The diffusion coefficient for ATP ( DATP ) at 24°C was chosen as an intermediate between the estimates obtained by two methods: ( 1 ) If DATP is 1 . 4×10−8 dm2/s at 16°C [12] and the Q10 of DATP is 1 . 4 [21] , then DATP would be 1 . 9×10−8 dm2/s at 24°C . ( 2 ) Since the molar mass of ATP is 507 . 18 g/mol , assuming a globular structure would allow the Stoke-Einstein equation to predict that DATP would be 1 . 4×10−8 dm2/s . The total concentration of ATP was set to 4 mM [12] , the majority of which was bound to Mg at rest ( Kon , Mg = 3 . 3×10−4/µM/s , Koff , Mg = 3/s , [22] ) . The on and off rates of Fluo-4 were also estimated from experimental data obtained at 16°C [23] using a Q10 of 2 . Dissociation constants of Fluo-4 and Fluo-5F were obtained from various in vitro calibrations [24] , [25] . DCaM was estimated from its Stoke's radius [26] . TnC contained three binding sites for Ca , where the high affinity sites ( TnCI , hi ) could also bind Mg ( Kon , Mg = 0 . 03/µM/s , Koff , Mg = 1 . 11/s , [27] , [28] ) . TnCII , lo bound exclusively to Ca [22] , [27] , [28] . Ca binding sites were also present on the sarcolemmal [29] and SR [30] membranes . Resting SR free [Ca] was 1 mM [31] , [32] . The jSR contained CSQ binding sites equivalent to 120 mM , due to a binding capacity of 30–40 Ca per molecule of CSQ [33] . The diffusion coefficient for Ca ( DCa ) in the SR was set to a value smaller than that in the cytosol to include the effect of tortuosity and Ca binding sites within the SR . Considering the SR network as a sheet with staggered holes as diffusion barriers , the tortuosity factor can be estimated from the ratio between the size of and distance between the ‘holes’ . Since SR tubules are ∼40 nm in diameter and the ‘holes’ in the SR network between adjacent tubules are ∼160 nm wide [34] , the diffusion coefficient would be reduced by 70% [35] . The binding of Ca to buffers in the SR lumen will also reduce the rate of Ca diffusion in proportion to the number of binding sites present . It is thought that ∼60% of Ca buffers in the network SR is SERCA [36] , with ∼1 . 7 mmol/LSR of SERCA in rat [37] . Assuming each SERCA molecule has two binding sites for Ca and free [Ca]SR is 1 mM and the other buffers have 1∶1 reactions with Ca , then the concentration of binding sites would be ∼2 . 8 mmol/LSR and diffusion would be reduced by ∼65% . Thus , the overall effect of tortuosity and Ca binding is a ∼90% reduction in effective DCa . In addition , the local network SR Ca flux into a jSR is likely to be further reduced due to reduced connectivity ( by only one or two tubules , [3] ) . This was accounted for by further reducing the diffusion coefficient between the last network SR element and the jSR element . If there were only one tubule of 40 nm diameter connecting these elements , then the rate of diffusion between them would be reduced by ∼95% [38] . Increased confidence in the value used was provided by the ability of the model to reproduce the time course of Ca blink recovery ( e . g . [5] ) . The diffusion coefficient of Fluo-5N within the SR was assumed to be 5 times smaller than for Fluo-4 in the cytoplasm [39] . Using different intra-SR Ca diffusion coefficients had relatively small effects on the simulated Ca spark ( Supporting Information Fig . S1 , red crosses ) . For example , when DCa , SR was increased 10-fold , peak fluorescence and FWHM increased by less than ∼10% , while the time-course was prolonged ( time to peak increased to ∼13 ms from 7 . 5 ms and time to half decay ∼doubled ) . On the other hand , altering the intra-SR Ca diffusion coefficient had large effects on Ca spark restitution ( not shown ) and Ca blink recovery . With the value ( s ) used here , experimental results were reasonably reproduced ( see below ) . Two three-dimensional ( 3D ) Gaussian functions were used to simulate the PSFs recorded at the coverslip and on tops of cells . For the coverslip PSF , the Gaussian function had a FWHM of 0 . 25 µm in the focal plane ( x , y ) and 0 . 6 µm along the optical axis ( z ) . For cell top PSF , the Gaussian function had a FWHM of 0 . 35 µm in x , y and 1 . 2 µm in z . The Ca-bound Fluo-4 signal has to be spatially blurred at each computational time-point to simulate an experimentally recorded Ca spark in a line-scan image . This raises a computational problem because efficient solution of the reaction diffusion equations needs to exploit symmetry ( where possible ) to reduce the dimensionality , while the PSFs are asymmetric . Furthermore , during parameter fitting , very large numbers of simulation runs may be carried out , so computational efficiency is highly desirable and suggests that limiting the problem to a spherical coordinate system might be advantageous . While re-gridding the solution at each time point to Cartesian coordinates for convolution by the PSF would be possible , this time-consuming operation was avoided by transforming sets of PSF weights from Cartesian to radial ordinates , each corresponding to a particular position of the PSF convolved with a spherical shell . This radial PSF could be pre-computed ( to save time ) and the convolution performed in FACSIMILE . This entire process was repeated at each time-point to generate the blurred signal , which was equivalent to a confocal line-scan image . The complete line-scan image was then used during least squares fitting ( see above ) . The optical distortion of the Ca-bound Fluo-5N signal during the formation of a Ca blink is not the same that for a Ca spark due to different geometry of the SR with respect to the confocal PSF . The volume of the signal of interest is much smaller and the signal is likely to be contaminated by signal from the network SR . To simulate this situation , the SR signal was distributed as an extruded “X” shaped spatial region centered on the jSR to mimic the SR network wrapping around the myofilaments ( see Fig . 1A and Fig . 1B ) . To test the sensitivity of the model to chosen parameters , parameters were independently altered and the simulated Ca2+ spark compared to a ‘standard’ using transport parameters given in Table 1 which: reached a peak F/F0 of 1 . 8 in 7 . 5 ms , had a time to half decay of 17 ms and a FWHM of 1 . 2 µm . The effects of altering [ATP] in the cytosol , [CSQ] in the jSR , jSR volume , [Fluo-4] , S , kon and koff are shown in Fig . S1 . From these analyses , we can predict how the release flux time-course would have to be altered to match experiments; for example an increase in peak amplitude would require correspondingly larger flux ( and deeper SR Ca depletion ) . An increase in time to peak would require a corresponding decrease in speed of decay of the release flux . Note that increasing the volume of the jSR ( red triangles ) had a similar effect to increasing [CSQ] ( see [40] ) since this also increases the amount of Ca available for release . The effect of altering the shape and amplitude of the Ca release function ( i . e . S , kon , koff ) , as occurred during curve-fitting , are also shown . The effect of altering SR Ca diffusion on Ca spark fits is illustrated in Supporting Fig . S2 .
Images of yellow-green beads located on the coverslip ( Fig . 2i ) of a perfusion chamber and on top of live myocytes ( Fig . 2ii ) for both water- ( Fig . 2A ) and oil- ( Fig . 2B ) immersion objectives are shown . It is notable the recorded PSF is clearly distorted along the optical axis , an effect which we attribute to refractive index mismatch ( es ) . A summary of PSF dimensions is given in Table 2 , and it is clear that the distortion in z is most pronounced when using the oil-immersion objective ( as might be expected ) , where the FWHM of the PSF was almost doubled . It is clear from these data that assumption of an idealized ( i . e . diffraction limited ) PSF during Ca spark and Ca blink recording would be erroneous . Two 3D Gaussian profiles based on our PSF measurements were used to simulate the effect of PSFs that are on the coverslip or on top of cells . A high signal-to-noise spontaneous Ca spark is shown in Fig . 3A and was used as the data set for parameter-fitting . Fig . 3B shows the coverslip PSF ( shown in in x-y and x-z views ) and the Ca spark generated by the computer model . The image on the right shows the absolute difference between the simulated and recorded events , showing a good fit with no systematic residuals . Fig . 3C shows the results of using the cell-top PSF , where the model could also fit the recorded event with a larger release flux ( see below ) . The measured ( black lines ) and fitted time ( Fig . 3D ) and spatial ( Fig . 3E ) profiles of the Ca sparks are also shown , colored according to the markers shown in Fig . 3A–C . These profiles also show that the fitted and measured events are in reasonable agreement . The un-blurred Fluo-4 dye signal at the center of the Ca spark is shown in Fig . 4A , as calculated if the recorded Ca spark occurred at the cell-top ( black lines ) or near the coverslip ( red lines ) , respectively . The dashed lines correspond to the blurred dye signals , where they been scaled to the amplitude of the un-blurred signals to allow comparison of the effect of blurring on time-course . Despite being in-focus , optical blurring in both cases caused the recorded Ca spark to be slower than that of the underlying signal . For a Ca spark that originated at the cell-top , the time of peak fluorescence was increased by ∼1 ms . This effect was smaller when the Ca spark was at the coverslip . The FWHM of the Ca spark was doubled due to blurring ( not shown ) . The fluxes that were required to produce the same recorded Ca spark ( Fig . 3 ) were different due from using the two different PSFs ( Fig . 4B ) . When the cell-top PSF was used , the calculated peak release flux was ∼11 . 8 pA , while when the smaller , coverslip PSF was used , only ∼8 . 7 pA was required to produce the blurred Ca spark . Since these values represent two extreme cases for Ca sparks recorded in-focus , but from different locations within a cell , it is likely that an average Ca spark would be associated with an intermediate peak current ( e . g . ∼10 pA ) . Note that the different PSFs did not markedly change the time-course of the calculated release flux . Fig . 5A shows the simulated line-scan images of [Ca]SR , Ca-Fluo-5N and the Ca blink ( optically blurred Ca-Fluo-5N ) signals generated from using the cell-top PSF marked by purple , green and red bars , respectively . Depending on the degree of optical blurring determined by the orientation of the network SR relative to the optical axis ( see Fig . 1B ) , Ca blink depletions ranged between 25–35% , which are within the range of values reported in other studies [3] , [5] , [39] . Importantly , the underlying non-blurred Ca-Fluo-5N signal showed much more extensive depletion compared to the corresponding Ca blink . For example , the Ca-Fluo-5N signal had decreased by ∼70% at its minimum , more than double that suggested by the blurred signal . Additionally , Ca-Fluo-5N also under-reported the underlying depletion of [Ca]SR , which was actually ∼90% . Overall , dye kinetics and optical blurring caused a near 3-fold under-estimation of the true extent of [Ca]SR depletion . Dye kinetics also had an effect on the ability of Ca blink signals to correctly report the time-course of [Ca]SR changes ( right panel , Fig . 5B ) . Though there were only small distortions to the time to minimum , the time taken for [Ca]SR recovery was under-estimated by Ca-Fluo-5N by ∼40 ms and which was slightly lengthened by optical blurring . Overall , this led to a ∼35% under-estimation of [Ca]SR recovery time . Fig . 6A and B show further examples of model fits to a number of experimentally recorded Ca sparks with different amplitudes and time-courses . The ability of the model to fit this range of Ca sparks is notable . The computed Ca release functions are shown in Fig . 6C , where peak flux occurred before the peak of the Ca spark . The calculated SR Ca signals in Fig . 6D show that [Ca]SR depletion was large compared to their associated Ca blink signals ( dashed lines ) . In addition , the duration of the computed release flux was always shorter than the duration of the fitted permeability function , where peak flux always occurred before RyR open probability had even begun to decline ( Fig . 6E ) , showing jSR depletion plays an important role in reducing release flux prior to closure of the RyR channels . The mean peak flux was 7 . 9±2 . 9 pA ( s . d . ) for Ca sparks with maximum F/F0 of 3 . 0±0 . 6 . Panel F shows average Ca blinks compared to average Ca-Fluo-5N and [Ca]SR signals , consistent with the trends described in Fig . 5 . The average ( blurred ) Ca blink signal was ΔF/F0 = 0 . 24±0 . 098 ( s . d . ) , compared to the corresponding SR depletion to an average minimum of 210±130 µM .
The model presented here shows that the Ca spark signal is distorted by the confocal PSF even when it is in-focus , so that its amplitude was approximately halved and width doubled . This is consistent with calculations reported previously , albeit with different PSF and a more narrow modeled spark FWHM of ∼1 µm [30] , [41] . The calculated peak release flux is dependent on the degree of spatial blurring , but our estimate is in reasonable accord with previous models [10] , [41] . This concordance might not have been expected when many models do not reproduce the spatio-temporal properties of recorded Ca sparks . This can be explained by the peak flux being the primary determinant of the peak change in fluorescence ( which all models fit well ) , which is largely dominated by flux amplitude . Initial analyses of Ca sparks ( with a mean F/F0 of ∼2 . 0 ) in early studies gave a flux estimate of 4 pA for 10 ms [2] and it is notable that initially estimated integrated flux , ∼40 pA . ms has not changed a great deal in subsequent analyses ( e . g . see [29] , [41] ) . This is due to the need to preserve total Ca release ( from integrated peak fluorescence ) during a Ca spark . In these simulations , a Ca spark of F/F0 ∼2 . 7 yields a flux integral of ∼40 pA . ms which is in good agreement with previous estimates , given the somewhat higher peak F/F0 recorded here . The distortion introduced by microscope blurring was more pronounced for Ca blinks compared to Ca sparks . This problem arises from: ( 1 ) the width of the PSF ( as is the case for Ca sparks ) , as well as ( 2 ) the spatially restricted nature of the underlying signal source , ( 3 ) the non-linear dye response; and ( 4 ) the high [Ca] in the extended network SR . The overall effect of these factors was that the Ca blink amplitude must under-estimate the extent of jSR depletion even when the microscope is focused on a z-line and at a junction . Our average estimated [Ca]jSR signal fell to ∼20% of the resting level , which is much lower than the decline to estimated previously [5] , [39] , even after including a correction for fractional jSR volume [3] . Since many Ca sparks are out of focus , this value should represent an upper limit for the actual SR depletion . Taking the brightest 5 Ca sparks as representative of in-focus events gave a jSR depletion to 8±6% ( mean ± s . d . ) of the resting level . The actual depletion in [Ca]jSR is certainly larger than directly inferred from the fluorescence signal due to the non-linear dye response , which will always cause the dye signal to underestimate true average Ca change ( i . e . dF/dCa≪1 ) . Furthermore , the network SR supplies a high Ca signal that changes more slowly than the jSR . Although this component might be captured by a correction for relative volume ( as noted by [3] ) , the extent of the correction is critically dependent on the spatial distribution of intra-SR gradients and the relative volume of network to jSR for a particular junction ( e . g . ∼30% further under-estimation if the SR and myofilaments were rotated 90° relative to the PSF , Fig . 1B ) . That the model reproduces Ca blink signals after blurring indicates that the profound jSR depletion we predict is completely consistent with experimental data and shows that previous estimates of jSR Ca depletion are almost certainly too small . The depth of jSR Ca depletion is affected by the rate at which the jSR is refilled by the network SR . If DCa in the SR is increased 10-fold , the time-course of recovery of Ca blinks is not reproduced ( Fig . S2 ) . Although such a change in SR DCa did not prevent fitting the Ca spark shown in Fig . 3 , the corresponding Ca blink was small ( ΔF/F0 ∼0 . 15 , Fig . S2C , dotted lines ) and recovered in 12 . 5 ms , which is much faster than any Ca blinks reported to date . Despite this much larger rate of diffusion within the SR , profound depletion of Ca in the jSR was still observed ( ∼60% , Fig . S2 , solid lines ) and the Ca blink was still spatially restricted ( Fig . S2D ) . An independent test of the calculations is provided by comparing the average SR Ca depletion that would occur during an evoked Ca transient or during a Ca wave . Assuming the unit of release includes the jSR and the network SR limited to a radius of ∼0 . 9 µm ( from consideration of the distance from the release site to the middle of the sarcomere ) , the model predicts that the jSR ( z-line ) Ca depletion would be ∼3-times larger than that at the m-line . However , during synchronous release , the m-line becomes depleted more deeply as more CRUs draw Ca from it . Using our model , a first order solution to this problem is provided by assuming that each CRU is not affected by release from adjacent sites so that the depletion is given by restricting the SR volume to the equivalent volume that would serve each CRU . CRU's form an approximately hexagonal lattice with 0 . 7 µm between them in the z-plane and 1 . 8 µm along the cell axis [42] . Thus , the equivalent volume of the SR source Ca in the spherical model would have a radius of 0 . 52 µm , and the model predicts that the m-line would deplete to level much closer ( 90% ) to that seen at the z-line . Therefore , if all CRU's are activated , the z- and m-line depletion signals are very similar ( albeit with slightly different time-courses ) and there would be ( essentially ) no detectable gradient along the sarcomere at typical Fluo-5N signal-to-noise ratios , as observed [13] , [43] . The steep Ca gradient across the jSR membrane is reduced rapidly during a Ca spark ( Fig . 6 ) . The effect of this local jSR Ca depletion has a profound effect on the time-course of Ca release . As shown in Fig . 3–5 , Ca release flux had decreased to half its peak value ( at ∼5 ms after peak flux ) , the Ca gradient from the jSR to the cytosol had decreased to ∼64% of the value at the time of peak flux . The small difference between the flux and gradient reflects the minor contribution of changes in RyR n·PO to the declining flux at this time , accounting for ∼12% of the total flux decline . Therefore , the decline in flux is not dominated by changes in RyR gating but rather the loss of driving force for Ca release at this time ( Fig . 6E ) . The smaller decrease in n·PO at this time may be explained by the RyRs operating in a near-saturated regime with regard to either jSR or cytoplasmic Ca control of RyR open time . Though not modeled here , it is highly likely that the reduction in release flux due to local Ca depletion will be transduced via the steep cytoplasmic Ca-dependence of RyR gating to reduce n·PO and eventually stop CICR [44] . That reduced jSR [Ca] affects CRU gating has been explored by Sato and Bers [45] , who concluded that SR depletion can prevent Ca spark initiation . This is in accord with the idea that the SR Ca depletion described here inhibits the regeneration inherent in CICR ( and will therefore contribute to Ca spark termination ) . As shown in Fig . 2 and Table 2 , the properties of a confocal PSF can deviate markedly from theoretical values when imaging occurs through a living cardiac myocyte . This is not simply due to spherical aberration ( since this should be negligible with the water immersion lens ) and the PSF is larger than the ∼0 . 2×0 . 7 µm expected for an oil-immersion lens focused into 10 µm of water ( see also Table 20 . 2 in [46] ) . Visual inspection of the fine structure of the PSF suggests that other problems beyond simple spherical aberration exist: the bending of the PSF axis and structure in the field suggest that the cell is behaving as a complex phase object rather than as a simple body of fluid . In connection with this point , it is known that the cell surface is quite uneven ( e . g . [47] ) and the presence of myofilaments , mitochondria and even the nucleus ( although the latter was avoided here ) add complex structures that are barely resolved yet contribute to the phase shift of the marginal rays that are critical for tight PSF formation . This model accurately reproduced the reported spatial and temporal properties of Ca blinks and Ca sparks . The under-estimation of [Ca]jSR depletion by Ca blink signals is somewhat larger than the results of a recent computer modeling study by Hake , et al . [9] would suggest . Although the latter study did not use experimental data sets nor microscope blurring ( and slightly different parameters ) , the constraint of geometry and microscope performance on the accuracy of recorded signals is clear . The profound SR Ca depletion we calculate strongly affects SR release time-course and shows that the declining Ca flux during a Ca spark is not simply due to RyR closure . By fitting a model with realistic geometry to recorded data , we limit the effects of noise that would preclude inverting the equations for diffusion and buffering . It was notable that despite using an extensive dataset , all solutions converged toward similar time-courses for the release flux , suggesting an unexpected invariance of RyR gating behavior during SR release . | Calcium levels inside myocytes regulate the heart's force of contraction . Calcium is released from the primary intracellular store called the sarcoplasmic reticulum . Calcium release was directly observed as ‘calcium sparks’ using fluorescent calcium indicators inside the cell . More recently , calcium levels inside the store have been measured as calcium ‘blinks’ . These suggest that some depletion of store calcium occurs during cell excitation; however , the actual extent of depletion is made uncertain by the complex sarcoplasmic reticulum shape , dye saturation and optical properties of the microscope . While previous studies have assumed idealized microscope properties , we measured microscope blurring and applied it to a computer model of calcium movements inside the cell . In this model , calcium release was adjusted to match the simulated blurred calcium signals to experimental results . The calculations show that the depth of local sarcoplasmic reticulum calcium depletion is much greater than inferred from calcium blinks and that the time-course of calcium release is affected by this depletion . An estimate for the time-course of gating of the ion channels that regulate calcium release inside the cell was also calculated . We suggest that the time-course of SR Ca release arises from a complex interaction of Ca depletion and channel gating . | [
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"phy... | 2013 | Extraction of Sub-microscopic Ca Fluxes from Blurred and Noisy Fluorescent Indicator Images with a Detailed Model Fitting Approach |
There are currently a large number of “orphan” G-protein-coupled receptors ( GPCRs ) whose endogenous ligands ( peptide hormones ) are unknown . Identification of these peptide hormones is a difficult and important problem . We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules , in particular peptide hormones , via cross-genomic sequence comparisons . The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models . This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level . Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins . In this proof of concept , we identified 45 out of 54 prohormones with only 44 false positives . The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides . Finally , in order to validate the computational methodology , we present the basic molecular biological characterization of the novel putative peptide hormone , including its identification and regional localization in the brain . This species comparison , HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences . This novel putative peptide hormone is found in discreet brain regions as well as other organs . The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development .
G protein coupled receptors ( GPCRs ) probably represent the largest gene family , making up 3% of the mammalian genome [1] . These proteins are made up of several subfamilies , including Class A rhodopsin-like , Class B secretin-like , Class C metabotropic glutamate/pheromone-like , and other nonmammalian receptors . Within each class , there is a very large number of smaller subclassifications , such as a family of receptors for peptide hormones within rhodopsin-like receptors . There are approximately 1 , 000 GPCRs , the vast majority of which are olfactory receptors , with more than 650 GPCRs in the rhodopsin family alone [2] . A large number of these receptors have been identified only by computational methods , while others have been cloned and transfected into cells; however , the cognate neurotransmitter and the receptor functions for many GPCRs are currently unknown . Any receptor for which the native neurotransmitter is unknown is considered an orphan receptor . Of all the orphan receptors that remain , some percentage represents receptors for peptide hormones . This large family of proteins is important not only from a basic science perspective , but because of their extracellular sites of action and importance as first messengers for cellular signaling , GPCRs have become a primary target for drug development . In fact , over 30% of all pharmaceuticals act either as agonists or antagonists of GPCRs [3] . Many pharmaceutical companies are identifying , cloning , and patenting new orphan GPCRs , with the hope that orphan receptors will ultimately lead to new drug development and new pharmaceutical agents . Although the identification of putative GPCRs can be accomplished relatively easily , the discovery of the endogenous ligands that activate these receptors is far more difficult . These ligands can exist as small molecules , lipids , peptides , or proteins [4] , [5] . Many , such as ATP , may have important functions other than activating a GPCR . Even within a class of hormones , there are seldom obvious clues that identify a new candidate . This is particularly true within the family of peptide hormones , as they are processed from a larger species known as preprohormones [6] . Peptide hormones , or neuropeptides , are a string of amino acids ranging from approximately 3 to 50 residues . They are found within a larger protein ( a preprohormone ) , and the production of the actual hormone usually follows specific rules . Preprohormones are secreted proteins , and each has a signal sequence that is necessary for the transport of the protein out of the Golgi complex into a secretory vesicle for processing and secretion where the signal sequence is removed , revealing the prohormone [7] . In general , hormones are surrounded by a pair of basic residues , i . e . Arg-Arg , Arg-Lys , Lys-Arg , or Lys-Lys , which are found directly adjacent to the putative hormone . These double basic residues act as recognition sites for processing enzymes , usually serine proteases that cleave the prohormone to liberate the active peptide [7] , [8] . In many cases , there is more than a single active peptide within one precursor protein [6] . Even with these common features , the identification of a peptide hormone from a DNA or protein sequence is very difficult . Even though all of the GPCRs are obviously related based upon DNA or protein sequence , the neuropeptides that bind to the receptors are only obviously related within discrete families of prohormones . For instance , the family of opioid-like peptides has four members . These prohormones , proopiomelanocortin ( POMC ) , proenkephalin , prodynorphin , and pronociceptin ( proN/OFQ ) , share similar genomic structures and a very slight similarity of protein sequence , most notably the Y ( F ) GGF of enkephalin , β-endorphin , dynorphin , and N/OFQ [9] , [10] . However , if one were to conduct a BLAST search in Genbank for DNA sequences similar to proenkephalin , one would not find any other neuropeptide . Simple search strategies within Genbank are not adequate for identifying novel neuropeptides , especially those not belonging to known neuropepeptide families . There is an additional feature of neuropeptides that may more clearly differentiate them from other types of molecules . Neuropeptides are usually well conserved among various species ( rat , mouse , human ) , while the intervening sequences , presumably because they are simply discarded , are not well conserved [11] . Here we describe a novel Hidden Markov Model ( HMM ) -based computational framework , the Match Profile HMM ( MPHMM ) method for neuropeptide identification based upon an approach that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species , and show how such models can be used to discover new functional molecules via cross-genomic sequence comparisons . This computational tool was used to identify a novel prohormone , NPQ , containing up to four potential neuropeptides [12]
The extended list of matches , the GUI SequenceMatcher , and the HIGHER tools will be made are available at http://www . cslu . ogi . edu/people/sonmezk/hormone . Initially , we will enable the visualization of our ENSEMBL and CELERA runs via the GUI . The next version will allow evolutionary HMM searches specified by the user . The HIGHER codebase will also be made available at the website once it is ready for release .
We have presented a computational framework that is capable of accounting for protein structure and cross-species evolutionary divergence simultaneously . By aligning low-level evolutionary HMM modules within a high-level functional-element grammar , it is possible to build precise models of the effects of evolutionary pressures on genomic structures . In particular , we have applied this technique to modeling of prohormones across species with the goal of identifying novel prohormones and associated peptide hormones based on their evolutionary divergence profiles and genomic structures . This technique has resulted in high accuracy detection in a known dataset and led to putative hormones in a set of hypothetical proteins . Biochemical validation of the findings has resulted in the initial characterization of the prohormone preproNPQ , containing four potential previously undiscovered neuropeptides .
In order to determine if the putative transcript named preproneuropeptide Q ( preproNPQ ) is found in the brain , we performed PCR using rat , human and mouse specific primers with their corresponding cDNAs . The sequences of the primers used were: Rat Forward Primer 5′-GAAGGGGCCGAGCATCCTGG-3′ and Reverse Primer 5′-CACCAGTAAAAGCGTCTGTCTTC-3′; Mouse Forward Primer 5′-GGACAGGGTCGGAACATGAAG-3′ and Reverse Primer 5′-GTGTTTTCACCAGTTGAAGAGTC-3′; Human Forward Primer 5′-ACGCAGAACATGAAGGGACTCAGA-3′ and Reverse Primer 5′-CCAGTATATTTTCACCAGTTAAGC-3′ . Advantage Genomic Polymerase Mix enzyme ( BD Biosciences Clontech , CA ) was used for PCR , according to manufacturer's instructions . Approximately 200–300 ng cDNA was used for each 50 ml reaction , along with 10 mM of specific forward and reverse primer , 2 . 2 ml magnesium acetate and dNTPs ( 10 mM ) . The annealing temperature was set at 53°C , and after 25 cycles of amplification , the PCR products were run on a 1 . 5% agarose gel and visualized using ethidium bromide . A positive control PCR reaction was also performed at the same time , using rat brain cDNA and specific primers for the prepronociceptin gene , and the reaction product was run on the gel . In order to determine if the preproNPQ transcript could be detected in various human tissues , we used Ambion's First Choice Human Blot ( a nylon membrane bound with 3 mg RNA from various human tissues , Ambion Inc , TX ) . The blot was prehybridized and probed with human NPQ cDNA prepared using the above preproNPQ human primers and the human DNA clone in pOTB7 vector from ATCC ( Cat # 6710068 , Manassas , VA ) . This clone contained the putative sequence for human preproNPQ , and the primers were used to isolate a 370 bp preproNPQ sequence that was used as the cDNA probe for hybridization to the RNA . Random-prime labeling of approximately 20–30 ng DNA was performed using 32P-dCTP and Klenow DNA polymerase , and after purifying the labeled probe on a G-50 column , the labeled DNA probe was hybridized to the nylon membrane overnight at 42°C . The membrane was washed and exposed to film . | Peptide hormones , or neuropeptides , are made up of a string of amino acids ranging from approximately 3 to 50 residues . These peptides are processed from a larger protein called a prohormone and activate a class of proteins called G-protein-coupled receptors ( GPCRs ) . Neuropeptides signal neurons and other cells leading to changes in cellular biochemistry and potentially gene expression . There are a number of “orphan” GPCRs , i . e . , receptors that have been discovered either by genomic sequence or by cloning , in which its respective peptide hormone is unknown . We have devised a computational method that models patterns in protein sequence simultaneously with evolutionary differences across species in order to identify previously unknown peptide hormones . We have used this computational methodology to identify a previously unknown putative prohormone that contains up to four potential neuropeptides , and we have characterized this prohormone with respect to location in rat brain and various human tissues . This computational technique will be useful for the identification of additional neuropeptides and help to characterize orphan GPCRs . Because roughly half of all pharmaceuticals act through activation or inhibition of GPCRs , this technique should lead to the identification of additional pharmaceutical targets and ultimately clinically used drugs . | [
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"biolo... | 2009 | Evolutionary Sequence Modeling for Discovery of Peptide Hormones |
Long noncoding RNAs ( lncRNAs ) participate in various biological processes such as apoptosis . The function of lncRNAs is closely correlated with their localization within the cell . While regulatory potential of many lncRNAs has been revealed at specific subcellular location , the biological significance of discrete distribution of an lncRNA in different cellular compartments remains largely unexplored . Here , we identified an lncRNA antisense to the pro-apoptotic gene PYCARD , named PYCARD-AS1 , which exhibits a dual nuclear and cytoplasmic distribution and is required for the PYCARD silencing in breast cancer cells . The PYCARD-regulated apoptosis is controlled by PYCARD-AS1; moreover , PYCARD-AS1 regulates apoptosis in a PYCARD-dependent manner , indicating that PYCARD is a critical downstream target of PYCARD-AS1 . Mechanistically , PYCARD-AS1 can localize to the PYCARD promoter , where it facilitates DNA methylation and H3K9me2 modification by recruiting the chromatin-suppressor proteins DNMT1 and G9a . Moreover , PYCARD-AS1 and PYCARD mRNA can interact with each other via their 5′ overlapping region , leading to inhibition of ribosome assembly in the cytoplasm for PYCARD translation . This study reveals a mechanism whereby an lncRNA works at different cellular compartments to regulate the pro-apoptotic gene PYCARD at both the epigenetic and translational levels , contributing to the PYCARD-regulated apoptosis , and also sheds new light on the role of discretely distributed lncRNAs in diverse biological processes .
The great majority of mammalian genomes are pervasively transcribed , giving rise to tens of thousands of noncoding transcripts , especially long noncoding RNAs ( lncRNAs ) [1] . LncRNAs participate in a large range of biological processes such as cell differentiation , apoptosis and proliferation , and most of them function by participating in the regulation of gene expression [2 , 3] . Unlike mRNAs , which must be localized to the cytoplasm for protein synthesis , lncRNAs exhibit diverse subcellular distribution patterns , ranging from predominant nuclear foci to almost exclusively cytoplasmic localization , and exert distinct regulatory effects at their particular site of action [4 , 5] . Thus , the subcellular localization of lncRNAs is critical for their biological function . A number of studies have implicated the regulatory potential of lncRNAs at specific subcellular location . Determined by the site of action where they are located , lncRNAs may work in cis on neighboring genes or in trans to regulate distantly located genes or molecular targets in the nucleus or cytoplasm [5 , 6] . Also , our related studies showed that several nuclear lncRNAs drive tumorigenesis by sequestering the activity of PSF protein in repression of proto-oncogene transcription [7 , 8] , and revealed that the OCC-1 transcripts localized in cytoplasm inhibit cell cycle transition by modulating the stability of HuR protein [9] . Current studies on lncRNAs have greatly advanced our knowledge of their physiological and pathological roles . Nevertheless , a substantial proportion of lncRNAs are revealed to exhibit a discrete subcellular distribution [10] , and the biological significance of discrete distribution of an lncRNA in different cellular compartments remains largely unexplored . The pro-apoptotic gene PYCARD encodes a signaling factor that consists of an N-terminal PYRIN-PAAD-DAPIN domain ( PYD ) and a C-terminal caspase-recruitment domain ( CARD ) and operates in the intrinsic and extrinsic cell death pathways [11 , 12] . PYCARD was originally identified to undergo the DNA methyltransferase-1 ( DNMT1 ) -mediated epigenetic silencing in a wide range of human tumors [13–17] , and subsequent studies showed that its inactivation is also associated with other epigenetic events such as H3K9 dimethylation [18] . In this study , we further report an lncRNA antisense to PYCARD , named PYCARD-AS1 , which exhibits a dual nuclear and cytoplasmic distribution and is required for the PYCARD silencing in breast cancer cells . PYCARAD-AS1 is functionally involved in the PYCARD-regulated apoptosis , and regulates apoptosis in a PYCARD-dependent manner , indicating that PYCARD is a critical function mediator of PYCARD-AS1 . Mechanistically , PYCARD-AS1 not only acts in cis to facilitate the recruitment of DNMT1 and histone H3K9 methyltransferase G9a to the PYCARD locus , but also inhibits ribosome assembly in the cytoplasm for PYCARD translation , leading to coordinated regulation of PYCARD expression at the epigenetic and translational levels . Our findings highlight the notion that the transcripts of a specific lncRNA may operate in biological processes by exerting distinct regulatory effects at different cellular compartments .
Through a GenBank search , we identified the gene C16orf98 on the opposite strand of the PYCARD gene , which produces a transcript in a head-to-head orientation relative to the PYCARD mRNA ( Fig 1A ) . Based on the sequence of C16orf98 , the experiments of 5′- and 3′-RACE were initiated with total RNA from SKBR3 cells and resulted in a 1 , 095-nucleotide ( nt ) antisense transcript of PYCARD ( Fig 1B ) , which is the same as the transcript annotated as PYCARD-AS1 . PYCARD-AS1 originates in the second intron 892 nt downstream of the transcription start site ( TSS ) of PYACRD , ends at nt 676 upstream of the PYCARD TSS , and consists of two exons ( Fig 1C ) . A “directional” RT-PCR assay , which was set up by using gene-specific reverse primers in RT reaction ( AS-R and S-R , shown in Fig 1C ) , showed that neither PYCARD-AS1 nor PYCARD produces longer RNA species spanning their 3’ non-overlapping regions ( Fig 1D ) . In addition , the RT-PCR detection initiated by different primers ( S1A Fig ) , as well as the 3′-RACE experiment , indicated that PYCARD-AS1 is poly ( A ) -tailed , belonging to an mRNA-like transcript . We next examined the sensitivity of PYCARD-AS1 transcription to the Pol II inhibitor α-amanitin . Following α-amanitin treatment , the levels of PYCARD-AS1 and ACTB transcripts were reduced , whereas those of pre-tRNAtyr and 45S pre-rRNA showed no change ( S1B Fig ) , indicating that PYCARD-AS1 is transcribed by Pol II . PYCARD-AS1 is annotated as a noncoding transcript in GenBank . In addition , the Coding Potential Calculator tool [19] predicted that PYCARD-AS1 displays no protein-coding potentiality ( S1C Fig ) . Although UniProt showed a putative protein prediction of 204 amino acids for PYCARD-AS1 ( S1D Fig ) , we found that the putative open reading frame ( ORF ) of PYCARD-AS1 could not be expressed as an N-terminal enhanced green fluorescent protein ( EGFP ) fusion protein ( S1E and S1F Fig ) . By RNA FISH assay , PYCARD-AS1 was revealed to exhibit a dual nuclear and cytoplasmic distribution ( Fig 1E ) . Moreover , cellular fractionation assay showed that the distribution of PYCARD-AS1 is clearly distinct from that of the nuclear-localized U1 snRNA , the mitochondrially retained 12S rRNA and the protein-coding GAPDH mRNA ( Fig 1F ) . We further separated the chromatin fraction , and found that approximately 25% of nuclear-localized PYCARD-AS1 transcripts were chromatin-enriched ( S1G Fig ) . Since antisense lncRNAs have been implicated as regulator of their sense counterparts [2 , 5] , we set out to analyze whether PYCARD-AS1 can regulate PYCARD expression . The transcription level of PYCARD-AS1 was first measured in four breast cancer cell lines , namely , SKBR3 , MCF7 , MDA-MB-231 and T47D . PYCARD-AS1 transcription was detectable in all cell lines , with SKBR3 expressing it most ( S2A Fig ) . Next , shRNAs were designed to silence PYCARD-AS1 and detect the effect of PYCARD-AS1 knockdown on PYCARD expression in SKBR3 cells . The shRNAs were shown to reduce the levels of total and cytoplasmic PYCARD-AS1 and efficiently act on the nuclear-retained and chromatin-associated PYCARD-AS1 ( Fig 2A–2F ) . Concomitantly , we observed that PYCARD-AS1 silencing increases the PYCARD mRNA and protein levels ( Fig 2G and 2H and S2B Fig ) , indicating a negative regulation of PYCARD by PYCARD-AS1 . In parallel , we knocked down PYCARD mRNA with specific shRNA , and found that PYCARD silencing didn’t change the PYCARD-AS1 level ( S2C Fig ) . In addition , PYCARD-AS1 knockdown was shown to have no effect on the mRNA levels of FUS , TRIM72 and PYDC1 , three genes neighboring PYCARD and PYCARD-AS1 ( Fig 1A and S2D Fig ) , suggesting that PYCARD-AS1 specifically regulates PYCARD . Also , the negative PYCARD regulation by PYCARD-AS1 was confirmed in MCF7 , MDA-MB-231 and T47D cells ( S2E–S2G Fig ) . The silencing of PYCARD is closely correlated with the defective apoptosis of tumor cells , and its reactivation was revealed to increase the susceptibility of tumor cells to cytotoxic agents [20] . Confirming the regulatory effect of PYCARD-AS1 on PYCARD expression prompted assays on the contribution of PYCARD-AS1 to PYCARD-regulated apoptosis . We tested whether PYCARD-AS1 regulates the sensitivity of SKBR3 cells to paclitaxel , which is also associated with the expression level of PYCARD [21] . Knockdown of PYCARD-AS1 increased PYCARD expression ( S2H Fig ) , and the PYCARD-AS1-knockdown SKBR3 cells showed increased sensitivity to paclitaxel treatment compared with the control cells ( Fig 2I and S2I Fig ) . Remarkably , simultaneous PYCARD knockdown , which neutralized the increased PYCARD expression ( S2H Fig ) , was shown to largely weaken the sensitivity of SKBR3 cells to paclitaxel increased by PYCARD-AS1 silencing ( Fig 2I and S2I Fig ) , suggesting that PYCARD-AS1 regulates apoptosis in a PYCARD-dependent manner . SKBR3 cells included in the above apoptosis assay were also subjected to global gene expression analysis . Microarray data revealed hundreds of genes that were induced or suppressed more than twofold as a consequence of PYCARD-AS1 knockdown , and showed that the expression of a significant proportion of these differentially expressed genes ( 91 of 175 PYCARD-AS1 knockdown-induced genes and 37 of 59 PYCARD-AS1 knockdown-suppressed genes ) could be reversed at least 1 . 5-fold in PYCARD-AS1 and PYCARD double-knockdown SKBR3 cells compared with that in PYCARD-AS1 knockdown SKBR3 cells ( Fig 2J and S1 Table; GEO accession number: GSE85032 ) . Several target genes identified by microarray were randomly selected for qRT-PCR confirmation ( Fig 2K and S2J Fig ) . The above data indicate that PYCARD-AS1 is involved in the PYCARD-regulated apoptosis , and that PYCARD is a critical downstream target of PYCARD-AS1 . Following on from the above studies , we attempted to determine how PYCARD-AS1 suppresses PYCARD by using SKBR3 cells . DNA methylation and histone H3K9 dimethylation are two types of epigenetic modifications contributing to the PYCARD silencing in tumor cells [13 , 18] . Since shRNAs were shown to act on the PYCARD-AS1 in nucleus ( Fig 2C and 2D ) , we first analyzed whether PYCARD-AS1 knockdown can impact the methylation status of the PYCARD promoter , with the PYCARD-AS1 promoter being detected as a control . The results from bisulfite sequencing showed that the PYCARD promoter , but not the PYCARD-AS1 promoter , was partially demethylated by PYCARD-AS1 knockdown ( Fig 3A ) , leading to a relatively hypomethylated state . We also performed ChIP assay to examine the distribution of H3K9me2 across a genomic region of approximately 2 . 7 kb that covers the PYCARD and PYCARD-AS1 loci . The decrease of H3K9me2 occupancy , which occurred upon PYCARD-AS1 knockdown , was only detected in the region near the PYCARD TSS ( Fig 3B , upper ) . Histone modification H3K27me3 was included as a ChIP control , and the result showed that it was rarely enriched across this genomic region compared with H3K9me2 ( Fig 3B , lower ) . As detected by Pol II ChIP assay , PYCARD-AS1 knockdown also resulted in an elevation in the initiating Pol II occupancy at the 5′ PYCARD region , as well as an elevation in the elongating Pol II occupancy at the 3′ PYCARD region and the total Pol II occupancy at the 5′ and 3′ PYCARD regions ( Fig 3C ) , indicating an enhanced PYCARD transcription initiation . The effect of PYCARD-AS1 on PYCARD transcription was further dissected via nuclear run-on assay , and the results revealed an increased production of nascent transcript for PYCARD when PYCARD-AS1 was knocked down ( Fig 3D ) . Altogether , the findings described above suggest that PYCARD-AS1 knockdown in SKBR3 cells changes the chromatin status from an inactive state to an active one , thereby inducing PYCARD transcription . In addition to DNMT1 , which had previously been identified [22] , G9a , a histone methyltransferase responsible for H3K9 dimethylation , was also revealed to occupy the PYCARD promoter in SKBR3 cells ( S3A Fig ) . PYCARD expression was induced by DNMT1 knockdown , G9a knockdown , or DNMT1 and G9a double-knockdown ( S3B Fig ) . Moreover , PYCARD-AS1 knockdown was shown to cause simultaneous loss of the DNMT1 and G9a occupancy at the PYCARD promoter without influencing their expression levels ( Fig 4A and 4B and S3C Fig ) . These findings indicate that PYCARD is under the control of DNMT1 and G9a , and that PYCARD-AS1 contributes to the recruitment of DNMT1 and G9a to the PYCARD promoter . A native RIP assay using DNMT1- and G9a-specific antibodies retrieved a substantial amount of PYCARD-AS1 ( Fig 4C ) , revealing a PYCARD-AS1 association with DNMT1 and G9a . Meanwhile , ChIRP assay using tiling oligos against PYCARD-AS1 ( Fig 4D ) specifically retrieved the PYCARD promoter DNA ( Fig 4E ) , indicating that PYCARD-AS1 is also associated with the PYCARD locus . In accordance with the finding that shRNAs can efficiently knock down the nuclear-retained and chromatin-associated PYCARD-AS1 transcript ( Fig 2C and 2D ) , PYCARD-AS1 knockdown was shown to decrease the PYCARD promoter DNA associated with PYCARD-AS1 transcript ( Fig 4F ) . Together , the above results suggest that PYCARD-AS1 may recruit DNMT1 and G9a to PYCARD promoter via the intermolecular interactions . We proceeded to elucidate the molecular mechanism whereby PYCARD-AS1 epigenetically regulates PYCARD . Although lncRNAs contain functionally redundant sequences , it is acknowledged that their core functionality generally depends on particular functional region [23] . To map the DNMT1- and G9a-interacting region within PYCARD-AS1 , nuclear extract from SKBR3 cells was subjected to limited RNase T1 digestion , so that G residues protected by an RNA binding protein would remain preferentially uncleaved . After DNMT1 or G9a RIP , the enriched RNA fragments , which were associated with DNMT1 or G9a and protected from degradation , were identified by qRT-PCR analysis using primer sets that scanned the PYCARD-AS1 transcript in overlapping ~150 nt-long segments ( Fig 5A ) . As shown in Fig 5B , the PYCARD-AS1 transcript from nt 631 to 1 , 095 was enriched by DNMT1- and G9a-specific antibodies after RNase T1 treatment , suggesting that the 3′ PYCARD-AS1 domain is the major region responsible for the DNMT1 and G9a interaction . The above finding was confirmed by an RNA pull-down assay using an in vitro-generated biotinylated 3′ PYCARD-AS1 region , which was shown to bind DNMT1 and G9a with equal efficiency as full-length PYCARD-AS1 ( Fig 5C ) . However , the effort to further narrow down and distinguish the DNMT1- and G9a-binding regions was failed because neither DNMT1 nor G9a could be retrieved by any of the biotinylated RNA segments derived from the 3′ PYCARD-AS1 region ( Fig 5C ) . In addition to the consensus primary sequence , a proper secondary structure is also critical for the function of lncRNAs such as protein binding [24 , 25] . Thus , further truncation of the 3′ PYCARD-AS1 region might have destroyed the necessary secondary structure and thus impair its property of association with DNMT1 and G9a . On the other hand , given that DNMT1 and G9a are reported to interact with each other directly [26] , it could be speculated that DNMT1 and G9a form a unique complex prior to their binding to PYCARD-AS1 . Consistently , our IP assay showed that RNase treatment didn′t destroy the DNMT1–G9a interaction ( S4A Fig ) . Since there is a substantial degree of reverse complementarity between PYCARD-AS1 and PYCARD sequences , we tested whether the association of PYCARD-AS1 with the PYCARD locus , as demonstrated by ChIRP assay ( Fig 4E and 4F ) , is mediated by direct base pairing , which results in formation of an RNA/DNA hybrid ( i . e . R-loop ) . Total RNA extracted from RNase H- or RNase inhibitor-treated SKBR3 cells was subjected to region-specific qRT-PCR as well as primer walking assay . These analyses indicated that the region of potential RNA–DNA interaction is likely to be contained in a 317-nt fragment within exon 2 of PYCARD-AS1 ( nt 576/892 with respect to the PYCARD-AS1 TSS ) , which is complementary to a region of the first exon of PYCARD ( nt 1/317 with respect to the PYCARD TSS ) , because this PYCARD-AS1 region is sensitive to the RNase H treatment , which degrades RNA in RNA/DNA hybrids ( Fig 5D and 5E and S4B Fig ) . We next established an RNase-ChIP assay to test whether R-loop formation contributes to the DNMT1/G9a occupancy at the PYCARD promoter , which was revealed to be PYCARD-AS1-dependent in SKBR3 cells ( Fig 4A and 4B ) . RNase T1 treatment , which would degrade the single-stranded PYCARD-AS1 sequence lying outside the 3′ DNMT1/G9a-binding region , led to a separation of the 3′ PYCARD-AS1 region from the PYCARD DNA that abrogated the association between DNMT1/G9a complex and PYCARD promoter; if PYCARD-AS1 interacts with the PYCARD DNA sequence to recruit DNMT1 and G9a as expected , treatment with RNase H would result in the significant release of DNMT1 and G9a from the PYCARD promoter ( Fig 5F ) . We next tested several other DNMT1- and G9a-targeted loci that include KCNQ1 and CDH1 , two genes characterized to also associate with the lncRNA KCNQ1OT1 or NEAT1 [27–30] . The results showed that the R-loop-dependent genomic recruitment of DNMT1 and G9a is not confined to the PYCARD locus ( S4C and S4D Fig ) . As PYCARD-AS1 and PYCARD mRNA constitute a pair of head-to-head overlapping transcripts , we next sought to determine whether they can interact with each other . Affinity pull-down assay showed that the in vitro-generated biotinylated full-length PYCARD-AS1 retrieved a substantial amount of PYCARD mRNA compared with the negative controls including beads alone or EGFP transcript ( Fig 6A ) . In parallel , the deletion mutant of PYCARD-AS1 that lacks the overlapping region ( PYCARD-AS1ΔOS , shown in Fig 6C , upper ) was tested , and the result showed that deletion of the overlapping region led to an impaired association with PYCARD mRNA ( Fig 6A ) . The interaction between PYCARD-AS1 and PYCARD transcripts was also confirmed within the cellular context by a MS2-RIP assay ( Fig 6B ) ; furthermore , an RNase-A assay indicated that the overlapping region of this pair of transcripts was resistant to RNase degradation ( Fig 6C , lower ) . PYCARD-AS1 was revealed to exhibit a dual nuclear and cytoplasmic distribution ( Fig 1E and 1F ) . Interestingly , by cellular component-specific RNase-A assays , the PYCARD-AS1–PYCARD interaction was detectable in both the nucleus and the cytoplasm ( S5A Fig ) . The interaction between PYCARD-AS1 and PYCARD transcripts implies a post-transcriptional regulation of PYCARD by PYCARD-AS1 . lncRNAs have been involved in the stability control of their paired mRNAs [31–37] . However , it seems unlikely that PYCARD-AS1 operates through this strategy because its depletion didn′t change the half-life of PYCARD mRNA in SKBR3 cells , which were treated with α-amanitin in advance to block new RNA synthesis ( S5B Fig ) . In addition , PYCARD-AS1 was demonstrated to have no effect on the subcellular distribution of PYCARD mRNA ( S5C Fig ) . We next tested whether PYCARD-AS1 gets connected to the PYCARD translation by monitoring the association of PYCARD mRNA with polysomes , the translational entity , in SKBR3 cells with or without PYCARD-AS1 knockdown . SKBR3 cell lysates fractionated through sucrose gradients were subjected to RNA isolation ( Fig 6D ) , and the isolated RNA samples were used to measure the recruitment of PYCARD mRNA on polysomes by qRT-PCR . As shown in Fig 6E ( upper ) , PYCARD-AS1 knockdown drove a shift of PYCARD mRNA toward heavier polysomes , in keeping with increased translation . As a negative control , the distribution of GAPDH mRNA in separation fractions did not change ( Fig 6E , lower ) . Given that the overlapping region of this pair of sense–antisense transcripts is localized at their 5′ ends , we reasoned that PYCARD-AS1 can influence the ribosome assembly on PYCARD mRNA when they interact with each other . To test this , we established an RIP assay to determine whether PYCARD-AS1 knockdown can affect the ratio of ribosome-occupied PYCARD to total PYCARD mRNA , which was expected to eliminate the "contaminating" effect of PYCARD-AS1 knockdown on PYCARD transcription . The results clearly showed that occupancy of RPS6 and L26 , the integral components of ribosomal subunits , on PYCARD mRNA was increased by PYCARD-AS1 knockdown ( Fig 6F , upper ) . In parallel , PYCARD-AS1 knockdown was shown to have no effect on the RPS6 and L26 occupancy on GAPDH mRNA ( Fig 6F , lower ) . To further address this issue , we performed a compensation experiment by infecting the PYCARD-AS1-knockdown SKBR3 cells , which had increased mRNA and protein levels of PYCARD ( Fig 6G and 6H ) , with lentivirus expressing the 5′ overlapping region of PYCARD-AS1 ( PYCARD-AS1OS ) or the PYCARD-AS1ΔOS , which contains several point mutations that disrupt the shRNA target . Neither of the transcripts could compensate for the effect of PYCARD-AS1 knockdown on PYCARD mRNA level ( Fig 6G ) . However , the PYCARD-AS1OS , but not the PYCARD-AS1ΔOS , has the ability to neutralize the PYCARD translation increased by PYCARD-AS1 knockdown ( Fig 6H ) . Collectively , the results suggest that PYCARD-AS1 also suppresses the translation of PYCARD in addition to its transcription .
LncRNAs have been implicated in a large range of biological processes such as apoptosis , and their function is closely correlated with the cellular compartments where they are located . It is interesting that a sizable proportion of lncRNAs are discretely distributed in different cellular compartments [10] . PYCARD-AS1 reported here is such a "discrete" lncRNA , which exhibits a dual nuclear and cytoplasmic distribution , and our current study describes a model whereby the discretely distributed PYCARD-AS1 transcripts link different effector mechanisms to simultaneously operate in the different aspects of PYCARD regulation ( Fig 7 ) , contributing to the PYCARD-regulated apoptosis . We first showed that PYCARD-AS1 can regulate PYCARD at the epigenetic level . Current evidence indicates that interaction with DNA methyltransferase or histone modifier is the major mechanism through which lncRNAs function in epigenetic regulation [2 , 38] . Nevertheless , PYCARD-AS1 represents one of the few lncRNAs , which include KCNQ1OT1 and NEAT1 [27 , 28 , 30] , identified to simultaneously modulate DNA methylation and histone modification at the loci of regulated genes . Biochemical interactions between DNA and histone methyltransferases were thought to provide a molecular explanation for the combinatorial pattern of DNA and histone modification in chromatin [26 , 39 , 40] . The current data further indicate that PYCARD-AS1 interacts with the DNMT1/G9a complex and aids in its recruitment to the PYCARD promoter . The core functionality of lncRNAs relies heavily on the cooperative action of their dispersed functional regions [23 , 41] . In the case of PYCARD-AS1-mediated epigenetic regulation of PYCARD , PYCARD-AS1 interacts with DNMT1/G9a complex via its 3′ region , and localizes at the PYCARD promoter via its 5′ region , thereby facilitating the location-specific DNMT1/G9a recruitment . While the detailed mechanism underlying the lncRNA-mediated R-loop formation is not fully understood [42] , our results showed that the R-loop structure formed by the 5′ PYCARD-AS1 region substantially contributes to the PYCARD-AS1-mediated PYCARD regulation . On the other hand , the interactions between lncRNA and DNMT1 are documented to have either positive or negative effect on the activity of DNMT1 [43–45] . The "guiding" lncRNAs , including PYCARD-AS1 reported here , can facilitate the DNMT1-mediated DNA methylation by recruiting DNMT1 , whereas some other lncRNAs , such as ecCEBPA and Dali , were found to sequester the DNMT1 activity as they compete with the DNA substrate of DNMT1 for DNMT1 binding [46 , 47] . PYCARD-AS1 also exerts inhibitory effect on PYCARD translation . Recent studies have shown that through binding mRNAs , lncRNAs , especially antisense lncRNAs , can repress or promote mRNA translation [35 , 48–50] . The distinct effects may depend on specific binding sequences and embedded elements in lncRNAs . In the case of PYCARD-AS1-mediated repression of PYCARD translation , PYCARD-AS1 activity depends on the 5′ overlapping sequence , which interferes with the ribosome assembly on PYCARD mRNA . The feature of 5′ overlapping sequence is shared by many other natural antisense transcripts . Nevertheless , the antisense Uchli RNA was reported to exert an opposite effect on the translation of its sense counterpart due to the presence of an embedded inverted SINEB2 element , which facilitates the ribosome binding to mRNA [49] . The paired antisense lncRNA may also affect certain other steps in protein translation . For instance , a binding of the PXN mRNA by its antisense lncRNA PXN-AS1-S can reduce PXN protein synthesis by inhibiting translational elongation [35] . In addition , a recent study reported that through competitive RNA–RNA interaction , an lncRNA is able to attenuate the activity of its paired antisense lncRNA in repression of mRNA translation [50] , thereby constituting a finely tuned lncRNA/antisense lncRNA/mRNA translational regulatory axis . The cluster of natural antisense transcripts comprises a surprisingly large fraction of lncRNAs [51]; moreover , the lncRNA–mRNA gene pairs are prevalent in mammalian genomes [52] . Thus , effect on mRNA translation might be a common effector mechanism employed by lncRNAs , especially antisense lncRNAs , to function in biological processes . Taken together , this study provides an example of how an lncRNA works at different cellular compartments to regulate a specific target gene at multiple levels , contributing to the regulation of apoptosis . The feature of discrete distribution in different cellular compartments is not exclusively exhibited by PYCARD-AS1; instead , it is extensively shared by other lncRNAs [10] . We propose that , as with PYCARD-AS1 , many other discretely distributed lncRNAs should also be multifunctional within the cell , and that elucidating their different functionalities at distinct distribution sites will greatly broaden our knowledge of lncRNA biology and provide new insights into their physiological and pathological roles in depth .
PYCARD-AS1 cDNA was synthesized from SKBR3 cells by RT-PCR . For the test of protein-coding potentiality of PYCARD-AS1 , the EGFP-coding sequence was inserted into the 3′ end of the putative PYCARD-AS1 ORF , and the fusion gene PYCARD-AS1-EGFP was cloned into the restriction sites Nhe I and Xho I of plasmid pcDNA3 . 1 ( Invitrogen ) . For lentivirus-mediated RNA interference , complementary sense and antisense oligonucleotides encoding short hairpin RNAs ( shRNAs ) targeting PYCARD-AS1 , PYCARD , DNMT1 and G9a transcripts were synthesized , annealed and cloned into the Age I and EcoR I sites of plasmid pLKO . 1 ( Addgene ) . For compensation experiment , cDNA corresponding to the PYCARD-AS1OS or PYCARD-AS1ΔOS was PCR-amplified from the PYCARD-AS1 cDNA and cloned into the Xho I and EcoR I sites of plasmid pLVX ( BD Clontech ) . For MS2-RIP experiment , the MS2-6× fragment was synthesized and fused to the 5′ end of PYCARD-AS1 or PYCARD-AS1ΔOS cDNA , and the resulting fusion constructs ( MS2-PYCARD-AS1 and MS2-PYCARD-AS1ΔOS ) were cloned into the Xho I and EcoR I sites of plasmid pcDNA3 . 1; the sequence encoding MS2 coat protein was PCR-amplified from plasmid pMS2-GFP ( Addgene ) and fused to the 3′ end of FLAG tag-encoding sequence , and the resulting FLAG-MS2 construct was cloned into the Hind III and Xho I sites of plasmid pcDNA3 . 1 . The primers and oligonucleotides used for plasmid construction are shown in S2 Table . HEK293T , SKBR3 , T47D , MDA-MB-231 and MCF7 cells were obtained from the ATCC and cultured in DMEM , MEM or RPMI1640 supplemented with 10% FBS in a 5% CO2 incubator at 37°C . To block cellular transcription by Pol II , SKBR3 cells in culture media were treated with 50 μM α-amanitin ( Sigma-Aldrich ) . For the apoptosis analysis , SKBR3 cells were treated with 5 nM paclitaxel ( Sigma-Aldrich ) for 72 h . Plasmid transfections were performed using Lipofectamine 2000 ( Invitrogen ) . For lentivirus infection , shRNA-encoding pLKO . 1 , or pLVX encoding specific PYCARD-AS1OS or PYCARD-AS1ΔOS , was co-transfected with psPAX2 and pMD2 . G plasmids ( Addgene ) into HEK293 cells; the infectious lentivirus was harvested 2 days post-transfection , filtered through 0 . 45-μm PVDF filters and transduced into SKBR3 , T47D , MDA-MB-231 or MCF7 cells . After lentivirus infection , the resulting cell population , but not the isolated single clones , were used for subsequent assays to avoid clone-specific effects . The full-length PYCARD-AS1 was obtained using 5′- and 3′-RACE System for Rapid Amplification of cDNA Ends ( Invitrogen ) in accordance with the manufacturer′s instructions . RACE PCR products were separated on a 1 . 5% agarose gel . Gel products were extracted with a Gel Extraction kit ( Foregene ) , cloned into pMD18-T vector and sequenced bidirectionally using M13 forward and reverse primers . The primers used in the RACE experiments are shown in S2 Table . RNA FISH was conducted using QuantiGene ViewRNA ISH Cell Assay Kit ( Invitrogen ) in accordance with the manufacturer′s instructions . In brief , SKBR3 cells cultured on cover slips were fixed , permeabilized and digested by protease to allow target accessibility . A probe set specific for PYCARD-AS1 ( designed and supplied by Invitrogen ) was added to the cells and hybridization was performed at 40°C for 3 h . After a series of signal amplification with Pre-Amplifier Mix , Amplifier Mix and Label Probe Mix supplied in the kit , cells were counterstained with DAPI and then detected using a fluorescent microscope ( Leica ) . A total of 1 × 107 cells were washed twice in cold PBS and then incubated in hypotonic buffer ( 50 mM HEPES , pH 7 . 5 , 10 mM KCl , 350 mM sucrose , 1 mM EDTA , 1 mM DTT and 0 . 1% Triton X-100 ) on ice for 10 min . After 5 min of centrifugation at 2 , 000 g , the supernatant was collected as the cytoplasmic fraction , and after additional washing , the remainder was considered as nuclear pellets , which could be resuspended in lysis buffer ( 10 mM HEPES , pH 7 . 0 , 100 mM KCl , 5 mM MgCl2 , 0 . 5% NP-40 , 10 μM DTT and 1 mM PMSF ) to prepare the nuclear lysate . To isolate the chromatin-enriched RNA , the chromatin pellets , as well as the soluble nucleoplasm , was prepared from the nuclear extract as described [53] . RNA samples were prepared from whole cell lysate or specific subcellular fractions using TRIzol reagent ( Invitrogen ) . RNA levels for a specific gene were measured by qRT-PCR ( starting with 50–100 ng RNA sample per reaction ) using Real-Time PCR Easy ( Foregene ) , in accordance with the manufacturer′s instructions . The qRT-PCR data were normalized to ACTB mRNA , 18S rRNA , mitochondrial-retained 12S rRNA , nuclear-localized U1 snRNA , or chromatin-associated XIST RNA , or presented as a percentage of the total amount of detected transcripts . The primers used in the qRT-PCR are shown in S2 Table . For global gene expression analysis , 15 μg of biotinylated cDNA synthesized from total RNA was hybridized to the Affymetrix GeneChip® PrimeView™ Human Gene Expression Array at 45°C for 16 h with Affymetrix GeneChip Hybridization Oven 640 . After washing and staining with Affymetrix Fluidics Station 450 , GeneChips were scanned by the Affymetrix GeneChip Command Console installed in the GeneChip Scanner 3000 7G . Hybridization data were analyzed with the Robust Multichip Analysis ( RMA ) algorithm using the default Affymetrix settings . Values are presented as log2 RMA signal intensity . A total of 5 × 105 cells treated with paclitaxel were harvested and stained with Alexa Fluor® 488 Annexin V/Dead Cell Apoptosis kit ( Invitrogen ) , in accordance with the manufacturer′s instructions . Flow cytometry analysis was carried out using an Accuri C6 flow cytometer ( BD Biosciences ) . Genomic DNA was extracted with the Genomic DNA Isolation kit ( Foregene ) and the bisulfite conversion reaction was performed using CpGenome Turbo Bisulfite Modification kit ( Millipore ) , in accordance with the manufacturer′s instructions . PCR amplification of bisulfite-treated DNA was carried out with PCR Easy ( Foregene ) . The amplified products were cloned and sequenced . The primers used in the PCR amplification are shown in S2 Table . The Abs used for immunoblotting were rabbit anti-PYCARD Ab ( 13833 , Cell Signaling ) , mouse anti-actin Ab ( sc-130301 , Santa Cruz Biotechnology ) , mouse anti-DNMT1 Ab ( ab13537 , Abcam ) and goat anti-G9a Ab ( sc-22879 , Santa Cruz Biotechnology ) . The Abs used for IP analysis were mouse anti-DNMT1 Ab ( ab13537 , Abcam ) and normal mouse IgG ( sc-2025 , Santa Cruz Biotechnology ) . The Abs used for ChIP analysis were rabbit anti-H3K9me2 Ab ( 4658 , Cell Signaling ) , rabbit anti-H3K27me3 Ab ( 9733 , Cell Signaling ) , rabbit anti-RNA polymerase II CTD repeat YSPTSPS ( S5P Pol II ) Ab ( ab5131 , Abcam ) , rabbit anti-RNA polymerase II CTD repeat YSPTSPS ( S2P Pol II ) Ab ( ab5095 , Abcam ) , mouse anti-RNA pol II Ab ( 39097 , Active Motif ) , mouse anti-DNMT1 Ab ( ab13537 , Abcam ) , rabbit anti-G9a Ab ( ab40542 , Abcam ) , normal mouse IgG ( sc-2025 , Santa Cruz Biotechnology ) and normal rabbit IgG ( sc-2027 , Santa Cruz Biotechnology ) . The Abs used for RIP analysis were mouse anti-DNMT1 Ab ( ab13537 , Abcam ) , rabbit anti-G9a Ab ( ab40542 , Abcam ) , mouse anti-p53 Ab ( P6874 , Sigma-Aldrich ) , rabbit anti-RPS6 Ab ( ab70227 , Abcam ) , rabbit anti-L26 Ab ( ab59567 , Abcam ) , normal mouse IgG ( sc-2025 , Santa Cruz Biotechnology ) and normal rabbit IgG ( sc-2027 , Santa Cruz Biotechnology ) . The Ab used for nuclear run-on assay was anti-BrdU Ab ( ab1893 , Abcam ) . A total of 5 × 106 cells were washed twice in cold PBS and pelleted . The pellet was resuspended in lysis buffer ( 10 mM HEPES , pH 7 . 0 , 100 mM KCl , 5 mM MgCl2 , 0 . 5% NP-40 , 10 μM DTT and 1 mM PMSF ) , incubated on ice with frequent vortexing for 15 min and then the lysate was obtained by centrifugation at 12 , 000 g for 10 min . Protein concentrations of the extracts were measured by the bicinchoninic acid assay ( Pierce ) . Forty micrograms of the protein was used for immunoprecipitation , or was fractionated by SDS-PAGE , transferred onto PVDF membranes and then blotted . For immunoprecipitation assays , protein samples were incubated with a specific antibody or control IgG overnight at 4°C . Subsequently , the samples were incubated with 50 μl of protein A agarose beads ( Invitrogen ) for 4 h at 4°C and then washed three times in washing buffer ( 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM MgCl2 and 0 . 5% NP-40 ) . Finally , protein complexes were eluted by SDS buffer ( 120 mM Tris-HCl , pH 6 . 8 , 20% glycerol and 4% SDS ) and then detected by immunoblotting . For native RIP assays , RNase OUT ( 50 U/ml , Invitrogen ) and a protease inhibitor cocktail ( Roche ) were added to the lysis buffer , and Ribonucleoside Vanadyl Complex ( 10 mM , NEB ) was added to the washing buffer ( the buffer mentioned here is the same as that used in immunoblotting and immunoprecipitation ) . Following the addition of antibody to the lysate , samples were incubated with 50 μl of protein A agarose bead ( Invitrogen ) for 4 h at 4°C and then washed three times in washing buffer . The beads were resuspended and treated with proteinase K at 45°C for 45 min . Coprecipitated RNAs were extracted using TRIzol reagent , ethanol-precipitated with Glycoblue ( Invitrogen ) as a carrier and then detected by qRT-PCR . The data of retrieved RNAs are presented as a percentage of the amount input . For RIP-based mapping assays , lysates were first mixed with RNase T1 ( 1 U/ml , Thermo Fisher Scientific ) , after which standard native RIP assays were performed using an antibody against DNMT1 or G9a . Following extraction of the coprecipitated RNA , the PYCARD-AS1 segments bound by DNMT1 and G9a , and hence protected from RNase T1 digestion and immunoprecipitated , were identified by qRT-PCR analysis using primer sets that scanned the PYCARD-AS1 transcript at ~150-nt-long , overlapping intervals ( S2 Table ) . For MS2-RIP , pcDNA3 . 1-MS2-PYCARD-AS1 , or pcDNA3 . 1-MS2-PYCARD-AS1ΔOS , was co-transfected with pcDNA3 . 1-FLAG-MS2 into SKBR3 cells . After 48 h , cells were subjected to RIP assay with Anti-FLAG M2 Magnetic beads ( Sigma-Aldrich ) in accordance with the manufacturer′s instructions . A total of 1 . 5 × 107 cells were washed with cold PBS and harvested in cold douncing buffer ( 5 mM MgCl2 , 0 . 5% NP-40 , 10% glycerol , and 50 mM Tris-HCl , pH 7 . 4 ) . After 10 min of incubation on ice , cells were disrupted with 30 strokes of a Dounce homogenizer , centrifuged at 3 , 300 g for 5 min and washed four times with 1 ml cold douncing buffer . Microscopic analysis indicated that samples contained intact nuclei , cellular debris and some intact cells . The crude nuclei were then resuspended in 100 μl nuclear run-on buffer ( 5 mM MgCl2 , 150 mM KCl , 0 . 1% sarbyl , 10 mM DTT and 50 mM Tris-HCl , pH 7 . 4 ) , and were mixed with 1 μl each of 10 mM ATP , GTP , CTP , 1 μl 10 mM BrUTP ( Sigma-Aldrich ) and 1 μl RNase inhibitor ( Thermo Fisher Scientific ) . Reaction mixtures were pre-incubated on ice for 30 min , then at 28°C for 5 min . The RNA was isolated by TRIzol reagent ( Invitrogen ) , and DNA was removed by DNase I ( Promega ) treatment . Nascent transcripts were immunoprecipitated with anti-BrdU antibody ( Abcam ) and subjected to qRT-PCR assays with primers listed in S2 Table . ChIP analyses were performed as described [7] . For RNase-ChIP assays , 1 × 106 cells were collected by centrifugation , permeabilized in 1 ml of PBST ( PBS containing 0 . 05% Tween 20 ) , and treated with 1 , 000 U/ml RNase T1 ( Thermo Fisher Scientific ) , 1 , 000 U/ml RNase H ( Thermo Fisher Scientific ) or 1 , 000 U/ml RNase inhibitor ( Thermo Fisher Scientific ) for 4 h at 25°C . The following procedures were carried out in accordance with the standard ChIP protocol . The genomic DNA in the precipitate was detected by qPCR using the primers shown in S2 Table , and the DNA precipitated by each antibody , including IgG , is presented as a percentage of the amount input . The collected cells were subjected to permeabilization treatment and then treated with RNase H or RNase inhibitor as described in RNase-ChIP assay . RNA was extracted from the treated cells and subjected to region-specific qRT-PCR as well as primer walking assay using primers listed in S2 Table to test the abundance of specific PYCARD-AS1 regions . Cells were cross-linked by 1% glutaraldehyde at room temperature for 10 min , followed by three washes in cold PBS . After being snap-frozen by liquid nitrogen and stored at −80°C , cross-linked cells were resuspended in nuclear lysis buffer ( 10 mM EDTA , 1% SDS and 50 mM Tris-HCl , pH 7 . 5 ) supplemented with a protease inhibitor cocktail ( Roche ) , and sonicated until DNA was in the size range of 100~500 bp . Cell lysates and a set of biotin-labelled antisense probes ( 20 nt in length ) were then incubated at 37°C for 4h , with the corresponding sense probes being included as a control . Streptavidin-coupled Dynabeads ( Invitrogen ) were added to pull down the probes . After washing , the retrieved DNA was isolated using the ChIP DNA Clean & Concentrator kit ( Zymo Research ) and subjected to qPCR analysis . The probes used for ChIRP are shown in S2 Table . To synthesize biotin-labelled transcripts , PCR fragments were prepared using forward primers harboring the T7 RNA polymerase promoter . Following purification of the PCR products , biotinylated transcripts were synthesized using MaxiScript T7 kit ( Ambion ) . Biotinylated RNA was heated to 85°C for 2 min , placed on ice for 2 min , supplied with RNA structure buffer ( 0 . 1 M KCl , 10 mM MgCl2 and 10 mM Tris-HCl , pH 7 . 0 ) and then incubated at room temperature for 20 min . Cell lysates were incubated with 10 pmol of biotinylated transcripts for 3 h at 25°C . Complexes were isolated with streptavidin-coupled Dynabeads ( Invitrogen ) . The retrieved protein and RNA were detected by immunoblotting and qRT-PCR , respectively . RNase-A assay was performed as described [31] . Briefly , cell lysates were treated with 20 ng/ml RNase A ( Thermo Fisher Scientific ) at 37°C for 30 min . RNA was extracted from the resultant sample , and subjected to qRT-PCR using primers shown in S2 Table to detect the association between PYCARD-AS1 and PYCARD transcripts . Polysome analysis was performed as described [48] . A total of 5 × 106 cells were preincubated with 100 mg/ml cycloheximide ( Sigma-Aldrich ) for 15 min . Cytoplasmic lysates were prepared and then fractionated by ultracentrifugation through 15%–50% linear sucrose gradients . Twelve fractions were collected , and RNA extracted from each fraction was subjected to qRT-PCR detection for specific transcript . Student′s t-test was performed to compare the differences between experimental groups relative to their paired controls . The data were presented as the mean ± SD and p-values of < 0 . 05 or below were considered statistically significant . | As the reveal of tens of thousands of long noncoding RNAs ( lncRNAs ) from mammalian genomes , there is increasing interest to understand how these transcripts function in physiological and pathological processes . The regulatory potential of many lncRNAs has been revealed at specific subcellular location . Nevertheless , many lncRNAs are discretely distributed in different cellular compartments , and the related biological significance remains largely unclear . PYCARD-AS1 is such a “discrete” lncRNA , which exhibits a dual nuclear and cytoplasmic localization . Here , we provide a comprehensive picture of how PYCARD-AS1 works at different cellular compartments to regulate the pro-apoptotic gene PYCARD at both the epigenetic and translational levels , thereby contributing to the apoptotic regulation . Our study adds a different layer to the lncRNA-mediated regulation of gene expression , and strongly suggests that the transcripts of a specific lncRNA may have distinct functionalities at different cellular compartments where they are located . | [
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"... | 2019 | A long noncoding RNA distributed in both nucleus and cytoplasm operates in the PYCARD-regulated apoptosis by coordinating the epigenetic and translational regulation |
Ductal carcinoma in situ ( DCIS ) is a heterogeneous group of non-invasive lesions of the breast that result from abnormal proliferation of mammary epithelial cells . Pathologists characterize DCIS by four tissue morphologies ( micropapillary , cribriform , solid , and comedo ) , but the underlying mechanisms that distinguish the development and progression of these morphologies are not well understood . Here we explored the conditions leading to the emergence of the different morphologies of DCIS using a two-dimensional multi-cell lattice-based model that incorporates cell proliferation , apoptosis , necrosis , adhesion , and contractility . We found that the relative rates of cell proliferation and apoptosis governed which of the four morphologies emerged . High proliferation and low apoptosis favored the emergence of solid and comedo morphologies . In contrast , low proliferation and high apoptosis led to the micropapillary morphology , whereas high proliferation and high apoptosis led to the cribriform morphology . The natural progression between morphologies cannot be investigated in vivo since lesions are usually surgically removed upon detection; however , our model suggests probable transitions between these morphologies during breast cancer progression . Importantly , cribriform and comedo appear to be the ultimate morphologies of DCIS . Motivated by previous experimental studies demonstrating that tumor cells behave differently depending on where they are located within the mammary duct in vivo or in engineered tissues , we examined the effects of tissue geometry on the progression of DCIS . In agreement with our previous experimental work , we found that cells are more likely to invade from the end of ducts and that this preferential invasion is regulated by cell adhesion and contractility . This model provides additional insight into tumor cell behavior and allows the exploration of phenotypic transitions not easily monitored in vivo .
The mammary gland is a highly organized , branched ductal network of luminal epithelial cells surrounded by myoepithelium and basement membrane embedded in stroma [1] , [2] . Reciprocal signaling between the cells and their surrounding microenvironment maintains the organization and function of the mammary epithelium . Disruption of these cues and the resulting architecture leads to ductal carcinoma in situ ( DCIS ) and invasive ductal carcinoma ( IDC ) [1]–[3] . DCIS is defined as increased proliferation of ductal epithelial cells in the absence of basement membrane degradation [4]–[6] . Whereas DCIS is not life-threatening , some of these lesions may progress to IDC if left untreated [7] , [8] . Pathologists classify DCIS by four morphologies: micropapillary , cribriform , solid , and comedo . Micropapillary tumors contain additional epithelial cells within the lumen of the duct ( Fig . 1A ) . Cribriform tumors are characterized by ducts filled with cells that form multiple lumena ( Fig . 1B ) . Solid tumors have completely filled ducts ( Fig . 1C ) . Comedo tumors are solid with a necrotic core resulting from nutrient insufficiency ( Fig . 1D ) [6] , . Of these four morphologies , comedo lesions have the greatest risk for recurrence after breast-conserving surgery [11] . Due to the increased use of mammographic screening , the number of observed incidences of DCIS has increased dramatically , by 500% and 290% between 1983 and 2003 for women over 50 and under 50 , respectively [12] . DCIS currently accounts for ∼20% of all breast cancers diagnosed in the U . S . [8] . It remains unclear how DCIS evolves into invasive breast cancer . In most cases , DCIS is detected by mammography in an otherwise asymptomatic patient; the lesions are then removed surgically after detection and so the natural history of the lesion cannot be monitored in vivo [5] . Of these lesions , invasive carcinomas develop more frequently in patients treated with biopsy alone than in patients who receive lumpectomy followed by radiation treatment [13] , [14] , highlighting the need for prognostic stratification and diligent monitoring . Published clinical studies show that 14–53% of DCIS originally misdiagnosed as benign breast disease later develops into invasive breast cancer [8] . Furthermore , DCIS and invasive cancers often have the same morphological appearance and genetic profile , suggesting that they originate from the same source , and DCIS and invasive morphologies are often present in the same lesion [8] , [15] . Computational models may help to predict which conditions lead to the development of the various morphologies of DCIS , and perhaps suggest plausible mechanisms by which DCIS evolves to invasive carcinoma . Recent work has emphasized the profound effects that the cellular , chemical , and physical properties of the tumor microenvironment can have on tumor progression [16]–[23] . In some instances the microenvironment provides a tumor-suppressive role , as autopsies have revealed that 20% of young and middle-aged women have clinically occult breast tumors [24] , whereas in other instances tumors readily progress to malignant carcinoma . Previously , we found that the mechanical properties of the host epithelium play a critical role in establishing or suppressing a tumorigenic phenotype in cells with a tumorigenic genotype . We incorporated human breast tumor cells into engineered tissue mimetics comprised of non-malignant host mammary epithelial cells , and observed that the tumor cells proliferated or invaded only when they were located at the ends of these tissues [25] . These sites of tumor cell invasion corresponded to regions of high endogenous mechanical stress . Furthermore , this dependence of tumor cell phenotype on location within the tissue could be modulated by altering the contractility , and thus the mechanical stress profile , of the host epithelium [25] . These location-dependent differences in tumor cell behavior strengthen the importance of studying tumorigenesis in the context of the tissue and its mechanical microenvironment [26] . Modeling DCIS within a sphere or within the circular cross-section of a single duct fails to capture these architecture-dependent variations in the microenvironment . More than 90% of all human mammary carcinomas originate in the epithelial ducts rather than the surrounding connective tissue [27] , and the majority of these arise from the terminal ductal lobular unit [28] , suggesting that the microenvironment around the terminal ends of the ducts is more supportive ( or less suppressive ) of tumor formation . Recent experimental work has revealed that tissue geometry establishes varying levels of mechanical stress and morphogen concentrations within mammary ducts [29]–[31] . These variations may establish regions that are preferential for tumor cell proliferation and invasion [25] . In addition , patients diagnosed with DCIS have frequently been found to have lesions with heterogeneous morphology [32] , suggesting that a radial cross-section of a single duct in silico cannot accurately predict tumor formation . In this study we begin by exploring the behavior of DCIS in a two-dimensional circular cross-section of a duct , thus allowing us to compare the results of our model to those of previously published studies . We then expand our model and vary the geometry of the tissue to examine regional differences in tumor cell behavior . The computational model presented here was developed using the cellular Potts model ( CPM ) implemented through the CompuCell3D modeling platform . The CPM is a multi-cell lattice-based model that uses fairly few parameters to describe effective interactions and constraint energies within biological systems [33] , [34] . CPM has been used to study both normal developmental processes including morphogenesis of the embryonic limb bud [35] as well as pathological processes associated with tumor behavior [36]–[38] . Andasari et . al . developed a multi-scale model to examine cancer growth and invasion resulting from intracellular dynamics of E-cadherin and β-catenin and found that lowering cell adhesion caused increased cell invasion [39] . Steinkamp et . al . used both mouse tumor models and a computational model to better understand ovarian tumor growth and morphology due to oxygen availability and tumor cell adhesion . These authors found that strong homotypic adhesion and weak heterotypic adhesion are required for cancer cells to form spheroid aggregates . Furthermore , variations in cell adhesion led to the establishment of different tissue microenvironments; cancer cells invaded preferentially into the microenvironment of the mesentery , omentum and spleen and did not invade into that of the stomach and small intestine [40] . Here we used CPM to explore the conditions that lead to the development of the different morphologies . We observed several plausible progressions between these four morphologies of DCIS . We also examined variations in phenotype that result from geometric features of mammary epithelial ducts , and observed that some regions are preferential for tumor cell invasion and that this invasion can be modulated by tuning cell adhesion and contractility .
We used Glazier and Graner's cellular Potts model ( CPM ) , implemented through the open-source simulation environment CompuCell3D ( http://www . compucell3d . org ) , to create a two-dimensional ( 2D ) model of DCIS . In this framework , each agent is a domain of pixels given a unique index , σ representing a cell compartment , cell , or tissue . As the model progresses , agents attempt to extend their boundary . One Monte Carlo step ( MCS ) is defined as one attempt for each pixel in the model to alter its location . The success of these attempts is given by the probability:where ΔH is the change in effective energy , which we describe below , Tm is the parameter “temperature , ” corresponding to the intrinsic agent motility , and the indices ( i , j , k ) specify lattice sites ( pixels ) [33] , [34] , [41] . The change in effective energy is defined by three main terms as shown below:where τ denotes the type of agent . The first term describes the surface adhesion energy between agents and their neighbors , essentially establishing the degree to which agents sort from one another . The second term defines the volume and the compressibility of the agent . The difference between the volume ( V ) and the target volume ( Vt ) is multiplied by a parameter that describes the stiffness of the agent ( λV ) . The third term of the Hamiltonian represents the elasticity of the agent membrane , where S is the surface area and St is the target surface area [33] , [34] , [41] . An in-depth review of the Glazier-Graner CPM model framework is given in Ref [34] . Parameters used here are summarized in Table 1 and discussed below . Mechanical links between cells were established through the adhesion coefficient , which is defined as the adhesion energy per unit contact area and lowers the effective energy of the system when bonds form between cells . A hierarchy of surface adhesion coefficients leads to cell sorting [45] . Experimentally , it has been shown that the physiological organization of LEP surrounded by a layer of MEP results from differential expression of E-cadherin [44] . We modeled strong cellular adhesion using negative values for the adhesion coefficients and modeled the differential expression of E-cadherin by setting JLEP , LEP<JMEP , LEP<JMEP , MEP . Adhesion coefficients for agent pairs that include culture medium or necrotic cells were set to zero , since cells that undergo necrosis continue to occupy space as debris , but no longer bind with other cells . This is equivalent to placing the cells in a very deformable stroma , which was not included per se in the current model . Additionally , cell attraction and repulsion were modeled using the focal point plasticity ( FPP ) plugin , which creates a link between the centers of mass of neighboring agents . The energy term is defined as , where Lij is the target distance between the agents' centers of mass , lij is the actual distance , and λij is equivalent to a spring constant . We established cell polarity by allowing epithelial cells to form links with only two neighbors [10] . We set the λij parameter for homotypic cellular interactions to be 10-fold greater than the λij parameter for heterotypic cellular interactions [10] . In order to mimic cell contraction , the target length between cells was set to be less than the resting length . For the more contractile MEP , we set the target length at 5 pixels while for LEP we set the target length at 8 pixels . When FPP parameters are set too high , cells achieve their target length by becoming fragmented . FPP parameters for agent pairs that include culture medium or necrotic cells were set to zero . We considered two types of cell death , apoptosis and necrosis . During normal development a luminal epithelial cell undergoes apoptosis when it experiences mechanical stress from being overcrowded , and is then extruded from the tissue [52] , [53] . In our model , we first checked whether a cell was overcrowded by counting the number of neighbors that were within 2 . 5 cell diameters . If there were 10 cells in this vicinity , the luminal epithelial cell was specified as overcrowded and apoptosis was inscribed with a given probability ranging from 0 to 1% . When a cell underwent apoptosis it was removed from the simulation . Necrosis results from a lack of nutrient availability . Since DCIS does not involve angiogenesis into the duct [54] , [55] , the closest nutrient source would be immediately adjacent to the duct itself . Here we specified that a cell would undergo necrosis when it was ten or more cell diameters away from the myoepithelial layer ( ≥100 µm ) , a distance roughly equal to the diffusion limit of oxygen . When a cell became necrotic , it no longer interacted with other cells and all cell-cell adhesion parameters were set to zero; however , the necrotic cell continued to occupy space .
We first explored the changes in mammary ductal morphology that resulted from altering rates of cell proliferation and apoptosis ( Fig . 2 ) . To determine the predominant morphology , we ran each simulation at least 20 times . If 80% or more of these simulations led to the same morphology , we classified it as such . Otherwise , we concluded that the conditions led to a mixed morphology . We found that the micropapillary morphology emerged under most conditions , with notable exceptions . In the absence of apoptosis ( Fig . 2A , left column ) , we observed both the solid and comedo morphology , depending on the frequency of cell division . For 10 mitotic events , the LEP completely filled the lumen . As the cells divided they imposed an outward force on the walls of the duct causing the duct to expand . When there were 15 or more mitotic events , the contractility of the myoepithelial layer could not balance this outward force , and we observed gaps in the MEP layer as well as the presence of a necrotic core . Notably , even though the LEP breached through the MEP layer , the duct still maintained a circular cross-sectional morphology; such gaps in the MEP layer have been observed in histological sections of human breast tumors diagnosed as DCIS [56] , [57] . Similarly , when the probability of apoptosis was set to 0 . 5% ( Fig . 2A , center column ) , solid and comedo morphologies were established under conditions of high proliferation ( 25 or 30 mitotic events ) whereas the micropapillary morphology emerged under conditions of low proliferation ( 20 or fewer mitotic events ) . When the probability of apoptosis was increased to 1% ( Fig . 2A , right column ) , micropapillary morphologies were established . The cribriform morphology was observed in a few simulations; however , it was not the predominant morphology under any of these conditions . For example , with 1% probability of apoptosis and 25 mitotic events , one in twenty simulations resulted in a cribriform morphology ( Fig . 2B ) . This result was surprising , given that 16–20% of all cases of DCIS have been described as cribriform in morphology [58] , [59] . These data suggest that the morphology of DCIS may depend on the balance between cell division and apoptosis , which is supported by collapsing the data into a single ratio of proliferation to apoptosis ( S1 Figure ) . The lumen fills and eventually becomes necrotic when this balance is shifted toward proliferation . The lumen remains patent , albeit abnormal , when this balance is shifted toward apoptosis . The low incidence of cribriform morphology observed in these simulations suggests that additional cellular behaviors are required to generate this architecture . As described above , there are two possible mechanisms by which cells in a normal duct can undergo growth arrest . In one , normal epithelial cells lose the ability to proliferate when they form tight junctions with their neighbors [47] , [48] . In the other , cells continue to proliferate but any daughter progeny that occupy the lumen immediately undergo apoptosis [49] . Thus , we next explored how the axis of cell division affects the morphology of the simulated duct . In the simulations described above ( Fig . 2 ) , we had specified the axis of cell division to be perpendicular to the epithelial cell layer ( Fig . 2C ) ; next we investigated the effects of cell divisions that introduced progeny into the lumen which were protected from undergoing apoptosis ( 0% probability; Fig . 3A , B ) , or allowing the cells to undergo random cell division thereby resulting in a loss of tissue polarity ( Fig . 3C , D ) . Regardless of the axis of cell division , solid and comedo morphologies were established under combinations of high proliferation ( 25 or more mitotic events ) and low apoptosis ( 0 . 5% probability ) . When the cell division axis was random or such that daughter cells were placed into the lumen , the duct appeared to expand slightly more than when the division axis was perpendicular to the epithelial cell layer . The former caused a small lumen to appear in conditions that otherwise led to a solid morphology ( compare Fig . 2A solid morphology to Fig . 3B solid morphology ) . For example , with 0 . 5% apoptosis and 25 mitotic events , the duct became almost completely filled with LEP; however , in half of the simulations a very small lumen remained . Although the cribriform morphology did not arise as the predominant morphology under any of these conditions , we found many ducts containing small lumena , particularly when the axis of cell division was parallel to the epithelial layer ( see white arrows in Fig . 3B and 3D ) . These results are consistent with observations that cells maintain apicobasal polarity in cribriform lesions [60] . We did not characterize the duct as cribriform unless cells extended completely across the diameter of the duct ( Fig . 2B ) . As the number of mitotic events increased , the number of ducts with cribriform morphology also increased . With 1% apoptosis , the percentage of cribriform ducts increased from 2% to 6% to 18% for 20 , 25 and 30 mitotic events respectively . This suggests that the cribriform morphology may occur more readily over a longer time span with more cell divisions . These data again suggest that the morphology of DCIS depends on the balance between cell division and apoptosis . Whereas it is difficult to explore the transitions between DCIS morphologies in intact tumors in vivo , this is readily achieved in silico . Examining intermediate time steps and running simulations for up to 3000 MCS , we observed multiple transitions between morphologies . As LEP accumulated in the lumen , the micropapillary morphology was the first to emerge . In the absence of apoptosis , or at low levels of apoptosis ( 0 . 5% probability ) with high proliferation , the micropapillary morphology progressed to solid and ultimately to comedo as the force of proliferating cells caused the duct to expand outward ( Fig . 4A ) . At higher levels of apoptosis ( 1% probability ) and high levels of proliferation , the micropapillary morphology progressed to cribriform ( Fig . 4B ) . With low levels of proliferation , the morphology remained micropapillary ( Fig . 4C , D ) . Increasing apoptosis from 0 . 5% probability to 1% probability did not affect the outcome of these simulations . Notably , high levels of apoptosis or low levels of apoptosis balancing low levels of proliferation caused the duct to remain fairly uniform in size ( Fig . 4B–D ) . Given that the cells continued to proliferate , we had anticipated that the lumen would fill completely and the cribriform morphology would ultimately progress to a solid morphology . Surprisingly , however , the morphology remained cribriform even after 3000 MCS under conditions of 1% probability of apoptosis and high proliferation ( Fig . 4B ) . This suggests that over longer periods of time , comedo and cribriform may be the ultimate morphological outcomes of DCIS , with apoptosis being the deciding factor . Importantly , we found that LEP were able to break through the MEP layer into the surroundings from any of the four morphologies ( Fig . 4E–H ) . For our purposes here we refer to this phenotype as invasion; however , we note that physiological invasion in vivo requires deterioration of the basement membrane , which is not included in the present model . Experiments in culture have revealed that asymmetries in tissue geometry lead to regional differences in endogenous mechanical stress , which result from the concentration of mechanical stresses by otherwise isotropically contracting cells in the tissue [29] , [30] . Furthermore , tumor cells have been observed to proliferate and invade preferentially from regions of high mechanical stress both in culture and in vivo [25] . We next explored whether tissue geometry affected the morphology that emerged by modeling a cross-section through a cylindrical ( ductal ) tissue . Throughout the tissue , the morphology of DCIS that emerged appeared to be fairly consistent; however , we observed that cells invaded more frequently from the ends than from the center of the duct ( Fig . 5A , B , E–G ) . Previously , we found experimentally that tumor cells proliferate almost twice as frequently when they are located at the ends of ducts engineered in culture [25] . When we included this pattern of proliferation in our model , we observed an increase in the number of tissues in which cells invaded from the ends ( Fig . 5C , D , E–G ) . We also noticed some differences in morphology . For example , in the simulations shown in Fig . 5C , the duct region of the tissue develops into a cribriform morphology while the end region becomes comedo with invasion . Experimentally , preferential invasion has been attributed to elevated levels of mechanical stress [25] , possibly due to mechanical regulation of YAP/TAZ [61] . When cells push and pull on each other within a tissue , varying levels of endogenous mechanical stress will emerge across the tissue due to asymmetries in the tissue geometry [29] , [31] . Therefore , we next explored the effect of altering tissue contractility in this model . In our model , the ability of cells to adhere to each other is regulated by the cell adhesion and FPP parameters . In the absence of proliferation , changing these parameters did not significantly alter the structure of the tissue ( Fig . 6A , B ) . However , as cells proliferated and produced an outward force , the roles of these parameters became more significant . When the value of the cell adhesion parameter was decreased , the cells no longer adhered to each other and invasion was observed around the entire periphery of the tissue ( Fig . 6C , E ) . When the value of the cell adhesion parameter was increased , the strength of cell adhesion prevented invasion ( Fig . 6D , E ) . Notably , high adhesion caused the morphology to remain micropapillary , whereas low adhesion led to the development of a cribriform morphology ( Fig . 6D , E ) . In addition to controlling cell adhesion , the FPP parameter also modulates tissue contractility . In our model , tissue contractility was approximated by a balance of FPP and λv parameters . The FPP parameter connects the centers of each cell with a spring , the target length of which is set to be less than the resting length , thereby creating an attractive pull between two cells . As the cells are pulled together , the λv parameter creates an outward push by imposing a penalty when the cell deviates from its target volume . When these two forces are balanced , the result is a tissue in a state of tension ( Fig . 6B , center tissue ) . When we lowered these parameters , essentially eliminating contractility from our model , the cells did not invade ( Fig . 6F , H ) . We did observe breaks in the myoepithelial layer in most tissues; however , since the LEP cells did not extend past the periphery of the tissue , we did not characterize these breaks as invasion . Increasing the value of these parameters caused cells to invade from the entire periphery of the tissue ( Fig . 6G , H ) . These results are in agreement with our previously published experimental results , in which we found that reducing tissue contractility eliminates tumor cell invasion , whereas increasing contractility permits tumor cells to invade from the duct region of the tissue [25] . Again there were notable differences in DCIS morphology . Low contractility caused the morphology to remain micropapillary , whereas high contractility led to the development of a cribriform morphology with necrotic cells in the center of the tissue ( Fig . 6F , G ) . The increased accumulation of cells in the interior of the tissue is in agreement with a recent agent-based model that showed that clusters of tumor cells grow faster as the λv parameter is increased [62] . We previously used a transgenic mouse expressing an inducible form of the kRas oncogene under control of the mouse mammary tumor virus ( MMTV ) promoter to observe tumor development in vivo in the post-pubertal mammary gland . These studies revealed that tumors form more frequently at the ends of the complex network of epithelial ducts in adult mice [25] . We thus expanded our computational model to examine tumor growth in a bifurcating duct , and observed that tumor cells invaded more often from the ends of the bifurcating duct . Using the same parameters for low and high contractility described above and presented in Fig . 6 , we explored the effect of altering tissue contractility . Low contractility caused the morphology to remain micropapillary , whereas high contractility led to the development of a cribriform morphology with necrotic cells in the center of the tissue ( Fig . 7A ) . Again we found that invasion was reduced by decreasing contractility and delocalized by increasing contractility ( Fig . 7 ) . Agreement between these in vivo and computational results suggests that this model could be expanded to predict tumor cell behavior in increasingly complex physiologically relevant geometries .
In order to better understand the development of breast cancer , it is beneficial to investigate the mechanisms by which the ductal architecture of the normal mammary gland is established and maintained . Computational models have given researchers an efficient method by which to formulate hypotheses that can be tested experimentally . Discrete and hybrid models have been used to capture cell-level interactions . For example , a recent agent-based model of the normal morphogenesis of mammary epithelial acini explored the relative roles of apoptosis , proliferation , and apico-basal polarity in maintaining a physiologically normal epithelium . This model suggests that apoptosis is necessary and sufficient for lumen formation and that apico-basal polarity is required to establish the physiologically normal morphology of the epithelium [49] . A similar model examined the role of mammary progenitor cells in development of DCIS , and found that progenitor cells lead to greater genetic heterogeneity and faster formation of DCIS [63] . A recent study also explored tumor growth in a cylindrical domain and proposed a patient-specific model calibration protocol [51] . These and other models [47] , [48] have provided valuable insight into the possible mechanisms underlying normal and abnormal development . With few exceptions [51] , [64] , most computational models of DCIS have focused on a spherical tissue or circular cross-section of a duct . Here we established a 2D multi-cell lattice-based model of DCIS that incorporates cell proliferation , apoptosis , necrosis , adhesion , and contractility . All four morphologies ( micropapillary , cribriform , solid and comedo ) emerged in our model . High proliferation with low apoptosis led to the emergence of solid and comedo morphologies , low proliferation with high apoptosis led to the micropapillary morphology , and high proliferation with high apoptosis led to the cribriform morphology . Given that the morphology is established through a balance between proliferation and apoptosis , monitoring this in DCIS lesions could be a possible prognostic indicator of eventual progression to IDC . The parameters that led to the development of each morphology were similar qualitatively to those reported previously by others , with one notable exception: we found that the cribriform morphology resulted from cells dividing perpendicular to the epithelial layer , whereas a previously published model required the inclusion of an elevated pressure within the lumen of the duct , a so-called intraductal pressure [10] . We did not include intraductal pressure in our model since we could find little support for the existence of such a pressure in the literature . The citations discussed by Ref [10] in support of elevated levels of intraductal pressure in fact document increased interstitial fluid pressure ( IFP ) . The presence of an IFP would impose forces directing inward on the epithelial duct , and not a force from the lumen that pushes outward as proposed by Ref [10] . In addition to suggesting regimes of parameters that lead to the four morphologies of DCIS , our model suggests probable transitions between these morphologies during breast cancer progression . Our model is unique in that , unlike most computational models of DCIS which examine cells arranged in a circular or spherical geometry , we also explored cell behavior in more physiologically relevant cylindrical and bifurcating duct geometries . The results of our model are consistent with immunohistochemical studies that show high proliferation in comedo and solid morphologies compared to micropapillary and cribriform morphologies [58] , [60] , [65] , [66] . As an example , Albonico et al found that 65% of cells in comedo lesions were positive for the proliferation marker Ki67 , whereas only 3% of cells in cribriform lesions were Ki67-positive . Furthermore , we found that when proliferation was balanced by apoptosis , these lesions did not advance over time and remained either micropapillary or cribriform . Consistently , 100% of cells in cribriform lesions were found to express the apoptosis regulator Bcl-2 , whereas this was reduced to 36% of cells in comedo lesions [65] . Our results are also consistent with clinical data showing that less than 50% of low-grade DCIS ( lesions with a low proliferation rate ) develop into invasive breast cancer over 25–30 years [67] . Similar to a recent computational study , we observed that increases in cell proliferation lead to the development of aberrant phenotypes and that disrupting proper cell division alignment can cause multiple lumena to form [68] . These results are congruent with those of a recent computational model that found that the ratio of tumor cell proliferation to apoptosis was a strong predictor of tumor volume [50] , although this parameter does not correlate with histological grade . In the cylindrical and bifurcating duct geometries , the patterns that emerge in our model are consistent with our previously reported experimental results that show increased invasion from regions of high mechanical stress , more specifically from the ends of these tissues . The ability of cells to invade can be modulated by altering cell adhesion or contractility . Experimentally we have observed increased proliferation at the ends of these tissues . Interestingly our model showed increased invasion from the ends of tissues with and without preferential proliferation . This suggests that enhanced proliferation at the ends is not the cause of the invasion in these regions; experimental testing of this hypothesis would require the ability to spatially modulate cell proliferation , which is not yet possible . Importantly , we also observed that more than one morphology emerged simultaneously in these asymmetric tissue geometries , but not in the circular tissues . Different morphologies of DCIS have been frequently observed in histological sections of individual lesions [32] , suggesting that future computational models of the mammary duct should incorporate more complex tissue geometries . In order to accurately model the transition to invasive breast cancer in future simulations , it will be necessary to include loss of basement membrane integrity as a parameter . Furthermore , it would be beneficial to include extracellular matrix ( ECM ) regions to more rigorously incorporate cell-ECM interactions and explore the effect of heterogeneity in the ECM microenvironment on tumor cell invasion; early models that treat the ECM as a continuum have suggested an important role for crosstalk between the tumor cell and its surrounding stroma in tumor development [69] , consistent with experimental results from mouse models of breast cancer [70] , [71] . Here we assumed that cells become necrotic when they are 10 cell diameters away from the MEP layer . While this is a good average approximation based on clinical observations , it is important to note that cells do not always become necrotic at a given distance . Cells become hypoxic due to limitations in oxygen diffusion; however , ducts with diameters up to 500 µm have been observed without a necrotic core [42] . Furthermore , necrotic regions of tumors are heterogeneous and although apoptosis and necrosis are considered to be distinct modes of cell death , recent studies have suggested that they may lie on a continuum [72] . We focused on the morphology or architectural pattern of DCIS , which is characterized in the clinic using histology . It is important to note , however , that histological characterization ( micropapillary , cribriform , solid , comedo ) is not as accurate of a prognostic indicator of disease progression as classification systems that also take into account nuclear morphology or mitotic index ( for example , the Nottingham [73] or Van Nuys [74] prognostic index ) . Ultimately , it would be beneficial to develop a multi-scale model of breast cancer that includes both cellular and subcellular features and behaviors . The mechanism by which cells and the ECM transmit mechanical cues and establish the mechanical profile of a tissue is incredibly complex [75] . To capture this complexity an ideal model would link lattice-based cellular behaviors with continuum biomechanical models and the subcellular machinery of the cytoskeleton to provide valuable insight into both normal and aberrant tissue behavior . Combining such computational models with recently developed engineered tumor models [76] may permit the successful integration of theoretical predictions with experimental validation . Our findings that tissue geometry-related mechanical stress plays a major role in the phenotypic evolution of DCIS point to the need to incorporate tissue structure information into individualized risk assessments , which could be accomplished with advances in high-resolution X-ray tomographic imaging . | Breast cancer is a complex disease that affects women worldwide . One heterogeneous group of lesions , ductal carcinoma in situ ( DCIS ) , often begins as a nonmalignant disease but can readily progress if left untreated . The progression of this disease is not well understood because DCIS is typically removed upon detection . Therefore , computational models might help predict whether DCIS will remain nonmalignant or progress towards invasive ductal carcinoma . Here we used a multi-cell lattice-based model to explore the relative effects of cell proliferation , death , division axis , adhesion and contractility on the development and progression of DCIS . We also examined the emergence and progression of DCIS in physiologically relevant geometries of the mammary duct . Our model suggests several plausible progressions between morphologies of DCIS , and predicts that some regions of a duct are preferential for tumor cell invasion . | [
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] | 2014 | Lattice-Based Model of Ductal Carcinoma In Situ Suggests Rules for Breast Cancer Progression to an Invasive State |
The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability . We show that the coding space of a given protein family—the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occuring amino acid sequences . We analyzed and calculated the size of three abundant repeat proteins families , whose members are large proteins made of many repetitions of conserved portions of ∼30 amino acids . While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences , we found that correlations between amino acid usage at different positions significantly impact that diversity . We quantified the impact of different types of correlations , functional and evolutionary , on sequence diversity . Analysis of the detailed structure of the coding space of the families revealed a rugged landscape , with many local energy minima of varying sizes with a hierarchical structure , reminiscent of fustrated energy landscapes of spin glass in physics . This clustered structure indicates a multiplicity of subtypes within each family , and suggests new strategies for protein design .
Natural proteins contain a record of their evolutionary history , as selective pressure constrains their amino-acid sequences to perform certain functions . However , if we take all proteins found in nature , their sequence appears to be random , without any apparent rules that distinguish their sequences from arbitrary polypeptides . Nonetheless , the volume of sequence space taken up by existing proteins is very small compared to all possible polypeptide strings of a given length [1] , even more so when specializing to a given structure [2] . Clearly , not all variants are equally likely to survive [3–5] . To better understand the structure of the space of natural proteins , it is useful to group them into families of proteins with similar fold , function , and sequence , believed to be under a common selective pressure . Assuming that the ensemble of protein families is equilibrated , there should exist a relationship between the conserved features of their amino acid sequences and their function . This relation can be extracted by examining statistics of amino-acid composition , starting with single sites in multiple alignments ( as provided by e . g . PFAM [6 , 7] ) . More interesting information can be extracted from covariation of amino acid usages at pairs of positions [8–10] or using machine-learning techniques [11] . Models of protein sequences based of pairwise covariations have been shown to successfully predict pair-wise amino-acid contacts in three dimensional structures [12–17] , aid protein folding algorithms [18 , 19] , and predict the effect of point mutations [17 , 20–22] . However , little is known on how these identified amino-acid constraints affect the global size , shape and structure of the sequence space . Accounting for these questions is a first step towards drawing out the possible and the realized evolutionary trajectories of protein sequences [23 , 24] . We use tools and concepts from the statistical mechanics of disordered systems to study collective , protein-wide effects and to understand how evolutionary constraints shape the landscape of protein families . We go beyond previous work which focused on local effects—pairwise contacts between residues , effect of single amino-acid mutations—to ask how amino-acid conservation and covariation restrict and shape the landscape of sequences in a family . Specifically , we characterize the size of the ensemble , defined as the effective number of sequences of a familiy , as well as its detailed structure: is it made of one block or divided into clusters of “basins” ? These are intrinsically collective properties that can not be assessed locally . Repeat proteins are excellent systems in which to quantify these collective effects , as they combine both local and global interactions . Repeat proteins are found as domains or subdomains in a very large number of functionally important proteins , in particular signaling proteins ( e . g . NF-κB , p16 , Notch [25] ) . Usually they are composed of tandem repetitions of ∼30 amino-acids that fold into elongated architectures . Repeat proteins have been divided into different families based on their structural similarity . Here we consider three abundant repeat protein families: ankyrin repeats ( ANK ) , tetratricopeptide repeats ( TPR ) , leucine-rich repeat ( LRR ) that fold into repetitive structures ( see Fig 1 ) . In addition to interactions between residues within one repeat , repeat protein evolution is constrained by inter-repeat interactions , which lead to the characteristic accordeon-like folds . Through these separable types of constraints , as well as the possibility of intra- and inter-familly comparisons , repeat proteins are perfect candidates to ask questions about the origins and the effects of the constraints that globally shape the sequences . A recent study [26] addressed the question of the total number of sequences within a given protein family , focusing on ten single-domain families . They took a similar thermodynamic approach to the one followed here , but had to estimate experimentally the free energy threshold ΔG below which the sequences would fold properly . Here we overcome this limitation by forgoing this threshold entirely . Instead we determine the sequence entropy directly , which is argued to be equivalent to using a threshold free energy by virtue of the equivalence of ensembles . We precisely quantify the sequence entropy of three repeat-protein families for which detailed evolutionary energetic fields are known [27] . We explore the properties of the evolutionary landscape shaped by the amino-acid frequency constraints and correlations . We ask whether the energy landscape , defined in sequence space of repeat proteins , is made of a single basin , or rather of a multitude of basins connected by ridges and passes , called “metastable states” , as would be expected from spin-glass theory . Using the specific example of repeat proteins makes it possible to analyze the source of the potential landscape ruggedness , and use it to identify which repeat-protein families can be well separated into subfamilies . The rich metastable state structure that we find demonstrates the importance of interactions in shaping the protein family ensemble .
We start by building statistical models for the three repeat protein families presented in Fig 1 ( ANK , TPR , LRR ) . These models give the probability P ( σ ) to find in the family of interest a particular sequence σ = ( σ1 , … , σ2L ) for two consecutive repeats of size L . The model is designed to be as random as possible , while agreeing with key statistics of variation and co-variation in a multiple sequence alignment of the protein family . Specifically , P ( σ ) is obtained as the distribution of maximum entropy [28] which has the same amino-acid frequencies at each position as in the alignment , as well as the same joint frequencies of amino acid usage in each pair of positions . Additionally , repeat proteins share many amino acids between consecutive repeats , both due to sharing a common ancestor and to evolutionary selection acting on the protein . To account for this special property of repeat proteins , we require that the model reproduces the distribution of overlaps ID ( σ ) = ∑ i = 1 L δ σ i , σ i + L between consecutive repeats . Using the technique of Lagrange multipliers , the distribution can be shown to take the form [17]: P ( σ ) = ( 1 / Z ) e - E ( σ ) , ( 1 ) with E ( σ ) = - ∑ i = 1 2 L h i ( σ i ) - ∑ i , j = 1 2 L J i j ( σ i , σ j ) + λ ID ( σ ) , ( 2 ) where hi ( σ ) , Jij ( σi , σj ) , and {λID} , ID = 0 , 1 , … , L , are adjustable Lagrange multipliers that are fit to the data to reproduce the experimentally observed site-dependent amino-acid frequencies fi ( σi ) , joint probabilities between two positions , fij ( σi , σj ) , and the distribution of Hamming distances between consecutive repeats P ( ID ( σ ) ) , which is equivalent to maximize the likelihood of the data under the model . We fit these parameters using a gradient ascent algorithm: we start from an initial guess of the parameters , then generate sequences via Monte-Carlo simulations and update the parameters proportionally to the difference between the empirical and model generated observables f i ( σ i ) - f i model ( σ i ) , f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) and P ( ID ( σ ) ) − P ( ID ( σ ) ) model . We repeat the previous steps until the model reproduces the empirical observables defined above , with a target precision motivated according to the finite size of our original dataset , as in Ref . [17] . See Methods for more details . We tested the convergence of the model learning by synthetically generating datasets and relearning the model ( see Methods ) . By analogy with Boltmzan’s law , we call E ( σ ) a statistical energy , which is in general distinct from any physical energy . The particular form of the energy ( 2 ) resembles that of a disordered Potts model . This mathematical equivalence allows for the possibility to study effects that are characteristic of disordered systems , such as frustration or the existence of an energy landscape with multiple valleys , as we will discuss in the next sections . Eq 2 is the most constrained form of the model , which we will denote by Efull ( σ ) . One can explore the impact of each constraint on the energy landscape by removing them from the model . For instance , to study the role of inter-repeat sequence similarity due to a common evolutionary origin , one can fit the model without the constraint on repeat overlap ID , i . e . without the λID term in Eq 2 . We call the corresponding energy function E2 . One can further remove constraints on pairwise positions that are not part of the same repeat , making the two consecutive repeats statistically independent and imposing hi = hi+L ( Eir ) , or only linked through phylogenic conservation through λID ( Eir , λ ) . Finally one can remove all interaction constraints to make all positions independent of each other ( E1 ) , or even remove all constraints ( Erand ≡ 0 ) . The evolutionary information contained in multiple sequence alignments of protein families is summarized in our model by the energy function E ( σ ) . Since this information is often much easier to access than structural or functional information , there is great interest in extracting functional or structural properties from multiple sequence alignments , provided that there exists a clear quantitative relationship between statistical energy and physical energy . Such a relationship was determined experimentally for repeat proteins by using E ( σ ) to predict the effect of point mutations on the folding stability measured by the free energy difference between the folded and unfolded states , ΔG , called the unfolding energy [17 , 20] . Synthetic sequences with low E ( σ ) have also been shown to reproduce the fold and function of natural sequences [29] . Here , extending an argument already developed in previous work [30–33] , we show how this correspondance between statistical likelihood and folding stability arises in a simple model of evolution . Evolutionary theory predicts that the prevalence of a particular genotype σ , i . e . the probability of finding it in a population , is related to its fitness F ( σ ) . In the limit where mutations affecting the protein are rare compared to the time it takes for mutations to spread through the population , Kimura [34] showed that the probability of a mutation giving a fitness advantage ( or disadvantage depending on the sign ) ΔF over its ancestor will fix in the population with probability 2ΔF/ ( 1 − e−2NΔF ) , where N is the effective population size . The dynamics of successful substitution satisfies detailed balance [35] , with the steady state probability P ( σ ) = ( 1 / Z ) e 2 N F ( σ ) . ( 3 ) Again , one may recognize a formal analogy with Boltzmann’s distribution , where F plays the role of a negative energy , and N an inverse temperature . If we now assume that fitness is determined by the unfolding free energy ΔG , F ( σ ) = f ( ΔG ( σ ) ) , then the distribution of genotypes we expect to observe in a population is P ( σ ) = ( 1 / Z ) e 2 N f ( Δ G ( σ ) ) . ( 4 ) Note that a similar relation should hold even if we relax the hypotheses of the evolutionary model . While in more general contexts ( e . g . high mutation rate , recombination ) , the relation between ln P ( σ ) and F ( σ ) may not be linear , such nonlinearities could be subsumed into the function f . Identifying terms in the two expressions ( 1 ) and ( 3 ) , we obtain a relation between the statistical energy E , and the unfolding free energy ΔG: E ( σ ) = - 2 N f ( Δ G ( σ ) ) . ( 5 ) For instance , if we assume a linear relation between fitness and ΔG , f ( ΔG ) = A + BΔG , then we get a linear relationship between the statistical energy and ΔG , as was found empirically for repeat proteins [17] . Strikingly , the relationship f does not have to be linear or even smooth for this correspondance to work . Imagine a more stringent selection model , where f ( ΔG ) is a threshold function , f ( ΔG ) = 0 for ΔG > ΔGsel and −∞ otherwise ( lethal ) . In that case the probability distribution is P ( σ ) = ( 1/Z ) Θ ( ΔG − ΔGsel ) , where Θ ( x ) is Heaviside’s function . Using a saddle-point approximation , one can show that in the thermodynamic limit ( long proteins , or large L ) the distribution concentrates at the border ΔGsel , and is equivalent to a “canonical” description [30 , 31 , 33]: P sel ( σ ) = ( 1 / Z ) e Δ G ( σ ) / T sel , ( 6 ) where the “temperature” Tsel is set to match the mean ΔG between the two descriptions: ⟨ Δ G ⟩ T sel = Δ G sel . ( 7 ) This correspondance is mathematically similar to the equivalence between the micro-canonical and canonical ensembles in statistical mechanics . Statistical energy and unfolding free energy are linearly related by equating ( Eq 1 ) and ( Eq 6 ) : E ( σ ) = E 0 - Δ G ( σ ) / T sel , ( 8 ) despite f being nonlinear . Eq 8 is in fact very general and should hold for any f in the thermodynamic limit in the vicinity of 〈E〉 . There are several ways to define the diversity of a protein family . The most intuitive one , followed by [26] , is to count the total number of amino acid sequences that have an unfolding free energy ΔGsel above a threshold ΔGsel [2] . This number naturally defines a Boltzmann entropy , S = ln N ( σ : Δ G ( σ ) > Δ G sel ) . ( 9 ) Alternatively , starting from a statistical model P ( σ ) , one can calculate its Shannon entropy , defined as S = - ∑ σ P ( σ ) ln P ( σ ) , ( 10 ) as was done in Ref . [27] . What is the relation between these two definitions ? By the same saddle-point approximation as in the previous section , the two are identical in the thermodyamic limit ( large L ) , provided that the condition ( Eq 7 ) is satisfied . We can thus reconcile the two definitions of the entropy in that limit . To calculate the Boltzmann entropy ( Eq 9 ) , one needs to first evaluate the threshold Esel in terms of statistical energy . This threshold is given by Esel = E0 − ΔGsel/Tsel , where E0 and Tsel can be obtained directly by fitting ( Eq 8 ) to single-mutant experiments . Esel can also be obtained as a discrimination threshold separating sequences that are known to fold properly versus sequences that do not [26] . In that case , assuming that the linear relationship ( Eq 8 ) was evaluated empirically using single mutants , this relationship can be inverted to get ΔGsel in physical units . Calculating the Shannon entropy Eq ( 10 ) , on the other hand , does not require to define any threshold . However , the threshold in the equivalent Boltzmann entropy can be obtained using Eqs 7 and 8 , i . e . Esel = 〈E〉 , where the average is performed using the distribution defined in Eqs 1 and 2 . To compare how the different elements of the energy function affect diversity , we calculate the entropy of ensembles built of two consecutive repeats from a given protein family for the different kinds of models described earlier , from the least constrained to the most constrained: Erand , E1 , Eir , Eir , λ , E2 , Efull . In the case of models with interactions , calculating the entropy directly from the definition Eq ( 10 ) is impossible due to the large sums . A previous study of entropies of protein families used an approximate mean-field algorithm , called the Adaptive Cluster Expansion [27] , for both parameter fitting and entropy estimation . Here we estimated the entropies using thermodynamic integration of Monte-Carlo simulations , as detailed in Methods . This method is expected to be asymptotically unbiased and accurate in the limit of large Monte-Carlo samples . The resulting entropies and their differences are reported in Table 1 and Fig 2 . All three considered families ( ankyrins ( ANK ) , leucine-rich repeats ( LRR ) , and tetratricopeptides ( TPR ) ) show a large reduction in entropy ( ∼40 − 50% ) compared to random polypeptide string models of the same length 2L ( of entropy Srand = 2L ln ( 21 ) ) . Interactions and phylogenic similarity between repeats generally have a noticeable effect on family diversity , although the magnitude of this effect depends on the family: ( S1 − Sfull ) /Sfull = 7% for ANK , versus , 13% for LRR , and 16% for TPR . Thus , although interactions are essential in correctly predicting the folding properties , they seem to only have a modest effect on constraining the space of accessible proteins compared to that of single amino-acid frequencies . However , when converted to numbers of sequences , this reduction is substantial , from e S 1 ∼ 3 · 10 54 to e S full ∼ 2 · 10 50 for ANK , from 1039 to 1034 for LRR , and from 7 ⋅ 1050 to 4 ⋅ 1042 for TPR . By considering models with more and more constraints , and thus with lower and lower entropy , we can examine more finely the contribution of each type of correlation to the entropy reduction , going from E1 to Eir to Eir , λ to Efull . This division allows us to quantify the relative importance of phylogenic similarity between consecutive repeats ( λID ) relative to the impact of functional interactions ( Jij ) , as well as the relative weights of repeat-repeat versus within-repeat interactions ( Fig 2 ) . We find that phylogenic similarity contributes substantially to the entropy reduction , as measured by Sir − Sir , λ = 4 . 5 bits for ANK , 4 . 3 bits for LRR , and 10 . 7 bits for TPR . The contribution of repeat-repeat interactions ( Sir , λ − Sfull ∼ 5 bits for all three families ) is comparable or of the same order of magnitude as that of within-repeat interactions ( S1 − Sir = 4 . 3 bits for ANK , 6 . 9 bits for LRR , and 11 . 4 bits for TPR ) . This result emphasizes the importance of physical interactions between neighboring repeats in the whole protein . On a technical note , we also find that pairwise interactions encode constraints that are largely redundant with the constraint of phylogenic similarity between consecutive repeats , as can be measured by the double difference Sir − Sir , λ − S2 + Sfull > 0 ( Fig 2 , orange bars ) . This redundancy comes from the fact that , in absence of an explicit constaint on P ( ID ) in E2 , the interaction couplings Ji , i+L ( σ , σ ) between homologous positions in the two repeats is expected to favor pairs of identical residues to mimic the effect of λID . This redundancy motivates the need to correct for this phylogenic bias before estimating repeat-repeat interactions . Comparing the three families , ANK has little phylogenic bias between consecutive repeats , and relatively weak interactions . By contrast , TPR has a strong phylogenic bias and strong within-repeat interactions . We wondered whether interactions constraining the space of accessible proteins had a characteristic lengthscale . To answer this question , for each protein family in Fig 1 , we learn a sequence of models of the form Eq 2 , in which Jij was allowed to be non-zero only within a certain interaction range d ( i , j ) ≤ W , where the distance d ( i , j ) between sites i and j can be defined in two different ways: either the linear distance |i − j| expressed in number of amino-acid sites , or the three-dimensional distance between the closest heavy atoms in the reference structure of the residues . Details about the learning procedure and error estimation are given in the Methods; see also S1 Fig for an alternative error estimate . The entropy of all families decreases with interaction range W , both in linear and three-dimensional distance , as more constraints are added to reduce diversity ( Fig 3 for ANK , and S2 Fig for LRR and TPR ) . The initial drop as a function of linear distance ( Fig 3A ) is explained by the many local interactions between nearby residues in the sequence . The entropy then plateaus until interactions between same-position residues in consecutive repeats are included in the W range , which leads to a sharp entropy drop at W = L . This suggests that long range interactions along the sequence generally do not constrain the protein ensemble diversity , except for interactions at exactly the scale of the repeat . This result suggests that the repeat structure is an important constraint limiting protein sequence exploration . These observations hold for all three repeat protein families . The importance of 3D structure in reducing the entropy can also be appreciated in the entropy decay as a function of physical distance ( Fig 3B for ANK ) where most of the entropy drop happens within the first 10 angstroms , indicating that above this characteristic distance interactions are not crucial in constraining the space of accessible sequences . The energy function of Eq ( 2 ) takes the same mathematical form as a disordered Potts model . These models , in particular in cases where σi can only take two values , have been extensively studied in the context of spin glasses [36] . In these systems , the interaction terms −Jij ( σi , σj ) imply contradictory energy requirements , meaning that not all of these terms can be minimized at the same time—a phenomenon called frustration . Because of frustration , natural dynamics aimed at minimizing the energy are expected to get stuck into local , non-global energy minima ( Fig 4 ) , significantly slowing down thermalization . This phenomenon is similar to what happens in structural glasses in physics , where the energy landscape is “rugged” with many local minima that hinder the dynamics . Incidentally , concepts from glasses and spin glasses have been very important for understanding protein folding dynamics [37] . We asked whether the energy landscape of Eq ( 2 ) was rugged with multiple minima , and investigated its structure . To find local minima , we performed a local energy minimization of Efull ( learned with all constraints including on P ( ID ) , but taken with λID = 0 to focus on functional energy terms ) . By analogy with glasses , such a minimization is sometimes called a zero-temperature Monte-Carlo simulation or a “quench” . The minimization procedure was started from many initial conditions corresponding to naturally occuring sequences of consecutive repeat pairs . At each step of the minimization , a random beneficial ( energy decreasing ) single mutation is picked; double mutations are allowed if they correspond to twice the same single mutation on each of the two repeats . Minimization stops when there are no more beneficial mutations . This stopping condition defines a local energy minimum , for which any mutation increases the energy . The set of sequences which , when chosen as initial conditions , lead to a given local minimum defines the basin of attraction of that energy mimimum ( Fig 4 ) . The size of a basin corresponds to the number of natural proteins belonging to that basin . Performing this procedure on natural sequences of consecutive repeat pairs from all three families yielded a large number of local minima ( Fig 5 ) . To control for the phylogenetic bias that links natural sequences , we repeated this analysis on sequences synthetically generated from the model ( Efull ) , and obtained very similar results ( see S6 Fig for ANK ) . When ranked from largest to smallest , the distribution of basin sizes follows a power law ( Fig 5A for ANK and S3A and S4A Figs for LRR and TPR ) . The energy of the minimum of each basin generally increases with the rank , meaning that largest basins are also often the lowest . Despite this multiplicity of local minima , the Monte-Carlo dynamics that we used in previous sections for learning the model parameters and for estimating the entropy did not get stuck in these minima , suggesting relatively low energy barriers between them . The partition of sequences into basins allows for the definition of a new kind of entropy Sconf = −∑b P ( b ) ln P ( b ) called configurational entropy , based on the distribution of basin sizes , P ( b ) = ∑σ∈b P ( σ ) , where σ ∈ b means that energy minimization starting with sequence σ leads to basin b . This configurational entropy measures the effective diversity of basins , and is thus much lower than the sequence entropy Sfull , while the difference Sfull − Sconf measures the average diversity of sequences within each basin . We find Sconf = 5 . 1 bits for ANK , 6 . 0 bits for LRR , and 10 . 4 bits for TPR . As each basin corresponds to a distinct sub-family within each family [32] , this entropy quantifies the effective number of these subgroups . While basins are very numerous , they are also not independent of each other . An analysis of pairwise distances ( measured as the Hamming distance between the local minima ) between the largest basins reveals that they can be organised into clusters ( panels B of Fig 5 , S3 and S4 Figs ) , suggesting a hierarchical structure of basins , as is common in spin glasses [36] . The impact of repeat-repeat interactions on the multi-basin structure can be assessed by repeating the analysis on the model of non-interacting repeats , Eir . In that model the two repeats are independent , so it suffices to study local energy minima of single repeats—local minima of pairs of repeats follow simply from the combinatorial pairing of local minima in each repeat . The analyses of basin size distributions , energy minima , and pairwise distances in single repeats are shown in panels C and D of Fig 5 , S3 , and S4 Figs . We still find a substantial number of unrelated energy minima , suggesting again several distinct subfamilies even at the single-repeat level . For comparison , the configurational entropy of pairs of independent repeats is 6 . 9 bits for ANK , 6 . 7 for LRR , and 7 . 6 for TPR . While for ANK and LRR repeat-repeat interactions decrease the configurational entropy , as they do for the conventional entropy , they in fact increase entropy for TPR , making the energy landscape even more frustrated and rugged . Note that the independent sites model E1 defines a convex energy landscape with a single local minimum—the consensus sequence—as all constraints hi can be optimized independently . To address how the interactions contribute in shaping the sequence space , going from a convex to a rugged landscape , we repeated the analysis with a limited linear interaction range W of 3 and 10 ( models of Fig 3A ) . We find that the more interactions we add , the more local minima we find ( S5A and S5B Fig for ANK with W = 3 , and C and D for W = 10 ) . The minima cluster into clearer sub-blocks structure as the interaction range is increased , consistent with the entropy reduction observed in Fig 3A . In summary , the analysis of the energy landscape reveals a rich structure , with many local minima ranging many different scales , and with a hierarchical structure between them . Lastly , we compared the statistical energy landscapes of different repeat families . Specifically , we calculated the Kullback-Leibler divergence between the probability distributions P ( σ ) ( given by Eqs 1 and 2 ) of two different families , after aligning them together in a single multiple sequence alignment ( see Methods ) . We find essentially no similarity between ANK and TPR , despite them having similar lengths: DKL ( ANK||TPR ) = 227 . 6 bits , and DKL ( TPR||ANK ) = 214 . 1 bits . These values are larger than the Kullback-Leibler divergence between the full models for these families and a random polypetide , DKL ( ANK||rand ) = 122 . 8 bits , and DKL ( TPR||rand ) = 157 . 6 bits . LRR is not comparable to ANK or TPR as it is much shorter , and a common alignment is impractical . These large divergences between families of repeat proteins show that different families impose quantifiably different constraints , which have forced them to diverge into different troughs of non-overlapping energy landscapes . This lack of overlap makes it impossible to find intermediates between the two families that could evolve into proteins belonging to both families .
Our analysis of repeat protein families shows that the constraints between amino acids in the sequences allows for an estimation of the size of the accessible sequence space . The obtained numbers ( ranging from 141 bits to 167 bits , corresponding to 1036 to 1050 sequences ) are of course huge compared to the number of sequences in our initial samples ( ∼20 , 500 for ANK , ∼18 , 800 for LRR , and ∼10 , 000 for TPR ) , but comparable to the total number of proteins having been explored over the whole span of evolution , estimated to be 1043 in Ref . [1] . In particular , we have quantified the reduction of the accessible sequence space with respect to random polypeptides . While most of this reduction is attributable to conservation of residues at each site , interactions between amino acids , both within and between consecutive repeats , significantly constrain the diversity of all repeat families . The break-up of entropy reduction between the three different sources of constraints—within-repeat interactions , between-repeat interactions , and evolutionary conservation between consecutive repeats—is fairly balanced , although TPR stands out as having more within-repeat interactions and more conservation between neighbours , suggesting that it may have had less time to equilibrate . All studied repeat families have rugged energy landscapes with multiple local energy minima . Note that the emergence of this multi-valley landscape is a consequence of the interactions between amino acids: models of independent positions ( E1 ) only admit a single energy minimum corresponding to the consensus sequence . This multiplicity of minima allow us to collapse multiple sequences to a small number of coarse-grained attractor basins . These basins suggest that mutations between sequences within one coarse-grained basin are much more likely than mutating into sequences in other basins . In general , our results paint a picture of further subdivisions within a family , and define sub-families due to the fine grained interaction structure . Going beyond single families , this analysis suggest a view in which natural proteins all live in a global evolutionary landscape , of which families would be basins , or clusters of basins , with a hierarchical structure [32] . This overall picture of the sequence energy landscape is reminiscent of the hierarchical picture of the structural energy landscape of globular proteins , an overall funneled shape with tiers within tiers [38] . The form of the energy landscape forcibly shapes the accessible evolutionary paths between sequences . The rugged and further subdivided structure shows that the uncovered constraints are global , and not just pairwise between specific residues . Therefore even changing two residues together , as is often done in laboratory experiments , is not enough to recover the evolutionary trajectories . While other approaches have explored local accessible directions of evolution [39] , our results suggest more global , non local modes of evolution between clusters . Interestingly , the sequences that correspond to the energy minima of the landscapes are not found in the natural dataset . This observation can be either due to sampling bias ( we have not yet observed the sequence with the minimal energy , although it exists ) , or this sequence may not have been sampled by nature . Alternatively , there may be additional functional constraint that are not included in our model to avoid these low energy sequences ( e . g . a too stable protein may be difficult to degrade ) . Even more intriguingly , sequences with minimal energy do not correspond to the consensus sequence of the alignment ( whose energy is marked by a gray line in panel A of Fig 5 , S3 , and S4 Figs ) , suggesting that the consensus sequence can be improved upon . All three repeat protein families studied here have been shown to be amenable to simple consensus-guided design of synthetic proteins . Synthetic proteins based on the consensus sequences of multiple alignments [40] were found to be foldable and very stable against chemical and thermal denaturation . Mutations towards consensus amino acids in the ANK family members have been experimentally shown to both stabilize the whole repeat-array and they may tune the folding paths towards nucleating folding in the consensus sites [41 , 42] . Our results suggest that interactions may play an additional role in stabilizing the sequences , and propose alternative solutions to the consensus sequences in the design of synthetic proteins .
We use a previously curated alignment of pairs of repeats for each family [17]: ANK ( PFAM id PF00023 with a final alignment of 20513 sequences of L = 66 residues each ) , LRR ( PFAM id PF13516 with a final alignment of 18839 sequences of L = 48 residues each ) and TPR ( PFAM id PF00515 with a final alignment of 10020 sequences of L = 68 residues each ) . Those multiple sequence alignments of repeats were obtained from PFAM 27 . 0 [6 , 7] . In order to improve the data obtained from the PFAM database , we used original full protein sequences available in UniProt database [43] to add available information using the headers of the original alignement . Firstly , to decrease the number of gaps positions , misdetected initial and final amino acids in repeats were completed with residues from full sequences . Secondly , individual repeats which appeared consecutively in natural proteins were joined into pairs . Finally , positions with more than 80% of gaps along the alignment were removed , eliminating in this way insertions . From the multiple sequence alignement of each family , they were calculated the observables that we use to constrain our statistical model . Particularly , we calculated the marginal frequency fi ( σi ) of an amino acid σi at position i and the joint frequency fij ( σi , σj ) of two amino acids σi and σj at two different positions i and j . These quantities were calculated using only sequences selected by clustering at 90% of identity computed with CD-HIT [44] and then normalizing by the amount of sequences . In this way , the occurrences of residues in every position are not biased by overrepresentation of proteins in the database . Furthermore , to take into account the repeated nature of the protein families that we are considering , an additional observable was calculated , the distribution of sequence overlap between two consecutive repeats , P ( ID ( σ ) ) , with ID ( σ ) = ∑ i = 1 L δ σ i , σ i + L . In order to obtain a model that reproduces the experimentally observed site-dependent amino-acid frequencies , fi ( σi ) , correlations between two positions , fij ( σi , σj ) , and the distribution of Hamming distances between consecutive repeats , P ( ID ( σ ) ) , we apply a likelihood gradient ascent procedure , starting from an initial guess of the hi ( σi ) , Jij ( σi , σj ) and λID ( σ ) parameters . At each step , we generate 80000 sequences of length 2L through a Metropolis-Hastings Monte-Carlo sampling procedure . We start from a random amino-acid sequence and we produce many point mutations in any position , one at a time . If a mutation decreases the energy ( 2 ) we accept it . If not , we accept the mutation with probability e−ΔE , where ΔE is the difference of energy between the original and the mutated sequence . We add one sequence to our final ensemble every 1000 steps . Once we generated the sequence ensemble , we measure its marginals f i model ( σ i ) and f i j model ( σ i , σ j ) , as well as Pmodel ( ID ( σ ) ) , and update the parameters of Eq 2 following the gradient of the likelihood . The local field and λID ( σ ) are updated along the gradient of the per-sequence log-likelihood , equal to the difference between model and data averages: h i ( σ i ) t + 1 ← h i ( σ i ) t + ϵ m [ f i ( σ i ) - f i model ( σ i ) ] , ( 11 ) λ ID ( σ ) t + 1 ← λ ID ( σ ) t - ϵ ID [ P ( ID ( σ ) ) - P ( ID ( σ ) ) model ] . ( 12 ) As the number of parameters for the interaction terms Jij is large ( = 212 L2 ) , we force to 0 those that are not contributing significantly to the model frequencies through a L1 regularisation γ∑ij , σ , τ|Jij ( σ , τ ) | added to the likelihood . This leads to the following rules of maximization: If Jij ( σi , σj ) t = 0 and | f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) | < γ J i j ( σ i , σ j ) t + 1 ← 0 . ( 13 ) If Jij ( σi , σj ) t = 0 and | f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) | > γ J i j ( σ i , σ j ) t + 1 ← ϵ j [ f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) - γ sign ( f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) ) ] . ( 14 ) If [ J i j ( σ i , σ j ) t + ϵ j [ f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) - γ sign ( J i j ( σ i , σ j ) t ) ] ] J i j ( σ i , σ j ) t ≥ 0 J i j ( σ i , σ j ) t + 1 ← J i j ( σ i , σ j ) t + ϵ j [ f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) - γ sign ( J i j ( σ i , σ j ) t ) ] . ( 15 ) If [ J i j ( σ i , σ j ) t + ϵ j [ f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) - γ sign ( J i j ( σ i , σ j ) t ) ] ] J i j ( σ i , σ j ) t < 0 J i j ( σ i , σ j ) t + 1 ← 0 . ( 16 ) The optimization parameters were set to: ϵm = 0 . 1 , ϵj = 0 . 05 , ϵID = 10 , and γ = 0 . 001 . To estimate the model error , we compute f i ( σ i ) - f i model ( σ i ) and f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) . We also calculate the difference of generated and natural repeat similarity distribution for all the possible repeats Hamming distances , penalized by a factor 5 to better learn the parameter λID: 5 ( P ( ID ( σ ) ) − P ( ID ( σ ) ) model ) . We repeat the procedure above until the maximum of all errors , | f i ( σ i ) - f i model ( σ i ) | , | f i j ( σ i , σ j ) - f i j model ( σ i , σ j ) | and 5|P ( ID ( σ ) ) − P ( ID ( σ ) ) model| , goes below 0 . 02 , as in Ref . [17] . Using this procedure we can calculate the model defined in Eq 2 with different interaction ranges used in the entropy estimation in Fig 3A . We start from the independent model hi ( σi ) = log fi ( σi ) . We first learn the model in Eq 2 with J = 0 . We then re-learn models with interactions between sites i , j along the linear sequence such that |i − j| ≤ W , in a seeded way starting from the previous model . The first and last point of Fig 3 correspond to the independent site model with λID and the full model in Eq 2 The entropy in Fig 3B is calculated in the same way as in Fig 3 , but now interactions are turned on progressively according to physical distance in the 3D structure rather than the linear sequence distance . In order to obtain the physical distance between residues we use as a reference structure the first two repeats of a consensus designed ankyrin protein 1n0r [45 , 46] , which have exactly 66 amino-acids . We define the 3D separation between two residues as the minimum distance between their heavy atoms in the reference structure . To learn the Potts model without λID ( E2 ) we remove λID from Eq 2 and re-learn the Potts field using the full model parameters as initial contition . To learn the single repeat models with and without λ ( Eir and Eir , λ , we take as initial condition the model with interactions below the length of a repeat ( W = L − 1 , dashed vertical line in Fig 3 ) , and then learn a model removing all the Jij terms between different repeats . We also impose that the hi fields and intra-repeats Jij terms are the same in each repeat , and the experimental amino-acid frequencies to be reproduced by the model are the average over the two repeats of the 1- and 2-points intra-repeats frequencies fi ( σi ) and fij ( σi , σj ) , such that f i ′ ( σ i ) = f i + L ′ ( σ i ) = 1 2 ( f i ( σ i ) + f i + L ( σ i ) ) , ( 17 ) and f i j ′ ( σ i , σ j ) = f i + L , j + L ′ ( σ i , σ j ) = = 1 2 ( f i j ( σ i , σ j ) + f i + L , j + L ( σ i , σ j ) ) , ( 18 ) if i and j represent sites within the same repeat . In this way we obtain a model for a single repeat that can be extended to both the repeats in the original set of sequences of our dataset . In practice to calculate the entropy S of the protein families we relate it to the internal energy E = −log p ( σ ) and the free energy F = −log Z: S = ⟨ E ⟩ - F = ∑ σ p ( σ ) E ( p ( σ ) ) + log Z = - ∑ σ p ( σ ) log p ( σ ) , ( 19 ) We generate sequences according to the energy function in Eq 2 and use them to numerically compute 〈E〉 . To calculate the free energy we use the auxilliary energy function: E α ( σ ) = - ∑ i h i ( σ i ) + α [ - ∑ i j J i j ( σ i , σ j ) + λ ID ] , ( 20 ) where the interaction strength across different sites can be tuned through a parameter α that is changed from 0 to 1 . We generate protein sequence ensembles with different values of α and use them to calculate F as a function of α , F ( 1 ) = F ( 0 ) + ∫ 0 1 d α d F d α: F ( 1 ) = F ( 0 ) + ∫ 0 1 d α ⟨ - ∑ i j J i j ( σ i , σ j ) + λ ID ⟩ α , ( 21 ) where the average over α is taken over the sequences generated with a certain value of α , characterized by the ensemble with probability p α ( σ ) = ( 1 / Z α ) e - E α ( σ ) . F ( 0 ) is the free energy for an independent sites model: F ( 0 ) = - ∑ i log ∑ σ i e h i ( σ i ) , ( 22 ) where the first sum is taken over protein sites and the second over all possible amino-acids at a given site . Eqs 22 and 19 result in the thermodynamic sampling approximation for calculating the entropy [47]: S = ⟨ E ⟩ + ∑ i log ∑ σ i e h i ( σ i ) - ∫ 0 1 d α ⟨ - ∑ i j J i j ( σ i , σ j ) + λ ID ⟩ α . ( 23 ) We generate 80000 sequences using Monte Carlo sampling for the energy in Eq 20 with 50 different α values , equally spaced between 0 and 1 at a distance of 0 . 02 , and then numerically compute the integral in Eq 23 using the Simpson rule . The entropy estimate is subject to three sources of uncertainty: the finite-size of the dataset , convergence of parameter learning , and the noise in the thermodynamic integration . We estimate the contribution of each of these errors using the independent sites model . In the independent sites model each site i is simply described by a multinomial distribution with weights given by the observed amino-acid frequencies in the datasets . The variance in the estimation of the frequencies from a finite size sample is Var ( fi ( σi ) ) = ( pi ( σi ) ( 1 − pi ( σi ) ) ) /Ns and the covariance between the frequencies of different amino-acids σ and σ′ at the same site i is Cov ( f i ( σ i ) , f i ( σ i ′ ) ) = - ( p i ( σ i ) p i ( σ i ′ ) ) / N s where Ns is the sample size and pi ( σi ) are the weights of the true multinomial distribution sampled . Through error propagation from these quantities we calculate the variance in the entropy of the independent sites model , to first order in 1/Ns: Var ( S i n d e p ) = 1 N s [ ∑ i ∑ σ i p i ( σ i ) log p i ( σ i ) 2 - S i n d e p 2 ] + O ( 1 N s 2 ) . ( 24 ) Evaluating this equation using the empirical frequencies p = f assuming they are sampled from an underlying multinomial distribution , gives an estimate of the standard deviation of 0 . 05 . We assume that the interaction terms do not change the order of magnitude of this estimation . Also the standard deviation in the averages in Eq ( 23 ) scales as 1 / N s with Ns = 80000 . The parameter inference is affected not only by noise , but also by a systematic bias depending on the parameters of the gradient ascent described above and the initial condition that we chose to start learning from . S1 Fig shows the average entropy of 10 realizations of the learning and thermodynamic integration procedure for the ANK family and its standard deviation as error bars . If we learn the models with an increasing W window progressively we get a different profile than learning each point starting from the independent model , and above L these two profiles are more distant than the magnitude of the standard deviation , signalling a systematic bias . S1 Fig also shows that progressively learning the model results in a better parameters convergence to values that give lower entropy values . In order to estimate how this bias is reflected in the entropy estimation we take the single-site amino-acid frequencies produced by the inferred energy function in the last Monte-Carlo phase of the learning procedure and calculate the corresponding entropy for this independent-sites model . We compute the absolute value of the difference between this estimate of the entropy and the independent-sites entropy calculated from the dataset . Again in doing this we assume that neglecting the interaction terms does not change the order of magnitude of this error . These procedure results in the errorbars shown in Figs 3 and 2 , Table 1 , S2 Fig . We repeat 10 realizations of both the parameter inference procedure and the entropy estimation , and in Fig 3 we show the average entropy of these 10 numerical experiments for the ANK family where error bars are estimated as explained above to sketch the order of magnitude of the error coming from systematic bias in the parameters learning . S1 Fig shows the mean entropy of ANK as in Fig 3A with the standard deviations of the realizations entropy as error bars , to give an idea of the combined noise in the thermodynamic integration and in the gradient descent , starting from the same initial conditions and with the same update parameters ( see Section ) . The combined noise is smaller than the entropy decrease at 33 residues , showing the decrease is real . To further check the robustness of the entropy estimation procedure , we generate two synthetic ANK datasets , one with an independent sites model , the other with a model of two non-interacting repeats obtained as explained above , and relearn the model from the synthetic datasets . Repeating the learning and entropy estimation procedure on each on the synthetic protein families gives results that are consistent with the model used for the dataset generation . The entropy of the model learned taking an independent sites dataset does not decrease with the interaction range W and the entropy of the model learned taking a non-interacting repeats dataset does not show any drop around the repeat length . We repeat the procedure described for the LRR and TPR repeat-proteins families reaching similar conclusions ( S2 Fig ) . In order to characterize the ruggedness of the inferred energy landscapes and the sequence identity of the local minima , we start from all the sequences in the natural dataset as initial conditions and for each of them we perform a T = 0 quenched Monte-Carlo procedure . Repeating this analysis on sequences synthetically generated from Efull yields very similar results ( see S6 Fig for ANK ) We perform this energy landscape exploration learning the parameters of the Hamiltonian in Eq 2 ( refer to Section for the learning procedure ) , and then set λID = 0 in the energy function because we want to investigate the shape of the energy landscape due to selection rather than the phylogenic dependence . We scan all the possible mutations that decrease the sequence energy and then draw one of them from a uniform random distribution . The possible mutations are all single point mutations . If the same amino-acid is present in the same relative position in the two repeats we allow for double mutations that mutate those two positions to a new amino-acid , that is identical in both repeats , at the same time . We do this so that the phylogenetic biases that are still partially present in the parameters of the model do not result in spurious local minima biasing the quenching results . The Monte-Carlo procedure ends when every proposed move results in a sequence with an increased energy , and the identified sequence is a local minimum of the energy landscape . To explore how turning on interactions makes the energy landscape more rugged , we perform the same procedure with the Hamiltonian corresponding to two intermediate interaction ranges in Fig 3A . That is Eq 2 , in which Jij was allowed to be non-zero only within a certain interaction range W . We picked W = 3 and W = 10 . In order to assess what is the role of the inter-repeat interactions we repeat this T = 0 quenched Monte-Carlo procedure on single repeats , with all the unique repeats in the natural dataset as initial condition . The learning procedure of the Hamiltonian for a single repeat is explained in Section . In this single repeat case the possible mutations are just the single point mutations . Once we have the local minima of the energy landscape , we obtain the coarse-grained minima using the Python Scipy hierarchical clustering algorithm . In this hierarchical clustering the distance between two clusters is calculated as the average Hamming distance between all the possible pairs of sequences belonging each to one cluster . As a result we plot the clustered distance matrix , the clustering dendogram and the basin size corresponding to the distance matrix entries . In the end we can repeat the quenching procedure described above for LRR and TPR families . The result are sketched in S3 and S4 Figs and lead to similar conclusions as for the ANK family . The Kullback-Leibler divergence between two families A and B is defined as DKL ( A||B ) = ∑σ pA ( σ ) log2 pB ( σ ) /pA ( σ ) . We can substitute the sequence ensembles for ANK and TPR in the definition of the probabilities obtaining: D KL ( A N K | | T P R ) = ⟨ E TPR - E ANK ⟩ ANK + F ANK - F TPR , ( 25 ) D KL ( T P R | | A N K ) = ⟨ E ANK - E TPR ⟩ TPR + F TPR - F ANK , ( 26 ) where the notation 〈〉ANK means that the average is calculated over sequences drawn from the ANK ensemble: P ( σ ) ANK = ( 1 / Z ANK ) e - E ( σ ) ANK . Therefore 〈ETPR〉ANK is the average TPR energy function evaluated , via the structural alignment between the two families , on 80000 sequences generated through a Monte Carlo sampling of the ANK model 2 ( and analogously for 〈EANK〉TPR ) . The terms FANK and FTPR are calculated in the same way as when estimating the entropy through Eqs 21 and 22 , as explained in Section . For the control against a random polypeptide of length L we use DKL ( FAM||rand ) = log Λ − S ( FAM ) , where Λ = 21L is the total number of possible sequences of length L . | Natural protein molecules are only a small subset of the possible strings of amino acids . This naturally calls the question of how many protein sequences theoretically exist that are functional , and how many have already been explored by nature . To help answer this question , we developed a statistical method to calculate the total potential number of protein sequences of a given family , focusing on three families of repeat proteins , which play important roles in a variety of cellular processes . The number of sequences that we compute is limited by functional interactions between the residues of the protein , as well as its evolutionary history . Applying techniques from the physics of disordered systems , we show that the space of sequences has a rugged structure , which could hinder their evolution . Individual proteins can be organised into distinct clusters corresponding to basins of attraction of the landscape , suggesting the existence of subfamilies within each family . | [
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"co... | 2019 | Size and structure of the sequence space of repeat proteins |
Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics , but the prediction of viral cross-protection remains an important unsolved problem . For foot-and-mouth disease virus ( FMDV ) research in particular , improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate , as well as for deciding whether appropriate vaccine ( s ) exist and how much they could mitigate the effects of any outbreak . To identify antigenic relationships and their predictors , we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data . We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity . These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology . Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes . Furthermore , for the SAT1 serotype , for which epitopes have never previously been identified , we provide strong evidence – by controlling for phylogenetic structure – for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization . Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage . These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out . As the parameterization uses pre-existing datasets , this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease .
The genetically highly variable nature of RNA viruses [1] has been extensively documented in pathogens such as foot-and-mouth disease virus ( FMDV ) and influenza virus . A direct consequence of this phenomenon is that inactivated or attenuated vaccines derived from some such highly variable viruses confer protection only against closely related field strains [2] , as has been amply demonstrated during the 2009 influenza A ( H1N1 ) pandemic [3] . This feature of the viruses makes it particularly important to estimate the cross-reactivity , and therefore the likely cross-protection , between sera derived from the vaccine strain and field viruses [4] , [5] . The emergence of antigenically novel viruses , against which existing vaccines do not provide adequate protection , may require the selection of new vaccine seed strains . Currently , where no appropriate vaccine exists , field isolates are , when possible , adapted for vaccine production , amplified and then processed into vaccines [6] . Only at this stage can the new vaccines be inoculated into animals and tested for efficacy in vitro and subsequently in vivo . Due to the time and expense required , there is a limit to the number of isolates that can be submitted to undergo this procedure , and a sub-optimal choice of vaccine strain may therefore be made . An in silico predictor that identifies those strains likely to provide the broadest cross-protection could therefore substantially enhance capacity to develop appropriate vaccines rapidly and effectively , whilst minimising the cost and the need for animal experimentation . FMDV is ideal for such an approach to vaccine strain selection . It is a positive-sense , single-stranded RNA virus , the prototype member of the genus Apthovirus of the family Picornaviridae . It exhibits great genomic variability , with 32–33% and 53% amino acid variability in our data , within and between serotypes respectively , in the immunogenically important structural proteins , VP1-VP3 ( similar variability has previously been observed in VP1 across all serotypes [7] ) . Its seven serotypes are not cross-reactive , but individual vaccines can often protect against large groups of genetically diverse viruses within a serotype . Nevertheless there are also antigenically distinct subtypes within each serotype , and this should allow the discrimination of antigenically important changes from other substitutions . The virus is endemic in sub-Saharan Africa where six of the seven serotypes occur , and the South African Territories ( SAT ) types 1 , 2 and 3 display appreciably greater intratypic genomic variation than the traditional “Euro-Asian” types [8]–[13] . Indeed , distinct genetic variants exist within these serotypes , with the serotypes being divided into topotypes based on genetic differences [7] . The variability of all SAT FMDV serotypes requires both a range of vaccines to provide protection within serotypes and accurate cross-reactivity testing to guide vaccine selection , with implications for the control of the disease by vaccination if either of these is not available . Despite similar genomic variability , SAT2 exhibits significantly higher intratypic antigenic variability than SAT1 [11] . Such a pair of serotypes with similar genetic but different antigenic characteristics provide an excellent testing ground for studies of the genetic basis of antigenic variability . Furthermore , SAT2 viruses are the causative agent in most outbreaks of FMDV in cattle in sub-Saharan Africa , and SAT1 is also widely dispersed , though mostly maintained through persistent infections of African buffalo . This makes them the most important serotypes to study in the region . The outer capsid proteins – VP1 , VP2 and VP3 – are directly involved in antigenicity and a large proportion of residues are exposed on the virion surface ( 40% in the structure used in this paper ) . Amongst the exposed residues are epitopes recognised by the host immune system . All serotypes are believed to share the major antigenic site on the flexible G-H loop of the VP1 protein , which is highly variable between even closely related strains . This is the only site to have been identified with monoclonal antibody ( MAb ) escape mutants for a single SAT2 virus [14] , and none have been for SAT1 . However , this and at least four additional sites have been implicated as neutralising epitopes for serotype O [15]–[17] , and further epitopes have been mapped for viruses from serotypes A , O and C using MAbs [15]–[24] . This antigenic variability is reflected in the virus neutralisation ( VN ) titres [25] , which provide an in vitro measure of whether the sites that contribute to the neutralization of the virus remain sufficiently similar to cross-react . Virus neutralisation is not the only important determinant of protection [26]; nevertheless the VN test ( VNT ) is one of the standard tests for cross-reactivity and it is considered to provide the most definitive serological results [6] . Specifically , the current approach uses VNTs to quantify antigenic relationships through “r1-values” – the ratio of the heterologous to homologous titres , with a ratio close to 1 indicating the viruses are antigenically similar . Generally r1-values in the range of 0 . 4–1 . 0 are considered to be indicative of reasonable levels of cross-protection , whilst all values being below 0 . 2 for a given isolate indicate the need for new vaccine strain development [27] , with 0 . 3 also proposed as a single threshold [28] . Many sources of variation are known to influence the neutralisation titres . However , standard approaches to obtaining r1-values do not fully account for these different sources of variability [29] . In order to maximise information available from neutralisation tests we developed a simple statistical methodology using multiple data sources , combining data from multiple experiments conducted at different times . The availability of sequence data and related titres from VN testing provides the opportunity to directly relate cross-reactivity to sequence variation . This relationship would allow prediction of an important component of vaccine efficacy for candidate vaccine seed strains , and rapid identification of vaccine match without the need for new serology work for existing vaccines . The aim of the current study is to develop an in silico tool to predict vaccine efficacy using sequence data , neutralising titres and structural information , and use this tool to identify and quantify the significance of epitopes of the viruses . We have obtained a broad spectrum of SAT1 and SAT2 isolates which were sequenced , and have generated sera from representative viruses . An extensive serological dataset was generated from VNTs . We have also used a novel , and currently the only , crystallographic structure for any SAT serotype to identify surface-exposed residues on the capsid . Specific objectives were to ( i ) generate improved statistical methods of estimating r1-values that maximise the efficient use of available experimental data , ( ii ) relate these estimated antigenic differences to sequence variation , ( iii ) use this relationship to predict vaccine match for viruses from sequence information , and to predict neutralisation titres , cross-reactivity and hence coverage for vaccine strains , and then ( iv ) to identify areas of the capsid containing epitopes .
Twenty SAT1 and twenty-two SAT2 viruses ( Table 1 ) representative of different topotypes were selected , and full capsid sequences were generated where not already available . This collection constitutes fully two thirds of all isolates for which full capsid sequences exist . Cattle sera were prepared against three SAT1 and four SAT2 strains . VNTs were carried out with 138 different pairs of protective strain and challenge virus , 59 of the 60 possible SAT1-SAT1 pairs and 83 of the 88 SAT2-SAT2 , with between 1 and 11 repeats of each , giving a total of 246 SAT1 and 320 SAT2 titres . This included replicates within individual experiments , and repeats with different sera and in different batches ( see Materials and Methods for an explanation of the terminology ) , in order to determine the significant sources of variability . A key feature of our analysis was to develop a formal approach for including data from multiple sera , experiments and batches . The use of homologous and heterologous titres from the same serum to calculate an r1-value controls for between-serum variability [25] , and in order to control for between-experiment variation , at least three repetitions are officially advised [6] – when more than one of either the homologous or heterologous VNT is carried out , then the results are usually averaged [30] . Our aim was to go beyond this , and combine all available titres measured in all batches for every pair of protective strain and challenge virus to produce a coherent set of best estimates for all of the true underlying r1-values simultaneously . This was achieved by first determining the presence or absence of and then estimating the magnitude of any consistent inter-experiment , inter-batch or other variability in the data . We built a linear mixed-effects model with log titre of the challenge virus versus protective strain as the response variable . The challenge virus ( p = 10−44 ) , protective strain ( p = 10−12 ) and their interaction ( p = 10−27 ) were significant fixed effects , but neither serotype nor whether sera were prepared by vaccination or infection was found to be significant . We would not have expected to see this latter effect since it was confounded with protective strain as all sera for each strain were generated by only one of vaccination or infection . A random effect at the level of experiment accounted for the inter-experiment variability . By comparing models with random effects to allow for other sources of variability , we determined that there was consistent variability between sera ( p = 10−15 ) , but not between batches ( see Materials and Methods ) . Apart from the one identified above , other interactions between these effects ( protective strain , challenge virus , serum and experiment ) were not found to be significant . This “best consensus estimate” model could thus be written as: ( 1 ) where tp , c is the titre for a neutralisation test for protective strain p and challenge virus c , μp , c is the mean log titre , εS and σS2 are the best linear unbiased predictor and associated variance for the random effect of serum , εE and σE2 are the equivalent measures for experiment , and εR and σR2 are the model residuals and associated variance . Estimates of these variances were used to examine the expected uncertainties associated with standard methods of estimating r1-values . The between-serum variance was 0 . 072 , which gives a 95% confidence interval around the estimate of +/−0 . 53 log titres due to inter-serum variability; as was noted earlier , this is eliminated by always using homologous and heterologous titres from the same serum to calculate an r1-value . However , the remaining ( between-experiment and residual ) variances sum to 0 . 287 , which gives a 95% confidence interval around the estimate of an individual serological r1-value ( i . e . using 1 homologous titre and 1 heterologous titre from the same serum in the same batch , as is usually the case ) of +/−1 . 49 log titres . With test variability this high it is clear that an improved method of estimating r1-values that makes use of all available data would be valuable . Estimates of μp , c had 95% confidence intervals ranging between +/−0 . 50 to +/−1 . 17 log titres , depending on the number of titres available ( the greatest uncertainty being associated with r1-values estimated from only a single homologous and heterologous titre ) . These narrower confidence intervals show that our new techniques for estimating μp , c provide a substantial improvement on existing methods for the same number of titres . These best consensus estimates of the true means were therefore used as our gold standard for subsequent analyses ( Figure 1 and Dataset S1 ) . Structural data were used to identify candidate areas of the capsid that might be antigenically significant ( 29 and 28 areas for SAT1 and SAT2 respectively – see Materials and Methods for details ) . These provide the starting point for a related linear mixed-effects approach used to predict r1-values from the sequence data and , ultimately , to identify antigenically significant areas of the capsid . The mean log titre , μ – the fixed effect term in the estimating model ( Equation 1 ) – was replaced with a predictive term based on differences between capsid-coding sequences of the protective and challenge strains . Removing this fixed effect also necessitated the inclusion of the additional random effect of challenge virus ( , p<10−10 ) , and the final model took the form: ( 2 ) where k0 is the average titre and di is a raw count of the number of amino acid changes between the protective strain and challenge virus in a single candidate area identified from the structural modelling ( the ith out of a total of N areas identified as potentially antigenically significant – see Materials and Methods for the model selection process ) , with ki the regression coefficients . Two different predictions are of interest: first , in an outbreak , the vaccine that best matches a given challenge virus , and second , to judge breadth of coverage of a candidate vaccine strain – the range of r1-values that it will produce for selected challenge viruses . Correspondingly , models were validated using two measures of quality of a predictor of antigenic distance: ( i ) the number of times the protective strain with the predicted highest r1-value for a given challenge virus matches the strain that would be selected using the serology data; and ( ii ) the difference between the predicted r1-values for specific pairs of protective strains and challenge viruses and our best consensus estimate r1-values for those pairs . In the former case only those challenge viruses that might have appropriate protective strains are considered ( we chose those with an estimated r1≥0 . 2 ) since we are not interested in whether the model correctly chooses the least worst vaccine when none could possibly be effective . Specifically , candidate models selected by the model-building process were cross-validated by estimating parameters in two different ways , corresponding to our two different requirements: ( i ) using datasets missing all data for each challenge virus in turn , and comparing the vaccine choice with that obtained using our gold standard estimates; or ( ii ) using datasets missing all data for each protective strain in turn , and comparing the r1-values generated for that missing protective strain compared with our gold standard estimates . The best models for each serotype are reported below . Eighteen out of the 20 SAT1 viruses but only 9 out of the 22 SAT2 viruses had protective strains close enough to offer some cross-reactivity ( r1≥0 . 2 ) , and so were included in the cross-validation . For SAT1 the best model after cross-validation contained two terms , the number of amino acid changes in the VP1 G-H loop and beyond ( residues 132–174 , which contains the major antigenic site for FMDV as well as sites in the H-I loop ) , and in the VP3 H-I loop ( residues 191–202 , which contains amino acids identified by MAb escape mutant studies as part of the epitope labelled Site 3 on A10 [24] ) – see Figure 2 , blue , for visualization . The formula for the r1-value predictor is: ( 3 ) where the εC are the best linear unbiased predictors from Equation 2 . For SAT2 the best model contained three terms: the number of amino acid changes in the VP1 C terminus ( residues 200–224 , which contains Site 1b on O , Sites C and D on serotype C , Site 2 for A10 , Sites 3 and 4 for A12 and Site 2 for A5 – [21] , and references therein ) , in the VP2 B-C loop ( residues 70–82 , which contains Site 2 on O , Site 3 on A10 , Site 1 on A5 and another part of Site D on C – [21] , and references therein ) , and residue 178 in the VP1 H-I loop ( the H-I loop as a whole contains Site 1 for A12 and Site 4 for A10 – [21] , and references therein ) – see Figure 2 , red , for visualization . The formula is: ( 4 ) The predictive models successfully identified 13 SAT1 matches ( 72% ) and all 9 SAT2 matches ( 100% ) ( Figure 3 ) . This accuracy is comparable with that obtained using the individual serological measurements , which , by bootstrapping the raw titres to generate individual serological r1-values , we estimated would correctly identify the best strain 70% of the time for SAT1 and 83% for SAT2 ( a multinomial test on these values shows that there is no significant difference between the serological and predicted values ) . Though additional serological and sequence data would ultimately improve the predictive model , it currently performs at least as well as standard serological approaches . A small bias was observed for all of the candidate models in their predictions for heterologous titres relative to homologous titres . This does not affect the vaccine match experiments where the aim is to reduce relative error ( in the differences between r1-values for different protective strains using the same challenge virus ) potentially at the expense of absolute error ( in the r1-values themselves ) . In this case , however , for accurate r1-value prediction the aim is to reduce this absolute error . Adding a term to the models that explicitly distinguished homologous and heterologous titres removed this bias , and so it was included in all of the candidate models . The best predictive model of r1-values following cross-validation for SAT1 contained the same two terms as before ( the number of amino acid changes in the VP1 G-H loop and beyond , and the number in the VP3 H-I loop ) , together with a term that is present when titres are heterologous . The formula is: ( 5 ) Ninety eight percent of the predictions ( Figure 4 , SAT1 , black crosses ) are within the 95% confidence limits of the gold standard estimates ( dashed lines ) , which is significantly better than achieved by individual serological r1-values ( grey dots ) at 87% ( Fisher's exact test , p<0 . 01 ) . The variance around the gold standard estimates is significantly lower for the predictions than the individual serological r1-values ( 0 . 09 rather than 0 . 18 , Bartlett test , p<0 . 01 ) . The best predictor of r1-values for SAT2 also contained the same three terms as before ( the VP1 C terminus , the VP2 B-C loop , and a single residue in the VP1 H-I loop ) , again together with a term for heterologous titres . The formula is: ( 6 ) For SAT2 , 77% of the predicted r1-values were within the confidence limits of the gold standard estimates ( Figure 4 , SAT2 , black crosses ) , which is not significantly different from 66% for individual serological r1-values ( grey dots ) . Variances were also not significantly different ( 0 . 40 compared to than 0 . 43 ) . Both predictions and serological measurements are less accurate than those for SAT1 due to the lower repeatability of SAT2 serology ( Figure 4 , grey dots ) . The above predictive models identify those areas that are correlated with loss of cross-reactivity . To identify those areas that are directly responsible for antigenic variability it is necessary to develop models that additionally control for the phylogenetic relationships between virus strains . The phylogenetic control extends Equation 1 in an analogous manner to the predictive model ( Equation 2 ) : ( 7 ) where δi is a delta function which is 1 if p and c are separated by branch i of the phylogenetic tree and 0 otherwise . Loss of cross-reactivity is caused by amino acid substitutions in the capsid proteins , and any individual substitution must occur in a specific branch of the phylogenetic tree ( though we may not be able to determine which ) . Each branch partitions the tree into two groups , and where a branch effect represents changes that impact significantly on cross-reactivities , they will be higher between viruses within the groups than those between groups ( after controlling for other effects ) . For instance , where a terminal branch is identified , the fixed effect of that branch specifies an amount by which the virus to which it leads ( the first group ) is antigenically distant from all other viruses ( the second group ) . A significant internal branch , similarly , identifies a clade that is antigenically distant from the rest of the tree . By building a model containing all of the branches in the tree , and then using a stepwise elimination procedure to remove branches which do not significantly improve the model fit ( p>0 . 05 ) , we are left with the set of branches that , when traversed , significantly account for reductions in antigenic cross-reactivity . Twelve phylogenetic branches are significant in SAT1 and twenty-one in SAT2 ( black lines , Figure 5 ) . For SAT1 these are six branches that each partition individual topotypes from the rest of the tree , and five terminal branches that lead to viruses for which large numbers of titres have been obtained ( including the three protective strains ) as well as one that is antigenically very distant from the protective strains ( ZAM/2/93 , which has no r1-value above 0 . 2 ) . For SAT2 , there are six internal branches throughout the tree and fifteen terminal branches leading to ten of the thirteen viruses that are antigenically distinct ( again , all r1-values are below 0 . 2 ) and five other viruses ( including the four protective strains ) . Any model containing these terms controls as completely for the phylogeny as is possible with the data available , and should a model have a significantly better fit than the phylogenetic model on its own , it must achieve this by some mechanism other than phylogenetic correlation . A simple combination of Equations 2 and 7 provides a potential model , with an additional term for the raw count of the number of amino acid changes between the protective strain and challenge virus in a single candidate area: ( 8 ) where d1 is the count of substitutions at a specific site , and k1 the associated regression coefficient . The phylogenetic control terms account for repeated measurement of all significant shared phylogenetic history . However , in doing so , they remove all significant direct effects of substitutions at individual branches of the tree , but are not designed to capture the interactions involved in multiple and/or convergent substitutions at the same sites in different branches . Consequently , the substitution count in any area significantly improves the model if it corresponds to this substitution structure . Parallel and/or back-mutations , relatively frequent in such highly variable viruses , are therefore strong signals used by the model to determine antigenically significant areas . The phylogenetic control is therefore conservative in that significant sites with substitutions at only one branch in the tree will not be identified , as the different substitutions in that branch cannot be readily disambiguated . Nevertheless , after controlling for phylogeny , the twenty-nine SAT1 areas tested with the model were collectively significant predictors ( p<0 . 05 [31] ) , but the twenty-eight areas for SAT2 were not significant ( collectively or individually ) . Because substitutions are ultimately responsible for the loss of cross-reactivity , the substitutions contributing to counts in these SAT1 areas must be responsible for this loss unless they are co-occurring with causative substitutions . Any such causative substitutions should , however , be identifiable because substitution counts in areas containing them will improve the model fit . Comparing the individually best SAT1 areas from above with bootstrapped random sequences of the same length from other parts of the capsid , however , fails to identify other causative substitutions , and eight areas were instead found to be significant after a Holm-Bonferroni correction for the number of terms [32] ( p<10−12 collectively ) . Of these eight terms , seven were individually significant in the previous test ( p<0 . 05 ) . These seven terms consisted of five that corresponded exactly to the five areas identified as the constituent parts of the Site 3 conformational epitope for A10 [24] ( p<10−8 collectively ) , one was the VP1 G-H loop ( p<0 . 001 ) , and the last was the VP3 G-H loop , previously identified as Site 3 on A12 [18] ( p<0 . 01 ) . To identify the specific residues responsible for these drops in antigenic cross-reactivity , Equation 8 is trivially modified to test substitutions to the 62 individually variable residues in the seven areas identified ( instead of the 29 candidate areas ) . Again , this is a conservative test , as it will only identify residues where multiple/convergent substitutions occur at different branches in the phylogeny . Collectively , changes to the residues are significant after controlling for phylogeny ( p<0 . 005 ) , but only two residues are individually significant ( p<0 . 05 ) . Bootstrap comparisons with other residues showed these to be the two most significant predictors of loss of cross-reactivity out of all the residues in the capsid , and both are adjacent to residues identified by MAb escape mutant studies on A10 as part of Site 3 [24] . These were residue 138 on the VP3 E-F loop and residue 198 on the VP2 H-I loop – see Dataset S2 for alignment , and Figure 2 , white , for visualization – and the expected effects of substitutions at those residues are a reduction in cross-reactivity of 25% ( 95% CI 8%–40% ) and 16% ( 95% CI 0%–30% ) respectively . To test whether areas and individual residues vary in their significance across the whole serotype or are conserved , a random effect ( κ ( r ) in Equation 9 ) that allows the count ( k1 in Equation 8 ) to vary in significance for different protective strains , challenge viruses or sera ( r ) can be added to the model: ( 9 ) For SAT1 none of these effects improve the model ( p>0 . 1 for all areas and individual residues ) . For SAT2 , however , although no areas or residues are significant individually , thirty-one of them have significant interactions ( p<0 . 05 ) with at least one of these ‘r’ terms , which suggests that there may be some variability in the significance of parts of the capsid for loss of cross-reactivity within the serotype .
The identification of antigenic sites on individual FMDV isolates is time consuming , with the consequence that data are not available for all serotypes , much less for all isolates . Indeed , very little is known about the important epitopes for SAT1 and SAT2 viruses , impacting on the potential to both design vaccines with broader or better targeted antigenic cover and predict the efficacy of a particular vaccine strain against circulating viruses in the field . We have identified seven areas containing what we believe to be three epitopes for SAT1 , and we provide evidence that these are conserved across our whole sample . We have further quantified the effect of substitutions at two specific residues in one of these epitopes . The conservative phylogenetic control employed throughout the analysis means that this may not be an exhaustive list of antigenically significant areas of the capsid , and is almost certainly not for residues , as it will only identify ones where multiple/convergent substitutions occur at different branches in the phylogeny . The areas that are identified do , however , correspond to epitopes identified by MAb escape mutants for other serotypes , and both of the specific residues found are ( after alignment ) adjacent to ones which are part of Site 3 on A10 [24] . Confirmatory evidence that the phylogenetic control is acting as expected is provided by the fact that for SAT1 all of the internal branches that are identified as antigenically significant correspond to previously identified antigenically important events , that is to say branches that partition individual topotypes from the rest of the tree . For SAT2 , we have identified variability in the significance of different sites for different protective and challenge strains , and even different sera , that may indicate epitopes are present on some viruses but not on others . This may help to explain the much greater observed antigenic variability in SAT2 compared to SAT1 despite their similar genomic variability [11] , as well as the absence from our analyses of identifiable epitopes that are conserved across the serotype . We have also used cross-reactivity data generated for SAT1 and SAT2 FMDV to develop a linear mixed-effects model that uses replicates and repeated experiments to more accurately account for variability in measurement and so generate better estimates of cross-reactivity for FMDV . We have enhanced this model with sequence and structural data to identify surface-exposed residues that correlate with loss of cross-reactivity and then built models using counts of amino acid substitutions in selected areas to predict cross-reactivity . We note that all of the areas used in these models are associated with epitopes identified by MAb escape mutants for other serotypes . Furthermore , for SAT1 they also correspond to parts of the new epitopes identified above . These predictive models were used to successfully identify the efficacy of novel candidate vaccines against the virus isolates , with 98% of SAT1 predictions within the 95% confidence intervals for our gold standard estimator of true r1-values , and 77% of SAT2 predictions . For SAT1 this was significantly better than individual serological r1-values despite the predictions being made without the use of any sera from the protective strains . Related models were also used to predict the best vaccine match for novel virus isolates . For the 9 SAT2 virus isolates for which any match existed in the data ( r1≥0 . 2 ) , the model correctly predicted all 9 , and for the 18 SAT1 isolates , the model predicted 13 . In both cases there is no significant difference between the model predictions and the serological results . The uncertainty inherent in the VNT and the variability between different experiments balance any inaccuracies in the predictive model , making it at least as effective a measure of r1-value and vaccine match as serology itself unless the latter is repeated multiple times . Improving the serological tests is an area of active research [33] , and we anticipate being able to improve the predictive models further by exploiting such improved serological datasets . The accuracy of the r1-value predictions and the inaccuracy of the matching for SAT1 ( relative to SAT2 ) may have the same cause – two of the anti-sera were raised against viruses of the same topotype ( topotype 1 – constituting 70% of the titres ) , producing a good model for that topotype , but with little power to generalise and predict cross-reactivity for significantly different viruses clustering in other topotypes . Consequently , it identified 8 out of 10 correctly when the answer was a topotype 1 vaccine , but only 5 out of 8 for the other topotype . Conversely , the relative inaccuracy of the r1-values but the accuracy of the matching for SAT2 may share the opposite cause – 4 anti-sera were raised against 4 different topotypes , giving a better estimate of which areas were antigenically significant in general and thereby allowing better vaccine matching . However , because of the relative sparsity of data from any individual topotype ( there were at most 92 titres for any individual topotype for SAT2 , against 170 for SAT1 ) , we obtained a poorer estimate of the relative importance of each area , and therefore a less accurate r1-value prediction . The greater inherent variability in SAT2 titres also necessitated more data to acquire an accurate estimate . These complementary results suggest that to refine the model further more data from different topotypes will improve the vaccine matching in SAT1 , and more from the same topotypes will improve the estimates of cross-reactivity in SAT2 . The VP1 G-H loop is known to contain major epitopes in all serotypes of FMDV where MAb studies have been conducted; substitutions in it are therefore considered to be a significant determinant of loss of cross-reactivity . Our SAT1 epitope analysis identified this loop and the model used it to predict cross-reactivity . However , our SAT2 model did not find the number of amino acid substitutions in the candidate area containing the loop to be in general a good correlate . There are three potential explanations . First , the G-H loop has a high substitution rate , so high indeed that we suggest that the epitope ( s ) may very often not cross-react even between closely related strains , giving the model little data with cross-reacting epitopes from which to identify a pattern . Second , the candidate area is much bigger than just the G-H loop , therefore including many residues that are not antigenically significant . Finally saturation can occur , causing the actual count even inside the G-H loop to cease to be meaningful . The combination of these effects makes it very difficult to detect the signal of epitope loss from the noise of other substitutions . Better predictors might be obtained by selecting smaller candidate areas . However , there is a risk of “fishing” until an appropriate sub-sequence is discovered , leaving the generality of the technique uncertain . Related work on influenza A [34] , [35] , which was the first to attempt this kind of prediction , examined 101 residues ( chosen based on previous laboratory identification of antigenic sites ) and built models containing up to 19 terms , testing orders of magnitude more models than our 2-/3-term out of 28-/29-area models . The advantage of the approach taken here is that the areas to be examined were determined by a single a priori criterion , the strategy for choosing models to test was fixed in advance , and the relatively small number of models examined in the model-building process was easily controlled for by a simple statistical correction . A further strength of our approach is that it identifies antigenically significant areas . These can be validated entirely independently through comparison with MAb escape mutant-derived epitope information . Of the 5 areas identified here , all have been identified by previous MAb work in other serotypes . Furthermore , the two SAT1 areas are specifically identified in separate analyses as containing epitopes for this serotype , which provides further strong evidence that the model is indeed predictive rather than merely correlative . For SAT2 the evidence is weaker since the epitopes have only been identified on other serotypes , and the evidence that there may be epitopic variability within the serotype suggests that more caution should be applied in using them to predict the cross-reactivity of distantly related isolates . This work could be further validated by a reverse-genetics approach targeting the specific SAT1 residues identified as antigenically significant . Our methodology could be used to evaluate the effect of amino acid substitutions by predicting coverage against a panel of circulating viruses , allowing potential vaccine candidates that are expected to better match the panel to be easily identified . Interestingly , because of the high substitution rate obscuring any signal in the VP1 G-H loop , identified substitutions would probably not be located in the major antigenic site of FMDV but instead be found in other antigenically important areas . The technique developed here can be used directly for any FMDV serotype and potentially for any similar virus where cross-reactivity , sequencing and structural studies have been carried out , both to identify epitopes , and to predict vaccine match for new isolates and estimate efficacy of new candidate seed strains . This can be done by exploiting historical datasets , and is therefore a quick , low cost and valuable method for better understanding antigenic relationships . In summary , the use of sequence data to predict antigenic relationships is a powerful tool that has the potential to be applied to a variety of different infectious agents .
All procedures were approved by the Onderstepoort Veterinary Institute Animal Ethics Committee according to national animal welfare standards . The viruses were either supplied by the World Reference Laboratory for FMD at the Institute for Animal Health , Pirbright ( United Kingdom ) or form part of the virus databank at the Transboundary Animal Diseases Programme ( TADP ) , Onderstepoort Veterinary Institute ( South Africa ) . Viral RNA was extracted from cell-culture-adapted isolates and cDNA synthesised [36] . The sequences for the P1-2A-coding regions were obtained via RT-PCR of viral genomic RNA using existing primers [36]–[39] . Direct DNA sequencing of the P1-2A region derived from a given FMDV isolate yielded a master sequence representing the most probable nucleotide for each position of the sequence . Due to the quasispecies nature of FMDV populations , polymorphisms were detected in some nucleotide positions . Nevertheless , all positions could be unambiguously assigned to a single nucleotide due to the high degree of redundancy generated by a genome-walking approach . Contigs for the ca . 2 . 2kb region were compiled using Sequencher™ vs4 . 7 ( Gene Codes Corporation ) . Since these are protein-coding regions , the amino-acid sequences were aligned with ClustalW ( v . 1 . 83 ) and this alignment was then used to align the nucleic-acid sequences . The antigenic diversity of the field isolates was determined using virus neutralisation assays in micro-titre plates using IB-RS-2 cells as the indicator system [25] . Cattle sera against reference SAT1 and SAT2 viruses were prepared by two consecutive vaccinations ( vaccinated at day 0 , boosted at day 28 and bled at day 38 ) using the following vaccine strains: SAT1: SAR/9/81 and KNP/196/91 ( both topotype 1 , see Table 1 ) ; SAT2: ZIM/7/83 ( topotype II ) and KNP/19/89 ( topotype I ) or convalescent sera obtained from 21 days post-infected cattle for SAT1: NIG/5/81 ( topotype 7 ) ; SAT2: RWA/2/01 ( topotype VIII ) and ERI/12/89 ( topotype VII ) . Cattle were housed in the isolation facility at TADP and all procedures were approved by the Onderstepoort Veterinary Institute Animal Ethics Committee . The neutralisation assays were performed against viruses from the various topotypes as indicated in Table 1 after their adaptation on IBRS-2 cells . The end point titre of the serum against homologous and heterologous viruses was calculated as the reciprocal of the last dilution of serum to neutralise 100 TCID50 in 50% of the wells ( ibid . ) . The crystal structure of the SAT1 BOT/1/68 capsid was solved at a resolution of 3Å ( Fry et al . , unpublished ) ( Protein Data Bank ID: 2wzr , r2wrsf ) . This is the only structure in existence for any SAT1 or SAT2 virus . SAT1 amino-acid sequence alignments were compiled with ClustalW . Structures were visualised and the surface-exposed residues identified with the PyMol Molecular Graphics System v1 . 2r0 ( DeLano Scientific LLC ) . Exposed regions of SAT2 were approximated by alignment with the SAT1 structure . The aligned SAT sequences were classified according to whether they coded for surface-exposed residues or not as determined from the above structure . Those that did were grouped into the longest possible contiguous sub-sequences where all of the residues were surface exposed . Forty-three such sub-sequences were found , which broadly corresponded to the loops and termini of the VP1 , VP2 and VP3 proteins , though some loops were not exposed , and some divided into more than one sub-sequence separated by hidden sections . This division was chosen as the simplest way of breaking the full sequence down into a number of candidate areas each of which might be implicated in one or more antigenic site ( s ) . Of these , 14 of the sub-sequences were invariant in SAT1 , as were 15 of the areas in SAT2 , leaving 29 and 28 areas respectively in the two serotypes to test as possible predictors ( see Dataset S2 for the areas identified ) . Although the areas identified for SAT2 were only an approximation to the true surface exposed areas for that serotype , residues from every known epitope of FMDV were contained within these regions [14]–[24] , and so the areas are likely to be sufficient . A phylogenetic tree was generated from the nucleotide sequence data using a relaxed uncorrelated exponential clock , and a GTR+CP112+Γ112+I nucleotide model [40] . This was identified as the best model using Bayes Factor analysis [41] , although all similar models produced the same tree topology . All of these models and analyses are included in BEAST version 1 . 5 . 3 and Tracer version 1 . 5 . 0 [42] . The experimental variables used in this study are described below . The neutralisation tests were grouped into experiments where the same serum was used at the same time with the same challenge virus ( tests within an experiment are replicates ) ; different experiments were grouped into batches by the time at which they were performed . Possible variability at these levels was investigated when building the model . Our gold standard best consensus estimates of r1-values are available in Dataset S1; raw serological data is available on request . There were five stages to the statistical modelling: | New strains of viruses arise continually . Consequently , predicting when past exposure to closely related strains will protect against infection by novel strains is central to understanding the dynamics of a broad range of the world's most important infectious diseases . While previous research has developed valuable tools for describing the observed antigenic landscapes , our ability to predict cross-protection between different viral strains depends almost entirely on cumbersome and expensive live animal work , often restricted to model species rather than the natural host . The development of computer-based approaches to the estimation of cross-protection from viral sequence data would be hugely valuable , and our study represents a significant step towards this research goal . | [
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] | 2010 | Sequence-Based Prediction for Vaccine Strain Selection and Identification of Antigenic Variability in Foot-and-Mouth Disease Virus |
The architectonic type principle relates patterns of cortico-cortical connectivity to the relative architectonic differentiation of cortical regions . One mechanism through which the observed close relation between cortical architecture and connectivity may be established is the joint development of cortical areas and their connections in developmental time windows . Here , we describe a theoretical exploration of the possible mechanistic underpinnings of the architectonic type principle , by performing systematic computational simulations of cortical development . The main component of our in silico model was a developing two-dimensional cortical sheet , which was gradually populated by neurons that formed cortico-cortical connections . To assess different explanatory mechanisms , we varied the spatiotemporal trajectory of the simulated neurogenesis . By keeping the rules governing axon outgrowth and connection formation constant across all variants of simulated development , we were able to create model variants which differed exclusively by the specifics of when and where neurons were generated . Thus , all differences in the resulting connectivity were due to the variations in spatiotemporal growth trajectories . Our results demonstrated that a prescribed targeting of interareal connection sites was not necessary for obtaining a realistic replication of the experimentally observed relation between connection patterns and architectonic differentiation . Instead , we found that spatiotemporal interactions within the forming cortical sheet were sufficient if a small number of empirically well-grounded assumptions were met , namely planar , expansive growth of the cortical sheet around two points of origin as neurogenesis progressed , stronger architectonic differentiation of cortical areas for later neurogenetic time windows , and stochastic connection formation . Thus , our study highlights a potential mechanism of how relative architectonic differentiation and cortical connectivity become linked during development . We successfully predicted connectivity in two species , cat and macaque , from simulated cortico-cortical connection networks , which further underscored the general applicability of mechanisms through which the architectonic type principle can explain cortical connectivity in terms of the relative architectonic differentiation of cortical regions .
One potent explanatory framework that imposes order onto the tangle of cortico-cortical connections is the so-called structural model [27] ( reviewed in [28 , 29] ) , also termed architectonic type principle ( ATP ) . This principle describes the patterns of cortical projections and their laminar origins and terminations in terms of the relative architectonic differentiation of brain areas . Briefly , graded differences in cortical architecture have been found to account for the graded patterns observed in the distribution of projection origins and targets across cortical layers [27 , 30–36] . Moreover , greater similarity in the architectonic differentiation of cortical areas has been found to be associated with higher connection frequency between them , above and beyond the explanatory power of spatial proximity [34 , 36 , 37] ( see [29 , 38] for reviews ) . Originally described for ipsilateral connections of the macaque prefrontal cortex [27] , the ATP has since been confirmed for a considerable number of brain systems and species , as well as contralateral connections [30–37 , 39–42] , suggesting a mammalian-general organisational principle . The general applicability of this principle was further supported in a recent study which performed prediction analyses that transferred information across mammalian species [43] . Specifically , by training a classifier on the relationship between cortical structure and connections in a first species , area-to-area connectivity in a second species could be reliably predicted from structural variations of cortical areas in the second species without making changes to the classifier . Moreover , in the human brain , a similar association has been observed , whereby less differentiated agranular or dysgranular areas have the highest amount of functional connectivity [44] . The architectonic type principle , thus , allows the prediction of cortico-cortical connectivity from brain architecture regularities . Further substantiation of the ATP calls for a mechanistic explanation of how the described relationships between brain architecture and connectivity may emerge . From early on , the origin of this relationship has been hypothesized to be linked to developmental events [27] . Specifically , the observed close relationship between variations in cortical structure and axonal connections may arise from an interplay between the ontogenetic time course of neurogenesis and concurrent connection formation [29 , 35 , 45] . Areas which develop during different time windows were suggested to be afforded distinct opportunities to connect , with self-organisation rather than precisely targeted connection formation leading to the strikingly regular final connectivity patterns ( cf . [46] ) . Put differently , it has been hypothesized that spatiotemporal interactions in the forming tissue , and specifically the relative timing of neurogenesis across the cortex , determine the connectivity patterns between cortical areas . Empirically , such a relationship has , for example , been observed in the olfactory system of the rat [47] . Here , using systematic computational simulation experiments , we explored whether this suggested mechanism may be capable of generating cortico-cortical connectivity consistent with empirical observations and the architectonic type principle ( Fig 1 ) . To this end , we implemented an in silico model of the growing two-dimensional cortical sheet of a single cerebral hemisphere that was progressively populated by neurons and divided into cortical areas . Model neurons randomly grew their axons across the cortical sheet and stochastically formed connections with potential postsynaptic targets ( similar , for example , to simulation experiments in [48] and [49] ) . We assessed the resulting network of simulated structural connections between cortical areas in the same way as in previous experimental studies ( e . g . , [34 , 36] ) and compared the results to the empirical observations . Since we constrained the model to a single hemisphere , the simulated connections represent ipsilateral connectivity . Following this general approach , we characterized a number of variants of the in silico model of the growing cortical sheet , which differed in their adherence to empirical observations about developmental processes , specifically the spatiotemporal sequence of neurogenesis across the cortex . By comparing the networks generated from these variants , we could infer which aspects of the proposed mechanistic underpinnings of the ATP , particularly , which neurodevelopmental assumptions , were necessary to approximate empirical ipsilateral cortical connectivity . We explicitly incorporated three aspects of corticogenesis in our simulations which are briefly described here . First , the cortical sheet is established through neurogenesis spreading out from spatial origins , or primordial points ( where the earliest neuronal populations are observed on the developing cortex ) , so that the surface of the cortex expands over time . This expansion is accompanied by a gradient in the time of onset of neurogenesis across the cortical sheet , which we refer to as the planar gradient of time of neurogenesis [50–60] . Developmental studies indicate that neurogenesis proceeds from at least two points of origin [57 , 60 , 61] , with new neurons successively increasing the extent of cortical tissue between these neurogenetic origins . This progression entails that areas formed earlier become further separated on the cortical sheet as new areas are generated . Moreover , there is a superimposed radial gradient in the progression of neurogenesis [50 , 51 , 53 , 62 , 63] ( which was not included in our in simulations ) , resulting in the characteristic inside-out generation sequence of neurons across layers ( meaning that , with the exception of neurons in layer I , neurons in lower cortical layers are generated before neurons in upper cortical layers ) . In contradistinction to the findings outlining a planar gradient in the onset of neurogenesis , as described above , it has also been suggested that the onset of neurogenesis is simultaneous across the cortex [64 , 65] . To contrast these two interpretations , we included both alternatives in our simulation experiments , as described in more detail below . Second , cortical areas that are generated later are generally more architectonically differentiated [45 , 60 , 66 , 67] ( also briefly reviewed in [35] ) . Gradual changes in cortical architecture along two trends were described already several decades ago [68–72] ( reviewed in [29 , 38] ) . In brief , the two foci of least differentiated cortex are the allocortical three-layered archicortex ( hippocampus ) and paleocortex ( olfactory cortex ) . These cortices are surrounded by periallocortex , where additional layers can be discerned , but without the clear laminar organisation found in the isocortex . Proisocortex , the next stage of differentiation , has a definite laminar organisation , but is missing a well-developed layer 4 . Finally , there are different levels of isocortex with increasing demarcation of laminar boundaries and prominence of layer 4 . More recently , changes in cell cycle kinetics across the forming cortical sheet and genetic correlates of the neurogenetic gradients have been described [58 , 59 , 73–75] , which elucidate how gradual changes in cortical architecture are effected and provide an association between time of origin and architectonic differentiation . Particularly , a lengthening in the cell cycle along the planar neurogenetic gradient is accompanied by a successive increase in the proportion of progenitor cells differentiating into neurons with each cell cycle . In combination with the mentioned relation between time of origin and final laminar position of neurons , this mechanism results in a relatively increased number of supragranular layer neurons in later generated sections of the cortical sheet . Thus , a positive correlation can be observed between time of origin and neuron density across the cortex [67] . This link has been corroborated by findings in the human cortex , which directly traced systematic architectonic variation of the cortex to the timing of development [45] . A lengthening of the overall developmental time period , and with it the neurogenetic interval , appears to be responsible for increased neuron numbers both within the cortex of a given species , as well as across species which differ in their overall level of architectonic differentiation [66 , 67 , 76] . In fact , it has been suggested that cortical architecture correlates not only with neurogenetic time windows during ontogenesis , but also with the succession of architectural differentiation observed during brain evolution [60 , 71] . This finding suggests that phylogenetic age has a bearing on architectural gradients . As mentioned above , it has repeatedly been reported that areas at similar points in the architectonic differentiation spectrum , as well as within the two described trends of architectonic progression , are preferentially linked , even if they are dispersed throughout the brain ( also reviewed in [38] ) . The link to phylogeny , added to this correlation between architectonic progression and associated connectivity , thus , further points towards a developmental origin of the interrelations captured by the architectonic type principle . The third aspect of neurogenesis which we incorporated into our simulations is that axon outgrowth starts concurrently with , or immediately after , neuronal migration [74 , 77–80] , and appears to be largely unspecific spatially [81] . We , therefore , assumed that connection formation starts as soon as neurons were placed in the cortical sheet . Further assumptions derived from these observations were that axons grow randomly across the cortical sheet ( i . e . , with no particular spatial orientation ) and that they indiscriminately form connections once they are close enough to a potential target neuron , a mechanism that has been named Peters’s Rule [82 , 83] . Thus , the process of connection formation can be described as stochastic , and has been simulated in this way in previous computational models of connection development , such as [49] . This mechanism entails that the probability of a neuron forming a connection is only dependent on the probability of its axon finding a target neuron . Since neurons that are far apart are separated by a larger number of neurons that could accommodate the axon , the probability of connecting to a target neuron is lower , the larger the distance between two neurons is . In effect , there is a positive correlation between the spatial proximity and connection probability of different neurons . The spatiotemporal dynamics of corticogenesis that emerge from the combination of these empirically grounded assumptions were hypothesized to result in the establishment of realistic cortico-cortical connectivity . In particular , we expected interactions between the spatial and temporal aspects of neurogenesis to lead to the formation of connections which are consistent with the predictions of the architectonic type principle concerning the relationship between areas’ relative architectonic differentiation and connection frequencies . Our simulation experiments , thus , contribute the first systematic exploration of the neurodevelopmental mechanisms that have been hypothesized to underlie the ATP [27 , 29 , 35 , 40] . In summary , we implemented several aspects of neurogenesis in an in silico model of the growing mammalian cerebral cortex . These aspects were then modified in some variants of the model , so that they either corresponded to , or violated , empirically observed phenomena . This strategy allowed us to compare the cortico-cortical connectivity resulting from hypothetical variants that differed in their assumptions , where some of these assumptions were empirically grounded and others were not . The approach enabled us to assess the merits of mechanisms which have been proposed to link cortical structure and connectivity through the ATP .
We simulated the growth of cortico-cortical connections between areas of different neuron density according to a constant set of growth rules . We evaluated how closely the simulated connectivity corresponded to empirical observations made in mammalian connectomes when the physical substrate of the connections , that is , the simulated cortical sheet , developed along different spatiotemporal trajectories . To this end , we systematically varied the settings of our in silico model to construct a number of variants , which we refer to as spatiotemporal growth layouts . We considered five sets of growth layouts: ( 1: realistically oriented density gradient ) planar growth of the cortical sheet , such that cortical areas were added around neurogenetic origins , with new areas having an increasingly higher neuron density ( i . e . , neuron density increased with distance from a point of origin ) ; ( 2: inverse density gradient ) planar growth of the cortical sheet , such that cortical areas were added around neurogenetic origins , but with new areas having increasingly lower neuron density ( i . e . , neuron density decreased with distance from a point of origin ) ; ( 3: radial ) no planar growth of cortical areas on the fringes of the cortical sheet , but gradual addition of neurons at a constant rate across the cortical sheet , which resulted in an ordered gradient of area neuron density that was the same as in sets 1 and 4; ( 4: static ) no planar growth of cortical areas , but the same final gradient of area neuron density as in sets 1 and 3; ( 5: random ) planar growth of the cortical sheet , such that cortical areas were added around neurogenetic origins , but with no ordered gradient of area neuron density , instead neuron density varied randomly between locations on the cortical sheet . For all five sets , we implemented three growth modes: ( 1D 1row ) one-dimensional growth implemented with one row of areas; ( 1D 2rows ) one-dimensional growth implemented with two rows of areas; and ( 2D ) two-dimensional growth . For all five sets , all three growth modes were implemented with planar growth around two neurogenetic origins . For set 1 ( realistically oriented density gradient ) , we additionally implemented each growth mode with one neurogenetic origin as well as three ( 1D growth ) or four ( 2D growth ) neurogenetic origins . Thus , in total , we considered 21 growth layouts , grouped into five sets according to the spatiotemporal trajectory the cortical sheet traversed ( see Fig 2 and Table 1 for an overview ) . We first present some general statistics of the simulated connectivity and then go on to characterize how well the relationship between connectivity and the two factors of ( relative ) neuron density and spatial distance corresponded to previously published empirical observations for the different growth layouts . Finally , we assess how well the different growth layouts predicted empirical connectivity , as an indication of how realistic the simulated connectivity was for a given growth layout . Fig 3 provides an outline of this procedure . Table 2 gives an overview of all results . The cortico-cortical networks resulting from the simulations showed realistic levels of overall connectivity , with between 39% and 66% of possible connections present ( Fig 4A , Table 3 ) . Previously , between 50% and 77% of connections were reported to be present in the macaque and cat cortex [22 , 34 , 84] . Some 2D growth layouts reached higher levels of connectivity , with up to 87% of possible connections present . This connection density translated into several hundreds of inter-areal connections ( Fig 4B , Table 3 ) , with between 250 and 400 connections for growth mode 1D 1row and between 900 and 1500 connections for growth mode 1D 2rows . Due to the large number of areas , connection numbers were much higher for 2D growth layouts , between 8000 and 18600 . We first checked how well the simulated networks corresponded to the empirical observations that a larger fraction of connections is present between regions that are more similar in neuronal density , as suggested by the architectonic type principle , and spatially closer to each other . To this end , we computed the relative frequency of present connections ( Fig 5 , Table 3 ) . We then examined how well both factors , absolute density difference and distance , enabled the reconstruction of the simulated networks using logistic regression . Specifically , we assessed these relations by computing McFadden’s Pseudo R2 statistic , which provides a measure of the increase in the model log-likelihood with inclusion of either or both factors compared to a null model ( Fig 6 , Table 3 ) . Another property of the simulated networks that we compared to empirical observations was area degree ( i . e . , the number of connections per area ) . We previously reported that , in biological cortical networks , the number of connections maintained by an area is negatively correlated with the area’s cytoarchitectonic differentiation [34 , 36] . Here , we show an analogous analysis for the simulated networks ( Fig 7 , Table 3 , S3 Fig ) . For random , static and radial growth layouts , area degree was not significantly correlated with neuron density , with the exception of 2D growth layouts , which showed a positive and significant correlation in each case . Growth layouts with realistically oriented density gradients showed a strongly negative , significant correlation between area degree and neuron density , with median correlation coefficients between -0 . 42 and -0 . 79 for both 1D growth modes . Conversely , for growth layouts with an inverse density gradient , area degree was strongly positively correlated with neuron density . For 2D growth along a realistically oriented density gradient , the observed effect was more variable . Correlation coefficients were of weak to moderate magnitude , and the correlation was not significant for 2D growth around one origin ( 2D 1origin: median ρ = 0 . 03 , median p > 0 . 05; 2D 2origins: median ρ = 0 . 17 , median p < 0 . 05; 2D 4origins: median ρ = 0 . 34 , median p < 0 . 05 ) . This observation was in contrast to the strongly positive and significant correlations observed for the 2D growth layouts without oriented growth , where median correlation coefficients were larger than 0 . 50 ( random 2D: median ρ = 0 . 54; static 2D: median ρ = 0 . 62; radial 2D: median ρ = 0 . 59 ) . We , therefore , concluded that the effect of oriented growth along a realistically oriented density gradient on area degree , as observed for both 1D growth modes , persisted in the 2D growth mode , but that it was not strong enough to completely abolish the tendency for a positive correlation between area degree and neuron density inherent to the 2D growth layouts , instead only diminishing the positive correlation . In summary , the empirically observed negative correlation between area degree and neuron density was only reproduced for the growth layouts with a realistically oriented density gradient . We cannot rule out that there existed a minor contribution of geometric centrality to this relationship . However , taking into account the results for the radial and static growth layouts made clear that such an effect , if there was any in the realistically oriented gradient growth layouts , could only be secondary . Without expansive , planar growth , there is no temporal advantage helping earlier-formed areas to accrue more connections . Any negative correlation between neuron density and area degree would , thus , be caused by geometric centrality . Fig 7 illustrates that no such correlation arises for the radial and static growth layouts , where instead area degree appears to vary randomly with neuron density . In the previous sections , we showed that empirically observed regularities , particularly a close relationship between connection existence and the two factors of relative neuron density and spatial distance , could be reproduced in silico . We further characterized how well the simulation captured this phenomenon by predicting empirical connectivity using classifiers trained on the simulated networks . Classification performance was used as a measure of how well the properties of the artificially generated networks reflected the characteristics of empirical brain networks , in particular , the macaque and cat cortical connectomes . We report two measures of classification performance , accuracy and the Youden index , J . Accuracy was calculated as the percentage of predictions that were correct , while the Youden index is a summary measure that takes into account both the rate of true positives and the rate of true negatives and indicates divergence from chance performance . As seen from Figs 8 and 9 , classification performance was generally higher for the macaque connectome than for the cat connectome . However , the described differences between growth layouts held for both species . We also provide the fraction of the available empirical connections that were classified in each species ( Fig 10 , Table 4 ) . Generally , between 30% and 60% of the empirical connections were classified , with some growth layouts reaching up to 86% ( Fig 10 ) . However , for some growth layouts , nearly no empirical connections reached posterior probabilities of at least 0 . 75 ( the minimal threshold applied for assigning a predicted label ) , and , thus , very low fractions of the available empirical connections were classified . Specifically , this applied to random growth layouts ( median fraction classified between <0 . 01 and 0 . 14 ) and the inverse 2D growth layout ( median fraction classified macaque: 0 . 08 , cat: 0 . 05 ) . The overall low posterior probabilities for these growth layouts and the resulting small fraction of classified empirical connections already suggested that the properties of those layouts did not correspond well to the properties of the empirical networks . This impression was corroborated by the classification performance measures ( see below ) .
We considered different spatiotemporal trajectories of how neurons populated the simulated cortical sheet . To recapitulate , simulated histogenesis proceeded according to five different sets of growth rules , with three to nine specific implementations per set ( a total of 21 different growth layouts ) . These five sets were ( 1: realistically oriented density gradient ) planar , expansive growth of the cortical sheet , with newer areas having successively higher neuron density; ( 2: inverse gradient ) planar , expansive growth of the cortical sheet , with newer areas having successively lower neuron density; ( 3: radial ) instead of planar growth , neurons started to populate all areas simultaneously and were added at a constant rate across the whole cortical sheet until each area reached its predetermined complement of neurons , with a final neuron density gradient identical to sets 1 and 4; ( 4: static ) all neurons of the cortical sheet formed simultaneously , with a neuron density gradient identical to the final gradient of sets 1 and 3; ( 5: random ) planar , expansive growth of the cortical sheet , with no ordered gradient of area neuron density around the two origins . To exclude effects specific to any particular implementation of these sets of growth rules , we considered three growth modes for each set: one-dimensional growth with one row of areas , one-dimensional growth with two rows of areas , and two-dimensional growth . For set 1 , with a realistically oriented density gradient , we considered growth around one origin and three or four origins ( for one-dimensional and two-dimensional growth modes , respectively ) additionally to the growth around two origins that was used in all five sets . These distinct spatiotemporal trajectories of cortical sheet growth led to considerable differences in the properties of the generated networks of structural connections . See Table 2 for an overall assessment of the results . While all growth layouts exhibited a clear decline in the relative frequency of present projections across larger distances , this measure correlated with absolute density difference only for a subset of growth layouts ( Fig 5 ) . Particularly , there was no consistent relationship for the random , static and radial growth layouts , while for oriented growth , both along a realistically oriented density gradient and along an inverse gradient , the relative frequency of present connections decreased with larger absolute density differences between areas . A more precise assessment of the extent to which distance and density difference determined connection existence was obtained by predicting simulated connectivity using binary logistic regression . Here , a similar picture as for relative connection frequency emerged from comparing McFadden’s Pseudo R2 values across growth layouts ( Fig 6 ) . Distance was a better-than-chance predictor of connection existence for most growth layouts , as shown by the performance increase compared to a constant-only null model that is measured by McFadden’s Pseudo R2 . In contrast , inclusion of absolute density difference increased prediction performance only for the layouts with oriented growth ( both along realistically oriented and inverse density gradients ) , but not for the random , static or radial growth layouts . Finally , the growth layouts differed in whether neuron density correlated with area degree ( Fig 7 ) . As before , for random , static and radial growth layouts , there was no consistent effect of neuron density on the measure of interest , in this case area degree . In contrast , there was a significant correlation with neuron density for layouts with oriented growth . This correlation was negative , as observed empirically , for growth layouts with a realistically oriented density gradient , but positive for growth layouts with an inverse density gradient . In combination , these results demonstrate that the relation between neuron density , which is one crucial feature of the physical substrate in which connections are embedded , and cortico-cortical connections is strongly influenced by the precise spatiotemporal trajectory of cortex growth , which coincides with the time of connection formation . By manipulating where and when areas of varying neuron density were generated , we could observe a change in the extent to which connections of the simulated network were accounted for by the two factors of spatial proximity on the fully formed cortical sheet and the relative neuron density , indicating relative architectonic differentiation of areas . As described above , the extent to which spatial proximity and relative neuron density determined simulated connectivity strongly depended on the specific spatiotemporal trajectory of the simulated growth of the cortical sheet . Growth layouts that more closely mirrored the biological developmental trajectory of the mammalian cortical sheet led to closer correspondence of the simulation results with empirical observations on adult connectivity . This finding became particularly apparent when we predicted empirical connectivity in two different mammalian species , cat and macaque , from regularities that were extracted from the simulated connectivity generated by the different growth layouts . Applying the regularities that emerged in our simulations to empirical data afforded a strong test of whether the simulations adequately captured ontogenetic processes and produced realistic networks . Our results showed that both of the aspects that were manipulated across growth layouts , the temporal trajectory of area growth as well as the direction of the neuron density gradient , were relevant for how well simulated connectivity allowed to predict empirical connectivity ( Figs 8 and 9 ) . First , it could be observed that growth layouts in which areas appeared successively around origins of neurogenesis ( i . e . , the realistically oriented density gradient growth layouts ) , were much better able to predict empirical connectivity than growth layouts with the same final neuron density gradient , but without the observed link between time of origin and neuron density ( i . e . , static and radial growth layouts ) . Second , in the presence of planar growth around origins , the direction of the neuron density gradient was crucial . This finding was indicated by the large decrease in prediction performance when comparing the realistically oriented gradient growth layouts with the random and inverse density gradient layouts . These sets of growth layouts both followed the same time course of cortical sheet expansion as the realistically oriented gradient , but with no relationship between time of origin and neuron density or a negative correlation between time of origin and neuron density , which contradicts the empirically observed positive correlation of time of origin with neuron density . Hence , the extent to which neuron density is well suited as a predictor of connectivity could be due to it reflecting neurodevelopmental time windows . Third , our analyses revealed that the number of neurogenetic origins , around which new areas grew , influenced the correspondence to empirical connectivity ( Tables 5 and 6 ) . Growth around two origins arguably led to the best prediction performance: it was superior to growth around one origin for both accuracy and Youden index , and performed better than growth around three or four origins in terms of accuracy . For the Youden index , this performance difference was present , but too small to be meaningful or statistically significant . Thus , while correspondence between simulated and empirical connectivity clearly increased with the addition of a second origin of neurogenesis , there was at the very least no further performance increase with the addition of a third or fourth origin . Fourth , we observed that the overall level of prediction performance for the realistically oriented density gradient growth layouts was quite high , indicating that they afforded a good correspondence with empirical connectivity not only relative to the other growth layouts , but also in absolute terms . Therefore , a dual origin of neurogenesis and the resulting cytoarchitectonic gradients arguably are necessary components of the developmental mechanism for the empirically observed relations to hold ( Fig 11 ) . These findings stress the importance of the theory of the dual origin of the cerebral cortex [38 , 71] and the presence of multiple gradients of neurogenesis [57 , 85] , for the configuration of connectivity in the adult cortex . Collectively , the presented results suggest that planar cortical sheet growth around two origins of neurogenesis and a systematic increase in neuron density with later time of origin are crucial determinants of the development of realistic cortico-cortical structural connections . Conversely , assuming that connection formation is a stochastic process with few constraints , as simulated here , the assumptions underlying the spatiotemporal growth trajectories of the random , static , radial and inverse growth layouts were shown not to mirror actual principles of cortical organisation . With the postulation of the architectonic type principle it was suggested that a close relationship between cortico-cortical connections and architectonic differentiation of the cortex might arise from the timing of neurogenesis [27] , a process that occurs in close temporal proximity to the formation of connections . Specifically , it has been hypothesized that the relative time of generation of areas of different neuron densities affords them with different opportunities to connect with each other , thus imposing constraints on stochastically formed connections [29 , 35] . This mechanism would be in line with findings in Caenorhabditis elegans [86] and rat cortex [47] . Moreover , a previous computational study demonstrated that topological features , such as modular connectivity , may arise from the growth of connectivity within developmental time windows [87] . Thus , the main premise of this study , that spatiotemporal interactions in the forming cortex determine connectivity , has long been under consideration . Here , we provide the first systematic exploration of the possible mechanistic underpinnings of the ATP . We simulated multiple combinations of spatiotemporal growth trajectories of the cortical sheet and neuron density gradients , to probe from which of the combinations realistic connectivity emerged . Our results showed that , indeed , of the wide variety of examined spatiotemporal growth trajectories , the variant of the in silico model that led to the best correspondence with empirical observations was the one that was based on the same assumptions as the mechanism proposed to underlie the realization of the ATP . Hence , the underlying assumption that differences in neuronal density correspond to distinct time windows was not refuted in the model , and neuron density carried predictive power with respect to connectivity features only if such a relation between density and neurogenetic timing held . Our systematic simulation experiments , thus , distinctly corroborate the previously hypothesized mechanistic underpinnings of the ATP and contribute a conceptualization that can be scrutinized for similarities with , and distinctions from , actual ontogenetic processes . This approach opens up the possibility of characterizing in more detail how correlations between the structure of the cortex and cortical connections emerge , because all aspects of the process are observable . Further refinement of the simulation , for example by introducing species-specific histogenetic time courses , will enable the exploration of species differences or potentially the demonstration of invariance to changes in some aspects of ontogenesis . Another factor that could be probed is how robust the emergence of realistic connectivity is against changes in absolute neuron density , which varies considerably across species [66 , 88] . From our simulations , it appears that temporal proximity of areas during neurogenesis underlies the positive relationship between similar neuron density and high connection probability . The close correlation between time of origin and architectonic differentiation described empirically ( see Introduction ) leads to a derivative correlation between temporal proximity of neurogenetic time windows and relative differentiation of cortical areas . Independent of this correlation , on a cortical sheet that expands around the origins of neurogenesis , areas with closer neurogenetic time windows tend to be spatially closer as well . Assuming that connection formation is a stochastic process , which implies that connection probability declines with spatial distance , this process leads to a higher connection probability between areas that are generated during nearby time windows . Temporal proximity during neurogenesis would , thus , be the common antecedent determining both relative architectonic differentiation and connection probability , while those two factors would only be indirectly related . Temporal proximity , however , is difficult to measure , and it is , therefore , no surprise that the correlation between its two direct consequences has been empirically observed first . This chain of reasoning reveals how our modulation of the relationship between temporal proximity during neurogenesis and relative architectonic differentiation in the considered growth layouts could have caused the vastly different outcomes in connectivity described here . In our simulations , we observed a relationship between the spatial proximity of areas and their likelihood to be connected , which appears to be an epiphenomenon of stochastic connection growth within a physically embedded system ( c . f . [49 , 89] ) . Distance is an inherent property of a spatially embedded system that cannot be removed from the implementation of spatial growth . However , in our simulation of cortical growth , the final distance between areas was not always an accurate measure of their distance during the time period of connection formation , which would be the factor that mattered principally for determining the likelihood by which two areas became connected . Since this distance during cortical sheet growth is correlated with the areas’ final distance , there was also a correlation between final spatial proximity and connection probability . But this correlation does not genuinely describe the dependency of the stochastic growth process on distance , because inter-areal distance was not static , as implied by this measure . The distance measure relevant here , namely distance at the time of connection formation , would be challenging to measure empirically . Therefore , relying on measures of final , adult distance and assuming a strong correlation between the two distance measures appears as a pragmatic strategy for empirical analyses . The present simulation experiments were designed to allow for the analysis of connection existence , that means , whether a possible connection between a pair of areas is present or absent in the final network . Naturally , axonal connections have many further properties beyond their simple existence; one prominent feature being the laminar distribution of the projection neurons’ somata and axon terminals in the areas of origin and termination , respectively . Laminar patterns of projection origin and termination are very well explained by the architectonic type principle ( reviewed in [28 , 29] ) , as has been demonstrated extensively in different species and cortical systems [27 , 30–33 , 35 , 36 , 39 , 41 , 42] . These conspicuous regularities most likely arise from fundamental developmental mechanisms , since they are ubiquitous and quite robust . This aspect becomes strikingly apparent in reeler mutant mice , where laminar connectivity patterns are largely correct [90–92] ( shortly reviewed in [74] ) , despite a systematic inversion ( to ‘inside-out’ ) of neurons’ final laminar positions relative to the regular order that neurons typically assume according to their time of origin ( ‘outside-in’ ) [53 , 90 , 93–96] . However , the precise mechanisms through which laminar projection patterns become established are still under investigation . Further simulation experiments could , therefore , be helpful in evaluating potential candidate mechanisms . Expanding the simulation of cortical sheet growth to take into account the radial distribution of neurons across layers , as would be required for assessing laminar projection patterns , will afford the introduction of spatially and temporally more fine-grained features of neurogenesis and final architectonic differentiation . In addition to the planar gradient in neurogenetic time windows , which was taken into account in the present simulation experiments , this could mean to include the radial gradient in neurogenetic time windows that characterises neurons populating different layers [50 , 51 , 53 , 62 , 63] . Beyond the density of neurons in any given area , as considered here , structural variation could include a number of cellular morphological measures which have been shown to change systematically with overall density ( e . g . , [25] ) . In the human brain , cellular morphological measures have also been shown to associate with how densely connected a cortical area is , both in the healthy brain and in the context of brain disorders [97 , 98] . Another feature that could prove to be relevant for the establishment of laminar projection patterns is the relative neuron density of cortical layers . As overall neuron density increases across the cortex , layer 2/3 becomes successively more pronounced [38 , 99] and neuron density increases more in the supragranular layers than in the infragranular layers [67] . Thus , there is a shift in when , and in which layers , the majority of neurons is generated for areas of different overall density , which could affect laminar patterns , especially in interaction with the sequential growth of areas across the cortical sheet . Further , there is systematic variation in the size of pyramidal cell somata across the cortex , a phenomenon termed externopyramidization [71 , 100] . Specifically , the ratio of soma size in supragranular to infragranular layers is larger in areas of strong architectonic differentiation than in weakly differentiated areas ( i . e . , in weakly differentiated areas , infragranular pyramidal cells tend to be larger than supragranular cells , while the reverse is true for strongly differentiated areas ) . Hence , it seems that the laminar position of projection origins is aligned with relatively larger cell size in the candidate population for cortico-cortical connections , pyramidal cells . Since maintenance of long-distance connections between cortical areas is metabolically expensive [101 , 102] , relatively larger cell size conceivably is advantageous for their maintenance . Thus , as hypothesized before [43 , 103] , externopyramidization might be linked to a shift in projection origins . In addition to the above-mentioned properties of the cortical sheet itself , there are potential modifications of the stochastic formation of connections to be considered . First , the pruning of connections during later stages of development [104] was not taken into account in the present simulations . Laminar projection patterns may conceivably be affected by selective elimination of some axon branches but not others [105 , 106] . Moreover , it has been observed that the time course of connection formation is not the same for all types of cells . Callosal projection neurons can reach their target areas without actually invading the gray matter , instead remaining in the white matter for a waiting period of days [107–109] . Similarly , waiting periods below the gray matter have been described for infragranular neurons projecting to area V4 from multiple areas in the ipisilateral hemisphere in macaques [110] . In contrast , supragranular neurons in the same tract-tracing experiments were found to invade the gray matter early , but many of them formed only transient projections that were subsequently eliminated . More generally , these and similar tract-tracing experiments have been interpreted to demonstrate different developmental profiles for axon outgrowth and connection formation in infra- and supragranular neurons [110–113] . In ‘feedback’ pathways , which according to the ATP can be conceptualised as projecting towards a relatively more differentiated area , extensive remodelling of laminar projection patterns until long after birth has been observed in a number of species ( mouse , cat , macaque , human ) and target areas [110–120] . This remodelling has been linked to activity-dependent maturation of pathways and the emergence of more refined perceptual capabilities [111 , 120 , 121] ( for example , reviewed in [122 , 123] ) . This observation suggests that not all factors contributing to adult laminar projection patterns may be accessible in simulation experiments with time frames that are restricted to corticogenesis and initial axon outgrowth . A further potential determining factor in the establishment of laminar projection patterns that warrants exploration is the possibility of genetic specification . The laminar position of projection targets might be regulated by genetically encoded factors . Numerous layer-specific transcription factors and neurotrophins have been described , which afford a precise targeting of specific layers or even cell types and cellular compartments ( reviewed , e . g . , in [124 , 125] ) . Co-culture experiments using cortical explants have shown that appropriate laminar position of axon terminals was retained outside of the ontogenetic growth environment , that is , in the absence of regular temporal and spatial relationships . Accurate laminar specificity has been demonstrated , for example , for thalamo-cortical , geniculo-cortical , and cortico-spinal connections in co-culture ( e . g . , reviewed in [124] ) . Similarly , connections formed in co-culture of rat visual cortex explants were shown to conform to organotypic laminar distributions [126 , 127] . Castellani and Bolz [128] elegantly demonstrated that organotypic and cell type specific projection patterns could be induced by membrane-associated factors through both induction and prevention of axon ingrowth and branching . Moreover , it has been shown that transcription factors can have population-specific effects , enlarging the range of potential interactions . For example , Castellani and colleagues [129] found that the membrane-bound protein Ephrin-A5 functioned as a repulsive axonal guidance signal in neurons destined to migrate to layer 2/3 , while acting as a ‘branch-promoting’ signal in neurons destined for layer 6 . These observations suggest that laminar patterns of projection terminations may not be entirely explicable by spatiotemporal interactions in the forming tissue , but are regulated by more prescriptive determinants . Our results illustrate how a mechanism linking the temporal order of neurogenesis across the cortex with the architectonic differentiation of areas could come to shape cortico-cortical connectivity such that it resembles the empirically observed connectivity of mammalian connectomes . However , simulation experiments , as performed here , can only assess whether a suggested mechanism is principally feasible , and explore what its essential components might be . That is , such computational experiments put a candidate mechanism to the test and allow drawing some inferences about possible ( and , importantly , impossible ) ingredients , but they do not establish biological facts by themselves . Ultimately , only empirical observation of the ontogenesis of the cortex can establish how this developmental process unfolds . The possibility cannot be excluded that there may exist an unrelated mechanism working through features not considered here , which could cause the phenotype of interest , in our case the close relation between architectonic differentiation and connectivity . Generally , incorporating more empirical anchor points in a model gives the conclusions of a simulation study more significance . To triangulate a likely solution to the developmental puzzle of how axonal connections are organized , it is necessary to constrain potential mechanisms by as many observable features as possible . As discussed above , more processes that shape connectivity could be included in our in silico model of neural development , such as waiting periods for connection formation , a differential ability of cortical layers to retain connections ( possibly linked to externopyramidization ) , the pruning of established connections , or the action of signalling molecules in attracting and repelling axons during connection formation . By integrating such processes , new insights could be gained into the emergence of further connection features such as laminar projection patterns and projection strengths . We constrained our in silico model to represent a single cerebral hemisphere , hence our results only apply to ipsilateral , intra-hemispheric connections . Contralateral , inter-hemispheric axonal connections have also been reported to be well represented by the architectonic type principle [31 , 37] , although at generally lower connection strengths . The in silico model could be expanded by a second hemisphere which develops simultaneously . Since similar types of cortex in the two hemispheres would be formed at nearby points in time , but further apart in space , this setup would be expected to lead to the observed pattern of ATP-consistent , but weaker connectivity , if the principle holds that spatiotemporal interactions govern connectivity patterns . We modelled the developing cortex as a two-dimensional sheet , across which axons grew until they met a target soma and formed a connection . In reality , the mammalian cortical sheet is not flat , but becomes at least curved , and often intricately folded , during corticogenesis . Moreover , axons are not positioned exclusively within the grey matter , but instead cover large distances through the white matter . These shortcuts between distant points on the cortical sheet imply that representing projection length as Euclidean distance between points on a flat cortical sheet is not accurate . Yet , regardless of how the concurrent processes of neurogenesis , axon formation and cortical folding affect each other [130 , 131] , measuring the precise lengths of projections in the adult cortex has so far not been straightforward . Hence , approximate measures have been employed , such as border distance on a cortical parcellation , Euclidean distance in three-dimensional space , or geodesic distance which accounts for some of projections’ confinement to white matter tracts . Euclidean distance on the simulated two-dimensional cortical sheet may , therefore , be a suitable surrogate measure for these approximate empirical measures . In line with this assumption , if cortical folding had a strong impact on our prediction of empirical data , it would be expected that performance in the less folded cat cortex would be better than in the more strongly folded macaque cortex . As this was not the case , we suspect that cortical folding and the resulting changes in projection lengths do not dramatically alter the spatiotemporal interactions which we hypothesize link architectonic differentiation and cortical connectivity . To further test this expectation , it would be interesting to predict connectivity data from a wider range of species , such as lissencephalic rodents and humans , whose cortex is even more strongly folded than the macaque cortex . Lastly , applying the classifier which was trained on simulated network data to predict empirical connectivity data resulted in better prediction performance for the macaque cortex than the cat cortex . Ultimately , there might be two reasons for this finding: Either the architectonic type principle characterises connectivity better in one of these species than the other , or the empirical measures that were used more faithfully capture the true structure in one of the species . Conceivably , adherence to the ATP might not be as pronounced in the smaller cat cortex , where both distances are shorter and therefore less distinctive , and there is less variation in total neuron number within the cortex due to a shortened neurogenetic interval [67] . Regarding the second possible reason , the structural measures from which we predicted connectivity were more detailed in the macaque cortex ( neuron density and Euclidean distance ) than in the cat cortex ( structural type and border distance ) . Further experiments are therefore required to distinguish between these two explanations . Indeed , it would be intriguing to expand the prediction of empirical connectivity data from simulated networks to other species , preferably to mammals whose cortex is on either side of cat and macaque on the scales of size and degree of architectonic differentiation . Just as for assessing the impact of cortical folding , rodents and humans would be good candidates to identify the source of the observed difference in prediction performance . We performed simulations of cortical sheet growth and the concurrent formation of cortico-cortical connections , systematically varying the spatiotemporal trajectory of neurogenesis as well as the relation between architectonic differentiation and time of origin of neural populations . Our results showed that , for realistic assumptions about neurogenesis , successive tissue growth and stochastic connection formation interacted to produce realistic cortico-cortical connectivity . This finding illustrated the fact that precise targeting of interareal connection terminations was not necessary for obtaining a realistic replication of connection existence within a cortical hemisphere . Instead , spatiotemporal interactions within the structural substrate were sufficient if a small number of empirically well-grounded assumptions were met , namely ( i ) planar , expansive growth of the cortical sheet as neurogenesis progressed , ( ii ) stronger architectonic differentiation for later neurogenetic time windows , and ( iii ) stochastic connection formation . We , thus , demonstrated a possible mechanism of how relative architectonic differentiation and connectivity become linked during development . These findings support hypotheses advanced in previous reports about the mechanistic underpinnings of the architectonic type principle [27 , 29 , 35 , 40] . The successful prediction of connectivity in two species , cat and macaque , from our simulated cortico-cortical connection networks further underscores the generality of the ATP and the wide applicability of its explanation of connectivity in terms of relative architectonic differentiation .
The generation of the cortical sheet across time was simulated in a number of different settings of the in silico model , here called variants or growth layouts . These growth layouts systematically differed in where and when neurons were generated on the forming cortical sheet , that is , they had different spatiotemporal growth trajectories . Below , we describe all growth layouts and their correspondence to neurodevelopmental findings in detail . An overview is provided in Table 1 , and Fig 2 as well as S1 Fig give a visualisation of cortical sheet development over time for the different growth layouts . All considered spatiotemporal growth trajectories were grouped into five sets of growth layouts . These sets differed with respect to whether cortical areas were generated by planar , expansive growth , whether there was radial growth , and in the final gradient of neuron density around neurogenetic origins . In growth layouts with planar growth , the cortical sheet expanded , as , with each growth event , new cortical areas emerged around neurogenetic origins . Each new cortical area was grown within one time step , thus all constituent neurons appeared on the cortical sheet simultaneously . Neurogenesis occurred on the outer fringes of the portion of the cortical sheet already generated around each origin of neurogenesis . For more than one neurogenetic origin , this process entailed that newly generated areas moved previously generated areas apart on the cortical sheet , increasing the spatial distance in between them . Thus , planar growth mimicked the empirically observed planar gradient in onset of neurogenesis ( see Introduction ) . Radial growth , in contrast , did not expand the cortical sheet over time . Here , the cortical sheet had its final dimension already at the start of corticogenesis and cortical areas did not differ with respect to the time of onset of neurogenesis , but instead in the length of their neurogenetic interval . During each growth event , neurons were added at a constant rate across the entire cortical sheet . Areas with lower neuron density finished generating their complement of neurons earlier in time than areas with a higher neuron density , which needed to generate a larger number of neurons . Radial growth thus reproduced an alternative interpretation of studies of neurogenetic timing ( see Introduction ) . Growth events , during which the cortical sheet was generated , were distributed across the fixed simulated length of time . For both planar and radial growth , they were timed in such a manner that all neurons had grown after one third of the simulation length , and the remaining time steps could be used for connection formation by all neurons . These three main properties of spatiotemporal growth of the cortical sheet were combined in the five sets of growth layouts , with each set containing three ( or in one case nine ) growth layouts , as follows: The first set , the realistically oriented density gradient growth layouts , grew by planar growth . Here , newly generated areas were of higher neuron density than previously grown areas . That is , there was a positive correlation between time of origin and neuron density , which appeared as a distinct gradient in neuron density around the neurogenetic origins on the final cortical sheet . The second set , the inverse neuron density gradient growth layouts , grew by planar growth like sets 1 and 5 . However , in these inverse gradient growth layouts , newly generated areas were of lower neuron density than previously grown areas , that is , there was a negative correlation between time of origin and neuron density . The third set , the radial growth layouts , grew by radial growth . The final density gradient was identical to sets 1 and 4 , but for the radial growth layouts , this pattern was caused by a positive correlation between length of the neurogenetic interval and neuron density , instead of a correlation between the time of onset of neurogenesis and neuron density . The fourth set , static growth layouts , did not in fact grow at all . All neurons were grown during the first growth event , thus the cortical sheet was fully formed from the beginning of the simulation . The final density gradient was identical to sets 1 and 3 . Finally , in the fifth set , the random growth layouts , the cortical sheet grew by planar growth . The resulting final cortical sheet had no directed gradient of neuron density around the neurogenetic origins . Instead , each newly generated area was randomly assigned a neuron density . Possible density values were drawn from the neuron densities found on the final cortical sheet of the first set , realistically oriented neuron density gradient . For each of these five sets , we implemented three different growth modes to mitigate influences of any specific choice of spatial implementation . Each growth mode was implemented around two neurogenetic origins . The three growth modes were as follows: First , one-dimensional growth with one row of areas ( 1D 1row growth layouts ) , where new areas grew to the left and right of neurogenetic origins ( i . e . , along the x-dimension of the cortical sheet ) and there was only one row of cortical areas . Second , we implemented one-dimensional growth with two rows of areas ( 1D 2rows growth layouts ) , where , again , areas were added to the left and right of neurogenetic origins , but there were two rows of areas stacked in the y-dimension of the cortical sheet . Third , we implemented two-dimensional growth ( 2D growth layouts ) , where new areas were added on all sides of neurogenetic origins ( i . e . , in both the x- and y-direction of the cortical sheet ) . In this growth mode , each successive growth event led to an exponentially increasing number of added areas , and for set 1 , realistically oriented density gradient , an unproportionally high number of areas of the highest neuron density , which did not accurately reflect the composition of the mammalian cerebral cortex . However , as stated above , we simulated the different growth modes to alleviate side-effects that might unintentionally arise from any particular spatial layout . Considering results across these specific implementations vastly reduced the risk of misinterpretation . We therefore included the two-dimensional growth mode despite its unrealistic rendering of the cortical sheet as a further control . As mentioned before , each of the 15 growth layouts that were described so far was implemented around two origins of neurogenesis ( 5 sets x 3 growth modes x 1 number of origins ) . For set 1 , realistically oriented neuron density gradient , we additionally considered two different numbers of origins for each growth mode . Specifically , we included growth around one neurogenetic origin and growth around three or four neurogenetic origins for 1D and 2D growth modes , respectively . These further six growth layouts allowed us to test whether the exact number of neurogenetic origins meaningfully influenced final connectivity . Thus , we considered a total of 21 growth layouts ( 5 sets x 3 growth modes x 1 number of origins + 1 set x 3 growth modes x 2 numbers of origins ) . We simulated 100 instances of the spatiotemporal development of each of these 21 growth layouts . Axons randomly grew across the cortical sheet and stochastically formed synaptic connections ( similar to , e . g . , [49]; also see [46] ) . Each neuron was assigned one axon terminal , which was initially located at the respective soma position . With each time step of the simulation , the axon extended by a fixed length at a random angle , and the position of the axon terminal changed accordingly . Once axon terminals left the cortical area their parent soma was located in , they were free to form a synapse with any neuron soma they encountered . Since both terminals and somata were defined by point-coordinates , a synapse was formed once the axon terminal approached a soma closer than a defined maximal distance . Upon synaptic contact , an axon stopped growing and the now occupied axon terminal remained at the location of the contacted soma for the remainder of time steps . To further increase stochasticity , we imposed a connection probability of 90% on potential synaptic contacts . Thus , in 90% of cases , a synapse successfully formed once the terminal was close enough to a soma , but in a randomly chosen 10% of cases , no synapse formed at this time step and the axon continued to grow . If soma positions changed because the cortical sheet grew , axon terminals ( both occupied and unoccupied ) were shifted with the cortical area they found themselves in at the time , and synaptic contacts were retained . This procedure of axon growth and synapse formation was not modified across variants of the in silico model . Different parameters of the axon growth process interacted to determine how fast axon terminals made synaptic contacts . This included for example the increase in axon length per time step and the maximal distance for synapse formation . In pilot runs of the simulation , we calibrated the relevant parameters such that after the fixed simulated length of time , most axon terminals ( >99 . 9% ) had made synaptic contact and final interareal connectivity fell into a range comparable to empirical reports [22 , 34 , 84] . This calibration resulted in slightly different parameter values for 1D and 2D growth modes , but the same values were used in all simulation instances within these growth modes . From the final state of the simulated cortical sheet , we extracted a number of features that were analogous to measures used in previous analyses of the mammalian cortex . First , we collapsed the axonal connections between individual neurons into a simulated connectome , which contained information about the existence of all possible area-wise connections . Thus , we constructed a complete binary connectivity matrix where connections were coded as either absent or present . Second , we extracted the two relevant structural measures from the final cortical sheet . The first measure was each area’s neuron density , and derived from that the difference in neuron density between area pairs , where density difference = densityarea of origin − densityarea of termination . For most analyses , we considered the undirected equivalent , the absolute value of density difference , which indicates the magnitude of the difference in neuron density between two areas . These two measures were equivalent to measures of architectonic differentiation previously used in studies examining mammalian cortical connectivity , such as neuron density difference ( e . g . , [35] ) , the log-ratio of neuron densities [36] , or difference in cortical type , which is an ordinal measure of architectonic differentiation ( e . g . , [33–35] ) . The second measure was the spatial proximity between pairs of areas , which we calculated as the Euclidean distance between areas’ centres of mass . This measure was equivalent to measures of spatial proximity we used in previous empirical studies ( e . g . , [35 , 36] ) . Since distance is an undirected measure , each analysis that included distance required the use of the undirected measure of neuron density difference , its absolute value . We performed the described analyses for each of the 100 instances that were simulated for each growth layout and aggregated results across instances . For the simulations and analyses we used Matlab R2016a ( The MathWorks , Inc . , Natick , MA , USA ) . | The mechanisms that govern the establishment of cortico-cortical connections during the development of the mammalian brain are poorly understood . In computational simulation experiments reported here , we explored the foundations of an architectonic type principle , which attributes adult cortical connectivity to the differences in architectonic differentiation between cortical areas . Architectonic differentiation refers , among other characteristics , to the cellular composition of cortical areas . This architectonic type principle has been found to account for diverse properties of cortical connectivity across mammalian species . Our in silico model generated connectivity patterns that were consistent with the architectonic type principle , as they are typically observed in mammalian cortices , if model settings were chosen such that they corresponded to empirical observations of cortical development . Our computational experiments systematically evaluated previously proposed mechanisms of cortical development and showed that connectivity consistent with the architectonic type principle arose only from realistic assumptions about the growth of the cortical sheet . | [
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... | 2018 | Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle |
All materials enter or exit the cell nucleus through nuclear pore complexes ( NPCs ) , efficient transport devices that combine high selectivity and throughput . NPC-associated proteins containing phenylalanine–glycine repeats ( FG nups ) have large , flexible , unstructured proteinaceous regions , and line the NPC . A central feature of NPC-mediated transport is the binding of cargo-carrying soluble transport factors to the unstructured regions of FG nups . Here , we model the dynamics of nucleocytoplasmic transport as diffusion in an effective potential resulting from the interaction of the transport factors with the flexible FG nups , using a minimal number of assumptions consistent with the most well-established structural and functional properties of NPC transport . We discuss how specific binding of transport factors to the FG nups facilitates transport , and how this binding and competition between transport factors and other macromolecules for binding sites and space inside the NPC accounts for the high selectivity of transport . We also account for why transport is relatively insensitive to changes in the number and distribution of FG nups in the NPC , providing an explanation for recent experiments where up to half the total mass of the FG nups has been deleted without abolishing transport . Our results suggest strategies for the creation of artificial nanomolecular sorting devices .
The contents of the eukaryotic nucleus are separated from the cytoplasm by the nuclear envelope . Nuclear pore complexes ( NPCs ) are large protein assemblies embedded in the nuclear envelope and are the sole means by which materials exchange across it . Water , ions , small macromolecules ( <40 kDa ) [1] , and small neutral particles ( diameter <5 nm ) can diffuse unaided across the NPC [2] , while larger macromolecules ( and even many small macromolecules ) will generally only be transported efficiently if they display a particular transport signal sequence , such as a nuclear localization signal ( NLS ) or nuclear export signal ( NES ) . Macromolecular cargoes carrying these signal sequences bind cognate soluble transport factors that facilitate the passage of the resulting transport factor–cargo complexes through the NPC . The-best studied transport factors belong to a family of structurally related proteins , collectively termed β-karyopherins , although other transport factors can also mediate nuclear transport , particularly the export of mRNAs ( reviewed in [1 , 3–6] ) . NPCs can pass cargoes up to 30 nm diameter ( such as mRNA particles ) , at rates as high as several hundred macromolecules per second—each transport factor–cargo complex dwelling in the NPC for a time on the order of 10 ms [7 , 8] . Here we focus on karyopherin-mediated import , although our conclusions pertain to other types of nucleocytoplasmic transport as well , including mRNA export . During import , karyopherins bind cargoes in the cytoplasm via their nuclear localization signals . The karyopherin–cargo complexes then translocate through NPCs to the nucleoplasm , where the cargo is released from the karyopherin by RanGTP , which is maintained in its GTP-bound form by a nuclear factor , RanGEF . The high affinity of RanGTP binding for karyopherins allows it to displace cargoes from the karyopherins in the nucleus . Subsequently , karyopherins with bound RanGTP travel back through the NPC to the cytoplasm , where conversion of RanGTP to RanGDP is stimulated by the cytoplasmic factor RanGAP . The energy released by GTP hydrolysis is used to dissociate RanGDP from the karyopherins , which are then ready for the next cycle of transport . Importantly , this GTP hydrolysis is the only step in the process of nuclear import that requires an input of metabolic energy . Overall , the energy obtained from RanGTP hydrolysis is used to create a concentration gradient of karyopherin–cargo complexes between the cytoplasm and the nucleus , so that the process of actual translocation across the NPC occurs purely by diffusion [1 , 3–6 , 9–15] . Conceptually , nuclear import can be divided into three stages: first , the loading of cargo onto karyopherins in the cytoplasm , second , the translocation of karyopherin–cargo complexes through the NPC , and , third , the release of cargo inside the nucleus ( Figure 1 ) . The first and last stages have been the subject of numerous studies , and are relatively well understood , being soluble-phase reactions amenable to biochemical characterization ( reviewed in [1 , 3–6 , 15] ) . The intermediate stage of transport is much less understood . Nevertheless , it is clear that the ability of karyopherins ( and other transport factors ) to bind a particular class of NPC-associated proteins containing phenylalanine–glycine ( FG ) repeats , known collectively as FG nups , is a key feature of the transport process , and allows them to selectively and efficiently pass with their cargoes through the NPC . In particular , experiments in which the FG nup–binding sites on the karyopherins were mutated show that disrupting the binding of karyopherins to FG nups impairs transport [6 , 12 , 13 , 15 , 16] . Current estimates of the binding affinity of karyopherins to most FG nups are in the range 1–1 , 000 nM ( or 10–30 kBT per binding site ) , depending on the FG nup and karyopherin type [18–20] . Each FG nup usually carries a small region that anchors it to the body of the NPC , and a larger region characterized by multiple FG repeats . These FG repeat regions are natively disordered flexible chains or filaments that contain binding sites for transport factors ( including karyopherins ) and also appear to set up a barrier at the entrance of the NPC for macromolecules that cannot bind them [1 , 3 , 4 , 9 , 13 , 14 , 21–23] . The detailed physicochemical nature of this barrier is still under active study , although FG nups have been shown in vitro to form flexible polymer brushes when grafted to a surface [22] or gels in bulk solution [24] . Importantly , it has been repeatedly demonstrated that individual FG repeat regions can have a long reach , on the order of many tens of nm , within the NPC [3 , 22 , 23 , 25] . What is still needed is a quantitative theoretical explanation that can account for the observed characteristics of facilitated nuclear–cytoplasmic transport . Here , we develop a diffusion-based theory to explain the mechanism of the intermediate stage of nucleocytoplasmic transport—i . e . , translocation through the NPC . A useful theory of NPC-mediated transport should provide insight into several major unresolved questions , including: ( i ) How does the NPC achieve high transport efficiency of cargoes of variable sizes and in both directions , through only diffusion of the transport factor–cargo complexes ? ( ii ) How does binding of transport factors to FG nups facilitate transport efficiency while maintaining a high throughput ( up to hundreds of molecules per second per NPC ) [7 , 8 , 12 , 13 , 26] ? ( iii ) NPCs largely exclude nonspecific macromolecules in favor of transport factor–bound cargoes ( reviewed in e . g . , [4] ) . How is this high degree of selectivity achieved ? ( iv ) Neither deletion of up to half the mass of the FG nups' filamentous unfolded regions , nor deletion of asymmetrically disposed FG nups' filamentous regions that potentially set up an affinity gradient , abolish transport [27] . Directionality of transport across the NPC can even be reversed by reversing the concentration gradient of RanGTP [28] . How can we account for such a high degree of robustness ? Several theoretical models have been proposed for the mechanism of transport through the NPC . These include the Brownian Affinity Gate model [4 , 14] , Selective Phase models [12 , 13 , 29 , 30] , the Oily Spaghetti model [1] , Affinity Gradient models [10 , 11 , 15 , 20 , 31] , the Dimensionality Reduction model [32] , and most recently a Two-Gate model [48] . All these models can be thought of as viewing the NPC as a “Virtual Gate” [4 , 14] , where the FG nups set up a barrier for entrance into the NPC and transport through the NPC involves facilitated diffusion controlled by association and disassociation of transport receptors with FG nups . They differ only in specific assumptions , such as the conformation and spatial deployment of the FG nups , their physicochemical state , or the distribution of affinities of binding sites ( reviewed in [6] ) . The aim of the present paper is to establish a general quantitative framework for NPC transport that is consistent with well-established structural and functional properties of the NPC and its components . We explain how the binding of karyopherins to the FG nups' flexible filaments inside the NPC can give rise to efficient transport . We demonstrate that competition for the limited space and binding sites within the NPC leads to a novel , highly selective filtering process . Finally , we explain how the flexibility of the FG nups could account for the high robustness of NPC-mediated transport with respect to structural changes [27] . We conclude by discussing verifiable experimental predictions of the model .
The NPC contains a central channel ( approximately 35 nm in diameter ) that connects the nucleoplasm with the cytoplasm . The internal volume of this channel , as well as large fractions of the nuclear and cytoplasmic surfaces of the NPC , is occupied by the flexible FG-repeat regions of the FG nups ( i . e . , that portion in each FG nup containing multiple FG repeats ) . Since these FG-repeat regions also protrude into the nucleus and the cytoplasm , the effective length of the NPC is estimated to be 70 nm [1 , 3 , 4] . The details of the distribution of the FG-repeat regions inside the central channel and the external surfaces of the NPC , as well as the exact number of binding sites on the karyopherins and the number of the FG-repeats on the FG-repeat regions that are accessible for binding , have not yet been well-established ( although the number of the FG repeats is in the range of 5–50 per FG nup [6 , 27] ) . We made no specific assumptions about the distribution of FG nups , interactions between them , and their density , degree of flexibility , or conformation within the NPC . As we will discuss , the general features of transport through the NPC appear relatively insensitive to these details . We represent transport through the NPC as a combination of two independent processes contributing to the movement of the karyopherin–cargo complexes through the central channel of the NPC: ( i ) the binding and unbinding of the karyopherins to the FG-repeat regions , and ( ii ) the spatial diffusion of the complexes , either in the unbound state or while still bound to a flexible FG-repeat region . The complexes entering the NPC from the cytoplasm thus stochastically hop back and forth inside the channel until they either reach the nuclear side , where the cargo is released by RanGTP , or return to the cytoplasm . Detachment from the FG-repeat regions and exit from the NPC can be either thermally activated , or catalyzed by RanGTP directly at the nuclear exit of the NPC [1 , 4] . A schematic illustration of transport through the NPC is shown in Figure 1 . It is important to distinguish between two different properties of the transport process , namely , ( i ) the speed with which individual complexes traverse the NPC , and ( ii ) the probability that complexes , entering from the cytoplasm , arrive at the nuclear side [1 , 4 , 9 , 33–35] . As we discuss below , binding of karyopherins to the FG nups increases the probability of the karyopherins traversing the NPC , i . e . , their transport efficiency; in the absence of such binding , the probability of traversing the NPC is low . For simplicity , we assume that the unbinding and rebinding occur faster than the lateral diffusion of karyopherin–cargo complexes along the channel ( although our conclusions were verified by computer simulations for any ratio of binding–unbinding rate to diffusion rate , unpublished data ) . In this limit , movement through the NPC can be approximated by diffusion in an effective potential as explained below . The strength of the effective potential depends on the relative strength of two effects . The first effect is the entropic repulsion between karyopherin–cargo complexes and FG-repeat regions and between the FG-repeat regions themselves , as the karyopherin–cargo complexes have to compress and displace the FG-repeat region filaments to enter the channel . The second effect is an attraction due to the binding of karyopherin–cargo complexes to the FG-repeat regions , as illustrated in Figure 2 . We represent the transport of karyopherin–cargo complexes through the NPC as diffusion in a one-dimensional potential , U ( x ) ( expressed in units of kBT ) , in the interval 0 < x < L ( Figure 2 ) . The shape of the potential in the NPC is determined by the distribution of the FG nups along the channel ( an issue we address later ) . The actual length of the NPC corresponds to the interval from x = R to x = L − R , and the regions of length R ( on the order of the width of the channel [33 , 36] ) at both ends of the interval correspond to the distance outside the NPC over which the particles diffuse into either the nucleoplasm or the cytoplasm . We did not directly model the diffusion of complexes outside of the NPC . Instead , we assumed that karyopherin–cargo complexes stochastically entered the NPC from the cytoplasm , with an average rate J at x = R , where J is proportional to the concentration of the karyopherin–cargo complexes in the cytoplasm [26 , 36] . Because of the random nature of movement inside the channel , a certain fraction of the complexes impinging on the channel entrance will not reach the nucleus and eventually return to the cytoplasm . We modeled this event by imposing absorbing boundary conditions at x = 0 and x = L that correspond to a karyopherin–cargo complex returning back to the cytoplasm , or going through to the nucleus , respectively . Thus , the entrance current , J , splits into J0 and JM , corresponding to the flux of complexes returning to the cytoplasm and going through to the nucleus , respectively . Active release of the karyopherin–cargo complexes from the NPC by the nuclear RanGTP is modeled by imposing an additional exit flux , Je ( proportional to the nuclear concentration of RanGTP ) , at a position x = L − R . Therefore , the transmitted flux , JM , splits into Je and JL , which correspond respectively to the flux of karyopherin–cargo complexes released from the FG-repeat regions by RanGTP and to thermally activated release , as shown in Figure 2 . The efficiency of the transport through the NPC is determined by the fraction of the complexes that reach the nucleus , JM/J . We emphasize that we did not study the equilibrium thermodynamic properties of the channel , but rather the steady state , out-of-equilibrium behavior . We neglected possible differences in the diffusion coefficient of the complexes inside and outside the NPC to focus on the role of karyopherin binding in the import process . We also assumed that no current enters the NPC from the nucleus as the cargoes are released from the karyopherins in the nucleus by RanGTP . Finally , we neglected variations of the potential in the direction perpendicular to the channel axis . The effects of these factors do not change our conclusions , and will be studied in detail elsewhere . Under the above assumptions , the model can be solved using standard theory of stochastic processes [34] . Importantly , the model can be solved for a potential of an arbitrary shape , allowing us to model different distributions of binding sites within the NPC . The transport of the karyopherin–cargo complexes through the NPC was then described by the diffusion equation for the density of complexes inside the channel , ρ ( x ) where the local flux of the complexes within the NPC , J ( x ) , is given by: The first term in Equation 2 describes the random thermal motion of the complexes , and the second term stands for the variations in the flux due to local variations of the potential U ( x ) ; D is the diffusion coefficient of the complexes inside the channel . The steady state density of the complexes in the channel , obtained by solving Equations 1 and 2 , with entrance flux J and satisfying the boundary conditions ρ ( 0 ) = ρ ( L ) = 0 , is The sum of the flux of karyopherin–cargo complexes going through the NPC , and of that returning to the cytoplasm , is equal to the total flux of complexes entering the NPC; hence |J0| + JM = J; similarly , JM − JL = Je . The flux , Je , is proportional to the number of complexes present at the nuclear exit , and to the frequency , Jran , with which RanGTP molecules hit the nuclear exit of the NPC: Je = Jranρ ( x = L −R ) R . Recalling that the potential outside the channel is zero ( U ( x ) = 0 ) for 0 < x < R and L − R < x < L , and using the continuity of ρ ( x ) at x = L − R , one obtains for Ptr , the probability of a given karyopherin–cargo complex reaching the nucleus ( i . e . , the fraction of complexes reaching the nucleus ) : where K = JranR2/D exp ( −U ( L−R ) ) . Equation 4 is the main result of this section and has several important consequences . The probability of traversing the NPC , Ptr , defines the transport efficiency . This efficiency is seen to increase with the potential depth E , ( defined as E = −minxU ( x ) , Figure 2 ) , proportional to the binding strength of the karyopherin–cargo complexes to the FG-repeat regions . In the absence of binding , Ptr is small ( ∼R/L ) , so that a complex will , on average , return to the cytoplasm soon after entering the NPC . Notably , an attractive potential inside the NPC increases the time the complex spends inside the NPC and thus increases the probability that it reaches the nuclear side , rather than returns to the cytoplasm . When RanGTP only releases cargo from its karyopherin , but not from the FG-repeat regions ( i . e . , Je = 0 ) ; the maximal translocation probability , Ptr , is 0 . 5 . However , in the case when RanGTP also releases karyopherin–cargo complexes from FG-repeat regions , the translocation probability , Ptr , can reach unity . Importantly , the latter effect is more pronounced for a large K , that is , for strong binding at the exit . We shall discuss the practical implications of this result later . The second important consequence of Equation 4 is that Ptr depends only weakly on the shape of the potential , U ( x ) . This can account for why the transport properties of the NPC are relatively insensitive to the details of the distribution of FG-repeat regions inside the NPC , and to the distribution of the binding sites on the FG-repeat regions . The previous section does not take into account the interference between karyopherin–cargo complexes inside the channel . Although a large interaction strength , E , increases transport efficiency , this increase is at the expense of an increased transport time T ( E ) , which grows roughly exponentially with E ( Text S1 ) , and leads to an accumulation of karyopherin–cargo complexes inside the channel . However , the space and the number of available binding sites inside the channel are limited . As the number of these complexes in the channel increases , they start to interfere with the passage of each other due to molecular crowding . This molecular crowding results from two different sources . One is the repulsion that the entering macromolecules feel from the FG nups that set up the permeability barrier . The second is competition for the limited space inside the channel between the karyopherin–cargo complexes themselves , and which we now demonstrate can determine the selectivity . In effect , these two factors represent the entropic exclusion that we have discussed previously [1 , 3 , 4 , 9 , 13 , 14 , 21–23] . To quantitatively investigate how mutual interference between translocating karyopherin–cargo complexes and molecular crowding affect transport efficiency , we performed dynamic Monte Carlo simulations of the diffusion of complexes inside the NPC , in the potential U ( x ) , using a variant of the Gillespie algorithm [37–39] . The simulations are a discrete version of the continuum formulation of the previous section . The interval [0 , L] is represented by N discrete positions , which is a standard way to approximate the continuous diffusion; it is important to appreciate that these sites do not represent the actual binding sites , but correspond to the length of a diffusion step . We allowed only a limited number , nmax , of complexes at each position at any moment of time , which models the competition between complexes for the limited space and the accessible binding sites inside the channel . In line with our analytical model above , karyopherin–cargo complexes were deposited at the position iR if it was unoccupied by a complex , with a probability of JL2/ ( DN2 ) per simulation step . When a complex reached position i = 0 ( cytoplasm ) or i = N ( nucleus ) , it was removed from the channel . In addition , the complexes present at the position i = N − iR could be removed directly , with the probability JranL2/ ( DN2 ) , which models the effect of the release of the complexes from FG-repeat regions by nuclear RanGTP . Once inside the channel , a complex present at site i could hop to an adjacent unoccupied site , i ± 1 , with the following probability: where xi is the site occupancy: xi = 0 if the site is unoccupied , xi = n if n complexes are present at the position i , up to the nmax complexes . The transition rates from a site i to a site i ± 1 were , if xi±1 < nmax , and zero , if xi±1 = nmax [37–39] . The results of our simulations for the experimentally relevant range of interaction strength E and incoming flux J are shown in Figure 3 ( see Discussion for an explanation of our choice for the parameter values ) . Figure 3 shows the results for nmax = 1; the results did not change substantially for higher allowed local occupancy ( Text S1 and Figure S5 ) . For low interaction strength , E , the translocation probability curves for all entrance fluxes , J , collapse onto a single line ( which is predicted by the analytical solution from the previous section ) , because in this regime there are few complexes simultaneously present in the channel , and molecular crowding is negligible . For stronger binding , karyopherin–cargo complexes accumulate inside the channel , blocking the inflow of additional complexes , and the channel becomes jammed as reflected in the decrease of the probability to traverse the NPC . The main conclusion of the simulations , as shown in Figure 3A , is that transport through the NPC is maximal for an optimal value of the interaction strength Ec . Therefore , our theory rigorously confirms the intuitive notion of the existence of an optimal binding that balances increased transport probability with increased time spent within the NPC . In our simulations , the optimal binding strength depends on the entrance flux , and thus on the abundance of complexes of a particular type in the cytoplasm . In particular , the optimal interaction strength is higher for low entrance fluxes , as illustrated in Figure 3B . Existence of an optimal interaction strength provides a mechanism for the selectivity of NPC-mediated transport . Karyopherin–cargo complexes tuned for a particular strength of interaction with FG-repeat regions have a high translocation probability , while macromolecules that do not interact with FG nups are less likely to cross . We elaborate on this finding in the next section . As discussed in the previous section , the binding of karyopherins to FG-repeat regions provides a mechanism of selectivity . However , the maximum depicted in Figure 3A is broad; the translocation probability is significant even for binding strengths considerably lower than the optimal one . For instance , if the optimal interaction strength is E = 15kT ( Kd ≈ 300 nM ) , macromolecules whose interaction strength is 7kT ( Kd ≈ 1 mM ) [17] have a probability of reaching the nucleus that is more than half the optimal one . On one hand , this broad maximum allows NPC-mediated import to function efficiently across a broad range of transport factor binding strengths . On the other hand , this might also permit passage of macromolecules that bind nonspecifically to FG-repeat regions ( e . g . , due to electrostatic interactions ) . However , proper functioning of living cells requires a high selectivity of the NPC—how might this be achieved ? So far , Figure 3 only takes into account the competition between complexes of identical binding strength for space inside the channel . However , in a situation where optimally binding karyopherins compete for space and binding sites inside the channel with other , weakly binding macromolecules , the passage of the latter is sharply reduced , which significantly increases the selectivity of the NPC . Qualitatively , because the strongly binding karyopherins and karyopherin–cargo complexes spend more time in the NPC , a weakly binding macromolecule entering the channel will—with high probability—find it occupied by karyopherin–cargo complexes . Therefore , because the residence time of a low affinity macromolecule is relatively short , there is a high probability that it will return to the cytoplasm before the channel clears . On the other hand , if a karyopherin–cargo complex enters a channel that is already occupied by other strongly binding complexes , there is still a high probability that , due to its relatively high residence time , it will reside inside the channel long enough for the complexes that are already inside the NPC to get through . As free karyopherins exchange back and forth across the NPC constantly , there will always be karyopherins ( or other transport factors ) binding in the NPC and so excluding nonspecific macromolecules , making the NPC a remarkably efficient filter . These heuristic arguments were verified via computer simulations , using the algorithm of the previous section , adapted to account for two species of particles of different binding strengths . Two species of particles of different binding affinities ( representing a karyopherin–cargo complex and another macromolecule that can bind nonspecifically ( and weakly ) to the FG-repeat regions ) , are deposited stochastically at the NPC entrance with the same average rate J . As in the previous section , the particles diffuse inside the channel until they either reach the nucleus , or return to the cytoplasm . Due to limited space , each position can be occupied by only a limited number of particles ( see Text S2 for the actual code ) . As Figure 4 shows , competition for the space inside the channel between the translocating particles dramatically narrows the selectivity curve as compared with Figure 3 . This effect is a novel mechanism for the enhancement of transport selectivity beyond what is expected from the equilibrium binding affinity differences alone . In contrast to other mechanisms of specificity enhancement ( e . g . , kinetic proofreading [40] ) , no additional metabolic energy is required for this enhanced discrimination . Instead , selectivity is achieved by competition producing a differential NPC response to two ranges of binding affinities . There remains a broad range of higher binding affinities , occupied by transport factors , wherein passage across the NPC is efficient; however , in the low range of affinities , transmission is effectively prevented . We emphasize that this is an essentially nonequilibrium effect , and the selectivity enhancement goes far beyond the difference in the equilibrium binding affinities . Importantly , the enhancement of the selectivity persists even when high local occupancies are allowed ( Text S1 and Figure S4 ) . In the previous sections , we used a continuous potential to model transport through the NPC . However , in reality the translocating karyopherin–cargo complexes likely hop between discrete binding sites that are located on the separate ( or on the same ) flexible FG-repeat regions , which fluctuate in space around their anchor points due to thermal motion [3 , 22 , 23] . This flexibility allows the complexes to diffuse along the channel while remaining bound to an FG-repeat region . A complex can also unbind from an FG-repeat region and rebind again to the same or a neighboring FG-repeat region , moving while unbound by passive diffusion . In this section , we elucidate how the number of FG nups inside the channel affects transport . Under these assumptions , the translocation of karyopherin–cargo complexes through the NPC can be described as diffusion in an array of potentials , as illustrated in Figure 5 , where each potential well Ui ( x ) of a width pi represents an FG-repeat region . The shape of each well depends on the number of the binding sites on each FG-repeat region , the binding strength of the karyopherin–cargo complex , and the rigidity and the length of an FG-repeat region , which determine the cost of its entropic stretching in the process of spatial fluctuations . This description allows for the possibility of having several binding sites on an FG nup which affects only the shape of the wells ( Text S1 and Figure S1 ) . The blue line in Figure 5 corresponds to the unbound state . Although for the purposes of illustration all the wells are shown to have the same form , the subsequent results are valid for an arbitrary distribution of potential shapes . We shall denote the density of the karyopherin–cargo complexes in the i-th well as ρi ( x ) and the density of unbound complexes as ρ0 ( x ) . The lateral diffusion of the complexes , combined with the binding and unbinding to the FG-repeat regions is then described by the following equations [41]: The first term in the first equation describes the diffusion in the unbound state , while the second and the third terms describe the unbinding and the binding to the wells . Similarly , the second equation describes the diffusion while still bound to the i-th well ( FG-repeat region ) . The local unbinding and binding rates from the i-th well are ri0 ( x ) and r0i ( x ) , respectively . They are related by the detailed balance condition , r0i ( x ) /ri0 ( x ) = e−Ui ( x ) +U0 ( x ) , which reflects the energy difference between the bound and unbound states . If the unbinding rates are fast compared with the diffusion time across the wells , the relative densities of bound and unbound complexes are at their local equilibrium Boltzmann ratio [41]: , where ρ ( x ) = ∑iρi ( x ) is the total density of the complexes at a position x . Adding up Equations 6 , one obtains an equation for the total density of the complexes at position x , ρ ( x ) : where [41] . Thus , the process of translocation through an array of flexible FG nups within the NPC can be described as simple lateral diffusion in the effective potential Ueff , which leads to Equations 1 and 2 in the first section of this manuscript , which are essentially identical to Equation 7 , even though they are written in a slightly different form . As we have proposed in previous sections , the transport properties of the NPC are relatively insensitive to the detailed shape of the effective potential . It follows from Equations 6 and 7 that the shape of the effective potential depends only weakly on the number and the shape of the overlapping potential wells corresponding to the FG-repeat regions . Even more strikingly , in our model the transport properties of the NPC are not very sensitive to the number of the FG-repeat regions . This is robust with respect to the variations in the number of the FG-repeat regions [27] . This was in striking contrast to the case of the inflexible FG-repeat regions when the binding sites are sparsely distributed without a large degree of overlap ( Text S1 and Figure S2 ) . We illustrate this point through a limiting case where the FG-repeat regions barely touch , represented by the potential shown by the blue line in Figure 6A . The flat central part of each potential well corresponds to where the karyopherin–cargo complex is diffusing in the channel while bound to an FG-repeat region , and the sharply rising regions at the borders correspond to unbinding of the complex and its transfer to the next filament . Narrow wells correspond to filaments with limited reach , while wide wells correspond to filaments that can stretch a long distance without significant entropic cost . The potential wells can have different widths , pi , so that their combined width is equal to the total length of the channel , . All the potential wells have the same shape , U0 , re-scaled to the width of an individual well , so that the potential at a point x = ∑j<ipj + Δx is: U ( x ) = U0 ( Δx/pi ) . Crucially , the transport properties of the potential shown in Figure 6A do not depend on the number of wells . Both the translocation probability and the residence time are equivalent for the multiwell potential shown in blue , and the single-well potential shown in red , obtained by rescaling an individual blue well to the whole length , L − 2R , of the channel . Indeed , it follows from Equation 4 that the translocation probability for the multiwell potential with n wells , is which is independent of the number and the width of the wells because ∑ipi = L − 2R; as before , K = JranR2/D exp ( −U ( L − R ) ) . We prove in Text S1 that the residence time is similarly independent of the number of wells . Since both translocation probability and residence time are independent of the number of wells , the transport properties do not depend on the number of wells , even for high entrance rates or binding strengths , when jamming becomes important , as verified by computer simulation ( Figure 6B ) . This result highlights the robustness of our model of NPC transport; in multiwell potentials of this type , the NPC's transport properties do not depend on the specific number of FG-repeat regions , so long as they are flexible enough for their fluctuation regions to overlap , permitting complexes to freely transfer from one filament to the next , which might explain the puzzling degree of robustness of the NPC transport with respect to the deletion of FG repeat regions [27] .
Several conceptual models have been proposed to describe transport through the NPC [1 , 3–6 , 9–15 , 29–32 , 48] . Most propose that this transport relies on diffusion of the transport factor–cargo complexes in the environment of flexible FG-repeat regions of the FG nups , controlled by transient binding to the FG nups these flexible regions [1 , 3–6 , 9–11 , 14–16] . We have formulated and solved a rigorous mathematical model of transport through the NPC that depends on the physics of diffusion in a channel combined with binding to the flexible filamentous FG-repeat regions ( without making detailed assumptions about the conformation and distribution of FG-repeat regions inside the channel ) . Our model applies to both export and import processes and explains the main features of NPC-mediated transport; namely , its high selectivity for cargoes bound to transport factors , its efficiency and directionality , and its robustness to perturbations . We propose that the selectivity of the NPC arises from a balance between the probability ( efficiency ) and the speed of transport of individual karyopherin–cargo complexes . Analogous ideas have been suggested to account for the transport properties of ion channels and porins [14 , 33–35 , 42] . In our model , the probability of a karyopherin–cargo complex reaching the nucleus increases with the binding strength of the transport factors to FG-repeat regions , but at the expense of increased residence time inside the NPC; eventually , complexes spend so much time in the NPC that they impede the passage of other complexes through the channel . Therefore , there is an optimal value of the binding strength of karyopherins to FG-repeat regions that maximizes their transport efficiency through the NPC . For karyopherin–cargo complexes with lower entrance fluxes , the optimal binding strength is higher because at low fluxes the accumulating complexes can reside longer in the channel without blocking it ( Figure 3 ) . This correlation of optimal binding strength and entrance flux could explain why there are different karyopherin types; the binding strength of each karyopherin type might be related to its cargoes' relative flux and hence the abundance of its cargoes . By considering known parameters of the nuclear transport machinery , we can test whether our simulations are consistent with the experimentally observed values of the flux through the NPC , and the residence time inside it . We take the effective length of the NPC as L ∼ 70 nm and its effective passive diffusion diameter as R ∼ 7 nm , within the range observed for different NPCs [1] . The diffusion coefficient D of the complexes inside the channel can be estimated to lie in the range 1–10 μ2/s , typical for protein diffusion in the crowded environment of the cytoplasm [43 , 44] . In vivo , the total cargo flux through an NPC ranges from several molecules per second up to several hundred molecules per second [12 , 26 , 45–47] . Accordingly , we performed simulations for values of the incoming flux J in the range 0 . 3–10 in units of ( 10−416D/R2 ) , which corresponds to a flux through the NPC in the range of 10–1 , 000 molecules per second . This results in predicted residence times in the NPC ( given by LR/Dexp ( E/kT ) ) of approximately 0 . 01–1 s , consistent with experimentally determined residence times [7 , 8] . For incoming flux values in this range , our model predicts optimal interaction strengths in the range of 5–15kBT ( Figure 3 ) . Molecular dynamics calculations predict higher binding energies [18] , but the effective interaction strength in our model is reduced by entropic effects from the flexible FG-repeat regions . Our model can also explain the high specificity of facilitated transport through the NPC , wherein each NPC permits the passage of transport factor–cargo complexes but efficiently filters out macromolecules that do not bind specifically to the FG-repeat regions . The difference in binding energy between specifically and nonspecifically binding macromolecules can be as little as a few kBT , which may not seem enough for such efficient discrimination . However , we have uncovered an additional mechanism that we believe significantly enhances the specificity of NPC transport . This mechanism relies on the direct competition between transport factors and nonspecifically binding macromolecules; they compete for space and binding sites in the channel . As a consequence of their stronger binding , transport factors have a longer residence time within the channel as compared with nonspecifically binding macromolecules , which are therefore outcompeted for space and binding sites within the channel . The constant flux of cargo bound or free transport factors between the nucleus and cytoplasm therefore effectively excludes nonspecifically binding macromolecules from the channel . We emphasize that this selectivity enhancement is essentially a nonequilibrium kinetic effect . Hence , although no metabolic energy is expended in this filtering process [40] , the resulting selectivity is much higher than might be expected from just the different binding affinities of transport factors and nonspecific macromolecules ( Figure 4 ) . In the case of karyopherin-mediated import , the transport efficiency is enhanced when RanGTP directly releases karyopherins from their binding sites on FG-repeat regions at the NPC exit [1 , 4 , 20 , 31]—an enhancement that increases with the binding strength at the nuclear exit . High affinity binding sites at the nuclear exit of the NPC decrease the probability of return , once a complex has reached the nuclear side . This result may account for the observed high affinity binding sites that are localized at the nuclear side on the NPC in import pathways and at the cytoplasmic side in export pathways [1 , 4 , 20 , 31] . Although the transport properties of the NPC depend strongly on the magnitude of the interaction strengths between transport factors and FG-repeat regions , we predict that transport depends only weakly on spatial variations of the binding strength along the channel . In particular , a gradient of binding affinity across the NPC should not , by itself , increase throughput compared with a uniform distribution of the same sites . This could explain how transport can be reversed across the NPC simply by reversing the gradient of RanGTP [28] . Only a high affinity trap at the exit of the NPC in combination with the action of RanGTP in releasing the karyopherins from this trap can improve the throughput through the NPC . Although transport relies on the flexibility of the FG-repeat regions , it is relatively insensitive to the number of flexible FG-repeat regions inside the NPC—as long as their fluctuation regions can overlap ( Figures 5 and 6 ) . This could account for recent experiments in which up to half the total mass of the flexible FG-repeat regions in NPCs were deleted without abrogating nucleocytoplasmic transport [27] . In particular , it follows from Equation 4 that the probability of traversing the NPC is low if the binding sites are sparse and stationary , unless they are so dense that they occupy almost all the available length of the channel . However , in this case , transport is sensitive to the number of sites . Thus , a theory that neglects the flexibility of the FG nups is incapable of explaining how the NPC can sustain a high throughput and be relatively insensitive to the removal of up to half the binding sites . Importantly , this result does not depend on the speed of the diffusion of the karyopherin–cargo complexes between the binding sites . By contrast , a model that relies on stationary binding sites predicts that transport will not be robust to deletion of half the binding sites ( Figures S2 and S3 ) . Hence , we show that not every diffusion-based mechanism can explain the robustness of transport with respect to deletion of the FG-nups . Moreover , different karyopherins can bind different specific FG-nups . Thus , they can follow different pathways within the NPC channel , each reliant on a small subset of specific FG-nups [27]; we thus predict that only removal of this small subset would prevent that karyopherin from transiting the NPC . One of the conclusions drawn by [27] from their results is that the lethal deletions have removed all the preferred FG-binding sites on an essential pathway; our model is thus completely in line with their work . Experimental tests for our model's predictions include varying the effective potential experienced by transport factor–cargo complexes inside the NPC by systematically introducing mutations into the binding sites [16] , changing the cargo size [2] , or using cells with genetically modified numbers of the FG-repeat regions [27] . Finally , any device built according to the principles outlined above would possess the transport properties described by our model , suggesting strategies for the creation of highly selective artificial nanomolecular sieves .
The simulations were written in C language and run on a cluster of UNIX processors . The simulation algorithm is described in the text; see Text S2 for the actual code . Analytical calculations were in part performed with pencil and paper , or in some cases using Mathematica version 5 . 1 . | The DNA at the heart of our cells is contained in the nucleus . This nucleus is surrounded by a barrier in which are buried gatekeepers , termed nuclear pore complexes ( NPCs ) , which allow the quick and efficient passage of certain materials while excluding all others . It has long been known that materials must bind to the NPC to be transported across it , but how this binding translates into selective passage through the NPC has remained a mystery . Here we describe a theory to explain how the NPC works . Our theory accounts for the observed characteristics of NPC–mediated transport , and even suggests strategies for the creation of artificial nanomolecular sorting devices . | [
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] | 2007 | Efficiency, Selectivity, and Robustness of Nucleocytoplasmic Transport |
Septins are critical for numerous cellular processes through the formation of heteromeric filaments and rings indicating the importance of structural regulators in septin assembly . Several posttranslational modifications ( PTMs ) mediate the dynamics of septin filaments in yeast . However , little is known about the role of PTMs in regulating mammalian septin assembly , and the in vivo significance of PTMs on mammalian septin assembly and function remains unknown . Here , we showed that SEPT12 was phosphorylated on Ser198 using mass spectrometry , and we generated SEPT12 phosphomimetic knock-in ( KI ) mice to study its biological significance . The homozygous KI mice displayed poor male fertility due to deformed sperm with defective motility and loss of annulus , a septin-based ring structure . Immunohistochemistry of KI testicular sections suggested that SEPT12 phosphorylation inhibits septin ring assembly during annulus biogenesis . We also observed that SEPT12 was phosphorylated via PKA , and its phosphorylation interfered with SEPT12 polymerization into complexes and filaments . Collectively , our data indicate that SEPT12 phosphorylation inhibits SEPT12 filament formation , leading to loss of the sperm annulus/septin ring and poor male fertility . Thus , we provide the first in vivo genetic evidence characterizing importance of septin phosphorylation in the assembly , cellular function and physiological significance of septins .
Septins are highly conserved GTP-binding proteins in eukaryotes that polymerize into heteromers and further higher-order structures , such as filaments , rings and gauzes . These structures are required for a wide range of cellular processes , including cell cycle regulation , cytokinesis , spermiogenesis , ciliogenesis and neurogenesis [1–7] . Accordingly , misregulation of septin filaments is associated with diseases including cancer , male infertility and neurodegenerative disorders [8] . Given the critical roles of higher-order septin structures in diverse cellular processes , it is important to investigate the regulation of septin assembly . Posttranslational modifications ( PTMs ) of yeast septins , such as sumoylation , phosphorylation and acetylation , modulate the dynamics of septin filament formation during the yeast cell cycle [9] . For example , the Saccharomyces cerevisiae septins are sumoylated during the cell cycle , and mutation of sumoylation sites interferes with the disassembly of the septin ring at a previous site of division [10] . In addition , the loss of PP2A-dependent dephosphorylation of yeast Shs1 results in failure to the remove septin ring at the bud neck at the completion of cytokinesis [11] . Interestingly , different phosphomimetic mutations in yeast Shs1 lead to no filament formation or distinct organizations of higher-order structures , such as rings and gauze-like structures [12] . However , the mechanism underlying the contribution of PTMs to the dynamics of higher-order structures remains unknown . In mammalian cells , several studies have identified PTMs on septins , but little is known about the roles of PTMs in septin assembly . Previous findings have implicated the phosphorylation of SEPT3 by cGMP-dependent protein kinase I ( PKG-I ) in nerve terminals in presynaptic plasticity [13 , 14] . SEPT4 is phosphorylated by GSK3 , and during the epididymal transit of spermatozoa , inhibition of SEPT4 phosphorylation through Wnt/GSK3 signaling establishes the function of membrane diffusion barrier in the sperm tail [15] . SEPT5 , which may be involved in synaptic vesicle transport , is ubiquitinated by E3 ligase Parkin for subsequent degradation [16] . However , whether these modifications mediate septin assembly or disassembly of higher-order structures remains largely unknown . More importantly , the in vivo significance of PTMs on septin assembly and function has not been explored . The sperm annulus is a septin-organized fibrous ring in the sperm tail , and defects in the annulus have been associated with asthenoteratozoospermia and male infertility [17 , 18] . Several septins , including SEPT 1 , 2 , 4 , 6 , 7 , and 12 , have been found to localize to the sperm annulus , and sterile men exhibiting abnormal sperm structure and motility display reduced septin signals[3 , 5 , 19 , 20] . Additionally , Sept4 null mice show reduced sperm motility , bent sperm tails and male infertility , and electron microscopy analysis of the sperm tail revealed the complete absence of an annular structure [3 , 5] . These data suggest that septins are fundamental subunits for annular establishment and function . Notably , SEPT12 is specifically expressed in male germ cells and organizes as SEPT12-7-6-2 and SEPT12-7-6-4 filaments at the sperm annulus [6 , 19] . A mutation of the SEPT12 GTP-binding site ( SEPT12D197N ) that abolishes SEPT12 polymerization into filaments leads to the absence of an annulus , abnormal structure and poor motility of spermatozoa in both infertile men and knock-in ( KI ) mice [6 , 21] . These findings suggest that SEPT12 filament formation is required for septin ring/annulus establishment and proper sperm function . Taken together , these results indicate that sperm annulus/septin ring formation requires the polymerization of septin subunits into filaments and is involved in the normal function and structure of sperm and robust male fertility . Although the composition and importance of the sperm annulus have been investigated , the regulation of annular formation remains unknown . In the present study , we showed that SEPT12 was phosphorylated at Ser198 , and we generated KI mice harboring a phosphomimetic mutation to imitate constitutively phosphorylated SEPT12 . Phosphomimetic SEPT12 KI mice displayed poor male fertility , abnormal sperm structure and motility , and loss of the annulus/septin ring . Moreover , we investigated how SEPT12 phosphorylation contributes to the absence of the annulus/septin ring and identified the regulatory kinase involved . In conclusion , we have established the first genetic model for characterizing the importance of mammalian septin phosphorylation in vivo and identified the mechanism underlying the role of phosphorylation in septin assembly .
The GFP-hSEPT12 protein was expressed in cells , and SEPT12 was immune-precipitated using the anti-GFP antibody followed by SDS-PAGE . The band corresponding to GFP-SEPT12 was excised , digested in-gel with trypsin and subjected for analysis by mass spectrometer . The MS/MS spectrum showed a phosphorylated SEPT12 peptide “196-ADSpLTMEER-204” indicating a phosphorylation site at Ser198 residue of SEPT12 ( S1A Fig ) . The Ser198 residue is located in the SEPT12 GTP-binding domain and is highly conserved in mammals , as shown through multiple sequence alignment ( Fig 1A ) . To investigate the impact of SEPT12 phosphorylation in vivo , we established KI mice harboring a phosphomimetic mutation at the conserved Ser198 site to imitate constitutively phosphorylated SEPT12 . KI mice carrying a mutation resulting in a change of the 196th amino acid from serine to glutamate ( SEPT12S196E ) were generated , and the genotypes of the mice were determined through sequencing ( Fig 1B and S1B Fig ) . The heterozygous KI males showed normal fertility ( Table 1 ) . However , the mating of homozygous KI males with distinct wild-type ( WT ) females for at least 4 months revealed a marked decrease in number of delivered litters and born pups compared with those of heterozygous males ( Table 1 ) . These data indicated SEPT12S196E/S196E males displayed poor fertility . Analysis of the reproductive organs revealed no gross differences in testicular and epididymal morphology between WT and SEPT12 KI mice ( Fig 1C ) . Additionally , histological analysis of these mice showed a similar germ cell population in the testis and densely packed tubules in the epididymis ( Fig 1D ) . These findings are consistent with the absence of a significant difference in the testis/body weight ratio and sperm count ( Fig 1E ) . However , a majority of spermatozoa from homozygous KI males exhibited abnormal structures and lacked motility ( Fig 1E ) . Thus , SEPT12 homozygous KI males displayed poor fertility due to defective sperm morphology and motility , rather than abnormal reproductive organs . Mature spermatozoa are comprised of head and tail which divided into a midpiece , principal piece and end piece ( Fig 2A ) . The tail contains a central bundle of microtubules , the axoneme , enclosed by a mitochondrial sheath in the midpiece and a fibrous sheath in the principal piece . Between the mitochondrial and fibrous sheaths is the annulus , a septin ring [18] . Immunofluorescence analysis of WT spermatozoa showed SEPT12 and SEPT4 signals at the distal end of mitochondria , in the region of the annulus . However , these two signals were not detected in homozygous spermatozoa ( Fig 2B and 2C ) . Statistical analysis showed that >80% of homozygous KI spermatozoa from epididymal cauda exhibited defects in the annular region , and these spermatozoa were either sunken or bent in the annular region observed by phase-contrast microscopy ( Fig 2D and S2A Fig ) . Assaying sperm from epididymis reveals a significantly increase in hairpin-like tail bend during epididymal transit ( S2B Fig ) . The phenotype of SEPT12 KI spermatozoa is similar with several mutant mice carrying defective sperm annulus , such as Septin4- and Tat1- and Ccny1-null mice [3 , 5 , 15 , 22 , 23] . These results indicate that phosphomimetic SEPT12 KI mice have a defective annulus devoid of essential septin subunits leading to sperm deformation . To examine the sperm annulus structure in more detail , electron microscopy was performed . WT spermatozoa exhibited electron-dense wedge-shaped annulus located between the mitochondrial and fibrous sheaths ( Fig 2E ) . However , the annulus was completely lost in phosphomimetic SEPT12 KI spermatozoa . Interestingly , the KI sperm displayed a greater distance and a sink between the mitochondrial sheath and the fibrous sheath . These results suggested that SEPT12 phosphorylation regulates the formation of the sperm annulus that is involved in the structural support of the sperm tail . To identify the annular defect in SEPT12 KI spermatozoa , we investigated the biogenesis of the annulus during spermatogenesis using SEPT4 as a representative annular marker [3 , 5] . Spermatogenesis includes the mitotic phase , meiotic phase , and spermiogenesis and is divided into 12 stages in the seminiferous tubule , with each stage being characterized by a specific combination of germ-cell type [24] . Sperm annulus biogenesis only occurs in the spermatid during spermiogenesis , which can be subdivided into 16 steps in mice . Immunohistochemistry analysis of WT testicular sections showed that the annulus accumulates at the caudal pole of the nucleus in step 13 spermatids ( Fig 3A; stage I ) and retains its position until step 15 spermatids ( Fig 3A; stage II–IV ) . Subsequently , the annulus continually moves down the sperm tail and arrives at the midpiece-principal piece junction in step 15–16 spermatids ( Fig 3A; stage V-VIII ) . Strikingly , the annular signal was absent in all SEPT12 homozygous KI spermatids , indicating a lack of septin ring assembly at the beginning of annulus formation ( Fig 3B ) . However , examination of SEPT4 expression revealed no significant difference between WT and SEPT12 KI spermatozoa ( S3A Fig ) , and annular components including septin 2 , 4 , 6 , 7 and 12 showed similar expression in WT and SEPT12 KI testis ( S3B Fig ) . These indicate loss of sperm annulus in SEPT12 KI mice do not result from defective septin expressions . Collectively , these data suggest that SEPT12 phosphorylation interferes with the initial assembly of the septin ring , leading to a loss of sperm annulus formation . To explore the molecular mechanism of SEPT12 phosphorylation in the assembly of higher-order structures , the effect of phosphomimetic SEPT12 on SEPT12 filament formation was examined in NT2/D1 cells , a human testicular carcinoma cell line . The Ser198 residue of human SEPT12 was substituted with aspartate ( S198D ) or glutamate ( S198E ) to mimic the constitutively phosphorylated protein , or replaced with alanine ( S198A ) to imitate the unphosphorylated protein . GFP-SEPT12WT and S198A assembled into filamentous structures , but GFP-SEPT12S198D and S198E formed irregular aggregates in NT2/D1 cells ( Fig 4A ) . Counting the cells with GFP-labeled filament fibers revealed that both SEPT12S198D and S198E resulted in no filament formation , indicating that Ser198 phosphorylation disrupts SEPT12 filament formation ( Fig 4B ) . We next asked whether the mimetic phosphorylated Ser198 of SEPT12 could counteract filament formation by wild-type SEPT12 . A constant amount of the GFP-SEPT12WT plasmid mixed with increasing doses of the phosphomimetic SEPT12S198E construct were transfected into cells , and the cells with filamentous structures were counted ( Fig 4C ) . The results showed that quantity of SEPT12 filaments was gradually reduced , suggesting that elevated Ser198 phosphorylation might impair SEPT12 filament formation in a dose-dependent manner . The effect of SEPT12 S198 phosphorylation was further examined through 3D structure prediction using homology modeling revealing the phosphorylation of the SEPT12 Ser198 residue located at the SEPT12-SEPT7 interface ( Fig 5A ) . It is known that SEPT12 polymerizes into SEPT12-7-6-2-2-6-7-12 and SEPT12-7-6-4-4-6-7-12 octamers , and these complexes further assemble into filaments through end-to-end associations [6] . Thus , we investigated whether Ser198 phosphorylation affects this assembly process , thereby disrupting SEPT12 filament formation . Immunoprecipitation analysis showed that SEPT12WT and S198A associated with SEPT2 , SEPT6 and SEPT7 , whereas the interaction between phosphomimetic SEPT12 ( S198D and S198E ) and these septins was profoundly or moderately perturbed ( Fig 5B ) . These data suggested that Ser198 phosphorylation at the SEPT12-SEPT7 interface results in dissociation of SEPT12 from SEPT7 , SEPT6 and SEPT2 . Moreover , SEPT12 WT and S198A formed filamentous structures with SEPT7 , SEPT6 and SEPT2 , whereas phosphomimetic SEPT12 ( S198D and S198E ) showed no filament formation and disassociated from these septins . Importantly , SEPT7 , SEPT6 and SEPT2 remained co-localized with each other , suggesting dissociation of phosphomimetic SEPT12 from the entire SEPT7-6-2-2-6-7 complex ( S4 Fig ) . Collectively , these results indicated that SEPT12 Ser198 phosphorylation disrupts filament formation by interfering with the SEPT12-SEPT7 interaction , thereby inhibiting the association between SEPT12 and the SEPT7-6-2-2-6-7 complex . Similarly , phosphomimetic SEPT12 ( S198D and S198E ) inhibited the interaction of SEPT12 with SEPT4 , SEPT6 and SEPT7 and resulted in dissociation of SEPT12 from the SEPT7-6-4 complex ( S5A and S5B Fig ) . In support of these findings in NT2/D1 cells , pull-down assay showed that His-tag SEPT7 recombinant protein interacted with SEPT12 , SEPT2 and SEPT6 in wild-type testis . In SEPT12 KI testis , however , the association between phosphomimetic SEPT12 and His-SEPT7 was abolished , but SEPT7 still interacted with SEPT2 and SEPT6 ( Fig 5C ) . Additionally , co-immunoprecipitation of SEPT2 indicated that SEPT2 still interacted with SEPT6 and SEPT7 in SEPT12 KI testis ( S6 Fig ) . Thus , SEPT12 phosphorylation disrupts association between SEPT12 and SEPT7 , but pre-complex SEPT7-6-2 still assemble in vivo . Together , these results suggest that SEPT12 Ser198 phosphorylation disrupts sperm annuls formation by abolishing assembly of SEPT12 with pre-complex SEPT7-6-2 . In contrast , phosphomimetic SEPT12 ( S198D and S198E ) did not affect the SEPT12-SEPT12 interaction , which is required for the association between octamers ( S7A Fig ) . Additionally , phosphomimetic SEPT12 ( S198D and S198E ) remained co-localized with SEPT12WT ( S7B Fig ) . In conclusion , SEPT12 phosphorylation destroys filament formation by disrupting the intra-complex association , rather than the inter-complex association . Inspection of the SEPT12 Ser198 residue and its flanking sequence identified a consensus target motif of protein kinase A ( PKA ) , [R/K]-X-X-[pS/T] ( Fig 1A ) [25] . PKA is a heterotetramer comprising two regulatory and two catalytic subunits; upon cAMP stimulation , the catalytic subunits dissociate from the regulatory subunits , thereby activating downstream cascades . We examined whether PKA is responsible for SEPT12 Ser198 phosphorylation using a phospho-Ser198 antibody that specifically recognizes SEPT12 phosphorylation at the Ser198 residue ( S8 Fig ) . Cells were treated with an increasing dose of cAMP followed by western blotting revealing that SEPT12 phosphorylation at the Ser198 residue ( p-Ser198 ) was elevated in a dose-dependent manner ( Fig 6A ) . In addition , inhibition of PKA activity by H89 reduced p-Ser198 signal in both 293T and NT2/D1 cells ( Fig 6B ) . These findings suggested a positive correlation between PKA activation and Ser198 phosphorylation status . The catalytic subunit alpha 2 of PKA ( PKACA2 ) is exclusively expressed in the testis and is the predominant catalytic form of PKA during spermiogenesis , specifically , in the stage of annular biogenesis [18 , 19 , 26] . Thus , we examined whether PKACA2 promotes SEP12 phosphorylation . Overexpression of PKACA2 markedly increased the p-Ser198 signal of SEPT12WT , but not that of S198A , in both the 293T and NT2/D1 cell lines , indicating that PKACA2 could facilitate SEPT12 phosphorylation at the Ser198 residue ( Fig 6C ) . Moreover , immunoprecipitation and immunofluorescence analyses showed SEPT12 co-precipitated and co-localized with PKACA2 , demonstrating that SEPT12 physically interacted with PKACA2 ( Fig 6D and 6E ) . Taking these findings together , we concluded that PKACA2 associated with SPET12 and phosphorylated its Ser198 residue . Notably , homology alignment among human septins revealed a consensus PKA target sequence at the Ser198 analog site in a majority of human septins , except for SEPT1 , SEPT5 and SEPT6 ( S9 Fig ) . Thus , it is likely that a majority of human septins serves as downstream targets of PKA . We further examined the effect of PKA-dependent Ser198 phosphorylation on SEPT12-organized structures . Cells expressing GFP-SEPT12 were treated with cAMP , and the cells with GFP-labeled filaments or aggregates were subsequently counted . Elevating the dose of cAMP treatment gradually reduced the number of SEPT12 filaments and increased SEPT12 aggregates , suggesting that PKA activation determines the formation of SEPT12-organized structures ( Fig 7A and 7B ) . Consistent with these findings , overexpressed PKACA2 decreased the filament formation of SEPT12WT , but not that of unphosphorylated SEPT12S198A in 293T cells ( Fig 7C ) . In contrast , PKACA2 enhanced the aggregate accumulation of SEPT12WT , but not that of S198A ( Fig 7D ) . Similar results were observed in NT2/D1 cells , in which PKACA2 altered SPET12WT-mediated , but not S198A-mediated structures ( S10 Fig ) . These data indicated PKACA2 disrupts SEPT12 filament formation and promotes SEPT12 aggregate accumulation through the Ser198 residue . These findings correlated with the mimetic phosphorylated Ser198 of SEPT12 ( SEPT12S198D and S198E ) formed aggregates , but not filamentous structures ( Fig 4A ) . In conclusion , PKA-dependent Ser198 phosphorylation impairs SEPT12 filament formation .
Here , we identified a phosphorylation site in SEPT12 and investigated its biological function during spermatogenesis ( Fig 8 ) . Sperm annulus , a septin ring , consists of SEPT12 filament , which is polymerized by end-to-end association of SEPT12-7-6-2-2-6-7-12 and SEPT12-7-6-4-4-6-7-12 octamers [6] . Upon phosphorylation through PKA at Ser198 residue , phospho-SEPT12 dissociates from SEPT7-6-2 and SEPT7-6-4 complexes , thereby disrupting SEPT12 filament formation . In wild-type mice , sperm annulus is assembled at sperm neck in elongating spermatid and subsequently migrates along axoneme to the mid-principal piece junction in spermatozoa . In phospho-SEPT12 knock-in mice , disorganized septin complexes are unable to polymerize into SEPT12 filament resulting in loss of sperm annulus , and leading to deformed sperm with weak motility and poor male fertility . In the present study , we provide the first in vivo evidence indicating phosphorylation as a structural determinant for septin assembly into higher-order structures , and the importance of phosphorylation in septin-mediated cellular and physiological function . SEPT12 KI mice display defective sperm annulus devoid of SEPT4 and SEPT12 subunits ( Fig 2B and 2C ) , is it possible that these KI mice could function as SEPT4 and/or SEPT12 KO mice ? Although SEPT12 KI mice share several features with SEPT4-null mice , SEPT12 KI mice do not have all defect of SEPT4-null mice , for example , mitochondrial defects [5] . In addition , the phenotype of SEPT12 KI mice is less severe than SEPT12 knock-out chimera mice , which display decrease in sperm count , defects in several sperm compartments and maturation arrest of germ cells at the spermatid stage [19] . Moreover , similar septin expressions including SEPT2 , 4 , 6 , 7 and 12 between WT and SEPT12 KI testis indicate the absence of sperm annulus do not result from the loss of septin expression ( S3B Fig ) . We conclude that phosphomimetic SEPT12 KI mice appears to be a mouse model with presence of septin subunits but loss of septin-septin interaction and septin filament assembly . Electron microscopy analysis of the sperm annulus revealed an electron-dense ring structure between the midpiece and principal piece of all mammalian spermatozoa [27] . High-magnification images showed that the annulus/ring structure consisted of highly packed filamentous structures . Consistent with the observed ultrastructure of the annulus , SEPT1 , 2 , 4 , 6 , 7 and 12 have been shown to localize at the sperm annulus , and septins are known to polymerize into filaments and bundles of filaments that can further form rings [3 , 5 , 6 , 28] . These results suggest that the architecture of the annulus/ring comprises multiple layers of septin filaments . Strikingly , spermatozoa carrying a phosphomimetic mutation in SEPT12 displayed a complete loss of the annulus , implying that a single phosphorylation site in SEPT12 controls the formation of multiple septin filamentous layers , a ring structure . Therefore , the present study highlights the importance of PTMs in the assembly of higher-order septin structures in vivo . Morphological description of the sperm annulus was performed decades ago , and studies concerning its composition and function are accumulating . Three functions of the annulus/septin ring of sperm have been demonstrated: ( 1 ) structural and mechanical support; ( 2 ) acting as a diffusion barrier to confine proteins to a specific membrane domain; and ( 3 ) establishment of the mitochondrial distribution [3 , 5 , 6 , 29 , 30] . In the present study , phosphomimetic SEPT12 KI spermatozoa with no annulus were found to show malformation and lack of motility , consistent with the known annulus function ( Figs 1E and 2 ) . Interestingly , the KI spermatozoa exhibited a greater distance between the mitochondrial and fibrous sheaths , causing a sink and hairpin-like bend in the tail . We proposed that the annulus/septin ring might serve as an adhesive connecting these two compartments protecting sperm from deformation . The role of the annulus in determining the mitochondrial distribution is controversial . In the midpiece of SEPT4-null spermatozoa , which have no annulus , the overall mitochondrial alignment is established , although the mitochondrial architecture is defective in terms of the cristae and size [5] . Here , we observed that SEPT12 KI spermatozoa exhibit normal mitochondrial morphology and distribution in the midpiece ( Fig 2E ) . These indicate that mitochondrial defect is a SEPT4-null-specific phenotype , and the annulus is not required for establishing the mitochondrial distribution . In addition to providing insight into annular function , the present study shows that phosphomimetic mutation in SEPT12 disrupts annulus formation in KI sperm . These findings further suggest that SEPT12 phosphorylation serves as a structural determinant controlling sperm annular establishment . During sperm transit in the epididymis , annulus could be a fence that close to act as membrane diffusion barrier confining proteins to specific region , or open as one-way gate for passage of proteins in one direction [29] . For example , basigin is restricted in the principal piece by annulus in caput sperm; however , it passes through the annulus and relocates to midpiece in cauda sperm [29 , 31] . Koch et al . showed that in Ccny1-null sperm , which is deficient in Wnt signaling , SEPT4 phosphorylation through GSK3 leads to loss of high-molecular-weight SEPT4 complexes and impair barrier function of sperm annulus [15] . These results suggest that septin phosphorylation determines barrier function through affecting annulus structure . In the present study , we further demonstrate that SEPT12 phosphorylation interferes with septin-sepitn interaction leading to disrupt filament assembly of annulus structure . Collectively , we speculate that during epididymal transit of spermatozoa , septin phosphorylation status regulates septin assembly and disassembly leading to affect state of annulus fence ( diffusion barrier and open gate ) for protein restriction or translocation . Higher-order septin structures , including filaments , rings and gauzes , are built for various cellular functions in organisms ranging from yeast to humans . However , the molecular mechanisms underlying the regulation of higher-order septin structures remain largely unknown . According to structural studies , all septins comprise highly conserved G domains and divergent N- and C-termini [28] . Electron microscopy analyses of the septin complexes of C . elegans , S . cerevisiae and H . sapiens indicate that septins self-polymerize into core complexes with mirror symmetry and alternative G- and NC-interfaces [32–34] . Additionally , septin complexes join end-to-end to form filaments under a low ionic strength in vitro or by membrane-directed annealing in yeast [32–35] . These results imply identical assembly fashion to all septin complexes and higher-order septin structures . Thus , given the shared domain structure and mode of assembly , the basic mechanism in regulating dynamics of septin complexes and/or higher-order structures might be common to septins . In the present study , we observed that SEPT12 Ser198 lies in the conserved G domain , and Ser198 phosphorylation abolishes the SEPT12-SEPT7 association and thereby disrupts SEPT12 filaments . Thus , it is likely a general phenomenon that septin PTMs in a conserved G domain regulate assembly of complex and higher-order structures through the direct modulation of SEPT-SEPT association . We observed that the SEPT12 Ser198 residue was phosphorylated by PKA to impair SEPT12 filament formation and promote the accumulation of SEPT12 aggregates . Notably , the PKA target motif is found not only in SEPT12 but also in other human SEPTs , suggesting that these human septins are subject to PKA phosphorylation and might undergo changes in organized structures ( S9 Fig ) . Thus , it is likely that PKA enables a majority of human septins to achieve structural diversity associated with different functional properties . In conclusion , the results of the present study provide in vivo physiological evidence of the PTM importance in the assembly , cellular functions and physiological impact of mammalian septins . In addition , we identified the mechanism by which PTMs contribute to septin assembly . These findings highlight the involvement of mammalian septin PTMs in higher-order structure assembly and provide insight into the regulation of septin assembly .
The GFP-hSEPT12 plasmid was transfect into the 293T cells for 40 hours . SEPT12 was immune-precipitated using the anti-GFP antibody followed by SDS-PAGE and Coomassie Blue analysis . The band corresponding to GFP-SEPT12 ( ~72 kDa ) was excised , digested in-gel with trypsin and subjected for analysis by mass spectrometer . Nanoflow liquid chromatography and tandem mass spectrometry ( LC-MS/MS ) was performed by coupling a linear ion trap mass spectrometer ( LTQ Velos Pro , Thermo Fisher Scientific ) to a nanoflow LC system ( nanoACQUITY UPLC , Waters ) using a tunnel frit trap column [36] packed ( 180 μm × 20 mm ) with 5 um Symmetry C18 beads ( Waters ) and an analytical column ( BEH130 C18 , 1 . 7 um , 75 um × 250 mm , Waters ) . An acetonitrile/water gradient of 12–80% for 22 min was used for analysis of tryptic phosphopeptides . For MS analysis , up to ten ion-trap MS/MS spectra were acquired per data-dependent cycle from MS scan range 400 to 1600 m/z with the ion charge 2+ , 3+ , and 4+ . The MS/MS spectra were converted into a peak list file and searched against the human International Protein Index database ( version 3 . 61 ) using the MASCOT search algorithm ( version2 . 3 . 0; Matrix Science ) . The mass tolerance in the database search for peptide MS and MS/MS were ±0 . 5 and ±1 . 5 Da , respectively . Methionine oxidation and phosphorylation on serine , threonine , and tyrosine residuals were set as variable modifications; cysteine methylthiolation was set as fix modification . The Mascot Delta ( MD ) score was obtained from the Mascot search result files by calculation the best and second best Mascot ion scores for the correct and alternative phosphorylation site localizations on an otherwise identical peptide sequence . The false position rate ( FPR ) of phosphorylation site determination was lower than 0 . 01 by using MD score higher than 19 which were published in 2011[37] . The Institutional Review Boards of National Cheng Kung University Medical Centre and National Taiwan University Medical Center approved all animal procedures . The entire Septin12 genomic fragment in the bMQ251k06 BAC clone was obtained from the BACPAC Resource Center , and the fragment containing exon 2 to exon 10 was cloned into the pL253 targeting vector ( Fig 1B ) . A targeting construct with a serine-to-glutamate ( S196E ) substitution was introduced into exon 6 through site-directed mutagenesis , and a loxP-neo-loxP cassette was inserted between exon 7 and exon 8 of Septin12 . The targeting construct was linearized and electroporated into mouse embryonic stem cells to replace the wild-type allele of Septin12 . Cell clones with the targeted allele were selected through Southern blot analysis and transfected with the Cre recombinase-expressing plasmid to remove the cassette , resulting in the KI allele . Cell lines with the KI allele were confirmed through genotyping and injected into C57BL/6J blastocysts to generate chimeric mice . Male chimeric mice were mated with wild-type females to transmit the KI allele to their offspring . The heterozygous KI mice were bred to yield homozygous KI mice . The following primers were used for the genotyping of WT and KI mice: forward—5’-TTTCTTCCCTCACTCATCCAC-3’ , and reverse—5’-TCTACAGCATCTTACCCGAATC-3’ The generation of all septins constructs and its mutant constructs was previously described [6] . NT2/D1 and 293T cells were cultured in Dulbecco’s minimal essential medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and 1% antibiotics . For transient transfection , NT2/D1 and 293T cells were transfected using Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instructions . For immunofluorescence analysis , mouse spermatozoa and NT2/D1 cells were fixed with 4% paraformaldehyde in phosphate-buffered saline ( PBS ) , permeabilized with 0 . 1% triton X-100 in PBS , and blocked with the antibody diluent ( Dako ) . Spermatozoa and cells were subsequently incubated with an anti-SEPT2 ( 1:100 , Proteintech ) , anti-SEPT4 ( 1:100 , Santa Cruz Biotechnology ) , anti-SEPT12 ( 1:100; Abnova ) , anti-FLAG ( 1:200 , Sigma-Aldrich ) , anti-Myc ( 1:200 , Genetex ) or anti-HA ( 1:200 , Covance ) antibody at 4°C overnight , followed by washing three times and Alexa Fluor 350- , Alexa Fluor 488- , Alexa Fluor 568- or Alexa Fluor 660-labeled antibody staining ( 1:200; Molecular Probes ) . Nuclei were counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI ) . For immunohistology , the testis and epididymis were fixed in Bouin’s solution overnight and embedded in paraffin blocks . The blocks were sliced into 5-μm thin sections and applied to glass slides . The sections were deparaffinized , rehydrated and stained with hematoxylin/eosin or incubated with the anti-SEPT4 ( 1:100 ) antibody and lectin peanut agglutinin ( PNA ) -conjugated with Alexa Fluor . For immunoprecipitation analysis , 2 μg of an anti-GFP or anti-FLAG antibody was incubated with Dynabeads protein G ( Thermo Fisher Scientific ) at room temperature for 15 min on a rotator , and the cell lysates were immunoprecipitated with the bead-antibody complex at 4°C overnight . The beads were collected after brief centrifugation and washed three times with wash buffer ( 100 mM Tris , 150 mM NaCl , 2 mM EDTA , 0 . 5% Tween-20 , and 0 . 01% NP-40 ) . The precipitates were resuspended in SDS-sample buffer and denatured at 95°C for 10 min . For western blotting , the proteins were separated through sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and blotted onto PVDF membranes ( Millipore ) . The blots were then incubated with an anti-SEPT2 ( 1:1000 ) , anti-SEPT4 ( 1:250 , sigma ) , anti-SEPT6 ( 1:1000 , Santa Cruz Biotechnology ) , anti-SEPT7 ( 1:1000 , Santa Cruz Biotechnology ) , anti-His ( 1:10000 , Abcam ) , anti-GFP ( 1:4000 ) , anti-FLAG ( 1:1000 ) , anti-Myc ( 1:5000 ) , anti-HA ( 1:5000 ) or anti-phospho-Ser198 ( 1:1000 ) antibody . Anti-phospho-Ser198 was purchased from Kelowna International Scientific , Inc . and was generated through immunizing rabbits with a phosphorylated peptide ( RADpSLTMEEREA , amino acids 195 to 206 in NP_653206 . 2 ) of SEPT12 . His-tagged SEPT7 recombinant protein was purchased form MyBiosource and 5 μg His-SEPT7 pre-incubated with 30 μl Ni-NTA beads ( Qiagen ) under 5mM imidazole for an hour at 4°C . Testicular lysate were then incubated with His-SEPT7-beads complexes under 5mM imidazole overnight at 4°C . The mixtures were washed three times and subjected to western blot analysis . A total of 106 mouse spermatozoa were collected from the cauda epididymis and pre-fixed with 2 . 5% glutaraldehyde at 4°C overnight . The prefixed spermatozoa were post-fixed in a 1% OsO4 solution at room temperature for 1 h , washed three times with ddH2O , dehydrated using ethanol and propylene oxide , embedded in Epon at room temperature , and polymerized in an oven at 55°C for 1 day . Finally , 80 nm thin sections were collected on grids , stained with lead citrate and uranyl acetate and examined through transmission electron microscopy ( JEM-1400 , JEOL ) . The human SEPT12 orthologous proteins in various species were aligned using the ClustalW2 program provided by EMBL-EBI ( http://www . ebi . ac . uk/ ) . The accession numbers for SEPT12 proteins of different species were as follows: Homo sapiens ( isoform 1: NP_001147930 . 1; isoform 2: NP_653206 . 2 ) , Pan troglodytes ( isoform 1: XP_001169473 . 1; isoform 2: XP_001169539 . 1; isoform 3: XP_001169556 . 1 ) , Bos Taurus ( NP_001091612 . 1 ) , Mus musculus ( NP_081945 . 1 ) and Rat tusnorvegicus ( XP_343860 . 3 ) . The accession numbers for SEPTIN proteins of Homo sapiens were as follows: SEPT1 ( NP_443070 . 1 ) , SEPT2 ( NP_001008491 . 1 ) , SEPT3 ( isoform A: NP_663786 . 2; isoform B: NP_061979 . 3 ) , SEPT4 ( isoform 1: NP_004565 . 1; isoform 2: NP_536340 . 1; isoform 3: NP_536341 . 1 ) , SEPT5 ( NP_002679 . 2 ) , SEPT6 ( isoform A: NP_665798 . 1; isoform B: NP_055944 . 2; isoform D: NP_665801 . 1 ) , SEPT7 ( isoform 1: NP_001779 . 3; isoform 2: NP_001011553 . 2 ) , SEPT8 ( isoform A: NP_001092281 . 1; isoform B: NP_055961 . 1; isoform C: NP_001092282 . 1; isoform D: NP_001092283 . 1 ) , SEPT9 ( isoform A: NP_001106963 . 1; isoform B: NP_001106965 . 1; isoform C: NP_006631 . 2; isoform D: NP_001106967 . 1; isoform E: NP_001106964 . 1; isoform F: NP_001106968 . 1 ) , SEPT10 ( isoform 1: NP_653311 . 1; isoform 2: NP_848699 . 1 ) , SEPT11 ( NP_060713 . 1 ) and SEPT14 ( NP_997249 . 2 ) . The above information was adopted from the NCBI database ( http://www . ncbi . nlm . nih . gov/ ) . All data are presented as the means ± SEM . Statistical differences were analyzed through one-way analysis of variance ( ANOVA ) , combined with the Tukey’s Multiple Comparison Test for posterior comparisons . P-values were considered significant at *P< 0 . 05; **P< 0 . 01; ***P<0 . 001 . | Septin polymerization into filaments and rings are built for numerous cellular processes , and misregulation of septin filaments is implicated in several human diseases . Therefore , it is important to identify structural regulator of septin assembly . We showed that SEPT12 was phosphorylated on the Ser198 residue , and phosphomimetic SEPT12 KI mice display defective male fertility , impaired sperm motility and loss of septin-organized sperm annulus . Homozygous KI spermatids exhibited complete absence of septin ring assembly during annulus biogenesis . Additionally , SEPT12 phosphorylation through PKA interfered with SEPT12 polymerization into complexes and filaments . Thus , SEPT12 phosphorylation regulates SEPT12 polymerization that is involved in the septin ring assembly/sperm annulus establishment , the structural and mechanical support of sperm and robust male fertility . The present study provides novel insight into spermatogenetic regulation , highlights the importance of phosphorylation as structural regulator of septin assembly in vivo , and identifies the mechanism by which phosphorylation contributes to septin assembly . | [
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... | 2017 | SEPT12 phosphorylation results in loss of the septin ring/sperm annulus, defective sperm motility and poor male fertility |
Mosquito-borne viruses have been estimated to cause over 100 million cases of human disease annually . Many methodologies have been developed to help identify areas most at risk from transmission of these viruses . However , generally , these methodologies focus predominantly on the effects of climate on either the vectors or the pathogens they spread , and do not consider the dynamic interaction between the optimal conditions for both vector and virus . Here , we use a new approach that considers the complex interplay between the optimal temperature for virus transmission , and the optimal climate for the mosquito vectors . Using published geolocated data we identified temperature and rainfall ranges in which a number of mosquito vectors have been observed to co-occur with West Nile virus , dengue virus or chikungunya virus . We then investigated whether the optimal climate for co-occurrence of vector and virus varies between “warmer” and “cooler” adapted vectors for the same virus . We found that different mosquito vectors co-occur with the same virus at different temperatures , despite significant overlap in vector temperature ranges . Specifically , we found that co-occurrence correlates with the optimal climatic conditions for the respective vector; cooler-adapted mosquitoes tend to co-occur with the same virus in cooler conditions than their warmer-adapted counterparts . We conclude that mosquitoes appear to be most able to transmit virus in the mosquitoes’ optimal climate range , and hypothesise that this may be due to proportionally over-extended vector longevity , and other increased fitness attributes , within this optimal range . These results suggest that the threat posed by vector-competent mosquito species indigenous to temperate regions may have been underestimated , whilst the threat arising from invasive tropical vectors moving to cooler temperate regions may be overestimated .
Mosquito-borne viruses are an increasing global health problem , predominantly in developing tropical countries , where billions of people are at risk of disease . For example , the World Health Organisation estimates that there are between 50–100 million cases of dengue each year , and the problem is continuing to worsen [1] . Furthermore , over the last few decades , people in the developed world are also becoming increasingly at risk from many arboviruses; for example , West Nile virus has become endemic in the USA [2] , and appears to be spreading within Europe [3] . Additionally , recently discovered mutations in the chikungunya virus may have increased its potential for transmission in the more temperate adapted Ae . albopictus , which may increase transmission risk in temperate regions in the future [4] . The risk of virus transmission by a competent vector is determined by how many feeds it takes on susceptible hosts in its remaining lifespan after the point at which it becomes infectious . The time to becoming infectious ( after the vector has taken an infected blood feed ) is termed the extrinsic incubation period ( EIP ) . During the EIP of mosquito vectors , the virus spreads from the gut through the body to the salivary glands , and replicates in order to be transmitted; typically , this process takes several days [5] . Both mosquito longevity and virus EIP are highly sensitive to temperature [5 , 6 , 7] . An increase in temperature generally reduces both the EIP and longevity , but not necessarily by the same amount . If the EIP decreases more than longevity , more time is available for virus transmission and thus transmission risk will theoretically increase; if longevity decreases more than EIP , less time is available for virus transmission and transmission risk will theoretically decrease . In addition to the effect on longevity , temperature is also known to affect other components of vector capacity such as biting rate and larval density [8 , 9] . There is not , therefore , a straightforward relationship between temperature and arbovirus transmission risk; rather , it is largely determined by the complex interplay between the competing effects of temperature on the EIP and components of vectorial capacity including longevity . Most current mathematical models of arbovirus disease risk identify the suitable ranges for viruses and their vectors separately [10 , 11 , 12] , or identify temperature suitability for the vector within a static set of virus requirements [13 , 14] . Here , we consider this dynamic interplay between the opposing effects on virus and vector which highlights that the same virus may have different optimal temperatures in different vectors . An observable effect of this interplay is that in some cases , vector ranges extend beyond the ranges of the viruses they transmit–usually into cooler areas . For example , Culex pipiens extends farther north across North America , Europe and Asia than does West Nile virus ( WNV ) ; cooler regions such as these , with vector but no virus , are indicative of a limit created by EIP increasing more than longevity . A possible example of the converse–a vector being found in hotter climates than the virus it transmits–may be the biting midge Culicoides obsoletus sl , which is found across Europe and north Africa , and which has transmitted bluetongue virus serotype 8 ( BTV-8 ) extensively in northern Europe , but little or not at all in southern Europe [15] , where the Afrotropical Culicoides imicola is the dominant vector . C . obsoletus appears to prefer a cooler climate range [16] , with higher densities in northern rather than southern Europe , and the higher BTV-8 transmission in northern Europe suggests that its increased longevity at lower ( northern ) temperatures may outweigh the increased EIP of BTV-8 . These observations led us to hypothesize that the optimal climate conditions for the transmission of an arbovirus by a vector are a compromise between the optimal conditions for the survival of the vector and the effect of the temperature on the EIP of the virus . Here , we apply this hypothesis to the mosquito-borne arboviruses dengue , West Nile and chikungunya viruses . We predict that cooler-adapted mosquitoes have a higher vectorial capacity in a cool climate than would warm-adapted mosquitoes . This higher vectorial capacity towards the optimal temperature conditions of the species in question , we propose , would lead to increased transmission risk in cool areas by cooler-adapted mosquitoes ( Fig 1 ) . Underpinning this hypothesis is the assumption that cool-adapted species will have a proportionally greater increase in longevity at colder temperatures than would warmer-adapted species ( Fig 2 ) . To test this hypothesis , we used published geolocated data to identify temperature and rainfall ranges in which a number of mosquito vectors are observed to co-occur with a range of arboviruses . We investigated whether different mosquito species have different climatic ranges for co-occurrence with the same virus , and related this to the average temperature ranges of the areas in which the different vector species are found .
Species locations , vector-virus relationships , and country and “sub-country” temperature and rainfall data were mined from the ENHanCEd Infectious Diseases ( EID2 ) database ( Eid2 . liverpool . ac . uk ) [17] . We employed the CRUTS3 . 1 climate dataset , based on monthly means calculated over the period 1950–2000 [18] . “Sub-countries” are defined as first administrative divisions ( e . g . states in USA , counties in UK , states in India , prefectures in Japan ) . This definition is used in the EID2 database and the list of these divisions was taken from: http://www . geonames . org/export/codes . html EID2 is an open-access and evidence-based web-fronted database that provides a repository of organisms , their interactions ( in particular the host-pathogen interactions ) , and their spatial ( at country and sub-country levels ) , and temporal distributions [17] . EID2 has been built and populated with minimum human intervention , by automatically gathering data from open-access online resources , and then extracting interactions accordingly . This was based on a three-step process: EID2 automatically retrieves data from NCBI PubMed and Nucleotide Sequences Database , and as such relies entirely on the meta-data provided at submission to identify the sub-species . Of the five mosquito and three virus species used in this study , only dengue virus had a practical proportion of the data described to sub-species level ( 56 . 1% of data entries ) ; all other species had less than 3% of their data entries described to sub-species level . Consequently , it was decided to use species level , as the sub-species sample size would be too small for meaningful analysis . Occurrence and absence data from the EID2 database ( at the sub-country level ) was validated against external observed published datasets . For this validation we used the data for Ae . aegypti , Ae . albopictus and DENV as these are the most well studied and have relevant recent publications . For Ae . albopictus and Ae . aegypti we compared the EID2 data with point occurrence data published by Kraemer et al . [19] . The dengue virus occurrence data was compared to data published by Bhatt et al . [20] . Statistical analysis was performed using the R software . Maps were generated using EID2 database grid information ( 25km x 25km grid with each square associated with a country and where possible a sub-country , or water body ) and custom C# code to assign colours to the squares . The temperature experienced by a mosquito vector in a given location ( sub-country ) varies throughout the year , and it is not straightforward to capture this in a single variable . For our purposes , we first define the four-month period of the year in each sub-country when adult vectors are most likely to be active , based on temperature and rainfall data , and then we extract the mean temperature of those four months only . We call these four months the Optimal Mosquito Season ( OMS ) . To find a sub-country’s OMS we used both monthly average temperature and rainfall data . We selected the warmest consecutive four-month period ( months 1 to 4 ) which also had at least 24% of the sub-country’s total annual rainfall within a period of four months starting from the preceding month ( months -1 to 3 ) . For example , if the warmest four-month period was March to June , this would be the OMS only if the total rainfall in February to May was at least 24% of the total annual rainfall . If not , the next warmest four-month period was checked for appropriate rainfall . The one month rainfall offset is to account for much of the larval development ( for which rainfall is most important ) occurring before and very little larval development occurring towards the end of the mosquito season . The above parameters were chosen as they appear to result in the best fit of the model with general published observations on mosquito seasons around the world ( e . g . www . nhstateparks . com/mosquitos . html , http://www . fitfortravel . nhs . uk/destinations/ , www . mosquitoreviews . com ) ; as well as published surveys ( for Thailand [21] , Ivory Coast [22] , Republic of Korea [23] and Brazil [24] ) . Given the lack of published models or databases for mosquito activity by month , our validation was restricted to manually comparing to published sources . The mean OMS temperature for all sub-countries that a vector species inhabits was determined in order to estimate the optimal temperature for each vector species . West Nile virus ( WNV ) , chikungunya virus ( CHIKV ) and dengue virus ( DENV ) were chosen for analysis to provide a range of globally important viruses transmitted by a wide variety of mosquito vectors . The list of known and potential vectors , as defined by the ENHanCEd Infectious Diseases ( EID2 ) database , for each virus is shown in Table 1 . Selection criteria for further analysis were volume of data available for analysis ( >10 sub-countries in which it is the sole vector species ( whether virus is present or not ) , and >3 sub-countries in which it is the sole vector and co-occurs with the virus ) . The requirement for the mosquito to be the sole vector species in analysed sub-countries was because it was assumed that for these sub-countries , the vector in question is likely to be responsible for most of the virus transmission , and thus analysis could be performed with fewer concerns over contributions to transmission made by other vector species . There were many sub-countries where more than one vector species for the respective virus is recorded in the EID2 database; these records were ignored in our analysis , as it was not possible to ascertain which of the vectors are transmitting the virus in those sub-countries ( 94/326 for CHIKV; 89/272 for DENV; 179/387 for WNV , numbers include all sub-countries for all vectors , not just vectors used in the analysis here ) . Whilst we focused on a small number of possible vectors ( shown in bold ) , we defined “sole vector” as being the only recorded vector from the full list , not just from the analysed subset . For each virus , we then extracted the mean temperature of the OMS for every sub-country for which the EID2 database has recorded the presence of a single vector species for that virus . For each vector species for each virus , OMS temperatures were compared between sub-countries with vector only and sub-countries with vector/virus co-occurrence . The OMS temperature ranges were then compared statistically to examine whether two or more vectors co-occur with the same virus at significantly different OMS ranges , whilst accounting for the different temperature ranges at which each vector occurs ( i . e . does a cooler-adapted vector co-occur with virus more frequently at the cool end of its range vs . a warmer-adapted vector ? ) . This was achieved by standardizing the temperature data for each vector distribution , before conducting the test as described below . Assume mosquito j lives in nj different sub-countries , then the standardised temperatures: t*ij , i ∈ ( 1… , j ) for this sub-country are: tij*=tij−t¯jsd ( tj ) , where t¯j is the mean and sd ( tj ) the standard deviation of the raw set of temperatures tij . Following standardisation , each vector’s sub-country-level temperature data have means of zero and standard deviations of 1 . Two-tailed Wilcoxon rank-sum tests were then performed on the subset of standardised data for the sub-countries containing both virus and vector . Kernel density estimation was used to estimate the empirical density of mosquito/virus co-occurrence without assuming a known underlying distribution . The underlying formula to estimate the distribution f of a variable X , of which we have n observations x1 , … , xn is ( Eq 1 ) : f^ ( x ) =1hn∑i=1nK ( x−xih ) Eq 1 With K being an appropriate kernel function and h being the bandwith for the evaluation of K . In this case a Gaussian kernel was used , i . e . the function K ( u ) is ( Eq 2 ) : K ( u ) =12πexp ( −u2u ) Eq 2 The bandwidth h was calculated adaptively . For the subsets of the data the estimated density was multiplied by the proportion of data in each subset .
See supplementary materials ( S1 , S2 and S3 Figs and S1 Table ) for details about EID2 data validation; briefly , 89 . 8% , 91 . 8% and 70 . 6% of observational points ( as published in [19 , 20] ) were inside EID2 polygons for Ae . aegypti , Ae . albopictus and DENV respectively . The periods of the OMS as defined by our parameters in different sub-countries are shown in Fig 3A . The mean temperature of the four months was taken and defined as that sub-country’s OMS temperature; the values of this temperature in different sub-countries are shown in Fig 3B . A detailed list of all sub-countries with their OMS months and temperatures can be found in the supporting information ( S2 Appendix ) . For CHIKV , the standardised temperature ranges at which Ae . aegypti and Ae . albopictus co-occur with virus were found to be highly significantly different ( two-tailed Wilcoxon rank-sum test , p<0 . 0001 ) , the peak for Ae . albopictus being at ~18°C , whilst the Ae . aegypti peak is at ~28°C ( Fig 4 ) . For DENV , both Ae . aegypti and Ae . albopictus have a peak for co-occurrence with virus at around 24–26°C; while Ae . albopictus has an additional , and larger , peak at about 18°C . However , overall there was no significant difference ( two-tailed Wilcoxon rank-sum test , p = 0 . 3442 ) . For WNV , three vectors were analysed , therefore two-tailed pairwise Wilcoxon rank-sum tests with Holm-adjusted p-values were performed . A significant difference was found between the standardized temperature ranges of Ae . vexans and Cx . quinquefasciatus ( p = 0 . 0068 ) . No significant difference was found between pairwise comparisons of either Cx . pipiens and Cx . quinquefasciatus or Cx . pipiens and Ae . vexans ( both p = 0 . 0806 ) due to overlap between their ranges . However , taken together , the data appear to show a descending order of temperature range of 1 ) Cx . quinquefasciatus , 2 ) Cx . pipiens , 3 ) Ae . vexans . In order to estimate the optimal temperature for each vector species , the mean OMS temperature for all sub-countries that a vector species inhabits was determined . These temperatures are shown in Table 2 . The mean OMS temperature of single-vector sub-countries for each of these vectors are shown to identify any bias in the use of single-vector sub-countries . For four of the five vectors there was no significant difference between the two temperatures ( Wilcoxon rank-sum tests ) ; Ae . albopictus however , shows a significant decrease in temperature for mean single-vector sub-country OMS ( P<0 . 001 ) . The mean OMS temperatures were compared for species that are vectors of the same virus; allowing identification of pairs of vectors that have both a difference in their virus co-occurrence temperature range and a difference in their optimal vector temperature range . For the CHIKV and DENV vectors Ae . aegypti and Ae . albopictus , there was found to be a significant difference in the mean OMS temperatures ( two-tailed Wilcoxon rank-sum test , p = 0 . 0031 ) . Again , two-tailed pairwise Wilcoxon rank-sum tests with Holm-adjusted p-values were performed for the three vectors of WNV . No significant difference between the mean temperatures of Cx . pipiens and Ae . vexans was found ( p = 0 . 95 ) ; however , highly significant differences were found between the mean OMS temperatures for Cx . pipiens and Cx . quinquefasciatus , and Ae . vexans and Cx . quinquefasciatus ( both p<0 . 0001 ) .
We have shown that different mosquito vectors appear to co-occur with the same virus at different temperatures , despite extensive overlap in the temperature ranges occupied by the vectors themselves . For example , our data show that Ae . albopictus is the primary vector for CHIKV in Regione Lombardia , Italy , which has an average OMS temperature of 17 . 3°C; this is outside of the OMS temperature range in which we observe Ae . aegypti as the sole CHIKV transmitter , but well within the observed OMS temperature range of the vector . Conversely , the State of Andhra Pradesh , India , whose average OMS temperature is 29 . 8°C , is shown here to have CHIKV primarily transmitted by Ae . aegypti; again , this OMS temperature is above that in which we observe Ae . albopictus as the sole vector , but within the vector’s own habitable range . Furthermore , we show that this pattern is broadly in line with that of the mean temperatures of the ranges the vectors inhabit–in other words , species that are found , on average , in cool regions tend to co-occur with virus at the cool end of their temperature range . Taken together , the data presented here suggest that mosquitoes are more competent vectors in climates that are more similar to those which they are adapted to . That is , the same virus can have different optimal temperatures/climates depending on the vector in question . We hypothesise that this effect is primarily the result of a proportionally greater increase in vector longevity versus EIP duration in the vectors’ preferred climate , as well as the effect of temperature on other components of vector capacity such as vector density . Thus , whilst great care must be taken when extrapolating from these results , we further suggest that the current perceived threat of invasive tropical vectors to temperate regions might be an over-estimation . Conversely however , we suggest that lab-competent mosquitoes native to temperate regions [25 , 26] potentially pose a much greater risk to temperate regions than is currently perceived , primarily due to their adaptation to their native environment . We believe that this is the first study to consider the interplay between the optimal climates for vector and virus using observational data; our results show a distinct difference between these optimal transmission climates . In this study we have used the EID2 database to look at sub-countries with records of only a single vector for a given virus . It was assumed that for these sub-countries , the vector in question is likely to be responsible for all or most of the virus transmission , and thus analysis can be performed with fewer concerns over contributions to transmission made by other vector species . All data procured from EID2 were manually spot-checked for accuracy by checking the evidence paper and/or sequence file . Unexpected data were verified , corrected or deleted e . g . removal of false positive species location data such as WNV presence in England and Wales ( in this particular example , EID2 reported the presence of WNV in England and Wales as a result of multiple papers reporting the absence or potential of WNV in these countries ) . With the exception of a misclassification error ( ‘Culex pipiens fatigans’ appearing when searching for ‘Culex pipiens’ and not appearing when searching for ‘Culex quinquefasciatus’ ) , only eight out of the 1000 spot-checked sub-country calls were found to be inaccurate . In addition , the location data for Ae . aegypti , Ae . albopictus and DENV were validated to recently published data sets ( see supplementary materials ) . Despite the statistical significance of our results , we acknowledge certain limitations to the data and analyses used here that restrict our ability to draw firm conclusions . In this section we discuss the following limitations to the data and methods: ( i ) The mean OMS temperature of all sub-countries containing a vector was compared to the mean OMS for single-vector sub countries in order to identify any bias introduced during this ‘sub-sampling’ of the data . For four of the five vectors , there was no significant difference , with only Ae . albopictus showing a significant change . This is likely the result of the exclusion of sub-countries on the warmer end of the Ae . albopictus range in which the vector more frequently co-habits with Ae . aegypti ( the larger numbers of single-vector sub-countries containing only Ae . aegypti appear to have minimised this effect on Ae . aegypti OMS temperatures ) . We acknowledge that this represents a possible source of bias in the Ae . albopictus analyses , which may have biased the results showing Ae . albopictus to co-occur more towards the cool end of its range . However , we do not think that this is a major concern . Firstly , there is still very significant overlap between the vector temperature ranges of Ae . albopictus and Ae . aegypti using single-vector sub-countries , and with the chikungunya data , the majority of co-occurrence of vector and virus is below the temperature where co-occurrence begins for Ae . aegypti: even if large amounts of data from the overlapping range of Ae . albopictus has been lost , this peak would still show a marked difference . Secondly , the pattern of vectors found in cool regions transmitting at cooler temperatures is still seen in the three-way comparison for West Nile virus , despite none of these vectors showing skewing of their data . ( ii ) A shortfall of the EID2 database is the ‘patchy’ data; some countries and sub-countries are under-represented in terms of sequence uploads to Genbank . Indeed , there is a correlation between wealth of a region ( as measured by the gross domestic product or GDP ) and the number of sequences it generates and uploads [27] . This , combined with the correlation of cooler climate and wealth , may result in a greater number of cooler sub-countries being analysed . However , this effect is expected to be consistent across all species of vector and virus from within the same sub-country , which would minimise any potential bias . In sub-countries where there are no mosquito species data , these sub-countries are not included in the analysis , minimising the effect of patchy data on the analysis beyond a reduction in statistical power . Furthermore , a large proportion of the sub-countries that do have mosquito species data do so because of NCBI papers and/or sequences pertaining to a general mosquito survey , decreasing any potential problems due to incomplete reporting of the species present in a given sub-country . Consequently , the set of sub-countries where there is assumed to be only one vector is likely to be adequately reliable for the scope of this study . As a result of using only single-vector sub-countries to define virus/vector co-occurrence temperature ranges , a proportion of the data was unusable . A related shortfall is that the majority of data in Pubmed and Genbank does not contain subspecies information; consequently , our analysis was limited to the species level and may have missed important distinctions between different subspecies . This is one possible explanation for the two peaks evident in the temperature range of Ae . albopictus ( Fig 4 ) , although there may be others . Furthermore , within species there can exist a population structure which correlates with environmental conditions , such as climate . This population structure can result in local adaptations ( to both vector and virus ) which may make different populations from the same species more suited for transmission in different climates . This cannot be accounted for using the methodology in this study as we have no information on the population structure and associated differences in competence . Instead we were restricted to analyse species as single units with no regard for population structure . Whilst this may have an effect on our results , we theorise that this type of population structure would serve to increase local vector and virus fitness in their respective local climates , consequently , it would likely lead to a non-directional increase in competence throughout all climate ranges and thus would not skew our findings . ( iii ) Due to the lack of a finer geo-location scale ( as a result of meta-data limitations ) , sub-country level data were used in our analysis . Whilst this is the finest-scale that could be used for this analysis , a small number of these sub-countries are relatively large ( such as Texas , Alaska , Queensland , Western Australia etc . ) , and there is substantial ecological variation within some of these territories . However , this applies to only a relatively small number of sub-countries and we believe it is unlikely to significantly affect the conclusions . The vast majority of sub-countries are considerably smaller and less ecologically heterogeneous than those mentioned above . ( iv ) As mentioned , many current models rely mainly or solely on temperature data for their predictions , and generally consider viruses and their vectors separately rather than considering the interplay between them . Our methodology addresses these issues by defining an optimal mosquito season ( OMS ) taking into account both temperature and rainfall . In addition , unlike most previous models , our methodology is not limited by laboratory-confined experimental data . All inferences about the climate ranges of vectors and viruses come from published geolocated field data , removing the need for experimental data , which may lack ecological validity , and allowing us to more accurately predict real-world climate ranges of mosquito species . One significant limitation of the OMS method however , is that it produces a single mean temperature for the four-month season , and loses for example any information about variation of temperature and rainfall within the season , which may be important for transmission . Using the described methodology , our model predicts that for the WNV vectors Ae . vexans and Cx . quinquefasciatus , Ae . vexans has both a significantly cooler ideal temperature range than Cx . quinquefasciatus and also a significantly cooler range in which it transmits ( co-occurs with ) the virus . We propose that the cool climate adaptation of Ae . vexans increases its vectorial capacity at cooler temperatures–possibly via a disproportional increase in longevity compared to EIP–over that of Cx . quinquefasciatus in such regions . This is in line with previous research showing that the EIP of WNV is very strongly affected by temperature , with the EIP of WNV in Cx . tarsalis being approximately seven times longer at 14°C than at 26°C [28] . Similar findings are also seen for CHIKV; the two major vectors ( Ae . aegypti and Ae . albopictus ) have highly significantly different temperature ranges , and , the cooler-adapted Ae . albopictus has a significantly cooler virus transmission temperature range than the warmer-adapted Ae . aegypti . However , the same is not seen for DENV , where the transmission temperature ranges of the two vectors ( again Ae . aegypti and Ae . albopictus ) are not significantly different . A possible biological explanation for the lack of an effect for DENV could be the effect of temperature on the EIP of the virus . A recent meta-analysis of the effect of temperature on the EIP of DENV [29] shows a very strong reduction of the EIP with increasing temperature at high temperatures ( 25–30°C ) , but little effect of temperature on the EIP at lower temperatures ( <20°C ) . This limited effect on EIP at lower temperatures means that even warm-adapted species , such as Ae . aegypti , may still be able to transmit the virus at lower temperatures despite the temperature being sub-optimal for the vector; similarly , at high temperatures , the large reduction in EIP means that even cool-adapted vectors such as Ae . albopictus survive long enough to transmit . Taken together , the EIP-temperature profile of DENV is in line with the wider competent temperature ranges for vectors , particularly outside of their optimal temperatures . A recent meta-analysis of vector longevity at different temperatures [30] predicts a higher longevity of Ae . aegypti at high temperatures ( ~>35°C ) than Ae . albopictus , whilst Ae . albopictus has a higher longevity at lower temperatures . Given this , along with the CHIKV data and modelling presented here , the evidence is in line with our hypothesis that at their respective optimal temperatures , Ae . albopictus and Ae . aegypti live significantly longer and have higher capacity to transmit the virus . A third temperature-sensitive factor , important in determining the vectorial capacity of a species , is the feeding interval . However , while investigation of the effect on feeding interval is beyond the scope of this study , there is evidence that increasing temperature increases blood-feeding frequency in a range of mosquitoes [31] . This factor could decrease the risk of transmission in cool areas , however , more research would be needed , especially for native temperate mosquito species , as to whether this holds true for cool-adapted species . Taken together , these data , and the wider literature , are in line with the proposed hypothesis that the optimal climate conditions for the transmission of an arbovirus are due in part to a compromise between optimal conditions for the growth , longevity and competence of the vector and the effect of the temperature on the EIP of the virus . The recent example of BTV-8 transmission by the cool-adapted midge Culicoides obsoletus sl , in northern Europe , but not southern Europe , despite being present in both , is consistent with this hypothesis . To our knowledge , this is the first study using observational data that demonstrates different mosquito vectors have different competent climate ranges for the same virus . For CHIKV and WNV , their respective vectors appear to have a higher competence in temperature ranges to which they are more adapted , suggesting that the threat from arbovirus transmission is greater from vectors native to the respective region ( or invasive species from a region with a similar climate ) . This has important implications in the estimation of risk from vector/virus combinations , especially in more temperate regions which may be at greater risk from competent native temperate vectors than is currently believed . | Mosquito-borne viruses , such as dengue , are believed to cause over 100 million cases of human disease annually . Current mathematical models that aim to predict risk of virus transmission are generally either highly “mosquito-centric” or “virus-centric” . For virus transmission to occur , conditions need to be suitable for both mosquito and virus: hence , we propose a novel approach that considers the interplay between the different optimal conditions for the mosquito and the virus . Our findings indicate that warmer- or colder- adapted mosquitoes are significantly more efficient vectors in warmer or colder climates respectively . Consequently , we propose that there is currently an underestimation of risk to temperate regions from their native and cooler adapted mosquitoes . | [
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"animals"... | 2017 | Co-occurrence of viruses and mosquitoes at the vectors’ optimal climate range: An underestimated risk to temperate regions? |
The Cut homeobox 1 ( CUX1 ) gene is a target of loss-of-heterozygosity in many cancers , yet elevated CUX1 expression is frequently observed and is associated with shorter disease-free survival . The dual role of CUX1 in cancer is illustrated by the fact that most cell lines with CUX1 LOH display amplification of the remaining allele , suggesting that decreased CUX1 expression facilitates tumor development while increased CUX1 expression is needed in tumorigenic cells . Indeed , CUX1 was found in a genome-wide RNAi screen to identify synthetic lethal interactions with oncogenic RAS . Here we show that CUX1 functions in base excision repair as an ancillary factor for the 8-oxoG-DNA glycosylase , OGG1 . Single cell gel electrophoresis ( comet assay ) reveals that Cux1+/− MEFs are haploinsufficient for the repair of oxidative DNA damage , whereas elevated CUX1 levels accelerate DNA repair . In vitro base excision repair assays with purified components demonstrate that CUX1 directly stimulates OGG1's enzymatic activity . Elevated reactive oxygen species ( ROS ) levels in cells with sustained RAS pathway activation can cause cellular senescence . We show that elevated expression of either CUX1 or OGG1 prevents RAS-induced senescence in primary cells , and that CUX1 knockdown is synthetic lethal with oncogenic RAS in human cancer cells . Elevated CUX1 expression in a transgenic mouse model enables the emergence of mammary tumors with spontaneous activating Kras mutations . We confirmed cooperation between KrasG12V and CUX1 in a lung tumor model . Cancer cells can overcome the antiproliferative effects of excessive DNA damage by inactivating a DNA damage response pathway such as ATM or p53 signaling . Our findings reveal an alternate mechanism to allow sustained proliferation in RAS-transformed cells through increased DNA base excision repair capability . The heightened dependency of RAS-transformed cells on base excision repair may provide a therapeutic window that could be exploited with drugs that specifically target this pathway .
Oncogenic potential of RAS signaling is frequently activated in human cancers as a result of point mutations in RAS genes or alterations in upstream or downstream signaling proteins ( reviewed in [1] , [2] ) . Oncogenic RAS cannot , however , transform primary culture cells alone but requires cooperation with other oncogenic stimulants , a finding that contributed to the concept of multistep tumorigenesis [3] . Subsequent studies have revealed that oncogenic RAS , as well as other oncogenes , cause senescence in both rodent and human primary cells [4] . The concomitant accumulation of p53 , p21CDKN1A , and p16INK4a , together with the finding that proliferation arrest could be bypassed by inactivating the Rb and p53 pathways , promoted the concept that oncogene-induced senescence was a component of the DNA damage response ( DDR ) that evolved as a tumor suppression mechanism [5] . RAS-induced senescence results from the heightened production of reactive oxygen species ( ROS ) [6] , [7] through increased expression and activity of NADPH oxidases [8] , [9] . Among the most deleterious of ROS-induced DNA adducts is 7 , 8-dihydro-8-oxoguanine ( 8-oxoG ) , which can mispair with adenine to cause G-C to T-A transversion mutations [10] . The well-conserved cellular defence system against 8-oxoG involves three main enzymes: MTH1 ( MutT in bacteria ) , a triphosphatase that hydrolyses 8-oxo-dGTP to remove it from the dNTP pool; MYH1 ( MutY in bacteria ) , a DNA glycosylase that catalyzes the excision of adenine from 8-oxoG·A mispairs; and OGG1 , a DNA glycosylase that excises 8-oxoG opposite cytosine [11] . The critical role played by 8-oxoG in triggering senescence was demonstrated in experiments where shRNA-mediated knockdown of MTH1 in human skin fibroblasts led to an increase in 8-oxoG levels and caused a senescent phenotype that was associated with several salient features of oncogene-induced senescence including senescence-associated beta-galactosidase ( SA-βgal ) activity , elevation of p53 , p21CKI , and p16INK4a proteins , and accumulation of DNA damage [12] . Conversely , MTHI overexpression prevents RAS-induced DDR and the associated premature senescence without affecting ROS levels [13] . In light of these findings , the elevated MTH1 expression in cancers with frequent activating RAS mutations appears to represent a case of nononcogene addiction [14] , [15] . This concept posits that tumor cells are acutely dependent on heightened expression or activity of proteins that are not themselves classical oncogenes [16] . High MTH1 expression in tumor cells likely provides a mechanism of adaptation to prevent senescence in response to excessive amount of ROS . The Cut homeobox 1 ( CUX1 ) gene has been implicated in cancer as both a potential tumor suppressor and an oncogene ( reviewed in [17]–[19] ) . On the one hand , CUX1 is located in the 7q22 . 1 chromosomal region , which is the target of loss-of-heterozygosity in a number of cancers [20]–[22] , and recent studies have pointed to CUX1 being as the putative tumor suppressor on 7q22 . 1 [23]–[26] . Yet no mutation has been found in the remaining allele [27]–[30] . The accumulated evidence supports a model of haploinsufficiency whereby the reduced expression of CUX1 would contribute to the development of the disease [20] . On the other hand , elevated CUX1 expression is frequently observed in various cancers and is associated with shorter disease-free survival ( [31]–[33] , reviewed in [18] ) . In particular , the comprehensive molecular characterization of human colon and rectal cancer rated CUX1 as the fifth most highly relevant gene ( p value = 3×10−10 ) on a scale showing the correlation between tumor aggressiveness and gene expression/somatic copy number alterations [31] . The dual role of CUX1 in cancer is illustrated by the fact that most cell lines with LOH of CUX1 display amplification of the remaining allele ( http://cancer . sanger . ac . uk/cancergenome/projects/cell_lines/ ) , suggesting that decreased CUX1 expression facilitates tumor initiation while increased CUX1 expression is associated with tumor progression . Indeed , CUX1 was found in a genome-wide RNAi screen to identify synthetic lethal interactions with oncogenic RAS [34] . CUX1 encodes two main isoforms that exhibit strikingly different DNA binding and transcriptional properties ( reviewed in [17] ) . The full-length protein , p200 CUX1 , contains four evolutionarily conserved DNA binding domains consisting of three Cut repeats ( CR1 , CR2 , and CR3 ) and a Cut homeodomain ( HD ) [35]–[38] . p200 CUX1 is an abundant protein that binds to DNA with extremely fast kinetics ( rapid “on” and “off” rates ) [39] . These properties are not consistent with a role as a classical transcription factor , which are present in low abundance and bind stably to DNA . In mid-G1 phase , 1% to 5% of p200 CUX1 is proteolytically processed by a nuclear cathepsin L isoform to produce the p110 CUX1 isoform [40] , [41] . This shorter CUX1 isoform stably interacts with DNA and , depending on promoter context , can function as transcriptional repressor or activator [42] , [43] . Another isoform that is aberrantly expressed in human breast cancers , p75 CUX1 , was found to exhibit DNA binding and transcriptional properties similar to that of p110 CUX1 [44] . Transcription and cell-based assays demonstrated a role for CUX1 in cell cycle progression and cell proliferation [45] , [46] , strengthening of the spindle assembly checkpoint [47] , DDRs [48] , cell migration and invasion [32] , [43] , resistance to apoptotic signals [33] , and dendrite branching and spine development in cortical neurons [49] . The role of CUX1 in many processes was demonstrated using knockdown or genetic inactivation approaches . Knockdown and genetic inactivation approaches have revealed multiples roles of CUX1 , but which CUX1 isoform is active in each process could not be established from these methods [32] , [33] , [49]–[51] . Overexpression studies have demonstrated that p110 CUX1 can stimulate cell cycle progression and cell motility , while the p200 CUX1 isoform is inactive in these assays [43] , [45] . Early studies described p200 CUX1 as a transcriptional repressor that functions in precursor cells to down-regulate the expression of genes that become expressed only in terminally differentiated cells [52]–[56] . However , immunohistochemical evidence demonstrates that CUX1 is highly expressed in terminally differentiated cells of several tissues including neurons of the cerebral cortex [33] , [49] , [51] . The molecular and cellular functions of p200 CUX1 remain to be established . Moreover , while the stimulation of proliferation , cell motility , and resistance to apoptosis provide mechanisms by which CUX1 may contribute to tumorigenicity [32] , [33] , [43] , [45] , we have yet to identify a molecular function that could explain the status of CUX1 as a haplo-insufficient tumor suppressor gene . To define and compare the oncogenic potential of CUX1 isoforms without interference from integration site effects or transgene copy number , we used the method of “specific transgenesis” whereby MMTV-p110 CUX1 or MMTV-p200 CUX1 transgenes were integrated by homologous recombination into the hprt locus . We previously reported that MMTV-p110 CUX1 transgenic mice develop mammary tumors of various histological types after a long latency [57] . Here we show that MMTV-p200 CUX1 transgenic mice develop mammary tumors with the same penetrance and similar long latency , however a major difference between these transgenic models is that activating mutations in Kras were observed in 45% of mammary tumors that developed in MMTV-p200 transgenic mice . Using lentiviral infections in the lung , we confirmed that p200 CUX1 cooperates with activated Kras in tumor formation . Cell-based assays showed that CUX1 accelerates the repair of oxidative DNA damage and prevents RAS-induced senescence in primary fibroblasts . Mechanistic studies revealed that CUX1 functions in base excision repair as an ancillary factor that stimulates the activity of the OGG1 DNA glycosylase . The heightened DNA repair capability conferred by high CUX1 expression is needed to enable the proliferation of RAS-transformed cells in the presence of elevated ROS . On the other hand , the role of CUX1 in base excision repair may explain how haplodeficient expression of CUX1 may contribute to tumor initiation .
Characterization of the mammary tumors that developed in the MMTV-p75 and MMTV-p110 CUX1 transgenic mice has previously been reported [57] . To assess and compare the oncogenic potential of p200 CUX1 with that of p75 and p110 CUX1 , as previously we used site-specific transgenesis into the hprt locus to generate transgenic mice expressing p200 CUX1 under the control of the mouse mammary tumor virus long terminal repeat ( Figure S1A ) [58] . This strategy minimizes variation from copy number and integration site effects , thus ensuring that each transgene is under the influence of the same regulatory sequences . Transgene expression was detected during pregnancy ( Figure S1B ) . We observed increased ductal branching and budding at 3 months and during pregnancy in transgenic mice ( Figure S1C , 3 months , 7 . 5 and 13 . 5 days ) . Moreover , involution appeared to be delayed in transgenic mice ( Figure S1D , 1 day involution ) . Cohorts of multiparous MMTV-p200 CUX1 ( n = 129 ) and wild-type FVB mice ( n = 128 ) were monitored for tumor incidence over 2 years ( Figure 1A , Kaplan-Meier plots ) . Tumors were detected primarily in the mammary glands and lungs ( Table 1 ) . Mammary tumors developed in 20 . 9% of p200 CUX1 transgenic lines as compared to 2 . 4% of wild-type FVB/N mice ( Table 1 ) . Histopathological analysis revealed that mammary tumors were of diverse histopathological types ( Figure 1B and Table S1 ) . Adenosquamous carcinoma , solid carcinoma , carcinoma papillary , or carcinoma cribiform were observed ( Figure 1B ) . As expected , CUX1 transgene mRNA was detected in all mammary tumors ( Figure 1C ) . In summary , mammary-specific p200 CUX1 expression increased the incidence of late-onset mammary tumors of various histological types . We investigated CUX1 expression and DNA binding activity in mammary tumors and normal mammary glands of age-matched transgenic littermates ( Figure S2A ) . Western blot analysis of normal mammary gland tissues revealed a major band of apparent M . W . of 150 kDa and a few other weaker bands of lower M . W . ( Figure S2A , lanes 3 and 4 ) . None of these proteins were able to bind to DNA as judged from a Southwestern assay using a CUX1 consensus binding site ( Figure S2B , lanes 3 and 4 ) . These results are consistent with those of a previous study that described a C-terminally truncated p150 CUX1 isoform in differentiated mammary glands [59] . In contrast , in tumor samples and in cell lines derived from MMTV-p200 tumors , we observed expression of a CUX1 protein of apparent M . W . of 200 kDa as well as many bands of lower M . W . including a species migrating at 110 kDa ( Figure S2A , lanes 1 , 2 , 5 , 6 , 7 , 8 ) . This species was also recognized by an HA-specific antibody ( unpublished data ) . Moreover , proteins of apparent M . W . of ∼200 , ∼140 , and ∼110 kDa possessed CUX1-site-specific DNA binding activity as revealed by Southwestern blotting ( Figure S2B , lanes 1 , 2 , 5 , 6 , 7 , 8 ) . The presence of an active p110 CUX1 species carrying an HA tag in tumor samples and tumor-derived cell lines led us to assess cathepsin L expression in mammary tumors from MMTV-p200 , p110 , and p75 CUX1 transgenic mice . Cathepsin L mRNA was elevated in the majority of MMTV-p200 CUX1 mammary tumors , however the same was not true for mammary tumors from MMTV-p110 and MMTV-p75 CUX1 transgenic mice ( Figure S2D ) . These results indicate that cathepsin L expression was elevated specifically in MMTV-p200 CUX1 transgenic mice . Elevated cathepsin L expression has been observed in RAS-transformed cells [60]–[63] . Moreover , retroviral expression of an activated RAS oncogene leads to a rapid increase of both cathepsin L expression and CUX1 proteolytic processing [63] . We therefore performed cDNA sequencing to look for the presence of activating mutations in genes implicated in the RAS pathway . No mutation was found in Nras , Hras , Braf , Pik3ca , Pten , or Mek1 ( n = 11 ) . However , mutations within the Kras genes were identified in 5 out of 11 mammary tumors from MMTV-p200 CUX1 transgenic mice ( Table 2 ) . These mutations replace a glycine with an aspartic acid at codon 12 ( G12D ) or a glutamine with a leucine at codon 61 ( Q61L ) . Such mutations were previously reported to maintain KRAS in an active GTP-bound state ( reviewed in [2] ) . Forty-five percent of mammary tumors from MMTV-p200 CUX1 transgenic mice sustained activating Kras mutations . This finding suggested that CUX1 and activated KRAS cooperate in tumor development . As a rapid assay to test this hypothesis , we infected the lungs of mice with lentiviruses expressing CUX1 , KRASG12V , or both CUX1 and KRASG12V ( Figure 2A ) . CUX1 expression failed to cause tumors to form when assessed at 19 wk postinfection . KRASG12V expression resulted in an average of two small tumors per mouse developed in four out of seven mice . In contrast 3 . 6 large tumors per mouse were observed in 8 out of 10 mice that received both CUX1 and KRASG12V ( Figure 2C , D ) . In summary , although a small number of mice were assessed , the calculated summed area of all tumors indicated that the total tumor burden was 7 . 5-fold higher in mice infected with a lentivirus expressing both CUX1 and KRASG12V than in mice that received KRASG12V alone ( p<0 . 05 , Mann-Whitney test ) . Moreover , pathophysiological analysis of these tumors demonstrates that whereas the KRASG12V mice solely developed grade 1 adenomas ( or adenomas with grade 1 nuclear atypia ) , mice expressing both CUX1 and KRASG12V developed higher grade adenomas ( grades 1 and 2 ) and one large adenocarcinomas ( Figure 2B ) . Oncogenic RAS cannot itself transform primary culture cells but induces senescence in both rodent and human primary cells [4] . The repeated finding of activating Kras mutations in MMTV-p200 CUX1 transgenic mice suggested that the CUX1 transgene provided a terrain in which rare cells that spontaneously acquire an activating Kras mutation could proliferate and evolve to become tumorigenic . To test this notion , we examined the proliferation of IMR90 human primary lung fibroblast cells following infection with retroviruses expressing HRASG12V , p200 CUX1 , or control virus ( Figure 3A ) . Cells infected with the retrovirus expressing HRASG12V fail to proliferate and stained positive for senescence-associated β-galactosidase ( SA-βgal ) activity ( Figure 3A , B ) . Co-expression of p200 CUX1 enabled RAS expressing cells to proliferate normally ( Figure 3A ) and prevented SA-βgal activity ( Figure 3B ) . RAS-induced senescence has been linked to the accumulation of DNA damage caused by ROS or replicative stress [6] , [9] , [13] . As reported [9] , HRASG12V expression in IMR90 cells resulted in DNA damage as assessed with single cell gel electrophoresis ( comet assays ) , and immunofluorescence microscopy for phospho-H2AX ( γ-H2AX ) antibody indicated that higher levels of DNA damage accumulated in IMR90 cells expressing HRASG12V ( Figure 3C , D ) . Co-expression of p200 CUX1 with HRASG12V , however , completely abrogated the increase in DNA damage and greatly reduced the proportion of cells with more than 5 γ-H2AX foci ( Figure 3C , D ) . We considered two mechanisms by which p200 CUX1 might mitigate DNA damage: it may reduce ROS levels or it may accelerate the repair of oxidative DNA damage . ROS measurements indicated that p200 CUX1 does not reduce but rather increases ROS levels ( Figure 3E ) . Therefore , this is not the mechanism by which CUX1 prevents RAS-induced senescence . To evaluate the effect of p200 CUX1 on oxidative DNA damage repair , IMR90 cells carrying an empty vector or expressing p200 CUX1 were treated with peroxide and allowed to recover for various periods of time before their DNA was assessed for damage in comet assays . Comet assays conducted under alkaline conditions ( pH>13 ) detect double-strand and single-strand breaks , abasic sites , and several types of altered bases that are intrinsically labile at high pH . When performed at pH 10 , this assay only detects double- and single-strand breaks . However , at pH 10 addition of DNA glycosylases allows the detection of specific types of altered bases . The formamidopyrimidine DNA-glycosylase ( FPG ) cleaves the DNA at 8-oxoG ( the most abundant oxidized base ) , formamidopyrimidines , a number of oxidized pyrimidines , and apurinic sites [64] . Comet assays at pH>13 indicated that total DNA damage was repaired more rapidly in cells expressing p200 CUX1 ( Figure 3F ) . Similar results were obtained in REF52 rat embryo fibroblasts ( Figure S3 ) . Comet assays at pH 10 in the presence of FPG demonstrated that repair of oxidized bases was accelerated by p200 CUX1 ( Figure 3H ) . Results of comet assays at pH 10 indicated that most additional single-strand break damage was repaired at 15 min ( Figure 3G ) . However , because base excision repair generates single-strand breaks as intermediates , increased damage was observed at 30 and 60 min in the vector cells ( Figure 3G ) . Together these results suggest that elevated CUX1 expression enables RAS-transformed cells to rapidly repair oxidative DNA damage , thereby allowing cells to avoid senescence and continue to proliferate . In support of this notion , expression of ectopic human 8-oxoG DNA glycosylase , OGG1 , prevented RAS-induced growth arrest and reduced the proportion of cells exhibiting SA-βgal activity both in IMR90 and REF52 cells ( Figures 3I and S3E ) . A genome-wide RNAi screen to identify synthetic lethal interactions with the KRAS oncogene identified CUX1 among many other candidates [34] , [65] . To validate these findings , we obtained the same pair of cell lines that had been employed in one of these studies [34] . DLD-1 cells encode a KRASG13D oncogene , whereas the DKO-4 cell line was derived from DLD-1 by inactivating the mutant KRAS allele [66] . Both cell lines were infected with a lentivirus expressing a doxycycline-inducible shRNA targeting CUX1 . CUX1 mRNA and protein expression were substantially reduced in both cell lines following treatment with doxycycline ( Figures 4A and S4A ) . CUX1 shRNA significantly reduced cell proliferation in DLD-1 cells , but not in DKO-4 cells ( Figure 4A ) . Reduced proliferation in the presence of CUX1 shRNA was confirmed using a tracking dye to enable measurement of cell division numbers ( Figure S4B ) . Comet assays revealed that DNA damage was increased following the knockdown of CUX1 , particularly in DLD-1 cells ( Figure 4B , C ) . In contrast , ROS levels were not significantly increased by CUX1 knockdown ( Figure S4C ) . Similar results were obtained using two distinct CUX1-specific shRNAs as well as an independent pair of cell lines . CUX1 knockdown caused increased oxidative DNA damage and inhibited the proliferation of Hs578T mammary tumor cells , which harbor an HRASG12D oncogene , but not of Hs578Bst cells , which are normal mammary epithelial cells obtained from the same patient ( Figures 4D , E and S4D , E ) [67] . DNA damage was also measured in Hs578T cells where we additionally analyzed cells following restoration of CUX1 expression via doxycycline withdrawal . DNA damage increased following CUX1 knockdown and decreased upon CUX1 restoration ( Figure 4E ) . As an adjunct to comet assay analysis to assess DNA damage , we have performed ELISA assays using 8-oxoG–specific antibodies , and found that the 8-oxoG levels in genomic DNA increase following CUX1 knockdown in DLD-1 and Hs578T cells ( Figure 4F ) . These results indicate that CUX1 knockdown causes an increase in oxidative DNA damage and reduces cell proliferation in RAS-transformed cells . We next verified whether genetic inactivation of the Cux1 gene would impair DNA repair . Mouse embryo fibroblasts ( MEFs ) from Cux1+/+ , Cux1+/− , and Cux1−/− mice were treated with H2O2 and submitted to single cell gel electrophoresis ( comet ) assays after different recovery periods . Prior to treatment , Cux1−/− MEFs exhibited higher levels of DNA damage than wild-type Cux1+/+ MEFs , while heterozygous Cux1+/− MEFs displayed intermediate levels of DNA damage ( Figure 5A ) . Consistent with this observation , following treatment with H2O2 DNA repair was delayed in Cux1−/− MEFs relative to Cux1+/+ MEFs ( Figure 5A ) . Interestingly , Cux1+/− MEFs displayed an intermediate phenotype indicating that these cells were haploinsufficient for DNA repair . RT-PCR and immunoblotting analyses demonstrated that Cux1+/− MEFs express intermediate levels of CUX1 ( Figure 5B and C ) . Importantly , OGG1 and APE1 protein expression was similar in the three cell populations ( Figure 5C ) . The acceleration of DNA repair in cells overexpressing CUX1 could be explained , at least in part , by the role of p110 CUX1 as a transcriptional activator of many genes involved in the DDR [48] . However , we consider it unlikely that such a mechanism could explain the effects of p200 CUX1 overexpression on DNA repair . The full-length CUX1 protein does not function as a transcriptional activator and very little of p200 CUX1 is proteolytic processed to produce p110 CUX1 in cells that are infected with a retrovirus expressing p200 CUX1 . On the other hand , the abundance of p200 CUX1 and its extremely fast DNA binding kinetics are compatible with a direct role in DNA repair [39] . These considerations led us to explore the possibility of a nontranscriptional role of CUX1 in DNA repair . To test this hypothesis , we expressed a recombinant protein encompassing the Cut repeats 1 and 2 fused to a nuclear localization signal , CR1CR2-NLS in DLD-1 cells ( Figure 6A ) . Since this protein exhibits very fast DNA binding kinetics and lacks the amino acids required for transcriptional activation , we expected that it would not function as a transcriptional activator [39] , [68] . Indeed , gene expression analysis confirmed that transcriptional targets of p110 CUX1 that are involved in DDRs and genes of the base excision repair pathway were not up-regulated in cells stably expressing CR1CR2-NLS ( Figure 6C ) . Despite its inability to activate transcription , CR1CR2-NLS reduced DNA damage in DLD-1 cells ( Figure 6B ) , and accelerated the repair of oxidative DNA damage following treatment with peroxide ( Figure 6B ) . These findings suggest that CUX1 may play a direct role in the repair of oxidized bases . The effects of CUX1 overexpression ( Figure 3H ) and knockdown ( Figure 4B and E ) on the repair of oxidized lesions and in particular of 8-oxoG ( Figure 4F ) led us to investigate this process in vitro . It is possible to reproduce a portion of the base excision repair process in vitro using cell extracts or purified DNA glycosylases together with double-stranded oligonucleotides containing an 8-oxoG residue . The efficiency of the reaction can be assessed by comparing the signals generated from the substrate and the product after separation on a denaturing gel . This in vitro reaction was first performed using whole cell extracts from Hs578T cells before and after induction of CUX1 shRNA . Immunoblot analysis confirmed the CUX1 knockdown , whereas the steady-state level of OGG1 remained unchanged ( Figure 7A ) . Extracts from CUX1 knockdown cells were less efficient at removing 8-oxoG and making a single-strand cut ( Figure 7B ) . In agreement with these results , 8-oxoG cleavage was more efficient with cell extracts from DLD-1 cells expressing CR1CR2-NLS than from cells carrying the empty vector ( Figure 7C ) . Next , we performed the 8-oxoG cleavage assay using purified human OGG1 in the presence of BSA , various recombinant CUX1 proteins , or another transcription factor as a control ( Figure 7D ) . The enzymatic activity of OGG1 was greatly stimulated by recombinant CUX1 proteins containing one or more Cut repeat domain ( s ) : CR2CR3HD , CR3HD , and CR1CR2 . In contrast , OGG1 activity was not stimulated by the full-length estrogen-related receptor alpha ( ERRα-FL ) , ERRα DNA binding domain ( ERRα-DBD ) , or the homeodomain protein B3 ( HOXB3 ) ( Figures 7D and S5C ) . OGG1 activity was stimulated by increasing amounts of CR2CR3HD up to a ratio of 1∶1 ( Figure S5E ) , and time-course analysis showed that equimolar amount of CR2CR3HD accelerated cleavage by OGG1 ( Figure S5F ) . Pull-down assays indicated that CR2CR3HD and OGG1 can interact in the absence of DNA ( Figure S5J , lane 3 ) . Importantly , CR2CR3HD alone did not cleave DNA containing an 8-oxoG ( Figure S5G , lanes 4 to 7 ) or an abasic site ( Figure S5H , lanes 3 to 7 ) . To investigate the effect of recombinant CUX1 proteins on the interaction between OGG1 and DNA , electrophoretic mobility shift assays were performed using identical oligonucleotides containing either an 8-oxoG or a normal guanine base . In this case , proteins and DNA were incubated for only 15 min and at 25°C to avoid cleavage of the probe . OGG1 , either alone or with a CUX1 protein , generated a stronger retarded complex with the 8-oxoG–containing probe than with the probe containing a normal G ( Figure 7E , OGG1 alone: compare lanes 2–5 , 9–12 , and 6–19; with CUX1 , lanes 3–5 , 8–10 , and 13–15 ) . The retarded complexes formed by OGG1 increased in intensity upon addition of Cut repeat proteins , but their mobility was not affected ( Figure 7E , compare lanes 2–3 , 9–10 , 12–13 , 16–17 , and 19–20 ) . These results indicate that Cut repeat proteins stimulate the binding of OGG1 to DNA without forming a ternary complex with OGG1 and DNA . The ERR full-length protein like CUX1 stimulated the binding of OGG1 to the 8-oxoG– or G-containing probes ( Figure S5I ) , but in contrast to CUX1 did not increase its catalytic activity ( Figure 7D , compare lanes 3 and 7 ) . In summary , results from in vitro assays demonstrate that CUX1 stimulates the DNA binding and catalytic activities of OGG1 .
The design of MMTV-p75 , p110 , and p200 CUX1 transgenic mice involved specific integration of each transgene into the same locus ( hprt ) to permit a direct comparison of CUX1 isoform oncogenic potentials without interference from integration site effects or transgene copy number . Another important aspect of our experimental design was to refrain from introducing additional mutations that cause the inactivation of a tumor suppressor or the activation of an oncogene , a manipulation that would have increased tumor burden and shortened the latency period . We reasoned that such an unbiased approach would better recapitulate the process of tumor development as it occurs in humans and therefore reveal significant genetic and epigenetic changes that cooperate with CUX1 overexpression in tumor development . Molecular analysis of mammary tumors from MMTV-p200 CUX1 mice revealed the presence activating Kras mutations in 45% of mammary tumors , suggesting that activated RAS and CUX1 cooperate in tumor formation . This hypothesis was verified by performing lentiviral infections in the lung of mice . Indeed , a higher tumor multiplicity , higher grade benign tumors , greater benign tumor burden , and the only adenocarcinoma was observed when CUX1 was co-expressed KRASG12V ( Figure 2 ) . Experiments in primary human and rodent cells suggested that CUX1 increases the number of lung adenomas , when expressed together with KRASG12V , by reducing oxidative DNA damage and preventing cell senescence . Co-expression of CUX1 with HRASG12V in IMR90 and REF52 primary fibroblasts led to a concomitant decrease in DNA damage ( Figures 3C and S3B ) , DNA damage foci ( Figure 3D ) , and SA-βgal activity ( Figure 3B ) and enabled RAS-expressing cells to proliferate normally ( Figures 3A and S3A ) . The mechanistic link between efficient oxidative DNA damage repair and continuous proliferation in the presence of a RAS oncogene was confirmed by showing that co-expression of human OGG1 with HRASG12V reduced SA-βgal activity and promoted rapid proliferation in both IMR90 and REF52 cells ( Figures 3I and S3E ) . Evidence from a number of studies indicates that senescence can occur in benign tumors . Several senescence-associated markers were found to be expressed in lung adenomas that develop in conditional knock-in mice carrying an endogenous KrasV12 oncogene [69] . Similarly , senescence-associated markers were expressed in pancreatic intraductal neoplasias that developed when the Lox-Stop-Lox/KrasV12 transgenic mice were crossed with mice that express Cre in the pancreas . These results have been extended to the BRAFV600E knock-in model [70] . Importantly , cell senescence is not restricted to mouse models , but has also been reported in premalignant human colon adenomas [71]–[73] , and human benign lesions caused by the BRAFV600E mutation [74] , or NF1 inactivation [75] . In summary , many studies indicate that most human and mouse tumor cells stop proliferating and undergo senescence at the premalignant stage , suggesting that it is at this stage that senescence-inducing signals reach sufficient intensity to be effective ( reviewed in [76] ) . During the course of this study , we became aware that a genomic RNAi screen to identify synthetic lethal interactions with an activated RAS oncogene tentatively identified CUX1 ( supplementary table 1 in [34] ) . We validated the synthetic lethality of CUX1 knockdown in two syngeneic pairs of cell lines that carry or not a RAS oncogene ( Figure 4 ) . We noted , however , that proliferation of DKO4 control cells was also slowed down , albeit to a lesser extent , by CUX1 knockdown . This became particularly evident when using the CFSE staining assay , which calculates the proportion of cells having progressed through any number of cell generations ( Figure S4B ) . A negative effect of CUX1 knockdown on DKO4 control cells had also been observed in the original RNAi screen [34] . These findings are consistent with the demonstrated role of CUX1 in cell cycle progression . Notably , Cux1−/− MEFs display a longer G1 phase and proliferate more slowly than their wild-type counterparts [45] . In addition , we cannot exclude that the role of CUX1 in DNA repair is also needed for normal cells to proliferate , as suggested from comet assays at pH 10 with DKO4 cells ( Figure 4C ) . Indeed , a significantly higher proportion of Cux1−/− MEFs exhibit SA-βgal activity in 20% than in 3% oxygen , whereas ectopic expression of p200 CUX1 is able to reduce the proportion of cells that display β-gal activity ( Figure S6 ) . Co-expression of CUX1 enables the proliferation of primary fibroblasts carrying a RAS oncogene ( Figures 3 and S3 ) , whereas CUX1 knockdown inhibits cell proliferation in DLD-1-KRASG13D and Hs578T-HRASG12D cells ( Figure 4 ) . Therefore , CUX1 is not only needed at the start of the transformation process , but persistent CUX1 expression also is required for long-term proliferation of RAS-transformed cells . Importantly , since CUX1 reduces the steady-state level of DNA damage such that checkpoint controls are not activated , the survival and continuous proliferation of tumor cells does not require the inactivation of p19ARF/p53 checkpoint controls . Indeed , cell lines established from mammary tumor cells that developed in MMTV-CUX1 transgenic mice display a wild-type p53 and a functional p53/p21CDKN1A axis that can be activated by ionizing radiations ( Figure S7 ) . Most studies investigating RAS-induced senescence in tissue culture originally focused on HRAS , however many studies clearly showed that KRAS can also increase ROS and induce senescence . Overall , the literature suggests that KRAS and HRAS both increase ROS and induce senescence ( [77]–[82]; reviewed in [76] , [83] ) . Whether RAS oncogenes must be overexpressed to induce senescence is somewhat controversial and merits some discussion . A KrasV12 knock-in was shown to induce senescence in lung adenomas and in pancreatic intraductal neoplasias [82] . As the KRAS oncogene was expressed from its own promoter , it can reasonably be assumed that it was expressed at the physiological level . Another group showed that mouse embryonic fibroblasts expressing the same KrasV12 knock-in did not undergo senescence and expression of KrasV12 throughout the body failed to induce unscheduled proliferation [84] . As only a fraction of lung bronchiolo-alveolar cells underwent malignant transformation , Kras-induced transformation was proposed to depend on cellular context . Importantly , none of these two studies documented the levels of expression of the KrasV12 allele in normal cells or in tumors . The apparent discrepancy between results obtained with similar mouse models could be resolved if we accept the multistep model proposed by the Chodosh group [85] . Using transgenic mice expressing HrasG12V from a doxycycline-inducible promoter , they observed that low levels of HrasG12V expression did not induce senescence or tumorigenicity , but spontaneous up-regulation of HrasG12V expression occurred at low frequency and was associated with senescence and tumor formation . Hence they proposed that Ras-induced tumorigenesis involves at least two steps consisting of the initial activating Ras mutation and then overexpression of the activated Ras allele . We consider it likely that a similar sequence of events occurred with the KrasG12D and KrasQ61L oncogenes that arose spontaneously in our MMTV-p200 CUX1 transgenic mice . Why Kras but not Hras spontaneous mutations were found in tumors from MMTV-CUX1 transgenic mice is not obvious . We note that spontaneous mutations in Kras , but not in Hras , were also found in mammary tumors that developed in transgenic mice expressing a c-Myc transgene in the mammary gland [86] . In the absence of evidence for functional differences between the two oncogenes [87] , we are left to speculate that spontaneous mutations in Kras may be more frequent than in Hras , that their expression levels differ , or that a functional interaction exists specifically between Kras and c-Myc or CUX1 . Together our results suggest that elevated CUX1 expression accelerates DNA repair in RAS-transformed cells , thereby mitigating DNA damage to a level that is compatible with continuous cell proliferation . Using FPG DNA glycosylase in comet assays , we were able to show that CUX1 specifically accelerates the repair of 8-oxoG lesions ( Figures 3H and 4B and 4E ) . Ultimately , using purified human OGG1 , we found that purified CUX1 proteins containing one or more Cut repeat domains were able to stimulate the enzymatic activity of OGG1 , whereas other transcription factors and DNA binding domains were inactive in this assay ( Figure 7C ) . These results demonstrate that CUX1 plays a direct role in the repair of oxidative damage by stimulating the action of OGG1 . We cannot , however , exclude the possibility that CUX1 plays additional roles in DNA repair as suggested from the results of comet assays at pH 10 ( Figures 4C and 4E and 6B ) , and the identification of CUX1 as one of the major substrates of PARP1 following treatment with a DNA damaging agent [88] . We noted that the expression of several CUX1 isoforms was elevated in cell lines as compared to the corresponding tumor samples . Two factors may explain this observation . First , tumor samples are obviously heterogeneous and may include cells that express lower CUX1 levels . Secondly , it is likely that cells with higher CUX1 expression are selected in tissue culture . Previous studies demonstrated that the p110 CUX1 isoform can accelerate cell cycle progression and stimulate cell proliferation [45] . Cells expressing more p110 CUX1 would therefore gradually overtake the rest of the population . Moreover , p200 CUX1 itself may confer an advantage in tissue culture by accelerating the repair of oxidative DNA damage . The discovery that CUX1 can accelerate the function of a DNA glycosylase has important implications in two areas of science . First , the possibility that the function of DNA glycosylases could be facilitated by ancillary factors apparently has not been thoroughly investigated in previous studies . Indeed , there is probably no need for ancillary factors to stimulate base excision repair in short-lived organisms with a small genome . The precedent of CUX1/OGG1 will justify further investigations into distinct classes of DNA binding proteins that participate in the repair of specific types of base damage . Second , to our knowledge , this study describes the first case of nononcogene addiction where transformed cells are dependent for their survival on the heightened activity of a normal protein that plays a direct role in DNA repair . In the context of tumor development and progression , mutations are believed to accumulate owing to compromised DNA repair functions [89] . Therefore , it is generally accepted that defects in DNA repair , whether transient or permanent , contribute to tumor development and progression . Yet , to replicate their DNA and proliferate , cancer cells need DNA repair mechanisms , perhaps even more than do normal cells . Based on our results , we propose that one adaptive response to oxidative stress in RAS-transformed cells is the up-regulation of the pathway that repairs oxidative DNA damage . In support of this notion , we note that among the synthetic lethal interactions with KRAS discovered in the genome-wide RNAi screen conducted by the Elledge group were four other genes that code for proteins involved in base excision repair: NEIL2 , XRCC1 , Pol beta , and LIG3 [34] . Overall , next to mitotic functions , base excision repair is one of the cellular processes that appears to be essential for the survival of KRAS-transformed cells . Many studies concur to suggest that CUX1 may function as a haploinsufficient tumor suppressor [20]–[26] . However , none of the reported functions of CUX1 in stimulating cell cycle progression , cell proliferation , cell motility , and resistance to apoptosis is consistent with a role as a tumor suppressor [32] , [33] , [43] , [45]–[47] . In a recent study , the authors claimed that 9 out of 10 unlisted cell cycle genes were inversely correlated with CUX1 expression , thereby implying that its tumor-suppressing function involved the repression of cell cycle genes [25] . Notwithstanding that one cannot judge this claim without knowing the identity of the genes in question , this notion runs counter to a large number of studies from several groups ( [17] , [90] , [91] and references therein ) . Our results showing that CUX1 knockdown or genetic inactivation of one Cux1 allele impairs DNA repair revealed a molecular activity that could explain how haploinsufficiency of CUX1 may contribute to tumor initiation by promoting the acquisition of mutations in genes and pathways that are involved in the transformation process ( Figures 4 and 5 ) . Future experiments should verify whether CUX1 hemizygosity indeed causes an increase in mutations and DNA rearrangements that predispose cells to tumor development . In addition , the fact that most cell lines with LOH of CUX1 display amplification of the remaining allele ( http://cancer . sanger . ac . uk/cancergenome/projects/cell_lines/ ) raises the intriguing possibility that tumor cells with increased CUX1 expression are later selected during tumor progression . The successful use of a PARP1 inhibitor for the treatment of tumor cells in which BRCA1 or BRCA2 is inactivated has provided a paradigm for the therapeutic exploitation of cancer cell addiction to a specific DNA repair pathway [92] . In the case of BRCA1–2 mutant cancer cells , permanent inactivation of a DNA repair pathway offered the opportunity for therapeutic intervention based on the concept of synthetic lethality [93] . The situation we observe in RAS-transformed cells is different . No obvious DNA repair defect is evident . On the contrary , to proliferate in the presence of elevated ROS and oxidative DNA damage , RAS-transformed cells have adapted by increasing their capacity to repair oxidative DNA damage . Yet this is where the Achilles' heel of these cancer cells may reside . The difference in the frequency of oxidative DNA damage between RAS-transformed cells and normal cells produces an increased dependency on base excision repair which may provide a therapeutic window that could be exploited with drugs that specifically target this pathway .
The p200-CUX1 transgenic mice were generated using the human CUX1 cDNA as described in [94] , and integrated by site-specific transgenesis into the Hprt locus , which resides on the X-chromosome . Two independent lines were backcrossed for at least seven generations with mice of the FVB strain , and as expected , transgene expression in the FVB genetic background was found to be identical in the two lines . To study tumor burden , we generated cohorts of female mice carrying one copy of the transgene on one chromosome X . As a result of random inactivation of one X chromosome in each cell , the transgene would be expected to be expressed in approximately 50% of cells in females . Hematoxylin and eosin staining and immunohistochemistry were performed as previously described [95] . The following primary antibodies were used: rabbit anti-CUX1 1300 ( 1∶500 ) [40] and mouse HA . 11 ( Covance , 1∶250 ) . Immunofluorescence microscopy for γ-H2AX was performed as previously described [48] . Visualization was done using an Axiovert 200M microscope with an LSM 510 laser module ( Zeiss ) . Images were analyzed using ImageJ64 software . Inguinal mammary gland number 4 was spread on a glass slide , air dried , fixed overnight in acetone , and stained as previously described [57] . Frozen tissue samples were crushed in liquid nitrogen and total RNA was extracted using QIAzol lysis reagent and RNeasy Lipid Tissue Mini Kit ( Qiagen ) following the manufacturer's instructions . Total RNA from cell lines and RT-qPCR was performed as described by [48] . Primers used are listed in . Mutations were identified by PCR amplification followed by DNA sequencing . Primers used for amplification and sequencing analysis are listed in Table S2 . Protein extraction and Western blotting were conducted as described [57] . The following antibodies were used: anti-CUX1 861 and 1300 ( 1/1 , 000 ) [40] , anti-HA . 11 ( Covance , MMS1∶1 , 000 ) , anti-RAS ( BD Transduction , 610001; 1∶1 , 000 ) , anti-OGG1 ( Pierce , PA1-31402; 1∶1 , 000 ) , anti-APE1 ( Santa Cruz , sc-5572 , 1∶1 , 000 ) , anti-p21 ( BD Transduction , 556431; 1∶1 , 000 ) , anti-tubulin ( Sigma , T6557; 1∶1 , 000 ) , and anti-lactate dehydrogenase A ( LDHA ) ( Cell Signaling , 2012; 1∶1 , 000 ) . South-western blotting was performed using a double-stranded oligonucleotide probe containing the CUX1 consensus-binding site: CGATATCGAT [57] . All cells were maintained in Dulbecco's modified minimum essential medium ( DMEM , Wisent ) supplemented with 10% Fetal Bovine Serum ( Tetracycline-free; Invitrogen ) and penicillin–streptomycin ( Invitrogen ) . All cells were grown at 37°C , 5% CO2 , and atmospheric O2 . Retroviruses were produced using 293VSV cells that were co-transfected with pLXSN-p200 CUX1-HA or pRev/TRE-p110 CUX1-HA and with packaging plasmids pVPack-GP and pVPack-VSV-G ( Stratagene ) . Retrovirus containing HRASG12V inserted in pBabe ( a kind gift from Dr . Scott Lowe ) was prepared in the same manner . Lentiviral vectors encoding KRASG12V-ires-eGFP , eGFP-ires-Cux1 , and KRASG12V-ires-Cux1 were produced via Gateway recombination into destination vector pLEG R1–R3 [96] . The lentiviral vector expressing human OGG1 was the Precision LentiORF Human OGG1 ( with native stop codon ) , Cat . No . OHS5897-202620019 , from Thermo Scientific . Lentiviruses were produced by co-transfecting 293-FT cells with plasmids encoding KRAS-ires-EGFP , EGFP-ires-Cux1 , KRAS-ires-Cux1 , and pTRIPZ-DoxOn-shCUX1 plasmid ( OpenBiosystems; Table S3 ) and packaging plasmid psPAX2 and envelop plasmid pMD2G . The medium of the transfected cells containing the retrovirus and lentivirus were collected for 5 and 3 d , respectively , starting 48 h posttransfection . Concentrated lentiviruses expressing KRAS-ires-EGFP , EGFP-ires-Cux1 , and KRAS-ires-Cux1 were titered by infecting 293T cells with 4 µg/ml of polybrene and counting the number of EGFP-positive cells by flow cytometry 72 h postinfection . Relative titers between all viruses were compared by quantifying virion RNA as described in [97] . FVB/NJ mice were anesthetized by intraperitoneal injection of 0 . 3 mg of avertin per gram of mouse weight . Using tracheal intubation as previously described [98] , [99] mice were administered 25 µl of 40 mM sodium caprate to enhance infection followed by 62 . 5 µl of lentivirus ( 108 infectious units ) 10 min later [100] . During the procedure and up until recovery , the mice were kept on a 37°C pad to prevent hypothermia . The mice were thereafter euthanized at 18 to 19 wk postinfection to harvest the lungs for analysis . Lungs were processed for histology as described in [70] . To quantify both tumor number and tumor burden paraffin , embedded blocks were serial sectioned with 100 µM steps . Tumor section area ( µm2 ) was obtained using Aperio ImageScope software after delineating tumor boundaries , using the maximal cross-sectional area obtained for each tumor from different sections . IMR90 and REF52 cells stably expressing either p200-CUX1-HA , p110 CUX1-HA , human OGG1 , or carrying an empty vector were plated at a density of 5×104 cells per well in a six-well plate . For the next two days , 2 . 5 ml of medium containing virus expressing either pBabe HRASG12V or an empty vector along with 6 µg/ml of polybrene ( Roche ) were added to the cells and spun at 1 , 200 g for 1 h . At 48 h after infection , cells were selected with appropriate concentration of puromycin . In all experiments , a parallel plate of uninfected cells was completely killed in selective media after 3 d . On the fifth day , hence designated as day 0 in proliferation assays , 2×104 cells per well were seeded in 12-well plates . Each day , cells were trypsinized and counted on a hemocytometer . The medium was replaced every 3 d . Each time point was done in triplicate , and the averages ± standard deviations were calculated . Experiments were repeated three times , and a representative experiment is shown . Cells were stained using the CellTrace carboxyfluorescein diacetate succinimidyl ester ( CFSE ) staining cell proliferation kit and were analyzed by flow cytometry with 488-nm excitation and emission filters appropriate for fluorescein , according to the manufacturer's instructions ( Molecular Probes/Invitrogen , C34554 ) . CFSE profiles were analyzed using the FlowJo software ( Tree Star Software ) . DLD-1 , DKO-4 , Hs578T , and Hs578Bst cells were infected with pTRIPZ-DoxOn-shCUX1 and selected with puromycin . Expression of CUX1-shRNA was induced in the stably infected cells by supplementing the growth media with 1 µg/ml of doxycycline . Cells grown in the absence of doxycycline were used as a control . Knockdown of the CUX1 gene was confirmed by qPCR and Western analysis . Equal number of cells were trypsinized , resuspended in PBS , and incubated with freshly prepared 10M 5- ( and-6 ) -chloromethyl-2′ , 7′-dichlorofluorescein diacetate ( CM-DCF-DA; Molecular Probes/Invitrogen , C6827 ) for 15 min at 37°C and analyzed by FACS . Geometric mean was determined using FlowJo software ( Tree Star Software ) . To measure DNA strand breaks , single cell electrophoresis ( comet assays ) was carried out using precoated slides ( Trevigen , MD ) . Total strand breaks were conducted in alkaline pH as described in [101] . Single and double DNA strand breaks as well as oxidative DNA damage were conducted using FPG enzyme in pH 10 as described by [102] . The slides were stained with propidium iodide and visualized with Axiovert 200M microscope with an LSM 510 laser module ( Zeiss ) . Comet tail moments were measured using the CometScore software ( TriTeck Corp ) . Comet tail moments were scored for at least 50 cells per condition . DNA was isolated from cells using a Qiagen DNeasy Blood and Tissue kit ( Qiagen , Valencia , CA ) , and DNase-free RNase was used to degrade RNA according to the supplier's protocols with some modifications . Briefly , diethylenetriamine pentaacetic acid ( 0 . 1 mM ) and ascorbic acid ( 2 mM ) were used to prevent possible background DNA oxidation during the genomic DNA isolation process [103] , [104] . The RNA-free DNA obtained was used to determine the 8-OHdG levels using Oxiselect oxidative DNA damage ELISA kit ( Cell Biolabs , San Diego , CA ) . We obtained 31-mer oligos containing 8-oxoG at position X and complementary oligos with a C opposite X from Integrated DNA Technologies ( Coralville , IA ) . The oligo sequence was 5′-GTGACTACGAGACCTXATGTGACTGAGAGAG- 3′ , as previously described [105] . Cleavage reactions with bacterially purified proteins were conducted using 50 nM of proteins and 0 . 08 U of human OGG1 ( New England Biolabs ) in 25 mM NaCl , 10 mM Tris ( pH 7 . 5 ) , 1 mM MgCl2 , 5 mM EDTA ( pH 8 . 0 ) , 5% glycerol , 1 mM of DTT , and 1 pmol of 32P radiolabeled double-stranded oligonucleotides containing an 8-oxoG base ( Figure S5 ) . Reactions with total cell extracts were performed as described by [106] . In both assays , cleavage reactions were performed at 37°C as previously described . The DNA was loaded on a prewarmed 20% polyacrylamide-urea gel ( 19∶1 ) and separated by electrophoresis in Tris-borate and EDTA ( TBE; pH 8 . 0 ) at constant 20 mAmp . The radiolabeled DNA fragments were visualized by storage phosphor screen ( GE Healthcare ) . EMSAs were performed as previously described with the following modifications [40] . Equimolar of bacterially purified proteins were used with or without OGG1 in the reaction together with 60 ng of poly ( dI-dC ) as a nonspecific competitor DNA . Gels were dried and visualized by storage phosphor screen ( GE Healthcare ) . | In the context of tumor development and progression , mutations are believed to accumulate owing to compromised DNA repair . Such mutations promote oncogenic growth . Yet cancer cells also need to sustain a certain level of DNA repair in order to replicate their DNA and successfully proliferate . Here we show that cancer cells that harbor an activated RAS oncogene exhibit heightened DNA repair capability , specifically in the base excision repair ( BER ) pathway that repairs oxidative DNA damage . RAS oncogenes alone do not transform primary cells but rather cause their senescence—that is , they stop dividing . As such , cellular senescence in this context is proposed to function as a tumor-suppressive mechanism . We show that CUX1 , a protein that accelerates oxidative DNA damage repair , prevents cells from senescing and enables proliferation in the presence of a RAS oncogene . Consistent with this , RAS-induced senescence is also prevented by ectopic expression of OGG1 , the DNA glycosylase that removes 8-oxoguanine , the most abundant oxidized base . Strikingly , CUX1 expression in transgenic mice enables the emergence of tumors with spontaneous activating Kras mutations . Conversely , knockdown of CUX1 is synthetic lethal for RAS-transformed cells , thereby revealing a potential Achilles' heel of these cancer cells . Overall , the work provides insight into understanding the role of DNA repair in cancer progression , showing that while DNA damage-induced mutations promote tumorigenesis , sustained RAS-dependent tumorigenesis requires suppression of DNA damage . The heightened dependency of RAS-transformed cells on base excision repair may provide a therapeutic window that could be exploited with drugs that specifically target this pathway . | [
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] | 2014 | RAS Transformation Requires CUX1-Dependent Repair of Oxidative DNA Damage |
Viral myocarditis is a serious disease , commonly caused by type B coxsackieviruses ( CVB ) . Here we show that innate immune protection against CVB3 myocarditis requires the IFIT ( IFN-induced with tetratricopeptide ) locus , which acts in a biphasic manner . Using IFIT locus knockout ( IFITKO ) cardiomyocytes we show that , in the absence of the IFIT locus , viral replication is dramatically increased , indicating that constitutive IFIT expression suppresses CVB replication in this cell type . IFNβ pre-treatment strongly suppresses CVB3 replication in wild type ( wt ) cardiomyocytes , but not in IFITKO cardiomyocytes , indicating that other interferon-stimulated genes ( ISGs ) cannot compensate for the loss of IFITs in this cell type . Thus , in isolated wt cardiomyocytes , the anti-CVB3 activity of IFITs is biphasic , being required for protection both before and after T1IFN signaling . These in vitro findings are replicated in vivo . Using novel IFITKO mice we demonstrate accelerated CVB3 replication in pancreas , liver and heart in the hours following infection . This early increase in virus load in IFITKO animals accelerates the induction of other ISGs in several tissues , enhancing virus clearance from some tissues , indicating that–in contrast to cardiomyocytes–other ISGs can offset the loss of IFITs from those cell types . In contrast , CVB3 persists in IFITKO hearts , and myocarditis occurs . Thus , cardiomyocytes have a specific , biphasic , and near-absolute requirement for IFITs to control CVB infection .
Myocarditis , which can cause serious , and sometimes fatal , complications including heart failure , cardiac arrest , and dilated cardiomyopathy , is commonly caused by infection and , most frequently , by viruses including coxsackievirus B3 ( CVB3 ) [1 , 2] . This enterovirus infects mice and humans , replicates to high titers , and causes acute viral myocarditis through two major pathological mechanisms; virus-mediated direct lysis of the infected cells and immune-mediated tissue damage ( immunopathology ) . Limiting virus infection by activating the immune system through type I interferon therapy ( T1IFN ) has shown promise [3 , 4] , but comes with an increased risk of immunopathology , because T1IFNs have strong and pleiotropic biological effects . Therefore , it is important to better understand how T1IFNs exert their anti-enteroviral effects , with the aim of retaining their biological benefits while reducing concomitant immunopathology . T1IFNs ( mainly , ~12 subtypes of IFNα and the sole IFNβ in human and mouse ) are important innate immune mediators against virus infection . T1IFN production is initiated in a virus-infected cell by the tripping of series of innate immune sensors; the resulting downstream signaling upregulates the transcription of genes encoding T1IFNs and pro-inflammatory cytokines , which are secreted from the infected cell [5] . After secretion , all of the T1IFN proteins signal through a common heterodimeric receptor , the T1IFN receptor ( T1IFNR ) , expressed by the great majority of somatic cells . T1IFN binding to this receptor activates the JAK-STAT pathways , leading to the induction of a large number of interferon-stimulated genes ( ISGs ) , which then exert various effects including innate immune antiviral action and modulation of cytokine production . Mice lacking this receptor rapidly succumb to CVB3 infection [6 , 7] , as do IFNβ knockout ( KO ) mice [8] , demonstrating the essential role played by T1IFNs in protecting against this virus . We recently generated inducible conditional knockout mice ( CMMCM T1IFNRf/f mice ) in which the administration of tamoxifen efficiently deleted T1IFNR expression specifically in cardiomyocytes and , using these mice , we revealed the importance of local T1IFN signaling into cardiomyocytes during CVB3 infection . Without such signaling , at ~2–3 days post-infection ( p . i . ) we observed increased cardiac titers; myocarditis was accelerated , and virus clearance was delayed [7] . These data raised several questions: during CVB3 infection , which ISGs are induced in cardiomyocytes in response to CVB3 infection , which of these ISGs are needed to suppress virus replication , and which ISGs regulate the rapid influx of inflammatory cells into the heart ? We show here that , following CVB3 infection , IFIT ( IFN-induced with tetratricopeptide ) family genes are highly induced in cardiomyocytes in vivo . The IFITs are a large family comprising six murine ( Ifit1 , Ifit2 , Ifit3 , Ifit1b , Ifit1c and Ifit3b ) and five human ( IFIT1 , IFIT2 , IFIT3 , IFIT5 and IFIT1B ) members [9] . These genes exert antiviral responses against various different viral species by binding to both host and viral molecules [9–11] , but the role of IFIT locus genes in enterovirus infection and the consequent pathogenesis have not been previously investigated . In this study , we use mice lacking the entire IFIT locus ( IFITKO mice ) , several primary cell types from these mice , and cardiomyocytes modified by CRISPR/Cas9-mediated gene editing . Both of these approaches—in vivo and in vitro–indicate that the IFIT locus acts biphasically , and in a cardiomyocyte-specific manner . During the first phase , constitutive IFIT expression is required for suppressing early CVB3 replication in several tissues and cell types . In the second phase , which follows T1IFN signaling , the upregulation of IFITs is vital for CVB3 clearance from cardiomyocytes , and for the prevention of myocarditis . We conclude that the second phase of IFIT activity is cardiomyocyte-specific because the T1IFN-driven induction of IFITs is expendable in other cell types . In these cells–unlike in cardiomyocytes–other ISGs can provide compensatory anti-CVB3 activity , offsetting the absence of IFITs .
To identify which ISGs are expressed in normal cardiomyocytes during CVB3 infection in vivo , we exploited CMMCM T1IFNRf/f mice [7] . Two weeks after receiving a Tamoxifen injection , Cre- or CMMCM T1IFNRf/f mice were challenged with 500 PFU CVB3 intraperitoneally ( i . p . ) . Two days later , a time point when the cardiac virus titers are still comparable in both groups [7] , the animals ( and uninfected controls ) were sacrificed , and ISG expression in the hearts of the four different data groups ( Fig 1A ) was determined by PCR array . CVB3 infection induced multiple ISGs in the genetically-intact heart ( Fig 1B , left ) , but this was largely abolished by T1IFNR deficiency in cardiomyocytes ( Fig 1B , right ) , indicating that ISG upregulation in the heart is limited mainly to those cells . By comparing the PCR signals of Cre- and CMMCM hearts , we estimated the extent to which various ISGs were upregulated in cardiomyocytes ( Fig 1C ) . IFNβ mRNA was ~20-fold more abundant in the hearts of Cre- mice , demonstrating that ( i ) cardiomyocytes are the major source of IFNβ during CVB3 infection and ( ii ) CVB3 infection triggers abundant IFNβ production by cardiomyocytes only if these cells can receive ( or have already received ) T1IFN signals . This is consistent with a previous report in which , using HL-1 cells ( a murine cardiomyocyte cell line [12] ) , the authors showed that CVB3 infection did not directly trigger IFNβ production [13] . We have independently confirmed this finding ( see S1 Fig ) . In addition to ISGs that been described previously ( Ifnb1 , Socs1 , Isg15 and Il6 ) [8 , 14–16] , we found that several of the IFIT family genes were up-regulated in cardiomyocytes at 2 days p . i . ( small arrows , Fig 1C; this PCR array assayed only Ifit1 , Ifit2 and Ifit3 ) . As described above , a broad spectrum of cellular functions of individual IFIT family genes , including antiviral effects , has been reported previously [9–11] , but little is known about the collective importance of the IFIT locus , and less still about its role during enterovirus infection in vivo . Therefore , the remaining experiments reported herein focused on the role of this gene family in responding to CVB3 infection . To confirm the PCR array results in vitro , and to extend them to IFIT family members not covered in the PCR array , we isolated primary cardiomyocytes from C57BL/6 ( B6 ) mice . CVB3 could efficiently infect , and replicate in , this cell type ( Fig 1D ) . Real-time PCR analysis of CVB3-infected cardiomyocytes showed that , in the absence of exogenous T1IFNs , there is a substantial ( > 30 hour ) delay in IFIT expression , but by 72 hours p . i . there is robust induction of Ifit1 , Ifit2 and Ifit3 , and also of one previously-uncharacterized family member , Ifit3b . However , there was little , if any , induction of Ifit1b and Ifit1c at 72 hours p . i . ( Fig 1E ) . Thus , our in vivo and in vitro data indicate that , by 48–72 after CVB3 infection , 3–4 members of IFIT family genes ( Ifit1 , Ifit2 , Ifit3 and Ifit3b ) are highly induced in cardiomyocytes . Next , we infected B6 mice with CVB3 ( 104 pfu , i . p . ) . The animals ( and uninfected controls ) were sacrificed at 2 days after CVB3 infection , and IFIT family gene expression in 11 different tissues was analyzed by real-time RT-PCR ( Fig 1F ) . Similar to cardiomyocytes , at this time point after CVB3 infection , Ifit1 , Ifit2 , Ifit3 and Ifit3b were induced in most tissues . Up-regulation of the other two family members is more tissue-restricted: Ifit1b is induced almost exclusively in kidney , liver and pancreas , and Ifit1c in liver and pancreas . In uninfected B6 mice , expression of most of the IFIT family mRNAs was low but detectable in most tissues ( S2A Fig ) . We also determined the constitutive expression of the mRNAs in three primary cell types isolated from B6 mice ( cardiomyocytes , peritoneal macrophages and cardiac fibroblasts ) , and found that all were expressed in uninfected cells ( S2B Fig ) . The expression pattern of IFIT mRNAs differed between the two cardiac-derived cell types , reflecting others’ findings that basal levels of ISG expression–including IFIT1 –can be detected in both cell types , and that the expression pattern is cell-type-specific [17] . The relative levels of IFIT1 expression differed between the cited study and our own data; we speculate that this might result from unidentified differences in culture conditions . In contrast to the marked induction of most IFIT mRNAs at 48–72 hours after CVB3 infection in vivo and in vitro ( Fig 1B , 1C , 1E and 1F ) , barely any increase was observed in mouse hearts at 24 hours p . i . ( S2C Fig ) , supporting our observation that there was little increase at 30 hours p . i . ( Fig 1E ) . Taken together , these data indicate that CVB3 infection of cardiomyocytes does not , in itself , drive the rapid and abundant expression of ISGs ( including IFITs and T1IFNs ) ; rather , the induction of ISGs depends on the cardiomyocytes having been primed by T1IFN signaling . Basal levels of IFIT2 and IFIT3 proteins were detectable in the liver and heart of naïve B6 mice , and both were markedly up-regulated following the in vivo administration of recombinant IFNβ ( S2D Fig , left panels ) . Low constitutive expression of IFIT2 and IFIT3 proteins also was detectable in primary cardiomyocytes , and was up-regulated after 24 hours of IFNβ treatment ( S2D Fig , right panels ) . Thus , we conclude that: ( i ) IFITs are constitutively expressed in many tissues / cell types; ( ii ) the constitutive expression pattern of the various IFIT genes can vary among cell types; ( iii ) CVB3 infection causes a robust increase in expression of most of the IFIT family genes , but ( iv ) in cardiomyocytes , this takes at least 30 hours to occur , suggesting that the increase depends upon these cells having received signals by systemic T1IFNs which , perhaps originate from other cell types , e . g . dendritic cells in vivo . To study the antiviral effect of T1IFNs on CVB3 infection in vitro , we first employed HL-1 cells , which we modified using the CRISPR/Cas9 system [18] . To validate the approach , we began by transfecting HL-1 cells with a vector encoding a single guide RNA targeted to exon 2 of the Ifnar1 gene , which encodes one of the heterodimeric T1IFN receptor proteins ( Fig 2A ) . HL-1 cells did not expand after selection / single cell dilution , preventing us from developing HL-1 clonal lines . Therefore , we relied on bulk edited and selected HL-1 cells , in which T1IFNR protein expression was dramatically decreased ( Fig 2B ) . Effective functional depletion was demonstrated by treating these cells , or their wt counterparts , with IFNβ; Ifit1 , Ifit2 and Ifit3 mRNA induction was ablated in Ifnar1-edited HL-1 cells ( Fig 2C ) . Western blotting showed that IFIT proteins were constitutively expressed at similar levels in wt and Ifnar1-edited HL-1 cells , but that IFNβ-driven induction of IFIT family gene products was ablated in the latter ( Fig 2D ) . Finally , exogenous IFNβ pre-treatment reduced the production of infectious CVB3 by ~2 , 300-fold in WT HL-1 cells but had no suppressive effect in Ifnar1-edited HL-1 cells ( Fig 2E ) . A further conclusion can be drawn from the data in this panel . There is no statistically-significant difference in virus titers between the non-treated WT and Ifnar-1-edited HL-1 cells , indicating that , during the time of infection , the cells did not produce sufficient IFNβ to confer any antiviral effect . This provides additional evidence that CVB3 infection of resting cardiomyocytes does not directly trigger abundant IFNβ production; if it did so , then one would have predicted that this endogenously-synthesized IFNβ would have suppressed viral replication in the WT cells to a level below that observed in Ifnar-1-edited cells , which are unable to respond to the cytokine . These findings confirm the in vitro data in S1 Fig , as well as our in vivo observations made using the CMMCM T1IFNRf/f mice; ( i ) T1IFN signaling into cardiomyocytes markedly inhibits CVB3 infection [7] and ( ii ) unless they receive T1IFN signals , cardiomyocytes do not produce abundant T1IFNs following infection by CVB3 ( Fig 1 ) . When combined with our demonstration that T1IFNs drive the strong up-regulation of IFIT expression in cardiomyocytes , these data raise the question: do IFIT genes participate in the observed T1IFN-mediated protection of cardiomyocytes against CVB3 ? To address this question , we again applied the CRISPR/Cas9-mediated bulk gene-editing approach to HL-1 cells , this time deleting the entire IFIT locus . HL-1 cells were transfected with two CRISPR/Cas9 expression vectors expressing the indicated sgRNAs ( Fig 3A ) and , after drug selection , we confirmed the deletion of entire IFIT locus on genomic DNA of IFIT locus-edited HL-1 cells by PCR analysis ( Fig 3B ) ; the combination of primer P1 + reverse primer did not generate a detectable PCR product on WT DNA , because the two primers are separated by ~100 kbp , but a ~500 bp amplicon was present when DNA from the sgIFITs edited cells was used , indicating that these cells contained IFIT locus KO DNA . However , the bulk-edited and selected population was not 100% pure , because PCR using the reverse primer together with the P2 primer ( which is absent from the KO DNA ) produced an amplicon . To determine the impact of IFIT locus editing on IFIT protein content , IFIT2 and IFIT3 protein levels in HL-1 cells were determined by western blot without or with prior stimulation with recombinant IFNβ ( 100 U/ml ) . As shown in Fig 3C , robust induction of IFIT2 and IFIT3 was observed in WT cells , and this was markedly reduced in the sgIFIT population , although some protein was detected , consistent with the conclusion that the population contains some cells with intact IFIT genes . We then infected both populations of HL-1 cells with CVB3 ( multiplicity of infection ( MOI ) of 1 ) in the presence or absence of IFNβ pre-treatment ( 100 U/ml ) . 72 hours p . i . , virus titers in the supernatant of these cells were determined by plaque assays ( Fig 3D ) . When cells were not pre-treated with IFNβ , infectious virus yield in the supernatant of IFIT locus-edited HL-1 cells was higher than in WT HL-1 cells at all time points , and became ~70-fold higher at 72 hours p . i . ( p<0 . 0001 ) ( Fig 3D , left panel ) . As shown in Fig 3D , right panel , IFNβ pre-treatment of WT HL-1 cells very effectively suppressed the infectious virus yield throughout the 72 hour time course ( black symbols ) , whereas the virus still replicated to high titer in IFNβ-treated IFIT locus-edited HL-1 cells , in which the virus titer was significantly higher as early as 6 hours p . i . , and ultimately became ~40 , 000-fold higher than in IFNβ pre-treated WT HL-1 cells ( p = 0 . 0008 ) . This viral titer data was reflected in analyses of virus RNA ( Fig 3E ) . In the absence of IFNβ treatment , we found ~30-fold higher quantities of virus RNA in IFIT locus-edited HL-1 cells compared to WT cells ( p<0 . 0001 ) , and IFNβ pre-treatment reduced virus RNA in both cell populations , but much more so in the WT ( P < 0 . 01 ) . Parallel findings came from analyses of virus protein accumulation ( Fig 3F ) . In summary , these data indicate that control of CVB3 infection by IFITs in HL-1 cardiomyocytes is biphasic: in the first phase , the constitutive ( T1IFN-independent ) expression of IFITs helps to constrain viral replication and in the second phase , T1IFN-driven up-regulation of IFIT expression in cardiomyocytes maintains this protection . The minimal antiviral effect of IFNβ on IFIT locus-edited cells ( Fig 3D & 3E ) indicates that , in cardiomyocytes , other ISGs are unable to confer substantial protection against CVB3 . To corroborate our HL-1 cell findings , we isolated primary cardiomyocytes from B6 mice and from IFIT locus-deleted ( IFITKO ) mice , which were generated in the laboratory of one of the authors ( GCS ) and will be described in detail in a separate paper . These IFITKO mice lack all of the IFIT family genes ( Ifit1 , Ifit2 , Ifit3 , Ifit1b , Ifit1c and Ifit3b ) , and the successful deletion is demonstrated by the fact that cardiomyocytes isolated from these IFITKO mice did not express any of the IFIT family genes , even after recombinant IFNβ treatment ( S3 Fig ) . Primary cardiomyocytes from B6 and IFITKO mice were infected with CVB3 , then recombinant IFNβ was ( or was not ) added to the culture media . 72 hours p . i . , virus titers in the supernatant of these cells were determined ( Fig 3G ) . These data confirmed the biphasic activities of IFITs in cardiomyocytes . CVB3 titer in the supernatant of primary cardiomyocytes ( not treated with IFNβ ) was significantly higher in IFITKO than B6 mice , and IFNβ treatment suppressed infection in WT cells , but not in IFITKO cells . Thus , in primary cardiomyocytes as in HL-1 cells , ( i ) IFIT locus genes appear to constitutively suppress CVB3 replication , ( ii ) IFN-driven IFIT up-regulation is required to maintain and extend this protection , and ( iii ) other ISGs , induced by exogenous IFNβ , appear unable to exert independent antiviral effects . We executed similar experiments with peritoneal macrophages , and with cardiac fibroblasts , from B6 and IFITKO mice ( Fig 3H & 3I respectively ) . In both cell types , in the absence of exogenous IFNβ , virus yield was significantly increased in the IFITKO cells , similar to what was observed for cardiomyocytes . However , in contrast to cardiomyocytes , IFNβ treatment strongly inhibited infectious virus production by IFITKO macrophages and cardiac fibroblasts , although a stronger effect was observed for the former cell type . These data indicate that: ( i ) constitutive IFIT expression plays a role in suppressing CVB3 replication in cardiomyocytes , cardiac fibroblasts and peritoneal macrophages , and ( ii ) the second , T1IFN-induced , phase of IFIT activity shows cell specificity; if IFITs are absent , other ISGs can confer antiviral protection in macrophages , but not in cardiomyocytes , with cardiac fibroblasts showing an intermediate phenotype . Sherry and colleagues have previously proposed that cardiomyocytes , being non-replenishable cells , may have developed near-unique mechanisms to cope with viral challenges [19] , and our observations support and extend this suggestion . Studies are ongoing to identify the precise mechanism ( s ) by which the IFIT locus mediates its anti-CVB3 activity . Next , we investigated the in vivo consequence of the loss of the IFIT locus during CVB3 infection . B6 and IFITKO mice were challenged with 104 pfu of CVB3 , i . p . , and their body weights were monitored daily ( Fig 4A ) . An early , and statistically-significant , loss of body weight was observed in IFITKO mice . Nevertheless , IFITKO mice survived significantly longer than B6 mice ( Fig 4B ) . The CVB3-infected animals were sacrificed at 12 days p . i . , and the two strains displayed dramatic macroscopic differences in the small intestine , pancreas , and liver ( Fig 4C ) . The small intestine of CVB3-infected IFITKO mice was swollen and fulfilled with gas , and their pancreata were smaller than those of their B6 counterparts . In contrast , the livers of the IFITKO mice looked grossly normal , while those of B6 mice were pale , and confocal analyses revealed numerous apoptotic hepatocytes ( S4 Fig ) , possibly contributing to the higher mortality in the WT animals ( Fig 4B ) . This is consistent with the recent demonstration , by others , that hepatic disease may contribute significantly to the mortality associated with CVB3 infection [20]; we speculate that , in WT mice , the antiviral activity of IFITs in the liver may contribute to hepatic immunopathology that is detrimental to survival . No obvious macroscopic differences were observed in the hearts , but histological analyses were revealing ( see below ) . To analyze the in vivo impact of loss of the IFIT locus on CVB3 replication , we measured the virus titer in the pancreas , liver and heart of both groups at different time points over the course of CVB3 infection . At day 1 p . i . , there were higher amounts of CVB3 in IFITKO mouse pancreas , liver and heart ( Fig 4D to 4F ) , indicating that–as we showed for several IFITKO cell types in vitro ( Fig 3G–3I ) –IFIT locus genes are required for restricting very early CVB3 infection in vivo . However , at later time points ( days 7 , & 11–12 p . i . ) CVB3 appeared to be more rapidly cleared from the pancreata and livers of IFITKO mice; at day 7 p . i . , titers were ~300-fold lower in the IFITKO pancreata , and ~2 million-fold lower in the IFITKO livers ( Fig 4D & 4E ) . We also found accelerated virus clearance in the feces of IFITKO mice ( Fig 4G ) . These data indicate that , while the IFIT locus is required for immediate virus control in several tissues in vivo , its absence eventually leads to much more effective viral clearance from most of those tissues . The heart appears to be an exception: at day 7 p . i . , CVB3 titers were high in the hearts of both mouse strains the small difference observed at this time point ( ~2-fold higher in the B6 mice ) was far less than the differences observed in pancreata and livers and , by day 11–12 p . i . , titers in IFITKO pancreata and liver were far below those in WT tissues ( Fig 4D & 4E ) , while the titers in IFITKO hearts were ~20-fold higher than those in B6 mice ( Fig 4F ) . This conclusion was supported by studies in which mice were challenged with a lower dose of CVB3 ( 103 pfu ) ; IFITKO mice showed 100-fold higher cardiac titers compared to B6 mice ( P < 0 . 05; S5A Fig ) . Exogenous IFNβ treatment has been shown to improve CVB3-induced pathogenesis in mouse models [21 , 22] and in some human clinical trials [3 , 4] . Since our in vitro data ( Fig 3D & 3E ) had shown us that the IFIT locus was required for most of the T1IFN-mediated inhibition of CVB3 replication in cardiomyocytes , we next examined whether the IFIT locus is required for IFNβ-mediated beneficial effects in vivo . B6 and IFITKO mice were challenged with 104 PFU of CVB3 and , 24 hours p . i . , received a single i . p . injection of either PBS or recombinant IFNβ ( 2 × 104 units ) . We monitored the body weight loss of these animals , and found that IFNβ treatment protected B6 mice from weight loss and overt signs of disease over a 12 day period of CVB3 infection , but failed to do so in IFITKO mice ( Fig 4H ) . Surviving mice were sacrificed at day 12 p . i . , and virus titers were measured in the pancreas , liver , and heart . For the heart , data are shown in Fig 4I; data for pancreata and livers are shown in S6 Fig . Three conclusions may be drawn: ( i ) in both mouse strains , there was a clear relationship between substantial body weight loss and high cardiac titers; ( ii ) most of the B6 mice benefited from IFNβ ( cleared virus from the heart , and showed minimal weight loss ) ; and ( iii ) most importantly , for the purpose of our study , no such effects were observed in IFITKO mice ( only one of the IFITKO mice had cleared virus from the heart , and that mouse still had significant weight loss ) . These data are consistent with the near-complete requirement for IFITs in cardiac clearance . Similar conclusions can be drawn from the pancreatic data , but for the liver it is more difficult to interpret any relationships with virus titer , because the great majority of the livers in both strains scored negative at this time point p . i . ( S6 Fig ) . Next , we sought to determine why virus clearance is accelerated in pancreas and liver , but not in heart , of IFITKO mice at later time points ( days 7 & 11–12 p . i . ) . Since viral load at early time points is higher in IFITKO cells ( Fig 3D ) and in IFITKO mice ( Fig 4D–4F ) , we reasoned that this might lead to the more rapid induction of innate responses in these mice compared to B6 animals . Strikingly , at day 1 p . i . , several chemokine mRNAs were highly induced in the hearts of IFITKO mice , but only modestly so in B6 tissues ( S7 Fig ) . We chose to carry out a more detailed analysis at 48 hours p . i . , because we knew that , by this time point , systemic T1IFNs have driven the upregulation of many ISGs in the infected heart ( see Fig 1B & 1C ) . PCR array heat maps ( Fig 5A ) revealed that , at 48 hour p . i . many ISGs appeared to be expressed at higher levels in IFITKO tissues than in their WT counterparts . Of interest , too , the pattern of ISG induction differed among the three organs analyzed . To more readily visualize the overall differences between the two mouse strains , the data were re-plotted to compare the range and extent of ISG up-regulation in all three organs of both strains ( Fig 5B ) . In all three organs , the highest ISG up-regulation occurred in IFITKO conditions , and the overall range of ISG expression appeared higher in IFITKO pancreas and heart , and almost equivalent in livers from both mouse strains . Thus , we consider it likely that this increased ISG expression may underpin the faster resolution of infection in the pancreas and liver of IFITKO mice ( Fig 4D & 4E ) . This enhanced virus clearance does not occur in the IFITKO heart ( Fig 4F ) despite there being increased overall ISG expression ( Fig 5B ) , consistent with the notion that , at least in cardiomyocytes , other ISGs cannot functionally compensate for the loss of the IFIT locus . Taken together , these in vivo data indicate that constitutive IFIT expression plays a key role in restricting CVB3 replication in most/all tissues and , in its absence , virus RNA-driven induction of the T1IFN response is accelerated; this , in turn , leads to the rapid up-regulation of a variety of other ISGs in all tissues , and these ISGs quickly control CVB3 replication in all of the tested tissues , except the heart . Thus , we suggest that the IFIT locus is especially vital for protecting cardiomyocytes , because these cells lack the functional redundancy that the other ISGs can provide in most cell types . Finally , we investigated the impact of loss of the IFIT locus on the extent of CVB3-induced myocarditis . Mice were infected with 104 pfu CVB3 i . p . and , 12 days later , hearts were harvested , and paraffin sections were stained ( Fig 6A ) . Immune cell infiltration was quite limited in the B6 heart at 12 days p . i . , while numerous infiltrating cells , and collagen deposition ( light blue ) , were observed in the hearts of IFITKO mice . These findings were reproducible when mice were challenged at a dose of 103 pfu of CVB3 ( S5B Fig ) . Since we had observed enhanced chemokine expression in the IFITKO hearts soon after infection ( S7 Fig ) , we analyzed infiltration of macrophages by staining heart vibratome sections with an antibody against Iba-1 ( Ionized calcium-binding adaptor molecule 1 , also known as Aif-1 ) , a protein that is predominantly expressed on cells of the macrophage lineage [23] . At 12 days post-CVB3 infection ( 104 pfu ) , Iba-1 signals ( Fig 6B , red ) were brighter and more frequent in the hearts of CVB3-infected IFITKO mice compared to the WT animals . Our previous work showed that development of myocarditis in the mice lacking T1IFN signaling into cardiomyocytes was not only exacerbated but also accelerated [7] . Likewise , we found more myocardial inflammation in IFITKO than in B6 mice at 7 days p . i . ( Fig 6C , yellow arrows indicate immune infiltrates ) , a time point when the mice of both groups showed comparably high cardiac virus titers ( Fig 4F ) . Real-time RT-PCR analysis was applied to RNA extracted from the hearts of both mouse strains at d7 p . i . , to identify genes expressed by immune cells , and revealed a small but significant increase of Cd8 RNA , and a massive increase of Ly6G , a marker of granulocytes and monocytes , in IFITKO hearts ( Fig 6D ) . Therefore , in the absence of the IFIT locus , there is more rapid inflammatory cell infiltration into the heart . Taken together , these data indicate that the IFIT locus plays an important role in limiting CVB3-induced myocarditis .
The present study was aimed at identifying the genes responsible for T1IFN-dependent antiviral protection in the heart . We report: ( i ) that the IFIT locus plays a central role in controlling CVB3 infection in multiple tissues including the heart; ( ii ) that it does so in two distinct phases , separated by the onset of T1IFN signaling; ( iii ) that the first phase , which depends on constitutive IFIT activity , impacts all analyzed tissues; but ( iv ) that the second , T1IFN-induced , phase of IFIT activity is cell-specific , being almost indispensable in cardiomyocytes , and redundant in other cell types . Most cell types can respond to T1IFNs , thereby increasing their ability to resist virus challenge . However , prior to being stimulated by T1IFNs , many cells also have a constitutive capacity to withstand virus infection . This has been referred to as “intrinsic antiviral immunity” [24] , and here we demonstrate that the IFIT family genes play such a role in protecting many cell types and tissues against CVB3 infection . Our in vitro observations show that , in the absence of T1IFN treatment , CVB3 replicates to higher titers in IFIT-deficient cardiomyocytes ( both in HL-1 cells and in primary isolates ) , peritoneal macrophages , and cardiac fibroblasts ( Fig 3G–3I ) . Others have reported that , specifically in cardiomyocytes , the mitochondrial antiviral signaling ( MAVS ) pathway is spontaneously activated , resulting in increased basal levels of IFNβ [25] , suggesting the possibility that IFNβ might contribute to the intrinsic immunity of cardiomyocytes to CVB3 that we report herein . However , as noted above , CVB3 infection of primary cardiomyocytes does not trigger abundant IFNβ production . Furthermore , it is intriguing to note that some viral proteases have been shown to cleave MAVS protein , potentially nullifying the pathway’s activity; these include the 3C protease of CVB3 [26] , and the 3ABC complex of another picornavirus , hepatitis A virus [27] . Moreover , cytokine responses to CVB3 appear to be independent of MAVS , and CVB3 titers are not increased in MAVS-deficient mice [28] . Given that , both before and after IFNβ stimulation , cardiomyocytes display a near-absolute requirement for endogenous IFITs ( Fig 3D–3G ) , we consider it likely that one or more of the proteins in the IFIT family play ( s ) the key role in conferring both constitutive and inducible anti-CVB3 protection in these cells . The importance of constitutive IFIT expression was confirmed by in vivo studies . IFITs are constitutively expressed in many tissues and cell types ( S2 Fig panels A , B and D , and Fig 2D ) and , compared to WT mice , CVB3 titers at 1 day p . i . were markedly higher in multiple tissues of IFITKO mice ( Fig 4D–4F ) . By two days after CVB3 infection , mice have transitioned from the first phase of antiviral immunity ( cell-intrinsic resistance ) to the second , T1IFN-induced , phase . At this time point , genetically-intact hearts express many ISGs ( Fig 1B & 1C ) , one of which is IFNβ , whose abundance is reduced ~20-fold if cardiomyocytes are unable to respond to T1IFN ( Fig 1C ) . These data suggest that cardiomyocytes are , by far , the major source of IFNβ in the CVB3-infected heart . Moreover , they demonstrate that extensive T1IFN synthesis by cardiomyocytes requires that the cells be able to respond to the cytokine–i . e . , wt cardiomyocytes exhibit a positive feedback loop in vivo , leading to the escalation of local T1IFN concentration , with consequent rapid and marked induction of numerous other ISGs , including several IFITs ( Fig 1B and 1C ) . This is consistent with the in vitro and in vivo observations that suggest that there is a 1–2 day delay in IFIT up-regulation , followed by an explosive increase . What are the antiviral consequences of T1IFN signaling into cardiomyocytes ? As noted above , we have reported that T1IFNR-deficient cardiomyocytes show delayed clearance of CVB3 in vivo [7] , and here we confirm in vitro the importance of T1IFN signaling in cardiomyocytes; IFNβ treatment reduced the yield of infectious virus by ~2 , 300-fold in wt HL-1 cells , while no such effect was observed using CRISPR/Cas generated T1IFNR-deficient cardiomyocytes ( Fig 2E ) . Strikingly , this T1IFN-driven suppression of CVB3 infection in cardiomyocytes is almost entirely dependent on the IFIT locus . IFNβ-treatment of wt HL-1 cells or wt primary cardiomyocytes dramatically inhibited CVB3 replication , but equivalent treatment of IFIT-deficient cardiomyocytes had very little effect , suggesting a near-absolute requirement for the IFIT locus in this cell type ( Fig 3D to 3G ) . In contrast , IFNβ-treated IFITKO peritoneal macrophages very efficiently controlled the infection , while IFNβ-treated cardiac fibroblasts showed an intermediate phenotype ( Fig 3H & 3I ) indicating that , for both of these cell types , other ISGs could wholly or partially restore antiviral resistance . Thus , IFITs act in a biphasic manner following CVB3 infection , and the T1IFN-driven second phase shows cell-type specificity . This mirrors previous reports of neuron-specific antiviral activity of IFIT2 against vesicular stomatitis virus [29 , 30] . The biological importance of this T1IFN-driven shift , from intrinsic immunity ( phase 1 ) to ISG-mediated antiviral responses ( phase 2 ) at ~1–2 days p . i . is clearly shown by the prior observations that IFNβKO , T1IFNRKO and CMMCMT1IFNRf/f mice all failed to control CVB3 infection [6–8] , demonstrating that intrinsic immunity alone is insufficient . Furthermore , our data suggest that intrinsic immunity–or , at least , the intrinsic immunity conferred by the IFIT locus–can be lost without fatal effects . In IFITKO mice , virus titers were initially higher than in wt animals–perhaps explaining the early and transient weight loss that occurred in these mice ( Fig 4A ) –but at later time points virus clearance was accelerated in the pancreas , liver and feces ( Fig 4D , 4E & 4G ) . This enhanced virus clearance is probably attributable to the more rapid induction of T1IFNs and ISGs in IFITKO mice ( Fig 5 ) , presumably driven by the extremely high viral titers that were present at 1 day p . i . Consistent with our in vitro data , this accelerated CVB3 clearance showed substantial cell ( tissue ) specificity: it was not observed in the hearts of IFITKO mice which , at 12 days p . i . , still contained much higher levels of CVB3 than did WT hearts ( Fig 4F & 4I ) . In addition , cardiac CVB3 titers in IFITKO mice were prolonged even if the mice were treated with exogenous IFNβ ( Fig 4I ) . Taken together , these findings suggest that other ISGs can substitute for the absence of IFITs in many tissues , but not in the heart , because of cardiomyocytes’ requirement for IFITs . The inability of other ISGs to protect cardiomyocytes against CVB3 after ~day 2 p . i . , when the T1IFN system has exerted its effects , appears to render these cells a more hospitable environment for the virus . As mentioned in the Introduction , virus-mediated direct cell lysis and immunopathology are the two major pathological mechanisms of myocarditis . Previous studies in mice in which the viral receptor ( CAR ) has been deleted in cardiomyocytes found that the hearts of these mice are resistant to infection , and the mice are largely protected against cardiac disease [31 , 32] , demonstrating that virus replication in cardiomyocytes is a prerequisite for myocarditis . Our data showed that enhanced cardiac virus replication in IFITKO mice was accompanied by accelerated and exacerbated myocarditis ( Fig 6 and S5B Fig ) . However , in contrast to our tissue culture data , which show clearly that IFIT expression in cardiomyocytes is the key factor , these in vivo data in IFITKO mice–which show that IFITs protect against myocarditis–are open to interpretation . It is possible that , in genetically-intact mice , protection against myocarditis is mediated solely by IFITs in cardiomyocytes ( paralleling the tissue culture data ) , but it also is possible that protection is mediated , at least in part , by the early actions of constitutively-expressed IFITs in multiple tissues . This issue can be resolved in the future by generating mice carrying a floxed IFIT locus , and crossing them to CMMCM mice [which are described in ref 7] , allowing the inducible deletion of the locus specifically from cardiomyocytes . Whichever mechanism is in play , the primary means by which the IFIT locus prevents viral myocarditis in WT animals is to inhibit CVB3 replication , thereby reducing both virus-mediated direct cell lysis and the immunopathological damage caused by infiltrating cells . In addition , we observed that the increased early viral load in the IFITKO mice is accompanied by a more rapid T1IFN response ( Fig 5 ) . Interestingly , in Sendai virus ( SeV ) -infected Ifit2 KO mice , which show elevated T1IFN induction triggered by uncontrolled virus replication , both SeV infection and abnormal production of T1IFN are required for the virus pathogenesis [33] . Hence , during CVB3 infection of IFITKO mice , the rapid / robust induction of the T1IFN response together with the prolonged virus presence in the heart may synergistically promote the cardiac immune cell infiltration and contribute to the pathogenesis of myocarditis . In conclusion , we have revealed a key role for the IFIT locus in modulating CVB3 infection . Through analyzing genetically-manipulated cells and mice , we show that the IFIT locus constitutively limits early virus replication in many tissues , and that its subsequent upregulation by the T1IFN response plays an especially-important role in cardiomyocytes , delaying or preventing the development of myocarditis . Future studies to reveal the precise mechanism ( s ) by which the IFIT locus acts , and to determine the function of each IFIT family gene , may lead to new and improved strategies for treating enterovirus-induced disease .
All animal experiments were approved by The Scripps Research Institute ( TSRI ) Institutional Animal Care and Use Committee ( protocol number 09-0131-3 ) and were carried out in accordance with the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals . C57BL/6 mice were purchased from the TSRI rodent breeding colony . Generation of IFITKO mice by the Sen laboratory will be described in detail elsewhere . CMMCM T1IFNRf/f mice were described previously [7] . Mice serum was isolated using K3-EDTA coated microvette tubes ( Starstedt , Nümbrecht , DEU ) from the blood by centrifuging at 12 , 000 rpm for 15 min at room temperature , and stored at -20 °C until its use . The cardiomyocyte cell line HL-1 [12] was obtained from Drs . William C . Claycomb and Ikuo Tsunoda at Louisiana State University ( Shreveport , USA ) . The cells were cultured in Claycomb medium supplemented with 100 μM norepinephrin , 10% fetal bovine serum ( FBS ) and 4 mM L-glutamine , in 37°C , 5% CO2 atmosphere . The Pierce primary cardiomyocytes isolation kit ( Thermo Fisher Scientific , #88281 ) was used to isolate primary cardiomyocytes and primary cardiac fibroblasts , following the manufacturer’s instructions with some modifications [17] . In brief , day 2 post neonate mice were sacrificed and the hearts were isolated . After mincing each heart into 1–3 mm3 pieces , the tissues were washed twice with HBSS ( Hanks-based salt solution ) , resuspended and incubated in working solution including primary cardiomyocyte isolation enzymes 1 and 2 ( components of the kit ) at 37°C for 30 minutes . The tissues were washed with HBSS several times and then with DMEM . Cells were plated at a density of 1 . 25 x 106 per well in six-well plates for 2 hours in order to separate cardiac fibroblasts ( rapidly adhering ) from cardiomyocytes ( still floating at 2 hours after plating ) . Adherent cells were washed with PBS and cultured for nine days , changing media every 3 days . Then , cell clusters that do not include any beating cells were collected and used as primary cardiac fibroblasts . For cardiomyocyte culture , 24 hours after plating the cells , fresh medium was added , with cardiomyocyte differentiation supplement ( another component of the kit ) . After 7 days’ growth and differentiation with one medium change at day 4 , cells were used as primary cardiomyocytes . For peritoneal macrophage isolation , mice were injected with aged 3% thioglycollate medium ( SIGMA Aldrich , #T9032 ) i . p . . Three days later the mice were sacrificed , and peritoneal cells were recovered by lavage and seeded onto a tissue culture plate . The next day , floating cells were removed , and the adherent cells were used as macrophages . The wild-type CVB3 used in these studies is a plaque purified isolate ( designated H3 ) of the myocarditic Woodruff variant of CVB3 . Mice were infected intraperitoneally ( i . p ) with the indicated dose of CVB3 and their survival and body weight were monitored during the course of infection . At the indicated times p . i . , feces were collected , and at the time of sacrifice , pancreata , livers and hearts were isolated , weighed , and homogenized in 1 ml Dulbecco modified Eagle medium ( DMEM ) . Virus titers were assessed using standard plaque assays as previously described [34] . T1IFN-related gene expression in pancreata , livers and hearts was quantified using Mouse Type I interferon response RT2 Profiler PCR Array ( PAMM-016Z , SA Biosciences , Frederick , MD ) . For real-time RT-PCR analysis , RNA was isolated from tissue and cell suspensions using the RNeasy Mini Kit ( QIAGEN , # 74104 ) , and 1–2 μg of RNA was reverse transcribed using iScript Reverse Transcription supermix ( Bio-rad , #1708841 ) . Real-time PCR was performed using Power SYBR Green PCR mastermix reagent ( Applied biosystems , #4367659 ) with specific primer sets ( see S1 Table ) . All of the values in PCR array analysis and real-time PCR analysis were normalized to the values of Gapdh . 2 x 105 HL-1 cells were seeded onto gelatin/fibronectin-coated plate . 24 hours later , pX459 ( ver . 2 ) encoding either sgIfnar1 or sgIfit1 and sgIfit2 was transfected into HL-1 cells and incubated for further 24 hours . The sequences of the sgIFITs are shown in S1 Table . Then culture medium was changed to the media containing puromycin ( 3 μg/ml ) and Cas9-expressing cells were selected for three days . After drug selection , puromycin was removed from culture media and cells were recovered . These bulk gene-edited cells were used for in vitro studies at early passage numbers . To determine surface IFNAR1 protein expression , HL-1 cells were analyzed by flow cytometry . WT or Ifnar1-edited HL-1 cells were incubated in trypsin-EDTA at 37°C for 5 min . Then , the reaction was stopped by adding DMEM supplemented with FBS . After washing several times , isolated cells were incubated with PE-conjugated anti-IFNAR1 antibody or PE-conjugated isotype control on ice for 20 min . After washing several times with FACS buffer , IFNAR1 expression on HL-1 cells was analyzed by flow cytometry using an LSR II ( BD Bioscience ) . Cells were lysed in RIPA buffer ( Millipore , #20–188 ) . After centrifugation , cell debris was discarded and protein concentration in the supernatant was determined by Pierce BCA Protein Assay Kit ( Thermo Fisher Scientific , #23225 ) . Colorimetry was measured using plate reader , Victor X3 ( Perkin Elmer ) . 5 μg of total cell lysates were mixed with Laemmli Sample Buffer ( Bio-Rad , #161–0747 ) and 10% of 2-Mercaptoethanol ( SIGMA Aldrich , #M6250 ) and used for western blotting . Blotting of the proteins to membrane was performed using Trans-blot Turbo RTA Transfer Kit ( Bio-rad , #170–4272 ) as follows . After developed on SDS gels , proteins were transferred on PVDF membranes ( Bio-Rad , a component of the Transfer Kit ) by using Trans-blot Turbo system ( Bio-Rad ) . Membranes were then blocked with 1% skim milk for an hour , and overnight with the relevant diluted primary antibodies . Then , membranes were washed three times with Tris buffer Tween 20 ( TBST ) and incubated with diluted secondary antibodies . One hour later , membranes were washed again three times with TBST , then protein-antibody complexes were visualized by Super Signal ELISA Femto Maximum Sensitivity Substrate ( Thermo Scientific , #37074 ) . Mice were perfused with Dulbecco’s PBS ( DPBS ) , and tissues were harvested and fixed using buffered zinc formalin at room temperature ( RT ) overnight . For standard histological analyses , tissues were paraffin embedded , and 3-μm sections were cut and stained with hematoxylin-eosin or Masson’s trichrome . Images were captured at 10x magnification with an BZ-X710 inverted microscope ( KEYENCE ) using BZ-X Viewer software ( KEYENCE ) . For confocal studies , 70 μm sections of liver and heart were cut with a Leica VT 1000S vibratome . Sections were incubated with primary antibody for 1 hr at RT and then at 4°C overnight . After washing , they then were incubated with secondary antibody for 1 hr at RT , washed and then incubated with Phalloidin 488 at 4°C overnight to label F-actin . After incubation , sections were washed , counterstained with Hoechst 33342 and mounted with ProLong Gold Antifade Mountant for confocal microscopy . Confocal images were captured using a Zeiss LSM 710 Laser Confocal Scanning Microscope running Zen 2009 Zeiss software suite . Representative regions within each vibratome section of the tissues were scanned as 8-bit optical sections ( 1 , 024 × 1 , 024 image sizes ) and reconstructed for analysis . Exposure and image acquisition settings were identical for all sections . All data were analyzed using Prism software ( GraphPad Prism 8 ) . An unpaired , two-tailed t-test was used to determine statistically significant differences for in vitro experiments . The Mann-Whitney test was used to analyze differences in viral burden . Kaplan-Meier survival curves were analyzed by the log rank test . P values less than 0 . 05 were considered significant , and are indicated in figures as follows: * 0 . 05>p>0 . 01; ** 0 . 01≥p>0 . 001; *** 0 . 001≥p>0 . 0001; **** p≤0 . 0001 . | Viruses can infect the heart , causing inflammation–termed myocarditis–which is a serious , and sometimes fatal , disease . One way to combat the infection is by stimulating our immune system , encouraging it to fight the virus . However , the treatment that is currently used “revs up” many different parts of our immune system , including some that play little or no role in clearing the virus , and this wide-ranging activation increases the risk of potentially-harmful side effects . We want to identify the parts of the immune system that fight virus infections of the heart , so that we can improve the treatment of viral myocarditis by selectively stimulating only those immune responses , thereby retaining the benefit of treatment ( i . e . , clearing the virus ) while reducing its cost ( i . e . lowering the risk of harmful side effects ) . In this paper , we demonstrate that a family of proteins called IFITs play a role in protecting many tissues against these infections , but are particularly important in heart muscle cells , in which they are indispensable . Thus , IFITs represent a possible target for the treatment of viral myocarditis . | [
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... | 2019 | Biphasic and cardiomyocyte-specific IFIT activity protects cardiomyocytes from enteroviral infection |
One of the paramount goals of synthetic biology is to have the ability to tune transcriptional networks to targeted levels of expression at will . As a step in that direction , we have constructed a set of unique binding sites for E . coli RNA Polymerase ( RNAP ) holoenzyme , designed using a model of sequence-dependent binding energy combined with a thermodynamic model of transcription to produce a targeted level of gene expression . This promoter set allows us to determine the correspondence between the absolute numbers of mRNA molecules or protein products and the predicted promoter binding energies measured in energy units . These binding sites adhere on average to the predicted level of gene expression over orders of magnitude in constitutive gene expression , to within a factor of in both protein and mRNA copy number . With these promoters in hand , we then place them under the regulatory control of a bacterial repressor and show that again there is a strict correspondence between the measured and predicted levels of expression , demonstrating the transferability of the promoters to an alternate regulatory context . In particular , our thermodynamic model predicts the expression from our promoters under a range of repressor concentrations between several per cell up to over per cell . After correcting the predicted polymerase binding strength using the data from the unregulated promoter , the thermodynamic model accurately predicts the expression for the simple repression strains to within . Demonstration of modular promoter design , where parts of the circuit ( such as RNAP/TF binding strength and transcription factor copy number ) can be independently chosen from a stock list and combined to give a predictable result , has important implications as an engineering tool for use in synthetic biology .
The regulation of gene expression is one of the primary ways that cells respond to their environments . The quantitative dissection of the networks that control such expression as well as the construction of designed networks has been a central preoccupation of regulatory biology . As sketched in Figure 1 , the level of gene expression exhibited by a cell can be targeted at multiple levels along the path from DNA to protein . Key biological tuning variables include the copy number of the transcription factors that act on a gene of interest , the strength of their binding sites , the strength of RNA polymerase binding , the strength of ribosomal binding sites and the degradation rates of the protein products of the gene of interest . Many of these tuning parameters have been studied in quantitative detail . For instance , Salis et al . [1] developed a model to describe the interaction energy between the ribosomal binding site ( RBS ) of an mRNA transcript and the 30S ribosomal subunit , which they relate to translation initiation rate using statistical thermodynamics . Using this model , gene expression can be predictively tuned over 5 orders of magnitude by modulating translation efficiency for a given gene [1] , [2] . Translation initiation ( and hence protein expression ) is thus tuned by choosing an RBS sequence with the desired interaction energy . The rate of protein degradation is another key determinant of intracellular protein concentration . Protein degradation can be modulated by the use of degradation tags appended to the C-terminal domain of a given protein . The ssrA tag [3] , for instance , targets proteins for destruction by the E . coli degradation machinery , which includes proteases ClpXP , ClpAP and SspB [4] . This degradation system has been artificially implemented in yeast , where ClpXP is expressed from an inducible promoter , and degradation rates of ssrA-tagged proteins can be tuned over a factor of by controlling the ClpXP concentration in the cell [5] . Similarly , manipulating the decay rate of the protein's transcript allows for modulation of the steady-state protein copy number [6] , [7] . In this paper , we focus on two sets of these transcriptional parameters: namely , the strength with which polymerase binds the promoter , and the number of transcription factors present when that promoter is controlled by simple repression . We begin by focusing on the simplest case where there are no repressor proteins present in the cell . Our interest in such “constitutive” promoters ( those not regulated by transcription factors ) stems from the goal of creating a set of promoters in which we can systematically vary both the mean and the noise to test recent models of transcriptional kinetics [8] . These experiments are further motivated by measurements which question our understanding of how the mean and noise in transcription depend on the architecture of the promoter [9] . To test these ideas on noise in transcription , we must know how to predictively tune the binding strength of RNAP to the promoter . Precise physical modelling of protein-DNA interaction energies is a difficult problem involving many degrees of freedom . Such binding energies are at the heart of the molecular interactions which result in ( or , in the case of repressor transcription factors , prevent ) transcription events . Hence , precise control of protein-DNA binding is an essential prerequisite for quantitative control of transcription . Despite the complexity of protein-DNA interactions and numerous molecular mechanisms involved in transcription initiation [10]–[14] , simple linear models of sequence-dependent binding energies are often sufficient to describe the interactions of transcription factors ( TFs ) or RNAP with DNA [15]–[20] . A “linear model” treats each base along the binding site as independently contributing a defined amount to the total binding energy . The total binding energy is then the sum of the contributions from each base along the binding site . In one recent study , the authors inferred the parameters describing the interaction of RNAP holoenzyme with DNA [20] . This matrix is shown pictorially in Figure 2 and the numerical values are provided in Supporting Information ( SI ) Text S2 . Mathematically , the binding energy of RNAP to a specific sequence is calculated using a matrix of energy values where represents the base identity ( A , C , T , G ) , and represents the base pair position along the binding site . For instance , represents the contribution from having a “C” present at position 8 along the binding site . We represent a particular promoter sequence by a matrix which is unity if the base pair has identity and zero otherwise . The total energy of the sequence in question is the inner product of these matrices , namely , ( 1 ) For convenience , we have added a constant offset to the matrix such that the average value of across the E . coli genome is zero ( see SI Text S1 for the original matrix from ref . [20] , SI Text S2 for the adapted matrix , and SI Text S3 for the Python source code to perform the adaptation ) . Since only differences in energy ( such as between two different promoter sequences ) are physically meaningful , we can add the same constant value to each element of the matrix without affecting its physical interpretation . We use this correspondence between promoter sequence and RNAP binding affinity to generate a suite of promoters with a wide range of binding affinities . We then show how a simple thermodynamic model of transcription , which postulates that transcriptional activity is proportional to the probability of finding the RNAP bound at the promoter , accurately predicts the scaling of the expression with RNAP binding energy . In addition , these measurements allow us to determine the proportionality between RNAP binding probability and transcriptional output for our gene . With this information , we can make absolute predictions for the transcriptional output of our designed promoters under other regulatory conditions . We test and confirm these predictions by measuring the transcriptional output of some of our promoters in the architectural context of simple repression ( similar to Ref . [2] ) and show we are able to make accurate , absolute predictions of the transcription as a function of average repressor copy number .
To construct promoters with a targeted level of gene expression , we compute the RNAP binding probability using a simple thermodynamic model based upon the RNAP binding energy matrix from the work of Kinney et al [20] ( shown in Figure 2 ) . A schematic of the allowed microscopic states of the promoter in the constitutive expression system , along with their thermodynamic weights , is shown in Figure 4 . This model treats all non-specific binding sites ( i . e . , binding sites other than the promoter of interest ) as binding RNAP with a fixed energy . More nuanced treatments of the non-specific background can be found in Refs . [19] , [26] , [27] , for example . Consider a cell with RNAP molecules which can bind non-specifically with energy to non-specific RNAP binding sites and with energy to the promoter of interest [21]–[25] . The energy of the state in which the promoter is unoccupied is which can occur in unique configurations . Similarly , the energy of the state in which RNAP is specifically bound is given by , and its multiplicity is given by . The probability that RNAP is bound is the Boltzmann factor of the bound state normalized by the partition function of the system , which simplifies to ( 2 ) where and where we have used the fact that for . In the simplifying case of a “weak promoter” , where , this expression reduces to ( 3 ) Note that the microscopic language used to make these derivations is convenient for interpreting binding energies and the dependence on number of polymerases . However , all of these results can be naturally derived and written in the alternative language of dissociation constants without ever making reference to the nonspecific background [23] . For example , we can write ( 4 ) where is the in vivo dissociation constant for RNAP from the promoter of interest . With these results , we can now explore the connection between the measured and the corresponding predicted level of expression . Since gene expression is ( by assumption ) proportional to , we can use equation 3 to conclude that ( 5 ) where is an unknown constant of proportionality related to the number of mRNA or proteins expected from a promoter with . With this relation in hand , we are now equipped to take the predicted energy for each RNAP binding site and compare the resulting expression to that predicted from equation 5 . To test the predictive power of the binding energy model , we measured protein expression and mRNA copy numbers for constitutive expression from each of our unique promoters . Based on equation 5 , a semi-log plot of these data against their respective predicted binding energies in units of should fall along a straight line with slope equal to −1 , consistent with Boltzmann scaling . Indeed , with the unknown constant as our single fit parameter , we find that gene expression follows the exponential relation predicted from the thermodynamic model in equation 5 , as seen in Figure 5 . In this figure , we have taken the zero of energy to be the average energy of RNAP binding across the whole E . coli genome calculated from the energy matrix of Figure 2 , as detailed in the Methods section below . The root-mean-square deviations of our fits are 1 . 02 for mRNA and 1 . 06 for protein . Since these values are the deviations of the natural logarithm of gene expression , we must exponentiate them to get a sense of the deviation in physical units . We conclude that our design process accurately predicts expression to within a factor of over nearly three orders of magnitude . In addition , the table in Figure 3 shows the predicted energy for each promoter ( the column labelled “Model” ) , calculated using the matrix in Figure 2 , as well as the experimentally measured energies of each promoter . To compute these measured energies , we solve equation 5 for , yielding . We then plug in the measured expression for each promoter and the inferred value for ( the of the black line in Figure 5 ) to compute for each promoter . The measured values for the RNAP binding energies for the LacZ and mRNA data are listed in Figure 3 . The promoters with colored entries will be further examined in the context of simple repression later in this work . The direct correlation between these two measurements of gene expression are shown in SI Figure S1 where protein expression is plotted vs average mRNA copy number for every promoter strength , exhibiting an excellent linear relation between these two readouts of expression . Fitting the data in Figure 5 to the full form for in equation 2 , allowing both and the unknown proportionality constant between to vary , we find for both the mRNA and the protein data . This is consistent with typical values for RNA polymerase copy number and the length of the E . coli genome ( [28]–[31] and , respectively ) , and thus the weak promoter limit appears to hold over the range of promoter strengths tested . Since mRNA and protein are linked by translation , their levels for a given promoter should be related . Individual mRNAs can be translated multiple times and it has been shown that the number of translations per mRNA is well described by an exponential distribution with mean , known as the protein burst size , which is the average number of proteins produced per mRNA [8] , [32] , [33] . Using the data described above , we can extract the burst size , defined as the ratio of protein production rate and the mRNA production rate , [8] , [34] . The quantity we measure , however , is the steady-state copy number , where is the average rate of mRNA or protein production and is the associated decay rate . Figures 5A and B demonstrate that the copy number is well described by Boltzmann scaling with . Using this knowledge , we rewrite the burst size as ( 6 ) with [35] and ( equal to the inverse of the cell division time ) . This gives us a measurement of the LacZ activity ( measured in Miller units , described in the methods section ) per mRNA; from available biochemical data we convert from Miller units to number of LacZ tetramers [36]–[39] ( [39] ) . Plugging these values into equation 6 we find the protein burst size , , for the particular RBS we have used is roughly LacZ tetramers or individual LacZ proteins per mRNA . Our discussion so far has focused on the behavior of the designed promoters in the absence of any regulatory interventions . We were interested in examining the portability of these promoters to other contexts such as when they are regulated by transcription factor binding . In the E . coli genome , there are hundreds of genes that are regulated by motifs involving simple repression [40] . For these architectures , there is a single binding site for a repressor protein which reduces the expression from the gene of interest . Addition of a repressor which binds to a proximal binding site necessitates the addition of a term to the partition function of the RNAP binding probability given by equation 2 . This additional term corresponds to the probability of repressor binding and making the promoter unavailable to polymerase . The resulting expression level in the context of thermodynamic models is then given by ( 7 ) where is the number of repressors ( the factor of originates from the fact that LacI has two binding heads ) and is the binding strength of that repressor to the specific binding site [2] , [25] . In the weak promoter limit the expression can be simplified to , ( 8 ) where , , was determined in the previous section by fitting equation 5 to the constitutive expression data in Figure 5A . We therefore have an absolute prediction for the level of gene expression in our LacZ measurements . The prefactor is the constitutive ( R = 0 ) prediction for expression . It is a constant prefactor for all values of R ( at a given promoter strength ) and thus the model predicts that any discrepancies between predicted and measured RNAP binding energies will be inherited through all repressor concentrations . This point is illustrated in Figure 6 where we show how the repressor titration predictions depend upon how well the original constitutive promoters follow the simple Boltzmann scaling . In particular , we show the level of expression for three hypothetical promoters , one whose constitutive properties are underestimated , one whose constitutive properties are overestimated and one for which the Boltzmann scaling is obeyed precisely . What we see is that the repressor titration ( Figure 6B ) inherits the error already present in the constitutive promoters from incorrectly predicting the RNAP binding energy . In each of our strains , the LacI O2 binding site is present near the promoter ( see Figure 3 ) . We reintroduce the repressor into our strains by integrating a cassette into the genome which expresses LacI . Specific LacI concentrations are obtained through modulation of the ribosomal binding sequence of the LacI gene . Using this process we create unique strains with average LacI copy numbers between and repressors per cell . Using equation 8 , we can make parameter-free predictions for the overall level of gene expression as a function of promoter strength , repressor binding strength and repressor copy number for the simple repression architecture . In Figure 7A , we show a comparison between predicted and measured protein expression in the case of simple repression , as a function of repressor copy number and of predicted promoter binding strength ( using from the “model” column of Figure 3 , and as found in Ref . [2] ) . Our measurements ( using the same LacZ assay as for the constitutive data above ) for three distinct promoters along with data from the lacUV5 promoter ( from Ref . [2] ) are shown as points color coded by expression level; Figure 7B shows the same comparison between theory and experiment collapsed along the promoter-strength axis . Each color represents a different promoter strength , with points representing measurements and the solid line representing the theoretical prediction for that promoter . The data in Figure 7B show a clear trend , for any one promoter , to either over or under predict the expression as was sketched in Figure 6 . We attribute this to imperfect predictive powers of the RNAP binding energy model from Kinney et al ( shown in Figure 2 ) [20]: if the thermodynamic theory underpredicts the measured expression at R = 0 using the model value for the RNAP binding energy ( for instance , the magenta point in Figure 5A ) , the theory will continue to underpredict the measured expression as repressors are added ( as seen for the magenta points in Figure 7B ) . In Figure 7 ( C ) we show the result of using the measured RNAP binding energies ( from the column labelled “LacZ” in Figure 3 ) for the promoter binding strength and the accordance between theory and experimental data is evident . It is clear from these measurements that our promoter library exhibits the kind of “transferability” required in order to use them in different regulatory contexts . In particular , the comparison between theory and experiment is very favorable even for the repressed architectures and the imperfect agreement is actually primarily an inheritance of the imperfect accord between theory and experiment for the unregulated promoters themselves .
In this paper , we have shown how high throughput data obtained from experiments like those in Ref . [20] provide a foundation that , together with quantitative predictions from simple thermodynamic models [21]–[25] , can be used to predictively tune protein-DNA interactions to produce a desired output from a gene with high precision . This approach contrasts with previous promoter engineering efforts , which have typically relied upon generating promoter libraries using random mutagenesis , followed by selection for mutants with desired expression levels [41]–[43] . We believe that predictive , model-based engineering of promoters represents a significant technical improvement over random mutagenesis , and moreover points the way to simultaneously engineering multiple aspects of promoter function ( such as repressor or activator binding strengths ) in a scalable way . We demonstrate the validity of our approach by simultaneously varying RNAP-promoter binding strength and the copy number of a transcription factor that represses these promoters . In this case , we can predict the absolute level of gene expression ( once the conversion constant between binding probability and expression units , , is known ) as a function of transcription factor concentration . While the binding site design procedure described here focused on alterations to the −10 and −35 region of promoters , we have made preliminary studies in which promoters are subjected to more severe perturbations , which indicate that the energy function does not describe these situations nearly so well . It is clear that changes in the linker region can have subtle effects on the twist registry and absolute spacing of the −10 and −35 binding sites that are not well accounted for by a linear weight matrix , which ignores correlations in multiple basepair changes [44] . Despite these challenges , constitutive expression from promoters designed in this study agrees well with the scaling predicted from the simple thermodynamic model presented here , and we have shown that our knowledge of simple repression can be applied on top of our understanding of constitutive expression to accurately predict the absolute expression from a gene when repression is introduced .
The energy matrix from [20] is given in arbitrary energy units ( AU ) . To calibrate these arbitrary units to physical units , we need two known reference energies , since only differences in energy are physically significant . From [45] , we know that RNAP binds the wild-type ( WT ) lac promoter with a binding energy more favorable than the non-specific background . Using the matrix from [20] , we find that the wild-type lac promoter has a binding energy of , while the average binding energy of all 41 bp segments in the E . coli strain MG1655 is 91 . 3 AU ( recall that the more positive the energy value , the less favorable the binding interaction ) . To obtain this value , we began at the chromosomal origin of replication and applied the matrix sequentially to each 41 bp segment ( both forward and reverse strands ) around the chromosome , and computed the mean of the resulting energy values . Thus , we find that a difference of is equivalent to a difference of , providing us with a conversion factor of per . To see how this plays out in practice , consider a hypothetical sequence whose binding energy is computed to be . The number we are actually interested in is . For this promoter sequence , we find that . We used the same approach to convert from AU to the units on the of Figure 5 for each of our distinct promoter sequences . All strains used are wild-type E . coli ( MG1655 ) with a complete deletion of the lacIZYA genes [39] . Modified promoters are created through site-directed mutagenesis of plasmid pZS2502+11-lacz [2] , [46] , which has the lacUV5 promoter expressing LacZ ( our reporter gene ) . These constructs are then integrated into the galK region using recombineering [47] . A schematic of the integrated region is shown in Figure 3 . The end result is a strain with a desired , multi-basepair change to the lacUV5 promoter which expresses LacZ and a complete deletion of the LacI protein . Our designed promoters span roughly orders of magnitude in constitutive expression and vary from the wild-type promoter by as few as 1 or as many as 9 individual basepair changes . The site labelled “O2” is a binding site for the LacI repressor protein . For the strains involving simple repression , we took our constitutive expression strains and created as many as 8 different strains with the LacI cassettes from Ref . [2] integrated at the ybcN site . The cassettes contain LacI expressed from an unregulated tet promoter with unique ribosomal binding sequences to produce varying LacI copy numbers . The exception is the data point at average LacI copy number of , which corresponds to the native wild-type LacI gene . The measurements for repressors per cell are from quantitative immunoblots in Ref [2] . One of our strains , the one with repressors/cells , has not been characterized this way , but instead the repressors/cell has been inferred from the measured expression of the lacUV5 promoter . Cultures were grown overnight ( at least 8 hours ) in LB and diluted 1∶4000 into mL of M9 minimal media supplemented with 0 . 5% glucose in a baffled flask . Cells were grown approximately 8 hours and harvested in exponential phase when OD600 was reached . Our assay for measuring LacZ activity is the same as described in Ref . [2] , which is a slightly modified version of that described in Ref [36] . A volume of cells from each sample between and was added to Z-buffer ( , , , , , pH 7 . 0 ) to reach a total of . This volume is chosen to minimize the uncertainty in measuring the time of reaction ( of hours ) and the yellow color is easily distinguishable from a blank sample of of Z-buffer . The assay was performed in Eppendorf tubes . The cells were lysed by addition of of followed by of chloroform , mixed by a 10 s vortex . The reaction was started with the addition of of 2-nitrophenyl ( ONPG ) in Z-buffer . The developing yellow color ( proportional to the concentration of the product ONP ) was monitored visually . Once sufficient yellow had developed in a tube ( easily measurable by OD550 and OD420 , without saturating the reading ) , the reaction was stopped by adding of . ( Typically of a 1 M solution is added in other protocols , but this change allows for the entire reaction to take place in a Eppendorf tube . ) Once all samples were stopped , the tubes were spun at for 3 min in order to reduce the contribution of cell debris to the measurement . of each sample were loaded into a 96 well plate and OD420 and OD550 measurements were taken on a Tecan Safire2 with the Z-buffer sample as a blank . In addition , the OD600 of of each culture was taken with the same instrument . The absolute activity of LacZ is measured in Miller units , ( 9 ) where is the reaction time in minutes , is the volume of cells used in milliliters and OD refers to the optical density measurements obtained from the plate reader . The factor of accounts for the use of as opposed to which changes the concentration of ONP in the final solution . Our assay is based on that used in Ref . [9] . Once a culture reaches OD600 , it is immersed in ice for minutes before being harvested in a large centrifuge chilled to for minutes at . The cells are then fixed by resuspending in of formaldehyde in PBS which is then allowed to mix gently at room temperature for 30 minutes . Next , they are centrifuged ( 8 minutes at ) and washed twice in of PBS twice . The cells are permeabilized by resuspension in Ethanol which proceeds , with mixing , for 1 hour at room temperature . The cells are then pelleted ( centrifuge at for minutes ) and resuspended in of wash solution ( formamide , 20× SSC , water ) and resuspended in of DNA probes ( consisting of an mix of unique DNA probes , individual oligo sequences available as SI Text S5 ) labelled with ATTO532 dye ( Atto-tec ) in hybridization solution ( dextran sulfate , formamide , E . coli tRNA , 20× SSC , BSA , of Ribonucleoside vanadyl complex ) . This hybridization reaction is allowed to proceed overnight . The hybridized product is then washed four times in wash solution before imaging in SSC . Samples are imaged on a agarose pad made from PBS buffer . Each field of view is imaged with phase contrast at the focal plane and with nm epifluorescence ( Verdi V2 laser , Coherent Inc . ) both at the focal plane and in 8 z-slices spaced above and below the focal plane , sufficient to cover the entire depth of the E . coli . The images are taken with an EMCCD camera ( Andor Ixon2 ) . The phase image is used for cell segmentation and the fluorescence images are used in mRNA detection . A total of unique fields of view are imaged in each sample and a typical field of view has between and viable cells ( cells which are touching and cells that have visibly begun to divide are ignored ) resulting in roughly individual cells per sample . The FISH data is analyzed in a series of Matlab ( The Mathworks ) routines . The overview of the workflow is as follows: identifying individual cells , segmenting the fluorescence to identify possible mRNA , quantifying the mRNA which are found ( because of the small size of E . coli , at high copy number mRNA can be difficult to distinguish and count by eye ) . | One of the most fundamental tuning parameters governing expression of a given gene is the strength of its promoter . But what are the sequence rules that govern promoter strength ? Recent high throughput mutagenesis experiments present an improved method for constructing an energy function that maps sequence to protein-DNA binding energy . We use this energy function combined with a thermodynamic model to deliberately design different promoters with over three orders of magnitude difference in their mean expression , and measure the resulting level of expression at both the mRNA and protein level to test this design strategy . The designed promoters are used in an alternate regulatory architecture and can now serve as the basis for the systematic examination of how both the mean and noise in gene expression depend upon the regulatory parameters that have been subject to evolutionary and/or human change . | [
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] | 2012 | Tuning Promoter Strength through RNA Polymerase Binding Site Design in Escherichia coli |
Staphylococcus epidermidis remains the predominant pathogen in prosthetic-device infections . Ventricular assist devices , a recently developed form of therapy for end-stage congestive heart failure , have had considerable success . However , infections , most often caused by Staphylococcus epidermidis , have limited their long-term use . The transcutaneous driveline entry site acts as a potential portal of entry for bacteria , allowing development of either localized or systemic infections . A novel in vitro binding assay using explanted drivelines obtained from patients undergoing transplantation and a heterologous lactococcal system of surface protein expression were used to identify S . epidermidis surface components involved in the pathogenesis of driveline infections . Of the four components tested , SdrF , SdrG , PIA , and GehD , SdrF was identified as the primary ligand . SdrF adherence was mediated via its B domain attaching to host collagen deposited on the surface of the driveline . Antibodies directed against SdrF reduced adherence of S . epidermidis to the drivelines . SdrF was also found to adhere with high affinity to Dacron , the hydrophobic polymeric outer surface of drivelines . Solid phase binding assays showed that SdrF was also able to adhere to other hydrophobic artificial materials such as polystyrene . A murine model of infection was developed and used to test the role of SdrF during in vivo driveline infection . SdrF alone was able to mediate bacterial adherence to implanted drivelines . Anti-SdrF antibodies reduced S . epidermidis colonization of implanted drivelines . SdrF appears to play a key role in the initiation of ventricular assist device driveline infections caused by S . epidermidis . This pluripotential adherence capacity provides a potential pathway to infection with SdrF-positive commensal staphylococci first adhering to the external Dacron-coated driveline at the transcutaneous entry site , then spreading along the collagen-coated internal portion of the driveline to establish a localized infection . This capacity may also have relevance for other prosthetic device–related infections .
Staphylococcus epidermidis is a major cause of prosthetic device infections . This capacity appears due to its ability to colonize both the skin as commensal flora and prosthetic materials via its ability to adhere to device surfaces and form biofilms [1]–[3] . Ventricular assist devices ( VADs ) are relatively new cardiovascular prostheses that have become a major form of therapy for patients with end-stage congestive heart failure . Originally developed as a bridge to cardiac transplantation , they are increasingly used as “destination therapy” ( e . g . , a means to improve survival and quality of life in patients with heart failure refractory to medical therapy ) [4] . However , an important limitation to the use of VADs has been the high incidence of device-related infections , which occur in 18%–59% of patients [5]–[8] . These infections can affect different components of the device , such as the surgical site , the device pocket or the device itself and pose a major threat to survival since eradication usually requires device removal [5] , [9]–[11] . Although the clinical features of VAD-associated infections have been well described , less attention has been devoted to their pathogenetic processes [5] , [7] , [12] . The driveline ( DL ) is the most common site of VAD-related infections [6] , [9] , [13] , [14] . This is likely due to the transcutaneous entry site , continued exposure to commensal flora and the prolonged VAD implantation times [15] . DL infections may remain localized to the entry site or progress , via an ascending infection , to cause further complications including sepsis and endocarditis [7] , [8] , [16] . DLs are coated with a layer of highly textured polyethylene terephthalate ( Dacron ) , a polyester material that facilitates integration into the skin and soft tissues , protecting against bacterial entry . However , mobility of the DL frequently impedes healing and increases the possibility of colonization , biofilm formation and infection [11] . DL infections , like other prosthetic device infections , are most often caused by S . epidermidis [11] . The critical step in the initiation of these infections is bacterial colonization of host structures . Staphylococci , including both Staphylococcus aureus and S . epidermidis , possess a wide range of structurally-related surface proteins with redundant adhesive properties , many of them belonging to a structurally related family of microbial surface components recognizing adhesive matrix molecules ( MSCRAMMs ) , that facilitate this initial colonization step [17]–[21] . The goal of this study was to investigate the pathogenesis of this unique prosthetic device infection as well as to establish a biological model for the study of other prosthetic device infections . By examining the adherence of S . epidermidis proteins to the surface of explanted VAD drivelines and to implanted DLs in a murine model of infection we found that SdrF ( GenBank #AF245041 ) , a protein without a previously identified role in foreign body infections , mediates S . epidermidis adherence to both the external and internal host-tissue coated components of the DL .
Five DLs from Heartmate II VADs , implanted for different periods of time ( 13–30 weeks ) , were obtained from patients undergoing removal of the device for transplantation . During implantation , host matrix components are deposited on the surface of the DL modifying its appearance with respect to non-implanted DLs ( Figure 1A ) and mostly resulting in the DL material , Dacron , no longer being accessible to the bacteria . Trichrome staining of the outer surface of the implanted DLs demonstrated that collagen was the major host component on the surface of the internal DL ( Figure 1B ) . Fibroblasts were also detected interspersed within the collagen fibers ( Figure 1C ) . The genes coding for likely S . epidermidis ligands were selected for comparison [2] . SdrF was cloned in L . lactis MG1363 . Two additional S . epidermidis surface proteins , SdrG ( GenBank #AF245042 ) and GehD ( GenBank #AF090142 ) , as well as the icaADBC operon ( GenBank #DQ149646 ) , responsible for the synthesis of polysaccharide intercellular adhesin ( PIA ) and subsequent biofilm formation , were cloned in L . lactis NZ9000 ( Table 1 ) . Assessment of production and estimation of relative amounts of surface-bound recombinant SdrF , GehD and SdrG in these L . lactis strains were demonstrated by flow cytometry . All three recombinant proteins were exported and anchored to the lactococcal cell wall ( Figure 2A ) . Production of PIA was assessed in a microplate biofilm assay as previously described ( data not shown ) [22] . Subsequently , the binding capacity of these L . lactis strains , as well as that of L . lactis pOri-SdrF , which produces the S . epidermidis collagen-binding protein SdrF [23] , to explanted DLs was measured . The L . lactis strain producing SdrF on its surface adhered significantly better to explanted DLs than the control strain harboring the vector pOri23 alone ( Figure 2A ) . Similarly , the biofilm-producing L . lactis pOri-icaO-47 strain showed a significant increase in DL adherence , although this adherence was significantly lower than that of SdrF ( Figure 2A ) . The adherence of SdrG , a fibrinogen-binding surface protein , was no different than the control . Surprisingly , GehD , a collagen-binding protein , did not demonstrate significant adherence to the collagen-coated DLs when compared with the control ( Figure 2A ) . Gram stain of explanted DL sections incubated with L . lactis harboring pOri-SdrF showed bacteria attached to the collagen fibers coating the Dacron-textured surface ( Figure 2B ) . SdrF contains four regions: a putative ligand-binding A domain ( rASdrF ) , the recently identified collagen-binding B domain ( rBSdrF ) , a serine-aspartate repeat region , which is thought to act as a stem spanning the staphylococcal cell wall therefore allowing domains A and B to protrude from the cell surface , and a C-terminal region which anchors the protein to the peptidoglycan . Two L . lactis recombinant strains that produce truncated cell wall-associated forms of SdrF lacking either the A or B domains were constructed in an earlier study ( L . lactis pOri-SdrFNA18 and pOri-SdrFN856 respectively ) , where SdrF was found to bind collagen type I via its B domain [23] . Binding of L . lactis strains producing either A or B domains of SdrF on their surface to explanted DLs was compared to that of the full length SdrF . L . lactis cells producing the surface-associated B domain adhered significantly better to explanted DLs than to either the control strain or to cells producing the A domain on their surface ( Figure 3A ) . Purified antibodies directed against either the A or the B domain of SdrF were used to further assess the involvement of both regions in DL binding [23] . Control IgGs as well as anti-rASdrF IgGs did not affect L . lactis SdrF-mediated binding to explanted DLs . In contrast , the presence of anti-rBSdrF IgGs at low concentrations ( 50 ng/mL ) caused a significant reduction in binding ( Figure 3B ) . S . epidermidis , like S . aureus , has redundancy in its adherence capacity with more than one protein often mediating adherence to the same host molecule [20] , [23]–[26] . We therefore investigated the effect of antibodies directed against different regions of SdrF on adherence of S . epidermidis to explanted DLs . Anti-rBSdrF caused a significant reduction in the SdrF-prototype S . epidermidis 9491 [18] binding to explanted DLs ( Figure 3C ) . In addition , S . epidermidis 9491 cells pre-incubated with anti-rASdrF also showed reduced adherence , although this reduction was significantly less pronounced than in cells pre-incubated with anti-rBSdrF ( Figure 3C ) . Pre-immune IgGs had no effect on S . epidermidis adherence . Furthermore , SdrF-positive strains S . epidermidis 9491 and HB adhered with higher affinity to the explanted DLs than the SdrF-negative strains 9 and K28 ( Figure 3D ) . The highly textured Dacron surface covering the DL promotes tissue integration as a way to diminish the occurrence of infections . However , the effect of this material on bacterial adhesion has not been investigated . The binding capacity of S . epidermidis proteins to non-implanted DLs was measured using the different L . lactis constructs . L . lactis cells containing SdrF on their surface adhered at a significantly higher rate than the control strain ( Figure 4A ) . Similarly , cells producing truncated forms of SdrF lacking either the A or the B domains were able to adhere to Dacron-textured surfaces ( Figure 4A ) . The presence of either surface-associated SdrG or PIA did not mediate adherence to non-implanted DLs ( Figure 4A ) . Anti-rASdrF and anti-rBSdrF IgGs separately caused a significant reduction in L . lactis ( pOri-SdrF ) adherence to non-implanted DLs whereas control pre-immune IgGs did not affect binding ( Figure 4B ) . The role of SdrF in S . epidermidis adherence to Dacron was further analyzed by assessing the effect of anti-rASdrF and anti-rBSdrF IgGs on S . epidermidis binding to non-implanted DLs . A solution ( 1∶1 ) of these two anti-SdrF antibodies significantly reduced adherence of S . epidermidis 9491 cells to Dacron whereas control pre-immune IgGs did not cause any apparent effect ( Figure 4C ) . In addition we compared the relative adherence of four S . epidermidis strains . While all S . epidermidis strains were found to adhere to Dacron , K28 , an SdrF-negative strain , showed a significantly higher adherence level ( Figure 4D ) . In order to further assess whether the textured nature of the material was a factor in adherence to the Dacron , SdrF binding to a flat polymer surface was measured . SdrF was found to mediate L . lactis adherence to polystyrene surfaces whereas SdrG and GehD had limited binding ( Figure 5A ) . In addition the effect of anti-SdrF antibodies on binding to polystyrene was assessed . Both anti-rASdrF and anti-rBSdrF IgGs effectively reduced SdrF attachment ( Figure 5B ) . Pre-immune control IgGs again had no effect on adhesion to polystyrene ( Figure 5B ) . A recently developed transcutaneous DL murine model was used to further assess the role of SdrF in DL infections [27] . Skin surrounding the DL entry point was inoculated with L . lactis pOri23 ( control; n = 15 ) or L . lactis pOri-SdrF cells ( n = 15 ) . Both DLs and a sample of surrounding tissue were removed 48 hours after infection . The presence of SdrF on the lactococcal cell surface caused a significant increase in the number of bacterial cells attached to the internal portion of the DL as well as to the surrounding tissue with respect to the controls ( Figure 6A and 6B ) . In order to assess the role of wild type SdrF in DL colonization by S . epidermidis , a mixture ( 1∶1 ) of antibodies directed against the A and B domains were pre-incubated with S . epidermidis 9491 cells prior to DL inoculation ( n = 15 ) . Significantly fewer S . epidermidis cells pre-incubated with anti-SdrF IgGs than cells pre-incubated with control IgGs colonized the DL and the surrounding tissue ( Figure 7A and 7B ) . Moreover , SdrF-positive S . epidermidis 9491 was found to adhere significantly better to both DLs and tissue than the previously described SdrF-negative S . epidermidis 9 ( Figure 7C and 7D ) .
S . epidermidis prosthetic device infections are in general initiated by surface proteins that mediate bacterial attachment directly to the synthetic surface or indirectly via host components , such as fibrinogen , that are deposited on the surface of foreign material [25] , [26] . This is the first study to examine the role of S . epidermidis surface components in the pathogenesis of DL infections . After implantation , the highly textured Dacron driveline surface is coated by host matrix constituents , most notably collagen and cellular components , including fibroblasts and/or myofibroblasts . To overcome the inherent redundancy found in S . epidermidis adhesins we used a heterologous lactococcal expression system combined with a screening assay to test for the adherence of S . epidermidis surface proteins to DL surfaces . The intrinsic heterogeneity of the implanted DL surface , as well as the variation due to implantation time resulted in the qualitative nature of our assay . We found that SdrF mediated adherence to explanted DL disks , via its attachment to collagen fibers present on the DL surface . The formation by L . lactis of PIA-dependent biofilm also increased the number of bacteria bound to DL disks . This binding was significantly lower than that of SdrF , and a nonspecific interaction due to the clumping effect of the biofilm cannot be ruled out . SdrF consists of four main components: a ligand binding domain A , a collagen binding domain B , the serine-aspartate repeat region and the C-terminal region which anchors the protein to the peptidoglycan [18] , [19] , [23] . Using previously described recombinant L . lactis strains [23] we demonstrated that the B domain was responsible for SdrF binding to explanted DLs . This interaction seemed to be specific since antibodies against the B domain reduced DL binding in a dose-dependent , saturable manner . Binding studies using S . epidermidis , where other adhesins might also contribute to adherence , further confirmed the role of SdrF . Even at relatively high antibody concentrations , the reduction in S . epidermidis binding did not exceed 50% , further reinforcing the notion that other adhesins are likely to be involved in DL binding . One candidate would be the surface protein GehD , which was previously shown to have collagen-binding activity [20] . However , this protein did not cause any adherence when present on the L . lactis or the S . epidermidis cell wall . Comparison of adherence between S . epidermidis strains HB and K28 , two strains differing in their SdrF phenotype , also suggested that SdrF plays an important role in S . epidermidis adherence to DLs . However , binding differences due to differential production of other surface proteins cannot be ruled out . The highly textured Dacron outer layer of VAD drivelines promotes tissue integration and wound healing [15] . Rapid attachment of the bacteria to implanted surfaces appears to be the first step towards biofilm formation and subsequent colonization by S . epidermidis [3] . The S . epidermidis autolysin AtlE has been shown to mediate initial attachment to polystyrene surfaces via hydrophobic interactions , although it is unclear whether the autolysin plays a direct role in this event [25] . We investigated the interaction between SdrF and the polyester surface of the DL . SdrF bound with high affinity to exposed Dacron layer of the non-implanted DL . This phenomenon persisted when either the A or B domains were absent , suggesting that this interaction might be due to hydrophobic forces rather than to conformational features of the polypeptide . Adherence did not occur when other S . epidermidis surface factors such as SdrG and PIA were produced by L . lactis cells , suggesting that this is SdrF-specific . Interestingly , adherence to the non-implanted DLs was significantly greater than binding to collagen-coated explanted DLs . Anti-SdrF antibodies reduced adherence to Dacron , supporting the notion that SdrF plays a role in the initial attachment to the highly textured surface of DLs . SdrF-defective S . epidermidis strains 9 and K28 showed high adherence levels to Dacron , with K28 level being even higher than 9491 . These observations suggest that other surface components also play a role in initial rapid attachment to implanted DLs . We further investigated the capacity of SdrF to adhere to hydrophobic polymeric surfaces by testing the ability of L . lactis ( pOri-SdrF ) to adhere to flat polystyrene surfaces . The results demonstrated that SdrF also mediated binding to flat polystyrene surfaces . In addition , substantial amounts of antibodies against the A and B domains significantly reduced this binding . Our results suggest that SdrF mediates binding to synthetic materials as well as to collagen-covered surfaces . This ability to directly mediate adhesion to synthetic materials and extracellular matrix components has been previously observed in two cell wall-associated proteins from Erysipelothrix rhusiopathiae , a Gram-positive animal and human pathogen [28] . SdrF appears to play a key role in the initial attachment of S . epidermidis to newly implanted DLs . We speculate that this process occurs in a stepwise fashion with SdrF mediating attachment of the skin commensal S . epidermidis to the uncoated Dacron at the DL exit site . Although unproven as yet , the bacteria may then migrate up the device by forming a biofilm and attach to the collagen-coated internal DL component ( Figure 8 ) . To test this hypothesis we used a recently described murine VAD DL model [27] . After two days we found L . lactis cells producing SdrF in the internal ( subcutaneous ) part of the DL as well as in the surrounding muscle . We also observed that blocking cell surface-exposed SdrF with antibodies against the A and B domains reduced the ability of S . epidermidis to colonize both the DL and the surrounding tissue . Furthermore we found that SdrF-positive strain 9491 colonized both DL and tissue significantly better than SdrF-negative S . epidermidis strain 9 , suggesting that SdrF might be responsible for the differential adherence . However , due to the non-isogenic nature of these two S . epidermidis strains , involvement of other surface factors cannot be ruled out . The interaction of S . epidermidis SdrF with the DL material has a broader biological role than the VAD infections alone since other prosthetic material including tunneled intravenous catheters also utilize Dacron to foster tissue integration [29] . Future studies are needed to determine in a more precise manner the nature and characteristics of the interactions between SdrF and artificial surfaces as well as host factors . However , the high affinity for both Dacron and polystyrene demonstrated in this study strongly suggests a significant biological role in the pathogenesis of infections of indwelling medical devices by S . epidermidis . The strategy used in this study , i . e . the use of explanted devices , combined with heterologous expression systems , has proven to be a valid and useful in vitro approach to the study of bacterial adhesins in these complex interactions [12] , [30] , [31] .
All procedures were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee at Columbia University . This study was reviewed and approved by the Columbia University Institutional Review Board . Lactococcus lactis NZ9000 [32] and MG1363 [33] were grown at 30°C in M17 medium ( BD Biosciences ) supplemented with 0 . 5% glucose ( GM17 ) . S . epidermidis strains 9491 [18] , 9 [20] , HB [34] and K28 [18] were grown at 37°C in Tryptic Soy Broth ( TSB ) ( BD Biosciences ) supplemented with 0 . 25% glucose ( TSBG ) . Strain 9491 is the prototype ATCC strain for SdrF; strain 9 is a clinical isolate from the forearm and has been shown to be defective in SdrF production [23] , [35]; strain HB was obtained from a human patient with osteomyelitis; strain K28 is the prototype strain for SdrG [18] . Escherichia coli XL1-Blue ( Stratagene ) was used as an intermediate host in cloning experiments and was grown at 37°C in Luria Broth ( LB ) ( BD Biosciences ) . Solid media was prepared by supplementing the corresponding liquid media with 1% agar . Strains harboring plasmids derived from pOri23 [31] were grown in the appropriate media supplemented with Erythromycin ( Ery ) ( 5 µg/mL for L . lactis and 500 µg/mL for E . coli ) ( Sigma ) . L . lactis constructs containing recombinant plasmids expressing full length SdrF and truncated forms of this protein have been previously described [23] . Antibodies directed against S . epidermidis proteins GehD and Fbe ( homologous to SdrG ) were kindly provided by Gabriela Bowden and Jan-Ingmar Flock respectively . Rabbit IgGs directed against the A and B domains of SdrF ( anti-rASdrF and anti-rBSdrF , respectively ) and control , pre-immune rabbit IgGs have been previously described [23] . Recombinant plasmids used in this study are described in Table 1 . L . lactis strains containing recombinant plasmids harboring full length as well truncated forms of sdrF have been previously described [23] . Genes encoding S . epidermidis surface proteins were cloned into L . lactis NZ9000 using pORi23 as vector . S . epidermidis DNA fragments were obtained by PCR amplification with Platinum PCR Supermix High Fidelity ( Invitrogen ) according to the manufacturer's instructions . PCR products were purified with PCR Purification Kit ( Qiagen ) , digested with the appropriate restriction endonucleases ( Table 1 ) ( New England Biolabs ) and ligated to similarly digested pOri23 . All cloning reactions were performed following manufacturer's instructions . The ligation mixture was used to transform E . coli XL1-Blue competent cells ( Stratagene ) . Recombinant plasmids were assessed by restriction enzyme analysis and sequencing . Plasmids harboring the correct insert were purified and used to transform L . lactis NZ9000 and MG1363 competent cells as previously described [23] , [33] . Recombinant protein production in L . lactis NZ9000 strains was assessed as follows; production of surface-exposed SdrF , SdrG and GehD was demonstrated as previously described by flow cytometry using IgG fractions purified from sera using the ImmunoPure IgG purification kit ( Pierce ) [23] . PIA production in L . lactis NZ9000 harboring pOri-icaO-47 was assessed as previously described with minor modifications [36] . Briefly , bacteria were cultured in microtiter plates ( Nunc ) for 24 hours . Wells were washed with phosphate-buffered saline ( PBS ) ( Bio-Rad ) , air-dried and PIA was stained with 0 . 1% safranin . For the VAD driveline explantation procedure , the driveline was cut from the device at surgery and was removed by pulling , avoiding manipulation of the internal part . The driveline was immediately rinsed in sterile , cold PBS , fixed in 10% formaldehyde for 30 min at room temperature and washed in PBS . The driveline was then cut longitudinally to create a flat surface and 6 mm-diameter disks were obtained with the use of a sterile cork borer for their subsequent use in adhesion assays . Non-implanted DLs were provided by the manufacturer ( Thoratec , Inc . ) and were processed in the same manner as explanted DLs before their utilization in adherence assays . L . lactis or S . epidermidis cultures were grown to mid-log phase , harvested and resuspended in cold PBS , adjusted to OD600 = 0 . 1 corresponding to a count of 2–7×107 CFU/mL and incubated for 1 hour at 37°C with disks obtained from explanted or non-implanted DLs . After incubation , disks were transferred to 50-ml Falcon tubes and extensively washed with PBS . Viable adherent bacteria were then lifted off the membrane using three 7-minute-incubations at 37°C with a solution of Trypsin-EDTA ( 1× ) ( Gibco ) . Bacterial suspensions were then plated onto appropriate culture media and incubated for 24–48 hours . To test the effect of antibodies on bacterial adherence to drivelines , L . lactis or S . epidermidis cell suspensions mentioned above were incubated for 45 min with appropriate concentrations of antibodies before adding the DL disks . Binding of L . lactis and S . epidermidis to a polystyrene surface was measured as previously described with minor modifications [22] . Briefly , bacterial suspensions in PBS were adjusted to OD600 = 0 . 1 and incubated for 30 min at 37°C in polystyrene tissue culture Petri dishes ( BD Biosciences ) and washed five times with PBS . Adherent bacteria were stained with crystal violet and assessed by microscopic counting using a Nikon Eclipse E400 microscope . A 200×magnification was used . For each test , five random fields were counted using ImageJ 1 . 40 g software [37] . To measure antibody effect on adherence , bacteria were pre-incubated for 30 min with the appropriate concentration of purified IgGs before incubation in polystyrene dishes . Explanted DL samples were embedded in paraffin and sections ( 6 µm ) were stained using Trichrome Stain ( Masson ) kit ( Sigma ) or Gram Staining kit ( Sigma ) in accordance with the manufacturer's instructions . Micrographs were taken using a Nikon Eclipse E400 microscope . C57BL/6J mice were divided into groups of 15 and transcutaneous drivelines were implanted following our recently described model [27] . Briefly , murine drivelines were constructed by coating silicone solid tubing with Dacron cloth . Mice were shaved and a 15 mm piece of murine driveline was transcutaneously implanted at the base of the neck . The internal ( subcutaneous ) part of the driveline spanned 10 mm; while the external part measured 5 mm . Mice were provided with food and water ad libitum . At the time of infection , drivelines were twisted to open the healing wound and the skin surrounding the DL inoculated with logarithmic phase bacterial cell suspensions in PBS ( 50 µl ) . When necessary , bacterial cell suspensions were pre-incubated for 30 min with the appropriate amount of antibodies prior to DL inoculation . Each mouse received 0 . 5–1×108 CFU . The skin was covered with Tegaderm ( 3 M ) . Forty-eight hours after challenge the mice were euthanized , drivelines were explanted and their internal parts cut approximately 1 mm below the transcutaneous point . Samples of surrounding tissue were also obtained . Internal drivelines were transferred to a tube containing 1 mL sterile PBS and vortexed for 5 min . In order to ensure maximum release of adherent bacteria , the driveline was subjected to another vortexing cycle using fresh sterile PBS followed by incubation in a solution of Trypsin-EDTA ( 1× ) ( Gibco ) for 7 min at 37°C . For each driveline all three samples were independently analyzed by serial dilution , plating onto the appropriate solid media , incubation and colony counting . The tissue was weighed , homogenized and plated onto agar . The results were expressed as the number of bacteria per gram of tissue . Statistical significance was determined using Student's t-test or analysis of variance . P<0 . 05 was considered to be statistically significant . | This study investigates the manner in which heart pumps become infected . Infections involving the driveline , the cord that connects the pump to its external power source , are a major complication of implantation of these devices . In this study , we examined why Staphylococcus epidermidis , a bacteria that is commonly found on the skin , is responsible for the majority of these infections . The ability of different surface molecules of S . epidermidis to attach to drivelines removed from patients undergoing transplantation was tested . SdrF , one of the S . epidermidis surface proteins studied , showed the highest adherence to the drivelines . This binding appeared to involve attachment of the B subunit of SdrF to collagen , a molecule that coats the driveline following implantation . SdrF also mediated attachment to the non-implanted Dacron material that is used to coat the drivelines . A mouse driveline infection model also demonstrated that SdrF was sufficient to initiate a driveline infection . Driveline infection can therefore be hypothesized to occur via: 1 ) skin colonization by S . epidermidis; 2 ) adherence of SdrF-positive S . epidermidis to the Dacron surface at the skin entry site; 3 ) migration of bacteria along the driveline; 4 ) adherence to the internal driveline via the collagen coating its surface; and 5 ) initiation of infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/bacterial",
"infections",
"microbiology"
] | 2009 | SdrF, a Staphylococcus epidermidis Surface Protein, Contributes to the Initiation of Ventricular Assist Device Driveline–Related Infections |
To identify new proteins required for faithful meiotic chromosome segregation , we screened a Schizosaccharomyces pombe deletion mutant library and found that deletion of the dbl2 gene led to missegregation of chromosomes during meiosis . Analyses of both live and fixed cells showed that dbl2Δ mutant cells frequently failed to segregate homologous chromosomes to opposite poles during meiosis I . Removing Rec12 ( Spo11 homolog ) to eliminate meiotic DNA double-strand breaks ( DSBs ) suppressed the segregation defect in dbl2Δ cells , indicating that Dbl2 acts after the initiation of meiotic recombination . Analyses of DSBs and Holliday junctions revealed no significant defect in their formation or processing in dbl2Δ mutant cells , although some Rec12-dependent DNA joint molecules persisted late in meiosis . Failure to segregate chromosomes in the absence of Dbl2 correlated with persistent Rad51 foci , and deletion of rad51 or genes encoding Rad51 mediators also suppressed the segregation defect of dbl2Δ . Formation of foci of Fbh1 , an F-box helicase that efficiently dismantles Rad51-DNA filaments , was impaired in dbl2Δ cells . Our results suggest that Dbl2 is a novel regulator of Fbh1 and thereby Rad51-dependent DSB repair required for proper meiotic chromosome segregation and viable sex cell formation . The wide conservation of these proteins suggests that our results apply to many species .
During meiosis , haploid gametes are produced from diploid precursor cells . The reduction of chromosome number is achieved by a single round of DNA replication followed by two rounds of chromosome segregation , termed meiosis I and meiosis II . While meiosis II is similar to mitosis in that sister centromeres segregate from each other , centromeres of homologous chromosomes ( homologs ) segregate to opposite poles in meiosis I [1 , 2] . Three meiosis-specific features are essential for proper segregation of chromosomes during meiosis I–formation of crossovers that connect homologs , mono-orientation of sister kinetochores , and a stepwise loss of sister chromatid cohesion . The formation of crossovers , as a result of meiotic recombination , and the attachment of sister kinetochores to microtubules emanating from the same spindle pole ( mono-orientation ) ensure that homologous centromeres are pulled in opposite directions on meiosis I spindles [2 , 3] . Crossovers and cohesion between sister chromatids distal to crossovers are responsible for holding homologs together until the onset of anaphase I , when a protease called separase cleaves cohesin along chromosome arms [4–6] . This allows segregation of recombined homologs to opposite poles of the meiosis I spindle . During meiosis I , cleavage of centromeric cohesin is blocked by Sgo1 ( called Mei-S332 in Drosophila ) complexed with the protein phosphatase 2A ( PP2A ) [7–9] . Deprotection of centromeric cohesin and a second round of separase activation allow cleavage of the centromeric cohesin at the onset of anaphase II , which is followed by segregation of sister centromeres in meiosis II [10] . Homologous recombination involves programmed formation of DNA double-strand breaks ( DSBs ) and an evolutionarily conserved pathway for DSB repair , which operates in both mitotic and meiotic cells . To promote high-level recombination ( including crossovers ) in meiosis , programmed DSBs are made by the highly conserved topoisomerase-like protein Spo11 ( called Rec12 in the fission yeast Schizosaccharomyces pombe ) and several essential accessory factors [11–13] . These meiotic DSBs are repaired using the intact sister chromatid or the homolog as a template [14] . Only recombination between homologs can lead to formation of the crossovers required for proper segregation of chromosomes during meiosis I . During DSB formation , Rec12 becomes covalently attached to DNA 5’ ends and is subsequently removed by an endonuclease , Mre11-Rad50-Nbs1 ( MRN ) complexed with Ctp1 [15–17] . The DNA 5′ ends are further resected to generate long 3′ single-stranded DNA ( ssDNA ) overhangs [18] . These ssDNA ends are then coated by Rad51 and ( in some species ) meiosis-specific Dmc1 , both of which are homologs of the bacterial DNA strand-exchange protein RecA [19] . Rad51 promotes the formation of DNA joint molecules ( JMs ) between Rad51-ssDNA filaments and homologous double-stranded ( ds ) DNA [20] . Auxiliary proteins , called “recombination mediators” , such as Rad52 , Rad54 , Rad55 , Rad57 , Sfr1 , Swi5 , and Rdh54 , promote the formation and/or stabilization of Rad51-ssDNA filaments [20 , 21] . Other proteins , such as the F-box DNA helicase Fbh1 and ( in the budding yeast Saccharomyces cerevisiae ) the Srs2 helicase , are negative regulators of Rad51 . Members of the Swi2/Snf2 family of DNA motor proteins , Rad54 and Rdh54 , enhance Rad51-mediated formation of JMs but are also involved in the removal of Rad51 from DNA , suggesting that JM metabolism needs to be carefully regulated [22–26] . Rad51-ssDNA filaments invade homologous dsDNA to form a displacement loop ( D-loop ) . Subsequent DNA synthesis primed by the invading 3’ DNA end extends the invading strand [20] . The recombination reaction can then take one of two different paths . If the extended invading strand is displaced and anneals with the other DSB end , a non-crossover is produced in a hypothetical process called synthesis-dependent strand-annealing ( SDSA ) . Alternatively , the strand invasion intermediate is stabilized , and capture of the second DSB end leads to formation of a Holliday junction ( HJ ) [14] . HJs can be resolved by endonucleolytic activities such as Mus81-Eme1 , which is critical in S . pombe , where it is the only known complex involved in meiotic HJ resolution [27–29] . Regulation of the formation and processing of meiotic JMs , the subject of this report , is complex and incompletely understood . We report here a new role for Dbl2 , which was first identified in a screen for S . pombe mutants defective in chromosome segregation during meiosis [30] . It was later identified in a screen for proteins forming microscopic foci at HO endonuclease-induced DSBs [31] . Dbl2 is required for normal DSB targeting of the DNA-repair helicase Fml1 [31] . Here , we show that Dbl2 is required for proper segregation of chromosomes during meiosis by regulating Rad51 function and JM metabolism , apparently by promoting formation of the helicase Fbh1 foci at the sites of DSB repair to dissociate a minor class of JMs . We discuss molecular mechanisms by which JM formation and processing are properly regulated for successful meiosis and the conservation of these proteins among species .
To identify novel proteins required for faithful meiotic chromosome segregation , we screened a library of about 3200 S . pombe deletion mutants purchased from Bioneer . We found that deletion of the dbl2 gene frequently led to missegregation of chromosomes during meiosis . The dbl2 gene was also identified in our previous screening in which we deleted 180 functionally uncharacterized genes whose expression is upregulated during meiosis and screened for mutants defective in meiotic chromosome segregation [30] . To confirm that this phenotype is due to deletion of dbl2 , we deleted the dbl2 gene in a haploid homothallic ( h90 ) strain in which the centromere of chromosome 2 was marked with GFP ( cen2-GFP [32] ) . We sporulated mutant cells , stained nuclei with DAPI and scored segregation of GFP dots in asci with four nuclei . Indeed , we found that dbl2Δ mutant cells frequently missegregated chromosomes during meiosis ( Fig 1A ) . To investigate chromosome segregation directly in anaphase cells , we fixed cells and stained with antibodies against tubulin and GFP . In wild-type cells , homologous centromeres segregate to opposite poles during anaphase I . However , we frequently observed non-disjunction of homologous centromeres and lagging chromosomes in dbl2Δ anaphase I cells ( Fig 1B and 1C ) . In the majority of anaphase I cells with lagging chromosomes , telomeres of chromosome 1 ( sod2-GFP signals ) lagged , while centromeres of chromosome 2 ( cen2-GFP signals ) segregated to the poles ( S1 Fig ) . This indicates that in dbl2Δ mutant cells microtubules are frequently able to attach to kinetochores and pull the sister kinetochores to opposite poles , while the chromosomal arms and telomeres lag behind . Although we have analyzed only telomeres of chromosome 1 and centromeres of chromosome 2 , we suppose that this is representative also for other chromosomes and thus it is unlikely that chromosomes in dbl2Δ mutant cells lag because they do not attach to microtubules . In addition , in dbl2Δ mutant cells we frequently observed mononucleate cells containing one spindle and stretched but undivided chromatin ( Fig 1C ) as well as mononucleate cells containing more than one spindle or four spindle pole bodies ( SPBs ) ( Fig 1D and 1E ) . This result suggests that despite the failure to segregate chromosomes at meiosis I , dbl2Δ cells proceeded to form metaphase II spindles within a single nucleus . Live-cell imaging of dbl2Δ cells confirmed chromosome missegregation and failure to segregate chromosomes to opposite poles during meiosis I ( Fig 2A ) . Remarkably , deletion of rec12 ( spo11 homolog ) suppressed the meiosis I chromosome segregation failure in dbl2Δ cells ( Fig 2 ) . In 13 out of 26 dbl2Δ zygotes observed , chromosomes failed to segregate to opposite poles during meiosis I , whereas in all 17 dbl2Δ rec12Δ zygotes observed , chromosomes segregated during meiosis I ( Fig 2A and 2B ) . The suppression of the failure of meiosis I chromosome segregation in dbl2Δ rec12Δ zygotes was observed in both live and fixed cells ( Fig 2B ) . Because Rec12 is required for meiotic DSB formation and recombination [11 , 33 , 34] , these results imply that Dbl2 acts after the initiation of meiotic recombination to allow faithful chromosome disjunction . Failure to segregate chromosomes in meiosis I could also be caused by an inability to remove cohesin from chromosome arms , which physically links two homologs that have recombined until the onset of anaphase I [6 , 35 , 36] . However , we found no evidence for defective removal of cohesin in dbl2Δ mutant cells when we analyzed the Rec8-GFP cohesin subunit , which is cleaved to allow cohesin removal from chromosomes [35] ( S2 Fig ) . Failure of chromosome segregation in meiosis I has also been observed in mutants defective in mono-orientation of sister kinetochores . In this case , centromeric sister chromatid cohesion , which persists throughout the first meiotic division , prevents segregation of bi-oriented sister chromatids to opposite poles [37–41] . Mutants defective in mono-orientation attempt but fail to divide nuclei during the first meiotic division; elimination of centromeric cohesion allows them to undergo an equational meiosis I division [37–41] . If meiosis I nuclear division failure in dbl2Δ mutant cells were due to a defect in mono-orientation of sister kinetochores , then elimination of centromeric cohesion at the onset of anaphase I should allow dbl2Δ mutant cells to undergo an equational meiosis I division . Elimination of the centromeric cohesin protector Sgo1 in a dbl2Δ strain with only one copy of chromosome 2 marked with GFP ( cen2-GFP ) did not increase equational segregation of sister centromeres during meiosis I ( Fig 3A ) . Moreover , we frequently observed mononucleate cells containing more than one spindle in dbl2Δ sgo1Δ mutant cells , indicating that the meiosis I nuclear division failure was not suppressed by elimination of Sgo1 . These results are consistent with the idea that in dbl2Δ mutant cells the failure to segregate chromosomes during meiosis I is not caused by a defect in mono-orientation of sister kinetochores . In addition to defects in meiosis I , we observed a high frequency of lagging chromosomes and missegregation of sister centromeres in dbl2Δ cells during anaphase II and mitosis ( Fig 3B–3D ) . Thus , in addition to segregation of homologs during meiosis I , Dbl2 is required for proper segregation of sister chromatids during both meiosis II and mitosis . Our observation that the meiosis I chromosome segregation failure in dbl2Δ is suppressed by rec12Δ as well as a previous report that Dbl2 binds to DSBs to facilitate targeting of DNA repair helicase Fml1 [31] prompted us to analyze the role of Dbl2 in the formation of asci and viable spores and in recombination . The dbl2Δ mutant produced abnormal asci , which often contained fewer than four spores or unequal-size spores ( S3 Fig ) , in accord with the frequent chromosome missegregation noted above . The overall yield of viable spores per cell in the mating mixture was about 13 times less in the dbl2Δ mutant than in wild type ( Table 1 ) , indicating a severe defect in meiosis . Viability of the few spores produced was about 57% of that of wild type ( Table 2 ) . However , the frequency of both intergenic ( ade6 –arg1 ) and intragenic ( ade6 ) recombination in viable spores was similar to that in wild-type ( Table 1 ) . As expected from the nearly wild-type levels of recombination , Southern blot hybridizations of DNA extracted from dbl2Δ mutant cells induced for meiosis revealed no defect in the formation and disappearance of DSBs at six DSB hotspots spanning a 0 . 50 Mb interval , mbs1 and mbs2 being the most prominent ( Figs 4A and S4 ) . In rad50+ strains the timing of appearance and disappearance of DSBs was similar to that in wild type; in rad50S strains , in which DSB repair is blocked , DSBs accumulated to similar levels in wild type and the dbl2 mutant [27 , 42] . Similarly , Holliday junction intermediates of meiotic recombination were formed and resolved with nearly wild-type kinetics ( Figs 4B , 4C and S4 ) . These results are consistent with the nearly wild-type recombination levels in the dbl2 mutant . We noted that a low level of joint molecules ( JMs ) persisted in the dbl2 mutant longer than in wild type ( Fig 4B and 4C ) . JMs persisted for at least 8 hr after meiotic induction in dbl2Δ but were scarcely detectable at or after 6 hr in dbl2+ . Accumulation of these species was Rec12-dependent , indicating that these JMs arise from meiotic DSBs and might be related to recombination intermediates . Since many of these JMs migrated on the arc that includes Y-shaped molecules , such as replication forks , we suspect they are D-loops , or closely related structures , which are postulated to be precursors to Holliday junctions ( see Discussion ) [27] . These persistent JMs could account for the failure of chromosome segregation and lagging chromosomes noted above ( Figs 1–3 ) . These persistent JMs were seen at two different DSB hotspots: mbs1 ( Fig 4B ) and ade6-3049 ( S4 Fig ) . Although defects in processing Holliday junctions can lead to chromosome segregation defects [27 , 28 , 43 , 44] , we think it is unlikely that this is the case in dbl2Δ mutant cells . Consistent with the idea that Dbl2 has a function independent of the Mus81-Eme1 Holliday junction resolvase [27 , 28 , 43 , 44] is the synthetic mitotic growth defect of a dbl2Δ eme1Δ double mutant strain: the double mutant grew much more poorly than either single mutant ( S5 Fig ) . Moreover , expression of a wild-type E . coli Holliday junction resolvase RusA but not the nuclease-dead RusA-D70N mutant suppressed camptothecin-sensitivity of eme1Δ mutant cells , as previously reported , but no suppression was seen in fbh1Δ , rqh1Δ ( lacking the Rqh1 DNA repair helicase [45] ) or dbl2Δ mutant cells ( Fig 5A and 5B ) [28 , 43] . RusA expression failed to suppress the meiosis I chromosome segregation defect in dbl2Δ and fbh1Δ mutant zygotes but partially suppressed the eme1Δ mutant phenotype as assessed by scoring mononucleate zygotes containing more than one spindle ( Fig 5C ) . These data indicate that Dbl2 does not have a major role in DSB formation or disappearance , Holliday junction formation or resolution , or the formation of recombinants . This prompted us to investigate other roles of Dbl2 in meiotic recombination . Homologous recombination is promoted by Rad51 , which forms filaments with ssDNA that undergo strand exchange with homologous dsDNA molecules [19 , 20] . The formation and timely disassembly of Rad51-ssDNA filaments is essential for successful DNA repair by homologous recombination . In wild-type S . pombe cells , Rad51 foci are present during meiotic prophase , when DSBs are formed and repaired , but are no longer present in metaphase I cells [46] . In the absence of proteins required for timely removal of Rad51-ssDNA filaments , such as the F-box DNA helicase Fbh1 , Rad51 foci persist throughout both meiotic divisions [23] . We used an antibody raised against fission yeast Rad51 to visualize formation of Rad51 foci in meiotic cells . Interestingly , in dbl2Δ mutant cells Rad51 foci formed during meiotic prophase and , as in fbh1Δ mutant cells , remained visible during meiosis I and meiosis II and in mononucleate cells containing more than one spindle ( Fig 6 , Table 2 ) . Thus , the failure to segregate chromosomes in the absence of Dbl2 correlates with persistent Rad51 foci . If the failure to segregate chromosomes in the dbl2Δ mutant were due to a defect in removal of Rad51-ssDNA filaments or Rad51-dependent JMs , then elimination of Rad51 should allow dbl2Δ mutant cells to segregate chromosomes during meiosis I , though perhaps improperly due to unrepaired DSBs or reduced crossover numbers . Indeed , no mononucleate zygotes containing more than one spindle were observed in a dbl2Δ rad51Δ double mutant ( Table 2 ) . A similar suppression of the dbl2Δ mutation was observed upon deletion of any one of the genes encoding mediator proteins Rad52 , Rad55 , Rad57 , Sfr1 , and Rad54 that promote Rad51-ssDNA filament formation [47 , 48]; such suppression was not observed upon deletion of negative regulators of Rad51 function ( Fml1 or Fml2 ) , the meiosis-specific Rad51 paralog Dmc1 , or Rdh54 ( Table 2 ) . Consistent with the notion that the chromosome segregation defect in dbl2Δ mutant cells is Rad51-dependent , the synthetic lethality of the rqh1Δ dbl2Δ double mutant was suppressed by deletion of rad51 in mitotic cells . These data suggest that Rad51 prevents proper segregation of chromosomes in the absence of Dbl2 . Our observation that Rad51 foci persist in dbl2Δ mutant cells beyond meiotic prophase raises the possibility that Dbl2 regulates proteins responsible for disassembly of Rad51-ssDNA filaments . Fbh1 , an F-box helicase related to bacterial UvrD , negatively regulates Rad51-mediated homologous recombination by disrupting Rad51 nucleoprotein filaments [22] . Interestingly , the fbh1Δ mutant phenotype resembles that of dbl2Δ–meiotic DSB formation and repair as well as recombination are close to wild-type levels , chromosomes frequently fail to segregate during meiosis I , Rad51 foci persist beyond meiotic prophase and the fbh1Δ mutant phenotype is suppressed by rad51Δ , rad52Δ or rad57Δ [23 , 49–51] . We therefore tested whether Fbh1 foci , which co-localize with Rad51 foci after meiotic DSB formation [23] , are affected in dbl2Δ mutant cells . Because Fbh1 is not visible when tagged at the endogenous locus , we used a strain in which the endogenous fbh1 gene was deleted and the yellow fluorescent protein ( YFP ) -tagged Fbh1 was expressed from a strong nmt promoter [23] . Fbh1-YFP forms foci in response to Rec12-dependent DSBs that co-localize with Rad51 foci , suggesting that this construct is functional [23] . We used camptothecin ( CPT ) , a topoisomerase I inhibitor , to induce DNA lesions in vegetative cells [52] . Fbh1-YFP foci were visible in dbl2+ cells but were strongly reduced in dbl2Δ mutant cells ( Fig 7A and 7B ) . This reduction was not due to a higher frequency of plasmid loss in dbl2Δ mutant cells because the stability of the Fbh1-YFP plasmid was similar in both dbl2+ cells ( 5 . 8% plasmid loss per generation ) and dbl2Δ cells ( 6 . 7% plasmid loss per generation ) . We obtained similar results when we induced DNA lesions in vegetative cells using methyl methanesulfonate ( MMS ) [53 , 54] ( S6 Fig ) . Fbh1-YFP foci were also visible in rad51Δ , rad52Δ , rad55Δ , rad57Δ , sfr1Δ and rad54Δ mutant strains , but the frequency of Fbh1-YFP foci was decreased by the dbl2Δ mutation in each of these strains , both in the absence and presence of CPT ( Fig 7C ) . The reduction of Fbh1-YFP foci in dbl2Δ mutant cells was not due to reduced levels of DNA lesions because Rad52-mCherry foci , which represent sites of active DNA repair [55] , were not reduced in dbl2Δ mutant cells ( S7 Fig ) . To investigate whether localization of Dbl2 and Fbh1 to DSBs are interdependent , we analyzed Dbl2-YFP expressed from a strong nmt promoter [56] . A previous report showed that Dbl2-YFP formed a focus at an HO endonuclease-induced DSB [31] . We observed that Dbl2-YFP formed nuclear foci in wild-type cells when DNA lesions were induced by MMS or CPT ( Fig 7D , S6 Table ) . Deletion of fbh1 had no effect on the formation of Dbl2 foci ( Fig 7D , S6 Table ) . This suggests that Dbl2-YFP expressed from a strong nmt promoter is able to form foci in the absence of Fbh1 . However , we cannot exclude the possibility that this is due to overexpression of Dbl2-YFP or the presence of spontaneous suppressor mutations which may occur in fbh1Δ mutant cells [23] . If the role of Dbl2 were to promote accumulation of Fbh1 at DNA lesions , we would expect overexpression of Fbh1 to suppress the dbl2Δ mutant phenotype . Indeed , both CPT-sensitivity and the meiotic chromosome segregation defect in dbl2Δ mutant cells were nearly fully suppressed by expression of Fbh1-YFP from a strong nmt promoter ( Fig 8A and 8B ) . These data suggest that Dbl2 promotes accumulation of Fbh1 at DNA lesions , such as DSBs , independently of recombination proteins Rad51 , Rad52 , Rad55 , Rad57 , Sfr1 and Rad54 .
Dbl2 was identified in screenings for mutants defective in chromosome segregation during meiosis [30] and for proteins that localize to DNA double-strand breaks ( DSBs ) [31] . Here , we report that in the absence of Dbl2 , Rad51 foci and Rad51-dependent DSB-repair intermediates ( DNA joint molecules , or JMs ) persist and frequently prevent proper segregation of chromosomes during meiosis I . Also in the absence of Dbl2 , foci of the F-box helicase Fbh1 are less abundant than in the presence of Dbl2 . We propose that a subset of JMs requires Fbh1 for their reversal or processing into Holliday junctions ( HJs ) resolvable by Mus81-Eme1 and that Dbl2 is required to promote formation of Fbh1 foci at these JMs . As predicted , the phenotypes of dbl2Δ and fbh1Δ are similar , although not identical , as discussed below . These proteins , like the formation of JMs during meiosis , are widely conserved , suggesting that diverse species require Dbl2 for successful meiosis and reproduction . The data reported here can be accounted for by the following proposal . During DSB repair , a subset of JMs is not converted into HJs . Some of these JMs might be D-loops , since they migrate on the "Y-arc" characteristic of such JMs ( Figs 4 and S4 ) . Others might be related to HJs , such as hemicatenanes , since they migrate on the "X-arc" characteristic of such JMs . ( Figs 4 and S4 ) . The helicase Fbh1 , recruited by Dbl2 , either reverses these JMs to allow synthesis-dependent strand annealing ( SDSA ) or enables their extension and conversion into HJs , which can be resolved by the Mus81-Eme1 HJ resolvase . This proposal accounts for our observations as follows . We observed a rare class of JMs that persisted late in meiosis in dbl2Δ cells ( Figs 4B , 4C and S4 ) . These JMs account for about 0 . 3% of the total DNA in a 12 kb interval containing the mbs1 or the ade6-3049 DSB hotspot ( Figs 4C and S4 ) , which corresponds to about 3 JMs per cell . This estimate is uncertain , because the low level of these JMs is not far above the background level and because the density ( number per kb ) of these JMs might be less in DSB-cold regions . Furthermore , some of the persistent JMs detected might not block segregation . Nevertheless , this frequency of persistent JMs is compatible with the frequency of missegregating or lagging chromosomes or cells with one nucleus but two spindles , which are seen in 20–50% of cells ( Figs 1 , 2 and 3; Table 2 ) ; if Poisson distributed , an average of one persistent JM per cell would result in 63% of cells with failed chromosome segregation . Thus , these persistent JMs are likely the cause of the failure of chromosomes to segregate properly during both meiosis I and II , consistent with rec12Δ eliminating both the persistent JMs ( Figs 4 and S4 ) and chromosome segregation failure ( Fig 2 ) . These JMs might be between sisters or between homologs , but for the reasons below , we suspect they are primarily intersister JMs . During meiosis I , when homologous centromeres segregate , a persistent intersister JM distal to a crossover would prevent segregation in a similar way that non-cleaved cohesin mediating sister chromatid cohesion prevents segregation [35 , 57 , 58] . A persistent interhomolog JM would also prevent segregation . During meiosis II , when sister centromeres segregate , an intersister JM would directly prevent segregation if not aided by a DNA helicase , such as Fbh1 . Other meiotic phenotypes of dbl2Δ are accounted for by this proposal . The suppression , by elimination of DSBs ( rec12Δ ) , of the dbl2Δ chromosome segregation failure ( Fig 2 ) and of persistent JM formation ( Figs 4B , 4C and S4 ) is accounted for by the requirement for Rec12 and DSBs to form meiotic JMs . Similarly , the suppression of both Rad51-focus accumulation and chromosome segregation failure in dbl2Δ mutants by rad52Δ , rad55Δ , rad57Δ , sfr1Δ , and rad54Δ is accounted for by the enhancement of Rad51 strand exchange by the corresponding proteins ( Table 2 ) . It is particularly noteworthy that dmc1Δ does not suppress dbl2Δ but rad52Δ does ( Table 2 ) . Dmc1 is not required for formation of intersister JMs at the loci tested in S . pombe [59] or in S . cerevisiae [60] , and genetic data indicate that S . pombe Rad52 is required for intersister but not interhomolog JM formation [61] . Furthermore , purified S . pombe Rad52 stimulates Rad51 strand exchange , although it inhibits Dmc1 strand exchange [62 , 63] . Failure to convert intersister JMs into HJs would not reduce recombinant frequencies but would prevent proper chromosome segregation and decrease viable spore yield , as observed in dbl2Δ ( Table 1 ) . Failure to recruit Fbh1 to JMs in dbl2Δ cells would allow accumulation of Rad51 foci ( Fig 6; Table 2 ) and rare JMs ( Figs 4B and S4 ) . The mitotic phenotypes of dbl2Δ are similarly accounted for . After DNA damage , the appearance of Fbh1 foci is Dbl2-dependent ( Fig 7 ) . dbl2Δ mutants are sensitive to camptothecin ( CPT ) ( Fig 5 ) , which inhibits topoisomerase 1 and leaves DNA lesions that must be repaired [52] . Mitotic DNA repair is thought to be primarily with the sister chromatid [64] . If some intersister JMs other than HJs , such as D-loops and hemicatenanes , persist in dbl2Δ mutants , expression of the bacterial RusA HJ resolvase would not alleviate the problem , as observed ( Fig 5 ) . In contrast , the CPT-sensitivity and presumed accumulation of HJs in eme1Δ mutants , which lack the S . pombe HJ resolvase Mus81-Eme1 [28] , is suppressed by expression of catalytically active RusA ( Fig 5 ) . The phenotypes of dbl2Δ and fbh1Δ are remarkably similar . In both mutants during meiosis , DSB formation and repair as well as recombination are close to wild-type levels , chromosomes frequently fail to segregate during meiosis I , Rad51 foci persist beyond meiotic prophase , and chromosome missegregation is suppressed by rad51Δ , rad52Δ or rad57Δ ( Figs 1 , 2 , 3 , 4 , 6 and S1; Table 2 ) [23 , 51] . The slightly higher spore viability and milder chromosome segregation defects in dbl2Δ than in fbh1Δ may result from residual Dbl2-independent binding of Fbh1 to DSBs or JMs . Moreover , during mitotic growth , both dbl2Δ and fbh1Δ mutant cells are sensitive to camptothecin [65] and show negative genetic interaction ( phenotype of double mutant is stronger than single mutants ) with mutants defective in DNA repair such as srs2Δ and rsc4Δ and positive genetic interaction ( suppression of the mutant phenotype ) with rad55Δ and rad57Δ [66] . It is interesting that Dbl2 promotes the formation of Fbh1 foci in the absence of Rad51 after DNA damage ( Fig 7C ) . This result suggests that Dbl2 recruits Fbh1 to DNA lesions before Rad51-ssDNA filament and JM formation . This feature may reflect a type of “fail-safe” mechanism , in which the repair machinery is recruited even before it is needed . This appears to be the case for some other events in meiosis . For example , in some species , such as S . cerevisiae , the Mre11-Rad50-Nbs1 ( MRN ) complex is required for meiotic DSB formation even though its catalytic activity is apparently needed only after DSB formation , i . e . , for the processing of DNA ends to allow Rad51-ssDNA filament formation [67] . If Fbh1 is recruited to most meiotic DSBs before JM formation , it may play a more prominent role in meiotic DSB repair than discussed above: it may aid metabolism of JMs other than the rare ( few per cell ) persistent JMs detected in dbl2Δ mutants ( Figs 4B , 4C and S4 ) . Purified Fbh1 can remove Rad51 from Rad51-ssDNA filaments and has DNA helicase activity [22] . Therefore , both Rad51 foci and JMs should accumulate in dbl2Δ and fbh1Δ mutants , as observed ( Table 2; Figs 4B , 4C and S4 ) . This is not the case for other S . pombe helicase mutants , fml1Δ and fml2Δ , or a chromatin remodeling mutant , rdh54Δ ( Table 2 ) . Thus , Fbh1 has a special role , which may be related to its having a second activity , ubiquitin ligase as part of the SCF complex [68] . We cannot distinguish whether the phenotypes observed here are due to loss of the helicase or the ligase activity , or both , but the accumulation of JMs ( Figs 4B , 4C and S4 ) suggests that the Fbh1 helicase is important for meiotic joint molecule metabolism . The ligase activity may also be important to remove Rad51 , but this action might be a result of the helicase alone . Previous work has shown that during meiosis JMs can be processed in a variety of ways into chromosomes suitable for segregation . In S . cerevisiae at least six mechanisms have been shown or inferred to convert JMs into recombinant chromosomes [69 , 70] ( and references therein ) . In contrast , in S . pombe only one mechanism has been described–the formation of HJs and their resolution nearly exclusively by the Mus81-Eme1 resolvase [27–29 , 44 , 71] . Here , we propose an additional mechanism–Fbh1 , recruited by Dbl2 , acts on D-loops to reverse them or to convert them into HJs suitable for Mus81-Eme1 resolution . This action is important , for in the absence of Dbl2 viable spore yields are reduced by a factor of 13 ( Table 1 ) and chromosomes missegregate in up to half of the cells ( Figs 1 , 2 and 3 ) . There is little effect on recombinant frequencies , however , because these JMs are rare ( Figs 4B and S4 ) and , we propose , many of these JMs are intersister , which cannot produce recombinants . Thus , studies of recombination have overlooked the important function of Dbl2 and Fbh1 for meiotic chromosome segregation . Dbl2 is predicted to encode a 78 kDa protein with a domain of unknown function ( DUF2439 ) at the N-terminus . Our bioinformatic searches revealed that within the DUF2439 region Dbl2 is highly conserved in fungi , animals and plants . Orthologs can be detected in Saccharomyces cerevisiae ( Mte1 , YGR042W ) , Homo sapiens ( ZGRF1 ) and Arabidopsis thaliana ( AT4G10890 ) ( S8 Fig ) . The only other sequence family that is significantly related to the DUF2439 domain is the one including Rdh54 ( S . cerevisiae , S . pombe ) and RAD54B ( H . sapiens ) ( S8 Fig ) . Interestingly , some proteins of both sequence families contain an AAA+ ATPase domain and/or a helicase domain ( ZGRF1 , Rdh54 , RAD54B ) at their C-termini . The C-terminal part of Dbl2 is conserved only within the Schizosaccharomyces species; it is enriched for polar residues and is predicted to be highly disordered and thus unlikely to form an active helicase . However , Dbl2 is required for targeting helicases Fml1 and Fbh1 to DSBs ( this work and [31] ) . Similarly , recent studies in S . cerevisiae showed that Mte1 is required for localization of Mph1 helicase at sites of DNA damage and regulates Mph1 activity [72–74] . Thus , we speculate that Dbl2 acts as an adaptor or recruiter for helicases Fml1 and Fbh1 and perhaps for other proteins . The mechanism may be similar to that of other adaptor proteins containing disordered C-terminal regions such as endocytic adaptor proteins Epsin1 and AP180 and Atg13 adaptor , which controls the initiation of autophagy [75 , 76] . What is the role of the DUF2439 domain ? Surprisingly , the N-terminal truncation that removes the DUF2439 domain had no effect on either Dbl2 focus formation at DSBs or on its ability to confer camptothecin-resistance [31] . Similarly , deletion of the budding yeast rdh54 confers sensitivity to MMS but N-terminal truncations of Rdh54 that remove or truncate the DUF2439 domain do not affect MMS sensitivity [77] . Interestingly , these Rdh54 truncation mutants lost their ability to interact with Rad51 and had impaired ability to dissociate Rad51-DNA complexes [77–79] . The reason why the DUF2439 domain is important for Rad51 interaction but not for resistance to camptothecin or MMS is not known , but one possible explanation is that the role of Dbl2 and Rdh54 in repair of MMS- or camptothecin-induced DNA damage may not be through Rad51 . The N-terminal domain of human RAD54B which includes the DUF2439 domain binds to branched DNA substrates and interacts with both RAD51 and DMC1 [80] . Similarly as in Rdh54 and RAD54B , the DUF2439 domain of Dbl2 may mediate interaction with Rad51 . This would allow Dbl2 to bring Fml1 and Fbh1 helicases directly to Rad51 nucleoprotein filaments . Consistent with this notion is our finding that Dbl2 interacts with Rad51 and weakly with Fml1 in yeast two-hybrid assays ( S9 Fig ) . Interestingly , our observation that the Fbh1 foci are formed in the absence of Rad51 ( Fig 7C ) indicates that , to some extent , Dbl2 is able to promote Fbh1 focus-formation at meiotic DSBs independently of Rad51 . We observed no interaction between Dbl2 and Fbh1 in yeast two-hybrid assays ( S9 Fig ) . These results do not , however , exclude the possibility that Dbl2 interacts directly with Fbh1 . Both Dbl2 and Fbh1 are evolutionarily conserved proteins present from yeast to humans , suggesting that our results may apply widely . A notable exception is the absence of an Fbh1 homolog in the budding yeast S . cerevisiae . However , it has been proposed that S . cerevisiae Srs2 helicase is a functional counterpart of Fbh1 [81] . The Dbl2 and Fbh1 proteins , like other proteins central to DNA repair , are widely conserved and play crucial roles in maintaining cell viability , genome integrity , and high fertility , making their further study important .
The genotypes of the yeast strains used in this study are listed in the S1 Table . Standard media ( rich YES and appropriately supplemented minimal EMM2 and sporulation SPA ) were used to maintain , grow and mate S . pombe strains [39 , 82–84] . To induce mating and meiosis ( Figs 1 , 2 , 3 , 6 and 7 and Table 2 ) , cells were grown in liquid YES to mid-log phase at 32°C , washed three times with water , mixed ( for heterothallic matings ) , transferred to EMM2-NH4Cl plates , and incubated at 25°C for 10–17 hr before examination [85] . DNA was extracted from meiotically induced cells and analyzed as described [86] . S . pombe was transformed using the lithium acetate method and genes deleted as described [87] . The immunostaining and microscopy used to analyze chromosome segregation and subcellular localization of Rec8 and Rad51 in S . pombe cells were performed as described [7] . Subcellular localization of Rad51 was determined using anti-Rhp51 polyclonal antibody ( Cosmo Bio ) diluted 1:500 . No foci were detected in rad51Δ mutant cells , indicating that in our assays anti-Rhp51 antibody specifically detected Rad51 ( S10 Fig and Table 2 ) . Live-cell imaging and spore viability determinations were performed as described [88] . Viable spore yields and recombinant frequencies ( Table 1 ) were determined as described [89] . fbh1∆ ( JG17775 ) and fbh1∆ dbl2∆ ( JG17777 ) cells harboring plasmid pMW651 ( expressing Fbh1-YFP and containing the LEU2+ marker , which complements the leu1-32 mutation in the parental strains ) were grown in selective EMM medium lacking leucine at 32°C to mid-log phase . The cells were washed with water and resuspended in nonselective YES liquid medium , incubated at 32°C for 18 hr ( 7 generations ) and then plated on YES plates . Colonies were formed after 3 days at 32°C and replica-plated onto selective plates ( EMM lacking leucine ) . The percentage of plasmid loss per generation was determined as described by Osman et al . [90] . Full-length coding regions for Dbl2 , Fbh1 , Fml1 and Rad51 were amplified from meiotic cDNA with primers that added a 5' SfiI and 3' SmaI or BamHI ( Fbh1 only ) restriction sites and then cloned into plasmid pGBKT7 ( Clontech ) for GAL4-DNA binding-domain bait constructs , or into plasmid pGADT7 ( Clontech ) for GAL4 activation domain prey constructs . Primers used were 5'-AAAAGGCCATGGAGGCCATGGATACAAGTTCCAATGTTTTTCATTATC-3' and 5'-AAAACCCGGGTCAAATAAAGTCACCATCTTCGTCCGAATC-3' for Dbl2 , 5'-AAAAGGCCATGGAGGCCATGAGTGCTCAACATTTACATAGCTGCAAAT-3' and 5’-AAAAGGATCCCTACTGATCATGTACAGCAAACAATTGATTTTCAATAAATAGCATCGATCTTTTAAGCCG-3' for Fbh1 , 5'-AAAAGGCCATGGAGGCCATGTCCGATGATTCTTTTAGTAGTGATGAAG-3' and 5'-AAAACCCGGGCTAAATCAGCATTCCTTTCATACGTTTCCTTTTC-3' for Fml1 , and 5'-AAAAGGCCATGGAGGCCATGGCAGATACAGAGGTGGAAATGCAAGTTAG-3' and 5'-AAAACCCGGGTTAGACAGGTGCGATAATTTCCTTGGGATCACCAACACC-3' for Rad51 . Bait and prey plasmids were introduced into S . cerevisiae strain PJ69-4a ( Clontech ) by standard lithium acetate-mediated transformation . Transformants were tested for bait-prey interaction by spotting onto SD minimal media lacking appropriate amino acids according to the manufacturer's instructions . We performed at least two independent transformations and growth tests . | Meiosis produces haploid gametes from diploid precursor cells . This reduction of chromosome number is achieved by two successive divisions after only a single round of DNA replication . To identify novel regulators of meiosis , we screened a library of fission yeast deletion mutants and found that deletion of the dbl2 gene led to missegregation of chromosomes during meiosis . Analysis of live dbl2Δ cells by fluorescence microscopy showed that chromosomes frequently failed to segregate during the first meiotic division . Further cytological and biochemical analyses revealed that this segregation defect is due to persistent intermediates of DNA double-strand break repair , also called DNA joint molecules . Our results indicate that Dbl2 is required for formation of Fbh1 DNA helicase foci at the sites of DNA double-strand break repair in order to process DNA joint molecules and allow segregation of chromosomes during meiotic divisions . Our bioinformatics searches revealed that Dbl2 is highly conserved in fungi , animals and plants , suggesting that Dbl2 plays a similar role in other organisms–the formation of viable sex cells and healthy progeny . | [
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... | 2016 | Dbl2 Regulates Rad51 and DNA Joint Molecule Metabolism to Ensure Proper Meiotic Chromosome Segregation |
Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections . Yet , the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood . We investigated the role of gamma band ( 50–80 Hz ) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer . We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex . The relationships between gamma phases at different sites ( phase shifts ) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer . We observed transient stimulus-related changes in the spatial configuration of phases ( compatible with changes in direction of gamma wave propagation ) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave . These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional connections .
Visual cortical processing is highly distributed and likely depends crucially upon the cooperative interactions between different groups of neurons . Although there is an extensive knowledge about how individual neurons encode specific features of visual stimuli [1 , 2] , how groups of neurons cooperate to give rise to a coherent perception of naturalistic scenes is still largely unknown [3 , 4] . One factor governing interactions among groups of neurons is their pattern of anatomical connections . This pattern in the primate primary visual cortex ( V1 ) includes a local recurrent microcircuitry involving both inhibitory and excitatory neurons [5 , 6] , as well as a larger network of horizontal connections spreading over several millimeters [1 , 7–11] . Such recurrent connectivity likely serves as an anatomical substrate to establish transient dynamic patterns of functional connectivity [12–15] , allowing selective communication between the populations of neurons involved in visual function [16–19] . However , the physiological mechanisms that may transiently modulate the effective strength of any given connection are largely unknown . One possibility is that transient interactions between neuronal groups depend upon the relative phase of the synchronization of gamma-band oscillations within each group [20–23] . Despite the growing support for a role of the relative temporal alignment of gamma oscillations in mediating communication , many questions about how they may operate remain unsolved [24] . In particular , while previous evidence linked gamma oscillations to symmetrical interactions between populations through synchronization [25] , it is not known whether they can establish a directional communication within a brain area and modulate it transiently according to the needs of stimulus processing or the demands of the task . Finding a mechanism for dynamic routing of information is of major importance for understanding intracortical communication . One reason why the above questions have not yet been fully clarified is that most previous studies considered symmetric measures of correlation ( or synchronization ) between neural populations [26] , which cannot provide information about the direction of interaction . We thus introduce here nonlinear information theoretic tools to quantify directed communication and its modulation by the stimulus to assess the role of the phase of gamma oscillations in modulating dynamically the routing of information and to analyze how this routing relates to the spatiotemporal pattern of gamma phase [27 , 28] . We analyze spiking activity and Local Field Potentials ( LFPs ) simultaneously recorded from locations separated by up to a few millimeters in the V1 of macaques during the presentation of naturalistic color movies . These stimuli present spatially extended visual features varying over a wide range of ecologically relevant time scales and are therefore ideally suited to both dynamically coactivate groups of neurons processing different regions of the visual field and to investigate how interactions among active groups of neurons may be modulated by changes in stimuli . We found that the local phase of gamma-band rhythmic activity exerts a dynamic , stimulus-modulated spatially asymmetric effect on the firing rate of spatially separated populations within V1 in a way that strongly suggests that directional information transfer is mediated by propagation of gamma oscillations . Further , differences in gamma phase across sites are transiently modulated by the visual stimulus ( with propagation of waves from the sending site accentuated when the sending site is strongly stimulated by the visual stimulus in its receptive field [RF] ) , despite the absence of reliable locking of gamma phase to the stimulus at any individual site . Finally , transient changes of phase differences across sites ( or spatial phase shifts ) co-occurred with changes in the causal interactions exerted by gamma oscillations onto spiking activity at other sites . These findings suggest that the dynamical relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional and effective network connections during information processing .
We recorded extracellular potentials in opercular V1 ( foveal and parafoveal representations ) of three anesthetized macaque monkeys with multiple electrodes positioned with a guide according to a 4x4 square grid with interelectrode spacing in the range 1–2 . 5 mm ( Fig 1A ) . From the extracellular potentials recorded at each electrode , we extracted two aspects of mesoscopic network activity . First , we extracted Multiple Unit Activity ( MUA ) by filtering the extracellular signal in the ( 1 , 000–3 , 000 Hz ) frequency range and computed the time-varying envelope of this oscillation . This signal is known to reflect the spike rate of neurons within 300 μm distance around the electrode tip [29] . The MUA was used here to measure the massed firing rate ( and thus the strength of local network activity ) at a given time and location . Second , we extracted LFPs by low-pass filtering the despiked extracellular potentials ( see Materials and Methods ) . Here , LFPs , which are known to provide a robust measure of network oscillations [30 , 31] , were used to measure the instantaneous phase and amplitude of network oscillations around the electrode location , using the Hilbert transform of the LFP band-passed signal ( S1 Methods ) . Activity was recorded both during binocular visual stimulation with 4–6 . 5 min long color Hollywood movie clips with 30 Hz frame rates ( with the same movie clip being presented over 30–120 repeated trials in the same session ) and during several 5 min long stretches without visual stimulation ( spontaneous activity ) . RFs were identified for each site using reverse correlation of the gamma power ( Materials and Methods , S1A and S1B Fig ) . RF distances for all electrode pairs were in the 0–4° range , and the majority of RF pairs had an overlap , with 46% of the data having a relative area overlap of 0 . 4 or less ( S1C and S1D Fig ) . To study neural tuning to visual features , we extracted visual features from the RFs ( primarily Orientation Activation—OA—and local Time Contrast—TC—see S1 Methods ) with computer algorithms . The correlation over time of visual features ( S1F Fig ) showed that , though most RFs had a positive correlation , due to their partial overlap , the visual features in different RFs were partly independent ( mean correlation 0 . 73 +/− 0 . 21 for TC , 0 . 4 +/− 0 . 30 for OA ) , thereby allowing some evaluation of how differences in visually-driven RF activation may modulate mesoscopic neural signals . The power spectrum of the changes of RF visual features over time ( S1E Fig ) showed that—in agreement with previous analyses of natural movies [32]—features varied slowly , with the most power in the low frequency range . Importantly , the properties of the presented movie imply that the gamma-band ( 50–80 Hz ) oscillations cannot simply reflect the entrainment from stimulus dynamics and must originate instead from neural interactions . Previous work showed that the LFP gamma-band was the one whose power carried more information about the movie stimulus ( [33] , see also S2B Fig ) , and whose power was proportionally more enhanced during movie stimulation with respect to spontaneous activity ( S3 Fig , see also [34–36] ) . To assess in which LFP frequency band visual stimulation elicited spatially organized oscillatory activity , we computed the spatial coherence of the multisite recordings [37] . This measure has been previously used to detect interesting spatiotemporal activity such as travelling waves [37 , 38] . We found ( Fig 1B and 1C ) that visual stimulation elicited a consistent increase of spatial coherence in all sessions ( t test; p < 0 . 001 ) only in the ( 50–80 Hz ) gamma-band , suggesting that sensory information elicited spatially organized oscillations in this band ( note the 60 Hz sharp coherence peaks correspond to harmonics of the 30 Hz frame rate of the movie and of the 60 Hz refresh rate of the monitor ) . Our aim was to investigate whether and how this stimulus-enhanced spatiotemporal coherent neural activity in the gamma-band mediates interactions and communication among neural populations at different locations . Relationships between gamma phases of different neural populations have been reported to influence mutual , symmetric , relationships between the firing of different neuronal populations [26 , 39] . To extend this understanding to the case of directed—rather than mutual—information exchanges , here we investigated whether the phase of gamma oscillations of a neural population has a directed effect onto the firing rate of other receiving populations . To address this question quantitatively , we used a direction-specific information theoretic analysis of the impact of phase on spiking activity at other sites during visual stimulation with natural movies . We measured whether the gamma phase of the sending population influences the spiking activity of the receiving population above and beyond what can be predicted by the past firing rate dynamics of the receiving population itself . That is , we computed the mutual information between the past gamma phase at a “sending” electrode and the spiking activity at a “receiving” electrode , conditioned upon the past spiking activity of the receiving population ( see illustration in Fig 2A ) . This quantity is called the Transfer Entropy ( TE ) from the gamma phase at the sending electrode to the spiking activity at the receiving electrode [28] . Significantly positive values of TE mean that the gamma phase of the sending population exerts a causal effect ( in the Wiener-Granger sense ) on variations in firing rate in the receiving location . Note that TE values are reported after subtracting out spurious amounts of causation due to effects including common visual stimulation that do not reflect genuine communication between sites and are Z-scored in SD units of this spurious magnitude of causation ( see S1 Methods and [40] ) . This TE analysis was performed for all available pairs of electrodes in each session , thereby running through all possible combinations of putative “sending” and “receiving” populations . Importantly , in this section and unless otherwise stated , we considered the overall information carried during the whole time of movie presentation . The analysis of how this is dynamically modulated during the presentation of the movie will be left for later sections . Mean values of TE across the entire dataset are reported in Fig 2B and show a high amount of TE ( with Z-scored values of the order of 8–10 significant at p < 10−7; t test ) from the gamma phase at the sending electrode to the spiking activity at the receiving electrode . TE values were significantly larger than zero ( using a t test and a False Discovery Rate control with q = . 05 ) for 84% of electrode pairs . Moreover , there was a more than 2-fold highly significant ( p < . 001; t test across pooled electrode pairs of all sessions ) increase in TE magnitude during movie stimulation with respect to spontaneous activity ( Fig 2B ) , suggesting these directed causal influences of phase relate to the processing of visual information . We then investigated whether gamma phase of the sending population has an effect on the firing of the receiving population that goes above and beyond the one exerted by the firing rate of the sending population . To quantify this , we computed the Lagged Conditional Information ( LCI ) , which is the mutual information between the past gamma phase at the sending electrode and the spiking activity at the receiving electrode , conditioned upon the past spiking activity of the sending population . The results ( S4 Fig ) showed a highly significant ( with Z-scored values of the order of 8–10 significant at p < 10−7; t test ) LCI , showing that the relationship between gamma phase of the sending population and the spiking activity of the receiving population cannot be accounted for by the relationship between spiking activity and gamma phase at the sending location . To further corroborate this conclusion , we computed how correlated across all pairs of electrodes were the values of TE from the gamma phase of the sending electrode to the spiking activity of the receiving electrode with the TE from the spiking activity of the sending electrode to the spiking activity of the receiving electrode . We found that there was a significant ( p < . 05 ) but very small and negative correlation ( Pearson ρ = − . 12 ) , again supporting the conclusion that gamma phase of a population exerts an effect on the firing rate of another receiving population that is largely different from that exerted by its firing rate . Given that TE is a directed and potentially asymmetric measure that can detect a leading direction of communication , we quantified the degree of spatial asymmetry in our information measures . We define the spatial asymmetry index as the ratio between the absolute value of the difference in TE in both directions and the maximal information in one of the two directions . This index takes values from 0 to 1 , with near-zero values indicating perfect symmetry and near-one values indicating prevalence of one-directional communication ( See S1 Methods ) . In our dataset , we found that ( Fig 2C ) both during spontaneous activity and movie stimulation , the average asymmetry index was large—in the range 0 . 6–0 . 7 , suggesting a prevalence of directed asymmetric effects of gamma phase on spiking activity of other sites over symmetric communications such as mutual interactions . Moreover , this asymmetry increased significantly during movie stimulation ( p < . 05; t test across pooled electrode pairs across sessions ) . We next investigated how these directed interactions depend on the distance between recording sites . Fig 2D reports the histogram of TE values of the pairs of electrodes across all sessions . The data were partitioned into four equipopulated ranges of interelectrode distances . While a larger number of high TE values could be observed at short distances ( <2 . 24 mm ) , several pairs with large interelectrode distances also exhibited large interactions . In addition , electrode pairs exhibiting very low values could be found at all distances , supporting that the TE is not a simple function of distance and arguing against that they could be ascribed to external artifacts or volume conduction . We further checked how asymmetry of causal interactions is influenced by distance by selecting pairs with both a sufficiently strong causal interaction ( we eliminated in each session the 20% of electrodes pairs with the lowest TE values ) and a direction of dominant causal interaction ( one direction of causation 10 times bigger than the opposite direction ) . We call the so-defined pairs “strongly asymmetric pairs” ( or in short asymmetric pairs ) . The distribution with respect to interelectrode distance of such strongly asymmetric pairs ( S1 Table ) shows that they are proportionally more frequent at larger distances , whereas “symmetric pairs” ( defined as pairs whose relative difference between TE values of both directions is less than 20% , also eliminating the 20% of electrodes pairs with the lowest TE values ) are proportionally more frequent at shorter distances . Finally , we studied how frequency-specific is the causal influence of the phase of the sending population onto the spiking activity of the receiving population . To do so , we band-passed the LFP into four other lower-frequency bands ( 2–4 Hz , 5–15 Hz , 15–30 Hz , and 30–50 Hz ) , we computed the instantaneous phase and repeated the same TE analysis for the phase of each band . Comparisons of results across bands ( Fig 2E ) show that , although lower frequency bands had a larger causal effect during spontaneous activity , during visual stimulation with Hollywood movies the highest information values were obtained for the gamma-band . This suggests that gamma-band has an important role in transmitting and routing across sites the information needed for stimulus processing . Overall , our information theoretic measures of interactions suggest that during stimulation with naturalistic movies , gamma phase of a primary visual cortical population exerts a genuine directed effect on the level of firing rate at other receiving locations within V1 , and that this effect goes beyond what may be due to the level of firing rate at the receiving or the sending location . Previous studies suggested that symmetric mutual interactions among neural populations depend on the phase relationships between the rhythmic activity of the interacting neural populations rather than on the phase of one population only [26] . In the light of these observations , we asked the following questions: does the directed causal effect of the phase of gamma oscillations of a neural population onto the activity of other receiving populations depend on the phase relationships of gamma oscillations at different sites ? If so , what are the specific phase relationships that correspond to larger causal effect of gamma phase on spiking activity at other locations ? To address these questions , we investigated the relationship between the spatiotemporal distribution of gamma phases and directed information transfer . We defined instantaneous phase shifts between two electrodes as the difference at each time point between the instantaneous gamma phases computed from the band-pass-filtered LFP at each electrode , and we quantified the circular mean across time of these phase shifts ( see S1 Methods ) . To relate phase shifts to our measure of information transfer , we used the convention of measuring them as the difference between the phase at the “sending” electrode and the phase at the “receiving” electrode ( the electrode at which the effects on MUA activity are considered ) , exactly as defined above when quantifying TE across sites . With this definition , a positive phase shift means that the oscillation in the sending electrode precedes the oscillation in the receiving electrode ( see Fig 3A for an illustration ) . In this section , we first considered the phase shift averaged over the entire time of movie presentation in all trials . ( The dynamical changes of phase shifts over the time of movie presentation and their relationship to dynamic changes of information transfer will be addressed in later sections ) . Most electrode pairs showed absolute values of movie-averaged phase shifts distributed between 0° and 80° ( S5 Fig ) . Moreover , 100% of the electrode pairs had a significantly nonuniform distribution over time of phase shifts ( p < 0 . 01; Rayleigh test ) , meaning that the phase relationships among all electrodes were not random . To test the relationship between phase differences and causal effects of gamma phases onto receiving sites , we computed the Spearman correlation between the movie-averaged phase shift of an electrode pair and the amount of causal effect ( TE ) exerted by gamma phase of the sending population onto spiking activity at the receiving electrode . The results ( Fig 3B ) show—consistently in all sessions—a significant ( p < 0 . 05 with Bonferroni correction ) positive Spearman correlation of phase shifts with TE . In other words , positive ( respectively negative ) phase differences corresponded to higher ( respectively lower ) values of TE between gamma phase at the sending location and spike rates at the receiving location . This is further illustrated by computing the histograms ( cumulated across sessions ) of the movie-averaged phase shifts for the above defined strongly asymmetric pairs . The result ( Fig 3C ) shows mostly positive phase differences . We found that the above-defined symmetric pairs had mean phase shifts distributed around the value 0° , corresponding to a zero-lag synchrony ( Fig 3C ) . A simple , yet accurate way to summarize these results is that the sign of the phase shifts indicates the dominant direction of interaction . In other words , causation predominantly flows from the location with the earlier phase to the location with the later phase . This pattern of phase differences is thus consistent with a simple compact description of the causal interactions as a propagating gamma wave . We finally checked whether the relationships between phase differences and the magnitude and direction of causal effect of phase of rhythmic activity were specific to the gamma-band . We performed the same correlation analysis on the phase of LFP bands of frequencies lower than the gamma range ( the 2–4 Hz , 5–15 Hz , and 16–50 Hz bands ) . As shown in S6 Fig , the correlations between the phase shifts and the TE between lower frequency phases and spiking activity were weaker ( and not significant in all sessions ) than the one found for the gamma phase , suggesting that the relationships between phase shifts and the magnitude and direction of causal effects of the phase of rhythmic activity were specific to the gamma-band . Given that phase shifts indicate the direction of causation in neural activity and that they can be described as travelling waves , it is tempting to speculate that these shifts are associated to a propagation of gamma oscillations along the horizontal connections of V1 . In the following , we investigated the extent to which these phase shifts are compatible with known physiological and anatomical properties of lateral connectivity . Propagation of waves across space and time can be investigated by analyzing the patterns of phase differences across electrodes [37 , 38 , 41] . As illustrated in an example from our data ( Fig 3D ) , if direction of gamma causation and gamma wave propagation are aligned , recording sites positioned along a line of prevalent TE flow ( i . e . , recordings sites that have asymmetric TE with their neighbors all pointing to a leading direction of causation ) should also have gamma-band time lags and phase shifts distributed according to their algebraic positions along the direction of propagation . We estimated from this pattern the putative speed of propagation . Since the speed of a propagating wave is inversely proportional to the spatial derivative of the phase along the direction of propagation , we estimated the spatial phase shifts values against the distance along the directions defined by strongly asymmetric pairs in each session ( assuming these pairs are prone to be oriented along the direction of propagation ) . For each single strongly asymmetric pair , designated as the “reference causal pair” , we studied the propagation along the line passing through both electrodes of this pair . We thus computed the phase shift between the receiving site of the considered reference causal pair ( for the leading direction of causation ) and all the other recording sites lying along the propagation axis defined by the line crossing both sites of the considered reference causal pair ( see S1 Methods and S7 Fig ) . The algebraic propagation distance corresponding to each measured phase shift values was computed by projecting the interelectrode distance over the axis defined by the reference causal pair . The origin of the x-axis indicates the position of the receiving site . When the receiving site was not achieving a minimum ( zero ) phase shift with respect to the other electrodes , but instead this minimum was achieved by the receiving site of another strongly asymmetric pair , this latter receiving site was chosen as the reference of the x-axis , such that the origin always indicates the final target of the wave propagation , and not an intermediate site lying on the propagation trajectory . The results for each reference causal pair are plotted on Fig 3E in gray . To estimate the spatial derivative of the phase , these data points were used to fit a spline regression model ( Fig 3E , in blue ) , showing a steep negative slope close to the origin and a progressive flattening further away from the origin . The decrease in the slope might reflect that the waves quickly attenuate as they propagate along the cortical surface; alternatively , it can possibly arise from interferences between travelling waves propagating on overlapping parts of this surface . To minimize those effects in the speed estimation , we estimate the speed of propagation when it is closest to its target receiving population located at the origin in our representation . We thus estimated the propagation speed from the spatial derivative of the phase where it is larger: at null interelectrode distance , using spline interpolation ( see S1 Methods ) , leading to an average propagation speed of 36 ± 4 cm/s ( mean ± bootstrap estimated SD ) . This propagation speed is similar in magnitude to the signal propagation speed along axons of excitatory horizontal connections reported in the literature [42–45] . We also investigated whether causal interactions were more prominent among pairs with similar orientation preference . For this , we first estimated the RF of each recording site by reverse correlation using the responses to the movie . We then estimated , by extracting the orientation content of each movie frame and correlating it with neural activity , the orientation tuning curve of multiunit spiking activity at each recording site ( see [46 , 47] and Materials and Methods ) . The Spearman correlation between orientation tuning similarity ( measured by covariance between the curves ) and TE , across all electrode pairs from all sessions was positive ( ρ = 0 . 24 , p < 0 . 01 ) . Since horizontal connections are more likely among populations with similar orientation preferences [11 , 48 , 49] , this finding is compatible with the hypothesis that gamma phase shifts may reflect causal interactions propagating along horizontal connections . The above findings demonstrated that the direction and strength of the causation exerted by gamma phase on spiking activity at other receiving sites correlates , on average , over long periods of dynamic visual stimulation , with the difference in gamma phase at both sites . It has been suggested that patterns of phase relationships may act as a dynamical gain factor that weights the effect of the anatomical connection infrastructure and therefore allows to modulate rapidly the strength of interactions among populations [23] . Evidence in support of this theory has been reported at the level of mutual symmetric interactions [26] . To understand whether this dynamical modulation may apply also to the case of directed interactions documented here , we asked whether changes in gamma phase shifts are related to changes in the stimulus , and whether they are accompanied by a readjustment of the magnitude of causal interactions among sites . In this section , we begin by studying whether gamma phase shifts can be dynamically and reliably modulated by the stimulus . An example of the time course over the movie presentation of the phase shift between two example electrodes in individual trials is shown in Fig 4A . To aid visualization , only 25 s of movie presentation were shown , and phase shifts were temporally smoothed by computing circular statistics over a 300 ms sliding window ( we chose a window length of 300 ms because it is a time scale with high power of stimulus variations in these natural movies [32 , 34 , 50] ) . For a given window , circular mean of the phase shift was encoded in Fig 4A by the hue , while the consistency of the shift was quantified by the Phase Locking Value ( PLV ) and encoded by the intensity ( see S1 Methods ) . Phase shifts were not constant in time , but varied in value and sign within each individual trial of the movie presentation ( Figs 4A and 5A ) . Notably , there were specific time points ( or scenes ) during the movie presentation in which either positive or negative phase differences were elicited reliably across trials ( see the red arrows in Fig 4A for examples of such periods ) , indicating that the dynamics of phase shifts is modulated by the properties of the visual stimulus . To systematically quantify the relationship between phase shifts and visual stimuli , we computed the mutual information that phase shifts carry about which scene of the movie was being presented . Mutual information is a principled and comprehensive way to quantify whether the considered neural response varies reliably across trials from scene to scene ( see Materials and Methods ) . In our experimental setting , it quantifies ( in units of bits ) how much the observation of a neural response reduces the uncertainty about which scene of the movie is shown . The information about the movie scenes carried by phase shifts ( averaged over all electrode pairs for each session ) is shown in Fig 4B . Information in phase shifts was significant in all sessions ( t test with False Discovery Rate control q = . 05 ) , meaning that phase shifts are indeed reliably modulated by the stimulus . In contrast , and consistently with previous reports [51] , the gamma phase of each individual electrode carried very little information about the movie ( Fig 4B ) . The fact that reliable modulation of phase shifts by the visual stimuli happens in absence of reliable modulations of the phase at each individual site means that the stimulus modulation of phase shifts cannot be explained by stimulus-evoked changes in the phase of individual sites . This suggests that stimulus-modulated spatial phase patterns reflect an emergent property of the relative dynamics and interactions between different cortical sites . We further checked whether information in phase shifts could be explained by the stimulus modulation of the gamma-band power in each individual electrode [34 , 52] . Although the gamma power in each site carried significant stimulus information in this dataset ( S2B Fig ) , the information in the joint observation of power at a given site and phase shifts with respect to another site was much higher in each session ( paired t test and False Discovery Rate correction with q = . 05 ) than information carried by power or by phase shifts alone ( Fig 4B ) . This means that the gamma phase shifts and gamma power at each site are modulated by the stimulus in a largely complementary way . Similarly , we found ( S2C Fig ) that the information in gamma phase shifts was also complementary to that of the firing rate from the same electrode ( note that under these stimulation and recording conditions , the local firing rate and gamma power are coupled quite tightly [34] ) . These findings imply that movie modulations of phase differences cannot be explained by modulations in local power or firing rate alone . To understand how frequency specific were these phase modulations , we computed sensory information for phases and phase shifts in the lower frequency bands , 2–4 Hz , 5–15 Hz , 16–50 Hz ( S2A Fig ) . Sensory information of phases in individual recording sites were on average larger or at least comparable to the information of phase shifts between sites at the same frequency , and were significant ( p < . 001 , t test w . r . t . bootstrapped values; Bonferroni-corrected ) in all sessions and frequencies , barring one single exception ( phase at 15–50Hz for c08nm1 ) . These results obtained for lower frequencies ( <50 Hz ) are in sharp contrast with results reported above for the gamma phase ( Fig 4B ) . Thus , the emergence of stimulus-dependent phase shifts between sites in absence of a stimulus modulation of phases at individual recording sites is specific to gamma-band oscillations . Results presented above showed the average phase differences computed over the entire period of movie stimulation strongly correlate with the dominant direction of interaction computed over the entire movie presentation . Concurrently , our mutual information analysis showed ( Fig 4 ) phase differences between recording sites can vary reliably across different movie scenes . A natural question is whether such dynamic stimulus-induced changes in phase differences lead to dynamic changes in the strength of directed interactions between neural populations . We addressed this question by studying , for each pair of electrodes , the relationship between changes over time of the phase shifts between the sites and the changes over time in causation ( measured as TE ) exerted by the gamma phase at one location to the spiking activity at another receiving location . We first individuated , for each pair of electrodes , blocks consisting of periods of dominant positive and negative phase shifts . The segmentation , illustrated in Fig 5A for an example pair of electrodes , was implemented as follows . We first computed the sign of phase difference for a given pair of electrodes at all time-points in each trial . Then we labelled time points with a majority of positive shifts across trials as positive phase shift points . Conversely , we labelled time points with a majority of negative phase shifts across trials as negative points . We then considered for further analysis only blocks made of continuous time segments with at least 300 ms of either entirely positive ( “positive blocks” ) or entirely negative ( “negative blocks” ) phase shift . We first considered the strongly asymmetric pairs defined in our previous analysis over the entire period of movie presentation . For simplicity , in reporting the results of this analysis , for each pair we ordered the electrodes according to the dominant direction of TE computed across all the movie presentation time . The distribution of phase for such an electrode pair is plotted on the right-hand side of Fig 5A . As illustrated by this example , and as shown above ( Fig 3C ) , almost all electrode pairs with strongly asymmetric causal interactions have an average phase lag aligned with the leading direction of causation , and thus had a positive average phase shift restricted to the 0–45° range . However , the time-varying phase shift in positive and negative blocks covered a broader range: across all recordings , 50% of the average phase shifts in a positive or negative block were contained between −20 to 105 degrees , suggesting that phase shift values in this range can modulate information transfer . For each electrode pair , we then computed TE between gamma phase at the sending location and spiking activity at the receiving location using only positive-phase or negative-phase blocks respectively . Due to their bias towards positive shifts , there was a larger total duration for positive than for negative phase blocks . To make the quantitative comparison of TE computed in this way as fair as possible , we randomly down-sampled the number of blocks such that approximately the same time length was used to compute TE in each condition . We then investigated whether the amount of TE ( and thus the strength of causation ) between gamma phase and spiking activity at a receiving location was stronger during blocks of positive phase . We considered separately the modulation with the sign of the phase shift of TE in either the leading or the weaker direction of causation ( Fig 5B ) . We found that the values of TE along the leading direction of causation were more than four times larger ( p < . 001; sign test ) when the phase lags were positive ( i . e . , consistent with the gamma wave propagating along to the leading direction ) than when the phase values were negative ( i . e . , consistent with the gamma wave propagating opposite to the leading direction ) . In contrast , we found that values of TE against the leading direction of causation were approximately twice larger ( p < . 05; sign test ) when the phase lags were negative ( i . e . , consistent with a gamma wave propagating against the leading direction ) than when the phase values were positive . In other words , when the phase shifts transiently point toward the direction of causation that is the leading one , on average over the experiment , then TE in the dominant direction is transiently enhanced and the one in the weaker direction is transiently suppressed . This effect is reversed when the phase shifts transiently point against the direction of causation that is the leading one on average . All in all , these results suggest not only the time-averaged phase shift points toward the overall dominant direction of communication across an entire experiment , but that transient stimulus-related changes in phase shifts are accompanied by a relative increase in causation strength along the spatial direction indicated by the sign of the phase shift . In other words , transient changes in the direction of propagation of the putative travelling gamma waves correspond to transient relative increases of information transfer in the direction of putative wave propagation . We finally studied the dynamic modulations of TE for electrode pairs that were classified above as having “symmetric interactions” , i . e . , for pairs of electrodes classified as having comparable TE values in both directions ( according to the criteria defined in previous section ) . For these symmetric electrode pairs , we found that the amount of causation was the same ( p > . 05; sign test ) for both positive and negative phase shifts ( Fig 5B ) . This suggests that for these pairs of sites their interactions remain “mutual” ( i . e . , symmetric ) independently of transient changes of phase shifts . The above analysis suggests that shifts in gamma phases , whose sign indicate the instantaneous direction of the gamma wave propagation and correlate with the TE , may modulate information transfer . A natural question is what kind of visual information about the stimulus is conveyed by these phase shifts . To address this question , we extracted various movie features estimated from the RFs of each channel ( see S1 Methods ) , and then we correlated these movie features both with MUA firing rate in the same channel and with gamma phase shifts in the corresponding strongly asymmetric channel pairs . ( see illustration in Fig 6A ) . We first considered the tuning to the local TC in the RF . This was the RF visual feature that correlated the most with the MUA firing rate in the same site , with an increase in TC leading to an increase of MUA firing rate ( Fig 6E ) . We found ( Fig 6D ) that both the TC at each individual site and its sum correlated positively with the phase shift in asymmetric pairs . Given that positive time shifts ( i . e . , time shifts along the overall prevalent direction of gamma wave propagation ) lead to relative increases of TE in the overall dominant direction , this positive correlation between TC and phase shifts suggests that larger firing rate activation due to increase of TC ( either in one of the RFs or in their sum ) leads the gamma wave to travel from the sending to the receiving site and TE to increase in the direction of propagation . Given that the TC in the two RFs is partly correlated ( S7 Fig ) , it is difficult to assess whether phase shifts are more modulated by the TC in one of the two RFs or by their sum . Interestingly , though , we found ( Fig 6D ) that the phase shift correlated positively with the difference between TC in the sending and the receiving site , which suggests that the direction of the travelling wave is influenced not only by the individual RF features but also by their combination ( Fig 6D ) , with gamma waves more likely to travel from the sending site when the sending RF is more activated than the receiving one . We repeated the analysis considering the OA in the RF , defined as the squared cosine of the difference between the orientation of the gradient in the RF and the preferred orientation of the corresponding channel . We found ( Fig 6D ) that OA modulated MUA firing rate and gamma phase shifts in a way similar to TC , although the correlation between OA and neural activity was overall much less strong than that of TC , and ( unlike for TC ) the correlation between difference of OA across RFs and phase shifts ( though positive ) did not reach statistical significance . To gain insights into the overall effect on wave propagation of all the various visual features entering the movie , we computed the correlation between the trial-averaged MUA firing rate ( a robust and general marker of the overall effectiveness of the visual stimulus drive at any given time point ) and the phase shifts . We found ( Fig 6F ) that phase shifts correlated positively and significantly with the MUA in each RF , as well as with the sum and the difference between MUA in the sending and receiving RFs . The strongest correlation was found to be with the MUA in the sending RF . This , together with the positive correlation found with the difference between MUA in the sending and receiving RFs ( Fig 6F ) , suggests that wave propagation from the sending RF and causation in the direction of the travelling wave is more likely when the sending RF contains one of its preferred features , and that the wave propagation is enhanced when the receiving RF is less activated by the stimulus . A simple interpretation of these results in terms of exchange of visual information is that the causal gamma waves propagate to communicate to nearby RFs the presence of their preferred feature , and that causation is particularly effective on the receiving site when the latter is not shown an optimal stimulus . In addition to RF features mentioned above , we also computed a more global visual feature: the optic flow in the movie [53] . An example of estimated optic flow for one movie frame is given in Fig 6B . The optic flow in the movie generates an apparent movement in the visual field of the observer . The distributions of resulting speeds for all stimuli are presented in S8 Fig . The median values for the apparent speed were in the range 2 . 12–5 . 6 deg/s . Assuming the scaling of the parafoveal representation of the visual field in V1 is approximately 0 . 35 deg/mm , a purely feedforward mapping of object motion on the cortical tissue would result in propagating speeds in the range 6–16 mm/s . These values are much smaller than the speed values estimated in our analysis ( approximately 360 mm/s ) . This suggests that optic flow generated by moving objects in the movie cannot generate patterns of gamma waves by simple feedforward entrainment to object motion . However , endogenously generated gamma waves might still play a role in the processing of this kind of motion . To test this hypothesis , we studied the relationship between the optic flow traversing the sending RF in the direction of the receiving RF , that we call “directed motion , ” and the phase shift in asymmetric pairs ( see illustration Fig 6B and S1 Methods ) . We found a positive correlation between the directed motion from the sending to the receiving RF and the gamma phase shift ( Fig 6C ) . This shows that gamma wave propagation is modulated by extra-RF visual properties and corroborates the view that the travelling waves may communicate the presence of information that is salient to the sending RF .
Gamma oscillations have been traditionally associated with symmetric interactions among neural populations , such as synchronization and coherence [23 , 54] . Such synchronized elements can naturally support important computations , such as tagging groups of neurons that participate in encoding of the same percept [17 , 55] . In our data , we found a proportion of site pairs exhibiting symmetric causal interactions accompanied by zero-lag synchronization between populations , supporting this view about the function of gamma oscillations . However , we also found a more prominent proportion of recording pairs exhibiting a systematic nonzero lag gamma phase shift among spatially separate sites , that were accompanied by direction specific , rather than symmetric , communication between the sites . Our finding that these directed communications and the accompanying gamma phase shifts can be dynamically modulated by the stimulus suggests an important potential function for the phase relationships among oscillatory properties of different networks: the dynamic routing of the directional flow of information according to the needs of stimulus processing . This function cannot be easily accommodated by mutual zero-lag synchronization . While more theoretical work is needed to understand the importance and computational abilities of these dynamic directed interactions [56 , 57] , it seems apparent that the simultaneous presence of functions such as dynamic one-directional routing and tagging of groups that process information together can only increase the range of sensory computations that can be implemented by the same anatomical network . Another interesting question for future theoretical research is to study the conditions under which realistic network models can reproduce such a rich and heterogeneous dynamics with both symmetric and directional communication . It remains a theoretical challenge to understand how both phenomena may coexist within the same primary cortical network . An important finding is that gamma phase shifts between two sites can reliably change with the stimulus , and do so accompanied by simultaneous changes in the communication between the sites , in absence of a stimulus modulation of gamma phase at either site . This suggests that spatial gamma phase shifts reflect an emergent cooperative property of cortical dynamics that cannot be accounted for by feedforward sensory influences on individual sites . It is thus tempting to hypothesize that dynamic gamma phase shifts mediate dynamic stimulus- and direction-specific communication across cortical sites . A computational advantage of this putative mechanism of network reconfiguration is that it can control the cooperation among neuronal groups partly independently from the individual stimuli in the RF of each site , allowing flexible changes in neural communication depending on e . g . , extra-RF sensory features and other contextual influences . Our findings are indeed consistent with theoretical work suggesting that ongoing trajectories of network state reconfigurations participate in the mechanisms of processing of complex stimuli , such as those used in our experiment [58] . Our experimental observation moreover parallels the predictions of a recent modeling study [57] , showing that modification of phase differences between interacting neural populations can lead to a rapid reconfiguration of their effective connectivity pattern . Although we set out to investigate the specific hypothesis that directed network communication is modulated by gamma-band activity during sensory processing [26] , we investigated a wide range of LFP frequencies . It is worth examining the implications of differences across frequencies in the results we found . TE causation values were significant for all bands , although the gamma-band had the largest causal effect during visual stimulation . Thus , our results support the notion that all bands can , in principle , be involved in information transfer . However , two key findings are specific to the gamma-band . First , a consistently significant and positive correlation between phase shifts and travelling waves ( Fig 3B and S6 Fig ) , together with consistent stimulus-induced spatial coherence increases ( Fig 1B ) is found only for the gamma phase . Thus , the relationship between causation and stimulus-related travelling waves is strongly supported only for the gamma-band . Second , for the gamma-band , we can safely conclude that the propagating waves originate from neural dynamics rather than from spatiotemporal correlations in the movie , because the latter are too slow to account for the propagation of information by gamma waves . In particular , the same cannot be said about low frequency LFPs . Indeed , the spectral power of the movie features is highest in the low frequency range in which LFPs carry visual information [34] . Our previous modeling of visual cortex [59] , as well as work on auditory cortex [60 , 61] , suggest that the visual information in low frequency LFPs reflects the entrainment to the slow dynamics of natural stimuli , implying that low frequency waves may reflect stimulus dynamics rather than neural dynamics . Great care should be taken when comparing the results obtained at different frequencies . On the one hand , the causal effect of lower frequency bands may be overemphasized with respect to that of the gamma-band , because lower frequency bands have often larger spatial coherence and likely capture the activity of neural populations of a larger size [31] . This caveat , while it does not affect the conclusion that gamma activity has the highest causal effect during visual stimulation , complicates the interpretation of the amount of causation across bands . On the other hand , a potentially larger spatial spread of lower frequency LFPs may penalize the ability to detect travelling waves at low frequencies , because it could compress the range of phase shifts attainable by low frequency oscillations ( though we note that in our data , [2–4 Hz] phase shifts spanned a range similar to that of gamma phase shifts ) . All in all , the caveats in comparisons across frequencies suggest that , although our results support the hypothesis of causal travelling waves in the gamma range , we cannot rule out that causal travelling waves might exist in other bands than gamma . Thus , while a long line of evidence links specifically gamma oscillations to the relative timing of interactions between local inhibitory and excitatory neurons [23 , 24 , 26 , 62–71] , our results cannot support that the neural mechanism associated to the generation of gamma band oscillations are also exclusively responsible for the modulation of directed information flow . The spatiotemporal pattern of gamma phases that we reported is consistent with the idea that gamma oscillations mediate direction-specific interactions by propagating along specific directions and over distances of several millimeters . Existence of functionally relevant travelling waves has been hypothesized in visual cortex [37 , 72] . An interesting question regards the possible anatomical substrate of this propagation of gamma-band activity . Our finding that sites with stronger causation tend to have similar orientation tuning and our estimation of propagation speed are compatible with the hypothesis that such interactions may travel along horizontal connections . The phase of gamma-band oscillations has been recently implicated in mechanisms for feedforward transmission of information across different areas in the visual cortical hierarchy [73 , 74] . Our results suggest that phase of gamma-band oscillations may also be involved in directional information transfers within a brain area , and that they may do so by modulating the propagation of information along lateral connections . Our data , however , do not speak on how this process may interact with top-down modulations of sensory processing from higher cortical areas; as such top-down contributions are likely to be minimal under the conditions of opiate anesthesia used for our data collection . We consider possible confounding factors in our measures of directed causal interactions between gamma phase and spiking activity at other sites . One possibility is that these relationships are not actually causal but they are rather due to cross-talk between signal at different locations or even to volume conduction . However , both the strong spatial asymmetry of the causal interactions that we observed and the finding that gamma-band interactions increase during stimulus presentation speak against such potential artifacts . Another eventuality is that the causal effect of gamma phase may only show up artificially because firing rate and gamma phase of the sending population are correlated either because of locking of spike times to genuine network oscillations [23] or because of a spike-shape bleed-through on the LFP trace [75 , 76] . In other words , the reported causal effects of gamma phase may actually capture the effect of the level of firing rate of the sending population . The finding that LCI values are significantly positive ( and thus that the same rate of the sending population may elicit a different firing rate in the receiving population depending on the gamma phase of the sending population ) , and the relatively small correlation between causation values exerted by spiking activity and gamma phase argue against this explanation . In addition , we note that we minimized spike bleed-through by removing the spike shapes prior to the LFP computation [76] . An important practical consequence of our results is that phase shifts across multichannel recordings can be taken as meaningful markers for dynamic functional connectivity in distributed networks . Such phase relationships are easier to compute and much less data intensive than detailed measures of functional connectivity such as TE , and have been used as markers of interactions among areas [77–79] . The results we presented in this article elucidate some of the neural information transmission mechanisms that may be captured by observation of relationships between phases of the oscillatory activity of spatially separated neural populations , and provide a simple way to interpret the time-resolved sign of these measures in terms of directionality of dynamic information flow . Our study suggests that the dynamical relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional network connections .
The data analyzed here was recorded as part of previous studies [34 , 40] . Recordings were obtained from the visual cortex of adult rhesus monkeys ( Macaca mulatta ) using procedures described below . All procedures were approved by local authorities ( Regierungspräsidium Tübingen ) , were in full compliance with the guidelines of the European Community ( EUVD 86/609/EEC ) and were in concordance with the recommendations of the Weatherall report on the use of nonhuman primates in research . Extracellular potentials were recorded in the V1 of three anesthetized monkeys for a total of four recording sessions ( d04nm1 , d04nm2 , g97nm1 , c98nm1 ) , each performed on a different day . The experimental procedures and recording setup have been described elsewhere [34 , 40] . In each session , 6 to 11 tungsten electrodes were positioned according to a 4 x 4 square matrix ( minimal interelectrode distance varied from 1 to 2 . 5 mm ) . During each session , between 30 and 120 trials of stimulation with color movies ( duration ranging from 4 to 6 . 5 min ) were recorded , as well as 5–10 trials of spontaneous activity ( 5 min duration ) . Spiking activity of neurons in the vicinity of each electrode was measured by extracting the MUA signal by high-pass filtering the extracellular potential above 1 , 000 Hz and subsequent rectification . The MUA signal obtained in this way measures the massed firing rate of a group of neuron in the vicinity of the electrode [30] . We used this measure of spiking activity because its values can be modeled by continuous random variables and are thus better suited to our information theoretic measures of spike field relationships than signals based on spike detection [40] . We extracted the LFP as follows . We cleaned the extracellular signal from spike bleed-through following the methodology proposed in [76] and code available at http://apps . mni . mcgill . ca/research/cpack/lfpcode . zip ( we used multiunit spikes detected with a threshold of 3 SD for this purpose ) . We then down-sampled the extracellular signals ( originally sampled at 20 , 835 Hz ) to 1 , 000 Hz and low-pass filtered with a cutoff frequency of 100 Hz to obtain broad-band LFP . From this signal , we extracted band-specific LFPs by band-passing it using a zero lag 8th order Butterworth FIR filter in the specified frequency range ( most analysis was done using the 50–80 Hz gamma-band ) . We extracted instantaneous phase and power of the considered oscillatory band using Hilbert transforms as detailed in S1 Methods . To compute how different neurophysiological signals ( such as phase or power at individual sites , or phase differences among sites ) were modulated by the movie stimulus , we computed the Shannon Information ( abbreviated as Information in this paper ) between the set of stimuli S and the neural response R , defined as I ( S;R ) =H ( R ) −H ( R|S ) ( 1 ) where H stands for the Shannon entropy . The first term on the right hand side of Eq 1 is called the response entropy and quantifies the variability of the neural response R across all trials and stimuli , while the second term is called the noise entropy and quantifies the residual variability of R for a given stimulus S . Information thus measures the reduction of uncertainty on R when S is known [80] . To apply this approach to a complex , time-varying stimulus such as a naturalistic movie , following previous work [34 , 51 , 81] , we divided the movie presentation time into nonoverlapping “scenes . ” Scenes were 300 ms long except for individual phases , which are fast time-varying and thus studied using scenes of one oscillation period duration . We considered each such scene and the associated neural response as stimulus response-pairs in Eq 1 . Thus , we computed how much information the neurophysiological response carried about which movie scene was presented . As such , it is a meaningful measure of how well and reliably the neural response is modulated during the movie . Information was computed by first binning the responses into a number of equipopulated bins and then applying statistical corrections to remove the limited sampling bias , using the Information Breakdown toolbox [82] available at http://www . sicode . eu/results/software . html ( see S1 Methods for full details ) . All variables ( except individual phases which vary at shorter time scales and were thus evaluated at a single point at the center of each scene , scene length having the duration of one period of oscillation ) were smoothed using a 300 ms rectangular sliding time window and binned into four bins ( we kept a low number of bins because of the low number of trials ) . TE is an information-theoretic measure of the causal dependency between the time series of a putative cause X and the time series of a putative effect Y in the framework of Wiener-Granger causality , stating that a signal Y is causing X if the knowledge of the past of Y reduces the uncertainty about the future of X . TE , along with other causal measures derived from the Wiener-Granger principle such as Granger Causality , is a widely used tool to infer causal functional connectivity from brain recordings . In many cases , the network inferred from these techniques matches well the patterns of anatomical connectivity [83] although the functional causal measures are also sensitive to the effect of dynamical variables such as the state of the network nodes that cannot be captured by anatomical connectivity [84] . The uncertainty of Xt , the present value of X , is quantified by its entropy H ( Xt ) . Using this definition , TE compares the uncertainty of Xt given its past Xpast with the uncertainty of Xt given both its past and the past of Y . The difference of these quantities defines the TE T ( Y→X ) =H ( Xt|Xpast ) −H ( Xt|Xpast , Ypast ) ( 2 ) It can be shown [28 , 85] that the above equation corresponds to the mutual information between present activity of Y and past activity of X , conditioned on the past activity of Y: T ( Y→X ) =I ( Xt;Ypast|Xpast ) ( 3 ) In our calculations , we almost always computed TE between the time series Y of the gamma-band phase at a putative sending location and the time series X of the spiking activity at a putative receiving location . Although , in one control analysis we used the spiking activity of the sending location as signal Y . TE was computed by binning the responses into a number of equipopulated bins and then using statistical corrections to remove the limited sampling bias , using the Information Breakdown toolbox [82] available at http://www . sicode . eu/results/software . html . The routine for TE estimation itself is provided as supplementary material ( http://dx . doi . org/10 . 6084/m9 . figshare . 1460872 ) . Finally , TE values were Z scored to the bootstrapped values of TE that would be obtained in case of no causation between the time series only because of a common time history of sensory stimulation ( See S1 Methods for details ) . We note that TE is similar in concept to Granger causality [27] but has the additional advantage over Granger causality that ( being based on mutual information ) it captures all possible kinds of relationships between the variables . We estimated the RF location using reverse correlation [86] between the gamma power in each channel and movie luminance . To ease computation , movie frames were spatially smoothed using an average over a 6 x 6 sliding window and then spatially down-sampled by a factor of 4 . When using reverse correlation with natural stimuli , the obtained RF is likely to be larger than the true RF because of the spatial correlations in the stimulus [87 , 88] . To achieve better localization of RFs , we thus minimized large correlation between pixel luminance across the frames in some cases by removing the first one or two largest singular value decomposition ( SVD ) components of the spatiotemporal time course of the movie luminance . Correlation values were computed across time for each experiment between each pixel luminance and gamma power , using time series subsampled at 66Hz , and taking into account a time lag of 60ms between the stimulus and the neural response in V1 , matching the order of magnitude reported in previous literature [88 , 89] . Correlation values were then Z-scored across experiments with the same movie stimulus , and the resulting maps for each movie were averaged together to get a final correlation map for each electrode in each recording session . An example of such correlation map is shown in S1B Fig . The RF center was chosen as the pixel achieving the maximum of this map , the RF shape was assumed square with vertical and horizontal borders , while the RF size was the smallest one achieving below 75% of the maximum value of the map on its border . | Complex and flexible behavior likely results from the ability of groups of neurons in the brain to reconfigure dynamically the information flow across different brain areas , depending on what the brain is engaged in ( processing a stimulus or carrying out a task ) . Here , we investigate how oscillations of cortical activity in the gamma frequency range ( 50–80 Hz ) may influence dynamically the direction and strength of information flow across different groups of neurons . By recording neural activity and measuring information flow between multiple locations in visual cortex during the presentation of Hollywood movies , we found that the arrangement of the phase of gamma oscillations at different locations indicated the presence of waves propagating along the cortical tissue . These waves were observed to propagate along the direction with the maximal flow of information transmitted between neural populations . The gamma waves changed direction during presentation of different movie scenes , and when this happened , the strength of information flow in the direction of the gamma wave propagation was transiently reinforced . These findings suggest that the propagation of gamma oscillations may reconfigure dynamically the directional flow of cortical information during sensory processing . | [
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"Methods"
] | [] | 2015 | Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer |
The control of Aedes albopictus , a major vector for viral diseases , such as dengue fever and chikungunya , has been largely reliant on the use of the larvicide temephos for many decades . This insecticide remains a primary control tool for several countries and it is a potential reliable reserve , for emergency epidemics or new invasion cases , in regions such as Europe which have banned its use . Resistance to temephos has been detected in some regions , but the mechanism responsible for the trait has not been investigated . Temephos resistance was identified in an Aedes albopictus population isolated from Greece , and subsequently selected in the laboratory for a few generations . Biochemical assays suggested the association of elevated carboxylesterases ( CCE ) , but not target site resistance ( altered AChE ) , with this phenotype . Illumina transcriptomic analysis revealed the up-regulation of three transcripts encoding CCE genes in the temephos resistant strain . CCEae3a and CCEae6a showed the most striking up-regulation ( 27- and 12-folds respectively , compared to the reference susceptible strain ) ; these genes have been previously shown to be involved in temephos resistance also in Ae . aegypti . Gene amplification was associated with elevated transcription levels of both CCEae6a and CCEae3a genes . Genetic crosses confirmed the genetic link between CCEae6a and CCEae3a amplification and temephos resistance , by demonstrating a strong association between survival to temephos exposure and gene copy numbers in the F2 generation . Other transcripts , encoding cytochrome P450s , UDP-glycosyltransferases ( UGTs ) , cuticle and lipid biosynthesis proteins , were upregulated in resistant mosquitoes , indicating that the co-evolution of multiple mechanisms might contribute to resistance . The identification of specific genes associated with insecticide resistance in Ae . albopictus for the first time is an important pre-requirement for insecticide resistance management . The genomic resources that were produced will be useful to the community , to study relevant aspects of Ae . albopictus biology .
The Asian Tiger mosquito Ae . albopictus is a major vector for a variety of viral diseases , such as dengue fever and chikungunya , which threaten over 2 . 5 billion people worldwide . Trade and climate changes have opened new ecological niches to this highly invasive species in temperate areas of the world . In Europe it was first detected in Albania in 1979[1] and since then it has spread to all Mediterranean/S . European countries including Greece , as well as Germany , Switzerland and The Netherlands[2] . Its invasive success has been associated with its ability to survive under cooler temperatures , compared to other mosquito species[3 , 4] . Cases of epidemics of viral transmission ( chikungunya ) that recently appeared in Europe and elsewhere ( La Reunion Island in 2005 and 2006; Italy 2007 , France and Croatia 2010 , Portugal 2012 ) were directly associated with the expansion of Ae . Albopictus [5] . Ae . albopictus is also a severe nuisance for humans , as it is an extremely aggressive exophilic feeder , biting throughout the day . The control of Ae . albopictus relies on clean-up campaigns that reduce the larval breeding sites , repellents ( spatial or personal ) , and insecticides ( both larvicides and adulticides ) . Temephos is an organophosphate ( OP ) larvicide which has been used for many decades to control Ae . albopictus mosquitoes , and the often sympatric Ae . aegypti , in several geographical regions , such as Asia and S America [6] . However , resistance against temephos has been selected and it already compromises the efficiency of the control intervention against Ae . aegypti [6 , 7] , two regions that harbor the greatest burden of viral diseases transmitted by Aedes vectors . The levels of temephos resistance in Ae . albopictus seem to be relatively low at present , however there are indications that the trait is evolving[7] . As only a limited number of larvicides are available on the market , temephos resistance is an important issue for several countries where it remains a main active ingredient . It is also a concern for regions that have banned its use , such as Europe: this molecule is a potential reliable reserve , for emergency epidemics or new invasion cases . Understanding insecticide resistance mechanisms is an important pre-requirement for the subsequent development of tools and practices that can improve the management and sustainability of control programs . There are two main molecular mechanisms responsible for insecticide resistance: target site resistance , due to mutations that reduce the binding affinity of the insecticide with its molecular target , and metabolism-based resistance , due to changes in detoxification enzymes ( such as cytochrome P450s , carboxyesterases ( CCEs ) , Glutathione S-transferases ( GSTs ) , ABC transporters , and UGD-transferases ( UGTs ) ) , which sequester , metabolise or facilitate the secretion of the insecticide molecules , thus preventing them from reaching their target [8–10] . The G119S substitution in acetylcholinesterase-1 ( AChE1 ) has been documented to confer resistance against OPs and carbamates ( CARB ) insecticides in Culex pipiens and Anopheles gambiae mosquitoes [11 , 12] . Metabolic resistance to OPs has also been associated with the over-production of CCEs , such as the Est-2 and Est-3 in Culex pipiens complex and the CCEae3a and CCEae6a , more recently , in Ae . aegypti [13] . Gene amplification and transcriptional up-regulation , alone or in combination , seem to be responsible for the increased production of esterases in insecticide resistant mosquitoes [14 , 15] . Here , we report the use of classical approaches , combined with transcriptomics to investigate the molecular mechanisms of temephos resistance identified in an Ae . albopictus population isolated from S . Europe . Significant genomic resources have been developed , and genes strongly implicated in Ae . albopictus temephos resistance have been identified .
Three different Ae . albopictus strains were used in this study: Lab , a reference susceptible strain , which was originally collected in Malaysia[16] was very kindly provided by Dr Charles Wondjii ( Liverpool School Tropical Medicine , UK ) ; Par-GR , a strain derived from an Ae . albopictus population collected in Athens ( Greece ) in 2010 using ovitraps ( dark plastic cup with a piece of wooden stick over the inner part of the cup and filled with tap water ) placed in putative oviposition sites of Ae . albopictus; Tem-GR , a strain that was derived from the Par-GR strain by temephos selection using standard WHO larval bioassays [17] and at least 1000 larvae in each generation for 12 generations at a dose killing 80% ( LC80 ) of the insects . Mosquitoes were reared in standard insectaries conditions ( temperature: 27°C; relative humidity: 80%; photoperiod: 12 hours day/night ) . Standard WHO larval bioassays on late 3rd /early 4th instars larvae were conducted to detect the level of susceptibility to temephos [17] . At least three replicates of 20 larvae were used for each concentration . Mortality was recorded after 24 hours . To determine the LC50s and confidence intervals , data were analyzed using the Polo plus 2002–2014 LeOra software and the R script BioRssay v . 6 . 1[18] . Enzyme activity measurements were carried out in 96-well plates ( NuncMaxiSorp ) using a Spectra Max M2e multimode microplate reader ( Molecular Devices , Berkshire , UK ) , following mosquito-specific assay protocols [19 , 20] , with slight modifications . Briefly , for the carboxylesterase activity assay , individual larvae of each strain were homogenized in 600μl of 0 . 1M sodium phosphate buffer pH 7 containing 1% Triton X-100 . 2μl from this homogenate were transferred in triplicates to a 96-well microplate and 200μl of 0 . 3mM α- or β- naphthyl acetate diluted in 0 . 02M sodium phosphate buffer pH 7 . 4 were added to each well . After 20min incubation , 50μl of 6 . 4mM Fast Blue B salt ( Sigma ) diluted in 35mM sodium phosphate buffer pH 7 containing 3 . 5%SDS were added to each well . Absorbance was measured at 570nm after five minutes of incubation . For AChE1 activity and inhibition assays , individual larvae were homogenized in 100μl extraction buffer ( 0 . 1Μ sodium phosphate buffer pH 7 containing 1% Triton X-100 ) and the supernatant obtained was used as the enzyme source . The reaction was conducted in 205μl substrate–reagent solution of 0 . 1M sodium phosphate buffer pH7 containing 25μl enzyme source , 5 , 5′-Dithiobis ( 2-Nitrobenzoic Acid ) ( DTNB ) and acetylthiocholine ( ATCHI ) , in final concentration of 0 . 5 mM and 1 . 2 mM , respectively , in the presence of different concentrations ( 5 to 30 μΜ ) of the analytical-grade inhibitors propoxur and paraoxon . AChE1 activity and residual activity ( percentage inhibition ) was measured and determined after 25min incubation time at 405nm . The protein concentration in the enzyme source for all biochemical assays was determined according to Bradford ( 1976 ) , using bovine serum albumin as a standard , to normalize activities for protein concentration . At least three biological replicates for each strain of at least 20 larvae were tested . The mean activity values were compared between the resistant and the susceptible strains , by Mann-Whitney test and differences were considered significant at a p<0 . 05 . Several batches of five to ten larvae from each strain were used for gDNA extraction using the DNeasy Blood and Tissue Kit ( Qiagen ) and the Cethyl Trimethyl Ammonium Bromide extraction method , as described in [21] . The resulting DNA was resolved in 100μl or 20μl water , respectively and samples were treated with RNase A ( Qiagen ) to remove RNA . Several batches of five to ten larvae in late third to early fourth stage were used for RNA extraction respectively , using the Arcturus Picopure RNA Extraction Kit ( Arcturus , California , USA ) . RNA was treated with DNAse I ( RNase–Free DNase Set Qiagen ) to remove genomic DNA contamination and subsequently used for cDNA synthesis , either using Superscript III reverse transcriptase and Oligo-dT 20 primers ( Invitrogen ) ( qPCR ) , or using Mint-Universal cDNA Synthesis kit . For Illumina sequencing , libraries were prepared in accordance with the Illumina Tru Seq RNA sample preparation guide ( May 2012 , rev . C ) for Illumina Paired-End Indexed Sequencing http://www . biotech . wisc . edu/Libraries/GEC_documents/TruSeq_RNA_SamplePrep_v2_Guide_15026495_C . pdf . Briefly , poly-A mRNA were first purified using Illumina poly-T oligo-attached magnetic beads and two rounds of purification . During the second elution of the poly-A-RNA , the mRNA was also fragmented and primed with random hexamers for cDNA synthesis . Cleaved mRNAs were reverse transcribed into first strand cDNA using reverse transcriptase and random primers . The RNA template was then removed and a replacement strand synthesized to generate double-stranded cDNA . Ends were subsequently repaired , dA base added , and Illumina indexing adapters were ligated . Finally , cDNA fragments that have adapter molecules on both ends underwent 15 cycles of PCR to amplify the amount of prepared material . The resulting libraries were validated using the Agilent 2100 bioanalyser to confirm the concentrations and size distribution . Samples were quantified using a Qubit 2 . 0 Fluorometer , before normalizing the concentration and pooling the samples , prior to validating the pool to be sequenced using qPCR . The pool was loaded at a concentration of 8pM onto 1 lane of an Illumina flow cell v3 . The sample was then sequenced using the Illumina HiSeq2000 , 100bp paired end run . Two libraries ( two biological replicates ) derived from independent RNA preparations from each strain , and were sequenced twice ( two technical replicates ) using Illumina platform , with sequenced paired end reads size equal to 100 bases . The levels of selected transcripts were measured by quantitative PCR ( qPCR ) . Amplification reactions of 25μl final volume were performed on a MiniOpticon Two-Color Real-Time PCR Detection System ( BioRad ) using 2μl of cDNA ( diluted 25 times ) , 0 . 2μM primers ( S1 Table ) and Kapa SYBR FAST qPCR Master Mix ( Kapa-Biosystems ) . Two housekeeping genes histone 3 and the ribosomal protein L34 were used as reference genes for normalization [29] . A fivefold dilution series of pooled cDNA was used to assess the efficiency of the qPCR reaction for each gene specific primer pair . A no template control ( NTC ) was included to detect possible contamination and a melting curve analysis was done in order to check the presence of a unique PCR product . Experiments were performed using four biological replicates and two technical replicates for each reaction . Relative expression analysis was done according to Pfaffl [30] and significance of calculated differences in gene expression was identified by a pair-wise fixed reallocation randomization test . Quantitative PCR reactions for the gDNA analysis were performed as described above , using gDNA as template . Histone 3 was used as a reference gene for the analysis of the gDNA [30] . Approximately fifty resistant females ( Tem-R ) were crossed to fifty susceptible ( Lab ) males ( Fem Res x Male Sus ) and fifty susceptible females to fifty resistant males ( Fem Sus x Male Res ) , in two replicates . Susceptibility to temephos of the F1 generation from both crosses was determined with a bioassay , as described above and dose-response curves were produced as described in the BioRssay manual [18] . Late third to early fourth instar larvae ( F1 generation ) of the Fem Res x Male Sus cross were selected with 0 . 05ppm temephos , a concentration which kills >90% of the susceptible individuals . This selection step was introduced to ensure that only heterozygous , but no susceptible individual would be isolated , in case the resistant strain is not completely homogenous . After that , F1 survivors were intercrossed and their eggs were collected and let to hatch . Late third to early fourth instar larvae of the F2 generation were selected with 0 . 12ppm temephos and larvae which died after 4 hours of exposure ( approximately 60 to 80% ) , as well as larvae which survived after 24 hours of exposure were collected . Genomic DNA was extracted from individual larvae and used as template in a quantitative real time PCR , performed as described above , in order to compare copy numbers of particular genes between dead ( the most susceptible ) and surviving ( the most resistant ) larvae . In this case results were expressed as the reverse ratio of the esterase gene Ct over the histone 3 Ct . Ct refers to the cycle at which the fluorescence for each gene rises appreciably above the background fluorescence .
The Ae . albopictus susceptible reference laboratory strain ( Lab ) that was obtained in order to facilitate the resistance analysis has an LC50 to temephos of 0 . 020ppm , while the most susceptible field population ( Field-S-IT ) from the Mediterranean region ( Genoa , Italy ) has an LC50 of 0 . 003 ppm ( Table 1 , [7] ) . The field resistant population collected from Greece ( parental strain , Par-GR ) has an LC50 of 0 . 048ppm ( resistance ratio 16-fold , as compared to the Field-S-IT population ) . The selected resistant strain Tem-GR , obtained from selection of Par-GR with temephos , has an LC50 of 0 . 128ppm ( resistance ratio 42 . 6-fold and 6 . 4-fold , as compared to the Field-S-IT and the Lab strain , respectively ) ( Table 1 ) . Resistance mechanisms were investigated in the Tem-GR strain and compared to the Par-Gr and to the Lab strains . In order to determine the relative role of the most relevant carboxylesterase detoxification system , based on previous studies on temephos resistance in other species [13] versus possible alterations in the AChE target site of temephos in the resistant phenotypes , we analysed enzymatic activities of the different strains . No significant differences in the AChE—ATCHI activity and/or inhibition patterns with paraoxon and propoxur , were observed among the three strains ( Lab; Par-GR; Tem-GR ) tested . However , α- and β-esterase activities with the substrates α- and β-naphthyl acetate were substantially different ( Fig 1 ) among the three strains: the resistant strain Tem-GR had the highest activity followed by the Par-GR and the reference strain ( Lab ) , in line with the temephos LC50 values . In order to investigate the underlying molecular mechanisms responsible for the temephos resistance phenotype , we used a high throughout IlluminaHiSeq2000 sequencing approach . Both Par-GR and Tem-GR were reared in parallel in the lab for 12 generations . The Par-GR was used as a “susceptible” population , in order to minimise the stochastic variation , i . e . genetic background , geographical differences and impact of extended laboratory colonisation . Homology searches focusing on genes which encode detoxification enzymes revealed a large number of putative P450 unigenes ( 203 unigenes ) , ABC transporters ( 140 unigenes ) , esterases ( 113 unigenes ) , GSTs ( 41 unigenes ) and UGTs ( 4 unigenes ) ( Tables 2 and S2 ) . Several transcripts that encode putative targets of insecticides were also identified , including AChEs , the target site of OPs and CARBs , sodium channel , the target site of pyrethroids , nicotinic acetyl-choline receptors ( nAChRs ) , target site of neonicotinoids , and chloride channels , the putative target site of avermectins ( Tables 2 and S2 ) . Differential transcription analysis between the resistant and the parental strains was performed on the 146 , 372 unigenes that were identified by the de novo assembly of the Illumina transcriptome . A total of 1 , 659 unigenes ( S3 Table ) were considered as differentially transcribed in the Tem-GR resistant strain , compared to the Par-GR parental strain , using a >4-fold regulation biological relevant threshold either direction and FDR < 0 . 05 . Out of 1 , 042 transcripts which were upregulated in the resistant strain , 309 transcripts had a match with known proteins . Among them , 17 unigenes that encode putative detoxification enzymes were identified ( Table 3 ) . Genes were named based on best blast hits with Ae . aegypti . Three of them encode CCEs , the CCEae3a ( in Ae . aegypti AAEL005112 ) ( 6 . 6-folds ) , the CCEae6a ( in Ae . aegypti AAEL005122 ) ( 6 . 1-folds ) and the AAEL015578 ( 5 . 6-folds ) CCE . Eight cytochrome P450s , primarily members of the CYP6 family , were also upregulated ( Table 3 ) , suggesting the involvement of monooxygenase metabolic pathways in temephos resistance . The UDP-glycosyltransferases ( UGTs ) AAEL003079 and the AAEL001533 , showed the most striking up-regulation ( 16 . 7 and 13 . 2-folds , respectively ) , among all detoxification genes , while the UGTs AAEL003076 , AAEL001822 , AAEL014371 also showed remarkable overexpression ( Table 3 ) . Other up-regulated transcripts that do not belong to detoxification gene families , but have been associated with insecticide resistance in other mosquito species , include transcripts with similarity to cuticle proteins ( AAEL002441 , CPIJ003474 ) ( S3 Table ) , and proteins involved in lipid biosynthesis ( such as the putative fatty acid synthase genes AAEL002204 and AAEL002227 ) . Although several detoxification genes were found up-regulated we focused on CCEs , as their involvement in the resistant phenotype was suggested also by the biochemical analysis and previous works had indicated their involvement in temephos resistance [13 , 31] in the closely related Ae . aegypti ( divergence dates estimated as 59 ± 19 My ) [32] . Out of 617 unigenes which were down-regulated in the resistant strain , 338 transcripts had a match with known proteins . Eight cytochrome P450s were present in this group ( S3 Table ) , with the AAEL002067 and the AaCYP9J24 putative homologue showing the highest down regulation ( 7 . 8 and 6 . 5-fold , respectively ) , and the AaCYP325AA1 , and AaCYPJ22 putative orthologues , showing also significant levels of down regulation ( 5 . 6 and 5 . 3-folds , respectively ) . A possible explanation for the down-regulation of P450s , in relation to organophosphate resistance might be the possible involvement of those P450s in the activation pathways of the pro-insecticide temephos , however more work is required to investigate this hypothesis . Quantitative PCR was used to validate the up-regulation of the carboxylesterases CCEae3a , CCEae6a and AAEL015578 in the temephos selected strain ( Tem-GR ) . As shown in Fig 2 , the levels of CCEae3a , CCEae6a and AAEL015578were confirmed to be significantly up-regulated in the temephos resistant ( Tem-GR ) strain compared to the parental ( Par-GR ) strain , at very similar levels: the up-regulation of the CCEae3a was estimated at 6 . 1-fold by Illumina and at 4 . 3-fold by qPCR , the CCEae6aat 6 . 6-fold and 5-fold , and the AAEL015578 at 5 . 6-fold and 5 . 5-fold respectively . In addition , the CCEae3a was upregulated 27-fold in the TemR , the CCEae6a 12-fold and the AAEL0155787 . 3-fold , compared to the reference strain ( Lab ) , respectively . This finding , is in good agreement with the bioassay and biochemical data , and indicates the involvement of the CCEae3a , the CCEae6a and the AAEL015578in the temephos resistant phenotype . Finally , quantitative PCR was used to compare the CCEae3a , the CCEae6a and the AAEL015578 gene copy number among the Tem-GR , the Par-GR and the Lab strains . A gene amplification of approximately 10-folds was observed for the CCEae3a and the CCEae6a in the Tem-GR , compared to the Lab strain . In contrast , copy numbers were not different for the AAEL015578 gene ( Fig 3 ) . A small and not statistically significant difference of approximately 1 . 8-fold was found for the CCEae3a and the CCEae6a copy numbers between the Tem-GR and the Par-GR strains , which might be due to the removal of susceptible individual mosquitoes during the selection of the heterogeneous field population . Genetic crosses were performed in order to investigate the inheritance of the temephos resistant phenotype and the genetic association of the amplified CCEae3a and CCEae6a in Ae . albopictus . Resistant females ( Tem-GR ) were crossed to susceptible ( Lab ) males ( Fem Res x Male Sus ) and susceptible females to resistant males ( Fem Sus x Male Res ) . F1 progeny of both crosses were tested for their susceptibility to temephos . An LC50 ( 95%CI ) equal to 0 . 082 ( 0 . 072–0 . 094 ) was estimated for progeny of the Fem Res x Male Sus cross and an LC50 ( 95%CI ) of 0 . 062 ( 0 . 038–0 . 091 ) for progeny of the Fem Sus x Male Res cross ( Fig 4 ) . Quantitative measurement of dominance , using Falconer’s formula [33] , indicated that resistance to temephos is inherited in both cases as a co-dominant trait ( D = 0 . 52 for Fem Res x Male Sus and D = 0 . 21 for the Fem Sus x Male Res , where -1 indicates complete recessive and 1 complete dominant genotype ) . Subsequently F1 individuals of the Fem Res x Male Sus cross were intercrossed and F2 progeny was obtained and selected with 0 . 12ppm temephos . Ten ( 10 ) dead larvae after 4h of exposure and 10 survivors after 24h of exposure were collected and quantitative real time PCR was performed using genomic DNA from individual larvae . Results showed that on average surviving larvae have statistically significant ( Welsh test , p value<0 . 05 , ) more copy numbers of both CCEae3a and CCEae6a esterases compared to dead larvae ( Fig 5 ) .
A mosquito population of the vector of dengue fever and chikungunya virus Ae . albopictus , with reduced susceptibility to the larvicide temephos was isolated from Greece . The operational impact of resistance is not known as the study did not directly assess this issue . However , the levels of resistance observed in the field and subsequently obtained by artificial selection ( 16- and 42 . 6-folds respectively , compared to Field-S-IT ) are significant . However , they may yet not substantially affect temephos performance if such resistant phenotypes are present in the field , in areas where temephos is still in use , or in cases when temephos would be required as an emergency tool ( i . e . epidemics or invasion cases ) , given that the LC95 of the resistant population ( 0 . 6 ppm ) is below the target field dose of temephos used ( 1 ppm ) , under optimum spraying conditions . The selection of resistance to temephos could be associated with the extensive use of this larvicide in Greece over the past years [34] . However , this resistance could also have been pre-selected already in other regions , and carried along the invasive routes of the Ae . albopictus population that established in Greece . Resistance mechanisms were subsequently investigated in a resistant strain obtained by brief laboratory selection with temephos ( Tem-GR ) , in comparison with the colonized parental strain ( Par-GR ) and an “independent” reference laboratory strain ( Lab ) . Biochemical data suggested that metabolic pathways ( CCEs ) , but not target site resistance ( altered AChE ) were present and active against temephos . Illumina transcriptome analysis was used to identify genes encoding detoxification enzymes , and quantify their expression levels in the resistant strain compared to the susceptible . A large Ae . albopictus mosquito larvae transcriptome dataset , consisting of 254 , 336 contigs and 146 , 372 unigenes was produced . The full set of the raw reads have been deposited in the Sequence Read Archive ( SRA ) ( http://www . ncbi . nlm . nih . gov/bioproject/282718; Submission ID: SUB923821; BioProject ID: PRJNA282718 ) . The transcriptome was found to contain a number of detoxification enzymes and a number of transcripts that encode putative insecticide target subunits ( Tables 2 and S2 ) . These genomic resources will be useful to the community investigating this major vector . Differential expression analysis revealed a number of genes with significantly differently expressed transcripts between the temephos selected ( Tem-GR ) strain and the parental ( Par-GR ) , a comparison that was chosen in order to minimize stochastic variation and isolate the resistance trait in the comparison . Three CCEs , CCEae6a , CCEae3a and AAEL015578 were among the most upregulated hits in the transcriptomic comparison between the Par-GR and the selected and more resistant Tem-GR . The up-regulation was confirmed by qPCR , and also against an independent reference strain ( Lab ) . This finding indicates that CCEs were selected by temephos , a result that is in tight correlation with the biochemical data , and indicated the involvement of CCEs in the resistance phenotype . These three CCEs , CCEae6a , CCEae3a and AAEL015578 encode putative members of the alpha esterase clade , a group of catalytically active CCEs that has been associated with xenobiotic detoxification functions and insecticide/organophosphate resistance , via sequestration [8] . Interestingly , CCEae6a and CCEae3a ( 30% and 44% identity between Ae . albopictus and Ae . aegypti , respectively ) were also recently found implicated in temephos resistance in Ae . aegypti from Thailand [13] . Gene amplification was subsequently found to be associated with elevated levels of CCEae6a and CCEae3a , but not AAEL015578 transcripts . Genetic crosses confirmed the link between the amplified CCEae3a and CCEae6a with temephos resistance , by demonstrating a significant association between survivorship and gene copy numbers in the F2 generation . Gene amplification of CCEs associated with OP resistance have also been reported in other insects [35 , 36] and mosquito species , such as Culex sp [37] and more recently , Ae . aegypti where the amplification of CCEae3a was associated with temephos resistance [13] , in line to our study . The levels of the elevated CCEae6a and CCEae3a gene copy numbers were lower than the respective up-regulation of the transcripts in the Tem-GR resistant strain , compared to both the Lab and the Par-GR strains , indicating that additional mechanisms may also contribute to the elevated levels of the CCE transcripts . The genetic analysis in Ae . albopictus also indicated that gene regulation might have an important role in the OP resistance for some individual mosquitoes . The operation of both gene amplification , transcriptional and translational control mechanisms to regulate the expression of CCE genes involved in insecticide resistance has been previously shown [38] , and it is not clear whether gene regulation or amplification ( or both ) is the determining factor in resistance . Furthermore , it has been shown in population studies that amplification levels vary between individuals over time through variation in organophosphate selection pressure , and that the loss or gain of gene copies , possibly through unequal sister-chromatid exchange is also a common phenomenon in mosquitoes and aphids [35 , 39] . The gene amplification that was identified in this study provides a gDNA marker that can be utilized to follow such dynamics of resistance alleles in the field , and investigate their origin and selection under various environmental contexts ( geographic and selective pressure histories ) . Based on Ae . aegypti genome , CCEae3a and CCEae6a ( but not AAEL015578 , which belongs to a different contig , NW_001835964 . 1 http://www . ncbi . nlm . nih . gov/gene/ ? term=AAEL015578 ) are clustered together within 18 . 214bp on the genome ( contig NW_001810264 . 1 within a 2 . 3Mbp length; CCEae3a , from 531 . 089 to 541 . 615 and CCEae6a from 559 . 829 to 567 . 390 bp , http://www . ncbi . nlm . nih . gov/gene/ ? term=AAEL005112 ) , which might explain the apparently equal co-amplification of those two genes ( approximately 10-fold for both the CCEae6a and CCEae3a ) in the Ae . albopictus resistance strain ( providing that synteny is maintained between the two species ) . The on-going efforts for sequencing Ae . albopictus genome will help to investigate further the mechanism of the amplification , and the striking similarities between the temephos resistance mechanisms identified in Ae . aegypti and Ae . albopictus . Finally , several genes were also found upregulated in the Tem-GR strain , such as cytochrome P450s , members of the CYP6 family that has been implicated in insecticide resistance in other mosquito species [40] , and UDP-glycosyltransferases ( UGTs ) , enzymes that are known to participate in Phase II detoxification of xenobiotics by catalyzing their conjugation with uridine diphosphate ( UDP ) sugars[10] . Genes of these families have also been found co-up-regulated with putative primary phase I detoxification enzymes in other resistance studies [10] , including temephos resistance in Ae . Aegypti [13] . This indicates that the co-evolution of multiple mechanisms , which may act in a coordinated manner to accelerate the detoxification , may be responsible for insecticide resistance . However , the relative contribution and/or redundancies of such individual genes and pathways in the resistance phenotype have not been studied in detail as yet . | Some of the most immediate challenges that the world faces are caused by insecticide-resistant mosquitoes that seriously threaten human health , via the diseases they transmit . Temephos is a major larvicide that has been used extensively for the control of Ae . albopictus and its often sympatric Ae . aegypti . Here we identified temephos resistance , and showed that specific carboxylesterase genes are overexpressed in the resistant strain through gene amplification . It is striking that exactly the same CCE genes , namely CCEae6a and CCEae3a , which are clustered in Ae . aegypti genome , have also been found associated with temephos resistance in this species . Identification of genes responsible for insecticide resistance is a key step in order to make careful risk assessments regarding the emergence of resistance and to design effective and sustainable vector control strategies . The gDNA—resistance associated marker ( i . e . : the gene amplification which was confirmed to be genetically linked with the phenotype ) can be used to follow the dynamics of resistance in the field , as well as facilitate population genetic studies for this highly invasive vector . The transcriptomic data that were produced represent a significant genomic resource , which will facilitate molecular studies in Ae . albopictus . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Transcriptome Profiling and Genetic Study Reveal Amplified Carboxylesterase Genes Implicated in Temephos Resistance, in the Asian Tiger Mosquito Aedes albopictus |
The well-studied DNA replication origins of the model budding and fission yeasts are A/T-rich elements . However , unlike their yeast counterparts , both plant and metazoan origins are G/C-rich and are associated with transcription start sites . Here we show that an industrially important methylotrophic budding yeast , Pichia pastoris , simultaneously employs at least two types of replication origins—a G/C-rich type associated with transcription start sites and an A/T-rich type more reminiscent of typical budding and fission yeast origins . We used a suite of massively parallel sequencing tools to map and dissect P . pastoris origins comprehensively , to measure their replication dynamics , and to assay the global positioning of nucleosomes across the genome . Our results suggest that some functional overlap exists between promoter sequences and G/C-rich replication origins in P . pastoris and imply an evolutionary bifurcation of the modes of replication initiation .
Eukaryotic DNA replication initiates at multiple genomic loci termed replication origins . While the initiation of DNA replication at origins is a key regulatory feature of genome replication in all organisms studied , the structural components of these cis-acting elements are remarkably diverse [1] . Yeast origins are generally short , intergenic , A/T-rich DNA elements . In contrast , metazoan and plant origins are large , poorly-defined zones enriched for genes and G/C-rich DNA [2]–[6] . In addition , while metazoan origin activity correlates with expression of adjacent genes [2] , [3] , [7] , no such correlation is seen in yeast . Though much has been learned about DNA replication using the highly tractable yeast models , these differences have limited the usefulness of yeast for the study of some aspects of mammalian DNA replication . Replication origins have been best defined in the budding yeast Saccharomyces cerevisiae , where origin fragments shorter than 100 bp can act as autonomously replicating sequences ( ARSs ) sufficient for episomal plasmid maintenance [8] . The 17 bp ARS Consensus Sequence ( ACS ) motif is required for the interaction with the six-subunit Origin Recognition Complex ( ORC ) that recruits downstream initiation factors [9] . In addition to the primary ACS , origin function requires flanking DNA elements that include transcription factor binding sites [10]–[12] , nucleosome depletion regions [13] , [14] , and helically unstable DNA [15] . While the dynamics of chromosome replication in S . cerevisiae are the product of a temporal timing program acting on origins with variable initiation efficiencies , the underlying regulators of replication dynamics are incompletely understood [12] , [16]–[20] . Another well-studied origin model is the fission yeast Schizosaccharomyces pombe where longer ( 500 bp to 1 kb ) stretches of A/T DNA are stochastically recognized by a domain of nine AT-hooks on the N-terminus of one of the ORC subunits—Orc4 [21]–[23] . Replication origins in metazoans have not been delineated to the same extent as in yeast . Metazoan replication initiates in broad replication zones that range up to 500 kb in length . Replication timing is controlled by both stochastic and regulated forces and is highly plastic throughout developmental transitions [24] . To date no clear sequence-specific binding sites for ORC have been detected in animals ( or plants ) though G/C-rich elements such as unmethylated CpG islands have been suggested as potential ORC targets [25] . ORC binding close to transcription start sites ( TSSs ) has been reported in both insects and mammals [3] , [26] . Indeed there is a clear association between origin activity and local gene expression in metazoans , and the DNA viruses that infect them , which is not seen in either of the major yeast models . Recent studies in non-canonical yeast species have elucidated that , even in related species , a diversity of consensus motifs are implicated in origin function . All budding yeast species tested so far have short A/T-rich origins with different consensus motifs . Kluyveromyces lactis has a 50 bp ARS consensus motif that can be accurately used to predict origin locations [27] . Conversely , Lachancea kluyveri recognizes sequences similar to the S . cerevisiae ACS , but with a much relaxed requirement for specific sequences [28] . Interestingly , its close relative L . waltii requires a consensus motif that bears similarities to aspects of both the S . cerevisiae and the K . lactis ACS motifs [29] . Recent profiling of replication initiation in non-canonical fission yeasts S . japonicus and S . octosporus implicated G/C-rich elements in origin function [30] . In this study we have comprehensively profiled replication origin location , structure , and dynamics in the methylotrophic budding yeast Pichia pastoris ( Komagataella phaffii ) [31] , [32] using a number of massively parallel sequencing techniques . In addition , we generated a genome-wide profile of nucleosome occupancy . Our findings show that this yeast , which is commonly used for industrial production of recombinant proteins [33] , employs at least two distinct types of DNA sequences to initiate replication . Approximately one third of P . pastoris ARSs require a G/C-rich motif that closely matches one form of the binding site of the well-studied Hsf1 transcriptional regulator [34] . The remaining origins use A/T-rich sequences for initiation . Genome regions near G/C-rich origins replicate significantly earlier than regions near the other class of origins and have a unique pattern of nucleosome organization . Their organization suggests that local transcriptional regulation may be linked in some way to replication timing at these sites . Furthermore , the most common plasmid vector used in P . pastoris contains a member of the AT-rich class of origin , suggesting that use of plasmids bearing a G/C-rich origin will yield immediate improvements for strain engineering .
The classic ARS screen identifies sequences sufficient for the initiation of replication of plasmids [35] , [36] by assaying for colony formation on selective medium . Non-replicating plasmids do not yield colonies . An early study identified two regions of the P . pastoris genome that have ARS function , but do not have ACS elements seen in S . cerevisiae ARSs [37] . To generate a comprehensive map of ARSs in the genome of P . pastoris ( PpARSs ) we utilized ARS-seq , a high-throughput ARS screen combined with deep sequencing ( Figure 1A ) [38] . A ∼15× library of genomic DNA fragmented by one of four “four-cutter” restriction enzymes was constructed in a non-replicating URA3 shuttle vector . A P . pastoris ura3 strain ( JC308 ) was transformed with this library and plated on medium lacking uracil ( C-Ura ) resulting in ∼20 , 000 colonies from an estimated 2–3×106 transformants . Colonies were replica-plated on C-Ura plates and grown for four additional days before the growing colonies were pooled . Total DNA was extracted from pooled cells . ARS inserts were amplified using vector-specific Illumina primers and sequenced using paired-end deep sequencing . The sequencing reads were assembled into 971 unique genomic fragments ( averaging 661 bp in length , Figure S1 ) and 358 overlapping contigs ( Table S1 ) . The data were filtered both computationally and by manual verification ( Methods ) resulting in a final list of 311 ARS loci . To delineate the functional regions of P . pastoris ARSs with greater precision we used miniARS-seq , a follow-up ARS screen where the input library is constructed from short subfragments of ARSs isolated from the initial ARS-seq screen ( Figure 1A ) [38] . The miniARS-seq screen returned 14 , 661 functional ARS fragments that were filtered and assembled into contigs ( Methods ) . This procedure narrowed the functional regions of 100 ARSseq contigs to ∼150 bp ( Table S2 ) . We have previously shown that ARS regions can be accurately narrowed by inferring functional “cores” based on regions of overlap among multiple ARS-seq/miniARS-seq fragments [38] . We combined data from both screens to generate a high-resolution map of ARS sites in the P . pastoris genome ( Table S3 ) . Identification of conserved motifs within a set of sequences with a shared function is one of the cornerstones of comparative genomics . The S . cerevisiae ACS motif is present in all S . cerevisiae ARSs and is easily recognizable by motif discovery algorithms [39]–[42] . The same is also true for L . waltii [29] , and in K . lactis the ACS motif can additionally be used to predict accurately genomic ARS locations [27] , [43] . We used the de novo motif discovery tool MEME [44] to identify conserved motifs of varying lengths within the entire set of P . pastoris ARSs using the zero or one occurrence per sequence ( zoops ) setting . MEME identified a 20 bp G/C-rich consensus motif ( “GC-ACS , ” E-value = 1 . 3e-248 ) with a TYGAAC core ( Figure 1B ) . However , not all PpARSs have a significant match to this motif . To determine the subset with a GC-ACS , we used the MAST algorithm to assign a score to the best occurrence of the motif within each sequence . The bimodal distribution of motif scores ( Figure 1C ) indicated that 107/311 ( 34 . 4% ) of the ARSs have much stronger matches to the motif than the remaining 204 ARSs . We were unable to detect any conserved motifs that were present among these 204 sequences . We found that P . pastoris ARSs were significantly enriched for G/C-content relative to combined intergenic sequences ( binomial P = 1 . 778e-06 ) . Furthermore , the 107 ARSs bearing the GC-ACS motif ( “GC-ARSs” ) were significantly enriched ( binomial exact test P = 2 . 825e-15 ) for G/C-content relative to the 204 ARSs without the motif ( “AT-ARSs” ) . In fact , the AT-ARSs alone are not significantly enriched for G/C or A/T content relative to all of intergenic DNA ( two-sided binomial exact test P = 0 . 46 ) , suggesting that GC-ARSs are chiefly responsible for the overall G/C enrichment in the ARS dataset . Additionally , while both classes of ARSs are predominantly intergenic , GC-ARSs associate with longer intergenes whereas AT-ARSs do not . The median length of all intergenes in the P . pastoris GS115 strain background is 216 bp [31] , whereas the median length of GC-ARS intergenes is 869 bp , an enrichment that cannot be explained by the length of intergenes alone ( Monte Carlo simulation P<0 . 01 ) . In contrast , the median AT-ARS intergene at 566 bp is not significantly longer than the background ( Monte Carlo simulation P = 0 . 85 ) . Another difference between the GC- and AT-ARSs is that the average combined ARS-seq read depths for individual ARSs of the AT- class are lower than for those of the GC-ARS class ( Figure S1B , one-tailed T-test P = 0 . 035 ) . This difference is most noticeable in that 61/204 AT-ARSs have a read depth <20 , while all GC-ARSs have higher read depths , and only 9/107 GC-ARSs have read depths of <300 . We validated a number of these low read depth AT-ARSs to ensure that they are not all false positives ( Table S3 ) . This discrepancy in read depth between GC- and AT-ARSs suggests that the AT-ARS dataset may be enriched for ARSs that replicate less efficiently in this plasmid vector context . Similarly to other budding yeast ARSs , PpARSs are predominantly intergenic ( hypergeometric test P<2 . 2e-16 ) . However , unlike S . cerevisiae , where replication origins are enriched in convergently transcribed intergenes ( where both adjacent genes are transcribed toward the intergene ) , P . pastoris ARSs are depleted in convergent intergenes ( Chi-squared P = 4 . 749e-05 , Figure S2 ) . To confirm that both GC-ARSs and AT-ARSs are bona fide replication origins in their chromosomal context , we assayed genomic origin firing by 2D-gel electrophoresis at two genomic loci ( Figure 1D ) . Replication intermediates were isolated from exponentially growing cells in YPD medium , subjected to 2D-gel electrophoresis as described [45] , and probed for a GC-ARS locus ( C379 ) and an AT-ARS locus ( A2772 ) . The presence of an upper arc on a 2D-gel blot results from replication bubble intermediates ( Figure 1D , red arrows ) and is indicative of replication initiation at the probed locus . We detected such “bubble arcs” at both loci , suggesting that members of both classes of sequences can function as replication origins in the genome . To test whether the GC-ACS identified from the sequence analysis is required for GC-ARS function , we used site directed mutagenesis to disrupt the motif within twelve different GC-ARSs and tested the effect of these mutations on ARS function ( Figure 2 ) . We replaced the central GA dinucleotide within the best match of the GC-rich motif with a CC dinucleotide to disrupt the motif ( TYGAAC was changed to TYCCAC , Table S4 ) . We ligated short DNA fragments ( 125 bp ) bearing both wild type and mutant alleles of each ARS into a URA3 plasmid and tested the resulting plasmids for ARS function by transformation of the P . pastoris ura3 strain ( Figure 2 ) . Multiple individual clones of all plasmids carrying wild-type ARS alleles yielded colonies on selective media indicating ARS activity . All clones were functional , regardless of the relative orientation of the ARS insert within the vector . Three of the twelve wild-type ARSs ( ARS-B1605 , ARS-C937 , and ARS-D781 ) showed a noticeably weaker ARS activity indicated by slower colony growth . This slow growth is likely due to the short fragment length of ARSs tested , since multiple flanking elements are commonly required to support or enhance ARS function . None of the clones bearing mutant ARS alleles showed colony formation indicating the absence of ARS function independent of insert orientation within the vector . Additionally , in all twelve cases , the wildtype ARSs retained function despite the GC-ACS being positioned <15 bp from the 5′ end of the ARS fragment . These results indicate that the GC-ACS motif is required for GC-ARS function whereas sequences flanking the motif on the 5′ side are not . While the GC-ACS motif is not present in all PpARSs , the fact that it is present in over a third of ARS fragments and is essential for ARS function in the subset of GC-ARSs tested suggest that it plays an important role in ARS function . This hypothesis is further supported by the fact that ARS-seq identified most of the intergenic matches of this motif ( 106/134 ) across the genome . The remaining twenty-eight intergenic occurrences of this motif that were not detected by ARS-seq have significantly lower match scores than the motifs within ARS fragments ( T-test P = 1 . 49e-07 ) suggesting that strong matches to the GC-ACS are good indicators of ARS activity . To assay directly the sequence determinants of ARS function , we applied a deep mutational scanning [46] , [47] approach , mutARS-seq [38] , to 100 bp fragments of P . pastoris ARS-C379 and ARS-A2772 . This method involves competitively growing yeast transformed with a library of randomly mutagenized variants of a given ARS and measuring the enrichment of each allele through paired-end deep sequencing of samples over time ( Figures 3A , S3 , S4 , and S5 ) . Stronger ARS variants increase in population frequency over the course of the competition and are given positive enrichment scores , whereas deleterious mutations result in depletion of these alleles and are given negative enrichment scores . We constructed mutARS-seq libraries for ARS-C379 and ARS-A2772 using oligonucleotides synthesized with a 2% chance of bearing a random mutation at each position . Each library contained >20 , 000 inserts . A ura3 strain of P . pastoris was transformed with the two libraries separately ( two biological replicates for each library ) . Resulting colonies on selective medium plates ( ∼100 , 000 transformants for each experiment ) were pooled and the cell mixture was used to inoculate a 1 L culture of liquid selective medium . The culture was grown at 30°C and the abundance of each ARS variant at different times was measured by 101 bp paired-end sequencing . The results of mutARS-seq show a striking difference in the sequences required for function of the two types of PpARSs . ARS-C379 shows a zone of constraint within the region corresponding to the match of the GC-ACS motif ( Figures 3B and S3 ) further supporting that the GC-ACS motif is required for ARS-C379 function . In contrast , ARS-A2772 does not have a GC-ACS and shows a region of constraint at a repetitive A/T-rich sequence that is not present in ARS-C379 ( Figures 3C and S4 ) . In searching for matches to the A/T-rich motif within the ARS set we were able to detect strong matches within only two sequences , one of them being ARS-A2772 . This result suggests further complexity within the AT-ARS functional determinants . Alternatively , this motif may be inherently elusive to alignment-based methods due to its repetitive A/T-rich structure . Our findings demonstrate that P . pastoris can utilize at least two different non-overlapping sequence motifs for the initiation of DNA replication . We also found that these ARSs retained function in both orientations within the vector , on different length inserts , and in other plasmid contexts ( data not shown ) , suggesting that at least one of these sequences , or an equivalent , must be present for the initiation of plasmid replication and that each is sufficient for initiation . While the ARS assay can be used for high-precision mapping of sequences required for replication initiation , it is not an accurate measure of origin activity in the genomic context . No correlation between ARS activity and genomic replication timing has been detected in either S . cerevisiae or S . pombe , presumably due to higher-level regulation of timing that is absent on plasmids . To overcome this limitation of the ARS assay , we used an approach that combines cell sorting and deep sequencing [17] , [48] , [49] to map the temporal patterns of replication within the P . pastoris genome . This method calculates the DNA copy number ratio between S phase and G1 phase cells in sliding windows across the genome . Since a replicated region is present in twice the copy number of a non-replicated region , this copy number ratio is proportional to the relative mean replication time of a given locus [49] , [50] . Approximately 1 . 5 million G1 and S phase cells were sorted from an exponentially growing culture using FACS . Total genomic DNA was isolated , randomly sheared , and sequenced to high coverage to measure the relative DNA copy number of all genomic loci . The ratios of sequence reads between G1 and S phase samples were calculated in non-overlapping 1 kb sliding windows across the genome and normalized based on the total number of reads within each sample ( Methods ) . The resulting ratios from biological replicates were LOESS smoothed , yielding highly reproducible replication timing curves ( Pearson and Spearman cor >0 . 94 , Table S5 ) . To generate a composite replication timing profile , the unsmoothed ratios from both replicates were averaged , normalized to a baseline value of 1 and smoothed ( Methods , Table S5 ) . Visual inspection of the chromosome replication profiles revealed ∼100 significant peaks corresponding to early replicating regions , or replication origins ( Figures 4A and S6 , Table S5 ) , as well as valleys that reflect replication termination loci . Additionally , we detected numerous small peaks and “shoulders” ( small peaks at the edges of larger peaks ) that we interpret to be later firing or less efficient origins . Quantitative analysis identified 176 peaks in replication timing peaks ( Figures 4 and S6 , Table S6 ) . Overlaying ARS coordinates with the replication curve showed that all large peaks except one contained at least one ARS . Examination of the sequence within the lone ARS-less peak ( near position 1 , 565 , 000 on chromosome 1 ) revealed two strong matches to the GC-ACS motif within 2 kb of the peak . Manual validation of 200 bp fragments centered on each of the motif occurrences revealed them both to have ARS function indicating that they are ARS-seq false negatives . We also used the replication timing data to further validate the ARS screen to remove false positives . We manually validated low coverage ARS-seq fragments that did not appear to map at a replication peak . From forty-nine fragments with a read-depth 2–10 ( fragments with read-depth 1 are filtered out at the ARS-seq stage; see Methods ) eleven did not appear close to peaks and were manually tested for ARS function . Among these eleven ( none of which had GC-ACS motifs ) , ARS activity was detected for only three ( Table S4 ) . To test whether ARSs bearing the GC-ACS motif are regulated differently than those without , we compared the replication curve values between the two classes of ARSs ( Figure 4B ) . Our data show that while GC-ARS regions are replicated significantly earlier than the background genomic distribution , AT-ARSs are not ( T-test P<2 . 2e-16 and 0 . 0699 respectively ) . Consistently , GC-ARSs are replicated earlier than AT-ARSs ( T-test P<2 . 2e-16 ) . This result holds true even if only loci without neighboring ARSs ( within a two-sided 40 kb window ) are compared ( T-test P = 6 . 267e-07 ) . Chromosomal regions with single isolated AT-ARSs replicate significantly later relative to the pool of all AT-ARSs ( T-test P = 0 . 0003 ) , suggesting that clustering of these elements increases their local replication signal . This effect was not seen at the GC-ARS loci ( T-test P = 0 . 88 ) , indicating that clustering does not significantly affect their timing . Another way to detect differences in replication timing between the two classes of ARSs is to measure the effect of removing their signals from the genomic dataset ( Figure 4C ) . Removing all points within 30 kb windows centered on GC-ARSs significantly shifted the distribution of remaining replication timing signals in the “later” direction ( T-test P<2 . 2e-16 ) . On the other hand , removing signals around AT-ARSs did not significantly affect the distribution of remaining points ( T-test P = 0 . 07094 ) . When signal was removed around all ARSs , it shifted the distribution relative to removing just GC-ARSs ( T-test P<2 . 2e-16 ) , consistent with the AT-ARSs occupying a lower tier in the hierarchy of origin activation times . Additionally , we found the distance from each ARS to the nearest replication peak and plotted histograms of these distances for AT- and GC-ARS's ( Figure 4D ) . We find that both types of ARSs are significantly associated with peaks ( Kolmogorov-Smirnoff test , P = 7 . 18×10e-5 for GC-ARSs and P = 0 . 0293 for AT-ARSs ) . GC-ARS's were significantly closer to peaks than AT-ARS's ( Kolmogorov-Smirnoff test , P = 6 . 13×10e-7 ) . Taken together , our data suggests that while both types of ARSs correlate with genomic replication origins , GC-ARSs are more often found associated with early origins and early replicating regions , whereas AT-ARSs show the opposite tendency . One common feature of replication origins is a nucleosome depletion region ( NDR ) close to the site of initiation [13] , [14] , [26] , [30] , [51] , [52] . To investigate whether this feature holds true for P . pastoris , we generated a complete map of nucleosome positions within the P . pastoris genome by sequencing genomic DNA digested with micrococcal nuclease [53] . Our results revealed gross nucleosome positioning features similar to those seen in other yeasts , such as an NDR at transcriptional start sites ( TSS ) followed by regularly positioned nucleosomes within the body of transcripts [54] , [55] ( Figure 5 , Table S7 ) . This result suggests that our experimental methods accurately captured the positions of nucleosomes in this strain . We also detected NDRs at replication origin sites; however , GC-ARS and AT-ARS sites showed striking differences in nucleosome occupancy relative to other budding yeasts [13] , [14] , [29] . When centered on the GC-ACS , we observed a relative depletion in nucleosome occupancy approximately 40 bp to the 5′ side of the motif ( in the TYGAAC orientation ) . However , unlike other yeast origins where the NDR spans the length of approximately one nucleosome , the P . pastoris GC-ARS depletion region spans approximately 450 bp and appears to be excluding three nucleosomes ( Figure 5 ) . On the other hand , AT-ARS sites showed a nucleosome depletion region of ∼150 bp in length , a pattern more closely resembling that in other budding yeasts . However , this NDR was not flanked by well-ordered nucleosomes at all AT-ARS sites and suggests either that there are key regulatory differences with other budding yeasts or that not all AT-ARSs use the same sequence determinant for origin firing . The underrepresentation of GC-ARSs in convergently transcribed intergenes ( Figure S2 ) suggests that these elements may be associated with promoters . As in promoters , the NDR near GC-ACS sites is followed by regularly spaced nucleosomes . To test the putative association of the GC-ACS with gene promoters , we searched for this motif in the regulatory motif databases and found that it is a match to one of the motifs annotated as the binding sites of the human Hsf1 [34] heat shock factor ( HSF ) transcriptional regulator [56] ( http://www . factorbook . org/mediawiki/index . php/HSF1 ) . Additionally , when centered on the GC-ACS motif ( in the TYGAAC orientation ) , GC-ARSs show a pronounced poly ( dA ) region around 10 bp to 35 bp upstream of the motif ( Figures 6A and S7A ) . Notably , this poly ( dA ) tract is not present near the non-ARS occurrences of this motif and is not required for ARS function ( Figure 2 ) . It has been previously shown that such a neighboring poly ( dA ) region is a conserved feature of Hsf1 binding sites in the sensu stricto group of budding yeasts [57] , though we note that the TYGAAC portion of the motif does not match the canonical budding yeast HSF motif . To determine whether the GC-ACS is likely to be a binding site for Hsf1 or one of its homologs , we aimed to test whether this motif is overrepresented in promoters of genes likely to be regulated by HSF . We used BLAST to identify homologs of S . cerevisiae genes regulated by HSF [58] and filtered the list to include only strong matches ( PBLAST E-value<1e-10 ) , resulting in a set of 120 gene homologs . We used the FIMO algorithm to identify significant matches to the GC-ACS within 500 bp regions upstream of all 5037 P . pastoris genes . We identified 451 genes that had GC-ACS motifs and 716 genes with matches to the HSF binding site ( the Heat Shock Element , HSE [56] , [59] ) , within 500 bp upstream of the start codon . In our set of 120 potential HSF-regulated P . pastoris genes , 45 had at least one match to the HSE ( hypergeometric test P = 3 . 1e-11 ) and 16 genes had GC-ACSs within 500 bp upstream of the start codon ( hypergeometric test P = 0 . 037 ) . We also used an independent approach to test whether GC-ACS motifs associate with HSE motifs throughout the genome . We mapped separately all occurrences of the GC-ACS and of the HSE . We then assigned to each motif occurrence the nearest annotated gene . There are 5037 annotated genes in P . pastoris . From these , 1 , 188 unique genes were assigned as closest gene to an occurrence of the GC-ACS and 1 , 236 unique genes were assigned as closest to an HSE . A significant number ( 524 ) of unique genes were present in both lists , suggesting an association between GC-ACS and HSE motifs ( hypergeometric test P = 4 . 6e-67 ) . While HSF function in P . pastoris has not been studied , these results show an enrichment of GC-ACS motifs in regions likely to be regulated by HSF . Furthermore , the GC-ACS motif is positioned close to TSSs ( Figure 6B ) and ORF start sites ( Figure S8 ) upstream of the motif suggesting some functional overlap between transcription and early origin firing . Since the GC-ACS is associated with promoters , it raises the possibility that transcription is required for origin activation . If this possibility were true , then the DNA between the GC-ACS and the TSS may be required for ARS function . Since miniARS-seq screens large numbers of randomly sheared ARS sub-fragments , we were able to test this possibility by determining what sequences flanking the GC-ACS are required for ARS function . Using the full list of inferred functional ARS cores we calculated the length of sequence between the edge of the consensus motif and the edge of the ARS core on either side of the motif ( Figure 6C ) . The distributions of 5′ and 3′ lengths show that several GC-ARSs require <10 bp of sequence on the 5′ of the GC-ACS while more ARS sequence is required on the 3′ side of the motif . In fact , the fragment of ARS-C379 that was used for mutARS-seq ( Figure 3A ) retained function with only 2 bp of ARS sequence to the 5′ side . Additionally , the twelve wild-type ARS fragments that were tested for activity ( Figure 2 ) all contained <15 bp of sequence to the 5′ of the GC-ACS . The fact that all tested ARSs retained function in the absence of 5′ flanking DNA shows that this region , and the 5′ poly ( dA ) sequence , are not required for GC-ARS function . While it is possible that transcription can initiate at ectopic sites in the plasmid , these results suggest that transcription per se may not be required for GC-ARS function in P . pastoris . Consistent with these findings , we have been unable to detect a correlation between expression and replication initiation/timing ( data not shown ) . The majority of ARSs in budding yeast require sequences on the 3′ side of the ACS ( on the T-rich strand ) collectively called “B-elements” [38] , [42] , [60] . Our data show that GC-ARSs also require flanking sequence on the 3′ side of the GC-ACS motif ( in the TYGAAC orientation ) for ARS function . This result is supported by our mutARS-seq data where we detected a minor region of constrained nucleotides ∼50 bp to the 3′ side of the GC-ACS in ARS-C379 ( Figure 3B ) . The required flanking DNA lies distal to the TSS and may explain the extended nucleosome depletion regions ( Figure 5 ) seen at these loci .
Faithful genome duplication is essential to all living organisms . Like many other cellular processes , DNA replication is primarily regulated at the initiation step . Understanding the regulation of initiation at replication origins is therefore key to understanding how different species replicate their genomes . The extensively studied yeasts S . cerevisiae and S . pombe have yielded great insights into origin function , but lack several properties exhibited by metazoan origins . For one , metazoan origins have G/C-rich signatures whereas all yeast origin sequence determinants described to date are A/T-rich with the possible exception of fission yeast S . japonicus , where GC-rich motifs have been implicated in origin function through sequence analysis . Another key difference between yeast and metazoan origins is the connection between replication initiation and transcription . While promoter-associated origins tend to be early-firing in metazoans , this phenomenon has not been previously described in yeast . These discrepancies limit the value of most yeast species as models for the study of replication origins from higher eukaryotes . A better model would ideally possess the beneficial characteristics of yeast ( genetic and molecular tools ) while also recapitulating more of the traits displayed by metazoans . In this study we generated a comprehensive profile of replication origins in P . pastoris , a budding yeast that is very distantly related to both the S . cerevisiae and S . pombe yeasts [61] . This methylotrophic budding yeast has traditionally been utilized as an industrial organism valued for its ability to convert methanol to biomass and for its ability to produce and secrete recombinant proteins in high yields [33] . An early study showed that two native P . pastoris ARSs did not function in S . cerevisiae , suggesting key mechanistic differences in replication initiation between the two species [37] . We identified 311 ARSs in P . pastoris and were able to delineate the essential functional regions to <200 bp in most cases . As in other budding yeasts we found PpARSs to reside predominantly in intergenic regions . However , unlike other studied yeasts , P . pastoris displayed a conserved G/C-rich motif ( GC-ACS ) in approximately 35% of its ARSs . In fact , almost all strong intergenic matches to this motif were isolated in our ARS screen , suggesting a causal role for this motif in origin function . We were unable to detect a strong conserved motif within the other origins ( AT-ARSs ) . It is possible that the AT-ARSs function with an ill-defined sequence determinant similar to those seen in S . pombe and L . kluyveri [22] , [28] or that the sequence required for AT-ARS function is innately elusive to traditional alignment-based methods due to its nucleotide composition . To identify experimentally the nucleotides required for ARS function , we used mutARS-seq , a massively parallel approach that allows simultaneous measurement of the effects of all mutations on the function of an ARS [38] . This approach showed that the GC-ACS is indeed required for GC-ARS function ( Figure 3B ) . Notably , the GC-ACS was the most constrained element within the ARS tested , suggesting that this motif is the primary element used for ARS function and not a supporting element akin to S . cerevisiae “B-elements” . The fact that the GC-ACS motif retains function within different plasmid contexts supports this hypothesis . The mutARS-seq experiment on ARS-A2772 , an AT-ARS , revealed a very different region of functional constraint ( Figure 3C ) . A repetitive A/T-rich element was required for the function of this ARS . Other than its general A/T-richness , this element is significantly different from all previously identified ACS elements . Similarly to the GC-ACS , this motif is also the only strong region of functional constraint within the ARS and functions within different plasmid contexts , suggesting that it is a primary ARS element . While it is tempting to speculate that both of these motifs act as ORC binding sites ( or in some other way recruit relevant protein factors ) , we have no direct evidence to this effect . To our knowledge P . pastoris is the first organism that simultaneously uses such diverse sequences as ARS elements . The dynamics of replication in this species showed a surprising difference in replication timing between GC-ARSs and AT-ARSs ( Figure 4 ) . While both types of origins exist within replication peaks , as a class , GC-ARS sites replicate significantly earlier and/or more efficiently than AT-ARS sites—although there are individual exceptions to this general categorization ( Figure 4B ) . Our data also show that while the timing/efficiency of AT-ARS benefits from clustering with other ARSs , GC-ARSs are not affected by clustering , suggesting that they are operating at maximal initiation potential . While it is not yet clear how such a hierarchy of replication timing is achieved mechanistically , in metazoan cells promoter-associated origins fire earlier than the others and this difference is usually attributed to increased chromatin accessibility at transcription start sites [1] . Our findings are consistent with the difference in timing being associated with differences in chromatin structure . We assayed global positioning of nucleosomes in P . pastoris by sequencing mononucleosomal DNA from MNase-treated chromatin . The results of this experiment ( Figure 5 ) showed an atypical pattern of nucleosome depletion at GC-ARSs that resembles the depletion pattern seen at TSSs , but with two additional nucleosomes depleted upstream of the TSS . In contrast , nucleosome depletion at AT-ARSs resembles the S . cerevisiae ARS pattern with a single nucleosome depleted close to the location of the A/T-rich functional element . It should also be noted that while the A/T-rich motif identified by mutARS-seq is essential for the function of ARS-A2772 , it is possible that other AT-ARSs use other elements . This possibility is supported by the fact that many AT-ARSs do not have strong matches to the motif generated from the mutARS-seq data despite showing a nucleosome depletion region at the site of best match . Combined , our findings suggest that P . pastoris can utilize at least two distinct sequences for origin selection and activation . One group of origins is A/T-rich and their replication times are distributed across S phase . The other type of origin is G/C-rich , disproportionally early replicating , and shows a close association with transcription start sites , properties usually associated with metazoan origins . In fact , the conserved motif required for GC-ARS firing is a very close match to the binding site of the human Hsf1 transcriptional activator [34] . Additionally , we have detected a statistical association between GC-ACS motifs and genes likely to be regulated by Hsf1 or its homologs . While the mechanistic nature of GC-ARS function will require additional investigation , our data could suggest that the Hsf1 binding site in P . pastoris is capable of recruiting either directly or indirectly the replication initiation machinery . Our data also suggest that transcription per se may not be required for GC-ARS function ( Figure 6C ) , as sequences between the GC-ACS and transcription start sites are not required for ARS function , but are likely to be required for transcription . Consistent with this model , we have not been able to detect a correlation between gene expression and replication timing , but this lack of correlation may also be due to a combination of subtle regulation patterns and scarcity of available expression data . It is worth noting that the GC-ACS motif does not match the well-defined S . cerevisiae Hsf1 binding site that has the sequence structure TTCTAGAAnnTTCT [62] and is often represented as three evenly-spaced trinucleotides TTCnnGAAnnTTC [59] . However , Hsf1 is known to directly regulate genes lacking this motif , suggesting an ability to interact with diverse sequences [58] . Barring a mis-annotation , it is possible that in P . pastoris at least one of the four Hsf1 homologs is able to interact with and recruit ORC whereas the single Hsf1 protein in S . cerevisiae cannot bind to this atypical motif and thus relies exclusively on A/T-rich ARSs . This hypothesis would imply that the ability to use G/C-rich motifs for replication initiation is an ancestral trait that was lost in the lineage leading to the Saccharomyces , Lachancea , and Kluyveromyces clades . Whether other budding yeasts can utilize G/C-rich sites for initiation is not yet known . Alternatively , since a connection between Hsf1 and replication initiation has not yet been described , it is possible that this novel function is specific to the Pichia ( Komagataella ) genus , or perhaps only P . pastoris . Another observation that points to this motif being used for multiple functions is that a G/C-rich motif constructed from mutARS-seq data ( Figures 3B and S7B ) is less information-rich than the motif obtained from alignment ( contrary to the case of the A/T-rich motif which is difficult to produce by alignment , but is very obvious in the mutARS-seq data ) . While the optimal bases within the mutARS-seq data perfectly match the alignment-based motif , the cost of changing to a sub-optimal nucleotide is lower at most positions than the alignment-based motif would suggest . This observation can be explained by hypothesizing that this GC-motif is used for both origin activity as well as transcriptional regulation . If transcriptional regulation of the genes affected by this motif is evolutionarily more constrained than is ARS activity , then we would expect that the G/C-rich motifs would be selected upon primarily for their regulatory function . Additionally , it is possible that GC-ACS motifs act as enhancer elements to other , potentially A/T-rich primary elements . Transcription factors such as Fkh1 , Abf1 , and Mcm1 have been previously shown to enhance origin activity in S . cerevisiae [10]–[12] . This model would argue that the G/C-rich motif does not act as a primary site of initiation , but enables nearby dormant elements to initiate DNA replication possibly through the chromatin-modifying activity of Hsf1 . However , the fact that approximately one-third of all active origins have the same G/C-rich motif and that almost all intergenic occurrences of this motif are in ARSs is very different from what has been previously observed in other yeast models where connections between ARSs and transcription factors are much less obvious . In addition to elucidating the features of replication dynamics , our data offer useful tools and data resources for this industrially important yeast . We anticipate that our nucleosome position map will be useful for studies of chromatin and gene expression , especially when combined with transcriptome data [55] , [63] . More practically , replication origins are regulators of genome duplication and cell cycle progression , and are essential for episomal plasmid maintenance [64] . Current episomal vectors used in P . pastoris contain the original PARS1 ( ARS-B413 in our data ) , an ARS discovered almost three decades ago [37] , [65] . Our data show that PARS1 is one of the less efficient AT-ARSs ( Figure S6 ) [64] , suggesting that using a different ARS may result in improvements in plasmid stability . Previously , we used mutARS-seq data to optimize ARS function in S . cerevisiae [38] and this approach can potentially be used to further improve plasmid maintenance in P . pastoris , facilitating strain engineering efforts in this system .
The P . pastoris strain used in these studies was JC308 ( James Cregg ) , a ura3 auxotroph of the GS115 background strain . All yeast growth was performed at 30°C; all bacterial growth was performed at 37°C . The plasmid vectors used in this study were previously described [38] . All E . coli work was done using Alpha-Select Gold Efficiency competent cells ( Bioline ) . All enzymes used were from New England Biolabs unless otherwise noted . Primers were purchased from IDT unless otherwise noted . PCR purification and purification of digested plasmids was done using the DNA Clean and Concentrator-5 Kit ( Zymo Research ) . Plasmid DNA was purified using the Wizard Plus SV Miniprep Kit ( Promega ) . ARS-seq and miniARS-seq screens were performed largely as described [38] . P . pastoris genomic DNA was isolated from cells grown in YPD using a phenol/chloroform bead-disruption method followed by ultracentrifugation in a CsCl gradient ( to remove mitochondrial DNA ) followed by EtOH precipitation . Genomic DNA was fragmented and ligated as described [38] . Cloning efficiencies of resultant libraries were verified by colony PCR and P . pastoris cells were transformed with libraries using a custom lithium acetate protocol as follows . To make competent cells yeast were grown in YPG medium ( 10 g/L yeast extract , 20 g/L Peptone , 3% v/v glycerol ) until OD600 density of 1 . Cells from 1L of culture were spun down , rinsed and resuspended in 10 mL of TE/LiOAc ( 10 mM Tris-HCl , 1 mM EDTA , 100 mM lithium acetate ) . Cell suspensions were incubated at 30°C with shaking for 30 minutes , dispensed into 100 µL aliquots and frozen at −80°C . For transformations competent cells were thawed at room temperature , mixed with 1–5 µg of plasmid DNA , 600 µL of “two-step” transformation buffer ( 40% polyethylene glycol-4000 , 100 mM LiOAc , 10 mM Tris-HCl , 1 mM EDTA , 12 mM DTT , 0 . 12 mg/mL fish sperm carrier DNA ) and incubated at 30°C with gentle rotation for 30 minutes . The cell mixture was then heat-shocked at 42°C for 30 minutes and plated . Cells were grown for five days , replica-plated , and grown for three more days before cells were pooled for plasmid extraction . DNA shearing for miniARS-seq , plasmid recovery from yeast , and Illumina sequencing were performed as described [38] . Illumina paired end sequencing reads were uniquely mapped to the GS115 genome [31] using Bowtie version 0 . 12 . 7 . Custom Python scripts were used to detect relevant restriction sites at the ends of all mapped fragments that were extended to remove truncation products . Overlapping fragments were assembled into contigs . Contigs that had a combined read-depth of 1 were removed from the dataset . Cases where multiple discontinuous contigs were joined by overlapping fragments were manually resolved based on read depth . To maximize miniARS-seq data recovery , 101 bp paired end reads were mapped in full and unmapped reads were trimmed to 50 bp and mapped again . Resulting fragments with read depth >1 were assembled into contigs and contigs consisting of fewer than three unique fragments were removed . Both ARS-seq and miniARS-seq fragments were used to delineate minimal overlapping regions ( “inferred functional cores” ) . To prevent data loss , cores that were <150 bp in length were extended bi-directionally to a final length of 150 bp . mutARS-seq was performed largely as described [38] . Mutagenized oligos of ARS-C379 and ARS-A2772 were synthesized by Trilink Biotechnologies . The resulting libraries contained 24 , 000—40 , 000 ARS inserts . Yeast were transformed with mutagenized libraries as described above in two biological replicate pools each containing ∼100 , 000 transformed colonies . After five days of growth on selective agar plates , colonies were pooled and inoculated into 1 L cultures of liquid selective medium . Cultures were grown for 36 hours with periodic dilution to prevent saturation . Samples were taken at 0 , 12 , 24 , and 36 hours . Sequencing data were analyzed using the Enrich software package [66] . For maximum separation averaged data from the 36-hour samples are shown in Figure 3 . To create a position-weighted matrix from mutARS-seq data , the enrichment ratio values within the constrained region were converted into relative allele frequencies after an arbitrary cutoff minimum of 0 . 2 was applied . Logo images were generated using Weblogo software [67] . ARS sequences bearing mutations ( Table S4 ) were ordered as custom designed double stranded gBlock DNA fragments ( Integrated DNA Technologies ) . The gBlocks were used as PCR templates to amplify the mutant alleles prior to cloning . Wild type ARS alleles were PCR amplified from the gDNA of the parent strain ( JC308 ) . The MEME de novo motif discovery tool [44] was applied to identify conserved motifs within the entire set of PpARSs using the 5th order Markov background model and the entire set of P . pastoris intergenic sequences . Both MAST [68] and FIMO [69] programs from the MEME suite were used to map motif occurrences within different sets of ARS sequences . A 1 L culture of P . pastoris was grown to early log phase in YEPD and harvested for genomic DNA isolation [70] . Approximately 8 µg of DNA was cleaved with NcoI or StuI to release genomic fragments of 4 . 575 kb or 4 . 043 kb containing ARS-C379 or the ARS-A2772 , respectively . Replication intermediates were separated on a first dimension gel of 0 . 4% ME agarose in 1×TBE for 20 hours at 1 V/cm . Lanes for the second dimension gel were sliced from the gel and encased in a second gel of 0 . 9% ME agarose in 1×TBE with 0 . 3 µg/ml . Electrophoresis for the second dimension was carried out for 4 . 5 hours at 5 . 5 V/cm at 4°C . The genomic fragments were detected on Southern blots using 32P-dATP labeled PCR probes . Replication timing experiments were performed largely as described [48] . Exponentially growing ( in YPD medium ) P . pastoris cells were subjected to flow sorting using standard techniques on a BD FACsAria II cell-sorter . The purity of each sorted sample was determined to be ∼95% . Genomic DNA from 1 . 5–2 million G1 and S-phase cells was isolated using the YeaStar Genomic DNA Kit ( Zymo Research ) . Randomly fragmented sequencing libraries were prepared using the Nextera DNA Sample Preparation Kit ( Illumina ) [71] . Approximately 29 million 50 bp reads were recovered for each sample of each replicate . More than 90% of the reads in all samples were mapped to the P . pastoris GS115 reference genome and ∼1% of the reads in each sample were removed due to multiple mapping sites . After processing , 25–27 million reads were assigned to 1 kb bins across the genome resulting in average count-depth of 2936 reads/bin for G1 sample of replicate 1 , 2796 reads/bin for G1 sample of replicate 2 , 2843 reads/bin for S sample of replicate 1 , and 2913 reads/bin for S sample of replicate 2 . Reads were mapped using Bowtie and custom scripts were used to generate replication timing profiles as described [48] . The total number of reads for each replicate was equalized in each sample and a ratio of S/G1 reads was calculated for each replicate . These ratios were multiplied by 1 . 5 to account for the fact that the average cell in the middle of S-phase will have replicated half of its DNA . We fitted a loess curve to the mean of the two replicate ratio measurements , then found peaks along this curve using the turnpoints ( ) function from the R package , pastecs . The resulting curves were normalized to a baseline value of 1 . Nucleosome positions were mapped similarly to the method described [53] . Two colonies were grown in 400 mL of YPD media until an OD600 of 1 and then cross-linked with formaldehyde . The two samples were bead disrupted in 10 mM Tris-HCl pH 8 . 0 with 1 mM CaCl2 . Visually lysed samples were then MNase digested for 30 minutes at increasing concentrations of MNase . Cross-links were removed by overnight incubation at 65°C followed by DNA extraction with phenol/chloroform . Extracted DNA was separated using a 2% agarose gel to visualize the mononucleosome enriched band . DNA corresponding to ∼150 bp was then extracted and sequenced using the Illumina HiSeq platform . The samples were divided in half to provide technical replicates . All sequencing data presented are available from the National Center for Biotechnology Information Sequence Read Archive ( ARS-seq - SRP031643; miniARS-seq - SRP031646; mutARSseq - SRP031760; replication timing - SRP031759; nucleosome mapping - SRP031651 ) . | Genome duplication in eukaryotes initiates at loci called replication origins . Origins in most budding and fission yeasts are A/T-rich DNA sequences , while metazoan origins are G/C-rich and are often associated with promoters . Here we have globally mapped replication origins and nucleosome positions in an industrially important methylotrophic yeast , Pichia pastoris . We show that P . pastoris has two general classes of origins—A/T-rich origins resembling those of most other yeasts , and a novel , G/C-rich class , that appear more robust and are associated with promoters . P . pastoris is the first known species using two kinds of origins and the first known budding yeast to use a G/C-rich origin motif . Additionally , the G/C-rich motif matches one of the motifs annotated as binding sites of the human Hsf1 transcriptional regulator suggesting that in this species there may be a link between transcriptional regulation and DNA replication initiation . | [
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] | 2014 | GC-Rich DNA Elements Enable Replication Origin Activity in the Methylotrophic Yeast Pichia pastoris |
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms . Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions . In this paper , we introduce k-OptForce , which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest . It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes ( for reactions with available kinetics ) and reaction flux changes ( for reactions with only stoichiometric information ) . Application of k-OptForce to the overproduction of L-serine in E . coli and triacetic acid lactone ( TAL ) in S . cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds . In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds , whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions . A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations . Furthermore , k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest , which cannot be captured by stoichiometry-alone analysis . This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects .
Bio-production is emerging as a competitive strategy for the production of a wide range of chemicals ranging from biofuels , precursor chemicals and bioactive molecules ( see [1]–[3] for detailed reviews ) . The use of metabolic modeling and computations is increasingly becoming instrumental in deciding how to engineer the production strain [4]–[11] . Computational strain design generally involves solving an optimization problem which optimizes a specific performance requirement ( e . g . , maximum flux of desired product ) while minimizing the total number of genetic alterations in the metabolic model . Depending on the adopted description of metabolism strain design computational tools could be broadly categorized as based on stoichiometry-alone or kinetic models of metabolism [12] . Kinetic models of metabolism require quantitative expressions that link reaction fluxes and metabolite concentrations . A system of ordinary differential equations ( ODEs ) is typically solved to obtain the time-dependent variation in metabolite concentrations and reaction fluxes . Different forms of mechanistic expressions have been used extensively such as Michaelis-Menten or Hill Kinetic expressions [13] , [14] . These expressions require a priori knowledge of detailed enzyme function mechanism and characterization [15] , [16] . Alternatively , various approximate kinetic forms such as lin-log [17]–[19] and log-lin [20] kinetics , power law kinetic expressions such as the S-system [21] and Generalized Mass Action [22] , and other forms of cooperativity and saturation [23] , [24] and convenience rate laws [25] have been proposed to reduce the number of kinetic parameters and complexity of the rate expressions . In addition , Varner and Ramkrishna [26]–[28] pursued the development of kinetic descriptions inspired by cybernetic modeling and optimality concepts . A number of review articles highlight the merits and demerits of various kinetic modeling formalisms [17] , [29] , [30] . Uncertainty in the assignment of kinetic parameter values has motivated the development of approaches that do not fix the parameter values but rather sample from a probability distribution [31]–[34] . Even though the use of kinetic models have led to some successes for strain design [20] , [35]–[43] the relative small scope of the employed models , difficulties in obtaining kinetic expressions and questionable portability of kinetic expressions across microbial production platforms have so far limited wide applicability and acceptance . The introduction of genome-scale models of metabolism [44]–[46] and the use of Flux Balance Analysis ( FBA ) to assess their maximum theoretical yields [47] , flux ranges [48] and trade-offs between growth and productivity [49] led to a flurry of computational strain design approaches [50] , [51] that used a purely stoichiometric description of metabolism . The advantage of using stoichiometry alone supplemented with some regulatory information is that the widest possible range of potentially feasible metabolic phenotypes could be accessed . The linearity of the underlying FBA description also affords significant computational savings and tractability even for genome-scale models . The downside is that identified flux redirection predictions ( especially knock up/downs ) are sometimes hard to translate into an actionable genetic intervention . For example , it is unclear if a desired metabolic flux up-regulation is achievable or even consistent with enzyme kinetics and/or whether it may lead to physiologically problematic metabolite concentrations . Stoichiometry-based strain design algorithms are often structured as bilevel mixed integer linear optimization problems ( MILP ) [4] , [8] , [9] , [11] , [52] . The outer level optimizes the biotechnological objective ( i . e . overproduction of target chemical ) through metabolic interventions , while the inner level optimizes the cellular objective that tries to counteract any external imposed genetic or environmental modifications [53] , [54] . Different fitness functions have been identified to simulate the cellular objective including maximization of biomass yield [4] , [8] , minimization of metabolic adjustment [5] , [54] , regulatory on-off minimization [55] , [56] , worst-case scenario [9] , [52] , or a combination thereof [11] , [57] , [58] . Details of these procedures have been reviewed elsewhere [50] , [51] . Even though they may lack important information on the enzyme kinetics of reactions these procedures have been successfully employed for the strain design of many important chemical products [59]–[64] . The need to integrate the mechanistic detail ( whenever available ) of kinetic expressions with the genome-scale scope of stoichiometric models has been recognized early by the community . Dynamic Flux Balance Analysis ( dFBA ) [65] integrates uptake kinetic expressions of the carbon substrate while optimizing biomass at every time step to apportion fluxes to the rest of the metabolic network . Several other researchers [66]–[68] extended this approach to incorporate kinetic expressions of multiple carbon sources and other nutrients into their quasi steady-state formalisms . Zhuang et al [69] and Salimi et al [70] developed the dynamic multi-species metabolic modeling ( DMMM ) approach to incorporate uptake kinetics of metabolites in stoichiometric models of a microbial consortium . Alternatively , steady-state flux distributions ( from FBA ) and stoichiometric information have been used to parameterize genome-scale kinetic models valid for small perturbations [22] , [71]–[73] . For example , Fleming et al [74] incorporated lin-log kinetic expressions from a small E . coli model ( comprised of 76 equations ) to constrain an FBA simulation . Similarly , Cotton et al [75] performed Flux Variability Analysis ( FVA ) for each flux in a small kinetic model ( by allowing the kinetic parameters to vary about their steady-state values ) and used the tighter bounds on kinetic parameters to refine flux estimation in genome-scale models . Despite these advancements on the modeling front , the use of hybrid stoichiometric/kinetic models has been left largely unexplored in the context of strain design . Here , we introduce k-OptForce , which extends the previously developed OptForce procedure [9] by bridging this gap between stoichiometry-only and kinetics-based descriptions of metabolism . This procedure seamlessly integrates the mechanistic detail afforded by kinetic models within a constraint-based optimization framework tractable for genome-scale models . Instead of relying on surrogate fitness functions such as biomass maximization or worst-case simulation for predicting flux re-directions , k-OptForce uses kinetic rate expressions to ( re ) apportion fluxes in the metabolic network . Using mechanistic models available in literature ( for example kinetic models for the central metabolism of E . coli [13] , [76] , [77] and S . cerevisiae [14] , [78] , [79] ) the allowable phenotype of both the reference and the engineered strain are characterized to be consistent with the allowable kinetic space . Subsequently , alternative genetic intervention strategies consistent with the restrictions imposed by maximum enzyme activity and kinetic regulations , as well as with the worst-case scenario of production of the desired chemical are identified using a bilevel optimization framework . We benchmarked the k-OptForce protocol for the microbial overproduction of L-serine in E . coli , and triacetic acid lactone ( TAL ) in S . cerevisiae . For the former , k-OptForce identified key regulatory bottlenecks in upper and lower glycolysis that must be overcome to redirect more flux towards L-serine , which regular OptForce fails to pinpoint . In addition , k-OptForce removed interventions identified by regular OptForce that resulted in kinetically infeasible flux re-distributions . Application of the k-OptForce for the microbial overproduction of TAL in S . cerevisiae revealed the impact of additional kinetic constraints in alleviating a severe worst-case simulation of regular OptForce , resulting in a higher prediction of TAL yield from fewer interventions as compared to regular OptForce predictions . The introduction of kinetic expressions in strain design can significantly affect the identified interventions in sometimes non-intuitive ways . In some cases additional modifications are needed to substitute interventions that cause enzyme saturation or concentration bound violations . The mechanism of action of these modifications is often subtle by alleviating substrate inhibition or draining away cofactors from competing pathways . In other cases , kinetic expressions shape flux distributions so as to favor the overproduction of the desired product requiring fewer direct interventions .
The system of ODEs is solved first to obtain steady-state fluxes for reactions in Jkin . The phenotypic space of the reference strain is then identified by iteratively maximizing and minimizing the flux of each reaction in Jstoic while keeping the fluxes of reactions in Jkin fixed at their steady-state values , and restricting the flux of any other reaction for which any experimental data is available at their experimentally determined values or ranges ( Figure 2A and Supplementary Text S1 ) . The flux ranges of the strain consistent with a desired overproduction target are similarly constructed by successively maximizing and minimizing the flux of network reactions subject to network stoichiometry , overproduction target , while also incorporating the kinetic expressions for reactions in Jkin as additional constraints . The resulting optimization formulation is shown qualitatively in Figure 2B ( see Supplementary Text S1 for detailed formulation ) . Up/down flux regulations within Jkin are modeled by modulating the corresponding maximum enzymatic reaction rates using a scalar z to denote the maximum allowable departure from their reference values . A value for z of zero denotes a knock-out , a value less one implies a down-regulation whereas a value greater than one an up-regulation . The metabolite concentration ranges involved in the kinetic expressions are selected by selecting a percent allowed departure from the values obtained by solving the system of ODEs ( e . g . , +/−50% ) and/or experimentally derived measurements . Nonlinear kinetic formalisms give rise to nonconvex nonlinear optimization problems ( NLP ) for identifying the flux ranges of the overproducing strain . These problems are solved to optimality using the global optimization solver BARON [80] accessed through GAMS . It is important to note that the kinetic expressions significantly restrict the range of allowable flux values consistent with experimental data and/or an overproduction target compared to flux ranges constrained only by stoichiometry . We quantify the average extent of this contraction by defining the Average Bound Contraction ( ABC ) factor:Here and denote the respective lower and upper bounds for the fluxes consistent with the overproduction target when only stoichiometry is used and and represent the same bounds upon the incorporation of kinetic information . Interestingly , kinetic information for reactions in Jkin propagates into Jstoic leading to bound contraction even for reactions with stoichiometry-only description . Tighter description of allowable flux ranges for the reference and overproducing strains allow for sharper elucidation of reactions whose flux must change to meet the imposed target ( i . e . , MUST sets ) . Similar to the OptForce procedure [9] , by contrasting the flux space of the wild-type network with that of the overproducing strain , the sets of reactions that must be up-regulated ( MUSTU ) , down-regulated ( MUSTL ) , or be knocked out ( MUSTX ) are identified ( see supplementary text of Ranganathan et al [9] for details ) . This procedure could be extended to identify higher order MUST sets ( e . g . , MUST Doubles , Triples etc . ) where instead of comparing the flux ranges for individual reactions , we contrast the sum and/or difference of two or more fluxes ( depending on the order ) between the reference and the desired phenotypes . For example , this procedure can elucidate MUSTUU , MUSTLL and MUSTUL sets ( see supplementary text of Ranganathan et al [9] for details ) . FORCE set is the minimum set of reactions ( and by extension genetic ) manipulations selected from within the MUST sets whose direct manipulation ( i . e . , updating of lower or upper bounds ) ensures production of the desired chemical beyond a desired target even under the worst-case scenario where fluxes are re-apportioned to drain flux away from the target product . The worst-case scenario is mathematically described by extending the bilevel optimization problem used for original OptForce [9] , as shown in Figure 3 ( also see Supplementary Text S1 for the detailed procedure ) . The outer problem maximizes the flux towards the desired chemical consistent with reaction kinetics and stoichiometry . Binary variables yL , kin and yU , kin associated with the MUSTL and MUSTU sets of reactions in Jkin respectively , are used to control the effect of engineering modifications to the corresponding . If yL , kin = 1 then for that reaction can be down-regulated to a value between 0 and its wild-type . If yU , kin = 1 then for that reaction can be up-regulated to a value between and . Otherwise , is kept unaffected at the reference value . As in the original OptForce procedure , the inner problem simulates the worst-case scenario by minimizing product formation but only for the reactions in Jstoic . A separate set of binary variables yL , stoic and yU , stoic identify interventions in Jstoic required to guarantee a non-zero yield of the target chemical consistent with the flux distribution in Jkin . It is important to note that the metabolic fluxes in Jkin remain unaffected by the worst-case simulation of the reactions in Jstoic in the inner problem . This bilevel formulation is converted into a single level mixed integer nonlinear problem ( MINLP ) using the conditions of strong duality . We construct the dual of the inner problem ( called the primal ) , and add all the dual constraints , along with those of the primal , to the outer optimization problem . Since all the nonlinear kinetic expressions are present in the outer problem , the inner ( primal ) problem is linear in the reaction fluxes vj . The fitness function of the inner problem is imposed setting the objectives of the primal and the dual equal to each other . It is to be noted here that the dualization of the flux variable for each reaction in Jkin introduces a bilinear term to the single-level formulation . This can be avoided by imposing the Karush-Kuhn-Tucker ( KKT ) conditions of complementary slackness between the primal constraints and their dual variables . This leads to the introduction of a binary variable for each constraint in the primal problem which is generally more tractable than the original bilinear constraints ( see Supplementary Text S1 for detailed formulations ) . The above described sequence of equivalent problem re-formulations yields a single-level nonlinear MINLP ( k-OptForce ) . The single-level k-OptForce optimization is successively solved using the global solver BARON [80] for an increasing number of interventions ( by increasing ) until the target yield is met . Due to the nonconvex nature of the kinetic expressions and the large number of binary variables the resulting MINLP equivalent representation of the bilevel optimization problem may become computationally intractable . For these cases , we exploit the natural hierarchy of the model by first selecting interventions within Jkin and subsequently within Jstoic ( see Figure 4 and Supplementary Text S1 for the algorithmic details ) . In the first step , a nonconvex optimization problem is solved to identify the minimum number of manipulations in Jkin that are consistent with the overproduction target ( Figure 4A ) . Keeping the fluxes in Jkin fixed at their optimized values the flux ranges of the overproducing strain are then re-calculated and the FORCE set for reactions in Jstoic are then identified ( see Step 2 ) as in regular OptForce [9] ( Figure 4B ) . By solving two separate problems the computational burden is significantly reduced at the expense of potentially missing synergistic interventions that share reactions between Jkin and Jstoic . It is to be noted that while the illustrated formulation only targets changes in for reactions in Jkin the same analysis could be applied for the modulation of other kinetic parameters ( e . g . , Km , KI etc . ) in the model . Once the FORCE set of interventions are identified ( along with alternative manipulation strategies ) , it is important to manually curate the results to understand the underlying metabolic reason behind each intervention . This is necessary since k-OptForce makes use of not just stoichiometry which imposes straightforward connections between reactants and products but also kinetics that introduce complex nonlinear couplings often between distal reactions through metabolite pools . While it is not possible to put forth an invariant set of rules for all applications , the following checks can be useful in general: ( 1 ) check whether any metabolite participating in affected reactions is hitting lower or upper concentration bounds , ( 2 ) identify if a common metabolite is fixing the branching ratio of fluxes in two pathways , ( 3 ) resolve whether a metabolite is limiting the flux of a reaction through substrate-level inhibition , ( 4 ) confirm if the flux in a pathway has been restricted because the vmax of one of the reactions has hit its upper bound , and , ( 5 ) analyze all alternate intervention strategies to identify common metabolites and/or enzymes that are being targeted .
L-Serine is a non-essential amino acid and a precursor for other amino acids such as cysteine , tryptophan and glycine . It also participates in the biosynthesis of purines and pyrimidines , and serves as an intermediate for phospholipids , sphingolipids and folate synthesis for several microorganisms [81] , [82] . The synthesis of L-serine in microorganisms such as Escherichia coli and Corynbacterium consists of a three-step pathway branching out of the glycolytic intermediate 3-phosphoglycerate ( 3pg ) . 3pg is converted to 3-phosphohydroxypyruvate ( 3php ) by phosphoglycerate dehydrogenase ( PGCD , EC 1 . 1 . 1 . 95 ) , and phosphoserine transaminase ( PSERT , EC 2 . 6 . 1 . 52 ) catalyzes the conversion of 3-phosphohydroxypyruvate ( 3php ) to L-phosphoserine ( pser-L ) using L-glutamate as the amino acid donor . In the last step , phosphoserine phosphatase ( PSP , EC# 3 . 1 . 3 . 3 ) catalyzes the final conversion to L-serine ( see Figure 5 ) . We used the genome-scale iAF1260 model of E . coli [83] as the stoichiometric model for our simulations . The kinetic rate expressions for reactions of central metabolism were extracted from Chassagnole et al [76] . This kinetic model , which has been used before in variety of studies [5] , [36] , [39] , [84] , consists of 25 metabolites and 25 reactions from glycolysis and pentose phosphate pathway ( see Supplementary Material S1 ) . All simulations were carried out in aerobic minimal medium with glucose as the sole carbon source . The reference strain ( i . e . , wild-type E . coli ) flux ranges were identified by finding the maximum flux variability in the entire network while keeping the fluxes in Jkin fixed at the steady-state values obtained by solving the system of ODEs for the kinetic model ( see Supplementary Figure S1 ) . The L-serine overproducing network flux ranges were calculated for a target of 90% maximum theoretical yield ( i . e . , 180 mol/100 mol glucose uptake ) . The minimum biomass production was kept at 10% of its maximum achievable . The maximum enzyme activity vmax of reactions in Jkin was allowed to vary from zero to two-fold up-regulation of its reference activity ( i . e . , z = 2 ) . Also , the concentration of metabolites in Ikin was allowed to vary within a two-fold range from their steady-state concentrations in the reference strain . Figure 6 illustrates the reduction in flux ranges in the overproducing phenotype after the introduction of the kinetic constraints when compared with a stoichiometry-only description . The average bound contraction ( ABC ) was 52% for the fluxes in Jkin . For example , the flux of glucose 6-phosphate dehydrogenase ( G6PDH ) in oxidative pentose phosphate ( PP ) pathway consistent with the imposed L-serine overproduction ranged from 0 to 136 mmol gDW−1hr−1 when constrained by only stoichiometry and from 0 to 62 mmol gDW−1hr−1 when imposing also kinetic information . This range reduction is due to the limitations of the maximum enzyme activity as well as metabolic concentrations of glucose-6-phosphate ( g6p ) regulating G6PDH flux . The restrictions implied by kinetics also propagate throughout the stoichiometric part of the network leading to an on average ten percent range contraction for reactions in Jstoic . For example , the fluxes ranges in 2-oxogluterate dehydrogenase ( AKGDH ) and succinate dehydrogenase ( SUCD ) in the TCA cycle decreased 7 . 4% and 6% , respectively as a direct consequence of the flux range reduction for pyruvate dehydrogenase ( PDH ) in Jkin which supplies acetyl-CoA ( accoa ) to TCA cycle . As a result of the tighter flux ranges in the overproducing network ( and characterization of base strain ) many more reactions are identified that must depart from their original ranges ( i . e . , MUST sets ) compared to regular OptForce both in MUSTU ( 38 vs . 3 ) and MUSTL ( 293 vs . 108 ) sets . For example , up-regulation of glucose-6-phosphate isomerase ( PGI ) in upper glycolysis supplies more flux towards 3pg and L-serine production . The flux range for PGI in the overproducing phenotype ( −36–100 mmol gDW−1hr−1 ) was wide enough to overlap with its reference flux value ( 35 mmol gDW−1hr−1 ) suggesting PGI up-regulation is not necessary for L-serine overproduction . In contrast , using k-OptForce we find that the flux range of PGI in the overproducing phenotype is restricted to 38–98 mmol gDW−1hr−1 which does not include the reference value of 35 mmol gDW−1hr−1 . This implies that it is impossible to produce L-serine at 90% theoretical yield without directly ( or indirectly ) increasing the flux through PGI which becomes a member of MUSTU . We also observe a significant increase in the number of reactions in MUSTL . This is because the kinetic expressions in Jkin fix the branching ratios for fluxes emanating from metabolites in Ikin . As a result , many reactions in Jstoic involving metabolites participating in reactions from Jkin appear as down-regulations . For example , k-OptForce identifies that the acetyl-CoA carboxylase ( ACCOAC ) flux , which branches away from pyr and accoa towards membrane lipid metabolism must decrease ( i . e . , MUSTL ) as it goes from ( 9–527 ) mmol gDW−1hr−1 in the reference strain to ( 2 . 1–2 . 3 ) mmol gDW−1hr−1 in the overproducing strain . Figure 7 summarizes the FORCE set of reactions as predicted by the original and k-OptForce . As expected , the first intervention suggested by both procedures is an at least 20-fold up-regulation in the activity of one of the three fluxes that directly lead to the synthesis of L-serine ( i . e . , PGCD , PSERT and PSP ) . However , the remaining interventions follow completely different strategies . k-OptForce emphasizes the need to remove substrate-level inhibition by making relatively small flux changes on a number of reactions to maintain concentrations within the imposed bounds ( i . e . , two-fold changes from wild-type measurements ) . Figure 7b illustrates that it is necessary to up-regulate upper glycolysis and down-regulate lower glycolysis to divert flux towards L-serine . The upper glycolytic pathway is tightly regulated by both product metabolites and nadh [85] . The kinetic expressions in Chassagnole et al [76] encode inhibition of PGI and phosphofructokinase ( PFK ) by 6-Phospho-D-gluconate ( 6pgc ) and phosphoenolpyruvate ( pep ) . The high activity of the PP pathway and lower glycolysis in the wild-type requires elevated intracellular levels of 6pgc ( 0 . 8 mM ) and pep ( 2 . 86 mM ) to supply the fluxes through phosphogluconate dehydrogenase ( GND ) ( 63 mmol gDW−1 hr−1 ) and pyruvate dehydrogenase ( PDH ) ( 93 mmol gDW−1 hr−1 ) reactions . The high concentrations of 6pgc and pep both prevent the up-regulation of upper glycolysis and the down-regulation of lower glycolysis . To alleviate this , k-OptForce suggests removal of PDH coupled with an at least three-fold down-regulation in the activity of either transaldolase ( TALA ) or transketolase ( TKT1 ) reactions in PP pathway . Removal of PDH also allows the concentration of pep to be reduced from 2 . 8 to 1 . 43 mM thus alleviating its inhibitory effect on PFK . Likewise , down-regulation of the PP pathway activity reduces the flux in GND lowering the concentration of 6pgc ( from 0 . 8 to 0 . 4 mM ) . Removal of substrate inhibition on PFK and PGI by pep and 6pgc leads to increased flux towards L-serine . The original OptForce procedure cannot identify such interventions , as substrate inhibition is not captured through stoichiometric modeling . The inhibitory effect of 6pgc and pep cannot be completely removed due to their prescribed lower limits in concentration ( 0 . 4 mM and 1 . 43 mM respectively ) . Moreover , the upper limits on concentration of metabolites involved in upper glycolysis put an upper bound on the amount of flux that can be carried by upper glycolysis . Therefore , additional interventions are needed to meet the L-serine target yield by modulating pathways away from glycolysis . k-OptForce suggests the reversal of glycine hydroxymethyltransferase ( GHMT ) thus converting glycine to L-serine ( see Figure 7b ) . In contrast , the original OptForce predicts that the entire amount of flux required for L-serine can be supplied through the up-regulation of the serine synthase pathway as no inhibitory effect or concentration bound is considered . It is to be noted here that the forward activity of GHMT is essential in vivo [86] , [87] . If , however , the lower limits on the concentration of 6pgc and pep are reduced to 0 . 35 mM and 1 . 3 mM respectively , their inhibitory effect on upper glycolysis is alleviated sufficiently to route all the flux required for L-serine production through the serine synthase pathway . The upper glycolytic flux of PGI increases from 78 to 80 mmol gDW−1 hr−1 and the PP flux is down-regulated further ( from 22 to 20 mmol gDW−1 hr−1 ) to provide the extra flux for L-serine ( results not shown here ) . As a result , k-OptForce suggests down-regulation of GHMT by at-least 3 folds from its reference flux instead of its reversal . All other interventions remain un-altered . The remaining interventions suggested by k-OptForce aim at preventing the drain of metabolic flux from L-serine . Removal of L-serine deaminase ( SERD_L ) prevents the conversion of L-serine to pyruvate . This is followed by an at least six-fold down-regulation ( from 60 to 9 mmol gDW−1 hr−1 ) of either citrate synthase ( CS ) or succinate dehydrogenase ( SUCD ) reactions to reduce the TCA cycle activity which arrests ATP production in the network . This prevents the conversion of L-serine to acetaldehyde whose activity requires five units of ATP per unit of flux . The original OptForce achieves the same goal by simply down-regulating the transport of oxygen and up-regulating the transport of ammonium into the cell . These interventions were not chosen by k-OptForce as they lead to flux values that are inconsistent with the kinetic expressions in Jkin . Consistent with k-OptForce predictions , metabolic engineering studies on C . glutamicum have revealed that overexpression of the serA/B and C encoding for the three enzymes in the L-serine production pathway have a positive , though small , effect on L-serine production [88] , [89] . Removal of sdaA encoding for the SERD_L reaction , coupled with up-regulation of the L-serine pathway have been reported to lead to higher L-serine yields [89] consistent with k-OptForce predictions . Other studies have shown that down-regulation of GHMT reaction through the removal of glyR regulator further improves L-serine production [90] . In a recent study , overexpression of pgk was shown to divert more flux towards L-serine in C . glutamicum [91] . This could be viewed as an alternative strategy to the one suggested by k-OptForce involving alleviation of the substrate level inhibition of upper glycolysis through down-regulation of PP and lower glycolytic flux . It must be emphasized that the k-OptForce results depend heavily on the accuracy of the rate expressions of the kinetic model . For example , it has been found in both E . coli and C . glutamicum , that the activity of PGCD is feedback inhibited by L-serine [81] , [92] . Alleviating this feedback regulation significantly improves production of L-serine [91] . However , k-OptForce cannot capture this regulation since the adopted kinetic model does not include this inhibitory effect . Accordingly , k-OptForce predictions must be carefully scrutinized to identify the driving forces for the identified interventions ( e . g . , substrate inhibition removal , ATP drain , cofactor sequestering , concentration increase , etc . ) and the reason for the omission of seemingly straightforward interventions ( e . g . , concentration bound violation , inadequate vmax , lethal deletion , cofactor imbalance , etc . ) . In addition to suggesting intervention strategies consistent with the kinetic constraints in the network , k-OptForce also pinpoints which ones and how original OptForce interventions violate network kinetics . For example , the original OptForce framework suggested the reversal of lower glycolytic reactions that converge to 3pg . This is accomplished by removing PGI and either GND , TKT1 or TALA in PP pathway to reroute the metabolic flux toward 3pg and pyruvate through E-D pathway by using 2-dehydro-3-deoxy-phosphogluconate aldolase ( EDA ) . Reversal of enolase ( ENO ) and phosphoglycerate mutase ( PGM ) in lower glycolysis converts pyruvate to 3pg . k-OptForce finds that this redirection is not feasible . Reversible reactions PGM and ENO rely on the relative concentrations of its reactants and products to inform their directionality . Their reversal , to the extent suggested by the original OptForce procedure requires the respective metabolic levels of 2-phosphoglycerate ( 2pg ) and pep to increase to 1 . 162 and 6 . 05 mM respectively , beyond the imposed upper limits of ( 0 . 856 and 4 . 726 mM respectively ) . Therefore , k-OptForce provides both a check on stoichiometry-only derived interventions and more importantly quantifies the impact of flux redirections on metabolite concentrations and required enzyme levels . Previous reports [93] on the sensitivity analysis of the E . coli kinetic model by Chassagnole et al [76] showed that simulation results are only sensitive to the values of nine ( out of 25 ) enzyme activities in the model . In light of this analysis , we perturbed the enzyme activities of two sensitive ones ( and ) and two rather insensitive ( and ) by +/−20% from their reference levels and repeated the k-OptForce calculations . Results showed that apart from up-perturbation , the remaining interventions ( both up-perturbation and down-perturbation ) were identical to the original results . Increasing the value of , which is one of the most highly sensitive parameters in the model [93] , increased the PP flux for the reference phenotype by 11% ( from 63 mmol gDW−1hr−1 to 70 mmol gDW−1hr−1 ) , as the glycolytic pathway was inhibited by increased 6pgc concentration . As a result , down-regulation of GND was suggested as an additional intervention to reduce the increased PP activity and route more flux from glycolysis towards L-serine . In all other cases metabolite concentrations and fluxes in the kinetic model were minimally affected by perturbations in enzyme activity . 4-Hydroxy-6-methyl-2-pyrone , commonly known as TAL , is a precursor for the production of phloroglucinol [94] , which is an intermediate for many products such as 1 , 3 , 5- triamino-2 , 4 , 6-trinitrobenzene ( TATB ) and resorcinol [95] . Synthesis of TAL [96] in both E . coli and S . cerevisiae has been explored [97] [98] . Because neither E . coli nor S . cerevisiae can natively synthesize TAL , production routes for TAL rely on the heterologous expression of non-native enzymes such as 2-pyrone synthase ( 2-PS ) ( found in Gerbera hybrida ) [96] or a genetically modified 6-methylsalycilic acid synthase ( 6-MSAS ) [96] , [97] and fasB [96] , [98] with their ketoreductase domains deactivated . These efforts have led to TAL yields in S . cerevisiae of only up to 6% of the theoretical maximum ( with a titer of 1 . 8 g/l ) in glucose medium . Figure 8 shows the targeted pathway for TAL synthesis in S . cerevisiae . We used the iAZ900 model [99] of S . cerevisiae as the stoichiometric network of metabolism . The kinetic expressions for reactions in central metabolism were imported from the kinetic model of central metabolism of S . cerevisiae described by van Eunen et al [79] . The model consists of twelve metabolites and twelve reactions , for the glycolytic pathway and the conversion of pyruvate to ethanol . Since the kinetic model did not include drains for amino acids from the central metabolic pathway metabolites ( g6p , f6p , g3p , 3pg , pep ) , we added drains ( similar to the method used in Chassagnole et al [76] ) using MFA information of S . cerevisiae central metabolism from Gombert et al [100] to ensure biomass production ( see Supplementary Material S1 ) . As in the first example , we allowed for up to two-fold changes in the metabolite concentrations and vmax from their wild-type values . Contrary to the previous example , here the kinetic expressions do not restrict further the flux ranges as the ABC metric ( see Methods ) for all Jkin and Jstoic fluxes is zero . As a result , we find no difference in the MUSTU ( 19 reactions ) and MUSTL ( 61 reactions ) sets for kinetic and original OptForce . This is due to the relatively few fluxes with kinetic expressions and the already fairly tight flux ranges achieved by stoichiometry-alone . For example , the PGI flux varies within the narrow range between 91 and 97 mmol gDW−1hr−1 for a 90% yield for TAL even when no kinetic expressions are used . This is because the imposed high production target for TAL largely fixes the flow in glycolysis . As a consequence of negligible contraction in flux ranges due to the kinetic constraints , no difference in predicted MUST sets by regular and k-OptForce is observed . Figure 9 compares the FORCE sets and the respective guaranteed yield of TAL as suggested by original and k-OptForce . In general , both procedures suggest strategies that increase the availability of precursors accoa and malonyl-CoA ( malcoa ) , up-regulating glycolysis , down-regulating PPP , and reducing nadph . However , while the original OptForce suggests that at least four interventions are required to achieve a 35% yield for TAL , k-OptForce suggests that a yield of 90% is achievable by only two interventions . Not surprisingly , both procedures suggest the up-regulation of the ACCOAC ( by at least nine-fold of its reference flux ) to increase the availability of the direct TAL precursor malonyl-CoA . The glycolytic flux is also up-regulated to divert flux from the PP pathway towards TAL . k-OptForce identifies that the kinetic expressions work in concert with the overproduction goal ( given the imposed concentration ranges ) without the need for any direct enzymatic interventions for reactions in Jkin . Figure 10 illustrates the required changes in metabolite concentrations in the overproducing network as predicted by k-OptForce . Elevated concentrations of metabolites in glycolysis lead to an increase the flux towards TAL . For example , the concentration of g6p in the upper glycolysis is increased by 8% ( from 1 . 41 to 1 . 55 mM ) leading to more flux through PGI ( from 78 to 96 mmol gDW−1 hr−1 ) . This eliminates all flux through G6PDH and PP ( from 19 to 0 mmol gDW−1 hr−1 ) to maintain the steady-state metabolite balance of g6p . Without the benefit of any kinetics , the original OptForce suggests the removal of G6PDH reaction as a requirement for down-regulating the PP pathway . k-OptForce also requires that the concentrations of glyceraldehyde 3-phosphate ( g3p ) and 3pg be elevated by 6% and 31% , respectively from their reference states to up-regulate the lower glycolysis flux from 168 to 192 mmol gDW−1 hr−1 . This re-direction in glycolysis prevents the loss of metabolic flux towards glycerol synthesis . Instead , the original OptForce procedure suggests the removal of glycerol-3-phosphatase ( G3PT ) in the glycerol synthesis pathway to serve the same purpose and channel the flux towards lower glycolysis and TAL . The fatty acid synthase is in direct competition to TAL production . It uses the same precursors as TAL ( i . e . , accoa and malcoa ) to form medium chain fatty acids . Activity of fatty acid synthase requires the cofactor nadph for the reductive steps in the pathway . Not surprisingly , both kinetic and original OptForce identify strategies to lower the availability of nadph . k-OptForce achieves this by suggesting a 20 fold up-regulation ( from 3 . 5 to 69 mmol gDW−1 hr−1 ) in the flux of aldehyde dehydrogenase ( ALDD ) which converts acetaldehyde towards acetate . The major routes of acetaldehyde production in S . cerevisiae are either from direct decarboxylation of pyruvate through pyruvate decarboxylase ( PYRDC ) or through the alternate threonine synthesis pathway followed by the cleavage of threonine by threonine aldolase ( THRD ) to acetaldehyde and glycine ( see Figure 9B ) . The threonine synthesis pathway is favored in TAL overproduction as it consumes one unit of nadph for every unit of flux . We note that the fluxes in PYRDC and alcohol dehydrogenase ( ALCD ) are fixed by the kinetic constraints in Jkin . Therefore , up-regulation of ALDD causes most of pyruvate to be routed through the threonine production pathway ( to maintain the steady-state conservation of acetaldehyde ) resulting in a decrease in nadph levels . The original OptForce does not arrive at this intervention as the kinetic control on the fluxes of PYRDC and ALCD is not captured . It instead suggests the removal of cytosolic isocitrate dehydrogenase ( ICDHy ) to reduce the nadph production , and thus arrests fatty acid synthesis . Unlike the previous example where k-OptForce required more interventions for the same overproduction target than the original OptForce , here the reverse trend is observed . The predicted yield for TAL by the original OptForce is only 35% of its theoretical maximum after four interventions whereas k-OptForce reaches 90% of theoretical maximum with only two manipulations . This is because the incorporation of kinetic information pushes metabolic flux in the direction that is needed for overproduction and away from the “worst-case” behavior . The steady-state balances of metabolites in a kinetic model [76] , [79] ( with as many metabolites as rate equations ) form a square-system of equations with zero degrees of freedom . Assuming that there are no multiple steady-states due to the nonlinearity of the kinetic expressions , steady-state fluxes or concentrations cannot change unless accompanied by alterations in kinetic parameters ( e . g . enzyme activities vmax ) . However , when a kinetic model is integrated with a stoichiometric genome-scale model , reactions in Jstoic that involve metabolites present in Ikin add in effect additional degrees of freedom to the square-system of equations thus decoupling metabolite concentrations from enzyme activities . As a result metabolite concentrations can change in such a way that fluxes are altered without requiring any enzymatic interventions as observed in the TAL overproduction example . In the absence of kinetic expressions for all reactions associated with the metabolites in Ikin a number of degrees of freedom remain for the metabolite concentrations . To avoid drastic concentration changes in response to the overproduction goal we explored penalizing deviations of metabolite concentrations from their reference steady-state values using a weight penalty factor ε . This posture in essence imposes a homeostasis term in the optimization objective function . The outer objective function of the bilevel formulation for identifying FORCE sets is thus modified as follows:The first term in the objective function is the flux of the desired chemical , scaled by its theoretical maximum flux in the network . The second term is the average fractional departure of the metabolites in Ikin ( ) from their reference values ( ) . Mkin represents the total number of metabolites in Ikin . When using the two-step procedure for identifying MUST sets ( see Methods and Supplementary Material S1 ) , the objective function for the first step is modified as follows:In this objective function , the first term is the sum of interventions in Jkin , scaled by the total number of reactions in Jkin identified in MUST sets ( MUSTkin ) . No other changes are made in the formulation of the algorithms . We tested this modified formulation on TAL production in S . cerevisiae . Figure 11 describes the effect of penalizing concentration departures on enzymatic interventions in Jkin for the overproduction of TAL in S . cerevisiae . We varied ε from 0 . 1 ( low penalty ) to 0 . 9 ( very high penalty ) on the identified interventions . Up to a ε value of 0 . 6 , the penalty is not high enough to require direct interventions instead of concentration changes . For a ε value of 0 . 7 k-OptForce identified up-regulation of enolase ( ENO ) while maintaining the average deviation in concentration to 0 . 1053 . Note that without the use of the penalty term the concentrations for 3pg and 2pg have to be elevated by 31% and 27% respectively , from their reference levels to redirect more flux through the lower glycolytic reactions of glyceraldehyde-3-phosphate dehydrogenase ( PGM ) and ENO . For ε = 0 . 8 the metabolite concentrations remain even closer to their reference levels ( average deviation is 0 . 0754 ) thereby requiring the up-regulation of glyceraldehyde-3-phosphate dehydrogenase ( GAPD ) in addition to ENO . For ε equal to or greater than 0 . 9 at least 6 additional enzymatic interventions in Jkin are necessary to increase glycolytic flux while concentrations remain very close to their reference values . By increasing the value of the penalty ε enables drawing trade-offs between allowable concentration changes and minimality of needed interventions for an overproduction goal while also providing a prioritization strategy for implementing engineering interventions . The conversion from cytosolic pyruvate to acetyl-CoA ( precursor for TAL ) in yeast follows a long and tightly regulated path involving the intermediate production of acetaldehyde and acetate [101] . We sought to computationally explore the use of a direct route from pyruvate to acetyl-CoA by adding a heterologous cytosolic PDH from E . coli in S . cerevisiae that directly converts pyruvate to acetyl-CoA . Note that S . cerevisiae has a pyruvate dehydrogenase activity in mitochondria but not in cytosol . The PDH complex in E . coli uses nad as the cofactor , however , an nadp-dependent PDH enzyme ( constructed by site-directed mutagenesis in the fold of the nad-binding domain of dihydrolipoamide dehydrogenase ) has also been expressed in E . coli with identical kinetic properties [102] . The maximum theoretical yield of TAL using the nadp-dependent PDH enzyme increased by 40% . By bypassing the multi-step conversion of pyruvate to acetyl-CoA , two ATP equivalents of energy are conserved . No such maximum yield improvements are found for the nad-dependent PDH due to nad imbalance in the cytosol . The kinetic expression for PDH was extracted from the kinetic model of E . coli proposed in Chassagnole et al . [76] . The interventions predicted by k-OptForce for maximizing TAL production are shown in Figure 12 . Upon addition of the heterologous PDH , the entire amount of flux towards TAL production is routed through PDH . This eliminates the ACS activity that previously drained ATP . Pyruvate decarboxylase ( PYRDC ) is now down-regulated , but its activity is not reduced to zero . The entire flux of PYRDC goes towards ethanol production to regenerate NAD and maintain the cofactor balance in cytosol [101] . Instead of up-regulating ALDD , k-OptForce identifies an alternative intervention to lower nadph availability by up-regulating either aspartate kinase ( ASPK ) , threonine synthase ( THRS ) or cystathionine synthase ( METB1 ) in the hydroxybutyrate production pathway .
k-OptForce integrates kinetic relations ( whenever available ) with stoichiometry based models to identify genetic perturbations that are consistent with enzyme expressions and metabolite concentrations . The resulting optimization problems pose significant computational challenges due to the bilevel nature of the formulation and the nonconvex terms in the objective function and constraints . We introduced tractable solution workflows for recasting the problems as equivalent single-level mixed-integer nonlinear optimization problems ( MINLP ) solved using the global optimization solver BARON to optimality . A hierarchical decomposition approach is also introduced for first identifying interventions within the kinetic part of the model followed by the interventions in the stoichiometry-only part of the model . As with other computational algorithms that make use of kinetics the results can be dependent upon the kinetic model structure and parameterization . Computational results show that the introduction of kinetic expressions in strain design can significantly affect the identified interventions in sometimes non-intuitive ways . In some cases additional modifications are needed to substitute interventions that cause enzyme saturation or concentration bound violations . The mechanism of action of these modifications is often subtle by alleviating substrate inhibition or draining away cofactors from competing pathways . In other cases , kinetic expressions shape flux distributions so as to favor the overproduction of the desired product requiring fewer direct interventions . Uncertainties in both the accuracy of the kinetic models and allowable concentration ranges imply that predicted interventions need to be carefully scrutinized to pinpoint the reasons for their inclusion . An important finding in this study was that concentration ranges have a very significant effect on the identified interventions . By penalizing departures of concentrations from the reference strain values substantial re-arrangements in the predicted interventions are observed . Each one of these changes can be analyzed and the underlying reason for its inclusion can be identified . A key contribution of kinetic descriptions is that they can attribute performance bottlenecks to specific concentration bounds and/or enzymatic parameter ranges bottlenecks revealing avenues for model improvement and strain optimization . The case study for L-serine overproduction in E . coli provides an example of how k-OptForce can be used to both identify interventions and trace the reason ( s ) for the exclusion of others . k-OptForce revealed that inhibition of upper glycolysis by pep and 6pgc must be alleviated to route more flux towards L-serine . This is achieved through removal of PDH and down-regulation of TALA or TKT1 respectively . Flux analysis on single gene mutant strains of E . coli show that deletion of either tala or tktA increases the flux through PGI [103] corroborating k-OptForce predictions . However , MFA data for the lpdA mutant encoding the PDH enzyme in E . coli [104] showed that the upper glycolysis is down-regulated and that the flux through PP pathway is up-regulated , contrary to k-OptForce predictions . A possible reason for this discrepancy could be due to insufficiencies in the kinetic expressions used to describe the reactions in Jkin . Alternatively , since lpdA also encodes for the activity of ICDHy and the glycine cleavage system ( GLYCL ) , its removal could be have a combined effect on down-regulating the flux in upper glycolysis , which is not captured by the kinetic model . k-OptForce , however , correctly predicts that PDH removal down-regulates lower glycolysis which is observed in the lpdA mutant strain [104] . Down-regulation of lower glycolysis is necessary to prevent the flux towards L-serine from draining away towards pyruvate . k-OptForce also sets an upper limit on the activity of the L-serine synthase pathway that the original OptForce procedure failed to pinpoint . In addition , k-OptForce prevents rearrangement of fluxes that would violate kinetic constraints and metabolite concentration limits . The original OptForce suggested reversal of lower glycolysis by rerouting metabolic flux through ED pathway . However , such re-distribution results in the upper and lower glycolysis to operate in opposite directions which cannot be achieved as the same regulator , ( i . e . , cra ) , determines the directionality of both upper ( i . e . , PFK ) and lower glycolysis ( i . e . , PYK ) [105] , [106] and represses the ED pathway upon reversal of glycolysis . k-OptForce may require fewer direct interventions if the kinetic expressions shape fluxes so as to favor the desired overproduction product as observed for the production of TAL where up-regulation of ALDD was suggested to redirect flux from pyruvate to acetyl-CoA . This is consistent with an experimental study for isoprenoid overproduction in S . cerevisiae [107] which demonstrated that overexpression of ald6 ( which encodes for the ALDD enzyme ) increases flux towards acetyl-CoA . However , a fraction of the flux from pyruvate to acetaldehyde was routed through threonine degradation without the requirement of any additional interventions . This direct intervention-free flux redistribution may be an artifact of the kinetic model and may require direct manipulations to engineer . Metabolome studies on single-gene mutant analysis in E . coli [103] revealed that , on average , internal metabolite concentrations were minimally altered from their reference concentrations as a result of the genetic perturbations . Changes in metabolic fluxes were largely the result of changes in enzyme activities . In response to this we postulated the use of a penalty term for violating homeostasis of metabolite levels . Alternatively , one could employ the method described in Smallbone et al [73] to formulate approximate lin-log expressions for all reactions associated with metabolites in Ikin that do not have a kinetic expression ( i . e . not part of Jkin ) . This would restore the square-system of equations in Jkin and recouple all metabolite concentrations with enzyme activities . The k-OptForce procedure is versatile enough to incorporate additional omics information , whenever available , to further improve prediction fidelity . For example , MFA data for reactions can be included as additional constraints to further tighten flux ranges . k-OptForce can also capture other types of metabolic regulation and select from a wider palette of direct interventions ( e . g . , enzymatic changes and transcriptional control ) such as the dynamic hybrid model of E . coli metabolism by Lee et al [108] that integrates signaling and transcriptional regulation with FBA . Temporal consideration can also be addressed be deploying k-OptForce within the dFBA framework [65] to explore the variation of metabolic interventions as a function of time alluding to RNAi type of interventions . We expect that k-OptForce predictions will help improve the breadth and accuracy of kinetic modeling descriptions by providing the quantitative means to assess model accuracy ultimately leading to improved fidelity of metabolic descriptions . | Computational strain design procedures aim at assisting metabolic engineering efforts by identifying metabolic interventions leading to the targeted overproduction of a desired chemical using network models of cellular metabolism . The effect of metabolite concentrations and substrate-level enzyme regulation cannot be captured with stoichiometry-only metabolic models and analysis methods . Here , we introduce k-OptForce , an optimization-based strain design framework incorporating the mechanistic details afforded by kinetic models , whenever available , into a genome-scale stoichiometric-based modeling formalism . The resulting optimization problems pose significant computational challenges due to the bilevel nature of the formulation and the nonconvex terms in the constraints . A tractable reformulation and solution procedure is introduced for solving the optimization problems . k-OptForce uses kinetic information to ( re ) apportion reaction fluxes in the network by identifying interventions comprised of both direct enzymatic parameter changes ( for reactions with available kinetics ) and reaction flux changes ( for reactions with only stoichiometric information ) . Our results show that the introduction of kinetic expressions can significantly alter the identified interventions compared to those identified with stoichiometry-alone analysis . In particular , additional modifications are required in some cases to avoid the violation of metabolite concentration bounds , while in other cases , the kinetic constraints yield metabolic flux distributions that favor the overproduction of the desired product thereby requiring fewer direct interventions . | [
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... | 2014 | k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design |
Amino-terminal acetylation is probably the most common protein modification in eukaryotes with as many as 50%–80% of proteins reportedly altered in this way . Here we report a systematic analysis of the predicted N-terminal processing of cytosolic proteins versus those destined to be sorted to the secretory pathway . While cytosolic proteins were profoundly biased in favour of processing , we found an equal and opposite bias against such modification for secretory proteins . Mutations in secretory signal sequences that led to their acetylation resulted in mis-sorting to the cytosol in a manner that was dependent upon the N-terminal processing machinery . Hence N-terminal acetylation represents an early determining step in the cellular sorting of nascent polypeptides that appears to be conserved across a wide range of species .
The mechanism of translational initiation dictates that eukaryotic proteins are synthesized with an amino-terminal methionine residue . In 80% of yeast proteins studied , the initiating methionine is removed to reveal a new amino-terminal residue [1] , and some 50% of proteins have their amino-terminal residue acetylated [2] , [3] . Hence rather few proteins possess an unmodified N-terminus . However , while N-terminal processing is widespread , its biological significance is not well understood . It has been suggested to contribute to differential protein stability and has recently been shown to function as a degron for certain cytosolic proteins [4] , [5] , while in a small number of cases the processed N-terminus is known to contribute directly to protein function [6]–[9] . Methionine cleavage is catalysed by methionine aminopeptidases ( MetAPs ) that act co-translationally as the N-terminus emerges from the ribosome [1] , [10] . MetAPs exhibit substrate specificity and are strongly influenced by the residue at position 2 ( P2 ) , with cleavage favoured by P2 residues with small side chains such as glycine , alanine , or serine [11] , [12] . Yeast and humans each possess two MetAPs ( MetAP1 & 2 ) , and while yeast can tolerate the loss of either enzyme , the double mutant is lethal demonstrating that methionine processing is a vital function [13] . Interestingly , MetAP2 is the target for the potent anti-angiogenic compound fumagillin that exhibits anti-tumourigenic properties [14] , [15] . Protein N-termini can also be modified by acetylation of the free α-amino group by N-α-acetyl transferases ( NATs ) . Five distinct NATs have been identified with different substrate specificities . NatA normally acetylates N-terminal G , S , A , and T residues exposed by MetAP cleavage , whereas NatB acetylates methionine residues that are followed by either D , E , or N at P2 [3] , [16] , [17] . NatC acetylates certain methionines with either L , I , W , or F at P2 , but other sequence elements influence processing in this case [18] . NatD appears to be specialised for histone N-acetylation [19] and finally NatE acetylates substrates with Leucine at P2 and Proline at P4 [20] . While most proteins remain in the cytoplasm after synthesis , others are targeted to different compartments . Those destined for the secretory pathway typically possess an N-terminal signal-sequence which directs them to the endoplasmic reticulum ( ER ) [21] . These proteins are translocated into the lumen of the ER , via the Sec61 translocon , whereupon their signal-sequence is removed by signal peptidase [22] . A subset of membrane proteins can be targeted to the ER via non-cleaved internal signal anchor or C-terminal trans-membrane segments , which act as both targeting and membrane-integration signals . N-terminal signal sequences are degenerate in primary structure but are typically 15–30 residues long , and usually comprise charged/polar residues , followed by 6–15 hydrophobic residues and a polar C-terminal region containing the cleavage site for signal peptidase [23] , [24] . In yeast , there are two pathways by which secretory proteins are targeted to the ER . The co-translational pathway is mediated by Signal Recognition Particle ( SRP ) , which recognises a signal sequence emerging from the ribosome and targets the ribosome-nascent chain ( RNC ) complex to the translocon via SRP-receptor ( SR ) [25] , [26] . The targeted ribosome then binds tightly to the cytosolic surface of Sec61p allowing the elongating polypeptide chain to be delivered directly into the translocation channel [27]–[29] . The alternative “post-translational” pathway is independent of SRP/SR [30] and targets full-length polypeptides in a reaction that requires cytosolic chaperones that maintain precursors in a translocation-competent conformation [31]–[33] . Translocation occurs via the same Sec61-channel , but in this case , targeting requires the essential integral membrane protein Sec62p that interacts with precursor and may constitute a specific receptor [34] . Mammalian cells possess a homologue of SEC62 , but this mode of translocation remains poorly characterized in metazoans [35] , [36] . Properties of the signal sequence , and in particular the hydrophobicity of the central core , determine which pathway a substrate will access , with more hydrophobic signal sequences utilizing the SRP pathway [30] . Cleavage of the signal sequence reveals a novel N-terminus for the mature translocated protein , which is located in the ER lumen and so inaccessible to the N-terminal processing enzymes . The processing status of the initiating methionine of signal sequences has largely been ignored , particularly as such N-termini are not detected in proteomic analyses . We therefore decided to investigate the N-terminal processing of signal sequences using a combination of bioinformatic and experimental approaches and find that N-terminal modification is incompatible with targeting to the ER .
Signal sequence recognition and N-terminal processing both occur co-translationally as the nascent chain emerges from the ribosome [10] , [37] . We therefore decided to investigate whether secretory proteins might be subject to N-terminal processing in a similar manner to their cytosolic counterparts . As the P2 residue is the major determinant of N-terminal processing , we first surveyed the amino acid frequency at this position for signal sequence-containing proteins versus cytosolic proteins ( Figure 1A ) . Surprisingly , we found a significantly different frequency distribution between the two sets ( p<0 . 0001 , according to the χ2 test with 18 degrees of freedom ) . Lysine , leucine , and arginine were most frequent at P2 in signal sequences but were rarely found at this position in the cytosolic set . Conversely , while serine and alanine were most frequent at P2 in cytosolic proteins , these were less evident in signal sequences . A clear pattern emerged when the ratio of frequencies were compared between the two classes of proteins ( Figure 1B ) ; small and acidic residues were strongly biased towards cytosolic proteins , whereas large and basic ones were favoured in signal sequences . The frequency of small residues at P2 in cytosolic proteins predicts that ∼72% of these proteins would be substrates for MetAP cleavage ( Figure 1C ) , in good agreement with empirical data from proteomic studies [2] . In contrast only 23% of signal sequences would be predicted to be MetAP substrates ( Figure 1C ) . Hence our data reveal that for signal sequences there appears to be a strong selection for P2 residues that would maintain the original N-terminal methionine . We next addressed whether this bias was of functional significance for ER translocation . The signal sequence of Carboxypeptidase Y ( CPY ) [38] begins with “MK” and so , like most secretory proteins in our analysis , is predicted to remain unprocessed . Rather than mutating the native P2 residue we chose to insert one of seven different amino acids between the initiator methionine and the following lysine residue ( Figure 2A ) . We then assessed the translocation efficiency of these mutants in vivo by monitoring their ER-dependent glycosylation ( Figure 2B ) . Insertion of arginine or valine had no effect on the efficiency of translocation , demonstrating that an insertion at this position does not inherently perturb signal sequence function . However , the other five insertions tested all resulted in translocation defects indicated by the accumulation of the cytosolic precursor form of preproCPY ( ppCPY ) . The most significant defects were observed for glycine , serine , and glutamate , which are three of the four residues most biased in their frequency distribution towards cytosolic proteins ( Figure 1B ) . Thus the bias observed in our bioinformatic analysis correlates with defects in translocation , thereby implying an important role for P2 in a functional signal sequence . The inhibitory effects of these various P2 residues might reflect either some simple perturbation of the signal sequence or their predicted impact on N-terminal processing . We reasoned that if processing alone were responsible for the effects , then inhibiting MetAP activity might restore translocation of the mutant proteins . We therefore analysed translocation in wild-type and Δmap1 cells in the presence of the Map2p inhibitor fumagillin ( Figures 2C and S1 ) . In wild-type ( MAP1 ) cells , fumagillin had little or no effect on the translocation of native ( MK ) CPY nor the translocation defects observed for the various insertion mutants . Similarly , the absence of Map1 alone ( Δmap1 ) had no discernible effect on any of the translocation substrates . In contrast , when Δmap1 cells were treated with fumagillin we found almost complete restoration of translocation for the MA , MC , MG , and MS mutants . All four are predicted substrates for Met-cleavage , and our data demonstrate that their inhibitory effects are entirely dependent upon MetAP activity . In contrast , ME is not a substrate for MetAP and we found that the translocation defect for this mutant persisted under these conditions . The effect of fumagillin was therefore substrate-specific , correlating precisely with the known specificity of MetAPs [11] . We therefore conclude that MetAP-dependent cleavage of a signal peptide's initiating methionine has a strong inhibitory effect on the translocation of CPY . In our analysis , the ME and MS mutations had the strongest effects on translocation ( Figure 2B ) and these P2 residues displayed extreme bias against their occurrence in natural signal sequences ( Figure 1B ) . While “ME” is not a substrate for MetAP , it is known to promote N-α-acetylation of the N-terminal methionine by NatB [6] . Likewise , the P2 serine , once revealed by MetAP , is predicted to be N-α-acetylated by NatA . We therefore tested whether acetylation might be the key determinant affecting translocation by analysing translocation efficiencies in either NatA ( Δard1 ) or NatB ( Δnat3 ) -deficient strains ( Figure 3 ) . In Δard1 cells , translocation of MS-CPY appeared largely restored while the ME mutant remained unaffected . The converse was observed in the Δnat3 strain . Importantly , the ability of the different Nat mutants to rescue precursor translocation matched precisely the substrate specificities of NatA and NatB for MS and ME , respectively . Moreover , the observation that inhibition of MAP activity specifically rescues the translocation of NatA substrates is entirely consistent with methionine cleavage being a prerequisite for NatA-dependent acetylation . Thus , it is the N-α-acetylation of these substrates that is the major determinant in the inhibition of translocation in vivo . We next examined the effect of mutants predicted to induce acetylation of two independent ER translocation substrates , namely Pdi1p and prepro-alpha factor ( ppαF ) ( Figure 4A and 4B ) . The signal sequence of Pdi1p begins MK and hence is not predicted to be a substrate for MetAP or N-acetylation [2] . MSK and MEK mutations both led to accumulation of non-translocated precursor and a reduction of fully translocated glycosylated Pdi1p at steady state . Furthermore , analysis by mass-spectrometry confirmed that the MSK mutant of pPdi1 was methionine-processed and N-acetylated in vivo , as predicted ( Figure S2 ) . No peptides corresponding to an unmodified N-terminus were detected . Wild-type ppαF , which begins MR , is efficiently translocated and secreted . In contrast an MS mutant , which is a predicted substrate for NatA , accumulated in cells , as the non-translocated precursor . Hence , the inhibitory effect of acetylation appears widespread and not restricted to CPY . Next we sought to reconstitute this phenomenon in vitro using ppαF . We translated both wild-type ( MR ) and MS mutant forms of ppαF in reticulocyte lysate and then incubated these precursors with yeast microsomes ( Figures 4C and S3 ) . We observed microsome-dependent translocation and glycosylation of wild-type ppαF but found no evidence of translocation of the MS mutant . Thus the inhibitory effect of the P2 Serine can also be reconstituted in vitro . Our data thus far indicate that MS-ppαF would be acetylated following processing by MetAP . To verify this directly we performed in vitro translations in the presence of 1-[14C]-acetyl-CoA and detected incorporation of radiolabel into MS-ppαF but not wild-type ( Figure 4D ) . For this experiment , we utilised a ppαF variant where all lysines have been mutated to arginine; hence , the only primary amine potentially available for acetylation is the N-terminal αNH2 group . These in vitro data demonstrate directly that the MS mutant form of ppαF is indeed acetylated as predicted and support our hypothesis that N-terminal acetylation inhibits ER translocation . Charge distribution across the signal sequence has been shown to affect translocation efficiency [39] . N-α-acetylation of the signal peptide would reduce the overall positive charge of the N-terminus by +1 , and therefore one potentially trivial explanation might be that it is the loss of positive charge , rather than acetylation per se , that inhibited translocation . However , we can exclude this possibility given that the insertion of an additional arginine residue at position 3 ( MSRR ) , which restores the overall charge of the N-region following N-α-acetylation , also failed to translocate ( Figure S3 ) . We next wished to assess the stage at which the translocation of an acetylated MS substrate is blocked . We incubated in vitro translated wild-type ( MR ) ppαF with yeast microsomes in the absence of ATP , which permits targeting to Sec61 , but not subsequent translocation . Using site-specific photocross-linking probes incorporated into the signal sequence , we could detect a complex spectrum of uv-induced adducts as has been reported previously ( Figure 4E; [40] ) . An adduct of ∼50 kD could be readily immunoprecipitated with Sec61p antisera , indicating the engagement of precursor with the translocon . In striking contrast , the MS mutant completely failed to crosslink with Sec61p . Hence we conclude that targeting arrests at a step prior to the interaction of the precursor with the translocon . There are two pathways by which secretory precursors can be targeted to the ER; some precursors follow a post-translational Sec62p-dependent pathway , while substrates with more hydrophobic signal sequences utilise a co-translational SRP-dependent mechanism [25] , [30] . As CPY , Pdi1p , and ppαF are all translocated post-translationally , we therefore sought to compare the behaviour of an SRP-dependent substrate . We chose the well-characterised SRP-dependent substrate OPY , a variant of CPY in which the endogenous signal sequence is replaced with that of Ost1p [41] . The OPY signal sequence begins MR , and so should remain unprocessed , enabling us to perform a precisely parallel mutational analysis to that for CPY ( see Figure 2C ) . In striking contrast to CPY , we found that the introduction of various processable residues at P2 had no effect on the translocation of OPY ( Figure 5A and 5B ) . Thus the observed inhibitory effect of an MS mutation on translocation can be suppressed in the context of an SRP-dependent signal sequence . This property was not limited to the Ost1p signal sequence; co-translational translocation of the SRP-dependent substrate DHC-αF [30] , [42] into yeast microsomes using a yeast translation extract was also unaffected by the incorporation of a potentially acetylatable serine residue at P2 ( Figure S4 ) . Moreover , the well-characterized SRP-dependent substrates Sec71 and Dap2 ( DPAP B ) [30] , [43] have P2 residues of S and E , respectively , entirely consistent with our finding that NAT substrates can be tolerated by the SRP pathway . These data suggest either SRP can successfully target an acetylated substrate or alternatively such substrates might not be processed as expected . Therefore , to address this point we assessed whether or not the Ost1p signal sequence was N-terminally processed . We tested the MS mutant for the presence of any unmodified N-termini using a biotinylation assay to detect free α-NH2 groups in a protein completely lacking lysine residues . We observed no difference in the efficiency of biotinylation between wild-type ( MR ) and mutant ( MS ) suggesting that in the context of an SRP-dependent signal sequence , and contrary to expectation , the MS amino-terminal was not acetylated ( Figure 5C ) . This effect of SRP might go some way to explain the small , but not insubstantial , minority of secretory proteins predicted to be processed in our bioinformatic analysis . Consistent with this idea , we found that average peak hydrophobicity of signal sequences among this minority was significantly greater than for the majority subset of sequences ( Figure S5 ) . Overall , more than 99% of signal sequences were either not predicted to be acetylated or were sufficiently hydrophobic to interact with SRP . Having validated the biological significance of the bias observed in our bioinformatic study , we extended our analysis from yeast to higher eukaryotes ( Figure 6 ) . The pattern observed in nematodes and insects was remarkably similar to that seen in yeast , with ∼70% of signal peptides predicted to retain an unprocessed methionine compared to only 20% for the proteome as a whole [2] . The trend was similar in humans and plants , albeit less pronounced , with ∼50% of secretory N-termini predicted to remain unprocessed compared to 15% for the proteome as a whole [2] . Thus this phenomenon appears not to be restricted to fungi but is very widely conserved .
Here we describe the striking observation that yeast signal sequences display a profound bias against N-terminal processing . The bias is precisely converse to that observed in cytosolic proteins where N-terminal processing is highly favoured . Moreover , we show that this bias is of functional significance as introduction of residues at position 2 which promote N-terminal processing inhibits translocation in to the ER . Importantly this inhibition can be reversed by blocking N-terminal processing , confirming that it is the processing itself that leads to the block in translocation . The bias against N-terminal processing is not restricted to yeast but is also observed across eukaryotes , suggesting this is a widely conserved phenomenon . It is possible that other factors distinct from N-terminal processing might affect the observed bias in amino acid frequency at position 2 . We considered the potential effect of the Kozak consensus sequence that favours a G at the +4 position ( corresponding to the first base of codon 2 ) in genes optimised for translation efficiency [44] . However , while this might contribute to the bias observed among cytosolic proteins , it is unlikely to be the dominant feature since it does not explain the predominance of Serine at position 2 . Furthermore , the Kozak consensus does not have such a strong effect in yeast and it has recently been reported that the effect of the +4 position may be more important in promoting N-terminal modification than in influencing initiation efficiency [45] . A second possible factor influencing the P2 frequency distribution could be the previously reported bias for an adenine-free stretch within the signal-sequence coding region of a secretory mRNA , which is important for its nuclear export [46] . However , this also seems an unlikely explanation as lysine , with its A-rich codon ( AAA/AAG ) , is actually more frequent at position 2 of signal sequences as compared to cytosolic proteins . Critically , however , both translation initiation and nuclear mRNA export operate independently of N-terminal processing and so would not lead to translocation defects that could be reversed by N-terminal processing mutants , as we observe . Furthermore the restoration of translocation in such processing mutants shows a precise substrate dependency , ruling out rescue of translocation by some indirect effect . Hence , while we would not completely exclude a minor role for the Kozak consensus or mRNA elements in influencing the P2 residue of signal sequences , the strong correlation and clear functional effects make a bias against N-terminal processing the simplest and most likely explanation of the relative P2 residue frequency . A trivial explanation for the inhibitory effect of acetylation could be the change in charge distribution across the signal sequence , which is known to be important for targeting [39] . However , this appears unlikely , firstly as insertion of an additional positively charged residue to counteract the loss of the +1 charge following acetylation of the free amino terminus did not restore translocation ( Figure S3 ) . Secondly , translocation of the ME CPY mutant can be restored in a strain lacking NatB activity ( Δnat3 ) , which results in the same net N-terminal charge as is present in the acetylated MS CPY mutant , which fails to translocate ( Figure 3 ) . Hence simple charge distribution alone cannot explain the inhibitory effects of N-acetylation . Overall our data indicate that N-acetylation inhibits ER translocation and that most secretory proteins avoid this by virtue of a P2 residue that prevents processing . Interestingly , SRP-dependent substrates appear to evade this effect as SRP blocks N-terminal N-acetylation even in the presence of a P2 residue predicted to be a NatA substrate . SRP and NatA are both thought to contact the ribosome via the same site ( ribosomal protein Rpl25/L23a ) [47]–[49] . Hence competition for this site would provide a potential mechanistic explanation for this phenomenon . This finding also predicts that while the P2 residue is the major determinant of N-acetylation by NatA , there are scenarios where N-acetylation does not occur , despite the presence of an appropriate P2 residue . Empirical evidence for this prediction was recently provided by the global analysis of N-acetylation of the drosophila proteome [50] . Comparison of predicted N-terminal processing of signal sequences across other species indicates an almost identical bias for nematodes and drosophila as seen in yeast . In plants and humans , the bias is still present but is less marked . Interestingly , a bias against predicted N-terminal processing ( 73% ) has also been noted for prokaryotic signal sequences [51] . Hence the bias against processing of signal sequences appears widespread and not restricted to yeast . Current dogma suggests that the SRP-dependent targeting pathway is more pervasive in mammals . As SRP appears to allow substrates to evade the effects of acetylation , this may well explain why the bias against N-terminal processing is less pronounced in humans . Nevertheless , homologues of the SRP-independent pathway components Sec62 and Sec63 are present in mammals and form complexes with the Sec61 translocon [35] , [36] . Furthermore , both mammalian and drosophila Sec62 can functionally replace their yeast counterpart [52] , [53] . These observations , combined with our observed bias against N-terminal processing in these organisms , suggest that although SRP-dependent targeting is perhaps more dominant , Sec62-dependent translocation still likely occurs . Identification of substrates for this pathway remains an important question to be addressed in the future . What might be the reason as to why secretory and cytosolic proteins have a precisely converse bias for N-acetylation ? Cytosolic proteins , once synthesized , typically fold rapidly to their final tertiary structure in the cytoplasm . In contrast , secretory precursors must reach the translocon in an unfolded state in order to be competent for translocation . Post-translationally translocated substrates achieve this by their interactions with cytosolic chaperones that prevent their folding within the cytoplasm [32] . SRP-dependent substrates are targeted co-translationally and so reach the translocon as short nascent chains , thus eliminating the possibility of folding in the cytoplasm . It is not known what causes translocation substrates to recruit these chaperones , but our data allow us to propose a model in which acetylation determines the fate of nascent polypeptides . We speculate that acetylation identifies nascent polypeptides , very early in their synthesis , as being destined to fold in the cytoplasmic compartment . Most secretory proteins are unmodified and so would be delayed in their folding sufficiently to facilitate their functional interaction with the translocon . This would be entirely consistent with our finding that acetylation blocks secretory substrate interaction with Sec61 , arresting the protein in the cytosol . Not all proteins that fold and remain in the cytosol are acetylated . It may be that such modification would be incompatible with function , but it might also be that such proteins have more complex folding requirements; for example , they might be required to fold more slowly , perhaps relying on the recruitment of specific cytosolic chaperones . An alternative biological explanation for this phenomenon could relate to a proofreading step for Sec62-dependent substrates . Unlike their SRP-dependent counterparts , Sec62-dependent signal sequences are only modestly hydrophobic [30] . It is quite likely , therefore , that globular cytosolic proteins may contain internal regions of similar hydrophobicity , which upon folding form the hydrophobic core of such proteins . Clearly , it is critical that these proteins do not translocate into the ER and become mis-sorted . Entirely consistent with this idea , it has been shown that randomly selected regions of the mature domains of both CPY and invertase ( Suc2 ) can promote translocation , albeit inefficiently , when positioned at the N-terminus [54] , [55] . A requirement for a free N-terminus proximal to the hydrophobic region could provide a mechanism to prevent internal regions of non-secretory proteins engaging the translocation machinery . Modification of the N-termini of cytosolic proteins would also help prevent mis-sorting . Internal ER targeting sequences of course exist , but they tend to be trans-membrane domains which act as signal anchor sequences; hence they are much more hydrophobic and thus promote targeting via the SRP pathway [30] . In summary , our finding that N-terminal processing inhibits ER translocation of secretory proteins identifies a non-acetylated N-terminus as a hitherto unappreciated yet general feature of signal sequences , which is necessary to promote efficient targeting of substrates to the ER translocon .
The set of S . cerevisiae signal sequence-containing proteins was obtained from the signal peptide database ( SPdb ) v 5 . 1 [56] . This set of 291 sequences was manually filtered for duplicates , dubious ORFs ( as defined by SGD ) , and proteins known to be localized to mitochondria , to yield a final filtered set of 277 ORFs . For a complete list of ORFs , see Table S1 . The P2 amino acid frequency distribution did not differ significantly between the filtered and unfiltered sets ( χ2 = 5 . 17 , 19 df ) . Graphical and statistical analysis was performed using Prism 4 . 0 ( GraphPad ) . MetAP cleavage was assumed for P2 residues A , C , G , P , S , V , and T [11] , [12] . The yeast cytosolic dataset ( Table S2 ) was generated by random selection from SGD of proteins with known cytosolic localization . Prediction of N-acetylation was performed as described previously [2]; where appropriate , the P3 residue was also taken into consideration . MN , which is only predicted to lead to N-acetylation in 55% of cases [2] , was scored as acetylated . Human and Caenorhabditis elegans signal sequence datasets were also obtained from the signal peptide database ( SPdb ) v5 . 1 [56] . Drosophila melanogaster and Arabidopsis thaliana datasets were obtained from the signal peptide website ( www . signalpeptide . de , accessed March 2010 ) . Peak hydrophobicity was determined by Kyte-Doolittle using a window size of 11 [30] , [57] . Yeast strains in this study are listed in Table S7 . GFY3 was constructed by mating Δpep4 and Δprc1 strains , sporulation of the diploid , and selection of tetrads , which had three G418-resistant spores; spores were scored for null mutations by PCR and western blotting . GFY7 was made by PCR amplification of the pFA6a-His3MX6 module [58] with appropriate primers ( Table S8 ) ; the PCR product was used to transform GFY3 and His+ colonies selected . GFY11 and GFY12 were made by PCR amplification of pAG26 [59] with appropriate primers ( Table S8 ) ; the PCR products were used to transform Δprc1 followed by selection on Hygromycin B . All deletions were confirmed by PCR . Yeast strains were grown in either YPD ( 1% yeast extract , 2% peptone , and 2% glucose ) or YNB ( 0 . 67% yeast nitrogen base , 2% glucose , and appropriate supplements ) at 30°C , with the exception of pulse-labelling of MWY63 ( sec61-3 ) , which was grown at 30°C , then shifted to 17°C for 2 h . The constructs which express ppCPY and ppOPY with position 2 insertion mutations of the signal sequence listed in Table S9 were made using the respective pairs of primers ( Table S8 ) to perform site-directed mutagenesis of pMW346 or pOPY , respectively . pGF22 , the PsiI/SphI fragment of pA11-k5 , was cloned into pEH3 to replace this portion of wild-type ppαF and thus making a lysine-free ppαF . pGF24 and pGF25 were constructed by PCR ( Table S9 ) of the Ost1 signal sequence from pOPY and pOPY-S , respectively . The PCR products were digested with EcoRI/HincII and cloned into pGF23 ( Table S9 ) to replace the ppαF signal sequence with that of Ost1 or the serine mutant version , respectively . PDI1 was amplified from genomic DNA with appropriate primers ( Table S8 ) that introduce a single C-terminal c-myc-tag . The PCR products were digested with PsiI/BamHI and were then ligated into BstZ171/BamHI sites of pMW346 , placing the PDI1-myc ORF under the control of the PRC1 promotor . pPPαF-2myc constructs were generated in a similar manner except that they contain two c-myc-tags and the PCR products generated were digested with BstZ171/BamHI . Yeast cells expressing wild-type CPY or signal sequence mutants ( Table S7 ) were grown in YNB medium with appropriate supplements to an OD600nm = 0 . 2 , where stated cells were treated with 3 µM Fumagillin ( Fluorochem ) for 30 min at 30°C prior to radio-labelling . Pulse-labelling was initiated by addition of 10 µCi of [35S] Methionine/Cysteine mix ( Perkin Elmer ) per OD600nm units of cells for 5 min at 30°C ( 20 min at 17°C for sec61-3 ) . Labelling was terminated by addition of ice cold sodium azide to a final concentration of 20 mM . For each sample 5 or 10 OD600 units of cells were harvested . Radiolabelled yeast cells were spheroplasted prior to addition of lysis buffer ( 1% SDS , 50 mM Tris-HCl , pH 7 . 4 , and 5 mM EDTA ) and then incubated at 95°C . Samples were then diluted with 5 volumes of immuno-precipitation buffer ( 62 . 5 mM Tris-HCl , pH 7 . 4 , 1 . 25% ( v/v ) Triton-X-100 , 190 mM NaCl , 6 . 25 mM EDTA ) , pre-cleared for 1 h , and then antiserum ( anti-CPY or anti-αF [60] , [61] ) added to the supernatant . After 1 h , immune complexes were recovered with Protein A sepharose for a further hour and then washed extensively prior to elution with SDS-PAGE sample buffer . Samples were then analysed by SDS-PAGE and visualised either by phosphorimaging or autoradiography . Quantification was performed with Aida image-analyzer software ( Raytek ) . Subsequent statistical analysis was performed using Prism 4 . 0 ( GraphPad ) . Samples for scintillation counting were dissociated from the sepharose with 3% SDS for 5 min at 95°C . Dissociated protein was dried onto Whatman glass GF/A filter discs and placed in 4 . 5 mL of scintillant and counted in a Tricarb 2100TR liquid scintillation counter ( Packard ) . Templates for transcription of various ppαF mRNAs were generated by PCR from plasmids pEH3 or pGF22 using appropriate primers ( Table S8 ) and transcription carried out with SP6 polymerase . Transcriptions of OpαF mRNAs were from pGF24 or pGF25 for MR and MS OpαF , respectively , and were carried out with T7 polymerase . Translations were performed in rabbit reticulocyte lysate system ( Promega ) for 30 min with the inclusion of either 2 . 04 µCi [35S] Methionine or 0 . 04 µCi 1-[14C]-Acetyl Coenzyme A ( Perkin Elmer ) per 10 µL of reaction . Translation was terminated by addition of 2 mM cycloheximide . Co-translational translocation of DHC-αF into yeast microsomes was performed using translation extracts from a strain over-expressing SRP , as described previously [42] . Preparation of yeast microsomes from a Δpep4 strain was carried out as previously described [62] . For translocation assays; 10 µL of translation reaction was incubated with 2 µL microsomes for 20 min at 30°C . Wild-type and MS K5K14ppαF were translated in rabbit reticulocyte lysate as above but in the presence of ε-4- ( 3-trifluoro-methyldiazirino ) benzoic acid ( TDBA ) -lysyl-tRNA and then used for photocross-linking assays as described [63] . Briefly , translations were terminated with 2 mM puromycin for 10 min at 30°C , and then treated with 0 . 5 mg/mL RNase A for 5 min on ice prior to depletion of ATP from the translation reaction and yeast microsomes by treatment with hexokinase/glucose . The microsomes and translation reaction were then combined , allowing targeting to occur for 15 min at 30°C . Microsomes were re-isolated by centrifugation and resuspended in membrane storage buffer . Samples were irradiated with uv light ( 365 nm , 15 mW/cm2 ) twice for 5 s and then precipitated with ethanol and analysed directly or following denaturing immuno-precipitation with Sec61 antiserum [64] . In vitro translations ( 20 µL scale ) , programmed with lysine-free OpαF mRNAs , were performed as above in the presence of [35S] methionine . Proteins were sequentially precipitated with ammonium sulphate , then ethanol . The samples were then denatured in PBS+1% SDS for 10 min at 65°C . Free N-termini were modified by treatment with 1 mM sulpho-NHS-SS-Biotin ( Pierce ) for 20 min at 37°C . After removal of free biotinylation reagent by acetone precipitation , samples were resuspended in PBS+0 . 1% SDS and then biotinylated proteins recovered on immobilized-streptavidin beads ( Pierce ) . Beads were washed 5 times with PBS+0 . 1% SDS and bound protein eluted in SDS-PAGE sample buffer . | The eukaryotic cell comprises several distinct compartments , called organelles , required to perform specific functions . The proteins in these compartments are almost always synthesised in the cytoplasm and so require complex sorting mechanisms to ensure their delivery to the appropriate organelle . Of course , not all proteins need to leave the cytoplasm since many remain there to perform cytoplasmic functions . It is well known that many proteins are modified by acetylation of their amino-terminus at a very early stage in their synthesis . We have discovered a profound difference between the likelihood of such a modification on cytoplasmic proteins and on those destined for one of the major organelles , the endoplasmic reticulum ( ER ) : whereas cytoplasmic proteins are typically acetylated , those bound for the ER are largely unmodified . Moreover , when specific ER proteins were engineered to induce their acetylation we found that their targeting to the ER was inhibited . Our data suggest that N-terminal acetylation is a major determinant in protein sorting in eukaryotes . | [
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] | 2011 | N-Terminal Acetylation Inhibits Protein Targeting to the Endoplasmic
Reticulum |
New strategies to eliminate dengue have been proposed that specifically target older Aedes aegypti mosquitoes , the proportion of the vector population that is potentially capable of transmitting dengue viruses . Evaluation of these strategies will require accurate and high-throughput methods of predicting mosquito age . We previously developed an age prediction assay for individual Ae . aegypti females based on the transcriptional profiles of a selection of age responsive genes . Here we conducted field testing of the method on Ae . aegypti that were entirely uncaged and free to engage in natural behavior . We produced “free-range” test specimens by releasing 8007 adult Ae . aegypti inside and around an isolated homestead in north Queensland , Australia , and recapturing females at two day intervals . We applied a TaqMan probe-based assay design that enabled high-throughput quantitative RT-PCR of four transcripts from three age-responsive genes and a reference gene . An age prediction model was calibrated on mosquitoes maintained in small sentinel cages , in which 68 . 8% of the variance in gene transcription measures was explained by age . The model was then used to predict the ages of the free-range females . The relationship between the predicted and actual ages achieved an R2 value of 0 . 62 for predictions of females up to 29 days old . Transcriptional profiles and age predictions were not affected by physiological variation associated with the blood feeding/egg development cycle and we show that the age grading method could be applied to differentiate between two populations of mosquitoes having a two-fold difference in mean life expectancy . The transcriptional profiles of age responsive genes facilitated age estimates of near-wild Ae . aegypti females . Our age prediction assay for Ae . aegypti provides a useful tool for the evaluation of mosquito control interventions against dengue where mosquito survivorship or lifespan reduction are crucial to their success . The approximate cost of the method was US$7 . 50 per mosquito and 60 mosquitoes could be processed in 3 days . The assay is based on conserved genes and modified versions are likely to support similar investigations of several important mosquito and other disease vectors .
The survival of mosquitoes to a relatively old age is required for the transmission of mosquito-borne diseases . Mosquito-borne pathogens such as dengue viruses and malaria parasites require a period of development or multiplication inside the mosquito ( extrinsic incubation period; EIP ) before transmission can occur . Female mosquitoes ingest the pathogen when taking a blood meal from an infected host . The pathogen must then penetrate the midgut , escape from the midgut , multiply and disseminate through the mosquito before infecting the salivary glands . Transmission may then occur when the female subsequently bites a naïve host . For many of the world's most important mosquito-borne diseases ( malaria , dengue and lymphatic filariasis ) , the EIP of the parasite or virus is long relative to the lifespan of the mosquito vector . The EIP of the dengue viruses in the primary mosquito vector , Aedes aegypti , is approximately 12–16 d [1] . Landmark epidemiological studies identified mosquito survival as a target for the prevention of mosquito borne disease and a sensitive indicator of disease activity [2]–[4] . However , the importance of mosquito longevity has seldom been directly tested because few tools exist that can accurately determine the age of wild caught mosquitoes . Mosquito control strategies adopting various microbial agents aim to reduce mosquito longevity to impact disease transmission [5]–[9] . Successful infection of Ae . aegypti with a life-shortening strain of the intracellular bacteria Wolbachia has recently been reported [9] . The infection causes a 50% reduction in Ae . aegypti longevity , is maternally inherited and has the capacity to be driven through wild mosquito populations through the mechanism of cytoplasmic incompatibility . Implementation of these strategies will require rapid and high throughput age determination of the targeted mosquito vectors to evaluate the efficacy of control . Traditional dissection based methods of age grading mosquitoes fall well short of the required accuracy and throughput required , and biochemical approaches such as the measurement of cuticular hydrocarbons lose accuracy beyond 15 d old [10]–[13] . We previously reported a method of predicting the age of adult female Ae . aegypti mosquitoes using the transcriptional profiles of age responsive genes [14] , [15] . Quantitative Reverse Transcriptase PCR ( qRT-PCR ) was used to measure the transcriptional profiles of these genes from the head and thorax of individual mosquitoes . Age predictions were then derived from the transcriptional profiles using multivariate calibration . An initial validation of the method was performed on mosquitoes that had been maintained inside 13 m3 capacity dome tents positioned inside and adjoining an elevated residence in Cairns , northern Australia . An age prediction model incorporating eight genes was reported that facilitated age predictions of female Ae . aegypti to within ±5 d of their actual age up to 19 d of age . For large scale studies , a reduced model incorporating three genes was recommended . 74 . 99% of the variation in the transcription of these genes was explained by mosquito age . However , the activity of mosquitoes in cages may have been reduced , affecting their capacity to seek out preferred micro-habitats with unknown consequences for the aging assay . Similarly , the influence of major physiological changes during the mosquito blood feeding - egg development cycle is unknown . Physiological processes associated with the mosquito gonotrophic cycle involve extensive changes in gene expression [16]–[19] . Here we report the evaluation of transcriptional age grading on Ae . aegypti that were entirely uncaged as adult mosquitoes ( called free-range mosquitoes ) . By conducting the experiment at an isolated homestead without resident Ae . aegypti , a large cohort of mosquitoes could be released unmarked and recaptured at known ages to 29 d old . To facilitate high throughput age grading , a multiplexed assay incorporating Taqman probes [20] was used for quantitative PCR analysis . Several physiological parameters ( including body size , blood digestion and ovary development ) were investigated as possible sources of variance in our age predictions . We have demonstrated that mosquito age estimates generated from transcriptional profiles under natural conditions can be applied to identify changes in mean mosquito population life expectancy . By incorporating Taqman probes , the method could be scaled-up to facilitate the expected increases in requirements for mosquito age grading when new mosquito control strategies are implemented .
Human ethics approval for allowing colonized ( dengue-free ) mosquitoes to feed on the investigators was obtained from James Cook University ( Human ethics approval H2250 ) . Blood feeding was considered to cause a medium risk of allergic reaction and provision was in place that individuals were excluded if they reacted strongly to bites . Written consent was obtained acknowledging the right to refuse or withdraw . Aedes aegypti were collected as eggs from ovitraps set at Machans Beach ( 16° 51′ 14″ S , 145° 44′ 55″ E ) , a suburb of Cairns , Queensland , Australia . G1 eggs were hatched in hay-infused water and reared on a diet of dry adult cat food ( Friskies; North Ryde , Australia ) under ambient conditions and low densities to ensure synchronous development . Pupae were transported to a homestead in an isolated rainforest located 13 km from Cairns . The homestead consisted of a cluster of three buildings; a single story , one bedroom , unscreened wooden house and two open-sided shelters . Ae . aegypti were known to be absent from the site and this was confirmed by attempts to trap Ae . aegypti using three adult mosquito traps ( BG-Sentinel traps; Biogents , Regensburg , Germany ) at the site over a four week period before release . The pupae were randomly divided into a “free-range” group for release and a “sentinel-cage” group . A third group was used to determine the sex ratio ( n = 83 ) . The free-range pupae were transferred to open 9 L release containers that were maintained at the site for 24 hr . Over this time the adults emerged and dispersed . The number of adult Ae . aegypti released was estimated by counting the pupal exuviae in the release containers at the end of the release period . Mosquitoes were allowed free movement around the property and volunteers residing at the site provided blood meals at 2 d intervals . The property was supplemented with additional larval habitats ( tyres , buckets and pot-plant bases ) and all larval habitats/potential oviposition sites were flushed-out and cleaned every 5 to 6 d to prevent emergence of any adult mosquitoes . Fifteen resting adult females were recaptured at 2 d intervals from 1 to 29 d from various sites around the field house using mechanical aspirators . Temperature and humidity was recorded throughout the experiment using Hobo data loggers ( Onset Computer Corporation , Pocasset , U . S . A ) . The sentinel cage group was placed into two cages ( 450×450×450 mm ) that were maintained on-site for the duration of the experiment and 10 mosquitoes were sampled from these at 4 d intervals from 1 to 29 d . At the time of capture , mosquitoes were briefly anaesthetized at −20°C for 5 min . The heads and thoraces of individual mosquitoes were dissected from abdomens , wings and legs on a glass slide using fine tweezers and a scalpel . Heads and thoraces were rapidly placed in 300 µl of RNAlater ( Ambion , Austin , U . S . A ) and stored as per the manufacturer's protocol . Abdomens of recaptured mosquitoes were stored at −20°C until dissections were performed to determine physiological status . At the conclusion of sampling , mosquitoes were exhaustively removed by aspirator collections and using four BG-Sentinel traps and 18 sticky-ovitraps positioned around the homestead over a three week period . No Ae . aegypti were collected after the second week or have been observed since , indicating that the mosquitoes did not become established . Various physiological characteristics of the free-range females were determined by dissection . Mating status was determined from the presence or absence of sperm in the spermathecae . The midgut was observed and females were classified as to whether blood was present or absent . Ovarian development was graded according to Christophers' stages [21] and parity by the presence or absence of ovary tracheolar skeins [22] . Wing length was used as a proxy for body size and was measured as the distance from the axial notch to the wing tip , excluding the fringe scales [23] . Samples were removed from RNAlater and transferred to 1 . 5 ml screw-capped plastic vials with a single 3 mm silica glass bead and 0 . 5 ml Trizol reagent ( Invitrogen , Carlsbad , U . S . A . ) . Samples were mechanically homogenized in a Minibeadbeater ( Biospec ) for 1 . 5 min , transferred to 1 . 5 ml microfuge tubes and centrifuged at 17 , 000×g for 10 min at 4°C to pellet the chitinous material . The supernatant was transferred to a new 1 . 5 ml microfuge tube . Trizol extraction was performed according to manufacturer's instructions; however , isopropanol precipitation was performed overnight at −30°C . Total RNA pellets were reconstituted in 20 µl RNAse-free water . Total RNA was quantified by absorbance readings using a Nanodrop spectrophotometer ( Biolab , Scoresby , Australia ) . Total RNA was standardized at 500 ng and treated with 0 . 2 U recombinant RNAse-free DNase ( Roche ) as per the manufacturer's protocol . A previous report [14] proposed a reduced set of three gene expression ( GE ) measures ( Aedes aegypti calcium binding protein [Ae-15848; XM_001653412] , Aedes aegypti pupal cuticle protein 78E [Ae-8505; XM_001656550] and Aedes aegypti cell division cycle 20 [cdc20; fizzy][Ae-4274; XM_001664201] ) all normalized to a housekeeping gene Aedes aegypti 40S ribosomal protein S17 [Ae-RpS17; AY927787] ) . Primer and dual-labelled Taqman probe sets ( Table S1 ) were designed for this gene set , using web-based assay design software RealTimeDesign ( http://www . biosearchtech . com/products/probe_design . asp; Biosearch Technologies Novato , CA ) , to allow multiplex qRT-PCR assays to be developed . Gene-specific Taqman probes were labeled with different fluorophores that had minimal spectral overlap to minimize “cross-talk” between color channels . Four different fluorophores were used to label Taqman probes specific to each gene . Gene-specific labeling was incorporated into the Taqman assay to allow for the possibility of a triplex , excluding the housekeeping gene , or quadraplex assay to be designed . However , these initial attempts to co-amplify three or four PCR products were unsuccessful as discussed below . Instead , two duplex assays were optimized to co-amplify: ( 1 ) Ae-RpS17 and Ae-15848 , and ( 2 ) Ae-4274 and Ae-8505 . Multiplex reactions were validated by comparing the Ct values obtained from the duplex and single-plex assays across a 107-fold dilution series ( 100–107 copies ) . The dilution series was constructed using a mixture of linearized plasmids containing inserts for each gene of interest . For the construction of plasmids , PCR products were amplified from a pool of Ae . aegypti cDNA , gel purified , ligated into pGEM-Teasy ( Promega , Madison , U . S . A ) and transformants cultured . Mini-preps were digested with AatII for 2 h to linearize plasmids , which were then quantified and serially diluted . All qRT-PCR assays contained 500 nM of each primer , 200 nM Taqman probe , 6 mM MgCl2 and 2 µl template cDNA , and were amplified with the following cycling conditions: 50°C , 2 min; 95°C , 2 min; then 50 cycles of 95°C for 10 s; 60°C for 20 s; fluorescence acquisition . All qRT-PCR assays were run in triplicate on the Corbett Rotorgene 6000 real-time PCR platform ( Corbett Research , Sydney , Australia ) . Ct values were calculated as the second derivative maximum of the fluorescence curve using the comparative quantification analysis module in the Rotorgene software ( Corbett Research , version 1 . 7 ) . Mean Ct values were calculated from replicate reactions and used to construct standard curves for single and duplex reactions . Single and duplex standard curves were analyzed with the ANCOVA procedure in SAS ( version 9 , SAS Institute , Cary , U . S . A . ) to determine that they were comparable in terms of slope . Reverse transcription was performed using 500 ng of DNAse-treated total RNA , anchored oligo ( DT ) 20 priming , 20 U RNaseOut ( Invitrogen ) and 100 U Superscript III reverse transcriptase ( Invitrogen ) based on the manufacturer's protocols . cDNA was diluted 5-fold to minimize the influence of PCR inhibitors . A random sample of 15 RT reactions were re-synthesized as negative RT controls ( no reverse transcriptase ) . These were screened for genomic DNA contamination by standard PCR with Ae-RpS17 primers ( 95°C , 3 min; 95°C , 30s; 60°C , 30s; 72°C , 1 min; 35 cycles; 72°C , 10 min ) . All RT negative controls tested negative . Transcriptional profiling of Ae-RpS17 , Ae-15848 , Ae-8505 and Ae-4274 was performed using the multiplex qPCR assay described above . ANOVA was applied to investigate the effects of adult mosquito age and confinement ( sentinel cage versus free-range ) on total RNA yield ( µg ) from the head and thorax of all Ae . aegypti females . Age was log10 transformed to account for curvature in the change in total RNA yield at younger ages . The influence of mosquito physiological parameters on total RNA yield was evaluated for free-range females . ANOVA was performed on total RNA yield with presence of blood in the midgut ( no blood or some blood ) and Christophers' ovarian development stage ( stage I to V; stage G females were omitted due to the disproportionate statistical influence of this group ) as factors and log-age and wing length as covariates . Estimated means for Christophers' ovarian stage groups were then calculated and compared . ANOVA was implemented in SPSS ( SPSS Inc . , Chigago , U . S . A ) . Gene transcription measures for Ae-15848 , Ae-8505 and Ae-4274 were normalized to the expression of the housekeeping gene ( Ae-RpS17 ) by calculating log contrast values for each gene [15] ( log10 of the ratio of the Ct value to the Ct value of Ae-RpS17 ) . The effects of adult mosquito age , grouping , wing length , blood presence and ovary development on the log contrast gene transcription measures was determined using ANOVA as described above for total RNA yield . An age prediction model was constructed using a multivariate analysis procedure that extracts a linear variable from multiple gene transcription measures [14] , [15] . Briefly , log contrast values of test mosquitoes ( sentinel cage females ) were entered into canonical redundancy analysis to reduce the dimensionality of the data by creating new variables called redundancy variates . The age prediction calibration model was created by regression of the first redundancy variate against adult mosquito age for the sentinel cage females . A non-parametric bootstrapping procedure was used to predict the age of each free-range female . This analysis was implemented in SAS ( version 9 . 1; SAS Institute , Cary , U . S . A ) using a SAS editor syntax that is provided in Cook et al . [15] . The sentinel cage dataset was input as the training dataset ( n = 72 ) and log contrast values for all free-range females were input as the test dataset ( n = 145 ) . The mean of 1000 bootstrap age predictions for each free-range female was reported as its predicted age . Alternative models were investigated for the prediction of mosquito age from the transcriptional measures . A Poisson regression model with a logarithmic link function was applied because predictions were constrained to positive values . Mosquito age was analyzed so that the regression coefficients described the log of the relative risk of the independent variables ( log contrast values for Ae-15848 , Ae-8505 and Ae-4274 with or without total RNA yield ) . Poisson regression with logarithmic link models were implemented in WinBUGS [24] . In addition , the redundancy variate model was repeated as described but with total RNA yield as an additional independent variable . Sources of variance in age prediction accuracy of the redundancy variate three gene model were investigated by performing ANOVA on the age prediction residuals ( predicted age minus actual age ) with blood digestion and ovary development as factors and log-age and wing length as covariates . The accuracy of mean life expectancy ( ex ) estimates that would be derived by applying our grading technique was investigated using Monte Carlo simulations . In particular , we tested our age grading method for the ability to differentiate two mosquito populations with a two-fold difference in mean life expectancy . This difference was chosen to test the ability to detect a 50% lifespan reduction induced by Wolbachia wMelpop infection . Two populations of 10 , 000 mosquitoes were created for which survival was described by exponential mortality models with ex set at 5 ( probability of daily mortality [α] of 0 . 1 ) and 10 days ( α = 0 . 2 ) , respectively . Samples of 100 , 200 , 300 , 400 and 500 mosquitoes of defined ages were randomly removed from the population . The age of each mosquito was predicted by randomly sampling from normal cumulative distributions of ages described by the mean and standard deviation of the experimentally predicted age estimates at each age . The distributions for even-aged mosquitoes were defined by the interpolated mean and standard deviations from the adjacent experimentally determined values . Mortality rates were estimated by calculating the regression coefficient of the natural log of the proportion of the population predicted to be within 24 hr age intervals against the age of each class . Age classes containing <3 mosquitoes were omitted from the calculations . Estimated ex values were then calculated ( 1/- αestimated ) . Sampling , age predictions and ex predictions were iterated 999 times using the Monte Carlo simulation function in the PopTools add-in in Microsoft Excel ( http://www . cse . csiro . au/poptools/ ) . Ninety five percent confidence intervals for ex were determined from the 2 . 5 and 97 . 5 percentiles of the resulting distributions .
Approximately eight thousand newly emerged Ae . aegypti adults ( 4804 female , 3203 male ) were released inside and around an isolated homestead near Cairns , north Queensland , Australia . These free-range mosquitoes were free to engage in natural mosquito behavior including human blood feeding , mating , oviposition and harborage in natural micro-habitats . Females from this cohort were recaptured at known ages ( 2 d intervals from 1 to 29 d ) and in varied physiological states , which provided an ideal sample to validate transcriptional mosquito age grading . Transcriptional profiles from the sentinel-caged mosquitoes were used to produce an age prediction model for the free-range females in an approach that may be applied to determine the age structure of wild Ae . aegypti populations . Mild conditions prevailed during the experiment , with average ambient temperatures of 20 . 9°C ( range 14 . 7–26 . 5°C ) in the house and 20 . 3°C ( 13 . 2–27 . 5°C ) in an open sided shelter and average relative humidity of 86 . 2% ( 51 . 2–99 . 7% ) . Exhaustive sampling at the conclusion of the experiment collected 286 female and 11 male Ae . aegypti . Dissection of free-range mosquitoes to determine physiological status showed that no female had mated by 1 d old ( n = 10 ) , 90% females had mated by 3 d ( n = 10 ) and all females ≥5 d old were mated ( n = 120 ) . The first peak in blood feeding activity occurred when females were 30 hr old ( ±12 hr ) . This was reflected by high percentages of free-range females containing blood when recaptured at 3 and 5 d old ( Figure S1A ) . The percentage of females with blood varied between 10% and 90% for subsequent samples . In some females , ovary development had advanced as far as Christophers' stage IV by 3 d old ( 28 hr after the first blood feeding peak; Figure S1B ) . The proportion of gravid females ( those containing a clutch of mature , stage V ovaries ) was 10% at 5 d , increased to 80% by 9 d and fell in subsequent days as females oviposited and blood feeding recommenced . For the females in which ovarian skeins could be visualized , all females ≤3 d old were nulliparous ( n = 14 ) and all older females were parous ( n = 48 ) . The mean wing length of the free range female Ae . aegypti was 3 . 01 mm , SE = 0 . 01 , significantly larger than previously recorded for wild Ae . aegypti collected under equivalent seasonal conditions in Cairns ( 2 . 85 mm , SE = 0 . 02; F = 71 . 40 , df = 256 , P<0 . 001 ) . A preliminary step in the transcriptional age grading assay is the isolation of total RNA from the mosquito head and thorax and reverse transcription of a standard quantity of total RNA to cDNA . Early indication that a specimen was relatively young was gained at this stage because total RNA yield decreased with age in Ae . aegypti females from 1 to 5 d old , with RNA levels stabilizing in later samples ( Figure 1 ) . The total RNA yield was not significantly different between sentinel cage and free range females ( ANOVA , n = 214 , P = 0 . 13 ) but was strongly influenced by log-age ( P<0 . 001 ) . We then examined potential physiological factors that may influence mosquito total RNA quantity and found that as well as log-age , the yield was highly influenced by ovary development ( ANOVA , n = 145 , P<0 . 001 ) and wing length , measured as a proxy for body size ( P = 0 . 002 ) , but was not influenced by the presence of blood in the midgut ( P = 0 . 61 ) . The model was fitted with an interaction between blood presence and ovarian development that was not significant ( P = 0 . 15 ) . A comparison of the main effects of ovarian development showed that egg maturation was associated with an increase in total RNA quantity in the head and thorax , with the rate steadily increasing from stage I to stage IV before total RNA levels dropped at the completion of egg development at stage V ( Figure S2 ) . Having previously identified a set of four genes that facilitated age predictions of Ae . aegypti ( three age responsive genes; Ae-15848 , Ae-8505 and Ae-4274 and a housekeeping gene , Ae-RpS17 [14] ) , we applied Taqman-probes targeting transcripts of these genes to streamline the determination of mosquito age . Ae-RpS17 and Ae-15848 are more highly expressed than Ae-4274 and Ae-8505 , with cycle threshold ( Ct ) values differing by approximately 10 cycles . Attempts were made to primer limit the amplification of Ae-RpS17 and Ae-15848 , and allow for the efficient amplification of the other two amplicons in later cycles . However , the co-amplification of all amplicons could not be achieved despite efforts to optimize MgCl2 and primer concentration . The similarity of transcript abundance between Ae-RpS17 and Ae-15848 , and Ae-4274 and Ae-8505 allowed for the development of a duplex Taqman assay without the obstacle of preferential amplification of highly abundant cDNA templates . Duplex assays were validated across a 107-fold dilution series of template abundance . Standard curves were constructed for the single and duplex Taqman assays , by plotting linear regressions of Ct value against the log concentration of linearized plasmid template . The slopes of the regression ( PCR efficiency ) for the single and duplex standard curves were determined to be equivalent by ANCOVA . Ae-4274 was the only amplicon where the duplex reactions were significantly different from the single-plex reactions across the 107-fold dynamic range examined ( n = 53 , df = 1 , P<0 . 05 ) . Age related variation was evident from the transcriptional profiles of the age responsive genes in Ae . aegypti head and thorax tissue ( Figure S3 ) . Transcription is represented as the log contrast of the qPCR Ct values of the gene relative to the housekeeping gene ( Ae-RpS17 ) . Log contrasts are inverse measures of transcript abundance , increasing as transcription is decreasing . Log contrast values describing the transcription of Ae-15848 showed a four-fold increase from 1 to 29 d old ( Figure S3A ) . The effect of log-age was highly significant ( ANOVA , n = 217 , P<0 . 001 ) but there were no differences between sentinel cage and free-range females ( P = 0 . 73 ) . For Ae-8505 , log contrast values increased rapidly from 1 to 3 d and increased at a more gradual rate with age in older females ( Figure S3B ) . Similarly , the effect of log-age was highly significant ( P<0 . 001 ) and differences between caged and free-range mosquitoes were not significant ( P = 0 . 23 ) . Log contrast values for Ae-4274 decreased gradually with age ( Figure S3C ) ; however the effect of log-age was highly significant ( P<0 . 001 ) . No significant differences were observed between caged and free-range mosquitoes ( P = 0 . 65 ) . We then examined the influence of the measured physiological factors on the transcription of the genes as a further test of their robustness as biomarkers of age . We analyzed the effects of log-age , wing length , presence of blood in the midgut , ovary development stage and an interaction between blood presence and ovary development on the log contrast values of Ae-15848 , Ae-8505 and Ae-4274 . The effect of log-age was highly significant for all genes , however; none of these factors or the interaction was significant ( Table S2 ) . The sentinel cage females were used as training samples to establish an age-prediction calibration model . The log contrast values for each female were entered into canonical redundancy analysis to produce a single redundancy variate . The analysis indicated that 68 . 8% of the variance in the gene transcription measures was explained by age . An age prediction calibration model was constructed from the regression of the redundancy variate for each female against adult age ( Figure S4 ) . An important outcome was that there was a strong linear component to the model ( Linear regression R2 = 0 . 688 , n = 72 , P<0 . 001 ) . The free-range Ae . aegypti females were treated as age-blinded test specimens . Transcription was quantified and log contrasts were calculated as for the sentinel cage mosquitoes . Canonical redundancy analysis was used to derive a redundancy variate for each individual and age was predicted using a bootstrap procedure that applied inverse regression of the sentinel cage model from this redundancy variate . The predicted ages were then compared to the actual ages of these females , known from the time of recapture . This comparison showed a strong , near-linear relationship between the predicted ages and the actual ages of the free-range females ( Figure 2; R2 = 0 . 62 , n = 145 , P<0 . 001 ) . Negative ages were predicted , because the normal distribution used to model age is not constrained to positive values . A Poisson model with logarithmic link function was applied to the training and test datasets because predicted values were constrained to positive values; however no overall gains in precision or accuracy were made over the redundancy variate model ( Fig . S5A ) . Interestingly , inclusion of total RNA yield as a predictor variable increased the fit of the Poisson regression model ( Fig . S5B ) ; however no gains were made by including total RNA in the redundancy variate model ( Fig . S5C ) . Negative predictions were therefore manually reset to zero days . Moderate accuracy of age predictions was achieved , with 31 . 0% of ages predicted to within 2 d of the actual age , 55 . 9% to within 4 d and 77 . 2% to within 6 d . However , 8 . 3% of predictions were greater than 10 d from the actual age . Two of these samples , obvious from Fig . 2 , were 7 d old females that had log contrast values for Ae-4274 that were greater than three standard deviations from the mean of all free-range females . None of the physiological factors measured ( blood digestion , ovary development and body size ) had a significant effect on the age prediction accuracy ( Table S3 ) . However , slight age prediction bias was observed as indicated by a significant effect of age on the error ( P<0 . 001 ) . Inspection of age prediction residuals against predicted age ( Fig . S6 ) showed no clear trend indicating that an appropriate model had been fitted . Monte Carlo simulation enabled us to model the application of transcriptional age grading to the estimation of population ex values using the experimental error distributions obtained from the release-recapture experiment ( Fig . 3A ) . First , we determined the error distribution that would be expected through sampling error alone , assuming that all mosquitoes sampled were age graded with 100% accuracy ( Fig . 3B ) . As expected , very little bias was observed in the estimates of ex . The estimates for the two populations were clearly differentiated and the precision of the estimates increased with increasing sample sizes . Second , we examined the estimates of ex from these same populations that would result if age estimates were derived by applying transcriptional age grading . For the population with ex of 10 d , the predicted ex values were not significantly different from the actual ex for sample sizes of 100–300 mosquitoes but an underlying bias towards underestimation of ex became evident at greater sample sizes ( Fig . 3C ) . However , estimates for the ex = 5 days population were significantly greater than the actual ex values at all sample sizes . An important outcome was that the two populations could be significantly differentiated from each other when estimates of ex were based on the age predictions of >200 mosquitoes .
We have shown that age grading mosquitoes based on gene transcription can be successfully applied to adult female Ae . aegypti maintained in the wild . The ability to determine the age of female Ae . aegypti to an accuracy of ±6 d for 72 . 2% of females under field conditions is a valuable asset for investigations of mosquito population age structure . One of many applications of mosquito age grading is for the assessment of the efficacy of mosquito control interventions , whether testing the capacity of an entomopathogenic micro-organism to shorten the mean life expectancy of a mosquito population , or to test the fitness of a transgenic mosquito with impaired ability to transmit pathogens in comparison to wild type mosquitoes . We demonstrated the capacity for transcriptional age grading to differentiate between two populations of mosquitoes having mean life expectancies of 5 and 10 d . A 2-fold difference was chosen to test the capacity of the model to identify a 50% lifespan reduction of Ae . aegypti females that is expected to result from a dengue control intervention based on Wolbachia intracellular bacteria [6] , [8] , [9] . Although bias was evident from absolute estimates of ex derived from transcriptional age estimates , a relative comparison of the predicted life expectancies enabled successful differentiation of the populations . We have increased the throughput of transcriptional age grading by applying duplex Taqman probe assays to the quantification of gene transcripts which is highly desirable for investigations of mosquito population age structure in which hundreds of specimens potentially require age grading . We applied a stringent test for validation of transcriptional age grading by releasing free-range Ae . aegypti , thereby allowing mosquitoes to engage in normal mosquito behavior , including blood feeding , dispersal in search of natural oviposition and resting sites and thermoregulation through harborage in typical microhabitats . As a result , females were recaptured in various physiological states at each age . However , of several physiological factors assessed by dissection ( presence of blood in the midgut , ovary development and body size ) , none affected the transcriptional profiles of the age responsive genes or age prediction accuracy . Similarly , transcriptional profiles did not differ between females from the sentinel cages or the free-range females which is important from an applied perspective as it indicates that age prediction models for wild caught mosquitoes can be calibrated on known-age mosquitoes maintained in captivity . There was some decrease in accuracy when compared to our previous application of the three gene age prediction model to Ae . aegypti maintained in field cages up until 19 days old [14] . However , we have increased the sample size of test specimens in the present study ( 30 to 145 ) and have extended the maximum age of samples to 29 d here . A Poisson log link model and alternative combinations of genes and total RNA yield predictor variables were tested; however , no improvements in accuracy were achieved over the three gene redundancy variate model . Outliers were attributed to aberrant gene transcription values , in some cases greater than three standard deviations from the mean of the group . The reasons for these extreme observations could not be determined . However , these outliers comprised less than 5% of all predictions . Environmental variation or other physiological factors not measured could account for the differences . The free-range females were larger than wild specimens collected under equivalent seasonal conditions; however , investigations in the laboratory have shown that transcription of the age responsive genes is robust to body size variation induced by varying the quantity of food provided to larvae ( LEH , unpublished data ) . We have also shown that a yield of total RNA from the head and thorax of an adult female above a threshold ( 4 . 4 µg in our experiments ) provides early indication that the specimen is a newly emerged , <3 d old adult ( a teneral adult ) . A 40% decrease in total RNA abundance with age from 1 to 7 d old has been previously observed from Ae . aegypti females [20] . High levels of total RNA in 1–2 d old adults followed a peak in total RNA abundance during the pupal stage , and were probably a residual effect of high transcription rates during metamorphosis . However , age related changes to total RNA yields have not previously been utilized for mosquito age grading assessments . In D . melanogaster , total RNA abundance decreased by 60% at a constant rate from 2 to 40 d of age [21] . Total ribosomal RNA , transfer RNA and mRNA levels decrease rapidly from emergence to 10 d in Drosophila and are thought to be due to down-regulation of RNA polymerase I , II and III mediated transcription [22] . Total RNA yield is dependent on the method of extraction used and we have shown that total RNA yield in the adult head and thorax increases with body size and during ovary development . These factors should be standardized in age grading assessments of wild mosquitoes based on total RNA yield . The ability to accurately determine the ages of wild caught mosquitoes is crucial for investigations into the population dynamics and vulnerabilities of important mosquito vectors . In particular , the capacity to differentiate two populations of mosquitoes based on changes to mean life expectancy will be critical for evaluating new dengue control interventions targeting mosquito longevity . We estimate that adopting the Taqman probe multiplexing approach saves approximately 20% in reagent and 30% time savings when compared to the equivalent Sybr green based approach . The cost of reagents required to derive age predictions was approximately US$7 . 5 per mosquito . Improvements in accuracy and throughput can be expected if additional age responsive genes are identified and included in the model . Optimized Taqman reactions could enable these transcripts to be measured in triplex or quadriplex qPCR assays . The genes on which our age prediction assay is based are conserved and therefore there is a large potential for the development of transcriptional age grading methods for other insect vectors of tropical diseases . | Once infected with dengue virus , a female Aedes aegypti mosquito must survive longer than twelve days before it can transmit the virus to an uninfected person . New dengue control strategies therefore aim to circumvent dengue transmission using entomopathogenic microorganisms that shorten mosquito lifespan . Accurate methods to determine the age of individual mosquitoes are required for these and other mosquito control interventions . We have previously shown that mosquito age can be predicted from the transcription of specific genes . Here we demonstrate that this can be achieved under natural conditions when mosquitoes are uncaged and free to engage in natural behavior . To do this , we produced “free-range” female mosquitoes by releasing 8007 mosquitoes at an isolated location and recapturing the females of known ages . We developed an age prediction model from gene transcription measures of mosquitoes maintained in small “sentinel cages” maintained onsite . We then used this model to predict the ages of the free-range mosquitoes , based on their own transcription measures . Age predictions were robust to physiological changes associated with blood feeding and egg development . We show that the technique could be applied to identify a 50% reduction in mosquito population survival that is expected from infection with entomopathogenic Wolbachia bacteria . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biochemistry/transcription",
"and",
"translation",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"ecology/population",
"ecology"
] | 2010 | Field Validation of a Transcriptional Assay for the Prediction of Age of Uncaged Aedes aegypti Mosquitoes in Northern Australia |
Following an epidemiological study carried out in 2006 showing a high prevalence of blinding trachoma in the Far North Region of Cameroon , a trachoma elimination programme using the SAFE strategy was initiated: three yearly trachoma mass treatments were to be performed . The entire district population ( 120 , 000 persons ) was treated with azithromycin 1 . 5% eye drops in February 2008 and January 2009 . To assess the effect of treatment on the prevalence of active trachoma , three epidemiological studies were conducted on a representative sample of children aged between 1 and 10 years . The first study was performed just prior to the first treatment , the second just prior to the 2nd treatment and the third one , one year later . The prevalence of active forms of trachoma ( TF + TI ) dropped from 31 . 5% ( 95%CI 26 . 4–37 . 5 ) before treatment to 6 . 3% ( 95%CI 4 . 1–9 . 6 ) one year after first treatment; a reduction of nearly 80% . One year after the second treatment , the prevalence decreased to 3 . 1% ( 95%CI 2 . 0–4 . 9 ) , a total reduction of 90% . Furthermore , there were no more TI cases ( only TF ) . There was no report of serious or systemic side effects . Tolerance was excellent . Active trachoma mass treatment with azithromycin 1 . 5% eye drops is feasible , well tolerated , and effective .
Trachoma is caused by Chlamydia trachomatis and it is spread by direct contact with eye , nose , and throat secretions from affected individuals or by contact with objects , such as towels and/or washcloths , which have had similar contact with these secretions [1] , [2] . Flies can also be a route of mechanical transmission [1] . Children are the most susceptible to infection but the blinding effects or more severe symptoms are often not felt until adulthood . Infection is frequently passed from child to child but also from child to mother . Indeed , women are nearly two times [3] more affected than men by trachoma and trichiasis , probably because one of the primary activities of girls is taking care of their younger family members . This activity continues into adulthood , with women carrying the main responsibility of caring for children . Trachoma remains the leading infectious cause of blindness in the world [4] . Mass oral azithromycin distribution has been used in several programs . At present , azithromycin is available in a 1 . 5% eye drop formulation in Europe , Maghreb and French speaking African countries . The National Blindness Prevention Programme and a Vision 2020 plan were established as a result of the prevention policy of the Republic of Cameroon . , In December 2006 , in the Kolofata Health District ( Far North Cameroon ) , a study assessing the prevalence of active and scarring trachoma , signalled the presence of endemic trachoma with significant blinding potential [5] . The National Blindness Prevention Programme decided to plan an elimination program by implementing the SAFE ( Surgery , Antibiotics , Facial cleanliness , and Environmental change ) strategy [6] , addressing the “A” ( antibiotic ) component by conducting mass treatment targeting the entire district population and using azithromycin 1 . 5% eye drops . The objective of this study was to assess the feasibility , tolerance and effectiveness of repeated topical mass treatment with azithtromycin 1 . 5% eye drops , used for the first time on a large scale to reduce the prevalence of active forms of trachoma in a population . The first year , mass treatment gave promising results with a decrease in trachoma prevalence of more than 25% ( from 31 . 5% to 6 . 3% ) [7] . This article presents results after two rounds of treatment .
In accordance with the WHO recommendations [8] , the trachoma control programme in the Kolofata Health District called for one mass treatment per year for three years . The treatment consisted in one instillation of azithromycin 1 . 5% in both eyes in the morning and in the evening during three consecutive days . The study received authorisation from the Cameroon Ministry of Public Health in February 2008 . The first round of treatment began on 23 February 2008 and ended on 10 March 2008 , and the second one was undertaken between the 5th and 20th of January 2009 ( Figure 1 ) . Each year , Théa Laboratories donated 120 , 000 complete treatments ( 720 , 000 single doses ) of azithromycin 1 . 5% eye drops , and sent them by air from Europe to Yaoundé , Cameroon , and by train and truck from Yaoundé to Kolofata . The target population was all residents of the Kolofata Health District [9] . The entire population was treated , but only children between 1 and 10 year olds were examined for active trachoma . During the 15 days preceding the beginning of treatment , the local community health workers , helped by a literate second-level community health worker , conducted an exhaustive door-to-door census of all residents of the Kolofata Health District . Each of the 250 local community health workers was assigned a village or neighbourhood of 400 to 500 residents . The local community health workers then administered treatment by visiting each household morning and evening for three consecutive days . Ophthalmic nurses were supervising each day the mass treatment in the different villages . A briefing was organized in Kolofata hospital each evening where the supervisors reported day after day their assessment of the mass treatment . As described by Huguet [7] , among children aged between 1 and 9 year olds ( up to their 10th birthday ) , three descriptive cross-sectional studies , to assess the effectiveness of treatment on the prevalence of active forms of trachoma in the population ( trachomatous inflammation—follicular ( TF ) and/or trachomatous inflammation—intense ( TI ) [10] , [11] ) , were conducted in the Kolofata Health District . The first was conducted prior to treatment in February 2008 , the second prior the second treatment in January 2009 , and the third in January 2010 , one year after the second treatment ( Figure 1 ) . The standard WHO protocol for trachoma prevalence surveys was used [12] . The population studied was chosen at random and was based on the exhaustive list of villages and demographic statistics gathered in 2006 for the national census [9] and revaluate annually by a census at Kolofata sanitary district level only ( 118 , 617 inhabitants in 2009 ) . Assuming a prevalence of less than 5% at the end of the study ( one year after the third treatment ) , it was necessary to include 2 , 400 children between 1 and 10 years in the study to obtain a precision of approximately 1 . 5% with a two-sided 95% CI and a cluster effect of 4 [13] . The 2 , 400 children were divided into 40 clusters , with 60 children per cluster chosen randomly . All target-population residents ( defined as children over 1 year and less than 10 years old [up to their 10th birthday] who had lived in the village for at least six months prior to the study date ) in randomly selected households were registered and included in the population to be examined . When a family had left the community more than 6 months before the visit and the household remained empty , that household was replaced by the household nearest to it . A household that was only “temporarily” empty ( less than 6 months ) was not replaced . The research team returned up to three times to examine any subject absent during the preceding visit . If after the third visit the missing person was not found , that person was declared absent and not replaced . Any family in the selected population whose head of household refused consent to participate in the study was not replaced [12] . This population corresponds to the enumerated population . The examined population corresponds to the population effectively examined . A two-day training session helped assure standardization of procedures for conducting the census , examining subjects , and collecting and recording data . A post-training test was conducted on 50 trachoma patients to determine whether the trainee had mastered the WHO simple grading system and to confirm that each future examiner had a concordance of more than 80% with an expert examiner for each key sign . A pilot study was conducted in two villages not included in the current studies . All children included in the study were examined by a senior nurse who everted the upper eyelid and examined the conjunctiva with a 2 . 5 magnifying glass and a torch held by an assistant who also recorded the data . The examiner changed gloves after the examination of each patient . Before examining the next person , the examiner verified that the assistant had filled out the study sheet in accordance with study protocol guidelines . Data were compiled and analysed using EPIINFO 6 software . Estimated confidence intervals took into account the composition of sample clusters . All subjects provided informed consent . As people were illiterate , informed consent was read to people and if they agreed to participate the participant or legally acceptable representative put their fingerprints on the informed consent and a literate witness signed on behalf of the participant . National ethics committee of Yaounde approved the way to collect consent and the study before the beginning of the study .
During the first annual treatment , azithromycin 1 . 5% eye drops were administered by the local community health workers to 111 , 340 of the 115 , 274 people counted in the census ( coverage 96 . 6% ) [7] . During the second round of treatment 105 , 802 people ( 45 , 288 adults and 60 , 514 children; 50 , 846 males and 54 , 956 females ) received the full 6-dose treatment of azithromycin eye drops . In addition , 41 , 376 doses were administered to others who did not complete the full 6-dose treatment i . e . to people who were absent during at least one of the treatment administration visit . The number of children examined during the study relative to the number counted in the random selected population is presented in Table 1 . During the study , age and sex distributions were similar in the sample populations before and after treatments ( p>0 . 05 ) ( Table 2 ) . In February 2008 , before all treatment , the prevalence of active forms was estimated to be 31 . 5% ( 95%CI 26 . 4–37 . 5 ) . One year after the first mass treatment ( January 2009 ) , this prevalence dropped significantly to 6 . 3% ( 95%CI 4 . 5–8 . 6 ) ( p<0 . 0001 ) . One year after two rounds of topical treatment ( January 2010 ) , prevalence dropped to 3 . 1% ( 95%CI 2 . 0–4 . 9 ) ( p<0 . 0001 ) ( Table 3 ) , a decrease of 90% . The prevalence of TF in the study sample was estimated to be 24% ( 95%CI 20 . 7–27 . 5 ) before treatment , it decreased significantly to 5 . 8% ( 95%CI 4 . 1–8 ) one year after first annual treatment ( p<0 . 0001 ) and again significantly ( p = 0 . 0001 ) to 3 . 1% one year after the second round of treatment ( Table 3 ) , either a decrease of 87% . The prevalence of TI was estimated to be 7 . 5% ( 95%CI 5 . 7–10 ) before treatment and disappeared after two annual treatments and ( 0 . 5% after 1st treatment ( p<0 . 0001 ) and 0% after second one ( p = 0 . 0005 ) ( Table 3 ) ( Figure 2 ) . Questionnaires concerning side effects of treatment were administered by community health workers during daily visits . According to WHO recommendations , if TF is 10% or more in children 1–9 years old , a mass treatment with antibiotic should be conducted throughout the district . Furthermore , as efficacy of the treatment was already assessed in a Phase III study , it was ethical that people of the communities were all treated , so there is no control group to assess the reliability of side effects . The few complaints recorded were local and brief ( blurred vision lasting several minutes following instillation of eye drops or transient burning sensation in the eyes ) . There were no reported serious ocular or systemic side effects .
A statistically significant ( p<0 . 0001 ) increase in trachoma prevalence was observed between the first prevalence study conducted in 2006 ( 26% ) and the study conducted prior to the first treatment in 2008 ( 31 . 5% ) . It suggests that there was no secular decline trachoma in this area . However , according to the WHO recommendation , as it was unethical to not treat some people of this area , the mass treatment protocol do not planned to have a control group . Thus the prevalence reduction ( from 31 . 5 to 3 . 1% ) of active trachoma among children of this study is likely to be a result of the two mass treatments with azithromycin 1 . 5% eye drops and prevention campaigns . By WHO definition , the current prevalence of 3 . 1% indicates that “trachoma as a blinding disease is being controlled” ( TF<5% and TI<0 . 2% ) [4] . The fact that no more TI grade ( severity factor of the disease ) was observed is particularly encouraging , since TI patients are those most likely to suffer blindness as the disease evolves [14] , [15] , [2] , [12] , [16] , [17] , [18] , [19] , [20] . Apart from minor complaints , treatment was accepted and well tolerated by both children and adults . The importance of endemic trachoma in the district of Kolofata justified the mass treatment of the entire population with azithromycin 1 . 5% eye drops as part of the SAFE strategy and in accordance with WHO recommendations [8] . The apparent coverage decreased between 2008 and 2009 ( 97% to 89% ) is an artefact produced by a change in reporting: in 2009 , unlike 2008 , only people who had completed all 6 doses were counted . The most common drug currently used in trachoma mass treatment campaigns is azithromycin 20 mg/kg taken orally . In Niger from 2002 to 2005 , SAFE strategy was implemented and three mass treatments using oral azithromycin were performed in 2 districts with 72 villages . Surveys were conducted 3 years apart ( before and after program ) The prevalence of TF among children decreased from 62 . 3% and 49 . 5% to 7 . 6% and 6 . 7% in three years [21] , a reduction of 89% in one village and 85% in the other . At the same period , in Mali , mass treatment using oral azithromycin was conducted in 7 districts . The prevalence of TI among children decreased from 33% to 2 . 5% in 3 years [22] , a reduction of 92 . 4% . In a study in Nepal where nearly 40% of children had active trachoma , three rounds of treatment directed at children age 1 to 10 years reduced clinically active trachoma to approximately 13% one year after the first treatment and to 4% one year after the second one . Furthermore , this three annual treatments were successful in reducing infection and disease in these children up to 6 months after the third last treatment ( 4% of children with active trachoma ) [23] . However , is 6 months long enough to determine of the reduction of the disease in a population . To assess this , one study conducted in trachoma-hyperendemic communities in Tanzania determined , after two rounds of mass treatment with oral azithromycin spaced 18 months apart , the rate of trachoma and infection at 5 years , either 3 . 5 years last treatment . Results showed that 3 . 5 years after two rounds of mass treatment , trachoma was not eliminated but antibiotherapy appeared to be associate with lower disease prevalence: from 39 . 2–80 . 6% ( according age groups ) at baseline to 7 . 7–49 . 1% 5 years after baseline [24] . Considering oral azithromycin studies published , mass treatment with azithromycin 1 . 5% eye drops , with a reduction about 90% of active trachoma , is at least as effective as treatment with oral azithromycin . Moreover , a phase III clinical trial showed that topical azithromycin 1 . 5% twice a day for 3 days has a similar efficacy as a single oral 20 mg/kg dose of azithromycin for the treatment of active trachoma in children [25] . The prevalence study planned for 2011 , one year after the third annual mass treatment with azithromycin eye drops , should be conclusive . Finally , trachoma persists where people live in poverty without water , sanitation , [26] , [1] , [27] and proper waste disposal [26] , [27] . Transmission of trachoma occurs where these conditions exist and should be expected to return after antibiotic treatment if the conditions are not changed . Improvements like construction of household pit latrines and hand-dug wells will bring about sustainable elimination of trachoma . However , the Ultimate Intervention Goal for Environmental improvements ( UIG-E ) defined by the WHO [4] is difficult to set up . Indeed , in 2008 and 2009 , only four borehole pumps were installed by the Cameroon government , and two wells were built by OSF . Thus , we are far from UIG-E , i . e . easy access to safe water for 80% of the population [1] . Following encouraging results from the first two mass treatment campaigns with azithromycin 1 . 5% eye drops , one additional mass treatment campaign was planned . The third campaign took place in January 2010 . A fourth study to track the evolution of active trachoma prevalence among children is planned for January 2011 . If the success of these first trachoma mass treatments with eye drops is confirmed , eye drops treatments would be a supplementary tool to fight trachoma in particular in young children and pregnant women . | Trachoma is the leading cause of infectious blindness worldwide , accounting for 1 . 3 million cases of blindness . Although it has disappeared in many regions of the world , trachoma is still endemic in Africa , Eastern Mediterranean , Latin America , Asia , and Australia . The WHO has currently set a target of 2020 for controlling trachoma to a low enough level that resulting blindness will not be a major public health concern . Topical tetracycline was for a long time the recommended treatment for active trachoma , but compliance to the regimen is extremely poor . Azithromycin has properties that make it an ideal treatment for Chlamydia trachomatis: high efficacy , intracellular accumulation , and a long tissue half-life . There is now a new mass treatment of trachoma by azithromycin 1 . 5% eye drops which is as effective as the oral route . In the test health district of Kolofata , Cameroon , the prevalence of trachoma among children dramatically decreased from 31% to less than 5% after 2 treatments . A third treatment was performed in January 2010 . An epidemiological surveillance is implemented to see if this removal will be permanent . It also avoids misuse of oral azithromycin and the eye drops are directly treating the site of the infection . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"ophthalmology/eye",
"infections"
] | 2010 | Elimination of Active Trachoma after Two Topical Mass Treatments with Azithromycin 1.5% Eye Drops |
The fitness effect of mutations can be influenced by their interactions with the environment , other mutations , or both . Previously , we constructed 32 ( = 25 ) genotypes that comprise all possible combinations of the first five beneficial mutations to fix in a laboratory-evolved population of Escherichia coli . We found that ( i ) all five mutations were beneficial for the background on which they occurred; ( ii ) interactions between mutations drove a diminishing returns type epistasis , whereby epistasis became increasingly antagonistic as the expected fitness of a genotype increased; and ( iii ) the adaptive landscape revealed by the mutation combinations was smooth , having a single global fitness peak . Here we examine how the environment influences epistasis by determining the interactions between the same mutations in two alternative environments , selected from among 1 , 920 screened environments , that produced the largest increase or decrease in fitness of the most derived genotype . Some general features of the interactions were consistent: mutations tended to remain beneficial and the overall pattern of epistasis was of diminishing returns . Other features depended on the environment; in particular , several mutations were deleterious when added to specific genotypes , indicating the presence of antagonistic interactions that were absent in the original selection environment . Antagonism was not caused by consistent pleiotropic effects of individual mutations but rather by changing interactions between mutations . Our results demonstrate that understanding adaptation in changing environments will require consideration of the combined effect of epistasis and pleiotropy across environments .
The extent to which mutations interact with their genetic background ( epistasis ) and the role such interactions play in evolution is not well understood [1] , [2] . Initial expectations were that epistatic interactions , defined as non-additive interactions among mutations , were common , causing fitness landscapes to be rugged and limiting the number of selectively accessible mutational paths [3] . Although early work revealed few such interactions ( reviewed in [4] ) , more recent studies of defined combinations of mutations have revealed abundant epistasis in a range of systems [5]–[17] . Studies that focused on interactions among beneficial mutations have often found a tendency for antagonism [18]–[21] , which is consistent with epistasis having a predictable influence on the curvature of fitness peaks in constant environments [22] , [23] . In addition to interactions within a genome , mutations can also interact with the external environment [24]–[29] . Moreover , phenotypic plasticity and epistasis can combine so that the fate of a mutation depends on both the environment and its genetic background [27] , [30] . This kind of dependence can have important evolutionary consequences . For example , Wright considered how fluctuating conditions — most often population size , but also the external environment — could change the sign of epistatic interactions and allow populations to evolve along otherwise maladaptive paths [31]–[35] . Relatively few studies have examined how epistasis and plasticity combine to influence mutational effects . One study that did found that all of 18 transposon insertion mutations were affected by either epistasis or plasticity , with half being affected by both [28] . It remains unclear , however , how interactions between beneficial mutations , which might be expected to depend strongly on a particular selective environment , will be affected by changes in the external environment . Such an understanding is vital for addressing questions concerning the course of adaptation in fluctuating conditions . For example: can the magnitude and sign of epistasis change with the external environment ? If so , are there any overarching features of mutation interactions , e . g . , a tendency towards antagonism , that nevertheless remain consistent ? A few studies have begun to address these questions by examining how epistasis between pairs of mutations changes with genomic [36] , [37] and environmental [38] contexts . Here , we expand this investigation of how the external environment affects epistatic interactions between five beneficial mutations that fixed in one population of a long-term Escherichia coli evolution experiment [19] . We first screened the response of the ancestor and the evolved genotype having all five mutations over a total of 1 , 920 external environments . Next , we measured the fitness of a set of 32 ( = 25 ) strains comprising all mutation combinations in the two environments with the most extreme opposing plasticity . These measurements allowed us to isolate effects of epistasis ( GxG ) , interactions between mutations and the environment ( GxE ) , and interactions between epistasis and the environment ( GxGxE ) on the fitness of defined genotypes . More generally , we investigated how the adaptive landscape , and the indirect consequences of each mutational step , might change with the external environment .
To determine how phenotypic plasticity changes following an adaptive walk , we used Biolog plates to compare the respiration ( a measure of catabolic activity ) of the ancestor and the strain containing five beneficial mutations ( hereafter , rtsgp , where each letter indicates a mutation in a gene or gene region as follows: r = rbs , t = topA , s = spoT , g = glmUS and p = pykF ) in 1 , 920 different environments . On average , rtsgp exhibited enhanced respiration over the ancestor ( mean = 84 . 37±1 . 39 ( SEM ) compared to 72 . 45±1 . 14 ( SEM ) ; paired t-test , t1919 = 26 . 28 , P = <0 . 0001 ) with significant differences in 203 environments ( see Materials and Methods for criteria ) , involving 171 gains of function and 32 losses of function ( Table S1 ) . These environments contained 28 alternative carbon sources , 34 alternative nitrogen sources , five alternative phosphate sources , ten nutritional supplements , and 126 “stressors” including antibiotics and other potentially toxic chemicals . Providing a useful control , one of the carbon sources in which the rtsgp strain had decreased respiration was D-ribose , which was expected due to a large deletion of the rbs operon in this strain [39] . We confirmed that measured respiration changes reflected growth rate changes in eight of the environments ( six gains of function and two losses of function ) by direct growth comparisons ( Table S2 ) . We focused on two environments that revealed large differences in respiration between rtsgp and the ancestor to examine how genotype and environment interact to affect fitness . The largest relative increase in respiration was in the presence of EGTA , a Ca++/Mg++chelator [40] . The largest decrease was in the presence of guanazole , a ribonucleotide DP reductase inhibitor [41] . In direct fitness competitions comparing rtsgp to its ancestor , rtsgp was significantly more fit in the environment containing the original selection medium ( DM25 , a minimal salts medium supplemented with glucose ) supplemented with EGTA than in the environment containing the selection medium alone ( DM25+EGTA: fitness = 1 . 497±0 . 068 ( 95% CI ) ; DM25: 1 . 299±0 . 061 ( 95% CI ) , t9 = 4 . 973 , P = 0 . 0008 ) . By contrast , rtsgp was less fit in DM25 supplemented with guanazole than in the selection environment ( DM25+guanazole: fitness of 1 . 116±0 . 029 ( 95% CI ) ; DM25: 1 . 299±0 . 061 ( 95% CI ) , t9 = −5 . 779 , P = 0 . 0003 ) . To examine the underlying genetic basis of this phenotypic plasticity , we quantified the fitness effect of each individual mutation in all three environments . The relative fitness of three of the five mutations significantly depended on the external environment ( rbs: F2 , 11 = 1 . 100 , P = 0 . 367; topA: F2 , 8 = 15 . 506 , P = 0 . 002; spoT: F2 , 10 = 31 . 389 , P<0 . 0001; glmUS: F2 , 10 = 3 . 513 , P = 0 . 070; pykF: F2 , 10 = 149 . 730 , P<0 . 0001 ) ( Figure 1 ) . In summary , three of the individual mutations present in the rtsgp genotype produced effects that differed significantly across environments . The above results demonstrate that the effect of individual mutations depend on the environment ( i . e . , G×E ) . However , it is also possible that interactions between mutations depend on the environment ( i . e . , G×G×E ) , which would further influence the topology of the fitness landscape and make it much more difficult to predict the influence of environmental changes on evolutionary outcomes . To examine G×G×E we measured the fitness of all combinations of the five beneficial mutations in the two focal environments ( i . e . , the selection environment supplemented with EGTA and guanazole ) . Fitness of each of the 32 genotypes comprising each mutation combination was quantified in both novel environments and compared with prior findings in the original selection environment [19] ( Figure 2 ) . To get some overall indication of the influence of environment on mutation effects we compare the number of “selectively accessible” mutational paths connecting the ancestor and rtsgp [42] . Although a different set of beneficial mutations would presumably be followed in guanazole and EGTA environments , considering a common set of genotypes allows a direct comparison of the effect of environment in altering selection pressures as a result of GxE and GxGxE . Of the 120 ( = 5 ! ) paths connecting the ancestor and rtsgp , 86 had monotonically increasing fitness in the selection environment [19] . By contrast , only 43 paths in EGTA and 2 paths in guanazole are selectively accessible ( Figure 2 , Tables S3 , S4 ) . ( The small number of selectively accessible paths in guanazole reflects , in large part , that rts and not rtsgp was the most fit genotype ( Table S6 ) . ) In all , nine mutational steps became significantly deleterious , six in the EGTA environment and three in the guanazole environment ( Tables S3 and S4 ) , although only three of these steps in the EGTA environment remain significantly deleterious when we correct for multiple comparisons . Nevertheless , differences in the number of selectively accessible paths available in different environments clearly indicate that environment affects landscape topology and selective constraints . To further examine the patterns of epistasis in the novel environments we focused on the effect of epistasis in determining the fitness of individual genotypes ( Tables S5 , S6 ) . In the EGTA environment mean epistasis was slightly , but not significantly , negative ( mean absolute epistatic deviation , εm = −0 . 039±0 . 046 ( 95% C . I . ) , t25 = −1 . 740 , P = 0 . 094 ) ( Figure 3 ) . In the guanazole environment mean epistasis was significantly positive ( εm = 0 . 057±0 . 022 ( 95% C . I . ) , t25 = 5 . 303 , P<0 . 0001 ) ( Figure 3 ) . In total , 16 and 5 genotypes exhibited significant epistasis in EGTA and guanazole , respectively ( Tables S4 , S5 ) . Both environments also displayed markedly different effects of higher-order epistasis involving interactions between at least three mutations . In the EGTA environment , genotypes tended to be more fit than expected from the sum of the relevant lower-order interactions ( mean higher-order epistatic deviation = 0 . 229±0 . 191 ( 95% C . I . ) , t15 = 2 . 556 , P = 0 . 022 ) . The opposite effect was seen in the guanazole environment ( mean higher-order deviation = −0 . 247±0 . 196 ( 95% C . I . ) , t15 = 2 . 693 , P = 0 . 017 ) . Considering only the mean effect of epistasis can miss other underlying patterns . For example , we previously found that the strength of negative epistasis between the five beneficial mutations increased with the expected fitness of the genotype in the selection environment , despite a lack of any mean effect [19] . This pattern has been reported in several other studies [18] , [20] , [21] and is consistent with interactions between beneficial mutations acting to slow the rate of adaptation . In the guanazole environment we found the same negative correlation between epistasis and expected fitness that was seen in the selection environment ( r = −0 . 748 ) ( Figure 4 , Figure S1 , Table S5 and S6 ) . The same correlation was only weakly negative in the EGTA environment ( r = −0 . 281 ) ( Figure 4 and Figure S2 ) . We also evaluated whether interactions between mutations and the environment , either EGTA or guanazole , contributed significantly to the overall variation in fitness , and found significant interactions between mutations ( GxG ) , mutations and the environment ( GxE ) , and interactions of both ( GxGxE ) ( overall model: F63 , 261 = 58 . 439 , P<0 . 0001 , Table S7 , see Materials and Methods ) . Using variance partitioning , we determined that GxGxE interactions explained approximately 8% of the variance in fitness observed in our complete data set ( Table S8 ) . Correlations between epistasis and expected fitness could reflect a general trend but could also be leveraged by outlying fitness or epistatic effects of an individual mutation . To distinguish between these possibilities we performed a series of ANCOVA analyses to test whether the presence or absence of each focal mutation influenced the overall relationship between epistasis and expected fitness ( Figure S3 and S4 ) . Only the pykF mutation explained a significant portion of the variation in the relationship between epistasis and expected fitness in the guanazole environment ( Figure S3 ) . Genotypes with this mutation tended to be more fit while the negative correlation , consistent with diminishing returns epistasis , with or without this mutation was retained . In the EGTA environment , considering genotypes distinguished by the presence or absence of either topA or pykF mutations revealed their significant contributions to the overall pattern of epistasis ( Figure S4 ) . The topA mutation tended to effect epistasis so as to decrease fitness ( mean epistasis of genotypes with topA = −0 . 088 compared to mean epistasis of genotypes without = 0 . 042 , t24 = 3 . 272 , P = 0 . 003 ) whereas pykF altered epistasis to generally increase genotype fitness ( mean epistasis of genotypes with pykF = 0 . 007 compared to mean epistasis of genotypes without = −0 . 087 , t24 = −2 . 156 , P = 0 . 041 ) . Genotypes lacking the rbs mutation again displayed a strong negative correlation between epistasis and expected fitness ( r = −0 . 783 ) , but adding the rbs mutation weakened the negative association between epistasis and expected genotype fitness without changing mean epistasis among these genotypes ( Figure S4 , genotypes with rbs , r = 0 . 089 , compared to without P = 0 . 033; mean epistasis with rbs = 0 . 0001 compared to mean epistasis of genotypes without = −0 . 078 , t24 = −1 . 720 , P = 0 . 098 ) . We used a higher-throughput approach using overall population growth ( AUC , see Materials and Methods ) as a proxy for fitness to assay for epistasis in nine additional environments . Seven of these environments were not expected to interact with the five mutations based on the initial Biolog screen comparing the rtsgp and ancestral strains ( Table S1 , Materials and Methods ) . In each environment , the growth of each single mutant was compared with the ancestor , rtsgp , and a randomly selected double-mutant , gp ( Figure 5 ) . In seven of nine environments , growth of either rtsgp or gp differed significantly from additive expectations assuming no epistasis ( Figure 5 , Tables S9 , S10 ) . The nature of these interactions also changed with the environment . For example , gp was significantly less fit than expected in two environments and significantly more fit than expected in four environments ( Table S9 ) . In summary , the sign and magnitude of epistasis among generally beneficial mutations may vary widely even with relatively small changes in the external environment .
Recent theoretical work has applied population genetic models to empirically constructed fitness landscapes to make basic predictions about the likelihood of particular evolutionary outcomes [8] , [14] . These outcomes depend crucially on the shape of the fitness landscape , which is determined by the form and extent of epistatic interactions between mutations . How sensitive these interactions , and therefore the repeatability of evolutionary outcomes , are to environmental change remains uncertain . To address this point experimentally we analyzed a set of strains including all combinations of the first five beneficial mutations that fixed during the adaptation of a population of E . coli to a constant laboratory environment ( Table 1 , [19] ) . By measuring the fitness of these strains in contrasting environments we generated two new empirical fitness landscapes that reveal how epistasis may change with the environmental context . Comparing these landscapes to the one determined in the original selection environment , we found interactions between mutations and their environment to be both common and complex . Previous work has shown that the diet breadth of 12 E . coli populations , including the population that was the source of the mutations used in our experiments , declined substantially during long-term evolution in a constant environment with a single carbon source [39] , [43] , [44] . However , it is difficult to distinguish if this trend was caused by few mutations of strong pleiotropic effect or if the beneficial substitutions display antagonistic pleiotropy in general . In an effort to distinguish these explanations , one study specifically focused on pleiotropic effects of beneficial mutations in five different environments . Mutations that were beneficial in the selected environment tended to be beneficial in others , and although there were exceptions , limited antagonistic pleiotropy was observed [45] , [46] . Here , we also report limited antagonistic pleiotropy with five beneficial mutations with an increased sample size of 1 , 920 environments from our initial Biolog screen ( Table S1 ) . This result supports the inference that antagonistic effects may be limited to a subset of beneficial variation . Since both studies focused on a collection of beneficial mutations contributing to initial adaptation to a minimal glucose environment , we speculate that early adaptation may be characterized by niche expansion with limited cost [47] . Epistasis was frequent in all environments and generally followed a pattern of diminishing returns . Nevertheless , both the individual effects of mutations and their interactions were environmentally dependent , in several cases resulting in mutations changing from being beneficial to deleterious or neutral ( Figure 1 , Figure 4 , Figure S2 and S3 ) . Perhaps most strikingly , different numbers of paths to the rtsgp genotype were found in each environment , one of which featured a different global peak . Our results also suggest that selective constraints in fluctuating environments may depend on how the environment influences epistasis between contending adaptive alleles , and not just the pleiotropic effects of individual mutations alone ( Table S5 ) . For example , the topA and glmS mutations were more beneficial in the EGTA environment alone ( positive pleiotropy ) ( Figure 1 ) , but in combination the tg genotype was much less fit than expected ( Table S4 ) . Since the fitness of this genotype did not significantly deviate from expectations in the guanazole environment ( Table S5 ) , environmental effects on epistasis ( GxGxE ) were not predicted by GxE interactions . More broadly , these results indicate that variable fitness of a genotype under different conditions can arise from altered interactions among the alleles comprising that genotype and not from any single mutation . This conclusion is robust to fitness measurements in environments not found to effect rtsgp respiration , suggesting that it is not dependent on our initial focus on the two environments in which GxE was most extreme ( Figure 5 ) . These interactions may be especially important in determining evolutionary outcomes given initially rugged fitness landscapes [14] , [48] , [49] or in naturally variable environments . In one study [17] , a more beneficial allele was eventually outcompeted by a less fit allele because of epistatic limits to the adaptive path of the former allele . However , a different outcome may have occurred in a fluctuating or seasonal external environment . Given prevalent genotype-by-environment interactions , epistatic interactions producing low fitness intermediates could be alleviated in alternative environments and allow new combinations of alleles to overcome evolutionary dead-ends and rise to fixation . This process could represent a mechanism for maintaining conditional , yet beneficial , variation in the population [50]–[52] . As evidence , the three significantly maladaptive steps in the EGTA environment are alleviated by a shift to either the guanazole or the original selection environment ( Figure 2 ) . Fluctuating environments may therefore provide a solution to evolutionary dead-ends in an inherently rugged fitness landscape . In summary , the combination of phenotypic plasticity and epistasis can strongly influence how an organism adapts to a new environment . Although the five mutations examined here would not likely be the same favored in these new environments , our results demonstrate that epistatic interactions are not static and can determine which trajectories are selectively accessible during an adaptive walk in a fluctuating environment . As a result , the fate of a mutation depends on its individual effect , epistasis with preexisting mutations and on interactions with the prevailing environment . With growing opportunities to survey dynamics of many genotypes within evolving populations , studies of both inherent properties of individual alleles and effects of their interactions in multiple conditions would address how frequently pleiotropy and epistasis guide adaptive evolution .
Twelve populations of E . coli have been propagated for more than 50 , 000 generations in Davis Minimal ( DM ) medium supplemented with 25 µg/ml glucose ( DM25 ) in a long-term evolution experiment studying the dynamics and genetic basis of adaptation [43] , [53]–[59] . Mutations identified in one of these populations , as described previously , are studied here [39] , [57] , [60]–[62] . Other types of media used in this study include Tryptic soy ( Tsoy ) broth , tetrazolium-arabinose ( TA ) agar plates , and DM media supplemented with sugars other than glucose or with glucose and additional compounds . E . coli strains were grown in rich Tsoy liquid media overnight from −80°C freezer stocks . Aliquots of overnight culture were transferred to 10 mL DM25 media to precondition the cultures for 24 hours prior to growth curves or fitness assays . To identify external environments that interact with the five mutations ( Table 1 ) , the respiration of the rtsgp genotype was compared to the ancestor of the long-term evolution experiment , REL606 , using Biolog's Phenotypic Microarray Services in duplicate ( Biolog , Hayward CA ) . This method utilizes a high-throughput approach to compare respiration of two strains in 1 , 920 different environments , consisting of a variety of carbon , nitrogen , phosphorous and sulfur sources , differences in pH , and an assortment of chemical agents that target a variety of cellular processes . This approach uses the reduction of a tetrazolium dye as a terminal electron acceptor to assess respiratory activity . The amount of respiration was quantified by the extent of color production taking readings every 15 mins and graphed as a kinetic response curve . Incubation , recording and quality control analysis of PM plates 1–20 were performed by Biolog staff using an OmniLog instrument . Relative respiration in each environment was compared using the average height of the kinetic response curves ( h ) . The two strains were considered to have differential growth in an environment if h differed by more than 3 standard deviations of the means of h for both strains . Since differences in respiration do not necessarily reflect differences in growth or fitness , growth rates and in some cases relative fitness ( see below ) of the rtsgp strain was compared with the ancestral strain in a variety of these external environments to confirm that respiration was representative of growth or fitness . These follow-up growth rate assays were confirmatory and qualitative , not quantitative ( Table S2 ) . The fitness of each constructed strain was determined relative to the ancestor by direct competitions as described previously [53] . Briefly , competitions were typically carried out at 37°C in 10 mL of DM25 , the same medium used in the original long-term evolution experiment , in 50 mL flasks with 10 mL beakers as covers . For some competitions glucose was replaced with another carbon source ( β-methyl-D-glucoside ) or supplemented with another compound at various concentrations ( all others ) . These compounds and concentrations were as follows: 1 . 25% β-methyl-D-glucoside , 0 . 5 mM 3-0-β-D-galactopyranosyl-D-arabinose , 50 µM Ara-Ser , 3 mg/mL guanazole , 25 µg/ml EGTA , 100 µM Trp-Ser , 12 µg/mL piperacillin , 100 µM sodium orthovanadate , 32 µg/mL novobiocin , and 10 mM sodium nitrite . The constructed strains were competed against a marked Ara+ ancestral strain ( REL607 ) that is able to utilize the sugar arabinose . The arabinose utilization phenotype was found to be neutral in each of these competitions but allowed for the two different cell types to be easily distinguishable on TA agar plates . Competitors were pre-conditioned in the medium used for the competition for 24 hours prior to all competitions . Each competitor was then standardized based on OD600 values and added to the competition environment . Competitions were typically carried out for three days with a 1∶100 mixture transferred to fresh media every 24 hours . Since the fitness effect of some mutations was small , multiday fitness assays were used to amplify subtle advantages . Mixtures of competing strains were plated on TA agar at the start and end of each competition to determine fitness . Relative fitness ( w ) was calculated as the ratio of natural logarithms of realized growth by each competitor over three days of competition . Assays were typically carried out with five-fold replication and no less than three-fold replication . The fitness values of genotypes in the selective environment assayed in our lab were generally lower than previously reported in a study carried out at the University of Houston with these strains [19] . We do not know the reason for this discrepancy , though lab-specific differences in fitness effects , for example due to differences in water source , have been seen previously [63] . We also tested whether different preconditioning methods influenced the outcome of these fitness assays ( that is , preconditioning cultures in the original evolution environment ( DM25 ) or under competition conditions ) . We found no significant difference in the fitness of two genotypes , tp and sgp , when competed against the ancestor under either preconditioning method ( tp , F2 , 6 = 0 . 667 , P = 0 . 244; sgp , F2 , 6 = 0 . 047 , P = 0 . 258 ) . Notwithstanding the difference , relative features of the fitness landscape do not seem to have changed ( all five beneficial mutations remained beneficial in the selective environment ( DM25 ) ( Figure 1 ) ) . We note also that the analyses reported in this work generally consider the fitness effects of genotypes within a single environment or across two novel environments ( DM25 glucose supplemented with EGTA or guanazole ) used in the experiments carried out at the University of New Hampshire . Importantly , the key result observed in the dataset reported by Khan et al . [19] , that epistasis was negatively correlated with expected fitness , is also seen in the work presented here . Relative fitness , w , was calculated as described above based on the change in the relative density of strains in direct competition with one another . The terms that we use to describe and quantify epistasis were adopted from da Silva et al . [15] . The effect of the interactions among adaptive mutations on relative fitness was calculated as absolute epistasis: ( 1 ) where is the set of mutations , is the fitness of the genotype with the entire set of mutations , and is the relative fitness of a mutant with mutation from that set . The null model assumes no interactions and under this model the fitness of a combination of beneficial mutations is equal to the product of the fitness of those mutations individually . We refer to this null hypothesis as the expected fitness of any combination of mutations . Any significant difference between the observed and expected fitness of a genotype indicates the presence of epistatic interactions . Moreover , the sign of the absolute epistasis is important , suggesting either a negative or positive interaction on the fitness of the genotype . Genotypes consisting of more than two adaptive mutations were further analyzed for net higher-order epistatic interactions , defined as epistasis that occurs between three or more mutations that cannot be explained as the result of constituent lower-order interactions . As a result , net higher-order epistasis was calculated by subtracting the effect of lower-order interactions as shown in equation 2 , ( 2 ) where represents the number of mutations present and represents the fitness of a subset of the mutations present . We used this combination of methods to determine what types of interactions are most important in producing the observed phenotypes . Given the error inherent to calculations of expected fitness and hence , we used the method of error propagation to approximate the error of both parameters [64] . Since expected fitness of a particular genotype is equal to the product of the fitness of those mutations individually , the error ( ) is calculated from the sum of the relative errors of the individual mutations as shown in equation 3 , ( 3 ) where is the standard deviation of single-mutation fitnesses present . Since the uncertainty of ε depends on both and , the error of is the summation of the uncertainty of both as shown in equation 4 , ( 4 ) Epistasis was considered significant using a t-test with the t-statistic calculated as the ratio of the mean relative fitness to its standard deviation and the degrees of freedom based on the number of replicate assays to determine significance ( Table S5 and S6 ) . To identify potential epistatic interactions among the five beneficial mutations in different environments , growth over 24 hours was quantified for the constructed strains containing only one of the five beneficial mutations and compared to both the ancestral strain and the constructed strain containing all five mutations , rtsgp . Cells were grown in 200 µL of DM25 media in 96-well plates with 12 replicates per strain . Relative growth was quantified as AUC based on OD600 measured every 15 minutes for 24 hours , compared to the ancestor , REL606 , and averaged across replicates . Average relative growth of genotypes containing only a single mutation were then used to calculate an expected additive value for gp and rtsgp assuming no epistatic interactions between mutations . The error for expected values was approximated using the method of error propagation described above . Observed and expected relative growth for both gp and rtsgp was compared in each environment using a t-test with the t-statistic calculated as the ratio of the mean relative growth to its standard deviation and the degrees of freedom based on the number of replicate assays to determine significance ( Table S9 , S10 ) . | The fitness effect of beneficial mutations can depend on how they interact with their genetic and external environment . The form of these interactions is important because it can alter adaptive outcomes , selecting for or against certain combinations of beneficial mutations . Here , we examine how interactions between beneficial mutations favored during adaptation of a lab strain of Escherichia coli to one simple environment are altered when the strain is grown in two novel environments . We found that fitness effects were greatly influenced by both the genetic and external environments . In several instances a change in environment reversed the effect of a mutation from beneficial to deleterious or caused combinations of beneficial mutations to become deleterious . Our results suggest that a complex or fluctuating environment may favor combinations of mutations whose interactions may be less sensitive to external conditions . | [
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] | 2013 | The Environment Affects Epistatic Interactions to Alter the Topology of an Empirical Fitness Landscape |
Zinc ( Zn ) is essential for the optimal growth of plants but is toxic if present in excess , so Zn homeostasis needs to be finely tuned . Understanding Zn homeostasis mechanisms in plants will help in the development of innovative approaches for the phytoremediation of Zn-contaminated sites . In this study , Zn tolerance quantitative trait loci ( QTL ) were identified by analyzing differences in the Bay-0 and Shahdara accessions of Arabidopsis thaliana . Fine-scale mapping showed that a variant of the Fe homeostasis-related FERRIC REDUCTASE DEFECTIVE3 ( FRD3 ) gene , which encodes a multidrug and toxin efflux ( MATE ) transporter , is responsible for reduced Zn tolerance in A . thaliana . Allelic variation in FRD3 revealed which amino acids are necessary for FRD3 function . In addition , the results of allele-specific expression assays in F1 individuals provide evidence for the existence of at least one putative metal-responsive cis-regulatory element . Our results suggest that FRD3 works as a multimer and is involved in loading Zn into xylem . Cross-homeostasis between Fe and Zn therefore appears to be important for Zn tolerance in A . thaliana with FRD3 acting as an essential regulator .
Zinc ( Zn ) is an essential micronutrient for a wide range of cellular functions [1] , yet if present in excess , it causes drastic toxicity symptoms resulting in yield reduction and stunted growth [1] . Soil mineral nutrient content varies greatly and plants have adapted to such varied environments in different ways . In highly Zn-contaminated soils only a small range of plants can develop . These Zn-tolerant plants may also hyper-accumulate Zn , like Arabidopsis halleri and Noccaea caerulescens [2] . For phytoremediation , for instance replanting in Zn-contaminated soils , it is crucial to understand the molecular bases of such adaptative processes . A . halleri and N . caerulescens have received extensive attention and various Zn transporters , such as HMA4 and MTP1 , and the Zn chelator nicotianamine were shown to be important for Zn tolerance and accumulation [2]–[6] . However , genetic approaches are not straightforward in these species , mainly because of the lack of available molecular tools , so it may be difficult to find additional genes involved in Zn tolerance in these species . To identify new genes involved in Zn tolerance , an alternative genetic approach is to benefit from how one species , Arabidopsis thaliana , has adapted to the environment and characterize the genetic factors underlying this natural variation . In A . thaliana , the numerous available natural accessions and RIL populations facilitate traditional linkage analyses and genome wide association studies . In particular QTL mapping analyses have already been successful in identifying genetic factors controlling metal tolerance such as aluminum and copper tolerance [7] , [8] . In A . thaliana , the natural variation in Zn accumulation in different organs and growth conditions has been characterized [9]–[15] . Although no Zn tolerance mechanism has been identified yet , A . thaliana accessions display substantial natural variation in their response to excess Zn [16] . In the presence of an excess of a given mineral element in the medium , from a nutritional point of view , there is some competition between this element and related elements; plants need to coordinate homeostasis to avoid ion imbalances . For instance , in A . thaliana , in the presence of an excess of Zn , plants reduce iron ( Fe ) accumulation in shoots becoming prone to Fe-deficiency [17] . In A . halleri , a fine regulation of Fe homeostasis contributes to Zn tolerance [17] . Many studies have revealed the importance of the coordination between homeostatic mechanisms for different metals , but the identification of the corresponding molecular components is rare [18] . Here , we describe the mapping of a QTL for the Zn root response which led to the identification of the AtFRD3 gene , which plays a role in Fe nutrition [19] , as being responsible for the natural variation in the root response to excess of Zn between the Bay-0 and Shahdara A . thaliana accessions . Results of this study confirmed the importance of the coordination between Fe and Zn homeostasis for the tolerance to Zn excess , with FRD3 being a major regulator of the cross homeostasis .
To explore the molecular bases of Zn tolerance variation , a QTL analysis was performed in the Bay-0×Sha recombinant inbred line ( RIL ) population [20] where the trait considered was the relative primary root length ( RelPR150 ) between high Zn ( 150 µM Zn ) and low Zn ( 1 µM Zn ) control conditions ( Figure 1A , 1B , Table S1 , Figure S1 ) . Three QTLs named ZnT1 , ZnT2 , and ZnT3 were localized on chromosomes 1 , 3 and 5 , respectively . No epistasis was observed either between the identified QTLs or between the QTLs and other regions of the genome . The ZnT1 and ZnT2 QTLs were confirmed by analyzing relative primary root length in the progeny of heterogeneous inbred families in which either the Sha or Bay allele was fixed ( Figure S2 ) . ZnT2 was the main-effect QTL explaining 29% of the total phenotypic variation ( Table S1 ) ; the recessive Sha allele ( Figure S3 ) was responsible for the greater reduction in primary root length observed in response to 150 µM Zn compared to the Bay-0 ( Bay ) allele ( Table S1 ) . A mapping population of 2 , 296 plants derived from a near-isogenic line ( NIL ) bearing a Sha introgression at the ZnT2 QTL in an otherwise Bay background ( NIL[Sha]; Figure S4 ) was analyzed and ZnT2 was mapped to a 23-kb region spanning 7 genes ( Figure 1C ) . Among these genes , the FERRIC REDUCTASE DEFECTIVE3 ( FRD3 ) gene was considered a good micronutrient-related candidate as it encodes a citrate efflux transporter involved in Fe nutrition [19] , [21]–[23] . To confirm a functional role of FRD3 in the Zn response by genetic complementation , wild-type Col-0 and the frd3-7 knock-out mutant were both crossed separately with Bay-0 and NIL[Sha] plants and the primary root lengths of F1 progeny were measured . The primary root growth of NIL[Sha] , inhibited in the presence of 150 µM Zn , was complemented with the FRD3Col allele but not with the frd3-7 mutant allele . The primary root length of Bay-0 was little affected by the presence of either allele . This indicates that the ZnT2 QTL is likely to be allelic to the FRD3 candidate gene ( Figure S1 , Figure S5 ) . Independently , the dominant FRD3Bay allele was introduced into NIL[Sha] plants by genetic transformation . This resulted in the conversion of the recessive Zn-sensitive phenotype of NIL[Sha] plants into a more Zn-tolerant phenotype ( Figure 2 ) . Together , these two approaches demonstrate that FRD3 has a functional role in determining the Zn-responsive ZnT2 trait . Sequencing the FRD3 gene in the Bay-0 and Shahdara accessions revealed differences due to multiple nucleotide substitutions , deletions and insertions ( Figure 3A , Figure S6 ) . The main variations differentiating the FRD3Bay and FRD3Sha alleles were 27-bp and 28-bp indels in the promoter region , two non-synonymous substitutions in exon 2 in the FRD3Sha allele and a 12-bp indel in the last intron . Other differences were a 4-bp indel in the 3′ UTR , ten 1- or 2-bp indels in non-coding regions , 5 synonymous SNPs in coding regions and 48 SNPs in non-coding regions . The non-synonymous substitutions in exon 2 induce N116S and L117P substitutions in the FRD3Sha polypeptide compared to FRD3Bay and FRD3Col polypeptides . Based on the analysis of the five main allelic variations in 109 A . thaliana accessions , five haplotypes were found ( Figure S7 , Table S2 ) . Interestingly , the 28-bp deletion present in the promoter and the two non-synonymous substitutions in exon 2 were always associated with each other and this haplotype was found in 6% of the 109 accessions ( Figure S7 ) . No haplotype was found in which the promoter deletion and non-synonymous substitutions were separate . We hypothesized that the 28-bp deletion present in the promoter and/or the two non-synonymous substitutions present in exon 2 , as found in the FRD3Sha allele , are the allelic variations responsible for the Zn-sensitive phenotype . A review of the types of nucleotide polymorphisms that underlie QTLs revealed that such a combination of nucleotide polymorphism in both promoter and coding regions , altering both gene expression and protein function , is not rare [24] . AtFRD3 is reported to be a citrate efflux transporter [22] , so the functionality of the different protein variants was tested using a citrate efflux assay in Xenopus oocytes . The FRD3Bay or FRD3Col proteins were found to mediate citrate efflux , but the N116S and L117P substitutions abrogated this function in the FRD3Sha protein ( Figure 3B ) . The FRD3Bay and FRD3Sha genes are also expressed differently . Expression of FRD3Bay was induced by an excess of Zn and by Fe shortage ( FRD3 plays a role in Fe deficient conditions [19] , [21]–[23] ) , while FRD3Sha was expressed at a markedly lower level under the same conditions ( Figure 3C ) . Genotype-dependent expression of FRD3 under Fe deficiency has already been observed in natural accessions [25] . Thus the FRD3Sha gene , the Zn-sensitive allele , not only encodes a non-functional transporter but is also weakly expressed in the presence of high concentrations of Zn in the medium . FRD3 belongs to the MATE family , one of the multidrug transporter families encountered in all living organisms [26] . Little is known of how MATE transporters work . When FRD3Col was co-expressed with FRD3Sha in Xenopus oocytes the ability of the FRD3Col protein to efflux citrate was completely lost ( Figure 3B ) . This would suggest first that FRD3Sha interacts with FRD3Col , indicating that the FRD3 transporter functions as a multimer , and second that FRD3Sha is a dominant negative isoform . The discovery that N116 and L117 are crucial for FRD3 function and that FRD3 likely functions as a multimer is thus an important step in understanding the general mode of action of MATE transporters . The apparent dominant negative effect of FRD3Sha in the citrate efflux assay in Xenopus oocytes may seem inconsistent with the observed recessive character of the Zn-sensitive phenotype associated with the FRD3Sha allele in planta . Compared to the control condition , transcript levels of the FRD3Sha allele are 2 . 5 to 5 times lower in response to Zn excess , while transcript levels of the FRD3Bay allele are 1 . 5 to 2 times higher in the same condition ( Figure 3C ) . We therefore favor the hypothesis that in planta , the effect of down regulation of the FRD3Sha transcript predominates over the dominant negative effect of the FRD3Sha protein . On this point , no accession harboring only the non-synonymous substitutions leading to the non-functional FRD3Sha isoform was found after screening 109 A . thaliana accessions . This suggests that these mutations are strongly selected against and that a mutation that reduces transcription is needed to overcome the dominant negative effect of the non-synonymous substitutions . FRD3 transcript accumulation was compared in NILs and natural accessions in relation to the haplotype . While FRD3 expression was induced by Fe deficiency and by Zn excess in Bay-0 , NIL[Bay] and Col-0 , regulation occurred in the opposite sense in Sha and NIL[Sha] ( Figure 3C ) . When correlated to the presence of certain polymorphisms in the gene particularly within the FRD3 promoter , the results point to the likely existence of local Zn- and Fe-responsive , possibly cis-acting , regulatory elements ( Figure 3C , Figure S7 ) . The differential induction of FRD3Bay and FRD3Sha transcripts by Fe deficiency was tested through allele-specific expression assays in F1 individuals . The differential induction was maintained in the presence of the contrasting allele indicating that the response is controlled in cis ( Figure S8A ) . The cis-acting differential regulation was confirmed in a cross between Ct-1 and Ita-0 ( Figure S8B ) , where Ct-1 is a Bay-like accession in terms of FRD3 transcriptional induction and sequence ( Table S2 ) , and Ita-0 only differs from Ct-1 at the 27bp-polymorphism ( Figure S7 ) and is not transcriptionally responsive to Fe deficiency . Therefore , although we cannot totally exclude the possibility that polymorphisms outside the sequenced region play a role , the indel sequences identified are probably important . None of the known Zn- or Fe-responsive cis-elements from plants is present in the Bay or Col alleles of the FRD3 promoter . The region around the 27-bp sequence in the FRD3 promoter region may therefore represent a new type of metal-responsive cis-regulatory element . FRD3 is known to be involved in Fe homeostasis [19] , [21]–[23] . More precisely , FRD3 releases citrate into the xylem so Fe can be solubilized , transported to the shoots and loaded into leaf cells . Variation at the FRD3 locus might therefore be expected to affect the Fe content of xylem sap . As expected , the Fe concentration in xylem sap of Sha plants and NIL[Sha] plants , which have the non-functional FRD3Sha allele , was much lower than in plants harboring the FRD3Bay-0 allele ( Figure 4A ) . However , unlike frd3 mutants in the Col-0 background [19] , [23] , [27] , they were neither dwarf nor chlorotic , and under control conditions , the shoot and root Fe content was normal . Also no Fe overload was observed in the root stele of plants carrying the FRD3Sha allele ( Figure 4B , Figure S9 , Figure S10 ) . No indication of Fe deficiency , such as increased mRNA levels or constitutive activity of the root ferric reductase oxidase FRO2 , was detected in plants harboring the FRD3Sha allele grown in control conditions , in contrast to what has been observed in frd3Col mutants [19] ( Figure S11 , Figure S12 ) . Xylem exudates of NIL[Sha] plants did not contain less citrate than Bay-0 plants ( Figure S13 ) . Altogether these observations may suggest that FRD3Sha is a leaky allele of FRD3 . Even an inefficient transport activity may be sufficient to avoid the severe phenotypes that are observed with the frd3 knock-out mutation . However , this hypothesis is not in agreement with the oocyte assay data showing that FRD3Sha is not functional . An alternative hypothesis could be that FRD3Sha is a strong mutant allele and that another mechanism is responsible for loading citrate or another Fe-chelating agent into the xylem to compensate for the lack of FRD3-driven citrate delivery in Shahdara and Bay-0 . To test the latter hypothesis , F2 plants issuing from a cross between Shahdara and frd3-7 ( KO mutant in Col-0 background ) , were assayed for Fe overaccumulation in the root stele using Perls' stain as a rapid assay of the functionality of FRD3 [21] . A quarter of the F2 population showed Fe overload in the root stele , indicating that this trait was under the control of one recessive locus ( Table S3 ) . Importantly , some of the F2 plants showing Fe overaccumulation in the root stele were not homozygous for the frd3 knock-out and two plants showing no Fe overload in the root stele were homozygous for the frd3 knock-out ( Table S3 ) . This result means that it is unlikely that FRD3Sha is a leaky allele of FRD3 and that a still uncharacterized mechanism can compensate , at least partially , for the lack of functionality of FRD3 , and is thus also involved in the translocation of Fe and Zn ions from the roots to the shoot . This mechanism is present in Shahdara , but it is missing in Col-0 , thus explaining why the frd3 mutant phenotype is stronger in the Col-0 background than in the Shahdara background . This compensatory mechanism is also most likely active in the four other accessions that harbor the FRD3Sha haplotype , two of which ( Hiroshima and 9481B ) being even more tolerant to Zn excess than Bay-0 ( Table S2 ) . We investigated whether FRD3 has a direct impact on Zn homeostasis . The Zn concentration in the xylem exudates of NIL[Sha] plants was lower than that of Bay-0 plants in control conditions ( Figure 4C ) , indicating that FRD3 plays a role in the loading of Zn into the xylem . In addition , in the presence of high concentrations of Zn in the culture medium , shoots of NIL[Sha] plants contained less Zn than Bay-0 shoots ( Figure 4D ) . This clearly indicated that FRD3 is involved in the translocation of Zn from the roots to the shoot in A . thaliana . The mechanism by which FRD3 is involved in Zn tolerance in roots is in contrast more difficult to infer from the data . A positive correlation between translocation of Zn from root to shoots and Zn tolerance in roots has already been established from the analysis of heavy metal atpase 4 ( hma4 ) mutant lines and HMA4-overexpressing lines [28]–[30] . The interpretation was that reducing Zn translocation from the roots to the shoot resulted in an increase in the Zn content in roots that would be the cause of an increased sensitivity to Zn in roots . This interpretation may not be of great help to interpret our data . Whatever the Zn concentration in the medium , the presence of the FRD3Sha allele does not induce any increase in the Zn content in roots compared to the FRD3Bay allele ( Figure S9 ) . It is possible that although the Zn content is similar in roots of plants harboring the FRD3Sha or FRD3Bay allele the Zn distribution is different within the roots and that this difference would results in a difference in the root sensitivity to Zn . An alternative hypothesis could however be that the impact of FRD3 on Zn tolerance results from an impairment in Fe homeostasis . We therefore investigated the phenotypic relationship between Zn and Fe homeostasis . High concentrations of Zn in the medium induced a decrease in the shoot Fe content ( Figure 4B ) , mimicking Fe limiting conditions ( Figure 3C , Figure S11 ) . Vice versa increasing the Fe concentration in the medium resulted in a decrease in shoot Zn content ( Figure 4D ) . Similar data have already been reported [17] . In response to the Zn constraint , the shoot Fe content in plants harboring the FRD3Sha allele was more reduced than in plants harboring the FRD3Bay-0 allele ( Figure 4B ) . Also , in the presence of excess Zn , more FRO2 ( Ferric Reduction Oxidase ) transcripts were expressed in NIL[Sha] plants than in Bay-0 plants ( Figure S11 ) , indicating that FRD3Sha confers sensitivity to Zn-induced Fe deficiency . Therefore we have shown that FRD3 acts at an intersection between Fe and Zn nutrition . As shown above , FRD3 controls the loading of both Zn and Fe into the xylem and these two metals appear to compete for root-to-shoot transport . This conclusion leads to the hypothesis that the primary reason for which FRD3 has an impact on Zn tolerance would be that excess Zn impairs Fe nutrition and that FRD3 is an important control point in the Zn-Fe relationship . This is in agreement with , and may explain , recent observations on how the cross-homeostasis between Fe and Zn deals with excess Zn in A . halleri and A . thaliana [17] . In particular , exposing A . halleri plants to high Zn concentrations induced neither a marked alteration in Fe root-to-shoot transport nor Fe deficiency [17] , which can be related to the fact that FRD3 expression is 45 times higher in A . halleri than in A . thaliana [31] , and thus would not be a limiting factor in the transport of either Zn or Fe from the roots to the shoot . In conclusion , we show that the cross-homeostasis between Fe and Zn is important in the A . thaliana response to excess Zn and that FRD3 is an essential regulator of this cross homeostasis . This new perspective on FRD3 , including the potential multimeric topology of the FRD3 transporter and the presence of an Fe deficiency-responsive element in the promoter , provides clear directions for further study of how FRD3 contributes to regulating plant micronutrient status .
QTL were identified from 165 Bay-0×Shahdara RIL lines [20] and validated using the HIF004 , HIF044 and HIF338 lines available from INRA Versailles Genomic Resource Centre ( http://dbsgap . versailles . inra . fr/vnat/ ) . NIL[Bay] and NIL[Sha] were obtained from RIL112 and RIL070 following three successive back-crosses with the Shahdara and Bay-0 parental lines , respectively . After each backcrossing step , lines were selected with 38 microsatellite markers [20] . At the end of the process , NIL[Bay] plants had a Bay allele at marker ATHCHIB2 and Sha alleles at the other 37 markers . The reverse was true for NIL[Sha] plants . Seeds of frd3-7 plants were kindly provided by C . Curie ( BPMP , Montpellier , France ) . Zn tolerance phenotypes were determined as previously described [16] . Plants were grown in the presence of 1 µM ZnSO4 ( control condition ) and in the presence of 150 µM ZnSO4 ( high Zn condition ) . For each genotype the primary root length was measured in both conditions to obtain the relative primary root length ( RelPR150 = ( PR150/PR1 ) ×100% ) . For Fe-response assays , plants were grown on control medium for 7 days and transferred to either Fe-deficient ( Fe 0 ) or Fe-sufficient ( Fe 50 ) medium for 4 days before collecting roots for RNA preparation or ferric reductase assays . The Fe-deficient medium was control medium without NaFeEDTA but with 300 µM ferrozine . For the sampling of xylem exudates and Perls staining , plants were grown in compost for 6 or 3 weeks respectively in a growth chamber ( 20°C , 180 µmol . m−2 . s−1 and an 8-h light/16-h dark photoperiod ) . Xylem exudates were collected by removing rosettes with a scalpel then placing a glass capillary tube on the root after discarding the first droplet exuded . After 30 min or 2 h of sap collection , xylem exudates were placed on ice then stored frozen at −20°C . The citrate content of xylem exudates ( 50 µl to 100 µl from the 2-h collection ) was analyzed by high performance ionic chromatography ( LC20 , Ionex ) using an IonPac AS11 column and a 1 mM to 22 mM NaOH gradient . The Zn and Fe contents of xylem exudates ( 5 µl from the 30-min collection ) were estimated by atomic absorption spectrophotometry using graphite tube atomizers GTA220 ( Varian ) with omega platform tubes for Zn and partitioned tubes for Fe . Zn concentration in plant tissues was assessed as previously described [16] and Fe concentration in plant tissues was measured by the absorbance of Fe2+-o-phenanthroline at 510 nM [32] . Core-Pop165 plants from the Bay-0×Shahdara RIL population [20] were grown on agar plates as previously described [16] . Primary root lengths were determined for ten plants from each of control ( 1 µM ZnSO4; PR1 ) and excess Zn ( 150 µM ZnSO4; PR150 ) agar plates . Relative primary root length ( RelPR150 = ( PR150/PR1 ) ×100% ) was analyzed using QTLCartographer ( http://statgen . ncsu . edu/qtlcart/ ) . Composite interval mapping ( CIM ) was performed using model 6 and the LOD significance threshold was obtained from permutation analyses . The percentage of variance explained by each QTL and its predicted allelic effect were obtained from QTLCartographer . Fine-mapping populations were obtained by crossing NIL[Sha] plants with Bay-0 plants , producing an F1 population which was self-pollinated to produce an F2 population . DNA was extracted from plants ( grown on soil in the greenhouse ) by freeze-drying and grinding cotyledons in 300 µl buffer ( 100 mM Tris HCl , 1 . 5 M NaCl , 20 mM EDTA , 2% ( w/v ) mixed alkyltrimethyl ammonium bromide ( Sigma ) , 0 . 5% ( w/v ) sodium sulfite , 1% ( v/v ) PEG6000 ) . After chloroform extraction , DNA was precipitated using isopropanol , dried then dissolved in 50 µl water . Fine-mapping was done in two steps; first 624 plants were screened for recombination between MSAT302422 and CAPS7012599 , then 1 , 672 plants were screened for recombination between MSAT302503 and MSAT2630717 . Markers used for genotyping are described by INRA Versailles Genomic Resource Centre ( http://dbsgap . versailles . inra . fr/vnat/ ) or in Table S4 , for those newly identified here . FRD3 alleles were amplified from genomic DNA of Bay-0 and Shahdara using KlenTaq LA DNA Polymerase Mix ( #D5062 , Sigma Aldrich ) with primers promo1F , promo2F , exon2R , intron4F , exon12R and post3 ( see Table S5 for primer sequences ) and three independent PCR products were sequenced . The genomic sequences of FRD3 including the promoter were amplified from Bay-0 and Shahdara genomic DNA ( Table S5 ) , cloned in the pGEM-T vector , verified by resequencing , then cloned in the pGREEN0179 vector [33] . Binary recombinant vectors were introduced into Agrobacterium tumefaciens strain GV3101 , which was used to transform NIL[Sha] plants with either FRD3Bay or FRD3Sha by the floral dip method [34] . Transformants were selected on MS/2 agar plates with 50 mg L−1 hygromycin . Single T-DNA insertion and homozygous lines were successively selected during segregation analysis . RNA extraction , cDNA preparation and real-time quantitative RT–PCR were done as previously described [16] . Primers used are listed in Table S5 . Three independent biological experiments were done to analyze transcript levels , once relative to ACT2/ACT8 [35] transcript levels and once relative to transcript levels of ACT2/ACT8 [35] , clathrin [36] ( At4g24550 ) , At5g12240 [37] and PP2A [37] ( At1g13320 ) . Similar results were observed in the two technical replicates . Two pairs of accessions were analyzed using different SNPs , Bay-0 versus Shahdara and Ct-1 versus Ita-0 . Parents and F1 individuals ( from reciprocal crosses ) were grown in vitro as described above and transferred onto Fe-deficient media . RNA was extracted from roots and cDNA prepared as described above . Pyrosequencing reactions were set up around SNPs in the parental coding sequence of FRD3 to assess the relative contribution of each allele to the mRNA population of mRNA [38] . Pyrosequencing was performed on F1 cDNA , on 1∶1 mixtures of parental cDNA , and on F1 genomic DNA as a control to normalize the ratios against possible pyrosequencing biases . Anything significantly driving allele-specific expression in hybrids is , by definition , acting in cis as F1 nuclei contain a mix of all trans factors [39] . In the Bay/Sha experiment , SNP1 and SNP2 interrogate ACA[A/G]GA[T/C]TGG with primers PyroSNP1-2_F and PyroSNP1-2_R-biotin for the PCR and PyroSNP1-2_Seq for the pyrosequencing reaction . In the Ct-1/Ita-0 experiment , SNP3 interrogates AA[C/T]GAT with primers PyroSNP3_F-biotin , PyroSNP3_R and PyroSNP3_Seq; and SNP4 interrogates TC[G/A]TTA with primers PyroSNP4_F , PyroSNP4_R-biotin and PyroSNP4_Seq ( Table S5 ) . The predicted cDNAs of the different FRD3 alleles ( Figure 4 , Figure S6 ) were obtained from FRD3Col cDNA ( G14324; pENTR223_FRD3Col ) by site-directed mutagenesis using the QuikChange Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA , USA ) . We obtained FRD3Bay cDNA by introducing a single base modification in exon 9 of FRD3Col cDNA and FRD3Sha cDNA by introducing the two SNPs in exon2 of FRD3Bay cDNA ( see Table S5 for primer sequences ) . All mutated cDNAs were sequenced to verify the mutation ( s ) and the complete sequence . These cDNAs were transferred from pENTR to the pGEM-GWC vector by recombination using LR-clonase ( Life Technologies , Grand Island , USA ) . The different FRD3 cDNA clones were linearized with PstI then transcribed using mMessage mMachine T7® Ultra Kit ( Life Technologies ) . Xenopus laevis oocytes ( CRBM , CNRS , Montpellier , France ) were prepared as previously described [40] and injected with 20 ng of RNA coding for FRD3Col , FRD3Bay or FRD3Sha using a micropipette ( 10–15 µM tip diameter ) and a pneumatic injector . Three days after injection , 14 oocytes ( either injected with FRD3 cRNAs or uninjected ) were placed in modified Ringer solution ( in which HEPES was replaced by 1 mM Tris ) supplemented with 10 mM citrate . Each batch of oocytes was injected with 25 nl of 100 mM 13[C]-citrate ( Sigma ) . After 5 min of recovery , oocytes were washed five times in 15 ml of cold modified Ringer solution ( pH 6 . 5 ) and placed in 1 ml of modified Ringer buffer for efflux measurement . After 20 min , 100 µl of efflux buffer was sampled in three replicates and the 13[C] abundance ( atom % ) was analyzed by continuous-flow mass spectrometry using the Euro-EA Eurovector elemental analyzer coupled to an IsoPrime mass spectrometer ( GV Instruments ) . Once the normality of residues had been tested , either one-way ANOVA and Tukey tests ( for parametric comparison of means ) or Kruskal-Wallis test followed by a non-parametric comparison of means were used . All these tests used an α-value of 0 . 05 and were done with the R software ( using shapiro , bartlett , aov , TukeyHSD , kruskal and nparcomp functions of R; R Development Core Team ) . Two-way ANOVA was used to test the interaction between genotypes in F1 progeny using the aov function of R software . To test differences in relative primary root growth values , confidence intervals ( α-value of 0 . 05 ) were calculated after ln transformation of data [41] . The genomic DNA sequences of FRD3 reported in this paper have been deposited in the EMBL Nucleotide Sequence Database: HE803766 ( Bay-0 ) and HE803767 ( Shahdara ) . | Plants are adapted to soils in which the amounts of different nutrients vary widely , like Zn-deficient or Zn-contaminated soils . Exploring the molecular bases of plant adaptation to Zn-contaminated soils is important in determining strategies for phytoremediation . Here , we describe the mapping and characterization of a QTL for Zn tolerance in A . thaliana that underlies the natural variation of the root response to excess Zn . This physiological variation is controlled by different alleles of the AtFRD3 gene , which codes for a citrate transporter that uploads citrate into the xylem sap , hence playing a role in Fe homeostasis . In the Zn-sensitive accession Shahdara , the expression of AtFRD3 is drastically reduced and the protein encoded is unable to efflux citrate in vitro . Less Fe and Zn are found in Shahdara root exudates , and less Fe and Zn are translocated from root to shoot when Zn is in excess . We deduce that a fine-tuned Fe and Zn homeostasis is crucial for Zn tolerance in A . thaliana . Finally , as a range of alleles were identified , some rare , it was possible to define a sequence motif that is a putative metal-responsive cis-element and demonstrate that two amino acids are essential for the function of the FRD3 transporter . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"plant",
"science",
"plant",
"biology",
"plant",
"genetics",
"biology",
"plant",
"physiology"
] | 2012 | Natural Variation at the FRD3 MATE Transporter Locus Reveals Cross-Talk between Fe Homeostasis and Zn Tolerance in Arabidopsis thaliana |
Parkinson’s disease ( PD ) is a neurodegenerative disorder characterized by the loss of dopamine ( DA ) neurons in the substantia nigra pars compacta ( SNc ) . Rare genetic mutations in genes such as Parkin , Pink1 , DJ-1 , α-synuclein , LRRK2 and GBA are found to be responsible for the disease in about 15% of the cases . A key unanswered question in PD pathophysiology is why would these mutations , impacting basic cellular processes such as mitochondrial function and neurotransmission , lead to selective degeneration of SNc DA neurons ? We previously showed in vitro that SNc DA neurons have an extremely high rate of mitochondrial oxidative phosphorylation and ATP production , characteristics that appear to be the result of their highly complex axonal arborization . To test the hypothesis in vivo that axon arborization size is a key determinant of vulnerability , we selectively labeled SNc or VTA DA neurons using floxed YFP viral injections in DAT-cre mice and showed that SNc DA neurons have a much more arborized axon than those of the VTA . To further enhance this difference , which may represent a limiting factor in the basal vulnerability of these neurons , we selectively deleted in mice the DA D2 receptor ( D2-cKO ) , a key negative regulator of the axonal arbour of DA neurons . In these mice , SNc DA neurons have a 2-fold larger axonal arborization , release less DA and are more vulnerable to a 6-OHDA lesion , but not to α-synuclein overexpression when compared to control SNc DA neurons . This work adds to the accumulating evidence that the axonal arborization size of SNc DA neurons plays a key role in their vulnerability in the context of PD .
PD is a neurodegenerative disorder primarily characterized by a massive loss of DA neurons in the SNc that is also thought to be accompanied by the loss of other types of neurons in a select subset of brain regions including the locus coeruleus and the pedunculopontine nucleus [1] . Canonical symptoms include a range of motor deficits , but PD patients also often suffer from non-motor symptoms including olfactory deficits and constipation . Inherited mutations in gene products such as Parkin , Pink1 , DJ-1 , α-synuclein , LRRK2 or GBA are found in approximately 15% of cases . These gene products are involved in basic cellular processes including mitophagy , oxidative stress handling , mitochondrial antigen presentation , vesicular trafficking and lysosomal function . One of the key unanswered questions in PD research is why alterations in such ubiquitous processes lead to selective degeneration of a select subset of neuronal populations in the brain including SNc DA neurons . A striking example of this selectivity is the much higher resilience of the neighboring DA neurons of the ventral tegmental area ( VTA ) , which are far less affected than SNc DA neurons in PD [1] . In the last few decades , many hypotheses have been raised about the core characteristics of SNc neurons that are responsible for their large bioenergetic requirements and that could explain their selective vulnerability . These include , but are not limited to , pacemaking activity [2] , high DA- and iron-related toxicity [3 , 4] and possessing a highly elaborate , long-range axonal arborization [5–8] . All these characteristics are thought to exert an important pressure on the capacity of these cells to efficiently produce energy and cope with the associated oxidative stress . In this context , any other subsequent cellular stresses associated with some of the genetic alterations mentioned above , as well as aging and exposure to environmental toxins could trigger the disease . We have previously showed in vitro that SNc DA neurons have a higher basal rate of mitochondrial oxidative phosphorylation and ATP production and a smaller reserve capacity compared with the less vulnerable DA neurons of the VTA , characteristics that appear to be the result of the highly complex axonal arborization of these neurons [8] . We therefore postulated that the size of this axonal arborization might be a significant contributor to the differential vulnerability of SNc and VTA DA neurons in PD . Based on our previous work and on modelling of the impact of the axonal arborization size on energy requirements [9 , 8] , it is possible that the relatively small size of the axonal arborization of mouse DA neurons compared to humans ( 10 fold smaller ) could explain the apparently high resilience of mouse DA neurons and the associated difficulty to produce optimal animal models in this species . Indeed , mouse models with genetic deletions of the key genes found in familial forms of the disease generally do not present age-dependent neuronal loss [10–14] . If the smaller axonal arborization size of mouse DA neurons is a key limiting factor for their vulnerability , it might be possible to increase this vulnerability by increasing their axonal arborization size in vivo . In order to reach this objective and test our hypothesis , we generated mice with a conditional deletion of the DA D2 receptor in DA neurons ( D2-cKO ) . Increased DA terminal density has been suggested to occur under chronic D2 antagonist administration [15 , 16] and in the constitutive knockout model of this receptor [17] and D2 agonists have been shown to reduce the density of axon terminals established by DA neurons [15 , 18] . Here we surmised that a cell-specific knockout of this receptor in DA neurons should lead to an increased size and complexity of the axonal arborization of these neurons and increase their intrinsic vulnerability . We find that in the intact mouse brain , the axonal arborization size of SNc DA neurons is 3-fold larger than that of less vulnerable VTA DA neurons . We further demonstrate that in D2-cKO mice , the axonal arborization size of SNc DA neurons is 2-fold larger relative to control mice , a phenotype associated with impaired evoked DA release and increased vulnerability to 6-OHDA , but not to α-synuclein overexpression . This work provides strong evidence in favor of the hypothesis that the axonal arborization size of SNc DA neurons plays a key role in regulating their basal vulnerability in the context of PD .
If axonal arborization size is a critical determinant of the selective vulnerability of SNc DA neurons , a prediction is that the axonal domain of these neurons should be more arborized than that of the more resilient VTA DA neurons in vivo . Since there is no specific axonal marker to distinguish between SNc and VTA DA neurons , we injected a small amount of floxed AAV2-eYFP in the SNc or the VTA of adult DATCre/+ mice to label a few hundred ( ~300–1000 ) DA neurons from one or the other population ( Fig 1A ) . For more examples of SNc targeted infections , see . S1 Fig . After immunolabeling and manual counting of the infected neurons , we quantified the extent of the related axonal arborization within the striatum using confocal imaging to systematically sample images from slices throughout the rostro caudal axis ( Fig 1B ) . As expected , the majority of SNc DA neurons projections were found in the dorsal striatum and the majority of VTA DA neurons projections were found in the ventral striatum . We next extrapolated the arborization density obtained from each slice to the size of the striatum on that slice and normalized it by the number of infected neurons . Finally , we plotted the arborization area obtained as a function of bregma coordinates for VTA ( Fig 1C ) and SNc ( Fig 1D ) targeted infections . Comparing the extent of the total arborization revealed a 3-fold larger axonal arbour for SNc compared to VTA DA neurons ( Fig 1E and 1F ) . Because an increase in axonal arbour size could increase the vulnerability of DA neurons , we aimed at increasing the axonal arborization of DA neurons by selective genetic deletion of the DA D2 receptor . To do so , we crossed DATIRES-Cre mice with Drd2loxP mice and generated DATIRES-Cre/+; Drd2loxP/loxP mice as previously described [19] . Control mice were heterozygotes for Cre expression ( DATIRES-Cre/+; Drd2+/+ ) . In these D2-cKO adult mice , we examined the axonal varicosities of DA neurons by measuring TH and DAT immunolabeled structures using confocal imaging in the ventral and dorsal striatum ( Fig 2A and 2B ) . We observed no change in the area covered by TH signal ( Fig 2C ) , the TH mean signal intensity ( Fig 2D ) or total TH signal ( Fig 2E ) in any part of the striatum . However , we observed an increased area covered by the DAT signal in the dorsal striatum ( Fig 2F ) with an increased DAT signal intensity ( Fig 2G ) , which resulted in a more than 2-fold increase in total DAT signal ( Fig 2H ) . No changes were observed in the ventral striatum . This increased DAT signal in the dorsal striatum was not the result of changes in the number ( Fig 2I ) or size ( Fig 2J ) of striosomes and was not a result of an increased number of DA neurons in the SNc , VTA or retrorubral field ( RRF ) , as determined by unbiased stereological counting ( Fig 2K ) . To confirm that this increased dorsal striatal DAT signal was the result of an increase in the axonal arborization size of SNc DA neurons , we again used conditional viral labelling to visualize the axonal domain of SNc and VTA DA neurons in D2-cKO mice . We observed a 2-fold increase in the axonal arborization size of SNc DA neurons in D2-cKO mice ( Fig 3A ) , with no change for VTA DA neurons ( Fig 3B ) . Comparing axonal arborization size of SNc and VTA DA neurons from control mice again showed a 3-fold difference between the two populations ( Fig 3A vs 3B ) . To better characterize this expanded axonal arbour originating from SNc D2-cKO DA neurons , we next measured the level of colocalization of virally-expressed YFP with DAT or TH ( Fig 3C ) . There was an increased colocalization of TH or DAT with the YFP-labelled axonal varicosities of D2-cKO mice and a general increased colocalization of TH and DAT inside these processes ( Fig 3D ) . To evaluate if these new processes were likely to be functional and able to release DA , we measured the colocalization of VMAT2 with YFP ( Fig 3E ) and found it to be unchanged ( Fig 3F ) . We also found an increased colocalization of VMAT2 and DAT inside these processes . An increased density of dopaminergic axonal fibers in the striatum , as well as the genetic removal of the pre-synaptic D2R known to control DA synthesis and release , could lead to increased DA release . Alternately , the enhanced bioenergetic requirements associated with a broader axonal arbour could lead to impaired DA neurotransmission . To distinguish between these possibilities , we quantified DA release evoked by single electrical pulses in acutely prepared striatal brain sections from D2-cKO and control mice using fast-scan cyclic voltammetry ( Fig 4A ) . We found that DA release was significantly reduced in the dorsal and ventral striatum ( Fig 4B ) . However , this difference was greatly diminished following incubation with the DAT antagonist nomifensine ( Fig 4C ) . This observation of a partial rescue with nomifensine , coupled with our observation of increased striatal DAT immunoreactivity ( Fig 2C–2E ) could imply that increased DAT function in D2-cKO mice was the cause of the reduced activity-dependent DA overflow . Alternately , as DAT blockers including nomifensine and cocaine have been reported to also promote DA release though other mechanisms [20–22] , the apparent rescue could result from an enhancement of DA release and not reuptake . To distinguish between these two possibilities , we examined the kinetics of DA release . Comparing D2-cKO and control mice , we found no change in kinetics of DA reuptake ( tau ) or in the maximal rate of reuptake ( Vmax ) in the dorsal ( Fig 4D ) , or ventral ( Fig 4E ) striatum , suggesting no robust change in DAT function in D2-cKO mice . To further address this issue , we also performed a surface biotinylation assay from the striatum of a separate cohort of control and D2-cKO mice and confirmed that there were no significant changes in surface DAT levels in the striatum in the absence of D2 autoreceptors ( Fig 4F ) . As an increase in axonal arbour size in D2-cKO SNc DA neurons is predicted to induce a larger bioenergetic burden on these neurons , we next examined their vulnerability in two different mouse models of PD: the α-synuclein viral overexpression model and the intra-striatal 6-OHDA model . AAV-mediated wild-type α-synuclein overexpression was achieved by stereotaxic injection into the mesencephalon ( Fig 5A ) . Three months after virus injection , stereological counting revealed a loss of 25–35% of DA neurons in the SNc and RRF ( Fig 5B and 5C ) , with no change in the number of non-DA neurons ( Fig 5D ) and no significant change in the VTA ( Fig 5E ) . This cell loss in the SNc and RRF was not significantly different in D2-cKO mice compared to control mice . We also observed the presence of phosphorylated α-synuclein positive cell bodies ( Fig 5A ) , a good indicator of the toxicity induced by the overexpression . In the dorsal striatum only ( Fig 5F ) , we observed a small 20% decrease in TH signal area ( Fig 5G ) and total signal ( Fig 5H ) with no change in signal intensity ( Fig 5I ) . At the behavioral level , mice overexpressing α-synuclein only showed a modest increased preference for ipsilateral paw use ( S2A Fig ) , with no change in the total number of steps and no difference between D2-cKO and CTL mice ( S2B Fig ) . In the rotation test , neither basal nor amphetamine-induced rotational preferences were altered ( S2C and S2D Fig ) , with amphetamine inducing an expected increase in total number of rotations ( S2E Fig ) . These finding are in keeping with the modest level of cell loss and striatal denervation in this model . We next examined the vulnerability of DA neurons using a second , different model of PD using the DA neuron-specific toxin 6-OHDA . Unilateral injection in the dorsal striatum at a low dose ( 1 . 5μg in 0 . 5 μL ) ( Fig 6A ) was performed in order to produce a partial loss of dopaminergic cell bodies ( Fig 6B ) . In control mice , one month after the 6-OHDA lesion , we measured an approximate 40% loss of DA neurons in the SNc ( Fig 6C ) and the RRF ( Fig 6D ) , with no significant loss in the VTA ( Fig 6E ) or for non-DA of the SNc neurons ( Fig 6F ) . Interestingly , in the D2-cKO mice , approximately 60% of SNc DA neurons were lost , representing almost 50% more neurodegeneration than for control mice ( 60% loss vs 42% loss for CTL ) ( Fig 6C ) . As for axon terminals , TH signal area ( Fig 6G ) and total TH signal ( Fig 6H ) were both reduced by approximately 50% in the dorsal striatum , with no change detected in the ventral striatum , confirming the specificity of the lesion . In addition , DAT signal area ( Fig 6J ) and total signal ( Fig 6K ) were reduced by approximately 75% in the dorsal striatum . There were no significant changes in TH and DAT signal intensity ( Fig 6I and 6L ) , suggesting loss of axonal terminals rather than simply reduced TH and DAT levels . Even if more neurons were lost in the SNc in D2-cKO mice compared to control mice , no significant difference was observed between the two genotypes ( Fig 6G–6L ) at the terminal level , compatible with compensatory axonal sprouting . In line with the modest decrease in TH signal within the striatum of these mice and the absence of genotype effect in striatal denervation , we failed to detect a difference between D2-cKO mice and controls in motor behaviors ( Fig 7 ) . However , the 6-OHDA lesion caused an increased preference for the ipsilateral paw in the stepping test ( Fig 7A ) with no change in total number of steps ( Fig 7B ) . In the rotation test , we observed no changes in rotational preference at basal levels ( Fig 7C ) , but an increased preference for ipsilateral rotations under amphetamine was detected ( Fig 7D ) . Finally , the total number of rotations was significantly increased following amphetamine administration ( Fig 7E ) .
The size of the axonal arbour of SNc DA neurons was measured previously in the intact rat brain [24 , 25] , but no direct comparison of this parameter with less vulnerable VTA DA neurons was available prior to the present work . However , by dividing the estimated number of terminals in the rat ventral and dorsal striatum with the corresponding number of DA neurons in the VTA and SNc , it had been previously estimated that SNc DA neurons have an 8-fold broader striatal axonal arborization compared to VTA DA neurons [7] . In the present work , we directly measured axonal arborization size of both neuronal populations in the entire striatum and similarly found a much larger axonal ( 3-fold ) arborization for SNc compared to VTA DA neurons in mice . The smaller difference between our finding ( 3-fold ) and the previous estimate ( 8-fold ) could be due to the use of different species ( rats vs mice ) , but we additionally took into account that VTA DA neurons also project to the dorsal striatum; projections which were not considered in the previous estimation [7] . The projections of VTA neurons to the dorsal striatum were much more diffuse , but because of the much larger size of the dorsal striatum compared to the ventral striatum , they accounted for a significant amount of the total number of projections from the VTA . These projections were also previously examined in a single neuron tracing study in mice [26] , but in this work , the authors did not compare VTA to SNc neurons . They nonetheless confirmed that part of VTA DA neurons projections were outside of the ventral striatum , compatible with previous classical work describing mesocortical and mesolimbic pathways [27] . It is also possible that we underestimated the axonal arborization size of SNc DA neurons , since we were not able to selectively label neurons from the most ventral part of the SNc , who are known to be particularly vulnerable in PD [28] , since they were too close to the VTA . It is possible that these highly vulnerable neurons could have an even broader axonal arborization . Another potential caveat of this study is that we did not quantify axonal processes outside of the striatum , which could have led to an overestimated difference between SNc DA neurons and VTA DA neurons , since VTA neurons are known to also project to other brain regions such as the cortex , amygdala and septum . However , in initial experiments , a global evaluation of these regions revealed only a very low relative density of dopaminergic processes compared to the striatum . We thus limited our quantification to the striatum in the present study , which is the main projection site for both populations of DA neurons . Although this represents a limitation , we consider it unlikely that our estimates were significantly affected by this focus on striatal projections . In keeping with this possibility , the relative difference between the size of the axonal arborization of VTA and SNc DA neurons found in the present study is quite similar ( 2–3 fold larger for SNc compared to VTA ) to what we previously observed in vitro [8] . Another limitation of the present work is that due to the quantity and volume of injected virus , we were not able to separately quantify the axonal arborization size of different subtypes of SNc or VTA DA neurons . Considering that the ventral tier of the SNc is much more vulnerable in PD compared to the dorsal tier [28] and that projections from the different subpopulations of DA neurons reach different subregions of the striatum [29] , it would be of major interest in future work to examine axonal arborization size of different subpopulations of the SNc in relation to their differential vulnerability in PD . The use of intersectional genetic tools might be better suited than trying to reduce viral injection volume to tackle this question . In the present study , we used D2-cKO mice to examine the vulnerability of DA neurons under conditions where these neurons develop an even larger axonal arborization . Increased DA terminal density in the dorsal striatum had been previously described in a constitutive [17] knockout model of this receptor . In order to focus on cell-autonomous mechanisms of vulnerability , we deleted the D2R gene selectively in DA neurons by crossing Drd2loxP mice with DATIREScre mice . Using these DATIREScre/Drd2loxPmice , we surprisingly did not find any changes in TH signal in the striatum , an observation that could reflect the highly plastic and homeostatic nature of TH expression in response to perturbations such as neurotoxins , which might make it somewhat unreliable to assess the extent of loss of axonal processes [30–36] . On the other hand , we did observe a significant increase in DAT immunoreactive processes in the dorsal striatum , as shown previously in the constitutive KO model [17] , with no increase in the number of DA neurons . This is also similar to what has been observed previously in the hippocampus of this cKO model [19] . However , DAT expression and localisation can be altered by many mechanisms [37 , 38] . For example , this transporter is known to form protein-protein interactions with the D2 DA receptor , which is thought to promote DAT localisation to the plasma membrane [39–42] . For this reason , the lack of D2 receptors in our D2-cKO models could have altered the expression of DAT by compensatory mechanisms and not directly as a result of an increase in axonal arborization size . To evaluate if the increase in DAT immunoreactive processes reflected an increase in axonal arborization size and was originating from SNc DA neurons and not from DA neurons from other regions such as the VTA , we took advantage of a viral labelling strategy to conditionally express a fluorescent reporter protein in SNc or VTA DA neurons . Doing so , we confirmed that SNc but not VTA DA neurons have an increased number of axonal processes in the striatum of the D2-cKO mice . To validate whether expanded axonal domains contained terminals that were likely to release DA , we also quantified the presence of TH , DAT and VMAT2 in these virally-labelled axonal processes . We found that there was an increase in colocalization with TH and DAT and no change in VMAT2 density in axonal processes , arguing that the increase in axonal size did not come at the expense of a loss in neurochemical identity . We next used fast scan cyclic voltammetry to measure DA release in the striatum and to gain further insight into the functionality of dopaminergic axons in this model . We found a general decrease in DA release that was partially rescued in the presence of a DAT antagonist . Our finding of a decrease in evoked DA overflow , although somewhat counter-intuitive when considering the autoreceptor function of the D2 receptor , is in line with previous observations of constitutive or conditional D2R KO mice [43–47] ( but see [48] ) . While we found here that this reduced DA release could be rescued by nomifensine , in a previous study , the use of a DAT antagonist was not sufficient to return DA levels to normal in the engrailed1-based D2-cKO [44] . It should be noted however that in this later work , while the control condition had both alleles of englailed1 , the D2-cKO mice had only one allele of this transcription factor , which is otherwise critical for the development of DA neurons . Since knockout of even only one allele of engrailed1 has been shown to affect the number of DA neurons and the density of their terminals [49 , 50] , it is possible that DA release in this model was affected by both the KO of the D2 receptor and the reduced engrailed1 expression as well as by the possible removal of the D2 receptor in engrailed expressing non-DA neurons of the VTA and SNc . It is also important to note again that there have been reports that activation of D2 receptors in dopaminergic terminals regulates positively the localization of the DAT to the plasma membrane [39–42] . In our D2-cKO mice , although we detected an increase in DAT levels by immunofluorescence , we did not observe any significant change in reuptake kinetics as assessed from cyclic voltammetry recordings . We also did not detect a significant change in DAT surface levels using a DAT surface biotinylation assay . However , a reduction in Vmax has been observed in a previous study in which the D2 receptor was knocked down acutely using siRNA [47] , although reuptake kinetics ( tau ) were not reported . The difference with our data could be explained by the acute nature of the deletion in this previous study . In the context of the absence of a change in reuptake kinetics , our finding of an apparent rescue of DA release in the presence of the DAT blocker nomifensine is puzzling . One possibility is that nomifensine was able to rescue a deficit in axon terminal function at a step which is independent from DAT activity . Previous work has indeed shown that DAT blockers including cocaine and nomifensine are able to enhance the exocytotic release of DA through a mechanism that is not yet clearly defined but that has been suggested to involve synapsin [20–22] . The goal of this work was to provide a first in vivo test of the importance of axonal arborization size on the vulnerability of SNc DA neurons . We confirm here that D2-cKO mice represent a model in which an expansion of the axonal arborization of SNc DA neurons can be detected . Based on our previous work performed with primary DA neurons [8] , we predicted that this should lead to increased vulnerability of SNc DA neurons . In keeping with this hypothesis , we found that D2-cKO SNc DA neurons were more vulnerable to a 6-OHDA lesion initiated at the axon terminal level . An alternate interpretation of this increased neuronal loss in D2-cKO mice could be that the basal increase in DAT-positive varicosities observed in these mice led to an increased uptake of 6-OHDA . Although this possibility cannot be formally excluded , its likelihood is limited because our cyclic voltammetry reuptake kinetic measurements argue for an absence of change in DAT functionality at the plasma membrane , a finding that is in line with our observation of a lack of change in DAT surface levels in the striatum of D2 cKO mice . In the 6-OHDA model , we also observed a stronger loss of cell bodies than striatal terminals , with similar levels of striatal TH and DAT fiber density in D2-cKO mice compared to control mice . This finding argues for robust axonal sprouting from surviving neurons in the D2-cKO mice . This is in line with work showing presence of compensatory reinnervation in this lesion model [51 , 52] and is also supported by the absence of exacerbated 6-OHDA induced behavioral impairements in the D2-cKO mice . In future work , it would be of interest to look at the vulnerability of VTA DA neurons to 6-OHDA in the D2-cKO model using toxin injection targeted to the ventral striatum , as these neurons do not show any changes in their axonal arborization size , but are thought to participate in intense axonal spouting in this lesion model [51 , 53] . Because the D2 receptor regulates many cellular processes , we cannot completely exclude the possibility that lack of D2 receptors could have increased the vulnerability of SNc DA neurons through mechanisms other than the increased axonal arborization size . Future work will be required to determine the origins of this enhanced neuronal loss , but an increased level of basal oxidative stress in SNc DA neurons could be implicated and synergistically lead to sufficient oxidant stress to initiate apoptotic death of DA neurons [54–56] . ROS production induced by 6-OHDA has also been reported to impair axonal transport in dopaminergic neurons [57] and to deplete ATP content and antioxidant reserve [58] , which could affect to a greater extent D2-cKO SNc DA neurons since they have a larger axonal compartment to maintain . Additionally , increased phosphorylation of α-synuclein to its pSyn-129 toxic form has been reported in the 6-OHDA model [59] , which could play a role in the observed toxicity . However , it is unlikely that this effect on α-synuclein is the main mechanism leading to cell death in the present study because we failed to detect any change in vulnerability when we overexpressed α-synuclein , even if we observed the presence of pSyn-129 in surviving cell bodies . This lack of an increased vulnerability to α-synuclein overexpression in the present model is presently unresolved , but it might be explained by the fact that pathology is initially induced in the cell bodies in this model , as opposed to its initiation in the terminals in the 6-OHDA model and that the time course of neurodegeneration is much longer in the overexpression model ( months vs days for 6-OHDA ) . Additionally , the α-synuclein model is thought to trigger degeneration by causing pathological protein aggregation and impaired proteasome/lysosome function [60–62] , unlike the 6-OHDA model , which directly impairs mitochondrial function by inhibiting mitochondrial complex I and IV and by inducing oxidative stress [63 , 64] . However , it has been suggested that α-synuclein overexpression can also influence mitochondrial function , but through different mechanisms . It has been proposed that once oligomerized , α-synuclein influences mitochondrial fusion/fission , transport , clearance and protein import mechanisms [65 , 66] , as well as complex I and ATP-synthase function [67] and therefore increases oxidative stress [68] . Since α-synuclein oligomerization seems to be a necessary step for all these alterations , overexpression of WT α-synuclein should take much more time than 6-OHDA injections to elevate oxidative stress to critical levels . It should therefore also leave much more time for neurons to attempt to compensate for these changes compared to the 6-OHDA model where ATP and antioxidant depletion and oxidative stress are rapidly induced . In combination with the much more modest loss of striatal TH immunoreactive processes in the α-synuclein overexpression model , this could in part explain why behavioral alterations were almost absent in this model . Additionally , it is also possible that the potentially enhanced level of oxidative stress in the nigro-striatal system of D2-cKO mice was not sufficiently elevated to promote enhanced vulnerability in response to all triggers of PD pathology . In line with this possibility , a global assessment of superoxide anion production and NADPH oxidase activity in the striatum and mesencephalon of the D2 cKO mice failed to reveal an increased stress level ( S3 Fig ) . Further experiments would be needed to examine selective markers of oxidative stress in the axonal and somatodendritic compartment of DA neurons , without the confounding presence of signal originating in striatal neurons and glial cells . Interestingly , even in the absence of exogenous triggers such as 6-OHDA or α-synuclein overexpression , features of PD pathophysiology such as loss of processes and presence of α-synuclein aggregates in the dorsal striatum have been reported in aged constitutive D2-KO mice [69] . In the present work , we did not produce nor examine aged D2-cKO mice , but it is possible that similar pathology would be observed . In conclusion , this work demonstrates for the first time that SNc DA neurons in the intact brain possess a larger axonal arbour size compared to VTA DA neurons . This work also provides strong additional supportive evidence for the hypothesis that a very large axonal arbour places DA neurons at increased risk in PD .
All procedures involving animals were conducted in strict accordance with the Guide to care and use of experimental animals ( 2nd Ed . ) of the Canadian Council on Animal Care . The experimental protocols were approved by the animal ethics committee ( CDEA ) of the Université de Montréal . Housing was at a constant temperature ( 21°C ) and humidity ( 60% ) , under a fixed 12h light/dark cycle and free access to food and water . Initial comparisons of the axonal arborization size of SNc and VTA DA neurons was performed using DAT-Cre knock-in mice [70] . The rest of the experiments were performed using DATIREScre mice obtained from Jackson Labs [71] and crossed with Drd2loxP mice [48] . Mouse background was mixed 129SV/C57BL6 and both males and females were used . All animals were genotyped using a KAPA2G Fast HotStart DNA Polymerase kit from Kapa Biosystem . Primer used were: DAT-Cre DATIREScre Drd2loxP Two-month-old DAT-Cre or DATIREScre positive mice were anesthetized with isoflurane ( Aerrane; Baxter , Deerfield , IL , USA ) and fixed on a stereotaxic frame ( Stoelting , Wood Dale , IL , USA ) . Fur on top of the head was trimmed , and the surgical area was disinfected with iodine alcohol . Throughout the entire procedure , eye gel ( Lubrital , CDMV , Canada ) was applied to the eyes , and a heat pad was placed under the animal and kept warm . Next , bupivacaine ( 5 mg/ml and 2 mg/kg , Marcaine; Hospira , Lake Forest , IL , USA ) was subcutaneously injected at the surgical site , an incision of about 1 cm made with a scalpel blade , and the cranium was exposed . Using a dental burr , one hole of 1 mm diameter was drilled above the site of injection [AP ( anterior–posterior; ML ( medial–lateral ) ; DV ( dorsal-ventral ) , from bregma] . The following injection coordinates were used: Note that the coordinates for SNc and VTA injections were purposely 0 . 3 mm anterior to the center of the targetted region . These coordinates were adjusted to prevent infection of RRF , rostral linear nucleus ( RLI ) or caudal linear nucleus ( CLI ) DA neurons . Next , borosilicate pipettes were pulled using a Sutter Instrument , P-2000 puller , coupled to a 10 μL Hamilton syringe ( Hamilton , 701RN ) using a RN adaptor ( Hamilton , 55750–01 ) and the whole setup was filled with mineral oil . Using a Quintessential Stereotaxic Injector ( Stoelting ) , solutions to be injected were pulled up in the glass pipet . For the axonal arborization size quantification , 0 . 1 μL ( SNc ) or 0 . 05 μL ( VTA ) of sterile NaCl containing 1 . 15x1012 viral genome particles/mL of AAV2-EF1a-DIO-eYFP ( UNC Vector Core , Chapel Hill , NC , USA ) was injected . For α-synuclein over-expression , 0 . 8 μL of AAV2-CBA-alpha-Syn ( 3 . 8x1012 viral genome particles/mL , MJF Foundation , USA ) or AAV2-CBA-eGFP ( 2 . 0x1012 viral genome particles/mL MJF Foundation , USA ) was injected . For 6-OHDA lesions , 0 . 5 μL of 6-OHDA ( 3 mg/mL ) in 0 . 2% ascorbic acid solution was injected . Forty minutes prior to 6-OHDA injections , the norepinephrine transporter blocker desipramine ( 25mg/Kg ) was injected intraperitoneally to the animals to prevent lesions of the noradrenergic fibers . After the unilateral injection , the pipette was left in place for 10 min to allow diffusion and then slowly withdrawn . Finally , the scalp skin was sutured and a subcutaneous injection of the anti-inflammatory drug carprofen ( Rimadyl , 50 mg/mL ) was given . Animals recovered in their home cage and were closely monitored for 24h . A second dose of carprofen ( 5 mg/kg ) was given if deemed necessary . The brains were collected 1 month after the 6-OHDA injection ( P90 ) , 2 months after viral injection for axonal arborization labeling ( P120 ) or 3 months after viral injection for α-synuclein overexpression studies ( P150 ) . Mice were anesthetized using pentobarbital NaCl saline solution ( 7 mg/mL ) injected intraperitoneally and then were perfused with 50mL of PBS followed by 100 mL of paraformaldehyde ( PFA ) 4% using an intracardiac needle at a rate of 25 mL/min . The brains were extracted , placed 48h in PFA followed by 48h in a 30% sucrose solution and frozen in isopentane at -30°C for 1 minute . 40 microns thick coronal sections were then produced using a cryostat ( Leica CM1800 ) and placed in antifreeze solution at -20 oC until used . One out of every 6th slice was used for immunofluorescence . After a PBS wash , the tissue was permeabilized , nonspecific binding sites were blocked and slices were incubated overnight with a rabbit anti-TH antibody ( 1:1000 , AB152 , Millipore Sigma , USA ) , a rat anti-DAT antibody ( 1:1000 , MAB369; MilliporeSigma , USA ) , a chicken anti-GFP ( 1:2000 , GFP-1020; Aves Labs , USA ) , a mouse anti-p-S129-α-synuclein ( 1:2000 , 328100 , Invitrogen , USA ) , a chicken anti-α-synuclein ( 1:2000 , AB190376 , Cedarlane , USA ) and/or rabbit anti-VMAT2 ( 1:2000 , kindly provided by Dr . G . W . Miller [72] ) Primary antibodies were subsequently detected with a rabbit or chicken Alexa Fluor-488–conjugated secondary antibody , a rabbit Alexa Fluor-546–conjugated secondary antibody , and/or a rat Alexa Fluor-647–conjugated secondary antibody ( 1:400; Thermo Fisher Scientific ) . One out of every 6th slice was used for DAB immunostaining . After a PBS wash , the tissue was incubated for 10 min with 0 . 9% H2O2 solution , then washed with PBS again and incubated for 48h with a rabbit anti-TH antibody ( 1:1000 , AB152 , Millipore Sigma , USA ) at 4°C , 12h with goat anti-rabbit biotin-SP-AffiniPure secondary antibody ( 111-065-003 , Jackson ImmunoResearch Laboratories , USA ) at 4°C and 3h with horseradish peroxidase streptavidin ( 016-030-084 , Cedarlane , USA ) . The DAB reaction was carried out for 45s , then stopped by incubation with 0 . 1M acetate buffer and slices were mounted on Superfrost/Plus microscope slides . They were left to dry for 96h after which they were stained with cresyl violet and went through subsequent incubations with increasing concentrations of alcohol . After short isopropanol and xylene baths , slides were sealed with Permount mounting medium ( SP15-100 , Fisher , USA ) using glass coverslips . All of the imaging quantification analyses were performed on images captured using confocal microscopy . Images were acquired using an Olympus Fluoview FV1000 microscope ( Olympus ) . Images acquired using 488 and 546 nm laser excitation were scanned sequentially to reduce nonspecific bleed-through signal . For each slice , up to 4 images were acquired in the dorsal striatum and up to 2 in the ventral striatum . All image quantifications were performed using ImageJ ( National Institutes of Health ) software . We first applied a background correction and then measured the area and intensity of the signal . For quantification of TH , DAT and VMAT2 positive terminals in the ventral or dorsal striatum , images were acquired using a 60x oil-immersion objective and averaged from slices at bregma 1 . 18 , 0 . 14 and -0 . 94 mm . For axonal arborization size quantification with eYFP viral expression , images were acquired on one out of every 6th slice from bregma -2 . 2 to 1 . 94 mm using a 20x water immersion objective since the fibers were easily distinguishable at lower magnification . The proportion of the area covered by eYFP fibers was extrapolated to the size of the striatum for each slice based on The Mouse Brain in Stereotaxic Coordinates 3rd Edition by George Paxinos [73] normalized by the number of infected neurons counted manually ( 300–1000 neurons ) and plotted in relation to the bregma coordinates . Stereological counting was not used for this quantification since the number of neurons was too low to get a reliable count using random sampling . The volume of eYFP positive axonal arborization was then approximated using the area under the curve . The number of striosomes and their size was also quantified using the integrated particles analyzer in Image J . Colocalization measurements were performed using the Jacop plugin for ImageJ on 60x confocal images [74] . Mander’s M1 and M2 coefficients were obtained after manual thresholding of the images to remove background . A mask of the YFP signal was applied to the other signals for measurement of their colocalization inside YFP fibers . TH-immunoreactive neurons were counted in one out of every sixth section using a 100x oil-immersion objective on a Leica microscope equipped with a motorized stage . A 60 x 60 μm2 counting frame was used in the Stereo Investigator ( MBF Bioscience ) sampling software with a 12 μm optical dissector ( 2 μm guard zones ) and counting site intervals of 150 μm after a random start ( 100 μm intervals for unilateral lesion ) . Mesencephalic DA nuclei , including the VTA , SNc and RRF were examined . Stereological estimates of the total number of TH-immunoreactive neurons within each nucleus were obtained . The number of TH-negative neurons was also estimated similarly in each region based on cresyl violet staining . Acute brain slices from 3-month-old mice were obtained using a protective slicing method [75] . Matched pairs of CTL and D2-cKO mice were used on each experimental day . After intracardiac perfusion , brains were quickly dissected , submersed in ice-cold NMDG cutting solution and coronal striatal brain slices of 300 μm ( from bregma AP 1 . 34 to 0 . 98 mm ) were prepared with a Leica VT1000S vibrating microtome in ice-cold ( 0 to 4°C ) NMDG protective cutting solution . Slices recovered for 12 min in 32° NMDG solution and were then transferred to oxygenated HEPES-buffered resting solution at RT for at least 1h . For recordings , slices were put in a custom-made recording chamber superfused with artificial cerebral spinal fluid ( aCSF ) at 1 mL/min and maintained at 32°C . All solutions were adjusted at pH 7 . 35–7 . 4 , 300 mOsm/kg and saturated with 95% O2-5% CO2 at least 30 min prior to each experiment . Electrically induced DA release was measured by fast-scan cyclic voltammetry ( FSCV ) using a 7 μm diameter carbon-fiber electrode placed into the dorsal or ventral striatum ∼100 μm below the surface and a bipolar electrode ( Plastics One , Roanoke , VA , USA ) placed ∼200 μm away . Carbon-fiber electrodes were fabricated as previously described [76] . Electrodes were polished and filled with 4M potassium acetate and 150 mM potassium chloride . Carbon fibers were then cut using a scalpel blade to obtain maximal basal currents of 100 to 180 nA . Electrodes were finally selected for their sensitivity to DA using in vitro calibration with 1μM DA in aCSF before each experiment . Before and after use , electrodes were cleaned with isopropyl alcohol . The potential of the carbon fiber electrode was scanned at a rate of 300 V/s according to a 10 ms triangular voltage wave ( −400 to 1000 mV vs Ag/AgCl ) with a 100 ms sampling interval , using a CV203BU headstage preamplifier ( Axon instrument , Union City , CA ) ) and an Axopatch 200B amplifier ( Axon Instruments ) . Data were acquired using a Digidata 1440A analog to digital converter board ( Axon Instruments ) connected to a computer using Clampex ( Axon Instruments ) . Slices were left to stabilize for 20 min before any electrochemical recordings . After positioning of the bipolar stimulation and carbon fiber electrodes in the striatum , single pulses ( 400 μA , 1ms ) were applied to the nucleus accumbens core ( referred to as ventral striatum ) and then to the dorso-lateral part of the dorsal striatum to trigger DA release . Stimulations were applied every 2 min . After recording in the dorsal striatum , the media was changed to ACSF containing 5 μM of nomifensine ( Sigma ) and single stimuli were applied to the dorsal striatum . Electrode calibration was performed before and after the recording of each slices and the average value for the current at the peak oxidation potential was used to normalize the recorded ex vivo current signals to DA concentrations . DA release was analyzed as the peak height of DA concentrations and DA reuptake was determined from the clearance rate of DA which was assumed to follow Michaelis-Menten kinetics . A nonlinear least square optimization was applied to fit a three-parameter exponential function with baseline shift to the reuptake phase of the DA response . Uptake parameters ( tau and Vmax ) were calculated based on the exponential fitting . To determine whether DAT-mediated DA uptake was compromised in D2-cKO mice , the initial portion of the falling phase of single pulse evoked [DA]o curves was used to calculate the Vmax ( maximal rate of DA uptake ) after setting the Km parameter to 0 . 2 μM , based on the affinity of DA for the DAT , measured in mouse synaptosome preparations [77] and with the assumption that the Km is not altered in the KO mouse line . Surface biotinylation experiments were carried using a protocol modified from Rickhag et al . 2013 [78] . Brains from 3-month-old conditional D2-cKO mice and CTL littermates were rapidly dissected and submerged in pre-oxygenated ( 95% O2 and 5% CO2 ) ice-cold sucrose buffered artificial cerebrospinal fluid . Coronal striatal sections ( 300 μm ) were obtained using a vibrating blade microtome ( Leica VT1000 ) . The slices were allowed to recover in oxygenated aCSF ( without sucrose ) for 1h at room temperature . After surface biotinylation , slices were rinsed twice and excess biotin was quenched by two washes in glycine in oxygenated aCSF ( 4°C ) . The biotinylated slices from individual mice were pooled and homogenized in lysis buffer containing protease and phosphatases inhibitor . The homogenates were quickly incubated , gently mixed and centrifuged to remove debris ( 4°C ) . Protein concentrations were measured and adjusted to 1ug/ml , and 100 μl of total lysates were stored to allow determination of the total protein input . Biotinylated proteins were isolated by loading equal amounts of protein onto 175ul avidin beads ( Thermo Scientific ) followed by overnight incubation at 4°C . Beads were washed in lysis buffer before elution of biotinylated proteins . Avidin beads were removed by filtration , and surface and total DAT levels were evaluated by western blot analysis . Protein samples were separated by SDS-PAGE and transferred to membranes . The membranes were blocked and then incubated subsequently with antibodies against DAT ( Millipore MAB369 , 1:1000 ) and with horseradish peroxidase ( HRP ) -conjugated anti-rat antibodies . Surface DAT protein bands were visualized by chemiluminescence . Blots of surface protein samples were reprobed for Na+/K+-ATPase ( Abcam 1:500 ) to account for variation in biotinylated input while actin ( HRP-conjugated actin ( 1:10000 , A3854 , mouse monoclonal , Sigma ) was used as loading control for the total lysates . Band intensities were quantified using ImageJ gel analysis software . Basal superoxide anion production and NADPH oxidase activity in brain tissues were measured using the lucigenin‐enhanced chemiluminescence method with a low concentration ( 5 μmol/L ) of lucigenin , as described previously [79] . The tissues from control and D2-cKO mice were washed in oxygenated Krebs HEPES buffer and placed in scintillation vials containing lucigenin solution , and the emitted luminescence was measured with a liquid scintillation counter ( Wallac 1409; Perkin Elmer Life Science ) for 10 minutes . The average luminescence value was estimated , the background value was subtracted , and the result was divided by the total weight of tissue in each sample . The NADPH oxidase activity in the samples was assessed by adding 10 to 4 mol/L NADH ( Sigma‐Aldrich ) in the vials before counting . Basal superoxide–induced luminescence was then subtracted from the luminescence value induced by NADH . All mice were habituated to the user by handling them once a day during 3 consecutive days before experiments . Mice were moved to the experimental room 1h before the test . Mice first went through a stepping test recorded with a digital camera ( DMK 22BUC03 , ImagingSource ) and IC Capture 2 . 4 software . Mice were gently lifted by the base of the tail at one end of a 1-meter corridor leaving only forepaws touching the surface and were pulled backward for 4s over a distance of 1-meter . Recordings were then watched in slow motion and the number of steps of each forepaw was counted . After 1h of rest , animals were placed in a 4L beaker with the digital camera recording their movements from underneath to assess rotation . After 20 min , amphetamine 5 mg/kg was intraperitoneally injected and mice were placed back in the beaker for 40 min . Recordings were then watched to count the ipsilateral and contralateral rotations made by the mice during the first ( basal ) and the last ( amphetamine ) 20 min . All experiments were performed blind to the experimental groups , from surgeries to image analysis . Parametric statistical tests were used because samples contained data with normal distributions . Data were presented as mean ± SEM . The level of statistical significance was established at p < 0 . 05 in one or two-way ANOVAs or two-tailed t-tests with Welch’s correction when needed . A ROUT outlier analysis was performed when required ( Q = 1% ) . Statistical analyses were performed with the Prism 7 software ( GraphPad Software , p < 0 . 05 = * , p < 0 . 01 = ** , p < 0 . 001 = *** , p < 0 . 0001 = **** ) . The Tukey post-hoc test was used when all the means were compared to each other and the Sidak post-hoc test was used when only subsets of means were compared . | Parkinson’s disease motor symptoms have been linked to age-dependent degeneration of a class of neurons in the brain that release the chemical messenger dopamine . The reason for the selective loss of these neurons represents a key unsolved mystery . One hypothesis is that the neurons most at risk in this disease are those with the most extensive and complex connectivity in the brain , which would make these cells most dependent on high rates of mitochondrial energy production and expose them to higher rates of oxidative stress . Here we selectively deleted in dopamine neurons a key gene providing negative feedback control of the axonal arbor size of these neurons , in the objective of producing mice in which dopamine neurons have more extensive connectivity . We found that deletion of the dopamine D2 receptor gene in dopamine neurons leads to dopamine neurons with a longer and more complex axonal domain . We also found that in these mice , dopamine neurons in a region of the brain called the substantia nigra show increased vulnerability to a neurotoxin often used to model Parkinson’s disease in rodents . Our findings provide support for the hypothesis that the scale of a neuron’s connectivity directly influences its vulnerability to cellular stressors that trigger Parkinson’s disease . | [
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... | 2019 | Increased vulnerability of nigral dopamine neurons after expansion of their axonal arborization size through D2 dopamine receptor conditional knockout |
Leptospirosis is a potentially fatal bacterial zoonosis that is endemic throughout the tropics and may be misdiagnosed as dengue . Delayed hospital admission of leptospirosis patients is associated with increased mortality . During a concurrent dengue/leptospirosis epidemic in Puerto Rico in 2010 , suspected dengue patients that tested dengue-negative were tested for leptospirosis . Fatal and non-fatal hospitalized leptospirosis patients were matched 1:1–3 by age . Records from all medical visits were evaluated for factors associated with fatal outcome . Among 175 leptospirosis patients identified ( 4 . 7 per 100 , 000 residents ) , 26 ( 15% ) were fatal . Most patients were older males and had illness onset during the rainy season . Fatal case patients first sought medical care earlier than non-fatal control patients ( 2 . 5 vs . 5 days post-illness onset [DPO] , p < 0 . 01 ) , but less frequently first sought care at a hospital ( 52 . 4% vs . 92 . 2% , p < 0 . 01 ) . Although fatal cases were more often diagnosed with leptospirosis at first medical visit ( 43 . 9% vs . 9 . 6% , p = 0 . 01 ) , they were admitted to the hospital no earlier than non-fatal controls ( 4 . 5 vs . 6 DPO , p = 0 . 31 ) . Cases less often developed fever ( p = 0 . 03 ) , but more often developed jaundice , edema , leg pain , hemoptysis , and had a seizure ( p ≤ 0 . 03 ) . Multivariable analysis of laboratory values from first medical visit associated with fatal outcome included increased white blood cell ( WBC ) count with increased creatinine ( p = 0 . 001 ) , and decreased bicarbonate with either increased WBC count , increased creatinine , or decreased platelet count ( p < 0 . 001 ) . Patients with fatal leptospirosis sought care earlier , but were not admitted for care any earlier than non-fatal patients . Combinations of routine laboratory values predictive of fatal outcome should be considered in admission decision-making for patients with suspected leptospirosis .
Leptospirosis is an emerging zoonosis caused by infection with bacterial spirochetes of the genus Leptospira , and is endemic throughout the tropics where >1 million cases and ~60 , 000 deaths occur annually [1 , 2] . Human infection typically occurs through direct or indirect contact with the urine of infected animals [1] . Leptospirosis is typically a mild acute febrile illness ( AFI ) ; however , ~10% of patients progress to severe leptospirosis with acute kidney failure , jaundice , and/or pulmonary hemorrhage [1 , 3] . The case-fatality rate for patients with severe leptospirosis ranges from 5–20% [4–6] . Due to similar clinical presentations , leptospirosis may be misdiagnosed as dengue [7–9] . Delayed or misdiagnosis of leptospirosis patients has been associated with increased mortality , potentially due to delayed administration of antibiotics [10–15] . Therefore , identification of early clinical markers of patients at risk for severe disease to thereby enable earlier patient admission may result in improved outcome . Severe thrombocytopenia , increased serum creatinine or BUN , hemoptysis , dyspnea , and jaundice have been associated with severe or fatal outcome in leptospirosis patients [5 , 12 , 14–18]; however , few studies have captured data from patients’ entire clinical course to identify demographic characteristics , clinical findings , or missed opportunities in clinical management associated with poor outcome [12 , 14] . Consequently , early clinical indicators of patients that have or will develop severe disease have not been well elucidated . During 1990–2014 , a total of 729 leptospirosis cases were reported to Puerto Rico Department of Health ( PRDH ) , of which 78 ( 10 . 7% ) were fatal ( S1 Fig ) . Such surveillance enabled documentation of leptospirosis epidemics in 2006 , 2007 , and 2010 . However , because of underreporting of leptospirosis [19] , which is attributable in part to misdiagnosis as dengue [20–22] , it is unclear if these data represent the true epidemiologic trends of leptospirosis . Factors associated with severe or fatal outcome in leptospirosis patients have not previously been investigated in Puerto Rico . To better understand the epidemiology of leptospirosis during the 2010 dengue epidemic in Puerto Rico [23] , we conducted enhanced surveillance by performing leptospirosis diagnostic testing on specimens from suspected dengue patients . We also reviewed medical records from all health care visits of identified leptospirosis patients to identify demographic characteristics , clinical signs and symptoms , laboratory values , and clinical practices associated with fatal outcome .
This study was approved by the Institutional Review Board at the Centers for Disease Control and Prevention ( CDC ) ( protocol # 6285 ) . Leptospirosis cases in Puerto Rico in 2010 were identified from four sources . First , suspected dengue cases reported via the Passive Dengue Surveillance System ( PDSS ) [24] that had no evidence of dengue virus ( DENV ) infection by rRT-PCR or anti-DENV IgM ELISA [23] ( N = 2 , 519 ) were eligible to be tested for evidence of Leptospira spp . infection by microscopic agglutination test ( MAT ) [25] and polymerase chain reaction ( PCR ) with primers specific for Leptospira spp . LipL32 [26] . Specimens selected for leptospirosis testing ( n = 1 , 133 ) came from cases for which either: a ) paired acute and convalescent specimens were available ( n = 654 ) ; or b ) only a convalescent specimen was available and the case had reported fever , body pain or headache , and jaundice , hemorrhage , or pleural effusion ( n = 479 ) . Second , fatal leptospirosis cases were identified via the Enhanced Fatal AFI Surveillance System ( EFASS ) in which: a ) serum or tissue specimens collected during autopsy were tested by MAT , PCR , or immunohistochemistry ( IHC ) [27]; and b ) death certificates were reviewed for use of “leptospirosis” or “Weil’s disease” . Third , all leptospirosis cases reported to PRDH along with a positive diagnostic test result via the Notifiable Diseases Surveillance System ( NDSS ) were included . Last , two commercial laboratories were queried for leptospirosis cases that tested positive by IgM dot blot . Cases identified through more than one data source with matching names and dates of birth were considered a single case . A laboratory-positive leptospirosis patient was defined as a person that had evidence of infection with Leptospira spp . by detection of: i ) antigen in a tissue specimen by IHC; ii ) nucleic acid in a serum or tissue specimen by PCR; iii ) ≥4-fold rise in MAT titer in paired serum specimens; iv ) MAT titer ≥800 in a single serum specimen; v ) anti-Leptospira IgM antibody at a private laboratory; or vi ) MAT titer ≥100 but <800 in a single serum specimen . A confirmed leptospirosis patient was defined by any of criteria i–v; a probable leptospirosis patient was defined by criteria vi . A suspected fatal leptospirosis patient was a person who died in Puerto Rico in 2010 , had the word “leptospirosis” written on the death certificate , and had either: a ) no leptospirosis diagnostic testing performed; or b ) negative diagnostic testing performed at a commercial laboratory on a specimen collected within five days of illness onset . Each fatal , laboratory-positive leptospirosis patient ( i . e . , cases ) was matched by age within five years with up to three non-fatal , hospitalized , laboratory-positive leptospirosis patients ( i . e . , controls ) . All available medical records–including private office , out-patient clinic , emergency department , and inpatient hospitalizations–during the episode of illness were reviewed . Controls that left the hospital against medical advice or had incomplete medical records were replaced . The frequencies of clinical , demographic and laboratory data were calculated by performing descriptive analyses of all leptospirosis patients identified in 2010 and compared using Student’s t-test or Chi squared test . Rates of leptospirosis by age group and municipality of residence were calculated using data from the 2010 United States Census [28] . Statistical differences and modeling of matched case-control data were performed using exact conditional logistic regression . Due to a limited number of matched pairs , several combinations of clinical lab results were considered for independent predictors of fatal outcome . Normal limits of laboratory values were defined by accepted standards [29] . All data analyses were conducted using SAS version 9 . 3 ( SAS Institute Inc . , Cary , NC ) , graphs were produced in SAS and Microsoft Excel ( Microsoft Corp . , Redmond , WA ) , and maps were created using ArcView ( ESRI , Redlands , CA ) . Specimens were not anonymized prior to diagnostic testing to enable reporting of results to requesting physicians . Data included in the case-control study were anonymized prior to analysis .
Among 1 , 133 suspected but laboratory-negative dengue cases that were selected for leptospirosis diagnostic testing , 105 ( 9 . 3% ) were laboratory-positive ( S1 Table ) . Among 802 specimens from patients tested for leptospirosis at a private laboratory , 56 ( 7 . 0% ) were positive . A total of 57 non-fatal leptospirosis patients were reported via NDSS in 2010 , and laboratory diagnostic evidence was provided for 15 ( 26% ) . After consolidating individual patients identified by multiple systems , a total of 149 non-fatal , laboratory-positive leptospirosis patients were identified in Puerto Rico in 2010 ( 4 . 0 non-fatal patients per 100 , 000 residents ) , of which 91 ( 61% ) were confirmed and 58 ( 39% ) were probable leptospirosis patients . Dengue was ruled out for 134 ( 90% ) non-fatal leptospirosis patients by rRT-PCR and/or IgM ELISA [23]; one apparent co-infection was identified in which DENV-1 was detected by RT-PCR and anti-Leptospira spp . IgM antibody was detected at a private laboratory . A total of 26 fatal leptospirosis patients were identified ( 0 . 7 fatal patients per 100 , 000 residents ) , of which 21 were confirmed and five were suspected leptospirosis patients; only two ( 7 . 7% ) had been reported to PRDH . Fifteen fatal , laboratory-positive leptospirosis patients had available kidney and liver tissue specimens , and Leptospira antigen was detected by IHC in all 15 . Dengue was ruled out in 18 ( 86% ) of the fatal , laboratory-positive leptospirosis patients and in two ( 40% ) of the fatal , suspected leptospirosis patients . Two patients with fatal DENV/Leptospira spp . co-infection were identified [30] . Among all 26 fatal leptospirosis patients , the most common reported causes of death included respiratory , cardiac , or renal failure , and septic shock ( S2 Table ) . MAT-positive specimens ( n = 130 ) from laboratory-positive leptospirosis patients showed strongest reactivity to serogroups including Icterohaemorrhagiae ( 57% ) , Australis ( 11% ) , Mini ( 5% ) , Bataviae ( 4% ) , Canicola ( 4% ) , Cynopteri ( 2% ) , Pyrogenes ( 2% ) , Pomona ( 1% ) , Djasiman ( 1% ) , and Autumnalis ( 1% ) ; 12% had strongest reactivity against more than one serogroup . Of four PCR-positive serum specimens from one fatal and three non-fatal patients , multi-locus sequence typing [31] identified six of seven alleles suggestive of L . interrogans serovar Icterohaemorrhagiae/Copenhageni in the specimen from the fatal patient; MLST was not successful for the other specimens . Leptospirosis patients had illness onset in all months of the year ( Fig 1 ) . Peak incidence of identified fatal and non-fatal leptospirosis patients occurred in October , in association with the rainy season . Most ( 79% ) fatal and non-fatal laboratory-positive leptospirosis patients were male . Leptospirosis patients were identified in all age groups ( Fig 2 ) . Incidence was highest in individuals aged 40–69 years and lowest in individuals aged >80 years . Fatal patients were significantly older than non-fatal patients ( mean of 50 vs . 41 years; p = 0 . 02 ) . Confirmed and probable non-fatal leptospirosis patients were not significantly different by age ( p = 0 . 34 ) or month of illness onset ( p = 0 . 35 ) ; however , more confirmed than probable non-fatal patients were male ( 85% vs . 68%; p = 0 . 02 ) . Most non-fatal ( 59% ) and fatal ( 92% ) leptospirosis cases were reported to have been hospitalized . Mortality by age group was highest in those aged 60–69 years ( 1 . 8 per 100 , 000 residents ) . Fatal and non-fatal leptospirosis cases resided in both urban and rural municipalities across Puerto Rico ( Fig 3 ) . In the 59 ( 76% ) municipalities for which cases were detected , incidence was highest in Patillas in the rainy southeast–where enhanced dengue surveillance was conducted at a community health center in 2010 [23]–and in the mountainous , agricultural center of the island . Incidence was lowest in Cabo Rojo in the arid southwest . A case-control study was conducted in which data from medical records were compared between 21 laboratory-positive fatal cases and 52 age-matched , laboratory-positive , hospitalized but non-fatal leptospirosis controls . Cases and controls did not differ significantly by sex , occupation , or animal or environmental exposure history , nor by reported co-morbidities or chronic medical conditions ( S3 Table ) . Fatal cases first sought medical care sooner after illness onset than non-fatal controls , and more often sought care at a private or out-patient clinic ( Table 1 ) . Although controls first sought medical care at a hospital more frequently than cases , cases were more often admitted or referred for admission at the first visit . Cases and controls did not differ by day post-illness onset ( DPO ) of hospitalization or duration of hospital stay . Cases were more often admitted to the intensive care unit , intubated , and received hemodialysis ( p ≤ 0 . 02 ) . Blood products were administered to more than half of cases and controls . Cases more often had leptospirosis included in the differential diagnosis at first medical visit ( p = 0 . 01 ) , whereas controls more often had “dengue” ever mentioned in any medical record ( p < 0 . 01 ) . The timing with which “leptospirosis” and “dengue” were mentioned post-illness onset and post-hospitalization did not differ between cases and controls . Antibiotics were administered to >70% of cases and controls . Corticosteroids were administered to roughly half of cases and controls , most frequently on the day of admission . The frequency , clinical setting ( e . g . , out-patient clinic vs . hospital ) , and timing of administration of both antibiotics and corticosteroids did not significantly differ between cases and controls . Cases presented to first medical visit with either fever or cough less often than controls ( Table 2 ) . Similarly , cases less often developed fever throughout hospitalization . Most cases developed jaundice , edema , leg pain , hemoptysis , and altered mental status , while fewer than half of controls had these findings . Developing cyanosis and having a seizure were also associated with fatal outcome . DPO of first laboratory values did not differ significantly between cases and controls . As compared to controls , at first medical visit cases had significantly elevated white blood cell ( WBC ) count , proportion of neutrophils , BUN , creatinine , and total bilirubin , and decreased bicarbonate and albumin ( Fig 4 , S4 Table ) . For cases , these values were also more frequently outside of normal ranges . Throughout the clinical course , cases had significantly elevated WBC count , proportion of neutrophils , BUN , and creatinine , and decreased hematocrit , bicarbonate , albumin , prothrombin time ( PT ) , and partial thromboplastin time ( PTT ) . Because fever and cough were the only early clinical signs and symptoms that were associated with fatal outcome and may be spurious findings ( see Discussion ) , only laboratory values were included as parameters in the model . BUN and PTT were removed from the model due to higher specificity of creatinine for kidney injury as opposed to dehydration and infrequency of the test being requested at initial patient presentation , respectively . Clinical laboratory values significantly associated with fatal outcome at first presentation as compared to controls included: decreased serum bicarbonate with elevated serum creatinine , elevated WBC count , or decreased platelet count; and elevated WBC count with elevated serum creatinine ( Table 3 ) .
Enhanced surveillance demonstrated a high rate of morbidity and mortality due to leptospirosis in Puerto Rico in 2010 ( 4 . 7 and 0 . 7 cases per 100 , 000 residents , respectively ) . Comparable incidences have been observed in other regions of the Caribbean that have conducted enhanced surveillance [32–36] , which also demonstrated highest burden in older male agricultural workers and the unemployed [2 , 36] . Although the patients identified in Puerto Rico reflected the expected clinical characteristics of severe leptospirosis ( i . e . , pulmonary hemorrhage , acute kidney injury , and/or septic shock with multi-organ failure ) , under recognition and underreporting of leptospirosis cases was prominent , as one-third of patients were never diagnosed with leptospirosis and two-thirds were not reported to public health authorities . These findings together demonstrate that leptospirosis remains a neglected tropical disease in Puerto Rico . Several missed opportunities for early clinical intervention were identified in this study . First , although fatal cases sought care earlier and were more often diagnosed with leptospirosis at first medical visit; however , fatal patients less often first sought care at a hospital , and were not admitted to the hospital any sooner than non-fatal patients . Thus , delayed hospital admission may have contributed to fatal outcome , as has been previously reported [12 , 13] . However , we saw no evidence that this delay was associated with the timing of initiation of antibiotic therapy , which did not differ between cases and controls . Although prospective clinical trials of antibiotics have not demonstrated a clear benefit to leptospirosis patient outcome [37] , this should not preclude administration of antibiotics to patients with suspected leptospirosis [10] . Last , roughly half of all leptospirosis patients were given corticosteroids , which may result in increased risk of hemorrhage and immunosuppression . A recent systematic review demonstrated no clear benefit to leptospirosis patient outcome by administering corticosteroids [38]; however , prospective clinical trials have yet to be conducted . To improve recognition of leptospirosis and thereby mediate earlier admission for care , clinicians should be aware of patient characteristics and clinical indicators associated with severe leptospirosis . Most previous studies that identified risk factors associated with death due to leptospirosis relied on data collected during the final medical visit , which may be suboptimal for identification of early indicators of fatal outcome . After matching for age and status of hospitalization , no patient characteristics , including gender and history of smoking [15 , 39] , were significantly associated with fatal outcome in this study . Similar to previously studies [5 , 8 , 12 , 16–18] , we observed that jaundice , hemoptysis , acute kidney injury , and dyspnea or respiratory insufficiency were significantly associated with fatal outcome in this study , though not at initial medical visit . Therefore , the utility of these signs and symptoms may be limited in early identification of leptospirosis patients at risk for fatal outcome . Unexpected risk factors associated with fatal leptospirosis in this study were absence of cough and fever at first health care visit and lack of development of fever throughout hospitalization . Cough at initial presentation has been previously associated with protection from fatal outcome [12] , though for unclear reasons . Potential explanations for lack of fever being associated with fatal outcome include incomplete capture of fever history , self-administration of antipyretics , or earlier entry into decompensated shock . Further studies should address the association of these signs and symptoms with fatal leptospirosis . A prominent utility of this study was the association of common clinical laboratory values with fatal leptospirosis , specifically decreased bicarbonate with decreased platelet count and increased WBC count with elevated creatinine , all of which have been previously associated with severe leptospirosis [5 , 8 , 16–18 , 40] . However , we did not observe that elevated serum potassium either at first presentation or at any point during hospitalization was associated with fatal outcome , as has previously been reported [40–43] . Nonetheless , the values of the laboratory markers of fatal outcome identified in this study tended to be farther outside of normal ranges at first presentation in fatal as compared to non-fatal patients , suggesting that patients with fatal leptospirosis may have progressed to severe disease more rapidly . In line with this , fatal patients were more likely to be diagnosed with leptospirosis earlier than were non-fatal patients , who were more likely to ever be diagnosed with dengue . Because previous studies associated elevated WBC count and elevated serum creatinine with leptospirosis as compared to dengue [20 , 44–46] , these clinical laboratory values may have utility in not only differentiating leptospirosis patients from dengue patients , but also in identifying leptospirosis patients at risk for poor outcome . Future studies should evaluate the prospective benefit of using such combinations of laboratory values to improve patient outcome through early identification and admission . Compared to previous studies that have identified risk factors associated with severe or fatal outcome in leptospirosis patients , a major strength of this study was the design of the case-control study . By reviewing medical records from each health care visits made by patients included in the case-control study , and not solely those from the patients’ hospitalization , we avoided biasing results towards points in patients’ illness in which they were likely to be more clinically severe ( i . e . , at point of hospitalization ) . This also enabled identification of clinical indicators that would be of clinical utility before patients were hospitalized , which could thereby mediate more rapid diagnosis and/or hospitalization of patients at-risk for fatal outcome . Moreover , by closely matching patients by age we avoided identification of risk factors that may be associated with older populations . These aspects of study design together may account for some differences in factors associated with fatal outcome identified by this study as compared to previous studies that did not control for age [3 , 8 , 12 , 16 , 17 , 47] . Additional strengths of this study include: conducting surveillance for fatal leptospirosis cases by testing specimens collected during autopsy of patients that died following an AFI , without which many fatal cases would not have been diagnosed; and utilizing multiple surveillance systems to identify fatal and non-fatal leptospirosis patients and subsequently comparing them using a standardized instrument for chart abstraction . Conversely , one limitation of this study is potential misclassification of some probable leptospirosis patients due to the presence of pre-existing neutralizing antibody . However , because several thousand suspected but dengue-negative cases reported to PRDH in 2010 were not tested for evidence of leptospirosis , the incidence of leptospirosis identified herein is likely an underestimate . Also , although previous studies have demonstrated that predictors of fatal leptospirosis include oliguria [8 , 17 , 18 , 41 , 48 , 49] and anuria [12] , we were unable to explore these factors due to the unavailability of routine clinical data on urine output . Moreover , due to limited sample size , we were also unable to identify specific cut-offs of clinical laboratory values associated with fatal outcome . Last , we were unable to evaluate DENV/Leptospira spp . co-infection as a risk factor for death since most leptospirosis cases were identified by screening suspected dengue cases that tested laboratory-negative for dengue . Clinical trainings to improve early recognition of leptospirosis patients , interpretation of diagnostic test results , need for case reporting , and clinical management should be conducted among clinicians working in both out-patient and in-patient settings in Puerto Rico . Since improvements in case surveillance and clinical awareness have been associated with decreases in patient mortality due to leptospirosis [6] , such trainings may also be needed in other areas of the tropics where clinical under recognition of leptospirosis may be high [2] . Population-based serosurveys should be conducted to accurately quantitate the burden of leptospirosis and identify modifiable risk factors associated with infection , including identification of the animal reservoirs that transmit Leptospira spp . to humans . Such findings can be used to develop educational campaigns to inform the public of population-specific strategies that can be employed to reduce their risk of leptospirosis . | Leptospirosis is a common tropical illness that results from exposure to the urine of animals infected with Leptospira bacteria . Because leptospirosis shares signs and symptoms with other common tropical illnesses such as dengue , identification of patients with leptospirosis can be challenging . Early identification of patients with leptospirosis is necessary to initiate antibiotic therapy and in some cases provide in-hospital management . During an epidemic of leptospirosis in Puerto Rico that occurred during a concomitant dengue epidemic , we identified leptospirosis patients by screening specimens from suspected dengue patients . Of 175 leptospirosis patients identified , 26 ( 15% ) died . After comparing leptospirosis patients that died to patients of a similar age that were hospitalized but survived , we observed that fatal cases were more often sent home after their first medical visit . We next identified several routinely available laboratory values from patients’ first medical visit that were associated with patients that died . Clinicians can use such laboratory values to diagnose and hospitalize leptospirosis patients at increased risk for fatal outcome . | [
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"dis... | 2016 | Early Indicators of Fatal Leptospirosis during the 2010 Epidemic in Puerto Rico |
Chemosensory pheromonal information regulates aggression and reproduction in many species , but how pheromonal signals are transduced to reliably produce behavior is not well understood . Here we demonstrate that the pheromonal signals detected by Gr32a-expressing chemosensory neurons to enhance male aggression are filtered through octopamine ( OA , invertebrate equivalent of norepinephrine ) neurons . Using behavioral assays , we find males lacking both octopamine and Gr32a gustatory receptors exhibit parallel delays in the onset of aggression and reductions in aggression . Physiological and anatomical experiments identify Gr32a to octopamine neuron synaptic and functional connections in the suboesophageal ganglion . Refining the Gr32a-expressing population indicates that mouth Gr32a neurons promote male aggression and form synaptic contacts with OA neurons . By restricting the monoamine neuron target population , we show that three previously identified OA-FruM neurons involved in behavioral choice are among the Gr32a-OA connections . Our findings demonstrate that octopaminergic neuromodulatory neurons function as early as a second-order step in this chemosensory-driven male social behavior pathway .
Organisms live in complicated environments requiring successful interaction with their surroundings for reproduction and survival . Information about the environment is transformed into neural activity by specialized sensory organs that detect signals via touch- , taste- , vibration- , odor- and image-sensitive neurons . Pheromones commonly used as olfactory or contact signals in social behavior like courtship and aggression provide information about gender , receptivity , or conspecificity [1]–[3] . In many systems , chemosensory signal-detecting systems are regulated by biogenic amines including dopamine , serotonin , and norepinephrine ( or octopamine , its invertebrate analog ) acting as neuromodulators [4]–[6] . Despite extensive investigation in a wide variety of organisms , it has proven difficult to assign specific roles to individual amines in the circuitry concerned with social behavior [7]–[10] . In this study , we directly connect amine regulation to pheromonal communication by identifying specific chemosensory to octopamine neuron contacts and then investigating their tissue-specific functional roles in male aggression and courtship selection . In Drosophila , pheromonal signals are communicated primarily via cuticular hydrocarbons ( CHC ) and long carbon chain esters that trigger olfactory ( volatile ) or gustatory ( contact ) receiving pathways in conspecifics [11]–[13] . Contact pheromones are detected by gustatory receptor-expressing sensory neurons ( GRNs ) found in taste sensilla in mouth , leg , and wing segments . Despite the importance of this non-volatile sensory information , only a small number of gustatory receptors ( GRs ) have been reported to be involved in the perception of pheromones that regulate social behavior . In one well-studied example , the behavior of males lacking the gustatory receptor Gr32a is altered in at least three ways; levels of male courtship towards females are reduced , levels of male courtship towards second males are elevated , and aggression as measured by the numbers of lunges ( a key higher level behavioral pattern ) is reduced [14]–[16] . In addition , a recent study describes a role of tarsal/leg Gr32a-expressing neurons in the inhibition of interspecies courtship between Drosophila species [17] . To transduce pheromonal stimuli , axons of Gr32a-expressing neurons project to distinct zones in the suboesophageal ganglion ( SOG ) [15] , [18] , and other sites within the central nervous system [19] . The SOG is a central brain region that in addition to axons of gustatory neurons contains extensive neuronal processes of octopamine neurons [20]–[22] . Reduced levels of the amine octopamine ( OA ) yield phenotypes similar to those seen in flies lacking Gr32a function [23]–[25] . Males without OA exhibit increased male-male courtship [23] and a delay in the initiation of male aggressive behavior [25] , as do Gr32a loss-of-function flies [16] . OA function is also necessary for males to make correct choices between courtship and aggression [21] , [23] and OA has been suggested to be essential for the display of higher-level aggression [24] , [25] . As studies in multiple systems reveal that the context of sensory information and internal states are often shaped molecularly by neuromodulators , we tested the hypothesis that the structural composition of the Gr32a pheromonal network includes synaptic connections to OA neuromodulatory neurons . We used behavioral assays , Ca2+ imaging , and the GRASP ( GFP Reconstitution Across Synaptic Partners ) method [26] , [27] to demonstrate the existence of functional and putative synaptic connections between Gr32a neurons and octopaminergic SOG neurons . Removing Gr32a-expressing neurons , eliminating OA , and altering both simultaneously confirmed essential roles for these chemosensory and OA neuronal groups on male aggression initiation and courtship selection . A role for the labellar Gr32a subpopulation in male aggression was revealed by functionally and anatomically separating Gr32a-expressing neurons into mouth and leg populations . Ca2+ imaging experiments demonstrate that OA-expressing neurons in the SOG respond to male cuticular hydrocarbon extracts and this response is eliminated in the absence of Gr32a neurons . Finally , GRASP connectivity between Gr32a neurons and three OA neurons that co-express the male forms of Fruitless ( FruM ) , link anatomical characterization with previous functional data [21] and indicate that this small subset of aminergic neurons is important to provide male selective modulation of behavior . The results presented here begin to decipher social behavior at the level of small subsets of sensory and neuromodulatory neurons and provide insight into how amine-expressing neurons anatomically contribute to circuitry directing sex-specific behavior .
To test the hypothesis that OA neurons might anatomically function in the Gr32a pheromonal input pathway , we generated a Tdc2-LexA:VP16 line and utilized this tool with the split-GFP system developed in C . elegans [26] and adapted for Drosophila [27] . In invertebrates , OA is synthesized from the amino acid tyrosine via the action of tyrosine decarboxylase ( TDC ) and tyramine β-hydroxylase ( Tβh ) . The Tdc2 gene encodes the neuronal TDC [28] and the Tdc2-LexA line can be used to label and manipulate OA neurons ( [29] , Figure S1 ) and possibly a small population of tyramine ( TA ) -expressing neurons [20] . The Gr32a receptor is expressed in sensory neurons in the mouth ( labellum - a gustatory organ of the proboscis and pharynx ) and in tarsal segments of all three legs [14] , [15] , [30] . Axons of Gr32a-expressing neurons project through three peripheral nerves to the SOG ( Figure 1A , B ) [18] , [31]–[33] . Peripheral chemosensory neuron expression of OA has not been detected in this study or previously [28] . However , within the central brain , individual OA neurons project extensive arborizations targeting multiple neuropil regions including the SOG , which functions at least in part , to receive key contact pheromone information ( Figure 1C , D , Figure S1D ) [20] , [21] , [28] . To determine if Gr32a-expressing neurons directly contact OA neurons , we used the GFP Reconstitution Across Synaptic Partners ( GRASP ) method , which detects putative synaptic connections based on the reconstitution of two fragments of a split-GFP protein on the outer membrane of targeted neuronal populations [26] , [27] . We observed GFP reconstitution in a reproducible , distinct pattern within the central SOG ( Figure 1E–I ) in flies containing one fragment of split-GFP under Tdc2 ( OA/Tyramine ) control ( Tdc2-lexA; lexAop-CD4::spGFP11 ) and the second fragment driven by the promoter of Gr32a ( Gr32a-Gal4; UAS-CD4::spGFP1-10 ) . Little or no fluorescence was observed upon expression of either split-GFP fragment alone ( Figure S2 ) . To confirm that at least a portion of the fluorescence seen in contact zones is likely synaptic , we added the UAS-syt:HA reporter [34] ( Figure 1E–G , displayed as red puncta ) . The overall syt:HA pattern shows clear preferential localization of terminal regions of Gr32a neurons and an extensive overlap is seen between syt:HA localization and split-GFP reconstitution at both low and higher magnification ( Figure 1E–H ) . In the merged channels ( Figure 1E , F ) , regions of syt:HA expression where no GFP reconstitution is observed indicating that only specific neurons amongst the populations of Gr32a and OA neurons contact each other . In particular , the synaptic endings derived from Gr32a neurons that project directly to the ventrolateral protocerebrum region [15] do not express reconstituted GFP ( Figure 1E , arrow ) demonstrating specificity in the GFP reconstitution pattern and specificity in the Gr32a to OA neuronal connections . This anatomical data is consistent with a recent study suggesting a close , possibly synaptic , apposition of Gr32a-expressing axons with male mAL neurons [14] . Gr32a expression is seen in all bitter-sensing neurons within the sensilla of the labellum , usually accompanied by many additional gustatory receptors in most of the neurons [33] , [35] , [36] . In one subgroup of chemosensory neurons , the Gr22e ( 9 neurons ) and Gr59b ( 4 neurons ) receptors co-localize with Gr32a as has been reported previously [33] , while in another distinct group Gr32a and Gr47a co-localize ( 3 neurons ) [36] . Expressing Gr22e-Gal4 or Gr59b-Gal4 with Tdc2-lexA and the GRASP reporter transgenes resulted in split-GFP reconstitution in the SOG region as described above ( Figure 1 ) albeit with reduced GRASP expression likely due to co-expression in only a subset of the population of Gr32a neurons ( Figure S3 ) . We also examined whether OA neurons might receive synaptic input from the Gr47a/Gr32a neurons , a different subgroup of bitter-responsive neurons [31] , [37] . GFP reconstitution was not observed between the Gr47a-Gal4 labeled axons and OA neurons ( Figure S4 ) . Although definitive verification of the GRASP signals will require electron microscopy , our results suggest that a number of octopaminergic SOG neurons may serve as neuromodulatory links in the information pathways between specific Gr32a-expressing neurons and taste-related behavioral outputs . If a subset of Gr32a gustatory neurons are in synaptic contact with octopaminergic SOG interneurons , then removing the OA neurons might cause changes in the branching patterns of incoming Gr32a axonal projections . To test this hypothesis , we eliminated OA neurons by driving expression of the programmed cell death gene , head involution defective ( hid , UAS-hid ) , coupled with the UAS-Red Stinger reporter transgene in OA/TA neurons . The Tdc2-Gal4/UAS-hid UAS-Red Stinger combination allowed us to identify transgenic brains that retained OA neurons ( DsRed expression was observed ) and brains that were devoid of OA neurons ( DsRed and Tβh expression were absent ) ( Figure S5 ) . Gr32a neuronal projections entering the SOG were visualized using the Gr32a-I-GFP reporter construct ( Figure 2A–C ) which drives GFP expression as a direct promoter fusion [33] . The resulting GFP fluorescence is weaker than when amplified through the Gal4/UAS system , however when all OA neurons were eliminated , we observed a range of axonal projection defects including an absence of Gr32a-I-GFP immunoreactivity in the SOG ( data not shown , 31% ) or a severe reduction and disorganization of Gr32a leg and labellum termini in 69% of preparations ( n = 21 , Figure 2D ) . Since the adult brains were dissected 1–5 days after eclosion , the differing severity of the Gr32a projection phenotypes could be due to increased axonal disorganization in the absence of OA neuronal targets as the flies age . No similar disorganization of Gr32a axonal projections is observed in control brains during the 1–5 day time frame . We next asked if Gr32a axonal morphology is altered if OA neurons are present but lack OA due to a null mutation in Tyramine β-hydroxylase ( tβhnM18 ) . Using Gr32a-Gal4 to drive reporter GFP expression , the stereotypical projections of Gr32a-expressing neurons from control and OA deficient males were examined . Gr32a axons terminated in the SOG ( Figure S6 ) in heterozygous control adult brains ( tβhnM18/+;Tdc2-Gal4;20XUAS-6XGFP ) . Compiling the same number of confocal sections in controls and OA deficient male brains ( tβhnM18;Tdc2-Gal4;20XUAS-6XGFP ) indicates the majority of Gr32a projections reach the SOG as in controls . However , we observed aberrant termination of Gr32a axons in the antennal lobe region of OA deficient brains ( Figure S6C–E ) that is distinct from previously described projections into the ventrolateral protocerebrum [15] . The effects of eliminating production of OA on individual Gr32a-expressing neurons remains to be determined but results from these experiments suggest the correct differentiation of OA neurons is required for precise axon targeting by at least a subset of Gr32a chemosensory neurons . A previous study reported that the Gr32a receptor mediates aggression-inducing and courtship suppression effects of the male-enriched cuticular hydrocarbons , ( z ) -7-tricosene [16] . Results presented here indicate that Gr32a-expressing neurons contact OA neurons and suggest that octopaminergic signaling is one of the pathways through which Gr32a-mediated pheromonal information is conveyed to other brain or possibly ventral cord regions . To test this hypothesis , we first analyzed fighting defects in males with impaired Gr32a function in our aggression chambers . This data provides a baseline for calculating how removal of OA neuromodulation in addition to eliminating Gr32a-mediated pheromonal information may or may not further alter male aggression or courtship . We ablated Gr32a-expressing gustatory neurons through expression of Diphtheria Toxin ( UAS-DTI ) via the Gr32a-Gal4 driver line [38] . Pairs of UAS-DTI;Gr32a-Gal4 or transgenic control males were placed in an aggression chamber and latency to the first lunge ( a key aggressive pattern essential for the establishment of hierarchical relationships ) and total numbers of lunges were quantified . Consistent with a role of Gr32a-expressing neurons in perceiving pheromones utilized for sex and species recognition in males , the latency to first lunge was significantly longer in males without Gr32a neurons compared to parental controls ( Figure 3A ) . Moreover , a significant reduction in the number of lunges was also observed ( Figure 3B ) . Males without Gr32a neurons exhibited a reduction in aggressive behavior when paired with a single control male as demonstrated by few lunges per fight and a failure to initiate aggression ( Figure S7A–C ) . To test the behavioral consequences of removing both Gr32a-expressing neurons and OA , we added the UAS-DTI;Gr32a-Gal4 transgenes to males with either the w+ tβhnM18 null recombinant chromosome ) or the w+ tβhM6 recombinant control chromosome [23] . The resulting experimental males do not produce OA yet retain OA neurons and the Gr32a-expressing neurons are ablated . Similar to what was observed for flies without Gr32a neurons , flies without OA show a 2-fold increase in latency when compared to genetic control males ( Figure 3C ) . If the function of Gr32a and OA neurons in setting the timely onset of an aggressive response were independent , the absence of both Gr32a receptors and OA function should result in an additive effect on aggression latency as compared to single mutants ( flies lacking Gr32a-expressing neurons or OA only ) . Removing Gr32a signaling and OA via the tβhnM18 mutation did result in a small increase in the latency to the first lunge when compared to control males ( Figure 3C ) . However , the increased latency was not significantly different from that observed in males without OA only ( Figure 3C , ( Mann-Whitney U test , p = 0 . 4 ) . This equivalent aggression initiation delay exhibited by males without Gr32a neuronal function and tβhnM18;UAS-DTI;Gr32a-Gal4 males is the expected result if the aggression-promoting pheromonal signals transmitted by Gr32a neurons are at least partially conveyed via OA neurons . When males without OA and Gr32a neurons fight , the total lunges per fight are decreased ( Figure 3D ) , though , the reduction in lunge number is not substantially different from UAS-DTI;Gr32a-Gal4 males ( Figure 3B ) . Removing Gr32a neurons in males without OA significantly decreased lunge number ( Figure S7D ) , however this additive value in lunge number reduction is not observed in males with only the Gr32a receptor eliminated ( see below , Figure 3E ) . Males with lowered levels of OA have been reported to exhibit lower numbers of lunges [24] , [25] . Results in this study indicate that tβhnM18 mutant males take twice as long as controls to display their first lunges in fights ( Figure 3C , D , Figure S7E ) . We previously demonstrated that males without detectable OA exhibited elevated courtship behavior towards other males [23] . One possible explanation of these results is that OA deficient males have difficulty recognizing the sex or species of a second fly . A similar delay in initiation observed in fights between males lacking Gr32a receptor neurons may be for this same reason ( this study and [16] ) . Given such a large delay in the onset of aggression in OA mutant flies ( Figure 3C , D and [25] ) , at least two factors can impact how lunge numbers are counted . First , counting lunges for a set period of time beginning when flies are first introduced to a chamber can yield very different results from counting at the start of lunging behavior ( Figure S7E ) . A second consideration is the inclusion of male pairs that did not display lunges . If fights without lunges are scored as “zeros” , the numbers of lunges seen in fights between pairs of tβhnM18 males are significantly lower than the numbers seen in the genetic controls ( Figure S7F ) , when fights that do not exhibit lunging are excluded , significant differences between tβh control and experimental are not found ( Figure S7G ) . tβhnM18 males that exhibited low numbers of lunges also engaged in elevated levels of male-male courtship , which was not observed in tβhM6 controls while OA deficient males that exhibited high numbers of lunges engaged in male-male courtship at low levels . These results are displayed as a ratio of wing extensions ( singing ) divided by lunges ( Figure S7H ) . Thus the affects of removing OA on the intensity of aggression also include a critical delay in the onset of aggression and an increase in male-male courtship . To support the hypothesis that Gr32a receptor function itself is a key transducer of the aggression-enhancing stimuli regulated by OA , we tested males containing the Gr32a−/− mutation [15] in the tβhnM18 ( null for OA ) and tβhM6 ( control ) backgrounds . Males without the Gr32a receptor and males without OA and Gr32a exhibited a similar 2-fold increase in the latency to lunge ( Figure 3D ) . The number of lunges displayed by males without OA ( tβhnM18 ) , without Gr32a ( tβhM6;;Gr32a−/− ) , or without OA and the Gr32a receptor ( tβhnM18;;Gr32a−/− ) were each significantly reduced as compared to control males ( tβhM6 ) ( Figure 3E ) . Differences in lunge number between groups of experimental males were not observed ( Figure 3E ) providing further support that OA may be downstream of Gr32a sensory signaling processes . As separately removing OA and Gr32a receptor function has been reported to increase male-male courtship toward intact males [23] and decapitated males [15] , we quantified the occurrences of courtship to the second male within the aggression paradigm . Males without the Gr32a receptor , males without OA , and males without OA and Gr32a all displayed a significantly greater amount of male-male courtship to the second intact male compared to controls ( Figure 3F ) . As with parameters of aggression , removing OA in the context of the Gr32a−/− mutation does not increase the already elevated levels of male-male courtship suggesting that OA may modulate Gr32a sensory input related to suppressing conspecific male courtship and promoting male aggression as these two processes have been suggested to reflect independent , parallel processes [39] . To determine if OA-expressing neurons modulate male aggression and courtship behavior by responding to sensory information concerning sexual recognition , we expressed the genetically encoded calcium indicator GCaMP6 [40] , and assayed changes in intracellular Ca2+ responses evoked by application of CHC extracts to the male legs . Male CHC extracts evoked significant increases in GCaMP6s fluorescence in subsets of OA SOG neurons of Tdc2-LexA;20XLexAop2-IVS-GCaMP6s males ( Figure 4A–B , E , G , n = 8 ) . The response to male CHCs was abolished in males with Gr32a neurons eliminated via DTI expression ( Tdc2-LexA/UAS-DTI;Gr32a-Gal4/20XLexAop2-IVS-GCaMP6s ) ( Figure 4C–D , F , G , n = 10 ) or through UAS-hid expression ( Tdc2-LexA/UAS-hid UAS-RedStinger;Gr32a-Gal4/20XLexAop2-IVS-GCaMP6s , data not shown ) . Male CHC extracts were also applied to the forelegs of males expressing GCaMP3 . 0 in Gr32a neurons ( UAS-GCaMP3 . 0/Gr32a-Gal4 ) , however Ca2+ changes were not reliably detected in these foreleg neurons . As the cellular transduction mechanisms involved in Gr32a signaling are currently unknown , it is possible that Ca2+ changes may be near or below the detection threshold or that a response may not include a Ca2+ influx . Nevertheless , our physiological data support the hypothesis that sensory information received by Gr32a neurons is directly relayed to OA neurons in the SOG . Although a single receptor subtype , Gr32a , appears to mediate key pheromonal responses that inhibit interspecies courtship , promote male aggression , and suppress conspecific male-male courtship , different subpopulations of Gr32a-expressing neurons may be involved in each case . To test this idea , we selectively ablated Gr32a-expressing chemosensory neurons located in the mouth without removing the leg Gr32a neurons . For this purpose , we used the homeotic teashirt promoter driving Gal80 expression [41] to significantly block Gal4-mediated activation in regions outside of the head . Via this route Diphtheria Toxin expression ( UAS-DTI ) was prevented resulting in males lacking Gr32a-expressing neurons only in the labellum or mouth ( Figure S8 ) . As in experiments presented above , the latency to lunge was significantly longer in males without labellar Gr32a neurons ( Figure 5A ) and a significant reduction in lunge number was also observed ( Figure 5B ) . As increased male-male courtship to a second intact male is exhibited by males without the Gr32a receptor and without OA ( Figure 3F ) , we quantified the occurrences of courtship behavior ( wing extensions and abdomen bending ) . The male-male courtship levels of UAS-DTI;teashirt ( tsh ) -Gal80/Gr32a-Gal4 male pairs are lower than control levels ( Figure 5C ) yet experimental males court females and successfully copulate in courtship assays ( 92% , n = 13 ) albeit with a longer latency to initiate courtship ( Table S1 ) . The ability of experimental males to successfully copulate is in agreement with a report indicating the ablation of the entire Gr32a neuron population does not alter the courtship of conspecific females [17] . Our results thereby indicate that there are functional differences on male social behavior served by the two separate populations of Gr32a-expressing chemosensory neurons and that the labellar Gr32a subpopulation is important for male aggression . Experiments in this study do not exclude a role for Gr32a leg neurons in male aggression , however the functional importance of the tarsal Gr32a subpopulation on male interspecies courtship behavior has recently been described [17] . To identify subpopulation-specific synaptic contacts between Gr32a and OA neurons , we used the teashirt-Gal80 line in combination with the GRASP system . Recent studies using the Gr32a-Gal4 driver to express GFP indicated at least 38 neurons in the mouth ( 19 neurons per labial palp ) and 11 neurons located in the legs express the reporter [36] , [38] . Adding the teashirt-Gal80 transgene significantly blocked Gal4-mediated activation in the thoracic region resulting in a reduction of GFP expression in the SOG . Thoracic ganglia neuronal projections via the cervical connective are reduced or absent ( arrowhead in Fig . 1A , compare Figure 1A to Figure 6A ) . The reduction of GFP-expression in leg sensory neurons of UAS-nlsGFP; tsh-Gal80/Gr32a-Gal4 progeny ( 0 . 38 neurons per front leg , n = 8 ) , versus males without Gal80 expression ( 5 neurons per front leg , n = 8 ) is shown in Figure 6F , G . With the addition of teashirt-Gal80 to restrict split-GFP expression to mouth Gr32a neurons , GFP reconstitution is visible in a highly reproducible pattern that appears to be part of the GRASP reconstituted pattern observed when the entire Gr32a-Gal4 expressing population is labeled ( compare 6D with 1E ) . Furthermore , GFP reconstitution co-localizes with the UAS-syt:HA reporter added to visualize the presynaptic terminals of Gr32a-expressing neurons . ( Figure 6H–J ) . As Gr32a and OA neuronal function strongly influence male-selective social behaviors , the GRASP patterns of male and female progeny were carefully examined . No apparent sex-specific differences were observed . Results from these experiments suggest that distinct behavioral responses to sex pheromone ( s ) are provided by separate subsets of Gr32a-expressing chemosensory neurons , in both cases involving potential direct reinforcement by OA . We previously demonstrated that three OA neurons express the male form of Fruitless ( FruM ) , a neural sex determination factor that is a key determinant of male patterns of courtship and aggression ( Figure 7A ) [21] , [42] , [43] . The necessity of FruM expression in this small subset of OA neurons was evident as the absence of FruM resulted in an increase in male-male courtship in an aggression setting [21] . These results suggested that sexual specification of certain OA neurons might be involved in reliably establishing mate selection ( or reliably suppressing conspecific male-male courtship ) . To determine if Gr32a-expressing neurons establish synaptic contacts with FruM-OA neurons , Tdc2-LexA was used in conjunction with the recently generated restrictable split-GFP component , lexAop2->stop>CD4::spGFP11 ( María Paz Fernández , unpublished data ) . Selectively activating split-GFP11 expression in FruM neurons was achieved through the production of the FLP enzyme in Fruitless-expressing neurons via the fruFLP [44] line and putative synaptic connections were observed in male and female brains also expressing Gr32a-Gal4 driven UAS-CD4::spGFP1-10 ( Figure 7B–D ) . At this time , we cannot simultaneously restrict Gr32a-expressing and OA neuronal populations or as yet quantify any sex-specific connection differences that may exist . However , our experiments indicate the FruM-OA neurons that account for increases in male-male courtship are anatomically connected to Gr32a neurons and these may form a microcircuit that contributes to the context-specificity of male courtship behavior .
Studies on animal behavior have been ongoing for decades and these have resulted in identifying pheromones , hormones and neurohormones , neurons , circuits and more recently , genes , that cause or contribute to the expression of social behavior . Yet a broad gap still exists between the identification of neurons and circuits suspected of involvement in specific behaviors and an understanding of how these circuits orchestrate the many context-dependent complex decisions animals routinely make in their daily lives . In this study , we demonstrate a direct early sensory link to a neuromodulatory-signaling element concerned with male aggression and courtship behavior and show that the two are interconnected in the suboesophageal ganglion . Our results show that sensory neurons expressing Gr32a , a widely distributed gustatory receptor that plays a critical role in male social behaviors [14]–[17] , relays primary sensory information to the SOG where octopaminergic interneurons are contacted . The high density of putative GRASP connections we observe between receptor neurons expressing Gr32a , 22e , and 59b , and OA neurons in the SOG ( these are co-expressed in a subset of the labellar sensory receptor neuron pool ) [36] ) , suggests that amine-dependent modulatory steps may serve as important second order components in connecting signals from taste receptor neuron subtypes to taste-evoked behavior in flies [31] , [45] ( in vertebrates and other invertebrate systems see [46] , [47] , [48] ) . A separate study also identified putative synaptic connections between Gr32a axons and the total population of FruM-expressing neurons [17] . Whether Gr32a-expressing neurons solely contact the OA-FruM neurons or whether they contact additional FruM neurons remains to be determined . We do observe regions of Gr32a-driven syt:HA expression without GFP reconstitution to OA neurons suggesting the Gr32a-expressing neuron population likely contacts additional neuron subsets . The Gr32a receptor is categorized as a contact-based chemoreceptor and is required for physiological responses to caffeine and other aversive , bitter-tasting compounds [36] , [49]–[51] . Gr32a is also reported to mediate the behavioral effects of the male pheromone ( z ) -7-tricosene and regulate interspecies courtship [16] , [17] . ( z ) -7-tricosene application to male legs evoked an increase in Ca2+ signaling in OA neurons ( Andrews and Certel , unpublished data ) , although we were unable to identify a reliable response to ( z ) -7-tricosene in Gr32a foreleg neurons at this time . Reconciling behavioral and physiological roles of Gr32a-expressing leg and labellar neurons to individual CHCs will require further investigation . Nevertheless , application of male CHCs to male legs evokes significant increases in Ca2+ signaling in OA neurons and this response is eliminated in males with ablated Gr32a neurons ( Figure 4 ) . These results support the behavioral data that indicates male aggression is promoted through the Gr32a receptor ( this study and [16] and suggests that at least a portion of the sensory information mediated by Gr32a receptor-bearing sensory neurons and OA modulatory interneurons operate in a single circuit . The manipulation of neuronal populations by altering the expression of single molecular products like the Gr32a gustatory receptor or one of the monoamines , commonly yields multiple behavioral phenotypes [14]–[16] indicating that such populations are heterogeneous in function . Separation of the grouped neurons into small subgroups can clarify the roles of these neurons in behavior and ultimately is essential in defining the circuitry involved . Recent findings indicate the tarsal Gr32a neurons are necessary to mediate species recognition [17] . Our data demonstrate that the foreleg tarsi and mouth populations of Gr32-expressing neurons may exert separable functional differences on male aggression and courtship behavior with both populations involving direct reinforcement by OA . Although Gr32a-expressing neurons do not exhibit any obvious sexual dimorphism , it has been postulated that their postsynaptic targets are sexually dimorphic [14] . With the increasing genetic capabilities of individual neuron manipulation , it will be interesting to determine if sexually dimorphic connectivity between single Gr32a and FruM-OA neurons regulate distinct differences in social behaviors . Results from further anatomical studies could provide insight into how potential sexual modification of OA signaling links chemosensory input to sex-specific behavioral output . Neural networks mediating ever-changing environmental stimuli , context-specific social behavior , and internal states challenge us with the overwhelming structural and functional complexity of their interactions . To attempt to reduce network complexity , one common approach is to define network subunits and demonstrate their functional role by selective removal . It is well known that amine neurons can signal through hormonal volume transmission and act on targets at a distance [52] , [53] . However , biogenic amines are also released synaptically and act on local targets [54]–[58] . Whether amine neurons function in separate modulatory circuits that run parallel to and interact with hard-wired circuitries directing behavior , or whether they are an integral part of such circuitry remains to be determined . However , understanding the presynaptic sources or postsynaptic targets of OA neurons should provide useful insight into the “structural” embeddedness of single cells within a network . An anatomical analysis of individual components will be necessary as proximity-based neuron groupings break down with the addition of cell-specific markers ( like FruM ) and within amine neuron populations [59] . Network anatomical characterization that includes neuromodulatory neurons may also provide insight into the reinforcing or opposing actions of amines through second amines or peptide modulators [60] , [61] . For example , Burke et al . , recently demonstrated plausible sites of synaptic contact between OA and DA neurons in the Drosophila mushroom body and a role for OA in providing appetitive reinforcement by OA receptor-mediated actions on DA neuron populations [29] . Our study offers a valuable framework in which to undertake the characterization of sensory-driven neural circuits and the underlying neuromodulation of sexually dimorphic patterns of social behavior .
The dTdc2-lexA:VP16 transgenic line was generated by cloning the same regulatory region as described previously [28] into the pBS_LexA::VP16_SV40 vector . In the previous construct , the GAL4 was inserted immediately before the coding start , and the entire construct ( genomic segments interrupted by Gal4 ) was inserted into the polylinker of pCaSpeR4 [28] . To generate the dTdc2-lexA:VP16 construct , genomic DNA containing the region −3459 to +4530 was amplified with the Expand Long Template PCR system ( Roche Applied Science ) . Fragment “A” of the dTdc2 genomic region was amplified using the following primers , Tdc2A- Forward: GTCGCGGCCGCAAAAGTTATTGCACATTG , Tdc2A-Reverse: GGCCGGCCGTTTCGGTAGGTTTTCCAAATC , and fragment “B” with the following primers , Tdc2B Forward: GTCGGGCCCATGGACAGCACCGAATTTC , Tdc2B-Reverse: GGCCGCGGCCGCTTAGAACATATCGAGTTG . The dTdc2 fragment A PCR product was inserted directly into the pBS-LexA::VP16_SV40 vector via the Eag1 site . Fragment B was first inserted into the TOPO vector and digested with Apa1 , followed by ligation into to pBS-Tdc2fragmentA-LexA::VP16_SV40 using the Apa1 site on the vector . The fragment containing Tdc2 fragment A+ the LexA coding region+dTdc2 fragment B was subcloned into the Not1 site of pCaSpeR4 . The lexAop2-FRT-STOP-FRT-::spGFP11 line was generated by amplifying the spGF11 fragment through PCR from the previously described pLOT plasmid [27] . The FRT-STOP-FRT cassette was amplified from the pJFRC177 plasmid ( #32149 , AddGene ) and both the STOP cassette and the spGFP11 fragment were cloned downstream of the 13XLexAop2 sequence in pJFRC19 ( #26224 , AddGene ) . The amplified fragments were verified by sequencing . Transgenic flies were raised by standard procedures and lines screened for appropriate expression . Adult male and female dissected brains were fixed in 4% paraformaldehyde ( Electron Microscopy Sciences ) for 25 minutes and labeled using a modification of protocols previously described [23] . The following primary antibodies were used: rabbit anti-GFP monoclonal ( 1∶200 ) ( Life Technologies , G10362 ) , mouse anti-GFP ( 1∶200 ) ( Invitrogen , A-11120 , Lot 764809 ) , rabbit anti-FruM ( 1∶2000 ) [43] , rat anti-CD8 ( 1∶100 ) , rat anti-HA ( Roche , 1∶1000 ) , mAb nc82 ( anti-bruchpilot ) ( 1∶30 ) [65] , anti-Tβh ( 1∶400 ) [66] . Secondary antibodies include Alexa Fluor 488-conjugated goat anti-rabbit , Alexa Fluor 488-conjugated donkey anti-mouse , Alexa Fluor 594-conjugated donkey anti-mouse , Alexa Fluor 594-conjugated goat anti-rabbit , Alexa Fluor 647-conjugated donkey anti-mouse ( Invitrogen ) . Cross-adsorbed goat anti-rabbit fluorescein-conjugated secondary antibodies were used in multi-labeling experiments . Images were collected on an Olympus Fluoview FV1000 laser scanning confocal mounted on an inverted IX81 microscope and processed using ImageJ ( NIH ) and Adobe Photoshop ( Adobe , CA ) . All fly strains were reared on standard fly food ( medium containing agar , glucose , sucrose , yeast , cornmeal , propionic acid , and Tegosept ) . Flies were grown in temperature- and humidity-controlled incubators ( 25°C , 50% humidity ) on a 12-h light/dark cycle . To collect socially naïve adults , pupae were isolated in individual 16×100-mm glass vials containing 1 . 5 ml of food medium . Upon eclosion , flies were anesthetized with CO2 , painted on the thorax with acrylic paint for identification and returned to their isolation vials to allow for recovery from anesthesia a full 24 hours before testing . Live brain preparations were made by anesthetizing a fly on ice followed by placement within a pipette tip with the head protruding . The pipette was then sealed with nail polish and allowed to dry . Flies thusly secured were placed in a 1 mL well for electrophysiology at an angle and the region containing the head was flooded with 400 µL of oxygenated HL3 solution . Removal of the proboscis and front of the head cuticle allowed for imaging . Each preparation was equilibrated for 5 min after proboscis and cuticle dissection . Male cuticular hydrocarbon extract ( hexane extract from 150 male flies 3 days post eclosure ) , ( z ) -7-tricosene ( Cayman Chemical #9000313 Lot# 0406404-32 ) , or quinine ( Sigma-Aldrich #6119-47-7 Lot # STBD3004V ) dissolved in oxygenated HL3 solution was administered via syringe into the rear of the pipette tip . Administration of each compound occurred a minimum of 15 seconds apart . Flies received either male cuticular extract or ( z ) -7-tricosene first , followed by quinine . Analysis of ΔF/F values in regions of interest was calculated using Fiji and Prism 6 . 0 . Epifluorescene images were acquired at the rate of 1 image/ . 750s by Hamamatsu camera ( ORCA ER series , model C4742-95-12ERG ) . Acquired images were registered ( StackReg plugin , Fiji software ) and regions of interest were selected within the suboesophageal ganglion . Image processing and analysis was accomplished with ImageJ version 1 . 44/Fiji version 1 . 43 . Image subtraction was performed in Fiji using the image calculator . Intensity tables were exported to excel and ( ΔF−F ) /F calculated for each series of images . Traces were generated in Prism 6 . 0 . Peak analysis was performed between regions no more than 5 seconds post compound administration ( for post CHC ) and no later than 4 seconds prior to compound administration ( for pre-CHC ) . Aggression assays were performed in individual chambers of 12-well polystyrene plates containing a food cup in the center [67] . 4–5 day old males were transferred in pairs to assay chambers by aspiration . Experiments were performed at 25°C in a humidity controlled room ( 50% ) . Fights were videotaped for 90 minutes and lunges counted for 30 minutes from the first lunge unless otherwise specified . The time between introduction into the chamber and the onset of aggression ( first lunge ) was defined as the fighting latency . Lunging behavior was determined as previously described [68] . Courtship assays were performed in a 12 well polystyrene plate ( VWR #82050-930 ) with one Canton S virgin female ( aged 7–10 days ) and one 4–5 day old male . The period between introduction into the courtship chamber and the first male wing extension ( singing ) was defined as courtship latency . | To mate or fight ? When meeting other members of their species , male fruit flies must determine whether a second fly is male or female and proceed with the appropriate behavioral patterns . The taste receptor , Gr32a , has been reported to respond to chemical messages ( pheromones ) that are important for gender recognition , as eliminating Gr32a function impairs both male courtship and aggressive behavior . Here we demonstrate that different subsets of Gr32a-expressing neuron populations mediate these mutually exclusive behaviors and the male Gr32a-mediated behavioral response is amplified through neurons that contain the neuromodulator octopamine ( OA , an invertebrate equivalent of norepinephrine ) . Gr32a-expressing neurons connect functionally and synaptically with distinct OA neurons indicating these amine neurons may function as early as a second-order step in a chemosensory-driven circuit . Our results contribute to understanding how an organism selects an appropriate behavioral response upon receiving external sensory signals . | [
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] | 2014 | Octopamine Neuromodulation Regulates Gr32a-Linked Aggression and Courtship Pathways in Drosophila Males |
In contrast to human cells , very few HSV-1 genes are known to be spliced , although the same pre-mRNA processing machinery is shared . Here , through global analysis of splice junctions in cells infected with HSV-1 and an HSV-1 mutant virus with deletion of infectious cell culture protein 27 ( ICP27 ) , one of two viral immediate early ( IE ) genes essential for viral replication , we identify hundreds of novel alternative splice junctions mapping to both previously known HSV-1 spliced genes and previously unknown spliced genes , the majority of which alter the coding potential of viral genes . Quantitative and qualitative splicing efficiency analysis of these novel alternatively spliced genes based on RNA-Seq and RT-PCR reveals that splicing at these novel splice sites is efficient only when ICP27 is absent; while in wildtype HSV-1 infected cells , the splicing of these novel splice junctions is largely silenced in a gene/sequence specific manner , suggesting that ICP27 not only promotes accumulation of ICP27 targeted transcripts but also ensures correctness of the functional coding sequences through inhibition of alternative splicing . Furthermore , ICP27 toggles expression of ICP34 . 5 , the major viral neurovirulence factor , through inhibition of splicing and activation of a proximal polyadenylation signal ( PAS ) in the newly identified intron , revealing a novel regulatory mechanism for expression of a viral gene . Thus , through the viral IE protein ICP27 , HSV-1 co-opts both splicing and polyadenylation machinery to achieve optimal viral gene expression during lytic infection . On the other hand , during latent infection when ICP27 is absent , HSV-1 likely takes advantages of host splicing machinery to restrict expression of randomly activated antigenic viral genes to achieve immune evasion .
HSV-1 and HSV-2 , two closely related human herpes viruses , establish lifelong incurable latency in and reactivate preferentially from trigeminal ganglia and dorsal root ganglia to cause orofacial and genital herpes , respectively . Although infections are usually mild , these viruses can cause severe disease including encephalitis and neonatal herpes . During latency in terminally differentiated neurons , expression of viral genes is repressed , except for the latency-associated transcript ( LAT ) and latency-associated miRNAs [1–3] . During acute infection , herpesvirus genes are expressed in a coordinated temporal cascade characterized by three kinetic classes , immediate-early ( IE or α ) , early ( β ) , and late . Late genes are further divided into two subclasses: leaky-late ( γ1 ) genes that are expressed at very low levels at early times after infection and are dramatically upregulated at late times as a result of the increased number of genomes present after DNA replication , and true late genes ( γ2 ) that are expressed exclusively after and are dependent upon viral DNA replication . HSV infected cell culture polypeptide 27 ( ICP27 ) , along with ICP4 , are the only two IE genes essential for virus replication [1 , 4] . ICP27 , highly conserved between HSV-1 and HSV-2 , is also the only one of the five HSV-1 IE genes that has clear homologs in all characterized mammalian herpesviruses ( 8 ) . ICP27 is known to be required for efficient expression of some viral DNA replication-related early genes and late viral genes as well as for virus growth [5 , 6] . ICP27 plays a role in transcriptional regulation through association with the C-terminal domain of RNA polymerase II [7 , 8] and interacts with viral transactivating proteins encoded by immediate early genes including ICP4 and ICP0 [9–11] . ICP27 forms homo-dimers [12 , 13] , interacts with U1 snRNP through its C-terminal domain , and colocalizes with U1 and U2 snRNPs [14 , 15] . ICP27 also interacts with splicing factors such as SRSF1 , SRSF2 , SRSF3 , and SRSF7 through its C-terminal domain , and SR protein kinase 1 ( SRPK1 ) through its N-terminal RGG RNA-binding domain [16–19] . Recently , ICP27 was shown to inhibit splicing of certain introns and promote use of alternative 5′splice sites ( ss ) in a small percentage of cellular genes in a sequence specific manner [20] . ICP27 also promotes co-transcriptional cellular pre-mRNA 3’ end formation using cryptic polyadenylation signals ( PAS ) in proximal introns , generating hundreds of novel , intronless GC-rich cellular transcripts that resemble HSV genes [20] . Although HSV-1 pre-mRNAs are transcribed in the nucleus by host transcription and RNA processing machineries , only 6 genes out of at least 84 genes , including 3 out of the 5 immediate early genes ( ICP0 , ICP22 and ICP47 ) , a latently expressed gene ( the latency associate transcript , or LAT ) and two late genes ( UL15 and gC ) , have until now been identified as spliced genes [1 , 21] . Recently , a few novel splice isoforms including two antisense transcripts , UL41-42C ( transcript initiated antisense to UL41 ) and AST-2 ( transcript antisense to UL36 ) , as well as UL49sp ( splice site flanked by a unusual GC-AG intron ) were identified using high throughput long-read sequencing in HSV-1 infected Vero cells [22] . Since ICP27-targeted host genes contain high GC content and cytosine-rich sequences , resembling HSV genes [20] , we hypothesized that ICP27 likely co-evolved with the GC-rich viral genome and may have additional unknown viral targets . In this report , we further investigate the role of ICP27 in regulating pre-mRNA processing of viral genes . In addition to discovery of novel alternative splice sites for known viral spliced genes , we identify 22 novel viral spliced genes , most of which are tightly controlled by ICP27 . Furthermore , we find that ICP27 tightly regulates expression of monocistronic ICP34 . 5 mRNA by inhibiting splicing and activating a PAS in the newly identified proximal intron , which represents a novel regulation mechanism for viral gene expression .
HSV infects most cell types in vitro including human kidney HEK-293 cells , which have been widely used in previous pre-mRNA splicing and polyadenylation related studies . To further understand ICP27’s role in viral pre-mRNA processing in a way aligning directly to previous findings [20] , we performed RNA-Seq using poly ( A ) -enriched RNA purified from HEK-293 cells infected with wild-type HSV-1 strain KOS , or an ICP27 deletion mutant ( d27-1 ) in the presence or absence of the viral polymerase inhibitor phosphonoacetic acid ( PAA ) at 4 hours post infection ( hpi ) or 7 hpi . The RNA-Seq data were analyzed using CLC genomic Workbench and the HSV-1 consensus sequence without the terminal repeat sequences was used as the reference ( Fig 1 ) . In KOS infected HEK-293 cells , reads mapping to the HSV-1 consensus sequence increased from approximately 43 . 2% at 4 hpi to 73 . 6% at 7 hpi , a result similar to that previously reported in infected MRC-5 human fibroblast cells [23] . However , deletion of ICP27 reduced reads mapping to the HSV-1 genome to approximately 10 . 5% at 4 hpi and 15 . 4% at 7 hpi , a reduction much greater than that induced by the viral polymerase inhibitor phosphonoacetic acid ( PAA ) , which markedly reduced transcription only of γ2 genes ( the subset of viral DNA replication-dependent γ genes ) . Reads mapping to the IE gene UL54 ( ICP27 ) are not detectable in d27-1 infected cells since the coding region of ICP27 was deleted in d27-1 . Deletion of ICP27 does not appreciably affect other α viral gene expression , but reduced non-α ( i . e . , β and γ ) viral gene expression , confirming ICP27’s essential role in promoting β and γ viral gene expression . The RNA-Seq data were also analyzed using CLC Genomics Workbench and the human genome consensus sequence ( HG19 ) as the reference . Aberrant mRNA processing of ICP27-targeted cellular genes , previously identified as a result of ectopic HSV-2 ICP27 expression [20] , was also observed in HSV-1 infected cells ( with or without PAA ) , but not in d27-1 infected cells , confirming HSV-1 and ICP27’s role in mediating aberrant pre-mRNA processing in infected cells . The three previously described types of ICP27 mediated aberrant pre-mRNA processing ( aberrant polyadenylation , aberrant use of 5’ss and intron retention ) are apparent in three representative genes , PPTC7 , ZER1 and POLR2A , respectively ( Fig 2 ) . The RNA-Seq results for these three representative genes in KOS and d27-1 infected cells are consistent with RT-PCR and Northern blot results [20] . HSV-1 thus mediates aberrant pre-mRNA processing in a manner similar to ectopic expression of ICP27 alone . Expression of ICP27 in the context of viral infection appears to induce additional intron retention in POLR2A that was not observed with ectopic expression of HSV-2 ICP27 in transfected cells , suggesting either subtle differences between HSV-1 and HSV-2 ICP27 or that virus-produced ICP27 more efficiently inhibits splicing than does ectopic expression in transfected cells . We used CLC Genomics Workbench to further analyze the RNA-Seq data to view the splicing pattern of known viral spliced genes ( as presented in Fig 1 ) at a resolution of single genes . Expression of ICP27 does not appear to influence pre-mRNA splicing of ICP47 , ICP22 , or ICP0 intron 2 ( Fig 3A–3D ) . Slight retention of ICP0 intron 1 was observed in HSV-1 strain KOS-infected cells; however , retention of ICP0 intron 1 was accompanied by increased reads mapping upstream of the ICP0 transcription initiation site ( Fig 3C ) . Because this approach may not detect splicing patterns of other known spliced genes due to much lower expression levels or to complex transcription patterns , the high throughput data were also mapped to a 44-bp reference unspliced sequence containing 22 bp of sequence from each exon and 22 bp sequence from the adjacent intron , and a 44-bp reference spliced sequence containing 22 bp sequence from each of the two exons expected to be joined following splicing . Each result was manually examined to confirm the results . The 44-bp length of these reference sequences detected splices in known spliced genes including ICP47 , ICP0 , UL15 and gC . The percentage of intron removal was thus calculated for individual genes in each of the five RNA-Seq data sets based on the reads mapping to exon-exon junction sequence relative to total reads mapped to both exon-exon junction and exon-intron junction sequences . The quantitative data support the graphical results in Fig 3A , 3B and 3C . ICP27 does not significantly influence the splicing of IE genes , except for intron 1 of ICP0 ( Fig 3D ) . The splicing efficiency for ICP0 intron 1 appears to increase from approximately 85% in KOS-infected cells to over 99% in d27-1 infected cells . Increase of ICP0 intron 1 retention is coupled with an increase in reads mapping to sequences upstream of the ICP0 transcription initiation site . Co-transcriptional splicing of the first intron in a gene can be greatly enhanced by the RNA capping machinery and a large distance from a cap-proximal 5′ss to the RNA 5′ cap may reduce the chance of this splice site being recognized by cellular splicing machinery [24–26] . Taken together with the mapping results in Fig 3C , this implies that the observed retention of ICP0 intron 1 in KOS infected cells is likely in readthrough transcripts from upstream alternative promoters , for which splicing of ICP0 intron 1 is likely less efficient due to the increased distance between the 5’ cap and the 5’ss of ICP0 intron 1 . The actual impact of ICP27 on the splicing of monocistronic ICP0 intron 1 is thus minimal , consistent with previous observations [27] . To confirm ICP27’s role in viral gene expression and obtain more precise quantitative information , we also performed RNA-Seq using Vero cells ( monkey kidney cells that have been widely used in HSV studies ) infected with KOS or d27-1 ( S1 Fig ) . The infection was performed in triplicate in 6 well plates and the poly ( A ) selected RNA samples were prepared at 7 hpi . In this RNA-Seq data , the impact of ICP27 on overall viral gene expression was similar to the results obtained in infected HEK-293 cells as shown in Fig 1 . The mean relative splicing efficiency and standard deviation were calculated based on the mapping results as described above . Retention of ICP0 intron 1 , although less severe than in infected HEK-293 cells ( Fig 3C ) , was also coupled with an increase of reads mapping to the sequences upstream of the ICP0 transcription initiation site ( Fig 3E and 3F ) . Thus , ICP27 does not appear to inhibit splicing of IE genes . To understand ICP27’s role in global viral pre-mRNA processing , we further mapped potential viral splice junctions using MapSplice 2 , software that identifies potential splice junctions relative to a reference genome without relying on sequence annotations ( 31 ) , with the default parameter settings and the RNA-Seq data for d27-1 and KOS infected HEK-293 cells ( 7 hpi ) presented in Figs 1 , 2 and 3 . Possible splice junctions were detected relative to the HSV-1 reference sequence ( raw data are presented in S1 and S2 Tables ) . Most newly identified introns possess canonical splice junctions flanked by GT ( GU ) and AG . Although the total viral read counts in KOS infected cells are nearly 5-fold more than d27-1 infected cells ( Fig 1 ) , the total read counts of the splice junctions mapping to the viral genome were similar between KOS and d27-1 infected cells ( 148 , 652 for KOS infected cells , and 143 , 990 for d27-1 infected cells ) . A total of 1940 and 450 splice junctions mapping to the HSV-1 genome were identified in KOS infected cells and d27-1 infected cells , respectively . KOS infected cells contained significantly more rare splice junctions ( reads ≤ 2 ) than did d27-1 infected cells ( 85% vs 44% ) , suggesting that splicing of these predicted and known viral genes may be generally inhibited in wild-type HSV-1 infected cells as compared with ICP27 deletion mutant virus infected cells . We next mapped the predicted splice junctions listed in S1 and S2 Tables to the viral genome . Ten ( 10 ) out of eleven ( 11 ) previously identified splice junctions in six known HSV-1 spliced genes , including three IE genes ( ICP0 , ICP47 and ICP22 ) , two late genes ( UL15 and gC ) and one latent gene ( LAT ) , and two recently identified spliced transcripts ( UL41-42C and AST-2 ) [28] , are also identified by MapSplice 2 ( Tables 1 and S1 and S2 ) . Only very few reads ( <10 ) were identified for splicing of AST-2 intron 2 and no read was obtained for splicing of AST-2 intron 1 or UL41-42C intron 3 . No read was obtained for splicing of UL49sp , a newly identified spliced transcript with a 74 bp intron flanked by GC-AG [22] . Total reads for these known splice junctions identified in KOS and d27-1 infected cells accounted for approximately 92 . 3% and 89 . 5% of total viral splice junctions detected , respectively . In addition to the known spliced junctions identified , a total of 13 novel alternative splice junctions for transcripts of 7 of the known spliced HSV-1 transcripts , including LAT , ICP0 , UL15 , ICP22 , ICP47 , gC and UL41-42C were identified and the reads for these novel alternative splice junctions accounted for approximately 2 . 2% of the total junctions identified ( Tables 1 and S1 ) . We confirmed the novel splice junctions mapping to LAT , ICP0 and UL15 by RT-PCR and sequencing . During latency of HSV-1 , the most abundant viral transcript is the latency-associated transcript ( LAT ) , a noncoding RNA . Primary LAT is a low-abundance transcript of 8 . 5 kb in latently infected neurons . LAT is spliced , leading to accumulation of abundant 2 . 0 kb and 1 . 45 kb highly stable introns in the nucleus [29 , 30] and LAT-encoded miRNAs [2 , 3] . The 2 . 0 kb intron appears to be the major species in the latently infected neuron and the 1 . 45 kb intron flanked by “GC-AG” is produced via secondary pre-mRNA splicing using splice sites within the 2 . 0 kb intron [30] . The splice junction of the 2 . 0 kb LAT intron was identified by MapSplice 2 ( Table 1 ) , as was a novel splice junction flanking a 1 . 68 kb GC-AG intron ( Fig 4A ) . The 5’ss of the 1 . 68 kb intron is the same as previously described second 5’ss of the 1 . 45 kb intron , which was previously shown to be an intron within the 2 . 0 kb intron ( illustrated in Fig 4A ) . The alternative splice junctions for the 1 . 68 kb LAT was confirmed by RT-PCR and subsequent sequencing of the PCR fragments ( Fig 4A ) . However , neither the 1 . 68 kb LAT intron nor the previously reported 1 . 45 kb LAT intron was readily detectable in KOS or d27-1 infected Vero cells by Northern hybridization ( Fig 4A ) . Splicing of the 1 . 68 kb intron flanked by “GC-AG” was much less efficient as compared to the 2 . 0 kb intron ( Fig 4B ) . Reads mapping to the splice junction for the 1 . 45 kb intron were not found in the high throughput data in either KOS or d27-1 infected cells ( Fig 4B ) . ICP0 , one of the five IE genes , is an E3 ubiquitin ligase that promotes viral gene expression and inhibits host cell response . ICP0 is non-essential at high multiplicities of infection [1] . Use of an alternative 3’ss located within intron 2 of the most common ICP0 mRNA isoform generates a one amino acid polymorphism and was confirmed by RT-PCR and sequencing ( Fig 4C ) . The frequency of using the alternative 3’ss for intron 2 is unaffected by the presence or absence of ICP27 ( Fig 4D ) . An alternative minor 5’ splice site for ICP0 intron 1 was also confirmed by RT-PCR ( Fig 4C ) . UL15 , an essential γ gene , is required for viral DNA cleavage and packaging [1] . Four alternative 5’ss and one 3’ss were confirmed by RT-PCR and sequencing ( Fig 4G ) . Each of the 5 alternative splices in UL15 destroys or truncates the open-reading frame ( ORF ) of UL15 protein ( Fig 4E ) . Although these splice junctions map to UL15 , some splice junctions such as the junctions using 5’ss nt27264 and 28566 may represent readthrough transcripts transcribed from upstream promoters . The relative splicing efficiency of the known splice junction ( 2990^33581 ) of UL15 ( γ ) was modestly increased from approximately 80% in KOS-infected cells to over 98% in d27-1 infected cells . Alternative splicing of UL15 was very inefficient in KOS infected cells but was significantly increased in d27-1 infected cells ( Fig 4F and 4G ) . We next analyzed splice junctions listed in S1 and S2 Tables mapping to previously unknown spliced genes in both KOS and d27-1 infected cells . Since low abundance splice junction reads likely represent very low splicing efficiency , only splice junctions greater than 65 nt with more than 30 reads from either KOS or d27-1 infected cells were selected for further verification . Viral transcripts with at least one splice junction verified experimentally by RT-PCR and sequencing are summarized in Table 2 . Approximately 56 novel splice junctions were mapped to 20 viral genes , including in 2 previously uncharacterized viral transcripts mapping complementary to UL4 and UL22 , named UL4-5C and UL22C . These novel splice junctions accounted for approximately 3 . 3% and 1 . 1% of total viral splice junctions for d27-1 and KOS infected cell ( 7 hpi ) , respectively . Since the cut-off for further analysis of putative splice junctions was set to 30 reads , the actual number of novel viral spliced transcripts almost certainly exceeds 22 . These novel spliced genes include viral DNA replication-related early genes encoding UL5 protein ( helicase/primase ) , UL52 protein ( helicase/primase ) , UL12 protein ( exonuclease ) , TK protein ( thymidine kinase ) and ICP8 protein ( single-strand DNA-binding protein ) , as well as late genes encoding multiple glycoproteins ( gH , gL , gE , gD and gB ) , and virus-host interaction factors including ICP34 . 5 , US3 , UL37 , UL24 , US11 and the virus-host shut-off protein ( VHS ) that are key viral virulence factors ( Table 2 ) . Splicing of these pre-mRNA transcripts destroys , truncates or internally deletes the open-reading frame ( ORFs ) for the protein encoded by each of these genes , indicating that ICP27-mediated aberrant pre-mRNA processing contributes to efficient expression of full-length viral proteins encoded by these genes with hidden splices . All newly confirmed introns possess canonical splice junctions flanked by GT ( GU ) and AG except for three transcripts , including the LAT 1 . 68 kb intron and UL26 , for which splice junctions are flanked by GC and AG . Most of the splice junctions flanked by GC and AG including that of UL49sp , a recently identified spliced gene , could not be confirmed by RT-PCR in infected cells , suggesting that the majority of the predicted splice junctions flanked by GC-AG may represent sequencing error due to the high GC content of the HSV-1 genome . A GC-AG type intron was predicted for UL42; however , sequencing of the RT-PCR bands indicates that the “GT” located 4-bp downstream of the predicted “GC” is included in the intron . In contrast , the vast majority of splice junctions flanked by GT ( GU ) and AG could be verified by RT-PCR . Two novel splice junctions ( UL4-5C and UL22C ) were mapped antisense to the coding region of UL4-5 and UL22 , potentially representing read-through transcripts . While HSV-2 ICP34 . 5 contains a 154-bp intron within the coding region and splicing inhibition of the HSV-2 ICP34 . 5 pre-mRNA by ICP27 results in a truncated form of ICP34 . 5 [31 , 32] , HSV-1 ICP34 . 5 , encoding the major viral neurovirulence factor , was not previously known to be a spliced gene . Splices from a 5’ splice junction ( nt125650 ) in the coding region of ICP34 . 5 to 3’ splice junctions ( predominantly ) at nt124046 in the 5’ UTR region of ICP0 and ( less frequently ) at nt123186 , which is also the acceptor splice site of ICP0 intron 1 were identified ( Table 2 and Fig 5A ) . The predominant spliced ICP34 . 5 transcript isoform ( 125650^124046 ) was readily detectable in d27-1 infected cells by RT-PCR but was barely detectable in KOS infected cells ( Fig 5B ) . Two minor splice junctions ( 124467^124046 and 124467^123186 ) were also confirmed by RT-PCR ( Fig 5B ) . Expression of HSV-1 ICP34 . 5 protein was abolished in d27-1 infected cells ( Fig 5C ) . Further analysis of the splicing efficiency of these novel splice sites in the ICP34 . 5 region indicates that splicing of the ICP34 . 5 pre-mRNA is only efficient when ICP27 is absent ( Fig 5D ) . When ICP27 is absent , 12560^124046 is the major splice with approximately 62% intron removal efficiency , followed by 123402^123186 , 12560^123186 and 12560^124047 . Splicing of 124467^124046 and 124467^123186 is very inefficient ( below 0 . 5% ) in the presence of ICP27 or not . Splicing of these novel introns is more efficient when ICP27 is absent . As discussed in Fig 3 , splicing of ICP0 intron 1 and intron 2 is efficient with slight intron retention in ICP0 intron 1 in KOS infected cells . To be more rigorous , we also performed quantitative analysis of the novel alternative 5’ss ( nt123402 ) of ICP0 intron 1 . Splicing of this alternative ICP0 intron 1 appears to be enabled in the presence of ICP27 , similar to the case with the alternative 3’ss ( nt 122380 and 122377 ) of ICP0 intron 2 ( Fig 4D ) . We next performed Northern blot of viral RNAs containing ICP34 . 5 exon 1 in HEK-293 cells infected with wild-type HSV-1 or ICP27 mutants using a probe corresponding to ICP34 . 5 exon 1 ( Fig 5E ) . m15 has a two-amino acid mutation on the C-terminal domain and d4-5 contains deletion of the N-terminal RGG RNA binding domain /SRPK-1 binding domain . A ~1 . 3 kb band corresponding to the monocistronic ICP34 . 5 mRNA ( Isoform I in Fig 5A ) , for which the ICP34 . 5 polyadenylation signal ( PAS ) is within the newly identified intron , was abolished in d27-1 and m15 infected cells and significantly reduced in d4-5 infected cells , while a ~3 kb band corresponding to the novel ICP34 . 5-ICP0 splice isoforms ( Isoforms IV , III and II ) was only present in d27-1 infected cells , consistent with the RT-PCR and Western blot findings ( Fig 5C ) . A ~4 . 5 kb band corresponding to readthrough ICP34 . 5-ICP0 splice isoforms ( Isoforms X/XI/XII ) is present in cells infected with both wild-type and ICP27 mutants . Other ICP34 . 5 splicing isoforms at ~5 kb ( Isoforms XIV/XIII/XV ) predicted to exist based on these splice sites were also detected . Based on the relative splicing efficiency of these introns , the major products corresponding to the 1 . 3 kb , 3 kb , 4 . 5 kb and 5 kb Northern blot bands appear to be Isoform I , IV , X and XIV , respectively . Thus , the Northern blot results not only confirm ICP27-dependent splicing inhibition of ICP34 . 5 pre-mRNAs , but also demonstrate that cleavage and polyadenylation of the monocistronic ICP34 . 5 mRNA is dependent on ICP27 and that the ICP27-dependent effects are dependent on ICP27 C-terminal sequences , as is the case for ICP27-mediated premature termination of cellular mRNAs in HSV-2 ICP27 transfected cells [20] . A similar result was also obtained in infected Vero cells ( Fig 5F ) , confirming that expression of the 1 . 3 kb monocistronic ICP34 . 5 mRNA as well as splicing involving the ICP34 . 5 5’ss is significantly increased when ICP27 is absent . We further analyzed the relative splicing efficiencies of the novel splice sites mapping to the ICP34 . 5-ICP0 region using the RNA-Seq data obtained from infected Vero cells ( Fig 5G ) . The quantitative results from Vero cells infected and sequenced in triplicate are comparable to those obtained from HEK-293 cells ( Fig 5D ) . The splicing at these novel splice sites in the ICP34 . 5 region indicates that splicing of the ICP34 . 5 pre-mRNA is only efficient when ICP27 is absent ( Fig 5D ) , also confirming that the 12560^124046 is the major splice among these novel ICP34 . 5 isoforms . Thus , the RNA that encodes HSV-1 ICP34 . 5 protein is efficiently expressed only when ICP27 is present to inhibit its splicing and promote 3’ formation using its own PAS . Quantitative analysis also confirmed that alternative splicing of ICP0 intron 1 does not appear to depend on absence of ICP27 . These results in Vero cells demonstrate that ICP27-mediated aberrant viral pre-mRNA processing is not host cell type dependent . The latently and acutely expressed HSV-1 miR-H6 maps antisense to the LAT promoter regions and was reported to play a role in regulating expression of ICP4 , the other IE genes ( along with ICP27 ) required for viral replication [3] . While the primary miR-H6 ( pri-miR-H6 ) transcript remains unknown , transcripts in this region including AL and TAL antisense to LAT exon 1 were reported previously [33 , 34] . Data obtained from HSV-2 suggest that transcription of HSV-2 pri-miR-H6 is likely initiated just upstream ( relative to pri-miR-H6 ) of the LAT TATA box [35] . Here , we show that miR-H6 maps to newly identified introns that share a common 3’ss mapping to the C-terminus of UL1 encoding glycoprotein L ( gL ) ( Fig 6A ) , implying that pri-miR-H6 is also a spliced gene and miR-H6 is an intronic miRNA . This splice destroys the coding region of gL but the entire coding region of UL2 , which encodes uracil-DNA glycosylase ( UDG ) is maintained . Thus , these two previously unidentified splice isoforms are named ( pri-miR-H6/UDG ) . The gene structure of pri-miR-H6/UDG ( 7618^9770 ) resembles the gene structure of ICP34 . 5-ICP0 , since both involve an intronic PAS located within 1 kb of the 5’ss . Both spliced isoforms ( pri-miR-H6/UDG and the gL spliced isoforms ) were confirmed by RT-PCR in KOS infected cells but were more abundant in d27-1 infected cells ( Fig 6B ) . Quantitative splicing efficiency using the RNA-Seq data obtained from infected HEK-293 cells indicates that splicing of the pri-miR-H6/UDG transcript is efficient only when ICP27 is absent ( Fig 6C ) . Quantitative splicing analysis using RNA-Seq of infected Vero cells ( in triplicate ) confirmed the results in HEK-293 cells ( Fig 6D ) . UDG , which removes uracil that was mis-incorporated or arose by deamination in viral DNA in terminally differentiated neurons where endogenous cellular UDG activity is diminished , is required for DNA replication [36 , 37] . This splicing that bridges the pri-miR-H6 promoter and the UDG coding sequences may potentially provide a mechanism for expression of UDG from the miR-H6 primary transcript during latency or early reactivation when ICP27 is absent . A splice variant using the 5’ss ( nt3721 ) of ICP0 exon 2 and the 3’ss ( 9770 ) of UL1 of ICP0-UL1 ( 3721^9770 ) was very recently reported [38] . In d27-1 infected cells , there is indeed one read mapping to the splice junction of ICP0-UL1 ( 3721^9770 ) ; however , there are no corresponding reads identified in KOS infected cells ( S1 and S2 Tables ) . Splicing of ICP0-UL1 ( 3721^9770 ) is below the detection limit and estimated to be less than 0 . 03% of the ICP0 exon 2 and exon 3 splice junctions in both KOS and d27-1 infected HEK-293 cells ( Fig 6C ) . ICP0-UL1 ( 3721^9770 ) is also under the detection limit in both KOS or d27-1 infected Vero cells ( Fig 6D ) , suggesting splicing of ICP0-UL1 ( 3721^9770 ) is much less efficient compared to other splice variants involving either the ICP0 5’ss ( nt 3721 ) or UL1 3’ss ( nt 9770 ) . UL5 and UL52 are essential early genes encoding subunits of the viral helicase and primase complex , which is only weakly expressed in infected cells [39] . Splicing of UL5 and UL52 were confirmed by RT-PCR and sequencing ( Fig 7A ) . Splicing efficiencies of UL5 and UL52 are approximately 20%-30% in HEK-293 cells and approximately 14–16% in Vero cells when ICP27 is not present; however , in the presence of ICP27 , splicing of these two genes is efficiently inhibited ( Fig 7B and 7C ) . Expression of UL5 protein is much reduced in d27-1 infected Vero cells or L2-5 cells that stably express UL5 under the ribonucleotide reductase ( ICP6 ) promoter ( Fig 7D ) , consistent with previous findings on the role of ICP27 on UL5 expression [5] . The predicted protein corresponding to spliced UL5 was not detected by Western blot , suggesting that it may be unstable . In addition to UL5 and UL52 , DNA replication-related early genes including ICP8 , TK , and UL12 are also spliced genes and ICP27 appears to inhibit splicing of these transcripts ( Table 1 and Fig 8 ) . While not all of these genes appear to be efficiently spliced in the absence of ICP27 , the combined effect of splicing in several replication-related genes likely exceeds that in any single gene . The finding that in addition to increasing expression of specific viral DNA replication related early genes as reported previously [5] and as shown in Fig 2 , ICP27 also contributes to maintain the functional full-length ORFs of targeted early genes by preventing co-transcriptional pre-mRNA splicing reveals a new regulatory mechanism at the pre-mRNA splicing level by which ICP27 controls viral DNA replication , and thus also expression of genes from newly synthesized DNA . At least one of the splice junctions for each of the novel spliced genes listed in Table 2 was confirmed by RT-PCR and subsequent sequencing ( Fig 8A and 8B ) . Furthermore , splicing is also much more efficient in d27-1 infected cells for most of the novel spliced genes , consistent with our observations for ICP34 . 5 , UL5 , UL52 and UL15 ( Figs 4–6 ) . The RT-PCR splicing patterns of a few inefficiently spliced transcripts ( with relative splicing efficiency <5% ) including UL42 , UL26 , UL22C and UL4-5C were not obviously different in the presence vs . absence of ICP27 , and splicing of UL26C and UL4-5C appeared to increase in the presence of ICP27 ( Fig 8B ) . The relative splicing efficiency of these novel spliced transcripts were also quantified using the RNA-Seq data . Splicing of gC , AST-2 and UL41-42C was much less efficient in KOS infected cells and was significantly increased in d27-1 infected cells with only one major exception ( Fig 8C and 8D ) . Splicing of UL41-42C appears to be reduced in d27-1 infected cells ( Fig 8C ) ; however , transfection of a UL41-42C minigene with or without an ICP27 expression plasmid does not obviously increase splicing efficiency of intron 1 and 2 , suggesting that the discrepancy is likely due to extremely low abundance of UL41-42C transcripts in d27-1 infected cells . The splicing efficiency of gC , a previously identified spliced gene ( γ2 ) , increased dramatically from approximately less than 0 . 5% in KOS-infected cells to more than 50% in d27-1 infected cells , which is consistent with previous observations [31] . We made a similar observation for AST-2 , a spliced transcript identified recently by long-read high throughput sequencing in wild-type HSV-1 infected cells using the PacBio sequencing system [22] . MRC-5 diploid human fibroblast cells and Vero African green monkey kidney epithelial cells were infected with KOS and d27-1 . RT-PCR was performed using primers targeting representative novel spliced genes including ICP34 . 5 , UL5 , UL52 , gH , US3 and gE ( S2A Fig ) . Similar splicing patterns ( by RT-PCR ) were observed in Vero and MRC-5 cells , confirming that ICP27-mediated effects on pre-mRNA splicing is not cell type dependent , consistent with the Northern blot results for ICP34 . 5 mRNAs . Quantitative splicing efficiency analysis for the novel spliced genes ( described in Table 2 and not described in the previous figures ) using the RNA-Seq data obtained from infected Vero cells ( in triplicate ) was determined ( S2B and S2C Fig ) . The gB splice junction ( 54946^54453 ) was under the detection limit of the quantitative analysis; however , all of the other novel splice junctions ( presented in Table 2 ) were confirmed in the Vero RNA-Seq data , with splicing of most of these transcripts inhibited by ICP27 as in infected HEK-293 cells ( Fig 8C and 8D ) . The transcripts in Vero and HEK-293 cells for which splicing was not significantly influenced by ICP27 include UL41-42C , UL4-5C and UL22C , which are transcribed complementary to known viral genes and have unknown significance in viral pathogenesis . The overall splicing efficiency in Vero cells ( when ICP27 is absent ) appears to be lower than that in HEK-293 cells , suggesting that cell type specific splice factors may influence the alternative splicing process of ICP27 targeted spliced genes . Most viral transcripts except for IE transcripts are reduced in d27-1 infected cells compared to KOS infected cells ( Fig 1 ) . To further quantify the effect of ICP27 on the accumulation of ICP27-targeted genes , especially the functional open-reading frames , we further analyzed relative expression levels of ICP27 targeted novel spliced genes ( listed in Tables 1 and 2 ) using RNA-Seq data . In this analysis , the RNA-Seq data were mapped to reference sequences selected to represent the total expression level of different splice variants as well as the ORFs of these genes . ICP27 does not appear to affect the accumulation of IE gene transcripts including ICP0 , ICP22 , ICP4 , or ICP47 ( as indicated by the shared exon 2 sequence from US11 ) , which serve as an infection control ( S3A and S3B Fig ) . There is an approximately 8–10 fold reduction in levels of two γ2 late genes including gC and LAT in the presence of PAA , the DNA synthesis inhibitor , consistent with the previous finding that expression of gC and LAT in infected cell cultures depends on viral DNA replication [1 , 40] . Accumulation of γ1 late and β genes appears to be relatively unaffected by DNA replication inhibition with PAA ( < 2-fold changes ) ; however , expression of many of these genes are significantly reduced when ICP27 is absent , indicating that ICP27’s role in regulating expression of these genes goes beyond just its influence on DNA replication . Accumulation of all other ICP27-targeted genes ( ORFs ) was positively correlated with ICP27 . In d27-1 infected cells , reduced accumulation of ICP27 targeted transcripts ranged from approximately 470-fold for gC ( UL44 ) to 1 . 7-fold for UL15 ( S3B Fig ) relative to cells infected with strain KOS . The median fold-reduction of ICP27-targeted gene levels in d27-1infected cells was approximately 18-fold for infection at 4 hpi and 8-fold at 7 hpi . Substantial reduction of accumulation of the gC transcript in d27-1 infected cells is consistent with previous findings that ICP27 regulates gC mRNA accumulation through a responsive element ( also a C-rich sequence ) on the gC mRNA [41] , in addition to its role in splicing inhibition of the gC transcript [21] . Following gC , the other most responsive genes including UL26 , US3 , UL52 , UL42 , and LAT were more than 60-fold reduced in d27-1 infected cells . Genes including gH ( UL22 ) , UL37 , VHS ( UL41 ) and gE ( US8 ) were approximately 20-fold reduced at 5 hpi in d27-1 infected cells ( S3B Fig ) . Expression of ICP34 . 5 ( as a total of transcript variants ) was reduced by approximately 14-fold at 4 hpi and 9-fold at 7 hpi in d27-1 infected cells , consistent with the observation by Northern blot ( Fig 5E and 5F ) . Consistently with previous findings [5 , 42] , replication-associated spliced early genes including UL5 , UL52 , UL42 and TK were also reduced significantly in d27-1 infected cells ( 6- to 73 -fold ) , while expression of ICP8 ( β ) was only modestly reduced ( approximately 2 . 5-fold ) in d27-1 infected cells . Due to the complexity of the transcription patterns , including sharing of PAS by different viral genes , read-through transcripts and viral transcription in both directions in certain locations , the RNA-Seq data should be interpreted cautiously in the context of nearby viral gene structures . Thus , splice junctions mapping in unknown transcripts antisense to known ORFs including UL22C , UL4-5C , AST-2 and UL41-42C were not included in this analysis . Nevertheless , these data are consistent with previously described results describing ICP27’s impact on virus gene accumulation [1] , and illustrates an additional mechanism by which ICP27 regulates expression of its targeted viral genes .
By transcriptome analysis in cells infected with an HSV-1 ICP27-deletion mutant , we identified hundreds of novel splice sites mapping to the HSV genome and experimentally confirm at least 22 novel viral alternatively spliced genes , many of which are essential for efficient viral replication . We find that ICP27 inhibits splicing and promotes efficient polyadenylation using a proximal intronic PAS to facilitate expression of ICP34 . 5 and pri-miR-H6/UDG , both of which are novel spliced genes with intact ORFs . These findings not only fundamentally change our understanding of HSV-1 gene structure by quantitively mapping alternative splicing to more than one third of the known viral genes , but also reveal a novel mechanism by which ICP27 hijacks host splicing and polyadenylation for optimal viral gene expression . These findings also imply that during latent infection when ICP27 is absent , HSV-1 likely takes advantage of host splicing machinery to restrict expression of randomly activated antigenic viral genes to achieve immune evasion . Analysis of high throughput RNA-Seq data for splice variants remains challenging . In this study , we used MapSplice 2 software to map pair-ended high throughput RNA-Seq data to an HSV-1 reference genome for novel splice junction discovery . This powerful approach was able to identify nearly all previously known splice junctions , as well as hundreds of novel splice junctions . We did not attempt to verify every single one of the hundreds of splice junctions identified ( S1 and S2 Tables ) but chose novel splice junctions with ≥ 30 reads for further experimental verification . We were able to confirm at least 22 previously unidentified spliced genes , recognizing that splice junctions with lower read counts could also likely be verified experimentally . For example , the very recently reported ICP0-UL1 splice junction [38] was detected with 1 read in d27-1 infected HEK-293 cells ( S1 Table ) . However , no ICP0-UL1 splice junction read was identified using the RNA-Seq obtained in KOS infected cells or Vero cells infected with either KOS or d27-1 ( S2 Table and Fig 6E ) , suggesting splice junctions even with extremely low read counts ( e . g . , one read count as listed in S1 Table ) may be experimentally verifiable and thus that splicing is a widespread event in HSV-1 infected cells . Due to the high GC content in HSV , we found that determination of splicing patterns by RT-PCR favors smaller PCR fragments representing spliced products , which is prone to overestimating splicing efficiency , especially for larger introns . Thus , we established a relative quantitative method by calculating the percentage of the exon-exon junction reads among the total exon-intron junctions and exon-exon junctions for each splice based on RNA-Seq data . This quantitative analysis is in agreement with the RT-PCR results for most of the spliced genes and is also consistent with Northern blot results in this this and previous studies , providing a powerful high throughput tool for better understanding the nature of a splice junction . Little is known regarding to how HSV , a large DNA virus and known to contain very few spliced genes , escapes host pre-mRNA splicing machinery . Pre-mRNA splicing is a “default” process initiated by binding of U1 snRNP to the consensus 5’ss and of U2 snRNP to the consensus 3’ss . Splicing factors can regulate gene expression by influencing the inclusion or exclusion of particular exons in a gene’s mRNA [43] . We identified much more alternative splicing in a large group of novel viral spliced genes in infected cells when ICP27 is absent . When ICP27 is present , most of the splices in these novel viral spliced genes are largely silenced , suggesting the viral IE protein functions in a way analogous to a splicing factor to inhibit splicing both of its own transcripts and of a small percentage of host transcripts that resemble HSV genes in a gene/sequences specific manner [20] . Thus , HSV ICP27 likely coevolved with GC-rich viral genes that contain C-rich sequences and co-opts host splicing machinery to ensure the correctness of viral ORFs . Interaction of U1 snRNP with 5’ss and PAS near the transcription start site controls the length of cellular mRNAs and promoter directionality [44–47] . We showed previously that ICP27 counteracts U1 snRNA’s function and promotes expression of hundreds of cellular short intronless transcripts resembling HSV genes [20] . Here , we show that ICP27 also toggles expression of the HSV-1 monocistronic ICP34 . 5 mRNA that encodes the major viral neurovirulence factor by activating the proximal PAS located in the newly identified intron and inhibiting splicing of the newly identified intron ( Fig 5 ) . In ICP27 deletion mutant infected HEK-293 and Vero cells , approximately 64% to 75% of ICP34 . 5 transcripts are spliced , destroying the coding sequence . Interestingly , ICP34 . 5 is also negatively regulated by the two most abundant latently expressed miRNAs mapping antisense to its 5’ UTR and exon 1 [2 , 3 , 48 , 49] , further suggesting the importance to viral pathogenesis of tight regulation of HSV-1’s major neurovirulence factor . The distance between the newly identified 5’ss of ICP34 . 5 and its PAS , which is located in the newly identified intron , is within the range ( ≤ 1 kb ) typical for both ICP27 or U1 snRNP inhibitor mediated activation of intronic PAS of cellular genes [20 , 44–46] . Because inhibition of U1 snRNP’s binding to a 5’ss also typically relieves its inhibition of polyadenylation at a downstream PAS ( typically within 1 kb of the 5’ss ) [44–46] , and ICP27 is known to interact with U1 snRNP through its C-terminal domain , colocalizing with U1 and U2 snRNPs [14 , 15] , this suggests that U1 snRNP is likely to be involved in the mechanism of ICP27-mediated splicing inhibition and activation of intronic PAS . The primary transcript of miR-H6 , a latently and acutely expressed viral miRNA that was reported to target the key viral transactivator ICP4 [3] , has not been identified although it is certainly transcribed antisense to the LAT . There are reports of transcripts antisense to LAT such as AL RNA and TAL RNA antisense to LAT exon 1; however , the 5’ start sites and 3’ ends of these RNAs has not been determined [33 , 34] . Identification of novel splice junctions for pri-miR-H6 suggests that during latency when ICP27 is absent , pri-miR-H6 is likely a spliced transcript . The gene structure around the splice 7616^9770 appears similar to that of ICP34 . 5 , with a PAS mapping to approximately 700 bp downstream of the novel splice site ( nt7618 ) and with the splicing inhibited by ICP27 ( Fig 6 ) . Splicing of the pri-miR-H6-UDG transcript during latency when ICP27 is absent may potentially lead to expression of UDG , a critical viral DNA repair enzyme . It is known that the endogenous enzymatic UDG activity is absent in terminally differentiated neurons . In contrast to its role in virus-host shutoff by inhibiting expression of selected cellular transcripts [20] , ICP27-mediated aberrant pre-mRNA processing is required to efficiently express full-length viral ORFs of ICP27 targeted viral genes during the coordinated temporal cascade of gene expression to promote efficient viral replication . Thus , ICP27 ensures the quality of its targeted viral transcripts . For example , inhibition of splicing of the low-abundance UL5 and UL52 , both essential early genes that encode the primase/helicase complex , may also contribute to a “switch” effect by which ICP27 regulates viral DNA replication and the many viral genes that rely on viral DNA replication . Splicing control of critical virulence factors , such as ICP34 . 5 , as well as essential glycoproteins , such as gH and gC , likely also collectively contributes to the avirulent phenotype of ICP27 deletion mutants . High GC content ( approximately 68% to 70% in HSV ) can contribute to intron retention or reduced splicing efficiency of mammalian genes [50 , 51] and likely contributes to the fairly low observed baseline splicing efficiency of many of the novel spliced genes identified in this study . Even low splicing efficiency indicates that , these genes contain authentic U1 snRNP binding sites at 5’ss . U1 snRNP binding to 5’ss inhibits 3’ end formation at proximal PAS ( typically <~1 kb from the transcription start site ) , a mechanism used by the cell to define the length and direction of its transcripts . Persistent U1 snRNP binding to cryptic 5’ss in proximity to a PAS can prevent accumulation of certain adenovirus , polyomavirus and bovine papillomavirus ( BPV ) mRNAs [52–54] . We thus hypothesize that some of the HSV genes , such as intronless short transcripts with cryptic 5’ss near a proximal PAS are also subject to U1 snRNP-mediated restriction and that ICP27 is required to remove U1 snRNP-mediated suppression of polyadenylation , analogously to ICP27’s role in promoting expression of cellular intronless transcripts polyadenylated from proximal intronic PAS as well as the ICP34 . 5 monocistronic RNA [20] ( Fig 5 ) . Thus , the overall consequence of ICP27 mediated splicing inhibition during lytic infection likely ensures not only the quality ( correct ORF and stabilized monocistronic mRNAs ) but also the quantity ( abundance ) of certain ICP27-targeted viral genes , in addition to ICP27’s known role in RNA exporting and transcription [1 , 55 , 56] . While the virus might have been able to achieve expression of full-length genes via mutation of its splice sites , conserved splice site sequences among different HSV-1 strains suggests that ICP27-regulated aberrant posttranscriptional pre-mRNA processing likely has additional important functions . For example , this mechanism may also help reduce accidental expression of full-length viral antigens targeted by ICP27 during latency when ICP27 is absent . Indeed , recent studies revealed that HSV latency is not entirely quiescent and frequent switching on of certain antigenic lytic genes has been reported in immunological and molecular studies [57–59] . Thus , posttranscriptional regulation including splicing and the LAT-encoded miRNAs that disrupt major viral antigens and genes required for viral replication during latency when ICP27 is absent may contribute to immune-evasion and maintenance of viral latency . We also hypothesize that binding of U1 snRNP may play an important role in suppression of polyadenylation of certain ICP27 targeted viral genes during latency in the absence of ICP27 , further contributing to immune-evasion and maintenance of viral latency . Other viruses , such as papillomavirus , polyomavirus , adenovirus , retrovirus , and influenza virus , for which viral mRNA is transcribed in the nucleus , take advantage of the host pre-mRNA splicing and polyadenylation machinery to encode more viral products using limited viral DNA sequences through alternative splicing and polyadenylation to suit their viral life cycles [60] . Many of these viruses encode viral proteins to co-opt the cellular pre-mRNA processing machinery . For example , influenza NS-1 interacts with SRSR2 , U6 snRNA and NS1-BP and altering host and viral mRNA splicing [60 , 61] . NS-1 also interacts with CPSF30 , polyadenylation factor required for the 3’ end processing of cellular pre-mRNA , resulted in reduced expression of cellular antiviral genes but not viral genes [61] . In contrast to many other viruses , the HSV-1 life cycle contains both a lytic infection phase , characterized by a coordinated temporal cascade , and a latent infection phase , characterized by the absence of significant viral antigen expression and viral DNA replication . During lytic infection , through its IE protein ICP27 , HSV-1 activates PAS contained within the proximal intron and near the transcription start site of its genes , while inhibiting splicing of viral and cellular genes in a gene/sequence specific manner to achieve optimal viral gene expression . Many ICP27-targeted cellular genes are related to host immune response [20] . During latent infection in the absence of ICP27 , HSV-1 likely uses host RNAi and splicing machinery to restrict expression of randomly activated viral antigens to achieve optimal immune evasion . Further investigation of the details of ICP27 mediated aberrant pre-mRNA processing will likely yield insight both into mechanisms of viral pathogenesis , potentially leading to identification of new targets for antiviral strategies , and into the mechanisms by which the cell itself controls alternative polyadenylation and splicing of selected genes .
HEK- HEK-293 , MRC-5 and Vero cells were obtained from ATCC . L2-5 cells , a UL5 mutant complementary cell line established from Vero cells that stably expresses HSV-1 UL5 , were obtained from Dr . Sandra K . Weller [62] . HSV-1 strain KOS , HSV-1 ICP27 mutant viruses ( as shown in Fig 5E ) , and the V27 ICP27-complementing Vero cell line used to grow ICP27 mutant viruses were obtained from Dr . Stephen Rice ( University of Minnesota ) [63 , 64] . Anti-HSV ICP4 antibody ( Santa Cruz ) and anti-Flag antibody ( Sigma ) were sourced commercially . Anti-HSV-1 ICP34 . 5 antibody was obtained from Dr . Ian Mohr [65] . Anti-HSV-1 UL5 antibody was prepared from rabbits using peptides ( AGGERQLDGQKPGPP and LTSNPASLEDLQRR ) . HEK-293 cells were infected with HSV-1 KOS or an ICP27 deletion mutant , d27-1 at a MOI of 5 in the presence or absence of the viral DNA replication inhibitor , phosphonoacetic acid ( PAA ) , at 300 mg/mL . At four or seven hours post-infection ( hpi ) , total RNAs were purified with the All-Prep DNA/RNA Kit ( Qiagen ) . cDNA libraries were prepared from polyadenylated RNA using the TruSeq RNA sample Kit V2 ( Illumina ) and were sequenced on the NextSeq 500 according to the manufacturer’s instructions ( Illumina ) . The six samples shared a single sequencer lane . Vero cells ( in 6-well plates ) were infected with HSV-1 KOS or d27-1 at a MOI of 5 in triplicate . At 7 hpi , total RNAs were purified with the All-Prep DNA/RNA Kit ( Qiagen ) . cDNA libraries were prepared from polyadenylated RNA using the TruSeq RNA sample Kit V2 ( Illumina ) and were sequenced on the NextSeq 500 . A total of 18 samples shared the same sequencing lane . Viral gene expression profile was analyzed using CLC Genomics Workbench ( QIAGEN ) with an HSV-1 strain 17 ( NC_001806 . 2 ) consensus sequence without the terminal repeat sequences as a reference ( note: the genome sequence of strain 17 was only used in the CLC Genomics Workbench related analysis and all the exact splice site notations were based on HSV-1 KOS strain ( JQ673480 . 1 ) as described below . CLC Genomics Workbench mapping of RNA-Seq data to genomes was performed without strand specificity . The cellular gene expression profile was analyzed using CLC Genomics Workbench and the human HG19 consensus sequence as a reference . The RNA-Seq data were analyzed using MapSplice 2 , software developed for mapping RNA-Seq data to a reference genome for splice junction discovery [66] . The HSV-1 KOS genome ( JQ673480 . 1 ) was used as the reference sequence . Splice junctions with more than 30 reads were selected for further analysis . The RNA-Seq data were further mapped to the exon-exon junction ( 22 bp from each adjacent exon ) or splice site junction ( 22 bp from the exon and 22 bp from the 22 bp from the adjacent intron ) reference sequences using CLC Genomics Workbench . Each mapping result was visually checked to avoid partial or false alignments . Relative splicing efficiency was calculated using the percentage of exon-exon junctions reads in the total reads mapped relative to the total exon-exon junctions and splice site ( 5’ or 3’ ) junctions for each splice . For further analysis of relative expression of ICP27 targeted genes , 44 bp sequences from the N-terminus of coding sequences or the sequence upstream of the splice site of targeted genes were used as references . Read counts were normalized with the highest reads of KOS or d27-1 infected cells to generate the relative expression levels between KOS and d27-1 infected cells ( S3A Fig ) . Fold-reduction comparisons were generated based on relative expression levels ( S3B Fig ) . All identified novel 5’ss sequences ( 3 bases in exon and 6 bases in intron ) and the 3’ss sequences ( 20 bases in the intron and 4 bases in the exon ) were aligned to the genomic sequences of five commonly referenced laboratory and clinical HSV-1 strains including strain KOS , HSV-1 strain 17+ ( NC_001806 ) , strain F ( GU734771 ) , strain McKrae ( JX142173 ) , and strain H129 ( GU734772 ) . The strength of the splice sites were measured by MaxEntScan [67] . pICP27 , an HSV-2 expression vector , was described previously [32] . HSV-2 ICP27 mutant plasmids including pΔR2 and pM15 were obtained from Dr . Masatoshi Hagiwara ( Tokyo Medical and Dental University ) [68] . The HSV-1 ICP34 . 5-specific DNA probe template ( nt 125645–125827 ) containing 97 bp of the 5’ UTR sequence and 86 bp of the exon 1 sequence upstream of the novel 5’ss ( as illustrated in Fig 5A ) was prepared from plasmid constructed using PCR fragment by oST1076 and oST1075B . Oligonucleotide primers and synthesized DNA fragments are included in S3 Table . HEK-293 cells , MRC-5 cells , Vero cells or L2-5 cells were infected with viruses indicated in the figures at a multiplicity of infection ( MOI ) of 5 . Total protein or RNAs were prepared at different time points post inoculation . Western blot was performed using the antibodies described above . For RT-PCR , total RNAs were extracted using All-Prep DNA/RNA kits ( Qiagen ) . The primer sequences are listed in S3 Table . The RT-PCR bands shown in the figures that correspond to novel splice junctions were further confirmed by Topo cloning and sequencing . HEK-293 cells were transfected with plasmids indicated in Fig 6D using Lipofectamine 2000 ( Invitrogen ) . Total protein or RNAs were prepared 24 hours post transfection . For Northern blots , total RNAs were prepared from HEK-293 cells or Vero cells infected with HSV-1 KOS strain or ICP27 mutants by TRIzol ( Invitrogen ) . Approximately 30 μg of total RNAs were separated in a formaldehyde denaturing 1 . 2% agarose gel ( Life Technologies ) . After transfer to GeneScreen Plus hybridization transfer membrane ( Perkin-Elmer ) , the membrane was incubated in NorthernMax hybridization buffer ( ThermoFisher Scientific ) at 58°C overnight with an HSV-1 ICP34 . 5-specific probe labeled with [α-32P] dCTP using a random priming kit ( Promega ) . | Little is known regarding to how HSV , a large DNA virus and known to contain very few spliced genes , escapes host pre-mRNA splicing machinery . Here , by establishing a high throughput splice junction identification platform and quantitative analysis method to assess splicing efficiency based on high throughput data , we find that HSV-1 encodes hundreds of previously unknown alternative splice junctions; however , splicing of these novel spliced genes is largely silenced in wild-type HSV-1 infected cells , explaining why only very few spliced genes have been previously identified in HSV-1 . Moreover , ICP27 is required for splicing inhibition and 3’ end formation of ICP34 . 5 , the major viral neurovirulence factor and also the major target of latently expressed viral miRNAs . These findings not only fundamentally change the view of HSV gene structure , but also reveal a mechanism by which HSV employs host splicing and polyadenylation machineries to achieve optimal gene expression during acute infection and may also contribute to immune evasion during latency when ICP27 is not expressed . | [
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"me... | 2019 | Hidden regulation of herpes simplex virus 1 pre-mRNA splicing and polyadenylation by virally encoded immediate early gene ICP27 |
Mutations in the leucine-rich repeat kinase 2 ( LRRK2 ) gene are associated with late-onset , autosomal-dominant , familial Parkinson's disease ( PD ) and also contribute to sporadic disease . The LRRK2 gene encodes a large protein with multiple domains , including functional Roc GTPase and protein kinase domains . Mutations in LRRK2 most likely cause disease through a toxic gain-of-function mechanism . The expression of human LRRK2 variants in cultured primary neurons induces toxicity that is dependent on intact GTP binding or kinase activities . However , the mechanism ( s ) underlying LRRK2-induced neuronal toxicity is poorly understood , and the contribution of GTPase and/or kinase activity to LRRK2 pathobiology is not well defined . To explore the pathobiology of LRRK2 , we have developed a model of LRRK2 cytotoxicity in the baker's yeast Saccharomyces cerevisiae . Protein domain analysis in this model reveals that expression of GTPase domain-containing fragments of human LRRK2 are toxic . LRRK2 toxicity in yeast can be modulated by altering GTPase activity and is closely associated with defects in endocytic vesicular trafficking and autophagy . These truncated LRRK2 variants induce similar toxicity in both yeast and primary neuronal models and cause similar vesicular defects in yeast as full-length LRRK2 causes in primary neurons . The toxicity induced by truncated LRRK2 variants in yeast acts through a mechanism distinct from toxicity induced by human α-synuclein . A genome-wide genetic screen identified modifiers of LRRK2-induced toxicity in yeast including components of vesicular trafficking pathways , which can also modulate the trafficking defects caused by expression of truncated LRRK2 variants . Our results provide insight into the basic pathobiology of LRRK2 and suggest that the GTPase domain may contribute to the toxicity of LRRK2 . These findings may guide future therapeutic strategies aimed at attenuating LRRK2-mediated neurodegeneration .
Parkinson's disease ( PD ( OMIM #168600 ) ) is a common neurodegenerative movement disorder that is characterized by muscular rigidity , bradykinesia , resting tremor and postural instability [1] , [2] . Although typically a sporadic disease , mutations in the leucine-rich repeat kinase 2 ( LRRK2 , PARK8 , OMIM #607060 , GenBank #AY792511 ) gene have been identified as a cause of late-onset , autosomal dominant familial PD that is clinically and neurochemically indistinguishable from sporadic PD [3]–[7] . Importantly , LRRK2 pathogenic mutations also contribute to sporadic PD [4] , [8] . Mutations in LRRK2 are the most common cause of familial and sporadic PD identified to date [9] . The LRRK2 gene encodes a large protein of 2527 amino acids that contains multiple domains . These include a LRRK2-specific repeat region , multiple leucine-rich repeats , a Ras of Complex ( Roc ) GTPase domain , a C-terminal of Roc ( COR ) domain , and a protein kinase domain belonging to the tyrosine kinase-like protein kinase family [10] , [11] . LRRK2 exhibits kinase activity whereby it can undergo autophosphorylation and can phosphorylate generic substrates [12]–[18] . However , physiological substrates for the kinase activity of LRRK2 have not yet been identified . The GTPase domain of LRRK2 can mediate GDP ( guanosine-5′-diphosphate ) /GTP ( guanosine-5′-triphosphate ) binding as well as GTP hydrolysis albeit at a relatively slow rate compared to other small GTPases such as Ras [14] , [15] , [19]–[22] . Intriguingly , GTP binding markedly enhances the kinase activity of LRRK2 and is an essential requirement for kinase activity [14] , [15] , [21] , [22] . It is unclear at present how the GTP binding and GTP hydrolysis activities of LRRK2 are regulated . Disease-associated mutations located throughout the LRRK2 protein have been shown to variably alter GTP binding , GTP hydrolysis or kinase activity [14]–[24] . Thus , alterations in both GTPase and protein kinase activity are clearly important for the development of PD due to LRRK2 mutations . A number of useful models have been developed to investigate the pathobiology of LRRK2 disease-associated variants , including Drosophila , transgenic mice and primary neuronal models . Studies in cultured primary cortical neurons reveal that the exogenous expression of pathogenic mutant forms of full-length human LRRK2 ( i . e . G2019S , R1441C and Y1699C ) induces marked neuronal toxicity relative to the wild-type protein [21]–[23] . Wild-type LRRK2 can also induce neuronal toxicity but to a lesser degree . LRRK2-induced toxicity in this neuronal model is dependent on intact GTP binding and kinase activity [21]–[23] . In Drosophila models , expression of human LRRK2 variants induces selective dopaminergic neurodegeneration and motor dysfunction [25]–[27] . Mutant LRRK2 R1441G BAC transgenic and R1441C knock-in mice exhibit mild defects in dopaminergic neurotransmission and motor deficits [28] , [29] . These observations are consistent with a toxic gain-of-function mechanism for disease-associated LRRK2 variants . The molecular mechanism ( s ) and/or pathway ( s ) by which LRRK2 variants induce neuronal toxicity are poorly understood and how alterations in GTPase or kinase activities regulate the toxic effects of LRRK2 are not well defined . Model organisms including yeast , worms , flies and mice are commonly used to uncover the fundamental biology and pathobiology of proteins associated with neurodegenerative diseases , including poly-glutamine expansion disorders , Parkinson's disease , Alzheimer's disease , Prion diseases and Friedreich's ataxia . The baker's yeast Saccharomyces cerevisiae , a eukaryotic single-cell organism , provides a powerful experimental system in which to dissect complex biological pathways and processes . Major advantages of yeast include the high degree of conservation of pathways , processes and protein function with mammalian cells , and the accessibility of yeast cells to genetic manipulation and genome-wide screening approaches . For Parkinson's disease ( PD ) , yeast have provided unique insight into the basic biology and pathobiology of the α-synuclein protein that is associated with autosomal dominant familial PD [30]–[33] . Here , we have employed yeast as a model to further understand the basic pathobiology of LRRK2 . Expression of truncated human LRRK2 reduces yeast viability in a manner largely dependent on the GTPase domain of this protein . Reduced viability in this yeast LRRK2 model is independent of kinase activity and disease-associated mutations , but can be modulated instead by altering GTPase activity and is associated with defects in vesicular trafficking and autophagy . This yeast model provides insight into the basic pathobiology of LRRK2 and suggests that the GTPase domain may contribute to the cellular toxicity of LRRK2 . These findings may guide future therapeutic strategies aimed at attenuating LRRK2-mediated neurodegeneration .
To gain novel insight into the pathobiology of LRRK2 , we set out to develop a simple yeast LRRK2 model . Yeast cells were transformed with expression constructs that express at high copy V5-tagged full-length human LRRK2 under the control of the galactose-inducible GAL1 promoter . Expression of wild-type ( WT ) or G2019S LRRK2 variants fail to affect the viability of yeast cells , which is most likely due to the formation of large LRRK2-positive intracytoplasmic inclusions that are biochemically insoluble ( Figure S1 ) . The same results are observed with low copy expression constructs ( Figure S1 ) . Thus , we elected to examine the detrimental effects of various smaller protein fragments of human LRRK2 that contain different functional domains . Following galactose induction of high copy expression constructs , LRRK2 fragments minimally containing the GTPase domain markedly reduce yeast viability relative to control cells , with the most toxic fragment containing the central GTPase , COR and kinase domains ( GTP-COR-Kin ) of LRRK2 ( Figure 1A ) . A larger LRRK2 fragment additionally containing the C-terminus ( GTP-COR-Kin-CT ) reduces yeast viability to a similar extent . The GTPase domain alone is also sufficient to markedly reduce yeast viability ( Figure 1A ) . LRRK2 fragments containing the kinase domain alone ( Kin or Kin-CT ) or a fragment lacking the N-terminal region ( ΔN-LRRK2 ) , which is poorly expressed , are much less toxic to yeast ( Figure 1A ) . Western blot analysis confirms the expression of each LRRK2 fragment in yeast following galactose induction ( Figure 1B ) . LRRK2 fragments exhibit similar diffuse cytoplasmic localization patterns in yeast as revealed by fluorescence microscopy ( Figure S2 ) . The loss of viability due to the expression of each LRRK2 fragment is confirmed by monitoring the growth rate of yeast cells in liquid media following galactose induction ( Figure 1C ) . We focused further on the GTP-COR-Kin fragment of LRRK2 throughout this study since its expression is most toxic to yeast cells and because it permits further analysis of the contribution of both enzymatic domains . To test if the toxicity is dose-dependent , we also examined the effects of low copy expression of the GTP-COR-Kin fragment . A similar phenotype is observed as with high copy expression of the GTP-COR-Kin fragment ( Figure 1A and 1C ) . Thus , the protein length , expression levels or cellular localization of each LRRK2 fragment do not correlate with their effects on yeast viability suggesting that alterations in viability are dependent on the protein domain composition or activity of each LRRK2 fragment . Moreover , these data demonstrate that LRRK2 protein fragments that contain the GTPase domain , but not full-length LRRK2 , can reduce the viability of yeast cells . Since expression of the GTPase domain of LRRK2 is sufficient to markedly reduce yeast viability , we sought to determine whether alterations in GTPase activity could influence this growth deficit . A number of missense mutations were introduced into the GTPase domain within the GTP-COR-Kin LRRK2 fragment that are predicted to functionally alter enzymatic activity ( Figure 2A ) . Two mutations , K1347A and T1348N , disrupt the conserved guanine nucleotide phosphate-binding loop motif ( P-loop , residues 1341–1348 ) and prevent GDP/GTP binding to the GTPase domain [15] , [22] . Two other mutations , R1398L and R1398Q , were targeted at the R1398 residue , a highly conserved glutamine residue in most small GTPases ( i . e . Q61 in H-Ras ) . LRRK2 contains a highly conserved DFAGR motif ( residues 1394–1398 ) in the switch II region which is mainly responsible for GTP hydrolysis . The P-loop residue T1343 is a glycine residue ( G12 ) in H-Ras . In H-Ras , the combined G12V and Q61L mutations create a GTPase-inactive form of this protein , which is constitutively GTP-bound and active . We introduced these two key H-Ras residues into LRRK2 via the analogous mutations T1343G and R1398Q ( RQ/TG ) to create a Ras-like GTPase that leads to increased GTP hydrolysis activity ( Figure 2A ) [15] . Moreover , a common R1441C pathogenic variant was also introduced into the GTPase domain of LRRK2 . Expression of the GTP-COR-Kin fragment of LRRK2 containing each mutation was induced by spotting yeast cells onto galactose media . Remarkably , altering the GTPase activity of LRRK2 leads to marked changes in yeast viability ( Figure 2B ) . Compared to WT LRRK2 , the GTP binding-deficient mutants K1347A and T1348N cause a dramatic reduction in yeast viability whereas the mutant R1398L and Ras-like mutant RQ/TG partially improve viability ( Figure 2B ) . The disease-associated R1441C variant reduces yeast viability similar to WT LRRK2 ( Figure 2B ) . Western blot analysis reveals that each mutant LRRK2 fragment is expressed at similar levels , which excludes alterations in expression level as a cause of their differential effects on yeast viability ( Figure 2C ) . Furthermore , fluorescence microscopic analysis fails to reveal obvious differences in the cellular localization of truncated LRRK2 GTPase variants with each variant adopting a similar diffuse cytoplasmic distribution in yeast cells ( Figure S2 ) . Growth impairments induced by expression of each mutant LRRK2 fragment in yeast are further confirmed in liquid media following galactose induction ( Figure 2D ) . To determine how alterations in the GTPase activity of LRRK2 due to each functional mutation correlate with changes in yeast viability , we examined both the GTP binding and GTP hydrolysis activities of each mutant LRRK2 fragment . GTP binding was measured using an established GTP-sepharose pull-down assay on total yeast proteins expressing each LRRK2 fragment ( Figure 2E ) . WT and the disease-associated mutant R1441C LRRK2 bind to immobilized GTP to similar extents whereas surprisingly all other mutants exhibit significantly reduced GTP binding ( Figure 2E ) . Consistent with prior reports of full-length LRRK2 [14] , [15] , [19]–[22] , the P-loop mutations , T1348N and K1347A , impair the GTP binding of LRRK2 ( Figure 2E ) . Importantly , the GTP binding capacity of each LRRK2 GTPase mutant does not correlate with its effects on yeast viability . It is not currently possible to measure the capacity of each mutant LRRK2 fragment to bind GDP . It is likely that certain mutations ( i . e . the P-loop mutants K1347A and T1348N ) impair GDP/GTP binding whereas other mutations ( i . e . the Ras-like mutant RQ/TG and R1398L ) may alter the affinity for binding to GDP and GTP . The effects of each mutation on LRRK2-mediated GTP hydrolysis were also determined in vitro by measuring the release of the γ-phosphate moiety from GTP ( Figure 2F ) . Truncated WT LRRK2 displays detectable GTP hydrolysis activity whereas the R1441C mutant exhibits a small reduction in activity , similar to previous reports [14] , [19] , [20] . As expected , the Ras-like RQ/TG mutant leads to a marked increase in GTP hydrolysis activity but unexpectedly the R1398L mutant produces a similar increase in activity . The P-loop mutants K1347A and T1348N essentially abolish the GTP hydrolysis activity of LRRK2 as expected ( Figure 2F ) . Therefore , alterations in GTP hydrolysis activity of each truncated LRRK2 GTPase mutant correlate closely with their effects on yeast viability . In this case , increased GTP hydrolysis partially improves the viability of yeast compared to WT LRRK2 whereas impaired hydrolysis dramatically reduces yeast viability . Notably , alterations in kinase activity via introduction of kinase-impaired ( i . e . K1906M or T2031A/S2032A/T3035A ) or a kinase-hyperactive ( i . e . G2019S ) mutation fails to similarly influence LRRK2-induced toxicity in yeast ( Figure S3 ) . To further examine if GTPase activity plays a key role in the toxic process , we investigated the GTPase activity of full-length human LRRK2 harboring the most frequent mutations causing PD . Importantly , the mutations R1441C/G in the GTPase domain and Y1699C in the adjacent COR domain , significantly decrease GTPase activity ( Figure 2G ) although the mutations , G2019S and I2020T , in the kinase domain do not have a significant effect , suggesting that impaired GTP hydrolysis of LRRK2 can contribute to PD . In yeast cells expressing human α-synuclein ( SNCA , PARK1/4 , OMIM #163890 , GenBank #BC108275 ) , defects in vesicular trafficking have been shown to underlie the cytotoxic effects of this protein with the earliest defect being a block in ER-to-Golgi vesicular trafficking [30]–[32] . Since α-synuclein pathology is a common feature of patients with LRRK2 mutations [3] , [7] , [34] , vesicular trafficking was examined to determine whether similar defects could also underlie LRRK2-induced toxicity in yeast . The lipophilic fluorescent dye , FM4–64 , is useful for monitoring endocytosis in yeast . FM4–64 binds to the plasma membrane of yeast cells where it is internalized by endocytosis into vesicles that subsequently undergo trafficking to the vacuole via the early and late endosome compartments . Thus , FM4–64 dye selectively stains the yeast vacuolar membrane appearing as a large ring-like cytoplasmic structure . Yeast cells expressing truncated LRRK2 variants following galactose induction were incubated with FM4–64 dye and live-cell imaging was conducted by confocal fluorescence microscopy . WT LRRK2 expression partly disrupts the normal trafficking of FM4–64 to the vacuolar membrane relative to control cells , which exhibit normal ring-like vacuolar staining ( Figure 3A ) . WT LRRK2 expression results in the appearance of large cytoplasmic punctate structures in addition to normal vacuolar staining , suggesting a modest defect in trafficking of FM4–64-labeled vesicles to the vacuole leading to their accumulation in endosomes . Yeast cells expressing truncated LRRK2 containing the two most toxic GTPase mutations , K1347A and T1348N , which impair the GTP binding and hydrolysis activity of LRRK2 , exhibit severe defects in the endocytic trafficking pathway with a dramatic increase in the appearance of labeled punctate structures and the complete absence of normal vacuolar membrane staining ( Figure 3A ) . Truncated LRRK2 variants that partially improved yeast viability compared to WT protein ( i . e . RQ/TG and R1398L ) induce similar trafficking defects to WT LRRK2 ( Figure 3A ) . Normal FM4–64 labeling of vacuolar membranes is observed when yeast cells are grown in glucose media ( data not shown ) . DIC images show that cells expressing each of the LRRK2 fragments have normal vacuolar morphology ( Figure 3D ) . Quantitation of defective endocytic trafficking reveals that the toxic GTPase-inactive mutants , K1347A and T1348N , lead to a significant increase in the number and frequency of FM4–64-labeled punctate structures per cell compared to WT LRRK2 , whereas the GTPase-active mutants , RQ/TG and R1398L , display a small non-significant reduction in the number of punctate structures relative to WT ( Figure 3B and 3C ) . Punctate structures are not normally observed in control yeast cells ( Figure 3B and 3C ) . The vesicular trafficking defects induced by expression of each truncated LRRK2 GTPase variant in yeast do not correlate with alterations in their cellular localization ( Figure S2 ) . In particular , there is no specific enrichment in the vacuole or endosomal compartments of each LRRK2 variant that would obviously account for their differential effects on endocytic vesicular trafficking ( Figure S2 ) . These results indicate that the endocytic vesicular trafficking defect in yeast is associated with alterations in LRRK2 GTPase activity and likely underlies toxicity in yeast induced by truncated LRRK2 . To verify that the observed defects induced by LRRK2 expression in yeast are due to vesicular trafficking pathways rather than simply by protein aggregation , yeast cells expressing truncated LRRK2 variants following galactose induction were examined by transmission electron microscopy ( TEM ) ( Figure 4 ) . Interestingly , yeast cells expressing truncated LRRK2 containing the two most toxic GTPase mutations , K1347A and T1348N , which impair GTPase activity exhibit a significant increase of autophagic vacuoles ( AVs ) ( 74 . 7% in K1347A cells and 86 . 2% in T1348N cells ) compared to WT LRRK2 ( 19 . 4% AVs ) ( Figure 4A and 4B ) . In contrast , AVs were uncommon in yeast cells carrying empty vector ( 9 . 2% AVs ) ( Figure 4A and 4B ) . In accordance with fluorescence localization studies of truncated LRRK2 variants in yeast ( Figure S2 ) , protein aggregates or inclusions were not readily observed in the electron micrographs . Taken together these data indicate that LRRK2-induced trafficking defects are mediated at least in part by alterations in autophagy in addition to effects on the endocytic vesicular trafficking pathway . To provide insight into the mechanism of LRRK2-induced toxicity in yeast , and to determine whether there are differences or similarities with α-synuclein-induced toxicity , a small candidate genetic screen was performed in yeast focused on modifiers of α-synuclein-induced toxicity . We elected to analyze potent modifiers of human α-synuclein-induced toxicity Ypt1 ( GenBank #AAS56793 ) and Ykt6 ( GenBank #AAB32050 ) [30] , [31] , as well as Hsp31 ( Genbank #AAB64972 ) , the yeast ortholog of human DJ-1 [33] , a neuroprotective redox-responsive protein associated with familial PD ( PARK7 , OMIM #606324 ) [35] , [36] . Yeast cells were transformed with constructs expressing truncated WT LRRK2 , each candidate protein alone , or both proteins together under the control of the GAL1 promoter and viability was examined by spotting of yeast cells on to galactose media . Expression of WT LRRK2 alone reduces yeast viability , whereas co-expression with each of the three candidate proteins fails to suppress the LRRK2-induced growth deficit ( Figure S4 ) . The three candidate yeast proteins were also tested for their ability to suppress toxicity due to the expression of the truncated LRRK2 variants , K1347A and T1348N , which induce a more pronounced loss of viability in yeast than WT LRRK2 . Co-expression with each of the three candidate proteins also fails to suppress the K1347A- or T1348N-induced growth deficit ( Figure S4 ) . Collectively , our data demonstrate that known potent suppressors of α-synuclein-induced toxicity in yeast ( i . e . Ypt1 and Ykt6 ) do not specifically suppress LRRK2-induced toxicity in this model suggesting that α-synuclein and LRRK2 induce toxicity in yeast through distinct pathways . Following expression of truncated LRRK2 variants , we also fail to observe defects in the normal trafficking of carboxypeptidase Y ( CPY ) and alkaline phosphatase ( ALP ) proteins from the endoplasmic reticulum ( ER ) to the vacuole by pulse-chase analysis ( data not shown ) , which represent two distinct biosynthetic transport pathways that converge upon the vacuole in addition to the endocytic pathway . Notably , human α-synuclein expression in yeast manifests prominent defects in normal CPY and ALP trafficking consistent with derangements in ER-to-Golgi vesicular trafficking [30] . Accordingly , toxicity induced by LRRK2 and α-synuclein expression in yeast most likely occur via impairment of distinct vesicular trafficking pathways . In order to validate the observations from this yeast model of LRRK2 toxicity and determine its wider applicability to mammalian cells , we examined the effects of human LRRK2 domain fragments and GTPase variants on neuronal viability . Expression constructs containing LRRK2 fragments identical to those employed in yeast including the GTPase domain ( GTP ) , kinase domain ( Kin ) and the GTP-COR-Kin fragment as well as full-length WT or G2019S LRRK2 were individually co-transfected together with eGFP as a marker into mouse primary cortical neurons and their effects on neuronal viability were compared . A well-established assay was employed to examine the viability of eGFP-positive neurons containing LRRK2 based on neurite process length and fragmentation as a reliable indicator of neuronal viability [21] , [22] , [37] , [38] . Using this method , LRRK2 expression was confirmed in >95% of eGFP-positive cortical neurons that were also positive for the neuronal marker , MAP2 ( representative images in Figure S5A and S5C ) , and neuronal viability was also confirmed by TUNEL staining ( representative images in Figure S5B ) . Expression of the GTPase domain , the GTP-COR-Kin fragment and full-length WT LRRK2 induces significant and equivalent neuronal toxicity relative to control neurons expressing eGFP alone , with a 10–20% loss of viability ( Figure 5A and 5B ) . The kinase domain alone fails to significantly reduce neuronal viability . Full-length LRRK2 containing the common G2019S pathogenic variant serves as a positive control for toxicity and induces a ∼50% loss of neuronal viability compared to control neurons ( Figure 5A and 5B ) , as previously reported [21]–[23] , [37] . Full-length human LRRK2 was packaged into a Herpes Simplex Virus ( HSV ) amplicon that co-expresses eGFP to generate an HSV-WT-LRRK2/CMV-eGFP amplicon . Expression of LRRK2 by the HSV amplicon causes similar neuronal toxicity to that of full-length WT LRRK2 transiently co-transfected into neurons with eGFP ( Figure 5A and 5B ) , indicating that transient transfection is a reliable and valid method by which to assess LRRK2-induced toxicity . Thus , truncated LRRK2 proteins containing the GTPase domain produce similar neuronal toxicity to that induced by full-length WT LRRK2 implying that the GTPase domain may underlie the toxic effects of LRRK2 . To determine and compare the effects of truncated LRRK2 GTPase variants on neuronal viability , similar experiments were conducted with the GTP-COR-Kin LRRK2 fragment containing each mutation that was previously examined in the yeast model . Expression of the GTPase-active WT , R1398L and RQ/TG variants of LRRK2 induces a significant yet equivalent level of neuronal toxicity relative to control neurons characterized by a 10–15% loss of viability ( Figure 5C and 5D ) . Expression of the LRRK2 GTPase-inactive variants , K1347A and T1348N , enhances neuronal toxicity compared to other GTPase variants with a ∼18% loss of viability for the K1347A variant and ∼23% loss for the T1348N variant that is significantly increased relative to the WT protein ( Figure 5C and 5D ) . Thus , GTPase variants in truncated LRRK2 induce toxicity in neurons that closely parallel their toxic effects in yeast . Collectively , these data demonstrate the validity of the yeast model for accurately predicting the detrimental effects of truncated LRRK2 variants on neuronal viability . Taken together , these data reveal that alterations in GTPase activity contribute to LRRK2-induced neuronal toxicity . Since the LRRK2 yeast model indicates that truncated LRRK2 may function in vesicular trafficking pathways , including endocytosis , the effect of full-length human LRRK2 on endocytosis and exocytosis was monitored in primary neurons . Mouse hippocampal neurons at days in vitro ( DIV ) 12 were transduced with HSV-WT-LRRK2/CMV-eGFP or control virus and 48 hours later synaptic vesicle ( SV ) endocytosis and exocytosis were monitored by using the lipophilic fluorescent dye FM4–64 . Neurons were first exposed to FM4–64 in the presence of 90 mM KCl , which depolarizes the nerve terminal and induces vesicular recycling and subsequent loading of FM4–64 by SV endocytosis . SV exocytosis was then monitored in real time by depolarizing the nerve terminals to unload the FM4–64 dye . Based on comparison of the mean fluorescence intensity values , the synaptic boutons of neurons carrying HSV-WT-LRRK2/CMV-eGFP display an approximate 1 . 34-fold decrease in loading of FM4–64 by endocytosis compared to the HSV-PrPUC/CMV-eGFP control ( Figure 6A left panels , Figure 6B at time point ‘0’ sec , and Figure 6C: control , 133 . 99±5 . 897; WT LRRK2 , 100 . 23±7 . 098 ) . Following depolarization of the FM4–64-loaded SVs , the control boutons displayed about 99% unloading of FM4–64 after 8 mins , whereas the synaptic boutons overexpressing LRRK2 show delayed unloading with an approximate 72% decrease in FM4–64 signal ( Figure 6A right panels , Figure 6B at time point ‘480’ secs , and Figure 6C: control , 0 . 886±0 . 851; LRRK2 , 28 . 3±0 . 804 ) . These data indicate that overexpression of full-length LRRK2 causes defects in both synaptic vesicle endocytosis and exocytosis in neurons consistent with the observation that overexpression of truncated LRRK2 variants in yeast perturbs vesicular trafficking pathways . To define mechanisms underlying LRRK2-induced cytotoxicity in yeast , we performed an unbiased genome-wide genetic screen to identify yeast genes that could suppress or enhance toxicity . A similar approach has been effective at identifying modifiers of α-synuclein or mutant huntingtin toxicity in yeast [39] . We mated a haploid query strain , harboring the galactose-inducible WT LRRK2 GTP-COR-Kin fragment , to a collection of ∼4 , 850 viable yeast deletion mutants . Following sporulation and haploid mutant selection , we isolated deletion mutants that suppressed or enhanced LRRK2 toxicity . Of 4 , 850 mutants screened , we identified 2 gene deletions that enhanced LRRK2 toxicity ( Figure 7A , Table 1 ) and 7 deletions that suppressed toxicity ( Figure 7B , Table 1 ) . Furthermore , these 7 LRRK2 toxicity suppressors also suppressed toxicity induced by the LRRK2 mutants , K1347A and T1348N , in the GTP-COR-Kin fragment ( Figure 7C ) . This set of yeast genes that modify LRRK2 cytotoxicty function in a number of diverse pathways including transcriptional regulation ( AHC1 ( GenBank #CAA99213 ) and GCN4 ( GenBank #AAA34640 ) ) , MAP kinase signaling ( SLT2 ( GenBank #AAB68912 ) , small GTPase signaling ( GCS1 GenBank #CAA98805 ) and mitochondrial function ( CCE1 GenBank #AAB24906 ) ( Table 1 ) . To further determine if these genetic modifiers enhance or suppress LRRK2 toxicity by modifying trafficking defects in yeast , we performed the FM4–64 assay in the two enhancer deletion mutants carrying WT GTP-COR-Kin fragment and the 7 suppressor deletion mutants carrying the most toxic LRRK2 mutant , T1348N . Interestingly , both enhancer mutants promote the endocytic trafficking defect induced by WT LRRK2 with an increase in the appearance of labeled punctate structures ( Figure 7D ) while the 7 suppressor mutants at least partially rescue the T1348N LRRK2-induced endocytic trafficking defect with the appearance of normal vacuolar membrane staining and a decrease in punctate structures ( Figure 7E ) . These data suggest that the genetic modifiers can at least partially modulate vesicular trafficking pathways and genetically interact with LRRK2 to modify LRRK2-induced toxicity . Accordingly , these data suggest that vesicular trafficking defects in yeast underlie , in part , LRRK2-induced toxicity .
Here , we employ yeast cells to provide insight into the pathobiology of human LRRK2 , a protein that is associated with autosomal dominant PD . A number of important conclusions can be derived from this yeast model . First , expression of LRRK2 fragments containing the GTPase domain markedly reduces the viability of yeast cells relative to other protein domains of LRRK2 . The expression of full-length LRRK2 in yeast is problematic since it is highly insoluble and is sequestered into large cytoplasmic inclusions , which prevents its potential for inducing toxicity . Thus , it is only possible to develop a yeast model of LRRK2 pathobiology based upon protein domain fragments rather than the full-length protein . Second , consistent with a prominent role for the GTPase domain in mediating the toxic effects of LRRK2 in yeast , the viability of yeast cells can be modulated by alterations in GTPase activity due to several functional mutations . Notably , interfering with GTPase activity ( i . e . GTP hydrolysis ) but not GTP binding or kinase activity is sufficient to modify LRRK2-induced toxicity in yeast . The pathogenic mutants R1441C/G and Y1699C in full-length LRRK2 have significantly decreased GTPase activity consistent with the notion that reduced GTPase activity is toxic to cells . Importantly , however , pathogenic mutations associated with familial PD ( i . e . R1441C and G2019S ) do not influence the toxicity induced by truncated human LRRK2 in yeast which perhaps suggests that these mutations may only exert their deleterious effects in the context of full-length LRRK2 or in mammalian cells . Third , the expression of functional LRRK2 GTPase variants induce defects in the endocytic vesicular trafficking and autophagy pathways . Vesicular trafficking and autophagic defects closely correlate with the level of toxicity induced by each truncated GTPase variant suggesting that defects in trafficking may underlie LRRK2-induced toxicity in this model . Accordingly , genetic modifiers that suppress LRRK2 toxicity in yeast also suppress trafficking defects . Fourth , known suppressors of α-synuclein-induced cytotoxicity in yeast do not suppress LRRK2 toxicity suggesting that both proteins mediate their toxic effects through distinct trafficking pathways yet with the common outcome of impairing vesicular transport to the vacuole , the yeast equivalent of the mammalian lysosome . Thus , defects in vacuolar or lysosomal transport may commonly underlie the pathogenic effects of α-synuclein and LRRK2 . Fifth , the toxic effects of truncated LRRK2 GTPase variants are similar between yeast and neuronal models of LRRK2 pathobiology and truncated or full-length LRRK2 cause similar endocytic trafficking defects in both yeast cells and neurons , respectively , suggesting that the yeast LRRK2 model is predictive of mammalian cells . Finally , a genome-wide genetic screen identified potent modifiers of LRRK2 toxicity in yeast , which may provide novel clues to the underlying mechanism of LRRK2-induced toxicity . Neuronal toxicity induced by WT and pathogenic variants of full-length human LRRK2 critically requires intact GTP binding and kinase activity [21]–[23] . However , it has not yet been possible to distinguish , which , if any , of these activities actually mediates the downstream toxic effects of LRRK2 or whether they serve to auto-regulate an alternative function or effector domain of this protein . In yeast cells , the detrimental effects of expressing truncated LRRK2 variants are independent of kinase activity and are not influenced by two common pathogenic variants located either in the GTPase domain ( i . e . R1441C ) or the kinase domain ( i . e . G2019S ) . Instead , toxicity is dependent on GTP hydrolysis activity , but not GTP binding activity . In the context of the central GTP-COR-Kin fragment of LRRK2 that is used here to explore the effects of GTPase variants , mutations that impair GDP/GTP binding and are thus GTPase-inactive promote toxicity , whereas mutations that produce a hyperactive GTPase partially reduce the toxic effects of LRRK2 ( Figure 8 ) . The lack of effect of kinase activity or pathogenic mutations on yeast toxicity induced by truncated human LRRK2 , might suggest that they require the full-length protein or a mammalian cellular context to exert their effects on LRRK2-induced toxicity . In the context of full-length LRRK2 , the K1347A and T1348N mutations prevent GTP binding and are GTPase-inactive but also impair kinase activity , which partially prevents LRRK2-induced neuronal toxicity [14] , [15] , [19]–[23] . The RQ/TG mutation produces a Ras-like GTPase that also has impaired kinase activity owing to its increased turnover of GTP [15] , [40] , a feature reflected in our yeast model . The R1398L mutation also promotes GTP hydrolysis and accordingly we observe that introduction of this mutation into full-length LRRK2 produces a kinase-inactive variant ( data not shown ) . The effects of the hyperactive GTPase mutants , RQ/TG and R1398L , on neuronal toxicity induced by full-length LRRK2 have not been defined , but they are likely to be protective due to their impairment of kinase activity and enhancement of GTPase activity . Both R1398L and RQ/TG mutants are capable of hydrolyzing GTP but their affinity for binding to GTP is reduced suggesting that they most likely predominate in a GDP-bound inactive state . It is likely that GTPase-inactive variants of LRRK2 induce greater toxicity in yeast through a novel gain-of-function mechanism by interfering with a pathway or process , or sequestering one or more proteins , critical for yeast survival or growth . A dominant-negative mechanism for LRRK2-induced toxicity is unlikely since yeast do not contain an obvious ortholog of human LRRK2 . While the truncated LRRK2 protein used herein does not behave in a manner identical to full-length protein with regards to the regulation of cytotoxicity in yeast or neurons , it instead reveals a fundamental contribution of the GTPase domain and particularly GTP hydrolysis activity in mediating the toxic effects of LRRK2 . A major challenge in future experiments will therefore involve dissecting the precise contribution of GTPase activity , vesicular trafficking pathways and genetic modifiers to neuronal toxicity induced by full-length LRRK2 variants . The fact that LRRK2 kinase activity plays no role in yeast toxicity allowed us to reveal instead a major role for the GTPase domain in toxicity induced by truncated LRRK2 in both yeast and neurons . Fragments of other disease-causing gene products , such as in Huntington's disease or other poly-glutamine repeat disorders [41]–[44] , TDP-43opathies [45] , [46] and α-synucleinopathies [47] , [48] play prominent roles in neurodegeneration due to the pathogenic generation of these truncated proteins . Interestingly , putative truncation fragments containing the LRRK2 GTPase domain have been identified in PD brains [3] , [49] . In addition , E1874stop is a LRRK2 pathogenic mutation in which the protein lacks the kinase and WD40 domains [50] . Thus , understanding whether GTPase domain-containing truncated LRRK2 proteins are important for disease pathogenesis and how the GTPase domain modulates full-length LRRK2 activity are important avenues of investigation . Moreover , since the truncated GTPase domain-containing LRRK2 constructs are toxic in the absence of kinase activity , caution may be warranted by solely focusing on kinase inhibition as a therapeutic target for preventing LRRK2-induced neurodegeneration . Indeed , the GTPase-inactive K1347A mutant in the context of the full-length G2019S LRRK2 protein only partially rescues LRRK2 toxicity despite completely inhibiting kinase activity suggesting that perturbations in the GTPase domain may have deleterious consequences in the setting of full-length LRRK2 independent of kinase activity [21] . The mechanism by which truncated human LRRK2 is toxic to yeast is unclear . The GTPase domain would appear to play a key role in mediating toxicity but other protein domains may also contribute . LRRK2-induced defects in endocytic vesicular trafficking and autophagy may underlie toxicity in yeast , an observation supported by the actions of genetic modifiers of toxicity on vesicular trafficking . Consistent with the yeast LRRK2 model , full-length LRRK2 causes defects in synaptic vesicle endocytosis and exocytosis in neurons . Many other observations suggest that full-length LRRK2 may play a role in vesicular trafficking in mammalian neurons . LRRK2 is localized exclusively to a wide range of vesicular and membranous structures in neurons , including lysosomes , endosomes , multivesicular bodies , the ER , Golgi , mitochondria and microtubule transport vesicles [51] , [52] . The G2019S variant promotes the formation of LRRK2-positive axonal inclusions in neurons that are membrane-bound and contain swollen lysosomes , distended mitochondria associated with vacuoles , multivesicular bodies and disrupted cytoskeletal components [24] , perhaps suggestive of disruption of normal vesicular trafficking . Consistent with our studies , a potential role for LRRK2 in endocytosis has recently been described [53] . LRRK2 interacts and co-localizes with Rab5B on synaptic vesicles . Knockdown or over-expression of LRRK2 in rodent primary neurons impairs synaptic vesicle endocytosis that can be rescued by over-expression of Rab5B [53] , a GTPase involved in the early endocytic pathway from plasma membrane to early endosome . Studies in C . elegans with the human LRRK2 homolog , LRK-1 , reveal a role for this protein in regulating the proper transport of synaptic vesicles to axonal regions possibly by acting at the trans-Golgi network to sort vesicles away from an alternative dendrite-specific transport mechanism [54] . Thus , in yeast it is likely that truncated LRRK2 interferes with the endocytic trafficking and autophagic pathways through functionally interacting or competing with key proteins involved in as yet unspecified steps during the transport of vesicles or their protein cargo from the plasma membrane and/or autophagosomes to the vacuole . LRRK2-associated neurite shortening induced by the G2019S variant may be mediated at least in part by autophagy , since it is associated with the development of autophagic vacuoles and can be reversed by impairing autophagy and potentiated by activating autophagy [55] . In yeast , macroautophagy constitutes an additional pathway for vacuolar transport involving the formation and delivery of large double-membrane vesicles termed autophagosomes containing cytoplasmic constituents and organelles to the vacuole for degradation and recycling . The macroautophagy pathway is also perturbed in our yeast LRRK2 model in addition to the endocytic vesicular trafficking pathway . Consistent with our studies , a potential role for LRRK2 in the endosomal-autophagic pathway has recently been described [56] . Collectively , the observations from neuronal and yeast models tend to support a role for LRRK2 in regulating the sorting or transport of vesicles via endocytosis or autophagic pathways that possibly converge on the vacuole/lysosome ( Figure 8 ) . Further study of the biology and pathobiology of LRRK2 in regulating vacuolar/lysosomal function and dynamics may prove particularly insightful . In particular , it will be important to clarify whether derangements in endocytic and autophagic trafficking pathways critically underlie the neuronal toxicity induced by disease-associated full-length LRRK2 variants and the mechanism ( s ) involved in this pathologic process . The observation that GTPase activity plays a key role in LRRK2 toxicity may prove highly useful in dissecting the molecular mechanism ( s ) underlying LRRK2-induced cytotoxicity and in the identification of genes or small molecules that can directly or indirectly modulate the GTPase activity of LRRK2 . The relevance of such an approach would be to identify modifiers of GTPase activity that would additionally prevent kinase activation as an alternative novel strategy to inhibit the pathogenic effects of LRRK2 . The key demonstration that truncated LRRK2 variants have similar effects on the viability of both yeast and neuronal cells suggests that this yeast LRRK2 model could be predictive for identifying genetic and chemical modifiers of conserved pathways , processes or proteins that are relevant for LRRK2-induced toxicity in neuronal models including human neuronal models derived from iPS cells . Our genome-wide genetic screen to identify suppressors and/or synthetic sick or lethal interactions of LRRK2-induced toxicity in yeast identified modifiers in a number of diverse pathways including genes that are involved in transcriptional regulation , MAP kinase signaling , small GTPase signaling and mitochondrial function . These genes may play important roles in the pathobiology of LRRK2-linked PD . Notably , two of the deletion suppressors have human homologs . SLT2 has four human homologs , which are serine/threonine MAP kinases MAPK1 , 3 , 11 and 14 involved in the initiation of translation , meiosis , mitosis , and postmitotic functions in differentiated cells . In addition they mediate their response via activation by environmental stress , pro-inflammatory cytokines and lipopolysaccharide by phosphorylating a number of substrates . The human homolog of GCS1 is ADP-ribosylation factor GTPase activating protein 1 ( ARFGAP1 ) which plays a role in membrane trafficking and/or vesicle transport . These deletion suppressors may prove to be attractive drug targets and they may provide important insight into the function of LRRK2 . In summary , our results provide evidence that the GTPase domain may contribute to LRRK2-induced toxicity , with enhanced GTP hydrolysis leading to reduced LRRK2 toxicity and impaired GTP hydrolysis leading to enhanced LRRK2 toxicity . In addition , our identification of genetic modifiers of LRRK2-induced toxicity in yeast provides important clues to proteins or pathways that may play key roles in mediating LRRK2-induced toxicity in higher organisms .
All procedures involving animals were approved by and conformed to the guidelines of the Institutional Animal Care Committee of Johns Hopkins University . Yeast haploid strain BY4741 ( MATa , his3Δ1 , leu2Δ0 , met15Δ0 , ura3Δ0 ) obtained from Open Biosystems ( Huntsville , AL ) was used throughout this study . For the yeast genetic screen , the LRRK2 query strain was constructed in Y7092 ( MATα , can1Δ::STE2pr-Sp_his5 , lyp1Δ , his3Δ1 , leu2Δ0 , ura3Δ0 , met15Δ0 ) . Yeast manipulations were performed and media were prepared using standard procedures . Transformations of yeast were performed using a standard high efficiency lithium acetate procedure [57] . Yeast cells carrying galactose-inducible expression constructs were routinely grown in YPD or synthetic complete media lacking uracil ( SC-URA ) containing glucose ( 2% dextrose ) to repress the GAL1 promoter . Yeast cells were pre-grown in SC-URA containing 2% raffinose ( no repression of the GAL1 promoter ) prior to growth in medium containing 2% galactose ( to induce the GAL1 promoter ) , to allow rapid , synchronous induction of expression . Yeast cells that were co-transformed with two galactose-inducible constructs ( with URA3 or LEU2 markers ) were grown in SC-URA/-LEU to select for both plasmids . Human LRRK2 fragment cDNAs were amplified from a pcDNA3 . 1-LRRK2-Myc-His vector [18] by PCR with primer pairs specific for the different domains of LRRK2 ( refer to Figure 1A ) with incorporation of an optimal yeast Kozak sequence ( AAAAATGTCT ) surrounding an ATG start codon ( underlined ) . PCR products were first cloned into the pCR2 . 1-TOPO TA cloning vector ( Invitrogen , Carlsbad , CA ) before subcloning into the GAL1 promoter-based yeast expression vector pYES2/CT ( 2 µ ori , URA3; Invitrogen ) or p416GAL ( CEN ori , URA3; kindly provided by Martin Funk [58] ) containing a C-terminal V5 tag , or into mammalian expression vector pcDNA3 . 1-Myc-His ( Invitrogen ) via BamHI and XhoI restriction sites . Missense mutations were introduced into the GTP-COR-Kin fragment of LRRK2 by PCR-mediated , site-directed mutagenesis , using the QuickChange XL kit ( Stratagene ) , followed by sequencing of the entire cDNA to confirm their correct incorporation . Candidate genes ( YPT1 , YKT6 and HSP31 ) were amplified from yeast genomic DNA by PCR to also introduce a C-terminal V5 tag and stop codon , and resulting cDNAs were cloned into the GAL1 promoter-based yeast expression vector p425GAL ( 2 µ ori , LEU2 , kindly provided by Martin Funk [58] . All cDNAs were subjected to DNA sequencing to confirm their integrity . Mouse monoclonal antibodies to yeast 3-phosphoglycerate kinase ( PGK , clone 22C5 ) , anti-V5 and anti-V5-HRP were obtained from Invitrogen . Mouse monoclonal anti-myc antibody ( clone 9E10 ) was purchased from Roche Biochemicals . Rabbit polyclonal anti-GFP antibody ( NB 600-303 ) was obtained from Novus Biologicals . HRP-linked anti-rabbit or anti-mouse IgG antibodies were obtained from Jackson ImmunoResearch Labs ( West Grove , PA ) . AlexaFluor-488 anti-mouse IgG and AlexaFluor-594 anti-rabbit IgG antibodies were from Molecular Probes . Human LRRK2-specific antibody JH5517 has been described previously [18] , [59] . Yeast cells carrying galactose-inducible LRRK2 constructs were grown and induced as described for spotting experiments . Total RNA was isolated from yeast cells by hot phenol extraction [60] and further purified using the Qiagen RNEasy Mini kit ( Qiagen ) . Total RNA concentrations were determined with a Nanodrop spectrophotometer ( Nanodrop Technologies ) prior to RT-PCR . cDNAs were generated from total RNA using the OneStep RT-PCR kit ( Qiagen ) and oligo-d ( T ) . PCR was conducted on equal quantities of mRNA-derived cDNAs for 25 cycles with LRRK2-specific primers located within the kinase domain ( Forward: 5′-CCAGATCAACCAAGGCTCAC-3′ , Reverse: 5′-CCTGCTGTTGTGATGTGTAG-3′ ) or yeast actin ( ACT1 ) primers ( Forward: 5′-TCGATTTGGCCGGTAGAGATT-3′ , Reverse: 5′-AAGATGGAGCCAAAGCGGTGATT-3′ ) as a loading control . Yeast cells carrying galactose-inducible LRRK2 constructs were grown and induced as described for spotting experiments . Total proteins were extracted from yeast by a standard method using glass bead lysis . Briefly , yeast cells were pelleted and lysed in 1 ml lysis buffer ( 1 X PBS , pH 7 . 4 , 1% NP-40 , 1 x phosphatase inhibitor cocktail 1 and 2 [Sigma-Aldrich] , 1 x Complete mini protease inhibitor cocktail [Roche] ) by vigorous shaking with glass beads at 4°C for 15 min and lysates were clarified by centrifugation at 17 , 500×g for 10 min at 4°C . Supernatants were incubated with 50 µl γ-aminohexyl-GTP-sepharose bead suspension ( Jena Bioscience , Jena , Germany ) by rotating at 4°C for 2 hr . The sepharose beads were sequentially pelleted and washed twice in wash buffer ( 1 X PBS , pH 7 . 4 , 1% Triton X-100 ) and twice with PBS alone . GTP-bound proteins were eluted into 50 µl Laemmli sample buffer ( BioRad ) containing 5% 2-mercaptoethanol by heating for 10 min at 95°C . GTP-bound proteins or input controls ( 0 . 1% total lysate ) were resolved by SDS–PAGE and subjected to Western blot analysis with anti-V5 antibody . Bands were visualized by enhanced chemiluminescence ( Amersham ) . Quantification of protein expression was performed using densitometry analysis software ( AlphaImager , Alpha Innotech Corp . ) . GTP hydrolysis activity was measured by monitoring the release of free γ-phosphate ( Pi ) from GTP . Briefly , total proteins were prepared from yeast cells carrying galactose-inducible LRRK2 constructs as described for GTP binding assays . Soluble lysates were subjected to immunoprecipitation with anti-V5 antibody ( 1 µg ) pre-incubated with 50 µl Protein G Dynabeads ( Invitrogen ) by rotating at 4°C overnight . Dynabeads were stringently washed 5x with lysis buffer before being subjected to GTPase activity assay in 96-well plates using the colorimetric GTPase assay kit ( Innova Biosciences , Cambridge , UK ) as per manufacturers instructions to measure the concentration of free Pi with absorbance measured at 590–660 nm . LRRK2 immunoprecipitates ( anti-V5 ) were also analyzed by Western blot analysis with anti-V5 antibody to quantify the input levels of each LRRK2 variant for normalization purposes . Densitometric analysis was conducted on protein bands using appropriate software ( AlphaImager , Alpha Innotech Corp . ) . A similar procedure was employed for myc-tagged full-length human LRRK2 variants derived from HEK-293T cells to measure LRRK2 GTPase activity . The HSV amplicon platform was utilized to generate HSV-LRRK2 expression vectors containing full-length human LRRK2 [61] . TEM was performed on yeast cells expressing truncated LRRK2 variants as previously described [45] , [62] at the Integrated Imaging Center , Johns Hopkins University . Yeast cells carrying galactose-inducible LRRK2 constructs were grown and induced as described for spotting experiments . Following galactose induction for 6 hrs , 1 ml of culture was harvested by brief centrifugation , resuspended in SC-URA media containing galactose and 40 µM FM4–64 red fluorescent dye ( Molecular Probes ) and incubated at 30°C for 20 minutes to allow dye internalization by endocytosis . Cells were washed once in SC-URA media containing galactose before being dispersed and mounted onto microscope slides . Imaging of red fluorescence was conducted on a Zeiss LSM510 live confocal system . Mouse primary hippocampal neurons ( E15–16 ) were transduced by HSV-WT-LRRK2/CMV-eGFP and HSVPrPUC/CMV-eGFP virus at DIV 12 . After 48 hour transduction , cells were mounted in a laminar-flow perfusion chamber on the stage of a custom-built laser scanning confocal microscope using a calcium containing buffer ( Solution B: 119 mM NaCl , 2 . 5 mM KCL , 4 mM MgCl2 , 30 mM Glucose , 25 mM HEPES , 2 mM CaCl2 ) . After gently removing Solution B cells were then continuously perfused with Solution A ( Solution B without CaCl2 ) . The first stimulus was then applied with FM dye containing Solution D ( 90 mM KCl , 29 mM NaCl , 2 mM CaCl2 , 2 mM MgCl2 , 30 mM Glucose , 25 mM HEPES and 15 µM FM4–64 , Molecular Probes ) for 2 min . This step leads to presynaptic release , vesicle fusion and dye incorporation by synaptic vesicle endocytosis . Cells were then washed by perfusion with Solution A for up to 10 min to minimize background staining . After gently aspirating Solution A , Solution C ( Solution D without FM dye ) is applied to cause release of the FM dye by synaptic vesicle exocytosis . Images were acquired every 10 sec with a CCD camera . The fluorescence intensity of manually designated pre-synaptic regions was quantified . Primary cortical neuronal cultures were prepared and transiently transfected with LRRK2 or eGFP expression constructs as described previously [22] , [37] . Briefly , cortices were dissected from embryonic day 15–16 fetal mice ( CD1 strain ) , dissociated by a 12 min digestion in TrypLE ( Invitrogen ) , and neurons were seeded into 24-well plates coated with poly-L-ornithine . Neurons were routinely maintained in Neurobasal media ( Invitrogen ) containing 2 mM L-glutamine and 2% B27 supplement at 37°C in a 7% CO2 humidified incubator . Glial cell growth was inhibited by addition of 5-fluoro-20-deoxyuridine ( 5F2DU , 30 µM , Sigma ) to the media on days in vitro ( DIV ) 4 . Media was replaced once every third day . At DIV 10 , neurons represented >90% of total cells in the culture . To assess LRRK2-induced toxicity , neurons at DIV 10 were transiently co-transfected with LRRK2 and eGFP expression constructs at a molar ratio 10∶1 , respectively , using Lipofectamine 2000 reagent ( Invitrogen ) according to the manufacturer recommendations . At 48 hrs post-transfection ( DIV 12 ) , live fluorescent images were collected on a Zeiss Automatic stage microscope with Axiovision 6 . 0 software . Neurons with obvious neurite process and/or nuclear fragmentation were counted as non-viable cells by investigators blinded to the identity of the experiment . For each independent experiment , the percent viability of eGFP-positive neurons ( n = 200 ) was determined and data are presented as a percent of control neurons transfected with eGFP alone . Neurons were subsequently fixed with 4% paraformaldehyde and immunocytochemistry was conducted with anti-myc ( Roche ) and anti-GFP ( Novus Biologicals ) antibodies and appropriate fluorescent secondary antibodies . LRRK2 expression was confirmed in >95% of eGFP-positive neurons ( Figure S5A ) . The above transfected neurons were fixed in 4% paraformaldehyde ( PFA ) after 48 hrs transfection . TUNEL staining was performed using the In Situ Cell Death Detection Kit ( Roche ) as per the manufacturer's instructions . Yeast cells carrying galactose-inducible LRRK2 constructs were grown and induced as described for spotting experiments . Following galactose induction for 6 hrs , 1 ml culture was harvested by brief centrifugation , and fixed in 4% formaldehyde/PBS for 1 hr . Cell walls were digested by incubation with Zymolyase 20T solution ( ICN Biochemicals ) , as recommended . Following permeabilization , cells were gently washed twice in KS solution ( 100 mM potassium phosphate pH 7 . 0 , 1 M sorbitol ) , and then resuspended in KS solution . Immunostaining with mouse monoclonal anti-V5 antibody ( Invitrogen ) and AlexaFluor-488 anti-mouse IgG ( Molecular Probes ) was conducted as previously described [63] Cells were dispersed onto microscope slides and mounted using Vectashield mounting medium containing DAPI ( Vector Laboratories ) for nuclear visualization . Fluorescent images were collected on a Zeiss Automatic stage microscope with Axiovision 6 . 0 software . The yeast LRRK2 toxicity modifier screen was performed using synthetic genetic array ( SGA ) analysis [64] as previously described [65] . We used a Singer RoToR HAD yeast pinning robot for manipulating yeast colonies at high density . A MATα yeast haploid query strain , Y7092 , carrying WT LRRK2 GTP-COR-Kin fragment was mated with a haploid yeast gene deletion collection of 4850 viable mutants , sporulated and then underwent selection for haploid mutants that also harbored the LRRK2 plasmid on solid media containing G418 and lacking uracil . Haploid deletion mutants that also carried the LRRK2 plasmid were identified on selectable media containing glucose and then the expression of LRRK2 was induced by growth on galactose media . After comparing colony sizes on galactose plates to those on glucose plates and normalizing for differences in the growth of deletion mutants between carbon sources , genes that suppressed or enhanced LRRK2 toxicity were identified . Initial hits from the screen were independently verified by fresh transformations and spotting assays . | Parkinson's disease ( PD ) is the second most common neurodegenerative disorder . PD is considered to be caused by a combination of risk factors including environmental exposure , age , and a positive family history for disease . Several genes have been unambiguously implicated in PD . However , our knowledge is still limited about these genes and the disease mechanisms involved . Mutations in the LRRK2 gene account for up to 40% of PD in certain populations . Since a single-cell model , baker's yeast , has been employed successfully to study the function of genes related to PD and other neurodegenerative disorders , we developed a yeast model of LRRK2 cytotoxicity in this study to investigate the function of LRRK2 . We dissected the LRRK2 protein into different fragments including the various functional domains and found that fragments including the GTPase domain of LRRK2 are toxic . This toxicity can be modulated by alterations in GTPase activity and correlates with defects in cellular trafficking . These truncated LRRK2 variants induce similar toxicity and trafficking defects in both yeast and primary neuronal models . This yeast model reveals an important role of GTPase activity in the basic pathobiology of LRRK2 and may guide future therapeutic strategies for PD . | [
"Abstract",
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] | 2010 | GTPase Activity Plays a Key Role in the Pathobiology of LRRK2 |
Drug molecules not only interact with specific targets , but also alter the state and function of the associated biological network . How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low–side-effect drug design . The arachidonic acid metabolic network is the network that produces inflammatory mediators , in which several enzymes , including cyclooxygenase-2 ( COX-2 ) , have been used as targets for anti-inflammatory drugs . However , neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment . To gain more insights into the anti-inflammatory drug design , the authors have studied the dynamic properties of arachidonic acid ( AA ) metabolic network in human polymorphous leukocytes . Metabolic flux , exogenous AA effects , and drug efficacy have been analyzed using ordinary differential equations . The flux balance in the AA network was found to be important for efficient and safe drug design . When only the 5-lipoxygenase ( 5-LOX ) inhibitor was used , the flux of the COX-2 pathway was increased significantly , showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators . When both COX-2 and 5-LOX were blocked , the production of inflammatory mediators could be completely shut off . The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors . Their work provides an example for the integration of systems biology and drug discovery .
Nonsteriodal anti-inflammatory drugs ( NSAIDs ) ( e . g . , aspirin ) are widely used for the treatment of musculoskeletal pain and other conditions . In the US , more than 1% of the population uses NSAIDs daily [1] , and the market for NSAIDs now amounts to more than $6 billion annually worldwide [2] . Although NSAIDs do alleviate the aches and pains , these drugs have undesirable side effects on the gastrointestinal tract and the central nervous system in addition to the potential exacerbation of conditions such as asthma [1] . The findings that cyclooxygenase-2 ( COX-2 ) plays a major role in inflammation , and that inhibition of COX-1 causes gastrointestinal toxicity and mild bleeding diathesis [3] , had suggested that selective COX-2 inhibitor would be an effective anti-inflammatory drug with low gastrointestinal side effects [4] . Ironically , the unexpected cardiovascular side effects of selective COX-2 inhibitors have surfaced [5 , 6] . Thus , on September 30 , 2004 , Merck & Company announced a voluntary withdrawal of the company's COX-2 inhibitor , VIOXX ( rofecoxib ) [7] . Other FDA-approved COX-2 inhibitors , such as celecoxib ( Celebrex ) and valdecoxib ( Bextra ) , are being re-evaluated [8–10] . Despite years of studies , safe anti-inflammatory drug design remains a great challenge . Failures in anti-inflammatory drug design illustrate the limitations of the current drug discovery paradigm . A steady waning in the productivity of the pharmaceutical industry in the past decade has been observed . This decline coincides with the introduction of target-based drug discovery [11] . Recently , medicinal chemists have started to think about drug discovery from a systems biology perspective [12 , 13] . Studying the cross-talks between biological responses rather than one by one may provide a better understanding of disease development and achieve accurate evaluation on drug efficacy and toxicity [14 , 15] . This new approach has been applied to safe drug design [16 , 17] . For example , the former SmithKline Beecham ( now GlaxoSmithKline , http://www . gsk . com ) focused on the blood coagulation cascade biochemical network [18 , 19] . Armed with a good understanding of the disease from the regulatory network level , the company used model predictions to develop a fully humanized anti–Factor IX antibody that has entered clinical trials . Rajasethupathy et al . have recently reviewed advances in the practical applications of systems biology to drug discovery [20] . These researchers promote the development of network-based drug design , which devises drug-treatment strategies from the level of the disease system using computational models and high-throughput experiments . In this paper , we study the dynamic properties of the arachidonic acid ( AA ) metabolic network in human polymorphonuclear leukocytes ( PMNs ) in the hope of gaining more insights into anti-inflammatory drug design . An ordinary differential equation ( ODE ) model of the AA metabolic network was developed . Flux analysis and simulation on the addition of exogenous AA were performed to study the network balance . The therapeutic effects of anti-inflammatory inhibitors were simulated , and the difference between dual functional COX-2 and 5-lipoxygenase ( 5-LOX ) inhibitors and the mixture of these two types of inhibitors was studied . Corresponding experiments on the introduction of exogenous AA , COX-2 , and 5-LOX inhibitors were performed and were found to be consistent with model predictions . Our work shows that flux balance is important for the efficacy and safety of the drugs . Compared with traditional single-target drugs , drugs against multiple targets can control the network balance and lead to safer treatment .
Inflammation is a type of nonspecific immune response to infection , irritation , or other injury . It is characterized by redness , swelling , pain , and sometimes loss of function . When harmful agents invade the human body , inflammatory mediators are released by immune cells . This release causes vasodilation , emigration of neutrophils , chemotaxis , and increased vascular permeability . AA is the precursor of inflammatory mediators , including prostaglandins ( PGs ) and leukotrienes ( LTs ) . Extensive researches on the metabolism of AA in human PMNs have been performed . Thus , based on the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [21] and a survey of the literature ( Figure 1; see details in Protocol S1 ) , a computational model of the AA metabolic network in human PMNs was constructed ( Dataset S1 ) . The production of inflammatory mediators is initiated by inflammatory stimuli that activate phospholipase A2 . This enzyme catalyzes the hydrolysis of the sn-2 position of the membrane glycerophospholipids to release AA . Two separate pathways produce inflammatory mediators . One is initiated by COX-2 and produces PGs , while the other is initiated by 5-LOX and produces LTs . PGE2 , LTA4 , and LTB4 are the major inflammatory mediators in our model . Once PMNs have been activated by inflammatory stimuli , the cells release inflammatory mediators and cause accumulation and activation of other cells . A series of ODEs was established to simulate unicellular behavior ( see details in Materials and Methods ) , which included 24 initial concentrations and 45 reaction constants ( see details in Protocol S1 ) . A total of 23 reaction constants was taken from experimental values , while the others were obtained by fitting the calculated production of LTB4 and its ω-oxidation products ( ω-LTB4 ) to experimental data [22] ( Figure 2 and Tables S1–S2 ) . The parameter set that fit the experimental data well was chosen for further studies . Flux analysis was performed on the main network pathways , which included the 5-LOX , 15-LOX , and COX-2 pathways . The result of the 12-LOX pathway analysis was not shown here due to its weak effect on other pathways in the current network [23] . Figure 3A shows simulations of 30 min . In the first 5 min , following the activation by inflammatory stimuli , the flux of the 5-LOX pathway was the largest and was responsible for the major metabolism of AA . As the result of negative feedback regulations ( e . g . , the suicide inhibition of LTA4H by LTA4 ) , the flux of the 5-LOX pathway decreased along with time , while the flux of the 15-LOX pathway increased . Ultimately , the 15-LOX pathway flux became the largest after the first 5 min . During the 30-min simulation , the flux of the COX-2 pathway remained small and played the least important role in the AA metabolic network in human PMNs . The first 10 min was critical for the production of LTs , while PGs continued to accumulate with time . These findings are consistent with previous experimental data [24 , 25] that showed that LTs rather than PGs are the main inflammatory mediators produced in human PMNs . Further simulations with different exogenous AA concentrations were performed . It seems logical to postulate that more AA would produce more inflammatory mediators because AA was the only source of LTs and PGs in our model; however , the simulation results interestingly showed that the output of LTs decreased with the concomitant increase of exogenous AA ( Figure 4A and 4D ) . This decrease was the result of the negative-feedback mechanisms in the 5-LOX pathway . The additional AA is mainly metabolized through the 15-LOX pathway . Further experiments were performed to validate this assumption ( see details in Materials and Methods ) . A decrease of LTs was observed when more than 0 . 5 mM AA was added to PMNs . The output of ( 15S ) -hydroxy-5Z , 8Z , 11Z , 13E-eicosatetraenoic acid ( 15-HETE ) increased at the same time ( Figure 4C and 4B ) . Thus , the simulation predictions are qualitatively consistent with the experimental results . Simulations on widely used anti-inflammatory inhibitors , including 5-LOX inhibitor , COX-2 inhibitor , and a combination of the two , were performed ( Figure 3 ) . The COX-2 inhibitor increased the flux through the 15-LOX pathway , while the flux through the 5-LOX pathway remained almost constant . The reason is that the effect of COX-2 pathway in the current model is weak in the early stage of AA metabolism and becomes more evident with time . The 5-LOX inhibitor induced the peak flux immediately after initiating the metabolism in both the COX-2 and the 15-LOX pathways . When both the COX-2 and 5-LOX inhibitors were introduced , the COX-2 and 5-LOX pathways were shut off , and the majority of the flux went through the 15-LOX pathway . These simulation results showed that single-target anti-inflammatory drugs cannot stop the production of all inflammatory mediators , and that the combination of the 5-LOX and COX-2 inhibitors would likely yield better therapeutic results . All of the inhibitors increased the production of 15-HETE , which was consistent with our experimental results ( Figure 5 ) . We then did further simulation on the combination of 5-LOX inhibitor and COX inhibitor . There are two strategies for this combination: developing dual functional COX-2 and 5-LOX inhibitors , or using a mixture of these two types of inhibitors . A few papers have been published on the development of these two anti-inflammatory strategies , and some drug candidates have already been in clinical tests [26–31] . When using a combination of inhibitors , two issues need to be considered: one is the mixing ratio ( MR ) of different single-functional inhibitors , which makes great contributions to the efficacy and safety of the mixture; the other is the relative inhibition constant to different enzymes ( DR ) , which decides the therapeutic effect of the dual-functional inhibitor . The efficacy of the dual-functional COX-2 and 5-LOX inhibitor and the mixture with different concentrations and DR/MR values was investigated and compared . To ensure the equality in the comparison , the same total inhibition ability ( KiCOX-2 × Ki5-LOX; see details in Materials and Methods ) and the same total concentration of inhibitors were used in the simulations . The inhibition intensity on the production of LTs and PGs was calculated to evaluate the efficacy of inhibitors ( see details in Materials and Methods ) . As shown in Figure 6A , for the mixture , the inhibitors had the largest effective concentration region when MR was close to the relative activity ( ER ) of the two enzymes ( see definition in Materials and Methods ) ; that is , the enzymes can be inhibited to more than 90% if the total inhibitor concentration ( CIt ) was larger than approximately 1% of the total concentration of 5-LOX and COX-2 ( CEt ) . When CIt was less than 1% of CEt , the enzymes cannot be effectively inhibited . When MR deviated from ER , the effective concentration region of the inhibitors became much smaller ( e . g . , when MR deviated from ER by an order of 104 ) , strong inhibition ( >90% ) was achieved only when CIt was about ten times larger than the CEt . Davies et al . [32] have reviewed the clinical pharmacokinetics of a COX-2–selective inhibitor , meloxicam . The maximum plasma concentration of this compound is from 0 . 531 to 5 . 35 mg/l after application of clinical dosage ( 5–30 mg ) to volunteers , which is about 0 . 3 to 3 times the CEt in our model . Thus , in general clinical treatments , the mixture of compounds might be effective in a quite broad region of MR/ER ( about 0 . 001 to 1 , 000 ) . If MR deviates too much from ER , then the clinical dosage will not be effective . For the dual-functional inhibitor , the compound also had the largest effective concentration region when DR was close to ER ( Figure 6B ) . However , the low concentration limit was extended to as low as 10−4 ( CIt/CEt ) ; that is , the dual-functional inhibitor was much more effective compared with the mixture of inhibitors when the concentration was low ( Figure 6C ) . If the dual-functional inhibitors and the mixture have similar pharmacokinetic properties , and the same drug dose is applied to patients , the dual-functional inhibitors will be effective for a longer time than the mixture , as they are active at low concentration . When DR deviated from ER , the effective concentration region of the inhibitor also became smaller . There was a similar trend when DR was larger than ER . When DR was smaller than ER , noticeable inhibition could be observed at very low inhibitor concentration .
Development of safe anti-inflammatory drugs has been an especially tough problem for a long time . Failures in selective COX-2 inhibitors have provided a good lesson on the drug safety problem . Thus , network-based drug design , following the development of systems biology , should be useful for finding safe therapeutic strategies from the level of the disease network . In the current study , we used the AA metabolic network in human PMNs as an example for the network-based drug design study . A mathematical model for the AA metabolic network in human PMNs was built using ODEs to describe enzymatic reactions and feedback loops . Some of the parameters were taken from published experimental studies , and other parameters and initial conditions were determined by fitting to experimental curves of LTB4 and derivative production . The model was validated by experiments on the influence of introducing exogenous AA or inhibitors and was found to explain experimental data well . However , as the network is complex , its robustness and reliability need to be further validated in future studies . Nevertheless , our AA metabolic network model can enhance the understanding of the production time course of inflammatory mediators , including LTs and PGs , and can be used to assist anti-inflammatory drug design . Network dynamic properties were found to be important for anti-inflammatory drug design targeting the AA network . Results of flux analysis , simulations , and experiments on the effect of exogenous AA showed the complexity of a compound's behavior in a system . Due to the effect of feedback regulations and other pathways in the network , the effect of a molecule in a system may not be same as its effect on a single point reaction . Thus , designing drugs from the network system level is necessary . Performing model studies combined with experiments would be an effective way to find safe drugs . Single-target anti-inflammatory inhibitors were found to have limitations based on simulations and experiments in the current work . Single-target inhibitors cannot achieve full success in anti-inflammatory treatment because they cannot control the production of both LTs and PGs at the same time . Results in our studies suggest that the combination of the COX-2 inhibitor and the 5-LOX inhibitor would have better treatment . In fact , a dual COX-2/5-LOX inhibitor , licofelone ( ML3000 ) [33 , 34] , has been successfully completed phase III trials and is demonstrated to be superior in safety and equally efficacious for standard treatments of osteoarthritis . Furthermore , multitarget anti-inflammatory drug efficacy was investigated . We simulated and compared the efficacy of dual COX-2/5-LOX inhibitors and the mixture of single-functional COX-2 and 5-LOX inhibitors . The mixing ratio and the relative inhibition constant were found to be important to the efficacy of the mixture and the dual-functional inhibitor , respectively . Generally speaking , the dual-functional inhibitor was effective in a larger concentration range , making it more robust towards concentration fluctuations . Further studies are necessary to achieve a better understanding of the difference between the dual-functional inhibitor and the mixture of single-functional inhibitors .
The mathematical model of the AA metabolic network in PMNs . Based on KEGG and a survey of the literature , a group of ODEs were devised to develop the mathematical model of the AA metabolic network in human PMNs ( see details in Protocol S1 and Dataset S1 ) . Michaelis–Menten equations are used to describe the catalysis in the network: where [S] is the concentration of substrate , [Et] is the total concentration of enzyme , Kcat is turnover number , and Km is the Michaelis–Menten constant . If competitive reversible inhibitors are involved in the catalysis , the equation is: where [I] is the concentration of inhibitor and Ki is the inhibition constant , which is defined as: If the inhibitors are irreversible , we assume the enzymes would decay according to the following equation: where K is a constant . When activators are involved in the catalysis , we use the following equation: where [A] is the concentration of activator and KI is a constant . PGE2 can upregulate 15-LOX through transcription in this network . Based on the experimental data , we describe its effect with the following equation: where [g] is the concentration of PGE2 and K is a constant . The ode15s routine of Matlab 6 . 5 ( Mathworks , http://www . mathworks . com ) was used to solve the ODEs . LTB4 and ω-LTB4 metabolic curves under different exogenous AA concentrations were calculated . These calculated curves were fit to the experimental data by empirically modulating parameters that had no direct values from published experiments , while the other parameters remained fixed to their experimental values . The parameter set that fit the experimental data well was chosen for further studies . Simulating the therapeutic effects of the dual functional inhibitor and the mixture . The inhibition behavior on different enzymes is assumed to be independent and can be calculated by the following equations ( only competitive reversible inhibitors are studied here ) : where [I] is the concentration of inhibitor , and Ki is the dissociation constant . We use these equations with the consideration that the binding affinity of drugs is usually strong and the necessary concentration of inhibitors is in the same magnitude with the concentration of the enzyme . Then the enzyme-inhibitor complex cannot be neglected , and the above equations are required . To evaluate the efficacy of inhibitors , inhibition intensity on the production of inflammatory mediators is defined as: where [PGs]1 is the concentration of PGs after taking drugs , [PGs]0 is the concentration of PGs before treatment , [LTs]1 is the concentration of LTs after taking drugs , and [LTs]0 is the concentration of LTs before treatment . We use the same value of total inhibition ability and total concentration of the dual functional inhibitor and the mixture to ensure the equality in the comparison . The total inhibition ability is defined as the product of inhibit constant to COX-2 and 5-LOX ( KiCOX-2 × Ki5-LOX ) and is fixed to 1 × 10−14 in all simulations . ER is defined as: where A is the activity of enzyme , and C is the concentration . The ER value in the current model is 0 . 02 . Materials . LTB4 , 20-OH-LTB4 , 20-COOH-LTB4 , 5-HETE , 15-HETE , PGB2 , MK886 , CAY10404 , and AA were purchased from Cayman Chemical ( http://www . caymanchem . com ) . The calcium ionophore , A23187 , was obtained from Acros Organics ( http://www . acros . com ) . Concentrations of the LTs and hydroxyeicosatetraenoic acids were determined by UV spectroscopy at 270 nm and 236 nm , respectively . PGB2 was used as an internal standard , and its concentration was determined by UV spectroscopy at 278 nm [ɛ ( PGB2 ) = 26 , 000] . Following the reported procedure [35] , human PMNs were isolated from the venous blood of healthy volunteers who had not ingested any aspirin-like compounds in the preceding week . Blood was collected in the anticoagulant Na-EDTA and was centrifuged at 100g for 10 min . The platelet-rich plasma was discarded . The blood was then mixed with 6% dextran T-500 in 0 . 01 M PBS ( T500/PBS = 1:1 ) . After 45 min , the upper layer that contained the leukocytes was layered gently over lymphocyte separation medium and centrifuged at 550g for 30 min to pellet the cells . The residual erythrocytes were removed by osmotic hemolysis with ice-cold water for 20 s . The mixture was washed twice by centrifugation in 0 . 01 M PBS at 250g for 5 min and then resuspended in PBS ( >95% PMNs , <5% monocytes and lymphocytes ) . PMNs were preincubated with MK886 [36] ( an inhibitor of 5-LOX ) , CAY10404 [37] ( an inhibitor of COX-2 ) , and a combination of MK886 and CAY10404 at 37 °C for 10 min , respectively . To stimulate the PMNs , 10 μM A23187 , 2 mM Ca2+ , and 2 mM Mg2+ were added for 1 h at 37 °C . Incubations were terminated by the addition of ethyl acetate ( containing 160 μl acetic acid/40 ml ) . PGB2 ( 100 ng ) was added as an internal standard . The upper solvent was evaporated under a stream of nitrogen , and the residue was dissolved in 30 μl methanol . Reverse-phase high-performance liquid chromatography ( HPLC ) was used to assay 20-OH-LTB4 , 20-COOH-LTB4 , and 15-HETE [38] . HPLC was performed with an Agilent 1100 series instrument ( http://www . agilent . com ) . A column ( Retasil C18 , 4 . 6 × 200 mm cartridge , 5 μm particle size; Elite , http://las . perkinelmer . com ) was used for separation of the samples . Solvents A and B consisted of methanol–water–acetic acid ( 10:90:0 . 05 ) and methanol–acetonitrile–acetic acid ( 30:70:0 . 05 ) , respectively . Lipids were eluted at a rate of 1 . 0 ml/min with continuous monitoring for UV absorbance at 235 nm and 270 nm for detection of 15-HETE and LTs , respectively . The retention times for 20-COOH-LTB4 , 20-OH-LTB4 , PGB2 , and 15-HETE were 6 . 399 , 6 . 603 , 11 . 604 , and 17 . 899 min , respectively . Cells were preincubated with 0 . 1 mM , 0 . 5 mM , and 1 mM AA at 37 °C for 10 min . To stimulate PMNs , 10 μM A23187 , 2 mM Ca2+ , and 2 mM Mg2+ were then added for 1 h at 37 °C . Incubations were terminated by the addition of ethyl acetate ( containing 160 μl acetic acid/40 ml ) . PGB2 ( 100 ng ) was added as an internal standard . The upper solvent was evaporated under a stream of nitrogen , and the residue was dissolved in 30 μl methanol . Again , reverse-phase HPLC was used to assay 20-OH-LTB4 , 20-COOH-LTB4 , and 15-HETE as described above . | Inflammation is a basic way in which the body reacts to infection , irritation , or other injury . When it is uncontrolled and misdirected , it causes diseases such as rheumatoid arthritis , inflammatory bowel disease , asthma , and others . In the United States , more than 1% of the population uses nonsteroidal anti-inflammatory drugs , such as aspirin , ibuprofen , or naproxen , daily to relieve aches and pains . However , these drugs have undesirable side effects . The withdrawal of VIOXX ( rofecoxib; Merck , http://www . merck . com ) in 2004 has given a good lesson on safety problems . To assist the design of safe anti-inflammatory drugs , we have constructed a computational model of the arachidonic acid ( AA ) metabolic network in human polymorphous leukocytes . By analyzing the flux changes upon drug treatment in this metabolic network , drugs against multiple targets were found to be capable of reducing toxicity as they exhibited balanced control of the system . The model of the AA metabolic network provides helpful information for anti-inflammatory drug discovery . This work sets an example for the integration of systems biology and drug discovery . | [
"Abstract",
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] | 2007 | Dynamic Simulations on the Arachidonic Acid Metabolic Network |
Here we investigate the correlations between coding sequence substitutions as a function of their separation along the protein sequence . We consider both substitutions between the reference genomes of several Drosophilids as well as polymorphisms in a population sample of Zimbabwean Drosophila melanogaster . We find that amino acid substitutions are “clustered” along the protein sequence , that is , the frequency of additional substitutions is strongly enhanced within ≈10 residues of a first such substitution . No such clustering is observed for synonymous substitutions , supporting a “correlation length” associated with selection on proteins as the causative mechanism . Clustering is stronger between substitutions that arose in the same lineage than it is between substitutions that arose in different lineages . We consider several possible origins of clustering , concluding that epistasis ( interactions between amino acids within a protein that affect function ) and positional heterogeneity in the strength of purifying selection are primarily responsible . The role of epistasis is directly supported by the tendency of nearby substitutions that arose on the same lineage to preserve the total charge of the residues within the correlation length and by the preferential cosegregation of neighboring derived alleles in our population sample . We interpret the observed length scale of clustering as a statistical reflection of the functional locality ( or modularity ) of proteins: amino acids that are near each other on the protein backbone are more likely to contribute to , and collaborate toward , a common subfunction .
There has been an ongoing debate over the past few decades about the processes underlying protein evolution [1]–[5] . The neutral theory [1] posits that protein evolution is chiefly governed by the fraction of newly arising mutations that are not detrimental enough to be removed by natural selection . However , recent population genetic analyses of closely related Drosophila species suggest that protein divergence between species is substantially in excess of the neutral model's predictions [6] , [7] . Intriguingly , this protein divergence excess is consistent with an important role for positive selection in protein evolution [5] , [8] , [9] , although the contribution of weakly deleterious mutations to this pattern is still debated [10] , [11] . The dramatic shift in our view of the processes driving protein evolution in Drosophila highlights the deficiency in our understanding of the mechanisms responsible for the observed protein divergence excess . One reason for this deficiency is the explicitly sequence-based nature of the population genetic analyses used to describe the excess divergence . These methods were developed for the analysis of linear sequences of independently evolving amino acids , and quite generally ignore the fact that most proteins fold into complex three-dimensional structures , held together by interactions between amino acids and between amino acids and the surrounding medium . Protein function depends critically on this folded structure , e . g . the arrangement of specific amino acids at the active site of an enzyme [12] . This is reflected in protein evolution; both the structure and the function of homologous proteins are remarkably conserved over long times , even while primary sequences substantially diverge [13] . The maintenance of protein structure is possible because evolution preserves structurally important interactions , such as favorable biochemical interactions between amino acids in physical contact [14] . This preservation of structurally important interactions affects sequence-based analyses; the preferred state and variability of an amino acid will depend on amino acids elsewhere in the protein [15] . This study is motivated by the desire to more closely integrate protein structure and function into sequence-based inferences of selection . Correlations between substitution patterns and protein structure have yielded insights over many years , from the slower divergence of protein active sites [1] , [16] to recent results indicating a correlation between estimates of positive selection and secondary structure [17] . Work demonstrating the evolutionary consequences of interactions inferred from RNA structure [18]–[20] supported the application of sequence-based inference of functional interactions to proteins , where functional interactions are difficult to identify even when structure is known [21] . Under the assumption that functionally interacting residues coevolve , interactions can be identified if enough evolutionary trajectories can be sampled . In practice this has meant multi-alignments across many species of large protein families [22]–[25] , but alignments within populations of the highly mutable HIV have also been used [26] , [27] . These methods have been successfully used to identify pair-wise interactions between residues that contribute to protein function . As an example , the inclusion of interactions inferred from a multi-alignment was shown sufficient to produce a stable fold [28] . Here we develop a complementary approach intended to probe the level of influence interactions have on protein evolution . Instead of focusing on a single protein and specific pairs of interacting residues , we shall aggregate evolutionary information across proteins and use the increased statistical power to look for generic patterns . Specifically , we investigate the correlations in the substitution processes at residues a given distance from each other along the protein backbone , averaged over many proteins of D . melanogaster . Our rationale is as follows: residues that are near in the primary protein sequence are also likely to be near in the folded protein ( Figure 1A ) and therefore more likely to interact physically and/or belong to the same protein domain . Consequently , if correlated evolution in proteins is common , it should be detectable by an increase in evolutionary correlation between residues nearby in sequence , for which physical interaction in the folded protein is more likely . While we will be unable to identify particular interactions , our approach will be informative about the overall level of influence interactions have on the evolution of proteins . We find that amino acid substitutions cluster together on the protein sequence , i . e . amino acid substitutions are more frequent nearby other such substitutions . The strength of this effect decays exponentially with the separation between the residues along the protein sequence , with a characteristic length scale of about codons . We observe this clustering phenomenon in substitutions between D . melanogaster and several sister species ( Figure 1B ) as well as in polymorphisms within a Zimbabwean population sample of D . melanogaster . Clustering is absent when considering synonymous substitutions , implicating selection as the root cause . Furthermore , clustering is stronger between substitutions that arose along the same branch of the evolutionary tree than between substitutions that arose in different branches , and nearby derived alleles tend to cosegregate in our population sample . Additionally , pairs of substitutions within codons of each other that arose in the same lineage have a significant tendency to cause compensatory changes to the total charge of the protein . These lines of evidence lead us to conclude that epistasis between amino acid substitutions contributes significantly to clustering , and the substitution process as a whole .
Amino acid substitutions are not distributed uniformly along the protein sequence . The cPDF for non-synonymous substitutions , , is significantly peaked around in every species comparison we consider . This peak describes the tendency of non-synonymous substitutions to ‘clump together’ on the protein sequence , a phenomenon we call clustering . The shape of the clustering peak is well-fit by a decaying exponential with a characteristic length scale of about 10 codons . In sharp contrast , the cPDFs involving synonymous substitutions , and , have no clustering peak , indicating that synonymous substitutions are distributed uniformly along the protein sequence . The difference between non-synonymous and synonymous clustering is highly significant , the sampling p-value is essentially zero ( , chi-square test ) . The magnitude of clustering is large . The nearest neighbor of a codon with a non-synonymous substitution is roughly twice as likely to also have such a substitution than would otherwise be expected . The impact of clustering extends well beyond the nearest neighbor , and is appreciable out to a distance of at least codons from a focal non-synonymous substitution . We quantify the total magnitude of clustering by defining the ‘clustering count’ as the difference between the expected number of substitutions of type in the codons downstream of a focal substitution of type and the expected number in a codon sequence segment distant from the focal substitution ( Methods ) . More plainly , is the number of extra substitutions you find in the vicinity of an substitution because substitutions cluster instead of being distributed uniformly along the sequence . Graphically , is the area under the clustering peak ( and above the asymptotic value ) of the normalized cPDF , multiplied by the overall density of substitutions . We are particularly interested in , which we will simply denote . The shape of is very consistent between the different species comparisons tested , but the clustering count is not because it depends not only on , but also on the density of substitutions between the species being compared . ranges from in the D . melanogaster versus D . sechellia alignment to in the D . melanogaster versus D . ananassae alignment , as seen in Figure 3A . increases linearly with ( the fraction of substituted amino acids ) , this is consistent with a clustering pattern that remains constant as divergence increases with time . Clustering between nearby non-synonymous substitutions is strongly supported by the data , but it is not a priori clear whether it is the separation of amino acids along the protein backbone , or the distance in base pairs along the genome , that matters . To discriminate between these possibilities we repeated the correlation analysis including only those pairs of residues which spanned an intron . As a result the genomic separation between codons had a median increase of bp ( codons ) and a minimum increase of bp ( codons ) , while separation between the encoded amino acids along the protein backbone was unchanged . As shown in Figure 2B , the cPDFs estimated from these intron-spanning pairs of codons correspond closely with those estimated within exons , when separation along the protein backbone ( exonic distance ) is used in the estimation . We conclude that the clustering length scale is set by the distance along the protein backbone , not along the genome . Remarkably , the clustering between amino acid substitutions is not limited to substitutions between species . It is also apparent among polymorphisms within a population sample of D . melanogaster ( Methods ) . Figure 2C shows the estimated cPDFs between synonymous and non-synonymous polymorphisms ( and ) . The cPDFs estimated from polymorphisms are much noisier because our population sample sequencing spans only kb of coding sequence , as compared to Mb for the divergence data . Nevertheless , we find clustering between polymorphisms analogous to that between substitutions: non-synonymous polymorphisms cluster significantly ( , chi-square test ) , while synonymous polymorphisms do not . We tested for potential relationships between clustering and a number of genetic properties by estimating on subsets of the full set of coding sequences stratified by the property in question . Clustering is robust in the sense that it is not substantially affected by many of the properties we tested , such as chromosome ( including autosome versus X ) , recombination rate and the level of gapping in the alignment ( Figures S2 , S3 , S4 ) . We did find a systematic relationship between the GC content of coding sequence and clustering; higher GC content correlates with stronger clustering ( Figure S5 ) . A notable factor that influences clustering is the level of constraint under which a gene evolves , which we estimate by the fraction of substituted amino acids . Amino acid substitution are more clustered in constrained genes than they are in unconstrained genes , i . e . has a larger clustering peak when it is estimated from highly constrained ( low ) coding sequences , see Figure 3B . In the inset of Figure 3B we have plotted the estimated from each subset of coding sequences against the average of that subset . It is useful to compare this plot to the one in Figure 3A , which also is a plot of versus . The difference between these plots is that in panel A effectively measures divergence time and scales linearly with , while in the inset of panel B tracks the level of constraint and is strongly sublinear in . In fact , once constraint relaxes past a certain point , becomes roughly constant . This relationship suggests that substitutions in constrained genes occur in tight clusters , and that as constraint lessens the additional substitutions which accrue do so uniformly along the sequence . Non-selective mechanisms cannot account for both significant non-synonymous clustering and the absence of synonymous clustering . Having ruled out non-selective mechanisms , we now consider potential selective mechanisms that could cause amino acid substitutions to cluster . Perhaps the simplest explanation for clustering is that proteins have short segments , such as unstructured loops , that are under reduced purifying selection . These weakly constrained segments experience locally increased rates of amino acid substitution , which we then observe as clustering in both divergence and polymorphism data . There are also several ways in which positive selection could cause clustering . Clustering could be the result of localized ‘adaptive bursts’ , i . e . functional modules in which multiple independently adaptive substitutions became available ( perhaps due to a changed environment ) . Because amino acids close on the protein backbone are more likely to be in the same module , the resulting burst of adaptive substitutions would be clustered on the sequence . Amino acids that are close along the chain are also more likely to physically interact , even after protein folding . As a consequence , the fitness effect , and hence evolutionary fate , of nearby substitutions could be contingent on one another ( i . e . epistasis ) . In particular we might imagine common compensatory interactions between nearby substitutions , although all synergistic interactions would contribute to clustering . Finally , another potential mechanism is hitchhiking . In this scenario mildly deleterious amino acid polymorphisms are driven to fixation by the selective sweep of a linked allele , resulting in clustered substitutions . We will now attempt to disentangle the relative contributions of these different selective scenarios . We can polarize substitutions by the lineage on which they arose using an outgroup and then repeat our correlation analysis for pairs of substitutions which arose in the same lineage and for pairs which arose in different lineages ( Methods ) . This allows us to begin to distinguish between potential selective mechanisms of clustering . If spatial heterogeneity in the strength of purifying selection is responsible for clustering we expect equal clustering within and between lineages , since in this case the presence of a substitution simply informs as to the level of constraint in that region of the protein sequence . In contrast , the alternative selective mechanisms ( adaptive bursts , compensatory or synergistic mutations , and hitchhiking ) are lineage-specific , they only apply when substitutions occur in the same lineage and therefore can only cause clustering between same-lineage substitutions . We incorporate polarization into our analysis by extending the sequence features in our cPDFs with the specification of the species lineage on which a substitution arose , e . g . is a non-synonymous substitution in the Dmel , Dsec , Dsim , Dyak , Dere , Dana , Dpse lineage ( Methods ) . The non-synonymous cPDFs estimated for substitutions in the same and different lineage than the focal substitution are shown in Figure 4A for each species comparison . Clustering between substitutions is always significant whether substitutions arose in the same lineage or in different lineages , but clustering between same-lineage substitutions is always significantly stronger ( Table S1 ) . We argued above that spatially heterogeneous purifying selection would cause equal clustering within and between lineages . If this is so , the excess clustering within lineages must be generated by one of the lineage-specific alternatives . Excess lineage-specific clustering can be quantified with an extension of the clustering count . First we define the lineage-specific clustering count as an analog of with the difference that the cPDF from which derives is estimated using only substitutions in lineage . Therefore , is the increased number of -lineage DNs near a focal -lineage DN due to clustering . Next , the ‘lineage-specific excess clustering count’ is the portion of which is inconsistent with a lineage non-specific mechanism . We quantify this as the difference between the within- and between-lineage clustering over the first codons ( Methods ) . This corresponds graphically to the area between those cPDFs ( the red area in Figure 4A , ) , multiplied by the density of substitutions in the lineage . The lineage-specific excess appears to be a roughly constant fraction of the total lineage-specific clustering . The estimate of is plotted against the estimate of for both lineages of all our species comparisons in Figure 4B . This relationship is well-fit by a linear model , suggesting that approximately of clustering within a lineage is due to lineage-specific mechanisms , i . e . some combination of compensatory or synergistic mutations , adaptive bursts and hitchhiking . The D . simulans lineage is an outlier , Dsim is aberrantly high . This may be a consequence of details relating to this particular reference sequence: the D . simulans reference sequence has lower coverage and quality than the other reference sequences as well as being a ‘mosaic’ assembly constructed from multiple individuals [29] . The Dsim lineage is also picked out by the synonymous control , there is significant synonymous clustering in this lineage above that found in any other lineage we consider ( Figure S10 ) . If compensatory mutations are contributing substantially to lineage-specific excess one might find evidence of this in a physical or biochemical quantity associated with the compensation . For example , changes in volume , hydrophobicity , charge , etc . might anti-correlate if the substitutions are compensatory . We tested several amino acid properties for such a relationship but found only one that exhibited the hypothesized behavior: nearby substitutions have a significantly increased probability to cause compensatory changes in charge , but only when they arise in the same lineage ! We quantify this effect by estimating the fraction of substitutions which compensate the effect of a focal charge-altering substitution , as a function of distance from the focal substitution . In Figure 5 we see that the fraction of charge-compensating substitutions is significantly elevated near a focal charge-altering substitution , on roughly the clustering length scale of 10 codons . This compensation serves to partially conserve the total charge of the protein sequence within the clustering length scale . Local charge compensation is significant in every species comparison we considered , all p-values , chi-square test ( Table S2 ) . A measure of the magnitude of this effect is the fraction of charge-altering substitutions that that have their charge alteration compensated for by the net change in charge caused by the other substitutions within codons . This varies by lineage , but is always significant and increases with species divergence up to for the species comparison of D . melanogaster and D . pseudoobscura . Charge compensation is a lineage-specific effect , and it is responsible for a significant fraction of the lineage-specific excess we observe , roughly depending on lineage ( Table S2 ) . The observation of substantial charge compensation , and the lack of compensation of other amino acid properties , is consistent with previous observations which suggested charge compensation to be of greater significance in protein evolution than compensation of other amino acid characteristics [22] , [34] . Interestingly , while substitutions in different lineages do not exhibit the local compensation phenomenon , they do show a weaker , but statistically significant , increase in the fraction of nearby changes which alter charge in the same direction , perhaps indicating convergent evolution ( Table S2 ) . Non-synonymous polymorphisms cluster as well , and polymorphism data provides another avenue to distinguish between the possible selective mechanisms of clustering . Under a model of bursts of independent adaptive mutations , beneficial amino acid mutations can be incorporated sequentially , and would not be expected to segregate together in the population since beneficial mutations rapidly fix after arising . In contrast , if epistatic selection is driving the observed clustering we expect that a compensatory mutation will only be found on a chromosome that already carries the first mutation , i . e . we expect the derived states of nearby polymorphic sites to cosegregate . We can quantify this expectation by estimating the average polarized linkage disequilibrium [35] , [36] , i . e . the frequency of the doubly derived haplotype minus the product of the frequencies of the individual derived alleles averaged over all pairs of polymorphisms a distance apart . then indicates that derived alleles occur in coupling more often than would be expected if their fitnesses were independent . Consistent with the compensatory scenario , we find when estimated from amino acid polymorphisms within codons of each other , as seen in Figure 6 . We evaluate the significance of the cosegregation of nearby derived alleles by bootstrapping: we resample polymorphic sites from the full set of polymorphic sites in our population , pair them off into a number of pairs equal to the number of pairs of polymorphisms within codons of each other , and then estimate from this resampled ensemble . Repeating this process times yields a bootstrapped probability distribution which we compare to the estimated from the data , yielding a bootstrapping p-value of of observing an equal or greater by chance from our population sample . Again , only pairs of non-synonymous polymorphisms significantly cosegregate , supporting the contention that epistasis is responsible and arguing against purely genomic explanations . Although cosegregation is statistically significant , because our polymorphism data set is limited ( compared to whole-genome comparisons of divergence ) there is more uncertainty about these results , and it is worth noting that cosegregation does not seem to extend beyond three codons of separation .
There are a number of selective mechanisms that could cause amino acid substitutions to cluster , and the clustering we observe most likely has multiple causes . We will now try to reconcile the various observations made above with the different mechanisms that have the potential to cause clustering , and estimate their respective contributions . Potential selective mechanisms of clustering can be grouped into two classes: ( A ) Heterogeneity in the strength of purifying selection acting within an ORF leads to variation in the density of substitutions and polymorphisms , resulting in clustering . ( B ) Novel protein variants are selected for and this adaptation leads to clusters of substitutions . The latter class of mechanisms comes in several flavors: ( i ) A localized adaptive burst in which several nearby substitutions independently sweep to fixation . This might be a consequence of changes in selective pressure on a protein domain that requires multiple adaptive substitutions to reach the new optimum [40] . ( ii ) A complex adaptation , in which several dependent substitutions are required to achieve the selected effect . This case includes scenarios of compensatory mutations , i . e . a second mutation is necessary to compensate deleterious side effects of the first [41] , and evolutionary contingency , i . e . the first mutation is necessary for the second mutation to be beneficial [42] . ( iii ) Hitchhiking , the fixation of otherwise deleterious substitutions as a result of a selective sweep at a linked site [43] , [44] . Purifying selection prunes mutations that are detrimental , perhaps because they interfere with protein structure or stability . Given that protein structure is strongly conserved across different domains of life , it is reasonable to assume that purifying selection operates in a similar fashion on homologous regions of proteins in different branches of the Drosophila phylogeny . Adaptive evolution , however , depends on the ecological niche of the species and can depend strongly on previous substitutions in that species . Adaptive evolution is therefore expected to be lineage-specific , at least moreso than purifying selection . We observed that clustering exists between pairs of amino acid substitutions in different lineages as well as in the same lineage , the latter being consistently greater ( Figure 4 ) . Clustering across lineages implies that a substitution found in one lineage is predictive of the local substitution rate independent of lineage , which we understand as a lineage-non-specific local increase in the substitution rate . This is most consistent with a class ( A ) mechanism such as locally relaxed purifying selection , e . g . in an unstructured loop of a protein . The excess clustering within lineages must be caused by a lineage-specific mechanism such as the class ( B ) mechanisms described above . Purifying selection can of course also vary in a lineage-specific way . If mildly-deleterious substitutions were highly clustered , and a reduced effective population size rendered them effectively neutral , this could result in excess clustering in the lower population size lineage . However , this scenario is inconsistent with the fact that we observe excess clustering within all lineages , and that it is quantitatively similar between lineage pairs diverging from a common ancestor . Locus-specific variation in purifying selection is also possible , but in most cases will affect an entire gene ( e . g . via duplication or transformation into a pseudo-gene ) and therefore would not lead to clustering on short length scales . Given that excess lineage-specific clustering is a substantial fraction of the total clustering in every lineage , it does not seem likely that lineage-specific variation in the strength of purifying selection can account for it . We start by addressing the potential contribution of hitch-hiking to clustering . A selective sweep of a strongly beneficial substitution fixes a linked haplotype , converting a local snapshot of polymorphisms present in the population into substitutions . This hitch-hiking process does not affect the fixation probability of neutral ( and perhaps synonymous ) mutations [45] , but is expected to increase the fixation probability of nearby deleterious non-synonymous substitutions . However , several observations argue against hitchhiking as the main contributor to clustering . First , hitch-hiking predicts that the length scale of clustering is given by the typical size of hitchhiked region [46] . This implies clustering dependent on separation along the DNA sequence rather than along the protein backbone , contrary to our observations ( Figure 2B ) . Second , there is no correlation between clustering and the average recombination rate of a coding sequence , which would affect the size of hitchhiked regions ( Figure S3 ) . Finally , we can calculate a rough upper bound for the contribution of hitchhiking to lineage-specific clustering . Given a per-site heterozygosity , the expected population frequency of derived mutations per site is . Non-synonymous in D . melanogaster is per site [47]–[49] and thus per 4-fold codon ( and slightly higher for 2-folds ) . Given this , the probability of finding a derived amino acid substitution within codons of a focal site is . This serves as a very generous upper bound on the contribution of hitch-hiking to , since only if the focal site is always adaptive and the observed variation always deleterious will this value be approached . This estimate suggests that the contribution of hitchhiking to lineage-specific clustering is minor , since this upper bound is less than the range over which we observe lineage-specific excess , from to depending on lineage ( Figure 4B ) . The two remaining adaptive scenarios , adaptive bursts and complex adaptations , are difficult to distinguish in part because the boundary between them is not sharply delineated . Certainly , different substitutions within codons in the same protein are never going to be completely independent . The question rather is whether one of the mutations ‘substantially’ affected the probability of the other . Do localized adaptive bursts , loosely defined as substitutions within codons which all independently improve fitness , dominate our clustering signal ? Or are the interactions ( epistasis ) between nearby substitutions mainly responsible ? We cannot fully exclude either scenario , but there is evidence that local interactions play at least a significant role . Mutations of independent beneficial effect would not be expected to compensate each others effect on total charge . This requires epistasis between the substitutions , and implies that complex adaptations are responsible for at least of lineage-specific excess . Secondly , independent beneficial mutations are expected to either fix sequentially or , if they do occur simultaneously , to generally segregate in repulsion [50] . This is inconsistent with the preferential cosegregation we observe between nearby derived alleles ( Figure 6 ) . Furthermore , charge compensation is only one of many relevant interactions , albeit the one we most readily ascertained from the primary sequence data . So the contribution of charge compensation is only a lower limit for the influence of complex adaptation on the substitution process . While the possibility of interactions between amino acid substitutions has never been seriously questioned ( and has recently been demonstrated in a number of concrete examples[42] , [51] ) , the general importance of epistasis and compensation in evolution has been , and continues to be , controversial . We find evidence that a non-negligible fraction of substitutions are involved in patterns of adaptation suggestive of epistasis . If lineage-specific clustering is mostly due to epistasis , a scenario consistent with our results , we can use the lineage-specific excess to estimate the number of substitutions which owe their fixation to interactions with other substitutions . For example , the lineage-specific excess in the D . yakuba lineage is Dyak . If we attribute the entirety of this to epistasis we would conclude that of the substitutions on this lineage were contingent on another substitution . This estimate is clearly generous in the sense that we have not completely excluded the contribution of other processes , but it is also conservative in the sense that it only includes the effect of elevated local epistasis and excludes the contribution of long-range interactions . To account for interactions between amino acids distant in the protein sequence but nevertheless in close vicinity in the folded protein , one would need to incorporate protein structure explicitly . However , the probability for any random pair of residues to be involved in such interaction decays rapidly with their separation along the protein backbone , likely to an asymptotic value . Hence , in our analysis we expect correlations between distant pairs to be lost in the background , with only the enriched short range interactions observable as excess clustering of substitutions . The presence of this local enrichment is the enabling factor behind our approach . In agreement with this interpretation , the inferred length scale of clustering of 10 codons is consistent with the size of secondary structure elements in proteins ( e . g . 3 turns of an helix ) . While this manuscript was prepared for publication , another group also found clustering of positively selected amino acid substitutions [17] . Via a different approach , the authors show that the rate of evolution depends on elements of secondary structure and that nearby positively selected sites tend to cluster . Finally , while we have focused on the mode of evolution responsible for lineage-specific excess , the clear clustering which occurs across lineages is notable in its own right . We attribute this clustering to spatially heterogeneous purifying selection . The clustering length scale is extremely consistent across all the species comparisons we considered and the polymorphism data ( Figure 2 ) . This suggests that models of protein evolution might be improved by incorporating correlation between the rate of amino acid evolution along the sequence ( e . g . site-specific in PAML [52] ) . This is particularly true if the length scale we observe here can be shown to be consistent across phyla , demonstrating it as a generic property of proteins themselves .
We assess two ‘types’ of significance here , sampling significance and bootstrapping significance . The assessment of sampling significance is understood by recalling how cPDFs are estimated . is the mean of a set of trials which can have outcome either or ( Bernoulli random variables ) . is the same , it is just an average over a cPDF for . Trials consist of selecting a focal substitution of type , looking away on the sequence , and recording the presence ( ) or absence ( ) of a substitution of type . So , assessing the significance of values of or is equivalent to assessing the significance of sums of Bernoulli random variables , for which we used chi-square tests . Bootstrapping significance is also a measure of sampling significance , with the difference being that the effect of resampling is evaluated at the level of the largest unit in our analysis , the coding sequence . The probability distribution of a value of interest is constructed by resampling with replacement from the full set of coding sequences a ‘bootstrapped’ set of equal size , estimating the value of interest on that bootstrapped set , and repeating . Bootstrapping p-values are then determined from this estimate of the probability distribution . If the estimated distribution can be approximated as a gaussian , as is always the case here , the gaussian approximation is used to assign the p-value . A modification of this bootstrapping scheme was used for polymorphism cosegregation , and described there . | Genes are templates for proteins , yet evolutionary studies of genes and proteins often bear little resemblance . Analyses of gene evolution typically treat each codon independently , quantifying gene evolution by summing over the constituent codons . In contrast , studies of protein evolution generally incorporate protein structure and interactions between amino acids explicitly . We investigate correlations in the evolution of codons as a function of their distance from each other along the protein coding sequence . This approach is motivated by the expectation that codons near each other in sequence often encode amino acids belonging to the same functional unit . Consequently , these amino acids are more likely to interact and/or experience similar selective regimes , introducing correlation between the evolution of the underlying codons . We find codon evolution in Drosophilids to be correlated over a characteristic length scale of ≈10 codons . Specifically , the presence of a non-synonymous substitution substantially increases the probability of further such substitutions nearby , particularly within that lineage . Further analysis suggests both functional interactions between amino acids and correlation in the strength of selection contribute to this effect . These findings are relevant for understanding the relative importance of different modes of selection , and particularly the role of epistasis , in gene and protein evolution . | [
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A cluster of eleven patients , including eight family members and three healthcare workers with fever and thrombocytopenia occurred in Yixing County , Jiangsu Province , China , from October to November 1996 . However , the initial investigation failed to identify its etiology . Severe fever with thrombocytopenia syndrome ( SFTS ) is an emerging infectious disease caused by SFTS bunyavirus ( SFTSV ) , which was first discovered in 2009 . The discovery of novel SFTSV resulted in our consideration to test SFTSV on the remaining samples of this cluster in September 2010 . We retrospectively analyzed the epidemiological and clinical data of this cluster . The first case , one 55-year-old man with fulminant hemorrhagic diseases , died on October 14 , 1996 . His younger brother ( the second case ) developed similar hemorrhagic diseases after nursing him and then died on November 3 . From November 4 to November 15 , nine other patients , including six family members and three medical staffs , developed fever and thrombocytopenia after exposure to the second case . The sera of six patients were collected on November 24 , 1996 . IgM antibodies against SFTSV were detected in all of the six patients’ sera using enzyme-linked immunosorbent assay ( ELISA ) , while IgG antibodies were detected in one patient’s serum using an indirect immunofluorescence assay ( IFA ) . We also found that IgG antibodies against SFTSV were still detected in four surviving patients’ sera 14 years after illness onset . The mysterious pathogen of the cluster in 1996 was proved to be SFTSV on the basis of its epidemiological data , clinical data and serological results . It suggests that SFTSV has been circulating in China for more than 10 years before being identified in 2009 , and SFTSV IgG antibodies can persist for up to 14 years .
Severe fever with thrombocytopenia syndrome ( SFTS ) , an emerging haemorrhagic fever , was firstly confirmed among the rural areas in the central and eastern regions of China in 2009[1] . The main clinical features include fever , thrombocytopenia , leukocytopia , lymphadenopathy , and gastrointestinal symptoms . It has an average case-fatality rate of 12% but can be as high as 30%[2] . The causative agent , SFTS bunyavirus ( SFTSV ) , is classified into the Phlebovirus genus , Phenuiviridae family , Bunyavirales order . It was once called as fever , thrombocytopenia and leucopenia syndrome virus ( FTLSV ) [3] , or Huaiyangshan virus ( HYSV ) [4] . SFTSV is believed to be transmitted through tick bites[1 , 5 , 6] , direct contact with SFTS patients’ blood or bloody secretion[7 , 8] , and probable aerosol transmission[9] . SFTS cases outside China were first reported in North Korea in 2009[10] , South Korea in 2012[11] and Japan in 2013[12] . A closely related virus called Heartland virus was isolated from patients with similar symptoms in the United States [13] . Hence , SFTS was listed as one of the nine infectious diseases on the WHO priority list in 2017 because of its trend of wider distribution and rising threat imposed on global health . Serological investigation showed that SFTSV infection was widespread in domestic animals ( e . g . goats , sheep , cattle , dogs , etc . ) and wild animals ( e . g . rodent and shrews ) [14 , 15] . The seroprevalence of SFTSV in healthy people in China varies from 0 . 23% to 9 . 17% , depending on the investigated population and geography as well as the test reagent and methods , but only a small proportion of exposed persons develop clinical symptoms[16] . From the published documents , two SFTS cases in Japan in 2005 reported by Kurihara et al . have been regarded as the earliest cases in the world [17] . A recent phylogenetic study on SFTSV in China , South Korea , and Japan demonstrated that SFTSV could be divided into the Chinese clade and the Japanese clade , which may have evolved separately over time , except for the rare occasion of overseas transmission[18] . These results suggest that SFTS may have existed without being identified for some time . In this study , we completed a retrospective analysis of a cluster of eleven patients with unexplained fever and thrombocytopenia in China in 1996 to determine whether SFTSV was responsible for this cluster .
The cluster of eleven patients with unexplained fever and thrombocytopenia occurred from October to November in 1996 in a township in Yixing County , which is located in southern Jiangsu Province of China and is characterized by hilly terrain . When the cluster was detected , public health workers were dispatched immediately to record the clinical and epidemiological information of patients and explore its causative agent . Sera of six patients were collected on November 24 , 1996 . Although the delay between the illness onset and sampling ranged from 9 to 20 days , all efforts were made to explore the causative agent at that time . Common pathogens including Hantavirus , Crimea-Congo Hemorrhagic fever virus , Orientia tsutsugamushi , Spotted fever group rickettsiae , Coxiella burnetii , Rickettsia Prowazeki , Rickettsia Mooseri , Salmonella typhi and other bacteria were excluded by blood culture and antibody tests in December 1996 . Then , a small amount of the remaining samples were stored at a temperature of -80°C in the laboratory of Jiangsu Provincial Center for Disease Control and Prevention ( JSCDC ) . The discovery of novel SFTSV and impact of clinical manifestations of SFTS resulted in our consideration to test SFTSV on the remaining samples of this cluster in September 2010 . Meanwhile , a retrospective investigation was conducted through interviewing the patients’ family members , neighbors and medical staffs , cross-checking several written timelines of the cluster , and collecting surviving patients’ sera . Acute-phase sera of SFTS patients were detected for SFTSV-specific IgM antibodies using an ELISA kit ( Xinlianxin , Wuxi , China ) according to the manufacturer’s protocol [19] . In the initial screening , an undiluted serum sample was used to determine whether the sample was positive for antibodies against SFTSV . Positive serum samples were further diluted in 2-fold increments starting at 1:2 for titration of antibody titers with the same assay . SFTSV-specific IgG antibodies were detected in all human sera by IFA as previously described [20] . Twenty microliters of diluted ( 1:2 to 1:1280 ) serum samples were added to the cell-spotted coverslips with viral antigens and incubated for 45 minutes at 37°C . After washing , 20μL of FITC-conjugated goat anti-human IgG ( Abcam , UK ) diluted 1:80 with Phosphate Buffered Saline ( PBS ) containing Evans Blue ( 1:20 , 000 ) was added for further incubation for 30 minutes at 37°C . After washing for three times , the slides were mounted in glycerin and examined under an immunofluorescence microscope . The study was approved by the Ethics Committee of JSCDC and informed consent was obtained from the participants . All data were analyzed anonymously .
On October 2 1996 , a 55-year-old man developed dizziness , fatigue and sore throat , followed by fever and chills on October 4 . Then , he developed nausea , vomiting , hematemesis and melena on October 12 . Laboratory testing revealed that he had thrombocytopenia , leukopenia , elevated serum alanine and aspartate transaminase levels , proteinuria , and hematuria the next day . On the morning of October 14 , he was admitted to the local township healthcare center . Physical examination showed conjunctival congestion , scleral icterus , ecchymosis on the back of his right hand and right wrist joints , and sporadic hemorrhagic spots on his soft palate . He was administered with dexamethasone and oxygen . He died on that evening . Retrospective investigation of the patient’s family members revealed that he was a mine safety supervisor , and his hobby was hunting . He caught three hares in the woods near his residence approximately 30 days before illness onset . He had no wife or child . During his illness , his younger brother attended to him day and night . The second case was patient A’s younger brother . Patient B not only attended to patient A during his illness , but also cleaned his body and dressed him in funeral clothes before cremation . He had sudden onset of fever , chills , and headache on October 25 , 11 days after patient A’s death . He was admitted to the same township healthcare center on October 28 . On November 1 , he was transferred to People's Hospital of Yixing County because of the severe condition . Physical examination on admission revealed supraclavicular lymph node enlargement and hepatomegaly . On November 2 , he bled from his mouth and nose , and developed neurological symptoms such as seizures and extensive skin ecchymosis . He died the next morning despite the intensive care including transfusion and hemostatic therapy . From November 4 to November 15 , nine cases , including six family members and three medical staffs , all developed fever and thrombocytopenia . Patient C ( patient B’s brother ) developed symptoms on November 4 firstly , followed by patient D ( patient B’s doctor ) , patient E ( patient B’s daughter ) and patient F ( patient B’s doctor ) on November 7 , November 8 and November 10; patient G ( patient B’s doctor ) , patient H ( patient B’s elder son ) and patient I ( patient B’s younger son ) presented with fever on November 11; The last two patients , patient J ( patient B’s brother-in-law ) and patient K ( patient B’s nephew ) developed symptoms on November 14 and 15 , respectively . The mean age of these nine cases was 38 . 5 years ( ranged from 22 to 63 years ) . Eight patients were male and one patient was female . Compared to the two fatal cases ( patient A and B ) , eight follow-up cases had shorter duration from illness onset to admission with milder symptoms , and finally recovered after supportive treatment . The timeline of key events is shown in Fig 1 , and all patients’ demographic and clinical information is shown in Table 1 . Two doctors ( patient D&G ) worked in People's Hospital of Yixing County and lived in the center of Yixing County . Patient F was an intern doctor in 1996 . There was no clinical information about patient F in existing records , because he returned to his college in another city for medical treatment after his illness onset . Prior to the onset of the disease , the three medical staffs had provided medical services for patient B , while six family members had participated in attending to him in People's Hospital of Yixing County . Meanwhile , these nine cases had no contact with patient A before illness onset . Retrospective interviews showed that the three medical staffs had contact with blood or bloody secretion of patient B while rescuing the critically ill patient B on the evening of November 2; patient C and patient J cleaned up his body’s blood after patient B died; Detailed exposure histories of other patients were not remembered clearly by them and their family members . Sera from patient C , patient D , patient G , patient H , patient I and patient K were collected on November 24 , 1996 . No serum was available from patient A and patient B . The time span from illness onset to sampling ranged from 9 to 20 days . SFTSV-specific IgM antibodies were detected in all of the six patients’ sera by ELISA , and SFTSV-specific IgG antibodies were detected in the sera of patient D by IFA . However , no SFTSV was isolated by using Vero , Vero-E6 and BHK 21 cell culture and no viral RNAs were detected by real-time reverse transcription PCR from these serum samples . Sera of the four surviving patients were collected on September 17 , 2010 , nearly 14 years after illness onset . SFTSV IgG antibody titers were 1:80 in patient C , patient G and patient J , and 1:640 in patient D .
The cluster of eleven patients with unexplained fever and thrombocytopenia in 1996 occurred 14 years before the discovery of SFTSV . These cases were initially diagnosed as a viral haemorrhagic fever caused by Hantavirus or Crimea-Congo Hemorrhagic fever virus , which were known as the most common viruses causing severe hemorrhagic diseases in China , due to the main clinical manifestations including fever , thrombocytopenia and hemorrhages . However , the antibody test and nucleic acid test for these two viruses were negative and further analysis was carried out . Then , differential diagnosis including Orientia tsutsugamushi , Spotted fever group rickettsiae , Coxiella burnetii , Rickettsia Prowazeki , Rickettsia Mooseri , Salmonella typhi and other bacteria were considered , but the test results were all negative . Therefore , we suspected an outbreak of a severe transmissible infection of unknown etiology in People's Hospital of Yixing County and requested notification of all similar cases from the local medical institutions . However , there was no evidence of more cases at that time . Remaining serum samples from six cases were kept stored in a freezer at a temperature of -80°C from November 1996 . To test SFTSV on these samples was considered owing to the discovery of novel SFTSV in 2009 and the impact of clinical manifestations of SFTS . There are five reasons to extrapolate that SFTSV was the mysterious pathogen of the cluster . Firstly , all ten patients with medical records developed typical symptoms of SFTS such as fever and thrombocytopenia . Moreover , six of them had leucopenia , and two of them died with severe hemorrhage compatible with severe SFTS . Secondly , the cluster occurred in Yixing County , which was characterized by hilly terrain . Serological results showed that SFTSV had been circulating widely in Yixing County . The overall SFTSV seroprevalence in urban and rural residents in Yixing County in 2011 was 0 . 20% [21] . Average SFTSV seroprevalence in animal species in Yixing County in 2012 were: goats ( 66 . 8% ) , cattle ( 28 . 2% ) , dogs ( 7 . 4% ) , pigs ( 4 . 7% ) , chickens ( 1 . 2% ) , geese ( 1 . 7% ) , rodents ( 4 . 4% ) and hedgehogs ( 2 . 7% ) [14] . Thirdly , SFTSV infection was reported to occur from April to October annually in Jiangsu Province [22] . The index case had illness onset in October , which was in accordance with the seasonal pattern of SFTSV . Although the index case had no clear history of tick bite , he had high exposure risk for tick due to his occupation of a mine safety supervisor and his hobby of hunting . Fourthly , transmission was closely associated with blood or bloody secretion exposure from the index case or patient B , both of whom died of a fulminant febrile illness with hemorrhage . This is consistent with SFTSV transmission patterns reported in the previous literatures[7 , 8 , 23] . Last but not least , IgM antibodies against SFTSV were detected in the acute-phase serum samples of six patients by ELISA . Although SFTSV isolation and viral RNA detection are the gold standards for diagnosis , the appearance of anti-SFTSV IgM by ELISA is useful and has become one of the diagnostic criteria for a laboratory-confirmed SFTS case in China since the specificity and sensitivity of ELISA test is similar to those of the microneutralization assay and anti-SFTSV IgM exhibit no cross-reactivity with these antibodies to other closely related viruses such as hantavirus , Rift Valley fever virus , dengue virus , and so on[24–26] . Based on all of these findings , the cluster of eleven patients with unexplained fever and thrombocytopenia in China in 1996 was most likely caused by SFTSV . Although one recent research by Qing-Bin Lu et al . found that SFTSV specific IgM antibody could be detected at a median of 9 days and remained persistent until 6 months after disease onset[27] , it seems to be a theoretical concern more than a practical one ( i . e . , the chance of a person acquiring SFTSV infection during a given transmission season , maintaining a significant level of virus-specific IgM activity over the ensuing 6 months , and then again being re-exposed to SFTSV during the subsequent transmission season is highly unlikely , because our previous studies indicate that the seroprevalence rate of SFTSV in high risk population is less than 2% in Yixing County and the incidence of SFTSV infection is less than 5 cases/100 , 000 population in the highest incidence county[14 , 28] ) . Therefore , the appearance of anti-SFTSV IgM is still a possible indicative sign of the clinical disease . One highlight of our study was that SFTSV IgG antibody titers were detected in surviving patients with high titers 14 years after illness onset , suggesting that SFTSV IgG antibody could last for more than 10 years , perhaps even a lifetime after infection . At present , only one research about the persistence of SFTSV IgG antibodies found that SFTSV IgG antibody could be detected 3 years after infection[27] . It should be noted that the IFA used to detect IgG antibody against SFTSV in our study has good specificity and sensitivity compared to RT-PCR , and no any cross with other arbor-virus including hantavirus , and Japanese encephalitis virus , etc . The other highlight was that the cluster comprising eleven SFTS patients occurred in Yixing County , China , in 1996 , which preceded the cases in Japan in 2005 reported by Kurihara et al . that might be mistaken as the earliest SFTS cases worldwide[17] . Our result suggests that SFTS have existed for a long time without being recognized . Three limitations exist in our study . Firstly , no tissue or serum sample of patient B and patient F , especially of the index patient , was available for retrospective laboratory detection . Secondly , no viral RNAs were detected by real-time reverse transcription PCR from the six patients’ sera . This might be because the time of sera collection was later than the patients’ viremia period , or because the long preservation time and the sera freeze thawing resulting in viral RNA degradation . Thirdly , the cluster occurred such a long time ago that detailed disease-related exposure history could not be clearly remembered and completely recorded . Memory bias may exist in our research . Our findings suggest that SFTSV has been circulating in China for more than 10 years before being identified and SFTSV IgG antibodies can persist for as long as 14 years . | SFTSV was first discovered in 2009 . It can be transmitted through tick bites , direct contact with SFTS patients’ blood or bloody secretion , and probable aerosol transmission . SFTS was listed as one of the nine infectious diseases on the WHO priority list in 2017 because of its trend of wider distribution and rising threat imposed on global health . It is worth mentioning that a cluster of eleven patients including eight family members and three healthcare workers developed fever and thrombocytopenia in China , from October to November 1996 , but the initial investigation failed to identify its etiology . A retrospective analysis was conducted in September 2010 . Based on the epidemiological , clinical , and serological findings , we speculated that SFTSV was the mysterious pathogen of the cluster . Meanwhile , high-titer SFTSV IgG antibodies were detected in four surviving patients’ sera . These SFTS patients preceded two cases in Japan in 2005 reported by Kurihara et al . , which were once regarded as “the earliest SFTS cases worldwide” . These results suggest that SFTS should have existed without being diagnosed for a long time , and SFTSV IgG antibodies can persist long for 14 years , and perhaps even a lifetime after infection . | [
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... | 2018 | A cluster of cases of severe fever with thrombocytopenia syndrome bunyavirus infection in China, 1996: A retrospective serological study |
We assess the variability of protein function in protein sequence and structure space . Various regions in this space exhibit considerable difference in the local conservation of molecular function . We analyze and capture local function conservation by means of logistic curves . Based on this analysis , we propose a method for predicting molecular function of a query protein with known structure but unknown function . The prediction method is rigorously assessed and compared with a previously published function predictor . Furthermore , we apply the method to 500 functionally unannotated PDB structures and discuss selected examples . The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence , structure , and function . The GOdot method is available online ( http://godot . bioinf . mpi-inf . mpg . de ) .
Protein structure databases are growing at a rapid rate and , in recent years , structural genomics initiatives have increased the growth rate further . Yet many protein structures remain without functional annotations . Low coverage of functional annotations substantiates the necessity of reliable automated methods for predicting the functions of proteins . A widely accepted vocabulary for characterizing gene and protein function is maintained by the Gene Ontology ( GO ) Consortium [1] . To understand protein function , information is typically inferred from evolutionarily related proteins . Evolutionary relation can be determined by sequence similarity . Enzymes , for example , tend to have a conserved function , when they share more than 40%–50% sequence identity [2]–[4] . Inference according to only sequence similarity is not very reliable for accurate function prediction , in particular for remote homology [5] , [6] . Some function prediction methods transfer function from similar sequences , such as GOtcha [7] , Blast2GO [8] , or PFP [9] . Phylogenomic methods , such as SIFTER [10] and Orthostrapper [11] , additionally consider knowledge on the evolution of homologous proteins . Motif databases , such as EMOTIF [12] , PROSITE [13] , and PINTS [14] are used to extract functionally relevant signatures of proteins . Gene3D [15] compiles Hidden Markov Model signatures for CATH families and links these signatures to GO functions . FSSA [16] and PHUNCTIONER [17] use structural signatures derived from proteins of similar function to predict molecular function of uncharacterized proteins . Some approaches use different types of structural features to predict function [18] , [19] . Other methods employ sequence-derived protein features [20] , genomic context [21] , and GO term co-occurrence [22] . Some approaches to function prediction combine several features derived from the protein , or combine predictions from different methods [23]–[25] . Two recent reviews [18] , [26] provide an overview of state-of-the-art predictors and discuss many of the aforementioned methods in detail . The underlying idea of similarity based function transfer is that proteins with similar sequence and structural features are likely to perform the same function [27]–[29] . We take this principle one step further by examining groups of similar proteins . Such a group can be seen as a local region within the protein universe . A molecular function that is shared by all proteins in a local region is considered to be conserved . Local regions may be interspersed with proteins not annotated with this function and function conservation can vary between different regions [30] , [31] . Therefore , we use the frequency of functionally identical proteins within a local region to determine the extent to which a function is conserved in the respective region of protein space . The degree of local function conservation is regarded as a confidence measure for the prediction , high conservation implying high confidence that the respective function is correct . This quantitative estimate yields a differentiated view on function conservation , enabling us to predict protein molecular function more accurately .
The analysis is based on a set of 7290 representative protein domains with maximal 40% sequence identity as provided by the ASTRAL Compendium [32] . Molecular function annotations for the proteins were taken from the Gene Ontology Annotation ( GOA ) Project [33] , [34] ( see Methods for details ) . Of the 7290 representative protein domains , 86% are annotated with at least one molecular function GO term and 84% are annotated with a molecular function GO term from level three , or more specific ( see Methods for the definition of GO levels ) . Some GOA annotations cannot be resolved to domain precision . Therefore , we reduced the domain set to single domain structures ( see Methods for details ) . Out of this reduced set of 4099 single domain structures , 3449 ( 84% ) domains are annotated with molecular function GO terms . The subsequent analyses are performed on this set of 3449 protein domains . These 3449 protein domains are annotated with 0 to 11 level three GO terms ( with a first quartile of 1 , a mean of 1 . 96 , and a third quartile of 3 GO terms ) . The domains are compared against each other with different measures for protein similarity ( see Methods for details ) : for measuring similarity we use two sequence-based programs , namely local profile alignment ( LP ) and global profile alignment ( GP ) [35] , and two structure-based programs , namely Combinatorial Extension ( CE ) [36] and TM-align ( TM ) [37] . How reliably can functional annotations be inferred from the neighboring proteins of a protein according to each similarity measure ? This question is analyzed for GO level three . With a leave-one-out cross-validation for each protein we assess the errors made when inferring GO terms from the nearest neighbor to each protein . The average percentage of correct annotation inferences ranges from 51% to 62% , depending on the similarity measure ( 55% for CE , 51% for TM , 62% for LP , 62% for GP ) . Compared to other studies [28] , [29] , we observe slightly lower error rates . In Figure 1A the inferred annotations are sorted according to the similarity measures and then binned such that each bin contains an equal number of counts ( ca . 670 annotations ) . This allows for comparing the number of errors in inference according to different similarity measures , where the different similarity measures are operating at different scales . Even for very similar proteins , in the highest scoring bins , we observe a maximum of only 83% annotations being correctly inferred . Consequently , when inferring annotations from nearest neighbors without further analysis , at least 17% of the annotations are predicted falsely . The situation is even worse for lower similarity ranges . These errors can be attributed to the local properties in sequence and structure space . They demonstrate the difficulty of function annotation transfers at different similarity ranges . We broaden the above analysis to all GO levels , and examine to which extent function prediction can potentially benefit from combinations of protein similarity measures . The Venn diagram in Figure 2 shows how the set of GO annotations decomposes into subsets that can be inferred from protein neighbors according to different similarity measures . Altogether , there are 1806 distinct GO terms attached to 3449 proteins , yielding 28774 annotations . Of these , 8907 annotations are not found at a nearest neighbor according to any similarity measure . The remaining 19867 GO annotations are found at the nearest neighbor according to at least one similarity measure . The numbers of annotations that could be inferred by one similarity measure alone range from 15499 ( 53% for CE ) to 17216 ( 60% for GP ) . Thus , if only one of the similarity measures were used for function inference , one would miss between 2651 ( 9% ) and 4368 ( 15% ) correct annotations that could be inferred using all four similarity measures . The diagram demonstrates clearly that there is potential in the combination of several similarity measures for predicting GO terms . In the previous sections , we demonstrated that inferring function according to annotations attached to the nearest neighbors is useful but prone to errors . We also showed that combining different similarity measures yields a potentially better coverage of predicted GO terms . Here , we propose the GOdot method which combines the information from several similarity measures and assesses local function conservation in protein sequence and structure space in order to predict GO molecular function . To assess the performance of the GOdot method for function prediction , we compare four variants of function predictors: function inference based on protein similarity alone ( as discussed above ) , function inference based on raw function conservation scores , function inference based on selectively combined function conservation scores , and function inference based on consensus combined function conservation scores . GOdot was applied to 500 query proteins corresponding to PDB entries labeled with unknown function and obtained by structural genomics initiatives . We analyzed the GOdot results for the subset of these proteins having four or more GO terms with a consensus combined score >1–10−10 ( 49 in total ) . For 13 of these proteins the predictions included at least one problematic GO term . In most of these cases the problematic GO term was annotated to protein neighbors that were multidomain proteins . These GO terms corresponded to the molecular function of a particular domain outside the region of sequence or structure similarity . Four additional proteins remain uncharacterized according to public annotation databases . The corresponding GOdot predictions were not necessarily incorrect , but they only included GO terms that were not very informative . Most of the GOdot predictions ( 32 proteins ) were consistent with additional functional information that had been made available in the UniProt [38] database or in the literature . Direct experimental evidence for the function annotation was usually not available for these proteins with consistent GOdot predictions . One case with experimental evidence is Cytochrome P450 from Sulfolobus tokodaii [39] , PDB ID 1ue8 . In other cases the structural model provides direct evidence for the molecular function , for instance if the model includes a ligand binding to the protein . The PH0226 protein from Pyrococcus horikoshii ( PDB ID 1ve3 ) is such an example . The crystal structure includes the cofactor S-adenosyl-L-methionine ( SAM ) bound to the protein . The protein also shows significant structural similarity to other SAM-dependent methyltransferases , and is a member of the Methyltransferase homologous family , as identified by Pfam [40] . This evidence is consistent with the GOdot prediction of S-adenosylmethionine-dependent methyltransferase activity ( GO∶0008757 ) with a combined consensus score of 1–10−11 . This same query was used to illustrate the GOdot function prediction process in Figure 4 . In other cases the available annotation is scarce and relies heavily on the detection of relationships to other proteins using either sequence or structure comparison methods . GOdot complements these approaches by providing an estimate for the function conservation given the extent of sequence and structure similarity . The hypothetical protein TT1426 from Thermus thermophilus provides an example of GOdot results complementing previous functional analysis . TT1426 has been identified in Pfam as a member of the Phosphoribosyl transferase domain family . The structure has been determined [41] , PDB ID 1wd5 , and predicted to be a phosphoribosyl transferase type I based on structural similarity to other proteins of the same family . GOdot predicts TT1426 to have a glycosyltransferase activity ( GO∶0016757 ) with high reliability ( combined consensus score is 1–5·10−11 , as expected for a phosphoribosyl transferase . Figure 8A shows the structural relationships between the query and the structural neighbors according to TM , which are used to make GOdot predictions . The structural neighbors of the query are all glycosyltransferases , with structural subgroupings corresponding to distinct substrates . In Figure 8B , the structure of the query is compared to the nearest neighbor ( a xanthine phosphoribosyltransferase ) . Both , the fold and the phosphoribosyl pyrophosphate-binding motif are conserved in the two proteins indicating that they share a phosphoribosyltransferase function . The differences in peripheral secondary structure elements indicate that they might have different substrates . In summary , the manual inspection revealed 13 problematic cases ( out of 49 proteins ) for which a function was predicted falsely due to an invalid transfer of function from a multidomain protein . Four proteins could be neither confirmed nor refuted . For 32 proteins the GOdot predictions were manually confirmed with various other sequence or structure-based methods . See Table S1 for further details . We propose the GOdot method for predicting molecular function of proteins . The method uses functionally conserved regions as a new concept . These functional conservations are determined by statistical learning on a representative set of protein domains . Protein sequence and structure information of an unannotated protein are used as input to GOdot , which then predicts a list of GO terms . Each of the predicted GO terms has a reliability estimate attached which is computed based on the previously determined functionally conserved regions . Both , the assessment using cross-validation on a representative dataset and the comparison with PHUNCTIONER [17] demonstrate that the analysis of functionally conserved regions is a powerful tool for function prediction: reference function predictors are considerably outperformed by the GOdot method . A high function conservation score is shown to indicate a high likelihood that a predicted GO term is correct . Consequently , function conservation scores can be used as reliability estimates within the prediction procedure . To our knowledge , the GOdot method is the first approach that directly addresses the problem of estimating varying local function conservation in protein space with respect to different measures for protein similarity . For each similarity measure , each GO term and each protein domain in the representative training set , function conservation is captured with a logistic curve . The result is a large number of mutually intertwined and overlapping logistic curves . The set of logistic curves offers a new view on the relation between sequence and structure on the one hand and function on the other hand . We regard the analysis of functionally conserved regions as an important contribution to current function prediction efforts , and we expect forthcoming developments in this field to uncover more detailed insights into the sequence-structure-function space . Local function conservation within protein space can be determined with respect to other protein similarity measures , such as shape or surface properties of protein binding sites , for example . The GOdot method can be easily extended to include other quantitative measures of protein similarity . For any new similarity measure one would simply perform an all-against-all comparison on the training set of proteins . Local function conservation can then be determined for that similarity measure . We are working on extending the GOdot method with new similarity measures to further improve its performance . The GOdot method is available online as a web-server ( http://godot . bioinf . mpi-inf . mpg . de ) , to which one can submit uncharacterized PDB structures . The method performs sequence and structure comparisons of the query protein to each entry from the representative set of protein domains . GO terms are predicted and function conservation scores are computed as reliability estimates . A ranked list of predicted GO terms is the output of the web-server .
The analysis is based on a representative set of protein sequences and structures annotated with function data . We downloaded a set of 7290 protein domains with no more than 40% sequence identity from the ASTRAL compendium ( version SCOP 1 . 69 ) [42] . These protein domains were assigned to the respective PDB structures . The PDB structures were mapped to UniProt sequences using the PDBSWS [43] . UniProt sequences were annotated with GO terms using the Gene Ontology Annotation ( GOA ) UniProt Gene association file ( version 36 . 0 ) [33] , [34] . We removed all domains having no GO annotation or being part of multidomain proteins according to SCOP . This representative set comprises 3449 protein domains annotated with 1806 distinct GO terms . Similarity between proteins is measured using different distance measures . We refer to observing a specific protein function consistently within a neighborhood of proteins in protein space as function conservation . We used different measures of similarity between proteins and describe a mathematical model for capturing function conservation . This model can be computed in a pre-processing step and later be used to predict protein function . The GOdot method takes a query protein as input and predicts scores for a number of GO terms . For a query , we first predict scores representing the degree of GO function conservation . These scores are based on the local function conservation of the terms annotated to the query's nearest neighbors . The predicted scores are combined to account for multiple occurrences of related GO terms . Finally , ranking the GO terms according to the combined scores , the method produces a sorted list of GO terms . Using logistic curves to estimate local function conservation . A typical function prediction commences with a query protein q of unknown function . We identify q's nearest neighbor with respect to the similarity measures , for example with simCE as mentioned above . Let x = simCE ( q , r ) be the similarity between q and the nearest neighbor r . The logistic curve previously computed for the neighbor r and one GO term f is used to estimate the likelihood of the GO term f occurring at similarity x to r . For a given similarity x and one GO term f , the raw function conservation score is defined aswhere β0 and β1 are the parameters representing the logistic curve for the particular GO term f attached to the particular nearest neighbor r . Thus , ψf can be interpreted as estimated probability of q having the same GO term f , given a similarity x to the neighbor r . For the other similarity measures simTM , simGP , simLP , the raw function scores are defined accordingly . Combining raw function conservation scores along the GO graph structure . For a query protein q , the different similarity measures point to potentially different nearest neighbors . These nearest neighbors are annotated with one or several GO terms . For each of these GO terms the raw function score provides an estimate of the likelihood that the transfer to the query is valid , at the given similarity . Thus , for a specific GO term f , we have four raw function scores attached to a protein , which we refer to as support . As the GO terms are interconnected via the GO hierarchy , the support partially relates to each other and needs to be combined . We merge several raw function conservation scores into one combined function conservation score per GO term . To this end , we propose the following score combination schemes which are applied to each GO term and incorporate the raw conservation scores of descendant GO terms . These combination schemes also ensure that GO terms obtain scores that are at least as high as those of their descendants . The selective score combination scheme computes the combined function conservation of a GO term f as the maximum raw function conservation score within the support of all its descendants f′ as follows:This selective score combination scheme is illustrated in Figure 9A . The consensus score combination scheme computes the combined function conservation . As mentioned before , the function conservation scores can be interpreted as probabilities . The probability of a GO term being correct is computed from the probabilities of the descendant GO terms being correct . The probability of a term being correct is one minus the probability that all descendant terms are incorrect . Assuming independence , the probability for all descendant terms being incorrect is the product of their individual probabilities for being incorrect . Consequently , we define the combined consensus function conservation score asThe combined consensus score depends on the number of descendants and the support observed for the descendants . High combined scores are caused by many descendants with high raw scores . The consensus score combination scheme is illustrated in Figure 9B . Each of the combination schemes above produces one combined score per GO term . These combined scores are estimates of the reliability of the predicted GO terms . The GO terms predicted for one query are ranked with respect to the combined scores yielding a sorted list . We refer to a combination scheme producing such a list as predictor . The assessment of the predictors is described in the next section . We assess the GOdot method's performance by cross-validation . The selective and consensus predictors are compared to a baseline predictor using precision-recall graphs . Cross-validation scheme . We perform a leave-one-out cross-validation . Predictors are trained for each protein ignoring the annotations attached to that protein . In the Text S1 and Figure S1 , we perform an additional significance analysis using ten-fold cross-validation . Predicting functions for a new query protein requires comparing the query to the set of representatives . Comparison of one protein to all 3449 protein domains in the set of representatives takes on average 4 hours for CE , 5 minutes for TM , and 2 minutes for profile alignment on a modern PC . With a compute cluster as back-end to the GOdot web-server , we provide answers typically within 20 to 60 minutes . In the training stage , establishing the protein space requires all-against-all comparisons , which is quite expensive ( 300 CPU days ) . When the distances are available , computing the logistic curves for 28774 annotations ( of all-level GO terms to 3449 proteins ) takes 9 minutes and is negligible in comparison . | Proteins are an essential class of molecules playing a variety of roles within a cell . They can be described in various ways: amongst others , by sequence , structure , and function . Determining protein function by wet lab procedures is challenging and tedious . Simultaneously , sequencing and structural genomics projects turn out ever increasing numbers of protein sequences and structures , which are largely lacking functional characterization . As a consequence , there is a growing demand for computational methods that can assist human experts in the functional annotation of proteins . We present a method for protein function prediction based on a novel concept , called local function conservation . Local function conservation in sequence and structure is determined by rigorously analyzing the variability of protein function with respect to sequence and structure similarity . Our method predicts protein function even if the protein to be functionally annotated has only distant relatives . Furthermore , we estimate the reliability of the function prediction . With this approach , we advance automated function prediction and contribute to a better understanding of the complex relations between protein sequence , structure , and function . | [
"Abstract",
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"Results/Discussion",
"Methods"
] | [
"computational",
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"biology/systems",
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] | 2008 | Local Function Conservation in Sequence and Structure Space |
Human Cytomegalovirus ( HCMV ) infection induces several metabolic activities that have been found to be important for viral replication . The cellular AMP-activated protein kinase ( AMPK ) is a metabolic stress response kinase that regulates both energy-producing catabolic processes and energy-consuming anabolic processes . Here we explore the role AMPK plays in generating an environment conducive to HCMV replication . We find that HCMV infection induces AMPK activity , resulting in the phosphorylation and increased abundance of several targets downstream of activated AMPK . Pharmacological and RNA-based inhibition of AMPK blocked the glycolytic activation induced by HCMV-infection , but had little impact on the glycolytic pathway of uninfected cells . Furthermore , inhibition of AMPK severely attenuated HCMV replication suggesting that AMPK is an important cellular factor for HCMV replication . Inhibition of AMPK attenuated early and late gene expression as well as viral DNA synthesis , but had no detectable impact on immediate-early gene expression , suggesting that AMPK activity is important at the immediate early to early transition of viral gene expression . Lastly , we find that inhibition of the Ca2+-calmodulin-dependent kinase kinase ( CaMKK ) , a kinase known to activate AMPK , blocks HCMV-mediated AMPK activation . The combined data suggest a model in which HCMV activates AMPK through CaMKK , and depends on their activation for high titer replication , likely through induction of a metabolic environment conducive to viral replication .
Upon infection , viruses must create a cellular environment conducive to viral replication . While there are many different aspects of this virally-induced environment , a critical component of this cellular reprogramming is the diversion of cellular resources such as energy and molecular building blocks to the production of viral progeny . Numerous viruses , ranging from small , non-enveloped RNA viruses to large enveloped DNA viruses have been reported to target the host-cell metabolic machinery [1]–[9] . This suggests that cellular metabolic function is a key virus-host interaction . Human cytomegalovirus ( HCMV ) , a member of the betaherpesvirus family , is a major cause of birth defects upon congenital infection as well as morbidity in immunosuppressed populations [10]–[12] . HCMV has been found to cause drastic changes to the host cell metabolic network upon infection , increasing the concentrations of select glycolytic enzymes , the steady state levels of glycolytic metabolites , and the fluxes through glycolysis and the TCA cycle [13]–[14] . Though the exact mechanisms through which HCMV induces glycolytic activation are not clear , it has recently been shown that virally-mediated activation of glycolysis can be blocked through inhibition of CaMKK and that HCMV infection induces the expression of the Glut4 transporter [15]–[16] . Here we explore the role of the AMP-activated protein kinase ( AMPK ) during HCMV replication and HCMV-mediated glycolytic activation . AMPK is a heterotrimeric , serine-threonine kinase that functions as a major energy regulator for the cell . Low ATP levels result in increased concentrations of AMP through the action of adenylate kinase [17] . These increased AMP concentrations induce AMP binding to AMPK and subsequent stimulation of AMPK activity , primarily through either LKB1 or CaMKK-dependent phosphorylation [18]–[21] . Upon activation , AMPK works to restore the ATP pool by activating ATP-producing pathways while simultaneously inhibiting ATP-consuming pathways [22]–[23] . One pathway positively regulated by AMPK is glycolysis . Upon AMPK activation , glycolysis can be upregulated through several mechanisms . AMPK targets numerous key glycolytic enzymes including glucose transporters ( Glut1 and Glut4 ) , hexokinase and PFK-2 , to increase glycolytic flux [23]–[26] . Here we show that AMPK activity is increased throughout viral infection relative to mock-infected fibroblasts . Additionally , high-titer HCMV replication requires activated AMPK as pharmaceutical or RNAi-based inhibition of AMPK severely attenuates the production of viral progeny . Consistent with a role in glycolytic activation , AMPK inhibition also leads to an attenuation of HCMV-induced glycolytic flux . These results suggest that AMPK is a critical cellular protein targeted by HCMV infection .
To determine if AMPK might be responsible for the metabolic induction observed during HCMV infection , we analyzed mock or HCMV-infected extracts for AMPK activity using a well described in vitro AMPK activity assay [27] . To help verify the specificity of the measured AMPK activity , we also performed assays in the presence of Compound C , a specific AMPK inhibitor [28] . At 24 h post-infection both mock and HCMV-infected cells exhibited similar amounts of AMPK activity ( Fig . 1 ) . Compound C treatment suppressed the observed AMPK activity to a background level of ∼2000 CPM , which was a consistent background level at all time points examined ( Fig . 1 ) . At later time points , i . e . 48 and 72 h post-infection , the AMPK activity associated with mock-infected fibroblasts fell significantly to background levels ( Fig . 1 ) . In contrast , the AMPK activity associated with HCMV-infected lysates increased over this time frame with much greater AMPK activity levels observed in HCMV-infected lysates than in mock-infected lysates at the same time points ( Fig . 1 ) . These results indicate that HCMV induces AMPK activity upon infection . Previously we have found that HCMV infection increases the total levels of the fatty acid biosynthetic enzyme , acetyl-CoA carboxylase ( ACC1 ) as well as the amount of phosphorylated ACC1 [29] . AMPK has been shown to phosphorylate ACC1 at Ser79 , resulting in decreased ACC1 activity with the end-result being inhibition of fatty acid biosynthesis and conservation of ATP [17] . To determine if activated AMPK increases levels of phosphorylated ACC1 in MRC-5 fibroblasts , we treated cells with an AMPK activator , AICAR [30] . AICAR treatment resulted in substantial increases in the abundance of phosphorylated ACC ( Fig . 2A ) , consistent with activation of AMPK . Subsequently , we analyzed the levels of total ACC1 and Ser79 phosphorylated ACC1 in the presence of the AMPK inhibitor , Compound C , during HCMV infection . At 24 h post-infection , HCMV-infected lysates contained more Ser79-phosphorylated ACC1 than mock-infected lysates ( Fig . 2B ) . At this time , treatment with Compound C reduced the levels of Ser79-phosphorylated ACC1 to the levels found in uninfected cells , indicating that AMPK might be responsible for the increased abundance of phosphorylated ACC1 ( Fig . 2B ) . At 48 and 72 h post-infection , treatment with Compound C reduced the levels of Ser79-phosphorylated ACC1 in HCMV infected lysates but also decreased the levels of total ACC1 ( Fig . 2B ) . Using densitometry , the relative signal ratios of pACC to ACC in the HCMV infected lysates were examined ( Fig . 2B ) . Treatment with Compound C had the largest impact on the pACC/ACC ratio at 24 hpi , reducing it ∼10-fold . At subsequent times post infection , Compound C reduced the pACC/ACC ratio at every time point , consistent with decreased phosphorylation , albeit to a much lesser extent than observed at 24 hpi ( Fig . 2B ) . While the decreases in phospho-ACC1 relative to total ACC1 upon Compound C treatment are consistent with inhibition of AMPK-mediated phosphorylation of ACC1 , Compound C treatment also impaired the HCMV-induced accumulation of total ACC1 suggesting that Compound C treatment could be impacting normal HCMV infection . Previous reports indicate that HCMV infection induces the levels of the Glut4 glucose transporter and the tuberous sclerosis protein ( TSC1 ) , a negative regulator of mTOR signaling [15] , [31] . Activated AMPK has been shown to increase Glut4 expression [32] as well as TSC1 levels through prevention of proteosome mediated TSC1 degradation [33]–[34] . Our results indicating that HCMV infection induces AMPK activity suggest the possibility that the induction of these proteins during HCMV infection may result from increased AMPK activity . As previously reported [31] , in the absence of inhibitor treatment , HCMV infection has little impact on TSC1 levels at 24 h post-infection but significantly increased the levels of TSC1 at 48 h post-infection and 72 h post-infection ( Fig . 2C ) . Treatment of HCMV-infected cells with Compound C substantially reduced the levels of TSC1 at all time points compared to DMSO treated controls ( Fig . 2C ) suggesting that AMPK activity is necessary for HCMV-mediated induction of TSC1 levels . As was reported previously [15] , we also observed increases in Glut4 in HCMV-infected cells as compared to mock-infected cells at 48 and 72 h post-infection ( Fig . 2C ) . Treatment with Compound C inhibited this induction of Glut4 levels ( Fig . 2C ) suggesting that AMPK activity is important for the viral induction of Glut4 expression . Taken together , our results indicate that HCMV-infection activates AMPK which in turn is necessary for the induction of TSC1 and Glut4 levels . AMPK can be activated by phosphorylation at residue Thr172 mediated by either LKB1 or CaMKK [17] . As shown in Figure 2D , HCMV-infected extracts contained a higher level of total and phosphorylated AMPK then mock extracts at 24 , 48 and 72 h post-infection . In both mock and HCMV-infected cells , the amount of Thr172-phosphorylated AMPK appeared to be the greatest at 24 h post-infection and subsequently declined as infection progressed ( Fig . 2D ) . For uninfected cells , this decline in Thr172-phosphorylated AMPK correlated with the decreased AMPK activity observed in cellular lysates ( Fig . 2D ) . For the HCMV-infected cells , the increase in AMPK activity observed as infection progressed did not correlate with the levels of Thr172-phosphorylated AMPK although the levels of total AMPK remained elevated . This combination of increased abundance of total AMPK and increased AMPK Thr172 phosphorylation likely contribute to the observed increases in AMPK activity during HCMV infection , although as AMPK is reported to be regulated by multiple phosphorylation events [35]–[37] , other mechanisms of activation cannot be ruled out . Treatment of cells with Compound C did not appreciably impact AMPK Thr172 phosphorylation ( Fig . 2D ) , not surprising given that Compound C inhibits AMPK through competitive inhibition at its ATP binding site [28] . Taken together , our results suggest that HCMV activates AMPK during infection likely in part due to an increase in both the phosphorylation at Thr172 as well as the total abundance of AMPK . AMPK is a central metabolic regulator whose activation can activate glycolysis by targeting multiple steps within the glycolytic pathway including glucose uptake and phosphofructokinase activity [17] . To determine if AMPK is important for the induction of glucose import by HCMV , we treated mock or HCMV-infected fibroblasts with Compound C and analyzed glucose import using a radioactive glucose analog . Consistent with previous reports [15] , [26] , HCMV infection induced glucose uptake greater than 5-fold compared to mock-infected cells ( Fig . 3A ) . Treatment with Compound C almost completely reversed this increase ( Fig . 3A ) . Compound C had a negligible impact on glucose uptake in mock-infected fibroblasts ( Fig . 3A ) . These data indicate that HCMV relies heavily on AMPK activity to activate glucose import whereas AMPK is not critical for glucose uptake in uninfected fibroblasts . To further analyze how activation of AMPK contributes to HCMV-mediated glycolytic activation , we measured the rate of glycolytic labeling after treatment with Compound C using 13C-labeled glucose as a metabolic tracer . Specifically , utilizing LC-MS/MS we measured the rate of 13 C-fructose bisphosphate accumulation , a central glycolytic metabolite , after pulse with 13C-glucose . Treatment with Compound C led to an approximate 2-fold decrease in 13C-labeled FBP accumulation in HCMV-infected fibroblasts ( Fig . 3B ) . In contrast , inhibition of AMPK had little impact on the labeling rate of mock-infected fibroblasts ( Fig . 3B ) . Lastly , we measured how inhibition of AMPK impacted the most downstream glycolytic phenotype , accumulation of lactate in the media . Treatment with Compound C substantially reduced lactate secretion in HCMV-infected fibroblasts , but not mock-infected fibroblasts ( Fig . 3C ) . In all of the glycolysis assays tested , the inhibition of glycolytic flux upon Compound C treatment was specific for HCMV-infected cells . This suggests that AMPK is important for HCMV-induced glycolytic activation , but does not contribute appreciably to the glycolytic rate in uninfected fibroblasts , which is consistent with AMPK's described role as a stress-induced metabolic regulator [17] . Addition of Compound C immediately following adsorption blocked HCMV-mediated activation of glycolysis ( Fig . 3A–C ) . To determine whether this glycolytic activation was sensitive to AMPK inhibition after the establishment of infection , we treated cells with Compound C at 24 hpi , a time at which immediate early gene expression is peaking , early genes are being expressed and viral DNA replication is initiating [38] . Subsequently , we measured lactate excretion into the media from 40-58 hpi , and from 58–76 hpi . As compared to a DMSO-treated control , treatment with Compound C immediately following adsorption resulted in a ∼40% reduction in lactate excretion ( Fig . 3D ) . Treatment of cells at 24 hpi resulted in a ∼20% reduction in lactate excretion whether measured from 40–58 or 58–76 hpi ( Fig . 3D ) . These results suggest that while HCMV-mediated activation of glycolysis is more sensitive to AMPK inhibition at the very beginning of infection , the induction of glycolysis is still attenuated when AMPK is inhibited during an HCMV infection that has already been established . Given that HCMV-infection induces AMPK activity ( Fig . 1 ) , and that AMPK activity is important for HCMV-mediated glycolytic activation ( Fig . 3 ) , we next tested if AMPK inhibition impacts viral replication . We treated fibroblasts with DMSO or two different concentrations of Compound C , and analyzed the production of viral progeny by plaque assay . Treatment with increasing concentrations of Compound C resulted in a dose-dependent decrease in viral titers . A greater than 20-fold defect in production of viral progeny was observed at 2 . 5 µM Compound C and a greater than 1000-fold defect was observed in cells treated with 5 µM Compound C ( Fig . 4A ) . To exclude the possibility of toxicity from drug treatment , we also performed a Live/Dead assay which stains live cells green based on their esterase activity and stains the nucleic acids of dead cells red based on the breakdown of membrane integrity . Treatment with Compound C at the highest concentration ( 5 µM , Fig . 4B ) resulted in little to no red staining in both mock- and HCMV-infected fibroblasts ( <1% ) with ubiquitous green staining ( >99% ) suggesting that Compound C treatment is not toxic to MRC-5 fibroblasts up to a concentration of 5 µM . These results suggest that AMPK activity is important for HCMV replication and that inhibition of AMPK does not induce significant toxicity in MRC-5 fibroblasts . We were interested in determining at what point during the infectious cycle AMPK activity might be required for high-titer HCMV replication . To address this issue , we treated cells with Compound C at various points post-infection and analyzed viral replication . Addition of Compound C at 24 and 48 hrs resulted in a greater than 10 and 5-fold reduction in the production of viral progeny , respectively , compared to DMSO treated ( Fig . 4C ) . These are significant reductions in viral yield and suggest that AMPK is important throughout the duration of HCMV infection for peak viral production . However , the difference in viral growth between treatment at adsorption and treatment at 24 h is large ( ∼100-fold , Fig . 4C ) , suggesting that the major requirement for AMPK activity occurs during the first 24 h of infection . Addition of Compound C at 72 hrs post-infection had a negligible impact on HCMV viral growth ( Fig . 4C ) . This suggests that AMPK activity is not necessary during the late stages of growth . Furthermore , this lack of inhibition when added at 72 hpi suggests that Compound C is not blocking infectious HCMV production through some artifactual interaction with newly produced HCMV virions . With the observation that Compound C treatment decreases HCMV viral titers , we were interested in investigating how this inhibition impacted other aspects of the viral life cycle . We performed Western blot analysis and quantitative real-time PCR ( qPCR ) to determine the effects of Compound C on viral protein accumulation and viral DNA replication , respectively . The expression of the immediate early protein , IE1 , was largely unaffected by Compound C treatment ( Fig . 5A ) , however a large decrease in the abundance of the early protein UL44 and the late protein pp28 was observed ( Fig . 5A ) . These results suggest that AMPK is required for the transition from the immediate early to the early stages of infection . As many early genes are involved in the regulation of viral DNA replication , we hypothesized that this defect in early gene expression could result in decreased viral DNA replication . As shown in Figure 5B , when virally infected fibroblasts are treated with Compound C , a marked decrease in viral DNA accumulation is observed , most notably at 48 and 72 h post-infection . These findings suggest that AMPK inhibition affects the viral life cycle at early stages of infection and inhibits viral DNA replication . The finding that pharmaceutical inhibition of AMPK attenuates viral replication and HCMV-induced glycolytic flux suggests that AMPK plays an important role during viral infection . Despite reports that Compound C is a specific inhibitor of AMPK [28] , the possibility for off-target effects is always an issue with pharmaceutical inhibitors . To confirm the importance of AMPK for HCMV replication , we employed an AMPK-specific RNAi to decrease AMPK expression during infection . Transfection of RNAi specific for AMPK resulted in an ∼40% reduction in AMPK abundance in both mock and HCMV-infected fibroblasts at 24 h post-infection in comparison to control RNAi transfected cells ( Fig . 6A ) . Analysis of AMPK activity indicated that transfection of AMPK-specific RNAi prior to HCMV infection reduced AMPK activity by approximately 50% , comparable to mock levels ( Fig . 6B ) . AMPK-specific RNAi had a much smaller impact on the AMPK activity of mock-infected cells ( Fig . 6B ) , consistent with a relative lack of AMPK activity in mock-infected cells to start with . Analysis of media lactate accumulation indicated that AMPK-specific RNAi ablated the HCMV-mediated induction of lactate excretion , but had little impact on the lactate excretion of mock-infected cells ( Fig . 6C ) . Analysis of how RNAi-mediated AMPK inhibition impacted viral replication indicated a greater than 250-fold reduction in viral progeny production as compared to control cells ( Fig . 6D ) . In total , these results confirm our findings that AMPK is a critical cellular factor required both for HCMV-mediated glycolytic induction as well as for high-titer replication . Previously , we have shown that glycolysis is upregulated upon HCMV infection , and that CaMKK appears to be required for both productive viral replication and virally-induced glycolytic flux [16] . CaMKK has been shown to be involved in the regulation of AMPK activity under various conditions [18] , [39] . Given the observation that AMPK appears to be important for both productive viral replication and HCMV-induced glycolytic flux as well , it seemed likely that CaMKK could be responsible for activating AMPK during HCMV infection . In order to determine the importance of CaMKK for AMPK activation , we used the CaMKK-specific inhibitor STO-609 to treat fibroblasts and subsequently analyzed the impact on AMPK activity . As shown in Figure 7A , inhibition of CaMKK blocked the induction of AMPK activity during HCMV infection . Importantly , it has been previously reported that STO-609 treatment at a similar dosage does not impact AMPK activity directly or affect AMPK activation induced by another AMPK-activating kinase , LKB1 [19] , [40] . To further explore the impact of CaMKK inhibition on AMPK activity , we examined the accumulation and phosphorylation of AMPK and its substrates by Western blot after treatment with STO-609 . Pharmaceutical inhibition of CaMKK decreased the levels of phosphorylated AMPK , but also reduced the levels of total AMPK ( Fig . 7B ) . Analysis of the relative ratio of phospho-AMPK to total AMPK indicated STO-609 treatment shifted the ratio towards the unphosphorylated AMPK by 30% at 24 hpi ( Fig . 7B ) . The observed reductions in total as well as pAMPK upon STO-609 treatment likely contribute to the reduction in AMPK activity observed in STO-609-treated cells ( Fig . 7A ) . Analysis of Ser79 phosphorylated-ACC upon STO-609 treatment indicated a similar trend . The amounts of Ser79-phosphorylated ACC and total ACC were both reduced upon STO-609 treatment , with a reduction of 40-50% in the relative pACC/ACC ratio . Similar to the observation with AMPK inhibition , treatment with STO-609 also blocked the increases in TSC1 and Glut4 levels observed during HCMV-infection ( Fig . 7B ) . As we had previously analyzed the impact of STO-609 treatment on HCMV activated 13C-FBP labeling [16] , we wanted to extend these observations with respect to measuring lactate production . Consistent with our previous 13C-FBP labeling results , STO-609 treatment blocked the induction of lactate secretion associated with HCMV infection yet had no effect on the accumulation of lactate production in mock-infected cells ( Fig . 7C ) . Taken together , our results demonstrate that inhibition of CaMKK inhibits HCMV-mediated AMPK activation as well as the accumulation and phosphorylation of downstream AMPK targets which is consistent with a model in which CaMKK mediates AMPK activation during HCMV infection .
We have previously established that HCMV infection induces numerous changes to the host-cell metabolic network [13]–[14] . Induction of glycolysis has also been found to be critical for high-titer HCMV replication [16] , [41] . Here we report that HCMV activates AMPK , a metabolic stress kinase , and that HCMV depends on its activity for high-titer replication . HCMV requires AMPK activation to increase glucose import and drive increased glycolytic flux ( Fig . 8 ) . Inhibition of AMPK attenuated both early and late gene expression and markedly reduced viral DNA replication . These results suggest that AMPK is an important cellular factor for HCMV replication . Unstressed , uninfected cells do not normally utilize AMPK to activate glycolysis [17] . Our results support this view , as the inhibition of AMPK did not impact the import of glucose or the FBP labeling rate in uninfected cells ( Fig . 3 ) . In contrast to uninfected cells , HCMV infection induces the activation of AMPK , which is critical for HCMV-mediated glycolytic activation . Interestingly , activation of AMPK would be predicted to have several consequences that are detrimental to infection including inhibition of protein translation and fatty acid biosynthesis [42] . AMPK-mediated inhibition of translation occurs through induction of the TSC1/2 complex which in turn negatively regulates translation through inhibition of mTOR [33]–[34] . It has recently been shown that the HCMV UL38 protein can bind to the TSC1/2 complex and prevent its inhibitory activity on mTOR and translation initiation [31] , [43] . Taken together , it appears that HCMV infection induces AMPK activation which in turn drives glycolytic activation , yet blocks the anti-viral effects of AMPK activation through the action of specific gene products such as UL38 ( Fig . 8 ) . While the UL38 protein appears sufficient to block the inhibitory effects of AMPK activation on mTOR activity , it is less clear how HCMV infection blocks AMPK's inhibitory effects on fatty acid biosynthesis . We have previously found that HCMV induces fatty acid biosynthesis , and specifically induces the activity of acetyl-CoA carboxylase ( ACC ) , the rate-limiting enzyme of fatty acid biosynthesis [14] , [29] . ACC , and consequently fatty acid biosynthesis , is negatively regulated by activated AMPK [17] . Given that HCMV requires activated fatty acid biosynthesis and ACC activity for viral replication , it is likely that HCMV infection blocks the negative impact of activated AMPK on ACC activity , potentially through the activity of an HCMV viral protein . Our results suggest that inhibition of CaMKK blocks the down-stream effects associated with activated AMPK ( Fig . 7 ) . Previous reports suggest that pharmaceutical inhibition of CaMKK using STO-609 blocks CaMKK-mediated activation of AMPK but does not impact LKB1- mediated AMPK activation or activation of AMPK upon energetic stress , for example , upon treatment with glycolysis inhibitors [19] , [40] . Taken together , these results suggest that HCMV infection requires CaMKK activity to activate AMPK , though the exact mechanism responsible is unclear . Our results suggest that inhibition of CaMKK reduces the amount of Thr172-phosphorylated AMPK , a known CaMKK phosphorylation site , as well as the total amount of AMPK . Both of these effects would be predicted to contribute to a decrease in AMPK activity during HCMV infection . Other AMPK phosphorylation sites have been implicated in the regulation of AMPK activity [35]–[37] , thus CaMKK could potentially be modulating AMPK activity through phosphorylation of sites other than Thr172 as well . We have previously reported that inhibition of CaMKK blocks high-titer virus production [16] . Our current findings that AMPK inhibition blocks HCMV replication to a similar extent as CaMKK inhibition is consistent with a model in which HCMV-mediates AMPK activation through CaMKK ( Fig . 8 ) . How HCMV infection induces CaMKK activity still remains to be determined , although it has previously been reported that HCMV infection induces Ca2+ release from ER stores and we have found that HCMV infection induces CaMKK expression [16] , [44] , both of which would be predicted to increase CaMKK activity . Glycolysis has been shown to be important for HCMV replication , and glycolytic inhibition has a similar impact on HCMV as inhibition of AMPK and CaMKK [16] , [41] . The similarity is both quantitative , in terms of the magnitude of reduction in viral titers , as well as qualitative , in blocking viral DNA replication and late gene expression , attenuating early gene expression and having no detectable impact on immediate early gene expression [16] , [41] . Given these similarities , and combined with the observed necessity of AMPK and CaMKK for HCMV-induced glycolysis , the simplest model would be that CaMKK and AMPK activation are important for HCMV replication due to their activation of glycolysis . Despite these correlations , it remains to be determined how much of HCMV's reliance on CaMKK and AMPK activity is due to their activation of glycolysis . The possibility that these kinases contribute to viral infection through phosphorylation of other cellular or viral targets cannot be ruled out . In summary , we find that HCMV infection activates AMPK , which is required for HCMV-mediated glycolytic activation and high-titer HCMV replication . As AMPK activation signals metabolic stress to normal cells , HCMV has evolved mechanisms to block the anti-viral consequences of metabolic stress pathway activation . While some of these mechanisms are known , such as UL38 maintaining mTOR activation through interaction and inhibition of the TSC complex , others remain to be elucidated , such as maintenance of fatty acid biosynthesis . This stress response balancing act is representative of a recurrent theme in virus-host evolution . Replicating viruses must create a cellular environment conducive to viral replication , the efforts of which host cells have evolved to resist . In the case of AMPK , the evolutionary struggle is for the keys to the host-cell metabolic machinery . As the AMPK pathway is not normally activated in uninfected cells and inhibition of AMPK activity is tolerated in animal models [45]–[46] , targeting this pathway clinically could be therapeutically beneficial for preventing HCMV-associated disease .
MRC-5 fibroblasts were cultured in Dulbecco modified Eagle medium ( DMEM; Invitrogen ) supplemented with 7 . 5% fetal bovine serum . Cells were grown to confluence in either 10 cm or 6-well tissue culture plates . Once confluent , medium was removed and serum-free DMEM was added . Cells were maintained in serum-free medium for 24 h prior to infection . HCMV ( strain Ad169 ) was used to infect cells at a multiplicity of infection ( MOI ) of 3 for all of the current experiments . Mock-infected controls were treated with an equal volume of medium containing the same serum concentrations as virus-treated cells . Virus adsorptions were carried out for 90 min at 37°C , after which viral innocula were aspirated and serum-free DMEM was added back . Production of infectious virus was measured by standard viral plaque assay . STO-609 ( EMD Biosciences ) , a specific inhibitor of CaMKK and Compound C ( Calbiochem ) , a specific inhibitor of AMPK , were maintained in DMSO at concentrations of 5 mg/ml at −20°C and 10 mg/ml at 4°C , respectively . Labeled DMEM was prepared from glucose-free media by adding 10 mM HEPES and either labeled ( 13C ) or unlabelled ( 12C ) glucose to a final concentration of 4 . 5 gL-1 . For flux analysis , samples were switched to fresh , unlabelled medium 24 h and 1 h before final addition of 13C-labeled medium . Samples were labeled for 1 min and the reaction was quenched by the addition of 4 ml −80°C 80% methanol and incubation at −80°C for 10 minutes . Cells were then scraped in the methanol , centrifuged at 3000 rpm for 5 min at 4°C and the supernatant was collected . The pellet was extracted twice more in 500 µl cold methanol , adding the resulting supernatants to the previously collected supernatant . After extraction , the supernatants were dried down under nitrogen gas and resuspended in 175 µl of 50% methanol . Samples were subsequently spun down at full speed for 5 min at 4°C and the remaining supernatant was transferred to HPLC sample vials . The accumulation of fully-13C-labeled fructose 1 , 6-bisphosphate was monitored using liquid chromatography-tandem mass spectrometry ( LC-MS/MS ) as previously described [14] and is briefly discussed below . LC-MS/MS was performed using a LC-20AD HPLC system ( Shimadzu ) and a Synergi Hydro-RP column ( 150×2 mm with a 5 µm-particle size; Phenomenex ) coupled to a mass spectrometer . The LC parameters were as follows: autosampler temperature , 4°C; injection volume , 20 µl; column temperature , 40°C; flow rate , 15 µl/sec . The LC solvents were solvent A , 100% methanol; solvent B , 10 mM tributylamine and 15 mM acetic acid in 97:3 water:methanol . The gradient conditions were as follows: negative mode—t = 0 , 100% B; t = 5 , 100% B; t = 10 , 80% B; t = 20 , 80% B; t = 35 , 35% B; t = 38 , 5% B; t = 42 , 5% B; t = 43 , 100% B; t = 50 , 100% B . Mass spectrometric analyses were performed on a TSQ Quantum Ultra triple-quadrupole mass spectrometer running in multiple reaction monitoring mode ( MRM ) ( Thermo Fisher Scientific ) . Peak heights for fructose-1 , 6-bisphosphate-extracted ion chromatograms were analyzed using Excalibur software ( Thermo Fisher Scientific ) . Proteins from cell lysates were solubilized in 1X disruption buffer ( 50 mM Tris ( pH 7 . 0 ) , 2% SDS , 5% 2-mercaptoethanol , and 2 . 75% sucrose ) , separated by 10% SDS-PAGE and transferred to nitrocellulose in Tris-glycine transfer buffer . Blots were stained with Ponceau S to visualize protein and ensure equal sample loading . The membranes were blocked in 5% milk in TBST followed by incubation in primary antibody . After subsequent washes , blots were incubated in secondary antibody and protein bands were visualized using the ECL detection system ( Pierce ) . Antibodies used were specific for the following viral proteins: IE1 ( Shenk Laboratory , unpublished ) , UL44 ( Virusys ) , and pp28 [47] and the following cellular proteins: tubulin ( Epitomics ) , TSC1 ( Millipore ) , Glut4 ( Abcam ) , ACC and phosphor-Ser79-ACC ( Cell Signaling Technologies ) , AMPK and phospho-Thr172-AMPK ( Cell Signaling Technologies ) . Image densitometry of specific protein bands was performed with ImageJ , developed by Rasband , W . S . at the NIH ( http://imagej . nih . gov/ij/ ) , as per the ImageJ instructions . AMPK was assayed largely as previously described [27] . Briefly , cells were washed 3X with warm Krebs-Hepes buffer ( 20 mM Na Hepes , pH 7 . 4 , 118 mM NaCl , 3 . 5 mM KCl , 1 . 3 mM CaCl2 , 1 . 2 MgSO4 , 10 mM glucose , 1 . 2 mM KH2PO4 , 0 . 1% BSA ) and incubated in Krebs-Hepes buffer containing either DMSO , the AMPK inhibitor , Compound C ( 5 µM ) , for 1 h at 37°C , or the CaMKK inhibitor , STO-609 ( 10 µg/ml ) . Buffer was then aspirated and dishes were placed on ice with immediate addition of 0 . 25 ml ice-cold lysis buffer ( 50 mM Tris/HCl , pH 7 . 4 , 50 mM NaF , 5 mM Na pyrophosphate , 1 mM EDTA , 1 mM EGTA , 250 mM mannitol , 1% Triton X-100 , 1 mM DTT , protease inhibitors ) . Cells were scraped and the resulting lysates transferred to microfuge tubes and incubated on ice for 10 minutes . Lysates were then centrifuged for 5 min at 14000 xg and 4°C in preparation for use . The AMPK assay was composed of a total reaction volume of 25 µl that was incubated for 10 min at 30°C . Each reaction consisted of 2 . 5 µl lysate assay buffer ( 62 . 5 mM Na Hepes , pH 7 . 0 , 62 . 5 mM NaCl , 62 . 5 mM NaF , 6 . 25 mM Na pyrophosphate , 1 . 25 mM EDTA , 1 . 25 mM EGTA , 1 mM DTT , and protease inhibitor cocktail ( Roche ) ) , 2 . 5 µl of 100 µM [γ-32P]-ATP ( 1 µCi/µl ) in 25 mM MgCl2 , 2 . 5 µl of 2 mM AMP in lysate assay buffer , 5 µl of 1 mM SAMS peptide in lysate assay buffer , with either Compound C ( 5 µM final ) or the equivalent volume of DMSO and 12 . 5 µl cell lysate . The reaction mixture was spotted on P81 phosphocellulose paper which was washed with 1% phosphoric acid , water , and acetone . The radioactivity of the phosphorylated SAMS peptide was quantified by scintillation counting . Non-AMPK-mediated phosphorylation of the SAMS peptide was estimated by performing the AMPK activity assay in the presence of saturating amounts of the AMPK inhibitor , Compound C . MRC-5 fibroblasts were transfected with 150 pmol of either esiRNA ( pooled endoribonuclease-prepared siRNA ) specific to AMPK1 ( Sigma-Aldrich ) or a non-targeting siRNA ( Dharmacon ) using Oligofectamine per manufacturer's directions . Forty-eight hours after transfection , siRNA-transfected cells were serum-starved for 24 h and then either mock-infected or infected with HCMV ( MOI = 3 ) . Samples were harvested at 24 h and 72 h post-infection to monitor AMPK protein knockdown by Western blot . Additional samples were harvested 96 h post-infection to monitor viral titers by standard plaque assay . Viral and cellular DNA was harvested at various time points post-infection in lysis buffer ( 100 mM NaCl , 100 mM Tris-HCl , 25 mM EDTA , 0 . 5% SDS , 0 . 1 mg/ml proteinase K and 40 µg/ml RNase A ) , and viral DNA was quantified using the UL26 primer set ( below ) . Quantitative PCR ( qPCR ) was performed using Fast SYBR green master mix , a model 7500 Fast real-time PCR system and Fast 7500 software ( Applied Biosystems ) . For quantifying viral DNA aUL26-HCMV specific primer set was employed: 5_-AACATCGCGTCGGTGATTTCTTGC-3_ ( forward ) and 5_-ACAGCTACTTTGAAGACGTGGAGC-3_ ( reverse ) , GAPDH 5_-CATGTTCGTCATGGGTGTGAACCA-3_ ( forward ) and 5_-ATGGCATGGACTGTGGTCATGAGT-3_ ( reverse ) . Lactate was measured in media samples using the BioProfile 100 Plus/400 ( Nova Biomedical ) , which employs an enzyme dependent amperometric electrode . MRC-5 fibroblasts were cultured in serum free DMEM for 24 h before infection and either mock or HCMV-infected . Lactate excretion into the media was measured over an 18 h interval , starting with a media change . After 18 h , 600 µl of media was removed from each sample dish and analyzed according to the manufacturer's instructions ( Nova Biomedical ) . Further information regarding the genes/ proteins studied in this manuscript can be found at the NCBI Gene Database ( http://www . ncbi . nlm . nih . gov/gene ) . Specific database entries for cellular genes are as follows: AMPK = PRKAB1 , PRKAA1 , PRKAG1; CaMKK = CAMKK1 , CAMKK2; TSC1 = TSC1; TSC2 = TSC2; Glut4 = SLC2A4; ACC1 = ACACA . The viral genes mentioned include the following from HCMV ( also known as Human Herpesvirus 5 ) : UL38 = UL38; IE1 = UL123; UL44 = UL44; pp28 = UL99 . | Human Cytomegalovirus ( HCMV ) is a ubiquitous human pathogen that is a major cause of birth defects . HCMV can also cause severe disease in immunocompromised individuals including transplant recipients , leukemia patients and those infected with HIV . It is clear that upon infection , HCMV takes control of numerous cellular processes that are important for the virus to generate the next round of infectious virions . We have previously found that upon infection , HCMV reprograms the metabolic activity of the host-cell . Here , we find that this metabolic reprogramming largely depends on the viral activation of a cellular protein called the AMP-activated protein kinase ( AMPK ) . AMPK is a central regulator of cellular energy production that is typically only activated when cellular energy stores are very low . Our results indicate that HCMV-mediated activation of AMPK is necessary to flip the metabolic switch thereby driving host-cell metabolic activation and viral replication . As inhibition of AMPK blocked viral replication , and had little impact on uninfected host-cell metabolism , targeting AMPK could have therapeutic potential to treat HCMV-associated disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biochemistry",
"infectious",
"diseases",
"biology",
"microbiology",
"molecular",
"cell",
"biology"
] | 2012 | HCMV Targets the Metabolic Stress Response through Activation of AMPK Whose Activity Is Important for Viral Replication |
EMBRYONIC FLOWER1 ( EMF1 ) is a plant-specific gene crucial to Arabidopsis vegetative development . Loss of function mutants in the EMF1 gene mimic the phenotype caused by mutations in Polycomb Group protein ( PcG ) genes , which encode epigenetic repressors that regulate many aspects of eukaryotic development . In Arabidopsis , Polycomb Repressor Complex 2 ( PRC2 ) , made of PcG proteins , catalyzes trimethylation of lysine 27 on histone H3 ( H3K27me3 ) and PRC1-like proteins catalyze H2AK119 ubiquitination . Despite functional similarity to PcG proteins , EMF1 lacks sequence homology with known PcG proteins; thus , its role in the PcG mechanism is unclear . To study the EMF1 functions and its mechanism of action , we performed genome-wide mapping of EMF1 binding and H3K27me3 modification sites in Arabidopsis seedlings . The EMF1 binding pattern is similar to that of H3K27me3 modification on the chromosomal and genic level . ChIPOTLe peak finding and clustering analyses both show that the highly trimethylated genes also have high enrichment levels of EMF1 binding , termed EMF1_K27 genes . EMF1 interacts with regulatory genes , which are silenced to allow vegetative growth , and with genes specifying cell fates during growth and differentiation . H3K27me3 marks not only these genes but also some genes that are involved in endosperm development and maternal effects . Transcriptome analysis , coupled with the H3K27me3 pattern , of EMF1_K27 genes in emf1 and PRC2 mutants showed that EMF1 represses gene activities via diverse mechanisms and plays a novel role in the PcG mechanism .
Polycomb group ( PcG ) proteins are epigenetic repressors implicated in various developmental and cellular processes [1] , [2] . PcG proteins function in multi-subunit protein complexes: Polycomb Repressor Complex 1 ( PRC1 ) and PRC2 [3] , the core components of which are conserved from Drosophila to humans . PRC2 marks the target gene by trimethylating histone H3 at lysine 27 ( H3K27me3 ) through the E ( z ) SET domain [4] , [5] , [6] , [7] , [8] . PRC1 , which binds the H3K27me3 methyl marks and docks on nucleosomes modified by PRC2 , inhibits transcription and blocks remodeling of the target nucleosomes , resulting in gene silencing [9] , [10] , [11] . Genome-wide studies confirmed co-localization of PRC1 and PRC2 on target genes . However , there are also genomic sites bound by one , but not the other , PRC [12] and transcriptional networks differentially regulated by PRC1 and PRC2 [13] . PcG action is counteracted by Trithorax Group ( trxG ) protein complexes [14] . Together , PcG and trxG complexes maintain repressive and active states of chromatin , respectively [14] . Protein-protein interaction and gel filtration studies have identified three Arabidopsis PRC2-like complexes [15] , [16] , [17] . Two components , FERTILIZATION INDEPENDENT ENDOSPERM ( FIE ) [18] , and MULTICOPY SUPPRESSOR OF IRA1 ( MSI1 ) [19] , are present in all three putative PRC2s [17] . Small gene families of homologs of Drosophila Su ( z ) 12 , i . e . , EMBRYONIC FLOWER2 ( EMF2 ) [20] , FERTILIZATION INDEPENDENT SEED2 ( FIS2 ) and VERNALIZATION2 ( VRN2 ) [21] , and of E ( z ) , i . e . , MEDEA ( MEA ) [22] , CURLY LEAF ( CLF ) [23] , and SWINGER ( SWN ) [15] , generate variation in Arabidopsis complex composition for targeted PRC2 regulation of multiple pathways . The EMF2/FIS2/VRN2 homologs have diverse , and sometimes redundant , roles [24] , [25] , [26] . The VRN2-containing PRC2 , VRN2-PRC2 , is required for vernalization-induced flowering through the repression of FLOWERING LOCUS C ( FLC ) [21] . Impairments in FIS2-PRC2 function cause endosperm over-proliferation and seed abortion [26] . Impairments in the EMF2-PRC2 do not affect seed development , but the plants have a shortened vegetative phase or skip it altogether [18] , [20] , [23] , [27] . Hence , EMF2-PRC2 is considered responsible for vegetative development . EMF1 , another Arabidopsis gene required for vegetative development , encodes a plant-specific protein containing sequence motifs found in transcriptional regulators [28] . EMF1 mutant plants and plants impaired in components of EMF2-PRC2 have similar phenotypes . Weak emf1 mutants are emf2-like , while strong emf1 mutants have a more severe phenotype than emf2 and the transgenic lines impaired in FIE [18] , [27] , [29] , [30] . Tissue-specific removal of EMF1 activity from leaf primordia allows vegetative growth , but leads to early flowering plants with curly leaves similar to clf mutants [31] . The early flowering phenotype of plants impaired in EMF1 or EMF2-PRC2 components was attributed to the ectopic expression of flower organ identity or flower MADS box genes such as AGAMOUS ( AG ) , APETALA1 ( AP1 ) , AP3 and PISTILATA ( PI ) [32] , . However , these plants have pleiotropic phenotypes and the expression of many genes other than the flower MADS box genes is affected [33] , [34] , [35] . This suggests that EMF1 and EMF2-PRC2 regulate additional developmental processes . EMF1 interacts with AG , PI , and AP3 chromatin and displays characteristics similar to the Drosophila PRC1 component , Posterior sex combs ( Psc ) [36] . It is also required for Arabidopsis RING-finger protein-mediated Histone 2A lysine 119 ( H2AK119 ) ubiquitination [37] . Mammalian PRC1 contains the RING-finger proteins from an E3 ubiquitin ligase complex that monoubiquitinates H2AK119 [38] . Functional characterization of Arabidopsis RING-finger proteins provided biochemical , molecular , and biological evidence that they have a PRC1 role in maintaining differentiated cell fates [37] , [39] , [40] . Another Arabidopsis PRC1-like component , the LIKE HETEROCHROMATIN PROTEIN1 ( LHP1 ) , recognizes H3K27me3 and interacts with many H3K27 trimethylated target genes [41] , [42] . The RING-finger proteins interact with both LHP1 and EMF1; and EMF1 is required for the H2AK119 ubiquitination activity of the RING-finger proteins [37] . However , EMF1 also interacts with the PRC2 component , MSI1 , in vitro [36] as well as with multiple other proteins [43] . The role of EMF1 in the PcG mechanism remains unclear . To better understand the full impact of EMF1 on Arabidopsis growth and development and the mechanisms of EMF1-mediated gene repression , we performed genome-wide mapping of EMF1 binding and analyzed the H3K27me3 and expression patterns of EMF1 target genes in emf1 and PRC2 mutants . Our results demonstrate direct epigenetic regulation of key genes controlling developmental programs and specifying cell differentiation processes via their interaction with EMF1 . Based on the requirement of EMF1 for H3K27me3 and H2AK119 ubiquitination on different target genes , we discuss the roles of EMF1 in the PcG mechanism and propose a novel role for EMF1– acting as a linker between the two PcG complexes for genes that depend on EMF1 for both histone modifications .
We have previously shown that EMF1 regulates the flower MADS box genes AG , AP3 , and PI via direct interaction with their chromatin [34] , [36] . The large number of mis-regulated genes in emf1 mutants [33] , [34] indicates that EMF1 regulates many other genes directly or indirectly . To identify all EMF1 target genes in Arabidopsis seedlings , we performed Chromatin Immunoprecipitation ( ChIP ) followed by microarray analysis ( ChIP-chip ) , using a transgenic Arabidopsis with a functional transgene – EMF1 tagged with 3FLAG and expressed under its own promoter ( EMF1::EMF1-3FLAG ) that can rescue emf1 mutants [36] . A high-resolution genome-wide map of EMF1 binding sites in Arabidopsis seedlings was generated by affinity purifying 3FLAG tagged EMF1-bound chromatin and hybridizing the associated DNA to customized NimbleGen High Density 2 tiling microarrays ( HD2 , 2 . 1M array ) representing the entire Arabidopsis genome of 28 , 244 genes without gaps . Utilizing the ChIPOTLe peak finding algorithm we identified 8 , 541 binding sites ( p<10−6 ) distributed throughout all 5 chromosomes , enriched in the euchromatic regions and underrepresented in the pericentromeric region ( Figure 1A; Figure S1A ) . 6 , 317 of the EMF1 binding sites are located in the transcribed region of the annotated sequences ( −200 bp to the 3′ end ) of 5 , 533 genes . The remaining sites are in intergenic regions ( Figure 1B; Table S1 ) . The 5 , 533 include AG , AP3 and PI ( Figure 1C ) , the known EMF1 target genes that are up-regulated in emf1 mutants , as well as 7 other flower MADS box genes and CRABS CLAW ( CRC ) ( Figure S1B ) . This is consistent with EMF1 repression of the flower organ program in Arabidopsis seedlings . Other EMF1 target genes identified by ChIP-PCR by Kim et al . , [34] , namely , LONG VEGETATIVE1 ( LOV1 ) , FLC , and ABSCISIC ACID INSENSITIVE3 ( ABI3 ) , are EMF1 binding genes in our study . As negative controls , FLOWERING LOCUS T ( FT ) and PHERES1 ( PHE1 ) , which did not interact with EMF1 in ChIP-PCR experiments , are not enriched with EMF1 binding sites ( Figure 1C ) . We confirmed the ChIP-chip results by ChIP-PCR on an additional 9 randomly selected genes with various enrichment level of EMF1 binding ( Figure S2 ) . Thus binding sites identified by ChIP-chip likely represent in vivo EMF1-target genes interaction . Because of the functional similarity between EMF1 and PRC2 , we compared the EMF1 binding pattern and the H3K27me3 modification profile across the whole Arabidopsis seedling genome . To minimize variability due to sample and microarray differences , we mapped EMF1 binding targets , determined the H3K27me3 profile , and measured mRNA levels ( see below ) with the same NimbleGen HD2 arrays . The ChIPOTle peak finding program identified 11 , 067 H3K27me3 enriched peaks ( p<10−35 ) , which correspond to 7 , 751 genes that showed 85% overlap with an earlier study ( Table S2; [42] ) . As reported previously , H3K27me3 peaks tend to be broad , often covering the entire transcriptional unit ( Figure 2A and 2B; Figure S1B ) , hence we used a very strict statistical cutoff for peak identification . Globally , EMF1 binding and H3K27me3 modification profiles are well correlated ( Figure 2A ) . Both are found throughout euchromatin regions and are underrepresented in the centromeres of all 5 chromosomes . At the genic level , the EMF1 binding pattern resembles the H3K27me3 profile , covering the transcription unit with the strongest signal around the transcriptional start site ( TSS , Figure 1C ) . The EMF1 signal gradually declines towards the 3′ end in some genes and does not extend as far into the 3′ non coding region as H3K27me3 modification does , see , for example , SEEDSTICK ( STK ) , ARGONAUTE5 ( AGO5 ) , AP1 , and SEPALATA1 ( SEP1 ) ( Figure 2B; Figure S1B ) . To better understand the relationship between EMF1 and PRC2 , we mapped the H3K27me3 sites in emf1 , emf2 , and fie mutant plants . Because FIE is required during seed development and fie mutants are embryo-lethal , we used a transgenic line that expresses FIE only during the seed development stage to recover homozygous fie seedlings [18] . Relative to two-week old WT , plants impaired in each of these three genes have no petioles and rosette leaves , a short hypocotyl , and oval shaped cotyledons . emf2 and plants impaired in FIE are similar in phenotype . The emf1 allele used in this study , emf1-2 , is a strong allele with a more severe phenotype than emf2 ( [32]; Figure 2C ) . Among the 7 , 751 genes marked by H3K27me3 in WT , 44% show reduced H3K27me3 in emf1 mutants , 54% in emf2 , and 84% in fie ( Figure 2A , 2B , and 2D ) . This 84% H3K27me3 reduction is consistent with an earlier study [30] , in which a 75% loss in a different FIE-impaired transgenic plant was reported . The loss of H3K27me3 in fie mutant seedlings indicates that H3K27me3 requires a functional PRC2 complex . The moderate decline of H3K27me3 in emf2 could be due to partial replacement of EMF2 function by its homolog , VRN2 [15] , [16] . The partial requirement for EMF1 shows that H3K27me3 is less dependent on EMF1 than on PRC2 , indicating a site-specific EMF1-dependent H3K27 trimethylation . Nevertheless , 75% of the genes with reduced H3K27me3 in emf1 have reduced H3K27me3 in emf2 and fie ( Figure 2E ) , indicating that trimethylation on these genes requires coordinated action by EMF1 and PRC2 . Because peak calling necessarily involves arbitrary cutoffs , we supplemented the ChIPOTLe analysis that generated the H3K27me3 peaks by an unsupervised k-means clustering algorithm ( k = 2 , Figure 3A , left panel ) . The 28 , 244 Arabidopsis genes were aligned at the annotated TSS , the average H3K27me3 signal calculated in each 100 bp bin across the 6 kb region surrounding the TSS , and the data sorted into two clusters , high and low H3K27 trimethylation . High enrichment level of H3K27me3 in the transcribed , relative to the 5′ untranscribed , region is clearly seen in the highly trimethylated gene cluster ( Figure 3A , left panel ) . We then arranged EMF1 binding strength to match the H3K27me3 sorting order ( Figure 3A , right panel ) , and found that genes in the cluster of high H3K27me3 exhibit high enrichment level of EMF1 binding , while the cluster with low H3K27me3 genes show low enrichment level of EMF1 binding . We then arranged H3K27me3 levels in the three mutants according to the high and low H3K27me3 clusters ( Figure 3B ) . The H3K27me3 level is most drastically reduced in fie , less in emf2 and emf1 , consistent with the ChIPOTLe analysis shown in Figure 2B . Since the high H3K27me3 cluster of genes shows the most distinct pattern ( Figure 3A and 3B ) , we plotting the average H3K27me3 signal and the EMF1 binding pattern of this cluster of genes across the 6 kb region surrounding the TSS in WT and in the 3 mutants ( Figure 3C ) . The promoter regions of this highly trimethylated cluster of genes show minimal H3K27me3 modification , while it is highly enriched in the transcribed region . H3K27me3 enrichment is highest around the TSS , then declines slightly but is maintained throughout the 3 kb of the transcribed region . As expected , H3K27me3 enrichment is reduced in all three mutants , nearly absent in fie and partially lost in emf2 and emf1 . Despite the reduction in the mutants , the H3K27me3 pattern across the gene remains remarkably similar to WT . The EMF1 binding pattern of these highly methylated genes is similar to their H3K27me3 modification pattern in that EMF1 binds primarily the chromatin of the transcribed , rather than the promoter , region . However , EMF1 binding in this cluster of highly methylated genes shows a precipitous drop from the peak of binding at the TSS in the 3′ direction: the major binding is within 1 kb of the TSS ( Figure 3C ) . Results from the k-means clustering algorithm and the ChIPOTle method are consistent . We then used a Perl implementation of the ChIPOTle method to identify the EMF1-bound genes that are trimethylated and found 58% ( 3230 ) of the 5 , 533 EMF1-bound genes exhibit H3K27me3 peaks , called EMF1_K27 genes ( p = 6×10−184; Fisher's exact test , see gene list in Table S2 ) . Our subsequent analysis focused on the EMF1_K27 genes , highly trimethylated on H3K27 and enriched for EMF1 binding . Gene ontology ( GO ) analysis of the 3230 EMF1_K27 genes revealed that EMF1 and H3K27me3 co-localize at a remarkably high number of genes involved in transcription factor activity , developmental pathways , and microRNA ( miRNA ) gene silencing ( Table 1; Table S3 ) . Relative to the whole genome , there is a 2 . 5–5 fold enrichment in the genes belonging to the categories of transcription factor activity , miRNA regulation , and genes involved in leaf , vascular , root , meristem , and flower development . EMF1 binds preferentially ( p<0 . 05 ) genes involved in biotic and abiotic stresses and in the biosynthesis of , and response to , the major plant hormones: abscisic acid ( ABA ) , auxin , brassinosteroids ( BR ) , cytokinins ( CK ) , ethylene , gibberellic acid ( GA ) , jasmonic acid ( JA ) and salicylic acid ( SA ) , and genes involved in biotic and abiotic stresses . We next examined the annotated genes with known developmental functions ( Figure 4; Table S4 ) , beginning with the genes required for flower and seed development that are up-regulated in emf1 mutants [33] , [34] . We found that EMF1 binds a subset of these H3K27me3 modified genes ( Table S4 ) . For example , EMF1 binds 3 of the 4 major seed regulated genes marked by H3K27me3 , namely , FUSCA3 ( FUS3 ) , ABA INSENSITIVE3 ( ABI3 ) and 2 LEAFY COTYLEDON2 ( LEC2 ) [44] , as well as , a fraction of the downstream seed maturation genes that are trimethylated , e . g . , the LATE EMBRYO ABUNDANT ( LEA ) , OLEOSIN ( OLEO ) , and LIPID TRANSFER PROTEIN ( LTP ) , and seed storage protein genes ( Table S4 ) . It is worth noting that some genes in the same families are bound by EMF1 but are not marked with H3K27me3 ( Table S4 ) . EMF1 silences the flower developmental program by interacting with and repressing all known flower organ identity genes and other genes specifying flower organ development , e . g . , CRC , SUPERMAN ( SUP ) , and PETAL LOSS ( PTL , [45] , [46]; Figure 4 ) . Flower organ identity genes are all type II MADS box genes [47] . We found that EMF1 preferentially interacts with type II MADS box genes . EMF1 does not interact with the Type I MADS box genes that are important for female gametophyte and early seed development , e . g . , PHE1 ( AGL37 ) , PHE2 ( AGL38 ) , AGL23 , and AGL61 [48] , [49] , although they are H3K27 trimethylated in Arabidopsis seedlings ( Table 2; Table S4 ) . Vegetative development requires not only the repression of the seed and flower programs but also dynamic activation and repression of genes to specify cell fates in the meristems and to dictate organized cell growth and differentiation . Our study of seedling chromatin showed that EMF1 binds H3K27me3 marked genes that specify cell fates in shoot and root apices and control leaf polarity , e . g . , SHOOT MERISTEMLESS ( STM ) , CLAVATA3 ( CLV3 ) , and WUSHEL ( WUS ) ( Figure 4 ) . Shoot meristem and leaf primordia in the shoot apex are separated by the expression of the boundary-specific genes encoding the NAC domain transcription factors , NO APICAL MERISTEM ( NAM ) and CUP SHAPED COTYLEDONE ( CUC ) [50] , [51] , [52] , which are negatively regulated by the TEOSINTE BRANCHED1 , CYCLOIDEA , and PCF ( TCP ) genes . NAM , CUC2 , and CUC3 are all trimethylated and bound by EMF1 ( Figure 4 ) . EMF1 interacts with 9 of the 10 H3K27 trimethylated TCP genes . TCP14 affects internode length and leaf shape [53] . EMF1 interaction with TCP genes that affect diverse aspects of Arabidopsis shoot growth and architecture is consistent with the pleiotropic effect of EMF1 impairment on Arabidopsis shoot development that includes petiole-less cotyledons , short hypocotyl and short inflorescence stem , due to limited cell elongation in emf1 mutants [31] . Hormones mediate growth and differentiation after germination . H3K27me3 marks a full spectrum of genes involved in indole-3-acetic acid synthesis , transport and signaling [54] , most of them are EMF1-bound ( Figure 4; Table S4 ) . EMF1 also interacts with many other hormone genes marked with H3K27me3 , e . g . , CYTOKININ OXIDASE ( CKKX ) , GA OXIDASE , and genes involved in JA , BR , and ethylene synthesis and response ( Table S4 ) . Temporal and spatial regulation of these EMF1_K27 genes is critical for normal shoot and root architecture and growth patterns . MicroRNA ( miRNA ) regulation of target genes controls various aspects of developmental transitions [55] . The juvenile to adult transition of the vegetative shoot is coordinated by the antagonistic activities of miR156 and miR172 , through their opposite expression pattern and the antagonistic function of their target genes [56] . The miR319-TCP and miR164-CUC miRNA-target nodes are involved in regulated cell proliferation during leaf morphogenesis [55] . EMF1 interacts with about 50% of the miRNA genes marked by H3K27me3 ( Table S4 ) . The AGONOUTE ( AGO ) genes mediate gene silencing through small RNA-directed RNA cleavage and translational repression [57] . EMF1 interacts with all H3K27 trimethylated AGO genes , including AGO10/ZIWILLE ( ZLL ) ( Table S4; Figure 4 ) , which acts in the siRNA and miRNA pathways and is essential for multiple developmental processes in plants [57] . Thus EMF1 may mediate juvenile and adult growth , as well as , lateral organ enlargement through the regulation of AGO and miRNA genes . In summary , to promote vegetative development and to regulate cell differentiation during shoot and root organogenesis , EMF1 binds genes required for other developmental phases and genes specifying cell identities . These are primarily genes trimethylated by EMF2-PRC2 on their H3K27 . We examined H3K27me3 of the EMF1_K27 genes in emf1 mutants and found two groups of genes . Group I genes–the EMF1-dependent H3K27me3 genes – comprising 57% of the EMF1_K27 genes ( 1845/3230 ) , are not H3K27me3 enriched in emf1 mutants . Group II genes– EMF1-independent H3K27me3 genes– comprising 43% of EMF1_K27 genes' are trimethylated in emf1 mutants ( Figure 5A; Table S2 ) . To determine whether the H3K27me3 of EMF1-bound genes is mediated by PRC2 , we examined trimethylation in fie and emf2 mutants . Most EMF1-bound genes showed reduced methylation in fie –96% of Group I and 76% of Group II genes ( Figure 5A ) . Therefore , both Group I and Group II genes are indeed methylated by PRC2 . 83% of Group I and 23% of the Group II genes showed reduced methylation in emf2 . Methylation may be less affected in emf2 than in fie because of EMF2 and VRN2 redundancy , while FIE participates in both EMF2- and VRN2-PRC2 . EMF1 targets many chromatin protein genes marked by H3K27me3 in WT seedlings , including FIS2 , VRN2 , MEA , ULTRAPETALA1 ( ULT1 ) , and ULT2 [64] . ULT1 is a component of trxG , the complex that antagonizes PcG action . EMF1 binding apparently represses ULT1 , as its transcription is up-regulated in emf1 mutants ( Table S6 ) . EMF1 does not bind the chromatin of EMF2 or the PRC1-like components , LHP1 , AtBMI1A , AtBMI1B , AtRING1A , and AtRING1B ( Table S2; [41] , [65] ) , which are required during postembryonic development , as is EMF1 . EMF1 does bind AtRING1C , an imprinted gene expressed in the endosperm [66] . To investigate the epigenetic regulation of EMF1 , we examined EMF1 interaction with itself . Interestingly , EMF1 binds its own chromatin strongly . Figure 6 shows EMF1 enrichment of the transcribed region of the EMF1 gene ( p<10−20 ) . This high level of EMF1 enrichment on EMF1 chromatin is accompanied by H3K27 trimethylation in WT , which is reduced in emf1 mutants , thus placing EMF1 in the category of Group I genes . Furthermore , EMF1 is up-regulated in emf1 mutants ( Figure 6 ) , providing evidence of EMF1 autoregulation . EMF1 transcript level is also elevated in emf2 and fie mutants , indicating its repression via a PcG-mediated mechanism . In addition to the EMF1_K27 genes , we investigated the 2303 EMF1-bound but not trimethylated ( EMF1_no_K27 ) genes in WT seedlings to find out their functional categories and whether they are regulated by EMF1 and PRC2 ( Table S2 ) . GO analysis showed that the fraction of genes involved in transcription and developmental processes and genes encoding transcription factors is lower in the EMF1_no_K27 than the EMF1_K27 genes , while genes involved in cellular organization and biogenesis , cytosol , and chloroplast are over-represented in the EMF1_no_K27 genes ( Table S5 ) . The EMF1_no_K27 genes tend to be actively transcribed genes with high RNA levels . Their average transcript score is more than 4 times that of the EMF1_K27 genes –1 . 83 for the EMF1_no_K27 , relative to 0 . 42 for the EMF1_K27 , genes . Analysis of NimbleGen transcriptome data showed about 14% of the EMF1_no_K27 genes is up-regulated and 7% down-regulated in emf1 mutants . A high percentage of these genes are similarly up- and down-regulated in the emf2 and fie mutants , indicating a coordinated regulation of these genes by EMF1 and PRC2 ( Figure S4A ) . We have previously shown that many photosynthesis genes that encode chlorophyll a/b binding proteins and photosystem I and II proteins are down-regulated in emf1 and emf2 mutants [33] , [34] . Seventy two percent of these genes are EMF1-bound [34] , which are all EMF1_no_K27 genes and many are coordinately down-regulated in all three mutants ( Table S2; Figure S4A and S4B ) . These results suggest that EMF1 activates their expression in the absence of H3K27me3 . Indeed , PRC1 in fly and vertebrate are in some cases recruited to the target genes independent of PRC2 or H3K27me3 [67] , [68] . Alternatively , despite EMF1-binding , deregulation of these genes in emf1 , emf2 , and fie mutants are a consequence of severe phenotypic aberrations in response to loss of these central regulators of development .
Our genome-wide study provided new lines of evidence that support EMF1 acting via the PcG mechanism . First , EMF1 interacts mostly with euchromatic sites located on all 5 chromosomes , a pattern similar to H3K27 trimethylation . Second , on the genic level , the EMF1 binding pattern mimics that of the H3K27me3 in binding the transcribed , not the promoter , region with the peak binding activity at the 5′ TSS . Third , EMF1 represses the seed and flower development genes and cell fate determination genes that are also modified by H3K27me3 . Fourth , H3K27 trimethylation on EMF1-bound genes is mostly dependent on PRC2 and gene expression is coordinately regulated by EMF1 and PRC2 . These findings demonstrate that , for genes that are highly enriched for EMF1 binding and H3K27me3 , EMF1 functions in the PcG mechanism . We investigated H3K27me3 dependency on EMF1 binding and found two groups of genes . Group I genes are richer in transcription factors and their repression is more dependent on EMF1 and PRC2 than Group II genes . Most importantly , Group I genes are dependent on EMF1 for H3K27me3 modification , while Group II genes are not . For Group I genes , which require EMF1 for K27me3 , EMF1 may act prior to , or as a member of , PRC2 to trimethylate H3K27 . For Group II genes that do not require EMF1 for H3K27me3 , EMF1 may have a PRC1 function , or may be unrelated to PcG action . Since many Group II genes require PRC2 for H3K27me3 , EMF1 is likely to act via the PcG mechanism , functioning downstream of H3K27trimethylation , as does PRC1 . The characteristics of the four Arabidopsis RING-finger proteins , AtRING1A , AtRING1B , AtBMI1A , and AtBMI1B , are consistent with their functioning like the mammalian PRC1 uibquitin ligase , which monoubiquitinates H2AK119 [8] , [37] . EMF1 interacts with these proteins , and is required for these RING-finger proteins' monoubiquitination of H2AK119 ( H2Aub ) , thus implicating EMF1 in PRC1 activity . The RING-finger proteins also interact with CLF [40] , the PRC2 H3K27 trimethylase , and with LHP1 [37] , [41] , [70] . The EMF1 binding pattern is similar to that of H2Aub in mouse embryonic fibroblast cells [71] in that both EMF1 binding and H2Aub localization are enriched in the 1 kb 5′ coding region . It is proposed that H2A ubiquitination interferes with early transcript elongation [67] . EMF1 preferential localization in the 5′ coding region is consistent with its involvement in PRC1's role in blocking transcription elongation by preventing RNA polymerase movement through the compacted nucleosomes [67] . However , EMF1 appears to partner with these RING-finger proteins only on a select group of target genes . Most notably , the signature EMF1 targets , the flower organ identity genes AG and AP3 , are not regulated by the 4 Arabidopsis RING-finger proteins . However , the class I KNOX ( KNOX1 ) genes , including STM , KNAT1 , KNAT2 , and KNAT6 , as well as WUS , and the seed regulator , FUS3 , are negatively regulated by both EMF1 and the RING-finger proteins [37] , [40] . EMF1 is bound to all these genes in Arabidopsis seedlings . Their ectopic expression in loss-of-function mutants suggests that these genes are direct target genes of the RING-finger proteins . Interestingly , their H3K27me3 shows varying degrees of dependence on EMF1 . KNOX1 and WUS are Group I genes: H3K27 trimethylation depends on EMF1 . FUS3 belongs to Group II: EMF1-independent H3K27me3 ( Table S2 ) . EMF1 may act on Group II genes such as FUS3 by assisting the PRC1 activity of the RING-finger protein-LHP1 complex following H3K27 trimethylation by PRC2 . For Group I genes such as STM , EMF1 may participate in each PcG complex separately or may act like a linker protein that assists PRC2 in spreading H3K27me3 , while helping PRC1 monoubiquitinate H2A . EMF1 interaction with MSI1 [36] and with the RING-finger proteins [37] is consistent with its involvement in both PRC2 and PRC1 activities . CLF interacts with AtRING1A/1B in yeast 2-hybrid assays [40] . Our results , together with this finding indicate a close association of PRC2 and PRC1 in Arabidopsis . This might be indicative of evolutionary divergence of PcG mechanisms . In Drosophila PRC2 and PRC1 are separate functions . Our study indicates that in Arabidopsis PcG proteins can also participate in closely linked PRC2-PRC1 function . EMF1 and the PRC2 proteins have a different evolutionary history [72] , [73] . The PRC2 ancestral sequences existed prior to the divergence of the animals and plants . During plant evolution , gene duplication generated alternate PRC2 components that diversified to control different functions . EMF1 is a plant-specific gene with homologous sequences found only in higher plants . It might have functioned first as a general transcriptional regulator for genes involved in development and basic cellular and biochemical activities . Coupling EMF1 with H3K27 trimethylation could have led to an enhanced targeting of genes in development . The repression of flower development , which effectively lengthens the vegetative phase , coupled with elaborating plant architecture through the regulation of hormone and signaling genes , may have been instrumental in the evolution of organisms with multiple developmental phases and diverse signaling processes . This is suggested by the progressive increase in the representation of genes involved in transcription and developmental processes from the H3K27me3 modified genes to the EMF1_K27 to Group I genes ( Figure 5C; Table S5 ) . The fact that EMF1_K27 genes are highly enriched with H3K27me3 and EMF1 binding suggests an emphasis on this epigenetic mechanism through robust retention of repressive chromatin during cell differentiation . Similarly , in mammalian cells , some genes are controlled by PRC1 , independent of PRC2 , and others are coordinately controlled by PRC1 with PRC2 [13] . The vast majority of developmental regulator genes are bound by both PRC1 and PRC2 , while genes bound by only one PRC are enriched for the membrane proteins [12] . The similarity of mutant phenotypes suggests that EMF1 acts primarily with EMF2-PRC2 to mediate developmental processes in Arabidopsis . EMF1 also acts together with AtBMI1A/1B and AtRING1A/1B to regulate genes maintaining cell identity . This means that EMF1 should not silence the EMF2-PRC2 or the 4 RING-finger protein genes . Indeed , EMF1 does not target CLF , EMF2 , SWN , or the RING-finger protein genes . EMF1 does not interact with VRN2 either , which has similar , ubiquitous expression patterns as EMF1 and EMF2 . EMF1 interacts with the chromatin of FIS2 and MEA , components of FIS2-PRC2 , consistent with their inactivity after germination . So far , no up-regulation of these two genes has been detected in the absence of EMF1 . Thus , EMF1 binding may not be the sole factor responsible for their repression , or their expression may require activators that are absent after germination . EMF1 coordinates only with EMF2-PRC2 to regulate PcG target genes . Neither EMF1 nor EMF2–PRC2 regulate the Type I MADS box genes involved in female gametophyte and endosperm development ( Table 2 ) , including PHE1 and PHE2 , whose maternal inheritance is mediated by FIS2-PRC2 [74] . PHE1 and PHE2 do not interact with EMF1 and are not normally expressed post-germination . Their repression is not likely dependent on EMF1 or EMF2–PRC2 , for they are not ectopically expressed in emf1 and emf2 mutants , even though they are trimethylated on H3K27 ( Figure S3 ) . This is consistent with a close association of EMF1 with EMF2-PRC2 and its lack of involvement in FIS2-PRC2 mediated epigenetic repression . ULT1 interacts with ARABIDOPSIS TRITHORAX 1 ( ATX1 ) , thus is considered a component of the Arabidopsis trxG that acts to antagonize PcG action , as evidenced by ult1 mutants rescuing the clf mutant phenotype [75] . ULT1 and ULT2 , a homolog of ULT1 , are EMF1_K27 genes ( Table 2 ) , and considered to be anti-repressors of PcG genes . ULT1 is up-regulated in emf1 and emf2 ( Table S6 ) , and both ULT1 and ULT2 are up-regulated in fie [30] . The temporal and spatial differentiation of ULT1 and ULT2 expression patterns is likely to involve EMF1 , but its role in the fine tuning of the repressor and anti-repressor balance in regulating gene expression remains to be characterized . Similarly , EMF1 autoregulation must be a dynamic process in order to modulate its epigenetic regulatory activities at a cellular level . Indeed , although EMF1 transcripts and proteins have been found in all tissues and organs [28] , [34] , [36] , their expression pattern differs temporally and spatially in WT and emf1 plants [31] . This may result from EMF1's autoregulatory actions . Future investigation of cell- and tissue-specific EMF1 binding activities is needed to address these questions . Temporally , EMF1_K27's major role , as plants undergo seed , vegetative and reproductive phase transitions , is to maintain repression of the seed and flower genes so as to allow vegetative growth after germination . Thus , major seed regulators and flower organ identity genes are repressed . Spatially , EMF1 is involved in switching or maintaining differentiated cell states , such that EMF1 probably represses the leaf polarity genes , KANADI and YABBY , and the shoot meristem-specific genes , STM and KNAT2 , in the differentiated leaf , hypocotyl and root cells . The three genes specifying stomata development , SPCH , FAMA , and MUTE , are inactivated in most cells except during stomata differentiation in the leaves . PLT1 and PLT2 are silenced in the shoot and mature root so that meristematic growth is restricted to the root tip . Future investigation of gene expression in separated tissues or in situ assays on individual genes of interest will clarify the role of EMF1 binding on the regulation of genes that did not show apparent expression change in mutants .
WT and emf mutants , emf1-2 and emf2-1 , of Arabidopsis used in this study are from the Columbia ecotype background , and have been described [33] . The transgenic plants impaired in FIE was described in Kinoshita et al . , [18] , and the pEMF1::3FLAG-tagged EMF1 , called RM , in Calonje et al . , [36] . Seeds were surface-sterilized and plated on agar plates containing 2/5X strength Murashige and Skoog medium [76] . The plates were placed for 2 days at 4°C and then transferred to a short day growth room ( 8 hrs light/16 hrs dark ) at 21°C . WT , mutants , and transgenic plants were harvested after growth for 14 days for expression and ChIP experiments . ChIP experiment was performed according to published procedure [36] on WT , emf1 , emf2 , transgenic FIE , and transgenic plant harboring the EMF1-3FLAG construct grown in the short day growth condition for 14 days . Due to homozygous lethality of emf1 , emf2 , transgenic FIE mutants , seeds from heterozygous plants were germinated; mutants were separated from the WT-looking plants and harvested . Plants were vacuum infiltrated in 1% formaldehyde solution for half an hour to cross-link the chromatin . Tissues were ground in liquid nitrogen , nuclei isolated , and chromatin extracted according to Bowler et al . , [77] . Chromatin was sheared by sonication ( Microson , MS-50 ) , 10″ on and 10″ off for 10 times to generate 0 . 5- to 2 kb fragments . For immunoprecipitated chromatin ( IP ) , monoclonal anti-FLAG mouse antibody ( Sigma F1804 ) and polyclonal anti-H3K27me3 antibody ( Upstate , rabbit IgG , 07-449 ) were added to fragmented chromatin to precipitate EMF1-bound and H3K27me3 modified chromatin , respectively . The cross-linking of IP was reversed with 5M NaCl and DNA precipitated by 100% EtOH . For the Input control ( Input ) , 0 . 5% of total chromatin before immunoprecipitation was reverse cross-linked by 5M NaCl and DNA isolated by 100% EtOH . The relative amount of DNA was determined by PCR and spectrophotometry ( NanoDrop , ND1000 ) . ChIP-chip was performed according to the NimbleGen protocol ( Roche , www . nimblegen . com ) . IP and Input DNA were amplified using the Whole Genome Amplification kit ( Sigma , GenomePlex Kit , WGA2 ) , and labeled with CY5 and CY3 , respectively [78] , [79] . Combined samples , which include 10 ug of CY5-labeled IP and 10 ug of CY3-labeled Input DNA , were hybridized with NimbleGen HD2 arrays ( http://www . nimblegen . com/products/chip/custom/index . html ) , with 2 . 16 million , ∼50mer probes that allow coverage of the entire Arabidopsis genome without gaps . The hybridization and data extraction were performed at the Fred Hutchinson Cancer Research Center DNA array facility ( http://www . fhcrc . org/science/shared_resources/genomics/index . html ) . Microarray hybridization was repeated three times with independent biological samples . For global gene expression studies , total RNA was extracted from 14 old WT and mutants using trizol ( Invitrogen ) and converted into cDNA according to Moon et al . , [33] . Genomic DNA from WT and cDNA from mutants and WT were labeled with CY3 , and CY5 , respectively , and combined to hybridize with NimbelGen HD2 arrays as described . For microarray analysis , signal intensity data of microarrays are extracted from the scanned images of each array using NimbleScan , NimbleGen's data extraction software . For ChIP-chip data , each feature on the array is represented as log2-ratio of the input signals for the immunoprecipitated DNA . The log2-ratio is computed and scaled to center the ratio of data around zero . Peaks were derived using a Perl implementation of ChIPOTle ( https://sourceforge . net/projects/chipotle-perl/ ) [80] using a window size of 300 bp , step size 50 bp with specific cut-offs . The p-value of 1×10−35 and the peak length of 300 bp were applied as a cut-off for dataset of H3K27me3 , and the p-value of 1×10−6 and the peak length of 100 bp were applied as a cut-off for the EMF1 binding dataset , respectively . Peaks were annotated using TAIR 8 . Genome browser views were generated using the SignalMap software from NimbleGen . End analysis was done as described in Zilberman et al . , [79] . For RNA expression data , each feature on the array is represented as log2-ratio of the genomic DNA for the amplified cDNA from each mutant . After median normalization , each feature was annotated and scored using perl scripts . To find differentially expressed genes in mutants , the datasets of [mutant –WT] were generated by subtracting probe values in the mutant datasets from counterpart values in the WT datasets , and then , arbitrary cutoff of ±1 . 5sd was used to select differentially regulated genes . The functional categories of target genes were assigned based on the GO annotations from the TAIR website [81] . For functional categories of GO annotations , the significant difference of each category for each group compared to the whole genome in TAIR8 was calculated with a poisson p-value for data in Figure 5C and Table S5 . For the small number of genes in developmental process and transcription categories , Fisher's exact test was used for assessing the significance of data in Table 1 . To validate EMF1-bound genes identified by ChIP-chip , ChIP products from three independent biological samples were used to perform semi-quantitative PCR according to Moon et al . , [33] on genes with different p-values . The PCR bands were scanned and measured by ImageJ program ( http://imagej . nih . gov/ij/ ) . The input signal for each gene was normalized to 100 . The IP signal was calculated as % input . PHE1 , which is not bound by EMF1 , was used as negative control and its IP signal was subtracted from that of other genes and plotted on the graph . Primer sequences used for ChIP analysis are listed in Table S11 . Total RNA was extracted from WT , emf1 , and emf2 according to Moon et al . , [33] . Semi-quantitative PCR was performed as described previously [33] . Primers used were as follows: EMF1; ( AGGTGCTGCCAACGAGATTGAT and CTTTTGAGTTTGAATGCAGTCCAC ) , UBQ; ( GATCTTTGCCGGAAAACAATTGGAGGATGGT and CGACTTGTCATTAGAAAGAAAGAGATAACAGG ) . RT-PCR of all samples and reference controls were performed in 3 independent replicates and repeated at least three times with similar results . Sequences are deposited in Gene Expression Omnibus ( GEO ) with accession number GSE34689 . | Polycomb group ( PcG ) proteins are epigenetic repressors maintaining developmental states in eukaryotic organisms . Plant PcG proteins are expected to be general epigenetic repressors; however , their overall impact on growth and differentiation and their mechanism of repression are still unclear . Here we identified several thousand target genes of the EMBRYONIC FLOWER 1 ( EMF1 ) protein , which shares no sequence homology with known PcG proteins . EMF1 regulates developmental phase transitions as well as specifies cell fates during vegetative development . Trimethylation of histone 3 lysine 27 ( H3K27me3 ) and ubiqutination of lysine 119 of histone H2A are carried out by different PcG protein complexes . EMF1 is required for both histone modifications on genes specifying stem cell fate in plants , thus revealing a novel role of EMF1 in linking the PcG protein complexes . Our results have important implications for the evolution of PcG regulatory mechanisms . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biology"
] | 2012 | EMF1 and PRC2 Cooperate to Repress Key Regulators of Arabidopsis Development |
Loss-of-function mutations in PINK1 , which encodes a mitochondrially targeted serine/threonine kinase , result in an early-onset heritable form of Parkinson's disease . Previous work has shown that PINK1 is constitutively degraded in healthy cells , but selectively accumulates on the surface of depolarized mitochondria , thereby initiating their autophagic degradation . Although PINK1 is known to be a cleavage target of several mitochondrial proteases , whether these proteases account for the constitutive degradation of PINK1 in healthy mitochondria remains unclear . To explore the mechanism by which PINK1 is degraded , we performed a screen for mitochondrial proteases that influence PINK1 abundance in the fruit fly Drosophila melanogaster . We found that genetic perturbations targeting the matrix-localized protease Lon caused dramatic accumulation of processed PINK1 species in several mitochondrial compartments , including the matrix . Knockdown of Lon did not decrease mitochondrial membrane potential or trigger activation of the mitochondrial unfolded protein stress response ( UPRmt ) , indicating that PINK1 accumulation in Lon-deficient animals is not a secondary consequence of mitochondrial depolarization or the UPRmt . Moreover , the influence of Lon on PINK1 abundance was highly specific , as Lon inactivation had little or no effect on the abundance of other mitochondrial proteins . Further studies indicated that the processed forms of PINK1 that accumulate upon Lon inactivation are capable of activating the PINK1-Parkin pathway in vivo . Our findings thus suggest that Lon plays an essential role in regulating the PINK1-Parkin pathway by promoting the degradation of PINK1 in the matrix of healthy mitochondria .
The accumulation of defective mitochondria is strongly implicated in aging , as well as a variety of common age-related diseases [1] , [2] , [3] . To counteract this accumulation , cells have evolved a number of mitochondrial quality control pathways . While previous work has revealed molecular mechanisms involved in the prevention and repair of mitochondrial damage [4] , [5] , [6] , the mechanism by which defective mitochondria are selectively detected and degraded was unknown until relatively recently . Over the past several years , studies of the PTEN-induced putative kinase 1 ( PINK1 ) and parkin genes , loss-of-function mutations of which give rise to heritable forms of Parkinson's disease [7] , [8] , have demonstrated that these genes encode components of a mitochondrial quality control system that promotes the selective degradation of defective mitochondria [9] , [10] , [11] . These studies have led to a model in which PINK1 , a mitochondrially localized serine/threonine kinase , is constitutively degraded in cells with healthy mitochondria , but is selectively stabilized on the outer membrane of defective mitochondria . The accumulated PINK1 then recruits the cytosolic E3 ubiquitin ligase Parkin , which ubiquitinates proteins on the mitochondrial surface , leading to isolation of the defective mitochondria and their eventual degradation in the lysosome [9] , [10] , [11] . Although studies of the PINK1-Parkin pathway have dramatically advanced our understanding of the mechanisms underlying mitochondrial quality control , many questions remain unanswered . One of the most important of these questions concerns the mechanism of PINK1 degradation by healthy mitochondria . Constitutive elimination of PINK1 prevents healthy mitochondria from being inappropriately destroyed , but permits a rapid response when PINK1 degradation ceases due to mitochondrial dysfunction . Previous work has identified three mitochondrially localized proteases that appear to participate in PINK1 proteolytic processing: mitochondrial processing peptidase ( MPP ) [12]; AFG3-like AAA ATPase 2 ( AFG3L2 ) [12]; and Rhomboid-7/Presenilin-associated rhomboid-like protein , mitochondrial ( Rho-7/PARL ) [13] , [14] . MPP removes the N-terminal mitochondrial targeting sequence of PINK1 and many other mitochondrial proteins [15] . The processing event mediated by AFG3L2 is unknown , although previous work suggests that AFG3L2 may facilitate the Rho-7/PARL cleavage event [12] . Rho-7/PARL cleaves PINK1 within its transmembrane domain [14] , [16] , creating a form of PINK1 that is released to the cytosol and degraded by the proteasome [17] , [18] , [19] . However , not all PINK1 degradation is dependent on Rho-7/PARL [12] , [13] , [14] , suggesting that there are other mechanisms of PINK1 degradation . To explore the mechanisms by which PINK1 is degraded , we used the fruit fly Drosophila melanogaster to conduct a screen for mitochondrial proteases that affect PINK1 processing and stability in vivo . Our work indicates that PINK1 is directed to the mitochondrial matrix , where it is degraded by the matrix-localized Lon protease . We also found that inactivation of Lon leads to the accumulation of cleaved forms of PINK1 that are active in vivo . Thus , Lon appears to represent a critical component of the mitochondrial proteolytic machinery that opposes PINK1 accumulation on the surface of healthy mitochondria .
To identify proteases that influence PINK1 stability , we used the pan-neuronal driver elav-GAL4 to express RNA interference ( RNAi ) constructs targeting known mitochondrial proteases in a fly strain that also bears a transgene with a myc-tagged form of PINK1 ( Table S1 ) . To avoid overexpression artifacts associated with the UAS/GAL4 system , we used a transgenic line that expresses PINK1-Myc under the control of the endogenous PINK1 promoter [20] . Flies bearing this transgene have only a small increase in PINK1 expression and exhibit no detectable abnormalities ( Fig . S1 and data not shown ) . We then performed anti-Myc immunoblotting on head protein extracts from flies co-expressing the PINK1 transgene and the GAL4-driven RNAi constructs to assess the effects of protease knockdown on PINK1 processing and abundance . The anti-Myc antiserum detected four bands in protein extracts from flies expressing a control RNAi targeting an exogenous gene ( mCherry , hereafter Control-R ) ( Fig . S2A ) . We propose that the highest molecular weight ( MW ) PINK1 band corresponds to unprocessed , full-length PINK1 ( FL-PINK1 ) . The second highest MW PINK1 band differs from the highest MW band by a mass consistent with that of the predicted PINK1 mitochondrial targeting sequence , so we propose that this band is the MPP-processed form of PINK1 ( MPP-PINK1 ) . The third highest MW PINK1 band corresponds in size to the Rho-7/PARL–processed form of PINK1 ( Rho-PINK1 ) , as identified previously [13] . The origin of the lowest MW PINK1 band is uncertain , but appears to be dependent on the AFG3L2 protease , as this band was nearly absent in AFG3L2-deficient animals ( Fig . S2A , B ) . This finding led us to name the lowest PINK1 band AFG-PINK1 . Among the 13 mitochondrial proteases tested in our study , only RNAi constructs targeting AFG3L2 ( CG6512 ) and Lon protease ( CG8798 ) resulted in accumulation of PINK1 ( Table S1 ) . For the remainder of our studies we focused on Lon as Lon involvement in PINK1 processing had not been previously characterized . RNAi-mediated knockdown of Lon led to a dramatic accumulation of all three processed PINK1 isoforms ( Fig . 1A , B ) . Importantly , the effects of Lon knockdown were reproduced with two independent Lon RNAi constructs , and the magnitude of PINK1 accumulation correlated with the extent of Lon knockdown ( Fig . 1B–D ) , indicating that PINK1 accumulation is a consequence of Lon inactivation , rather than an RNAi off-target effect . Thus , our finding that MPP-PINK1 , Rho-PINK1 , and AFG-PINK1 accumulate upon Lon inactivation raises the possibility that these PINK1 isoforms are substrates of Lon-mediated matrix degradation . However , before testing this model further , we explored other possible explanations of our findings . Lon has been shown to degrade a number of mitochondrial proteins [21] , [22] , and is also implicated in mitochondrial DNA ( mtDNA ) stability [23] , [24] , [25] and in the mitochondrial unfolded protein stress response [26] . Recently published work also suggests that mitochondrial unfolded protein stress triggers activation of the PINK1-Parkin pathway [27] . Thus , PINK1 accumulation in response to Lon knockdown could be a downstream consequence of a generalized matrix protein degradation defect , mtDNA instability , or the mitochondrial unfolded protein response ( UPRmt ) . However , knockdown of Lon did not influence the abundance of the inner membrane–associated matrix proteins Complex V β ( Comp V β ) or NADH dehydrogenase ( ubiquinone ) Fe-S protein 3 ( NDUFS3 ) ( Fig . 2A ) , and only mildly affected the abundance of a known Lon substrate , Mitochondrial transcription factor A ( TFAM ) . Moreover , the effect of Lon knockdown on TFAM abundance was only seen in flies expressing the stronger Lon RNAi construct , Lon-R2 ( Fig . 1E ) . Knockdown of Lon also had no effect on mtDNA abundance ( Fig . S3 ) , or on the abundance of Heat shock protein 60 ( Hsp60 ) , a marker of the UPRmt ( Fig . 1C , F ) . Thus , PINK1 accumulation appears to be a relatively specific consequence of Lon inactivation rather than a downstream consequence of a general matrix protein degradation defect , mtDNA instability , or UPRmt activation . Because PINK1 accumulates in cell culture upon mitochondrial depolarization [10] , [11] , another potential explanation of our findings is that Lon inactivation triggers mitochondrial depolarization . To explore this possibility , we used a recently described procedure to measure mitochondrial membrane potential in the adult Drosophila nervous system [28] . Briefly , we dissociated the brains from flies expressing either a control RNAi or RNAi targeting Lon to create neural cell suspensions . We then stained the cell suspensions with the mitochondrial membrane potential–dependent dye tetramethylrhodamine , ethyl ester ( TMRE ) , and used flow cytometry to compare the distribution of mitochondrial membrane potential in these cell populations . Neither of the RNAi constructs targeting Lon caused a significant reduction of mitochondrial membrane potential ( Fig . 1G ) . Rather , there was a trend for the stronger Lon RNAi construct , Lon-R2 , to cause hyperpolarization . These findings indicate that the increased PINK1 abundance upon Lon inactivation is not due to decreased mitochondrial membrane potential . We therefore performed additional studies to explore the role of Lon in PINK1 processing . Because Lon is a matrix-localized protease , the simplest interpretation of our findings is that Lon promotes the degradation of PINK1 in the mitochondrial matrix . This model predicts that Lon inactivation should result in the accumulation of PINK1 in the matrix . To test this model , we first examined the subcellular localization of the accumulated PINK1 isoforms following knockdown of Lon , using differential sedimentation to generate mitochondrial and postmitochondrial supernatant fractions . Previous work has shown that Rho-PINK1 can be released into the cytosol , where it is degraded by the proteasome [17] , [18] , [19] . Consistent with this work , we found that most of the PINK1 protein in control animals consisted of Rho-PINK1 in the supernatant fraction ( Fig . 2A ) . We also detected lesser amounts of Rho-PINK1 in the mitochondrial fraction , and faint bands of MPP-PINK1 in the mitochondrial and the postmitochondrial supernatant fractions of control animals , suggesting that MPP-PINK1 can also be released from mitochondria ( Fig . 2A ) . RNAi-mediated inactivation of Lon resulted in the accumulation of all processed forms of PINK1 , including AFG-PINK1 , in both the mitochondrial and postmitochondrial supernatant fractions , particularly in the case of the stronger Lon-R2 construct ( Fig . 2 ) . However , by far the largest increases in PINK1 accumulation upon Lon inactivation were seen in the mitochondrial fraction ( compare 2B–D with 2E–G ) , consistent with the model that Lon promotes PINK1 degradation in the mitochondrial matrix . The finding that Lon inactivation also results in the accumulation of PINK1 in the postmitochondrial supernatant fraction raises the possibility that Lon is also required for the efficient import of PINK1 into the matrix . To test whether the PINK1 that accumulates in mitochondrial fractions from Lon-deficient animals resides in the matrix , we performed protease protection experiments on isolated mitochondria . Specifically , we compared the Proteinase K ( ProK ) digestion sensitivities of the various PINK1 isoforms to the sensitivities of Mitofusin ( Mfn ) , Complex V β ( Comp V β ) , and pyruvate dehydrogenase ( PDH ) , which reside on the outer mitochondrial membrane , on the matrix side of the inner membrane , and in the matrix lumen , respectively . Treating a mitochondrial fraction from control flies with a low concentration of ProK ( 0 . 5 µg/ml ) resulted in substantial degradation of Mfn but did not significantly influence the abundance of Comp V β , PDH , or PINK1 ( Fig . 3A–D ) . However , disrupting mitochondrial membranes with Triton X-100 followed by ProK treatment at a low concentration ( 0 . 5 µg/ml ) resulted in nearly complete degradation of Comp V β , PDH , and PINK1 in both control and Lon-deficient animals ( Fig . S4 ) , indicating that the resistance of these proteins to ProK degradation in intact mitochondria ( Fig . 3A–D ) is a consequence of their internal localization rather than of inherent ProK insensitivity . Upon treatment of mitochondria from Lon-deficient animals with a low concentration ( 0 . 5 µg/ml ) of ProK , the MPP-PINK1 and AFG3-PINK1 isoforms were 40% depleted ( Fig . 3E , F ) . The remaining 60% of MPP-PINK1 and AFG-PINK1 in mitochondrial fractions from Lon-deficient animals was substantially depleted only at ProK concentrations that also resulted in depletion of Comp V β and PDH ( 5 and 10 µg/ml; Fig . 3F ) . These findings indicate that PINK1 accumulates in both the outer membrane and the matrix upon Lon inactivation , consistent with the model that Lon protease promotes the import and degradation of PINK1 in the mitochondrial matrix . To confirm our finding that at least some of the PINK1 that accumulates in Lon-deficient animals resides in the mitochondrial matrix , we used confocal microscopy to examine the localization of PINK1 following Lon knockdown . We performed this experiment using thoracic muscle because the large mitochondria in this tissue are relatively easy to image . We compared the localization patterns of a mitochondrial matrix–targeted YFP ( Mito-YFP ) to that of a Myc-tagged form of Mitochondrial Rho ( Miro-Myc ) , a FLAG-tagged form of Optic atrophy 1 ( Opa1-FLAG ) , endogenous cytochrome c ( Cyto C ) , and PINK1-Myc ( Fig . 4 ) . Previous work has established that Miro localizes to the mitochondrial outer membrane [29] , Opa1 to the inner membrane [30] , and Cyto C to the intracristal space [31] . The Mito-YFP showed the characteristic , highly invaginated structure of the matrix . The Miro-Myc signal surrounded the Mito-YFP signal , as would be expected for an outer mitochondrial membrane protein ( Fig . 4A ) . Opa1-FLAG also surrounded the Mito-YFP and interleaved with it , as would be predicted for a protein localized to the inner membrane , particularly the necks of the cristae [30] ( Fig . 4B ) . Cyto C interleaved with the Mito-YFP , consistent with its localization within cristae [31] ( Fig . 4C ) . When we examined the localization of PINK1-Myc in control flies , we detected only a small amount of PINK1 signal , consistent with previous work demonstrating that PINK1 is rapidly degraded in healthy mitochondria ( Fig . 4D ) [10] , [11] . However , animals expressing the Lon RNAi construct exhibited a substantial accumulation of PINK1 that co-localized in part with matrix-targeted Mito-YFP ( Fig . 4E ) . The degree of co-localization between Mito-YFP and the various mitochondrial proteins analyzed in Figure 4 was measured by determining the percentage of volume that the red objects ( individual areas of signal from the various mitochondrial proteins ) shared with Mito-YFP . Those red objects that shared 60% or more of their volume with Mito-YFP were considered to be co-localized . This analysis revealed that PINK1 in Lon-deficient animals had the highest degree of co-localization with Mito-YFP , followed by PINK1 in control animals , and then Cyto C , Opa1-FLAG , and Miro-Myc in descending order ( Fig . 4F ) . This finding confirms our biochemical observations and offers further support for the model that Lon protease promotes the degradation of PINK1 in the mitochondrial matrix . Previous work has shown that the accumulation of PINK1 on the mitochondrial outer membrane triggers the recruitment of Parkin and the eventual degradation of mitochondria [10] , [11] . Thus , our finding that some of the excess MPP-processed and AFG3L2-processed PINK1 in Lon-deficient animals accumulates on the mitochondrial outer membrane raises the possibility that Lon inactivation might trigger PINK1-Parkin pathway activity . In potential support of this hypothesis , there is a trend towards increased mitochondrial membrane potential in Lon-deficient animals that mirrors the increased mitochondrial membrane potential seen in flies overexpressing PINK1 [28] . We performed several experiments to test this hypothesis directly . To determine whether inactivation of Lon influences PINK1-Parkin pathway activity in vivo , we first explored the influence of Lon deficiency on a PINK1 overexpression phenotype . We have previously shown that overexpressing PINK1 in a variety of Drosophila tissues is toxic , and that this toxicity can be suppressed by homozygous loss-of-function mutations in parkin and dramatically enhanced by co-overexpression of Parkin [32] . These and other findings indicate that the toxicity associated with PINK1 overexpression results from an amplification of PINK1-Parkin pathway activity . A convenient way to quantify this toxicity involves monitoring the extent to which the structure of the compound eye becomes disorganized when PINK1 is overexpressed in the eye using the ey-GAL4 driver . We used this approach to test the influence of Lon on PINK1-Parkin pathway activity . Both RNAi constructs targeting Lon significantly enhanced ( worsened ) the PINK1 overexpression phenotype ( Fig . 5A ) , as would be predicted if Lon normally acts to restrict PINK1 activity . Moreover , the enhancement produced by these RNAi constructs correlated with their efficiency in reducing Lon expression , and with their effectiveness in triggering PINK1 accumulation ( Fig . 1A , B ) . Neither of the Lon RNAi constructs detectably influenced eye structure when driven with the ey-GAL4 driver in an otherwise WT background ( Fig . S5 ) , suggesting that the enhancement of the PINK1 overexpression phenotype conferred by these RNAi constructs is not simply an additive effect of combining two unrelated perturbations that affect the eye . Next we examined the effects of a P-element insertion and a deletion targeting Lon on a thoracic indentation phenotype produced by PINK1 deficiency in the flight muscle . Previous work has shown that thoracic indentations serve as a reliable surrogate marker of muscle pathology in PINK1 and parkin mutants [20] , [32] , [33] , [34] . We used an RNAi line targeting PINK1 that yields a hypomorphic phenotype , because decreases in Lon activity would not be expected to improve a PINK1 null phenotype . We also avoided the use of RNAi constructs targeting Lon because the introduction of any additional UAS element suppressed the thoracic indentation phenotype caused by PINK1 RNAi , likely through a GAL4 dilution effect ( data not shown ) . We found that a heterozygous P-element insertion mutation in Lon suppressed the PINK1-RNAi thoracic indentation phenotype ( Fig . 5B ) but did not influence the thoracic indentation frequency of PINK1 null mutants , confirming that Lon deficiency suppresses the phenotype only when some PINK1 is present ( Fig . S6 ) . Additionally , a heterozygous deletion that completely removes Lon caused even stronger suppression ( Fig . 5B ) . Together , our findings indicate that the PINK1 isoforms that accumulate upon knockdown of Lon are capable of triggering PINK1-Parkin pathway activity in vivo , and demonstrate that Lon plays an important role in regulating the PINK1-Parkin pathway .
Previous work has shown that PINK1 protein is normally maintained at low levels in healthy cells [10] , [11] . While the proteasome appears to be responsible for the degradation of a cytosolic Rho-7/PARL–processed form of PINK1 [17] , [18] , and thus at least partially accounts for the low abundance of PINK1 in healthy cells ( Fig . 6A–C ) , whether PINK1 degradation also occurred in mitochondria remained unclear from previous work . Our study identifies Lon as a protease involved in PINK1 processing and degradation in the mitochondrial matrix ( Fig . 6D ) . Lon may also serve to assist the import of PINK1 into the matrix , given that MPP-PINK1 and AFG-PINK1 accumulate on the mitochondrial surface and in the cytosol upon Lon inactivation . Lon could facilitate PINK1 import into the matrix through its known unfoldase activity , ultimately delivering PINK1 to the Lon protease domain for degradation [35] . While a recent study in vertebrate cell culture also identified the mitochondrial matrix protein AFG3L2 as a PINK1 processing protease , the exact role of AFG3L2 in PINK1 processing was not established [12] . Our work suggests that AFG3L2 is responsible for producing a cleaved form of PINK1 , AFG-PINK1 . Moreover , our finding that AFG-PINK1 accumulates to a greater extent than other PINK1 isoforms upon Lon inactivation ( Fig . 2 ) further suggests that AFG-PINK1 is the preferred substrate for Lon . Together , our findings support a model in which AFG3L2 and Lon play important roles in mitochondrial quality control , by promoting degradation of PINK1 within the mitochondrial matrix to prevent healthy mitochondria from being targeted for mitophagy . Our finding that Lon plays a role in the degradation of PINK1 contrasts with two recent papers that examined the effect of Lon knockdown on PINK1 levels in cultured cells [12] , [27] . Neither Greene et al . [12] nor Jin et al . [27] observed dramatic accumulation of PINK1 when Lon was targeted by shRNA . There are several possible explanations for this discrepancy . One potential explanation is that mammalian Lon may be highly efficient at degrading PINK1 , such that even a small amount of Lon activity may be sufficient to fully degrade PINK1 . Another potential explanation is that the cultured cells were able to compensate for reduced Lon activity through increased cytoplasmic release of Rho-7/PARL-processed PINK1 for proteasomal degradation . It should also be noted that while Greene et al . and Jin et al . did not observe dramatic accumulation of PINK1 upon Lon knockdown , both these groups and others have reported PINK1 accumulation upon MG132 treatment [12] , [14] , [18] , [36]; while MG132 is best known as a proteasome inhibitor , it is also a potent inhibitor of Lon [37] . Moreover , Jin et al . observed PINK1 accumulation upon expression of an unfolded protein targeted to the matrix ( a deletion mutant of ornithine carbamoyltransferase , ΔOTC ) , and PINK1 accumulation was further enhanced by simultaneously knocking down Lon . From these findings Jin et al . posit that when the UPRmt is insufficient to reduce the unfolded protein stress , PINK1 import is inhibited through an unknown mechanism , thus triggering mitophagy . However , an alternative interpretation of their findings is that ΔOTC acts as a competitive inhibitor of PINK1 degradation by Lon , which is known to degrade unfolded proteins [35] . This model would account for both the accumulation of PINK1 seen upon expression of ΔOTC , and the increased accumulation of PINK1 and ΔOTC that is seen when Lon is knocked down . Further studies will be required to distinguish these models . Our findings also differ from those of previous biochemical studies concluding that PINK1 does not localize to the mitochondrial matrix [38] , [39] . However , the conflicting biochemical studies used cells with intact Lon protease . If PINK1 degradation by Lon is rapid and efficient , one would not expect to detect significant amounts of PINK1 in the mitochondrial matrix except when Lon function is impaired . Indeed , our biochemical experiments are in general agreement with previously published work , as we also detected little PINK1 in mitochondrial fractions from WT control animals . Although previous work indicated that the accumulation of FL-PINK1 on the outer surface of depolarized mitochondria triggers the activation of the PINK1-Parkin pathway [10] , [11] , it was unclear whether other processed forms of PINK1 could also trigger pathway activation . Our findings demonstrating that Lon inactivation results in the accumulation of MPP-PINK1 and AFG-PINK1 on the outer mitochondrial membrane , and causes increased PINK1-Parkin pathway activity in vivo , suggest that one or both of these forms of PINK1 are also capable of activating the PINK1-Parkin pathway . Recent work also suggests that the PARL-processed form of PINK1 can promote pathway activity , as it can associate with mitochondria in vitro and promote the recruitment of Parkin [40] . However , because FL-PINK1 appears to be the only form of PINK1 that accumulates upon mitochondrial depolarization [10] , processed forms of PINK1 may not normally participate in pathway activity . They may , however , have other important biological roles . In particular , recent studies implicate PINK1 in phosphorylation [41] and selective turnover [42] , [43] of matrix-localized proteins; our finding that processed forms of PINK1 localize at least transiently to the matrix raises the possibility that PINK1 directly mediates these processes . Future work will be needed to fully delineate these possibilities , as well as to explore the possible therapeutic benefits of Lon inhibition in treating the many diseases associated with accumulation of defective mitochondria , including Parkinson's disease .
Drosophila stocks were maintained on standard cornmeal-molasses food at 25°C . The PINK1-myc transgenic line , UAS-PINK1 transgenic line , UAS-Miro-myc transgenic line , Mhc-GAL4 driver line and Dmef2-GAL4 driver line have been previously described [20] , [34] , [44] , [45] , [46] . The following lines were obtained from the Bloomington Drosophila Stock Center: elav-GAL4 , ey-GAL4 , sqh-mito-EYFP , LonG3998 , Df ( 3L ) Exel9011 , P{VALIUM20-mCherry}attP2 ( Control-RNAi ) , and P{TRiP . HMS01060}attP2 ( Lon-RNAi2 ) . The P{GD11336}-v21860 ( PINK1-RNAi ) , P{KK101663}-v109629 ( AFG3L2-RNAi1 ) , and P{GD14030}-v36036 ( Lon-RNAi1 ) RNAi lines were obtained from the Vienna Drosophila Resource Center . All other RNAi lines tested were obtained from the stock centers indicated in Table S1 . For measurement of PINK1 mRNA , total RNA was extracted from elav-GAL4; Control-R/+ and elav-GAL4; Control-R/PINK1-myc using TRIzol ( #15596-026 , Life Technologies ) . Reverse transcription was done using the iScript cDNA Synthesis kit ( #170-8890 , Bio-Rad ) and diluted 1∶50 and 1∶300 before use in qPCR reactions . Primer sequences for PINK1 and Rap2l were obtained from the FlyPrimerBank [47] . Primer pairs PA60267 and PP23832 were used for PINK1 , and primer pair PP8673 for Rap2l . The log2 method was used to calculate fold change . Rap2l was used as the internal control , as the expression of this gene has been reported as the most invariant across different genotypes and ages [48] . qPCR was performed using Brilliant III Ultra-Fast SYBR Green QPCR Master Mix ( #600882 , Agilent Technologies ) and a Bio-Rad Opticon 2 machine . Mitochondrial and nuclear DNA abundance were measured by using the DNA extraction method and primers described in a published report [49] . qPCR of mitochondrial and nuclear DNA was performed as described above . Proteins were separated by SDS-PAGE on 10% Tris-acrylamide gels and electrophoretically transferred onto PVDF membranes . Immunodetections with commercial antibodies were performed at the following concentrations: 1∶1000 mouse anti-Myc 9E10 ( #M4439 , Sigma ) , 1∶500 rabbit anti-LONP1 ( #NBP1-81734 , Novus Biologicals ) , 1∶500 rabbit anti-TFAM ( #ab47548 , Abcam ) , 1∶1000 rabbit anti-Hsp60 ( #4870S , Cell Signaling Technology ) , 1∶1000 mouse anti-VDAC ( #MSA03 , MitoSciences ) , 1∶2000 mouse anti-OxPhos Complex V subunit β ( #A21351 , Molecular Probes/Life Technologies ) , 1∶1000 mouse anti-NDUFS3 ( #ab14711 , Abcam ) , 1∶3000 mouse anti-PDH ( #MSP07 , MitoSciences ) , 1∶50 , 000 mouse anti-Actin ( #MAB1501 , Chemicon/Bioscience Research Reagents ) . The rabbit anti-Mfn had been described previously [50] . The secondary antibody anti-mouse HRP ( Sigma ) was used at 1∶2500 for anti-Myc; 1∶10 , 000 for anti-VDAC and anti-NDUFS3; and 1∶7500 for anti–Complex V β , anti-PDH , and anti-Actin . The secondary antibody anti-rabbit HRP ( Sigma ) was used at 1∶10 , 000 . Signal was detected using Thermo Scientific electrochemiluminescence reagents . Densitometry measurements of the western blot images were measured blind to genotype and condition using Fiji software [51] . Normalized western blot data were log-transformed when necessary to stabilize variance before means were compared using Student t test . Each experiment was performed at least three times . Measurement of mitochondrial membrane potential in neural cells was conducted as previously described [28] , except that the dissections were conducted in DME/Ham's F-12 High Glucose media without phenol red ( Sigma ) with 20 mM HEPES ( Sigma ) , 2 . 5 mM glutamine ( Sigma ) , and 0 . 5% trypsin ( Invitrogen ) at 25°C . Briefly , four adult Drosophila brains per genotype were dissected , dissociated at 25°C , and labeled with 10 nM TMRE at room temperature ( Enzo Life Sciences ) . Samples were maintained at room temperature , in supplemented media with 10 nM TMRE , until flow cytometry analysis was performed . The effect of mitochondrial depolarization on TMRE accumulation was assessed by pretreating neural preps with 100 µM carbonyl cyanide m-chlorophenyl hydrazone ( CCCP ) for 10 minutes prior to the addition of TMRE . Flow cytometry was performed using a BD FACSCanto or a BD LSRII ( BD Biosciences ) , equipped with a 635 nm laser . The mean TMRE fluorescence of each experimental sample was normalized to the mean fluorescence of the control sample prepared and analyzed on the same day . Heads from 30 male and 30 female flies were manually homogenized with a pestle in isolation buffer ( 220 mM mannitol , 68 mM sucrose , 20 mM HEPES pH 7 . 4 , 80 mM KCl , 0 . 5 mM EGTA , 2 mM Mg ( CH3COO ) 2 ) containing a protease inhibitor cocktail ( #P8340 , Sigma ) . The homogenate was centrifuged at 1500 g for 5 minutes at 4°C to prepare a postnuclear supernatant . The postnuclear supernatant was then subjected to a further round of centrifugation at 10 , 000 g for 25 minutes at 4°C to pellet mitochondria . The mitochondrial pellet was then either suspended in isolation buffer without protease inhibitors and used in protease protection experiments ( see below ) , or solubilized in SDS-PAGE sample buffer and used in western blot analysis along with the supernatant fractions . Mitochondrial fractions in isolation buffer without protease inhibitors ( prepared as described above ) were divided in half . One half received a specific concentration of Proteinase K ( ProK ) ( #19131 , Qiagen ) and the other half received an equal volume of buffer lacking ProK . These samples were incubated on ice for 20 minutes , followed by the addition of phenylmethylsulfonyl fluoride ( PMSF ) to inhibit ProK . SDS-PAGE sample buffer was then added to the samples , which were then boiled and used in western blot analysis . For experiments involving Triton X-100 , equal amounts of ProK were added to both halves of the mitochondrial fraction . Following ProK addition , 1% Triton X-100 was added to one of the two samples , and both samples were incubated for 40 minutes on ice , followed by the addition of PMSF to inhibit ProK . SDS-PAGE sample buffer was then added to the samples , and the samples were boiled and used in western blot analysis . Adult thoracic muscle was dissected and then fixed in 4% paraformaldehyde in PBS . The tissue was blocked for one hour in PBS with 0 . 1% Triton X-100 and 10% fetal bovine serum , then incubated overnight in 1∶500 rabbit anti-GFP ( #A11122 , Life Technologies ) and either 1∶500 mouse anti-Myc 9E10 , 1∶500 mouse anti-FLAG ( #F3165 , Sigma ) , or 1∶2000 mouse anti-Cytochrome C ( Cyto C ) ( #556433 , BD Biosciences ) . The tissue was then washed in PBS with 0 . 1% Triton X-100 and incubated overnight with 1∶500 anti-rabbit Alexa 488 secondary antiserum ( #A11034 , Life Technologies ) and 1∶500 ( 1∶1000 for Cyto C ) anti-mouse Alexa 568 secondary antiserum ( #A11031 , Life Technologies ) . After final washes , the tissue was mounted in Prolong Gold ( #P36934 , Invitrogen ) and imaged sequentially with 488 nm and 561 nm lasers on an Olympus FluoView FV1200 ( Olympus America ) with a 60× oil objective and 15× digital zoom , taken at 1024×1024 pixels and 30 steps of 0 . 13 µm . Each stack of 30 images was deconvoluted using Huygens Professional 4 . 4 . 0-p8 software ( Scientific Volume Imaging ) , using a signal to noise ratio of 20 for the green channel and 18 for the red channel . Co-localization was calculated using the advanced Object Analyzer of the Huygens software , using a threshold of 270 or 2 times the standard deviation of the image , whichever was higher . The seed threshold for objects was set at 2% and the garbage threshold for objects at 500 voxels . At least 6 image stacks were analyzed per condition . Flies were assigned to one of three phenotypic categories by an investigator blinded to genotype . Each fly was scored according to the severity of the more greatly affected eye . The three categories were as follows: mild , which ranged from completely WT appearance to bristle misorientation and slight deviation in ommatidial row arrangements; moderate , which consisted of overall ommatidial disorganization and mildly to moderately decreased eye size; and severe , which ranged from greatly reduced eye size to completely absent eyes . The mild , moderate and severe categories were assigned scores of one , two , and three , respectively , and the mean score for each genotype was calculated . Means were compared using Student t test . Flies were individually examined under a dissecting microscope for the presence of thoracic indentations as previously described [32] . Flies with indentations were assigned a score of one . Flies with no indentations were assigned a score of zero . The mean scores of the experimental and control ( sibling ) genotypes were compared by Student t test . | Mitochondria are essential organelles that provide most of the cell's energy and perform many other critical functions . The gradual accumulation of defective mitochondria is thought to play a role in aging and in diseases of the nervous system , including Parkinson's disease . The selective elimination of defective mitochondria is therefore a vital task for the cell , and the protein PINK1 was recently identified as a critical player in this process . PINK1 accumulates on the surface of mitochondria after they are damaged , starting a process that leads ultimately to the elimination of defective mitochondria . Previous work indicated that PINK1 does not accumulate on healthy mitochondria because it is rapidly degraded . However , it was unclear exactly how and where this degradation occurred . Our work shows that Lon protease promotes the degradation of PINK1 in the mitochondrial matrix . This finding provides new insight into the mechanisms of mitochondrial quality control , and reveals a potential strategy for treating the many diseases associated with the accumulation of defective mitochondria . | [
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"science... | 2014 | PINK1-Parkin Pathway Activity Is Regulated by Degradation of PINK1 in the Mitochondrial Matrix |
Susceptibility to HIV has been linked to systemic CD4+ T cell activation in cohorts of seronegative individuals with high HIV-exposure risk . We recently described an increased risk of HIV transmission in individuals infected with Wuchereria bancrofti , the causative agent for lymphatic filariasis , in a prospective cohort study . However , the reason for this phenomenon needs further investigation . Two-hundred and thirty-five HIV negative adults were tested using Trop Bio ELISA for detection of W . bancrofti infection and Kato Katz urine filtration and stool based RT-PCR for detection of soil transmitted helminths and schistosomiasis . FACS analysis of the fresh peripheral whole blood was used to measure T cell activation markers ( HLA-DR , CD38 ) , differentiation markers ( CD45 , CD27 ) , markers for regulatory T cells ( FoxP3 , CD25 ) and the HIV entry receptor CCR5 . Frequencies of activated HLA-DRpos CD4 T cells were significantly increased in subjects with W . bancrofti infection ( n = 33 median: 10 . 71% ) compared to subjects without any helminth infection ( n = 42 , median 6 . 97% , p = 0 . 011 ) or those with other helminths ( Schistosoma haematobium , S . mansoni , Trichuris trichiura , Ascaris lumbricoides , hookworm ) ( n = 151 , median 7 . 38% , p = 0 . 009 ) . Similarly , a significant increase in HLA-DRposCD38pos CD4 T cells and effector memory cells CD4 T cells ( CD45ROposCD27neg ) was observed in filarial infected participants . Multivariable analyses further confirmed a link between W . bancrofti infection and systemic activation of CD4 T cells independent of age , fever , gender or other helminth infections . W . bancrofti infection is linked to systemic CD4 T cell activation , which may contribute to the increased susceptibility of W . bancrofti infected individuals to HIV infection .
The human immunodeficiency virus ( HIV ) epidemic and high HIV transmission rates continue to affect large parts of the world [1] . The disproportionately high prevalence of HIV in communities of sub-Saharan Africa has led to the hypothesis that concomitant helminth infections augment the risk of HIV transmission by increasing systemic immune activation [2–5] . Helminths are parasitic worms that primarily affect the world’s poor and cause chronic infections in one-fourth of the world's population [6] . Different helminth infections can induce a diverse array of clinical symptoms and pathology . In addition to their associated morbidity , several distinct immunological changes have been described for different helminth species [7] , some of which might potentially increase HIV susceptibility . Recently , we demonstrated for the first time an increased risk of HIV acquisition in individuals infected with the filarial nematode Wuchereria bancrofti , the agent that causes lymphatic filariasis [8] . Our data shows that W . bancrofti is an independent risk factor for HIV acquisition even when adjusting for sexual behavior , condom use , circumcision , marital status , age and gender , among other factors . Infections with W . bancrofti are chronic and persist over many years despite antihelminthic treatment . The majority of infected individuals remain asymptomatic; however , 10 to 30% develop filarial pathology with lymphedema or hydrocele . Different clinical outcomes are associated with distinct immunological phenotypes . The early phase of a filarial infection is characterized by proinflammatory cytokines [9] , followed by anti-inflammatory cytokines such as IL-10 and TGF- beta during the chronic phase of infection in clinically asymptomatic individuals [10 , 11] . In patients with chronic filarial pathology , a lack of regulatory T cells and increased Th1 and Th17 proinflammatory responses were documented [12] . Members of the family of vascular endothelial growth factors , as well as IL-1beta , TNF-alpha , and IL-6 have been implicated in lymphangiogenesis [13–15] . Individuals with lymphedema showed increased frequencies of HLA-DRpos CD8 T cells [16]; however no differences in CD4 T cell maturation or expression of HLADR have yet been described for W . bancrofti-infected individuals . Activated CD4 T cells are a major cellular reservoir for continuous HIV replication in vivo , as shown in several studies [17–20] . In order to address the hypothesis that the increased HIV transmission risk in W . bancrofti -infected subjects is linked to increased systemic CD4 T cell activation , we studied expression of activation ( HLA-DR , CD38 ) and maturation ( CD27 , CD45RO ) markers in CD4 and CD8 T cell populations , as well as T regulatory CD4 T cells and HIV entry receptor ( CCR5 ) in relation to W . bancrofti infection status .
This study was approved by the ethics committees of the Tanzanian National Institute for Medical Research , Mbeya Medical Research and Ethics Committee and Munich University Hospital , and was conducted according to the principles expressed in the Declaration of Helsinki . All participants recruited in the study were adults above 18 years of age who provided written informed consent before enrolment into the study . A total of 386 adult study participants from the ‘‘Evaluating and Monitoring the Impact of New Interventions” ( EMINI ) general population cohort from the Mbeya region in Southwest Tanzania were enrolled into the prospective Worm-HIV-Interaction-Study ( WHIS ) based on their helminth and HIV infection status between October 2009 and March 2012 [21] . After excluding 136 HIV-infected study participants , 2 individuals without filariasis results , and 13 participants with invalid flow cytometry data , results from 235 HIV negative volunteers ( 137 women and 98 men ) were analyzed . In the study area , governmental treatment of W . bancrofti started in 2009 . However , none of the 235 participants reported participation in the mass drug administration program . This study uses the blood , urine and stool specimens that were collected from each participant at baseline . HIV status was determined using HIV 1/2 STAT-PAK ( Chem-Bio Diagnostics Systems ) and positive results were confirmed using ELISA ( Bio-Rad ) with discrepancies being resolved by Western Blot ( MPD HIV Blot 2 . 2 , MP Biomedicals ) . Forty ml of venous blood were drawn from each participant using anticoagulant tubes ( CPDA , EDTA; BD Vacutainer ) . Fresh blood samples were processed within 6 hours of the blood draw at the NIMR-MMRC laboratories .
Fresh whole blood was used for flow cytometric assessment of activation and maturation markers on T cells . Frequencies of HLA-DRpos CD4 T cells were significantly increased in subjects with W . bancrofti infection ( n = 33 median: 10 . 71% ) compared to subjects either without any helminth infection ( n = 42 , median 6 . 97% , p = 0 . 016 Mann-Whitney test , univariable analysis , Fig 3A ) or the combined group of individuals harboring other helminths ( S . haematobium , S . mansoni , T . trichiura , A . lumbricoides , hookworms ) ( n = 151 median 7 . 38% , p = 0 . 009 ) . Polyparasitism was not significantly associated with increased frequencies of HLA-DRpos CD4 T cells ( Fig 3B ) . W . bancrofti infection was associated with increased frequencies of HLA-DRposCD38pos CD4 T cells ( n = 31 ) compared to helminth negative subjects ( n = 41median 2 . 84% versus 2 . 16% , p = 0 . 035 , Fig 4A ) . Again , polyparasitism was not significantly associated with increased frequencies of HLA-DRposCD38pos CD4 T cells ( Fig 4B ) . To adjust the above associations of filariasis infection with immune activation for potentially confounding factors , we estimated multivariable mixed effects models including age , gender , fever and the other helminth infections ( T . trichiura , A . lumbricoides , S . mansoni , S . haematobium and hookworm ) as fixed effects and study site as a random effect in linear regression models . Univariable analysis had demonstrated an influence of these factors on our outcome parameters ( Tables 2 and S1–S8 ) . The significant association of W . bancrofti infection with HLA-DRpos CD4 T cell counts was confirmed in the multivariable analysis ( Table 2 ) . Table 2 shows HLA-DRpos CD4 T cell counts of all tested helminths . Some other helminth species , for example A . lumbricoides and T . trichiura also show elevated HLA-DRpos CD4 T cells in uni- and multivariable analysis . This was described and discussed in a previous manuscript about this cohort [21] and is not repeated in this manuscript . In contrast , other helminth species , for example hookworms seem to have no impact on immune activation marker . Similar uni- and multivariable analyses were performed for HLA-DRpos CD8 T cell counts as well as for HLA-DRpos CD38pos CD4 and HLA-DRpos CD38pos CD8 T cell counts ( Tables 3 and S1 , S2 , S3 ) . The significant increase in activated T cells in W . bancrofti-infected individuals was confirmed for the HLA-DRpos CD38pos CD4 and CD8 T cells . Frequencies of HLA-DRpos CD8 T cell were increased in subjects with W . bancrofti ( mean 29 . 5% ) infection compared to subjects without filarial infection ( mean 20 . 8% ) . However , this difference was not significant after adjusting for study site and other confounding factors in the multivariable analysis ( Tables 3 and S2 ) . To give an overview of the different parameters associated with immune activation , which are influenced by infection with W . bancrofti , we summarized the results in Table 3 whereas a complete multivariable analysis showing the influence of other factors are given in the supplementary Tables S1–S8 . Apart from T cell activation , we also studied the memory phenotype of CD4 T cells using CD45RO and CD27 as markers for naïve ( CD27posCD45ROneg ) , ‘‘central memory-like" ( CD27posCD45ROpos ) and effector memory ( CD27negCD45ROpos ) CD4 T cells . Total memory CD4 T cells were defined as the sum of central memory ( CD27posCD45ROpos ) , effector memory ( CD27negCD45ROpos ) and “terminally differentiated” ( CD27negCD45ROneg ) CD4 T cells . W . bancrofti infection was associated with significantly increased frequencies of effector memory ( CD27negCD45ROpos ) CD4 T cells , in univariable ( mean 24 . 1% versus 20 . 0% , p = 0 . 0096 ) as well as multivariable analysis ( Tables 3 and S5 ) . This was not observed for other analyzed helminth species . Importantly , the effector memory CD4 T cell frequencies were positively correlated with the activation status ( Spearman’s rho = 0 . 617 , p<0 . 001 , Fig 5 ) . Regulatory CD4 T cells , defined as CD25highFOXP3pos CD4 T cells were found in similar percentages in all study groups ( Tables 3 and S4 ) with the exception of T . trichiura-infected individuals , who exhibited increased regulatory CD4 T cells compared to T . trichiura-uninfected individuals . Addressing the expression of HIV entry receptor CCR5 , no difference was found regarding the percentage of CCR5 positive CD4 T cells , CCR5 positive regulatory CD4 T cells , or the mean fluorescence intensity of CCR5 on memory CD4 T cells between the different helminth infections ( Tables 3 , S6 , S7 and S8 ) .
The importance of CD4 T cell activation for HIV susceptibility has been emphasized in several studies that focused on HIV exposed but seronegative individuals [18–20 , 29–31] . Activated CD4 T cells are a major cellular reservoir for continuous HIV replication in vivo . Recently , a study embedded into a phase 2B HIV vaccine trial and conducted in a high-risk setting examined a variety of different immune cell populations and phenotypes in peripheral blood in relation to HIV acquisition . The study showed that of all studied cell subsets , the frequency of activated HLA-DRpos CD4 T cells discriminated best between subjects who later HIV seroconverted and age- , ethnicity- and risk-matched controls who did not seroconvert , regardless of intervention arm [17] . To address potential mechanisms underlying the W . bancrofti-associated risk increase in HIV acquisition , which was recently described by our group [8] , an evaluation of different markers for maturation , immune activation as well as CCR5 expression was therefore performed . We found significantly higher frequencies of HLA-DRpos and HLA-DRpos/CD38pos as well as ‘‘effector-like” memory ( CD27negCD45ROpos ) CD4 T cells in participants infected with W . bancrofti compared to individuals with other helminth infections or without helminth infection . Univariable analysis results were supported by multivariable analyses , in which potential confounders were addressed . In our previous publications about the EMINI cohort , we had described different age and gender distributions for the detected helminth species [22–25] . We therefore included age , gender , and additionally fever ( during the last 24 hours as proxy for an acute illness ) into the model . In addition , we included the infection status of other helminths to adjust for potential confounding , since we had previously described a positive association of Trichuris trichiura and Ascaris lumbricoides with the expression of markers for systemic T cell activation [21] . The association between increased frequencies of activated HLA-DRpos and HLA-DRpos/CD38pos and effector memory ( CD27negCD45ROpos ) CD4 T cells in peripheral blood and W . bancrofti infection was independent of other host and environmental factors , suggesting a causal link between these two factors . The participants studied here had been selected from the large general population cohort ( EMINI ) based on their helminth infection status . These participants were selected in 9 different study sites with distinct geographical conditions . Participants with W . bancrofti and T . trichiura infection were almost exclusively found in Kyela site close to Lake Nyasa , whereas hookworm , A . lumbricoides and Schistosoma infections were found in several study sites . We therefore included residence in Kyela site as a random effect into our multivariable model . The positive association of W . bancrofti with HLA-DRpos and HLA-DRpos/CD38pos as well as ‘‘effector-like” memory ( CD27negCD45ROpos ) CD4 T cells was confirmed in the mixed linear regression models with area of residence as a random effect . In addition we measured increased percentage of HLA-DRpos/CD38pos CD8 T cells in W . bancrofti-infected individuals , however , there is no evidence that CD8 T cell may contribute to increased HIV susceptibility . Our study has some limitations that we tried to address during the analysis . Because our cohort did not include W . bancrofti mono-infected individuals , we compared not only to helminth negative individuals , but also to individuals with other helminth infections . The participants in the study group with “other helminths” were infected with similar species in an almost identical composition compared to the W . bancrofti group , providing an optimal “background” to study the effect of an additional helminth species ( Figs 3A and 4A ) . In addition , we showed that polyparasitism had no influence on immune activation ( Figs 3B and 4B ) . This approach seems appropriate , as we might have missed more parasites , like Onchocerca volvulus and Mansonella perstans , two other tissue nematodes . Increasing information about the modulation of the immune response to other pathogens in co-infected individuals is available [32–36] . However , the study area is not known to be endemic for O . volvulus and we did not measure M . perstans . In a previous publication [21] , we described differences between helminth species regarding their association with T cell activation . With adjusting for other helminths in our multivariate analysis , we clearly showed that the described CD4 T cell activation in W . bancrofti- ( but also in A . lumbricoides and T . trichiura ) -infected individuals was independent of concomitant infection with other helminths . W . bancrofti infection was diagnosed by retrospective testing of bio-banked samples using an ELISA specific for circulating filarial antigen in plasma ( TropBio Og4C3 ELISA , Townsville , Australia ) , which was an identical diagnostic approach to what was used in our previous study that showed significantly increased HIV acquisition risk in W . bancrofti-infected subjects [8] . It would have been interesting to characterize W . bancrofti infection in more detail , such as investigating the presence of microfilariae ( the offspring of the adult worms , responsible for W . bancrofti transmission from humans to mosquitos ) , which requires the nocturnal collection of blood . Unfortunately , we do not have information about microfilariae for our study participants . Because the clinical presentation of W . bancrofti infection shows such variety , the immunological background of these features has been studied extensively: Hyporesponsiveness was reported from in vitro studies of PBMC cultures of W . bancrofti-infected individuals [37 , 38] . Treatment of the filarial parasites resulted in recovery of antigen-specific responses supporting the direct causal relationship [39] . It was discovered that the hyporesponsiveness , which was described initially , was typical for the filarial infected but asymptomatic individuals , whereas in lymphedema patients Th1 and Th17 responses mediated the pathology [12] . However , the number of participants with pathology in our cohort was too small to describe differences among sub groups . Infected individuals , as measured by the presence of circulating filarial antigen , who do not have microfilariae in the night blood , display a distinct immunological profile , characterized by stronger filarial-specific interleukin-5 , IL-10 and TNF-alpha responses , compared to microfilaremic individuals [40] . Regulatory T cells , which are responsible for tolerance to self-antigens , show stronger activity in microfilaremic , compared to amicrofilaremic individuals [41–44] . In this context , it is possible that also HLA-DRpos and HLA-DRpos/CD38pos CD4 T cell frequencies , and also expression of HIV entry receptors , may differ in microfilaremic , amicrofilaremic or symptomatic filarial infected patients . Further studies with a better characterization of participants are needed . An increase of systemically activated CD4 T cells is likely to support early HIV dissemination because they are cellular targets for HIV replication [45 , 46] . However , it is unknown whether increased activation of circulating T cells in peripheral blood is also linked to increased activation of CD4 T cells at the mucosal site of viral transmission . In our study , the frequency of HLA-DRpos CD4 T cells strongly correlates with the frequency of effector memory CD4 T cells and both subsets are significantly increased W . bancrofti infected subjects . We have previously shown in the same cohort that both CD27 negative effector memory CD4 T cells and HLA-DR+ memory CD4 T cells express significantly higher levels of the HIV co-receptor CCR5 compared to CD27+ central memory or naive CD4 T cells [21] , which may facilitate better HIV entry into these specific cell subsets . These effector memory CD4 T cells may migrate into tissues [47] , such as the mucosal surfaces of the reproductive tract . Thus , future studies need to study HIV co-receptor expression and T cell activation in the reproductive tract in relation to W . bancrofti infection . In summary , our data clearly show that W . bancrofti infection is linked to systemic CD4 T cell activation , as determined by HLA-DR expression , providing a possible mechanism for the increased HIV-susceptibility observed in W . bancrofti-infected individuals [8] . Whether treatment of W . bancrofti could decrease immune activation and whether this in turn could reduce HIV transmission in areas where lymphatic filariasis is prevalent needs to be examined in future studies . | The importance of CD4 T cell activation for HIV susceptibility has been emphasized in several studies focusing on HIV transmission and prevention . Particularly , activated HLA-DR+ CD4 T cells may play a major role in HIV susceptibility . In this analysis we describe systemic activation of CD4 T cells in individuals infected with W . bancrofti the causative agent of lymphatic filariasis . This helminth disease leads to debilitating pathology in some of the individuals; however , the majority of infected persons remain asymptomatic . We recently described an increased HIV incidence in subjects infected with W . bancrofti compared to uninfected individuals from the same area . To decipher underlying reasons for this phenomenon , we measured immune activation parameters in CD4 and CD8 T cells . The increased percentage of HLADR positive and HLADR/CD38 positive CD4 T cells and also effector memory CD4 T cells that we describe here could be a possible mechanism to explain our previous findings of increased HIV incidence in individuals infected with this filarial nematode . | [
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"retroviruses... | 2019 | Wuchereria bancrofti infection is linked to systemic activation of CD4 and CD8 T cells |
The aim of this study was to evaluate the potential use of nasal , oral , and ear swabs for molecular diagnosis of canine visceral leishmaniasis ( CVL ) in an endemic urban area in Brazil . Sixty-two naturally infected and ten healthy dogs were enrolled in this study . Bone marrow aspirates , peripheral blood , skin biopsy , and conjunctival , nasal , oral , and ear swabs were collected . All samples , except blood , were submitted to conventional PCR ( cPCR ) and quantitative real time PCR ( qPCR ) to detect and quantify Leishmania infantum DNA , respectively . All dogs were submitted to thorough clinical analysis and were included based on a combination of serological ( ELISA immunoassay and immunofluorescent antibody test ) and parasitological methods . The cPCR positivity obtained from nasal swab samples was 87% ( 54/62 ) , equivalent to those from other samples ( P>0 . 05 ) . Positive results were obtained for 79% ( 22/28 ) in oral swabs and 43% ( 12/28 ) in ear swab samples . A significant difference was observed between these data ( P = 0 . 013 ) , and the frequency of positive results from oral swab was equivalent to those from other samples ( P>0 . 05 ) . The use of ear swab samples for cPCR assays is promising because its result was equivalent to skin biopsy data ( P>0 . 05 ) . The qPCR data revealed that parasite loads in mucosal tissues were similar ( P>0 . 05 ) , but significantly lower than the parasite burden observed in bone marrow and skin samples ( P<0 . 05 ) . Nasal and oral swab samples showed a high potential for the qualitative molecular diagnosis of CVL because their results were equivalent to those observed in samples collected invasively . Considering that mucosae swab collections are painless , noninvasive , fast and practical , the combination of these samples would be useful in massive screening of dogs . This work highlights the potential of practical approaches for molecular diagnosis of CVL and human leishmaniasis infections .
Visceral leishmaniasis ( VL ) is considered the most severe manifestation among the different clinical expressions of leishmaniasis in humans [1] , [2] . This potentially fatal disease is a zoonosis in the Americas [3] , and it is caused by Leishmania infantum ( = L . chagasi ) . From a veterinary perspective , the canine VL ( CVL ) is considered one of the most important diseases in dogs , which represent the main domestic reservoir of parasite and can present risk for human infection [4] , [5] , [6] . According to the World Health Organization , 3 primary measures should be applied for controlling VL: ( i ) insecticide-based control of sand flies , ( ii ) diagnosing and treating human cases , ( iii ) diagnosing and euthanizing seropositive dogs , although this option is very polemic and disputable [2] . Diagnosis is described in 2 of the 3 recommendations adopted for controlling the disease , demonstrating the strategic importance of proper diagnosis . A correct diagnosis in both humans or dogs is critical because it helps to make decisions more suitable to regions where control measures are more necessary [7] . Furthermore , accurate diagnosis is very desirable for identifying infected animals and avoiding elimination of non-infected dogs . Various techniques are available for diagnosing CVL infection , which are typically divided into parasitological , immunological , and molecular methods . The parasitological procedures allow the identification of the etiological agent and rely on tissue cultures and cytological or histological analysis based on optical microscopy [8] . The main immunological tests used for diagnosing CVL infection are based on serological methods [9] . In Brazil , enzyme linked immunosorbent assay ( ELISA ) and immunofluorescence antibody test ( IFAT ) have been used for CVL surveillance [10] . However , these tests have limitations such as low sensitivity in asymptomatic dogs [11] and cross-reactions with trypanosomiasis and cutaneous leishmaniasis ( CL ) [12] , [13] . The molecular methods are primarily based on polymerase chain reaction ( PCR ) and have been extensively described for qualitative CVL diagnosis exhibiting high sensitivity , specificity and reproducibility [14] , [15] , [16] . Specific DNA sequences are easily detected by conventional PCR ( cPCR ) . Quantitative real time PCR ( qPCR ) is a more recently developed technological approach that permits not only diagnosis but accurate parasite load estimation , and it has been applied for monitoring treatment efficacy [17] , [18] , [19] . The association of PCR with non-invasive sampling techniques represents a high potential for contributing to CVL diagnosis . Previous studies have described the use and feasibility of the conjunctival swab for detecting Leishmania DNA in dogs in Brazil [16] and Italy [20] . Therefore , swabs can be used to easily collect cells from the mucosa and possibly from other anatomical regions of dogs . Thus , the aim of this work was to evaluate oral , nasal , and ear swabs as alternative resources for carrying out qualitative molecular CVL diagnosis by cPCR and estimating parasite burden in these tissues by qPCR . This study is based on the need of more simplified methods for assessing the CVL infections in naturally infected dogs in endemic areas such as Latin America , by using techniques with higher sensitivity and specificity than serological methods used for CVL diagnosis .
Experiments with dogs were performed in compliance with the guidelines of the Institutional Animal Care and Committee on Ethics of Animal Experimentation ( “Comitê de Ética em Experimentação Animal” , national guidelines , Law number 11 . 794 , 8/10/2008 ) from Universidade Federal de Minas Gerais; approved protocol number: 183/08 . This study was designed in Belo Horizonte , the capital of Minas Gerais State , Brazil , an urban and endemic area that is considered one of the regions most affected by VL in Brazil [21] . Sixty-two naturally infected mongrel dogs of both sexes , unknown ages , and destined for euthanasia were collected in the Municipal Zoonotic Diseases Control Department of Belo Horizonte , MG . Infected animals enrolled in this study showed positive results in ELISA and IFAT , which were performed according to protocols recommended by specific Brazilian legislations . Additionally , inclusion of these dogs was based on a positive result in the parasitological culture test and/or simultaneous positivity of ELISA and IFAT techniques carried out in-house ( topic 5 ) . Samples from 10 healthy mongrel dogs of both sexes and free from Leishmania infection were used as negative controls and were provided by Federal University of Ouro Preto , MG . All animals were submitted to thorough clinical analysis . Samples were collected in two distinct moments . The first and second collections involved 34 and 28 naturally infected dogs respectively . Seven clinical samples were collected , including nasal , oral , ear , and conjunctival swab , skin biopsy , bone marrow and peripheral blood . All these samples were obtained from all animals ( n = 62 ) , except oral and ear swabs . These 2 samples were used only in the second collection ( n = 28 dogs ) . Previously , dogs were anesthetized using 2% xilazine ( 2 . 2 mg/kg , Syntec , Brazil ) and 2 . 5% thiopental ( 9 . 0 mg/kg , Cristália , Brazil ) . Sterile swabs for microbiological isolation ( Inlab ) were used to remove exfoliative cells from the ear epithelium and nasal , oral , and conjunctival mucosae . A swab was firmly rubbed against the oral mucosa , the inner nasal mucosa in both nostrils , and the lower eyelid of both eyes separately ( Figure S1 ) . To collect epithelial cells , a sterile swab was immersed in sterile phosphate-buffered saline and rubbed against the internal surface of the left ear , which had been cleaned with 70% ethanol . Swab tips were broken and transferred into the DNAse-free tubes . Skin biopsies were obtained from the internal surface of the right ear using 5 . 0-mm sterile punches . Bone marrow aspirates ( ∼1 . 0 mL ) were collected from the sternum using sterile 10 mL syringes and needles ( 18 gauges ) and divided in 3 fractions . Approximately 200 µL were transferred to DNAse-free tubes for DNA extraction . A drop was added and smeared on a clean slide , and the remaining volume was used for cultures ( topic 4 ) . Five milliliters of peripheral blood were collected from the jugular vein . One fraction was transferred into tubes containing ethylenediamine tetraacetic acid ( EDTA ) for DNA extraction . A second aliquot was stored in a tube without EDTA for obtaining serum . For DNA purification , all samples were immediately kept on ice for transportation and stored at −20°C until use . The presence of parasites was investigated using optical microscopy with 1000× magnification . Slides smears were stained using the modified Giemsa method ( Bioclin , Brazil ) . Bone marrow aspirates were added to Novy-McNeal-Nicolle medium containing 12% rabbit defibrinated blood and Minimum Essential Medium ( GIBCO BRL , USA ) containing 10% fetal calf serum ( CULTILAB , Brazil ) , penicillin ( 100 U/mL ) , and streptomycin ( 1 . 0 µL/mL; GIBCO BRL , Life Technologies USA ) . Cultures were examined by optical microscopy and subcultured thrice over a 10-day period , after which all culture tubes were reexamined . Sera from dogs were divided into aliquots and submitted to serological and biochemical tests . ELISA assays were carried out to measure total serum IgG as described elsewhere [22] . IFAT assays were performed based on a standardized protocol [23] . The cut-off value was ≥1∶40 , as recommended by the Brazilian legislation . In both serological tests antigens were prepared from cultured L . infantum promastigotes , MHOM/BR/1967/BH46 strain . Some biochemical parameters were measured to complement the clinical analysis . Serum albumin and globulins levels were assessed using Biuret reagent ( BIOCLIN , Brazil ) at an absorbance of 510 nm ( Epoch , Biotek , USA ) . The colorimetric kinetics method was used to measure serum creatinine ( Cobas Mira Classic , Roche , Germany ) . Finally , serum urea level was assessed using a colorimetric enzymatic assay ( BIOCLIN , Brazil ) . Each swab used in collections was immersed in a lysis buffer solution [50 mM Tris , 50 mM NaCl , and 10 mMEDTA ( pH 8 . 0 ) ] , 1% Triton X-100 , and proteinase K ( 250 mg/mL ) . This mixture was incubated at 56°C for 2 h , eluted from the cotton swab and transferred to 1 . 5 mL DNAse-free tubes . Then , the phenol-chloroform method was performed as described elsewhere [16] . Purified DNA was suspended in 30 µL of sterile H2O . DNA purification from bone marrow and skin biopsy samples was performed using the Nucleo Spin kit ( Macherey-Nagel , Germany ) according to the manufacturer's instructions . To detect L . infantum DNA , the following L . donovani complex-specific primers were used: [5′ ACG AGG TCA GCT CCA CTC C 3′] , [5′ CTG CAA CGC CTG TGT CTA CG 3′] . The cPCR reaction was conducted to amplify the kinetoplast DNA ( kDNA ) minicircle conserved region of 100 base pairs . For each sample , a master mix of 10 µL was prepared as follows: 1 . 0 µL of DNA preparation , 5 . 0 µL of Master Mix Go Taq ( Promega , USA ) each primer at 1 . 0 pmol/µL , and ultrapure H2O . In all cPCR runs , DNA purified from L . infantum at 1 . 0 ng/µL ( MHOM/BR/1967/BH46 strain ) and DNA from a recognized infected dog were used as positive controls . DNA extracted from a non-infected dog and water were used as negative controls to assess nonspecific annealing of primers and contamination , respectively . The cPCR reaction was carried out as previously described [24] . The final results were analyzed on a 5% polyacrylamide gel stained using AgNO3 . Parasite loads were estimated on the basis of absolute quantification using qPCR , as described previously [25] , [26] . Primers addressed to the DNA polymerase gene ( GenBank accession code AF009147 ) and canine β-actin gene ( GenBank accession code NM_001195845 . 1 ) were used [22] . This canine housekeeping gene was adopted as an endogenous control to verify DNA integrity and to normalize the calculations . Standard curves were generated using known amounts of TOPO PCR 2 . 1 plasmids ( Invitrogen , USA ) containing cloned canine genes of β-Actin ( 307 bp ) or L . infantum DNA polymerase ( 90 bp ) . Because these genetic sequences are single-copy genes , the final results were expressed as the number of parasites per canine cells . Reactions were carried out as described previously [22] using the ABI Prism 7500 Sequence Detection System ( SDS Applied Biosystems , Foster City , CA , USA ) . The frequencies of positive results were compared between paired clinical samples by using the chi-square test or by Fisher's exact test for a number of dogs above or below 30 individuals , respectively . Data distributions were evaluated using the Kolmogorov-Smirnov and D'Agostino-Pearson normality tests . Parasite burdens were compared in pairs using the Mann-Whitney U test . The significance level was set at 5% , and the differences were considered significant when the P value <0 . 05 .
According to the qualitative molecular diagnosis data , the frequencies of positive results were as follows: nasal swab , 87% ( 54/62 ) ; conjunctival swab , 76% ( 47/62 ) ; skin biopsy , 81% ( 50/62 ) ; bone marrow biopsy , 90% ( 56/62 ) . These cPCR results were compared considering paired samples . Based on the qualitative molecular diagnosis data , it was shown that the positivity obtained using nasal swabs was equivalent to those for samples obtained invasively and for conjunctival swab ( P>0 . 05 ) , ( Table 1 ) . This last clinical sample showed a frequency of positive results lower than that calculated for bone marrow samples ( P = 0 . 031 ) . Furthermore , the conjunctival swab data was equivalent to skin biopsy data ( P>0 . 05 ) , ( Table 1 ) . Oral and ear swab samples were collected from only the last 28 naturally infected dogs . Then , the cPCR frequencies of positive results were recalculated for other samples using 28 animals , and statistical analysis was performed . Positive results were as follows: oral swab , 79% ( 22/28 ) ; ear swab , 43% ( 12/28 ) ; nasal swab 75% ( 21/28 ) ; conjunctival swab 54% ( 15/28 ) ; skin biopsy , 68% ( 19/28 ) ; bone marrow biopsy , 79% ( 22/28 ) ( Tables 2 and 3 ) . The result obtained with oral swab samples was also statistically equivalent to those obtained with skin biopsy and bone marrow samples ( P>0 . 05 ) . Ear swab samples showed low positivity compared to bone marrow samples ( P = 0 . 013 ) . However , the ear swab performance was statistically equivalent to that obtained for skin biopsy ( P>0 . 05 ) , ( Table 2 ) . According to the comparisons between clinical samples obtained noninvasively , the frequency of positive results obtained with ear swab samples was lower than that calculated with nasal and oral swab samples ( P = 0 . 029 and P = 0 . 013 , respectively ) . For all other comparisons , no significant differences were observed between the frequency of positive results ( P>0 . 05 ) , ( Table 3 ) . The combination of 2 different mucosal swab samples showed frequencies of positive results as follows: nasal and conjunctival swabs , 90% ( 56/62 ) ; nasal and oral swabs , 93% ( 26/28 ) ; oral and conjunctival swabs , 86% ( 24/28 ) ( Figure 1 ) . The frequency of clinical signs observed in sites where swabs were applied was calculated . In all , 39% of dogs ( 24/62 ) showed signs on the ear , including exfoliative , nodular , and ulcerative lesions , desquamation , and hyperqueratosis . Signs in the eyes were observed in 44% of dogs ( 27/62 ) including uveitis , conjunctivitis , mucosa hyperpigmentation , hyperemia , and keratitis . Ten percent of dogs ( 6/62 ) showed signs in the mouth , including ulcers , mucosa hyperpigmentation , and nodules . Finally , only 3% ( 2/62 ) of dogs showed clinical signs on the nose , including epistaxis and pustules ( Figure 2 ) . From a quantitative point of view , parasite burdens were estimated in the nasal and conjunctival swabs , skin biopsy and bone marrow samples from 62 naturally infected dogs . The parasite loads obtained from conjunctival and nasal swab samples were equivalent ( P>0 . 05 ) . On the other hand , the parasitism in the ocular and nasal mucosae was lower than those estimated in the clinical samples obtained invasively ( P<0 . 05 ) . In fact , the highest parasite loads were detected in the bone marrow and skin biopsy , but no difference was observed between these samples ( P>0 . 05 ) ( Figure 3 ) . The oral and ear swabs were collected from the last 28 dogs . Then , the parasite loads in other clinical samples , except blood , were recalculated using this sample size . The low parasite burden in mucosae was confirmed , and there was no significant difference among nasal , oral , and conjunctival swab samples ( P>0 . 05 ) . Once more , parasite loads estimated in bone marrow and skin biopsy samples were equivalent ( P>0 . 05 ) . At the same time , the parasitism in these two samples was higher than those estimated for oral , nasal and conjunctival mucosae ( P<0 . 05 ) , ( Figure 4 ) . It was not possible to assess the parasite burden in ear swab samples .
According to our study , nasal and oral swabs samples showed a high potential for CVL molecular diagnosis by using cPCR because of their high positive indices , which were equivalent to those obtained from samples collected invasively . CVL is a systemic disease and the infection can occur in a wide variety of organs and tissues [27] , [28] , [29] . Considering that mucosae undergo high cellular proliferation and a constant generation of exfoliative cells , we focused on these tissues on the basis of swab practicability for collecting biological material . We used conjunctival swab samples in this work owing to its promising results for CVL molecular diagnosis described previously by our group [16] , [22] , [30] , [31] . Thus , the conjunctival swab sample was adopted as a reference sample collected noninvasively to compare with nasal , oral and ear swab samples . Conjunctival and nasal swab samples were collected separately from both eyes and nostrils , respectively , and treated as distinct samples . The simultaneous use of 2 ocular swabs and 2 nasal samples increased the positivity of cPCR ( data not shown ) and is highly recommended for screening dogs . Nonetheless , there was no significant difference between the cPCR positive results calculated for right and left nostrils or conjunctivas ( data not shown ) . Although the conjunctival swab positivity has been considered lower than that obtained from bone marrow , the combination of ocular samples provided a diagnostic result statistically equivalent to the samples obtained invasively ( data not shown ) . Anyway , in all paired comparisons , just one conjunctiva and one nostril was adopted in order to avoid undue favoritism for these swab samples . This study demonstrated Leishmania DNA detection from nasal swabs for the first time . This method increases the number of available options for detecting parasites in dogs by using cPCR . Considering that amastigotes within macrophages can reach the mucosae through the lymphatic and/or hematogenous route [32] nasal tissues are susceptible to parasite colonization owing to the large number of blood vessels in the mucosa . Furthermore , the muzzle is a favorable site for sand fly bites due to the absence of hair . The DNA yield obtained from nasal swabs was markedly high ( data not shown ) , demonstrating that these samples are rich source of DNA for molecular biology assays . As described for the nasal swab samples , oral swabs yielded an equivalent result compared to the samples collected invasively for diagnosis using cPCR . Particularly , the frequency of positive results was high in the bone marrow because parasites naturally migrate to lymphoid tissues [33] . Interestingly , the frequency of positive results obtained from oral swab samples was the same as that calculated for bone marrow samples . Thus , oral mucosa may be a more practical choice for collecting samples to be used for qualitative diagnosis by using PCR techniques . In our study , oral swab samples were used for molecular diagnosis of CVL for the first time in a Brazilian endemic region , and we confirmed a high potential for detecting DNA from L . infantum . This clinical sample was firstly evaluated in an endemic region from Italy using qPCR assays , but it showed low sensitivity for detecting Leishmania DNA in seropositive dogs [20] . According to these authors , the presence of Leishmania DNA in oral swabs may have implications on the transmission of parasites among dogs through licking and bites . Parasite transmission to dogs in the absence of phlebotomines has been confirmed through blood donation [34] , placenta ( vertical transmission ) [35] , [36] , and sex ( venereal transmission ) [37] . However , transmission through licking , bites , and wounds is still unproven [9] . Further studies to confirm this hypothesis are necessary . Various pathological processes associated with CVL have been detected in conjunctival , nasal , and oral mucosae . Nodular lesions , granulomatous , lymphoplasmacytic , and pyogranulomatous manifestations have been well described in these tissues [38] , [39] , [40] . In the context of CVL diagnosis , the use of swabs has been indicated for collecting samples particularly in the presence of dermatological lesions [41] . However , according to our data , most dogs had no lesions in conjunctival , nasal , or oral mucosae , and the L . infantum DNA was detected in these tissues with a high positivity . This result emphasizes the potential of these clinical samples , which were obtained using swabs for diagnosing the infection in dogs without clinical signs . In cPCR experiments , the ear swab presented low positivity . DNA extracted from this sample showed poor yield and purity . The absorbance measured using spectrophotometer indicated protein contamination in many samples ( data not shown ) , and the low quality of DNA likely affected cPCR and qPCR performances . On the other hand , the cPCR positive index obtained from ear swab samples was equivalent to that calculated for skin biopsy samples . This result is promising , and the DNA extraction from ear swab should be improved . In addition , ear swab should be evaluated in further studies , and it can be a more attractive choice to avoid the use of invasive methods such as skin scraps and biopsies for the molecular diagnosis of CVL . High frequencies of positive results were obtained using a combination of samples collected using swabs . This is particularly useful for screening dogs in large-scale studies . Sensitive methods are indicated to preliminary surveys for diagnosing infections in populations , and the sensitivity of the diagnostic techniques is one of the accuracy measures of interest to public health policymakers [42] . According to our results and considering the practicability of mucosae swabs , we strongly recommend the combination of these clinical samples for diagnosing CVL based on PCR assays . In this case , the swab samples could be mixed and processed as a unique sample in order to enhance the diagnostic sensitivity . Additionally , this procedure may simplify sample management and save time , permitting the analysis of a large number of dogs . We also analyzed parasitism levels in different tissues . Generally , parasite loads in mucosae were low indicating weak parasite colonization . Besides , bone marrow and skin biopsy samples showed high and similar parasite burdens . These results were the same when we separately compared parasitism in these different clinical samples in 62 and 28 dogs , thus reinforcing our conclusions . Parasites show natural tropism towards lymphoid tissues , and different studies have shown that bone marrow is a good source for Leishmania DNA detection and quantification in agreement with our study [43] , [44] , [45] . The high parasite load in the skin is an important characteristic that helps to explain the role of dogs as parasite reservoirs in endemic regions [22] , [46] , [47] . Therefore , we chose to examine the canine skin . As a matter of fact , it has been suggested that ear tissue should be used as the primary site for parasitological confirmation in dogs [48] . In addition , it was pointed as the better anatomical region from skin to perform biopsies and PCR assays for detection of Leishmania infection [49] . In our experimental context , there was no difference among nasal , oral , and conjunctival swab samples in detecting Leishmania infection in naturally infected dogs . All of these tissues permitted the evaluation of parasite load using qPCR . These results are relevant because distribution of parasites is not uniform in host organs and tissues [50] . Thus , combining different canine clinical samples would be useful for obtaining more conclusive diagnostic results by using PCR . Choosing appropriate samples for detecting parasites in different affected tissues of dogs is necessary for accurate diagnosis and/or prognosis . This is very important for the orientation of clinicians' work [41] . Hence , we recommend the use of nasal , oral , and conjunctival swabs on the basis of their practicability and potential for PCR assays . The use of swabs has several applications in the molecular diagnosis of human leishmaniasis as well . In Ecuador , 15 of 16 patients presenting CL tested positive by using lesion swabs and PCR [51] . In Colombia , human CL was diagnosed by combining PCR with conjunctival and nasal swabs [52] . Additionally , oral swab was used to successfully detect VL infection in patients from India [53] . These data extend the potential of these clinical samples for VL and CL molecular diagnosis . In summary , we demonstrated the utility of nasal , oral , and ear swab samples for detecting Leishmania infection in dogs . Because these collections are painless , noninvasive , fast , and practical , combining these samples would be useful for screening large numbers of dogs . Large-scale evaluations in the field considering epidemiological aspects and infected dogs without clinical signs should be conducted . | Visceral leishmaniasis ( VL ) is an important public health problem in different regions of the world . It presents high lethality in human cases without suitable treatment and is considered one of the most important disorders in dogs , the main domestic reservoir of the etiological agent of VL ( Leishmania infantum ) . Most cases of VL in Latin America occur in Brazil , and control campaigns have not shown satisfactory results . The diagnosis of human and canine infection is critical for making decisions regarding surveillance and control policies . In this work , we propose a non-invasive collection method of mucosal and epithelial cells for the molecular diagnosis of canine VL by conventional polymerase chain reaction ( cPCR ) and for the estimation of parasite load by quantitative real time PCR ( qPCR ) . We used nasal , oral , and ear swabs as practical , simple , painless and fast alternatives for collecting samples . These procedures are according to the need of more simplified methods for detecting L . infantum infection by using robust diagnostic techniques such as cPCR and qPCR . Additionally , potential applications for diagnosing human VL are highlighted . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
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"parasitology",
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"acids",
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] | 2013 | Nasal, Oral and Ear Swabs for Canine Visceral Leishmaniasis Diagnosis: New Practical Approaches for Detection of Leishmania infantum DNA |
Host shifts–where a pathogen jumps between different host species–are an important source of emerging infectious disease . With on-going climate change there is an increasing need to understand the effect changes in temperature may have on emerging infectious disease . We investigated whether species’ susceptibilities change with temperature and ask if susceptibility is greatest at different temperatures in different species . We infected 45 species of Drosophilidae with an RNA virus and measured how viral load changes with temperature . We found the host phylogeny explained a large proportion of the variation in viral load at each temperature , with strong phylogenetic correlations between viral loads across temperature . The variance in viral load increased with temperature , while the mean viral load did not . This suggests that as temperature increases the most susceptible species become more susceptible , and the least susceptible less so . We found no significant relationship between a species’ susceptibility across temperatures , and proxies for thermal optima ( critical thermal maximum and minimum or basal metabolic rate ) . These results suggest that whilst the rank order of species susceptibilities may remain the same with changes in temperature , some species may become more susceptible to a novel pathogen , and others less so .
Temperature is arguably the most important abiotic factor that affects all organisms , having both indirect and direct effects on physiology and life history traits [1–3] . There is much to be learned about the impact of climate change on infectious diseases [1 , 4 , 5] . Changes in temperature can impact both host and parasite biology , leading to complex and difficult to predict outcomes [2 , 6] . Host shifts , where a parasite from one host species invades and establishes in a novel host species , are an important source of emerging infectious disease [7] . A successful host shift relies on a number of stages occurring [8] . Firstly , exposure of the host to the new pathogen species must occur in such a way that transmission is successful . Secondly , the pathogen must be able to replicate sufficiently to infect the novel host . Finally , there must be sufficient onwards transmission for the pathogen to become established in the new host species [7 , 9 , 10] . Some of the most deadly outbreaks of infectious diseases in humans including Ebola virus , HIV and SARS coronavirus have been linked to a host switch event [11–14] and many others have direct animal vectors or reservoirs ( e . g . Dengue and Chikungunya viruses ) [15 , 16] . The potential for novel host shifts may increase with changing temperatures due to , fluctuations in host and/or parasite fitness , or changes in species distributions and abundances [17 , 18] . Distribution changes may lead to new species assemblages , causing novel contacts between parasites and potential hosts [19–21] . Susceptibility to infection is known to vary with temperature , due to within individual physiological changes in factors such as the host immune response , metabolic rate or behavioural adaptations [22–25] . Thermally stressed hosts may face a trade-off between the resource investment needed to launch an immune response versus that needed for thermoregulation , or behavioural adaptations to withstand sub-optimal temperatures [26–29] . Temperature shifts could also cause asymmetrical or divergent effects on host and parasite traits [30] . For example , changes in temperature may allow differential production and survival of parasite transmission stages , and changes in replication rates , generation times , infectivity and virulence [31–33] . Temperature is also known to impact vector-borne disease transmission through multiple effects on both vector life cycles and transmission behaviours [20 , 34–37] . Host shifts have been shown to be more likely to occur between closely related species [38–40] , but independently of this distance effect , clades of closely related hosts show similar levels of susceptibility [9 , 41] . Thermal tolerances − like virus susceptibility − are known to vary across species , with groups of closely related species having similar thermal limits , with a large proportion of the variation in these traits being explained by the phylogeny [42–45] . Previous studies on host shifts have assayed the susceptibility of species at a single temperature [9 , 39 , 41 , 46] . However , if the host phylogeny also explains much of the variation in thermal tolerance , then phylogenetic patterns in virus susceptibility could be due to differences between species’ natural thermal optima and the chosen assay temperatures . Therefore , for experiments carried out at a single temperature , phylogenetic signal in thermal tolerance may translate into phylogenetic signal in thermal stress . Any apparent phylogenetic signal in susceptibility could potentially be due to the effects of thermal stress , and may not hold true if each species was to be assayed at its optimal temperature . If this was indeed the case this would have implications for species distribution models that aim to use estimates of environmental conditions to predict host and pathogen ranges [5 , 47 , 48] . Here , we have asked how species’ susceptibilities change at different temperatures and whether susceptibility is greatest at different temperatures in different species . We infected 45 species of Drosophilidae with Drosophila C Virus ( DCV; Dicistroviridae ) at three different temperatures and measured how viral load changes with temperature . Viral load is used here as a measure of DCV’s ability to persist and replicate in a host , which has previously been shown to be tightly correlated to host mortality [41] . We are therefore examining one of the steps ( “ability to infect a novel host” ) needed for a host shift to successfully occur [7 , 9 , 10] . We also examine how proxies for thermal optima and cellular function ( thermal tolerances and basal metabolic rate ) relate to virus susceptibility across temperatures , as increasing temperatures may have broad effects on both host and parasite [43–45] . DCV is a positive sense RNA virus in the family Discistroviridae that was originally isolated from Drosophila melanogaster and in the wild has been found in D . melanogaster and D . simulans [49–51] . DCV infected flies show reduced metabolic rate and activity levels , develop an intestinal obstruction , reduced hemolymph pH and decreased survival [52–55] . This work examines how temperature can influence the probability of host shifts , and looks at some of the potential underlying causes .
We used Drosophila C virus ( DCV ) clone B6A , which is derived from an isolate collected from D . melanogaster in Charolles , France [56] . The virus was prepared as described previously [57]; briefly DCV was grown in Schneider’s Drosophila line 2 cells and the Tissue Culture Infective Dose 50 ( TCID50 ) per ml was calculated using the Reed-Muench end-point method [58] . Flies were obtained from laboratory stocks of 45 different species . All stocks were maintained in multi generation populations , in Drosophila stock bottles ( Dutscher Scientific ) on 50ml of their respective food medium at 22°C and 70% relative humidity with a 12 hour light-dark cycle ( Table A in S1 Text ) . Each day , two vials of 0–1 day old male flies were randomly assigned to one of three potential temperature regimes; low , medium or high ( 17°C , 22°C and 27 °C respectively ) at 70% relative humidity . Flies were tipped onto fresh vials of food after 3 days , and after 5 days of acclimatisation at the experimental temperature were infected with DCV . Flies were anesthetized on CO2 and inoculated using a 0 . 0125 mm diameter stainless steel needle that was bent to a right angle ~0 . 25mm from the end ( Fine Science Tools , CA , USA ) [9 , 41 , 57] . The bent tip of the needle was dipped into the DCV solution ( TCID50 = 6 . 32×109 ) and pricked into the pleural suture on the thorax of the flies . We selected this route of infection as oral inoculation has been shown to lead to stochastic infection outcomes in D . melanogaster [55] . However , once the virus passes through the gut barrier , both oral and pin-pricked infections follow a similar course , with both resulting in the same tissues becoming infected with DCV [55] . One vial of inoculated flies was immediately snap frozen in liquid nitrogen to provide a time point zero sample as a reference to control for relative viral dose . The second vial of flies were placed onto a new vial of fresh cornmeal food and returned to their experimental temperature . After 2 days ( +/- 1 hour ) flies were snap frozen in liquid nitrogen . This time point was chosen based on pilot data as infected flies showed little mortality at 2 days post infection , and viral load plateaus from day 2 at 22°C . Temperatures were rotated across incubators in each block to control for incubator effects . All frozen flies were homogenised in a bead homogeniser for 30 seconds ( Bead Ruptor 24; Omni international , Georgia , USA ) in Trizol reagent ( Invitrogen ) and stored at -80°C for later RNA extractions . These collections and inoculations were carried out over three replicate blocks , with each block being completed over consecutive days . The order that the fly species were infected was randomized each day . We aimed for each block to contain a day 0 and day 2 replicate for each species , at each temperature treatment ( 45 species × 3 temperatures × 3 experimental blocks ) . In total we quantified viral load in 12 , 827 flies over 396 biological replicates ( a biological replicate = change in viral load from day 0 to day 2 post-infection ) , with a mean of 17 . 1 flies per replicate ( range across species = 4–27 ) . Of the 45 species , 42 had 3 biological replicates and three species had 2 biological replicates . The change in RNA viral load was measured using quantitative Reverse Transcription PCR ( qRT-PCR ) . Total RNA was extracted from the Trizol homogenised flies , reverse-transcribed with Promega GoScript reverse transcriptase ( Promega ) and random hexamer primers . Viral RNA load was expressed relative to the endogenous control housekeeping gene RpL32 ( RP49 ) . RpL32 primers were designed to match the homologous sequence in each species and crossed an intron-exon boundary so will only amplify mRNA [9] . The primers in D . melanogaster were RpL32 qRT-PCR F ( 5’-TGCTAAGCTGTCGCACAAATGG -3’ ) and RpL32 qRT-PCR R ( 5’- TGCGCTTGTTCGATCCGTAAC -3’ ) . DCV primers were 599F ( 5’-GACACTGCCTTTGATTAG-3’ ) and 733R ( 5’CCCTCTGGGAACTAAATG-3’ ) as previously described [41] . Two qRT-PCR reactions ( technical replicates ) were carried out per sample with both the viral and endogenous control primers , with replicates distributed across plates in a randomised block design . qRT-PCR was performed on an Applied Biosystems StepOnePlus system using Sensifast Hi-Rox Sybr kit ( Bioline ) with the following PCR cycle: 95°C for 2min followed by 40 cycles of: 95°C for 5 sec followed by 60°C for 30 sec . Each qRT-PCR plate contained four standard samples . A linear model was used to correct the cycle threshold ( Ct ) values for differences between qRT-PCR plates . Any samples where the two technical replicates had cycle threshold ( Ct ) values more than 2 cycles apart after the plate correction were repeated . To estimate the change in viral load , we first calculated ΔCt as the difference between the cycle thresholds of the DCV qRT-PCR and the RpL32 endogenous control . For each species the viral load of day 2 flies relative to day 0 flies was calculated as 2-ΔΔCt; where ΔΔCt = ΔCtday0 –ΔCtday2 . The ΔCtday0 and ΔCtday2 are a pair of ΔCt values from a day 0 biological replicate and a day 2 biological replicate . Calculating the change in viral load without the use of the endogenous control gene ( RpL32 ) gave equivalent results ( Spearman’s correlation between viral load calculated with and without endogenous control: ρ = 0 . 97 , P< 0 . 005 ) We carried out two assays to measure the thermal tolerances of species; a cold resistance measure to determine critical thermal minimum ( CTmin ) under gradual cooling , and a heat resistance measure through gradual heating to determine critical thermal maximum ( CTmax ) . 0–1 day old males were collected and placed onto fresh un-yeasted cornmeal food vials . Flies were kept for 5 days at 22°C and 70% relative humidity and tipped onto fresh food every 2 days . In both assays individual flies were placed in 4 ml glass vials ( ST5012 , Ampulla , UK ) and exposed to temperature change through submersion in a liquid filled glass tank ( see Fig A in S1 Text ) . For CTmax the tank was filled with water and for CTmin a mixture of water and ethylene glycol ( 50:50 by volume ) was used to prevent freezing and maintain a constant cooling gradient . Five biological replicates were carried out for each species for both CTmax and CTmin . Temperature was controlled using a heated/cooled circulator ( TXF200 , Grant Instruments , Cambridgeshire , UK ) submerged in the tank and set to change temperatures at a rate of 0 . 1 °C/min , always starting from 22°C ( the rearing temperature for stock populations ) . Flies were monitored continually throughout the assay and the temperature of knock down was ascertained by a disturbance method , whereby a fly was scored as completely paralysed if on gentle tapping of the vial wall the fly did not move any of its body parts . To examine how cellular function changes with temperature , we estimated the resting metabolic rate of each species at 17°C , 22°C and 27 °C to examine if changes in general cellular processes were related to changes in viral load . Following the same methods as the viral inoculation assay , groups of 10 , 0–1 day old male flies from 44 species were acclimatised at the three experimental temperatures for 5 days ( D . pseudoobscura was excluded as not enough individuals could be obtained from stocks for sufficient replication ) . Every 2 days flies were tipped onto fresh vials of cornmeal food . This was repeated in three blocks in order to get three repeat measures of metabolic rate for each of the species , at each of the three experimental temperatures . Flies were collected in a randomly assigned order across the three blocks . Closed system respirometry was used to measure the rate of CO2 production ( VCO2 ) as a proxy for metabolic rate [59] . Flies were held in 10ml-3 airtight plastic chambers constructed from Bev-A-Line V Tubing ( Cole-Parmer Instrument Company , UK ) . All measures were carried out during the day inside a temperature controlled incubator , with constant light , that was set to each of the experimental temperatures that the flies had been acclimatised to . The set up followed that of Okada et al . ( 2011 ) [60] . Compressed air of a known concentration of oxygen and nitrogen ( 21% O2:79% N2 ) was scrubbed of any CO2 and water ( with Ascarite II & Magnesium Perchlorate respectively ) and pumped through a Sable Systems RM8 eight-channel multiplexer ( Las Vegas , NV , USA ) at 100 ml/min-1 ( ±1% ) into the metabolic chambers housing the groups of 10 flies . The first chamber was left empty as a reference cell , to acquire a baseline reading for all subsequent chambers at the start and end of each set of runs , therefore seven groups of flies were assayed in each run . Air was flushed into each chamber for 2 minutes , before reading the previous chamber . Readings were taken every second for 10 minutes by feeding the exiting air through a LiCor LI-7000 infrared gas analyser ( Lincoln , NE , USA ) . Carbon dioxide production was measured using a Sable Systems UI2 analog–digital interface for acquisition , connected to a computer running Sable Systems Expedata software ( v1 . 8 . 2 ) [61] . The metabolic rate was calculated from the entire 10-minute recording period by taking the CO2 reading of the ex-current gas from the chamber containing the flies and subtracting the CO2 measure of the incurrent gas entering the chamber . These values were also corrected for drift away from the baseline reading of the empty chamber . Volume of CO2 was calculated as VCO2 = FR ( Fe CO2 –Fi CO2 ) / ( 1-Fi CO2 ) . Where FR is the flow rate into the system ( 100ml/min-1 ) , Fe CO2 is the concentration of CO2 exiting and Fi CO2 is the concentration CO2 entering the respirometer . Species were randomly assigned across the respiration chambers and the order in which flies were assayed ( chamber order ) was corrected for statistically ( see below ) . To check for any potential effect of body size differences between species on viral load , wing length was measured as a proxy for body size [62] . A mean of 26 ( range 20–30 ) males of each species were collected and immediately stored in ethanol during the collections for the viral load assay . Subsequently , wings were removed and photographed under a dissecting microscope . Using ImageJ software ( version 1 . 48 ) the length of the IV longitudinal vein from the tip of the proximal segment to where the distal segment joins vein V was recorded , and the mean taken for each species . The host phylogeny was inferred as described in Longdon et al ( 2015 ) [41] , using the 28S , Adh , Amyrel , COI , COII , RpL32 and SOD genes . Briefly , any publicly available sequences were downloaded from Genbank , and any not available we attempted to Sanger sequence [9] . In total we had RpL32 sequences for all 45 species , 28s from 41 species , Adh from 43 species , Amyrel from 29 species , COI from 38 species , COII from 43 species and SOD from 25 species ( see www . doi . org/10 . 6084/m9 . figshare . 6653192 full details ) . The sequences of each gene were aligned in Geneious ( version 9 . 1 . 8 , [63] ) using the global alignment setting , with free end gaps and a cost matrix of 70% similarity . The phylogeny was constructed using the BEAST program ( version 1 . 8 . 4 , [64] ) . Genes were partitioned into three groups each with their own molecular clock models . The three partitions were: mitochondrial ( COI , COII ) ; ribosomal ( 28S ) ; and nuclear ( Adh , SOD , Amyrel , RpL32 ) . A random starting tree was used , with a relaxed uncorrelated lognormal molecular clock . Each of the partitions used a HKY substitution model with a gamma distribution of rate variation with 4 categories and estimated base frequencies . Additionally , the mitochondrial and nuclear data sets were partitioned into codon positions 1+2 and 3 , with unlinked substitution rates and base frequencies across codon positions . The tree-shape prior was set to a birth-death process . The BEAST analysis was run twice to ensure convergence for 1000 million MCMC generations sampled every 10000 steps . The MCMC process was examined using the program Tracer ( version 1 . 6 , [65] ) to ensure convergence and adequate sampling , and the constructed tree was then visualised using FigTree ( version 1 . 4 . 3 , [66] ) . All data were analysed using phylogenetic mixed models to look at the effects of host relatedness on viral load across temperature . We fitted all models using a Bayesian approach in the R package MCMCglmm [67 , 68] . We ran trivariate models with viral load at each of the three temperatures as the response variable similar to that outlined in Longdon et al . ( 2011 ) [9] . The models took the form: yhit=β1:t+bmrh∙β2+wingsizeh∙β3+CTminh∙β4+CTmaxh∙β5+up:ht+ehit Where y is the change in viral load of the ith biological replicate of host species h , for temperature t ( high , medium or low ) . β are the fixed effects , with β1 being the intercepts for each temperature , β2 being the effect of basal metabolic rate , β3 the effect of wing size , and β4 and β5 the effects of the critical thermal maximum ( CTmax ) and minimum ( CTmin ) respectively . up are the random phylogenetic species effects and e the model residuals . We also ran models that included a non-phylogenetic random species effect ( unp:ht ) to allow us to estimate the proportion of variation explained by the host phylogeny [9 , 41 , 69] . We do not use this term in the main model as we struggled to separate the phylogenetic and non-phylogenetic terms . Our main model therefore assumes a Brownian motion model of evolution [70] . The random effects and the residuals are assumed to be multivariate normal with a zero mean and a covariance structure Vp ⊗ A for the phylogenetic affects and Ve ⊗ I for the residuals ( ⊗ here is the Kronecker product ) . A is the phylogenetic relatedness matrix , I is an identity matrix and the V are 3×3 ( co ) variance matrices describing the ( co ) variances between viral titre at different temperatures . The phylogenetic covariance matrix , Vp , describes the inter-specific variances in each trait and the inter-specific covariances between them . The residual covariance matrix , Ve , describes the within-species variance that can be both due to real within-species effects and measurement or experimental errors . The off-diagonal elements of Ve ( the covariances ) can not be estimated because no vial has been subject to multiple temperatures and so were set to zero . We excluded D . pseudoobscura from the full model as data for BMR was not collected , but included it in models that did not include any fixed effects , which gave equivalent results . Diffuse independent normal priors were placed on the fixed effects ( means of zero and variances of 108 ) . Parameter expanded priors were placed on the covariance matrices resulting in scaled multivariate F distributions , which have the property that the marginal distributions for the variances are scaled ( by 1000 ) F 1 , 1 . The exceptions were the residual variances for which an inverse-gamma prior was used with shape and scale equal to 0 . 001 . The MCMC chain was run for 130 million iterations with a burn-in of 30 million iterations and a thinning interval of 100 , 000 . We confirmed the results were not sensitive to the choice of prior by also fitting models with inverse-Wishart and flat priors for the variance covariance matrices ( described in [9] ) , which gave qualitatively similar results ( 10 . 6084/m9 . figshare . 6177191 ) . All confidence intervals ( CI’s ) reported are 95% highest posterior density intervals . Using similar model structures we also ran a univariate model with BMR and a bivariate model with CTmin and CTmax as the response variables to calculate how much of the variation in these traits was explained by the host phylogeny . Both of these models were also run with wing length as a proxy for body size as this is known to influence thermal measures [59] . We observed significant levels of measurement error in the metabolic rate data; this was partially caused by respiratory chamber order during the assay . We corrected for this in two different ways . First , we fitted a linear model to the data to control for the effect of respiratory chamber number and then used this corrected data in all further models . We also used a measurement error model that controls for both respiratory chamber number effects and random error . Both of these models gave similar results although the measurement error model showed broad CIs suggesting the BMR data should be interpreted with caution . All datasets and R scripts with the model parameterisation are provided as supporting information ( S1 Text ) .
To investigate the effect of temperature on virus host shifts we quantified viral load in 12 , 827 flies over 396 biological replicates , from 45 species of Drosophilidae at three temperatures ( Fig 1 ) . DCV replicated in all host species , but viral load differed between species and temperatures ( Fig 1 ) . Species with similar viral loads cluster together on the phylogeny ( Fig 2 ) . Measurements were highly repeatable ( Table 1 ) , with a large proportion of the variance being explained by the inter-specific phylogenetic component ( vp ) , with little within species or measurement error ( vr ) ( Repeatability = vp/ ( vp + vr ) : Low = 0 . 90 ( 95% CI: 0 . 84 , 0 . 95 ) , Medium = 0 . 96 ( 95% CI: 0 . 93 , 0 . 98 ) , and High = 0 . 95 , ( 95% CI: 0 . 89 , 0 . 98 ) ) . We also calculated the proportion of between species variance that can be explained by the phylogeny as vp/ ( vp+ vs ) [71] , which is equivalent to Pagel’s lambda or phylogenetic heritability [69 , 72] . We found the host phylogeny explains a large proportion of the inter-specific variation in viral load across all three temperatures , although these estimates have broad confidence intervals due to the model struggling to separate the phylogenetic and non-phylogenetic components ( Low = 0 . 77 , 95% CI: 0 . 28 , 0 . 99; Medium = 0 . 53 , 95% CI: 0 . 31×10−5 , 0 . 85; High = 0 . 40 , 95% CI: 0 . 99×10−5 , 0 . 74 ) To examine if species responded in the same or different way to changes in temperature we examined the relationships between susceptibilities across the different temperatures . We found strong positive phylogenetic correlations between viral loads across the three temperatures ( Table 2 ) . Our models showed that the variance in viral load increased with temperature , however the mean viral load showed no such upward trend ( Table 1 ) . This suggests that the changes in variance are not simply occurring due to an increase in the means , that is then driving an increase in variance . The high correlations suggest the rank order of susceptibility of the species is not changing with increasing temperature . However , the change in variance suggests that although the reaction norms are not crossing they are diverging from each other as temperature increases i . e . the most susceptible species are becoming more susceptible with increasing temperature , and the least susceptible less so [73] . For example , D . obscura and D . affinis are the most susceptible species at all three temperatures . The responses of individual species show that some species have increasing viral load as temperature increases ( Fig 1 , e . g . Z . taronus , D . lummei ) , while others decease ( e . g . D . littoralis , D . novamexicana ) . The changes we observe could be explained by the increase in temperature effectively increasing the rate at which successful infection is progressing ( i . e . altering where in the course of infection we have sampled ) . However , this seems unlikely as at 2 days post infection at the medium temperature ( 22°C ) , viral load peaks and then plateaus [41] . Therefore , in those species where viral load increases at higher temperatures the peak viral load itself must be increasing , rather than us effectively sampling the same growth curve but at a later time point . Likewise , in those species where viral load decreased at higher temperatures , viral load would need to first increase and then decrease , which we do not observe in a time course at 22°C [41] . To check whether this also holds at higher temperatures we carried out a time course of infection in a subset of six of the 45 original experimental species at 27°C , where we would expect the fastest transition between the rapid viral growth and the plateau phase of infection to occur ( Fig B in S1 Text ) . This allowed us to confirm that the decreasing viral loads observed in some species at higher temperatures are not due to general trend for viral loads to decline over longer periods of ( metabolic ) time . We quantified the lower and upper thermal tolerances ( CTmin and CTmax ) across all 45 species with 3 replicates per species . Neither CTmax nor CTmin were found to be significant predictors of viral load ( CTmin -0 . 21 , 95% CI: -0 . 79 , 0 . 93 , pMCMC = 0 . 95 and CTmax 0 . 31 , 95% CI: -0 . 11 , 0 . 74 , pMCMC = 0 . 152 ) . When treated as a response in models we found the host phylogeny explained a large proportion of the variation in thermal maximum ( CTmax: 0 . 95 , 95% CI: 0 . 84 , 1 ) and thermal minima ( CTmin: 0 . 98 , 95% CI: 0 . 92 , 0 . 99 , see S1 Text Fig C ) . We also measured the basal metabolic rate of 1320 flies from 44 species , across the three experimental temperatures , to examine how cellular function changes with temperature . BMR was not found to be a significant predictor of viral load when included as a fixed effect in our model ( slope = 9 . 09 , 95% CI = -10 . 13 , 20 . 2689 , pMCMC = 0 . 548 ) . BMR increased with temperature across all species ( mean BMR and SE: Low 0 . 64 ± 0 . 02 , Medium 1 . 00 ± 0 . 04 , High 1 . 2 ± 0 . 04 CO2ml/min-1 , see S1 Text Fig D ) . When BMR was analysed as the response in models , the phylogeny explained a small amount of the between species variation ( Low 0 . 19 , 95% CI: 2 × 10−8 , 0 . 55 , Medium 0 . 10 , 95% CI: 5 × 10−7 , 0 . 27 , High 0 . 03 , 95% CI: 8 × 10−9–0 . 13 , S1 Text Fig E ) indicating high within species variation or large measurement error . Consequently the mean BMRs for each species , at each temperature , were used in the analysis of viral load will be poorly estimated and so the effects of BMR will be underestimated with too narrow credible intervals . To rectify this we ran a series of measurement error models , the most conservative of which gave a slope of -9 . 8 but with very wide credible intervals ( -62 . 5 , 42 . 6 ) . Full details of these models are given in the Supporting Information ( S1 Text ) .
We found that susceptibilities of different species responded in different ways to changes in temperature . The susceptibilities of different species showed differing responses as temperatures increased ( Fig 1 ) . There was a strong phylogenetic correlation in viral load across the three experimental temperatures ( Table 2 ) . However , the variance in viral load increased with temperature , whereas the mean viral load did not show the same trend . This suggests that the rank order of susceptibility of the species remains relatively constant across temperatures , but as temperature increases the most susceptible species become more susceptible , and the least susceptible less so . Changes in global temperatures are widely predicted to alter host-parasite interactions and therefore the likelihood of host shifts occurring [5 , 21 , 47 , 74 , 75] . The outcome of these interactions may be difficult to predict if temperature causes a different effect in the host and pathogen species [18 , 37 , 76–78] . Our results show that changes in temperature may change the likelihood of pathogens successfully infecting certain species , although they suggest that it may not alter which species are the most susceptible to a novel pathogen . The increase in phylogenetic variance with temperature is effectively a form of genotype-by-environment interaction [28 , 79–81] . However , it varies from the classically considered ecological crossing of reaction norms , as we do not see a change in the rank order of species susceptibly across the range of experimental temperatures . Instead , we find the species means diverge with increasing temperatures and so the between species differences increase [73 , 82] . It is also important to note that temperature may not simply be causing a change in effect size when considering the biological processes occurring during host-parasite interactions [22 , 83] . For example , virus replication may plateau at higher temperatures due to resource limitation . The observed level of susceptibility may be the combined outcome of both host and parasite traits , which may interact nonlinearly with temperature . We also note that by using a limited range of temperatures for practical reasons we may have not captured all unimodal relationships between viral load and temperature . As temperature is an important abiotic factor in many cellular and physiological processes , we went on to examine the underlying basis of why viral load might change with temperature . Previous studies that found phylogenetic signal in host susceptibility were carried out at a single experimental temperature [9 , 41] . Therefore , the patterns observed could potentially be explained by some host clades being assayed at sub-optimal thermal conditions . We used CTmax and CTmin as proxies for thermal optima which , due to its multifaceted nature , is problematic to measure directly [84–86] . We also measured basal metabolic rate across three temperatures to see if the changes in viral load could be explained by general increases in enzymatic processes . We found that these measures were not significant predictors of the change in viral load with temperature . This may be driven by the fact that all temperature related traits are likely to be more complex than what any single measure can explore . Traits such as host susceptibility are a function of both the host and parasite thermal optima , as well as the shape of any temperature-trait relationship [37 , 78] . The host immune response and cellular components utilised by the virus are likely to function most efficiently at the thermal optima of a species , and several studies have demonstrated the outcomes of host-pathogen interactions can depend on temperature [26 , 28 , 76 , 81] . However , the mechanisms underlying the changes in susceptibility with temperature seen in this study are uncertain and a matter for speculation . Our results show that in the most susceptible species , viral load increases with temperature; this may be due to the virus being able to successfully infect and then freely proliferate , utilizing the host cells whist avoiding host immune defences . In less susceptible species viral load does not increase with temperature , and in some cases it actually appears to decreases . Here , temperature may be driving an increase in biological processes such as enhanced host immunity , or simply increasing the rate of degradation or clearance of virus particles that have failed to establish an infection of host cells . We have investigated how an environmental variable can alter infection success following a novel viral challenge . However , temperature is just one of the potential environmental factors that will influence the different stages of a host shift event [8] . Using a controlled method of viral inoculation allows us to standardize inoculation dose so we can ask , given equal exposure , how does temperature affect the ability of a pathogen to persist and replicate in a given host ? However , in nature hosts will be faced with variable levels of pathogen exposure , infected through various modes of transmission and often by multiple strains or genotypes [87] . Such variables may have consequences for the establishment and subsequent infection success of any potential host shift event . It is known that oral infection by DCV is stochastic and immune barriers such as the gut are important [55 , 88 , 89] , therefore establishing the relevance of infection in the wild in this system would require further study using different potential routes of infection . The geographical distribution of a host will also influence factors such as diet and resource availability [28 , 90–93] , and so further work on the role of nutrient and resource availability would therefore be needed to further explore the impact of these on potential host shifts . In conclusion , we have found changes in temperature can both increase or decrease the likelihood of a host shift . Our results show the rank order of species’ susceptibilities remain the same across temperatures , suggesting that studies of host shifts at a single temperature can be informative in predicting which species are the most vulnerable to a novel pathogen . Changing global temperatures may influence pathogen host shifts; for example changes in distributions of both host and pathogen species may generate novel transmission opportunities . Our findings suggest that increases in global temperature could increase the likelihood of host shifts into the most susceptible species , and reduce it in others . Climate change may therefore lead to changing distributions of both host and pathogens , with pathogens potentially expanding or contracting their host range . Understanding how environmental factors might affect broader taxonomic groups of hosts and pathogens requires further study if we are to better understand host shifts in relation to climate change in nature . | Emerging infectious diseases are often the result of a host shift , where a pathogen jumps from one host species into another . Understanding the factors underlying host shifts is a major goal for infectious disease research . This effort has been further complicated by the fact that host-parasite interactions are now taking place in a period of unprecedented global climatic warming . Here , we ask how host shifts are affected by temperature by carrying out experimental infections using an RNA virus across a wide range of related species , at three different temperatures . We find that as temperature increases the most susceptible species become more susceptible , and the least susceptible less so . This has important consequences for our understanding of host shift events in a changing climate as it suggests that temperature changes may affect the likelihood of a host shift into certain species . | [
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"microbiology... | 2018 | Changes in temperature alter the potential outcomes of virus host shifts |
Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs . Yet fitness landscapes are vast and complex , and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics . Here , we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides , and develop a network model to describe the relationship between sequence and structure . We find that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics . A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes . This supports an “ascent of the abundant” hypothesis , in which evolution yields abundant phenotypes even when they are not the most fit .
Despite its familiar slogan—“survival of the fittest”— evolution by natural selection may not always yield optimal organisms . In particular , it will be fundamentally constrained by the variation introduced into populations by mutation or migration . If better traits never arise , then natural selection will never have the opportunity to favor them . Whereas adaptive constraints are central to evolutionary theory [1]–[3] , there have been relatively few empirical characterizations of them [4]–[8] . Several of these studies suggest that selection can overcome putative constraints [6]–[7] . Yet , one study of the enzyme beta-isopropylmalate dehydrogenase ( IMDH ) concludes that adaptation is constrained by its spectrum of mutations [8] . With the introduction of the fitness landscape metaphor , Sewell Wright was one of the first to argue for the importance of adaptive constraints [9] . In contrast to Fisher's panselectionist views [10] , Wright suggested that fitness valleys—low-fitness genotypes separating high-fitness genotypes—may preclude simple incremental evolution [9] . He argued that adaptation depends on both the structure of the fitness landscape ( that is , the spectrum of possible mutations ) and demographic conditions . Since the 1930s , the theory of evolutionary constraints has matured , but is largely premised on hypothetical fitness landscapes or very local estimates of mutational effects [11] , [12] . For most phenotypes of interest , we cannot yet model complete fitness landscapes . It requires knowing the fitnesses across large sets of genotypes , typically too vast to exhaustively study either empirically or computationally . There are , however , a few biologically important phenotypes for which this is tractable . In particular , Eigen and Schuster pioneered the study of RNA molecules , using RNA secondary-structure folding algorithms as tractable genotype-to-phenotype maps [12] , [13] . In their model , the genotype of a molecule is its primary sequence and the phenotype is its predicted minimum free energy secondary structure; fitness is based entirely on the similarity of a phenotype to an ideal target structure . Through extensive sampling ( that is , folding many diverse sequences ) and evolutionary simulations , this system has motivated and clarified several important ideas in modern evolutionary theory , including error catastrophes , quasispecies , neutral networks , and punctuated equilibria [14]–[23] . The most influential concept to emerge from these RNA studies is that of “neutral networks” , which are sets of genotypes with identical fitness that are interconnected by neutral mutations [15] . In the RNA model , the genotypes in a neutral network are sequences that fold into the same shape and are connected to each other by paths of neutral point mutations . The neutral networks of RNA and protein molecules appear to share three basic characteristics: ( i ) most neutral networks are small ( contain few genotypes ) , whereas relatively few are large ( contain many genotypes ) ; ( ii ) large neutral networks are mutationally adjacent to a greater diversity of phenotypes than small neutral networks; and ( iii ) large neutral networks span the entire sequence space [15] , [24]–[26] . Based on these characteristics , researchers have proposed that large neutral networks should facilitate evolution by allowing populations to explore vast regions of regions of fitness landscapes through neutral drift [15] , [18] , [24] , [26] , [27] . There is some evidence to support this assertion , though it is largely based on sampling studies [15] , [24] , [26] or simulation studies with strong assumptions about the fitness landscape [26] . Most recently , Wagner ( 2008 ) showed that populations evolving on large neutral networks sample more alternative phenotypes than those evolving on small neutral networks , yet these populations were constrained to explore a single neutral network . Whether large neutral networks actually facilitate the evolution of optimal phenotypes fundamentally depends on the global structure of mutational connections between different neutral networks . If large neutral networks are almost exclusively connected to other large neutral networks , then populations will easily move among common phenotypes , but be unable to evolve rare phenotypes . Theoretical and computational characterizations of RNA fitness landscapes suggest that this may , in fact , be the case . Yet , these predictions are largely based on relatively small samples of sequences which may include only the most common phenotypes in the fitness landscape [15] , [24] . Here , we use the RNA folding model to determine the complete structure of fitness landscapes and how neutral network size and adjacencies constrain evolutionary dynamics ( for better or for worse ) . Specifically , we fold all RNA molecules of lengths 12 to 18 nucleotides , and then develop a network model describing the patterns of mutational connectivity among the phenotypes produced by molecules of the same length . We build on previous characterizations of RNA neutral network structure [15] , [25] , [28] , [29] , and argue that the mutational connectivity among phenotypes follows simple predictable patterns that fundamentally constrain evolution .
RNA molecules fold into secondary structures that are the essential scaffolds for functional tertiary structures and are evolutionarily conserved for most functional RNA molecules [30] . The formation of secondary structures is relatively well understood and can be rapidly predicted using thermodynamic minimization [31]–[34] . We used the Vienna RNA folding software [version 1 . 6 . 1 with the default parameter set; [33] to predict the lowest free energy shapes of all RNA molecules of lengths 12–18 nucleotides . We assume that the shape of a molecule is a reasonable proxy for its fitness [19] , [21] , [23] and refer to each map from sequences of length n to their predicted shapes as an n-mer fitness landscape . We studied evolutionary dynamics on the 12-mer fitness landscape by computationally simulating a population of evolving RNA molecules . The molecules stochastically replicate at each discrete generation in proportion to their fitnesses , and evolve by point mutations . We and others have used similar models to study many aspects of RNA evolutionary dynamics [18]–[23] , [35] . An important feature of the RNA system is that the fitness effect of a point mutation stems from a biologically explicit model of molecular structure and is not simply selected from a probability distribution of mutational effects , as in simpler evolutionary models . To compute the fitness of a molecule , we first predict its minimum free energy secondary structure ( that is , its groundstate ) , and then compare this predicted structure with a pre-specified target structure . Specifically , if σ is the groundstate of a molecule m and t is the target structure , then the fitness of the molecule W is given by ( 1 ) where α = 0 . 01 and β = 1 are scaling constants , d ( σ , t ) is the Hamming distance between the parenthetical representations of σ and t , ( parenthetical notation represents paired bases with pairs of parentheses and unpaired bases with dots ( e . g . , ( ( ( . . . . ) ) ) is a simple stem-loop structure ) and L = 12 is the length of the sequence . The range of fitness values possible given our choice of parameters is 0 . 99 - 100 . 0; except the open-chain shape , which was assigned a fitness of zero . Several other studies using this computational model have shown that the qualitative results are largely insensitive to the choice of parameters and even the shape of the fitness function [18]–[21] , [23] . For every starting structure-target structure combination , we adapted 20 replicate populations for τ = 1 , 000 , 000 generations . The population size was held fixed at N = 1000 , which was chosen both for computational tractability and to limit the effects of genetic drift . The genomic mutation rate was maintained at U = 0 . 0003 ( NU = 0 . 3 ) for all bases in the RNA alphabet . We used soft-selection ( constant N ) to maintain the population size when genotypes that fold into the open-chain shape occasionally appear . The expansive and intertwining neutral networks smooth the fitness landscape so that virtually every phenotype can mutate to at least one fitter phenotype , except , of course , the optimal ( target ) phenotype . Yet the likelihood of finding a more fit mutation while drifting on a large neutral network may be exceedingly small . Specifically , 96 . 7% of all neutral networks have at least one beneficial mutation ( across all fitness functions considered in this study ) , and there always exists a path of beneficial and neutral mutations leading to the target phenotype . In our simulations , the average time to target was 339111 . 7 generations; and there is no significant correlation between time to target and the abundance of the target . The simulations were allowed to run for approximately three times longer than the typical time to acquire the target , and 100 times longer than the evolutionary simulations reported in other studies using this system [18]–[21] , [23] . Two sets of simulations with different parameter sets ( N = 500 , U = 0 . 05 , τ = 5 , 000; N = 1000 , U = 0 . 005 , τ = 250 , 000 ) produced similar results to those reported here ( not shown ) . The parameters were selected to be biologically reasonable and do not appear to strongly affect the outcome . Although even the most unlikely phenotype can evolve given infinite time , we believe that our results reflect the likely course of evolution . Rfam is a curated database of functional RNA genes , which are those genes in which the RNA molecule itself takes parts in a biological reaction [36] . Here , we used version 7 ( 2006 ) of the database . We restricted our analysis to families in which the predicted shape of each sequence in the family was at least 60% identical to the consensus structure , thereby minimizing the effects of folding inaccuracies . This included 239 Rfam families ( about 50% of the entire database ) with representatives of every functional class in the database . Abundance estimates were obtained by calculating contiguity statistics for the secondary structures predicted by thermodynamic minimization of each sequence in a family . We then determined the rank percentiles of these abundance estimates in a null distribution of abundance estimates from random sequences . To generate the null distributions , we randomized each sequence in a family 500 times ( preserving nucleotide composition ) , and then calculated the contiguity statistics of the ground-state shapes of these random molecules . We finally determined the fraction of contiguity statistics in the null distributions that were less than the contiguity statistic from the naturally occurring molecule ( Figure 1 ) . Receiver operating curve ( ROC ) analysis is a technique for assessing the performance of classifier models [37] . The area under an ROC gives the probability that a model correctly assigns a binary variable ( in this case , natural or random molecule ) to its proper group . We used ROC analysis to assess relative accuracies of thermostability and contiguity for classifying sequences as natural ( taken from the Rfam database ) or random , under the assumption that natural molecules will have higher contiguity and thermostability than random permutations of those molecules . Specifically , we performed logistic regressions of molecule class ( natural or random permutation ) on contiguity statistic and thermostability , and compute the area ( A ) under the ROC as:where P and N are the numbers of positive and negative instances in the data set , TP and FP are the counts of true positive and false positive classifications between indices i and j . We used the ROCR package to perform all such calculations in R 2 . 5 . 0 [38] .
We have predicted the groundstate structures of all RNA molecules of lengths 12 through 18 nucleotides; we refer to length n RNA molecules as n-mers . The map from sequences to shapes is extremely degenerate with large numbers of sequences ( genotypes ) giving rise to identical shapes ( phenotypes ) , as previously observed [15]–[17] . We found that the number of unique phenotypes approximately doubles with each single-base addition , from 59 unique 12-mer shapes to 3211 unique 18-mer shapes . Some of these shapes are quite common , with many unique genotypes folding into them , while others are quite rare , formed by few unique genotypes . We define abundance as the number of genotypes that produce a particular phenotype . The distributions of phenotype abundances appear similar across all lengths of molecules ( roughly exponential without the 10% of extreme values in each tail ) , with relatively few highly abundant phenotypes and many rare ones Figure 2 . This is qualitatively similar to the distributions reported previously for both protein and larger RNA molecules [15]–[17] , [29] . Figure 2 shows a portion of the abundance distribution and a sample of shapes present in the 12-mer fitness landscape . For the 12-mer to 16-mer sequence lengths , the landscapes are composed entirely of variations on stem-loop-structures . In the 17- and 18-mer landscapes , we observe the emergence of sequences folding into multi-loop shapes , albeit at very low frequencies ( on the order of 0 . 001% of all sequences ) . The relatively low structural diversity is consistent with known constraints on RNA structural motifs , for example , loops must contain at least three nucleotides [31] , [33] . A set of genotypes that shares a common phenotype is called the neutral network of that phenotype ( Figure 3 ) [15] . Neutral networks may be composed of one or more components . Within any component , all genotypes are connected to each other by a sequence of point mutations that remain within the component; these mutations are , by definition , neutral . For example , in the bottom network of Figure 3B , the red phenotype has a neutral network with two components , each of which consists of a set of red nodes interconnected by red edges . The abundance of a phenotype is precisely the size of its neutral network . Counterintuitively , there is only a weak positive relationship between the abundance of a phenotype and the number of distinct components in its neutral network ( r2 = 0 . 11 , P≈0 . 01 ) . The majority of the 12-mer RNA neutral networks are dominated by relatively few large components , which each contain approximately 8–10% of the sequences in the neutral network; together these large components account for at least 80% of the neutral network . Importantly , the large components share many of the same characteristics as the entire neutral network . In particular , they are each mutationally connected to the majority of the shapes that are adjacent to the entire neutral network ( typically >75% ) . Figure 2 also reports the number of components ( Nc ) , the maximum Hamming distance between a pair of sequences in a single component ( Dmax ) , and the maximum shortest path length between a pair of sequences in a single component ( Dspl ) for the neutral networks in the 12-mer landscape . The neutral networks for the most abundant phenotypes percolate through the entire space of genotypes . The various phenotypes within a fitness landscape are connected to each other by mutations . If we aggregate all genotypes into their respective neutral networks , we create a mutational network in which each vertex represents a distinct phenotype and edges connect pairs of vertices when there is at least one point mutation that converts one phenotype to the other ( Figure 3 ) . For example , consider a two-locus , two-allele , haploid model with genotypes AB , Ab , aB , and ab ( Figure 3A ) . There are three unique phenotypes–the two ( A- ) genotypes produce one phenotype ( blue ) , aB produces another phenotype ( green ) , and ab produces a third phenotype ( purple ) . Mutational networks , in turn , form the underpinnings for fitness landscapes , which depend on the map from phenotype to fitness . Figure 3B caricatures a higher dimensional genotype network and its projections to phenotype and fitness networks . For RNA molecules , the vertices in a mutational network represent unique shapes and the edges represent point mutations that cause a molecule to fold into a new shape . Roughly speaking , evolution by natural selection moves populations along the edges in a mutational network from one phenotype vertex to another . We are therefore interested in how the structure of mutational networks influences evolutionary dynamics . Intuitively , the structure of a mutational network may influence ( i ) the likelihood that a given phenotype will arise and , ( ii ) if it arises , the likelihood that the population can further evolve other , better phenotypes . Hereafter , we use accessibility to refer to the likelihood that a phenotype will arise , and evolvability as the likelihood that a phenotype can further evolve other , better phenotypes . The most straightforward measure of a phenotype's mutational connectivity is its degree in the mutational network , that is , the number of other phenotype that can be reached by a single mutation . For the 12-mer through 18-mer RNA molecules , there are significant positive correlations between phenotype abundance and degree [R = 0 . 88 ( 12-mer ) to R = 0 . 91 ( 18-mer ) ; P<2×10−16] . This has been observed previously and suggests that abundant phenotypes should be both more evolvable and more accessible than rare phenotypes [24] , [26] , [27] . The degree of a phenotype is , however , a crude indicator of its mutational connectivity to other phenotypes . It does not reflect the probability that a mutation will actually yield a new phenotype; this probability typically declines as the size of the neutral network increases . Furthermore , the degree does not quantify whether the non-neutral mutations off a neutral network are evenly divided among the set alternative phenotypes , or are biased towards a select few of these phenotypes . We therefore developed two novel statistics , which provide a more nuanced perspective on mutational connectivity . Both of these statistics use the quantity , where νij is the number of point mutations to genotypes in the neutral network for phenotype i that create a genotype in the neutral network for phenotype j , and is the total number of non-neutral point mutations to genotypes in the neutral network for phenotype i . Thus , fij is the fraction of non-neutral point mutations to genotypes in the neutral network for phenotype i that create genotypes in the neutral network for phenotype j . Large values of this fraction indicate that phenotype j is relatively easy to find ( via random mutations ) from phenotype i . Mutational proximity is often not symmetric ( that is , fij≠fji ) , because the denominators differ . The first statistic estimates the overall accessibility of phenotype i from other phenotypes in the landscape using . Large values of Ai indicate that phenotype i is relatively accessible from throughout the landscape . The second statistic quantifies the potential for evolution away from phenotype i using a variation on Simpson's diversity index: . This index indicates the diversity of other phenotypes that can be easily produced by mutations from a given phenotype , and thus may indicate the potential for further adaptation away from that phenotype . Specifically , it gives the probability that two randomly chosen non-neutral mutations to genotypes within a given neutral network will result in the same phenotype . The index is large for phenotypes that are adjacent to many other phenotypes , and its non-neutral mutations are fairly evenly divided among the adjacent phenotypes; it is small for phenotypes that primarily mutate to one or very few alternate phenotypes . In the 12-mer landscape , A increases significantly with the abundance of a phenotype ( Figure 4 , top pane ) . In other words , random mutations are more likely to move genotypes to a large neutral network than to a small neutral network . In contrast , E decays significantly with phenotype abundance ( Figure 4 , middle pane ) , suggesting that it may be more difficult to evolve away from large neutral networks than small neutral networks . To provide more insight into the mutational networks , we also calculated the average abundance of phenotypes reached by mutation from phenotype i using . We find that the average abundance of neighboring phenotypes significantly increases with the abundance of a phenotype ( Figure 4 , bottom pane ) , meaning that the majority of non-neutral mutations to abundant phenotypes produce other abundant phenotypes . Thus far we have characterized the mutational networks formed by single point mutations . If we instead considered the mutational networks formed by all combinations of one , two or three mutations , then the phenotype network becomes highly interconnected . The number of adjacent phenotypes significantly increases with multiplicity of mutations considered ( mean node degrees are 42 . 7 , 53 . 6 , and 57 . 2 for the one , two , and three mutant adjacencies , respectively; P<5×10−3 ) , and the network is nearly completely connected for triple mutations . Thus , under elevated mutation rates , populations may be able to attain rare phenotypes easier than expected based on point mutation adjacencies . In summary , these observations suggest that abundant phenotypes may be easy to find but difficult to escape , and thus the structure of a fitness landscape may significantly constrain evolutionary dynamics . Whereas the accessibility of abundant shapes is rather intuitive , the prediction that their vast neutral networks can hinder further evolution contradicts a large body of theory , which suggests that large neutral networks should enhance evolvability [18] , [26] , [27] . We note that this evolutionary constraint was previously proposed for a simple fitness landscape model [39] . To test the hypothesis that highly abundant phenotypes are readily accessible , yet poorly poised for further evolution , we ran stochastic simulations of an adapting population of 12-mer RNA molecules using an established model ( see Materials and Methods for details ) [18]–[21] , [23] . Since we are interested in the effect of phenotype abundance on the capacity of selection to acquire the optimal phenotype , we selected the phenotypes of the founding populations ( henceforth , founding phenotypes ) and target shapes to span the range of abundances found among the 12-mer phenotypes . We chose ten founding phenotypes [ranks ( abundance ) : 3 ( 183 , 791 ) , 8 ( 117 , 213 ) , 13 ( 76 , 478 ) , 18 ( 61 , 699 ) , 23 ( 39 , 740 ) , 28 ( 27 , 312 ) , 33 ( 11 , 354 ) , 38 ( 2 , 260 ) , 43 ( 1 , 299 ) , 48 ( 713 ) ] and randomly selected 20 genotypes from the neutral network of each founding phenotype to form 200 isogenic founding populations . Each founding population was composed of a single genotype and , therefore , a single phenotype . In essence , we simulated adaptation starting from 20 random points in the neutral network of each founding phenotype . We separately adapted each founding population to twelve target phenotypes [ranks ( abundance ) : 2 ( 218 , 576 ) , 7 ( 122 , 332 ) , 12 ( 93 , 866 ) , 17 ( 61 , 895 ) , 22 ( 41 , 092 ) , 27 ( 27 , 522 ) , 32 ( 15 , 348 ) , 37 ( 2 , 963 ) , 42 ( 1 , 368 ) , 47 ( 800 ) , 52 ( 240 ) , 57 ( 109 ) ] . We considered adaptation successful if the population ever acquired the target phenotype , regardless of its frequency in the population . In the successful runs , however , the target phenotype quickly dominates the populations and rises to frequencies of nearly N ( the population size ) . The mutational connectivity statistics described above ( Ai and Ei ) will only be good indicators of evolutionary dynamics if the probability of mutating from phenotype i to phenotype j correlates with the fraction of mutations to i that produce j ( fij ) . To test this basic assumption , we compared the phenotype mutation rates observed in the simulations ( fraction of mutations to i that produce j ) to fij ( the fraction of non-neutral point mutations to genotypes in the neutral network for phenotype i that create genotypes in the neutral network for phenotype j ) . In fact , we find an almost perfect relationship between the two quantities ( Figure 5A ) , suggesting that mutational network structure fundamentally constrains evolution and that Ai and Ei are good indicators of these constraints . Across the 2400 simulations , we observed a significant positive correlation between the abundance of the target phenotype and the likelihood that a population successfully evolved to the target ( Figure 6A ) . This is consistent with the positive relationship between phenotype abundance and mutational accessibility , as indicated by the A statistic ( Figure 4A ) . Phenotype abundance also positively correlates with the number of times a phenotype arises in the evolving populations ( Figure 7A ) . Taken together , these results support our hypothesis that abundant shapes are more likely to appear via mutation in evolving populations than are rare shapes . We did not , however , observe a relationship between the founding phenotype abundance and the ultimate evolutionary outcome ( Figure 6B ) . When a simulation failed to acquire the target , the population was primarily composed of phenotypes of greater abundance than both the target phenotype and the average abundance of a random phenotype , demonstrating that the structure of mutational networks can steer populations towards abundant , but non-optimal , phenotypes . As suggested by the negative relationship between abundance and the E statistic , evolution away from abundant phenotypes appears to be limited by the improbability of beneficial mutations . In support of this explanation , we also find a significant positive correlation between the abundance of a phenotype and the duration of the phenotype in the evolving populations ( Figure 7B ) . These observations appear to be inconsistent with the widely-held belief that neutral networks facilitate evolution by allowing populations to traverse large regions of fitness landscapes without reducing fitness [15] , [18]–[20] , [26] , [27] , [40] . In our simulations , populations readily evolve from one abundant shape to another ( that is , from one large neutral network to another ) , but are often unable to evolve rare phenotypes . Thus , while the hypothesis that neutrality ( the fraction of mutations that are neutral ) allows populations to explore phenotype space is true , the evolutionary outcome of such exploration is generally confined to other abundant phenotypes . Most of the prior studies addressing this hypothesis are based on relatively small random samples of sequences from large genotype spaces , which may consist of exclusively abundant phenotypes . The conclusion that neutrality facilitates evolution is reasonable when considering only abundant subsets of fitness landscapes , but is somewhat misleading when one considers the fitness landscapes in their entirety . These results suggest the following hypothesis: the evolution of phenotypes , whether complex whole-organism phenotypes or RNA shapes , may be biased toward abundant phenotypes , even if those phenotypes are not optimal . We cannot , however , test this hypothesis by directly measuring the abundances of complex organism-level phenotypes since we cannot yet completely characterize their fitness landscapes . As a first step in this direction , we have developed a simple structural statistic that allows us to indirectly estimate the abundances of naturally occurring RNA shapes , which are much larger and more complex than those considered thus far . Across the n-mer phenotypes , we observed that longer contiguous helical stacks ( stems ) form more frequently than shorter contiguous stacks and stacks that contain bulges ( which break up helices ) . We quantify this with a new statistic ( Figure 8 ) given by This contiguity statistic significantly correlates with log phenotype abundance in the 12- through 18-mer landscapes [r ranges from r = 0 . 71 ( P = 3 . 6×10−10 ) in the 12-mer landscape to r = 0 . 69 ( P<2 . 2×10−16 ) in the 18-mer landscape] . The utility of the contiguity statistic is that one genotype is sufficient to estimate the abundance of its phenotype . We conjecture , therefore , that we can use the contiguity statistic to ask whether naturally occurring RNA molecules are biased towards abundant shapes . We used the contiguity statistic to estimate the abundances of the RNA molecules in Rfam , a curated database of functional RNA genes [36] . The Rfam molecules are grouped into families , and every sequence in a family is thought to code for the same functional RNA . We compared the contiguity statistics calculated for the Rfam sequences to null distributions generated by calculating contiguity statistics for thousands of random permutations of those sequences . Specifically , for each naturally evolved molecule , we determined whether the contiguity statistics of their predicted shapes were significantly larger than the contiguity statistics of random molecules from the same fitness landscape ( see Methods for details ) . The structures of the natural RNA molecules indeed have larger contiguity statistics than randomly chosen structures from the same fitness landscapes ( Figure 1 ) . This observation supports an “ascent of the abundant” hypothesis in which the mutational networks connecting diverse phenotypes may steer populations toward abundant , though not necessarily optimal , phenotypes . Yet , Figure 1 ( red squares ) shows that natural molecules are also significantly more thermostable than random molecules . Thus one must ask whether the high contiguity values of natural molecules are simply byproducts of the evolution of thermostability ( or some other advantageous structural property ) or , in fact , exist because of mutational biases towards abundant shapes , or both . The abundances of the natural molecules ( as estimated by their contiguity statistics ) are even more statistically pronounced than their thermostabilities . We used logistic regression analysis to ask which of contiguity or thermostability better distinguishes naturally occurring molecules from their random permutations . We regressed molecule class ( natural or random permutation ) on contiguity statistic and ( separately ) on thermostability . The area under a receiver operating curve ( ROC ) gives the probability that a model correctly assigns a binary variable ( natural or random molecule ) to its proper group . The logistic model for contiguity yielded an area under the ROC of 0 . 82 , which is good; the model for thermodynamic stability yielded an area under the ROC of 0 . 62 , which is poor . Our results are therefore consistent with an apparent biases towards abundant phenotypes in both the small RNA landscapes and natural RNAs are not simply byproducts of natural selection for thermostability .
Evolutionary biologists have long appreciated that the evolutionary potential of a phenotype depends on the breadth of its neutral network . Eigen's error catastrophe theory , an extension of classic mutation-selection balance theory , argues that the evolutionary potential of a phenotype depends on both its fitness relative to alternative phenotypes and its robustness to mutations [41] . Under high mutation rates , only phenotypes with sufficiently large and connected neutral networks can persist . The phrase “survival of flattest” has been used to refer to the evolutionary success of low-fitness phenotypes with large neutral networks over higher-fitness phenotypes with small neutral networks [42] . Critically , this idea assumes that these diverse phenotypes compete directly with one another in an evolving population . The relationship between abundance and evolvability that we have described here is not a simple restatement of this idea . Instead , the evolutionary tendency towards abundant phenotypes results from a biased exploration of phenotype space . Abundant phenotypes are more discoverable ( random mutations are more likely to produce abundant phenotypes ) and more inescapable ( once abundant phenotypes evolve , it is very hard to mutate to other phenotypes ) . In our simulations , we observed that , when the populations failed to acquire the target phenotype , it was not due to the target shape being lost to mutation pressure or other forces . In the failed simulations , the target phenotype never appeared in the first place ( not shown ) . Our results extend ideas developed in prior studies of both RNA and protein structural evolution [15] , [29] . In particular , Schuster et al . argued that abundant RNA phenotypes are within a few mutations of almost any genotype in the landscape [15] , and Reidys et al . further demonstrated that only abundant phenotypes have neutral networks that percolate through the entire sequence space [24] . As a result , evolutionary biologists have proposed that large neutral networks greatly enhance the evolutionary potential of evolving populations [15] , [18] , [24] , [26] , [27] . Yet , these studies largely focused on the local structure of neutral networks and not global patterns of mutational connectivity . Here we have taken a global perspective and found that large neutral networks are more likely to impede than enable evolution . The probability of a non-neutral mutation and the diversity of phenotypes produced by such mutations both decline as neutral network size increases ( Figure 4 , middle ) . In our simulations , populations on large neutral networks were no more likely to evolve better phenotypes than populations on small neutral networks ( Figure 6 ) . Furthermore , these populations spent more time on large neutral networks than small neutral networks ( Figure 7B ) . Our results more generally suggest that the structure of RNA mutational networks favors the evolution of abundant phenotypes , even when rare phenotypes are more fit . Abundant phenotypes are more likely to arise via a random mutation than rare phenotypes , and , once established in the population , are more difficult to escape via subsequent mutations . This gives a new perspective on the widely-accepted hypothesis that large neutral networks facilitate evolution [15] , [18] , [24] , [26] , [27] . While large neutral networks enable populations to explore large regions of fitness landscapes via mutation , the outcome of such exploration is almost always evolution to another abundant phenotype rather than to a rare phenotype . Thus , in the larger scheme of things , neutrality may serve as a trap rather than a catalyst for evolution . While our study suggests that naturally occurring RNA molecules are biased towards abundant shapes , we recognize that abundance may have evolved as a byproduct of correlated biophysical or biochemical properties that enhance the functionality of molecules . We specifically address the possibility that the abundance bias may be driven by thermostability . Our simulation study shows that abundant shapes will evolve in the absence of natural selection for thermostability , and our analysis of natural RNA molecules indirectly suggests that thermostability alone cannot account for the bias toward abundant shapes . We believe that both processes have probably contributed to the prevalence of abundant shapes: ( i ) natural selection for thermostability and/or other beneficial molecular properties that correlate with abundance and ( ii ) the underlying structure of the mutational network . We contend that the second process is important and perhaps has precluded the evolution of functionally optimal molecules . In closing , we have further characterized the relationship between phenotype abundance and mutational connectivity , and explored its evolutionary implications . The abundance of a phenotype positively correlates with the probability of randomly mutating to that phenotype and negatively correlates with the probability of randomly mutating away from that phenotype to alternative phenotypes . Consequently , the evolutionary potential of a phenotype critically depends on its abundance , and mutational networks therefore can fundamentally constrain evolution . As we learn more about the structure of mutational networks , we can gain new perspectives on the history and function of natural systems and better methods for artificially selecting molecules with desired functions . Characterizing mutational networks remains a formidable challenge , particularly when we consider more complex phenotypes and sources of variation beyond simple point mutations . We can approach these larger landscapes using statistical shortcuts , like the contiguity statistic introduced here , that indirectly provide information about the global structure of the fitness landscape , or by designing farther-reaching mutagenesis experiments . | Evolutionary biology tells us much about the immediate fate of a mutation once it appears , but relatively little about its long-term evolutionary implications . Major evolutionary transitions from one trait to another may depend on a long sequence of interacting mutations , each arising by chance and surviving natural selection . In this study , we characterize the network of mutations that connect diverse molecular structures , and find that this network biases evolution toward traits that are readily produced by one or a short sequence of mutations . This bias may prevent the evolution of optimal traits , a phenomenon they call the “ascent of the abundant . ” | [
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] | 2008 | The Ascent of the Abundant: How Mutational Networks Constrain Evolution |
Plant pathogens secrete an arsenal of effector proteins to impair host immunity . Some effectors possess enzymatic activities that can modify their host targets . Previously , we demonstrated that a Phytophthora sojae RXLR effector Avr3b acts as a Nudix hydrolase when expressed in planta; and this enzymatic activity is required for full virulence of P . sojae strain P6497 in soybean ( Glycine max ) . Interestingly , recombinant Avr3b produced by E . coli does not have the hydrolase activity unless it was incubated with plant protein extracts . Here , we report the activation of Avr3b by a prolyl-peptidyl isomerase ( PPIase ) , cyclophilin , in plant cells . Avr3b directly interacts with soybean cyclophilin GmCYP1 , which activates the hydrolase activity of Avr3b in a PPIase activity-dependent manner . Avr3b contains a putative Glycine-Proline ( GP ) motif; which is known to confer cyclophilin-binding in other protein substrates . Substitution of the Proline ( P132 ) in the putative GP motif impaired the interaction of Avr3b with GmCYP1; as a result , the mutant Avr3bP132A can no longer be activated by GmCYP1 , and is also unable to promote Phytophthora infection . Avr3b elicits hypersensitive response ( HR ) in soybean cultivars producing the resistance protein Rps3b , but Avr3bP132A lost its ability to trigger HR . Furthermore , silencing of GmCYP1 rendered reduced cell death triggered by Avr3b , suggesting that GmCYP1-mediated Avr3b maturation is also required for Rps3b recognition . Finally , cyclophilins of Nicotiana benthamiana can also interact with Avr3b and activate its enzymatic activity . Overall , our results demonstrate that cyclophilin is a “helper” that activates the enzymatic activity of Avr3b after it is delivered into plant cells; as such , cyclophilin is required for the avirulence and virulence functions of Avr3b .
Plants have two layers of defense in response to microbial pathogen attacks [1] . Pattern-triggered immunity ( PTI ) is dependent on the recognition of microbe- or pathogen-associated molecular patterns ( MAMPs or PAMPs ) by surface-localized pattern recognition receptors ( PRRs ) [2] . The second defense system is effector-triggered immunity ( ETI ) , which relies on the perception of pathogen effectors [1] . ETI produces a faster and stronger resistance response than PTI and often induces localized cell death in the infected area [1 , 3] . Effectors are usually rapidly evolving in pathogens due to dynamic and sometimes opposite evolutionary forces during the arms race with the hosts [4] . Plant pathogens secrete a diverse repertoire of effectors to overcome plant immunity and enable infection [5 , 6]; however , the biochemical functions of most effectors , especially those produced by eukaryotic pathogens , remain largely unknown . To our knowledge , only a few effectors produced by phytopathogenic oomycetes and fungi have clear biochemical functions and manipulate their host targets by enzymatic activity . For example , Cmu1 produced by the fungal pathogen Ustilago maydis has chorismate mutase activity and affects salicylic acid ( SA ) levels in maize cells [7] . Similarly , PsIsc1 from the oomycete pathogen Phytophthora sojae and VdIsc1 from the fungal pathogen Verticillium dahliae also suppress SA-mediated immunity through their isochorismatase activity [8] . Phytophthora is a group of notorious plant pathogens that infect a wide range of crop , vegetable , horticultural , pasture plants , and forest trees . Like many other plant pathogens , Phytophthora secretes effectors to gain virulence . The functions of an increasing number of Phytophthora effectors , particularly the RXLR ( arginine , any , leucine , arginine ) effectors that can be delivered into host cell , have recently been reported [5] . Many RxLR effectors can suppress PTI and ETI . For example , various effectors from the soybean ( Glycine max ) pathogen P . sojae , including Avr1b , Avr3b and Avr1d , are able to abolish cell death triggered by other effectors and/or PAMP in soybean or Nicotiana benthamiana [9–12] . Functional analysis of thirty-three RXLR effectors from the potato and tomato pathogen Phytophthora infestans revealed eight of them that could suppress PTI triggered by the bacterial flagellin peptide flg22 in tomato protoplasts [13] . Further investigations have revealed the molecular basis by which RXLR effectors suppress plant immunity . For example , PexRD2 perturbs plant immunity-related signaling by interacting with the kinase domain of MAPKKKε and reducing the accumulation of phosphorylated MAPK [14] . AVRblb2 interacts with host papain-like cysteine protease C14 , which is a positive regulator of plant immunity , and prevents its secretion into apoplast [15] . Pi03192 targets NAC transcription factor NTP1 and NTP2 and prevents Phytophthora culture filtrate triggered re-localization of these proteins from the endoplasmic reticulum ( ER ) into the nucleus [16] . Another important discovery is that Phytophthora RXLR effectors could target RNA silencing pathway . Phytophthora suppressors of RNA silencing 1 and 2 ( PSR1 and PSR2 , respectively ) of P . sojae are powerful suppressors of RNA silencing in plants and enhance plant susceptibility to Phytophthora by inhibiting the biogenesis of small RNAs [17 , 18] . We previously demonstrated that the P . sojae RXLR effector Avr3b possesses the Nudix hydrolase activity and contributes to full virulence of P . sojae [19] . Nudix hydrolases hydrolyze a wide range of organic pyrophosphates , including nucleoside di- and triphosphates , dinucleoside and diphosphoinositol polyphosphates , nucleotide sugars and RNA caps , which may be toxic or have regulatory roles [20] . As such , Nudix hydrolases play a key role in signaling , house-keeping processes and maintaining cellular homeostasis [21] . In particular , Arabidopsis thaliana Nudix protein AtNUDT7 was found to be a negative regulator of the basal defense response . Loss-of-function mutation of AtNUDT7 results in higher levels of salicylic acid ( SA ) and constitutive expression of defense-related genes , and thereby enhanced resistance to the bacterial pathogen Pseudomonas syringae and the oomycete pathogen Hyaloperonospora arabidopsidis ( Hpa ) [22–25] . Besides Avr3b , Nudix effector CtNUDIX from the fungal pathogen Colletotrichum truncatum is exclusively expressed during the late biotrophic phase and elicits a HR-like cell death in tobacco leaves , suggesting that CtNUDIX may signal the transition from biotrophy to necrotrophy [26] . In addition , Hpx26 produced by the bacterial pathogen Ralstonia solanacearum shares homology with known Nudix hydrolase [27] . So far , the biochemical functions of CtNUDIX and Hpx26 are unclear . Cyclophilin ( CYP ) possesses peptidyl-prolyl cis-trans isomerase ( PPIase ) activity and catalyzes the isomerization of prolyl bonds in proteins [28 , 29] . CYP family members are involved in diverse aspects of cellular physiology including transcription , immune response , mitochondrial function , cell death , and chemotaxis [30–32] . Interestingly , CYP plays a critical role in the activation of bacterial effectors and the life cycle of viruses , especially hepatitis C virus ( HCV ) and human immunodeficiency virus ( HIV ) [30 , 33 , 34] . For example , AvrRpt2 from P . syringae is delivered into the plant cell as an inactive form and subsequently activated by host cyclophilin ROC1 ( Rotamase CYP1 ) [33] . During HCV infection of animal cells , cyclophilin B interacts with the HCV RNA polymerase NS5B and functions as a stimulatory regulator of NS5B in HCV replication machinery [35] . The Nudix hydrolase activity of Avr3b is important for P . sojae infection . However , recombinant Avr3b proteins produced in Escherichia coli had no hydrolase activity unless it was incubated with plant extracts , suggesting that unknown plant factors is required for the activation of Avr3b in host cells . Here , we identified the soybean cyclophilin protein GmCYP1 as an Avr3b-interacting protein and showed that its PPIase activity is responsible for the activation of Avr3b enzymatic activity . We further identified the Pro132 residue in a potential GP motif of Avr3b as a key residue involved in Avr3b-GmCYP1 interaction . The mutant Avr3bP132A , no longer possessing the hydrolase activity , is also abolished for its virulence activity in N . benthamiana as well as HR-triggering activity in a resistant soybean cultivar that produces the cognate resistance ( R ) protein of Avr3b , Rps3b . Moreover , application of a PPIase inhibitor , cyclosporine A ( CsA ) , or silencing of GmCYP1 greatly reduced the cell death triggered by Avr3b in soybean , suggesting that GmCYP1 is required for the maturation of Avr3b as a Nudix hydrolase and therefore playing an essential role in the virulence and avirulence activities of Avr3b in planta .
Previous study demonstrated that Avr3b is a functional Nudix hydrolase , and this enzymatic activity is essential for the virulence function of Avr3b [19] . To further elucidate the enzymatic activity of Avr3b , we expressed the recombinant GST-Avr3b protein in N . benthamiana and E . coli , respectively . Interestingly , unlike Avr3b expressed in N . benthamiana , which exhibited clear Nudix hydrolase activity , purified Avr3b produced in E . coli could not hydrolyze the substrate NADH ( Fig 1A ) . These results suggest that the enzymatic activity of Avr3b might need to be activated by some unknown factors from plants . To test this hypothesis , recombinant Avr3b produced by E . coli was incubated with dialyzed total protein extracts from P . sojae , soybean or N . benthamiana for 15 hours before the hydrolase activity was examined . Consistent with the previous results , addition of soybean or N . benthamiana extracts significantly promoted the hydrolase activity of Avr3b , while no change was observed after Avr3b was incubated with extracts from P . sojae mycelium ( Fig 1B ) . These results suggest that Avr3b is likely in an inactive form in P . sojae and activated after it is delivered into plant cells by a plant factor ( s ) . To investigate the underlying mechanism of Avr3b activation in plant cells , we performed yeast two-hybrid ( Y2H ) screens and identified plant proteins that associate with Avr3b using a soybean cDNA library . One protein that was repeatedly identified from three independent screens is a soybean cyclophilin protein GmCYP1 ( Gm11g10480 ) . We then focused our research on GmCYP1 because cyclophilin was shown to activate the cysteine protease activity of a bacterial effector AvrRpt2 in Arabidopsis [33 , 34] . The Avr3b-GmCYP1 interaction was validated using three independent assays . First , we cloned the full length cDNA sequence of GmCYP1 into the Y2H vector pGADT7 and confirmed its association with Avr3b in yeast ( Fig 2A ) . Secondly , we purified the recombinant GST-Avr3b , His-GmCYP1 proteins from E . coli and detected co-precipitation of His-GmCYP1 with GST-Avr3b in vitro , but not with GST , using glutathione resins ( Fig 2B and S1 Fig ) . Finally , we confirmed the interaction between Avr3b and GmCYP1 in N . benthamiana using co-immunoprecipitation . FLAG-Avr3b was co-expressed with GFP-GmCYP1 or GFP in N . benthamiana leaves using Agrobacterium-mediated transient expression . Enrichment of GFP-GmCYP1 was detected in the FLAG-Avr3b precipitates using anti-FLAG affinity gel from total protein extracts ( Fig 2C ) . Taken together , these experiments demonstrate that Avr3b directly interacts with GmCYP1 in vitro and in vivo . Because Avr3b could be activated by protein extract of N . benthamiana , we hypothesized that cyclophilin proteins in N . benthamiana could interact with Avr3b and activate its Nudix hydrolase activity . To test this hypothesis , we examined whether Avr3b interacts with GmCYP1 homologs in N . benthamiana . A phylogenetic tree was constructed based on the conserved cyclophilin domain of cyclophilin proteins from N . benthamiana , soybean and P . sojae ( S2 Fig ) . Five N . benthamiana cyclophilins ( NbCYP1—NbCYP5 ) were selected as they share the highest ( 74%-94% ) similarity to GmCYP1 in amino acid sequences . Among them , NbCYP1 ( NbC26100015g0001 ) and NbCYP2 ( NbS00003953g0001 ) are identical in their full-length amino acid sequences; NbCYP3 ( NbS00044621g0001 ) and NbCYP4 ( NbS00014422g0001 ) also share high identity ( 98% identity ) . These NbCYPs were then examined for their interactions with Avr3b . Using in planta co-immunoprecipitation assay , we were able to detect enrichment of NbCYP3 and NbCYP4 , but not NbCYP1 or NbCYP5 ( NbS00058430g0005 ) , in the FLAG-Avr3b precipitates ( Fig 2D ) . These results demonstrate that NbCYP3 and NbCYP4 can interact with Avr3b in planta . Taken together , these results suggest that Avr3b associates with specific cyclophilin proteins in plant cells , such as GmCYP1 in soybean and NbCYP3 and NbCYP4 in N . benthamiana . Cyclophilins serve as protein folding catalysts by regulating cis-trans isomerization of prolyl bonds [36] . We then confirmed that GmCYP1 possesses the peptidyl-prolyl cis-trans isomerase ( PPIase ) activity by the chymotrypsin-coupled assay using N-Succinyl-Alanine-Proline-Phenylalanine-P-Nitroanilide ( Suc-AAPF-pNA ) as the alpha-chymotrypsin substrate [37] . In the presence of functional PPIase , the Suc-AAPF-pNA prolyl bond is more rapidly converted to the trans conformation , which can be cleaved by chymotrypsin , leading to the formation of a colored product 4-nitroaniline [38] . As such , in the presence of active PPIase , we would observe a rapid increase in absorbance at 390 nm . As expected , Recombinant His-GmCYP1 protein purified from E . coli showed clear PPIase activity compared to GST , which served as a negative control ( Fig 3A ) . Based on previous studies , Arg62 of GmCYP1 might be essential for the PPIase activity [34 , 39] , we constructed the mutant GmCYP1R62A , which indeed lost the PPIase activity but still interacted with Avr3b ( Fig 3A and S3 Fig ) . In addition , the PPIase activity of GmCYP1 can be significantly inhibited in the presence of 20 μM cyclosporine A ( CsA ) , a chemical inhibitor of cyclophilin ( Fig 3A ) . These results demonstrate that GmCYP1 is a canonical cyclophilin with the PPIase activity . Next , we examined whether Avr3b is activated by GmCYP1 via its PPIase activity . Purified GST-Avr3b proteins produced by E . coli were incubated with His-GmCYP1 or His-GmCYP1R62A ( S4 Fig ) . The Nudix hydrolase activity of GST-Avr3b was significantly enhanced after incubation with His-GmCYP1 , but not with His-GmCYP1R62A ( Fig 3B and S1 Table ) . Consistently , activation of Avr3b Nudix hydrolase activity by GmCYP1 was significantly weakened in the presence of CsA ( Fig 3B ) . These results suggest that Avr3b is activated by GmCYP1 in a manner that is dependent on PPIase activity . To study the interaction between Avr3b and GmCYP1 in planta , Avr3b was co-expressed with GFP-GmCYP1 , GFP-GmCYP1R62A or GFP in N . benthamiana leaves . N . benthamiana leaves transiently expressing Avr3b and GmCYP1 showed a higher level of Nudix hydrolase activity ( Fig 3C and S1 Table ) and increased susceptibility to P . capsici ( Fig 3D ) . This difference was not due to different protein expression levels of Avr3b as shown by western blots ( S5 Fig ) . These results suggest that GmCYP1 can promote Avr3b maturation in planta . Our previous experiments showed that NbCYP3 and NbCYP4 interact with Avr3b in planta , we next determined whether NbCYP3 and NbCYP4 can activate Avr3b processing . NbCYP3 and NbCYP4 were silenced in N . benthamiana by virus-induced gene silencing ( VIGS ) using Tobacco Rattle Virus ( TRV ) -based vectors . Because NbCYP3 and NbCYP4 are highly similar , our TRV:NbCYP3 construct effectively silenced both genes , but the transcript levels of the homologous genes , NbCYP1 , NbCYP2 , and NbCYP5 , remained unchanged ( S6A Fig ) . These results confirmed that the silencing construct specifically knocked down the expression of the Avr3b-interacting NbCYP3 and NbCYP4 . Plants expressing the TRV:NbCYP3/4 constructs did not show any marked phenotypic alterations compared to the control plants expressing TRV:GFP ( S6B Fig ) . We then expressed Avr3b and RFP ( as a control ) in NbCYP3/4-silenced leaves through Agro-infiltration and analyzed the Nudix hydrolase activity . In contrast to the clear Nudix hydrolase activity detected in leaves expressing TRV:GFP , Avr3b did not exhibit any activity in NbCYP3/4- silenced leaves ( Fig 4A ) . This is not due to different protein expression levels of Avr3b in these leaves ( S6C Fig ) . Next , we infected these leaves with the P . capsici . In leaves that transiently expressed Avr3b , we observed significantly enlarged lesions in the TRV:GFP plants; however , this virulence activity of Avr3b was completely abolished in NbCYP3/4-silenced leaves ( Fig 4B ) . From these experiments , we conclude that silencing of NbCYP3 and NbCYP4 disrupted the activation of Avr3b in N . benthamiana . To verify whether NbCYP3 and NbCYP4 are responsible for activating the Nudix hydrolase activity of Avr3b , we co-expressed Avr3b with NbCYP3 , NbCYP4 or GFP in N . benthamiana . Over-expression of NbCYP3 and NbCYP4 in N . benthamiana led to further enhancement on the Nudix hydrolase activity of Avr3b ( Fig 4C ) , supporting a role of NbCYP3 or NbCYP4 in Avr3b activation in N . benthamiana . In soybean , resistance to P . sojae strains expressing Avr3b is conferred by the resistance protein Rps3b [19] . To test the role of GmCYP1 in Avr3b-induced cell death in resistant cultivars of soybean , CsA was used to suppress the PPIase activity in soybean leaves , which were subsequently tested for Avr3b-triggered HR . Our results showed that transient expression of Avr3b in Rps3b-producing soybean could induce cell death , which was readily blocked by spraying 20 μM CsA on the leaves ( Fig 5A and 5B ) . To rule out the possibility that application of CsA inhibits general cell death in soybean , another P . sojae effector Avr1b was tested using soybean carrying its cognate R protein Rps1b [40] . The same CsA treatment did not disturb Avr1b-triggered cell death in Rps1b-producing soybean , indicating that CsA specifically suppressed Avr3b-triggered cell death ( Fig 5C and 5D ) . These results suggest that the PPIase activity is important for the recognition of Avr3b , but not a general factor of ETI , in soybean . To further investigate the role of GmCYP1 in Rps3b-mediated cell death in soybean , a hairpin RNAi construct targeting GmCYP1 was introduced into soybean leaves together with Avr3b by co-bombardment ( S7A Fig ) . Quantitative RT-PCR data showed that GmCYP1 transcript level was significantly reduced in the delivery region two days after bombardment ( S7B Fig ) . On the contrary , the transcript levels of another two soybean cyclophilin genes , Gm04g00700 and Gm06g00740 , both of which share high sequence identity ( 83% and 84% in full-length nucleic acid sequences respectively ) with GmCYP1 , were not reduced in leaves expressing the GmCYP1-RNAi construct ( S7B Fig ) . The cell death assay indicated that Avr3b-triggered cell death was weakened in soybean when GmCYP1 was silenced . ( Fig 5E and 5F ) . Taken all the data together , our experiments demonstrate that GmCYP1 is required for the recognition of Avr3b by Rps3b , likely by modulating Avr3b protein structure via the PPIase activity . In general , cyclophilins preferentially bind to the Glycine-Proline ( GP ) motif in substrate proteins and catalyze prolyl bond isomerization [41 , 42] . Sequence analysis identified a potential GP motif ( G131P132 ) in Avr3b ( S8 Fig ) . To examine whether this putative GP motif is involved in the interaction between Avr3b and GmCYP1 , we generated the mutant Avr3bP132A and examined its Nudix hydrolase activity when expressed in N . benthamiana . Western blot data showed that Avr3b and the mutant Avr3bP132A was expressed in a similar level ( S9A and S9B Fig ) . Proteins extracted from tissues expressing Avr3bP132A showed the same low level of Nudix hydrolase activity as those extracted from GFP-expressing tissues , indicating that Avr3bP132A was no longer activated ( Fig 6A ) . We then determined that the interaction between GmCYP1 and Avr3bP132A was significantly weakened in yeast ( Fig 6B and S9C Fig ) , consistent with the notion that Pro132 plays a key role in mediating Avr3b-GmCYP1 interaction . Overall , these results demonstrated that Pro132 of Avr3b is required for Avr3b maturation , probably through direct interaction with GmCYP1 . To further detect the effect of GmCYP1 on the biological functions of Avr3b in planta , Avr3b and Avr3bP132A were expressed in N . benthamiana , and the detached leaves were subsequently challenged with P . capsici . P . capsici produced larger lesions on Avr3b-expressing leaves than on Avr3bP132A-expressing leaves ( S9D Fig ) . Quantitative PCR confirmed a higher biomass of P . capsici in Avr3b-expressing leaves , suggesting that Avr3bP132A was unable to promote P . capsici infection ( Fig 6C ) . Regarding to the avirulence role of Avr3b during soybean-P . sojae interaction , we tested whether Avr3bP132A could be recognized by soybean Rps3b plant . Transient expression of Avr3bP132A by bombardment could not induce Rps3b-mediated cell death on soybean leaves ( Fig 6D and S9E Fig ) . Previous studies have shown that Avr3b suppresses cell death induced by interactions between Avr1b and Rps1b [19]; thus , we examined whether Avr3bP132A still retains this virulence function using bombardment . Our data showed that , unlike Avr3b , Avr3bP132A was unable to suppress Avr1b-triggered cell death in soybean ( Fig 6E and S9F Fig ) . These results provide convincing evidence that Pro132 is essential for both avirulence and virulence functions of Avr3b , probably by mediating the interaction of Avr3b with GmCYP1 . Cyclophilins belong to a large protein family that is present in all cell types of all the organisms studied so far [43] . Previous results showed that Avr3b could not be activated by P . sojae extracts ( Fig 1B ) , suggesting that Avr3b may not interact with or be processed by P . sojae cyclophilins . By searching the genomes of soybean and P . sojae , we found 17 cyclophilin homologues in P . sojae and 72 cyclophilin homologues in soybean ( S2 Table ) . To test whether P . sojae cyclophilins could interact with Avr3b , we cloned the genes Ps108795 and Ps108195 , which are the most similar to GmCYP1 ( 79% and 60% identity in full-length amino acid sequences ) , for Y2H analysis . Our data showed that neither of these P . sojae cyclophilins could interact with Avr3b in yeast ( Fig 7A and 7B ) . This result supports a hypothesis that Avr3b is likely present as an inactive form before entering the plant cells . GmCYPs have diverse gene structures and subcellular localizations , indicating that they involve in a large variety of cellular functions [44] . Since GmCYP1 is predicted as a cytosolic cyclophilin [44] , we then selected ten soybean cyclophilins including other cytosolic cyclophilins that share high sequence similarity with GmCYP1 for interaction analysis with Avr3b . Interestingly , none of these tested GmCYPs could interact with Avr3b in yeast ( Fig 7A–7D ) . These results suggest that the enzymatic activity of Avr3b is specifically activated by GmCYP1 in soybean .
In this study , we report that a soybean cyclophilin protein GmCYP1 physically interacts with the Phytophthora effector Avr3b and activates the Nudix hydrolase activity of Avr3b , which is required for the full virulence of P . sojae . GmCYP1 possesses the PPIase activity that can be suppressed by the chemical inhibitor CsA . Application of CsA or mutation of catalytic residue Arg62 of GmCYP1 abolished its activating activity on Avr3b , suggesting that maturation of Avr3b in plant cells is dependent on PPIase activity of GmCYP1 . Moreover , we identified Pro132 of Avr3b as a key residue to mediate the interaction with GmCYP1 . In planta expression of Avr3bP132A lost its hydrolase activity and is no longer able to enhance Phytophthora infection when expressing in N . benthamiana . The mutation of Pro132 also abolished Avr3b-induced HR in Rps3b-producing soybean . Interestingly , we found Avr3b Nudix hydrolase activity can be activated by both N . benthamiana and soybean extracts , but not P . sojae extracts . Since the activation of Avr3b by N . benthamiana extract could be largely impaired by CsA treatment , N . benthamiana likely provides “helper” molecule ( s ) possessing PPIase activity for Avr3b activation ( S10 Fig ) . Our experiments further confirmed that two N . benthamiana cyclophilins , i . e . NbCYP3 and NbCYP4 , and one soybean cyclophilin , i . e . GmCYP1 , are likely the “helpers” that activate Avr3b in plant cells . One outstanding question is whether other GmCYP1 homologs can process Avr3b . Our large scale yeast two hybrid assays testing interaction between Avr3b and GmCYP1 homologs suggest that Avr3b specifically interacts with GmCYP1 , indicating substrate specificity in cyclophilin homologs in soybean ( Fig 7A–7D ) . This is consistent with previous reports that cyclophilins interact and process different classes of proteins through sequence-specific binding and diverse localization [41 , 43 , 45 , 46] . Previous studies showed that cyclophilins can regulate the protease activity of effector AvrRpt2 from bacteria , conformational change of plant ETI receptor RPM1-interacting protein RIN4 , and the binding activity of HCV RNA polymerase NS5B through their PPIase activity [31 , 33 , 35] . Our experiments showed that cyclophilins are also associated with the activation of Nudix hydrolase , and that GmCYP1 activates Avr3b in a PPIase-dependent manner . However , whether cis-trans isomerization of Avr3b protein actually occurs during Phytophthora infection requires further investigation . To our surprise , the site direct Avr3bP132A mutation not only impaired Avr3b Nudix hydrolase activity but also abolished the Avr3b triggered immunity , suggesting Pro132 is a key residue associated with Rps3b recognition . Interestingly , the investigation of P . sojae natural populations uncovered only two Avr3b natural alleles , namely the avirulence allele Avr3bP6497 and the virulence allele Avr3bP7076 . Sequence analysis of both alleles revealed a mutation on the Pro132 site in the avirulence sequence into an Alanine at the corresponding position in virulence allele Avr3bP7076 ( S11 Fig ) . This natural mutant and the phenotype of the Avr3bP132A mutant constructed in this study suggest that Pro132 is a key residue in Avr3b recognition event . On the other hand , our previous study indicated that the Avr3bQQQQ mutant ( Avr3bR220Q , E221Q , E225Q , E226Q , a non-functional Nudix hydrolase mutant ) could still trigger cell death in soybean , suggesting that Avr3b recognition by the cognate Rps3b receptor is independent on the Nudix hydrolase activity [19] . A possible explanation of these results is that P132 is required for not only the Nudix hydrolyase activity , but also other feature of Avr3b , such as protein conformation changes , which may be required for Avr3b-Rps3b recognition . In addition to P . sojae , Avr3b-like effectors are extensively present in other Phytophthora species including P . infestans , P . ramorum and P . capsici . Sequence alignment of Avr3b-like effectors indicated that the GP motif containing Pro132 is not conserved among these effectors , although other potential GP motifs are present in a few other effectors ( S11 Fig ) . These data suggested that effector processing by plant cyclophilin proteins might not be required for all the Phytophthora Nudix effectors . It has been widely accepted that pathogen effectors directly target host proteins and manipulate host targets [47] . However , host proteins also manipulate the activity of pathogen effectors . It has been proposed that some host proteins could act as “helpers” to facilitate effector functions , whereas others are “targets” [4] . Previous studies demonstrated that folding of bacterial effector facilitated by host factors might be important to regulate effector functions in host cells during pathogenesis . Previous studies showed that Arabidopsis cyclophilin ROC1 is required to activate the cysteine protease activity of the bacterial effector AvrRpt2 [33 , 34] . Our study demonstrated that a soybean cyclophilin GmCYP1 is responsible for the activation of a Phytophthora effector Avr3b as a Nudix hydrolase ( Fig 7E ) . In another word , GmCYP1 is the host helper recruited by Avr3b to become an active virulence protein in plant cells . These results suggest that recruitments of host factors as “helpers” is a common pathogenesis mechanism shared by eukaryotic and prokaryotic pathogens .
Plants were grown in greenhouse at 22–25°C with a cycle of 16 and 8 hours of high light intensity and darkness , respectively . P . sojae isolate P6497 and P . capsici used in this study were grown in 10% vegetable ( V8 ) juice agar medium at 25°C in the dark . Avr3b gene without the secretion signal was cloned into the yeast vector pGBKT7 ( Clontech ) . The expression of the BD-Avr3b fusion proteins has been verified by western blots ( S12 Fig ) . A soybean ( Glycine max , William 82 ) cDNA library was constructed in the Y2H vector pGADT7 using total RNA extracted from soybean hypocotyl tissues collected 12 and 24 hours after the plants were inoculated with P . sojae zoospores ( Clontech ) . Approximately 6×106 primary yeast clones ( three times coverage of the library ) were screened using Avr3b as the baits . Potential yeast transformants containing cDNA clones interacting with Avr3b were selected using the SD/-Trp/-Leu/-His/-Ade selective medium . To construct GST-fusion plasmids , Avr3b was inserted into the vector pGEX4T-2 ( GE Healthcare Life Science ) . To construct His-fusion plasmid , GmCYP1 was inserted into the vector pET28a . Pull-down assay was performed using ProFound pull-down GST protein-protein interaction kit ( Pierce ) according to the manufacturer’s instructions . GST , GST-Avr3b , and His-GmCYP1 was expressed in E . coli strain BL21 ( DE3 ) respectively . The soluble total proteins were incubated with 50 μl glutathione agarose beads ( Invitrogen ) for 2 hours at 4°C . The beads were washed five times and then incubated with equal amount of bacterial lysates containing His-GmCYP1 for another hour at 4°C . The beads were washed five times again , and the presence of His-GmCYP1 was detected by western blot using anti-His antibody . To analysis Nudix hydrolase activity of Avr3b , a general approach was used as described [23]; Reaction mixture ( 50 μl for each sample ) contained 50 mM Tris-HCl , pH 8 . 5 , 1 mM dithiothreitol , 5 mM MgCl2 , 2 mM substrate ( NADH ) , 2 units of calf alkaline phosphatase , and 2 μg of protein samples . After incubation for 30 min at 37°C , the reaction was stopped by adding 150 μl of 0 . 5 M H2SO4 , followed by addition of 100 μl of water . For color development , 700 μL of a freshly made mixture containing 600 μl of 0 . 42% ( w/v ) ammonium molybdate and 100 μL of 10% ( w/v ) ascorbic acid was used . The reaction tubes were incubated in 45°C water bath for 20 min for color development and then cooled down to room temperature . The solutions were measured using spectrophotometer ( Beckman , USA ) at A820 . The reaction mixture without protein was used as a blank control . For Nudix hydrolase activity assay of total plant protein extract , one N . benthamiana leaf was infiltrated with FLAG-GFP bacteria in one side and with FLAG-Avr3b or FLAG-Avr3bP132A in another side . A total of 0 . 2 g of GFP or Avr3b infiltrated leaf tissue was collected at 2 days post infiltration ( dpi ) . For both kinds of protein preparations , the relative hydrolase enzyme activity was calculated as the ratio of A820 reading value from sample mixture over control mixture . A standard SDS-PAGE protocol was performed for protein separation . Proteins were transferred onto polyvinylidene difluoride ( PVDF ) membranes using a semi-wet apparatus ( Bio-Rad , Hercules , CA , U . S . A . ) . Then , the membrane was blocked using phosphate-buffered saline ( PBS; pH 7 . 4 ) with 3% nonfat dry milk for 1 hour at room temperature . Antibodies were added to PBST with 3% nonfat dry milk ( PBSTM ) at a ratio of 1:4 , 000 and incubated at 4°C overnight , followed by three washes ( 10 min each ) with PBST . Then , the membrane was incubated with a goat anti-mouse IRDye 800CW ( Odyssey , number 926–32210; Li-Cor , Lincoln , NE , U . S . A . ) at a ratio of 1:10 , 000 in PBSTM at room temperature for 2 hours . The membrane was washed four times ( 10 min each ) with PBST , then visualized using Odyssey scanner with excitation at 700 and 800 nm [11] . Avr3b with a N-terminal FLAG-tag was inserted into pGR107 , and GmCYP1 was inserted into pBinGFP for expression in N . benthamiana . The Avr3bP132A and GmCYP1R62A mutants were generated by overlapping PCR using primer described in ( S3 Table ) . FLAG-tagged Avr3b and GFP-tagged GmCYP1 or GFP were co-expressed in N . benthamiana leaves by agroinfiltration . FLAG-tagged proteins were immunoprecipitated by anti-FLAG M2 affinity gel ( Sigma-Aldrich ) from total extracts harvested from N . benthamiana leaves at 2 dpi . The interacting GFP fusion proteins were detected as described above . The expression of FLAG- or GFP-tagged protein in total extract was confirmed by western blot using either anti-FLAG or anti-GFP antibody . Recombinant Avr3b and mutants were overexpressed in E . coli strain BL21 ( DE3 ) . E . coli clones were grown in LB medium to a density of OD600 = 0 . 5 , and then protein expression was induced overnight at 18°C with 0 . 4 mM isopropyl β-D-thiogalactopyranoside ( IPTG ) . Cells were lysed in a buffer containing 100 mM Tris-HCl , 150 mM NaCl , 1mM imidazole , 1 mM DTT ( pH 8 . 0 ) . Recombinant GST-Avr3b were affinity purified by Glutathione Sepharose affinity chromatography ( GE Healthcare ) and eluted from the Glutathione Sepharose with 10 mM reduced glutathione in 100mM HEPES , 150 mM NaCl , 2 mM DTT , and 10% glycerol ( pH 7 . 5 ) . Recombinant CYP1 or mutant proteins were expressed E . coli BL21 ( DE3 ) by pET28a and isolated under native conditions . E . coli clones were grown in LB to a density of OD600 = 0 . 6 . Protein expression was induced for 6 h at 28°C with 0 . 5 mM IPTG . Cells were lysed in a buffer containing 10 mM imidazole , 20 mM Tris pH 8 . 0 , 150 mM NaCl . Proteins were purified by Ni+-sepharose affinity chromatography and washed with the same buffer containing 50 mM imidazole . The proteins were eluted with 250 mM imidazole in the same buffer . PPIase assays were conducted essentially as previously described [28] . The mixture of following components were incubated on ice for 10 min: 975 μl HEPES Buffer ( 35 mM HEPES , 0 . 015% TritonX-100 , pH 8 . 0 ) , 20 μl of 5 mM N-succinyl-ala-ala-pro-pNa ( Baychem ) , and 8nM of His-GmCYP1 or the GST negative control . Each sample was placed in a spectrophotometer pre-cooled to 8°C . After the addition of 10 mM alpha-chymotrypsin ( Sigma ) , the absorbance at 390 nm was recorded every second for one min at 8°C immediately . To test the activation of Avr3b proteins by soybean extract , the recombinant Avr3b protein ( 2 μg ) produced and purified from E . coli was incubated with 100 μg of dialyzed soybean extract , 100 μg of dialyzed N . benthamiana extract or 100 μg of dialyzed P . sojae ( P6497 ) mycelium extract respectively in a total volume of 50 μl in buffer containing 50 mM HEPES , 10 mM sodium bisulfite , 10 mM sodium metabisulfite and 1mM DTT ( pH 7 . 5 ) at 25°C for 15 hours . To assess the activation of Avr3b by GmCYP1 , purified recombinant Avr3b ( 2 μg ) was incubated with 1 μg of E . coli expressed recombinant GmCYP1 protein in a total volume of 50 μl . For inhibition of PPIase activity , 20 μM of cyclosporine A ( Sigma ) was used in each reaction . 4 to 5 week old N . benthamiana were infiltrated with Agrobacterium carrying FLAG-GFP , FLAG-Avr3b or Avr3b mutant . The infiltrated leaves were inoculated with zoospore suspension of P . capsici 48 hours post Agro-infiltration . The total DNA isolated at 36 hours post inoculation . Then primers specific for P . capsici and N . benthamiana actin genes were used to quantify the relative biomass of pathogen by quantitative PCR ( S3 Table ) . PCR reactions were performed on an ABI Prism 7500 Fast real-time PCR System ( Applied Biosystems , Foster City , CA , U . S . A . ) . The GmCYP1 hairpin constructs were prepared in a two-step cloning process in pFF19::CHSA plasmid . The chalcone synthase ( CHSA ) intron was amplified from plasmid pFGC5941 . The CHSA intron amplicon was inserted into plant transient expression plasmid pFF19::GUS ( replacing GUS with CHSA intron , creating pFF19::CHSA ) . The PCR-amplified GmCYP1 cDNA fragments were first insert at outer restriction site ( BamHI ) and then the inner restriction site ( AscI ) on either side of the intron sequence . The construct were used by bombardment to induce transiently silencing GmCYP1 in soybean . We used the Tobacco Rattle Virus ( TRV ) -based VIGS system , which uses bipartite sense RNA1 and RNA2 viruses [48] , to silence cyclophilin genes in N . benthamiana . The NbCYP3 fragment was amplified by PCR and then cloned into pTRV2 vector . Primer pairs TRVNbCYP3F and TRVNbCYP3R ( S3 Table ) were used to amplify NbCYP3 . At four-leaf stage , the N . benthamiana plants were selected for Agro-infiltration . Prior to Agro-infiltration , A . tumefaciens strain GV3101 cells carrying pTRV1 and pTRV2 constructs were collected and resuspended in infiltration buffer ( 10 mM MgCl2 , 150 mM acetosyringone , and 10 mM MES , pH 5 . 6 ) and mixed in a 1:1 ratio . Plants were grown for 2 weeks after infiltration . The fully expanded six leaves of the silenced plants were then used for inoculation and quantitative PCR assays . TRV2:GFP vector was used as the negative control . Avr3b , Avr3bP132A and Avr1b genes were amplified using specific primers without signal peptide sequences and inserted into plant transient expression plasmid pFF19-GUS ( replacing GUS with the test genes ) . The double-barreled particle bombardment assays was performed on leaves to deliver a parallel control shot in every case that contained GUS DNA plus EV DNA as described [9] . For each paired shot , Specific cell death activity was calculated as the ratio of blue spot numbers for various test gene constructs compared to that of the control . After bombardment , the leaves were incubated for 2 days in darkness at 28°C . The leaves were then stained for 12 h at 37°C using 0 . 8 mg/mL X-gluc ( 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid , cyclohexylammonium salt ) , 80 mM Na phosphate , pH 7 . 0 , 0 . 4 mM K3Fe ( CN ) 6 , 0 . 4 mM K4Fe ( CN ) 6 , 8 mM Na2EDTA , and 0 . 1% ( v/v ) Triton X-100 and then de-stained in 100% ethanol . Nudix hydrolase homologs used in this study are acquired as described [19] . The P . sojae and soybean homolog searches were performed at the Joint Genome Institute database ( http://genome . jgi-psf . org ) and soybean genome ( http://www . phytozome . net/soybean . php ) . For N . benthamiana genome searching , the genome sequence was downloaded from the N . benthamiana database ( http://bti . cornell . edu/research/projects/nicotiana-benthamiana/ ) and a local BLAST search was conducted . Protein domain and motif analyses were conducted using the NCBI conserved domain database ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) and Motif Scan ( http://myhits . isb-sib . ch/cgi-bin/motif_scan ) . Sequence alignment was performed using BioEdit2005 . | Phytophthora sojae , an oomycete pathogen that causes the Phytophthora root and stem rot disease of soybean , delivers variety of effectors into host cell to reprogram host immunity . Genome sequencing uncovers that P . sojae genome encode several hundreds of effector genes . However , the mode of action of most of the P . sojae effectors remains unknown . The investigation of effector-interacting proteins provides opportunities to better understand the pathogenesis mechanism of the pathogen and the defense mechanism of the host plants . Previously , we reported that P . sojae avirulence effector Avr3b modulates plant immunity through its Nudix hydrolase activity . Interestingly , the enzymatic activity is required for its virulence but not required for recognition by resistant gene . In this study , we identified soybean cyclophilin protein GmCYP1 as an Avr3b interactor . The enzymatic activity of GmCYP1 is required for the maturation Avr3b , which is directly related to both virulence and avirulence functions of Avr3b . This work provides a novel insight into how Phytophthora pathogens recruit host proteins to activate the enzymatic activity of effectors in order to gain successful infection . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | The Activation of Phytophthora Effector Avr3b by Plant Cyclophilin is Required for the Nudix Hydrolase Activity of Avr3b |
Rift Valley fever virus ( RVFV ) , a member of the genus Phlebovirus within the family Bunyaviridae , causes periodic outbreaks in livestocks and humans in countries of the African continent and Middle East . RVFV NSs protein , a nonstructural protein , is a major virulence factor that exhibits several important biological properties . These include suppression of general transcription , inhibition of IFN-β promoter induction and degradation of double-stranded RNA-dependent protein kinase R . Although each of these biological functions of NSs are considered important for countering the antiviral response in the host , the individual contributions of these functions towards RVFV virulence remains unclear . To examine this , we generated two RVFV MP-12 strain-derived mutant viruses . Each carried mutations in NSs that specifically targeted its general transcription inhibition function without affecting its ability to degrade PKR and inhibit IFN-β promoter induction , through its interaction with Sin3-associated protein 30 , a part of the repressor complex at the IFN-β promoter . Using these mutant viruses , we have dissected the transcription inhibition function of NSs and examined its importance in RVFV virulence . Both NSs mutant viruses exhibited a differentially impaired ability to inhibit host transcription when compared with MP-12 . It has been reported that NSs suppresses general transcription by interfering with the formation of the transcription factor IIH complex , through the degradation of the p62 subunit and sequestration of the p44 subunit . Our study results lead us to suggest that the ability of NSs to induce p62 degradation is the major contributor to its general transcription inhibition property , whereas its interaction with p44 may not play a significant role in this function . Importantly , RVFV MP-12-NSs mutant viruses with an impaired general transcription inhibition function showed a reduced cytotoxicity in cell culture and attenuated virulence in young mice , compared with its parental virus MP-12 , highlighting the contribution of NSs-mediated general transcription inhibition towards RVFV virulence .
Rift Valley fever virus ( RVFV ) is the pathogen causing Rift Valley fever , which affects both humans and domestic ruminants , primarily in countries of the African continent and Middle East . The virus is an arbovirus and circulates between mosquito vectors and ruminants in endemic areas . RVFV causes high mortality rates in young ruminants and a high rate of abortions in pregnant ruminants [1] . Humans are infected with the virus either by mosquito bite or by direct contact with materials of infected animals . The majority of patients show influenza-like symptoms but few develop hemorrhagic fever , neurological symptoms , and ocular disease [2] . Due to its major impact on public health , RVFV is classified as a category A priority pathogen by the National Institute of Allergy and Infectious Diseases . Currently there is no approved vaccine available for humans and animals in non-endemic areas . RVFV belongs to the family Bunyaviridae , genus Phlebovirus . RVFV is an enveloped virus and carries 3 segmented RNA genomes , the L , M and S segments , which are of negative or ambisense polarity . The L segment encodes L protein , a viral RNA-dependent RNA polymerase . M RNA encodes 78kDa protein , NSm protein , Gn protein and Gc protein , the latter two of which are major envelope glycoproteins and generated by co-translational cleavage of precursor Gn/Gc polyprotein . 78kDa protein is dispensable for virus replication [3] , whereas it plays important roles in virus dissemination in mosquitoes [4 , 5] . NSm is a viral anti-apoptotic protein [6 , 7] and also is important for efficient virus replication in macrophage cell lines [5] . S RNA expresses a nucleocapsid ( N ) protein and a nonstructural protein NSs by using an ambisense coding strategy . The N protein encapsidates the viral RNA and forms a ribonucleocapsid complex with L protein [8] . RVFV NSs protein is a phosphoprotein with an apparent molecular weight of 31 kDa and is localized in both the cytoplasm and nucleus [9] . In the nucleus , NSs forms filament-like structures by self-dimerization through its C-terminal domain [10] . RVFV NSs protein is a major virus virulence factor and has various important biological functions , which are important for countering the host antiviral response . One of the NSs functions is suppression of IFN-β mRNA transcription . NSs binds to Sin3-associated protein 30 ( SAP30 ) , a subunit of a co-repressor complex , and maintains IFN-β promoter in a transcriptionally silent state , leading to suppression of IFN-β mRNA transcription [11] . In addition to the specific inhibition of IFN-β transcription , NSs suppresses general transcription; it has been proposed that NSs exerts suppression of general transcription by interacting with subunits of transcription factor II H ( TFIIH ) complex , p44 and p62 [12 , 13] . A recent study showed that the NSs-mediated general transcription inhibition contributes to the inhibition of IFN-β mRNA transcription [14] . Although NSs-mediated transcription suppression has been considered to be important in suppressing the host antiviral response , the exact effects of the NSs-mediated general transcription inhibition on virus virulence have not been defined . NSs promotes the degradation of double-stranded RNA-dependent protein kinase R ( PKR ) , an antiviral IFN-stimulated gene product , through a proteasome pathway to prevent phosphorylation of eIF2-α triggered by RVFV infection [15–18] . Furthermore , NSs contributes to cellular stress responses such as an increase in reactive oxygen species , activation of DNA damage signaling , cell cycle arrest , and activation of the p53 signaling pathway [19–22] . Although how NSs induces the cellular stresses remains largely unknown , these stress responses may contribute to RVFV-induced cell death . To elucidate the mechanisms of the different functions of NSs , it would be of great value to characterize a series of NSs mutants , each of which specifically lacks one of these NSs functions but retains other functions . However , introducing any short in-frame deletion in the RVFV NSs resulted in loss of all functions [23] , suggesting to us that the NSs protein structure is important for its biological activity . Hence , it has been challenging to generate NSs mutants that lack a specific biological function but retain its other functions . The lack of these NSs mutants has prevented a detailed mechanistic analysis of each biological function of the NSs . In this study , we generated two RVFV mutants , each carrying mutations in NSs , that specifically targeted its general transcription inhibition function , to delineate the mechanism of its inhibition by NSs . Furthermore , we examined the importance of the NSs-mediated inhibition of general transcription on virus-induced cytotoxicity and tested its role in RVFV virulence by using a young mouse model .
All mouse studies were performed in facilities accredited by the Association for Assessment and Accreditation of Laboratory Animal Care in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals ( Institute of Laboratory Animal Resources , National Research Council , National Academy of Sciences , 1996 ) . The animal protocol ( protocol number , 1105023A ) was approved by the Institutional Animal Care and Use Committee of The University of Texas Medical Branch . A standard recombinant PCR method , in which pProT7-S encoding antiviral-sense S RNA [24] served as a template , was used to generate pProT7-S carrying mutations in NSs coding region . The NSs coding region of pProT7-S was amplified by using primers carrying Mfe I or a Not I site . After digestion with Mfe I and Not I , the PCR fragment was cloned into Eco RI and the Not I site of a pCAGGS plasmid , resulting in the plasmids expressing NSs protein . For expression of human FBXO3 isoform 1 ( FBXO3/1 ) [14] , intracellular RNAs of MRC-5 cells were subjected to cDNA synthesis , and the FBXO3/1 gene was amplified with primers carrying Mfe I or the Not I site . After addition of an N-terminal V5 tag , the PCR product was cloned into EcoR I and the Not I site of pCAGGS . For expression of human SAP30 , PCR product encoding human SAP30 with N-terminal V5 tag was cloned into EcoR I and the Xho I site of pCAGGS . All of the constructs were confirmed by sequencing . BSR-T7/5 cells which stably express T7 RNA polymerase [25] were maintained in Glasgow’s minimal essential medium ( MEM ) ( Lonza ) containing 10% fetal bovine serum ( FBS ) , 10% tryptose phosphate broth , MEM Amino Acids Solution and geneticin ( 1 mg/ml ) . Vero E6 cells were maintained in Dulbecco’s modified MEM ( Gibco ) containing 5% FBS . MRC-5 cells were maintained in Eagle's MEM ( EMEM ) ( Gibco ) containing 10% FBS , MEM Non-Essential Amino Acids Solution ( Gibco ) , and 1% sodium pyruvate ( Sigma ) . HeLa cells were maintained in EMEM containing 10% FBS . MP-12 is a highly attenuated RVFV strain obtained after 12 serial passages of an RVFV ZH548 strain in the presence of 5-fluorouracil [26] . A recombinant MP-12 strain and other MP-12-derived mutants were rescued from cDNAs as described previously [24] , except that BSR-T7/5 cells were used in place of BHK/T7-9 cells . Titers of the rescued viruses were determined by using a plaque assay [24] . For the virus carrying R16H/M250K mutations in NSs , passage 0 ( P0 ) virus obtained from plasmid-transfected BSR-T7/5 cells were serially diluted and inoculated into VeroE6 cells . The highest titer of P1 virus was selected and used for the study . Anti-PKR rabbit polyclonal antibody , anti-Flag tag mouse monoclonal antibody , anti-Flag tag rabbit monoclonal antibody , and anti-V5 tag rabbit monoclonal antibody were purchased from Cell Signaling Technology . Anti-GTF2H1 ( p62 ) mouse monoclonal antibody , anti-GTF2H2 ( p44 ) mouse polyclonal antibody , and anti-XPD mouse monoclonal antibody were purchased from Abcam . Anti-TFIIH p44 ( N-17 ) goat polyclonal antibody , anti-β-actin goat polyclonal antibody and horseradish peroxidase ( HRP ) -labeled anti-mouse , anti-goat , and anti-rabbit secondary antibodies were purchased from Santa Cruz Biotechnology . Anti-GST-N rabbit polyclonal antibody was generated by inoculating a rabbit with GST-N fusion protein ( the entire N protein was fused with the C terminus of GST protein ) followed by affinity purification of the serum [3] . Anti-MP-12 mouse serum was provided by Dr . Robert B . Tesh at The University of Texas Medical Branch . The monoclonal antibody H2KkDk ( H2K ) , which is against major histocompatibility complex class I antigen , was obtained from Dr . Paul Gottlieb at The University of Texas at Austin . Cells were washed with phosphate-buffered saline ( PBS ) and suspended in SDS polyacrylamide gel electrophoresis ( SDS-PAGE ) sample buffer . Samples were boiled for 3–5 min and subjected to SDS-PAGE . Proteins were electroblotted onto polyvinylidene difluoride membranes ( immune blot: Bio Rad ) . After blocking with skim milk , the membranes were incubated with the primary antibody for 1 h at room temperature and with the secondary antibody for 1 h at room temperature . The proteins on the membrane were detected by using an ECL Western Blotting Detection Reagent ( GE Healthcare Life Sciences ) or ECL plus Western Blotting Substrate ( Pierce ) . Vero E6 cells were infected with either MP-12 or its mutants at a multiplicity of infection ( m . o . i . ) of 3 . At 16 h post infection ( p . i . ) , the culture media was replaced with methionine/cysteine-free medium . After starvation for 30 min , the infected cells were labeled with 100 μCi/ml of 35S-methionine/cysteine ( 1 , 000 Ci/mmol; MP Biomedicals ) for 1 h . The radiolabeled cells were suspended in 2x SDS-PAGE sample buffer , resolved by SDS-PAGE and visualized by Coomassie Blue staining or autoradiography . The Click-iT RNA Alexa Fluor 488 Imaging Kit was purchased from Thermo Fisher Scientific . Vero E6 cells were infected with recombinant viruses at an m . o . i . of 3 and incubated with 1 mM of 5-ethynyl uridine ( 5EU ) for 1 h at 16 h p . i . Cellular RNA was stained with Alexa fluor , 488-coupled azide for 30 min by following the manufacturer's protocol . For a negative control , mock-infected cells were treated with 5 μg/ml of actinomycin D ( ActD ) for 30 min prior to 5EU treatment and for 1 h during 5EU treatment . After the click reaction , RVFV N protein was stained with anti-GST-N rabbit polyclonal antibody followed by Alexa fluor 594 conjugated anti-rabbit antibody . Images were captured on a Zeiss Axiophot 2 fluorescence microscope with a 40x magnification lens and processed with the ImageJ software [27] . For flow cytometry analysis , the infected cells were detached from the dish by Accumax ( Innovative Cell Technologies ) after the 5EU treatment and suspended in culture media . After washing with PBS containing 1% bovine serum albumin ( BSA ) , cells were fixed with 2% formaldehyde/PBS for 30 min at room temperature and blocked with blocking buffer ( PBS containing 0 . 2% saponin and 1% BSA ) for 15 min on ice . Cellular RNA was stained by Alexa fluor 488-coupled azide for 30 min in the presence of 0 . 5% saponin and 1% BSA . After washing with the blocking buffer , RVFV N protein was stained by anti-GST-N rabbit polyclonal antibody for 30 min on ice followed by Alexa fluor 594 conjugated anti-rabbit antibody . The cells were washed with PBS containing 1% BSA , passed through a cell strainer ( BD Falcon ) , and analyzed on an LSRII Fortessa ( BD Biosciences ) . Single cells were gated based on their forward scatter and side scatter profile . More than 30 , 000 count of the gated single cells were analyzed for each experiment . Confluent Vero E6 cells grown in 96-well plates were either mock infected or with mutant virus at an m . o . i . of 3 . Cell viability was determined by using Viral ToxGlo ( Promega ) , which measures cellular ATP . At various times p . i . , ATP detection reagent was added to each well . After incubation for 10 min , luminescence was measured by SpectraMax M5e ( Molecular Devices ) . Total RNAs were extracted by using TRIzol reagent ( Invitrogen ) and subjected to Northern blot analysis as described previously [28] . Strand-specific digoxigenin-labeled RNA probes and a digoxigenin ( DIG ) system ( Roche ) were used to detect RNA . The 564-nucleotide-long , 293 cell-derived , and DIG-labeled IFN-β riboprobe [29] was used for IFN-β mRNA detection . Cells cultured on chamber slides were fixed in 4% paraformaldehyde for 15 min . The fixed cells were permeabilized with 0 . 2% TritonX-100 for 15 min and blocked with 1% BSA in PBS for 30 min . After the blocking , the cells were stained with primary antibody diluted with the blocking solution , followed by incubation with Alexa Fluor 488 or 594-conjugated secondary antibodies ( Molecular Probes ) . Images were captured by a Zeiss Axiophot 2 fluorescence microscopy and processed with ImageJ software [27] . To detect the interaction between NSs and p44 , we infected HeLa cells with MP-12-NSs-Flag , which expresses NSs with the C-terminal Flag tag or its mutant viruses at an m . o . i . of 3 . At 8 h p . i . , the cells were lysed in lysis buffer [50 mM Tris-HCl , pH 7 . 6 , 0 . 1% NP-40 , 150 mM NaCl , 1 mM EDTA , protease inhibitors ( Sigma ) , and 100 U/ml Benzonase ( Sigma ) ] . After 3 cycles of freeze-thaw , the cell lysate was cleared by centrifugation at 4°C and 100 , 000 x g for 1 h and incubated with Dynabeads protein G ( Life Technologies ) conjugated with anti-GTF2H2 ( p44 ) mouse monoclonal antibody or H2K antibody according to the manufacturer’s protocol . Precipitates were washed with the lysis buffer with 0 . 1% Tween 20 and analyzed by Western blotting using the anti-p44 goat polyclonal , and anti-p44 mouse monoclonal and anti-Flag mouse monoclonal antibodies . To detect the interaction between NSs and SAP30 , we transfected HeLa cells with pCAGGS-V5-SAP30 which encodes human SAP30 carrying an N-terminus V5 tag by using FuGENE HD transfection reagent ( Promega ) . At 16 h post transfection , the cells were infected with MP-12-NSs-Flag or its mutant viruses . The cells were lysed with lysis buffer ( 50 mM Tris-HCl , pH 7 . 5 , 100 mM NaCl , 1% NP-40 , 1 mM EDTA , and protease inhibitor cocktail ) [11] at 8 h p . i . The cell lysate was cleared by centrifuge at 21 , 000 x g and 4°C for 15 min and incubated with Anti-V5-tag mAb-Magnetic Beads ( MBL International ) according to the manufacturer’s protocol . Precipitates were washed with the lysis buffer containing 450 mM NaCl and analyzed by Western blotting by using anti-V5 tag rabbit monoclonal and anti-Flag mouse monoclonal antibodies . For detection of interaction between NSs and p62 , HeLa cells were infected with MP-12-NSs-Flag or its mutant viruses at an m . o . i . of 3 . At 5 h p . i . , the cells were lysed in lysis buffer [20 mM Tris-HCl , pH 7 . 6 , 0 . 1% TritonX-100 , 150 mM NaCl , 1 mM EDTA , protease inhibitors ( Sigma ) , and 100 U/ml Benzonase ( Sigma ) ] [13] . After 3 cycles of freeze-thaw , the cell lysate was cleared by centrifugation at 4°C and 100 , 000 x g for 1 h and incubated with Dynabeads protein G conjugated with anti-GTF2H1 ( p62 ) mouse monoclonal antibody according to the manufacturer’s protocol . Precipitates were analyzed by Western blotting using the anti-p62 mouse monoclonal and anti-Flag mouse monoclonal antibodies . To detect the interaction between NSs and FBXO , we transfected HeLa cells with pCAGGS-V5-FBXO3/1 , which encodes FBXO3/1 carrying an N-terminus V5 tag , by using FuGENE HD transfection reagent . At 16 h post transfection , the cells were infected with MP-12-NSs-Flag or its mutant viruses and lysed with lysis buffer ( 50 mM Tris-HCl , pH 7 . 5 , 0 . 2% NP-40 , 5% glycerol , 100 mM NaCl , 1 . 5 mM MgCl2 , and protease inhibitor cocktail ) [14] at 5 h p . i . The cell lysate was cleared by centrifugation at 21 , 000 x g and 4°C for 15 min and incubated with Anti-V5-tag mAb-Magnetic Beads ( MBL International ) according to the manufacturer’s protocol . Precipitates were washed with the lysis buffer and analyzed by Western blotting by using the anti-V5 tag rabbit monoclonal and anti-Flag mouse monoclonal antibodies . HeLa cells prepared in 12-well plates were transfected with 0 . 25 μg of the pCAGGS-based plasmid encoding either NSs or mutant NSs or an empty vector along with 0 . 1 μg of pRL-SV40 ( Promega ) which expresses Renilla luciferase under control of an SV40 promotor . Renilla luciferase activities in the transfected cells were measured by using a Renilla luciferase assay system ( Promega ) at 24h post transfection . Eighteen-day-old CD-1 mice were intraperitoneally inoculated with 104 PFU of MP-12 , MP-12-M250K or MP-12-R16H/M250K or with Hank's Balanced Salt Solution ( HBSS ) . The animals were observed for survival for 22 days post inoculation .
We previously generated and characterized an RVFV MP-12 strain-derived mutant virus , which is deficient for efficient virus genome co-packaging due to a large deletion in the 5’ untranslated region of M RNA segment [30] . To obtain virus variants , carrying mutations that can compensate for this deficiency , we serially passaged this mutant virus in type I IFN-incompetent Vero E6 cells; the virus inoculum , used for each passage , was diluted 10 times and the released viruses were harvested at 4 days p . i . The titer of the mutant virus progressively increased with each passage; at passage 18 , the virus titer was 2 . 9 x 106 PFU/ml , which was 2 logs higher than the initial titer of 2 . 0 x 104 PFU/ml prior to the serial passage . We isolated 34 plaque-cloned viruses from passage level 18 , and 28 had a large deletion in the NSs gene in the S segment , whereas 6 plaque-cloned viruses retained the full-length NSs gene . We determined the full genome sequence of 5 clones that retained the full-length NSs gene and found that all of the isolated viruses had several mutations in the L , M , and S segments . Table 1 shows mutations found in the NSs genes of 5 plaque-cloned viruses and the uncloned passage 18 virus . All five plaque-cloned viruses and the passage 18 virus had an amino-acid substitution at position 250 , including M250K or M250T mutations . Two plaque-cloned viruses and the passage 18 virus had a D100G substitution . Other mutations found in plaque-cloned viruses were R16H and A75E . Although K202N mutation was found in the uncloned passage 18 virus sample , this mutation was absent in the plaque-cloned viruses . To test whether the mutations affect NSs functions , we generated MP-12-based mutant viruses , each carrying R16H , R16H+M250K ( NSs-R16H/M250K ) , A75E , D100G , D100G+M250T , M250K ( NSs-M250K ) , or M250T mutations in the NSs , by using a reverse genetics system [24] . Accumulations of NSs protein in VeroE6 cells infected with MP-12 carrying the D100G mutation or the D100G+M250T mutations were significantly lower than those in MP-12 infected cells ( Fig 1 ) , indicating that the D100G mutation affected efficient NSs accumulation . Due to the poor accumulation of NSs , we excluded the MP-12 mutant carrying the D100G mutation and that carrying the D100G+M250T mutation from subsequent analysis . In cells infected with the other mutants , levels of NSs protein accumulation were similar to that in MP-12 infected cells . In this study , we refer to MP-12 NSs as wild type NSs ( wt NSs ) . To evaluate host transcriptional shut-off activities of these NSs mutants , levels of RNA synthesis in virus-infected VeroE6 cells were measured by labeling with 5EU from 16 h to 17 h p . i . Mock-infected cells alone , as well as those treated with ActD , and MP-12-infected cells and cells infected with MP-12ΔNSs , which lacks the NSs gene [24] , served as controls . 5EU-labeled RNA , which was detected as a fluorescence signal , was mainly observed in the nucleus but not in the cytoplasm , indicating that viral RNA synthesis , which occurs exclusively in the cytoplasm , was not very active at 16h post infection . As shown in Fig 2A , strong signals of fluorescent-labeled RNA were observed in the nucleus of mock-infected cells and MP-12ΔNSs-infected cells , demonstrating active host RNA synthesis . In contrast , ActD-treated cells showed very weak fluorescent signals , demonstrating ActD-mediated host transcriptional shut-off . MP-12-infected cells showed weaker fluorescent signals in the nucleus , compared with those in MP-12ΔNSs-infected cells , demonstrating an inhibition of host transcription by NSs . Fluorescent intensity observed in cells infected with virus carrying mutation M250T , R16H or A75E in NSs was similar to that in MP-12-infected cells , suggesting that host transcription was still inhibited by these NSs mutants . In contrast , cells infected with MP-12 carrying NSs-R16H/M250K ( MP-12-R16H/M250K ) had bright fluorescent signals , whose intensities were similar to those in mock-infected cells or MP-12ΔNSs-infected cells . Fluorescent signal intensities of cells infected with MP-12 carrying NSs-M250K ( MP-12-M250K ) were between those of MP-12-infected cells and MP-12ΔNSs-infected cells . We next used flow cytometry analysis to quantitatively measure the levels of 5EU-labeled RNAs in MP-12-M250K-infected cells and in MP-12-R16H/M250K-infected cells . We used the same controls as described above . Fig 2B shows the results using density plots , wherein 5EU incorporation and N protein levels are shown on the X and Y axes , respectively . In mock-infected and ActD-treated samples , most of the cells were found in quadrant 3 ( Q3 ) and Q4 , respectively , demonstrating active host transcription in the former , but not in the latter ( Fig 2B-a and 2B-b ) . In all infected samples , 86 . 8–93 . 4% of cells were RVFV N protein positive ( Q1+Q2 ) , demonstrating that most of the cells were infected . The level of 5EU-labeled RNA in MP-12ΔNSs-infected cells was similar to that in mock-infected cells , demonstrating similar RNA synthesis activity in these two samples ( Fig 2B-a and 2B-f ) . MP-12-infected cells showed lower levels of 5EU-labeled RNA compared with those in MP-12ΔNSs infected cells , demonstrating its lower RNA transcription activity ( Fig 2B-c and 2B-f ) . MP-12-R16H/M250K-infected and MP-12ΔNSs-infected cells showed similar density plot patterns , suggesting to us that host transcription inhibition did not occur in MP-12-R16H/M250K-infected cells ( Fig 2B-e and 2B-f ) . MP-12-M250K-infected cells showed lower levels of 5EU-labeled RNA compared with those in MP-12ΔNSs-infected cells ( Fig 2B-d and 2B-f ) . However , the percentage of MP-12-M250K-infected cells with low transcription activity was 43 . 0% ( Q1/Q1+Q2 ) , while in the MP-12 samples , it was 56 . 6% . These results suggested that MP-12-M250K replication suppressed host general transcription , and yet the inhibitory activity of MP-12-M250K was weaker than that of MP-12 . We next examined the effect of differences in the transcription inhibitory activities of the mutant viruses on global protein synthesis by incorporation of 35S-methionine/cysteine into newly synthesizing proteins in mock-infected cells and in cells infected with MP-12 , MP-12ΔNSs , MP-12-M250K , or MP-12-R16H/M250K ( Fig 2C ) . Consistent with our previous study [24] , MP-12 replication , but not MP-12ΔNSs replication , suppressed host protein synthesis . MP-12-R16H/M250K replication did not inhibit host protein synthesis , while MP-12-M250K replication moderately inhibited host protein synthesis . Taken together , these analyses showed that NSs-R16H/M250K lost the general transcription suppression activity , leading to efficient host protein synthesis in infected cells , while NSs-M250K exerted inefficient general transcription suppression activity , causing moderate levels of host protein synthesis inhibition in infected cells . Analysis of replication kinetics of MP-12 , MP-12ΔNSs , MP-12-R16H/M250K and MP-12-M250K showed that all viruses replicated efficiently with similar replication kinetics in Vero E6 cells ( Fig 3A ) . MP-12 formed clear plaques in Vero E6 cells , whereas MP-12-R16H/M250K and MP-12-M250K formed turbid-type plaques like MP-12ΔNSs ( Fig 3B ) , indicating that the R16H/M250K mutation and the M250K mutation in NSs affected virus-induced cytotoxicity . Cell viability assays showed that MP-12 replication caused a 90% reduction in cell viability as compared with mock-infected cells at 72 h p . i . ( Fig 3C ) , whereas MP-12ΔNSs-infected cells and mock-infected cells showed similar cell viabilities throughout the course of the experiments . MP-12-R16H/M250K-infected cells and MP-12-M250K-infected cells showed an 11% and 39% decrease in cell viability , respectively , as compared with that in mock-infected cells at 72 h p . i . , demonstrating that both of the mutant viruses were less cytotoxic than MP-12 . MP-12 , MP-12-M250K and MP-12-R16H/M250K had similar replication kinetics , regardless of their differential ability to induce cytotoxicity ( Fig 3 ) , implying that virus-induced cytotoxicity did not have a significant impact on RVFV replication kinetics in Vero E6 cells . Because others reported that MP-12 replication induces NSs-dependent p53 stabilization , which contributes to virus-induced cell death [21] , we next examined stabilization of p53 in cells infected with MP-12 and other mutant viruses ( Fig 3D ) . Amounts of p53 protein significantly increased in MP-12-infected cells , but not in MP-12ΔNSs-infected cells , confirming the NSs-dependent p53 stabilization in the infected cells . Accumulation of p53 also occurred in MP-12-M250K-infected cells , whereas the accumulation levels were lower than those in MP-12-infected cells . Very low levels of p53 accumulation occurred in MP-12-R16H/M250K-infected cells . NSs interacts with PKR and triggers degradation of the PKR by a proteasome pathway [15–18] . To investigate whether the NSs mutants retain the ability for PKR degradation , we compared the total amount of PKR in virus-infected cells . Similar levels of reduction in the amounts of PKR occurred in cells infected with MP-12 , MP-12-M250K or MP-12-R16H/250K , suggesting that NSs-R16H/M250K and NSs-M250K promoted PKR degradation as efficiently as wt NSs ( Fig 4 ) . Le May et al . proposed that interaction of NSs with SAP30 interferes with the recruitment of co-activator protein CBP at activation sites on the IFN-β promotor , leading to the inhibition of IFN-β mRNA transcription [11] . To investigate the ability of MP-12-R16H/M250K or MP-12-M250K to suppress IFN-β mRNA transcription , we examined the levels of IFN-β mRNA in MRC-5 cells infected with MP-12 , MP-12-ΔNSs , MP-12-M250K or MP-12-R16H/250K at various times p . i . ( Fig 5A ) . MP-12ΔNSs replication induced robust IFN-β mRNA transcription , whereas IFN-β mRNA was not detectable in MP-12- or MP-12-M250K-infected cells . In contrast , low levels of IFN-β mRNA accumulation occurred in MP-12-R16H/M250K-infected cells , demonstrating that the NSs-R16H/M250K was unable to efficiently inhibit IFN-β mRNA transcription . MP-12-M250K replicated as efficiently as did MP-12 in MRC-5 cells , whereas the virus titer of MP-12ΔNSs was significantly lower than that of MP-12 at 72 h p . i . ( Fig 5B ) , demonstrating the efficient inhibition of IFN-β production in the former , but not in the latter . Although MP-12-R16H/M250K replicated better than MP-12ΔNSs , it replicated less efficiently than MP-12 , suggesting to us that the low levels of IFN-β production in the MP-12-R16H/M250K-infected MRC-5 cells prevented efficient replication of the virus . Next , we examined the interactions of the NSs mutants with SAP30 . To detect NSs , we used viruses carrying an NSs with a C-terminal Flag tag ( MP-12-NSs-Flag , MP-12-M250K-Flag and MP-12-R16H/M250K-Flag ) . Cells , transiently expressing a V5-tagged SAP30 , were infected with these viruses , and the co-localization of the mutated NSs protein with SAP30 was examined by fluorescence microscopy analysis . Like wt NSs , both NSs mutants formed filament-like structures in the nucleus ( Fig 5C ) . The expressed SAP30 was mainly observed in the nucleus and was co-localized with wt NSs and both mutated NSs in the filaments . We also performed co-immunoprecipitation analysis to examine NSs-SAP30 interaction . As a negative control , V5-tagged Venus was expressed in place of V5-tagged SAP30 . Anti-V5 antibody co-precipitated wt NSs and both NSs mutants along with V5-tagged SAP30 , while the amounts of NSs-M250K that were co-immunoprecipitated with SAP30 were lower than those of wt NSs ( Fig 5D ) . Anti-V5 antibody did not co-precipitate wt NSs along with V5-tagged Venus ( Fig 5D ) . These data demonstrate that both NSs mutants bound to SAP30 in infected cells . Taken together , these data indicated that NSs-R16H/M250K was unable to completely block IFN-β transcription despite its ability to bind to SAP30 . Both NSs mutants retained some NSs functions , including degradation of PKR and SAP30 binding , and yet these NSs mutants and wt NSs showed different levels of general transcriptional suppression activities in virus-infected cells . Transcriptional suppression activities of the mutated NSs were also examined in cells transiently expressing NSs along with Renilla luciferase from co-transfected plasmids . Luciferase activity was strongly inhibited in the cells co-expressing wt NSs and Renilla luciferase ( Fig 6A ) . Consistent with the data obtained from infected cells ( Fig 2 ) , NSs-R16H/M250K expression did not inhibit luciferase activity , whereas NSs-M250K expression moderately inhibited the luciferase activity . Western blot analysis of nuclear and cytoplasmic fractions from virus-infected cells showed that like wt NSs , NSs-R16H/M250 and NSs-M250K were also distributed in both the nucleus and the cytoplasm ( S1 Fig ) , demonstrating that these mutations did not affect the subcellular localization of NSs . These data prompted us to further examine the interplay between these NSs mutants and a form of host transcription machinery , TFIIH , as other studies have shown an association of TFIIH with NSs in the NSs-induced host transcription shut-off . RVFV NSs binds to p44 , a subunit of TFIIH , and interferes with the formation of the TFIIH complex by inhibiting the subsequent interaction of p44 and XPD [12] . The reduction in the abundance of XPD and p44 also occurs upon RVFV infection , although the mechanisms that govern the reduction of these proteins are unclear [12] . In addition , NSs binds to both p62 , a TFIIH subunit , and FBXO3 , a component of E3 ubiquitin ligase , which leads to p62 degradation [13 , 14] . We first examined the abundance of TFIIH components , including p44 , p62 and XPD , in infected cells . Substantial reduction of p62 abundance and moderate reductions in the abundances of p44 and XPD occurred in cells infected with MP-12 , or MP-12-M250K at 16 h p . i . ( Fig 6B , left panels ) . In contrast , there was no substantial reduction in the abundance of these TFIIH components in cells infected with MP-12ΔNS or MP-12-R16H/M250K . The same results were obtained when viruses carrying Flag-tagged NSs were used . To exclude the possibility that different transcription suppression activities of these viruses affected the results , we repeated the experiments in the presence of ActD and obtained similar results ( Fig 6B , right panels ) . Next , we tested the interaction of mutated NSs with p44 and p62 . Cells infected with MP-12 or its mutants , all of which carried Flag-tagged NSs , were subjected to co-immunoprecipitation analysis using anti-p44 antibody ( Fig 6C ) . Anti-p44 antibody co-immunoprecipitated wt NSs and both mutant NSs along with p44 , whereas control anti-H2K antibody precipitated neither p44 nor NSs . These data demonstrated that both NSs mutants bound to p44 . To examine the interaction of p62 and NSs , cells were infected with the viruses as described above , and cell extracts were prepared at 5 h p . i . , when p62 was still detectable in MP-12-NSs-Flag-infected cells ( Fig 6D ) . Anti-p62 antibody , but not anti-H2K antibody , co-immunoprecipitated wt NSs and both mutant NSs along with p62 , demonstrating binding of the mutant NSs proteins to p62 . We noted that the amounts of NSs-M250K that were co-immunoprecipitated with p44 and with p62 were lower than those of wt NSs , implying that the binding efficiencies of NSs-M250K for p44 and for p62 were lower than those for wt NSs . FBXO3 is an interactor of RVFV NSs that is engaged in the degradation of p62; NSs interacts with the full-length FBXO3 protein ( FBXO3/1 ) as well as with a shorter splice variant of the FBXO3 that lacks the C-terminal acidic domain and poly ( R ) region [14] . Because NSs-R16H/M250K did not induce p62 degradation regardless of its ability to bind to p62 ( Fig 6D ) , we suspected that NSs-R16H/M250K would not interact with FBOX3 . To test this possibility , cells transiently expressing the V5-tagged FBXO3/1 were mock infected or infected with MP-12-NSs-Flag , MP-12-M250K-Flag , or MP-12-R16/M250K-Flag . At 5 h p . i . , cell extracts were prepared and subjected to co-immunoprecipitation analysis by using anti-V5 antibody ( Fig 6E ) . All of the NSs proteins , including NSs-R16/M250K , were co-immunoprecipitated with FBXO3/1 . Table 2 summarizes interactions of the NSs mutants with p44 , p62 and FBXO3 . NSs is a major virulence factor of RVFV [31] . The NSs-mediated inhibition of type I IFN production is thought to contribute to the virulence of the virus , yet it remains unclear whether the general transcription suppression function of NSs contributes to this virulence . We examined the importance of the NSs-mediated transcription inhibition in RVFV virulence by using a young mouse model . Although MP-12 is known as an attenuated strain , the intraperitoneal inoculation of 104 PFU of MP-12 into 18-day-old CD1 mice resulted in the death of 55% of the mice within 13 days p . i . ( Fig 7 ) . Under the same experimental conditions , none of the MP-12-R16H/M250K-inoculated mice and 20% of the MP-12-M250K-inoculated mice died . No obvious clinical signs , including neurological symptoms , were observed . These data demonstrated that the MP-12-R16H/M250K lacked virulence , and MP-12-M250K was less virulent than MP-12 in this young mouse model .
In this study , we demonstrated that the M250K and R16H/M250K mutations in NSs differentially reduced its inhibitory activity on host transcription without affecting its ability to inhibit IFN-β transcription , through its interaction with SAP30 , and induced PKR degradation . NSs-R16H/M250K , which completely lacked the activity to inhibit general transcription , correspondingly lost the ability to promote the degradation of p62 . Unexpectedly , NSs-R16H/M250K was able to interact with FBOX3 and p62 ( Fig 6 ) . These data possibly suggest that the FBXO3-NSs-p62 interaction may not be sufficient to trigger p62 degradation . NSs-M250K exhibited a partially reduced activity to inhibit general transcription . The results of co-immunoprecipitation assays showed that a reduced amount of NSs-M250K co-precipitated with SAP30 , p62 and p44 , compared with that in wt NSs ( Figs 5 and 6 ) . However , the slightly impaired ability of NSs-M250K to inhibit general transcription could not be solely attributed to the lower binding efficiency of NSs-M250K to p44 because NSs-M250K efficiently suppressed IFN-β transcription and induced p62 degradation , despite its lower efficiency of binding to SAP30 and p62 . Moreover , NSs-R16H/M250K , which lacked transcription suppression activity , efficiently interacted with p44 ( Fig 6 ) . These data bring into question the importance of NSs-p44 interaction for its host transcriptional shut-off function . One possibility is that the NSs-p44 interaction may only make a modest contribution towards its transcription inhibition activity and possibly could have additional , as yet unidentified , biological function ( s ) . A second possibility is that only wt NSs , but not the mutated NSs , is able to interfere with the formation of an active TFIIH complex . NSs competes with XPD for binding to p44 , resulting in inhibition of the TFIIH complex formation [12] . Although both NSs-M250K and NSs-R16H/M250K retained the ability to bind to p44 , it is possible that the binding of these mutated NSs to p44 did not exclude the binding of XPD . The R16H single mutation did not affect the ability of NSs to inhibit transcription ( Fig 2 ) . Although the M250K mutation is in close proximity to the ΩXaV motif located at the C-terminal region of NSs , which is essential for p62 degradation [32] , MP-12-M250K still retained the ability to degrade p62 ( Fig 6 ) . However , the R16H/M250K double mutant lost the ability to induce p62 degradation . These results imply that the combined mutations , R16H and M250K , induced an unfavorable structural alteration in NSs , which abolished its function to degrade p62 . MP-12-R16H/M250K replication induced low levels of IFN-β mRNA ( Fig 5 ) , indicating that NSs-R16H/M250K was not able to block IFN-β mRNA synthesis as efficiently as wt NSs despite its ability to interact with SAP30 ( Fig 5 ) . It has been reported that NSs-induced p62 degradation contributes to the inhibition of IFN-β production [14] . Accordingly , our data suggested that MP-12-R16H/M250K was unable to completely block IFN-β mRNA synthesis due to a lack of ability to promote the degradation of p62 ( Fig 6 ) . Although MP-12-R16H/M250K replication induced IFN-β mRNA synthesis , the level of the IFN-β mRNA was significantly lower than that in MP-12ΔNSs-infected cells ( Fig 5 ) , suggesting the importance of interaction of NSs with SAP30 for IFN-β inhibition . Taken together , the data shown here and those of others [11 , 14] strongly imply that NSs-SAP30 interaction and NSs-induced general host transcriptional suppression function are both necessary for efficient inhibition of IFN-β mRNA transcription . MP-12ΔNSs or MP-12 encoding a reporter gene in place of the NSs gene causes less prominent cytopathic effects than does MP-12 , demonstrating the contribution of the NSs towards the induction of cytotoxicity [24] . MP-12 replication induces NSs-dependent p53 stabilization , which contributes to virus-induced cell death [21] , and yet it was unclear which function of the NSs contributed to the induction of the p53-mediated cytotoxicity . As shown in Fig 3 , MP-12-infected cells showed the lowest cell viability , followed in order by MP-12-M250K , which moderately suppressed host general transcription , and MP-12-R16H/M250K , which did not suppress host transcription . Hence , there was a correlation between the strength of NSs-mediated , host transcriptional shut-off activity and cell viability in the infected cells ( Table 3 ) . Likewise , accumulation of p53 was the highest in MP-12-infected cells , followed in order by that in MP-12-M250K-infected cells and MP-12-R16H/M250K-infected cells , suggesting to us that p53 stabilization also correlates with the transcription inhibition activities of the different NSs mutants . These results indicate that the NSs-mediated host transcriptional shut-off triggered p53 stabilization , leading to p53-mediated cell death . Although MP-12 is an attenuated RVFV strain , 55% of 18-day-old CD1 mice died after intraperitoneal inoculation with 104 PFU within 13 days p . i . ( Fig 7 ) . As the immune system is not yet fully developed in young mice , it likely failed to prevent systemic infection by MP-12 . Consistent with this notion , intraperitoneal inoculation of MP-12 into severe combined immune deficiency mice also caused 100% mortality [33] . MP-12-R16H/M250K carrying NSs that lacks the host transcription inhibition function was completely attenuated in 18-day-old CD1 mice ( Fig 7 ) . MP-12-M250K carrying NSs with an impaired ability to inhibit transcription also exhibited reduced virulence when compared to that in its parental virus MP-12 . These data highlight the role of the host transcription inhibition function of NSs in RVFV virulence . Type I IFN has been shown to play a critical role in protecting the host from RVFV-induced disease in animal models [34] . Notably , both the NSs mutants , carrying either the M250K single mutation or the R16H/M250K double mutation , retained the ability to bind to SAP30 , the factor that is targeted by NSs to inhibit IFN-β mRNA transcription . However , the induction of IFN-β mRNA synthesis was not completely blocked in MP-12-R16H/M250K-infected MRC-5 cells , most probably due to the lack of inhibition of host transcription in these cells expressing the mutated NSs . These data suggest the possibility that the inability of MP-12-R16H/M250K to completely block the production of IFN-β in infected mice could have contributed towards its attenuation . In addition , the production of other antiviral and/or proinflammatory cytokines could have also contributed towards the attenuated phenotype of both of these mutant viruses , carrying NSs with an impaired ability to block host transcription . It is also possible that the lower levels of virus-induced cytocidal effects might have contributed to the lower virulence of these NSs mutant viruses , as both mutant viruses caused less severe cytopathic effects and cytotoxicity than did MP-12 in cultured cells ( Fig 3 ) . We believe that experiments using RVFV mutants , carrying the M250K single mutation and the R16H/M250K double mutation , in animal models would yield valuable information about the role of NSs-mediated host transcription inhibition in regulating host cytokine responses and its impact on the pathogenesis of RVFV . MP-12 is an attenuated live vaccine candidate , but it still harbors residual virulence in a young mouse model . Although further studies are required to test the immunogenicity and protective efficacy of MP-12-M250K and MP-12-R16H/M250K , there is a potential for developing these MP-12-derived NSs mutant viruses as safer live attenuated RVFV vaccine candidates . We found that the serial passage of an MP-12-derived mutant virus having a large deletion in the 5’ untranslated region of M RNA segment [30] in Vero E6 cells resulted in accumulation of variant viruses that were able to replicate better than the original mutant virus . Most of viruses in passage 18 had a large internal deletion in the NSs gene , which may mean their NSs proteins are biologically inactive . Others have reported that RVFV carrying large deletions in the NSs gene start accumulating from the 15th serial passage in BHK cells of the RVFV P strain , which is defective in IFN-α/β signaling [35] . In our experiment , the large deletions in the NSs gene were detected after the 5th serial passage of the virus . Although the cell lines used in the studies were different , the deletion of the NSs gene as early as after 5 passages implied that there was a selective pressure to remove the NSs gene from the virus genome during the passaging of the mutant virus . The full-length NSs gene of the uncloned passage 18 viruses had M250K , M250T , K202N and D100G mutations ( Table 1 ) . In the cells infected with MP-12 carrying NSs with a D100G mutation , the accumulation of NSs was poor ( Fig 1 ) , indicating that the D100G mutation affects the efficient accumulation of NSs . As two out of the five plaque-cloned viruses ( clones 3 and 4 , Table 1 ) also had the D100G mutation in NSs , this possibly affected its accumulation in infected cells . The remaining three clones carried NSs with a M250K or R16H/M250K mutation . Our results showed that these mutations in NSs partially or completely abolished its host transcription inhibition function without affecting its ability to interact with SAP30 to inhibit IFN-β mRNA synthesis . These data implied that the host transcription function of NSs was unfavorable for the mutant virus , carrying a deletion in the 5’ UTR , to replicate well in VeroE6 cells . MP-12-M250K and MP-12-R16H/M250K replication induced low cytotoxicity due to its lower inhibitory activity on host transcription ( Figs 2 and 3 ) . We suspect that the lack of NSs-induced host transcription suppression created a favorable cellular environment for the mutant virus , thereby allowing the emergence and accumulation of mutant viruses that lacked this function . Further studies are required to delineate the importance of NSs-mediated host transcription inhibition in the virus life cycle . | Rift Valley fever virus ( RVFV ) has a significant impact on the livestock industry because of its high mortality rate in young ruminants and causation of a high abortion rate in pregnant animals . Human RVFV infections generally manifest as self-limiting and non-fatal illnesses . However , a small percentage of patients develop encephalitis , vision loss and hemorrhagic fever with a high mortality rate . Currently , there is no commercially available vaccine for human use or effective antiviral drug for RVFV treatment . The non-structural protein NSs is a major virulence factor of RVFV , which mediates suppression of host general transcription , inhibition of IFN-β transcription and degradation of PKR , to block host antiviral responses . To examine the contribution of host transcription inhibition to RVFV virulence , we generated RVFV MP-12 strain-derived mutants that have attenuated inhibitory activity on host transcription due to amino acid mutations in NSs . The mutant viruses showed attenuated cytotoxicity in cell culture and attenuated virulence in young mice , demonstrating the contribution of NSs-mediated host transcription inhibition to the virulence of RVFV . | [
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... | 2016 | Mechanistic Insight into the Host Transcription Inhibition Function of Rift Valley Fever Virus NSs and Its Importance in Virulence |
The key molecular event that marks entry into the cell cycle is transcription of G1 cyclins , which bind and activate cyclin-dependent kinases . In yeast cells , initiation of G1 cyclin transcription is linked to achievement of a critical cell size , which contributes to cell-size homeostasis . The critical cell size is modulated by nutrients , such that cells growing in poor nutrients are smaller than cells growing in rich nutrients . Nutrient modulation of cell size does not work through known critical regulators of G1 cyclin transcription and is therefore thought to work through a distinct pathway . Here , we report that Rts1 , a highly conserved regulatory subunit of protein phosphatase 2A ( PP2A ) , is required for normal control of G1 cyclin transcription . Loss of Rts1 caused delayed initiation of bud growth and delayed and reduced accumulation of G1 cyclins . Expression of the G1 cyclin CLN2 from an inducible promoter rescued the delayed bud growth in rts1Δ cells , indicating that Rts1 acts at the level of transcription . Moreover , loss of Rts1 caused altered regulation of Swi6 , a key component of the SBF transcription factor that controls G1 cyclin transcription . Epistasis analysis revealed that Rts1 does not work solely through several known critical upstream regulators of G1 cyclin transcription . Cells lacking Rts1 failed to undergo nutrient modulation of cell size . Together , these observations demonstrate that Rts1 is a key player in pathways that link nutrient availability , cell size , and G1 cyclin transcription . Since Rts1 is highly conserved , it may function in similar pathways in vertebrates .
Entry into the cell cycle is initiated by G1 cyclins , which bind and activate cyclin-dependent kinases [1] . There are two cyclin-dependent kinases in budding yeast that function during G1 , called Cdk1 and Pho85 , which are activated by numerous different G1 cyclins [1] . Cdk1 is activated by the cyclins Cln1 , Cln2 , and Cln3 , while Pho85 is activated by Pcl1 and Pcl2 , as well as by additional cyclins that do not appear to directly regulate G1 events . The G1 cyclins are redundant: cells lacking any two of the cyclins Cln1 , Cln2 or Cln3 are viable , but loss of all three cyclins is lethal [2] , [3] . Similarly , cells lacking Cln1 and Cln2 or Pcl1 and Pcl2 are viable , but loss of all four cyclins is lethal [4] , [5] . The cyclin Cln3 plays a role in triggering transcription of a suite of genes required for initiation of G1 events , including the genes for Cln1 , Cln2 , and Pcl1 , which are often referred to as late G1 cyclins [5]–[9] . Transcription of the late G1 cyclins is generally considered to be the key molecular event that marks entry into the cell cycle [10] , [11] . The late G1 cyclins initiate growth of a new daughter bud and are also required for polar growth after bud emergence [4] , [12] . Production of late G1 cyclins is tightly regulated . Cyclin mRNA and protein undergo rapid turnover , so mechanisms that act at the level of transcription play an important role [13]–[15] . Transcription of G1-specific genes , including the late G1 cyclin genes , is dependent upon the SBF and MBF transcription factors . SBF and MBF each include a distinct DNA binding subunit , called Swi4 and Mbp1 , respectively , and a shared subunit called Swi6 . SBF and MBF are kept inactive early in the cell cycle by a repressor called Whi5 [16] , [17] . Loss of Whi5 causes transcription of late G1 cyclins to occur before the mother cell has completed growth , leading to premature bud emergence and a reduced cell size . Cdk1/Cln3 triggers transcription of late G1 cyclins by phosphorylating and inactivating Whi5 . Transcription of late G1 cyclins can also be triggered by a redundant Cln3-independent pathway that is dependent upon the Bck2 protein [18]–[21] . The late G1 cyclin Cln2 promotes its own transcription via a positive feedback loop , which ensures that initiation of G1 events occurs in a coordinated , switch-like manner [6] , [7] , [22] . Mechanisms that control G1 cyclin transcription play an important role in control of cell size . A cell size checkpoint links initiation of G1 cyclin transcription to cell size . Thus , transcription of late G1 cyclins is only initiated when the mother cell has reached a critical size , which contributes to cell size homeostasis . An interesting property of cell size control in yeast is that the critical cell size is modulated by external nutrients , such that cells growing in poor nutrients are significantly smaller than cells growing in rich nutrients [23] , [24] . It is thought that nutrients modulate cell size by rapidly changing the critical cell size for initiation of G1 cyclin transcription [11] . The mechanisms that link initiation of G1 cyclin transcription to cell size and nutrient availability are unknown . Interestingly , cln3Δ bck2Δ whi5Δ triple mutants , which lack all upstream regulators known to play an important role in the control of G1 cyclin transcription , undergo normal nutrient modulation of cell size [25] . Thus , the signals that control cell size by linking G1 cyclin transcription to nutrient availability must act by a different mechanism . The mechanisms that link G1 cyclin transcription to cell size and nutrient availability are likely to be a key to understanding cell size control . Here , we report that a specific form of protein phosphatase 2A ( PP2A ) is required for control of G1 cyclin transcription and nutrient modulation of cell size . PP2A is a trimeric complex that consists of a catalytic “C” subunit , a scaffolding “A” subunit , and a regulatory “B” subunit [26] . Binding of different B-type regulatory subunits is thought to direct PP2A activity toward different substrates . Thus , the key to understanding PP2A is to understand the function and regulation of specific regulatory subunits . In budding yeast , two B subunits called Cdc55 and Rts1 bind to PP2A in a mutually exclusive manner , forming two distinct PP2A complexes: PP2ACdc55 and PP2ARts1 [27] , [28] . We discovered a role for Rts1 in controlling G1 cyclin levels while characterizing a genetic interaction between RTS1 and the septin CDC12 . The septins are a conserved family of proteins that localize to the site of bud emergence in early G1 and to the bud neck during bud growth and cytokinesis [29] . The septins have been proposed to form a diffusion barrier between the mother and daughter cell , to serve as a signaling scaffold for activation of kinases , or to carry out functions in the secretory pathway . Temperature sensitive alleles of the septins cause cells to undergo a prolonged delay at G2/M while undergoing continuous polarized growth , leading to the formation of highly elongated cells [30] . The G2/M arrest is mediated by Swe1 , the budding yeast Wee1 homolog , which phosphorylates and inhibits Cdk1 to delay entry into mitosis [31] . The G2/M delay and the elongated cell phenotype are eliminated by swe1Δ . A number of kinases have been identified that regulate septin function and localization , and may in turn be regulated by the septins [31]–[36] . These kinases include Elm1 , Gin4 , Cla4 , and Hsl1 . Loss of these kinases can cause a Swe1-dependent G2/M delay and an elongated cell phenotype similar to septin mutants . Previous work found that rts1Δ increased the restrictive temperature of the cdc12-6 allele [37] . Loss of RTS1 also caused altered septin ring dynamics; however , it remained unclear whether the observed changes in septin ring dynamics were sufficient to explain the rescue of the cdc12-6 temperature sensitive phenotype . For example , it was possible that in addition to regulating septin ring dynamics , Rts1 played additional roles in pathways that promote polar growth or Swe1-dependent G2/M delays . We therefore further investigated the role of Rts1 in polar cell growth and cell cycle progression .
Since rts1Δ suppressed the temperature sensitivity of cdc12-6 , we tested whether rts1Δ also suppressed the elongated cell phenotype of these cells . We found that rts1Δ cdc12-6 cells showed reduced elongation compared with cdc12-6 cells when shifted to 30°C ( Figure 1A ) . In addition , rts1Δ significantly reduced the elongated cell phenotype caused by loss of GIN4 , ELM1 , and CLA4 ( Figure 1B ) . We next considered the possibility that rts1Δ rescued the elongated cell phenotype of these mutants by eliminating the Swe1-dependent G2/M delay . To test this , levels of the mitotic cyclin Clb2 were assayed by Western blotting during a synchronized cell cycle in wild type , elm1Δ , and elm1Δ rts1Δ cells ( Figure 1C ) . As previously shown , elm1Δ cells underwent a prolonged G2/M delay with elevated Clb2 levels when compared to wild type cells [35] . The prolonged G2/M delay was not eliminated by rts1Δ . Thus , although rts1Δ reduced the elongated cell phenotype caused by loss of CDC12 , GIN4 , CLA4 , and ELM1 , it did not appear to do so by reducing the Swe1-dependent G2/M delay . Since rts1Δ did not rescue the G2/M delay in elm1Δ cells , we considered the possibility that Rts1 plays a direct role in promoting polar growth . To test this , we utilized cells that over express SWE1 from the GAL1 promoter , which arrest at G2/M with high levels of G1 cyclins and undergo constitutive polar growth [12] , [38] . Wild type or rts1Δ cells carrying GAL1-SWE1 were released from a G1 arrest in the presence of galactose to induce expression of SWE1 . We then measured bud lengths at time intervals after induction of GAL1-SWE1 to determine the rate of polar bud growth . We found that polar bud growth in GAL1-SWE1 rts1Δ cells occurred at a slower rate than GAL1-SWE1 control cells ( Figure 2A and 2B ) . Controls showed that wild type and rts1Δ cells expressed similar levels of Swe1 protein ( Figure 2C ) . Similar results were also obtained by measuring rates of bud elongation after induction of GAL1-SWE1 in log phase populations of cells ( not shown ) . We next determined whether Rts1 plays a role in initiation of polar bud growth . To do this , we assayed initiation of bud growth in synchronized populations of wild type and rts1Δ cells . Cells were released from an early G1 arrest and the timing of bud emergence was determined ( Figure 2D ) . Cells lacking Rts1 showed a delay in bud emergence of 22 minutes ± 0 . 15 minutes compared with wild type cells . Together , these results demonstrate that Rts1 is required for both the timely initiation and the normal rate of polar bud growth . Since G1 cyclins are required for initiation and maintenance of polar bud growth , it seemed likely that Rts1 is required for functions mediated by G1 cyclins . To test this , we determined whether rts1Δ showed genetic interactions with the G1 cyclins . We found that cln2Δ significantly reduced the rate of proliferation of rts1Δ cells ( Figure 3A ) . Moreover , we failed to recover rts1Δ cln1Δ cln2Δ spores when rts1Δ was crossed to a cln1Δ cln2Δ strain , which suggested that rts1Δ is synthetically lethal with cln1Δ cln2Δ . To further test this , we created a GAL1-CLN2 cln1Δ rts1Δ strain , in which the expression of CLN2 could be repressed by switching from galactose-containing media to dextrose-containing media . This strain grew well on galactose , but was inviable on dextrose , which confirmed that rts1Δ is lethal in cln1Δ cln2Δ cells ( Figure 3B ) . To characterize the defects caused by loss of Rts1 , Cln1 , and Cln2 , we turned off CLN2 expression in the GAL1-CLN2 cln1Δ rts1Δ cells by shifting the cells to media containing dextrose for 8 hours ( Figure 3C ) . The GAL1-CLN2 cln1Δ rts1Δ cells arrested primarily as large , unbudded cells with a small percentage of budded cells ( 6 . 5% ) . Control cells carrying rts1Δ or cln1Δ GAL1-CLN2 had 35% and 15% budded cells respectively . The GAL1-CLN2 cln1Δ rts1Δ cells also became abnormally large , which is commonly observed in cells that fail to undergo bud emergence ( Figure 3C ) [39] , [40] . We next tested whether rts1Δ showed a genetic interaction with cln3Δ . When rts1Δ cells were crossed to cln3Δ cells , a small proportion of the expected progeny was recovered ( 2/80 spores were recovered rather than the predicted 20/80 ) , while the other progeny segregated according to the expected Mendelian ratios ( rts1Δ: 21/80 , cln3Δ: 20/80 , wild type: 18/80 ) . The few recovered rts1Δ cln3Δ cells formed colonies at the same rate as rts1Δ cells , but their low rate of recovery from the cross suggested that they could contain suppressor mutations . To analyze the viability of cells lacking RTS1 and CLN3 in a context unlikely to select for suppression , we utilized a GAL1-CLN3 rts1Δ strain . When switched to dextrose-containing medium we found that GAL1-CLN3 rts1Δ cells were viable , although they formed colonies at a slightly slower rate than rts1Δ cells ( not shown ) . It was previously reported that rts1Δ is synthetically lethal with the cyclin-dependent kinase Pho85 in the S288C strain background [41] . We were able to isolate a few rts1Δ pho85Δ spores in the W303 strain background , although they were poorly viable and grew significantly slower than either rts1Δ or pho85Δ cells ( Figure 4A ) . We were also able to recover pcl1Δ pcl2Δ rts1Δ cells from crosses . These grew more slowly than rts1Δ cells but were more robust than pho85Δ rts1Δ cells ( Figure 4B ) . Thus , the poor viability of pho85Δ rts1Δ cells is not strictly due to lack of Pho85 activity associated with the Pcl1/2 cyclins , and may indicate that additional Pho85/Pcl complexes are important for normal growth in rts1Δ strains . In contrast to cells lacking CLN1 , CLN2 and RTS1 , the pcl1Δ pcl2Δ rts1Δ cells did not show severe defects in bud formation , although they did become larger than the rts1Δ or pcl1Δ pcl2Δ cells . ( Figure 4C ) . In summary , rts1Δ showed genetic interactions with multiple G1 cyclins and cyclin-dependent kinases . Because the late G1 cyclins show extensive redundancy , mutations that cause reduced function of G1 cyclins should show synthetic interactions with mutations that cause a further reduction in cyclin levels . The fact that rts1Δ showed lethality when combined with cln1Δ cln2Δ , and reduced viability when combined with pcl1Δ pcl2Δ , suggests that Rts1 is required for the normal function of both pairs of cyclins , rather than mediating the functions of specific cyclins . The fact that rts1Δ also showed genetic interactions with cln3Δ , which is upstream of the late G1 cyclins , indicates that Rts1 does not act solely in a Cln3-dependent pathway that promotes transcription of the late G1 cyclins . We next determined whether accumulation of the G1 cyclin Cln2 occurred normally in synchronized rts1Δ cells . For these experiments , we utilized a 3XHA-tagged version of CLN2 expressed from the CLN2 promoter and quantitative Western blotting to assay Cln2 protein levels . These experiments revealed that the peak of Cln2 accumulation was delayed by 10–15 minutes in rts1Δ cells and that Cln2 failed to reach normal levels ( Figure 5A and 5C ) . The effects of rts1Δ on Cln2 accumulation were more severe at 34°C and 37°C ( Figure 5D ) . Accumulation of the mitotic cyclin Clb2 was correspondingly delayed and cells appeared to delay in G2/M , as revealed by sustained levels of Clb2 relative to the wild type control ( Figure 5A ) . Since the cells used in these experiments were synchronized with mating pheromone , it was possible that the delayed accumulation of Cln2 was due to delayed release from mating pheromone arrest . To determine whether rts1Δ cells underwent normal release from mating pheromone arrest , we assayed the phosphorylation state of Cdc24 , which is the guanine nucleotide exchange factor for Cdc42 [42] . In previous work , it was found that Cdc24 becomes hyperphosphorylated during mating pheromone arrest and undergoes dephosphorylation upon release from the arrest [12] , [43] . Cdc24 then undergoes hyperphosphorylation during G1 that is dependent upon the Cla4 kinase and Cdk1 [12] , [43] , [44] . We found that Cdc24 underwent normal dephosphorylation in rts1Δ cells after release from mating pheromone arrest , which suggested that rts1Δ does not cause delayed release from mating pheromone arrest ( Figure S1A ) . Cdc24 showed delayed phosphorylation in rts1Δ cells , consistent with delayed initiation of G1 events . To further rule out the possibility that the G1 delay was due to mating pheromone-induced arrest , we used an alternative method for cell synchronization . Cells can be arrested in mitosis by depletion of Cdc20 , which is required for proteolytic destruction of the mitotic cyclins [45]–[47] . CDC20 was placed under the control of the GAL1 promoter in wild type and rts1Δ cells . Synchronization in metaphase was achieved by shifting cells to media lacking galactose for 4 hours , followed by releasing cells into galactose-containing media to initiate synchronous exit from mitosis . Cells lacking Rts1 showed a 30–40 minute delay in Cln2 accumulation and reduced Cln2 levels under these conditions ( Figure 5B ) . We also tested whether the effects of rts1Δ on Cln2 accumulation were dependent upon the strain background . Several commonly used laboratory yeast strains contain different alleles of the SSD1 gene , which can cause significant differences in phenotypes [48] . However , we found that rts1Δ caused similar defects in Cln2 accumulation in both the W303 ( ssd1-d2 ) strain background and the S288C ( SSD1-v1 ) strain background ( Figure 5 and Figure S1B , respectively ) . Our finding that Cln2 accumulation was delayed and reduced in rts1Δ cells suggested an explanation for the reduced polar growth caused by rts1Δ in mutants that undergo excessive polar growth ( Figure 1A and 1B and Figure 2A ) . We hypothesized that rts1Δ leads to reduced and delayed Cln2 accumulation in these cells , thereby causing reduced polar growth . We tested this directly by assaying Cln2 accumulation in synchronized gin4Δ and rts1Δ gin4Δ cells . As expected , rts1Δ caused reduced and delayed accumulation of Cln2 , and a corresponding delay in Clb2 accumulation ( Figure S2 ) . We next tested whether rts1Δ affected levels of CLN2 mRNA or mRNAs encoding additional G1 cyclins . Northern blotting revealed that accumulation of CLN2 , CLN1 and PCL1 mRNA was reduced and delayed in rts1Δ cells ( Figure 5E and 5F and Figure S1C ) . Transcription of the late G1 cyclins is controlled by the SBF transcription factor . To test whether rts1Δ caused delayed transcription of MBF targets as well , we assayed RNR1 mRNA expression . RNR1 mRNA accumulation was reduced and delayed to a similar extent as CLN2 mRNA in rts1Δ cells ( Figure 5G and 5H ) . Together these results show rts1Δ causes reduced and delayed accumulation of both SBF and MBF-regulated transcripts . Since rts1Δ caused reduced and delayed transcription of G1 cyclins , the delayed bud emergence observed in rts1Δ cells could be due solely to a role for Rts1 in promoting G1 cyclin transcription . Alternatively , Rts1 could play diverse roles in regulating events required for bud emergence . To distinguish these possibilities , we tested whether expression of CLN2 from the GAL1 promoter could rescue the delayed bud emergence of rts1Δ cells . Wild type and rts1Δ cells carrying GAL1-CLN2 or an empty vector were released from a G1 arrest under conditions that induce expression of CLN2 , and the timing of bud emergence was assayed . We found that expression of CLN2 from the GAL1 promoter dramatically advanced the timing of bud emergence in rts1Δ cells , providing nearly complete rescue of the delay in bud emergence ( Figure 6 ) . This observation , combined with our previous observations , established that Rts1 functions in mechanisms directly involved in controlling transcription of the G1 cyclins . Expression of GAL1-CLN2 did not rescue the temperature sensitivity of rts1Δ cells , which indicated that the temperature sensitivity of rts1Δ cells must be due , at least in part , to additional functions of Rts1 ( not shown ) . Recent work found that Cln2 acts in a positive feedback loop to stimulate its own transcription [22] . Thus , the delay in accumulation of CLN2 mRNA could be due to a failure in mechanisms required for normal accumulation of the Cln2 protein , which would disrupt the feedback loop . Overexpression of CLN2 from the GAL1 promoter might be expected to rescue this kind of defect . Therefore , we tested whether Rts1 functions in several mechanisms known to regulate accumulation of CLN2 protein . Cln2 is a highly unstable protein and proteolysis plays an important role in regulation of Cln2 protein levels . Proteolysis of Cln2 is controlled by the SCF ubiquitin ligase complex , which recognizes phosphorylated sites at the C-terminus of Cln2 and targets Cln2 for destruction [13] , [49] . Cdc34 is the E2 ubiquitin conjugating enzyme component of the SCF complex . We hypothesized that the reduced Cln2 protein levels observed in rts1Δ cells could be caused by increased SCF-dependent proteolysis of Cln2 protein . Reduced protein levels , in turn , would disrupt the positive feedback loop that promotes CLN2 transcription , thereby causing reduced and delayed transcription of CLN2 mRNA . To test this possibility , we created an rts1Δ strain that also contained a temperature sensitive allele of CDC34 ( cdc34-2 ) . Cells were released from a synchronized G1 arrest into 37°C media , and Cln2 protein expression was followed by Western blotting ( Figure 7A ) . As previously reported , inactivation of Cdc34 in the control cells caused stabilization of Cln2 and a dramatic increase in Cln2 protein levels [13] . In the cdc34-2 rts1Δ cells , Cln2 protein levels were still reduced and did not accumulate to the high levels observed in cdc34-2 cells . This showed that the failure of rts1Δ cells to accumulate normal levels of Cln2 is due to a failure to produce Cln2 , rather than to increased SCF-dependent destruction of Cln2 . We also considered the possibility that Rts1 regulates Cln2 stability via SCF-independent mechanisms . The Cln2 protein has a short half-life of 8–10 minutes [13] , [49] . To determine whether rts1Δ decreased the half-life of the Cln2 protein , we expressed a burst of CLN2 from the GAL1 promoter and then measured the rate of destruction of Cln2 protein after shutting off the promoter . In wild type control cells , the half-life of Cln2 was 9 . 6±2 . 0 minutes , similar to previous reports . In rts1Δ cells , the half-life of Cln2 was 10 . 8±2 . 4 minutes , which showed that there is not a significant decrease in the stability of Cln2 protein ( Figure 7B ) . To further define the function of Rts1 , we tested whether it acts in pathways known to control G1 cyclin transcription . The Whi5 transcriptional repressor delays G1 progression by inhibiting transcription of G1 cyclins . Whi5 acts by binding and inhibiting the SBF and MBF transcription factors , which are required for transcription of the G1 cyclins [16] , [17] . The Cdk1/Cln3 complex relieves this inhibition by phosphorylating Whi5 , which triggers export of Whi5 from the nucleus . Thus , it was possible that Rts1 played a role in the inactivation of Whi5 . If this were true , whi5Δ should rescue the delayed expression of Cln2 observed in rts1Δ cells . However , we found that bud emergence and accumulation of Cln2 protein were still delayed in rts1Δ whi5Δ cells compared to whi5Δ cells ( Figure 8A , and data not shown ) . In addition , whi5Δ did not rescue the temperature sensitivity of rts1Δ cells ( Figure 8B ) . Loss of Whi5 advanced the production of Cln2 protein in rts1Δ cells , although not to the same extent observed in whi5Δ cells , which indicated that Whi5-dependent regulation of transcription occurs normally in rts1Δ cells . Thus , the delayed Cln2 expression in rts1Δ cells is not due to a failure to inactivate Whi5 . Bck2 acts in a redundant pathway that works in parallel to Cln3 to promote transcription of G1 cyclins [18]–[20] . To test whether Rts1 acts in this Bck2-dependent pathway , we crossed rts1Δ to bck2Δ to create rts1Δ bck2Δ cells . If Rts1 functioned solely in the Bck2-dependent pathway , we expected to see no additive phenotypic effects in the double mutant . All of the expected rts1Δ bck2Δ progeny were recovered from the cross . We found that bck2Δ increased the temperature sensitivity of rts1Δ ( Figure 9A ) . To test whether deletion of Bck2 altered the timing of G1 events in rts1Δ cells , we compared the timing of bud emergence and Cln2 accumulation in wild type , rts1Δ , bck2Δ , and rts1Δ bck2Δ cells . Cells lacking BCK2 delayed bud emergence to a similar extent as rts1Δ cells . Bud emergence was severely delayed in rts1Δ bck2Δ cells when compared to either single mutant , and a subset of cells failed to bud by 2 hours after release from G1 arrest ( Figure 9B ) . Cln2 accumulation peaked later in rts1Δ bck2Δ cells than in rts1Δ or bck2Δ cells , and accumulated to lower levels ( not shown ) . These observations are consistent with previous reports that CLN2 mRNA is reduced in bck2Δ cells [18] , [19] . When tested at 34°C , we saw results that were similar , although more severe ( not shown ) . We examined the phenotype of rts1Δ bck2Δ cells in log phase cultures that were grown at room temperature , and found that rts1Δ bck2Δ cells appeared much larger than either rts1Δ or bck2Δ cells ( Figure 9C ) . The strong additive effects of rts1Δ and bck2Δ rule out the possibility that Rts1 functions solely in the Bck2-dependent pathway that controls G1 cyclin transcription , although it remains possible that Rts1 contributes to both Bck2-dependent and independent pathways . We next determined whether we could detect a role for Rts1 in regulating SBF or MBF . The components of SBF and MBF are Swi6 , Swi4 and Mbp1 . The Stb1 protein also associates with SBF and MBF and regulates their activity [50] . We therefore generated 3XHA-tagged versions of these proteins and determined whether they showed Rts1-dependent changes in modification state or protein levels . Stb1-3XHA and Swi6-3XHA showed multiple forms on Western blots due to phosphorylation , as previously reported [50]–[52] . Loss of Rts1 caused no detectable changes in the levels of Stb1 modification during a synchronized cell cycle ( not shown ) . In contrast , Swi6-3XHA showed reduced phosphorylation in rts1Δ cells at 20 to 30 minutes after release from a mating pheromone arrest ( Figure 10A ) . Notably , the defect in Swi6 phosphorylation occurred at the time that cells would normally be initiating G1 cyclin transcription . We detected no changes in the protein levels of Swi4 or Mbp1 in rts1Δ cells during the cell cycle . Swi4 and Mbp1 both migrated as a single band , so electrophoretic mobility shifts could not be used to assay their modification states . We also tested for genetic interactions between rts1Δ and swi6Δ , swi4Δ or mbp1Δ . Previous studies found that swi6Δ is recovered poorly from genetic crosses ( 27% of expected swi6Δ progeny are recovered ) [53] . We obtained similar results , and were unable to obtain swi6Δ rts1Δ progeny , suggesting that swi6Δ is synthetically lethal with rts1Δ . We found that mbp1Δ rts1Δ cells grew more poorly than either single mutant ( Figure 10B ) . We detected no genetic interaction with swi4Δ ( Figure 10C ) . The synthetic lethal interaction with swi6Δ must be treated with caution because swi6Δ shows synthetic lethality with a surprisingly broad range of genes , including genes that do not appear to have G1-specific functions ( BioGRID database ) . Thus , the synthetic lethality may be due to functions of Rts1 that are not related to G1 functions . Our analysis of the role of Rts1 in control of G1 cyclins suggested that Rts1 does not function solely in the known critical pathways for control of G1 cyclin transcription that work through Cln3 , Whi5 , or Bck2 . This was an intriguing discovery , because previous work found that the mechanisms responsible for nutrient modulation of cell size do not control G1 cyclin transcription via Cln3 , Whi5 or Bck2 [11] , [25] . We therefore hypothesized that Rts1 controls G1 cyclin transcription in a distinct pathway that mediates nutrient modulation of cell size . To test this , we determined whether Rts1 is required for nutrient modulation of cell size . We grew wild type and rts1Δ cells in rich or poor carbon sources and measured cell size with a Coulter counter . We found that Rts1 is required for nutrient modulation of size ( Figure 11A ) . The slight shift in size observed for rts1Δ cells shifted to poor carbon sources is similar to the slight shift observed for sch9Δ and sfp1Δ , which have also been found to be required for nutrient modulation of cell size [25] . Since rts1Δ cells are abnormally large , we were concerned that they may already be above the critical size for initiation of G1 cyclin transcription , and therefore not subject to G1 size control . To test this , we used mih1Δ cells , which are abnormally large because they undergo extra growth during G2/M [54] , [55] . The mih1Δ cells showed normal nutrient modulation of cell size control ( Figure 11B ) . Furthermore , others have found that cln3Δ cells , which are also abnormally large , undergo normal nutrient modulation of cell size [25] .
Loss of Rts1 caused reduced and delayed expression of multiple G1 cyclins , and a corresponding delay in bud emergence . Overexpression of CLN2 from a heterologous promoter largely rescued the delayed bud emergence in rts1Δ cells . We found no evidence that Rts1 regulates Cln2 protein stability . Together , these observations demonstrate that Rts1 functions in a pathway that regulates G1 cyclins at the level of transcription . The results of genetic analysis further supported the conclusion that Rts1 works in a pathway that controls G1 cyclin levels . The genetic interactions that we observed for rts1Δ are summarized in Table 1 . In general , rts1Δ caused slow growth or lethality when combined with deletions of G1 cyclin genes or pho85Δ . Due to the redundancy of the G1 cyclins , a general reduction in levels of G1 cyclin expression caused by rts1Δ would be expected to cause additive effects when combined with gene deletions that further reduce levels of G1 cyclins . The finding that Rts1 is required for normal levels of Cln2 protein provides an explanation for why rts1Δ caused reduced polar growth in mutants that fail to properly inactivate Swe1 . Failure to inactivate Swe1 causes cells to arrest at G2/M with high levels of Cln2 protein [12] . Since Cln2 promotes polar growth , a reduction in Cln2 levels would be expected to cause reduced polar growth . Accordingly , we found that rts1Δ caused reduced and delayed accumulation of Cln2 in gin4Δ cells , which fail to inactivate Swe1 . Previous work found that G1 cyclin transcription is regulated by another PP2A-like phosphatase called Sit4 . Loss of Sit4 caused decreased transcription of late G1 cyclins and defects in bud emergence , similar to rts1Δ [56] . However , there are a number of significant differences in the G1 phenotypes caused by sit4Δ and rts1Δ . First , in contrast to rts1Δ , defects in bud emergence caused by sit4Δ are not rescued by expression of CLN2 from a heterologous promoter , which demonstrates that Sit4 carries out functions required for bud emergence beyond controlling G1 cyclin transcription [56] . Second , the phenotype of sit4Δ cells is strongly enhanced by the ssd1-d2 allele , whereas the phenotype of rts1Δ is not affected by the status of SSD1 [56] . Finally , sit4Δ cln3Δ cells are barely viable , whereas loss of RTS1 caused relatively mild effects in cln3Δ cells [56] . These phenotypic differences suggest that Rts1 and Sit4 may function in independent pathways that regulate late G1 cyclin levels in response to different signals , but do not rule out the possibility that they have overlapping functions . We used epistasis analysis to determine whether Rts1 regulates G1 cyclin transcription via known mechanisms . This analysis demonstrated that Rts1 does not function solely in a Bck2-dependent pathway , and ruled out a role for Rts1 in a pathway known to regulate G1 cyclin transcription via Cln3-dependent inactivation of the Whi5 transcriptional repressor . Cln3 may also promote G1 cyclin transcription in a Whi5-independent manner . Overexpression of CLN3-1 makes whi5Δ cells smaller , which suggests that Cln3 can drive G1 cyclin transcription by an alternative mechanism [16] . In genetic crosses , we found that most cln3Δ rts1Δ spores failed to germinate , and cells lacking both Rts1 and Cln3 showed slow growth when compared to either single deletion . Thus , Rts1 does not appear to function solely in a Cln3-dependent pathway that promotes G1 cyclin transcription . Together , these observations suggest that Rts1 does not function solely in one of the known pathways that play an important role in promoting G1 cyclin transcription . Our results do not rule out the possibility that Rts1 contributes to multiple pathways . We found that rts1Δ caused reduced and delayed expression of both SBF and MBF-dependent transcripts , which demonstrated that Rts1 acts in a pathway upstream of both transcription factors . We further found that rts1Δ caused a reduction in Swi6 phosphorylation at the time that cells initiate G1 cyclin transcription . Since Swi6 is the one shared component of SBF and MBF , this suggests that Rts1 regulates both transcription factors via Swi6 . In support of this , the pattern of genetic interactions observed for rts1Δ is similar to the pattern previously reported for swi6Δ . Both swi6Δ and rts1Δ cause slow growth in cln2Δ cells , lethality in cln1Δ cln2Δ cells , and slow growth or lethality in bck2Δ cells [57] , [58] . In addition , neither rts1Δ nor swi6Δ caused synthetic lethality in combination with cln3Δ cells [58] . In contrast , loss of SWI4 is lethal in combination with cln3Δ [58] . The fact that Swi6 undergoes reduced phosphorylation in rts1Δ cells , rather than hyperphosphorylation , indicates that it is unlikely to be a direct target of PP2ARts1 . Phosphorylation of Swi6 that can be detected by an electrophoretic mobility shift is dependent upon the MAP kinase Slt2 , and activation of Slt2 coincides with initiation of polar growth [51] , [59] . The Slt2 pathway is activated by Pkc1 , and overexpression of Pkc1 suppresses swi4 mutants , which demonstrates a role in controlling G1 cyclin transcription [60] . In previous work , slt2Δ was not found to cause reduction in the levels of CLN1 or CLN2 transcripts , but did cause a reduction in levels of PCL1 and PCL2 transcripts in cells grown at 37°C . These studies did not utilize synchronized cells and may therefore have missed effects of slt2Δ on levels of CLN1 and CLN2 transcripts . In addition , rts1Δ could lead to misregulation of Slt2 that causes effects on CLN1 and CLN2 transcripts that are more significant than the effects caused by slt2Δ . Further work will be necessary to test for possible roles of Rts1 in Slt2-dependent regulation of G1 cyclin transcription . Previous work found that nutrient modulation of cell size does not require Cln3 , Whi5 , or Bck2 , which suggests that it works via a distinct mechanism . The discovery that Rts1 does not function solely in one of the pathways known to play a critical role in controlling G1 cyclin transcription therefore prompted us to test whether Rts1 is required for nutrient modulation of cell size . We found that Rts1 is required for nutrient modulated control of cell size , which identifies Rts1 as a new component of the pathways that integrate nutrient availability , cell size , and entry into the cell cycle . Little is known about the pathways responsible for nutrient modulation of cell size . Two conserved pathways play broad roles in controlling nutrient sensing , nutrient utilization , cell growth and cell cycle entry [61] . In one pathway , the TOR kinases respond to the availability of nitrogen and trigger activation of pathways that control cell growth and nitrogen utilization . A second pathway responds to carbon source availability and regulates growth via activation of the Ras GTPase and protein kinase A ( PKA ) [61] . The Ras/PKA pathway is required for control of cell size: increased activity of the pathway leads to increased cell size , while decreased activity leads to reduced cell size [62]–[64] . However , the mechanisms by which Ras/PKA control cell size are unknown . A key target of both pathways is ribosome biogenesis . Two key regulators of ribosome biogenesis that are thought to regulate cell growth are Sch9 and Sfp1 . Sch9 is a member of the AGC kinase family and is closely related to vertebrate Akt kinase , while Sfp1 is related to transcription factors [65] . Sch9 and Sfp1 carry out overlapping functions in controlling transcription of genes required for ribosome biogenesis [25] , [66] , [67] . Loss of either protein causes reduced ribosome biogenesis , while loss of both is lethal . The TOR pathway appears to work through Sch9 , but the mechanisms by which the Ras/PKA pathway controls ribosome biogenesis are unclear [65] , [68] . It is thought that nutrient modulation of cell size is achieved by changing the critical cell size required for initiation of G1 cyclin transcription [11] . Thus far , only three proteins have been found to be required for nutrient modulation of cell size: Sch9 , Sfp1 and PKA [25] , [64] . Notably , all three converge on control of ribosome biogenesis . Moreover , mutants that cause reduced rates of ribosome biogenesis cause cells to enter the cell cycle at a reduced cell size , leading to an overall reduction in cell size [67] . Together , these observations suggest a model in which the rate of ribosome biogenesis sets the critical cell size [11] , [67] . According to this model , cells growing in rich nutrients have a high rate of ribosome biogenesis , which sends a signal that inhibits G1 cyclin transcription to allow the cell to reach a larger critical size . The mechanisms that link initiation of G1 cyclin transcription to ribosome biogenesis and nutrient availability are unknown . The identification of Rts1 as a new upstream regulator of G1 cyclin transcription that is also required for nutrient modulation of cell size is therefore a significant step towards understanding these pathways . Rts1 could function between ribosome biogenesis and G1 cyclin transcription . Alternatively , Rts1 could promote ribosome biogenesis , in which case the effects of rts1Δ could be due to decreased rates of ribosome biogenesis . Inactivation of factors that promote ribosome biogenesis cause reduced cell size , whereas rts1Δ causes increased cell size . However , we do not yet know what causes rts1Δ cells to have an increased cell size , and it could be due to a G2/M delay that occurs later in the cell cycle [28] . Since Rts1 is highly conserved , it may also play a role in mechanisms that integrate external signals , cell size , and G1 cyclin transcription in vertebrate cells . The signaling pathways that control G1 cyclin transcription in vertebrate cells are of considerable interest , since deregulation of these pathways contributes to cancer [69] .
The strains used for this study are listed in Table 2 . Cells were grown in yeast extract-peptone-dextrose ( YEPD ) media supplemented with 40 mg/liter adenine , unless otherwise noted . Full length CLN2 was expressed from plasmid pSL201-5[GAL1-CLN2-3XHA URA3] [49] . 3XHA-tagging of CLN2 was carried out by digesting plasmid pMT104 with PvuII and integrating at the Cln2 locus using standard yeast transformation techniques [9] , [70] . 3XHA-tagging of other genes was carried out as previously described [71] . Cells were grown overnight at room temperature on a shaking platform . Cells at an OD600 of 0 . 6 were arrested in G1 by the addition of 0 . 5 µg/ml α factor for 3 . 5 hours . Cells were released into a synchronous cell cycle by washing 3× with fresh YEPD pre-warmed to 30°C , and time courses were carried out at 30°C unless otherwise noted . To prevent cells from entering a second cell cycle , α factor was added back at 65 minutes . For metaphase arrest , strains containing GAL1-CDC20 were first grown overnight in YEP +2% raffinose +2% galactose and then washed into media containing 2% raffinose and allowed to arrest for four hours . Cells were released from metaphase arrest by adding 2% galactose and were then shifted to 30°C . At each time interval , 1 . 6 ml samples were collected in screw-top tubes . The cells were pelleted , the supernatant was removed , and 250 µl of glass beads were added before flash freezing . To lyse cells , 100 µl of 1× sample buffer ( 65 mM Tris-HCl ( pH 6 . 8 ) , 3% SDS , 10% glycerol , 50 mM NaF , 50 mM β-glycerolphosphate , 5% 2-mercaptoethanol , bromophenol blue ) was added . 2 mM PMSF was added to the sample buffer immediately before use from a 100 mM stock made in 100% isopropanol . Cells were lysed by shaking in a Biospec Multibeater-8 at top speed for 2 minutes . The tubes were immediately removed , centrifuged for 1 minute in a microfuge and placed in a boiling water bath for 5 minutes . After boiling , the tubes were again centrifuged for 1 minute and 10 µl was loaded on a gel ( 5 µl when probing for Nap1 ) . For microscopy , 180 µl samples were collected and fixed by adding 20 µl of 37% formaldehyde for 1 hour . Cells were washed twice in 1× PBS , 0 . 05% Tween-20 , 0 . 02% sodium azide and imaged by differential interference contrast microscopy . Bud emergence was quantified by counting the number of buds per 200 cells for each sample . Polyacrylamide gel electrophoresis was carried out as previously described [72] . Gels were run at 20 mA on the constant current setting . Western blots were transferred for 1 hour at 1 Amp at 4°C in a Hoeffer transfer tank in a buffer containing 20 mM Tris base , 150 mM glycine , and 20% methanol . Blots were probed overnight at 4°C with affinity purified rabbit polyclonal antibodies raised against Clb2 , Swe1 , Cdc24 , Nap1 or HA peptide . Blots were probed with an HRP-conjugated donkey anti-rabbit secondary antibody ( GE Healthcare ) . For quantitative western blotting , protein was transferred onto Millipore Immobilon-FL membrane . Before transfer , the membrane was first briefly wetted in 100% methanol . A Cy5-conjugated secondary antibody was used ( Affinipure goat anti-rabbit , Jackson ImmunoResearch ) and images were collected on a Typhoon 9410 variable mode imager . ImageQuant was used to quantify band intensity . Local background was subtracted from each band . An antibody that recognizes the Nap1 protein was used as a loading control . Since Nap1 migrates below Cln2 , the western blots could be cut into two pieces to independently probe Cln2 and Nap1 in the same samples . For each lane , the ratio of Cln2/Nap1 signal was determined to normalize for differences in loading . Graphing was done with GraphPad Prism version 4 . 00 for Mac [73] . Probes that specifically recognized CLN1 , CLN2 , PCL1 , RNR1 and ACT1 RNA were made using a gel-purified PCR product ( CLN1 oligos: AGAATGGTCCTGTAAGAGAAAGT , AGAAACTGATGATGAAGAGGCAT; CLN2 oligos: TGAACCAATGATCAATGATTACGT , TCAAGTTGGATGCAATTTGCAG; PCL1 oligos: ACTAGATTGGTCAGATACACCAA , TGGTTACATCTTTTAGCCTTCTTAGA; RNR1 oligos: ACTTAGGTGTCATCAAGTCATCA , TCTACCACCATGCTTCATGATATCTT; ACT1 oligos: TCATACCTTCTACAACGAATTGAGA , ACACTTCATGATGGAGTTGTAAGT ) and the Megaprime DNA labeling kit ( GE Healthcare Amersham ) . Total yeast RNA was extracted as previously described [74] and blotting was carried out using standard methods [75] . Blots were stripped and re-probed for ACT1 as a loading control . Images were collected on a Typhoon 9410 variable mode imager . ImageQuant was used to quantify band intensity . Local background was subtracted from each band . The amount of CLN1 , CLN2 , PCL1 , and RNR1 RNA was normalized to the amount of ACT1 RNA for each lane and used to determine relative expression level . Data from three independent time course experiments was used to determine error bars ( standard error of the mean ) . Samples of synchronized GAL1-SWE1 or GAL1-SWE1 rts1Δ cells were collected at 30 minute intervals following release from a G1 arrest into galactose-containing media . Cells were imaged using differential interference contrast microscopy and ImageJ 1 . 37v software was used to measure bud length and bud width for 100 cells for each sample [76] . The extent of polar growth was measured as the ratio of length/width , and was plotted for each time point . The half-life of the Cln2 protein was determined as previously described with the following modifications [49] . Cells were grown overnight in YEP media containing 2% glycerol/ethanol . GAL1-CLN2 transcription was induced by washing cells into YEP media containing 2% galactose for 1 hour . Expression of CLN2 was then repressed by washing cells into YEPD media . Quantitative Western blotting was carried out to quantify Cln2 levels over time . Non-linear regression curve fitting for one phase exponential decay was carried out using GraphPad Prism version 4 . 00 for Mac [73] . Cultures of cells were grown in triplicate overnight at 25°C in either YEP +2% dextrose or YEP +2% glycerol and 2% ethanol . A 1 ml sample of log phase ( OD600 = 0 . 60 ) culture was fixed with 1/10 volume 37% formaldehyde for 1 hour , then washed twice with 1× PBS+0 . 04% sodium azide +0 . 02% Tween 20 . Cell size was measured using a Channelizer Z2 Coulter counter as previously described [77] . 150 µl of fixed culture were diluted in 20 ml of Isoton II and sonicated for 20 seconds prior to Coulter counter analysis . Each plot is the average of 4 independent experiments in which 3 independent samples were analyzed . | A critical point in the cell cycle occurs in G1 phase , when cells must decide whether to enter a new round of cell division . At this time , cells assess nutrient availability to ensure that they have sufficient resources to complete cell growth and division . Vertebrate cells also assess growth factors that control cell growth and determine when and where cell division occurs in the context of a multi-cellular organism . A cell-size checkpoint acts during G1 to delay entry into the cell cycle if the cell is below a critical size . When the appropriate signals have been received , cells commit to entry into the cell cycle by initiating transcription of G1 cyclins . The mechanisms that integrate external signals , cell growth , cell size , and entry into the cell cycle are poorly understood and represent a fundamental unsolved problem in cell biology . We discovered that a specific form of protein phosphatase 2A ( PP2ARts1 ) functions in the pathways that integrate nutrient availability , cell size , and entry into the cell cycle . PP2ARts1 is highly conserved and may therefore carry out similar functions in all eukaryotic cells . | [
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] | 2009 | The Rts1 Regulatory Subunit of Protein Phosphatase 2A Is Required for Control of G1 Cyclin Transcription and Nutrient Modulation of Cell Size |
The protozoan parasite Plasmodium is transmitted by female Anopheles mosquitoes and undergoes obligatory development within a parasitophorous vacuole in hepatocytes before it is released into the bloodstream . The transition to the blood stage was previously shown to involve the packaging of exoerythrocytic merozoites into membrane-surrounded vesicles , called merosomes , which are delivered directly into liver sinusoids . However , it was unclear whether the membrane of these merosomes was derived from the parasite membrane , the parasitophorous vacuole membrane or the host cell membrane . This knowledge is required to determine how phagocytes will be directed against merosomes . Here , we fluorescently label the candidate membranes and use live cell imaging to show that the merosome membrane derives from the host cell membrane . We also demonstrate that proteins in the host cell membrane are lost during merozoite liberation from the parasitophorous vacuole . Immediately after the breakdown of the parasitophorous vacuole membrane , the host cell mitochondria begin to degenerate and protein biosynthesis arrests . The intact host cell plasma membrane surrounding merosomes allows Plasmodium to mask itself from the host immune system and bypass the numerous Kupffer cells on its way into the bloodstream . This represents an effective strategy for evading host defenses before establishing a blood stage infection .
Despite considerable research and eradication efforts , malaria remains one of the most debilitating infectious diseases in the developing world . In 2008 alone , 247 million cases and nearly one million deaths were recorded [1] . Malaria is caused by Plasmodium , a protozoan parasite that is transmitted by infected female Anopheles mosquitoes . Transmission occurs during a blood meal and the parasite ultimately enters the bloodstream as a sporozoite . After traveling to the liver , it infects hepatocytes and replicates into several thousand merozoites . At the end of the liver stage , these merozoites are packaged into merosomes , which facilitate shuttling into the bloodstream [2] . Upon reaching the lung capillaries , the merosomes rupture and release their cargo of infectious merozoites [3] to infect erythrocytes . This mode of transition to the blood stage has been described previously [2] and while the general process of merosome formation is understood , many of its details are still unknown . One major point of controversy is the origin of the membrane surrounding the clusters of exoerythrocytic merozoites before and after merosome formation . In principle , there are three membranes from which it could stem: the parasite membrane ( PM ) , the parasitophorous vacuole membrane ( PVM ) or the host cell membrane ( HCM ) . It has been hypothesized that the merosome membrane derives from the host cell because this would be most advantageous to the parasite [2] . For one , it would not have to waste energy building an additional membrane that is only needed for a relatively short time . More importantly , being wrapped in HCM would camouflage the parasite as self , serving as a kind of Trojan horse as the merosomes enter the bloodstream . The vast majority of parasite antigens would be masked until the merozoites are released in the lung capillaries , and even then exposure time would be very brief since invasion of red blood cells is expected to occur quickly . However , attempts to prove that the HCM surrounds detached cells and merosomes by staining for typical hepatocyte surface markers were unsuccessful [3] . It has therefore been suggested that the membrane of merosomes derives from the PVM [4] , [5] . Using live imaging we show that not the PVM but the HCM forms the membrane of detached cells and merosomes and that detachment of the infected hepatocyte begins almost immediately after the breakdown of the PVM . We also offer an explanation for the difficulty in detecting typical hepatocyte surface proteins in the merosome membrane: upon breakdown of the PVM , a transgenically expressed marker protein is quickly lost from the HCM . Protein biosynthesis appears to come to a halt , likely due to a lack of energy since we observe rapid disintegration of host cell mitochondria .
With the existence of a membrane verified , there were three candidate membranes from which it could originate: the PM , the PVM and the HCM . Moving from the inside out we first examined the fate of the PM during the late liver stage . HepG2 cells were infected with P . berghei-mCherry sporozoites and fixed at different time points after infection . They were then stained for immunofluorescence analysis with anti-MSP antibody to label the PM and anti-Exp1 antiserum to label the PVM . The parasite cytoplasm was visualized by staining with anti-RFP antibody and nuclei by staining with DAPI . Confocal images were taken and false-colored to facilitate assessment of colocalization: nuclei are shown in blue and the PVM in green while the PM is displayed in red and the parasite cytoplasm in cyan ( Figure 2 ) . In the schizont stage , both the PM and the PVM surround the syncytic parasite ( Figure 2A ) . Shortly afterwards , the PM begins to invaginate around groups of nuclei to form the cytomere stage ( Figure 2B ) . This invagination proceeds until the PM forms the membrane of the individual merozoites ( Figure 2C ) . We consistently observed that only once this process was completed , did we see PVM rupture as could be determined by the loss of a clear Exp1-staining pattern ( Figure 2D ) . This is the last step before cell detachment but no PM staining was detectable anywhere except around individual merozoites , discounting the PM as the membrane surrounding the detached cell and merosome . Even though MSP1 was exclusively detected in the merozoite membrane , it cannot be completely ruled out that the host cell membrane surrounding merosomes and detached cells could contain other parasite components through modification of the existing membrane . However , so far no proteins of parasite origin could be detected ( data not shown ) . In any case , the important notion is that the membrane of merosomes and detached cells is not of parasite origin . Next we investigated the fate of the PVM upon merozoite formation more closely . For live imaging we generated a transgenic P . berghei strain that expresses Exp1 , a protein that localizes to the PVM , fused to mCherry ( P . berghei-Exp1-mCherry , Figure 3A ) . Since constitutive expression of the fusion protein led to a developmental arrest of the parasite ( RRS , unpublished observation ) , we used a liver stage-specific promoter for expression [7] . Correct localization of the fusion protein was confirmed by immunofluorescence analysis of HepG2 cells infected for 48 hours with P . berghei-Exp1-mCherry parasites . Infected cells were stained with anti-RFP ( also detects mCherry ) and anti-Exp1 antibodies and imaged confocally , allowing visualization of both endogenous and recombinant Exp1 ( Figure 3B ) . Overlaying the confocal images shows almost complete colocalization , indicating that the P . berghei-Exp1-mCherry parasite line is a useful tool for imaging the PVM . We therefore infected GFP-expressing HepG2 cells with P . berghei-Exp1-mCherry sporozoites and observed parasite development in a confocal live setup starting at 62 hpi . Stills of a representative movie ( Video S1 ) are shown in Figure 3C . In the first image of the series , merozoites have already formed and can be seen in negative ( 3C , 0h ) . They are surrounded by the clearly delineated PVM , but are released into the host cell cytoplasm as the PVM breaks down ( 3C , 1 . 36h ) . The PVM disintegrated further ( 3C , 1 . 63h ) until all mCherry fluorescence was distributed equally throughout the host cell ( 3C , 2 . 72h ) . Detached cells and merosomes resulting from six independent P . berghei-Exp1-mCherry infections were examined and showed the same fluorescence pattern ( n = 20 ) . All cells were followed until detachment to ensure that the parasite contained within was viable and completed development . Additionally , detached cells and merosomes were collected from the supernatant of P . berghei-Exp1-mCherry-infected cell cultures that had not been imaged and examined for the presence of a PVM . In all cases , the PVM had disintegrated entirely ( data not shown ) . The complete breakdown of the PVM before detachment was also obvious when looking at the GFP fluorescence profile across a line through the host cell . GFP distribution at an earlier time point was restricted to the cytoplasm of the host cell and was excluded from the PV by the PVM ( Figure 3D , top panel ) . At a later time point , GFP was evenly distributed through both the host cell cytoplasm and the PV lumen , demonstrating that the PVM as a barrier had disappeared ( Figure 3D , lower panel ) . A similar phenomenon could be seen in reverse when examining the fluorescence profile of P . berghei-mCherry , which carries mCherry in the parasite cytosol . Initially , fluorescence was confined to a peak corresponding to the densely packed area of merozoites within the PV ( Figure 3E , upper panel ) . Later the fluorescence peaks were much more evenly distributed because merozoites had spread throughout the cell ( Figure 3E , lower panel ) . They appeared to move more freely and much faster ( Video S2 ) . This rapid movement of merozoites also served as a marker of parasite viability , as injection of detached cells or merosomes containing motile merozoites into mice reproducibly led to blood stage infections [8] . In summary , since the PVM disintegrates entirely before and during detachment of the host cell , it also can be dismissed as the origin of the detached cell and merosome membrane . As we could show that neither the PM nor PVM are the source of the detached cell and merosome membrane , the HCM was the obvious remaining candidate , despite the fact that previous attempts to stain hepatocyte surface markers were unsuccessful [3] . Therefore we chose a different approach by labeling the HCM with the general membrane stain Vybrant DiO before PVM breakdown and detachment . We confirmed that Vybrant DiO indeed labeled the hepatocyte membrane by transfecting HepG2 cells with pDisplay-mCherry , which encodes mCherry fused to a secretion signal sequence and a transmembrane domain that anchors it in the membrane ( Figure S1A ) . Co-staining of the transfected HepG2 cells with Vybrant DiO and subsequent confocal imaging showed that both the plasma membrane and intracellular membranes were clearly labeled ( Figure S1B ) . The overlay image shows colocalization of the Display-mCherry protein and Vybrant DiO in the HCM , therefore verifying the suitability of Vybrant DiO for our experimental setup . Additionally , infection of HepG2 cells with P . berghei-Exp1-mCherry , which has a red-fluorescent PVM , followed by staining with Vybrant DiO showed that the dye does not label parasite membranes but is host cell-specific ( Figure S1C ) . Live imaging of HepG2 cells infected with P . berghei-mCherry and stained with Vybrant DiO from 62 hpi allowed visualization of the membrane around the detaching cell ( Figure 4 and Video S3 ) . Since only host cell membrane material is stained , the membrane must be either the HCM or an additional internal membrane that is built specifically for this purpose . To exclude the presence of such a membrane , infected cells were examined by electron microscopy during the late liver stage ( Figure S1D ) . While the HCM , PVM and PM are easily detectable , there was no evidence for any additional pre-existing or arising membrane . Therefore , the membrane of detached cells and merosomes must be derived from the HCM . This led us to test why this membrane does not stain positive for hepatocyte surface markers . To address this issue , we transfected HepG2 cells with pDisplay-mCherry and then infected them with P . berghei-GFP sporozoites . Cells were then imaged in a confocal live setup beginning at 62 hpi . Stills of a representative movie ( Video S4 ) show that the Display-mCherry protein initially localized to the HCM as expected ( Figure 5A , 0h ) , but was quickly and progressively lost from the membrane upon PVM breakdown until it was distributed in patches throughout the cytoplasm of the detached cell ( 5A , 0 . 92h and 4 . 31h ) . For quantification , we compared infected cells before and around PVM breakdown . For this , we infected pDisplay-mCherry-transfected HepG2 cells with P . berghei-GFP sporozoites and fixed cells at 24 and 60 hpi . Samples were then stained for immunofluorescence analysis ( IFA ) with anti-RFP and anti-GFP antibodies . Cells that were fixed at 60hpi were additionally stained with an anti-Exp1 antiserum to determine if the PVM had already broken down . Because detached cells were floating they were collected from the supernatant of infected cell cultures at 65–68 hpi and examined directly . Localization of the Display-mCherry protein in infected cells was classified as either plasma membrane-bound ( if the membrane was clearly stained ) or entirely vesicular ( Figure 5B ) . Early after transfection most cells showed plasma membrane localization of the Display-mCherry protein . Over time , the balance shifted towards a vesicular distribution , probably due to constant endocytosis processes in living cells . At the end of the liver stage , we still saw clear membrane localization of the Display-mCherry protein in 43% of the infected cells . However , once the PVM broke down , membrane localization of the fluorescent fusion protein was lost in all cells examined , both at 60 hpi and in detached cells and merosomes . This confirms the live imaging results that the Display-mCherry protein is lost from the HCM upon PVM disintegration . To test whether this phenomenon also occurs for proteins that are native to hepatocytes , we transfected HepG2 cells with an expression plasmid that encodes the human insulin receptor , which is known to localize to the hepatocyte plasma membrane [9] , fused to GFP ( IR-GFP ) [10] . We confirmed the HCM localization by co-transfection with pDisplay-mCherry followed by confocal imaging ( Figure S2A ) . Stills of a representative movie ( Video S5 ) from time-lapse imaging of transfected , P . berghei-mCherry-infected HepG2 cells starting at 50 hpi showed that IR-GFP is found in the HCM during the schizont stage ( Figure S2B , 0h ) and early stages of merozoite formation ( Figure S2B , 13 . 94h ) . However , upon breakdown of the PVM and detachment of the host cell , the distribution of IR-GFP becomes granular ( Figure S2B , 16 . 55h ) and it gradually disappears from the HCM . This was confirmed by fixing transfected , P . berghei-mCherry-infected cells at 48 hpi and as detached cells and staining them for immunofluorescence analysis with anti-GFP antibody ( Figure S2C ) . While IR-GFP was present in the membrane of the host cell at the schizont stage ( left panel ) , it was not found in the membrane of detached cells and merosomes ( right panel ) . In addition , we also examined the fate of the major histocompatibility complex I ( MHCI ) protein . P . berghei-mCherry-infected cells were fixed at 48 hpi and as detached cells and stained for immunofluorescence analysis with anti-MHCI antibody ( Figure S2D ) . Again , the membrane protein was detectable in the HCM at the parasite schizont stage ( left panel ) , but was lost from the membrane of detached cells and merosomes ( right panel ) . In conclusion , both transgenically expressed marker proteins and endogenous hepatocyte proteins disappear from the HCM upon PVM breakdown and detachment of the host cell . We hypothesized that this might be due to an immediate arrest of protein biosynthesis upon PVM breakdown . To test this hypothesis , we treated P . berghei-infected HepG2 cells with cycloheximide , which blocks translational elongation and therefore protein biosynthesis in eukaryotes [11] . If protein biosynthesis in the host cell and the parasite indeed ceases towards the end of the liver stage , treatment with cycloheximide should have no effect on the development of the parasite and detached cells should form at a normal rate . Since most infected cells detach between 65 and 70 hpi , cells were treated between 62 and 70 hpi . As a control , infected cells were treated starting at an earlier time point ( 36–44 hpi or 36–70 hpi ) when the parasite actively replicates and protein biosynthesis is expected to be crucial . At 70 hpi , detached cells from all treatment regimens were collected from the cell culture supernatant , counted and graphed as percentages of an untreated control ( Figure 5C ) . As anticipated , both short- and long-term treatment at an earlier time point resulted in a marked decrease in the number of detached cells . In contrast , treatment during the late stage had no impact on detached cell formation . To ensure that the drop in detached cell formation after early-stage treatment was not due to host cell death , a live/dead-assay was performed on treated cells . No increase in host cell death was observed in relation to the untreated control ( data not shown ) . Protein biosynthesis can be inhibited during the normal course of apoptosis [12] and since host cell death during the late liver stage exhibits some features of apoptosis it is possible that this causes the observed loss of membrane proteins . However , since this mode of inhibition is usually dependent on caspase activation [12] , which has been shown to be absent at this stage [13] , other factors might play a role . We therefore examined if the apparent block in protein biosynthesis might be due to a lack of energy within the host cell . To test this , we infected HepG2 cells with P . berghei-Exp1-mCherry parasites and stained with MitoTracker GreenFM at 62 hpi to visualize mitochondria . Infected cells were then imaged in a confocal live setup . Stills of a representative movie ( Video S6 ) are shown in Figure 6A . While the PVM was still intact , the host cell mitochondria displayed their typical morphology of a branched network ( 6A , 0h ) . Once the PVM broke down , host cell mitochondria appeared to draw together and began to form highly fluorescent clusters ( 6A , 1 . 14h ) . This process continued until only a few clusters were left in the detached cell ( 6A , 1 . 99h and 4 . 55h ) . To further characterize these mitochondrial clusters we labeled host cell mitochondria by transgenic expression of either GFP or dsRed . For this we used a plasmid construct carrying the respective fluorescent protein fused to the targeting sequence of the mitochondrial protein Cox8 ( Figure S3A ) [14] . Correct localization of the fluorescence markers was confirmed by co-staining with either MitoTracker GreenFM or tetramethylrhodamine ethyl ester ( TMRE ) , a cell-permeant dye that stains active mitochondria ( Figure S3B and 3C ) . To confirm that the mitochondrial remnants observed in detached cells are indeed of host cell and not of parasite origin , we infected Cox8-dsRed-transfected HepG2 cells with PbcGFPMITO parasites , which express GFP fused to the targeting sequence of PbHsp60 [15] . Detached cells and merosomes were collected and confocal images taken ( Figure 6B ) . The overlay image clearly demonstrates that the mitochondrial remnants are solely red-fluorescent and therefore of host cell origin . Functionality of the host cell mitochondrial remnants was then tested by infecting Cox8-GFP-transfected HepG2 cells with P . berghei parasites and staining detached cells and merosomes with TMRE ( Figure 6C ) . It can be observed in the overlay image that in detached , infected cells very few host cell mitochondria were TMRE-positive , despite successful TMRE staining of parasite mitochondria . This indicates a massive loss of host cell mitochondria with intact membrane potential . This , in turn , means that most host cell mitochondria have lost their capability to produce ATP for intracellular processes like protein synthesis .
Examination of the three potential parent membranes - PM , PVM and HCM - clearly ruled out the PM: through repeated invaginations during the late liver stage , it becomes the membrane of merozoites , confirming the suggestion from a previous publication [17] . Additionally , typical parasite membrane proteins were absent from the membrane of detached cells and merosomes , which therefore does not appear to be extensively rebuilt by the parasite . Live imaging of a transgenic parasite strain whose PVM is visualized by expression of an Exp1-mCherry fusion protein then demonstrated that the PVM disintegrates entirely before detachment . It is therefore also not the membrane in question . This fits with the circumstantial evidence from previous publications [18] . Breakdown of the PVM was suggested as early as 25 years ago , after a mixture of host cell and parasite material had been observed within host cells by electron microscopy ( EM ) . These observations have recently been confirmed and , in addition , host cell organelles have been observed in intravital merosomes [3] . Furthermore , the abundant PVM protein UIS4 could not be detected on merosomes [3] , supporting our finding that the PVM is not the membrane surrounding detached cells and merosomes . Live imaging always carries the risk of artifacts generated by phototoxicity due to prolonged exposure times . However , we excluded that the observed breakdown of the PVM was a consequence of photodamage for several reasons . First , merozoites in the resulting detached cells moved rapidly ( compare Video S4 ) . This rapid movement is a marker of parasite viability since injection of illuminated detached cells and merosomes containing motile merozoites into mice consistently results in blood stage infection [8] . Interestingly , the movement of merozoites appears to become far more pronounced once the PVM has ruptured , indicating that active movement was constrained by the PVM barrier . Second , a cytosolic distribution of Exp1-mCherry , indicating PVM breakdown , is observed also in detached cells and merosomes that have developed without exposure to laser light ( data not shown ) . Finally , in our hands photodamage virtually always manifested as an arrest in development with the parasite exhibiting either a high level of vacuolization or undefined aggregates . Neither of these features are present in the parasites that successfully disrupt the PVM and enter the host cell cytoplasm . Also , it has been observed that merozoites that are finally liberated from merosomes are viable since they initially exclude propidium iodide and do not expose PS ( VH , unpublished observations ) . So far it is not known how breakdown of the PVM and egress of the parasite are triggered . The only protein known to be involved so far is the liver specific protein 1 ( LISP1 ) of P . berghei [19] . It associates with the PVM and is necessary for its disruption , but the underlying mechanism remains unclear . Other likely mediators are proteases since PVM breakdown is blocked by the general protease inhibitor E64 [2] . In T . gondii , a perforin-like protein and changes in intracellular ion concentrations have been shown to orchestrate the disintegration of the PVM [20] , [21] , [22] , and similar mechanisms might act in Plasmodium . Further insight could also be gained from the study of Plasmodium blood stage egress . Here the parasite is also surrounded by a PVM that is disrupted almost simultaneously with the HCM to liberate merozoites . This process likely involves a protease cascade [23] , [24] . Interestingly , while in the blood stage the HCM disintegrates shortly after the PVM breakdown , our data show that in the liver stage the HCM stays intact for an extended period of time after the merozoites have been released into the host cell cytoplasm . A premature breakdown of the HCM would result in merozoite liberation into the liver tissue and the space of Disse but not into the bloodstream . It is therefore essential for exoerythrocytic merozoites to preserve the HCM until merosomes are formed and transported across the endothelium to a blood vessel . The molecular effects on the host cell upon PVM breakdown have been discussed previously [2] , [3] . While the cell exhibits certain signs of apoptosis such as cytochrome c release and DNA condensation , important hallmarks such as caspase activation and the loss of phosphatidylserine asymmetry are absent [2] , [13] . We demonstrate here that soon after PVM breakdown , the mitochondrial network of the host cell begins to disintegrate . The few remaining mitochondria clusters only partially retain a membrane potential . This stands in contrast to EM images of in vivo merosomes , which are reported to show well-preserved mitochondria [3] . Since the EM observations have not been quantified , it might be that the provided image coincidentally shows one of the rare cases where we also see preservation of mitochondrial membrane potential but it might also reflect a general difference between in vivo and in vitro merosomes or even P . berghei and P . yoelii . The mechanism underlying the mitochondrial degeneration is unclear , but might be connected to the host cell cytoskeleton since a similar mitochondrial fragmentation is seen after the interruption of vimentin intermediate filaments [25] . Both the morphology and loss of membrane potential closely resemble characteristics of mitochondria during apoptosis [26] . Mitochondria are often central to the induction of apoptosis [27] and it is possible that the breakdown of the PVM results in their exposure to apoptotic triggers . Parasitophorous vacuole-localized proteases are likely released and activated and might cause host cell death . Also , specialized parasite proteins could actively disrupt mitochondrial integrity as known for several viruses and bacteria [28] , [29] , [30] . Interestingly , the observed parasite-dependent host cell death seems similar to the initial stages of apoptosis . It is possible that the quick disruption of mitochondria leads to the rapid depletion of ATP within the host cell and that this results in an early arrest of the apoptotic program due to its dependence on energy . Therefore , features that arise early during apoptosis are present but the cell lacks the energy to execute the final steps such as DNA fragmentation and caspase activation . A depletion of available energy in combination with inhibitory factors triggered during apoptosis [12] would also explain why we observe an arrest in protein biosynthesis around this time . This in turn might cause the change in distribution of the transgenically expressed membrane marker protein Display-mCherry and the hepatocyte membrane proteins IR-GFP and MHCI . After PVM breakdown , the proteins are quickly lost from the plasma membrane and are soon only present in some residual vesicles . It is possible that apoptotic membrane breakdown also plays a role in this process . Considering this , it is not surprising that it has been impossible to detect endogenous hepatocyte surface markers on the merosomal membrane [3] . Being wrapped in HCM is an elegant method of immune evasion by the parasite: to reach the lung capillaries , where merosomes rupture and release their cargo [3] , they must pass through liver tissue and blood vessels which are lined with Kupffer cells . The HCM protects the traveling merozoites from being recognized as foreign and thus prevents phagocytosis and the initiation of an immune response . The loss of MHCI from the membrane of detached cells and merosomes is especially interesting in this regard as it will likely prevent the recognition by TH1 cells . Therefore , the parasite is shielded from the host immune system until it has reached the lung capillaries and thus conditions that are favorable for the invasion of erythrocytes . Employing this Trojan horse strategy might also be advantageous to the parasite during subsequent reinfections: upon formation of merosomes in vivo , some parasite material remains in the liver [2] , [3] and immune cells are known to be recruited to these sites [3] , [13] . If the HCM simply ruptured to release the parasite , the remaining cell material would appear necrotic to immigrating phagocytes and could activate an inflammatory immune response [31] , [32] , [33] . Instead , though , it initially remains intact and only slowly begins to expose phosphatidylserine , whereupon it is recognized by phagocytes as apoptotic . Antigen that is taken up from apoptotic cells usually promotes tolerance and does not cause an immune response unless significant danger signals are present [34] , [35] , [36] . In combination with the suppressive effect of the parasite blood stages [37] , [38] , these mechanisms might limit the immune response against the liver stage and might aid subsequent infections . Therefore , Plasmodium might exploit the HCM both for short- and long-term protection from the host immune response . In conclusion , we demonstrate here that after the invagination of the PM around merozoites , the PVM breaks down and the merozoites are released into the host cell cytoplasm . At the same time , parasite-dependent host cell death is initiated that alters the host cell profoundly , including the rapid disintegration of mitochondria . It is suggested that this leads to an arrest in protein biosynthesis and an aborted version of apoptosis . Ultimately , the host cell detaches and merosomes bud off , while the intact HCM serves as protection from the host cell immune system , thus representing the final step in the exploitation of the hepatocyte by the parasite to its own survival . Clearly , the parasite modifies the host cell extensively both before and after PVM breakdown and further research is needed to understand the full extent of these manipulations .
This study was carried out in strict accordance with the guidelines of the German Tierschutzgesetz ( TierSchG; Animal Rights Laws ) . Mice were obtained from Charles River Laboratories . The protocol was approved by the Department of Veterinary Affairs of the Hamburg state authorities ( Permit Number: FI 28/06 ) . Blood feeding was performed under ketavet/rompun anesthesia , and all efforts were made to minimize suffering . All parasite strains used in the present paper have a P . berghei ANKA background . The wild type strain does not express any fluorescent proteins . P . berghei-mCherry and -GFP express mCherry or GFP under the constitutive eef1aa promoter and show cytosolic localization of the respective fluorescent protein [39] . In P . berghei-Exp1-mCherry the PVM protein Exp1 [2] , [13] , [40] is fused to mCherry and expression is driven by the late liver stage-specific promoter of the gene PBANKA 100300 ( PB103464 . 00 . 0 ) [7] , [41] . In the PbcGFPMITO parasite strain , GFP is targeted to mitochondria by fusion with the predicted mitochondrial targeting sequence of P . berghei heat shock protein 60 ( Hsp60 , PBANKA_121400 ) ; the sequence was amplified using the oligonucleotides ATATGGATCCATGCTATCTAGATTGTGTGGG and ATATGGATCCTATTACATTTCTTCCTTTGGGTC . Expression of the fusion protein in the parasite is controlled by the eef1aa promoter . Transgenic P . berghei strains were generated as described previously [16] . Briefly , expression plasmids were introduced into blood stage schizonts using electroporation and transfected schizonts were then injected intravenously into mice for replication and selected with pyrimethamine . HepG2 cells were purchased from the European cell culture collection and kept in Minimum Essential medium with Earle's salts supplemented with 10% FCS , 1% penicillin/streptomycin and 1% L-glutamine ( all from PAA Laboratories , Austria ) . They were cultured at 37°C and 5% CO2 and split using Accutase ( PAA Laboratories , Austria ) . HepG2 cells were harvested by Accutase treatment and 2x106 cells were pelleted by centrifugation at 160g . They were resuspended in Nucleofector V solution ( Lonza , Cologne , Germany ) and transfected with 3–5 µg plasmid DNA using program T-28 of the Nucleofector transfection device according to the manufacturer's instructions . The mCherry-pDisplay-plasmid was generated by inserting the coding sequence of mCherry into the pDisplay vector ( Invitrogen , Darmstadt , Germany ) and kindly provided by Isabelle Tardieux ( Universite Paris Descartes , Paris , France ) . The pDsRed1-N1-Cox8 and pEGFP-N1-Cox8 plasmids contain the respective fluorescence protein fused to the targeting sequence of the mitochondrial protein Cox8 . The hIR-GFP plasmid was purchased from addgene ( Addgene plasmid 22286 ) and originally created by Rowena Ramos [10] . 8x104 HepG2 or HepG2-GFP cells were seeded into either glass bottom dishes ( WillCo Wells BV , The Netherlands ) or on cover slips in 24-well plates . At 12–24 hours after seeding , P . berghei sporozoites were isolated from the salivary glands of infected Anopheles stephensi mosquitoes and added to the HepG2 cells . After an incubation period of 2 hours the sporozoite-containing medium was removed and fresh medium was added . At the indicated time points , infected cells were prepared for imaging . Detached cells and merosomes were collected from the supernatant of infected cell cultures as a mixture . Since merosomes have been shown to bud from detached cells [2] , [3] , they share the same membrane and were therefore examined as one . HepG2 cells were seeded at 8x104 cells per well into 24-well plates and infected with P . berghei sporozoites . They were then treated with 5 µg/ml cycloheximide ( Sigma-Aldrich , Taufkirche , Germany ) for the indicated time periods . The medium was changed twice daily and detached cells were removed and counted at 70 hpi . Hoechst 33342 was added at 1 µg/mL for 10 min at 37°C and 5% CO2 . MitoTracker Green ( Molecular Probes , Invitrogen , Darmstadt , Germany ) was added to a concentration of 300 nM and incubated for 30 min at 37°C and 5% CO2 . Propidium iodide ( Sigma-Aldrich , Taufkirche , Germany ) was added at a concentration of 3 µM for 15 min at room temperature . pSIVA ( kindly provided by Yujin Kim , University of Southern California ) [6] was added at 20 µg/ml and incubated for 1 h at 37°C and 5% CO2 . TMRE ( Molecular Probes , Invitrogen , Darmstadt , Germany ) was added to a concentration of 25 nM and incubated for 30 min at 37°C and 5% CO2 . Vybrant DiO ( Molecular Probes , Invitrogen , Darmstadt , Germany ) was added at 5 µl/ml for adherent and 10 µl/ml for already detached cells and incubated for 1 h at 37°C and 5% CO2 . For all stainings of adherent cells , the staining medium was removed after the incubation period and fresh medium was added . For detached cells , the staining solution was diluted by adding an equal amount of fresh medium before imaging . 8x104 HepG2 cells were seeded on cover slips in 24-well plates . At appropriate time points after infection cells were fixed in 4% paraformaldehyde/PBS for 20 minutes at room temperature . They were washed three times with PBS and subsequently incubated in ice-cold methanol for 10 minutes at −20°C . After washing with PBS , unspecific binding sites were blocked by incubation in 10% FCS/PBS for 1 h at room temperature . Primary antibodies were diluted in 10% FCS/PBS and incubated for 2 hours at room temperature . Cells were then washed three times with PBS and secondary antibodies in 10% FCS/PBS were added for 1 hour at room temperature . Cells were washed again with PBS and mounted on microscope slides with Dako Fluorescent Mounting Medium ( Dako , Cambridgeshire , UK ) . For the staining of detached cells and merosomes , the cell culture supernatant was collected at 68 hpi and centrifuged at 160g for 5 minutes . The cells were then resuspended in 4% PFA/PBS and incubated for 20 minutes at room temperature . Afterwards , they were spun down at 160g for 5 minutes and resuspended in a small amount of 4% PFA/PBS . The solution was subsequently spread onto a microscope slide using a cytospin centrifuge at 800g for 4 minutes . The detached cells and merosomes were incubated with 4% PFA/PBS for 15 minutes and then washed three times with PBS . They were then incubated with ice-cold methanol for 5 minutes at room temperature . After washing with PBS , they were blocked and stained immediately as described above . For anti-MHCI staining , cells were incubated with anti-MHCI antibody in culture medium on ice for 30 minutes . They were then washed three times with culture medium and fixed in 4% paraformaldehyde/PBS for 10 minutes at room temperature . After washing with PBS , they were incubated in 0 . 25% Triton X/PBS for 10 minutes at room temperature . They were again washed three times with PBS and subsequently blocked and stained as described above . The following antibodies were used: mouse FITC anti-MHCI antibody ( BD Biosciences , Heidelberg , Germany ) , rat anti-RFP antibody ( Chromotek , Planegg-Martinsried , Germany ) , chicken anti-Exp1 antibody ( Heussler Lab , BNI , Hamburg , Germany ) , mouse anti-MSP1 antibody ( kindly provided by Anthony Holder ) , anti-rabbit AlexaFluor594 antibody ( Invitrogen , Darmstadt , Germany ) , anti-mouse AlexaFluor488 antibody ( Invitrogen , Darmstadt , Germany ) , anti-rat AlexaFluor594 antibody ( Invitrogen , Darmstadt , Germany ) , anti-chicken Cy2 antibody ( Dianova , Hamburg , Germany ) and anti-mouse Cy5 antibody ( Dianova , Hamburg , Germany ) . Nuclei were stained with 1 µg/ml DAPI ( Invitrogen , Darmstadt , Germany ) during the secondary antibody incubation period . All images of fixed cells were acquired using a confocal laser point scanning microscope ( see below for specifications ) . 8x104 HepG2 or HepG2-GFP cells were seeded into glass bottom dishes and infected with P . berghei sporozoites . At 60–62 hpi they were either imaged directly or stained as indicated . All time lapse imaging was performed using a confocal laser line scanning microscope ( see below for specifications ) . Images were acquired every 10–20 minutes . HepG2 cells were infected with P . berghei-GFP parasites . They were subsequently FACS-sorted to enrich for infected cells and seeded on Thermanox cover slips . At 48 hpi cells were first washed twice with PBS and then fixed with 2% glutaraldehyde in sodium-cacodylate buffer pH 7 . 2 . This was followed by a postfixation step with 1% osmium tetroxide and dehydration at increasing ethanol concentrations and propylene oxide . Cells were then embedded in an epoxy resin ( Epon ) . Ultrathin sections were made with an Ultra Cut E microtome ( Reichert Microscope Services , Depew , USA ) and stained for imaging with uranyl acetate and lead citrate . Images were then acquired using a Tecnai Spirit transmission electron microscope ( FEI , Eindhoven , The Netherlands ) at an acceleration voltage of 80 kV . For wide-field ( WF ) imaging , an Axiovert 200 base was used in combination with an X-Cite 120 Fluorescence Illumination System ( EXFO , Mississauga , Canada ) . Excitation and emission light was passed through Chroma filter sets ( AHF Analysentechnik AG , Tuebingen , Germany ) . Images were taken using a FAST1394 QICam camera ( QImaging , Surrey , Canada ) and the Openlab software . For all images , a Zeiss 20x LD A-Plan objective was used . For confocal point scanning ( CPS ) microscopy , an Olympus IX81 microscope was used . Images were acquired using an Olympus 100x UPlanSApo 1 . 4 Oil objective and the Olympus Fluoview software version 1 . 7b . Fluorescence was excited by a 405-nm diode laser ( Olympus , Germany ) , a 488-nm argon laser ( Olympus , Germany ) , a 559-nm diode laser ( NTT Electronics , USA ) and a 635-nm diode laser ( Olympus , Germany ) . Emission light passed through a 405/Mar/5437635 filter set before detection via PMTs . Confocal line scanning ( CLS ) was performed with a Zeiss Observer . Z1 inverted microscope integrated into an LSM5 Live imaging setup . Images were acquired using a Zeiss 63x Plan-Apochromat 1 . 4 Oil objective and the Zeiss Efficient Navigation 2008 and 2009 software . Fluorescence was excited by a Sapphire 488-nm diode and a Compass 561-nm diode pumped laser ( both Coherent , USA ) and emission light passed through a 505-nm and a 575-nm long-pass filter before detection via a CCD line detector . During imaging , cells were kept in a CO2 incubator at 37°C . After acquisition , contrast and brightness levels were optimized using either the Zeiss Efficient Navigation 2009 software or Adobe Photoshop CS 8 . 0 . Images were only enhanced as a whole . Fluorescence profiles were generated using the Zeiss Efficient Navigation 2009 software . Cytochrome c oxidase subunit VIII ( Cox8 ) , Protein/Entrez ID CAG28615; exported protein 1 ( Exp1 ) , PlasmoDB ID PBANKA_092670; green fluorescent protein ( GFP ) , Protein/Entrez ID ADN93293; insulin receptor ( IR ) , GenBank/Entrez ID M10051; mCherry , Protein/Entrez ID ACY24904; merozoite surface protein 1 ( MSP1 ) , Protein/Entrez ID AAA66185 . | Malaria is one of the most important infectious diseases in the developing world . It is caused by Plasmodium parasites , which are transmitted by female Anopheles mosquitoes during blood feeding . In the mammalian host , Plasmodium first develops within liver cells , growing from one parasite into many thousands . After this extensive replication , the parasites are released into the blood stream in vesicles termed merosomes that are surrounded by membrane . However , the origin of this membrane was unclear due to the absence of typical host cell membrane markers . Here , we analyzed several parasite- and host cell-derived membranes and show that the merosome membrane is of host cell origin . We also demonstrate that characteristic markers are lost from the host cell membrane once the parasite is liberated from its enclosure within the cell and moves freely in the host cell . The disappearance of membrane markers seems to be a consequence of the host cell death that is triggered toward the end of parasite development in the liver cell . The simultaneous induction of host cell death and retention of an intact host cell membrane enables the Plasmodium parasite to hide from the host immune system and thus to escape elimination before establishing a blood stage infection . | [
"Abstract",
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] | 2011 | Hostile Takeover by Plasmodium: Reorganization of Parasite and Host Cell Membranes during Liver Stage Egress |
Detecting regular patterns in the environment , a process known as statistical learning , is essential for survival . Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short ( seconds to minutes ) temporal windows . Here , we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously , in a temporally sparse manner , across windows of minutes to hours . Using a combination of behavioral , electrophysiological , and molecular approaches , we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding . Analysis of population activity ( structural tuning ) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies . The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination . Gain modulation was accompanied by changes in gene and protein expression , indicative of long-term plasticity . Physiological changes were largely independent of corticofugal feedback , and no changes were seen in upstream cochlear nucleus responses , suggesting a key role of the auditory midbrain in sensory gating . Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning . Using longer timescales than previously explored , the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns , a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through .
As we interact with the environment , our brain is constantly detecting patterns—i . e . , regularities—in the sensory world . This capacity allows us to recognize surrounding stimuli and make predictions necessary for survival . Patterns in the sensory input are extracted through a process known as statistical learning [1] . Regularities in the continuous sensory input that fit relatively short windows , in the order of seconds to tens of seconds , can be encoded through neuronal adaptation of response gain in both subcortical and cortical structures [2–4] . However , little is known about the circuits that code patterns that are temporally sparse , i . e . , when the regularity is repeated discontinuously across time windows of minutes and hours . Statistical learning of sparse patterns is important for grammatical learning or musical sensitivity in humans [5 , 6] , both of which are achieved through exposures that occur across days to years . This type of learning is likely to involve long-term plasticity mechanisms , different from neuronal adaptation . Changes in neuronal response gain that reflect fast adaptation are ubiquitous in the auditory cortex ( AC ) [2 , 7 , 8] but can also be found in the inferior colliculus , a subcortical midbrain structure that is the first convergence station in the auditory circuit [9] . For example , stimulus probability selectivity [3 , 4 , 10 , 11] , as well as some forms of response selectivity to natural sounds [12–14] , is observed in some divisions of the inferior colliculus [4] . Correlations between inferior colliculus activity and temporal patterns , such as speech or rhythmic tapping , have also been described in humans [11 , 12] . We hypothesized that neuronal correlates of statistical learning of temporally sparse patterns can also be found in the inferior colliculus . The context can be a strong predictor of the soundscape . In real life , as animals move through the environment , they can reencounter the same context and its characteristic sounds in temporally spread bouts . Here , in order to understand the neuronal coding of temporally sparse patterns in the sensory input , we used context–sound associations as stimuli . Thus , we set out to specifically test ( 1 ) whether mice can detect temporally sparse context–sound associations and ( 2 ) whether this detection triggers changes in the response patterns of neurons in the inferior colliculus . To recreate a natural environment while maintaining control over the experimental variables , we used the Audiobox—a socially , acoustically , and behaviorally enriched environment in which mice lived in groups for up to 2 weeks [15] . Mice were exposed to sounds that were associated with the context , with different degrees of predictability . The consequence of this exposure was assessed at the behavioral , electrophysiological , and molecular levels . First , we measured the effect that temporally sparse sound exposure had on the response gain of collicular neurons by simultaneously measuring evoked responses across different frequency bands . We subsequently assessed the effect these changes had on frequency coding and discrimination before testing how physiological changes in sensory gating paralleled behavioral generalization measures . We then confirmed that plasticity-associated changes in gene and protein expression had taken place . Since conditioning-triggered midbrain plasticity can depend on corticofugal input [16] , we tested the dependence of the observed changes on cortical feedback . Finally , to ascertain the origin of changes in the activity of inferior colliculus neurons , we assessed the effect that sound exposure had on upstream and downstream structures .
We did not find changes in the animal’s behavior during sound exposure that could indicate learning of the context–sound association . In order to ascertain whether statistical learning had occurred , we tested the effect that the different exposure patterns had on subsequent conditioned frequency discrimination . For that purpose , we used latent inhibition ( LI ) [17 , 18] . LI is the effect by which exposure to a neutral , nonreinforced stimulus delays learning of a subsequent association between this stimulus and an aversive outcome . We have shown before [15] that the mere exposure to a sound in the corner elicits LI in the Audiobox when the sound is subsequently conditioned in the same place , indicating that the presence of the sound in the corner was learned . We now probed the conditions under which LI is observed by comparing the effect of predictable and random sound exposure . Following the predictable or random sound exposure phases ( 16 kHz; S1C Fig and Methods ) , all mice were conditioned to 16 kHz sound in some visits to the water corner , such that a nose-poke during conditioned visits would trigger the delivery of an aversive air puff ( S1D Fig ) . Mice needed to discriminate between safe visits and conditioned visits and refrain from nose-poking during the latter . On the first day of conditioning , the control ( never exposed to 16 kHz ) and random ( exposed to 16 kHz outside the corner ) groups showed successful avoidance when 16 kHz was present in the corner and good discrimination , as reflected in d′ values above 1 ( S1E Fig ) . The predictable group , on the other hand , had d′ values significantly below the other groups ( S1E Fig ) , indicating the failure to avoid nose-poking when 16 kHz was present , i . e . , the occurrence of LI . This indicates that mice had learned the association between the safe 16 kHz tone and the corner during the exposure phase . Note that random sound exposure in the food area had a mild effect on the levels of avoidance in the corner during conditioning ( S1E and S1F Fig , green triangles ) , and mice never reached the level of performance of the control group , suggesting that both forms of sound exposure influenced subsequent avoidance during conditioned visits , albeit with weaker effects when random . In summary , all three groups behaved identically during the exposure phase but showed three different patterns of behavior during subsequent conditioning of the 16 kHz sound in the corner . Thus , learning of the association between the predictable sound and the context where it was heard ( the water corner ) did occur even though it had no effect on behavioral measures during the exposure itself . We conclude that the exposure protocol constitutes a successful model of temporally sparse statistical learning . The inferior colliculus is an auditory subcortical station on which diverse sensory information converges [9] . It has been shown to be sensitive to short-term statistical learning through neuronal adaptation . We now investigated whether statistical learning of temporally sparse patterns could affect the coding properties of the inferior colliculus . We acutely recorded from the inferior colliculus of anesthetized animals exposed to predictable or random 16 kHz for 6–12 days ( Fig 1A and 1B ) . We recorded multiunit activity from well-separated spikes ( S2A Fig ) using linear multielectrode arrays ( 16 sites , 50 μm apart ) inserted dorsoventrally along the collicular tonotopic axis ( Fig 2A and 2B ) . The first electrode was on the dura , and the second electrode rarely gave reliable responses . We therefore characterized auditory-evoked responses to different tone frequency–intensity combinations simultaneously in the remaining 14 depths ( 100–750 μm , see Methods ) . Depths of 100 and 150 μm were considered to be putative dorsal cortex based on different response patterns [19 , 20] , and the remaining depths , the central nucleus . All experimental groups showed a dorsoventral axis of tonotopic organization in the inferior colliculus such that progressively higher frequencies elicited responses progressively deeper ( Fig 2C; representative example raster plots in S2B–S2D Fig ) , in agreement with previous studies [21 , 22] . Tuning was quantified using spikes evoked at 70 dB SPL ( behavioral mean exposure intensity was 68 dB ) by stimuli of 30 ms length ( see Methods ) . An increase in response gain was evident in the tuning curves of predictable animals with respect to control animals at multiple depths along the tonotopic axis of the inferior colliculus ( Fig 2C ) . The predictable group had homogenously high levels of activity across all depths ( see Fig 2C , red , for mean ) . The random group had high activity localized to the putative dorsal cortex ( <200 μm depth ) and to depths with best frequencies ( BFs; the frequency that elicits the strongest response in a given location ) around 16 kHz ( 500–550 μm: Fig 2C and S2E Fig , green ) . This pattern of responses in the predictable and random groups was confirmed by quantification of peak firing rates in depth zones ( S3A Fig ) . The overall mean peak of firing rate of the control group was similar to age-matched animals reared under standard conditions ( home cage group ) but significantly smaller than the predictable group ( S3B Fig ) . Thus , sound exposure , whether predictable or random , generated an increase in collicular evoked activity compared to control animals . While in the random group , the increase was localized to depths with good responses at and near 16 kHz; in the predictable group , it was homogeneously distributed . The effect was not dependent on the frequency of the exposed tone , since mice in a predictable group exposed to frequencies other than 16 kHz also showed an increase in response gain ( S3C Fig for group exposed to 8 kHz ) . The effect was not dependent on the number of exposure days ( 6–12 days ) in the Audiobox ( S3D and S3E Fig ) . When individual tuning curves were aligned by BF rather than depth , the overall increase in excitability in the predictable group remained ( S3F Fig ) . Experience-dependent plasticity , such as auditory conditioning , can induce transient shifts in the BF of collicular neurons [23–25] . Indeed , we noticed that the peaks of the tuning curves of the predictable group were shifted in multiple depths ( Fig 2C , e . g . , 300–500 μm ) compared to the control group . Unlike what has been reported before as a result of conditioning , the shift in BFs that resulted from sound exposure was not toward the conditioned frequency but toward higher frequencies , even in regions with BFs of 16 kHz or above . The average BFs were consistently higher in the predictable and , to a lesser extent , the random group than in the control and home cage groups ( Fig 2D ) . Further quantification of the mean difference in BF across depth with respect to the control group confirmed this effect ( Fig 2E ) . The BF shift was independent of the frequency of the sound played in the water corner area . We measured the BFs in animals that were exposed under identical conditions to frequencies different from 16 kHz ( either 8 kHz , 13 kHz , or a combination of 8 and 13 kHz ) . Except for the group exposed to 8 kHz alone , which did not show a reliable shift in BF with respect to controls ( but note shifts in this group at specific depths , S3C Fig ) , shifts were similar in magnitude to those observed in mice exposed to 16 kHz ( S4A Fig; see Methods ) . Interestingly , average BF at threshold intensities was similar between groups ( S4B Fig ) , indicating that the shift is in suprathreshold tuning rather than a real change in tonotopy . Care was taken during the probe insertion to ensure consistency in the location and depth of the electrodes ( see Methods ) , and small variations from animal to animal cannot explain the systematic group differences . Additionally , simultaneous recordings along the rostrocaudal axis of predictable and control animals ( S4C Fig; see Methods ) revealed that the upward shift was present throughout the dorsoventral axis in the rostral and caudal portions of the inferior colliculus . In summary , there was a homogenous , frequency-unspecific , and suprathreshold shift in tuning in both exposed groups . The shift was significantly stronger in the predictable group and , unlike previously described for conditioning paradigms [23–25] , the shift was not toward the exposed frequency but upward along the tonotopic axis . Experience-dependent plasticity often results in changes in response gain [26 , 27] , which can take the shape of changes in response reliability , spontaneous activity , signal-to-noise ratio ( SNR ) , and tuning bandwidth [28 , 29] . To evaluate which of these variables was responsible for the increase in response gain in the predictable and random groups in a frequency-specific manner , we divided recording sites in 2 equally sized regions: one of sites with a BF tuned around 16 kHz ( 14–19 kHz , “tuned” hereafter; Fig 3A ) and another with sites tuned to 10–13 kHz ( “adjacent” hereafter; Fig 3A ) . We first measured whether the increase in gain was the result of an increase in firing rate alone or also in the reliability of evoked responses ( defined as the percentage of trials with at least 1 spike during the evoked period , 0–80 ms from stimulus onset; example in Fig 3A , right ) . In both the tuned and adjacent regions , response reliability was stronger around the local BF and decreased toward the edges of the frequency range , mirroring tuning ( Fig 3B ) . In the tuned region ( Fig 3B , right ) , the reliability of the evoked responses was significantly higher in the random group compared to the other groups , as quantified for the peak of tuning ( Fig 3C , right; see example in Fig 3A , right ) . On the other hand , spontaneous activity was similar across groups in the tuned region but higher for the predictable group in the adjacent region ( Fig 3D; see example in Fig 3A , right ) . If only adjacent regions showed an increase in spontaneous activity , mice exposed to a tone in the low frequency range ( 8 kHz ) would show a converse pattern: an increase in spontaneous activity in the region that we now call tuned ( Fig 3E ) . Indeed , when mice were exposed to 8 instead of 16 kHz , we found that the spontaneous activity was increased in the area with BFs near 16 kHz and comparable in the regions with BFs near 8 kHz ( Fig 3F ) . The region-specific increase in spontaneous activity had a direct effect on the SNR ( evoked/spontaneous firing rate ) , which was significantly smaller in the adjacent region compared to the tuned region in the predictable group ( S5A Fig ) . We conclude that the SNR increased in the area that responds to the exposed tone , independently of its frequency , compared to the flanking regions . Finally , tuning bandwidth was increased in the predictable group with respect to both control and random groups . The effect was observed at both the base and half-maximum of the tuning curve ( Fig 3G , left and right respectively ) . Changes in gain were not the result of changes in overall excitability , since intensity thresholds were similar ( 35 dB ) in all groups ( S5B Fig ) . Additionally , we quantified response latency ( see Methods ) , which is known to decrease with the efficiency of the stimulus [30] . In the predictable group , latencies were similar in both regions compared to the control group ( S5C Fig ) . In the random group , latencies were lower than the control group in the adjacent region and lower than the predictable group in the tuned region ( S5C Fig ) . To conclude , the increase in response gain observed in the predictable and random groups resulted from different mechanisms ( Fig 3H ) . In the predictable group , the increase in response gain was frequency unspecific and affected the evoked and the spontaneous activity , as well as the tuning bandwidths . Moreover , spontaneous activity was reduced in the tuned region , resulting in a local increase in SNR . In the random group , the increase in evoked activity was centered around the exposure frequency and was , at least in part , the result of increased reliability without affecting either spontaneous activity or tuning bandwidth . Auditory input evokes responses throughout the tonotopic map . This is reflected in neither peri-stimulus time histogram ( PSTH ) nor tuning curves , both of which represent local responses . Since we recorded simultaneously from 14 locations along 700 μm of the inferior colliculus , we were able to quantify the simultaneous response to a given frequency along the collicular tonotopic axis . We will refer to this response as structural tuning ( Fig 4A and 4B ) . Unspecific increases in bandwidth , such as that observed in the predictable group , would have the effect of increasing the response gain to a given frequency tone throughout the tonotopic map ( Fig 4A , light red versus dashed structural tuning ) . Increases in reliability that are not accompanied by changes in tuning bandwidth , such as that observed in the random group , would have the effect of increasing a structural tuning curve’s gain at a local depth without much change elsewhere ( Fig 4B , light green versus dashed structural tuning curves ) . Indeed , sound exposure affected structural tuning curves of different frequencies for the predictable and random groups , which were more distinct across frequencies compared to those of control animals ( Fig 4C ) . The effect this has on coding will be assessed below . We assessed how different changes in response gain across groups both locally ( region specific , tuning curves ) and globally ( structural tuning ) affected frequency coding and discrimination . We measured between-frequency discrimination and within-frequency response consistency using receiver operating characteristic ( ROC ) curve analysis and classification accuracy measures , respectively . ROC analysis is used to assess discriminability between two stimuli [31] by comparing the cumulative probability distributions of responses to these stimuli for different discrimination criteria ( Fig 5A and 5B ) . For the local tuning , we used individual tuning curves with a BF of 11 . 3 kHz ± 1 . 1% ( adjacent region ) or 16 kHz ± 1 . 1% ( tuned region ) and generated ROC curves for comparison between the BF and the to-be-compared frequency ( f1 and f2 in Fig 5A ) . We then used the area under the ROC curve ( AUROCC , Fig 5B ) as the index of discriminability . ROC curves obtained from tuning curves in the adjacent region were not different between predictable and random groups ( Fig 5D ) . In the tuned region , however , the random group showed better discrimination ( larger AUROCC ) for all ΔFs than both the control and predictable groups , who do not differ between them ( Fig 5E ) . This region-specific increase in discriminability in the random group parallels the region-specific increase in both gain and reliability in this group , in the absence of a change in bandwidth . In the predictable group , there was no change in discriminability in either region , which is consistent with the region-unspecific increase in both gain and bandwidth ( Fig 5D and 5E ) . This consistency derives from the fact that ROC curves are not sensitive to changes in response size , only to changes in distributions , and these are not necessarily changed when gain and bandwidth increase together . We then performed the same analysis for the structural tuning . This was performed for individual responses to a given frequency compared to the mean response ( across trials ) to 11 . 3 kHz ( Fig 5F ) and 16 kHz ( Fig 5G ) . Here , the predictable group shows less discriminability between frequency pairs ( Fig 5F , in which f1 = 11 . 3 kHz , and Fig 5G , in which f1 = 16 kHz ) than both the random and control groups . This decrease in discriminability in the predictable group is consistent with the increase in bandwidth and the concomitant increase in activity throughout the structural tuning curve ( see Fig 4A ) , which ultimately changes response distribution across the tonotopic axis and increases overlap between structural tuning curves . To a certain extent , ROC analysis reflects the variability in the response to each of the stimuli compared . Yet this is not true for the structural tuning ROC curves , because their wide response distributions ( responses across all depths ) and their asymmetrical shapes ( Fig 5C ) increase the level of overlap between the distribution curves without reflecting the trial-to-trial variability at the peak of the distribution ( Fig 5H ) . Trial-to-trial response consistency can be measured using classification accuracy probabilities . We used structural tuning curves to train a classifier [32 , 33] to predict the played frequency ( see Methods ) . The probability of predicting a given frequency correctly was significantly higher in both predictable and random groups with respect to control . In both groups , accuracy was higher in the tuned versus the adjacent region ( Fig 5I ) . Overall , the data suggest that statistical learning is accompanied by changes in neuronal coding in the inferior colliculus that affect frequency discrimination and response classification accuracy . The described changes in frequency coding could , potentially , have different effects on behavioral measures of frequency discrimination . We next tested this using a behavioral measure of spontaneous frequency discrimination . We used the prepulse inhibition of the auditory startle reflex ( PPI ) , a behavioral assay that is known to engage the inferior colliculus [34 , 35] and has been successfully used to determine frequency discrimination acuity in mice in the absence of training ( Fig 6A ) . When assessed in the presence of a constant background tone , the percentage of PPI is proportional to the difference between the background and prepulse tones [36–38] . Predictable and random groups were exposed as before to a 16 kHz tone for 6–12 days in the Audiobox . PPI was then measured in a separate apparatus , using a background tone of 16 kHz and progressively different prepulse tones up to 1 octave ( see Methods ) . The percentage of PPI elicited was significantly smaller in the predictable group than in the control and random groups at multiple prepulse frequencies tested ( Fig 6B ) . Similarly , the average discrimination threshold ( 50% of inhibition of maximum response , see Methods ) of the predictable group was higher than both the control and random groups but only reached significance against the latter ( S5D Fig ) . The increased generalization in the predictable group was not specific to frequencies around 16 kHz . PPI measured with a background tone of 11 . 3 kHz in animals exposed to 16 kHz ( Fig 6D ) also showed a significant increase in frequency generalization ( Fig 6E ) . Thus , only predictable sound exposure resulted in greater frequency generalization . Next , we questioned whether changes in behavioral frequency discrimination were related to the collicular changes observed in frequency coding described above . We calculated ROC curves from the PPI data to be able to compare the behavioral and neuronal responses under the same method [31] . Surprisingly , the predictable and random groups showed larger AUROCCs when the background tone was 16 kHz , although the effect was not significant ( Fig 6C ) . This is surprising because lower PPI is typically attributed to decreased discrimination acuity . The effect was specific to the frequencies around the exposed tone . When the background tone was 11 . 3 kHz , the increased generalization observed in the PPI for the predictable group was paralleled by diminished discrimination , as reflected in the lower AUROCCs , in this group with respect to the control group ( Fig 6F ) . In conclusion , the increased generalization observed in the PPI in the predictable group is consistent with the ROC analysis of the structural but not the local tuning for the same group ( Fig 5F and 5G ) . This increase in generalization paradoxically did not reflect a decrease in discrimination , which was normal in both predictable and random groups for frequencies in the tuned region . That this effect was frequency specific , since discrimination was reduced for frequencies in the adjacent region , is consistent with the physiological classification accuracy measures ( Fig 5D , 5E and 5I ) . Auditory conditioning studies have shown that collicular plasticity depends on direct cortical feedback through descending projections from layer V of the AC [39 , 40] . To test whether the maintenance of the changes in collicular response that had been triggered by predictable sound exposure were also dependent on cortical feedback , we performed simultaneous inactivation of the AC with muscimol and recordings in the inferior colliculus on a subset of control and predictable animals ( see Methods , Fig 7A and S6A Fig ) . Cortical inactivation generated an increase in collicular evoked activity in both groups without affecting the differences in overall tuning between groups , including the BF shift ( see tuning curves at 600 μm in Fig 7B; and S6B and S6C Fig ) . The increase in the activity of individual recording sites before and after cortical inactivation was comparable between groups ( Fig 7C ) . Cortical inactivation affected neither reliability ( Fig 7D ) nor the difference in spontaneous activity in the adjacent region ( Fig 7E , left ) . However , upon cortical inactivation , spontaneous activity of the predictable group increased in the tuned region ( Fig 7E , right ) . This increase reveals a cortical control of collicular excitability that occurs specifically in the region tuned to the exposed sound . Cortical inactivation slightly increased the bandwidths for both groups without affecting the difference between them ( Fig 7F ) . In summary , cortical inactivation resulted in an overall increase in the amplitude of the tuning curves that did not affect the difference in gain between the groups . The relatively lower spontaneous activity in the tuned region disappeared after cortical inactivation , revealing a frequency-specific form of cortical control on the inferior colliculus SNR . These data suggest that cortical feedback plays a minor role in the maintenance of sound exposure–triggered collicular plasticity . We next asked whether the changes in evoked activity and frequency representation were the result of an overall increase in excitability throughout the auditory pathway . Single-unit recordings in the cochlear nucleus—the main ascending input into the inferior colliculus—of animals in the control and predictable groups were similar in tuning , evoked , and spontaneous activity ( Fig 8A–8C ) . Additionally , predictable sound exposure had no effect on either thresholds or bandwidths ( S7A–S7D Fig ) , suggesting that exposure-triggered changes in the inferior colliculus were not the result of upstream plasticity . Similarly , evoked responses recorded in the primary auditory cortices of control and predictable mice were similar in overall tuning , temporal response pattern , and BF distribution ( S7E–S7H Fig ) . Changes observed in the inferior colliculus were thus not inherited from the main upstream input , the cochlear nucleus . They also did not result in an obvious change in cortical tuning , although it is possible that more subtle effects would be observable in a behaving animal . Fast neuronal adaptation , previously described in the inferior colliculus [3 , 41] , occurs within tens of seconds and would not necessarily be expected to be accompanied by changes in gene or protein expression . Sparse sound exposure , however , requires the integration of information across minutes and over several visits to the context associated with the sound . To investigate whether the observed changes were paralleled at the molecular level after predictable exposure , our key experimental condition , we measured gene expression in the predictable and control groups , using the home cage group as reference . We assessed the expression of neuronal genes reported to change their expression levels upon sound exposure , acoustic learning , or environmental enrichment [42–47] . In most cases , the expression was similar between the control and predictable groups and different from the home cage group ( S1 Table ) , suggesting that the largest effect was triggered by the placement of animals in the Audiobox itself . Exceptions were the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) receptor subunits gria1 and gria2 and brain-derived neurotrophic factor ( BDNF ) , which were significantly reduced only in the control group with respect to the home cage group . The ratio between the expressions of the presynaptic markers glutamate vesicular transporter 2 ( vglut2 ) and the GABA vesicular transporter ( vgat ) showed a significant increase for control and predictable groups . To investigate whether the increase in the Vglut2/VGAT ratio at the level of gene expression were accompanied by molecular changes in protein expression at specific locations of the inferior colliculus , we measured immunoreactivity to VGAT and Vglut2 proteins at two depths ( 300 and 600 μm ) , corresponding roughly to the “adjacent” and “tuned” areas used before , in the central nucleus of the inferior colliculus of control and predictable animals ( S8A Fig , see Methods ) . This ratio was used as an expression of excitation/inhibition balance , since this ratio has been shown to change upon environmental manipulations and to be a signature of synaptic plasticity [46] . We found that the number of Vglut2 puncta in the dorsal ( “adjacent” ) area was similar between groups , while VGAT was significantly reduced in the predictable group . This resulted in a significant increase in the Vglut2/VGAT ratio ( S8B Fig , left ) . At 600 μm in depth ( “tuned” ) , there was a decrease in Vglut2 in the predictable animals but only a trend in the same direction for VGAT , with no difference in the Vglut2/VGAT ratio between groups ( S8B Fig , right ) . Thus , predictable and sparse sound exposure results in changes in gene and protein expression that are characteristic of long-term memory .
Statistical learning is essential for a correct interpretation of the sensory input . This form of learning is likely to be distributed throughout different brain regions , depending on the stimulus patterns to be learned , their modalities , and spatiotemporal combinations [48–50] . Some forms of statistical processing must happen at the level of subcortical structures as part of sensory gating . Neuronal adaptation—changes in firing rate as a result of continuous stimulation—is maybe the best-studied mechanism of experience-dependent plasticity believed to be underlying statistical learning of environmental regularities that occur within the recent stimulation history . It has been hypothesized to increase the dynamic range of neurons as well as gating of specific inputs [51] and is observed in cortical [2 , 7 , 52–54] and subcortical structures [2–4] . Meta-adaptation has been observed across 5-second windows in a continuously alternating sensory stimulation paradigm in the inferior colliculus [4] . Yet the circuits underlying statistical learning of temporally sparse patterns have not been characterized . This timescale of statistical learning is reflected in the sensitivity of neurons in the auditory system for natural sounds [12–14 , 55–58] . Neuronal adaptation is achieved through short-term plasticity [59–61]; therefore , it is unlikely to be the mechanism underlying the type of statistical learning that needs to be accumulated across bouts of exposure that are separated by minutes to hours , like the one we describe here . Using a combination of electrophysiological , behavioral , and molecular approaches , we show that the inferior colliculus , an auditory subcortical structure , was sensitive to statistical learning of temporally sparse auditory patterns . We exposed mice to sounds that were fully predictable ( predictable group ) . This exposure was self-initiated , limited to visits to the water corner ( context specific ) , and lasted only for the duration of the individual visits ( temporally sparse ) . Exposure to these patterns resulted in an increase in response gain that was frequency unspecific and was not due to mere sound exposure , since the random group ( exposed to a sound in a fixed context but at random time intervals ) showed a different pattern of collicular plasticity . Increase in response gain changed the pattern of population activity , resulting in increased between-frequency overlap in the structural tuning but a more consistent trial-to-trial within-frequency coding . These effects were paralleled at the behavioral level , at which increased response generalization was , paradoxically , not paralleled by a decrease in frequency discrimination as is discussed below . Cortical feedback played a minor role in the maintenance of collicular plasticity , and changes were not observed in the main input structure , the cochlear nucleus [62 , 63] . This suggests that plasticity was initiated in the inferior colliculus , as further supported by changes in gene expression indicative of long-term plasticity . The combined analysis of local ( region-specific tuning curves ) and global ( structural tuning ) neuronal responses allowed us to uncover 2 coexisting mechanisms of frequency coding in the predictable group . On one hand , consistency in frequency coding was increased , as reflected in frequency-specific increase in classification accuracy . On the other hand , the potential for increased generalization was reflected in the increased overlap between structural tuning curves in the predictable group . Both increased discrimination and increased generalization were paralleled at the behavioral level . While , typically , a decrease in PPI has been interpreted as a decrease in frequency discrimination , here we found that different prepulse tones can generate discriminable startle responses and yet be less effective in generating PPI near the background tone . Thus , at the behavioral level , increased generalization in the startle’s inhibition was found to coexist with normal frequency discrimination near the exposed frequency . This highlights the relevance of responses across spatially distributed neuronal populations , in which even increased responses away from the tuned region ( the tail of the structural tuning ) might have an impact on behavioral output . Predictable sounds , when highly repetitive and consistent , are less salient . It is maybe because of this that behavioral responses to pure tones are largely more inhibited in the predictable group . In striking contrast , mice in the random group showed no evidence of diminished discrimination at either the neuronal population level or behaviorally , probably reflecting the saliency of randomness . Indeed , in this group , changes in response gain were—unlike in the predictable group—typically constrained to the tuned region . Corticocollicular projections are believed to modulate collicular sensory filters [23 , 64–67] . The narrow corridors of the Audiobox prevented us from optogenetically modulating cortical activity during the exposure . Cortical inactivation during the recording , however , subtly increased the size of the evoked responses in both control and predictable groups and had no effect on either the suprathreshold tonotopic shift induced by sound exposure or the increase in bandwidth . However , it affected the levels of spontaneous activity . The frequency-specific low level in spontaneous activity in the tuned region disappeared upon inactivation , meaning that the cortical feedback can locally reduce spontaneous activity in one region of the inferior colliculus to increase the SNR . Nonetheless , overall , the cortical inactivation data suggest that the AC plays a small role in the maintenance of learning-induced plasticity and that this is limited to local modulations of spontaneous activity . Whether corticofugal feedback is required to initiate this plasticity in the early times of exposure will require further investigation . Recently , Slee and David [68] reported increases in spontaneous activity in the inferior colliculus that resulted in suppression of responses to the target sound during an auditory detection task . Differences in excitability can be attributed to changes in interactions within the local circuit . In the predictable group , we observed changes in excitation/inhibition ratios at the presynaptic level that had no parallel at the postsynaptic level . Together , this might reflect the implementation of a switch that can be either turned on or off depending on , for example , the presence of a global signal in the form of a neuromodulator or brain state [69 , 70] . Indeed , a frequency-specific decrease in spontaneous activity in the predictable group resulted in an increase in SNR ( evoked/spontaneous activity ) . SNRs have been studied in the context of speech saliency in noisy backgrounds [71–73] and have been hypothesized to contribute to compromised sensory gating in neuropsychiatric diseases , highlighting their importance for auditory processing [74] . Recordings were performed in anaesthetized animals , and although anesthesia does not prevent the expression of preattentive mechanisms , the exact implementation of the proposed switch might be different in the behaving animal [75 , 76] . In both exposed groups , we observed a surprising shift in suprathreshold tonotopy with respect to the control group . This was reflected in a homogeneous shift in BFs across all depths measured . This shift was significantly larger in the predictable group than in the random group . While reinforcement-driven plasticity is characterized by locally measured shifts toward a conditioned frequency in both inferior colliculus and AC [77 , 78] , spatially broad frequency shifts cannot always be measured . In the one case in which this was done [64] , the shift was also found to extend beyond the directly activated frequency band . Whether the inferior colliculus uses the BF shift as a coding mechanism or this is rather a byproduct of other plastic changes will require further investigation . In fact , BF might not be a very reliable coding variable [79 , 80] . Measurements such as structural tuning , in which simultaneous responses across a widespread neuronal population are measured , might better represent the information that the brain is using at any given point in time . Differences in sensory filtering at the level of the inferior colliculus are likely to influence how information is conveyed downstream to thalamus and cortex . Depending on whether the change impinges primarily on the excitatory or inhibitory ascending input into the thalamus , the overall effect might be either to enhance or suppress selective responses . The collicular inhibitory input into the thalamus acts monosynaptically on thalamocortical projecting neurons [81] , potentially regulating the magnitude and timing of cortical activity and thus playing a crucial role in sensory gating . We did not find obvious changes in excitability or frequency representation at the cortical level after predictable sound exposure . In the auditory system , which processes a constant input of stimuli arising from all directions , preselection of to-be-attended stimuli might happen at the level of subcortical structures . In other sensory systems , filtering of stimuli might involve different circuit mechanisms [82 , 83] . Taken together , our results demonstrate that the inferior colliculus , a subcortical structure , plays a significant role in the detection of statistical regularities that arise from temporally sparse interactions with a naturalistic environment . The effect this learning had on subsequent behavior suggests that the observed changes in coding modulate the filtering of the exposed sounds to control behavioral outcomes . Our study places the inferior colliculus as a key player in the processing of context–sound associations , which are of great relevance in sound gating . This role might be the basis for the link between the inferior colliculus and autism , in which patients exhibit alterations in sensory gating [84–86] . The finding that neuronal responses are sensitive to the context in which sounds appear suggests that the inferior colliculus might integrate stimuli across a parameter space that goes beyond the auditory domain . Thus , the inferior colliculus could be acting as an early multimodal warning system .
All animal experiments were approved by the local Animal Care and Use Committee ( LAVES , Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit , Oldenburg , Germany ) in accordance with the German Animal Protection Law . Project license number 33 . 14-42502-04-10/0288 and 33 . 19-42502-04-11/0658 . Female mice C57BL/6JRj ( Janvier labs , France ) between 5 and 8 weeks old were used for all experiments . A sterile transponder ( IS0 compliant 11784 transponder , 12 mm long , TSE , Germany ) was implanted subcutaneously in the back of the anaesthetized mice . The small wound caused by the injection was closed with a drop of a topical skin adhesive ( Histoacryl , Braun , United States of America ) . After 1 to 2 days of recovery , animals were placed in the Audiobox ( New Behaviour/TSE , Germany ) . The Audiobox is an automatic testing chamber consisting of 2 compartments connected by a corridor ( Fig 1A ) , where mice lived in groups of up to 10 animals . The first compartment—the “food area”—consists of a normal mouse cage , where animals have ad libitum access to food . Water was available in the second compartment—the “water corner”—located inside a sound-attenuated chamber . An antenna located in the entrance of the corner identified the individual mouse transponder . The individual visits to the corner were detected by coincident activity of a heat sensor and the reading of the transponder . Visits occurred mainly during the dark cycle [15] . A water port is present at either side of the corner and can be closed by a sliding door . To open the door and gain access to the water , animals needed to nose-poke . Nose-pokes were detected by a sensor located by the door . The end of the visit was signaled by deactivation of the heat sensor and the absence of transponder reading . Individual-mouse data ( start and end of visit , time and number of nose-pokes ) were recorded for each single visit . Visits to the corner could be accompanied by a sound , depending on the identity of the mouse . A loudspeaker ( 22TAF/G , Seas Prestige ) was located above the corner to present sound stimuli . The sounds presented were generated in MATLAB ( The MathWorks , USA ) at a sampling rate of 48 kHz and consisted of 30 ms pure tones with 5 ms slope , repeated at 3 Hz for the duration of the visit and at variable intensity of 70 dB ± 5 dB ( measured at the center of the corner in the predictable group or the center of the home cage in the random group ) . The sound intensity was calibrated with a Bruël & Kjaer ( 4939 ¼” free field ) microphone . The microphone was placed at different positions within the corner , as well as outside the corner , while pure tones ( 1–40 kHz ) were played at 60–70 dB . Microphone signals were sampled at 96 kHz and analyzed in MATLAB . Tones between 3 kHz and 19 kHz did not show harmonic distortions within 40 dB from the main signal . The sounds presented inside the corner were attenuated by over 20 dB outside the attenuated box . Since little attenuation occurred in the corridor located inside the attenuated box immediately connected to the corner , mice in this location could hear the sound played in the corner . All the experimental groups were first habituated to the Audiobox for 3 days without sound presentation . After the habituation phase , the exposed group heard a fixed-tone pip of a specific frequency for the duration of every visit , regardless of nose-poke activity and water intake . The random group was exposed to a fixed-tone pip in the mouse cage at random intervals . The sound was delivered by a loudspeaker located above the cage and calibrated such that sound intensity in the center of the cage was comparable to that inside the corner . The presentation of the sound was triggered by corner visits of a mouse living in another Audiobox , in a yoke control design . This ensured that the pattern ( mainly at night ) and duration of sound presentation in the cage was comparable to that experienced by each mouse in the predictable group when making corner visits . The control group consisted of age-matched animals that lived during the same amount of time in a different Audiobox without sound presentation . The number of mice reported in Fig 1C–1E corresponds to exposed animals to 16 kHz used for recordings in the inferior colliculus and AC . The sounds used during the exposure phase were fixed for each mouse and replication: 8 , 13 , or 16 kHz , depending on the experiment . One group of animals ( 8 and 13 kHz group ) was exposed in 71% of the visits to 8 kHz and the remaining 29% of the visits to 13 kHz , similar to the preconditioned phase of the LI protocol . The experiment consisted of 4 phases: habituation , safe , exposure , and conditioning [15] . Animals were divided in 3 different groups that differed only in the exposure phase before conditioning . During the habituation phase ( 3 days ) , no sound was presented , and the sliding doors remained open . In the safe phase ( 7 days ) , a safe tone of 8 kHz was paired with every visit to the corner , and the sliding doors opened only after nose-poke . In the exposure phase ( 5 days ) , groups were exposed to different frequencies as follows: ( i ) for the control group , 71% of the visits were paired with an 8 kHz tone , and 29% were paired with a 4 kHz tone; ( ii ) for the predictable group , 71% of the visits were paired with an 8 kHz tone , and 29% were paired with a 16 kHz tone; ( iii ) for the random group , 100% of the visits were paired with 8 kHz , and a 16 kHz tone—played in the home cage—was paired to 29% of the visits of a mouse living in another Audiobox to its corresponding corner . Up to this point , all nose-pokes resulted in access to water independently of the sound played . In the conditioning phase , 71% of visits were paired with an 8 kHz tone , and 29% were paired with a 16 kHz , which was conditioned such that a nose-poke resulted in an air puff and no access to water . During this phase , mice had to learn to avoid nose-poking when they heard 16 kHz ( conditioned visit ) . To assess discrimination performance , the discriminability index ( d’ ) was calculated . d’ used in signal detection theory is defined as d'=Z ( HR ) -Z ( FAR ) , in which Z ( p ) , p ∈ [0 1] is the inverse of the cumulative of the gaussian distribution; HR is the hit rate , in which a hit is the correct avoidance of a nose-poke during a conditioned visit; and FAR is the false alarm rate , in which a false alarm is the avoidance of a nose-poke during a safe visit . Since d’ cannot be calculated when either the hits or the false alarms reach levels of 100% or 0% , in the few cases when this happened , 99% and 1% , respectively , were used for these calculations . Mice were anesthetized with avertin before acute electrophysiological recordings in the inferior colliculus ( induction with 1 . 6 mL/100 grs and 0 . 16 mL/100 grs ip to maintain the level of anesthesia as needed ) . Anesthetized mice were fixed with blunt ear bars on a stereotaxic apparatus ( Kopf , Germany ) . The temperature of the animal was monitored by a rectal probe and maintained constant at 36 °C ( ATC 1000 , WPI , Germany ) . The scalp was removed to expose the skull , and bregma and lambda were aligned vertically ( ± 50 μm ) . A metal head-holder was glued to the skull 1 . 3 mm rostral to lambda to hold the mouse , and the ear bars were removed . To access the left inferior colliculus , a craniotomy of 2 . 8 × 3 mm was made , with the center 1 mm lateral to the midline and 0 . 75 mm caudal to lambda . The inferior colliculus was identified by its position posterior to the transverse sinus and anterior to the sigmoid sinus . The tip of the left inferior colliculus became visible after the craniotomy , and measurements from the rostrocaudal and mediolateral borders were made to place the recording electrode exactly in the middle of the inferior colliculus , targeting the central nucleus . The probe was inserted such that the most dorsal electrode was aligned with the dura ( Fig 2B ) , thus minimizing the error in depth alignment . An error in depth assessment might arise from the topmost recording site ( with a diameter of 13 μm ) not being exactly aligned with dura . Since the electrode sites are visible under microscope , the depth error is unlikely to have been more than ± 25 μm ( half the distance between electrode sites ) . Other measures were in place to ensure reliability of the positioning: ( 1 ) before inserting the probe , bregma and lambda were aligned to the same horizontal plane; ( 2 ) the probe was lowered at a fixed rostrocaudal and mediolateral position with respect to bregma; ( 3 ) the probe angle was 90° with respect to the bregma–lambda plane; ( 4 ) dura was intact; and ( 5 ) penetration was very slow . Extracellular multiunit recordings were made using mainly multielectrode silicon arrays ( Neuronexus Technologies , USA ) of 16 electrode sites in either a single shank ( most data; 177 μm2 area/site and 50 μm spacing between sites ) or 4 shanks ( rostrocaudal analysis; 150 μm intershank spacing ) . Glass-coated single electrodes were used to collect data on exposure to frequencies other than 16 kHz . These were either glass-coated tungsten electrodes with a typical impedance of 900 mOhm and an external diameter of 140 μm ( AlphaOmega , Germany ) or glass-coated platinum/tungsten electrodes with a typical impedance of 1 mOhm ( Thomas Recordings , Germany ) . The electrodes were inserted in the central part orthogonally to the dorsal surface of the inferior colliculus and lowered with a micromanipulator ( Kopf , Germany ) . In the case of single electrodes , recordings were made every 50–100 μm . When multielectrode silicon arrays were used , they were lowered ( at a rate of 100 μm/5 minutes ) until the upper electrode was in contact with the inferior colliculus surface , visualized with a microscope ( 750 μm depth ) . The electrodes were labeled with DiI ( 1 , 1'-dioactedecyl-3 , 3 , 3 , 3'-tethramethyl indocarbocyanide , Invitrogen , Germany ) to allow the reconstruction of the electrode track in postmortem sections using standard histological techniques ( Fig 2B ) . The electrophysiological signal was amplified ( HS-36 or HS-18 , Neuralynx , USA ) and sent to acquisition board ( Digital Lynx 4SX , Neuralynx , USA ) . The raw signal was acquired at 32 kHz sampling rate , band-pass filtered ( 0 . 1–9 , 000 Hz ) , and stored for offline analysis . Recording and visualization were made by Cheetah Data Acquisition System ( Neuralynx , USA ) . The sound was synthesized using MATLAB , produced by an USB interphase ( Octa capture , Roland , USA ) , amplified ( Portable Ultrasonic Power Amplifier , Avisoft , Germany ) , and played in a free-field ultrasonic speaker ( Ultrasonic Dynamic Speaker Vifa , Avisoft , Germany ) located 15 cm horizontal to the right ear . The sound intensity was calibrated at the position of the animal’s right ear with a Bruël & Kjaer ( 4939 ¼” free field ) microphone . Microphone signals were sampled at 96 kHz and analyzed in MATLAB . Tones between 2 kHz and 30 kHz did not show harmonic distortion within 40 dB from the main signal . Sound stimuli consisted of 30 ms pure-tone pips with 5 ms rise/fall slope played at a rate of 2 Hz . We used 24 frequencies ( 3 . 3–24 . 6 kHz , 0 . 125 octave spacing ) at different intensities ( 0–80 dB with steps of 5 or 10 dB ) played in a pseudorandom order . Each frequency-level combination was played 5 times . For the analysis of SNRs , data were bundled in “adjacent” and “tuned” regions . Each of these regions comprised 4 steps in the frequency sweep ( 14 . 6 , 16 , 17 . 6 , and 19 kHz for the tuned; 10 . 3 , 11 . 3 , 12 . 3 , and 13 . 4 kHz for the adjacent region ) and ranges of frequencies with a ΔF of 30% . For the two-tone inhibition protocol , a fixed tone ( 16 kHz , 50 dB ) was played simultaneously with a variable tone of a specific frequency-intensity combination ( 3 . 3–24 . 6 kHz , 0 . 125 octave spacing; 0–80 dB with steps of 5 or 10 dB ) . The stored signals were high-pass filtered ( 450 Hz ) . To improve the SNR in the recordings with the silicon probes , the common average reference was calculated from all the functional channels and subtracted from each channel [87] . Multiunit spikes were then detected by finding local minima that crossed a threshold that was 6 times the median absolute deviation of each channel ( S2A Fig ) . Recorded sites were classified as sound driven when they fulfilled 2 criteria: ( 1 ) Significant evoked responses: a PSTH was built , with 1 ms bin size , combining all the frequencies and the intensities above 30 dB . The overall spike counts over 80 ms windows before and after tone onset were compared ( p < 0 . 05 , unpaired t test ) . ( 2 ) Responses were excitatory: they crossed an empirically set threshold ( evoked spikes–baseline spikes ) of 45 spikes . Responses that were inhibitory ( less evoked spikes than baseline , <10% of cases ) were not used . Using these criteria , 85% of the recorded sites where classified as sound driven . In auditory-driven recording sites and for each testing protocol , the spikes across all the trials for each frequency-intensity combination were summed at 1 ms bins . Evoked firing rates were calculated in an 80 ms window , starting with stimulus onset expressed as spikes per second . This yielded a specific spike rate per each frequency-intensity combination that was used to build iso-intensity tuning curves . The peak in collicular activity for each group was computed by averaging the peak of the tuning curve at 70 dB for each recording site along the tonotopic axis . The BF ( frequency that elicited the best response in a given recording depth ) was selected as that with the highest spike count when responses were summed over all intensities . In the rare cases in which more than one frequency elicited the highest response , the mean was used as BF . The difference in BF along the tonotopic axis was computed as the mean across depths of each individual BF minus the average control BF at each depth . Reliability was calculated for recording sites with a BF within a specific range . For each selected site , reliability was calculated as the percentage of trials in which the BF in the selected range evoked at least 1 spike at 70 dB . The spontaneous activity was calculated as the firing rate within a window of 80 ms previous stimulus onset . The SNR was the ratio between the activity evoked by a specific frequency at 70 dB ( calculated as described above ) and the spontaneous activity . The intensity threshold—the lowest sound intensity that elicited a reliable response—was calculated from the FRA as the lowest sound intensity that elicited a spike count 1 . 5 times higher than the spontaneous activity [88] . The bandwidth at the base , for each sound intensity above threshold , was calculated from the smoothed FRA ( 4-point averaging [88] ) as the width in octaves of the frequencies that evoked at least 20% of the maximum response . The bandwidth at half-maximum , for each sound intensity above threshold , was calculated from the smoothed FRA as the width in octaves of the frequencies that evoked 50% of the maximum response at each intensity level . Only recording sites with a BF of 9 to 16 kHz were included in the analysis to avoid the inclusion of incomplete tuning curves due to the frequency range we used as stimuli . The intensity-specific BF corresponded to the frequency that elicited the strongest response at each sound intensity . Latencies corresponded to the time after sound offset of the first evoked spike . ROC analysis was used to assess the discriminability across frequencies in the tuning curves , across structural tuning curves , and across prepulse frequencies in the behavioral PPI . For the tuning curves ( local tuning ) , we generated response distributions ( perfcurve function , MATLAB ) based on the number of spikes elicited by a given tone across trials ( Fig 5A left ) . The probability that a given frequency f2 will be bigger than a growing criterion of number of spikes will go from 1 to 0 as the criterion traverses the range of spike numbers elicited by f2 ( Fig 5A right ) . For the blue f2 in the figure , the criteria that elicit probabilities above 0 will overlap with those of f1 ( yellow ) , while for the brown f2 , there will be no overlap . The ROC curve will therefore be largest for the comparison between the brown f2 and f1 and shallower for the comparison between the blue f2 and f1 ( Fig 5B ) . The ROC analysis of the structural tuning was based on the variability in the size of the response across depths ( 250 to 750 μm ) , rather than trials , and was calculated for structural tuning curves elicited by individual tone presentations ( trials , Fig 5C ) . The number of spikes was used to generate depth distributions in the same way that the number of trials was used to generate spike distributions for the local tuning . In this case , f1 was either the average structural tuning of 16 kHz or 11 . 3 kHz , while f2 was the trial-by-trial structural tuning of frequencies below f1 . The trial-by-trial ROC values for each frequency were averaged before they were plotted . The ROC analysis for the behavioral data was based on the variability in the startle response across prepulse presentations of a given frequency ( see PPI methods below ) . Distributions were constructed , like for the local tuning , from the individual trial values . For each PPI test , f1 was whatever frequency was the background frequency ( 16 or 11 . 3 kHz ) , and f2 varied across the range of prepulse frequencies . Structural tuning–based classification [32 , 33] was performed as follows . The input to the model is a spike-counts dataset of size S × T × N in which S is the total number of stimuli ( S = 24 frequencies ) , T is the number of repetitions for each stimulus ( T = 5 ) , and N is the number of recorded depths ( N = 14 ) . The vector Vs , t = ( Vs , t1 , … , Vs , tN ) represents a single-trial response of the neural population to stimulus s , in which s goes from 1 to S , and t goes from 1 to T . The model is then “trained” to create individual response templates for each stimulus s calculated by averaging the vector Vs , t over the T − 1 trials in the training set . The single trial left out of the training set is used to generate a prediction and classified as being generated by a given stimulus if the euclidean distance between the single trial and the template corresponding to that stimulus is minimal compared to all the other distances . We classified all S × T single trials using this scheme and summarized the results in a confusion matrix C of size S × S , in which the i , j-th element Ci , j is the fraction of trials with stimulus i being classified as stimulus j . The individual confusion matrices , representing the probability of correctly predicting the actual frequency , were averaged across groups and used to estimate classification accuracy . Animals were placed in a custom-made acrylic chamber of 12 cm long and 4 cm in diameter . Movement was detected by a piezoelectric sensor located below the chamber . The protocol was as previously reported by others [36 , 37] . The experiment was divided in 5 phases following one after the other uninterruptedly . ( 1 ) Chamber habituation: at the start of each session , animals were placed in the test chamber and allowed to habituate for 10 minutes; ( 2 ) Sound habituation: a constant background tone ( f1: 16 kHz , 70 dB SPL ) was played for 5 minutes; ( 3 ) Startle-only trials: 10 startle-only trials were presented on the background of 16 kHz to allow for short-term habituation to the startle sound; ( 4 ) Test phase: 10 pre-pulse trials and 10 startle only trials were presented to assess frequency discrimination; ( 5 ) Startle-only trials: 5 startle-only trials were presented to check for habituation over the duration experiment . Trials consisted of a frequency change from the background tone ( f1 ) to the prepulse tone ( f2 , 80 ms long , 1 ms ramp ) at constant 70 dB SPL ( Fig 1F ) . This was immediately followed by 20 ms broadband noise ( BBN ) at approximately 100 dB , which was in turn followed by the background tone at 70 dB until the following trial in a seamless manner . For the “startle-only trials , ” f1 and f2 were 16 kHz , and for prepulse trials , f2 was 15 . 92 , 15 . 84 , 15 . 68 , 15 . 472 , 15 . 2 , 14 . 72 , 14 , or 8 kHz , corresponding to Δf of 0 . 5% , 1% , 2% , 3 . 3% , 5% , 8% , 12 . 5% , and 50% , respectively , relative to f1 . For animals in which f1 was 11 . 3 kHz , f2 was 11 . 31 , 11 . 25 , 11 . 19 , 11 . 08 , 10 . 93 , 10 . 74 , 10 . 4 , 9 . 89 , or 5 . 65 kHz . Trials had pseudorandom lengths between 8 and 25 seconds . The mouse acoustic startle reflex was measured as the maximal vertical force exerted on the piezo within a 200 ms window starting with the onset of the startle noise , minus the mean of the force for 50 ms before the startle noise . For each animal , the startle-only trials of the test phase and the prepulse trials of each frequency were averaged . The percent of PPI for each prepulse frequency PPI ( % ) was calculated as follows: PPI ( % ) =100×ASRnopps-ASRppsASRnopps , in which ASRnopps is the mean response of the startle-only trials , and ASRpps is the mean response of the prepulse trials for that particular frequency . Discrimination thresholds for each animal , defined as the Δf that caused 50% of inhibition of the maximum response , were calculated from parametric fit to a generalized logistic function ( fit function MATLAB ) [37] PPI=-a2+a1+exp ( b+cΔf ) . Animals with a fit coefficient of the curve ( R2 ) below 0 . 7 were excluded from statistical analysis ( 3 control animals , 2 exposed animals , and 1 random animal ) . Additionally , the pooled data for each group were also fitted to a generalized logistic function . In a subset of the animals and after the surgery in the inferior colliculus , a 4x3 mm craniotomy medial to squamosal suture and rostral of the lambdoid suture was made to expose the left AC . The AC was located dorsal and posterior of the transverse sinus [89] . A small amount of Vaseline was applied to the boundaries of the craniotomy to form a well . A single electrode or a 16-channel multielectrode array was inserted . Evoked responses to the tone pips were constantly monitored . A small amount of volume of phosphate-buffered saline solution ( Sigma , USA ) was applied ( 3–5 μL ) every 10–15 minutes during baseline recordings in the inferior colliculus . Then , 3–5 μL of muscimol were applied over the AC ( 1 mg/mL , dissolved in phosphate-buffered saline solution , Sigma , USA ) . AC evoked activity was monitored using frequency sweeps at 70 dB SPL or BBN of different intensities every 5 minutes . AC was usually inactivated 15–20 minutes after muscimol application . Once cortical inactivation was confirmed , recordings in the inferior colliculus were repeated . Six to 12 days after the beginning of sound exposure ( 8 kHz ) , mice were removed from the Audiobox one at a time for acute electrophysiology . Mice were anesthetized with urethane ( 1 . 32 mg/kg , ip ) and xylazine ( 5 mg/kg , ip ) . Animal temperature was maintained at 36 . 5 °C using a custom-designed heating pad in a soundproof chamber with ambient temperature of 30 °C . A tracheotomy was performed , and the cartilaginous ear canals were removed before the mouse was positioned in a custom-designed head-holder and stereotaxic apparatus . Then , a craniotomy was performed on part of the occipital bone , and part of the cerebellum was aspirated to visualize the superior semicircular canal as a reference point . A glass microelectrode filled with 2 M NaCl and 1% methylene blue was advanced in 4 μm steps ( Inchworm micromanipulator , EXFO Burleigh , Germany ) , aiming for the anterior part of the anteroventral cochlear nucleus . Extracellular signals were amplified and band-pass filtered ( 300–3 , 000 Hz ) using an ELC-03X amplifier ( NPI Electronic , Tamm , Germany ) . Digitized signals ( TDT system 3 ) were saved for offline analysis using custom-written MATLAB software . Once a sound-responsive neuron was isolated , the spontaneous rate , CF , and best threshold were determined as described by Jing and colleagues [90] . Unit classification was based on the response pattern to 200 repetitions of 50 ms tone burst at CF ( 2 . 5 ms cos2 rise/fall , 10 Hz repetition rate ) , as described by Taberner and Liberman [91] . The analysis for “other cell types” includes mostly chopper units , some onset units , and a few pauser/build-up units . Likewise , responses to 8 kHz tone bursts were recorded , and the receptive area of each unit was mapped using 30 ms tone bursts at 70 dB ( 10 repetitions per sweep , 3 Hz repetition rate ) for a total of 13 frequencies ranging from 4 kHz to 30 kHz . A 4 × 3 mm craniotomy medial to squamosal suture and rostral of the lambdoid suture was made to expose the left AC . The AC was located dorsal and posterior of the transverse sinus [89] . Single-electrode penetrations ( 400–450 μm ) were made along the exposed cortical surface spaced between 200–250 μm . Auditory core fields ( A1 and AAF ) were identified according to their response latencies and tonotopic distribution [89] . Data acquisition and acoustic stimulation were similar as with inferior colliculus recordings . A separate set of mice was used for gene expression analysis . After 3 days of habituation and 7 days of sound exposure in the Audiobox , mice were anesthetized with avertin and killed by cervical dislocation; immediately , the brain was extracted; and both inferior colliculi were dissected and immediately frozen at −80 °C and stored for later analysis . RNA was isolated from inferior colliculi using the RNAeasy Kit ( Qiagen ) , following manufacturer’s instructions . cDNA was synthesized from 1 μg of RNA using the Superscript III Kit ( Invitrogen ) and random nonamer primers . For quantitative real-time PCR , SyBr Green Master Mix kit ( Applied Biosystems , Germany ) was used , and amplification reactions were run on a Roche LC480 Detection System ( 384-well plates ) or 7500 Fast Real-Time PCR System ( 96-well plates ) . Reactions were run in 4 replicates . The efficiency ( E ) of each pair of primers was estimated based on the slope ( m ) of a standard curve of the Ct values from 5 serial logarithmic dilutions of a template cDNA , using the following formula: E=10 ( -1m ) . The goodness of fit ( R2 ) of all the standard curves was >0 . 98 . We used the gene of the ribosomal protein L13a ( rpl13a ) as a reference gene , since it has been reported as the best candidate gene for brain gene expression analysis [92] . The relative expression of Rpl13a showed no change between the three groups tested ( F2 , 17 = 0 . 8 , p = 0 . 47 , n = 7 , 8 , and 5 for exposed , control , and home cage groups , respectively ) . Gene expression relative to the housekeeping gene ( Rpl13a ) was calculated with the method used by [93] , in which corrections for different efficiencies between target gene and housekeeping gene are made: RE=EkhgCThkgEtgCTtg , in which RE is the relative expression , Ekhg is the efficiency of the housekeeping gene , CThkg is the Ct value of the housekeeping gene , Etg is the efficiency of the target gene , and CTtg is the Ct value of the target gene . After testing for normality distribution using the Jarque-Bera test , group comparisons were made using multiple way ANOVAs , accordingly . For experiments with multiple measures per animal , we used mixed-design ANOVA , with mouse identity as a nested random effect . To test the effect of days on frequency representation and collicular activity , we used a linear mixed effects model ( fitlme , MATLAB , with mouse identity as a random effect ) . For data in which normality test failed , a Kruskal-Wallis test or wilcoxon signed rank test for paired data was used . Where possible , post hoc Bonferroni corrections for multiple comparisons were used . Means are expressed ± SEM . Statistical significance was considered if p < 0 . 05 . | Some things are learned simply because they are there and not because they are relevant at that moment in time . This is particularly true of surrounding sounds , which we process automatically and continuously , detecting their repetitive patterns or singularities . Learning about rewards and punishment is typically attributed to cortical structures in the brain and known to occur over long time windows . Learning of surrounding regularities , on the other hand , is attributed to subcortical structures and has been shown to occur in seconds . The brain can , however , also detect the regularity in sounds that are discontinuously repeated across intervals of minutes and hours . For example , we learn to identify people by the sound of their steps through an unconscious process involving repeated but isolated exposures to the coappearance of sound and person . Here , we show that a subcortical structure , the auditory midbrain , can code such temporally spread regularities . Neurons in the auditory midbrain changed their response pattern in mice that heard a fixed tone whenever they went into one room in the environment they lived in . Learning of temporally spread sound patterns can , therefore , occur in subcortical structures . | [
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"memory"... | 2018 | Auditory midbrain coding of statistical learning that results from discontinuous sensory stimulation |
Although the vitamin A metabolite retinoic acid ( RA ) plays a critical role in immune function , RA synthesis during infection is poorly understood . Here , we show that retinal dehydrogenases ( Raldh ) , required for the synthesis of RA , are induced during a retinoid-dependent type-2 immune response elicited by Schistosoma mansoni infection , but not during a retinoid-independent anti-viral immune response . Vitamin A deficient mice have a selective defect in TH2 responses to S . mansoni , but retained normal LCMV specific TH1 responses . A combination of in situ imaging , intra-vital imaging , and sort purification revealed that alternatively activated macrophages ( AAMφ ) express high levels of Raldh2 during S . mansoni infection . IL-4 induces Raldh2 expression in bone marrow-derived macrophages in vitro and peritoneal macrophages in vivo . Finally , in vivo derived AAMφ have an enhanced capacity to induce Foxp3 expression in CD4+ cells through an RA dependent mechanism , especially in combination with TGF-β . The regulation of Raldh enzymes during infection is pathogen specific and reflects differential requirements for RA during effector responses . Specifically , AAMφ are an inducible source of RA synthesis during helminth infections and TH2 responses that may be important in regulating immune responses .
Vitamin A ( retinol ) is a critical factor in protective immunity , as evidenced by the increase in infectious disease morbidity and mortality associated with its deficiency in the diet [1] . The biological activity of vitamin A requires intracellular oxidation of retinol to retinoic acid ( RA ) , a hormone-like metabolite that modulates the function of innate and adaptive immune cells [2] , [3] . The rate-limiting step in RA synthesis is catalyzed by three major isoforms of retinal dehydrogenase ( Raldh1-3 ) , a family of tightly regulated enzymes [4]–[6] . Homeostatic Raldh expression in immune cells is well described in gut-associated lymphoid tissues ( GALT ) [7]–[11] , where RA synthesis by antigen presenting cells ( APCs ) contributes to the recruitment and function of local lymphocyte populations . However , it remains unclear whether Raldh expression is an inducible component of effector immune responses during infection in other peripheral organs like the liver . Elucidating the regulation of RA synthesis by inflammatory cells is critical for understanding the role of RA signaling in shaping immune responses in vivo . While basal RA signaling is required for general T cell activation [12] , RA also acts in concert with other signals to mediate inflammatory [13] , [14] and regulatory [8] , [10] , [15] , [16] T cell functions . In the presence of IL-4 , a critical mediator of type-2 inflammation , RA favors T-helper ( TH ) 2 responses in murine [17] , [18] and human [19] CD4+ T cells by enhancing the expression of GATA-3 and type-2 cytokines while inhibiting T-bet and IFNγ expression . Accordingly , vitamin A deficiency attenuates eosinophilia , IgE responses , and type-2 cytokine expression in vivo [20]–[22] . TH2 cells mediate protective immunity to helminth parasites that are common in regions of the world where vitamin A deficiency is prevalent [23] , [24] . However , the importance of RA in the generation of TH2 responses during helminth infection is not well characterized and the population of cells responsible for RA synthesis in this setting has not been identified . In this study , we sought to determine whether RA synthesis is a regulated component of immune responses during infection . Based on the existing evidence that RA promotes TH2 responses , we hypothesized that Raldh expression is induced in APCs that are activated during TH2 inflammation . To address this hypothesis , we evaluated RA signaling and Raldh expression in mice infected with the parasitic helminth , Schistosoma mansoni , an important human pathogen that provides a well-characterized model of TH2 inflammation . Deposition of S . mansoni eggs in the liver and intestine drives a type-2 granulomatous response characterized by TH2 cells , AAMφ , and eosinophils [25] . In parallel , and in a model of TH1 responses , we evaluated mice infected with lymphocytic choriomeningitis virus ( LCMV ) . The broad tropism of LCMV allowed for the direct comparison of TH1- and TH2-polarized responses in the liver and intestine . Vitamin A deficient mice showed severely impaired TH2 but not TH1 responses in the liver , suggesting a role for RA synthesis during TH2 inflammation at this site . Raldh enzymes were highly expressed by AAMφ recruited to liver granulomas during S . mansoni infection , and Raldh2 expression in macrophages was induced by activation with IL-4 in vitro and in vivo . Thus , our findings demonstrate that helminth-elicited AAMφ are an inducible source of RA synthesis in the setting of retinoid-dependent TH2 inflammation and identify IL-4 activation as a selective mechanism for Raldh2 induction in these cells .
To assess the role of RA synthesis during infection , we first determined whether S . mansoni- and LCMV-elicited T cell responses are dependent upon vitamin A . Mice were maintained on a vitamin A deficient ( A− ) or control ( A+ ) diet beginning at day 10 of gestation . S . mansoni-infected mice were analyzed at week 7 post-infection , corresponding to the acute TH2 response that is elicited by egg deposition , while LCMV ( Armstrong strain ) -infected mice were analyzed at day 7 post-infection . Infections were timed such that all the mice were analyzed at 15 weeks of age . By this time , serum retinol levels in A- mice were reduced to ∼0 . 35 µM , a level defined by the World Health Organization as severe vitamin A deficiency [26] . Within the liver of S . mansoni-infected mice , eggs are deposited that evoke granulomatous eosinophilic inflammation , a process that is Th2-dependent . Although the livers of A+ and A− mice showed no differences in the numbers of eggs ( Figure 1A ) , A− mice had significantly smaller granulomas ( Figure 1B ) and reduced eosinophilic infiltration ( Figure 1C ) , similar to mice genetically deficient in TH2 responses ( IL-4−/− , Stat6−/− ) [27] , [28] . The diminished granuloma size in A− mice was associated with microvesicular damage in the liver ( Figure 1D ) . Unlike other models of more extreme liver pathology leading to mortality in S . mansoni-infected mice [27] , [28] , there was no difference in survival rates between A+ and A− mice . The characteristic expansion of IL-4+ TH2 cells associated with egg deposition in the liver and the intestine was significantly reduced in A− mice ( Figure 1E ) . Concomitantly , the numbers of IFNγ+ and TNFα+ CD4+ T cells ( Figures S1A and B ) were not decreased in A− mice , indicating a selective defect in the TH2 response induced by vitamin A deficiency . Similar to reports in previous studies [12] Foxp3+ T cells were actually increased in the A− mice , either infected with S . mansoni , LCMV or even in the uninfected control mice ( Figure 2 and S2 ) . When hepatic leukocytes were co-cultured with schistosome egg antigen ( SEA ) for 72 hours , we found that SEA-specific IL-4 and IL-10 responses were dramatically reduced in A− mice . By contrast , the production of IFNγ and TNFα was indistinguishable between samples from A+ and A− mice ( Figure 1F ) , although this response was not antigen specific and was probably not produced by CD4+ cells . By quantitative real-time PCR analysis ( qRT-PCR ) of isolated liver lymphocytes , we found that vitamin A deficiency significantly reduced the expression of IL-4 , IL-5 , and IL-13 but not of IFNγ ( Figure 1G ) . The effects of vitamin A deficiency were less pronounced in the draining mesenteric lymph nodes ( MLN ) than in the liver . Both the number of IL-4+ T cells analyzed ex vivo and the SEA-specific IL-4 and IL-10 responses were either unaffected or only slightly reduced by vitamin A deficiency ( Figures S1C and D ) . However , the expression of IL-5 and IL-13 was vitamin A-dependent ( Figure S1E ) . The majority of IL-4-producing T cells in lymph nodes responding to helminth infection are follicular helper-T cells ( T-fh ) , which are functionally distinct from TH2 cells [29] , [30] . In aggregate , these results suggest that RA signaling is critical for the expression of type-2 cytokines by TH2 cells recruited to sites of tissue inflammation , but is not essential for IL-4 expression by T-fh cells . In contrast to S . mansoni infection , we found that the numbers of GP61 and GP33 peptide-specific IFNγ- or TNFα-positive CD4+ or CD8+ T cells in the livers ( Figure 3A ) , spleens ( Figure 3B ) and MLN ( Figure S3 ) of LCMV-infected mice were unaffected by vitamin A deficiency . However , LCMV-specific ( Figures 3C and D ) as well as polyclonal ( Figure S4 ) TH1 responses in the intestine were significantly diminished by vitamin A deficiency , consistent with a defect in intestinal homing [7] . These results demonstrate that vitamin A deficiency does not impair all T cell responses to pathogens , but that higher levels of RA signaling are required to maintain intestinal homing of effector T cells and to support helminth-elicited TH2 responses . The vitamin A-dependency of S . mansoni-elicited TH2 responses suggested a critical role for RA during this infection . To determine whether RA signaling was directly targeted to CD4+ T cells during infection , we measured CCR9 expression by T cells as a surrogate marker of RA activity [7] , [13] . Baseline CCR9 expression on CD4+ T cells in naïve , uninfected mice was reduced as a result of vitamin A deficiency in the MLN and intestinal mucosa but not in the spleen , confirming previous reports that homeostatic RA synthesis is a selective function of APCs in the GALT [7] , [8] , [10] ( Figure 4 ) . In the liver , CCR9 was not induced by either S . mansoni or LCMV infection , but was diminished in A− mice ( Figure S5 ) . As expected , all mucosal CCR9+ T cells were CD62Lneg ( effector/memory subset ) , consistent with the possibility that these cells homed to the intestinal mucosa following antigen presentation . During LCMV infection , CCR9 induction was restricted to the intestinal tissues . During S . mansoni infection , by contrast , CCR9 expression was also induced in secondary lymphoid organs ( e . g . , the spleen ) ( Figure 4 ) . In each case , the increase in CCR9 expression was diminished in A− mice , indicating a dependency on vitamin A metabolites . These results indicate that S . mansoni infection requires vitamin A to drive RA signaling in T cells beyond the intestinal tissues . To determine which cells produce RA after infection , we used qRT-PCR to measure the three major Raldh isoforms that facilitate local RA synthesis in liver leukocytes isolated from S . mansoni- and LCMV-infected mice . Raldh2 and Raldh3 were expressed >50-fold higher in type-2 relative to type-1 inflammatory cells ( Figure 5A ) , despite a similar increase in the number of inflammatory cells in the liver during both infections ( data not shown ) . At day three post-infection with LCMV , when virus titers in the liver , intestine , spleen and MLN remain high , there was also no induction of any Raldh isoform expression in any of these tissues ( data not shown ) . The MLN and spleen have a slightly higher expression of Raldh2 in S . mansoni-infected mice; however , these differences were slight and more variable compared to the liver ( data not shown ) . No significant differences in Raldh2 expression were seen in the intestinal tissues of S . mansoni-infected mice ( data not shown ) . Notably , Raldh2 is the isoform constitutively expressed by GALT APCs , while a role for Raldh3 in immunity has not been described . S . mansoni egg-elicited granulomas are comprised of macrophages , eosinophils , and T cells [25] . To determine if myeloid cells are the source of Raldh expression , liver sections from S . mansoni-infected mice were co-stained with antibodies reactive for CD11b and Raldh . The Raldh antibody recognizes Raldh1 as well as Raldh2 . Hepatocytes stained brightly for Raldh ( Figure 5B ) , most likely reflecting expression of Raldh1 , a low efficiency isoform highly expressed in the liver . Raldh staining was also detectable within granuloma cells that co-stained for CD11b . To distinguish between expression of different Raldh isoforms in macrophages and eosinophils , which both express CD11b , liver leukocytes from S . mansoni-infected mice were sort-purified by fluorescence activated cell sorting ( FACS ) for qRT-PCR analysis ( Figure 5C ) . While expression of all three Raldh isoforms was detected in macrophages , eosinophils , and T cells , Raldh2 in macrophages was the most abundant source of Raldh expression . Similar results were obtained from sorted MLN cells ( Figure 5C ) . However , in this tissue the sorting strategy does not exclude CD11b+ dendritic cells ( DCs ) . Macrophages sorted from the whole livers of S . mansoni-infected mice may include inflammatory AAMφ recruited to granulomas as well as resident Kupffer cells . Recently , AAMφ have been reported to originate from the proliferation of tissue resident macrophages [31] , which may also occur in the liver granulomas . The CX3CR1-GFP reporter mouse has been used to track monocyte-derived DCs and macrophages in several different organs , including the liver and the intestinal tract [32] . During steady state conditions , the only GFP+ cells were “round” monocytes ( white arrows ) in the sinusoidal vessels ( Figure 6A ) [32] . Kupffer cells did not express GFP , making this a convenient model to distinguish between them and inflammatory macrophages . At seven weeks after infection with S . mansoni , almost all of the GFP+ cells found in the tissues had a morphology ( with multiple cellular processes; Figures 6B and 6C ) and a localization ( on the outer fringe of granulomas; Figure 6C ) consistent with that of AAMφ . To better define these cells , liver leukocytes were sort-purified into CD11b+ subpopulations that were either positive or negative for GFP ( Figure 6D ) . RNA was extracted from these fractions and the expression of arginase 1 , Ym1 , FIZZ1 as well as of Raldh2 was measured by qRT-PCR analysis within them ( Figure 6E ) . Compared to CD11b+CX3CR1-GFP− cells , CD11b+ CX3CR1-GFP+ cells expressed high levels of arginase 1 , Ym1 , FIZZ1 and of Raldh2 , indicating that CX3CR1-GFP+ cells are AAMφ and an important source of RA synthesis during S . mansoni infection . To further explore the regulation of Raldh expression by AAMφ , bone marrow-derived macrophages were treated with IL-4 or IFNγ in vitro and then assayed for expression of Raldh2 transcript using qRT-PCR . Stat6−/− macrophages were activated in parallel to confirm the specificity of IL-4 signaling . As expected , IL-4-induced arginase 1 expression was strictly Stat6-dependent while IFNγ-induced iNOS expression was unaffected in Stat6−/− macrophages ( Figure 7A ) . Raldh2 showed Stat6-dependent induction by IL-4 . By contrast , Raldh2 expression was inhibited by IFNγ and not affected by the regulatory cytokines , IL-10 and TGF-β1 ( data not shown ) . We did not detect Raldh1 or Raldh3 expression in bone marrow-derived macrophages under any of these culture conditions . These results support the conclusion that Raldh2 expression is a selective characteristic of AAMφ and not of classically-activated macrophages . Next , Raldh expression was assayed in AAMφ elicited in vivo by intraperitoneal administration of thioglycollate ( TG ) in combination with recombinant IL-4 complexed with anti-IL-4 antibodies ( IL-4c ) [31] . Raldh2 , like arginase 1 and also Ym1 and FIZZ1 ( Figure S6 ) , was highly expressed in peritoneal macrophages elicited by TG plus IL-4c treatment compared to treatment with TG or IL-4c alone , or to resident peritoneal macrophages ( PBS control; Figure 7B ) . Raldh2 induction by this method was abrogated in Stat6−/− mice ( Figure 7C ) . AAMφ elicited by TG plus IL-4c treatment were then assayed for aldehyde dehydrogenase ( ALDH ) activity by flow cytometry using the Aldefluor assay . Peritoneal F4/80+CD11b+ macrophages expressing the mannose receptor MRC1 had abundant ALDH activity that was blocked by the ALDH-specific enzyme inhibitor diethylaminobenzaldehyde ( DEAB ) ( Figure 8A and S6 ) . These results show that ALDH activity mirrors expression of Raldh2 in AAMφ and confirm that inflammatory AAMφ are an inducible source of RA synthesis . Inflammatory macrophages elicited by thioglycollate alone expressed MRC1 , but did not have ALDH activity . To determine if these AAMφ can induce Foxp3+ T cells , as has been previously demonstrated for RA-producing CD103+ lamina propria DCs [8] , [10] , naïve T cells were cultured with AAMφ elicited by TG plus IL-4c treatment , or with TG-elicited macrophages and resident peritoneal macrophages . As was shown previously for AAMφ elicited by Brugia malayi [33] and S . mansoni [34] , AAMφ elicited by TG plus IL-4c significantly inhibited the proliferation of naïve CD4+ T cells ( Figure 8B ) . By day 6 of co-culture , significantly more Foxp3+ CD4+ cells were detected after culture with AAMφ elicited by TG plus IL-4c ( Figure 8C , right panel ) , relative to culture with resident macrophages or TG-elicited macrophages indicating that AAMφ have an enhanced capacity to induce Foxp3+ CD4+ cells . TGF-β1 has been previously shown to enhance conversion of naïve CD4+ T cells into Foxp3+ cells , especially in the presence of CD103+ DCs . Addition of exogenous TGF-β1 to the cultures enhanced the induction of Foxp3+ CD4+ cells ( Figure 8C ) even with resident macrophages ( 12 . 3% ) and TG-elicited macrophages ( 21% ) , but was especially dramatic with AAMφ elicited by TG plus IL-4 ( 77 . 5% ) . When exogenous TGF-β1 was supplemented with exogenous RA , there was an even greater induction of Foxp3+ cells . Almost all of the CD4+ cells were Foxp3+ in cultures with AAMφ , compared with approximately half of the CD4+ cells being Foxp3+ in cultures with resident macrophages or TG-elicited macrophages . We then determined if the conversion of naive CD4+ T cells into Foxp3+ T cells by AAMφ could be inhibited by a synthetic RA receptor inhibitor LE540 ( Figure 8D and 8E ) . Whereas LE540 slightly increased the proportion of CD4+ CD25+ Foxp3+ cells when added to cultures with resident peritoneal macrophages or TG-elicited macrophages , LE540 very significantly blocked the induction of CD25+ Foxp3+ cells by AAMφ ( Figure 8D ) . When LE540 was added to the cultures along with TGF-β1 ( Figure 8E ) , there was a reduction in the number of Foxp3+ cells induced by AAMφ to a similar level ( 16 . 3% ) as TG-elicited macrophages ( 21 . 7% ) . There was also a reduction in the expression of α4β7 integrin , which was previously been shown to be induced by RA ( Figure 8E ) . Notably , the induction of α4β7 integrin by AAMφ was also reduced in the presence of LE540 . However , CCR9 was not induced by culture with AAMφ ( Figure 8D ) and even when RA was added exogenously to the cultures , fewer CD4+ cells ( 37 . 8% ) were CCR9+ when cultured with AAMφ compared to resident macrophages ( 70 . 9% ) or TG-elicited macrophages ( 68 . 6% ) . These results indicate that while AAMφ may induce Foxp3 expression through RA , they do not directly induce CCR9 expression on naïve CD4+ cells in vitro and may actually inhibit CCR9 expression .
The increase in infectious disease morbidity and mortality associated with vitamin A deficiency can be reduced by vitamin A supplementation , suggesting that vitamin A metabolites are important in reducing the pathogenic effects of infection [1] , [2] . RA mediates the effects of vitamin A in adaptive immunity [7] , [11] , [12] , [17]; however , the regulation of RA synthesis from retinoid precursors during infection remains poorly understood . In this study we show that AAMφ could be an important source of RA during a TH2 response against helminths . This study was aimed at determining whether RA synthesis is an inducible function of pathogen-elicited immune responses . We first evaluated the dependency of effector immune responses on dietary retinoids and identified two critical roles for RA in regulating T cell responses during infection: ( 1 ) in the gut , where RA is constitutively synthesized by GALT APCs and drives intestinal homing , both TH2 and TH1 responses were retinoid-dependent; and ( 2 ) the induction of systemic RA signaling during helminth infection corresponded to the retinoid-dependency of TH2 but not TH1 responses in the liver . The latter finding suggested that RA synthesis might be a specialized function of type-2 inflammatory cells . Indeed , we found that infiltrating leukocytes in the liver during S . mansoni infection expressed high levels of the RA-synthesizing enzymes Raldh2 and Raldh3 , with Raldh2 abundantly expressed by AAMφ that had been recruited to granulomas . AAMφ are a common feature of type-2 immune responses [35] and have been implicated in T cell regulation , fibrosis , and mucosal repair . The regulation of RA synthesis in DCs is much better understood than in macrophages . While the signals in the gut microenvironment that drive constitutive RA synthesis are not well understood , IL-4 [36] , GM-CSF [37] , and beta-catenin signaling [38] have been implicated in Raldh2 expression by GALT DCs . TLR2 stimulation [39] and activation of the peroxisome proliferator-activated receptor ( PPAR ) -γ [40] can also induce Raldh2 expression by DCs . More recently , RA itself has also been shown to induce Raldh enzyme activity in DCs [37] , [41]–[43] . Consistent with our finding that AAMφ represent an important Raldh2-expressing population during S . mansoni infection , we found that IL-4 drives Raldh2 expression in macrophages in vitro and in vivo . Raldh2 in macrophages appeared to be the dominant source of Raldh expression in type-2 inflammatory cells; however , the catalytic efficiency ( Vmax/Km ) of Raldh3 is ∼10-fold higher than Raldh2 [6] . It is accordingly possible that both of these enzymes are relevant sources of RA synthesis within S . mansoni granulomas . Raldh3 expression was nearly undetectable in the liver of uninfected and LCMV-infected mice , suggesting the specificity of this enzyme for type-2 inflammation . Further studies are needed to elucidate the signals mediating Raldh3 induction . While this study focused on the role of RA signaling in T cell responses , the induction of RA synthesis during helminth infection has important implications for other cell types involved in type-2 inflammation . For example , RA promotes eosinophil survival by inhibiting caspase-3 expression and function [44] . RA also inhibits IL-12 expression in DCs [45] and macrophages [46] , reducing the TH1-priming capacity of these cells . Interestingly , IL-3 activation has been shown to induce Raldh2 expression in human basophils in vitro , leading to both autocrine and paracrine RA signaling [47] . Further investigation into these RA-mediated effects in vivo may better define the role of vitamin A and AAMφ in protective immunity . While we identified AAMφ to be an important source of Raldh2 activity ( and hence a source of RA ) during S . mansoni infection , we have not addressed the relative contribution of DC-derived RA in regulating T cells during infection . DCs can also become alternatively activated during helminth infection [48] and the RA produced by AAMφ may act directly on DCs to enhance Raldh enzyme activity [41]–[43] through a positive feedback loop mechanism . Since DCs are much better at presenting antigen to naïve T cells in draining lymph nodes , they may be more important for regulating T cell differentiation through RA than AAMφ . Instead , AAMφ may condition DCs to produce RA when they migrate to draining lymph nodes during infection . We found that AAMφ could not induce CCR9 expression on naïve T cells in vitro , suggesting that DCs may be more important in performing this function . The generation of mice with macrophage- and DC-specific defects in Raldh expression will be critical in further exploring the relative contribution of RA synthesis from these two APC populations during immune responses to infections . These experiments could also provide more direct evidence for the promotion of TH2 responses by RA produced by AAMφ or DCs . RA promotes Foxp3+ regulatory T cell ( Treg ) induction in vitro [8] , [10] , [15] , [16] , and previous studies have highlighted the selective ability of Raldh2-expressing GALT DCs to induce Foxp3 expression in T cells in a RA-dependent manner [8] , [10] . In this study , we made the important observation that AAMφ , like lamina propria CD103+ DCs [8] , [10] can induce the differentiation of Foxp3+ T cells through an RA-dependent mechanism . While lamina propria macrophages have been described to induce Foxp3+ T cells [9] , this is the first time that AAMφ have been shown to be a source of RA and have the capacity to induce the differentiation of Foxp3+ T cells . Since AAMφ and Foxp3+ T cells are both important in regulating the immune response during helminth infection [24] it is perhaps not a surprise that AAMφ can induce the differentiation of Foxp3+ T cells . Future studies will determine if our observations made through an in vitro system are indeed functionally relevant during a complex in vivo infection process . It is unclear why vitamin A deficient mice have more Foxp3+ Tregs than mice on a control diet , either under baseline , uninfected conditions , or when infected with LCMV or S . mansoni . Other recent studies have also shown a higher frequency of lamina propria Tregs in vitamin A deficient mice and mice lacking RA receptor ( RAR ) -α [12] , [49] . While the higher frequency of lamina propria Tregs observed in vitamin A deficient and RAR-α−/− mice could be attributable to a loss of effector CD4+ T cells in this tissue rather than an increase in the number of Tregs [12] , we also observed higher Treg frequencies in the MLN and spleen of vitamin A deficient mice . Notably , vitamin A deficiency had no effect on thymic Treg frequency ( Figure S2 ) . Further studies are needed to determine the mechanism of expansion and suppressive function of Foxp3+ Tregs induced during vitamin A deficiency . Although it has previously been shown that TH1 and TH17 responses are attenuated in vitamin A deficient mice ( e . g . during infection with Toxoplasma gondii ) [3] , [12] , we find here that responses to LCMV are mostly intact , apart from the homing of activated T cells to the intestinal tissues . The predominantly CD8+ CTL response to LCMV may have different requirements for RA than intracellular parasite and bacterial pathogens that elicit TH1 responses . Future experiments with RAR-deficient mice may clarify the role of RA for CTL responses during viral infections such as LCMV . Vitamin A deficiency affects ∼200 million preschool age children and ∼19 million pregnant woman globally [26] , both of which populations are also at great risk for severe infections . The geographic distribution of vitamin A deficiency overlaps significantly with that of endemic helminth infections . We have demonstrated that RA-synthesizing enzymes are induced during retinoid-dependent type-2 immunity and our results support a role for RA in the generation of protective TH2 responses during helminth infection . Importantly , Raldh2 expression was found to be a selective function of AAMφ , an APC population that is common to a variety of helminth infections [50] , [51] and required for host protection during schistosomiasis [52] . It follows that the efficacy of vaccines aimed at eliciting protective TH2 responses against helminth parasites [53] may depend on both the vitamin A status of the host as well as on the ability to prime APCs such as AAMφ that are competent for RA synthesis .
Wild-type and Stat6−/− C57BL/6 mice were purchased from Jackson Laboratories . CX3CR1-GFP mice were kindly provided by Dr . Dan Littman ( Skirball Institute , NYU ) and were used as heterozygotes from crosses of CX3-CR1-GFP/GFP with wild-type C57BL/6 mice . For vitamin A deficiency experiments , timed-pregnant C57BL/6 dams were purchased from Charles River . Mice were maintained in a specific pathogen free UCSF Laboratory Animal Resource Center facility . Pregnant dams were fed a vitamin A deficient ( 0 IU/g , TD . 86143 Harlan Teklad ) or control ( 20 , 000 IU/g , TD . 93160 ) diet starting at day 10 of gestation and continuing through weaning . After weaning , mice were maintained on the same diet for the duration of the experiment . Animal protocols were approved by the UCSF Institutional Animal Care and Use Committee . Mice were infected subcutaneously with 150 Puerto Rican S . mansoni cerceriae harvested from laboratory-maintained Biomphalaria glabrata snails . This number was titrated to result in a consistent chronic non-lethal infection in C57BL/6 mice . The intensity of infection was determined by counting adult worms recovered by perfusion of the portal system at euthanasia . To determine hepatic egg burden , liver samples were weighed , homogenized , and digested with trypsin; eggs were then sedimented and counted under a dissecting microscope . 2×105 p . f . u . of LCMV-Armstrong was administered intraperitoneally . Mice were treated i . p . on day 0 and day 2 with IL-4c mixture containing 5 µg of recombinant murine IL-4 ( Peprotech ) and 25 µg of anti-IL-4 mAB ( 11b11 , BioXcell ) or PBS control , as described previously [31] . Mice were also treated i . p . with 3 ml of thioglycollate alone or in combination with IL-4c on day 0 for comparison . Following sacrifice on day 4 , cells were isolated from peritoneal exudate by washing the peritoneal cavity with cold PBS . Peritoneal exudate cells were treated with ACK lysis buffer ( Lonza Walkersville ) to lyse red blood cells and washed with PBS . Cells were either used immediately for further staining and analysis by flow cytometry or lysed with TRIzol for RNA extraction . To obtain single-cell suspensions , livers were minced and digested with 100 U/ml type 8 collagenase ( Sigma ) and 150 µg/ml DNase I ( Sigma ) for 1 hour at 37°C followed by dispersal over 70 µm filters . Hepatic leukocytes were enriched by density centrifugation over a 40/80% Percoll ( GE Healthcare ) gradient . Spleens and MLN were dispersed over 70 µm filters , followed by lysis of splenic red blood cells with ACK lysis buffer ( Invitrogen ) . Small intestine and colon tissue were first cleaned of mesentery , fat , and fecal contents , and then cut into ∼2 cm pieces . Tissue pieces were incubated with 1 mM DTT followed by two consecutive incubations with 30 mM EDTA and 10 mM HEPES to remove epithelial cells . The remaining intestinal tissue was then digested as described above , and leukocytes were enriched by density centrifugation over a 40/80% Percoll gradient . For histopathology , liver tissue was fixed in 10% formalin and paraffin-imbedded . Tissue sections were stained with hematoxylin and eosin for egg granuloma diameter measurements , eosinophil quantification , and scoring of microvesicular damage , as described [46] , by two individuals blinded to treatment . 5×105 cells were stimulated for 5 hours at 37°C in the presence of 10 µg/ml brefeldin A ( GolgiPlug , BD Pharmingen ) . Phorbol 12-myristate 13-acetate ( PMA , 10 ng/ml ) and ionomycin ( 1 µg/ml ) were used for polyclonal T cell stimulations . LCMV peptides GP61 and GP33 ( 10 µg/ml ) were used for antigen-specific CD4+ and CD8+ T cell stimulations , respectively . For detection of cytokines in culture supernatants , 5×105 cells were cultured for 72 hours in the presence of adult schistosome worm homogenate or schistosome egg homogenate at a protein concentration of 50 µg/ml . Cytokines were quantified using a multiplex bead-based assay ( TH1/TH2/TH17 Cytometric Bead Array , BD Biosciences ) , according to the manufacturer's instructions . Samples were acquired on an LSRII with FACSDiVa software ( BD Biosciences ) and data were analyzed with FCAP Array software . Cells were stained with PE-conjugated anti-Siglec-F antibody ( E50-2440 , BD Biosciences ) for 20 minutes at 4°C and then incubated with anti-PE magnetic beads ( Miltenyi Biotec ) . Siglec-F+ cells were positively selected on MS columns ( Miltenyi Biotec ) , according to the manufacturer's instructions; Siglec-F− cells were collected in the flow-through . Both fractions were stained with antibodies against CD3 , CD11b , and Siglec-F , and sorted directly into TRIzol ( Invitrogen ) using a BD FACSAria cell sorter . Tissue samples were homogenized in TRIzol . RNA was collected in the aqueous extraction phase and column purified using an RNeasy kit ( Qiagen ) . cDNA was generated using an Omniscript Reverse Transcription kit ( Qiagen ) with oligo-dT primers in the presence of RNasin Plus RNase inhibitor ( Promega ) . PCR reactions were carried out with Taqman primer/probe sets ( Applied Biosystems ) in a StepOne Plus machine ( Applied Biosystems ) . Sections of formalin-fixed , paraffin-imbedded tissue were deparaffinized and rehydrated according to standard protocols . Slides were immersed in citrate buffer ( pH 6 . 0 ) and heated in a pressure cooker for antigen retrieval . After blocking , tissue sections were stained for 1 hour at room temperature with antibodies against CD11b ( M1/70 , Abcam ) and Raldh ( Abcam ) followed by a 1-hour incubation with fluorochrome-conjugated secondary antibodies . Images were acquired on a Leica DM6000B microscope . CX3CR1-GFP/+ mice were anesthetized with a combination of ketamine , xylazine , and acepromazine injected intraperitoneally and were kept warm on a heating pad or a pre-warmed stage . Livers of anesthetized mice were exposed by carefully cutting through the skin and peritoneum just below the rib cage and gently coaxing out a lobe of the liver . Mice were then inverted onto a pre-warmed aluminum stage insert with a 2 . 5 cm hole fitted with a glass coverslip secured with vacuum grease and tape . The liver was stabilized with gauze soaked in PBS to limit movement during imaging and to keep the liver moist . Mice were injected retro-orbitally with 250 µg of Hoechst 33342 to visualize nuclei and 250 µg BSA conjugated to Alexa 647 to detect blood vessels . Mice were then transferred to a heated chamber that was used to keep the microscope , objectives , mice , and stage at 37°C during imaging . Images were acquired on a Leica SP2 inverted confocal microscope with light generated from UV , 488 nm , and 633 nm laser lines and detected using tunable filters . Macrophages were derived from bone marrow cells harvested from the femurs and tibias of C57BL/6 mice . Cells were differentiated for six days in the presence of fetal bovine serum ( FBS ) and 3T3 fibroblast supernatant containing M-CSF and cryopreserved . Thawed macrophages were rested for 12 hours , followed by activation with IL-4 ( 20 ng/ml ) or IFNγ ( 50 ng/ml; all cytokines were purchased from Peprotech ) . Cells were lysed in TRIzol ( Invitrogen ) at the indicated time points for RNA extraction . 4×105 naïve T cells isolated from lymph nodes using the Naive CD4+ T Cell Isolation Kit II ( Miltenyi Biotec ) were cultured together with 2×105 peritoneal macrophages and 1 µg/ml of soluble anti-CD3 and 5 ng/ml recombinant human IL-2 ( R&D ) in complete RPMI ( 10% FCS , 2 mM L-glutamine , 0 . 05 mM 2-mercaptoethanol , and 100 U of penicillin and streptomycin ) for 3 d or 6 d in 12-well plates . Cultures were supplemented with fresh medium containing 5 ng/ml IL-2 on day 3 . In some proliferation assays , T cells were labeled with Violet CellTracker ( Invitrogen ) to track cell division . On day 6 , cells were stained for flow cytometry and Foxp3+ cells were detected by intracellular nuclear staining ( see above ) . Under certain conditions , recombinant human TGF-β1 ( R&D Systems ) , all-trans RA ( Sigma-Aldrich ) , or the RA receptor inhibitor LE540 ( Wako Chemicals USA ) were added to culture wells . Statistical significance was determined with the unpaired Students's t test using Prism software ( GraphPad ) . Figure S1 shows that TH1 responses during S . mansoni infection are not dependent on vitamin A . Figure S2 shows that Foxp3+ Tregs are sustained in the thymus and small intestine during vitamin A deficiency . Figure S3 shows that LCMV-specific CD4+ and CD8+ T cell responses in the MLN are not dependent on vitamin A metabolites . Figure S4 shows that polyclonal TH1 responses during LCMV infection are only dependent on vitamin A in the intestinal mucosa . Figure S5 shows that retinoid-dependent expression of CCR9 on CD4+ T cells in the liver is not altered during infection with S . mansoni or LCMV . Figure S6 shows that ALDH activity and expression of Ym1 and FIZZ1 are upregulated in AAMφ induced by thioglycollate and IL-4 . | Vitamin A deficiency , a major global health concern , increases morbidity and death due to infectious diseases . For vitamin A to be utilized by the immune system , it must be metabolized into retinoic acid ( RA ) , its active form . RA is a key determinant of T cell activity . However , its contribution to protective immunity during infection is poorly understood , as is the regulation of its synthesis in this context . We examined RA synthesis by immune cells responding to helminth infection and virus infection . While intestinal T cell responses were vitamin A-dependent during both infections , only T cell responses elicited by helminth infection were vitamin A-dependent in the liver . Consistent with this finding , the enzymes necessary for RA synthesis were expressed by inflammatory cells recruited to the liver during helminth , but not virus , infection . We identified alternatively-activated macrophages as a source of RA synthesis within immune cells responding to helminth infection and find that they can induce regulatory T cells . Our findings provide a better understanding of vitamin A utilization during infection and demonstrate that RA synthesis is an inducible component of protective immunity . | [
"Abstract",
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] | [
"immunology",
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] | 2012 | Upregulation of Retinal Dehydrogenase 2 in Alternatively Activated Macrophages during Retinoid-dependent Type-2 Immunity to Helminth Infection in Mice |
Neuritis is a frequent complication of Myocobacteria leprae infection and treatment due to the variety of mechanisms through which it can occur . Not only can mycobacterial invasion into peripheral nerves directly cause damage and inflammation , but immune-mediated inflammatory episodes ( termed leprosy reactions ) can also manifest as neuritis at any point during infection . Treatment of leprosy reactions with thalidomide can also lead to neuritis due to an adverse drug effect . Neuritis can emerge years after initial diagnosis and treatment , although it is most frequently found at time of diagnosis or early into the treatment course . Treatment of neuritis is dependent on high-dose corticosteroid therapy as well as therapy for suspected underlying etiology . Here , we present a case of ulnar neuritis presenting in a patient with lepromatous leprosy four years after treatment of initial infection , with subsequent improvement after corticosteroid burst while maintained on thalidomide therapy .
Mycobacterium leprae is a non-motile , acid-fast bacteria that infects its host by invasion of Schwann cells , the principle support cell in the peripheral nervous system . The entry of M . leprae into peripheral nerves has been thought to cause nerve function impairment independently of the subsequent immune response to infection , although the direct effect of M . leprae on Schwann cells remains unclear . While in vitro studies suggest no loss of Schwann cells by the organism ( favoring cell survival ) , biopsies of human lesions have demonstrated Schwann cell apoptosis [1] . The significance of these findings relative to nerve injury is unclear , as subsequent inflammatory responses , termed leprosy reactions , are thought to be largely responsible for the neurologic manifestations of leprosy infection . The variability of clinical presentation is thought secondary to genetic variability determined by different biological pathways modulated by M . leprae , as well as the reprogramming of adult Schwann cells and interactions of innate and adaptive immunity [2] . Leprosy reversal reactions are immune-mediated inflammatory complications that occur in treated and untreated M . leprae infections , affecting 30–50% of all leprosy patients . Two subtypes of reactions are recognized , initially due to their differing clinical presentation and later their suspected immunopathology: type 1 reactions ( also referred to as reversal or downgrading ) and type 2 ( erythema nodosum leprosum , ENL ) [3] . The neurologic manifestations are secondary to peripheral nerve injury and are characterized by loss of sensation , paralysis , and deformity [4] . These reactions can occur before , during , or after multidrug therapy ( MDT ) treatment of underlying infection , and can both manifest with neuritis . Type 1 reactions are cell-mediated reactions characterized by inflammatory changes at sites of mycobacterial infection . Skin changes include the development of tender , erythematous , and swollen lesions at sites of high mycobacterial burden , while affected nerves can have altered sensory and motor function . Type 2 reactions ( ENL ) are immune-complex mediated reactions with systemic features to include painful , erythematous subcutaneous nodules as well as fever , lymphadenitis , arthritis , iridocyclitis , orchitis , and notably , neuritis . Whereas the recovery rates for type 1 reactions may be as high as 70% if treated within 6 months of onset , ENL may have a chronic or relapsing nature , causes long term neuropathy and subsequent disability . While both subtypes of reactions can cause nerve inflammation and damage , they are thought more common in type 1 reactions but have been found to occur in up to 35% of patients with ENL [4] . The treatment of type 1 reactions relies on corticosteroids ( often at high doses for prolonged periods ) while continuing to treat underlying M . leprae infection . Thalidomide is a highly effective treatment for ENL , while corticosteroids remains common primary or additional choice due to restrictions and/or unavailability of thalidomide . In cases of neuritis , corticosteroids are thought to reduce intraneural edema and reduce the inflammatory response to M . leprae antigens [5] . While MDT provides an effective and standardized regimen for treatment of underlying M . leprae infection , treatment doses/duration of corticosteroids for leprosy reactions remains dependent on clinical response . Many cases are complicated by reaction relapses in both subtypes , as well as a high degree of a chronic course of ENL . Here , we present a case of steroid-responsive ulnar neuritis occurring in a lepromatous leprosy patient on chronic thalidomide therapy several years after completion of MDT .
Patient study was routine in nature with guideline-directed medical therapy adhered to . No experimental treatment was pursued in treatment of this patient . Consent for publication of case report as well as images was obtained from patient both verbally and in written format , with the patient demonstrating full understanding and agreement with publication and dissemination of clinical course and treatment , with removal of personally identifiable information from case reports as well as images . Patient information , medical history , and clinical course were obtained via electronic medical records chart review of subspecialty documentation and radiology reports during the period September 2012 through May 2019 . All personally identifiable information ( PII ) was removed to maintain anonymity of patient . Patient images obtained upon informed consent of patient and supplied by authors of this manuscript . The Institutional Review Board was not involved in procedures , given the retrospective and non-experimental nature of this study .
A 33-year-old Micronesian man with lepromatous leprosy presented with one month of difficulty with writing and right hand/finger numbness in the ulnar distribution , four years after completion of MDT . He had been diagnosed by skin biopsy six years prior and completed two years of MDT with rifampin , dapsone , and clofazimine . His treatment course had been complicated by concomitant development of numerous erythematous and tender lesions on bilateral arms and legs ( Fig 1 ) consistent with ENL beginning one year into MDT . For ENL , he was treated with thalidomide 100mg daily during MDT with several failed attempts to discontinue secondary to recurrence of lesions on his upper and lower extremities ( ENL relapse ) . He had subsequently been restarted on thalidomide 100mg daily after three month thalidomide-free period due to ENL relapse one month prior to presenting with right hand numbness/weakness . Formal nerve conduction studies were obtained of the right arm to elucidate the underlying etiology of his weakness as well as potential burden of nerve impairment , which demonstrated right ulnar neuropathy at the elbow involving focal demyelination and severe ( 75% - 85% ) axonal loss . A right elbow MRI was obtained to assess for presence of nerve compression , which demonstrated focal area of ulnar nerve inflammation within the cubital fossa without mass effect . The patient was diagnosed with ulnar neuritis thought secondary to immune-mediated neuritis related to infiltration of M . leprae into the nerve and was treated with prednisone taper consisting of 1mg/kg for two weeks followed by 20mg for three months upon demonstration of clinical stability of right hand numbness . Prednisone was then discontinued after completion of a five-week taper . He remained on thalidomide 100mg daily throughout corticosteroid treatment as well as one year after completion of steroid taper , due to his history of frequent ENL relapses . The patient was followed monthly after completion of prednisone taper for monitoring of neuritis , as well as to manage his thalidomide treatment and monitor for ENL relapse . At three months following steroid treatment , he reported no difficulty in gross motor and ADL tasks but continued to have some residual difficulty with fine instrumented tasks with right hand . At one year after completion of the prednisone taper , the patient’s right hand numbness and weakness had resolved , with the patient demonstrating full strength , sensation , and range-of-motion on neurologic examination .
Peripheral neuritis remains a particularly challenging aspect of care for leprosy patients , as it is often difficult to determine whether the etiology of nerve injury is due to infiltration of mycobacteria into peripheral nerves causing damage the subsequent immune response ( as seen in both type 1 reactions and ENL ) , or both [1] . This patient’s neuritis developed substantially late in his clinical course compared to other documented neuritis cases: in cohort study of 78 patients , Raffe et al . report all cases of neuritis occurred either at time of leprosy diagnosis or within the first 12 months of treatment [4] . Given the late onset of this patient’s presentation as well as his frequent ENL recurrences , we feel this patient’s neuritis is most likely consistent as a complication of ENL . The patient had completed MDT approximately four years prior to presenting with new onset right hand weakness , so it is thought less likely due to damage from initial or persistent mycobacteria invasion into the nerve , as this would likely have presented earlier on . While it is unknown the exact trigger of type 1 reactions , they are seen more frequently in the borderline subtype than pure lepromatous subtype . This patient’s leprosy infectious was demonstrated to be lepromatous subtype on skin biopsy at time of diagnosis ( to be distinguished histologically from tuberculoid and borderline subtypes ) . Although nerve function impairment occurring lepromatous patients has been demonstrated to occur independently of leprosy reactions [6] , these were cases of early nerve function impairment , occurring around time of diagnosis/treatment of primary infection . Just as it remains difficult to elucidate the underlying etiology of peripheral neuritis in leprosy patients , management presents its own set of challenges . The treatment for leprosy reactions relies on thalidomide for ENL as well as high-dose steroids for both subtypes of leprosy reactions . ENL remains one of the few indications for thalidomide and requires frequent outpatient monitoring . Corticosteroids present their own set of challenges , as high doses and durations are often requires to achieve clinical stability/improvement . Prednisolone for five months was found superior to three months of treatment , with dosing of 60mg daily being marginally and non-statistically better than 30mg for this duration [7] . ENL cases may require higher doses/durations of steroids as refractory symptoms have been reported in patients on prednisone 60mg for three months [8] . This patient’s stability at three months post-corticosteroid taper suggests that initial treatment with high-dose prednisone at 1mg/kg for two weeks , then three months of 20mg prednisone followed by taper may offer an alternative treatment strategy . Methotrexate also remains an option for patients who cannot tolerate higher doses of corticosteroids or have refractory symptoms despite high doses . Treatment with prednisone 20mg daily with methotrexate 2 . 5 mg every 12 hours , three times per week demonstrated improvement in neural pain in nine-month period in patients with chronic neuritis secondary to leprosy ( regardless of subtype ) [5] . Given this patient’s prolonged exposure to thalidomide treatment over several years , it also remains possible that his neuritis is secondary to adverse drug effect of thalidomide , as peripheral neuropathy has been described in patients undergoing thalidomide treatment for leprosy [9] as well as multiple myeloma [10] . These patients demonstrated sensory length-dependent neuropathy presenting as painful paraesthesias or numbness , with greater degeneration correlating to total cumulative dose of thalidomide . The potential etiology for our patient’s neuropathy thus ranges from primary infection effect to leprosy reaction ( both type 1 and ENL ) to adverse effect from thalidomide . However , the patient’s improvement in right hand function , with full strength , sensation , and range of motion one year after completion of prednisone taper while remaining on daily thalidomide suggest it is less likely to be an adverse effect of thalidomide therapy . While a trial off of thalidomide treatment to observe its effect on his neuritis could be considered , his frequent ENL relapses while off of thalidomide prohibit discontinuation at this time . This patient’s clinical stability without progression of neuritis after completion of the corticosteroid taper provides an example of late-onset neuritis in leprosy patient , as well evidence for a reasonable treatment approach . Regardless , leprosy reactions , both of which can manifest with neuritis , remain a challenging complication of leprosy treatment that requires diligent monitoring for months to years following initiation of treatment . | Leprosy infection is frequently complicated by peripheral neuritis , but is often difficult to distinguish if it is due to persistent infection , an immune-mediated reaction , or adverse drug reaction . In this case , we present a patient with ulnar neuritis occurring four years after multidrug therapy , with improvement with high-dose corticosteroid therapy while remaining on thalidomide treatment . | [
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"sym... | 2019 | Late-onset ulnar neuritis following treatment of lepromatous leprosy infection |
Anaplasma phagocytophilum is an emerging pathogen that causes human granulocytic anaplasmosis . Infection with this zoonotic pathogen affects cell function in both vertebrate host and the tick vector , Ixodes scapularis . Global tissue-specific response and apoptosis signaling pathways were characterized in I . scapularis nymphs and adult female midguts and salivary glands infected with A . phagocytophilum using a systems biology approach combining transcriptomics and proteomics . Apoptosis was selected for pathway-focused analysis due to its role in bacterial infection of tick cells . The results showed tissue-specific differences in tick response to infection and revealed differentiated regulation of apoptosis pathways . The impact of bacterial infection was more pronounced in tick nymphs and midguts than in salivary glands , probably reflecting bacterial developmental cycle . All apoptosis pathways described in other organisms were identified in I . scapularis , except for the absence of the Perforin ortholog . Functional characterization using RNA interference showed that Porin knockdown significantly increases tick colonization by A . phagocytophilum . Infection with A . phagocytophilum produced complex tissue-specific alterations in transcript and protein levels . In tick nymphs , the results suggested a possible effect of bacterial infection on the inhibition of tick immune response . In tick midguts , the results suggested that A . phagocytophilum infection inhibited cell apoptosis to facilitate and establish infection through up-regulation of the JAK/STAT pathway . Bacterial infection inhibited the intrinsic apoptosis pathway in tick salivary glands by down-regulating Porin expression that resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection . However , tick salivary glands may promote apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway . These dynamic changes in response to A . phagocytophilum in I . scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and will contribute to characterize gene regulation in ticks .
Anaplasma phagocytophilum ( Rickettsiales: Anaplasmataceae ) is an emerging zoonotic pathogen transmitted by Ixodes ticks of which the major vector species are I . scapularis in the US and I . ricinus in Europe [1] . This intracellular bacterium infects tick midguts [2] and salivary glands [3] and vertebrate host granulocytes causing human , canine and equine granulocytic anaplasmosis and tick-borne fever of ruminants [4–8] . Human granulocytic anaplasmosis is the second most common tick-borne disease in the United States and tick-borne fever is an established and economically important disease of sheep in Europe [8 , 9] . The molecular mechanisms used by A . phagocytophilum to infect and multiply within vertebrate hosts including the inhibition of neutrophil apoptosis have been well characterized [5 , 10–14] . Anaplasma infection in the tick vector has been shown to modulate gene expression and tick proteins have been identified that interfere with bacterial acquisition and/or transmission [15] . However , little information is available on the impact of pathogen infection at both transcriptome and proteome levels and the molecular pathways affected by A . phagocytophilum to establish infection in ticks . Recently , Ayllón et al . [16] demonstrated that A . phagocytophilum infection inhibits tick intrinsic apoptosis pathway resulting in increased infection and Severo et al . [6] defined a role for ubiquitination during bacterial colonization of tick cells . However , as shown for other tick-pathogen models [17] , information is not available on the tick tissue-specific responses to A . phagocytophilum infection . These facts underline the importance of defining strategies by which these bacteria establish infection in the tick vector . As recently shown for Drosophila melanogaster , arthropod transcriptomes and proteomes are dynamic , with each developmental stage and organ presenting an ensemble of transcripts and proteins that give rise to substantial diversity in their profile [18] . Characterization of tissue-specific responses and cellular pathways in ticks in response to infection with A . phagocytophilum by use of high-throughput omics technologies is essential for understanding tick-pathogen interactions and to provide targets for development of novel control strategies for both vector infestations and pathogen infection/transmission [15 , 19 , 20] . However , the application of a systems biology approach to the study of non-model organisms such as tick-pathogen interactions poses challenges including the analysis of large datasets in order to extract biologically relevant information and interpret changes in gene expression in relation to simultaneous changes in the proteome [21–23] . The I . scapularis genome is the only tick genome sequenced ( GenBank accession ABJB010000000 ) but limitations in genome assembly and annotation add additional complexity to the characterization of the molecular events at the tick-pathogen interface [23–25] . Thus , the design of experiments combining tick transcriptomics and proteomics require the integration of these different datasets to identify relevant biological processes and molecules . This challenge can be addressed by assessing global transcriptome and proteome changes and studying specific pathways such as immune response and apoptosis that are important for pathogen infection and transmission by ticks . In this study , we characterized global tissue-specific response and apoptosis signaling pathways in I . scapularis infected with A . phagocytophilum . Apoptosis was selected for pathway-focused analysis due to its role in A . phagocytophilum infection of tick cells [16] . Nymphs and female midguts and salivary glands were selected for this analysis using a systems biology approach combining transcriptomics and proteomics data . These tick developmental stages and tissues were selected for this study because nymphs are the main vectors for pathogen transmission to humans and animals while midguts and salivary glands play a major role during pathogen acquisition , multiplication and transmission [15 , 26] . The hypotheses addressed in this study included that tick tissue-specific response to infection reflects pathogen developmental cycle and A . phagocytophilum infection impacts on tick apoptosis pathways in a tissue-specific manner . The results showed that A . phagocytophilum infection results in complex and dramatic tissue-specific changes of the tick transcriptome and proteome and further extended our understanding of the role of selected biological pathways during bacterial infection and multiplication in the tick vector .
A . phagocytophilum , as other obligate intracellular bacteria , evolved to manipulate host cells to establish infection [27] . Pathogen survival requires the alteration of cell native functions to allow infection , multiplication and transmission . The impact of pathogen infection on host cell function is reflected by changes in the transcriptome and proteome , something that was characterized here at tissue-specific level in ticks infected with A . phagocytophilum . Two independent samples were collected and processed for each tick developmental stage and tissue . After RNAseq , 2 . 1–4 . 1 Gbp ( Ave±SD; 2 . 8±0 . 6 ) high quality reads were obtained for nymph , adult female midgut and salivary gland samples in infected and uninfected ticks with 101±2 bp average read length and less than 10% ( 0–8% ) variation between replicates ( S1 Table ) . These reads were aligned to the I . scapularis reference genome using TopHat and resulted in 16083 , 12651 and 11105 gene transcripts in nymph , midgut and salivary gland samples , respectively with 16293 ( 99–231014 ) bp average length and 48±6%GC content ( S2 Table ) . The number of unique gene transcripts mapped among all samples ( 17503 ) represented 85% of the predicted 20486 protein-coding genes in the I . scapularis genome [28] . Of the mapped transcripts , 8516 ( 53% ) , 5394 ( 43% ) and 2487 ( 22% ) were differentially expressed in response to A . phagocytophilum infection in nymph , midgut and salivary gland samples , respectively ( P<0 . 05; Fig 1A and S2 Table ) . Probably due to the fact that whole internal organs were analyzed in nymphs , the number of differentially expressed genes in the nymphs was similar to the total number of differentially expressed genes in adult female midguts and salivary glands together ( Fig 1A ) , suggesting that other tissues did not contribute much to the transcriptome in nymphs . However , differences were observed in the number of up- and down-regulated genes in different samples with a higher number of down-regulated genes in nymphs and midguts while in salivary glands the number of up- and down-regulated genes was similar ( Fig 1A ) . We used P<0 . 05 for differential gene expression analysis , but a high proportion of the differentially expressed genes were significantly different between infected and uninfected samples at P<0 . 001 ( Fig 1A ) , providing additional support for the transcriptomics data . Proteomics analysis resulted in the identification of 7418 unique proteins , representing 36% of the predicted proteins encoded by the I . scapularis genome [28] . The number of proteins identified in nymphs ( 738 ) was lower than the number of proteins identified in midguts ( 4195 ) and salivary glands ( 6324 ) , but the fraction of proteins matching I . scapularis identifications was similar between samples ( 53–66% ) , supporting that sampling did not affect protein assignations . Of the identified proteins , 67 , 330 and 533 were differentially represented in response to A . phagocytophilum infection in nymphs , adult female midguts and salivary glands , respectively ( Fig 1B and S3 Table ) . Despite the difference between the number of mapped transcripts and proteins due to the lower resolution of protein identification when compared to transcriptomics [29] , the coverage of the tick proteome reported here was high for ticks [23] . Similar to the transcriptomics analysis , differences were observed in the number of over- and under-represented proteins in different samples with a similar number of over-represented proteins in midguts and salivary glands but 2 . 7-fold more under-represented proteins in the salivary glands than in the midguts ( Fig 1B ) . At the individual mRNA and protein levels , a moderate ( R2 = 0 . 4 ) correlation was obtained for the entire dataset but for genes and proteins highly up- and down-regulated/represented correlation did not exist ( S1 Fig ) . The discrepancy between mRNA and protein levels could be explained by delay between mRNA and protein accumulation which requires sampling at different time points and/or the role for post-transcriptional and post-translational modifications in tick tissue-specific response to A . phagocytophilum infection . For example , apoptosis is often regulated at the post-transcriptional level [30] . The analysis of the total number of differentially expressed genes and represented proteins identified in tick samples highlighted dramatic tissue-specific differences in tick response to A . phagocytophilum infection . To characterize the complexity of the effect of pathogen infection on tick tissues , gene and protein ontology ( GO ) analyses were conducted to allow for a better characterization of tissue-specific differences in response to infection . The GO analysis revealed that as expected for the incomplete annotation of the I . scapularis genome , many of the genes and proteins were assigned to unknown ( “Others” ) biological process ( BP ) or molecular function ( MF ) ( S2 and S3 Figs ) . Nevertheless , cellular process , metabolic process and regulation were the most represented BP while catalytic activity and binding were the most represented MF in all tissues for both transcripts and proteins ( S2 and S3 Figs ) . However , tissue-specific differences were also found that were more evident at the mRNA than at the protein level ( S2 and S3 Figs ) , thus illustrating the complexity of the tick tissue-specific response to A . phagocytophilum infection . The GO analysis is redundant because the same gene/protein may participate in more than one BP or MF , a problem that can be overcome in part by considering as one category in the analysis highly expressed/represented genes/proteins to reduce the number of entries per category . The analysis of tick genes/proteins whose expression/representation varied in more than 50-fold/5-fold further illustrated the complexity of tissue-specific differences in response to infection ( S4 and S5 Figs ) . The total number of highly differentially expressed/represented genes/proteins suggested that the impact of bacterial infection on tick gene expression was more pronounced in nymphs and adult female midguts than in adult female salivary glands ( Fig 2A ) . The hypothesis is that tick tissue-specific response to infection reflects pathogen developmental cycle . In adult female midguts , bacterial infection had the highest impact on tick gene/protein expression/representation through down-regulation of immune system and/or cellular process and up-regulation of metabolic process , while in salivary glands the bacteria had a lower impact on cellular processes because it does not need to replicate and are ready for transmission to vertebrate hosts by feeding ticks ( Fig 2B ) . However , as a dynamic process , bacterial replication at earlier developmental stages may also affect cellular processes in salivary glands . These results reflected A . phagocytophilum developmental cycle in adult female tick tissues in which the intracellular reticulated , replicative form more abundant in midgut cells converts to the non-dividing infectious dense-core form more abundant in the salivary glands where bacterial transcription and translation are more active than replication [26] . Apoptosis is one of the pathways affected by intracellular bacteria such as A . phagocytophilum to establish infection in vertebrate host cells [31] and preliminary results suggested a role for apoptosis during infection of tick cells [16] . Our hypothesis is that A . phagocytophilum infection impacts on tick apoptosis pathways in a tissue-specific manner . To test this hypothesis , putative apoptosis pathway genes were annotated and then used to characterize tissue-specific differential gene/protein expression/representation in response to bacterial infection in combination with functional analyses . The annotation of the putative apoptosis pathway genes in I . scapularis was based on sequence identity to genes reported in other organisms and used to characterize the tissue-specific response to A . phagocytophilum infection ( Figs 3A-3C and 4A and 4B; S4 Table ) . All apoptosis pathways described in other organisms were identified in I . scapularis ( Fig 4C ) . Each pathway requires specific triggering signals to begin an energy-dependent cascade of molecular events that activate the Caspase-dependent apoptosis execution pathway [32] . At least in humans , the Perforin/Granzyme pathway can only work in a Caspase-independent fashion through Granzyme A ( Fig 4C ) [32] . However , the ortholog for the Perforin gene was not identified in I . scapularis in these studies ( Fig 4A and 4C ) . Apoptosis pathway genes were differentially expressed in I . scapularis nymphs and adult female midguts and salivary glands with little overlapping between the different samples , thus providing additional evidences for the complexity of tissue-specific response to bacterial infection ( Figs . 3A-3C , 4A , 4B and 5A; S4 Table ) . Four , 18 and 22 apoptosis pathway components were identified in both transcriptome and proteome in nymphs , adult female midguts and salivary glands , respectively ( Fig 5B ) , and some of these molecules also showed differences between infected and uninfected samples at the protein level ( S4 Table ) . These results suggested a role for apoptosis pathways during A . phagocytophilum infection of I . scapularis . Some of the intrinsic apoptosis pathway components demonstrated a clear pattern of gene/protein differential expression/representation among the various samples ( Fig 5C ) . In nymphs and adult female midguts , a tendency was observed towards gene up-regulation without an effect on protein representation in response to infection . However , in adult female salivary glands genes were down-regulated in response to infection with Caspase protein under-represented in infected ticks . One of the problems associated with gene/protein annotations based on sequence identity is that function may not be necessarily identical between organisms . Therefore , functional characterization is ultimately needed to support gene/protein annotation and predicted function . In ticks , RNA interference ( RNAi ) is the most widely used technique for functional analysis [33] . The intrinsic apoptosis pathway has been implicated in A . phagocytophilum infection of tick cells [16] and was therefore selected for functional analysis using RNAi ( Fig 5D ) . The results revealed significant gene knockdown after dsRNA-mediated RNAi ( Table 1 ) . Gene knockdown for all selected intrinsic apoptosis pathway genes except Porin resulted in reduced tick weights ( Fig 6A ) . These results differed from previous experiments in which Porin knockdown did result in reduced tick weigh [16] . One likely explanation for this discrepancy is the difference in the percent of gene expression silencing obtained in midguts , the most important organ in tick feeding , between both experiments ( 93% in [16] vs . 73% here ( Table 1 ) ) , thus reinforcing that the role of Porin in tick feeding is marginal . The number of ticks that completed feeding was reduced in ticks injected with Bcl-2 and IAP dsRNAs ( Fig 6B ) and suggested a role for these genes during tick feeding . However , although a tendency was observed towards higher A . phagocytophilum DNA levels in ticks after RNAi for most of the selected intrinsic apoptosis pathway genes ( Fig 6C ) , this effect was statistically significant for Porin only when compared to control dsRNA-injected ticks ( Fig 6D and 6E ) . The results suggested that these genes do not have the same function reported in other organisms or the possible role of these genes on pathogen infection was not as relevant as that of Porin . Ayllón et al . [16] reported that A . phagocytophilum infection of tick cells results in down-regulation of mitochondrial Porin , thus providing a mechanism for subversion of host cell defenses to increase infection . This result was corroborated in these studies in which higher A . phagocytophilum DNA levels after Porin gene knockdown was found in both midguts and salivary glands ( Fig 6D and 6E ) . Interestingly , among selected intrinsic apoptosis pathway genes , Porin was the only one consistently showing higher mRNA levels in unfed than in fed tick developmental stages and tissues ( Fig 7A ) , suggesting an effect of tick feeding on Porin expression that may also contributed to Porin down-regulation in infected adult female salivary glands . Tick feeding and infection with A . phagocytophilum may also affect Cytochrome c expression as part of the effect on the intrinsic apoptosis pathway ( Fig 5C ) . Tick feeding resulted in variable Cytochrome c mRNA levels in different tissues and developmental stages with lower levels in fed larvae , nymphs and adult male midguts but not in female ticks and adult male salivary glands when compared to unfed ticks ( Fig 7B ) . Infection with A . phagocytophilum resulted in up-regulation of Cytochrome c in adult female midguts but down-regulation in the salivary glands ( Fig 7C ) , in agreement with Porin expression in response to infection ( Fig 5C ) . The knockdown of intrinsic apoptosis pathway genes did not affect Cytochrome c mRNA levels in adult female tick midguts , but the effect in salivary glands suggested a complex mechanism by which tick cells respond to changes in the expression of these genes ( Fig 7D ) . Taken together , these results showed a complex effect of tick feeding and A . phagocytophilum infection on Cytochrome c mRNA levels . Although Porin and Cytochrome c expression was down-regulated in infected tick salivary glands , differences in protein levels between uninfected and A . phagocytophilum-infected tick salivary glands were not found ( Fig 5C ) . These results were corroborated by immunocytochemistry ( Fig 8A ) , demonstrating that differences between infected and uninfected tick salivary glands were not at the protein level but in the localization of Cytochrome c ( Fig 8B ) . While Cytochrome c was distributed in the cell cytoplasm of uninfected tick salivary glands , in A . phagocytophilum-infected tick salivary glands Cytochrome c was mainly localized within organelles that probably correspond to mitochondria ( Fig 8C ) . Although the mechanism ( s ) regulating mitochondrial permeability and the release of Cytochrome c during apoptosis are not fully understood , Bcl-2 may acts through the voltage-dependent anion channel or Porin , which in turn may play a role in regulating Cytochrome c release [34] . Taken together , these results demonstrated that A . phagocytophilum infection results in down-regulation of Porin expression in tick salivary gland but not midgut cells , which did not translate in different protein levels but resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection ( Fig 8C ) . The extrinsic apoptosis pathway is composed of Death ligand/receptor interactions such as Fatty acid synthase ( FAS ) ligand ( FasL ) /receptor that activate apoptosis ( Fig 4C ) . A putative FasL-coding gene was not identified in the I . scapularis genome sequence , but the identification of the Fas apoptotic inhibitory molecule and Death receptors suggested that still uncharacterized FasL-like ligands may be present in ticks . Two genes were annotated as coding for Death receptors but except for down-regulation in nymphs for one of them , their expression did not change in response to pathogen infection and were not identified in the tick proteome ( S4 Table ) . FAS is a central enzyme in de novo lipogenesis [35] but the inhibition of FAS causes apoptosis [36–39] . Interestingly , 24 genes were annotated as FAS-coding genes ( Fig 3B ) . In general , most of the putative FAS proteins were not identified in the tick proteome , suggesting low protein levels or problems with the annotation of these genes ( Fig 9A ) . Nevertheless , 6 of the putative FAS genes were corroborated at the protein level ( Fig 9A ) . The analysis was then focused on the changes in FAS mRNA and protein levels in response to A . phagocytophilum infection that revealed different patterns in tick nymphs and adult female midguts and salivary glands ( Fig 9A ) . Thirteen FAS genes were down-regulated in tick nymphs while two FAS genes were up-regulated in adult female midguts in response to infection . In adult female salivary glands , FAS gene expression could not be assessed but A . phagocytophilum infection resulted in 4 under-represented FAS proteins ( Fig 9A ) . Two different mechanisms mediated by the extrinsic [36 , 37] and intrinsic [38 , 39] pathways have been proposed to explain the apoptosis induced by the inhibition of FAS . However , the activation of the intrinsic apoptosis pathway is associated with mitochondrial oxidative stress and respiratory chain impairment , independent of FAS inhibition [39] , thus suggesting that tick salivary glands may be responding to A . phagocytophilum infection by promoting apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway . In this way , activation of the extrinsic apoptosis pathway in infected salivary glands may serve to counteract , at least in part , bacterial inhibition of the intrinsic apoptosis pathway . The activation of the extrinsic apoptosis pathway after FAS inhibition may be mediated by different mechanisms including possible interactions between FAS and FasL [36 , 37 , 40] . Phylogenetic analysis of putative I . scapularis FAS proteins suggested functional redundancy ( Fig 9B ) , thus encouraging the use of FAS inhibitors and not RNAi for the functional characterization of these molecules during A . phagocytophilum infection of tick cells . Despite the increase in the number of apoptotic uninfected tick cells in culture , a 2 to 3 fold increase in the percent of apoptotic cells after 48 h of treatment with 5 , 10 or 20 μg/ml of the FAS inhibitor Cerulenin was observed ( Fig 9C ) . These results suggested that , as in other organisms [41] , Cerulenin had an effect on cultured tick cells by promoting apoptosis through FAS inhibition . After 48 h of A . phagocytophilum infection of tick cells , the percent of apoptotic cells decreased in the presence of 0 , 10 and 20 μg/ml of Cerulenin ( Fig 9D ) , probably reflecting the effect of bacterial infection on the inhibition of the intrinsic apoptosis pathway . However , as expected for the Cerulenin induction of the extrinsic apoptosis pathway , A . phagocytophilum DNA levels decreased after 48 h of treatment as compared with infection levels in the absence of Cerulenin ( Fig 9E ) . These results corroborated the effect of FAS inhibition on reducing A . phagocytophilum infection of tick salivary glands by activating the extrinsic apoptosis pathway in response to bacterial infection . The Janus kinase/signal transducers and activators of transcription ( JAK/STAT ) pathway has been implicated in apoptosis signaling in vertebrate hosts infected with A . phagocytophilum [13] , but has not been previously characterized in infected ticks . The JAK/STAT pathway genes were down-regulated in nymphs , up-regulated in adult female midguts and not affected by bacterial infection in adult female salivary glands ( Fig 10A ) . In vertebrate hosts , A . phagocytophilum infection activates the JAK/STAT pathway to inhibit neutrophil apoptosis while mycobacteria and Brucellae inhibit this pathway to overcome host adaptive immunity [13] . The results in ticks suggested that similar mechanisms might occur during A . phagocytophilum infection by decreasing immunity in nymphs while inhibiting cell apoptosis in midgut cells to facilitate and establish infection . However , none of the JAK/STAT pathway components were identified in the tick proteome ( S4 Table ) , thus precluding from comparing mRNA and protein levels in infected tick samples . To verify the possible role of the tick JAK/STAT pathway during A . phagocytophilum infection , a preliminary experiment was conducted treating infected tick cells with JAK and/or STAT inhibitors ( Fig 10B ) . The results showed that while the STAT inhibitor did not affect bacterial infection , treatment with the JAK inhibitor and the combination of STAT and JAK inhibitors did result in the reduction of A . phagocytophilum DNA levels when compared to control cells incubated with growth medium alone . These results supported a role for the tick JAK/STAT pathway during A . phagocytophilum infection . The validation of RNAseq and proteomics data is important in order to provide additional support for the results obtained in these studies . However , although real-time RT-PCR is easy to perform to validate RNAseq data , few antibodies are available against tick proteins for validation of proteomics data . Herein , 16 genes were selected to validate RNAseq results by real-time RT-PCR ( S6A Fig ) . Analysis of mRNA levels by real-time RT-PCR in individual samples from infected and uninfected tick nymphs , adult female midguts and salivary glands corroborated RNAseq results by demonstrating that gene up- or down-regulation was similar between RNAseq and RT-PCR analyses for most samples ( S6B Fig ) . The minor differences observed between the results of both analyses could be attributed to intrinsic variation in gene expression and the fact that approximately 85% of the ticks used for RNAseq were infected [42] while for RT-PCR all ticks were confirmed uninfected or infected with A . phagocytophilum before analysis . Nevertheless , a positive correlation was obtained for absolute differential expression values between RNAseq and RT-PCR ( S6C Fig ) . For the validation of proteomics data only nymph proteins were available after completion of the studies and two antibodies against intrinsic apoptosis pathway proteins , Porin and Cytochrome c , were used for Western blot analysis ( S6D Fig ) and immunofluorescence ( Fig 8A ) . The results corroborated proteomics results in adult female tick salivary glands and nymphs and showed that although Cytochrome c was not identified by proteomics in nymphs ( Fig 5C ) , Western blot results did not show any difference between infected and uninfected ticks ( S6D Fig ) . The experimental approach used in this study using systems biology showed a dramatic and complex tissue-specific response to A . phagocytophilum in the tick vector , I . scapularis . The results demonstrated that tick tissue-specific response to infection reflected pathogen developmental cycle and the impact of A . phagocytophilum infection on tick apoptosis pathways in a tissue-specific manner . All apoptosis pathways described in other organisms were identified in I . scapularis , except for the absence of the Perforin ortholog in the Perforin/Granzyme pathway , and tissue-specific differences were found in the response to A . phagocytophilum infection . Although an ortholog for FasL was not identified in I . scapularis , other Death ligand/receptor interactions may activate the extrinsic apoptosis pathway . Functional characterization using RNAi demonstrated that Porin silencing significantly increased tick colonization by A . phagocytophilum but did not affect tick feeding , thus illustrating how bacterial inhibition of Porin expression increases tick vector capacity for this pathogen . In tick nymphs , the results suggested a possible effect of bacterial infection on the inhibition of tick immune response but further experiments are required to address this hypothesis . In tick midgut cells , the results suggested that A . phagocytophilum infection inhibited cell apoptosis to facilitate and establish infection through up-regulation of the JAK/STAT pathway genes . Bacterial infection inhibited the intrinsic apoptosis pathway in tick salivary glands but not in midguts by down-regulating Porin expression that resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection . However , tick salivary glands may be responding to A . phagocytophilum by promoting apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway . In summary , the results suggested that A . phagocytophilum uses different mechanisms to establish infection in I . scapularis nymphs and adult female midguts and salivary glands ( Fig 11 ) , supporting the observation that the pathogen uses similar strategies to establish infection in both vertebrate and invertebrate hosts [16] . A . phagocytophilum has a type IV secretion system that translocates effector molecules to host cells to exert their activity on transcription and apoptosis and favor bacterial infection [27 , 31] . These effectors have not been fully characterized but may be responsible for some of the changes shown here to occur in tick transcriptome and proteome in response to bacterial infection . These dynamic changes in response to A . phagocytophilum in I . scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and will contribute to characterize gene regulation in ticks .
Animals were housed and experiments conducted with the approval and supervision of the OSU Institutional Animal Care and Use Committee ( Animal Care and Use Protocol , ACUP No . VM1026 ) . I . scapularis ticks were obtained from the laboratory colony maintained at the Oklahoma State University Tick Rearing Facility . Larvae and nymphs were fed on rabbits and adults were fed on sheep . Off-host ticks were maintained in a 12 hr light: 12 hr dark photoperiod at 22–25°C and 95% relative humidity . Nymphs and adult female I . scapularis were infected with A . phagocytophilum by feeding on a sheep inoculated intravenously with approximately 1x107 A . phagocytophilum ( NY18 isolate ) -infected HL-60 cells ( 90–100% infected cells ) [42] . In this model , over 85% of ticks become infected with A . phagocytophilum in nymphs , midguts and salivary glands [42] . Ticks ( 200 nymphs and 100 female adults ) were removed from the sheep 7 days after infestation , held in the humidity chamber for 4 days and dissected for DNA , RNA and protein extraction from whole internal tissues ( nymphs ) or midguts and salivary glands ( adult females ) . Adult midguts and salivary glands were washed in PBS after collection to remove hemolymphs-related cells . Uninfected ticks were prepared in a similar way but feeding on an uninfected sheep . Two independent samples were collected and processed for each tick developmental stage and tissue . Total RNA , DNA and proteins were extracted from uninfected and A . phagocytophilum-infected nymph , midgut and salivary gland samples using the AllPrep DNA/RNA/Protein Mini Kit ( Qiagen , Valencia , CA , USA ) . Ten individual nymphs and female ticks were dissected and samples collected to characterize A . phagocytophilum infection and the mRNA levels of genes selected after RNA sequencing ( RNAseq ) . Total RNA quality was evaluated using the Agilent 2100 Bioanalyzer RNA Nano Chip ( Agilent Technologies , Santa Clara , CA , USA ) . For RNAseq sample preparation , the TruSeq Stranded mRNA Sample Prep Kit ( Illumina , San Diego , CA , USA ) was used according to the manufacturer's protocol . Briefly , 10 μg of each total RNA sample was used for polyA mRNA selection using streptavidin-coated magnetic beads , followed by thermal mRNA fragmentation . The fragmented mRNA was subjected to cDNA synthesis using the SuperScript II reverse transcriptase ( Life Technologies , Grand Island , NY , USA ) and random primers . The cDNA was further converted into double stranded cDNA and , after an end repair process , was finally ligated to Illumina paired end ( PE ) adaptors . Size selection was performed using a 2% agarose gel , generating cDNA libraries ranging in size from 200–500 bp . Finally , the libraries were enriched using 15 cycles of PCR and purified by the QIAquick PCR purification kit ( Qiagen , Valencia , CA , USA ) . The enriched libraries were diluted with elution buffer ( Qiagen ) to a final concentration of 10 nM . Each library was run at a concentration of 7 pM on one Illumina Hiseq 2000 lane using 100 bp sequencing ( CD BioSciences , Shirley , NY , USA ) . In the case of paired-end reads , distinct adaptors from Illumina were ligated to each end with PCR primers that allowed reading of each end as separate runs . The sequencing reaction was run for 100 cycles ( tagging , imaging , and cleavage of one terminal base at a time ) , and four images of each tile on the chip were taken in different wavelengths for exciting each base-specific fluorophore . For paired-end reads , data were collected as two sets of matched 100-bp reads . Reads for each of the indexed samples were then separated using a custom Perl script . Image analysis and base calling were done using the Illumina GA Pipeline software . TopHat [43] was used to align the reads to the I . scapularis ( assembly JCVI_ISG_i3_1 . 0; http://www . ncbi . nlm . nih . gov/nuccore/NZ_ABJB000000000 ) reference genome . TopHat incorporates the Bowtie algorithm to perform the alignment [44] . TopHat initially removes a portion of reads based on quality information accompanying each read , then maps reads to the reference genome . TopHat allows multiple alignments per read ( up to 40 by default ) and a maximum of 2 mismatches when mapping reads to the reference genome . The mapping results were then used to identify “islands” of expression , which can be interpreted as potential exons . TopHat builds a database of potential splice junctions and confirms these by comparing the previously unmapped reads against the database of putative junctions . Default parameters for TopHat were used . Raw counts per gene were estimated by the Python script HTSeq count [http://www-huber . embl . de/users/anders/HTSeq/] using the reference genome . The raw counts per gene were used by DEGseq [45] to estimate differential expression at P<0 . 05 . Proteins were digested using the filter aided sample preparation ( FASP ) protocol [46] . The FASP method allows processing total SDS lysates of essentially any class of protein from biological samples of any origin , thus solving the long-standing problem of efficient and unbiased solubilization of all cellular proteins irrespective of their subcellular localization and molecular weight . Briefly , samples were dissolved in 50 mM Tris-HCl pH8 . 5 , 4% SDS and 50 mM DTT , boiled for 10 min and centrifuged . Protein concentration in the supernatant was measured by the Direct Detect system ( Millipore , Billerica , MA , USA ) . About 150 μg of protein were diluted in 8 M urea in 0 . 1 M Tris-HCl ( pH 8 . 5 ) ( UA ) , and loaded onto 30 kDa centrifugal filter devices ( FASP Protein Digestion Kit , Expedeon , TN , USA ) . With this method , the sample is solubilized in 4% SDS , then retained and concentrated into microliter volumes in an ultrafiltration device . The filter unit then acts as a ‘proteomic reactor’ for detergent removal , buffer exchange , chemical modification and protein digestion . Notably , during peptide elution , the filter retains high-molecular-weight substances that would otherwise interfere with subsequent peptide separation [46] . The denaturation buffer was replaced by washing three times with UA . Proteins were later alkylated using 50 mM iodoacetamide in UA for 20 min in the dark , and the excess of alkylation reagents were eliminated by washing three times with UA and three additional times with 50 mM ammonium bicarbonate . Proteins were digested overnight at 37°C with modified trypsin ( Promega , Madison , WI , USA ) in 50 mM ammonium bicarbonate at 40:1 protein:trypsin ( w/w ) ratio . The resulting peptides were eluted by centrifugation with 50 mM ammonium bicarbonate ( twice ) and 0 . 5 M sodium chloride . Trifluoroacetic acid ( TFA ) was added to a final concentration of 1% and the peptides were finally desalted onto C18 Oasis-HLB cartridges and dried-down for further analysis . For stable isobaric labeling , the resulting tryptic peptides were dissolved in Triethylammonium bicarbonate ( TEAB ) buffer and labeled using the 4-plex iTRAQ Reagents Multiplex Kit ( Applied Biosystems , Foster City , CA , USA ) according to manufacturer's protocol . Briefly , each peptide solution was independently labeled at room temperature for 1 h with one iTRAQ reagent vial ( mass tag 114 , 115 , 116 or 117 ) previously reconstituted with 70 μl of ethanol . Reaction was stopped after incubation at room temperature for 1 h with diluted TFA , and peptides were combined . Samples were evaporated in a Speed Vac , desalted onto C18 Oasis-HLB cartridges and dried-down for further analysis as previously described . Labeled peptides were loaded into the LC-MS/MS system for on-line desalting onto C18 cartridges and analyzing by LC-MS/MS using a C-18 reversed phase nano-column ( 75 μm I . D . x 50 cm , 3 μm particle size , Acclaim PepMap 100 C18; Thermo Fisher Scientific , Waltham , MA , USA ) in a continuous acetonitrile gradient consisting of 0–30% B in 145 min , 30–43% A in 5 min and 43–90% B in 1 min ( A = 0 . 5% formic acid; B = 90% acetonitrile , 0 . 5% formic acid ) . A flow rate of ca . 300 nl/min was used to elute peptides from the reverse phase nano-column to an emitter nanospray needle for real time ionization and peptide fragmentation on orbital ion trap mass spectrometers ( both Orbitrap Elite and QExactive mass spectrometers , Thermo Fisher Scientific ) . For increasing proteome coverage , iTRAQ-labeled samples were also fractionated by cation exchange chromatography ( Oasis HLB-MCX columns ) into six fractions , which were desalted and analyzed by using the same system and conditions described before . For peptide identification , all spectra were analyzed with Proteome Discoverer ( version 1 . 4 . 0 . 29 , Thermo Fisher Scientific ) using a Uniprot database containing all sequences from Ixodida ( May 17 , 2013 ) . For database searching , parameters were selected as follows: trypsin digestion with 2 maximum missed cleavage sites , precursor and fragment mass tolerances for the Elite of 600 ppm and 1200 mmu , respectively , or 2 Da and 0 . 02 Da , respectively for the QExactive , carbamidomethyl cysteine as fixed modification and methionine oxidation as dynamic modifications . For iTRAQ labeled peptides , N-terminal and Lys iTRAQ modification was added as a fixed modification . Peptide identification was validated using the probability ratio method [47] and false discovery rate ( FDR ) was calculated using inverted databases and the refined method [48] with an additional filtering for precursor mass tolerance of 12 ppm . Only peptides with a confidence of at least 95% were used to quantify the relative abundance of each peptide determined as described previously [49] . Protein quantification from reporter ion intensities and statistical analysis of quantitative data were performed as described previously using QuiXoT [50 , 51] . For iTRAQ data , only the intensity of the reporter ions within 0 . 4 Da windows around the theoretical values was considered for quantification . Reporter intensities were corrected for isotopic contaminants by taking into consideration the information provided by the manufacturer . The intensity of the reporter peaks was used to calculate the fitting weight of each spectrum in the statistical model as described previously [51] . Outliers at the scan and peptide levels and significant protein-abundance changes were detected from the z values ( the standardized variable used by the model that expresses the quantitative values in units of standard deviation ) by using a false discovery rate ( FDR ) threshold of 5% as described previously [51] . Results are the mean of two replicates . The gene and proteins ontology ( GO ) analysis for Biological Process ( BP ) and Molecular Function ( MF ) was done using the STRAP software ( Software for Researching Annotations of Proteins; [http://www . bumc . bu . edu/cardiovascularproteomics/cpctools/strap/] developed at the Cardiovascular Proteomics Center of Boston University School of Medicine ( Boston , MA , USA ) [52] . For annotation of selected pathways , gene identifiers were obtained from VectorBase ( www . vectorbase . org ) and compared to the corresponding pathways in D . melanogaster , Anopheles gambiae , Aedes aegypti and Homo sapiens . Regression analysis of biological processes in infected tick nymphs , adult female midguts and salivary glands was conducted using Excel normalizing against the total number of differentially expressed genes and represented proteins and excluding transcripts and proteins without known assignations . For RNAi , oligonucleotide primers containing T7 promoters ( S5 Table ) were used for in vitro transcription and synthesis of dsRNA as described previously [16] , using the Access RT-PCR system ( Promega ) and the Megascript RNAi kit ( Ambion , Austin , TX , USA ) . The unrelated gene Rs86 dsRNA was synthesized using the same methods described previously and used as negative control [16] . The dsRNA was purified and quantified by spectrophotometry . Unfed adult ticks ( N = 20 females per group ) were injected with approximately 0 . 5 μl dsRNA ( 5x1010-5x1011 molecules/μl ) in the lower right quadrant of the ventral surface of the exoskeleton of ticks [53] . The injections were done using a 10-μl syringe with a 1-inch , 33 gauge needle ( Hamilton , Bonaduz , Switzerland ) . Control ticks were injected with the unrelated Rs86 dsRNA or were left uninjected . After dsRNA injection , female ticks were held in a humidity chamber for 1 day after which they were allowed to feed on sheep inoculated intravenously with A . phagocytophilum ( NY18 isolate ) as described before with 20 male ticks per tick feeding cell [42] . Two sheep , Sheep 11 and Sheep 15 , were used with 11 cells each to feed ticks injected with gene-specific dsRNAs and the Rs86 dsRNA and uninjected controls . Ten female ticks per group were collected after 7 days of feeding and midguts and salivary glands dissected for DNA and RNA extraction using Tri Reagent ( Sigma-Aldrich , St . Louis , MO , USA ) following manufacturer instructions . RNA was used to characterize gene knockdown by real-time RT-PCR with respect to Rs86 control and DNA was used to characterize A . phagocytophilum infection by PCR [16] . Remaining ticks were allowed to feed until full engorgement and tick mortality and weight were determined in individual female ticks collected after feeding . Tick weight was compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by Student's t-test with unequal variance ( P = 0 . 05 ) . The number of ticks completing feeding was compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by one-tailed Fisher's exact test ( P = 0 . 05 ) . A . phagocytophilum DNA levels were characterized by msp4 real-time PCR normalizing against tick 16S rDNA as described previously [16] . Normalized Ct values were compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by Student's t-test with unequal variance ( P = 0 . 05 ) . The expression of selected genes was characterized using total RNA extracted from individual nymphs and/or female midguts and salivary glands . All ticks were confirmed as infected or uninfected by real-time PCR analysis of A . phagocytophilum msp4 DNA in midguts and salivary glands . Real-time RT-PCR was performed on RNA samples using gene-specific oligonucleotide primers ( S5 Table ) and the iScript One-Step RT-PCR Kit with SYBR Green and the CFX96 Touch Real-Time PCR Detection System ( Bio-Rad , Hercules , CA , USA ) . A dissociation curve was run at the end of the reaction to ensure that only one amplicon was formed and that the amplicons denatured consistently in the same temperature range for every sample . The mRNA levels were normalized against tick 16S rRNA and cyclophilin as described previously using the genNorm method ( ddCT method as implemented by Bio-Rad iQ5 Standard Edition , Version 2 . 0 ) [16] . Normalized Ct values were compared between test dsRNA-treated ticks and controls treated with Rs86 dsRNA or between infected and uninfected ticks by Student's t-test with unequal variance ( P = 0 . 05 ) . For analysis of mRNA levels in different tick developmental stages , total RNA was extracted from eggs ( three batches of approximately 500 eggs each ) , fed and unfed larvae ( three pools of 50 larvae each ) , fed and unfed nymphs ( three pools of 15 nymphs each ) , and fed and unfed males and females adults tick tissues ( 4 ticks each ) were used for real-time RT-PCR as described before but normalizing against tick cyclophilin and ribosomal protein S4 [GenBank: DQ066214] using oligonucleotide primers rsp4-F: 5’-GGTGAAGAAGATTGTCAAGCAGAG-3’ and rsp4-R: 5‘-TGAAGCCAGCAGGGTAGTTTG-3’ . Antibodies against Porin [16] and Cytochrome c ( H-104: sc-7159; Santa Cruz Biotechnology , Inc . Dallas , TX , USA ) were used for Western blot and immunofluorescence studies . Total proteins used for proteomics from infected and uninfected nymphs ( 2 μg from each sample ) were methanol/chloroform precipitated , resuspended in Laemmli sample buffer and separated on a 15% SDS-PAGE gel under reducing conditions . After electrophoresis , proteins were transferred to nitrocellulose membranes ( Bio-Rad , Hercules , CA , USA ) , blocked with SuperBlock blocking buffer in TBS ( Thermo Scientific ) and incubated overnight at 4°C with rabbit polyclonal anti-Porin ( dilution 1:1000 ) or anti-Cytochrome c ( dilution 1:200 ) antibodies . To detect the antigen-bound antibody , membranes were incubated with goat anti-rabbit IgG conjugated with horseradish peroxidase ( dilution 1:10 , 000; Sigma-Aldrich ) . Immunoreactive proteins were detected by chemoluminescence using the SuperSignal West Pico chemoluminescent substrate ( Thermo Scientific ) , visualized with an ImageQuant 350 Digital Imaging System ( GE Healthcare , Pittsburgh , PA , USA ) , quantified using the ImageQuant TL 7 . 0 software ( GE Healthcare ) and normalized against total proteins . Normalized protein levels ( N = 2 ) were compared between samples by χ2 test ( p = 0 . 05 ) . Positive controls ( C+ ) corresponded to recombinant I . scapularis Porin expressed in Escherichia coli ( 5 μg ) and human HL60 cells for Porin and Cytochrome c Western blots , respectively . For immunofluorescence , adult ticks were infected with A . phagocytophilum as described before . Female ticks were removed from the sheep 7 days after infestation , held in the humidity chamber for 4 days and fixed with 4% paraformaldehyde in 0 . 2M sodium cacodylate buffer , dehydrated in a graded series of ethanol and embedded in paraffin . Sections ( 4 μm ) were prepared and mounted on glass slides . The paraffin was removed from the sections with xylene and the sections were hydrated by successive 2 min washes with a graded series of 100 , 95 , 80 , 75 and 50% ethanol . The slides were treated with Proteinase K ( Dako , Barcelona , Spain ) for 7 min , washed with PBS and incubated with 3% bovine serum albumin ( BSA; Sigma-Aldrich ) in PBS for 1 h at room temperature . The slides were then incubated for 14 h at 4°C with primary antibodies diluted 1:100 to 1:300 in 3% BSA/PBS and after 3 washes in PBS developed for 1 h with goat-anti-rabbit IgG conjugated with FITC ( Sigma-Aldrich ) ( diluted 1:160 in 3% BSA/PBS ) . The slides were washed twice with TBS and mounted in ProLong Antifade reagent ( Molecular Probes , Eugene , OR , USA ) or in mounting medium containing DAPI ( Vector Laboratories , Peterborough , UK ) . The sections were examined using a Leica SP2 laser scanning confocal microscope ( Leica , Wetzlar , Germany ) . Sections of uninfected ticks and IgGs from preimmune serum were used as controls . The I . scapularis ISE6 tick cell line ( provided by U . G . Munderloh , University of Minnesota , USA ) was cultured in L15B300 medium and inoculated with the human NY18 isolate of A . phagocytophilum propagated in HL-60 cells as described previously [16] . Uninfected cells were cultured in the same way , except with the addition of 1 ml of culture medium instead of infected cells . Uninfected and infected cultures ( three independent cultures with approximately 5x105 cells each ) were seeded in 24 well plates and treated with FAS inhibitor Cerulenin ( Santa Cruz Biotechnology , Heidelberg , Germany ) at 0 , 5 , 10 and 20 μg/ml and sampled at 0 h and 48 h after treatment . Apoptosis was measured by flow cytometry using the Annexin V-fluorescein isothiocyanate ( FITC ) apoptosis detection kit ( Immunostep , Salamanca , Spain ) following manufacturers protocol . It detects changes in phospholipid symmetry analyzed by measuring Annexin V ( labelled with FITC ) binding to phosphatidylserine , which is exposed in the external surface of the cell membrane in apoptotic cells . Cells were stained simultaneously with the non-vital dye propidium iodide ( PI ) allowing the discrimination of intact cells ( Annexin V-FITC negative , PI negative ) , early apoptotic cells ( Annexin V-FITC positive , PI negative ) , late apoptotic/necrotic cells ( Annexin V-FITC positive , PI positive ) and dead cells ( Annexin V-FITC negative , PI positive ) . All samples were analyzed on a FAC-Scalibur flow cytometer equipped with CellQuest Pro software ( BD Biosciences , Madrid , Spain ) . The viable cell population was gated according to forward-scatter and side-scatter parameters . The percentage of apoptotic cells ( including early apoptotic , late apoptotic/necrotic and dead cells ) was determined by FACS after Annexin V-FITC and PI labeling . Total DNA was extracted from 200 μl of a tick cell suspension using the RealPure Spin Kit ( Durviz , Valencia , Spain ) following the manufacturer's recommendations . A . phagocytophilum DNA levels were characterized by msp4 real-time PCR normalizing against tick 16S rDNA as described before [16] . The percent of apoptotic cells and normalized A . phagocytophilum DNA levels were compared between cells analyzed at 0 and 48 h of Cerulenin treatment and/or bacterial infection by Student's t-test with unequal variance ( P = 0 . 05; N = 3 ) . The I . scapularis ISE6 tick cells were cultured and infected with the human NY18 isolate of A . phagocytophilum as described above . Infected cells were treated with 400 nM of the pan JAK inhibitor ( GLPG0634; MedChem Express , New Jersey , USA ) , 9 . 2 μM of the STAT3 inhibitor ( Cryptotanshinone; MedChem Express , New Jersey , USA ) or a combination of both at the same concentration . Control cells were incubated culture medium alone . The inhibitors were added at the same time as the bacteria and then sampled at 48 h to extract total DNA to determine A . phagocytophilum DNA levels as described above . A . phagocytophilum DNA levels were compared between treated and control cells by Student's t-test with unequal variance ( P = 0 . 05; N = 4 ) . FAS amino acid sequences were aligned with MUSCLE ( v3 . 7 ) configured for high precision [54] and the ambiguous regions were removed with Gblocks ( v0 . 91b ) [55] . The phylogenetic tree was reconstructed using the maximum likelihood method implemented in PhyML ( v3 . 0 aLRT ) [56 , 57] . Internal branch confidence was assessed by the bootstrapping method ( 1000 bootstrap replicates ) . Graphical representation and editing of the phylogenetic tree were performed with TreeDyn ( v 198 . 3 ) [58] . | The continuous human exploitation of environmental resources and the increase in human outdoor activities , which have allowed for the contact with arthropod vectors normally present in the field , has promoted the emergence and resurgence of vector-borne pathogens . Among these , Anaplasma phagocytophilum is an emerging bacterial pathogen transmitted to humans and other vertebrate hosts by ticks as they take a blood meal that causes human granulocytic anaplasmosis in the United States , Europe and Asia , with increasing numbers of affected people every year . Tick response to pathogen infection has been only partially characterized . In this study , global tissue-specific response and apoptosis signaling pathways were characterized in tick nymphs and adult female midguts and salivary glands infected with A . phagocytophilum using a systems biology approach combining transcriptomics and proteomics . The results demonstrated dramatic and complex tissue-specific response to A . phagocytophilum in the tick vector Ixodes scapularis , which reflected pathogen developmental cycle and the impact on tick apoptosis pathways . These dynamic changes in response to A . phagocytophilum in I . scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and contributes information on tick-pathogen interactions and for development of novel control strategies for pathogen infection and transmission . | [
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] | [] | 2015 | Systems Biology of Tissue-Specific Response to Anaplasma phagocytophilum Reveals Differentiated Apoptosis in the Tick Vector Ixodes scapularis |
During cell division the genetic material on chromosomes is distributed to daughter cells by a dynamic microtubule structure called the mitotic spindle . Here we establish a reconstitution system to assess the contribution of individual chromosome proteins to mitotic spindle formation around single 10 µm diameter porous glass beads in Xenopus egg extracts . We find that Regulator of Chromosome Condensation 1 ( RCC1 ) , the Guanine Nucleotide Exchange Factor ( GEF ) for the small GTPase Ran , can induce bipolar spindle formation . Remarkably , RCC1 beads oscillate within spindles from pole to pole , a behavior that could be converted to a more typical , stable association by the addition of a kinesin together with RCC1 . These results identify two activities sufficient to mimic chromatin-mediated spindle assembly , and establish a foundation for future experiments to reconstitute spindle assembly entirely from purified components .
The spindle is a highly dynamic structure composed of microtubule polymers and hundreds of other factors including motor proteins and microtubule-associated proteins ( MAPs ) [1] . Its purpose is to attach to chromosomes and accurately segregate them to daughter cells . Once thought to be passive participants , chromosomes are now known to play an active role in spindle assembly , since immobilized mitotic chromatin [2]–[4] , or chromosome fragments containing microtubule attachment sites ( kinetochores ) [5] , have been shown to direct the formation of spindle structures . However , the minimal chromosome components sufficient to generate a spindle have not been defined . One candidate enzyme associated with chromatin that could drive spindle assembly is RCC1 , the guanine nucleotide exchange factor ( GEF ) for the small GTPase Ran . RCC1 generates a steep gradient of RanGTP near chromosomes , activating a subset of mitotic motors and MAPs that are cargoes of the importin β family of nuclear transport receptors [6] . Addition of a hydrolysis-deficient mutant of Ran bound to GTP ( RanQ69L-GTP ) stabilizes microtubules that are organized by motor proteins into asters and small spindle-like structures in metaphase-arrested cytoplasmic extracts prepared from Xenopus laevis eggs [7]–[10] . RanQ69L-GTP disrupts the RCC1-generated RanGTP gradient and spindle assembly [11] , while flattening the gradient eliminates spindle assembly around chromatin beads [12] . These experiments demonstrate that a RanGTP gradient is required for chromatin-dependent spindle assembly in Xenopus egg extracts , but is it sufficient ? We set out to test whether immobilized RCC1 in the absence of other chromatin factors can reconstitute a mitotic spindle .
To test whether a single protein factor , RCC1 , is sufficient to direct spindle formation in egg extracts , we developed a novel substrate consisting of single 10 µm diameter porous NeutrAvidin beads . This approach alleviates the need for small beads to cluster or align by generating a high surface area to which biotinylated molecules can be tightly bound ( Figure 1A ) . RCC1 ( α isoform ) engineered to contain a single amino-terminal biotin was coupled to the beads , which were then incubated in metaphase-arrested egg extracts containing rhodamine-labeled tubulin and observed by fluorescence microscopy . Whereas uncoupled or bovine serum albumin ( BSA ) -coupled NeutrAvidin beads had no activity ( unpublished data ) , microtubule arrays formed around RCC1 beads that could be sorted into five major categories ( Figure 1B ) . Robust bipolar structures made up greater than 30% of the arrays , with a distribution of categories similar to single chromatin-coated beads under the same reaction conditions ( Figure 1C ) . Notably , however , RCC1 bead spindle morphology differed from that of individual chromatin beads , which induced larger spindles that contained more microtubules as determined by microtubule fluorescence intensity ( Figure 1D , E ) . Furthermore , whereas chromatin bead spindle microtubules were most dense in the center , RCC1 bead spindle microtubules had a higher density at the poles ( Figure 1D ) . Thus , immobilization of a single chromatin component , RCC1 , is sufficient to induce bipolar spindle formation in X . laevis egg extracts , but spindle morphology is different , suggesting that the pathway is not completely active , or that other chromatin proteins contribute to microtubule stabilization and organization . To determine whether RCC1 beads fully recapitulate RanGTP-driven mitotic cargo activation , we added the FRET probe Rango-2 , which measures the level of cargo release from importin β [11] , [13] . Rango gradients surrounding RCC1 beads appeared similar to those formed around chromatin beads , and both were further enhanced by addition of wild-type Ran , indicating that the RCC1 beads have similar activity compared to chromatin and that cargo gradients are limited by the amount of Ran that can be loaded with GTP ( Figure 2A ) . Consistent with this interpretation , varying the amount of RCC1 per bead 4-fold had little effect on the distribution of microtubule structures ( unpublished data ) , whereas addition of wild-type Ran up to three times its estimated endogenous concentration of ∼3 µM resulted in a dose-dependent decrease in monopolar and bad bipolar structures , while the percentage of multipolar structures indicative of enhanced microtubule polymerization increased ( Figure S1 ) . Further evidence of equivalent Ran pathway activity by RCC1 beads compared to chromatin was the similar localization of the cargo TPX2 to spindle microtubules , and sensitivity to spindle disruption by a truncation mutant of importin β ( amino acids 71–876 ) that still binds cargo but does not bind RanGTP ( Figure 2B; Figure S2 ) [14] , [15] . Despite their morphological differences , chromatin and RCC1 bead spindle dynamics and organization were similar . Both spindle types displayed poleward microtubule flux at similar rates , indicating kinesin-5 activity ( unpublished data ) [16] , [17] , and spindle morphology was similarly disrupted upon inhibition of the microtubule cross-linking spindle pole protein NuMA ( Figure 2C; Figure S2 ) [18] . Therefore , RCC1 beads appear to fully activate the RanGTP cargo release pathway and generate spindles that possess structural and dynamic features of spindles formed around sperm chromosomes or chromatin-coated beads . One distinctive behavior observed by time-lapse fluorescence microscopy was the tendency of single RCC1 beads to oscillate within the spindle from pole to pole , whereas chromatin beads were stationary ( Figure 3A , B; Video S1 ) . We observed a range in bead oscillatory activity , which sometimes dampened over time . Interestingly , when a monopolar microtubule array formed , the RCC1 bead moved unidirectionally , appearing to be pushed along by a trail of polymerizing microtubules ( Figure 3C; Video S2 ) . This motility was reminiscent of actin polymerization-driven propulsion of the bacterium Listeria monocytogenes or beads coated with its actin nucleation promoting protein ActA , which does not require motor activity [19] . We therefore propose that the oscillatory movement of an RCC1 bead occurs because the bead does not connect to spindle microtubules and is pushed from pole to pole by polymerizing microtubule plus ends . Analogous to a bead uniformly coated with ActA that induces polarized actin assembly , a symmetry-breaking event might initiate RCC1 bead motility [20] , [21] . Because of the antiparallel orientation of microtubules in bipolar structures , the RCC1 bead is driven towards the opposite spindle pole where it encounters a higher density of polarized microtubules polymerizing in the opposite direction . Such a polar ejection force of spindle microtubules has been well documented , although oscillatory chromosome movement also requires kinetochore fibers [22] . We cannot rule out that bead movement is regulated by egg extract factors , but biochemical association of specific extract proteins was not observed ( unpublished data ) . We therefore reasoned that additional chromatin factors normally act to stabilize interaction with spindle microtubules . Plus end-directed chromatin-associated kinesins ( chromokinesins ) contribute to multiple aspects of mitosis , regulating spindle microtubule dynamics and organization , as well as chromosome compaction and segregation [23] , and represent excellent candidate factors for mediating chromatin-spindle interactions . Furthermore , kinesin-coated beads can move directionally along microtubules in a reconstituted system [24] . To determine whether plus end-directed microtubule-based motor activity in the absence of other chromokinesin functions was sufficient to stabilize RCC1 bead spindles , we first coupled conventional kinesin-1 motor domain ( amino acids 1–560 ) together with RCC1 to the beads at a 1∶1 ratio of proteins . Although bipolar spindles initially formed around the hybrid beads , the poles eventually collapsed together and pushed the bead out of the spindle , generating a “push pole” morphology ( Figure 4A , Video S3 ) . These observations suggest that , like around chromatin and RCC1 beads , microtubules are nucleated in random orientations and quickly attain an antiparallel organization due to the activity of kinesin-5 and other motors [4] , [25] . Once microtubule plus ends become oriented toward the bead , however , the strong processive motor activity of kinesin-1 appeared to dominate microtubule organization , clustering plus ends at the surface of the bead . Interestingly , the glass bead often cracked once the poles were pushed together and outward , indicating that the kinesin motor can generate significant force against the bead ( Video S3 , right panel ) . In contrast to kinesin-1 , chromosomal kinesins -4 and -10 have slower motility and are weakly processive [26] . We therefore mutated the motor domain of kinesin-1 , changing residue 203 from arginine to lysine ( R203K ) to reduce its ATPase activity and motility , but preserve microtubule binding [27] . Remarkably , beads coated with a 1∶1 ratio of RCC1 and kinesin-1 ( R203K ) induced bipolar spindle assembly but were stationary like chromatin beads ( Figure 4B , Video S4 ) . Whereas no obvious effect on spindle morphology was observed , the percentage of bipolar spindles formed after 1 h increased by approximately 30% ( Figure 4C ) . These results show that by mediating bead-microtubule interactions and centering within the spindle , a non-motile kinesin strongly enhances the stability of bipolar microtubule arrays induced by RCC1 beads . To investigate whether other microtubule binding activities were sufficient to stabilize RCC1 bead spindles , we substituted the microtubule plus end binding protein EB1 for kinesin-1 ( R203K ) . EB1 associates with growing microtubules and functions to recruit a large set of proteins that regulate microtubule dynamics and interactions with cellular structures including kinetochores [28] . Unlike kinesin-1 ( R203K ) , the presence of EB1 on RCC1 beads did not decrease the percentage of beads oscillating or their movement amplitudes , which appeared indistinguishable compared to RCC1 beads ( Figures 4D , 3B; Video S5 ) . Nevertheless , the percentage of bipolar spindles formed at 1 h increased , similar to the RCC1/kinesin-1 ( R203K ) hybrid beads ( Figure 4C , E ) . EB1 may be mediating bead-spindle microtubule interactions that promote spindle bipolarity , but through a distinct microtubule binding mechanism . Alternatively , EB1 on beads may be acting directly or indirectly to stabilize spindle microtubules , thereby facilitating the activity of kinesin-5 and other motors to sort them into bipolar arrays . These results indicate that bead oscillations per se do not impair spindle bipolarity , and differential effects on the spindle likely reflect the distinct properties of EB1 and kinesin . We hypothesize that molecular motor domains provide the microtubule interaction best suited to stabilize RCC1 bead or chromosome arm position within a spindle . Evaluation of additional microtubule binding proteins or domains , and careful analysis of bead spindle phenotypes and dynamics will be necessary to elucidate underlying mechanisms . In summary , stable bipolar spindle assembly can be induced in the absence of chromosomes , chromatin , or kinetochores by coupling two proteins , RCC1 and kinesin-1 ( R203K ) , to beads . These results demonstrate that the anisotropy of RanGTP distribution in the cytoplasm is sufficient to drive mitotic spindle assembly . However , chromatin spindles are still larger and more robust , indicating that other chromatin-associated factors must also contribute to normal spindle morphology . One possible activity is the Aurora B kinase , which phosphorylates and inactivates microtubule-destabilizing proteins [29] , [30] and makes important contributions to kinetochore-driven spindle assembly [12] . Enrichment of mitotic kinases on beads in Xenopus egg extracts has previously been achieved by adding beads coupled with IgGs specific for Aurora A [31] , or for the chromosome passenger complex protein INCENP [12] , which binds Aurora B . While neither kinase was found to be sufficient for spindle assembly , they clearly play supporting roles . Crucial also may be bona fide chromokinesins such as Xklp1 , which has been shown to recruit the bundling MAP PRC1 to generate the antiparallel microtubule arrays of the central spindle [32] . Our system establishes a foundation to test the roles of other chromatin and spindle factors and forms the basis for spindle reconstitution entirely from defined components .
Slides were prepared in two different ways . Microscope slides and coverslips were placed in metal racks ( Electron Microscopy Sciences #72239-04 and #71420-25 ) rinsed thoroughly with water , then dip rinsed and stored in 10 M sodium hydroxide within a glass container . The container was then bath sonicated for 30 min . The slides and coverslips were kept within the container overnight . The following morning , slides and coverslips were rinsed with water and either ( 1 ) rinsed with ethanol , blown dry , and stored under vacuum or ( 2 ) dip rinsed in 0 . 2 M acetic acid for 1 min . Next they were rinsed with water . Excess water was removed by blowing with clean air , and then the racks were placed in ethanol . 1 . 5% hydroxy ( polyethyleneoxy ) propyltriethoxysilane ( Gelest , #SIH6188 . 0 ) in 93 . 5% ethanol , 5% acetic acid was added to the slides and coverslips in a new glass containers and rocked at room temperature for 2 h . The racks were then removed from the silane solution and dip rinsed in four baths of ethanol then one bath of acetone for 1 min each . After the acetone , the slides and coverslips were blown dry with clean air and baked at 107°C for 30 min . After cooling for 1 h , they were placed in a vacuum desiccator and used for the following 2 d . Approximately 10 µg ( 1 µl ) of beads were added to 29 µl of X . laevis egg extracts and stored on ice with gentle tapping every 10 min . After 30–60 min , 5 µl of the reactions were spotted on a slide under a 22×22 mm coverslip , put in a humidity chamber , and kept in the dark . After 30 to 60 min on the slide , the live reactions were observed by fluorescence microscopy . Beads , microtubules , and DNA were visualized by adding 2 µg/ml Streptavidin Alexa Fluor 488 conjugate ( Invitrogen #S-32354 ) , 50 µg/ml rhodamine-labeled tubulin , and 2 µg/ml Hoechst 33342 dye , respectively , to the extract . Human full-length RCC1 was cloned into pRSETA with a biotin acceptor peptide ( GLNDIFEAQKIEWHE ) and a spacer ( ASTPPTPSPSTPPT ) on the N-terminus [34] . RCC1 and the E . coli biotin ligase ( BirA ) were cotransformed into BL21 DE3 competent cells . Cells were outgrown for 1 . 5 h , added to 1 L of LB [35] with ampicillin and chloramphenicol , and grown overnight at 37°C . The next morning , 150 ml of culture was added to 1 L of fresh LB/amp/chlor ( 6 L total ) and grown at 37°C until OD600 = 0 . 4 . The media was brought to 50 µM biotin and protein expression induced with 0 . 3 mM IPTG overnight at 16°C . The next morning , cells were pelleted , then resuspended in 50 mM Tris pH 8 . 0 , 150 mM NaCl , 4 mM EDTA , 1 mM DTT , 1 mM PMSF , 15 mM MgCl2 , DNAse I , and lysed by French press . Lysate was filtered and run on a 5 ml SP HP column ( GE Biosciences ) with 20 column volumes ( cv ) of washes and then a gradient of 10% to 100% B for 10 cv ( Buffer A: 25 mM NaPO4 pH 7 . 8 , Buffer B: Buffer A+500 mM NaCl ) . RCC1-containing fractions were pooled and diluted down to 100 mM total salt with cold water and filtered . The protein was then loaded onto a 1 ml Q HP column ( GE Biosciences ) and run with the same gradient and buffers as the SP HP column . RCC1 fractions were pooled , brought to 10% glycerol , frozen in 10 µl aliquots ∼6 mg/ml in liquid nitrogen , and stored at −80°C . Human kinesin 1–560 wild-type and kinesin 1–560 ( R203K ) were purified and labeled by the same protocol . Plasmids were transformed into BL21 competent cells and grown on LB plates overnight . The next morning , several colonies were combined and shaken in 20 ml of LB for 10 min . 4 ml of the bacteria was then added to 1 L of 2xYT [35] with 0 . 2% dextrose ( 4 L total ) and grown at 37°C until OD600∼0 . 6 . Protein expression was induced overnight at 24°C with 0 . 1 mM IPTG . The next morning , cells were pelleted , then resuspended in 50 mM NaHPO4 pH 8 . 0 , 250 mM NaCl , 2 mM MgCl2 , 20 mM imidazole , 1 mM ATP , 0 . 2 mM TCEP , 1 mM PMSF , 10 µg/ml LPC , and lysed by French press . Lysate was filtered and run on a 1 ml Nickel HiTrap column ( GE Biosciences ) with washes of 10 cv of buffer ( 50 mM NaPO4 pH 7 . 2 , 250 mM NaCl , 1 mM MgCl2 , 0 . 1 mM ATP , 0 . 2 mM TCEP ) containing 20 mM imidazole followed by 20 cv with 50 mM % imidazole . Protein was eluted with 300 mM imidazole . Protein-containing fractions were pooled and diluted to 100 mM total salt with Mono Q Buffer ( 25 mM PIPES pH 6 . 8 , 2 mM MgCl2 , 1 mM EGTA , 0 . 1 mM ATP , 0 . 2 mM TCEP ) . The protein was then loaded onto a 1 ml Mono Q column ( GE Biosciences ) and eluted with a 0 . 1 to 1 . 0 M NaCl gradient over 20 cv . The highest concentration kinesin fractions were pooled and reacted on ice for 2 h with a 40 molar excess of Biotin PEG EZ link maleimide ( Pierce ) . The reaction was then desalted over 2 HiTrap desalting columns ( GE Biosciences ) into 25 mM Pipes pH 6 . 8 , 400 mM NaCl , 2 mM MgCl2 , 1 mM EGTA , 0 . 1 mM ATP , 0 . 2 mM TCEP , 20% sucrose , aliquoted , frozen in liquid nitrogen , and stored at −80°C . Biotinylated BSA was generated by reacting 1 . 25 ml of Albumin Standard ( Thermo Scientific ) with a 20× molar excess of EZ Link Biotin-PEO12-NHS ( Thermo Scientific ) on ice for 1 h . The excess biotin was removed by desalting the protein over three 5 ml HiTrap Desalting columns into PBS . Fractions were pooled , aliquoted , frozen in liquid nitrogen , and stored at −80°C . A 6xHis-tagged human Ran construct was transformed into BL21 competent cells and grown on LB plates overnight . The next morning , several colonies were combined and shaken in 150 ml of LB for 10 min . 20 ml of the bacteria was then added to 1 L of LB ( 6 L total ) and grown at 37°C until OD600∼0 . 4 . Protein expression was induced by 0 . 3 mM IPTG and grown at 25°C for 4 h . Cells were pelleted and stored at −80°C . Cells were resuspended in PBS , 1 mM MgCl2 , 0 . 1 mM GTP , 1 mM PMSF , and lysed with a French press . Lysate was filtered and run on a 5 ml Nickel HiTrap column ( GE Biosciences ) with washes of 10 cv 2% B and 20 cv 10% B . Protein was eluted with 60% B ( Buffer A: 50 mM NaPO4 pH 7 . 4 , 500 mM NaCl , 1 mM MgCl2 , 0 . 1 mM GTP , Buffer B: Buffer A+500 mM imidazole ) . The highest concentration fractions were pooled and desalted over 5 HiTrap desalting columns ( GE Biosciences ) into XB , 1 mM MgCl2 , 0 . 1 mM GTP . Desalted fractions were aliquoted , frozen in liquid nitrogen , and stored at −80°C . Human full-length 6x-His-tagged EB1 was cloned into the pAN6 vector ( Avidity ) , which contains a biotin acceptor peptide ( GLNDIFEAQKIEWHE ) on the N-terminus [34] . EB1 and the E . coli biotin ligase ( BirA ) were cotransformed into BL21 DE3 competent cells . Cells were outgrown for 1 . 5 h , added to 1 L of LB [35] with ampicillin and chloramphenicol , and grown overnight at 37°C . The next morning , 150 ml of culture was added to 1 L of fresh LB/amp/chlor ( 6 L total ) and grown at 37°C until OD600 = 0 . 4 . The media was brought to 50 µM biotin and protein expression induced with 0 . 3 mM IPTG overnight at 16°C . The next morning , cells were pelleted , then resuspended in PBS with PMSF , and lysed by French press . Lysate was filtered and run on a 5 ml Nickel HiTrap column ( GE Biosciences ) according to the manufacturer's instructions . Briefly , the lysate was loaded onto the column , washed with 20 cv of 100 mM imidazole , and EB1 was eluted with 300 mM imidazole . EB1-containing fractions were pooled and desalted into PBS with 3×5 ml HiTrap deslating columns ( GE Biosciences ) . EB1 fractions were pooled and frozen in 10 µl aliquots in liquid nitrogen , and stored at −80°C . We empirically determined how much of each protein to add to the beads to obtain the desired ratios by combining different amounts of the proteins with streptavidin resin beads to couple them . After thorough washing , the bound proteins were eluted and analyzed by SDS-PAGE and their ratios determined . | The mitotic spindle is a bipolar structure that is responsible for separating the two sets of duplicated chromosomes in a dividing cell , thereby delivering one set to each of the two daughter cells . It is built from dynamic filaments called microtubules , as well as hundreds of other components that contribute to the organization and dynamics of the microtubules and to chromosome movement . To understand which proteins are essential for spindle formation and function , we would like to be able to build it from purified components . As a step towards this goal , we coupled individual proteins to inert glass beads ( as a substitute for chromosomes ) , to determine what combination of proteins can induce spindle assembly in a complex cytoplasm derived from frog eggs . We found that a single enzyme called RCC1 is sufficient to activate a pathway that stabilizes and organizes microtubules into a bipolar structure around the bead , but that this bead then oscillated back and forth between the poles of the spindle . By coupling a microtubule-based motor protein together with RCC1 on the bead , we were able to balance the bead in the center of the spindle . Thus , two proteins immobilized on a bead can substitute for a chromosome and induce stable spindle formation . | [
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] | 2011 | Mitotic Spindle Assembly around RCC1-Coated Beads in Xenopus Egg Extracts |
Trials to reintroduce chloroquine into regions of Africa where P . falciparum has regained susceptibility to chloroquine are underway . However , there are long-standing concerns about whether chloroquine increases lytic-replication of Epstein-Barr virus ( EBV ) , thereby contributing to the development of endemic Burkitt lymphoma . We report that chloroquine indeed drives EBV replication by linking the DNA repair machinery to chromatin remodeling-mediated transcriptional repression . Specifically , chloroquine utilizes ataxia telangiectasia mutated ( ATM ) to phosphorylate the universal transcriptional corepressor Krüppel-associated Box-associated protein 1/tripartite motif-containing protein 28 ( KAP1/TRIM28 ) at serine 824 –a mechanism that typically facilitates repair of double-strand breaks in heterochromatin , to instead activate EBV . Notably , activation of ATM occurs in the absence of detectable DNA damage . These findings i ) clarify chloroquine’s effect on EBV replication , ii ) should energize field investigations into the connection between chloroquine and endemic Burkitt lymphoma and iii ) provide a unique context in which ATM modifies KAP1 to regulate persistence of a herpesvirus in humans .
Two earlier studies reported contradictory findings on the ability of chloroquine to lytically ( re ) activate Epstein-Barr virus ( EBV ) in human B lymphocytes [1 , 2] . This left open the debate on whether chloroquine might contribute to the high rates of endemic Burkitt lymphoma ( eBL ) in malaria holoendemic areas of Africa . eBL is almost uniformly associated with EBV and is thought to arise from germinal center B cells harboring clonal EBV in every cell of the tumor [3] . While we did not set out to address the possibility of a link between chloroquine and EBV lytic replication , our investigations into the property of partial permissiveness of EBV [4 , 5] , a member of the herpesvirus family and a WHO group I carcinogen , reveal that chloroquine activates EBV lytic cycle in eBLs . A key feature of herpesviruses is the ability to restrict the number of latently/quiescently infected cells that respond to lytic triggers by producing infectious virions . This property of partial permissiveness limits virus-mediated pathology while ensuring persistence in the cell [4–6] . In the case of EBV , this property also curbs approaches to effectively activate the virus into the lytic phase to kill cancers bearing EBV . Our efforts to reveal strategies to enhance lytic susceptibility of EBV have focused on identifying regulatory mechanisms of lytic susceptibility that are shared by members of the herpesvirus family . We previously reported that the transcription factor signal transducer and activator of transcription 3 ( STAT3 ) plays a key role in regulating susceptibility of both oncogenic human herpesviruses EBV and Kaposi’s Sarcoma Associated Herpesvirus ( KSHV ) to lytic signals [4 , 5 , 7] . For KSHV , STAT3 functions via the universal transcriptional co-repressor Krüppel-associated Box ( KRAB ) -associated protein ( KAP ) -1 [7]–prompting us to investigate the contribution of KAP1/tripartite motif protein 28 ( TRIM28 ) towards lytic susceptibility of EBV . KAP1’s ability to remodel chromatin is primarily regulated by post-translational modifications . KAP1 harbors an E3 ligase activity for Small Ubiquitin-like Modifier ( SUMO ) protein and is subject to constitutive SUMOylation within KAP1 oligomers . SUMOylation creates binding sites on KAP1 for two histone modifiers ( CHD3 and SETDB1 ) that mediate histone deacetylation and trimethylation at lysine 9 of histone 3 ( H3K9 ) respectively , consequently causing chromatin condensation and transcriptional repression [8 , 9] . Phosphorylation of KAP1 at S824 impairs SUMOylation of KAP1 and antagonizes its ability to condense chromatin . A key component of the DNA damage response triggered by double-strand DNA breaks , particularly in the context of heterochromatin , is phosphorylation of KAP1 at S824 resulting in remodeling , relaxation and repair of damaged DNA [10] . Although generally thought to be mediated via the PI3-kinase-related kinase ataxia telangiectasia mutated ( ATM ) [11–13] , whether ATM phosphorylates KAP1 or functions via an intermediate kinase is not clear . We now report that the cellular strategy of KAP1-mediated chromatin remodeling to repair DNA breaks in heterochromatin is hijacked by a ubiquitous cancer-causing virus to derepress viral chromatin , thereby regulating the balance between virus replication and persistence in the host . We also provide novel evidence for direct in situ interaction between endogenous ATM and KAP1 resulting in phosphorylation of KAP1 in lytic cells , even in the absence of observable DNA damage . Importantly , we demonstrate that the antimalarial agent chloroquine utilizes the above strategy to trigger EBV replication , thus resolving the controversy over whether chloroquine increases EBV replication . Its ability to expel EBV from latently infected B cells also makes chloroquine an attractive candidate to directly kill and elicit immune-mediated killing of tumor cells .
We first tested the effect of manipulating KAP1 on the EBV lytic cycle using HH514-16 eBL cells in which EBV is tightly latent but can be switched into the lytic phase using exogenous triggers [4–6] . Compared to scrambled siRNA-transfected cells , knockdown of KAP1 with two different siRNAs accompanied by exposure to the lytic cycle inducing agent sodium butyrate ( NaB; an HDAC inhibitor ) resulted in elevated levels of the EBV latency-to-lytic switch protein ZEBRA ( BZLF1 product ) , transcripts from viral lytic genes BZLF1 ( immediate early lytic gene ) , BMRF1 ( early lytic gene ) , and BFRF3 ( late lytic gene ) , and EBV load ( Fig 1A–1C and S1 Fig ) . In contrast , compared to empty vector-transfected cells , overexpression of KAP1 suppressed the levels of ZEBRA , transcripts from BZLF1 , BMRF1 , and BFRF3 , and EBV DNA copy number ( Fig 1D–1F ) . As expected , knockdown and overexpression of KAP1 were observed in Fig 1A and 1D , respectively . Thus , cellular KAP1 functions as a regulator of EBV lytic activation . Since KAP1 is able to regulate EBV lytic susceptibility and since the transcriptional repressive function of KAP1 is regulated by post-translational modifications , most commonly phosphorylation at S824 impairing KAP1’s ability to repress target genes [9] , we examined whether KAP1 underwent phosphorylation at S824 upon lytic activation . Exposure of HH514-16 cells to NaB resulted in phosphorylation of KAP1 at S824 , but only in lytic , i . e . ZEBRA+ and EA-D+ cells; refractory cells did not demonstrate p-KAP1 ( Fig 2A–2C ) . Importantly , treatment of BJAB cells , an EBV-negative B lymphoma cell line , with NaB did not induce phosphorylation of KAP1 , whereas exposure to the DNA damaging agent etoposide did ( S2 Fig ) . As an alternative to a chemical lytic trigger , we activated the lytic cycle by inducing expression of ZEBRA in HH514-16-derived CLIX-FZ cells that contain a stably-integrated doxycycline-inducible tagged BZLF1 gene , and detected phosphorylation of KAP1 at S824 , again only in lytic cells ( Fig 2D–2F ) . Notably , spontaneous lytic cells also stained for p-KAP1 while latent cells did not ( Fig 2C and 2F ) . In a parallel set of experiments , we examined the effects of lytic activation in an EBV-transformed lymphoblastoid cell line ( LCL ) . LCLs are generally resistant to commonly used lytic activation triggers but can be induced to support the EBV lytic cycle following introduction of the BZLF1 gene . Consistent with our findings with NaB-treated HH514-16 cells , we found increased phosphorylation of KAP1 at S824 , again primarily in ZEBRA+ and EA-D+ ( lytic ) LCLs following transfection with BZLF1 plasmid; very few refractory cells demonstrated pKAP1 ( Fig 3 ) . To investigate the contribution of the S824 residue towards latency-to-lytic cycle transition , we introduced wild-type versus mutant KAP1 ( S824A [phospho-dead] and S824D [phospho-mimetic] ) in HH514-16 cells . As expected , compared to empty vector-transfected cells , FLAG-KAP1-wt and S824A KAP1 each repressed ZEBRA protein , EBV lytic transcripts and EBV DNA copy number . In contrast , compared to FLAG-KAP1-wt , S824D KAP1 resulted in increased ZEBRA protein , EBV lytic transcripts and EBV DNA copy number ( Fig 4A–4C ) . Thus , lytic trigger-induced phosphorylation of KAP1 at S824 impairs KAP1’s ability to restrict EBV replication . To determine how KAP1 is phosphorylated during EBV lytic activation , we turned to the phosphatidylinositol 3-kinase-related kinase ( PIKK ) ataxia telangiectasia mutated ( ATM ) . A key event triggered by double-strand DNA breaks , particularly in the context of heterochromatin , is ATM-mediated phosphorylation of KAP1 at S824 which facilitates repair of damaged DNA [10–13] . We found that the presence of the ATM inhibitor KU-55933 during lytic activation resulted in impaired p-S824 KAP1 levels without affecting total KAP1 levels; simultaneously , the level of ZEBRA protein and the number of lytic cells were suppressed ( Fig 5A and 5B ) . Importantly , in cells with ZEBRA expression , treatment with KU-55933 completely abolished phosphorylation of KAP1 ( Fig 5B ) , indicating that ATM is required both for EBV lytic gene expression and phosphorylation of KAP1 at S824 . In contrast , the mTOR ( another PIKK ) inhibitor Torin1 impaired lytic susceptibility without affecting p-S824 KAP1 levels in lytic cells ( Fig 5A and 5B ) ; of note , inhibitors were used at the lowest concentrations at which there was discernible reduction in p-S824 KAP1 levels . In a complementary approach , we found that compared to scrambled siRNA-transfected cells , the level of p-S824 KAP1 decreased by 40% in cells transfected with siRNA to ATM ( Fig 5C ) . As expected , si-ATM transfected cells demonstrated lower amounts of ATM . Since lytic activation/ZEBRA expression results in phosphorylation of KAP1 ( Figs 2 and 3 ) and both ZEBRA expression and KAP1 phosphorylation require ATM ( Fig 5A–5C ) , we asked if ATM is also functionally interposed between ZEBRA and KAP1 . We therefore expressed ZEBRA in BL cells and LCLs while simultaneously treating with KU-55933 . We found that ZEBRA-expressing cells demonstrated substantially reduced amounts of p-S824 KAP1 in the presence of KU-55933 compared to solvent-treated cells ( Fig 5D and 5E ) , indicating that functionally , ATM could be placed downstream of ZEBRA but upstream of KAP1; moreover , KAP1 phosphorylation is dependent on ATM . Taken together , these results indicate that lytic activation/ZEBRA expression leads to ATM-mediated phosphorylation of KAP1 at S824 . Following DNA double-strand breaks in heterochromatin DNA , ATM induces phosphorylation of KAP1 [11–13]; however , whether ATM interacts with KAP1 or functions via intermediary kinases remains unclear . We found that compared to latently infected cells ( NaB-untreated ) , an antibody to ATM was able to co-precipitate KAP1 in cells exposed to the lytic trigger NaB; similarly , an antibody to KAP1 co-precipitated ATM in the same cells ( Fig 6A ) . We addressed whether endogenous ATM and KAP1 interact in situ in NaB-treated BL cells using proximity ligation assay ( PLA ) . Fig 6B shows that ATM interacted with KAP1 only in lytic cells , as seen by small spots of fluorescence; ATM-KAP1 interaction was not observed in refractory cells . PLA signal is typically absent if binding partners are more than 40 nanometer apart [14 , 15] , placing ATM and KAP1 in very close proximity . Thus upon activation , ATM directly interacts with KAP1 , resulting in p-S824 KAP1 and ultimately latency-to-lytic transition . Chloroquine , a well-characterized antimalarial agent , has been reported to activate ATM [16] . We asked if chloroquine could cause phosphorylation of KAP1 and activate EBV replication . Indeed in BL cells , chloroquine caused phosphorylation of KAP1 at S824 and lytic activation; moreover , pKAP1 was observed strictly in ZEBRA+/lytic cells . Importantly , phosphorylation of KAP1 and lytic activation in response to chloroquine were impaired in the presence of the ATM inhibitor KU-55933 ( Fig 7A ) . Chloroquine increased phosphorylation of KAP1 and expression of ZEBRA over time ( Fig 7B ) . Furthermore , expression of ZEBRA in response to chloroquine depended on phosphorylation of KAP1 at S824 as demonstrated by a drop in the ZEBRA level following overexpression of the phospho-dead mutant ( S824A ) of KAP1 compared to wt KAP1 ( Fig 7C ) . Treatment with chloroquine increased levels of EBV lytic transcripts as well as release of encapsidated EBV particles ( Fig 7D and 7E ) ; the latter indicates that chloroquine-activated EBV lytic cycle reaches completion and virus particles are released from lytic cells . While NaB clearly was more robust than chloroquine in its ability to increase the extracellular viral load , chloroquine caused a substantial increase in the number of released viral particles even at a concentration of 10μM . We also tested the effect of chloroquine on two other eBL cell lines , Jijoye and Raji , and found that chloroquine induced the expression of ZEBRA in both cell lines , although with different temporal patterns ( Fig 7F ) . Thus , chloroquine activates EBV replication in BL cell lines through ATM and KAP1 . Similar to BL cell lines , exposure of LCLs to chloroquine resulted in phosphorylation of KAP1 at S824 and ZEBRA expression ( Fig 8A ) . Interestingly , pKAP1 was observed earlier in LCLs compared to HH514-16 cells treated with chloroquine ( Fig 8A versus Fig 7B ) . Also consistent with our observation in chloroquine-treated HH514-16 cells , the EBV lytic cycle reached completion in LCLs , again releasing substantial numbers of viral particles into the supernatant , even at the 10μM concentration of chloroquine ( Fig 8B ) . At 10μM concentration , a serum level that results from medically relevant doses of chloroquine used for malaria treatment , HH514-16 cells released about 10-fold more virus particles compared to LCLs ( Fig 7E versus Fig 8B ) . Thus , chloroquine via ATM induces phosphorylation of KAP1 at S824 , thereby triggering the complete EBV lytic cycle in BL cells and LCLs . DNA damage , primarily double-strand breaks , activate ATM [17] . In examining whether ATM activation during EBV lytic cycle was associated with DNA double-strand breaks , we undertook staining for γH2AX , a substrate of ATM and a marker of DNA damage . We observed increased amounts of γH2AX only in lytic cells; however , instead of discrete foci indicative of areas of damaged DNA , the staining pattern appeared diffuse . In contrast , cells exposed to etoposide , a double-strand break inducing agent , showed γH2AX foci as expected , but no lytic activation ( Fig 9A and 9C ) . Importantly , the presence of KU-55933 during lytic activation resulted in loss of both lytic cells and γH2AX staining . Consistent with ATM activation , treatment with chloroquine also resulted in increased level of γH2AX in lytic cells that was inhibited in the presence of KU-55933 . Again , nuclear γH2AX staining was diffuse , consistent with lack of DNA damage ( Fig 9B and 9C ) . Notably , chloroquine treatment resulted in approximately 14–15% lytic cells ( Figs 7A and 9B ) . Thus , in response to lytic trigger or chloroquine , ATM is activated despite a lack of observable DNA damage; ATM then interacts with KAP1 to cause phosphorylation at S824 resulting in derepression of lytic genes . Taken together , our findings support a model in which KAP1 , un-phosphorylated at S824 , represses lytic genes in latently infected cells ( Fig 10-i ) . Lytic signals or ZEBRA cause ATM to phosphorylate KAP1 at S824; this modification renders KAP1 unable to repress EBV lytic genes , thereby resulting in expression of lytic genes of all kinetic classes ( Fig 10-ii ) . Chloroquine , by activating ATM , feeds into this pathway to trigger EBV lytic cycle and evicts the virus .
Our work demonstrates that EBV has appropriated components of the DNA repair machinery typically operational at heterochromatin DNA to regulate the balance between lytic ( re ) activation and persistence . While ATM has previously been shown to be required for efficient EBV lytic activation [18–20] , the precise mechanism and the substrate for its kinase activity were not known . We also demonstrate that endogenous ATM and KAP1 interact in situ resulting in phosphorylation of KAP1 . Notably , the signal that activates ATM during EBV lytic cycle does not require DNA double-strand breaks . Furthermore , DNA damage-mediated ATM activation does not necessarily induce EBV replication; either additional triggers are needed to activate transcription of viral genes or damage to viral DNA cripples lytic replication . In the setting of EBV lytic replication , we postulate that ATM may be activated by: i ) The EBV protein kinase , known to be expressed downstream of ZEBRA; lack of DNA damage foci is consistent with this option . ii ) Distortion of DNA caused by binding of ZEBRA , both a replication protein and a transactivator [21] , may be detected as DNA “lesions” leading to ATM activation; loss of ATM activation following transfection of DNA binding domain mutants of ZEBRA [20] supports options i and ii . iii ) Low levels of DNA damage that may escape detection by conventional means or specific types of DNA lesions may activate ATM; activation of EBV lytic cycle by DNA crosslinking agents supports the latter possibility [18] . Modification of the S824 residue of KAP1 represents a shared mechanism for regulating persistence versus lytic cycle activation in at least three members of the herpesvirus family; yet the responsible kinases are distinct: viral protein kinase in KSHV [22] , mTOR in HCMV [23] and ATM in EBV . KAP1 also participates during initial infection of KSHV , establishment of HCMV latency , and maintenance of EBV and KSHV latency [23–26] . How KAP1 is recruited to viral DNA remains an open question for both HCMV and EBV although in KSHV-infected cells , cellular Nrf2 and the viral protein LANA recruit KAP1 to viral DNA [26 , 27] . Since KAP1 is not a bonafide DNA binding protein , we suspect that it is recruited to specific sites on the viral genome via other proteins , with Krüppel associated box ( KRAB ) -zinc finger proteins being the most likely candidates [9] . Two recent studies have reported on the effect of chloroquine on lytic ( re ) activation of herpesviruses . The first study , consistent with our observation , found that the lytic cycle of HCMV was activated , albeit after 3 doses of chloroquine [23] . The second , found that chloroquine hindered virion production of KSHV and EBV in response to lytic cycle inducing agents [28] . Although in apparent contradiction to our findings , the latter study did not examine the effect of chloroquine alone beyond 6 hours; further , this study focused on the effect of chloroquine in the setting of known lytic cycle inducing agents . A multitude of studies have causally linked malaria to eBL . Malaria causes polyclonal activation of B cells resulting in their proliferation and differentiation [29] . Proliferation increases the number of B cells latently infected with EBV while differentiation results in antibody-producing plasma cells . Differentiation into plasma cells triggers lytic activation of EBV [30] . While such EBV lytic activity would normally be curtailed by T cells , malaria also suppresses EBV-directed T cell responses [31–33]; as a result , EBV lytic replication goes unchecked , ultimately resulting in an expansion of the latently infected B cell pool . The link between EBV lytic cycle and eBL development is further strengthened by the following observations: i ) increases in serum IgG antibodies to EBV lytic proteins preceded the development of eBL [34] and ii ) the sap of the milk bush found more often at the homes of eBL patients in Malawi and known to contain the parent compound of the EBV lytic cycle activating agent phorbol myristic acid , induced c-Myc translocations characteristic of BL in cord blood B cells simultaneously exposed to EBV [35 , 36] . Our discovery that chloroquine ( re ) activates EBV supports the idea that chloroquine may contribute to the development of eBL . A study in Tanzania from 1977 to 1982 reported a decrease in the incidence of eBL during the early years of chloroquine prophylaxis for malaria , followed by its resurgence after completion of the study [37] . This seeming disagreement with our findings , while difficult to resolve in the absence of virologic and immunologic data from the said study , may be fallacious because the decline in eBL incidence had started several years before chloroquine distribution among study subjects began [37] . Further , we postulate that the observed drop in eBL incidence was due to decreased malaria burden resulting in decline in pathologic B cell activation with associated improvement in EBV-directed T cell function . These effects would promote more physiologic numbers of latently infected cells and destroy B cells undergoing EBV lytic cycle activation . In contrast , the rebound in eBL incidence soon after the study could be attributed to the combined effects of increased malaria burden and associated impairment of T cell responses to EBV resulting from the observed emergence of chloroquine resistance during the study together with chloroquine-mediated EBV lytic activation . In a more recent study , chloroquine prevented the development of Myc-induced lymphomagenesis in a mouse model by inhibiting autophagy [38]; however , this experimental system is independent of EBV-infection and therefore models sporadic BL but not eBL . Our findings resolve a long-standing dispute by confirming the contribution of chloroquine to the EBV lytic cycle , and provide the mechanism . This knowledge is timely especially in light of ongoing efforts to re-introduce chloroquine into parts of Africa where P . falciparum has regained susceptibility to chloroquine . It is also notable that although chloroquine is no longer prescribed for P . falciparum malaria in most of Africa , over-the-counter use of chloroquine has continued over the last few decades because of its broad anti-pyretic and anti-inflammatory properties [39] . Whether chloroquine-mediated EBV lytic activation contributes to the development of eBL , particularly in the setting of malaria , warrants further investigation . On the other hand , in patients already diagnosed with eBL , chloroquine may be explored in an oncolytic approach to cause lytic EBV-mediated cell death and provoke an anti-tumor immune response . This approach may serve as an alternative to chemotherapy with multiple genotoxic agents , rarely feasible in the low resource settings of equatorial Africa .
Endemic Burkitt lymphoma cell lines HH514-16 ( a gift from Dr . George Miller , Yale University ) , Jijoye and Raji ( gifts from Dr . Janet Hearing , Stony Brook University ) were maintained in RPMI 1640 supplemented with 10% fetal bovine serum ( Gibco ) and 1% penicillin-streptomycin ( Gibco ) . CLIX-FZ ( Clone-HH514-16 transfected with pLIX_402-FZ ) cells were generated from puromycin ( P8833 , Sigma-Aldrich ) selected HH514-16 cells transfected with pLIX_402-FZ . Lymphoblastoid cell line ( LCL ) used was generated and maintained as described before [40] . Sodium butyrate ( NaB; 3mM; 303410 , Sigma-Aldrich ) , doxycycline ( 5μg/ml; D9891 , Sigma-Aldrich ) , KU-55933 ( 1μM; SML1109 , Sigma-Aldrich ) , Torin1 ( 0 . 5μM; S2827 , Selleckchem ) and chloroquine ( 200μM; C6628 , Sigma-Aldrich ) were used to treat Burkitt lymphoma cells except where specific concentrations are indicated . KU-55933 ( 1μM ) and chloroquine ( 100μM ) were used to treat lymphoblastoid cells except where specific concentrations are indicated . Plasmid pFLAG-CMV2-KAP1 was a gift from Professor Kum Kum Khanna [41] . Plasmids FLAG-KAP1-S824A and FLAG-KAP1-S824D were generated by replacing the corresponding fragment in pFLAG-CMV2-KAP1 with a product generated from a two-step PCR using pFLAG-CMV2-KAP1 as template and the following primers: forward primer–TCAGGGCTGGAGGTGGTGGCTCCTGAGGGTACC; reverse primers–ACCAGGGCCACCAGACAGCTCCTGGGCACTCAGGCCAGCACCAGGCAGGCT and CTTTAATAAGATCTGGATCTTCAGGGGCCATCACCAGGGCCACCAGACAGCTCCTG were used for construction of pFLAG-KAP1-S824A; forward primer–TCAGGGCTGGAGGTGGTGGCTCCTGAGGGTACC and reverse primers–ACCAGGGCCACCAGACAGCTCCTGGTCACTCAGGCCAGCACCAGGCAGGCT and CTTTAATAAGATCTGGATCTTCAGGGGCCATCACCAGGGCCACCAGACAGCTCCTG were used for construction of pFLAG-KAP1-S824D . Plasmid pHD1013-Z was a gift from Dr . Ayman El-Guindy and described before [42] . Plasmid pLIX_402-FZ ( FLAG-BZLF1 ) was constructed by inserting the BZLF1 coding sequence amplified from a cDNA library of HH514-16 cells with forward primer ATACATCTAGAGCCACCATGGATTACAAGGATGACGACGATAAGATGATGGACCCAAACTCGAC and reverse primer ATACAACCGGTGAAATTTAAGAGATCCTCGTG into pLIX_402 ( a gift from David Root [Addgene plasmid # 41394] ) at NheI and AgeI sites . BACmid p2089 was a gift from Professor Henri-Jacques Delecluse [43] . siRNAs targeting human KAP1 and ATM transcripts and scrambled/non-targeting siRNA were purchased from Santa Cruz Biotechnology ( sc-38550 , sc-29761 and sc-37007 ) and Dharmacon ( D-001206-13-05 and M-005046-01 ) and reconstituted with nuclease free water . HH5514-16 cells and lymphoblastoid cells ( 1 × 106 ) were transfected with 20 μg of plasmid or 200 picomoles of siRNA in Ingenio solution ( MIR50117 , Mirus ) using an Amaxa Nucleofector II ( program A-024 ) except where specific amounts of nucleic acids are indicated . Antibodies used include rabbit anti-KAP1 Ab ( A300-274A , Bethyl Laboratories ) , rabbit anti-ATM Ab ( A300-299A , Bethyl Laboratories ) , mouse anti-β-actin Ab ( AC-15 , Sigma ) , rabbit anti-p-S824-KAP1 Ab ( A300-767A , Bethyl Laboratories ) , mouse anti-EA-D Ab ( MAB8186 , EMD ) , mouse anti-FLAG Ab ( F3165 , Sigma ) , goat anti-ATM Ab ( ( A300-136A , Bethyl Laboratories ) , rabbit anti-p-S139-H2AX ( γH2AX ) Ab ( 9718P , Cell Signaling Technology ) , normal rabbit IgG ( sc-2027 , Santa Cruz ) , normal goat IgG ( sc-3887 , Santa Cruz ) and mouse anti-ZEBRA Ab ( a gift from Professor Paul Farrell ) , HRP conjugated goat anti-mouse IgG ( H+L ) ( AP308P , EMD Millipore ) , HRP conjugated goat anti-rabbit IgG ( H+L ) ( AP307P , EMD Millipore ) , Fluorescein isothiocyanate ( FITC ) conjugated donkey anti-rabbit IgG ( 31568 , Thermo Fisher ) , Phycoerythrin ( PE ) conjugated goat anti-mouse IgG ( sc-3738 , Santa Cruz ) , FITC conjugated goat anti-mouse IgG ( F0257 , Sigma ) , and Alexa Fluor 647 conjugated goat anti-rabbit IgG ( A-21245 , Thermo Fisher ) . Sodium butyrate ( NaB ) -treated or untreated HH514-16 cells in Fig 6A were harvested at 24 hours post treatment with RIPA buffer [50 mM Tris-HCl ( pH7 . 4 ) , 150 mM NaCl , 1% ( v/v ) NP40 , 1% ( w/v ) deoxycholate , 1 mM EDTA , 1X protease and phosphatase inhibitor cocktail ( catalog no . 5872 , Cell Signaling Technology ) ]; 10% of cell lysates was kept as input and the remaining was incubated with indicated antibodies and protein A/G agarose ( sc-2003 , Santa Cruz Biotechnology ) for 16 hours at 4°C . Immunoprecipitates were washed with RIPA buffer and subjected to immunoblotting for analysis . Immunoblotting experiments with indicated antibodies were performed as previously described [44] . Cells were fixed with BD Cytofix/Cytoperm solution ( 554722 , BD Bioscience ) at room temperature for 15 mins , washed with 1X BD Perm/Wash buffer ( 554723 , BD Bioscience ) and incubated with indicated primary antibodies for 1 hour at room temperature . After washing , cells were further incubated with corresponding secondary antibodies for another hour at room temperature and then subjected to flow cytometry or mounted using Prolong Gold Antifade with DAPI ( 4′ , 6-diamidino-2-phenylindole ) ( P36935 , Thermo Fisher Scientific ) for microscopy . Analysis gates for flow cytometry were determined based on parallel staining with isotype-matched control antibodies . Proximity ligation assay between ATM and KAP1 protein in sodium butyrate-treated HH514-16 cells was conducted according to manufacturer’s instructions with goat anti-ATM and rabbit anti-KAP1 antibodies ( A300-136A and A300-274A , Bethyl Laboratories ) , Duolink In Situ PLA Probe Anti-Goat MINUS plus Anti-Rabbit PLUS ( DUO92006 and DUO92002 , Sigma-Aldrich ) , Duolink In Situ Detection Reagents Green ( DUO92014 , Sigma-Aldrich ) and washing buffers ( DUO82049 , Sigma-Aldrich ) . qRT-PCRs were performed as previously described [5] and analyzed using the ΔΔCT method [45]; primers sequences included: forward primer–GTAACCCGTTGAACCCCATT and reverse primer–CCATCCAATCGGTAGTAGCG for 18S rRNA; forward primer–TTCCACAGCCTGCACCAGTG and reverse primer–GGCAGAAGCCACCTCACGGT for BZLF1; forward primer–ACCTGCCGTTGGATCTTAGTG and reverse primer–GGCGTTGTTGGAGTCCTGTG for BMRF1; forward primer , –AACCAGAATAATCTCCCCAATG and reverse primer–CGAGGCACCCCAAAAGTC for BFRF3 . Cell-associated EBV DNA was extracted as previously described [7] and viral DNA was quantitated using quantitative-PCR ( q-PCR ) by amplifying EBV BALF5 gene with forward primer–CGTCTCATTCCCAAGTGTTTC and reverse primer–GCCCTTTCCATCCTCGTC . In Figs 7 and 8 , released EBV particles were pelleted from supernatant , washed with 1X PBS and treated with DNase . Absolute EBV genome copy number was determined with a standard curve obtained through q-PCR using serially diluted BACmid p2089 as template and primers targeting the EBV BALF5 gene . P values were calculated by comparing the means of two groups of interest using unpaired Student t test . | Viruses that persist for the life of the host , like the herpesvirus Epstein-Barr virus ( EBV ) , tightly regulate lytic replication to reduce killing of host cells and ensure virus survival . We show that repression of EBV replication is disrupted by the antimalarial drug chloroquine which modifies an otherwise normal cellular mechanism that repairs DNA , to influence gene expression through a process known as chromatin remodeling . This finding a ) reveals a new connection between the DNA repair machinery and gene regulation and b ) resolves a long-standing dispute over whether chloroquine increases EBV replication , thereby contributing to endemic Burkitt lymphoma , a cancer almost uniformly associated with EBV . There are ongoing efforts to re-introduce chloroquine into parts of Africa where falciparum malaria has regained susceptibility to chloroquine . | [
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"immuno... | 2017 | Chloroquine triggers Epstein-Barr virus replication through phosphorylation of KAP1/TRIM28 in Burkitt lymphoma cells |
Following pathogen infection the hosts' nervous and immune systems react with coordinated responses to the danger . A key question is how the neuronal and immune responses to pathogens are coordinated , are there common signaling pathways used by both responses ? Using C . elegans we show that infection by pathogenic strains of M . nematophilum , but not exposure to avirulent strains , triggers behavioral and immune responses both of which require a conserved Gαq-RhoGEF Trio-Rho signaling pathway . Upon infection signaling by the Gαq pathway within cholinergic motorneurons is necessary and sufficient to increase release of the neurotransmitter acetylcholine and increase locomotion rates and these behavioral changes result in C . elegans leaving lawns of M . nematophilum . In the immune response to infection signaling by the Gαq pathway within rectal epithelial cells is necessary and sufficient to cause changes in cell morphology resulting in tail swelling that limits the infection . These Gαq mediated behavioral and immune responses to infection are separate , act in a cell autonomous fashion and activation of this pathway in the appropriate cells can trigger these responses in the absence of infection . Within the rectal epithelium the Gαq signaling pathway cooperates with a Ras signaling pathway to activate a Raf-ERK-MAPK pathway to trigger the cell morphology changes , whereas in motorneurons Gαq signaling triggers behavioral responses independent of Ras signaling . Thus , a conserved Gαq pathway cooperates with cell specific factors in the nervous and immune systems to produce appropriate responses to pathogen . Thus , our data suggests that ligands for Gq coupled receptors are likely to be part of the signals generated in response to M . nematophilum infection .
Animals have evolved multiple strategies for coping with the presence of pathogenic microbes . The best characterized is the immune response where animals activate their physical and cellular defenses to respond to invading microorganisms . The innate immune response is the first line of this defense , acting to recognize and eliminate pathogens [1] , [2] , [3] . Unlike adaptive immunity; which is only found in vertebrates , innate immunity is highly conserved throughout evolution with plants , invertebrates and vertebrates sharing surprisingly similar responses including expression of antimicrobial peptides and activation of phagocytosis . As a consequence of this , invertebrate model systems , including Drosophila and Caenorhabditis elegans , have provided important insights into the molecular mechanisms that underlie infection responses [4] , [5] , [6] , [7] C . elegans is able mount innate immune responses to both naturally occurring ( Nematocida parisii , Drechmeria coniospora and Microbacterium nematophilum ) and clinically important ( Pseudomonas aeruginosa and Staphylococcus aureus ) bacterial and fungal pathogens when they are provided as a food source [7] , [8] , [9] , [10] , [11] , [12] , [13] . Because it lacks professional immune cells and phagocytes C . elegans relies on epithelial innate immunity to mount a response that includes transcription of many host defense genes [14] including numerous anti-microbial peptides [15] . It is becoming increasingly clear that this type of epithelial immunity also plays an important role in the immune response of the mammalian intestine [16] . Changes in neuronal signaling also occur upon infection and neuronal signaling can modulate the innate immune response [17] . In addition , behavioral changes can also be triggered by exposure to pathogen . For example , avoidance of pathogens is likely to be an important part of the response to microbes in many animals and perhaps even humans [18] . Studies of pathogen avoidance have utilized C . elegans , which has evolved rapid avoidance behaviors allowing it to alter its locomotion in response to aversive cues in its environment [19] , [20] , [21] , [22] . Aversive cues such as serrawettin , a secreted surfactant produced by Serratia marcescens , are directly sensed by chemosensory neurons located in the animal's head [19] . The receptors for these pathogen-associated cues are unknown , however , the G-protein ODR-3 and the TAX-2/4 cGMP gated channel are required to mediate avoidance to S . marcescens [19] and TAX-2/4 is also required for animals to avoid M . nematophilum and P . aeruginosa [23] implicating G-protein coupled receptors ( GPCRs ) in at least some of these responses . A conserved MAP Kinase pathway including p38 MAPK has also been shown to regulate both the innate immune response and aversive behavior to Pseudomonas aeruginosa [24] and neuronal TGF-ß signaling is important for the induction of antimicrobial peptides upon infection by D . coniospora [25] . Some of these behavioral responses are likely to involve detection of pathogen by chemosensory neurons , for example , serotonin release from ADF chemosensory neurons is required for learnt aversive responses to pathogenic bacteria [22] , dopamine release from sensory neurons is required for behavioral responses to enteropathic E . coli [26] , and a p38 MAPK pathway is required in chemosensory neurons to mediate changes in egg laying in response to P . aeruginosa [24] . Behavioral changes such as aversion require changes in locomotion . Within C . elegans cholinergic motor neurons Gαq ( EGL-30 ) , Gα12 ( GPA-12 ) and Gαo ( GOA-1 ) comprise a G-protein coupled regulatory network that controls the release of acetylcholine ( ACh ) at the neuromuscular junction [27] by regulating diacylglycerol ( DAG ) levels at the synapse [28] . EGL-30 ( Gαq ) is central to this regulatory network and mediates DAG production through regulation of EGL-8 ( PLCß ) [29] . DAG produced by EGL-8 ( PLCß ) is also required for activation of the PKC homolog TPA-1 in the response to infection by the fungus D . coniospora [30] . However , this role for DAG in response to infection does not involve neurons . More recently Gαq ( EGL-30 ) has also been shown to regulate DAG destruction by directly activating the Trio ortholog UNC-73 ( RhoGEF ) resulting in activation of the small GTPase RHO-1 ( the single C . elegans Rho ortholog ) , which negatively regulates the diacylglycerol kinase DGK-1 [31] , [32] . Reduction-of-function mutations in EGL-30 ( Gαq ) are lethargic and gain-of-function mutants have hyperactive locomotion [33] . Animals with mutations in UNC-73 ( Trio ) also move lethargically [32] , [34] . Similarly , inhibiting endogenous RHO-1 signaling by expressing the Rho inhibitor , C3 transferase , in the cholinergic motor neurons leads to lethargic locomotion and a decrease in ACh release [31] . Thus , changes in Gαq-RhoGEF Trio-Rho signaling result in changes in ACh release and locomotion rate . Although a great deal has been discovered about the G-protein pathways that control neuronal activity in the cholinergic motor neurons less well understood are the signals that act upon the GPCRs to regulate G-protein signaling . Almost certainly changes in the environment will alter activity of the cholinergic motor neurons and thus locomotion . In its natural environment C . elegans is constantly sensing and responding to attractive and aversive signals by altering its locomotion and animals that have evolved effective mechanisms for interpreting and responding to environmental cues , such as the presence of pathogen , will have an evolutionary advantage . A recent study has shown that EGL-30 ( Gαq ) signaling in the chemosensory neuron , ASH , is required for the response to some aversive stimuli [35] . Is the Gαq-RhoGEF Trio-Rho pathway part of the signaling network that modulates neuronal activity and alters locomotion in response to the presence of pathogen , and if so in which cells is this pathway required ? In order to understand more about how the regulation of Gαq signaling modulates neuronal activity in response to pathogens we have investigated the role of EGL-30 ( Gαq ) in the response to infection by the nematode-specific pathogen M . nematophilum . M . nematophilum colonizes the rectum of C . elegans causing it to mount an innate immune response that includes the induction of several antimicrobial factors , swelling of the tail and an aversive behavior that causes animals to leave lawns of M . nematophilum [9] , [23] , [36] . Here we show that upon infection by M . nematophilum pathogen C . elegans alters locomotion behavior: we observe an increase in both ACh release and locomotion in response to infection that requires the Gαq-Rho GEF Trio-Rho signaling pathway in the cholinergic motorneurons and that this signaling is required for aversive behavior . We also show that the innate immune response to M . nematophilum infection requires the Gαq-Rho GEF Trio-Rho signaling pathway . Activation of this pathway in neurons is sufficient to trigger the behavioral response to pathogen , but in epithelial cells it must co-operate with a Ras signaling pathway to trigger the innate immune response . Thus , our studies demonstrate that the Gαq-Rho GEF Trio-Rho signaling pathway is a core pathway acting either alone or in combination with other pathways in a cell specific manner to trigger behavioral and innate immune responses to pathogen .
We , and others , have previously characterized an extensive network of G-protein signaling pathways that regulate ACh release and locomotion in the cholinergic motor neurons of C . elegans [28] . An important question is what are the environmental inputs into this network of neuronal signaling pathways that trigger changes in the activity of the cholinergic motor neurons ? One important environmental cue would be the presence of pathogens; it would be an advantage , upon infection , for animals to alter their locomotion to move away from the location of the pathogen and this has been demonstrated in a number of cases [37] . This proved to be correct as wild-type C . elegans increased their rate of locomotion upon exposure to the pathogen M . nematophilum relative to animals grown on control OP50 E . coli ( Figure 1A ) . Mutations in C . elegans EGL-30 ( Gαq ) ( egl-30 ( ad805 ) ) caused a decrease in locomotion and these mutants did not change their locomotion in response to exposure to M . nematophilum indicating that signaling via EGL-30 ( Gαq ) is required to alter locomotion behavior in response to exposure to M . nematophilum ( Figure 1A ) . It is possible that the reduced locomotion of egl-30 ( ad805 ) animals makes it impossible for us to detect small increases in locomotion caused by exposure to M . nematophilum . Mutations in the UNC-29 nicotinic ACh receptor ( unc-29 ( e1072 ) ) [38] cause a stronger reduction in locomotion than egl-30 ( ad805 ) , however , these mutants still increased rates of locomotion in response to exposure to M . nematophilum , confirming that we can detect differences in locomotion rate caused by exposure to M . nematophilum in mutants that move slowly . Increased locomotion of C . elegans relative to controls could represent a specific response to exposure to a pathogen or a non-specific difference between M . nematophilum and our control bacteria ( the OP50 E . coli strain ) as a food source , for example animals growing on M . nematophilum could be starved relative to animals growing on E . coli . To determine which explanation is likely to be correct we exposed C . elegans to the UV336 M . nematophilum strain , which is unable to infect C . elegans [39] . Animals grown on UV336 did not change their locomotion compared to controls suggesting that wild-type animals increased their rate of locomotion upon infection by M . nematophilum ( Figure 1A ) . We have previously shown that EGL-30 ( Gαq ) acts within the cholinergic motorneurons to regulate locomotion . Expression of EGL-30 from the unc-17 cholinergic motorneuron specific promoter ( MN::EGL-30 ) not only restored the locomotion of EGL-30 ( Gαq ) mutant animals it also caused the animals to move faster than wildtype animals . Expression of EGL-30 ( Gαq ) in just the cholinergic motorneurons also restored the increased locomotion response of animals in response to infection by M . nematophilum compared to E . coli ( Figure 1A ) . We next examined the effect of infection on ACh release at the C . elegans neuromuscular junction using the acetylcholine esterase inhibitor aldicarb . Aldicarb prevents the removal of endogenously released ACh causing it to build up and resulting in hyper-contraction of the body wall muscles that paralyses the animal with a time course dependent on the rates of release from the cholinergic motor neurons [40] . Animals with decreased levels of ACh release are resistant to aldicarb-induced paralysis [41] . Exposure of wild type animals to M . nematophilum resulted in an increase in ACh release as shown by hypersensitivity to aldicarb compared to animals grown on E . coli ( Figure 1B ) . Exposure to the avirulent UV336 M . nematophilum strain did not alter levels of ACh release suggesting that changes in ACh release are in response to infection by M . nematophilum . Reduction-of-function mutations in EGL-30 ( Gαq ) are resistant to aldicarb ( [29] and Figure 1B ) and infection of egl-30 ( ad805 ) did not result in an increase in ACh release ( Figure 1B ) indicating that EGL-30 ( Gαq ) signaling is required to increase ACh release and alter locomotion behavior in response to infection . Expression of EGL-30 ( Gαq ) only within the cholinergic motorneurons ( MN::EGL-30 ) rescued the decreased ACh release defect in egl-30 ( ad805 ) mutant animals and caused a level of ACh release higher than that of wild-type animals . Cholinergic expression of EGL-30 ( Gαq ) was also sufficient to restore the increased levels of ACh release in response to infection by M . nematophilum compared to E . coli ( Figure 1E ) . Thus , our results are consistent with a role for EGL-30 ( Gαq ) within the cholinergic motorneurons that is necessary and sufficient to mediate the increased locomotion response and the increased ACh release response of C . elegans infection by M . nematophilum . Upon M . nematophilum infection of wild type C . elegans the pathogen adheres to the cuticle around the rectal opening causing the animal to mount an innate immune response that includes swelling around this opening known as the Deformed anal region ( Dar ) phenotype [9] ( Figure 1C and G ) . While carrying out our locomotion assays we noticed that the Dar phenotype was significantly decreased in egl-30 ( ad805 ) animals following infection . The egl-30 ( ad805 ) mutation did not alter the ability of the pathogen to attach to the cuticle , as Syto13-labelled M . nematophilum was still observed adhering to the rectum ( Figure 1D and G ) . In addition to tail swelling , infection with M . nematophilum causes constipation [9] . This is exacerbated in animals that are defective in the Dar response [42] . Consistent with their decreased Dar response egl-30 ( ad805 ) animals became severely constipated following infection , but not when grown on E . coli ( Figures 1D and S1 ) . Thus , EGL-30 ( Gαq ) signaling is required for both behavioral and innate immune responses to infection . Expression of EGL-30 ( Gαq ) within the cholinergic motorneurons was unable to rescue the Dar response of egl-30 ( ad805 ) mutants ( Figure 1G ) . Expression of EGL-30 ( Gαq ) cDNA from a 1 . 3 Kb egl-5 promoter fragment that is expressed in the B , K , F , U , and P12 . pa rectal epithelial cells and in three posterior body wall muscles [43] ( Figure 1F ) did rescue the Dar phenotype in egl-30 ( ad805 ) animals , however , these animals remained constipated ( Figures 1G and S1 ) . egl-30 ( ad805 ) animals expressing EGL-30 ( Gαq ) in the rectal epithelial cells remained resistant to aldicarb and no increase in ACh release was observed following infection ( Figure 1E ) . Our data suggest the EGL-30 ( Gαq ) regulates behavioral responses to infection by M . nematophilum by acting in the cholinergic motorneurons and innate immune responses to infection by acting in the rectal epithelial cells . Expression of constitutively active EGL-30 ( Q205L ) in cholinergic motorneurons or from a heat shock-inducible promoter is sufficient to increase both locomotion and ACh release [29] . To determine whether EGL-30 ( Gαq ) signaling was sufficient to induce the Dar response in the absence of infection we generated transgenic animals that expressed constitutively active EGL-30 ( Q205L ) in adult animals ( using a heat shock-inducible promoter ) or in the rectal epithelial cells ( using a 1 . 3 Kb fragment of the egl-5 promoter ) . Over expression of activated EGL-30 ( Gαq ) from these transgenes resulted in tail swelling in the absence of infection ( Table 1 and data not shown ) suggesting that EGL-30 ( Gαq ) signalling in the adult rectal epithelial cells is sufficient to cause the Dar phenotype . A gain-of-function mutation in the chromosomal egl-30 ( egl-30 ( js126 ) ) gene has also been isolated [44] . In contrast to transgenic expression of activated egl-30 this chromosomal mutation did not trigger the Dar response ( Table 1 ) . The inability of the egl-30 ( js126 ) mutation to activate an innate immune response is in contrast to cholinergic motor neurons where this mutation is sufficient to increase locomotion and ACh release [29] , [32] . EGL-30 ( Gαq ) signaling in the cholinergic motor neurons activates at least two pathways to regulate ACh release [45] . Firstly EGL-30 ( Gαq ) activates the PLCß , EGL-8 , to increase diacylglycerol ( DAG ) production [29] and secondly it binds to and activates the RhoGEF UNC-73 ( Trio ) [32] to regulate signaling via RHO-1 and decrease DAG destruction [45] . Mutations in EGL-8 ( PLCß ) and UNC-73 ( Trio ) suppress the increased locomotion and ACh release caused by activation of EGL-30 ( Gαq ) [29] , [32] . Does EGL-30 ( Gαq ) utilise the same pathways during the Dar response to infection ? To determine whether UNC-73 ( Trio ) and EGL-8 ( PLCß ) are also required downstream of EGL-30 ( Gαq ) during the immune response we induced the Dar phenotype in the absence of infection using a heat shock-inducible gain-of-function EGL-30 ( Q205L ) . Following heat shock the Dar phenotype observed in these animals was suppressed by egl-8 ( md1971 ) and unc-73 ( ce362 ) mutants ( Table 1 ) placing PLß and Rho signaling downstream of EGL-30 ( Gαq ) in the immune response to infection . Consistent with our results mutations in EGL-8 ( PLCß ) were identified in a screen for suppressors of the infection-induced Dar phenotype [23] suggesting that conserved signaling pathways may act in multiple tissues to regulate different responses to infection . Here we investigate the role of UNC-73 ( Trio ) and its effector RHO-1 in the response to M . nematophilum infection . The C . elegans genome encodes 21 Dbl containing Rho GEF's several of which are required for viability [46] , [47] . To investigate whether UNC-73 ( Trio ) was the only Rho GEF required for the innate immune response we infected viable , fertile animals carrying mutations in 10 of the 21 known C . elegans Rho GEF's with M . nematophilum . Following infection only unc-73 ( ce362 ) and ect-2 ( ku427 ) significantly decreased the percentage of infected animals with a Dar phenotype , indicating that a subset of Rho signaling pathways are required for the pathogen-induced Dar response ( Figure 2A ) . UNC-73 ( Trio ) is a highly conserved RhoGEF related to mammalian Trio [34] . It contains two tandem RhoGEF domains: the N-terminal RHOGEF1 domain specifically activates Rac family GTPases , whereas the C-terminal RHOGEF2 domain specifically activates RHO-1 [48] ( Figure 2B ) . Mutations that selectively disrupted unc-73′s RacGEF activity ( e936 and ok936 ) [34] had a normal pathogen-induced Dar response , whereas mutations specific to the RhoGEF domain ( ce362 and ok317 ) [32] had a decreased response ( Figure 2C ) although pathogen was still able to attach to the cuticle , as Syto13-labelled M . nematophilum was observed adhering to the rectum of unc-73 ( ce362 ) animals ( Figure 2E ) . Furthermore , the pathogen-induced Dar could be rescued in unc-73 ( ce362 ) mutants by expressing UNC-73 ( Trio ) isoforms that only contain the RHOGEF2 domain [34] ( Figure 2B and D ) , confirming that RHO-1 , but not Rac , activation by UNC-73 ( Trio ) is required for the Dar response to pathogen . Henceforth all the UNC-73 ( Trio ) mutations used are in the RHOGEF2 domain that selectively blocks RHO-1 activation . Because UNC-73 ( Trio ) was required for the Dar phenotype and has previously been shown to regulate C . elegans locomotion under standard conditions [32] we next asked whether Rho signaling was also required to alter locomotion behavior and increase ACh release following infection . Unlike wild type controls , unc-73 ( ce362 ) animals did not increase their locomotion rate following infection ( Figure 3A ) . Expression of UNC-73E from a pan-neuronal promoter partially rescued the reduced locomotion phenotype and restored the increase in locomotion following infection . unc-73 ( ce362 ) animals were slightly resistant to aldicarb when grown on E . coli OP50 as has been observed previously [34] and ACh release was not increased following infection ( Figure 3B ) indicating that UNC-73 ( Trio ) is required for both the immune and behavioral responses to infection . Rho signaling is required throughout development [49] . Therefore to investigate whether UNC-73 was required in adult animals for the Dar phenotype we performed rescue experiments in unc-73 ( ce362 ) animals using a heat shock-inducible UNC-73 transgene . We were able to partially rescue the Dar phenotype in unc-73 ( ce362 ) adults by expressing UNC-73 10–18 hours prior to adulthood ( L3/L4 larval stage ) indicating that Rho signaling in adult animals is required for the response ( Figure 2D ) . To determine the site of action for UNC-73 ( Trio ) in both the behavioral and immune responses to infection we performed rescue experiments using UNC-73 expressed from either the neuronal specific promoter rab-3 or in the rectal epithelial cells using a 1 . 3 Kb egl-5 promoter fragment . Expression of UNC-73 in the rectal epithelial cells was sufficient to rescue the defective Dar response of unc-73 ( ce362 ) mutants ( Figure 2D ) however these animals remained resistant to aldicarb and no increase in neurotransmitter release was observed following infection ( Figure 3C ) . Conversely expression of UNC-73 in the nervous system failed to rescue the Dar response ( Figure 2D ) but wild type levels of neurotransmitter release were observed in these animals in the absence of infection ( Figure 3C ) . M . nematophilum infection of these animals resulted in an increase in ACh release that was identical to the one observed following infection of wild type animals ( Figure 3C ) . Taken together this data confirms that the Gαq-RhoGEF Trio signaling pathway acts in different tissues to mediate the behavioral and immune responses to infection . The simplest explanation for our results is that UNC-73 ( Trio ) activation of RHO-1 is required for immune and behavioral responses to infection . To confirm the requirement for Rho signaling in the rectal epithelial cells we inhibited endogenous RHO-1 in a subset of rectal epithelial cells ( K , F and U ) by expressing the Rho inhibitor , C3 Transferase , from the bus-1 promoter and found that this was sufficient to decrease the percentage of Dar animals ( Figure 4A ) . Conversely expression of activated RHO-1 ( G14V ) ( RHO-1* ) in adults using a heat shock-inducible transgene caused a strong Dar phenotype ( Figure 4B , C and D ) that was not observed when RHO-1* was expressed from a neuronal promoter ( Figure 4B ) , demonstrating a role for RHO-1 in adult C . elegans outside of the nervous system . Tail swelling was observed when RHO-1* was expressed from a 1 . 3 Kb egl-5 promoter fragment that is expressed in the B , K , F , U , and P12 . pa rectal epithelial cells and in three posterior body wall muscles [43] ( Figure 4B and E ) but not when RHO-1* was expressed in the body wall muscles and the B cell using a 469 bp fragment of the same promoter [43] ( Figure 4B ) . Thus , RHO-1 signaling in the adult rectal epithelial cells is sufficient to phenocopy the C . elegans response to infection . How does RHO-1 signaling in the rectal epithelial cells cause the Dar phenotype ? One well established role for Rho signaling is the regulation of cell shape [50] . Co-expression of mCherry together with RHO-1* using the same 1 . 3 Kb egl-5 promoter fragment allowed us to visualize cell shape changes in rectal epithelial cells . Activation of RHO-1* caused changes in cell morphology; cells appeared larger and were no longer organised around the rectal opening instead spreading towards the dorsal side of the animal ( Figure 4 F , G , J and K ) . These changes were also observed in the rectal epithelial cells of wild-type animals infected with Microbacterium nematophilum ( Figure 4F–I ) [51] . Thus , RHO-1* acts cell-autonomously to alter rectal epithelial cell morphology in a manner similar to the innate immune response to pathogens . Although inhibition of RHO-1 in a subset of the rectal epithelial cells ( the K , F and U cells ) using the bus-1 promoter reduced the Dar response , expression of RHO-1* in these same cells did not trigger the Dar response ( Figure 4B ) suggesting that coordinated activation of RHO-1 in multiple rectal epithelial cells is required for the Dar response . What is the physiological effect of increases in locomotion in response to infection by M . nematophilum ? Previous results have shown that if given a choice between lawns of E . coli and M . nematophilum then after 4 hours C . elegans have left lawns of M . nematophilum , and this is termed the aversion behavior . We have repeated these experiments and show that animals do avoid M . nematophilum but do not avoid the avirulent M . nematophilum strain UV336 suggesting that aversion requires infection of C . elegans ( Figure 5 ) . We also noticed that initially , after 30 minutes , animals show no aversion to M . nematophilum suggesting that aversion differs to that of repellents such as quinine , to which C . elegans responds to in seconds [52] . The M . nematophilum aversion behavior was lost in animals with mutations in EGL-30 ( Gαq ) ( egl-30 ( ad805 ) ) or UNC-73 ( Trio ) ( unc-73 ( ce362 ) ) . Expression of EGL-30 in cholinergic motorneurons ( MN::EGL-30 ) partially rescued the aversion behavior of the egl-30 ( ad805 ) mutants suggesting that at least some of the aversion response occurred independent of EGL-30 ( Gαq ) signaling in the sensory neurons ( Figure 5 ) . Expression of UNC-73 from pan-neuronal promoter ( N::UNC-73 ) rescued the aversion behavior of the unc-73 ( ce362 ) mutants demonstrating that neuronal RHO-1 signaling is required for aversion behavior . It was previously shown that the Raf/MEK/ERK MAPK pathway is necessary and sufficient for the Dar response: hyper activation of the pathway by the over expression of constitutively active forms of LIN-45 ( Raf ) , MEK-2 ( MEK ) , or MPK-1 ( ERK ) results in tail swelling in the absence of infection ( as we have shown for constitutively active EGL-30* and RHO-1* ) , while mutations in lin-45 , mek-2 or mpk-1 result in a defective Dar response [42] . Blocking MAPK signaling using the MEK inhibitor U0126 , RNAi for mpk-1 , or mutations in lin-45 ( sy96 ) , mek-2 ( n1989 ) , or mpk-1 ( ku1 ) significantly decreased the Dar response induced by RHO-1* . ( Table 2 and Figure S2 ) . In addition , we observed that loss of RHO-1 signaling , using an unc-73 ( ce362 ) mutant , was unable to suppress Dar induced by over expression of constitutively activated LIN-45 , MEK-2 , or MPK-1 ( Table 2 ) , demonstrating that Rho signaling acts upstream of Raf and its downstream effectors the MAPKs to trigger the Dar response . The small GTPase Ras activates the ERK MAPK pathway and , in mammalian cells , RhoA cooperates with Ras during cell transformation [53] . Therefore , we tested whether the C . elegans Ras genes ( let-60 , ras-1 , and ras-2 ) and RHO-1 cooperate during the Dar response . RHO-1*-induced Dar significantly decreased in animals with a reduction-of-function mutation in let-60 ( n2021 ) , indicating that RHO-1 acts either upstream , or in parallel to , LET-60 ( RAS ) during the Dar response ( Table 2 ) . Previous studies have reported a wild-type response to infection in let-60 ( n2021 ) mutants [42] , however , we observed that the Ras mutants let-60 ( n2021 ) and let-60 ( sy93 ) had a reduced Dar response when exposed to M . nematophilum ( Figure 6A ) . let-60 ( n2021 ) decreased M . nematophilum-induced tail swelling , however , bacteria ( labelled with SYTO13 ) were still observed adhering to the rectum ( Figure 6B ) demonstrating that mutations in LET-60 ( RAS ) do not block infection but do block the Dar response . Mutations in the other Ras genes , ras-1 ( gk237 ) or ras-2 ( ok628 ) , had no effect , suggesting that LET-60 ( RAS ) is the only RAS gene required during the Dar response triggered by infection or RHO-1* activation ( Figure 6A ) . Interestingly although Ras ( LET-60 ) signaling is required for the immune response to infection it is not required for the behavioral response . Although let-60 ( n2021 ) animals were slightly hypersensitive to aldicarb when grown on E . coli OP50 , infection by M . nematophilum increased ACh release in let-60 ( n2021 ) mutants in a manner similar to that observed in wild type controls ( Figure 6C ) . In C . elegans the function of LET-60 ( RAS ) has been best characterized during vulval formation , where a gain-of-function mutation in the chromosomal let-60 ( n1046 ) results in a multi-vulval phenotype [54] . Over expression of constitutively active LET-60 ( G12V ) ( LET-60* ) , either in adult animals ( using the heat shock-inducible promoter ) or in the rectal epithelium , did cause the Dar response ( Figure 6D ) , however , the chromosomal gain-of-function let-60 ( n1046 ) mutation was not sufficient to trigger the Dar response ( Table 1 ) . Expression of constitutively active LET-60 ( RAS ) in rectal epithelial cells did not cause as pronounced a Dar response as observed with constitutively active MEK-2 from the same promoter ( Compare Figure 6D and E ) and this could reflect differences in expression of the genes , the strength of the activating mutation or differences in the numbers of downstream LET-60 ( RAS ) pathways activated . Using the MEK inhibitor U0126 we were able to suppress the Dar response induced by either RHO-1* or LET-60* ( Table 2 and Figure S2 ) indicating that both of these pathways act upstream of RAF/MEK/ERK to mediate the Dar response and suggesting that at least one signal required for Raf activation in the rectal epithelia is RHO-1 dependent . Consistent with this the LET-60*-induced Dar response was significantly decreased in an unc-73 ( ce362 ) mutant ( Table 2 ) . The Dar response of the LET-60*; unc-73 ( ce362 ) animals was further reduced by addition of U0126 but this was not a significant change ( p = 0 . 18 ) . Our results could suggest that RHO-1 acts downstream of LET-60 ( RAS ) , but a mutation in x blocked the Dar response caused by RHO-1* ( Table 2 ) suggesting the RHO-1 and LET-60 ( RAS ) pathways act in parallel . Both RHO-1 and LET-60 ( RAS ) act upstream of Raf/MEK/ERK so our data suggests that these parallel pathways converge on Raf ( lin-45 ) to regulate ERK/MAPK signaling and trigger the Dar response during the innate immune response to pathogenic bacteria . Both the Gαq-RhoGEF Trio-RHO-1 and Ras signaling pathways act upstream of LIN-45 ( Raf ) to mediate the immune response to infection , however , chromosomal gain-of-function mutations in EGL-30 ( Gαq ) or LET-60 ( Ras ) were not Dar suggesting that levels of signalling from these mutations was not individually sufficient to trigger the immune response . To investigate whether simultaneous activation of these pathways was able to cause the Dar phenotype we over expressed constitutively active EGL-30 ( Q205L ) in the chromosomal gain-of-function let-60 ( n1046 ) mutant and observed an increase in the number of Dar animals when compared to expression of this transgene in wild type animals ( Table 1 ) . In addition we observed a number of animals with the Dar phenotype when we combined the chromosomal gain-of-function mutations in both egl-30 ( js126 ) and let-60 ( n1046 ) ( Table 1 ) . These two observations suggest that these pathways act in parallel to cause the Dar phenotype perhaps acting as a coincidence detector between two infection signals . However , in both of these experiments the increase in Dar animals was small suggesting that in wildtype animals additional factors are required to mediate a robust Dar response to M . nematophilum infection .
C . elegans display both behavioral and innate immune responses upon exposure to pathogenic M . nematophilum [9] , [23] . Avirulent strains of M . nematophilum fail to induce immune responses that include the Dar response [9] and expression of putative anti-microbial peptide genes [36] , [39] and here we show that the avirulent UV336 strain of M . nematophilum is also unable to trigger behavioral responses . The failure of the avirulent UV336 strain to increase locomotion or increase ACh release compared to the E . coli control makes it unlikely that behavioural responses are due to different nutritional values of M . nematophilum versus E . coli , i . e . animals growing on M . nematophilum receive less nutrition relative to animals growing on E . coli . The simplest explanation for these results is that C . elegans is capable of recognizing that it has become infected and coordinates behavioural and immune responses in response . What are the signals produced by infection ? Both the behavioral and innate immune response to infection require the conserved EGL-30 ( Gαq ) /UNC-73 ( Trio RhoGEF ) /RHO-1 ( RhoA ) signaling pathway ( henceforth referred to as the EGL-30 ( Gαq ) pathway ) . Defects in the EGL-30 ( Gαq ) pathway do not prevent infection by M . nematophilum , instead activation of the EGL-30 ( Gαq ) pathway is required in neurons and the rectal epithelial cells to trigger behavioral and Dar responses to infection respectively . Here we have only addressed locomotion , ACh release , aversion and the Dar response but infection can also triggers other changes , for example expression of anti-microbial peptides [36] , and the EGL-30 ( Gαq ) pathway could play a role in coordinating a wider range of responses to pathogen than studied here . Indeed , EGL-30 ( Gαq ) is required in the intestine for protection against P . aeruginosa , although it is unknown if RHO-1 signaling is also required [55] . Thus , our results demonstrate that in response to infection signals that activate Gq coupled GPCRs are at some point required . In which cells are the Gq coupled GPCRs that trigger behavioral and immune responses to infection located ? Cell specific rescue experiments show that cholinergic EGL-30 ( Gαq ) signalling is required for behavioural responses to infection whereas rectal epithelial EGL-30 ( Gαq ) signalling is required for the Dar response to infection . We have previously demonstrated a role for EGL-30 ( Gαq ) signalling in cholinergic neurons [29] , [31] and here we show that one mechanism by which the cholinergic EGL-30 ( Gαq ) pathway is activated is in response to infection . In the case of the immune response this is the first demonstration of a role for EGL-30 ( Gαq ) signalling in rectal epithelial cells for the Dar response . The rectal epithelial cells consist of five cells ( B , F , Y , U , and K′ ) , expression of activated RHO-1 in all five cells caused a Dar response in the absence of infection , whereas expression only in B failed to trigger the response . In contrast , inactivation of RHO-1 in just the B cell prevented a Dar response upon infection . Thus , coordinated EGL-30 ( Gαq ) signalling in multiple , if not all , rectal epithelial cells is required for the Dar response . Our results demonstrate separate sites of action for the EGL-30 ( Gαq ) signaling pathway in behavioral and immune responses to infection and argue against a model in which EGL-30 ( Gαq ) signaling acts in a single cell to produce further secreted signals that go on to trigger behavioral and immune responses to infection . Our data also argues against a model whereby the Dar response triggers behavioral changes and vice versa . We conclude that Gq coupled GPCRs present on the cholinergic motorneurons and on multiple rectal epithelial cells are required for the behavioral and immune responses of C . elegans respectively in response to infection by M . nematophilum . What is the physiological relevance of the behavioral response to infection by M . nematophilum ? We show that C . elegans , when infected by M . nematophilum , move faster and we show that this results in the animals leaving a lawn of M . nematophilum . Such a response is likely to lessen exposure to M . nematophilum and subsequent eggs laid will not hatch in the presence of pathogen . We also believe the behavioural changes we observe in response to infection explain the aversion of C . elegans to lawns of M . nematophilum . Yook et al . first demonstrated that given a choice between lawns of E . coli and M . nematophilum animals preferentially localized to the E . coli lawn after 4 hours [23] and this is termed the aversive response . Our results are consistent with the behavioral responses we report here as playing an important part in the aversive response . Firstly , neither the behavioral or aversive responses are triggered by the avirulent M . nematophilum strain UV336 . Secondly , both responses fail to occur in EGL-30 ( Gαq ) and UNC-73 ( TrioRhoGEF ) mutant animals . And thirdly , these responses are rescued by cholinergic motorneuron expression of EGL-30 and neuronal expression of UNC-73 . Evidence from aversive responses to other pathogens suggests that aversion can be a learnt response requiring both chemosensory neurons and interneurons [22] , [56] . We observe that C . elegans do not avoid lawns of M . nematophilum after 30 minutes but do so after 4 hours and this is consistent with , but does not prove , a learnt behavior . Our rescue experiments suggest that if chemosensory and interneurons are required for aversion to M . nematophilum then the pathways acting within those neurons can signal in the absence of EGL-30 ( Gαq ) signaling . However , the partial rescue of the aversion response by cholinergic motorneuron expression EGL-30 ( Gαq ) could indicate that the full aversion response does require additional EGL-30 ( Gαq ) signaling in other cells , for example the sensory neurons . Mutations in two components of a cyclic nucleotide gated channel , tax-2 and tax-4 , also prevent aversion of C . elegans to lawns of M . nematophilum [23] . tax-2 and tax-4 genes are required in sensory neurons to mediate aversive responses to S . marcescens [19] possibly suggesting that chemosensory neurons are also required for aversion to M . nematophilum . However , tax-2 and tax-4 mutants also fail to produce the Dar response to M . nematophilum infection and they have been reported to have cuticle defects [23] thus , currently we cannot determine if sensory neurons are required for the aversive or Dar response to M . nematophilum . Previously it has been shown that a conserved LIN-45 ( Raf ) /MEK-2 ( MEK ) /MPK-1 ( ERK ) MAPK pathway is required for the Dar response [42] . Raf is activated by Ras GTPases in other systems and here we have shown that LET-60 ( Ras ) mutations blocked the Dar response to pathogen whereas transgenic overexpression of activated LET-60 ( RAS ) in the rectal epithelial cells triggered the Dar response . As with the EGL-30 ( Gαq ) pathway , signalling by LET-60 ( Ras ) , LIN-45 ( Raf ) , MEK-2 ( MEK ) and MPK-1 ( ERK ) ( hereafter referred to as the LET-60 ( Ras ) pathway ) is required cell autonomously within the rectal epithelial cells for the Dar response . Two results suggest that within the rectal epithelial cells the EGL-30 ( Gαq ) pathway and LET-60 ( Ras ) converge on LIN-45 ( Raf ) to trigger the Dar response – Firstly , reductions in signaling of the MAPK pathway using either mutations in LIN-45 ( Raf ) , MEK-2 ( MEK ) , MPK-1 ( ERK ) , or chemical inhibition of MEK-2 ( MEK ) using U0126 blocked the Dar response triggered by transgenic expression of activated RHO-1 or LET-60 ( RAS ) . In contrast , mutations in RHO-1 signaling ( UNC-73 ( Trio ) ) or LET-60 ( Ras ) did not block the Dar response triggered by transgenes expressing gain-of-function mutations in LIN-45 ( Raf ) , MEK-2 ( MEK ) , and MPK-1 ( ERK ) . These results suggest that both RHO-1 and LET-60 ( RAS ) act upstream of LIN-45 ( Raf ) . Secondly , a mutation in UNC-73 ( RhoGEF ) blocked the Dar response triggered by transgenic expression of activated LET-60 ( RAS ) , and a mutation in LET-60 ( RAS ) blocked the Dar response triggered by transgenic expression of activated RHO-1 . Thus , for the Dar response , defects in RHO-1 or LET-60 ( Ras ) signaling co-suppressed each other suggesting that these two pathways act in parallel . The simplest model that explains our data is that in rectal epithelial cells the RHO-1 and LET-60 ( Ras ) signaling pathways converge on LIN-45 ( Raf ) ( Figure 7 ) . The requirement for convergent RhoA and Ras signaling for Raf activation has also been observed in mammalian cells , where dominant negative forms of RhoA blocked the ability of Ras to activate Raf , indicating that Rho signaling is required for Raf activation , although the mechanism is unknown [57] . Alternative interactions between Rho and Ras also exist . During C . elegans vulval formation RHO-1 appears to act upstream of LET-60 ( Ras ) [54] suggesting that the Rho and Ras signaling pathways can either act in parallel or in series depending on the cell type . Interactions between Rho and Ras pathways appear to be essential during cellular transformation [53] and co-activation of RhoA and Ras signaling can lead to different responses from those signaled by either pathway alone [58] . It will be important to identify the cell specific factors that control the interactions between the Rho and Ras signaling pathways . Infection of C . elegans with M . nematophilum provides a starting point for genetic screens to identify the molecular mechanisms by which RhoA and Ras act together to activate Raf . The factors that allow this co-operation are likely to be critical in C . elegans and mammals for signaling involved in innate immunity and oncogenesis . These results also demonstrate that in wildtype animals the Dar response requires two signals: one that activates GPCRs coupled to the EGL-30 ( Gαq ) pathway and a second that activates receptors that activate the LET-60 ( RAS ) pathway . Where are these signals likely to be produced ? One candidate is the hypodermal cells that are the focus of the M . nematophilum infection . Hypodermal signaling is required to induce expression of anti-microbial peptides in response to infection by D . coniospora [59] , [60] , which , like M . nematophilum , infects the hypodermis . This hypodermal signaling requires p38 MAPK signaling [59] and it will be interesting to test if the response to M . nematophilum also requires activation of the p38 MAPK pathway in hypodermal cells . Identifying the ligands that activate the receptors coupled to EGL-30 ( Gαq ) and LET-60 ( Ras ) will provide important clues to how C . elegans recognises it has been infected . Transgenic activation of genes in the EGL-30 ( Gαq ) and LET-60 ( Ras ) pathways led to the Dar response even in the absence of pathogen , however , gain-of-function mutations in the endogenous chromosomal genes did not cause the Dar response . For example the egl-30 ( js126gf ) gain-of-function mutation increased locomotion and ACh release [44] but did not trigger the Dar response and the let-60 ( n1046gf ) gain-of-function mutation results in multiple vulva formation [61] but did not trigger the Dar response . A small percentage of animals with both the egl-30 ( js126gf ) and let-60 ( n1046gf ) mutations did show a Dar response in the absence of pathogen but at a much lower rate than observed following transgenic expression of gain-of-function mutants of EGL-30 ( Gαq ) and LET-60 ( RAS ) . The n1046gf mutation causes a Gly to Glu change in LET-60 ( RAS ) at a position 13 [62] , an amino acid change known to cause oncogenic activation in mammalian RAS [63] , [64] . The js126gf mutation causes a Val to Met change in EGL-30 ( Gαq ) at position 180 [65] and this mutation is predicted to interfere with the GAP activity of EGL-30 ( Gαq ) . Both the n1046gf and js126gf mutations are semi-dominant [61] , [62] , [65] but the amino acid changes involved differ from the mutations used to cause constitutive activation in our transgenes ( EGL-30 ( Q205L ) and LET-60 ( G12V ) ) and it is unclear to what level these different mutations activate EGL-30 ( Gαq ) and LET-60 ( Ras ) . It appears likely that our transgenes cause higher levels of EGL-30 ( Gαq ) and LET-60 ( Ras ) signalling , either the mutations involved in the transgenes cause stronger activation , the increased level of expression from the transgenes results in stronger signaling , or a combination of these two possibilities . Perhaps strongly activating mutations in the chromosomal EGL-30 ( Gαq ) and LET-60 ( RAS ) genes cause lethality , whereas restricted expression of strongly activating EGL-30 ( Gαq ) and LET-60 ( RAS ) mutations from a transgene can be tolerated . Unlike the Dar response , both transgenic and chromosomal gain-of-function mutations in EGL-30 ( Gαq ) are sufficient to trigger changes in ACh release and locomotion , and these neuronal changes do not require inputs from the LET-60 ( RAS ) pathway . This suggests that the Dar response requires a higher level of EGL-30 ( Gαq ) signaling than the behavioral response to infection . In addition the Dar response requires coincident EGL-30 ( Gαq ) and LET-60 ( RAS ) signaling and this is not apparently required for the behavioral response to infection . Perhaps the consequences of inappropriate activation of the innate immune response are more severe than inappropriate activation of the behavioral response and animals may set a higher threshold for the Dar response than changes in behavior . This is the first demonstration for a role for RHO-1 in C . elegans innate immunity , however , its mammalian ortholog RhoA is a key regulator of mammalian immune responses acting to regulate Toll receptor signaling , leukocyte migration , and phagocytosis of pathogens [66] suggesting further parallels between mammalian and C . elegans innate immunity . Although less well studied than the immune response behavioral changes following infection play an important role in defending many species , including humans , from pathogen attack [18] . Coordination of these responses makes sense as it allows animals to mount an immune response to the immediate threat whilst simultaneously taking action to remove the pathogen , however , the complicated nature of the mammalian brain and immune system has made it difficult to identify the molecular mechanisms that mediate these interactions . With its simple , well described , nervous system and a rapidly growing understanding of its immune system , C . elegans provides a model to understand the role RhoA and Gαq signaling play in coordinating behavioral and immune responses to infection [67] .
C . elegans strains used in this study are detailed in Supplemental material . All strains were cultivated at 20°C on nematode-growth media ( NGM ) plates seeded with E . coli OP50 unless otherwise stated and maintained as described previously [68] . Plasmids ( listed as pRJM or SJN ) were constructed using standard techniques , and verified by sequencing . Transgenic strains ( listed as nzEx or impEx ) were isolated by microinjection of 100 ng/µl of plasmid unless otherwise described below together with ttx-3::gfp ( a gift of O . Hobert , Columbia University NY ) , unc-122::gfp ( a gift of P . Sengupta Brandeis University MA ) , rol-6 dominant marker , or acr-2::mcherry ( SJN445 ) at 50 ng/µl as a marker . Some cDNAs were obtained from Yuji Kohara at the Center for Genetic Resource Information , National Institute of Genetics , Research Organization of Information and Systems , Mishima , Japan . Unless otherwise stated all injections were performed into N2 animals . Plasmids and transgenic strains are described in Supplemental Methods . Infection with M . nematophilum was performed as described previously [42] with the following modifications . NGM plates were seeded with 10% M . nematophilum diluted in OP50 E . coli . Adult animals were transferred to infection plates and were maintained at 20°C or 25°C . F1 progeny were scored for the presence or absence of the Dar phenotype once they reached L4 or adult stages . In the case of hs::UNC-73;unc-73 ( ce362 ) animals synchronized populations of L1 animals were obtained by bleaching and these L1′s were transferred to infection plates . This generation was assayed for the presence of the Dar phenotype . SYTO13 staining was performed as described previously [42] . Adult animals were infected with 10% M . nematophilum diluted in OP50 E . coli and F1 progeny were assayed as one-day-old adults . Locomotion assays were performed as described previously [69] . Sensitivity to 1 mM aldicarb ( Greyhound Chromatography ) was determined by analysing the onset of paralysis as described previously [40] . For each experiment , at least 20 animals were tested and each experiment was repeated at least four times . Error bars indicate the s . e . m . Assays were performed essentially as described by Yook et al . ( 2007 ) with the following changes . Assays were performed on 60 mm plates with 40 µl of an overnight culture of bacteria grown in LB placed on opposite sides of the plate . Animals were washed in M9 and allowed to settle before aspiration , centrifugation of the animals was found to alter their behavior and was not used . A suspension of animals in a drop of M9 was placed equidistant from each bacterial lawn , numbers of animals varied from 25 to 100 . The chemotaxis index = ( number of animals on lawn A- number of animals on lawn B ) /number of animals on lawn A+B . In all experiments lawn A was OP50 except where both lawns contained M . nematophilum . Expression from the heat shock promoter was achieved using two rounds of heat shock for 60 min separated by 30 min at 20°C . Heat shock was performed on one-day-old adults or L4′s except for in hs::UNC-73E;unc-73 ( ce362 ) animals where heat shock was performed at 0 , 24 and 48 hours after transfer to M . nematophilum plates when animals were at approx L1 , L2/3 and L3/4 stage respectively . For transgenic animals containing hs::RHO-1* or hs::UNC-73E transgenes a heat shock temperature of 33°C was used . For all other transgenes heat shock was performed at 37°C . Animals were allowed to recover overnight at 20°C before scoring for the Dar phenotype . One-day-old adults were transferred to NGM plates seeded with OP50 containing 50 µM U0126 ( Sigma ) or DMSO ( as a control ) . Plates were incubated at 20°C for 2 hours and animals were heat shocked as described above . Animals were allowed to recover overnight at 20°C before scoring for the Dar phenotype . Animals were imaged by mounting on 2% agarose pads . DIC images were obtained using a Zeiss Axioplan microscope with ×40 objective . Digital images were captured using Openlab software ( Improvision ) and processed using ImageJ ( NIH ) . For fluorescence microscopy animals were viewed on a Leica TCS SP5 microscope with a Leica ×63 objective . Images were obtained using Leica Application Suite Microscope software . Digital images were processed to give maximum intensity projections or 3D projections of a Z-series using ImageJ ( NIH ) . In all cases statistical analysis was performed using an unpaired two-tailed t-test . P values between 0 . 05 and 0 . 001 ( significant ) are indicated on figures using one asterisk , and P values of 0 . 001 or less ( highly significant ) are indicated with two asterisks . No vertebrate animals were used for these studies and no ethical approval was required . | Once infected by a pathogen the nervous and immune systems of many animals react with coordinated responses to the danger . A key question is what are the pathways by which responses to infection occur and to what extent are the same pathways involved in differing responses ? Here we demonstrate that a Gαq-RhoA pathway is required for both behavioral and immune responses to infection in C . elegans . We show that Gαq-RhoA signaling is a late step in the response to infection and their site of action defines the cellular targets of signals generated internally in response to infection . One response is to move away from sites of pathogenic bacteria and Gαq-RhoA signaling acts in motorneurons to achieve this . A second response is an innate immune response where Gαq-RhoA signaling acts within cells close to sites of infection , the rectal epithelial cells , to cause major changes in their size and shape to mitigate the effects of infection . Our work demonstrates that ligands for Gq coupled GPCRs are likely to be required for response to infection . Identifying these ligands and the cells that release them will help define the mechanisms by which C . elegans recognizes pathogens and coordinates behavioral and immune responses to infection . | [
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] | 2012 | Behavioral and Immune Responses to Infection Require Gαq- RhoA Signaling in C. elegans |
Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes . Consequently , these studies are limited in their discovery of new and unknown disease relationships . We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network . We identified 4 , 620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases . Fourteen of the significant disease correlations also shared common drugs , supporting the hypothesis that similar diseases can be treated by the same drugs , allowing us to make predictions for new uses of existing drugs . Finally , we also identified 59 modules that were dysregulated in at least half of the diseases , representing a common disease-state “signature” . These modules were significantly enriched for genes that are known to be drug targets . Interestingly , drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins , highlighting the importance of these core modules as prime therapeutic opportunities .
Our understanding of the human disease state is incomplete without the knowledge of how various diseases relate to each other . Relationships between diseases have been used to gain insights into the etiology and pathogenesis of similar diseases [1] . Study of disease similarities has also led to the discovery of new causal genes for diseases [2] , [3] . Moreover , similarities between biological concepts such as genes have been used successfully in gene function prediction [4] . However , most of the early work on finding disease-similarity has been limited to studying the clinical phenotypes of the diseases . For instance , similarities in disease symptoms and pathological results have been used to ascertain similarities between Alzheimer's disease and vascular dementia [1] . These methods are not quantitative and cannot be used to compare the relative similarities between diseases . More recently , scientists have been able to explore the genetic similarity between diseases because of the availability of large-scale knowledge-bases such as the Online Mendelian Inheritance in Man ( OMIM ) [5] . In 2007 , Goh and colleagues created the first “Diseasome” , a network of human diseases [6] . This network consisted of human diseases/disorders as nodes and two diseases were joined by a link if they shared known disease genes ( data obtained from OMIM ) . Van Driel et al . [7] inferred disease-disease associations by an automated text mining of OMIM descriptions . Liu et al . [8] mined for disease etiologies from the Medical Subject Headings ( MESH ) [9] vocabulary and used it to reveal similarities between diseases . Although the above studies provided comprehensive views of disease interrelationships , they were mainly studying monogenic disorders and generally ignored the effect of the environment on these and other , more complex , diseases . They also relied heavily on information that is already known , such as known disease genes or known pathways . As a result , they were limited in their ability to uncover hitherto unknown relations between diseases . Advances in high-throughput molecular assay technologies , accompanied by declining per-sample costs , have given rise to numerous public repositories of biomolecular data such as mRNA expression profiles and protein interaction networks . In particular , the availability of these datasets for many different diseases presents a ripe opportunity to use data-driven approaches to advance our current knowledge of disease relationships in a systematic way . As a matter of fact , very recently , Hu and Agarwal [10] presented an approach to determine disease relationships using only gene expression data . In order to obtain the disease correlations , the authors excluded genes which don't change meaningfully using an arbitrary threshold . They also did not take advantage of the plethora of protein interaction data available for the human system . Protein networks represent the physical processes taking place inside a cell and are essential to acquire a complete understanding of any biological condition such as disease . Therefore , just as sequencing of genomes has enabled the reorganization of many species and provided quantitative metrics to appreciate their relationships , we believe an integrated approach combining both mRNA expression and protein interaction data will provide us a quantitative way to assess the correlation between diseases . Here , we present the first such systematic and integrated approach to explore the architecture of human diseases . In particular , we identified 4 , 620 functional modules analogous to important complexes and pathways in the human protein network and recorded how they varied in each of the 54 diseases using the mRNA expression data . This process provided a quantitative measure to describe the overall response of the human system to a given disease . Subsequently , we used these measures to identify 138 significant associations between diseases . We also discovered functional modules that are common to at least half of the diseases representing a common “disease-state” signature . These common disease-state modules were not only significantly enriched for genes that were known drugs targets , but their corresponding drugs were known to treat significantly more diseases than expected by chance highlighting their importance as therapeutic opportunities .
Protein complexes and pathways are accountable for most processes in the cell . Accordingly , we can gauge the response of a cell system to a certain perturbation ( such as disease ) by the measuring changes in the expression levels of various functional modules of the system . To this end , we first generated a catalog of 4 , 620 functional modules by querying the large-scale human protein interaction network ( see Methods ) . We then collected the mRNA expression arrays associated with each disease from the Gene Expression Omnibus ( GEO ) [11] . After several rounds of filtering the gene expression data for accuracy , reliability , and experimental context , we had microarrays representing 54 human diseases ( see Methods and Table S1 ) . Next , we combined the gene expression data and the 4 , 620 functional modules to generate a Module Response Score ( MRS ) for each module in each disease-state representing its activity level ( see Methods ) . Specifically , positive MRS values correspond to modules that are up-regulated and negative MRS values identify modules that are down-regulated in the disease-state as compared to the control ( healthy ) . Figure 1 gives an overview of the process to compute the MRS values for a given disease . In the end , we generated a matrix containing the MRS values for each module in each of the 54 diseases considered in this study . The relationships between different diseases were then ascertained by the Partial Spearman correlation coefficient of their MRS values ( see Methods and Figure S1 ) . Specifically , we calculated the Spearman correlation between two diseases conditioned on the responses of the functional modules in their respective control samples . The use of the Partial Spearman correlation coefficient instead of the generic Spearman correlation coefficient not only provided a quantitative metric to assess disease similarity but also explicitly factored out the possible dependencies between different gene-expression experiments due to their underlying tissue or cell types . Figure 2 ( A ) is the hierarchical clustering of diseases based on the correlations generated above . To assign significance to these associations , we randomized the gene to module assignments as well as the control and disease labels 100 times to generate a background distribution of disease correlations ( see Methods ) . We then selected only those disease correlations that passed the p-value threshold of 0 . 01 ( FDR = 10 . 37% ) resulting in 138 significant disease-disease similarity relationships . Immediately , we see that many expected disease associations such as the brain disorders like Alzheimer's disease , Bipolar disorder and Schizophrenia are pooled together in one sub-branch . We also see many novel and hitherto unknown significant correlations such as the similarity between uterine leiomyoma and lung cancer . We also created a network representation to display all the 138 significant disease correlations ( Figure 2 ( B ) ) . In this network , the nodes are diseases , while the thickness of the edges between two diseases represents their strength of correlation . This abstraction allows us to pick additional significant disease associations that were missing in the hierarchical clustering . For example , Crohn's disease and Malaria share a significant disease correlation . A listing of all the significant disease correlations is provided in Table S2 . Although the 54 diseases considered in this study cover many categories of diseases ranging from cancers to cardiomyopathies , some categories of diseases such as cancer are over-represented as opposed to others such as infectious diseases . Ideally , we would like to explicitly correct for this bias by down-weighing over-represented classes . However , the principle behind organizing diseases into categories such as cancers , infectious diseases and others is not the same . For instance , diseases are classified as cancers if their underlying pathology consists of a group of cells that show uncontrolled growth , invasion of nearby cells and metastasis . On the other hand , infectious diseases relates to diseases which are caused by pathogens and have the potential to spread from person to person . Lack of a common organization scheme prevents us from explicitly correcting for the observed over-representation . Moreover , there is considerable heterogeneity even among diseases of the same category . For instance , the category of cancers covers a wide variety of diseases affecting many different cell types and having many different biological causes ranging from mutations caused by chemical carcinogens to bacterial and viral infection . This heterogeneity is seen even at the transcriptional level [12] . We also have observed this heterogeneity in the results of our study as all the 17 cancers considered in our analysis did not cluster together ( Figure 2 ( A ) ) . By combining both mRNA expression and protein interaction data , we are providing one of the first ways to compare and classify diseases systematically . The common organizing principle here is the molecular pathology of a given disease . At the outset , we explored the genetic basis of the diseases in our study to explain and validate the observed disease correlations . Specifically , we aimed to test the hypothesis that diseases which are significantly associated through the MRS-based correlation coefficient also significantly shared disease genes . For this purpose , we collected a list of genes known to be associated with diseases , hereinafter as the Disease Gene List ( see Methods ) . We found known gene variants associated with only 31 of the 54 diseases in our study resulting in an overall total of 465 possible pair-wise disease comparisons . A pair of diseases was considered to significantly share disease genes only if the Hypergeometric p-value of the overlap was less than 0 . 01 . Eighty-two of the overall 465 comparisons significantly shared disease genes . On the other hand , only 73 of the 465 disease pairs were significantly associated using the MRS-based correlation coefficient . This gives rise to a contingency table as shown in Table 1 with a one-sided Fisher's Exact Test p-value of 0 . 033 . It suggests that the genetic similarity between diseases significantly contributes to the molecular pathological disease similarity observed in this study . Lack of a strong p-value might be explained by the fact that the number of known disease genes are much higher for well-studied diseases like Schizophrenia ( 345 genes ) as opposed to less well-studied diseases like Mixed hyperlipidemia ( 4 genes ) . Mapping of genes to diseases was also hindered due to fact that we used a very strict vocabulary to define diseases ( see Methods ) . Finally , this result might also allude to the role of environment in disease causation and similarity . A few of the significant disease correlations which also significantly shared disease genes is provided in Table 2 and the complete list is provided in Table S3 . In order to further understand the biology behind the observed disease correlations , we examined some of their underlying functional modules . First , we analyzed the sub-branch of brain disorders , Alzheimer's disease ( ALZ ) , Bipolar disorder ( BIP ) , Schizophrenia ( SCHZ ) , and Glioblastoma ( GLIO ) , in the hierarchical representation of the disease correlations ( Figure 2 ( A ) ) in more detail . Figure 3 ( A . i ) corresponds to the synaptic vesicle and was one of most down-regulated modules in all four diseases ( second lowest average MRS value ) . This module is a secretory organelle that stores neurotransmitters and releases them into the synapse . Loss of synaptic functions and more specifically , decreased expression of synaptic vesicle proteins such as SNAP-25 is one of the main effects of ALZ [13] , [14] . Decreased synaptic function has also been observed for both BIP and SCHZ [15] , [16] . In particular , the levels of protein SNAP-25 was shown to be reduced in both BIP and SCHZ [17] . The function of this module in GLIO is still to be explored . Uterine leiomyomas ( UTL ) are benign tumors affecting the uterus . As shown in Figure 2 ( A ) , UTL shares a strong correlation with lung cancers . Figure 3 ( A . ii ) corresponds to the DNA repair pathway which had the highest average MRS value for the three diseases . Polymorphisms in the genes involved in the DNA repair pathway such as PCNA , POLB have been associated with increased risk of lung cancer [18] . Moreover , the Arg399Glu allele of the XRCC1 gene has been shown to be a risk factor for lung adenocarcinoma [19] and lung squamous cell carcinoma [20] . Surprisingly , the same Arg399Glu polymorphism in the XRCC1 gene has also been associated with an increased risk of UTLs [21] giving causal genetic evidence for the correlation we observed between the diseases using microarray-based molecular pathological measurements . Knowledge of a comprehensive disease-similarity tree ( network ) based on molecular data could possibly be used in finding new uses for existing drugs . Similar diseases share similar molecular phenotypes and could potentially be treated by similar drugs . To explore this avenue , we collected a list of drugs , their corresponding target genes and the diseases they are known to treat ( US FDA approved indications ) or off-label uses . This information was obtained from the RxNorm from National Library of Medicine [22] , DrugBank [23] , National Drug File Reference Terminology ( ND-FRT ) [24] and MicroMedex [25] . Overall , 17 of the 138 significant disease correlations shared at least one drug in common and 14 of them had a significant Hypergeometric p-value less than 0 . 01 ( Table 3 , Table S4 ) . For instance , we found that the FDA approved drug Flouroucil , used to treat Actinic keratosis , has been shown to have positive indications for treating Malignant tumor of the colon [25] . Similarly , the drug Doxorubicin is FDA approved to treat both Urothelial carcinoma and Acute myeloid leukemia [25] . This number is a conservative estimate as the list of drugs used here is incomplete . Moreover , we used a very specific vocabulary to define diseases ( see Methods ) and accordingly mapped drugs to them . For instance , we found many drugs treating lung cancer; however in many cases , our combined knowledge base doesn't specify whether the cancer was an adenocarcinoma or a squamous cell carcinoma . In those cases , we excluded the drug from our consideration . A caveat to this approach is that drugs can be shared between diseases mainly because the corresponding diseases belong to the same category . For instance , drugs can be shared between two cancers etc . As a result , it is difficult to differentiate whether two diseases shared drugs due to the similarity in their molecular pathology or due to their underlying disease type . Moreover , the chemical similarity between drugs can also affect the reported p-values . Another consequence of elucidating and quantifying the response of the cell system to a disease is that we can use this methodology to find modules that are generally dysregulated ( activated or repressed ) in the disease-state . In other words , we used the MRS values to characterize a common “signature” across disease-states . In order to generate the set of modules that are commonly dysregulated in the 54 diseases considered in this study , we used a two-fold approach . Firstly , a module was selected if the median of its absolute MRS values across all diseases was significantly higher than expected at random . We generated a random background distribution of median scores by shuffling the gene to module assignments ( see Methods ) . Overall , at a p-value of 0 . 01 and associated FDR of 16 . 15% , we selected 286 modules . We then filtered the above set of 286 modules to only include those modules which were significantly differentially expressed in many diseases . A module was determined to be significantly differentially expressed in a given disease if the absolute value of its MRS was above 1 . 5 ( p-value = 0 . 028 ) . Finally , we selected 59 modules that were significantly differentially expressed in 20 or more diseases as the common disease state signature . These modules were not only dysregulated in at least half of the diseases each but were also significantly differentially expressed in more than 20 diseases . Moreover , these 59 modules taken together were dysregulated in 45 of the 54 diseases in our study . Figure S2 shows the combined illustration of all the 59 modules . They were mainly enriched for the functions of immune system response ( p-value = 6E-70 ) and DNA repair ( p-value = 4 . 1E-30 ) . A representative sample of 7 modules is shown in Figure 3 ( B . iii–vii ) . We investigated the 59 modules further by searching for known drug target genes/proteins . We obtained the list of drugs and their corresponding targets from the DrugBank database [23] . Overall , 70 genes/proteins within the 59 signature pathways were identified as targets of known drugs giving a Hypergeometric p-value of 1 . 8E-11 . Thus , the set of the signature modules was significantly enriched for drug target genes compared to that expected by chance . We then predicted that other genes/proteins in these modules would also serve as prime candidates for designing new drugs . Most existing drug target genes usually fall into a comparatively small set of gene families such as G protein coupled receptors , serine proteases etc [26] . Hence , new drug targets can be found by exploring other members of the protein families of the existing drug targets . We explored the 59 signature modules for genes which belonged to the same protein families as known drug target genes . For that purpose , we obtained a list of genes and their corresponding families and sub-families from the PANTHER database [27] . Overall , we found 241 genes among a total of 450 genes in the signature modules sharing the same protein families as the known drug target genes compared to a total of only 3 , 520 such genes in the whole human PPI giving a Hypergeometric p-value of 1 . 47E-12 . Therefore , the 59 signature modules were also significantly enriched for druggable genes . Further , we also counted the number of distinct diseases that are known to be treated by the drugs corresponding to each of the 70 known drug targets . We observed that drugs targeting these 70 genes are known to treat an average of 65 diseases each compared to an average of ∼42 diseases for all known drug targets ( p-value = 0 . 02 ) . These results provide evidence that the genes in the signature modules are more likely to be good drug targets and drugs that target these proteins are more likely to treat many diseases . Yildirim et al . [28] showed that most drugs seemed to be palliative and only cured the symptoms of the diseases rather than the diseases themselves . Therefore , the enrichment for drug target genes which treat many diseases might be due to the shared symptoms of the diseases . In summary , this study demonstrates the value of an integrated approach in revealing disease relationships and the resultant opportunities for therapeutic applications . Looking forward , we aim to incorporate more gene expression data from GEO and other similar repositories , and expand the set of diseases in our disease-similarity network .
The gene expression data used in this analysis was obtained from the NCBI Gene Expression Omnibus ( GEO ) [11] . In this study , we restricted to using only those microarrays that were curated and reported in the GEO Datasets ( or GDS ) . We selected for microarrays that were assigned to human disease conditions . These assignments were made by the method explained in Butte et al . [29] . Briefly , the experimental context of a collection of microarrays from GEO ( or GEO Series , GSE ) can be obtained from the Medical Subject Headings ( MeSH ) [9] terms associated with the records of corresponding publications in PUBMED . Subsequently , the MeSH terms were connected to disease concepts using the Unified Medical Language System ( UMLS ) [30] . The GDS curation provided more details such as the tissue or biological substance from which the samples were derived . We only included those GSEs in which both disease as well as their corresponding control condition was measured in the same tissue ( cell type ) in the same experiment , using a previously described method [31] . We also manually selected for GSEs in which the control was a healthy sample . Further , we removed all GSEs that included time-series data to avoid complications arising due to temporal changes in gene expression . For consistency , we also restricted the GSEs to only those arrays which used Affymetrix Gene Chip Human Genome U133 Array Set HG-U133A or U95 Version 2 platforms , which are among the most commonly used platforms , mapping to current gene identifiers as previously described [32] . As both of these platforms have shared probe-sets , the bias of the platform used on the overall analysis would be reduced considerably . We subsequently selected GSEs that had at least two disease samples and two control samples . GEO contains some experiments ( GSEs ) that have gene expression measurements for more than one disease but share the same control measurements . Such measurements might induce correlations between their component diseases , which are not necessarily biological . Thus , to avoid bias , in all such cases , we included only one representative disease for each set of control samples in contrast to Hu et al . [10] . This entire process yielded 54 diseases for our final analysis . The protein-protein interaction ( PPI ) data for human was obtained from the Human Protein Reference Database ( HPRD ) [33] . This database contains PPI obtained from the two high-throughput yeast two-hybrid experiments [34] , [35] as well as through literature curation . Further , HPRD contains the maximum number of PPI of any of publicly available literature-derived databases for human PPI [36] . We filtered the PPI for human proteins that had a corresponding Entrez Gene ID , yielding 34 , 998 unique protein interactions spanning 9303 proteins in human . Previously , Sharan et al . [37] presented the PathBLAST family of network alignment tools . Briefly , these methods help identify conserved modules between protein networks of two ( or more ) species . Suthram et al . [38] also used it effectively to identify dense subnetworks corresponding to functional modules within a protein network of a single species . Applying the same approach here , we identified 4 , 620 functional modules in the human PPI network . First , we normalized the gene expression data in each microarray sample ( disease state or control ) using the Z-score transformation . This transformation allows for the direct comparison of gene expression values across various microarray samples and diseases . Next , we computed the activity level of a gene i in disease k as the t-test statistic ( Sik ) of its Z-transformed score between the disease and the control samples for each disease . In cases where there was more than one experiment ( or GEO Series ) for a given disease , we employed a meta-analysis technique using linear regression to obtain a combined t-test statistic . This approach takes into account the variations between different experiments in the calculation of the gene activity score ( Sik ) ( see section below ) . Finally , the module response score ( Mik ) for each module i in a disease k is assigned to be the mean of the gene activity scores ( Sik ) of its component genes . In the end , we obtained a vector of module response scores ( Mik ) for each disease . The t-test statistic between two conditions can be represented using linear regression . For instance , let Yi and Xi be gene expression values and disease state ( disease has a value of 1 and control a value of 0 ) , respectively . Then , we have a linear regression model as follows:where and are the parameters of the model . The t-test statistic when estimating the value of is the same as the standard t-test statistic between disease and control states . The advantage of the linear regression model is that we can add more terms to the model to account for other sources of variation such as the experiment number . In the present work , we expanded on the above model as follows:where n is the number of different experiments for a given disease and , is an indicator variable which is 1 if the i'th gene expression measurement is from the experiment number k . Again , the and are the parameters of the model which need to be estimated . The addition of the new terms allows for explicitly accounting for the effect of the experiment on the gene expression value . This approach is similar to a mixed effects model for adjusting for the within-experiment dependencies , but is more aggressive in removing such effects . Consequently , the t-test statistic in the estimation of will now be a combined metric for the different studies . The partial correlation coefficient gives the correlation between two variables , say x and y , keeping a third variable , z , constant . This method tries to measure the similarity between x and y , over and above that caused by their common dependency on z . The partial correlation can be calculated as follows: The above formula can be expanded to condition on two variables as follows:In this study , we calculated the Partial Spearman correlation between two diseases conditioned on the responses of the functional modules in their respective control samples . The response of a functional module in the control samples for a given disease was calculated as the mean of the z-transformed scores of its component genes . As a result , the Partial Spearman correlation coefficient provided a quantitative metric to assess disease similarity and also explicitly factored out the possible dependencies between different gene-expression experiments due to their underlying tissue or cell types [39] . Our approach is consistent with the findings by Dudley et al . [40] that the disease signal in the GEO datasets is stronger than the tissue signal and hence , implying that the observed disease correlations reflect true biology . We used the R script provided by Kim et al . [41] for calculating the Partial Spearman correlation between two diseases . To assign significance to the observed disease correlations , we created a background distribution of disease correlations expected at random . First , we randomized the gene to module assignments . We envisioned the gene to module assignments as a bi-partite graph ( Figure S3 ) where there exists a link between a gene and a module if that gene is a member of that module . We then randomized the graph by randomly swapping links . This process preserved the number of modules , the number of genes assigned to a module as well as the number of modules a given gene belongs to . Next , we also shuffled the disease and the control sample labels . We then calculated the MRS values for the modules using the randomized data and computed the corresponding disease correlations . Finally , we repeated the whole process 100 times to create a background distribution of disease correlations . We built a comprehensive disease-associated gene database , referred to as the Disease Gene List , by collecting genes known to be associated with various diseases from literature curation and large databases . In particular , we first curated 37 , 953 disease Single Nucleotide Polymorphism ( SNP ) associations from 2 , 679 papers , mapping 10 , 167 specific SNPs from the SNP Database ( dbSNP ) to 748 diseases and phenotypes . We then annotated each SNP with its corresponding gene ( s ) using dbSNP ( Chen and Butte , unpublished data ) . Next , we extracted genes that are significantly associated with diseases in Genetic Association Database ( GAD ) [42] . These consisted of associations that were reported to be positive at least once . We also collected genes that are associated with disorders in the Online Mendelian Inheritance in Man ( OMIM ) [5] . Lastly , we retrieved genes that are associated with diseases in the professional version of Human Gene Mutation Database ( HGMD ) [43] . Finally , we combined disease genes obtained from the above four different sources by relating them to Entrez gene IDs and removing outdated Gene IDs using AILUN [32] . The module figures in the paper were drawn using the Cytoscape software [44] . | Many human diseases are related to each other through shared causes or even shared pathology . Knowledge of these relationships has long been exploited to treat similar diseases with the same therapies . However , most of the traditional approaches to discover these relationships have depended on subjective measures , such as similarity in symptoms , or incomplete knowledge , such as genes with mutations . Here we present the first approach integrating high-throughput datasets such as mRNA expression and large-scale protein-protein interaction networks to discover human disease relationships in a systematic and quantitative way . We discover 138 significant pathological similarities between 54 human diseases ranging from lung cancer , schizophrenia , and malaria . We also discovered a set of common pathways and processes within the cell that are dysregulated in at least half of the diseases . We infer that these processes correspond to a common response of the human system to a disease state . Interestingly , we find that many of the proteins in these pathways are already known to be targets of existing drugs . In fact , the drugs corresponding to these proteins are known to treat significantly more diseases than expected by chance highlighting the importance of these common molecular pathological pathways as prime therapeutic opportunities . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [
"computational",
"biology/systems",
"biology",
"pharmacology/drug",
"development",
"pathology/molecular",
"pathology"
] | 2010 | Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets |
Parasites of the Leishmania genus infect and survive within macrophages by inhibiting several microbicidal molecules , such as nitric oxide and pro-inflammatory cytokines . In this context , various species of Leishmania have been reported to inhibit or reduce the production of IL-1β both in vitro and in vivo . However , the mechanism whereby Leishmania parasites are able to affect IL-1β production and secretion by macrophages is still not fully understood . Dependent on the stimulus at hand , the maturation of IL-1β is facilitated by different inflammasome complexes . The NLRP3 inflammasome has been shown to be of pivotal importance in the detection of danger molecules such as inorganic crystals like asbestos , silica and malarial hemozoin , ( HZ ) as well as infectious agents . In the present work , we investigated whether Leishmania parasites modulate NLRP3 inflammasome activation . Using PMA-differentiated THP-1 cells , we demonstrate that Leishmania infection effectively inhibits macrophage IL-1β production upon stimulation . In this context , the expression and activity of the metalloprotease GP63 - a critical virulence factor expressed by all infectious Leishmania species - is a prerequisite for a Leishmania-mediated reduction of IL-1β secretion . Accordingly , L . mexicana , purified GP63 and GP63-containing exosomes , caused the inhibition of macrophage IL-1β production . Leishmania-dependent suppression of IL-1β secretion is accompanied by an inhibition of reactive oxygen species ( ROS ) production that has previously been shown to be associated with NLRP3 inflammasome activation . The observed loss of ROS production was due to an impaired PKC-mediated protein phosphorylation . Furthermore , ROS-independent inflammasome activation was inhibited , possibly due to an observed GP63-dependent cleavage of inflammasome and inflammasome-related proteins . Collectively for the first time , we herein provide evidence that the protozoan parasite Leishmania , through its surface metalloprotease GP63 , can significantly inhibit NLRP3 inflammasome function and IL-1β production .
Leishmania parasites , which are the causative agent of leishmaniasis , are able to both survive and proliferate within macrophages . The protozoan parasites evolved strategies to avoid phagocyte activation during infection by seizing control of key signaling pathways [1 , 2] . Studies previously implicated the metalloprotease GP63—a major virulence factor of Leishmania parasites—in a variety of parasite survival mechanisms . In this context , GP63 has been suggested to affect amongst others Leishmania binding to macrophages , phagocytosis of parasites , evasion of complement-mediated lysis and protozoan migration through the extracellular matrix [1 , 3] . Furthermore , GP63 has been identified as a key Leishmania virulence factor that modulates cellular signalling through the subversion of host protein tyrosine phosphatase ( PTP ) function [4 , 5 , 6] . In this context GP63-mediated PTP-cleavage , results in the activation of the respective phosphatases . This mechanism was identified for the SH2 domains-containing protein tyrosine phosphatase ( SHP-1 ) and protein-tyrosine phosphatase 1B ( PTP-1B ) [6] . Besides phosphatases , GP63 has been shown to cleave other targets within the cells including kinases like TAB 1 and transcription factors , including AP-1 and NF-κB [7 , 8] . The importance of the host PTP-modulation and the subsequent inhibition of signaling pathways is emphasized by the observation that key pro-inflammatory mediators such as nitric oxide ( NO ) , IL-6 and TNFα were subsequently downregulated by Leishmania [4 , 5 , 9] . Another factor of pivotal importance for inflammatory processes that has been studied in the past in the context of Leishmania infections is IL-1β . In this regard Leishmania infections have been reported in a variety of studies to alter IL-1β production dependent on the parasite species used [4 , 10 , 11 , 12 , 13] . This included the deregulation of the IL-1β release due to parasite infections upon usage of known IL-1β inducers like LPS , IFN-γ or nigericin . However , the means used by the parasites to interfere with inflammasome activation remain unclear to date . IL-1β is translated as an inactive precursor—pro-IL-1β ( 31 kDa ) , which is processed into active IL-1β by the multi-protein inflammasome-complex upon stimulation of the cells . Integral components of the inflammasome complexes are caspase-1 , responsible for proteolytic cleavage of the IL-1β precursor , [14] a member of the NOD-like receptor ( NLR ) family , which acts as the sensor component of the inflammasome and ASC , a CARD/PYD protein that serves as a docking and activation platform for caspase-1 and the respective NLR [14] . Dependent on the NLR-protein within the complex , inflammasomes have been shown to respond to a variety of stimuli including bacterial and viral pathogen associated molecular patterns ( PAMPs ) like microbial nucleic acids or proteins and danger associated molecular patterns ( DAMPs ) [15 , 16] . In context of the latter , the NLRP3-containing inflammasome was found to be critical for the recognition of inorganic crystals such as malarial hemozoin ( HZ ) , silica and asbestos , as well as other DAMPs like cardiolipin , ATP and uric acid ( MSU ) [17 , 18 , 19 , 20] . Canonical NLRP3-inflammasome activation requires two signals . The first signal results in an increased expression of pro-IL-1β and the NLR protein as basal levels are insufficient to facilitate inflammasome activation . Typical initial signals are relayed through pattern recognition receptors such as TLRs or receptors for cytokines ( e . g . type I IFNs ) [15] . Furthermore , inducers of PKC and MAPK-dependent signaling such as phorbol myristate acetate ( PMA ) have been used as first signals for inflammasome activation in vitro [21 , 22] . The second signal induces the oligomerisation and complex formation of the inflammasome that allows the processing of pro-IL-1β . Complex formation and activation can be triggered in different ways including ROS generation , potassium efflux , lysosomal damage and mitochondrial destabilization or damage [23] . Following this rationale of NLRP3 inflammasome organization and activation , modulation—for instance after Leishmania infections—may take place at the level of priming , complex assembly or complex activation as has been shown for a variety of other pathogens that interfere with inflammasomes . In this study we report that Leishmania parasites , through its virulence factor GP63 inhibit IL-1β production and secretion induced by different NLRP3 inflammasome activators . Importantly , this was observable in both a murine and human model system for Leishmania infection . Dysregulation is achieved due to GP63-dependent interference with signaling pathways upstream of the inflammasome , which affect ROS generation . In this context our data suggests PKC signaling and its downregulation is pivotal for the Leishmania-mediated downregulation of inflammasome activation . In addition , Leishmania GP63 also seems to specifically target components of the inflammasome for proteolytic cleavage that is most likely the basis for the suppression of IL-1β production after ROS-independent activation of the NLRP3 inflammasome .
L929 and THP-1 cell lines were obtained from ATCC ( Manassas , VA , USA ) . Cells were cultured in RPMI-1640 medium supplemented with Penicillin-Streptomycin-Glutamine ( PSG; both reagents from Wisent , Saint-Jean Baptiste , QC , Canada ) and fetal bovine serum ( FBS; Gibco Burlington , ON , Canada ) . In some experiment the culture medium was exchanged to MEM Alpha medium ( Gibco , Burlington , ON , Canada ) . Reagents used included linezolid , ATP , silica and hemin ( > 99% of purity; Sigma-Aldrich , Oakville , ON , Canada ) ; GÖ6983 ( Biomol , Farmingdale , NY , USA ) ; asbestos ( Structure Probe , West Chester , PA , USA ) , MSU ( Alexis Biochemical , Farmingdale , NY , USA ) ; protease inhibitor cocktail ( Roche , Mississauga , ON , Canada ) and PVDF membrane ( BIO-RAD , Mississauga , ON , Canada ) . All others unlisted or not indicated reagents were purchased from Sigma-Aldrich ( Oakville , ON , Canada ) Antibodies used in experiments included anti-human NLRP3 and ASC ( Alexis Biochemical , Farmingdale , NY , USA ) , anti-human pro-IL-1β , anti-human and murine caspase-1 ( Santa Cruz; Dallas , TX , USA ) , anti-phospho-tyrosine/HRP ( eBiosciences; San Diego , CA , USA ) , anti-human mature IL-1β ( Cell signaling Technology , Danvers , MA , USA; Rockland-Immunochemicals , Limerick , PA , USA ) , phospho ( Ser ) -PKC-substrate antibody ( Cell signaling Technology; Danvers , MA , USA ) , anti-GP63 ( obtained from Dr . McMaster , University of British Columbia , Vancouver , Canada ) and anti-murine IL-1β ( R&D systems , Minneapolis , MN ) . Synthetic HZ was generated as previously described [24 , 25] . Briefly , 0 . 8 mmol crystalline hemin ( >99% of purity ) was dissolved in degassed NaOH ( 0 . 1M ) for 30 minutes with stirring . After , the pH was adjusted with propionic acid to 4 and the material was allowed to anneal at 70°C for 18 hrs . The supernatant was removed and the crystals were incubated three times with NaHCO3 ( 0 . 1M ) for three hours . In between incubations , samples were briefly washed with milliQ H2O . Thereafter , the crystals were washed three times with methanol and milliQ H2O in an alternating fashion . Subsequently , the samples were dried in a vacuum oven overnight over phosphorous pentoxide ( Sigma-Aldrich , Oakville , ON , Canada ) . Synthetic HZ samples were analyzed by X-ray powder diffraction , scanning electron microscopy ( SEM ) , and infra-red spectroscopy to characterize the crystalline state of HZ . Leishmania major ( L . major ) , L . mexicana , L . major GP63 knock out ( GP63 KO ) , and L . major GP63 rescue ( the GP63 gene was inserted into L . major GP63 KO parasites [26] ) were used in different experimental setups . All Leishmania parasites were maintained at 25°C in SDM-79 culture medium supplemented with 10% FBS by bi-weekly passage and used for different applications after 6–7 days of culture ( stationary phase ) . Stationary phase parasites were either used to infect macrophages ( at a ratio of 20:1 ) , to recover the culture supernatant for GP63 ( L . mexicana ) purification or to generate parasite secretome or exosome preparations . To generate Leishmania-conditioned medium ( LCM ) , all species of Leishmania were adapted to grow in DMEM medium ( Wisent , Saint-Jean Baptiste , QC , Canada ) supplemented with 10% of FBS and 1% of PSG . After 7 days in culture , LCM was collected by centrifugation of parasite cultures ( 1 , 000 x g , 5 min ) and subsequent filtration with 0 . 22 μm filters . Leishmania GP63 was purified using an immunoaffinity column . The antibodies used to purify Leishmania GP63 were specific to L . mexicana GP63 [27] . The antibody was cross-linked using the Affi-Gel HZ Immunoaffinity kit ( BIO-RAD , Mississauga , ON , Canada ) . GP63 was purified from the supernatant of stationary L . mexicana cultures and concentrated by centrifugation using Amicon Ultra centrifugational filters ( EMD Millipore , Etobicoke , ON , Canada ) at 4 , 000 rpm for 10 minutes and stored at -80°C . Exoproteome was prepared as described previously [28] . Briefly , stationary L . mexicana promastigotes were washed 3 times with PBS , incubated in phenol red-free and serum free DMEM ( Wisent , Saint-Jean Baptiste , QC , Canada ) for 4 hrs and culture supernatants were centrifuged twice at 4 , 000 rpm for 10 min . Subsequently , the material was either concentrated using 10 kDa cut off Amicon Ultra centrifugational filters ( EMD Millipore , Etobicoke , ON , Canada ) and used as secretome preparations or centrifuged ( 100 , 000 x g , 60 min at 4°C ) to isolate exosomes . Protein concentration was determined using Bradford reagent ( BIO-RAD , Mississauga , ON , Canada ) . THP-1 cells were cultured with RPMI-1640 medium supplemented with 10% FBS , 1% PSG , 50 μM of 2-β-mercaptoethanol , 4 . 5 g/L of Glucose and 1 mM sodium pyruvate . For THP-1 differentiation 1 . 5 x106 cells/mL were incubated with 0 . 5 μM of PMA . After three hrs cells were washed , plated ( 0 . 75 x 106 cells/mL in 6 wells plates ) and incubated for 20–24 hrs . As a consequence the phagocytic properties of the cells were increased and expression of inflammasome proteins and pro-IL-1β was induced . In some experiments THP-1 cells were incubated with 50 ng/ml PMA for 24 hrs . Cells were infected with indicated Leishmania spp . at a ratio of 1:20 ( macrophages:parasites ) or incubated with purified GP63 , secretome preparations , exosomes preparations or Leishmania culture medium ( LCM ) . Cells were washed after times of infection dependent on the experimental setup and the medium was replaced with MEM Alpha medium without FBS . Cells were subsequently stimulated with indicated concentrations of HZ , silica , asbestos , MSU or ATP for 6 hrs . Linezolid was incubated for 18 hrs . Bone marrow cells were obtained by flushing out the femurs and tibias from 6 weeks old C57Bl/6 mice . Subsequently , erythrocytes were lysed using NH4Cl ( 155 mM ) in Tris/HCl ( 10 mM ) , pH 7 . 2 . Bone marrow derived cells were counted , seeded and incubated in RPMI-1640 medium supplemented with 1% of PSG , 10% FBS and 30% ( v/v ) L929 cell culture supernatant . Cells were cultured for 7 days , exchanging the culture media every second day . For assays , BMDM were harvested and seeded ( 0 . 75 x 106/mL ) in RPMI medium supplemented with 5% FBS and 1% of PSG . The following day , cells were primed with LPS ( 100 ng/ml , 3 hrs ) and infected with Leishmania spp . at a ratio of 1:20 ( macrophages:parasites ) . After infection for variable time periods dependent on the experimental setup , cells were washed , medium was replaced with MEM-Alpha without FBS and cells were stimulated with indicated concentrations of HZ or linezolid for 6 hrs or 18 hrs respectively . Supernatants were collected at indicated time points and proteins were precipitated with trichloroacetic acid at a final concentration of 10% . Precipitated proteins were dissolved in Tris/HCl 0 . 1 mM pH 8 . 0 and laemmli sample buffer [29] . Cell extracts were obtained by lysing cells with either Igepal ( Sigma-Aldrich , Oakville , ON , Canada ) containing lysis buffer ( 1% Igepal in PBS , 20% Glycerol , protease inhibitor cocktail , 2 mM Na3VO4 and 1 mM NaF ) or for caspase-1 detection Triton-X-100 ( Fisher Scientific , Walham , MA , USA ) containing lysis buffer ( 1% Triton-X-100 in 10 mM Tris/HCl pH 7 . 5 , 150 mM NaCl , 5 mM EDTA and protease inhibitor cocktail ) . Supernatant and cell lysate samples were subjected to SDS-PAGE and immunoblot analysis . SDS-PAGE/Immunoblot: SDS-PAGE and Immunoblot were performed following protocols previously published [30] . For the detection of caspase-1 p10 , 4–12% NuPAGE gels ( Invitrogen ) were used . After protein transfer onto PVDF membranes , detection of target proteins was achieved through specific primary antibodies and matched secondary HRP-conjugated antibodies . NUNC maxisorb 96 well plates ( Nalge NUNC , Richester NY , USA ) were coated with 100 μl/well of capture antibody ( SET TO GO kit , eBiosciences , San Diego , CA , USA ) overnight and blocked with 200 μl/well assay diluent solution 1 hr at RT . After blocking , 100 μl of standard proteins or samples were added to each well and incubated for 2 hrs . After 5 washes , 100 μl/well of detection antibody were added and incubated 1 hr at RT . For cytokine detection , 100 μl/well of Avidin-HRP were added and incubated for 30 min . Afterwards , 100 μl/well of substrate solution were added for 15 min . 50 μl of stopping solution were added and plates were read at 450 nm in an ELISA reader ( Elmer EnSpire Multimode Plate Reader , Perkin Elmer , Waltham , MA , USA ) and concentrations were calculated according to a standard curve . PMA-differentiated THP-1 cells ( 0 . 1x106 cell/100 μl ) were seeded in opaque 96 well plates . Cells were infected as indicated with Leishmania parasites for 2 hrs . Cells were washed with PBS and incubated with phenol red free RPMI ( Wisent , Saint-Jean Baptiste , QC , Canada ) containing 20 mM of 2 , 7-dichlorofluorescein diacetate—DCFH-DA ( Sigma-Aldrich , Oakville , ON , Canada ) for 10 min at 37°C . Subsequently , cells were stimulated as indicated adding inflammasome activators . The rate of DCFH-DA oxidation to DCF was observed with a SpectraMax M3 ( Molecular Devices , Sunnyvale , CA , USA ) fluorescent plate reader at a 488 nm excitation wavelength and a 525 nm emission wavelength . Unpaired Student’s t-test was used when comparing two groups . The differences were considered significant for p < 0 . 05 . Statistical analysis was performed using Prism 5 . 00 software ( GraphPad , San Diego , CA ) . C57BL/6 mice were purchased from Charles River Laboratories and Jackson Laboratories , and were kept in pathogen-free housing . All research involving mice was carried out according to the regulations of the Canadian Council of Animal Care and was approved by the McGill University Animal Care Committee under ethics protocol number 4859 . Mice were euthanized using CO2 asphyxiation followed by cervical dislocation .
Upon activation macrophages can produce a large array of pro-inflammatory molecules including IL-1β , which is produced by inflammasome complexes . The NLRP3 inflammasome acts as an intracellular signaling platform which is able to sense a variety of exogenous signals like asbestos , silica [17] as well as the malarial pigment HZ [20 , 31] and DAMPs such as ATP [18] and MSU [21] . In initial dose-response experiments using synthetic HZ we confirmed the ability of HZ to induce IL-1β maturation in PMA-differentiated THP-1 cells ( S1 Fig ) . The observed , dose-dependent IL-1β secretion was comparable to the results obtained in the case of silica or MSU treatment of cells . Infection of THP-1 cells with Leishmania did not induce IL-1β release as shown for L . mexicana ( S2 Fig ) . Leishmania parasites are well known for their ability to block and inhibit various microbicidal functions of macrophages [32] , therefore we sought to elucidate whether infections with Leishmania would inhibit IL-1β production by macrophages , which has been indicated to possibly serve a host protective role in murine models of infection [33 , 34] . As previously introduced the NLRP3 inflammasome activator HZ causes the IL-1β maturation and secretion in PMA-differentiated macrophages . Pre-infection with L . mexicana and L . major , revealed a parasite-dependent block of IL-1β maturation and release ( Fig 1A ) . The impaired production of IL-1β was not restricted to a system of human cell culture but was also observable when parasite infection preceded inflammasome activation in murine BMDMs ( Fig 1C ) . In those experiments due to the availability of antibodies the processing of caspase-1 into its active fragments p10 and p20 could be observed . Notably processing of caspase-1 was absent after infection of cells possibly due to the lack of inflammasome activation or complex formation . Identical experimental setups for infections as in Fig 1A using L . major GP63 KO and L . major GP63 rescue parasites support the hypothesis that Leishmania’s capability to inhibit IL-1β maturation and release was GP63-dependent ( Fig 1A–1E ) . Thus , IL-1β secretion was not impaired in the absence of the protease ( Fig 1A ) . This finding was further supported by experiments using pretreatment of THP-1 cells with Leishmania culture supernatant ( LCM ) instead of parasites ( Fig 1B ) . The attenuated IL-1β secretion coincided with the presence of GP63 in the LCM . We were able to show that LCM of L . mexicana and L . major GP63 wild type parasite cultures contained GP63 ( Fig 1D ) . To further evaluate the impact of secreted leishmanial factors like GP63 on IL-1β production the supernatant of L . mexicana cultures was concentrated and used in titration experiment on PMA-differentiated THP-1 cells ( Fig 1E ) . IL-1β maturation and release were stimulated by a variety of known NLRP3 inflammasome inducers , namely HZ , silica and asbestos . In all cases we observed an inhibition of IL-1β secretion in a dose-dependent manner by the L . mexicana culture supernatant . Leishmania GP63 can be found either intracellular in the protozoan endoplasmic reticulum , membrane bound via a GPI anchor or secreted without the GPI anchor . During infection GPI anchored , membrane bound GP63 ( GPI-GP63 ) can also be cleaved and released into the supernatant [2 , 35] . Therefore , the similarities of our results using either Leishmania infections or Leishmania culture supernatant are most likely to be attributed to the presence and activity of GP63 . In recent years , several studies have described the observation that proteins are secreted as exosecretome by Leishmania parasites upon 37°C temperature shock [28] or as exosomes during the culture of parasites [36 , 37] . Both Leishmania secretome and exosomes have been shown to contain GP63 . Thus , we evaluated whether leishmanial secretome or exosome preparations would inhibit IL-1β maturation and secretion . As expected the results were in accordance with the data acquired using parasite infections and culture supernatant treatment of cells , with both secretome and exosomes inhibiting IL-1β production induced by either HZ or MSU ( Fig 2C ) . The importance of the metalloprotease GP63 was clearly demonstrated through experiments using purified GPI-GP63 ( Fig 2A and 2B ) from L . mexicana stationary phase cultures to pretreat THP-1 cells . This resulted in an attenuation of the HZ-induced IL-1β maturation and its release ( Fig 2B ) identical to infection experiments previously shown . Collectively , these results provide convincing evidence that Leishmania GP63 is the causative factor for an impaired IL-1β production by the NLRP3 inflammasome complex , which was observed after infections with Leishmania parasites prior to cell stimulation . Activation of the NLRP3 inflammasome due to DAMPs is often associated with ROS production and ROS-induced or -dependent signaling [38 , 39] . In this context the molecular basis of ROS generation has been under debate for some time and recent hypothesis include damage to mitochondria as a possible ROS-source and propose thioredoxin-interacting protein TXNIP may act as a ROS-sensor [40] . Leishmania has been shown to interfere with the generation of ROS and other microbicidal molecules [15] and has been described to be involved in the inflammasome activation [15] . Using known danger molecules like HZ and silica we determined , that both crystalline agents readily induce the generation of ROS in THP1 cells ( Fig 3A ) . Therefore , we hypothesized that a Leishmania-dependent decrease of ROS-species or an impaired ROS production could be the basis of the diminished IL-1β maturation/release previously observed . Consequently , infection of THP-1 cells with L . mexicana led to an abrogated ROS production even after HZ or silica stimulation , supporting our hypothesis ( Fig 3B ) . As our previous results suggested the possibility of a GP63-mediated inflammasome suppression , we included purified L . mexicana GP63 ( pGP63 ) in our experimental setup . Pretreatment of THP cells with pGP63 from L . mexicana supernatant was also sufficient to reduce ROS levels to a similar extend as Leishmania infections ( Fig 3B ) . We previously presented evidence , that HZ-induced NLRP3 inflammasome activation is dependent on Syk activation and signaling [20] . Furthermore , in a variety of studies it has been suggested that Syk activation in turn can be coupled to PKC signaling [41] . PKC activation has previously been associated with ROS production [42 , 43] . Therefore , we sought to analyze , whether HZ affects PKC activation as well as PKC-mediated phosphorylation and if PKC-dependent signaling may be of importance for the HZ-driven ROS production and inflammasome activation . The analysis of PKC-dependent protein phosphorylation in PMA-differentiated THP-1 cells and BMDMs revealed , that HZ indeed led to an increased phosphorylation of PKC substrates ( Fig 4A and S3 Fig ) . Specific inhibition of PKC using the PKC-inhibitor GÖ6850 [44] was able to counteract the augmented PKC-substrate phosphorylation levels after application of HZ ( S4 Fig ) . The examination of ROS production after the loss of PKC-dependent phosphorylation and PKC-signaling revealed a significant decrease of intracellular ROS-generation in both THP-1 cells ( Fig 4B , upper panel ) and LPS-primed BMDM ( Fig 4B , lower panel ) . Additionally , to analyze whether PKC-mediated ROS generation was connected to the attenuated IL-1β maturation and/or release we investigated IL-1β levels after PKC-inhibition . In accordance with the data shown previously , IL-1β maturation is abrogated after suppression of PKC-dependent signaling through the application of the PKC inhibitor GÖ6850 ( Fig 4C ) . Interestingly , we already established in the past that PKC activation can be negatively modulated by Leishmania infections [45] . Consequently , experiments with L . mexicana preceding HZ stimulation showed that PKC-dependent phosphorylation in this context is clearly altered by Leishmania parasites presenting a possible explanation for our previous observations ( Fig 4D ) . Through the use of L . major GP63 wild type ( Lmj WT ) and L . major GP63-/- ( Lmj KO ) parasites we were able to further substantiate the dependency of PKC-dependent ROS-reduction on GP63 activity in both THP-1 and BMDM cells ( Fig 4E ) Taken together our results suggest that PKC activation is a signaling event upstream of IL-1β production after HZ stimulation , which is disrupted by Leishmania . In conclusion the analysis of ROS-production and PKC-signaling after infection , the use of pGP63 , GP63-/- parasites and the chemical inhibition of PKC , suggest that PKC-dysregulation most likely through GP63 impairs IL-1β release . Our previous data showed that Leishmania infections of macrophages prevent the maturation of pro-IL-1β to mature IL-1β upon stimulation possibly intervening with a host protective effect . Thus far , the impairment of IL-1β after infection was attributed to a suppression of PKC-dependent signaling and the loss of ROS production . As the enclosed data supports that GP63 is closely associated with these events , we wanted to examine if Leishmania parasites and GP63 may also interfere with inflammasome activation through proteolytic cleavage of inflammasome components . We and others demonstrated that GP63 can cleave targets containing the following amino acid-motives: polar/hydrophobic/basic/basic amino acids ( P1- P’1-P’2-P’3 ) [43 , 46] . A first indication for GP63-dependent cleavage of inflammasome components was obtained by experiments using BMDMs . There , we observed that after infection with Leishmania we were able to observe cleavage of pro-IL-1β ( Fig 1C ) . An in silico sequence analysis for putative GP63 cleavage sites revealed the possibility of additional GP63 cleavage sites in the sequences of inflammasome complex and associated proteins . Thus , the sequences of human and murine NLRP3; pro-IL1β and TXNIP—a protein that has been suggested to possibly be involved in ROS-mediated inflammasome activation [40]–contain putative cleavage sites for GP63 ( Fig 5A and 5C ) . As GP63 facilitated cleavage is not necessarily restricted to the proposed cleavage motif and to confirm our in silico findings we performed Western blotting analysis of infected THP1 and LPS-primed BMDM cells . In accordance with the in silico data , GP63 seemed to be able to directly interfere with the inflammasome complex . Thus , we observed cleavage of NLRP3 after infection with Leishmania ( Fig 5 ) . This process was GP63-dependent as illustrated by the results for L . major wt , GP63KO and GP63 rescue parasites . Although , the in silico analysis did not suggest a GP63-mediated cleavage of ASC or Caspase-1 we choose to analyze both as they are an integral part of the inflammasome complex . Neither pro-caspase-1 nor ASC showed any cleavage after infection . In addition , as anticipated cleavage and/or cleavage fragments were detected for pro-IL-1β and TXNIP in lysates of cells infected with L . mexicana , L . major wt or L . major GP63 rescue expressing GP63 , but not in cells infected with L . major GP63 KO . Taken together our data suggests that Leishmania is able to impair inflammasome activation through different GP63-dependent alterations of proteins and signaling pathways . A controversial question of inflammasome activation is the dependency of ROS for the activation and assembly of the complex . Recent data associates mitochondrial damage with the activation of the NLRP3 inflammasome . In this context cardiolipin seems to work as a DAMP , able to induce inflammasome complex formation and ultimately IL-1β . The previous data presented different ways how leishmanial GP63 can suppress inflammasome activity . This included the cleavage of NLRP3 . Therefore , we wanted to know whether these processes might also have a direct effect on IL-1β production . Thus , we observed the IL-1β generation in a ROS-independent experimental setup using the antibiotic linezolid [19] . Linezolid stimulation of either THP-1 or LPS-primed BMDM cells resulted in the maturation of IL-1β as previously published ( Fig 6A and 6B ) . When cells were infected prior to linezolid stimulation , IL-1β release appeared reduced for both murine and human cells . In accordance to previously shown data Leishmania infection alone did only lead to a minimal production of mature IL-1β ( Fig 6A ) . Taken together this finding may indicate , that the infection of cells with Leishmania can abrogate both ROS-dependent and -independent inflammasome activation , possibly through different mechanisms as both ROS-inducing signaling events are blocked and inflammasome components are cleaved due to the leishmanial protease GP63 .
Leishmania parasites have evolved many mechanisms to hijack macrophage microbicidal functions in order to survive and proliferate within the phagocytes . In the present work we addressed how Leishmania parasites can attenuate IL-1β production through the leishmanial virulence factor GP63 during infection . In the past , the activity of inflammasomes and the associated production of especially IL-1β has been correlated with the host protection against parasitic infections , for instance in the case of T . cruzi or T . gondii [47 , 48 , 49] . In the case of Leishmania parasites the importance and the role of inflammasomes and IL-1β is very controversially discussed , mainly due to the use of different Leishmania species , different leishmanial developmental stages and different infection models . Previous work indicated the possibility of a species-dependent dysregulation of inflammasomes and inflammasome-related pathways and implicated different leishmanial virulence factors . Reports showed that L . donovani and L . tropica do not induce IL-1β production , and negatively modulate the capacity of IFN-primed human or LPS-primed murine peritoneal macrophages to produce IL-1β upon activation [10 , 50 , 51] . The focus of several studies was a parasite-mediated dysregulation of IL-1β on a transcriptional level after infection of human [52 , 53] or murine phagocytes [54] . In this context , Hatzigeorgiou and collaborators [55] implicated a LPG-dependent interference with IL-1β mRNA that translated into both decreased stability and production of IL-1β mRNA and consequently reduced transcription of the IL-1β gene [52 , 55] . However , data of Cillari et al . [54] and Gurung et al . [56] indicated that L . major infections might increase inflammasome activity and cytokine production during long term infections . Some reports indicate that the role the inflammasome is dependent on the model for infection with a possible delay in the resolution of cutaneous lesions in the absence of IL-1β [33 , 34] . Thus indications exist for a possible inflammasome-mediated host protective mechanism in some murine models of infection . In contradictory reports working with the new world Leishmania species L . amazoniensis , parasites have been described to be able to both suppress and induce IL-1β secretion by infected cells . On the one hand a study by Ji et al . provided data that the infection of C57Bl/6 mice with L . amazoniensis led to a delay in the secretion of chemokines and cytokines including IL-1β in vivo [11] . On the other hand a recent report of Lima-Junior et al . presented data that inflammasomes and IL-1β are involved in the control of L . amazoniensis infections of C57Bl/6 mice as shown by in vitro and in vivo studies using mice and BMDMs of deficient in IL-1β production ( including caspase-1 and NLRP3 KO mice ) [13] . Our infection experiments with L . major and L . mexicana revealed an attenuated capability of macrophages to produce and secrete IL-1β when stimulated with the specific and well characterized NLRP3 inflammasome agonist HZ [20] . This we observed in C57Bl/6-derived BMDMs as well as after infection of human cells , which may indicate a role in the circumvention of a host protective mechanism by Leishmania . In PMA-differentiated THP-1 cells as well as in LPS-primed macrophages , L . major and L . mexicana inhibited NLRP3 inflammasome activation as indicated by reduced levels of secreted IL-1β . A possible explanation to the divergent result to previous reports , like Lima-Junior et al . [13] , could be the difference in Leishmania spp . used . As introduced before , especially for L . amazoniensis previous data has been controversial . Furthermore , it is to be noted , that L . amazoniensis exhibits a rather unique pathogenesis and a very peculiar intracellular compartmentalization after host infection , which is not observable with the species used in our report [57] . Another crucial difference is potentially the experimental setup , specifically the time of incubation used to detect IL-1β production . It is in fact conceivable that longer periods of infection as examined in the work of Lima-Junior et al . may affect the secretion of IL-1β through cell death related events [58 , 59] . On this note , data published by Gomes et al . is noteworthy [60] . In their experiments using L . braziliensis IL-1β production was dependent on the developmental form of the parasite used . Infections with amastigotes led to IL-1β maturation while promastigotes did not . Thus the transition from promastigotes to amastigotes during infection may be of the essence for a Leishmania-mediated effect on IL-1β maturation . In addition , we want to point out the fact that infection in our experiments preceded inflammasome activation while previous work predominantly analyzed IL-1β levels over time after infection in vivo or after stimulation of cells with an inflammasome inducing agent like LPS and subsequent infection in vitro [11 , 13] . Collectively , our results may indicate that an initial block of IL-1 maturation may prevent a host protective effect , thus facilitating parasite survival . Importantly , none of the previous reports analyzed the possibility of a GP63-mediated effect on IL-1β maturation . In our study , we were able to observe an attenuated IL-1β production using not only parasites but Leishmania culture supernatants , leishmanial secretome and exosome preparations as well , all of which have been shown to contain the metalloprotease GP63 [2 , 28] . Moreover , purified GP63 exhibited similar effects when used on human THP-1 cells prior to inflammasome activation with HZ . We previously showed that the malaria pigment HZ elevates ROS-levels leading to inflammasome activation [20] . ROS have been shown to contribute to parasite clearance and are inhibited by Leishmania parasites [1]—an effect that can be mediated through the activity of the metalloprotease GP63 . Although at this point we cannot rule out the involvement of other leishmanial factors in the inhibition of ROS generation , our data and the usage of purified recombinant GP63 strongly suggests an important role of the protease in this context . Our work identified PKC-signaling as the mechanism upstream of the observed ROS induction after treatment of THP-1 cells with HZ . In the past , different studies presented evidence for a role of PKC both upstream and downstream of ROS generation [42 , 43 , 61] . Our results show that PKC-signaling and especially PKC-dependent ROS-generation can mediate inflammasome activation . Some studies previously indicated similar implications for PKC in the activation of the NLRC4 inflammasome [62] . In this context PKCδ-mediated NLRC4-phosphorylation was suggested as the basis of the observed effects [62] . Thus , we here clarify how PKC signaling may also affect NLRP3 inflammasome activation in response to DAMPs like HZ . In agreement with previous data we were also able to establish that Leishmania is capable to alter the previously introduced HZ-mediated PKC activation after Leishmania infection . As a consequence stimulated cells exhibited a loss of IL-1β production . Initial reports using L . donovani suggested that LPG was involved in the alteration of PKC-signaling as purified LPG prevented PKC activation in macrophages after stimulation with either LPS or PKC-activators [63] . Nevertheless , it has been described in recent years that the inhibition of the oxidative burst in macrophages as well as the associated signaling events , including PKC , were in part mediated by the parasites surface molecules LPG and GP63 [45 , 64] after infection . Our findings are also corroborated by previous reports that revealed a GP63-mediated interference with PKC-signaling and PKC targets . In this regard , Corradin et al . presented evidence that the PKC substrates myristoylated alanine-rich C kinase substrate ( MARCKS ) -related protein ( MacMARCKS ) and myristoylated alanine-rich C kinase substrate ( MARCKS ) , the latter which is of importance for cell motility , adhesion , endo- , exo- and phagocytosis as well as for the interplay of calmodulin and PKC signaling , [65] are targeted by GP63 for cleavage [66 , 67] . The interference of pathogens with inflammasomes has been shown in a number of bacterial or viral infections . This includes the expression of decoy proteins that bind NLRs or ASC , factors that block caspase-1 activity or scavenger receptors for IL-1β [68] . Interestingly , this also includes Zmp1 a Zn2+-metalloprotease expressed by Mycobacteria spp . that interferes with caspase-1 activation [69] . The leishmanial Zn2+-metalloprotease GP63 has been shown to facilitate cleavage of a multitude of cellular substrates , most notably cellular phosphatases . In silico data using the GP63-cleavage motif [46] indicated a possible GP63-mediated processing of inflammasome or inflammasome-associated proteins , including NLRP3 and pro-IL-1β , which we were able to confirm in infection experiments of both murine BMDMs and human THP-1 macrophages . Interestingly , we observed that one of the GP63-cleaved proteins was TXNIP , which has been shown to facilitate ROS-dependent inflammasome activation [40] . Thus , our data indicates that Leishmania may employ different GP63-linked strategies to impair secretion and maturation of IL-1β during infection , the downregulation of ROS on the one hand and the cleavage of inflammasome and inflammasome-related proteins on the other hand . The relevance of the latter mode of inflammasome inhibition is illustrated by the diminished release of IL-1β after stimulation of cells with linezolid , a ROS-independent inflammasome inducer [19] . IL-1β has been associated with the control of parasitic infections possibly including various Leishmania species . Collectively , we here provide evidence that Leishmania major and mexicana parasites are able to dampen IL-1β secretion during initial stages of infection , rendering cells non-responsive towards stimulation of the NLRP3 inflammasome . This may substantiate a host protective mechanism that has been suggested previously [33] . Moreover , we here show that the observed reduction of IL-1β maturation after infection takes place in both a murine and a human infection model . Our finding that the parasites can impair cytokine secretion through both the downregulation of ROS and possibly the proteolytic cleavage of inflammasome and inflammasome-related proteins strongly supports an important role of this mechanism in the formation of infection . Thus , our data presents a novel way whereby Leishmania ensures the infection of their target cells emphasizing the parasites ability to overcome host protective functions during infection . | Leishmania parasites are the causative agent of leishmaniasis , a wide spread disease in tropical and subtropical areas . The microorganisms have been shown to be well-adapted to their hosts and are able to enter their target cells where they replicate themselves . To ensure these processes , Leishmania disrupts a multitude of cellular signals and protective mechanisms , which overall attenuates immune responses against the parasites . A key factor for inflammatory processes , also during infections , is IL-1β . As previous studies suggested a dysregulation of IL-1β levels after infection with Leishmania parasites , we herein investigated the underlying mechanisms . Our work reveals that Leishmania suppressing IL-1β production through its virulence factor GP63 . Furthermore , our data suggests that the parasites can dampen the maturation of IL-1β after different stimuli . In this regard we established a role for the suppression of the kinase PKC and the generation of reactive oxygen species , as well as the cleavage of cellular proteins that are important for IL-1β-generation . Thus , we here present a novel aspect for how Leishmania parasites can counteract host protective mechanisms . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [] | 2015 | PKC/ROS-Mediated NLRP3 Inflammasome Activation Is Attenuated by Leishmania Zinc-Metalloprotease during Infection |
Network-based infectious disease models have been highly effective in elucidating the role of contact structure in the spread of infection . As such , pair- and neighbourhood-based approximation models have played a key role in linking findings from network simulations to standard ( random-mixing ) results . Recently , for SIR-type infections ( that produce one epidemic in a closed population ) on locally tree-like networks , these approximations have been shown to be exact . However , network models are ideally suited for Sexually Transmitted Infections ( STIs ) due to the greater level of detail available for sexual contact networks , and these diseases often possess SIS-type dynamics . Here , we consider the accuracy of three systematic approximations that can be applied to arbitrary disease dynamics , including SIS behaviour . We focus in particular on low degree networks , in which the small number of neighbours causes build-up of local correlations between the state of adjacent nodes that are challenging to capture . By examining how and when these approximation models converge to simulation results , we generate insights into the role of network structure in the infection dynamics of SIS-type infections .
There is a strong and deep connection between networks and the spread of infectious diseases [1–9] . Virtually all infections can be thought of as propagating through a network of ( epidemiologically-relevant ) contacts between individuals in the population , with the structure of this underlying network determining much of the infection dynamics . Therefore an understanding of population-level transmission at the scale of individual hosts is closely linked to a study of the properties of the underlying transmission network . Recent advances in network science have highlighted how both local and global structure of the network are key in the dynamics of infection [2 , 10–13] . While networks are being increasingly used for airborne and close-contact infections ( such as influenza [14] and RSV [15] ) which spread through social contacts , the epidemiological network literature was originally formulated for sexually transmitted infections ( STIs ) where the network is generally more clearly defined . Classic examples include homosexual contact networks from early HIV studies [1] and the Colorado-Springs study of sexual contacts in the high-risk heterosexual population [16] . While a focus on STIs has substantial advantages in terms of determining the network , it also places constraints on the epidemiological dynamics that need to be considered . The overwhelming majority of STIs ( e . g . chlamydia or gonorrhoea , although not HIV ) can be approximated using the Susceptible-Infected-Susceptible ( SIS ) paradigm , where infected individuals are treated and recover to the susceptible state , and hence are able to be re-infected multiple times . Although SIS models are inherently lower-dimensional than their SIR ( Susceptible-Infected-Recovered ) counterparts , potential reinfection of the same individual ( multiple times ) leads to more complex dynamical behaviour between neighbouring nodes on a network and makes it more difficult to generate tractable results [17] . When details of the complete network are available , and we are dealing with a particular applied problem , then the most straightforward approach is to simulate the dynamics of infection on the given network ( e . g . [5 , 16 , 18] ) . However in anything but ideal circumstances simulation may be problematic . For example , using simulations alone: it may be difficult to understand sensitivity to elements in network structure or biases in the way the network connections were sampled; it is computationally challenging to infer epidemiological parameters; and it may be difficult to gain a robust understanding of the causal determinants of the observed dynamics . Approximations that maintain the analytic tractability of traditional ODE ( Ordinary Differential Equation ) models , but take account of elements of network structure provide a possible solution . These have been quite successful for ‘one off’ epidemics that obey the SIR paradigm , with notable advances including the ‘effective degree’ approach of Ball and Neal [19] , the probability-generating function approach of Volz [20] ( and its reduction to a single dynamical equation by Miller [21] ) and the model of Lindquist et al . [22] ( originally also called ‘effective-degree’ model , but to which we refer throughout as ‘neighbourhood model’ to avoid confusion with the earlier use of this terminology ) . Such models , together with pairwise or related approximations [23 , 24] discussed extensively in this paper , have been shown to be exact methods of calculation of marginal probabilities for the stochastic SIR model for finite explicitly known networks [25–28] . Such models can also reproduce the expected course of the stochastic SIR model with constant infection and recovery rates on large configuration model networks with with several recent asymptotic proofs of convergence published [29–32] . Much work has also focussed on extending such methods to weighted [33] and dynamic [34] networks , as well as to models with arbitrary duration of the infectious period [35–37] , with the common denominator that on clustered networks results from all approaches are only approximate . Despite all these successes concerning SIR models ( or related models such as SEIR , which includes an exposed period [17 , 24] ) , the same is not generally true for infections without long-lasting immunity , with realistic demographic turnover or with significant viral mutation [38 , 39]—i . e . , the majority of pathogens of interest . Here we focus on STIs since the motivation for use of a network is strong [40] . These diseases are of major public health importance and the appropriate modelling framework ( the SIS model on a network , also called the ‘contact process’ and frequently considered in theoretical studies ) is the most challenging for approximation models to capture . To fully predict the dynamics and hence the impact of control on a range of sexually transmitted infections requires mathematical models that can account for both network structure of sexual partnership and the complications that arise from reinfection that is associated with SIS-type behaviour [40] . Here we consider three distinct approaches to capture the dynamic build-up of correlations between nearby individuals on the network—each approximation methodology has an associated integer that can be increased to achieve greater levels of accuracy . We stress , however , that our approach does not rely on special features of the SIS model but can be applied to the full spectrum of disease-dynamics models used to inform applied epidemiology and public health ( including those with short-term immunity and hence SIRS-type dynamics ) . Although the long-term aim is to utilise such approximation techniques to gain a clear understanding of the dynamics of STIs ( as well as other infections that confer short-duration immunity ) on realistic networks , we focus this paper on understanding when simple modelling techniques fail . One occasion when simple approaches fail is in the case of extreme heterogeneity [41–44] . Although risk-structure ( or heterogeneity in network structure ) is a highly important aspect of modelling STI—especially in terms of defining individual risk—we argue that many studies have focussed on this aspect [45 , 46] and that it is usually possible to capture epidemiological effects of population heterogeneity by modestly increasing the system’s dimension through the introduction of multiple risk-groups ( e . g . low- and high-risk behaviour ) . In contrast to the heterogeneous case , the impact of a limited number of contacts and the build-up of dynamical correlations in the state of neighbouring nodes is a less studied issue that presents deep conceptual challenges , especially for SIS dynamics . We therefore focus on developing a better understanding of this problem by ignoring many realistic features of STIs ( we briefly comment on them in the discussion ) and considering the idealised case of a homogeneous degree or ‘k-regular’ networks with low connectivity and hence greater importance of the link with each contact ( in particular k = 3 and k = 2 ) . In these remarkably simple networks , the effects of local correlations are at their strongest and are not masked by the impact of degree heterogeneity . To illustrate this concept we compare simple risk-structured mean-field ( random-mixing ) models ( which account for degree heterogeneity within the network but not correlations that develop due to contact structure ) with results from stochastic network simulations of SIS infection dynamics ( Fig 1 ) . This example demonstrates that when either the mean degree or the variance in the degree distribution increases , so the standard risk-structured ODE model provides a better fit to the simulated dynamics . The agreement between these simple models and simulations is worst for a homogeneous degree 3 network and hence it is this test scenario we predominantly consider throughout this paper .
In this work , simulation models and traditional mean-field approximation models ( that ignore network structure ) represent two extremes in terms of analytic tractability and computational efficiency . Two approximate models for SIS-dynamics ( Materials and Methods ) have been developed that lie between these extremes: pairwise and neighbourhood approximations . Pairwise approximations [45 , 48–50] consider the dynamic states of pairs of individuals that are connected in the network and hence capture some of the build-up of local correlations within the network . Neighbourhood approximations [22] have appeared more recently and can be conceptualised as a more sophisticated , though higher-dimensional , extension to the pairwise approximation . Neighbourhood approximations model the number of connected individuals of each type around a central individual; for SIS dynamics this is simply the number of S and I connected to a central individual of a given state . This means that neighbourhood models capture higher-order correlations within the network , as they effectively model multiple chains of three connected individuals sharing the same central node . We consider methods to extend the pairwise and neighbourhood approaches; either increasing the size of the subgraph considered ( e . g . going from modelling pairs to modelling triple motifs ) or increasing the number of node states by counting infection events . Subgraph or motif expansions to the pairwise models track the dynamics of the possible states of increasingly larger motifs or subgraphs of m connected individuals within the network . Clearly as m becomes large , we precisely account for the full dynamics on larger sections of the network and hence expect our approximations to become more accurate . However with increasing m comes increasing number of motifs and also higher dimension dynamics . For degree k = 3 networks we consider motifs of size m = 1 ( the standard mean-field model ) , m = 2 ( the traditional pairwise model ) as well as m = 3 and m = 4; for the special case of degree k = 2 networks , we are able to consider larger motifs up to m = 16 due to the linear structure of all k = 2 motifs ( see Fig 2 ) . Similarly , it is feasible to expand the neighbourhood model , which we index by parameter n . Again we consider n = 1 to be the standard mean-field model , while n = 2 accounts for the states of all neighbours of a central individual , and expansions to neighbours of neighbours ( n = 3 ) is also possible although of very high dimensional for networks of k = 3 or above . The reinfection counting extension explicitly tracks the number of times an individual has been infected , effectively increasing the number of states for each individual ( i . e . disease state × number of times infected ) . To create a finite system , we track the infection times up to a maximum of L ( which now incorporates all those individuals infected L times or more ) . The motivation for this extension derives from a failure in traditional SIS pairwise models to account for the correlation between infected and newly recovered individuals . This extension should therefore improve the performance of the approximation model during the early stages of invasion when infection is rare . However , the long-term equilibrium dynamics when all individuals have been infected L times or more ( assuming the infection persists ) , will be identical to that of the pairwise model . Although we take the simulation model as our gold-standard , deriving precise values for particular quantities is often computationally intensive and naive methods can be improved . For early epidemic growth rates , we generate a finite Cayley tree ( thereby eliminating all clustering ) and study the dynamics until infection hits an outer leaf . For endemic prevalence it is not possible to use a Cayley tree; any finite Cayley tree must have lower degree at the outer leaves which would influence the dynamics . Instead , we generate large networks using the Molloy-Reed ( or configuration ) algorithm [47] , and ensure that self connections , multiple connections between nodes and short loops ( of five or less connections ) are removed by randomly shuffling connections . Moreover , far greater accuracy can be achieved when estimating quantities from simulation by realising that the expected rate of change of infection is determined by the state of the network and is given exactly by mechanistic models ( such as Eq 3 ) where the variables are taken directly from the simulation . This allows us to remove some of the effects of stochasticity from the calculation . We therefore use this expected rate of change to directly calculate early growth rates , and use the long-term relationship between prevalence and expected rate of change to find the endemic equilibrium prevalence ( see S1 Text ) . Fig 3 shows the advantage of this method , reducing the variance in our estimate of mean endemic prevalence and hence improving the accuracy of any fixed duration simulation . We now layout in some detail the different approximation models used within this paper: mean-field; standard pairwise; reinfection counting; motif models; and neighbourhood models . The elements captured in each approximation are illustrated in Fig 4 .
We begin by comparing the growth rates from four approximation models ( mean-field , pairwise ( motif m = 2 ) , pairwise with reinfection counting ( L = 50 ) and neighbourhood ( n = 2 ) , see S1 Text ) with those from stochastic simulations on a Cayley tree , for different values of the transmission rate τ substantially above the critical value that permits successful invasion ( Fig 5A , 5C and 5E ) . Unsurprisingly , the standard ODE model that ignores all elements of network structure ( and hence ignores the negative S-I correlations that build-up and reduce transmission within a network ) vastly over-estimates the early growth rate . Including some element of local structure , such as that captured by the pairwise ( motif m = 2 ) model substantially improves the prediction of the growth rate but still overestimates compared to the simulated value . Finally , adding additional structure , either in terms of the reinfection counting or neighbourhood expansion enhances the accuracy . On closer inspection ( Fig 5C and 5E ) we observe that away from the critical invasion point , the reinfection counting model provides a highly accurate prediction of the early growth rate , outperforming all other approximation methods . In addition , as indicated by Fig 1 , we find that for higher degree networks ( k = 6 , Fig 5E ) all models , even the standard mean-field ODE model , provide a more accurate estimate of the true behaviour . Turning our attention to the prevalence of infection ( Fig 5B , 5D and 5F ) , it is clear that all approximation models ( even the standard mean-field model ) perform reasonably well when comparing their equilibrium values with the numerical estimates of the expected prevalence . As mentioned before , we also note that the standard pairwise model ( m = 2 ) and reinfection counting pairwise model ( for any L ) have the same equilibrium prevalence—in the reinfection counting model all individuals will eventually be infected more than L times , thereby reaching the upper limit . However , even taking L very large , the same quasi-equilibrium prevalence is reached even before a significant fraction of individuals hit the upper reinfection counting limit L . This is related to the loss of local correlation structure as the network becomes saturated with infection and paths of infection meet through medium and long loops within the network . Comparing more closely results from the approximation models against simulated prevalence ( Fig 5D and 5F ) shows that the neighbourhood model ( n = 2 ) outperforms the pairwise models ( m = 2 ) . This is to be expected as the neighbourhood model captures higher-order spatial structure within the network , effectively capturing the status of k + 1 connected individuals . However , all approximation models perform worse as the expected prevalence drops and the critical transmission rate is approached . This comparison raises the question of how the motif and neighbourhood approximations perform as m and n are increased , incorporating more of the local network . We consider two cases . Firstly , k = 3 where only limited extensions of the models are feasible ( m = 3 , m = 4 and n = 3 ) as the dimension of the systems rapidly becomes large and the mathematical formulations are unwieldy . Secondly k = 2 ( which we note is a special case [55 , 56] ) where neighbourhood and motif models are equivalent for m = 2n − 1 , and where we can readily extend the approximation methods to extremely high orders ( S1 Text ) . Fig 6 demonstrates the impact of taking these higher order approximations . Considering the k = 2 case ( when the network is a linear system ) , increasing the order ( m = 1 to m = 16 ) leads to a drop in endemic prevalence and convergence of the critical transmission rate to the estimated value ( vertical line ) . At the critical transmission value ( estimated as τC ≈ 1 . 6489 [57] ) the error scales extremely slowly with the order of the model ( approximately O ( m−0 . 271 ) ) , Fig 6B ) . When returning to the case k = 3 that has been the main focus of this work ( where we estimate τC = 0 . 544 from numerous large scale simulations ) both the approximation methods behave far better ( motif error ∼O ( m−2 . 7 ) ; neighbourhood error ∼O ( n−3 . 2 ) ) , and offer reliable predictions of endemic prevalence even quite close to the critical point as the order of the approximations increases .
Moment closure approximations for the spread of infections on networks can be highly informative , especially when uncertainty in the underlying network structure precludes detailed simulation of a specific case . By generating relatively simple , tractable models ( in the form of ODEs ) , an intuitive understanding can be developed for the spread of infection that does not rely on precise measurement of network structure . This approach has been highly successful for infections with SIR-type dynamics [23–25] , where recovery leads to lifelong protection; however , for infections that obey the SIS paradigm and can therefore be contracted multiple times this closure approach does not have the same level of precision [40 , 45 , 48 , 49] . Most sexually transmitted infections are well approximated by SIS-type dynamics , and modelling sexually transmitted infections requires an appreciation of the dynamic implications of the sexual contact network , due to the relatively low numbers of sexual contacts at any time . Therefore , although closure approximations for SIS-type infections on networks is highly challenging , it is nevertheless an area of considerable applied importance . Several other recent studies have considered the behaviour of SIS models on networks [42 , 45 , 49–51 , 54 , 58–61] showing that this is a field of active research where there are substantial challenges in establishing rigorous analytical results and in matching approximations , simulations and real data . In this paper we have mainly focussed on homogeneous random networks where each individual has exactly k = 3 contacts , and all contacts are considered bi-directional . This restricts our attention to the highly challenging case of small and homogeneous degree , as higher mean degree or greater heterogeneity leads to infection prevalences that are closer to mean-field predictions that ignore the local correlations that arise from network structure . For homogeneous random networks of this type we show that , as expected , pairwise approximations ( that consider the state of two connected individuals ) outperform standard models ( that ignore any correlations within the network ) , while neighbourhood-based models ( originally called ‘effective degree models’ [22]—that consider the state of all neighbours around a central individual ) outperform pairwise models ( Fig 5A and 5B ) , and in turn extended neighbourhood models ( that consider neighbours of neighbourhoods ) are even more accurate ( Fig 6C ) . This is unsurprising since closing the approximation at higher orders , and therefore essentially modelling more of the underlying local behaviour , is always likely to provide a more accurate description of the population-scale dynamics . We also investigated extensions to the standard closure models , including a count of the number of times an individual has been infected . This removes some of the inaccuracies that pairwise ( and other ) approximation models suffer from when trying to capture the early stages of infection in a largely susceptible population . The results of this improvement to the pairwise model generates far better predictions for the early growth-rate of infection , offering a substantial improvement over both standard pairwise models and neighbourhood models ( Fig 5 ) . It would therefore seem prudent , although dimensionally-challenging , to combine reinfection counting with closure models that operate at the whole neighbourhood scale ( or even larger ) thereby enabling an approximation to both the early and endemic dynamics . However , it may be far simpler to use the model most appropriate to the setting , depending on whether it is early growth or endemic prevalence that is required . With moment closure models there is always the temptation to include higher order terms in the approximation and close at one order higher . We considered higher-order approximations to SIS dynamics on both k = 3 and k = 2 networks , noting that the SIS model on k = 2 is identical to classic 1-dimensional contact process [55 , 56] . For the 1-dimensional k = 2 case , we are able to extend the modelling approach to much higher orders , but find that these closures still overestimate the prevalence near to the critical point . For the k = 3 network , we are only able to extend the neighbourhood model one additional step ( considering neighbours of neighbours ) . However , as we capture the status of more contacts around the central individual , this extended neighbourhood model provides a very accurate approximation to the SIS behaviour , although it is complex to construct and relatively high-dimensional . Throughout we have focused on expanding our approximations to ever higher orders , but the upper bounds to what can be achieved differ between methods . For the reinfection counting model ( where dimension of the system is 2L2 − 1 and hence grows relatively slowly with L ) taking L past 50 had very little effect , so further expansion was irrelevant . For degree k = 2 networks , where motif and neighbourhood expansions are equivalent , the limiting factor was the dimension of the system . The ODEs were simulated up to m = 16 at which point the dimension is 2m−1 + 2M−1 − 1 = 32 , 895 ( where M = ⌈ ( m + 1 ) /2⌉ = 8 ) . For degree k = 3 the main limitation is not the dimension of the system but the complexity of closure approximations which utilise the probabilities of overlapping subgraphs; considering approximation higher than m = 4 or n = 3 is possible , but the gains in accuracy may not be worth the considerable effort . Finally , we consider the fact that sexual networks are highly heterogeneous ( with some individuals having many more life-time partners than others [3 , 62] ) . This risk heterogeneity is important for understanding who becomes infected , but the action of this heterogeneity is readily captured by traditional risk-structured mean-field models [63 , 64] that ignore network correlations ( Fig 1 ) . Extending all the models discussed in this paper to capture degree heterogeneity is possible although one needs to specify , in addition to the degree distribution , a degree correlation matrix—the choice of which can have a dramatic impact on the disease dynamics [9] . Furthermore , the dimensionality of the system can rapidly exceed currently available computational resources due to the combinatorial number of possible configurations , especially for the neighbourhood model . For example , considering neighbourhood models n = 2: 5 equations are needed for k = 2 networks and 7 equations for k = 3 networks; however when the degree is heterogeneous far more equations are required , in a network where all nodes are degree 1 or 2 then 27 equations are needed but if nodes can be degree 1 , 2 or 3 then the number of equations rises to 165 . For neighbourhood models approximated at the next order ( n = 3 ) the effect is even more dramatic; a heterogeneous networks where nodes can be degree 1 , 2 or 3 requires 65 , 015 equations . We note , however , realistic sexual networks may have a large proportion of population with relatively few connections and where infections spread poorly; this leads us to believe that large sections of sexual networks may behave more like the low degree ( k = 2 or k = 3 ) situations considered here where there is a strong need to accurately capture network correlations . As heterogeneity increases , pairwise models , which do not suffer such a pathological growth in dimensionality , become relatively accurate and , as was illustrated in Fig 1 , even simple ODE models can be highly effective at capturing the aggregate prevalence in highly heterogeneous populations . Depending on the applied problem , we argue that a combination of the systematic approximations here can be used as a trade-off between accuracy and computational complexity . Even if degree heterogeneity is captured by models , the network structure and infection dynamics used here are extreme simplifications of real-world behaviour: in particular , sexual networks are dynamic ( with most individuals practising serial monogamy [45] ) , and the natural history of infection is often far from the Markovian process with only two states ( S and I ) discussed here . In reality , for most STIs , there is likely to be a latent period following infection; detection , treatment and recovery will follow ( non-Markovian ) processes; and treatment is likely to offer some limited protection . These facets will act to prevent rapid reinfection of individuals , which , like heterogeneity , should improve the accuracy of approximation models . Future work should clearly focus on developing more sophisticated and realistic network-based simulation models for STIs and comparing these to a range of approximation methods; however , we believe that the careful exploration of the accuracy of approximate models performed here is a key step in this process . | Networks are now widely used to model infectious diseases , but have posed significant mathematical challenges . Recently analytic results have been obtained for ‘one-off’ network epidemics that follow the SIR paradigm , but these results do not carry over to other scenarios—most significantly to many sexually transmitted infections , where accounting for network structure is vital . Here , we show that it is possible to obtain the large-population dynamics of such diseases on networks through systematic approximations . We focus on a mathematically challenging case of SIS dynamics on networks with low degree . | [
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... | 2016 | Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks |
Glial cells play structural and functional roles central to the formation , activity and integrity of neurons throughout the nervous system . In the retina of vertebrates , the high energetic demand of photoreceptors is sustained in part by Müller glia , an intrinsic , atypical radial glia with features common to many glial subtypes . Accessory and support glial cells also exist in invertebrates , but which cells play this function in the insect retina is largely undefined . Using cell-restricted transcriptome analysis , here we show that the ommatidial cone cells ( aka Semper cells ) in the Drosophila compound eye are enriched for glial regulators and effectors , including signature characteristics of the vertebrate visual system . In addition , cone cell-targeted gene knockdowns demonstrate that such glia-associated factors are required to support the structural and functional integrity of neighboring photoreceptors . Specifically , we show that distinct support functions ( neuronal activity , structural integrity and sustained neurotransmission ) can be genetically separated in cone cells by down-regulating transcription factors associated with vertebrate gliogenesis ( pros/Prox1 , Pax2/5/8 , and Oli/Olig1 , 2 , respectively ) . Further , we find that specific factors critical for glial function in other species are also critical in cone cells to support Drosophila photoreceptor activity . These include ion-transport proteins ( Na/K+-ATPase , Eaat1 , and Kir4 . 1-related channels ) and metabolic homeostatic factors ( dLDH and Glut1 ) . These data define genetically distinct glial signatures in cone/Semper cells that regulate their structural , functional and homeostatic interactions with photoreceptor neurons in the compound eye of Drosophila . In addition to providing a new high-throughput model to study neuron-glia interactions , the fly eye will further help elucidate glial conserved "support networks" between invertebrates and vertebrates .
Glia have been recognized as a major and heterogeneous non-neuronal cell type in the nervous system for more than 150 years , but their chief homeostatic and regulatory roles in nervous system development and maintenance have only recently emerged [1–4] . Despite increasing interest in the functions of glia in health and disease , the molecular networks that orchestrate gliogenesis , glial functions , and glia-neuron interactions are still enigmatic . One of the first described glial subtypes was Müller glia . This specialized glial type is a radially-shaped macroglia that provides structural support , neuroprotection , and homeostatic recycling of energy , ions , and neurotransmitters for retinal neurons , some of the most active neurons in the body [5–7] . Müller glia are considered a specialized astrocyte , but have also been noted to share characteristics with oligodendrocytes [8 , 9] . Moreover , in some vertebrates ( e . g . zebrafish and embryonic chick ) , Müller glia can serve as a source of stem cells for retinal regeneration [10] , much like radial glia in other parts of the developing nervous system [11 , 12] . This suggests the presence of overlapping developmental and functional “networks” among different glial subtypes . For decades , Drosophila has served as an effective model for uncovering conserved genetic mechanisms involved in nervous system development and physiology [4 , 13–15] . The fly’s visual system is among the best-characterized experimental systems for studying neuronal function and dissecting neurodevelopmental and neurodegenerative processes . In this system , a cluster of photoreceptors ( PRs ) in each individual eye unit ( ommatidia ) captures and processes light within a prominent apical compartment ( rhabdomeres ) that extends along the neuronal cell body and is restricted to the retina proper . Basally , the PRs project axons that exit the retina and synapse with second order neurons in the underlying optic lobe [16] . Within the optic lobe , several subretinal glial subtypes have been identified which support PR axon guidance and ensheathment , neurotransmitter recycling and neuron survival [17–21] . However , potential support roles intrinsic to the fly retina proper remain largely undefined . The Drosophila retina contains two main non-neuronal “accessory” cell types: pigment cells and cone cells ( CCs ) ( Fig 1A ) [22 , 23] . Pigment cells prevent light scattering between ommatidia , and have been implicated in the visual cycle , maintenance of histaminergic neurotransmitter levels , and ROS-induced lipid peroxidation [18 , 24 , 25] . These cells have also been presumed to function in ion and energy homeostasis for PRs based on electrophysiological assays in the honeybee retina [26 , 27] . While CCs are primarily known for their developmental role in corneal lens formation [28–31] , they are also in close physical proximity to PRs , and hence are well-positioned to provide support to them . CCs radially span the retina , connecting an apical vitreous-like substance with a basal blood-retina barrier ( Fig 1A ) [28 , 30 , 32 , 33] . This configuration of corneagenous cells is highly conserved throughout arthropods , including the common house centipede in myriapods and the “living fossil” Limulus in chelicerates [34–36] , raising the possibility of deeply rooted interactions between CCs and PRs . Indeed , based on such interactions , investigators speculated a glial role for CCs nearly 50 years ago [37] . Here , we present molecular and functional evidence that ommatidial CCs serve multiple glial functions in the Drosophila compound eye . To test this possibility , we established a CC-targeted knockdown paradigm and analyzed neighboring PRs using histological and electrophysiological methods . Genetically , we demonstrate that CCs provide structural and functional support to PRs , and that these roles are differentially contributed by transcription factors that are also involved in vertebrate gliogenesis ( Pax2 , pros/Prox1 , and Oli/Olig1 , 2 ) . Using cell-specific transcriptomic approaches , we further document the CC-enriched expression of multiple candidate glial effector genes commonly associated with both Drosophila and vertebrate glia . Finally , using cell-targeted knockdowns , we demonstrate that CCs are involved in typical glial support functions , including the control of ion balance , energy resources , and sustained neurotransmission . Combined , our findings suggest that CCs serve as intrinsic retinal glia in the Drosophila compound eye , and establish a new , non-invasive experimental paradigm to dissect regulatory glial support modules .
Developing and adult CCs specifically express prospero ( pros ) and dPax2 ( a . k . a sv ) [31 , 38 , 39] ( S1A Fig ) , two transcription factors widely associated with glial development and/or function [40–54] . Previous studies on the role of pros and dPax2 in CCs showed that these factors function cooperatively to distinguish the non-neuronal CC fate from the fate of the last neuronal cell type ( the R7 photoreceptor ) via feedback control of Ras and Notch signaling [31] , a feature also common for neuron-glia fate decisions [55–60] . Importantly , individual pros and dPax2 mutants minimally affect CC specification [31 , 39] , allowing us to test the hypothesis that CCs serve glial-like support role in the fly retina through these factors . For these studies , we used the GAL4-UAS system to drive pros and dPax2-directed RNAi constructs in CCs and tested for the phenotypic consequences on neighboring neuron morphology and activity as 2 measures of common glial support functions . GAL4 expression was driven using a 275 bp pros enhancer that is expressed in CCs and R7s from early specification through adulthood , with little to no expression in the underlying optic lobe ( Fig 1B , S1B and S1C Fig ) . Importantly , we confirmed that animals lacking R7s ( sev14 mutants ) do not exhibit the morphological or electrophysiological phenotypes reported here [31 , 61–63] ( Fig 2K and 2N ) , allowing us to conclude that the ERG and morphology phenotypes described below are dependent on CC and not R7 function . We first histologically analyzed retinal integrity in adult eyes knocked down for pros and dPax2 using previously verified RNAi constructs [31] . In wild type and control ( prosPSG-GAL4>nGFP ) animals ( Fig 2A ) , the regularly spaced ommatidia centrally house a trapezoidal array of actin-rich rhabdomeres from the main PR neurons ( R1-R6 ) . The rhabdomeres and PR cell bodies span the depth of the retina , are caged by CCs , and are physically segregated from the brain and PR axons through a fenestrated membrane formed by the interommatidial pigment cells ( Figs 1A , 2B and 2G , S2A Fig ) [32] . Retinas from prosRNAi CC knockdowns appeared morphologically similar to controls ( Fig 2C and 2D; S2B Fig ) , while dPax2RNAi knockdowns showed severe defects in retinal and PR rhabdomere morphology ( Fig 2E , 2F and 2I; S2C Fig ) . These results are consistent with previous results using eye-specific mutants for these factors ( pros17 and svspapol ) [31 , 38 , 39 , 64 , 65] . Also similar to lens defects in dPax2spapol mutants [31 , 39 , 65] , dPax2RNAi CC knockdowns showed a variable severity in retinal defects ( Fig 2I , S2C Fig ) . This ranged from moderately affected regions with short , misshapen rhabdomeres that failed to extend through the retina ( Fig 2F and 2I; S2C Fig ) to more severe regions in which clusters of rhabdomeres were mislocalized beneath the fenestrated membrane , hence absent from the retina ( Fig 2E , 2F , 2F’ and 2I; S2C Fig ) [65] . Interestingly , rhabdomere clusters observed in the brain maintain a relatively normal ommatidial arrangement ( Fig 2F’ ) , suggesting that fully formed eye units had lost contact with the retina . To test if dPax2-negative CCs remained in contact with PRs , we performed immunostaining for Fasciclin 3 ( Fas3 ) , a transmembrane protein we identified in a larger screen for CC markers . In control eyes , Fas3 is exclusively localized to CC-CC interfaces as well as the interommatidial mechanosensory bristle ( Fig 2G and 2H ) . In dPax2-negative CCs , Fas3 immunostaining remained closely associated with actin-rich PR rhabdomeres at the top of the retina as well as with the mislocalized PRs beneath the retina ( Fig 2I and 2I’ ) . Together , these findings suggest that dPax2 is required in CCs for proper elongation of PR rhabdomeres and overall retinal architecture , but not for the intimate association of CCs and PRs . We next assayed the neuronal activity of PRs in prosRNAi and dPax2RNAi CC knockdown flies using electroretinogram ( ERG ) recordings ( Fig 2J–2N ) . Like wild-type animals [66 , 67] , dark-adapted control flies ( prosPSG>GFP ) ( Fig 2J ) and sev14 flies ( Fig 2K ) exhibited a strong PR-dependent depolarization ( ~10 mV ) in response to light , as well as second-order neuron responses in the optic lobe , detectable as “on” and “off” peaks ( transients ) before and after PR depolarization . Similar normal ERG traces were recorded from dPax2RNAi CC knockdowns ( Fig 2M ) , suggesting that despite the disruption in retinal architecture , the PRs in dPax2RNAi knockdown flies remain functional . In marked contrast , prosRNAi CC knockdowns showed a significant reduction in overall PR depolarization in dark-adapted flies ( Fig 2L and 2N; S3A Fig ) and almost no PR response in light-adapted flies ( S2D Fig ) , suggesting substantial visual loss . Importantly , eyes fully mutant for pros ( Minute clonal analysis with pros17 ) showed a similar reduction in PR activity as prosRNAi CC knockdown flies ( S2D Fig ) . No changes in the “on” transient peak was observed in prosRNAi CC knockdowns or pros17 mutant retinas ( S3B Fig ) when normalized to overall PR activity , suggesting that second-order neuronal activation is intact in these animals . Combined , these electrophysiological analyses reveal that pros , and not dPax2 , is necessary in CCs to sustain proper PR function . Further , these data indicate that CC-dependent support of PR structure and function are genetically separable by pros and dPax2 . Like vertebrate PRs , Drosophila PRs are susceptible to excitotoxic injury and degeneration under sustained bright light conditions [68–72] . While the abnormal retinal morphology in dPax2RNAi flies prevented reliable evaluation of light-induced degeneration , the healthy appearance of prosRNAi CC knockdowns under normal lab-rearing conditions allowed us to test for a possible role of CCs ( and pros ) in this process . Histological examination of control animals ( prosPSG>GFP ) raised 7 days in continuous darkness or sub-degenerative light conditions [72] displayed similarly intact rhabdomere morphology ( Fig 2O , S2E and S2H Fig ) , as did dark-raised prosRNAi flies ( Fig 2O , S2F and S2I Fig ) . In contrast , prosRNAi flies raised under continuous light showed signs of rhabdomere degeneration ( Fig 2O , S2G and S2J Fig ) . To quantify this pros- and light-dependent retinal degeneration , we made use of the deep pseudopupil ( DPP ) of the Drosophila compound eye . Visualization of the DPP allows for non-invasive detection of intact rhabdomere integrity in living flies , and its presence declines in flies undergoing retinal degeneration [71 , 73 , 74] . Consistent with the above histological analysis , DPP monitoring in animal populations raised for 7 days in continuous light showed intact DPPs in >95% control flies throughout the course of the experiment , whereas DPP loss was observed in >50% of prosRNAi flies by day 3 and in all prosRNAi flies by day 7 ( Fig 2P ) . Control and prosRNAi flies raised in total darkness also preserved DPPs throughout the experiment . Combined , these findings indicated that pros in CCs is essential for preventing light-dependent degeneration of PRs . To molecularly define CCs , we turned to a cell type-specific transcriptomic approach . For these experiments , we isolated CCs by fluorescence-activated cell sorting ( FACS ) from retinal tissue dissected at three developmental stages: specification ( larval CCs ) , maturation/differentiation ( pupal CCs ) , and terminal differentiation/maturity ( adult CCs ) ( see Methods ) . Adult PRs were isolated for comparisons ( S4A Fig ) . RNA isolated from these sorted cell populations was sequenced using Illumina HiSeq2500 , and transcript quantification using TMM normalization was applied to remove RNA compositional biases between samples and improve the compatibility of cross-sample analysis ( S1 Table ) . Validation experiments confirmed that multiple housekeeping genes were expressed equivalently across all transcriptomes , and that known PR differentiation and effector genes ( e . g . oc/Otd , ninaE/Rh1 , Arr1 and chaoptin ) were specifically enriched in PRs and not CCs ( S4B Fig ) . In addition , proteins previously shown to be expressed in mature CCs ( e . g . cut ( ct ) , eyes absent ( eya ) , and Drosocrystallin/Crys ) [29 , 75 , 76] were among the top 50 cell-specific genes expressed in our adult CC transcriptome ( S1 Table ) . Given the critical roles of glial cells missing/glide ( gcm ) and reversed polarity ( repo ) in the determination of most Drosophila glial cell fate decisions [77–81] , we first investigated their presence in our CC transcriptome data . Expression of both gcm and repo was appreciably higher in developing CCs compared with adult CCs and PRs ( S1 Table , Fig 3A ) . Peak pros and gcm levels preceded that of repo ( Fig 3A ) , consistent with previous findings that pros activates gcm , which in turn activates repo in subpopulations of Drosophila glia [44 , 49 , 58 , 82–85] . The transient expression of gcm and repo during CC development was further confirmed with immunostaining for a gcm reporter construct and Repo protein ( S4C–S4F Fig ) . Thus , both gliogenic genes associated with the Drosophila nervous system are transiently expressed in developing CCs . We next examined our larval , pupal , and adult CC transcriptomic data for the presence of orthologous factors that , like pros/Prox1 and Pax2 , are commonly associated with gliogenesis in the vertebrate nervous system . This analysis showed transient expression of the transcription factors Olig1 , 2/Oli , Rx/Rax , Hes1/dpn , Hes5/E ( spl ) , Hey2/Hey , Lhx2/ap , and Vsx2/Vsx2 in larval and pupal , but not adult , CCs ( Fig 3A ) . Oli expression was also specifically detected in PRs , consistent with previous studies reporting fly and vertebrate Oli gene expression in both early neural progenitors and specified neurons [86–89] . Further , we detected Sox100B expression in pupal and adult ( but not larval ) CCs ( Fig 3A , S1 Table ) , similar to the later onset of expression found for its ortholog Sox9 in vertebrate retinal glia [90 , 91] . These findings hence revealed that the developing CCs of the Drosophila compound eye express multiple , deeply conserved gliogenic factors . To functionally test whether candidate gliogenic genes besides pros and Pax2 are required in CCs for PR support , we focused on gcm and repo as bona fide Drosophila glial factors , and Oli , a known gliogenic gene in vertebrates and C . elegans [89 , 92–94] , but whose function in Drosophila has thus far only been analyzed in motor neuron development [86] . Applying the same targeted RNAi /ERG strategy as above , we tested two independent RNAi lines for each candidate gene , each pair yielding comparable results ( S3A Fig ) . Cone cell specificity was tested using either pan-photoreceptor ( otd1 . 6-GAL4 ) [95] or pan-glial ( repo-GAL4 ) [96] drivers . All gcm , repo , and Oli RNAi knockdowns developed eyes with a normal appearance of external lens facets and DPPs , indicating that similar to pros , depletion of these factors in the eye does not demonstrably affect cell type specification or ocular morphogenesis . ERG recordings from either gcm or repo CC knockdown animals ( pros>gcmRNAi , pros>repoRNAi ) revealed a similar phenotype as pros>prosRNAi flies: a significant reduction in PR activity ( ~60% below control values ) ( Fig 3B and 3C; S3A and S5 Figs ) and no detectable changes in normalized transient sizes ( S3C and S3D Fig ) . These results are consistent with gcm and repo lying downstream of pros in subsets of other developing glia [40 , 42 , 44 , 49] . A similar repo-dependent reduction in PR activity was previously reported for viable adult repo1 hypomorphs [78 , 97] . Notably , however , unlike our CC knockdowns of repo , repo1 mutants also exhibit reduced “on” transients [78 , 97] ( Fig 3C ) , suggesting defects in optic lobe glia . To validate the effectiveness of our repoRNAi approach , we 1 ) confirmed that Repo protein levels in pros>repoRNAi knockdown ommatidia were specifically reduced in CCs ( S4F Fig ) , and 2 ) knocked down repo in all glia using repo-GAL4 . In agreement with studies using null repo alleles , repo>repoRNAi animals raised at our normal experimental conditions ( 25°C ) showed early lethality , further confirming efficient repo knockdown [77 , 79] . In addition , knocking down repo later during eye development , making use of the temperature-sensitivity of GAL4 ( see Methods ) , led to both repo1-associated phenotypes [97]: reduced PR activity and “on” transients ( Fig 3C; S3B–S3D Fig ) . Other cell specificity control experiments showed that PR-specific repoRNAi expression had no effect on ERG activity , while PR knockdown of ninaE/Rh1 ( the primary opsin contributing to ERG activity ) reduced PR activity by >90% and CC knockdowns of Rh1 showed no changes in PR activity ( Fig 3C; S3A Fig ) . Combined , these data indicate that gcm and repo are required during CC development to promote support function ( s ) in the adult retina whereas repo-positive cells outside of the retina support “on” transient activity . Like pros , gcm , and repo knockdowns , ERG recordings of Oli CC knockdowns also showed a reduction in overall PR depolarization ( ~40% of control levels ) ( Fig 3B ) . In marked contrast to these other knockdowns however , OliRNAi CC knockdown flies also showed a reduction in the average “on” transient responses ( Fig 3C and 3D; S3B Fig ) . Further analysis of this outcome revealed a light-pulse dependent decay of the transient size ( Fig 3D , S3D Fig ) , a phenotype not observed with any of our other CC knockdowns . Selective knockdown of Oli in PRs on the other hand , resulted in ERG traces comparable to control animals ( Fig 3C; S5A Fig ) . These combined results suggest that the transient expression of Oli specifically in Drosophila CCs regulates developmental networks required for neuronal activity and sustained neurotransmission in neighboring PRs . To further probe the potential glial properties of CCs , we first identified the top 1000 most enriched genes expressed in developing , differentiating , and mature CC gene sets ( larva , pupal , and adult CCs , S2 Table ) . We then compared these CC-enriched gene sets with 109 genes previously identified in a genetic screen for roles in Drosophila glial differentiation ( http://www . sdbonline . org/sites/fly/aimorph/glia . htm ) [98] ( S3 Table ) . Such analysis revealed that 49% ( 53/109 ) of these genetically-defined glial genes are prominent in at least one stage of CC development . These included factors known to function downstream of pros , gcm and repo ( e . g . pnt , loco , and unc-5 ) and cell adhesion molecules commonly associated with glia ( e . g . wrapper , gliotactin ( Gli ) , neurexin IV ( NrxIV ) , and Contactin ( Cont ) ) ( Fig 4A ) [40 , 77 , 82 , 99–105] . To assess the cellular specificity of glial gene expression in CCs , we constructed corresponding enriched gene groups derived from adult PRs ( from this study ) and publicly-available central nervous system ( CNS ) and digestive system ( DS ) RNA-seq data sets ( see Methods ) . When compared to the 109 glial genes analyzed above , larval CCs showed the highest enrichment , followed by pupal CCs , adult CCs and the CNS ( Fig 4B ) . Of the neural cell populations analyzed , PRs showed the least significant enrichment , while consistent with its non-neural origin , the DS showed no significant enrichment . Similar enrichment patterns were observed when these samples were compared with 2309 Drosophila genes regulated by gcm in both loss- and gain-of-function paradigms [84] ( Fig 4B , S3 Table ) . Finally , comparison of the top 1000 genes most differentially expressed between adult CCs and PRs with both Drosophila glial gene sets showed a significant enrichment of CC- , but not PR-specific , genes ( Fig 4B ) . Combined , these molecular analyses indicate that fly CCs are specifically enriched for a broad panel of fly glia-associated gene products . Having established the expression of many developmental factors associated with vertebrate gliogenesis in developing Drosophila CCs , we next tested for overlap of our adult CC- and PR-specific gene sets with published glial- or neuronal-restricted effector genes from postnatal mouse retina and forebrain [106–108] . This analysis revealed that mouse orthologs of genes highly expressed in fly PRs were significantly enriched in all mouse neuronal cell populations analyzed , while those genes expressed in fly CCs showed the highest enrichment for murine astrocytes and Müller glia ( Fig 4C ) . Lower but significant enrichment was also observed between CCs and oligodendrocytes as well as retinal ganglion cells ( Fig 4C ) . Consistent with an enrichment of Müller glia and astrocyte genes in CCs , several common astroglial markers showed overlap among these three cell populations , including Sox2 , Hes1 , Cdkn1 ( p27Kip ) , Glul ( GS ) , and Rlbp1 ( CRALBP1 ) ( Fig 4D , S5 Table ) . In addition , the Kir4 . 1 inward rectifying potassium channel ( Kcjn10 ) and the chaperone Cryab were shared among the CC- , Müller glia- , astrocyte- and oligodendrocyte-enriched gene sets ( Fig 4D , S5 Table ) . These data suggest that fly retinal CCs share molecular signatures with Müller glia , astrocytes , and to some degree , oligodendrocytes . To functionally test for potential glial homeostatic functions in CCs , we focused on a subset of 11 candidate effectors: α- and β-subunits of the Na/K-ATPase ( a . k . a . the Na+ pump ) , K-inward rectifying channels ( Kir2 . 1/4 . 1 homologs ) , lactate dehydrogenase ( dLdh/Impl3 ) , glucose transporter 1 ( Glut-1 ) , the excitatory amino acid transporters Eaat1/Glast and Eaat2/Glt-1 , and the glutamate-ammonia ligase glutamine synthetase ( GS2 ) ( Fig 5A ) . Notably , these genes promote three important support functions that are conserved in the insect and vertebrate retina—ion balance , energy homeostasis , and glutamate recycling [109] . In addition , these genes are commonly used to identify vertebrate glial types , including Müller glia , astrocytes , and oligodendrocytes . Based on our transcriptome analysis , orthologs for each of these factors were expressed at relatively high levels in adult CCs ( Fig 5B ) . Also , because fly PRs are histaminergic and the CC-generated pseudocone serves as a reservoir for the histamine-associated metabolites β-alanine and carcinine , we analyzed β-alanine synthase/β-ureidopropionase ( Drosophila pyd3/vertebrate UPB/BUP1 ) , a deeply conserved factor necessary for histamine recycling , pyrimidine and vitamin B biogenesis , energy production , and antioxidant production [110–112] . Again , 2 independent RNAi lines were tested for each gene , both showing comparable effects on PR activity ( S3C Fig ) . Cell-specific knockdowns for a subset of factors was confirmed by immunostaining ( S6 Fig ) , and adult eyes from each CC knockdowns lacked detectable external defects in lens or PR morphogenesis . CC knockdowns for the subunits for the Na+ pump ( ATPα , nrv2 , nrv3 ) , as well as the Kir channel Irk2 resulted in significantly reduced PR responses relative to controls ( Fig 5C ) . In contrast , knockdown of Irk1 or Irk3 showed ERG recordings comparable to controls ( Fig 5C ) . CC knockdowns of energy-promoting factors dLdh and Glut1 each led to a ~50% reduction in overall PR responses ( Fig 5C; S3C Fig ) . And finally , CC knockdown of Eaat1 or pyd3 led to a significant reduction in PR activity , while Eaat2 or Gs2 CC knockdowns showed control levels of PR activity ( Fig 5C; S3C Fig ) . We did not detect significant changes in averaged “on” transient size for any of the effector genes tested ( S3B and S3D Fig ) . Combined , these data demonstrate that CCs require multiple but distinct glial effectors to specifically promote PR neuronal activity .
Recent advances in evolutionary biology have come to recognize deeply conserved gene networks [117] ( also termed “kernels” [118] or “character identity networks” [119] ) as a valuable means to detect and explore structural and functional homologies among tissues and cell types across animal phyla [120–122] . In the current study , we have identified several support roles that cone cells provide to their neighboring retinal neurons . Such roles include ionic and metabolic homeostasis , identified through functional tests of classic glial effectors such as the Na+/K+ ATPase , Kir channels , Eaat1 , Ldh , and Glut-1 . Our study complements seminal work in the honeybee compound eye , which based on electrophysiological and histological findings , already predict the use of such factors in undefined “retinal glia” [26 , 109] . More recent genetic studies in Drosophila demonstrated that interommatidial pigment cells mediate other support roles , including neurotransmitter storage , visual pigment recycling , and lipid peroxidation [18 , 24] . Combined with the present findings , these data suggest that both accessory cell types in the insect compound eye—CCs and interommatidial pigment cells—serve glial functions that resemble the overlapping support roles of Müller glia and the retinal pigmented epithelium in the vertebrate retina [7 , 123] . Complementing the homeostatic functions performed by CCs , our studies further reveal at least three genetically distinct transcriptional inputs that are pertinent to CC-dependent retinal support . Knockdowns for pros , gcm , and repo , for instance , show a requirement for this known gliogenic network in supporting PR activity , whereas Pax2 is critical for establishing proper retinal structure , and Oli is necessary for sustaining neurotransmission and PR activity . Other cell-selective knockdowns suggest that repo-positive optic lobe glia , but not CCs , promote histaminergic neurotransmitter recycling ( Fig 3C ) . Notably , while most mature fly glia express Repo , a subset of glia/support cells in peripheral sense organs ( sheath , or thecogen , cells ) are Repo-negative [77–79 , 124] , yet express both Pros and Pax2 [45 , 51–53 , 125–129] , much like mature CCs . Moreover , although previous studies on Drosophila Oli suggest that this factor—known to participate in gliogenesis in C . elegans and mice [87 , 88 , 94]—was not involved in fly gliogenesis , these conclusions were based on the restricted study of Repo-positive glia [86] . Our finding that Oli is an important transiently expressed transcription factor in CCs raises the possibility that this gene could be more generally important in insect glia than previously recognized . Further , given that the repo gene has been lost in the vertebrate lineage [130] , while pros , Pax2 and Oli play deeply conserved glial functions [40 , 43 , 47 , 48 , 50 , 51 , 89 , 94 , 131] , further examination of roles and the interplay among this ensemble of factors in fly CCs offers a system to uncover both lineage-specific and more deeply conserved “glial character networks” required for promoting neuronal support cell functions . Indeed , interesting context-dependent cross-talk has already been shown for Prox1 and Olig factors during vertebrate neurogenesis [132] , and ocular morphometric phenotypes are observed in patients with mutations in Pax2 [46 , 133] . In addition to defining a general glial-nature for CCs , the data presented here and elsewhere reveal a striking number of commonalities between insect CCs and vertebrate Müller glia at the developmental , structural , molecular , and functional levels . Many of the support functions identified here in CCs are common to other glial cell types across the nervous system . Nevertheless , both CCs and Müller glia represent specialized macroglial cells that specifically serve retinal neurons . Thus , highlighting these key common features provides a useful framework to further understand shared features with other glial subpopulations . Developmentally , both CCs and Müller glia are intrinsic to the retina , and adopt their non-neuronal fate from a pool of equipotential neurogenic precursor cells via Notch signaling [108 , 134 , 135] . dPax2 and pros are essential for these Notch-dependent decisions in CCs [31] , resembling their gliogenic roles in other parts of the fly and vertebrate nervous systems [40 , 50 , 51 , 57 , 58 , 136] . Interestingly , orthologs for both factors have also been reported to be expressed in Müller glia [131 , 137–139] , raising the possibility that , like in Drosophila CCs , mouse orthologs Prox1 and Pax2 may control overlapping but distinct roles in determining Müller glia fate and function in the vertebrate retina . Morphologically , CCs and Müller glia radially span the retina , in direct contact with all retinal neurons and connecting an apical vitreous-like substance with a basal blood-retina barrier . This is similar to other radial glial subtypes such as Bergmann glia and tanycytes . Additionally , Müller glia [7 , 140 , 141] and CCs both have the capacity to serve as intraretinal light guides . With this feature in mind , it is notable that small heat shock/ɑ-crystallin-related proteins are highly enriched in both CCs and Müller glia [142–144] ( S3–S5 Tables ) . This feature is also true for other glia , including oligodendrocytes , and has mainly been attributed to their role as chaperone proteins that provide neuroprotective support [145–147] . This property warrants investigation in Drosophila CCs , especially since one small heat shock protein ( hsp23 ) is known to be specifically upregulated in CCs under stress conditions [148] . However , it is also attractive to speculate that crystallins in Müller glia and CCs have been co-opted to help mediate light guidance , as has been proposed for the evolution of lenses across the animal kingdom [22 , 149] . And finally , at the molecular and functional levels , we find that CCs share considerable gene and physiological overlap with a number of glial types . These include factors commonly used to define Muller glia and are critical for their function [8 , 150] . In particular , we describe the requirement of conserved gliotypic effector genes such as the Na/K-ATPase , Kir channels , EAAT1 , Glut1 and Ldh in CCs for maintaining PR neuronal activity . It is possible that these detailed similarities between Müller glia and CCs independently evolved in response to common functional requirements of photoreceptors that are inherent to complex eye types . Nevertheless , comparative studies suggest that the earliest eyes in the bilaterian ancestors were minimally comprised of two cells: a photosensitive neuron associated with an accessory pigment cell [22 , 151–154] . This model is largely based on the requirement of light insulation for directional information , but this two-cell eye type also raises the possibility that essential “neuron-glia” neuroprotective support could have existed in simpler and/or ancestral visual systems [22] . The overlapping functions of CCs , Muller glia , and pigmented epithelial cells lays the groundwork to identify and test the possibility that diverse glial modules existed in sensory systems prior to the separation of vertebrates and invertebrates . Regardless of the evolutionary origin of CCs , their genetic and functional overlap with vertebrate and invertebrate glial subtypes provides a molecular framework to analyze and identify conserved elements of glial regulatory networks central for establishing and maintaining a healthy sensory system . Moreover , the described capacity to track the progression of neurodegeneration in vivo , using non-invasive , glia- and neuron-specific tools expands the use of the fly retina as an effective system to define deeply conserved mechanisms of neuron-glia biology .
A 275 bp prospero eye enhancer , which we called prosPSG , was PCR-amplified from genomic DNA extracted from yw67 flies with the following primers: CCGGAATTCATCTGTGACGAAGACACTCGTTT and CGCGGATCCTCGATTGCCAGGAAGTGC , using previous mapping studies [155] as a guide . To generate the prosPSG-GAL4 driver , the prosPSG fragment was cloned as an EcoRI/BamHI fragment ( sites underlined in primers ) into hs43GAL4-pCHAB [156] . To create prosPSG-GFP , the prosPSG enhancer and hs43 TATA box was PCR-amplified from prosPSG-GAL4 using the primers: CGAGAATTCGGTACCCGCCCGGGATCAGATC and TCGAAGATCTCTGCAGATTGTTTAGCTTGTTCAGCTGC and cloned as an EcoRI/BglII fragment into pStingerAttB-GFP ( gift from B . Gebelein , CCHMC ) at the EcoRI/BamHI sites ( underlined ) . Transgenic flies were generated using standard procedures ( Rainbow Transgenics ) in yw67 flies for the GAL4 line or the attB insertion site 51C ( containing M{3XP3-RFP . attP’} ) for the GFP line . The following alleles were used available through the Bloomington Stock Center ( BSC ) and the Vienna Drosophila Resource Center ( VDRC ) : gcm1 ( HM05124 ) & 2 ( JF01074 ) , repo 1 ( HMS02971 ) & 2 ( JF02974 ) , Oli1 ( HMJ02216 ) & 2 ( JF02001 ) , ATPα 1 ( HMS00703 ) & 2 ( JF02910 ) , nrv21 ( HMS01637 ) & 2 ( JF03081 ) , nrv3 1 ( HMS02961 ) & 2 ( JF03367 ) , Irk21 ( HMS02379 ) & 2 ( JF01838 ) , Irk3 ! ( KK107031 ) & 2 ( JF02262 ) , Irk11 ( HMS02480 ) & 2 ( JF01841 ) , dLdh1 ( KK102330 ) & 2 ( HMS00039 ) , Glut1 1 ( HMS02152 ) & 2 ( JF03060 ) , Eaat11 ( KK100187 ) & 2 ( HMS02659 ) , Eaat2 1 ( KK107989 ) & 2 ( HMS01998 ) , Gs21 ( HMS02197 ) & 2 ( GD9378 ) , pyd31 ( HMS01029 ) & 2 ( GD10029 ) , ninaE 1 ( JF01438 ) & 2 ( JF01439 ) , sev14 , svspapol , UAS-GFP ( BSC stock 5130 ) , FRT82Bubi-GFPnls , RpS3 ( BSC stock 5627 ) , repo-GAL4 ( BSC stock 7415 ) , repo1 ( BSC stock 4162 ) and otd1 . 6-GAL4 [156] . Other lines used were: prosPSG-GFP ( generated here ) , prosPSG-GAL4 ( generated here ) , dPax2RNAi [31] , prosRNAi [31] , FRT82B-pros17 . 15 [157] , the retina-specific flippase ey3 . 5-flp [158] , and the fate-mapping UAS-H2B:YFP reporter [159] . For CC knockdown experiments , flies with the genotype yw67/yv1; prosPSG-GAL4/+; UAS-RNAi/UAS-GFP flies were analyzed . Other genotypes included yw67; otd1 . 6-GAL4/+; UAS-RNAi/+ ( for PR knockdowns ) , yw67; +/+; repoGAL4/UAS-RNAi ( for repo knockdowns ) , sev14/+ ( control ) vs sev14/sev14 ( experimental ) , repo1/+ ( control ) vs repo1/repo1 ( experimental ) , yw67; prosPSG-GAL4/+; UAS-GFP/+ ( controls for pros and dPax2 RNAi knockdowns ) , yw67; otd1 . 6-GAL4/+; UAS-GFP/+ ( controls for ninaE RNAi ) and yv1; +/+; UAS-RNAi ( controls for all remaining RNAi lines ) . To analyze eyes almost entirely mutant for pros , eye-specific Minute clones using the null pros allele , pros17 , were generated as previously described [31] . Flies were raised on standard cornmeal-molasses food at 25°C in ambient light conditions unless otherwise noted . For repo>repoRNAi knockdown experiments , animals were either raised at 25°C from time of egg laying ( for reponull-like phenotypes ) , or were raised at 18°C until late 3rd instar stages and then shifted to 25°C ( for repo1-like phenotypes ) . For light-induced degeneration , 25 flies per experimental group were raised 12 inches from a 25-watt fluorescent light bulb for 7 days . In three separate experiments , daily analysis was conducted to quantity the number of flies with an intact deep pseudopupil [73 , 160 , 161] as an indicator of intact photoreceptor rhabdomeres . Statistical comparisons were performed using 1-way ANOVA . At the end of the 7 day experimental period , eyes were dissected , fixed and prepared for semi-thin plastic sections or whole mount phalloidin staining . For plastic and thin sections , adult eyes were dissected in PBS , fixed in 4% formaldehyde in PBS at RT for 30 min and post-fixed in 2% osmium tetroxide in PBS on ice for 60 min . Tissue was serially dehydrated in EtOH and infused with LR-white resin ( EMS ) overnight . The resin was polymerized in gelatin capsules at 65°C overnight . For semi-thin sections , 1μM sections were placed onto glass slides , stained with toluidine blue ( CCHMC Pathology Core ) and imaged on a Zeiss Axioplan2 . Thin EM sections were mounted on 200 mesh copper grids and stained with 2% uranyl acetate and lead citrate [162 , 163] ( CCHMC Pathology Core ) and imaged on a Hitachi H7650 TEM . For immunofluorescence , eyes were dissected in PBS , and either fixed in 4% formaldehyde at RT for 15 min or in -20°C methanol overnight followed by immunostaining procedures previously described [31] . Antibody concentrations used were: GFP ( rabbit , 1:500 , Invitrogen ) , GFP ( goat , 1:500 , Abcam ) , Elav ( rat , 1:200 , DSHB ) , Fas3 ( mouse , 1:50 , DSHB ) , ATPα ( mouse , 1:50 , DSHB ) , Nrv1 ( guinea pig , 1:150 , Paul et al , 1998 ) , Nrv2 ( rabbit , 1:150 , Sun et al , 1998 ) , Nrv3 ( rabbit , Baumann et al . 2010 ) , Repo ( mouse , 1:20 , DSHB ) , 22C10 ( mouse , 1:50 , DSHB ) , β-alanine ( rabbit , 1:1000 , Abcam ) . Donkey secondary antibodies were conjugated to AlexaFluor 488 , 555 or 647 ( Invitrogen ) . Actin-rich rhabdomeres were detected using AlexaFluor 555-conjugated phalloidin ( 1:50 , Invitrogen ) . Samples were imaged on a Nikon A1R multiphoton confocal , and image processing was performed using NIS-elements ( Nikon ) , Imaris ( bitplane ) and Photoshop CC ( Adobe ) . Unless otherwise stated , 1-day old flies were immobilized with CO2 , individually mounted onto plastic coverslips with dental wax , and dark adapted for 30 minutes . The electrophysiological setup included a faraday cage , a 1600 AM-Systems amplifier ( Sequim , WA , USA ) , and an iWorks AD board 118 with LabScribe2 software ( iWorks Systems , Dover , NH , USA ) . The recording electrode ( a PBS-soaked cotton wick connected to a silver wire ) was positioned on the surface of the eye , and the grounding electrode ( a PBS-soaked glass electrode connected to a silver wire ) was placed between the third and fourth abdominal segments . Signals were sampled at 10 , 000 Hz . To elicit responses from all PRs except the UV-sensitive Pros-positive R7s , 5 pulses ( 5secs on , 15sec off ) of blue-green light ( 490nm LED , LED supplies part #L4-0-T5TH15-1 ) were delivered via a fiber optic cable positioned immediately adjacent to the eye , delivering a light intensity of 3 . 55 x 1014 photons/cm2/sec . Data was collected from at least five flies per genotype from one RNAi line and three flies from a second RNAi line , with controls ( GAL4 or RNAi lines ) being recorded on the same day to avoid day-to-day variation . All genotypes were also initially tested using a white LED light delivering a light intensity of 2 . 77 x 1015 photons/cm2/sec , with similar results observed . To confirm that any reduction in photoreceptor response-strength was not simply due to a shift in relative light sensitivity , VlogI curves were generated with 150 msec pulses and 30s recoveries at different light intensities , with all reported recordings performed within the linear range of these curves ( S5B Fig ) . All data was analyzed with a custom Matlab program with the following parameters . First , data was first smoothed [filter{ones ( 1 , windowsize ) /windowsize , 1 data}] with a window size of 10 . The PR response amplitude was calculated as the absolute voltage difference between the average of 100 baseline values ( immediately prior to stimulation ) and the amplitude of the sustained negative response , measured as the average of 100 points immediately prior to stimulus termination . All test PR responses reported in Figs 3B , 3C and 5 were normalized to day and genotype-matched controls ( GAL4 or RNAi lines , described above ) . Raw values are plotted in S4 Fig . The “on” transient amplitude was calculated as the absolute voltage difference between the baseline and the maximum voltage reached during stimulation . We confirmed that these transients were linear with respect to PR activity at the light levels used in these experiments [164 , 165] . Therefore , to exclude the possibility that reduced transients did not merely reflect decreased PR receptor responses , relative transient size was calculated based on the ratio of the “on” transient strength to the maximal PR response . To assess changes in “on” transients in response to repeated stimuli , the ratio of the first and last transients was calculated . Absolute voltage values were normalized as percentages relative to day and genotype ( RNAi or GAL4 alone ) -matched controls ( Fig 3C ) . All captured data followed a normal distribution by Kruskal-Wallis tests . Stated significance was determined using multiple t-tests between each sample and its day/genotype-matched control . p-values were corrected using Benjamini and Hochberg’s false discovery rate ( Microsoft Excel and Prism v6 ) . For cell sorting experiments , eye tissue ( from which antennae , brain and lamina tissue were carefully removed ) from 45–75 animals was dissected from larva ( wandering late third instars ) , pupae ( 25% after puparium formation [25 hr at 25°C] ) and adults ( 1–2 days post-eclosion ) in ice-cold PBS during a period no longer than 2 hours . Tissue was dissociated by placing whole eyes in 0 . 5% trypsin for 15 min at room temperature . Single cell suspensions were made by pipetting dissociated tissue in PBS/1% FBS ( Gibco ) /1mM EDTA with a P200 tip coated with 1% BSA . For larval ( L ) and pupal ( P ) cone cells ( CCs ) , GFP-positive cells were sorted from R7-less sev-; prosPSG-GFP; TM2/TM6B animals . For adult ( A ) CCs , dissected whole-mounted retinas from yw67;sp/CyO;TM2/TM6B flies were dissociated and stained 10 min with pre-conjugated Fas3-Alexa555 ( 1:50 ) to mark CCs and 22C10-Alexa647 ( 1:50 ) to mark the photoreceptors and interommatidial bristle ( Fig 1G ) . Control sorts from unstained samples were performed using sev-; sp/CyO; TM2/TM6B animals for larval and pupal stages and yw67;sp/CyO;TM2/TM6B flies for adults . Fas3 ( 7G10 ) and 22C10 monoclonal antibodies were developed by Corey Goodman [166] and Seymour Benzer [167] , and obtained from the NIH/NICHD-created Developmental Studies Hybridoma Bank maintained at The University of Iowa , Department of Biology , Iowa City , IA 52242 . Antibody conjugations were performed using APEX labelling ( Invitrogen ) according to manufacturer’s suggestions . Photoreceptor RNAseq data was obtained from RFP-positive/GFP-negative gated cells from adult prosPSG-GFP retinas ( brain and lamina removed ) , making use of the attB docking site carrying the 3xP3-RFP transgene that is expressed in adult PRs [168 , 169] . Cells were sorted directly into lysis buffer and RNA was immediately extracted using the RNeasy Micro kit ( Qiagen , cat . 74004 ) and kept frozen at -80°C . RNA concentration and quality was assessed on an Agilent Bioanalyzer ( CCHMC Microarray Core ) . RNA amplification and cDNA synthesis was performed using the Ovation RNA Amplification System V2 ( NuGEN , cat . 7102–08 ) using manufacturer’s suggestions . Nextera library preparations was performed by the CCHMC Microarray Core and sequenced by the CCHMC DNA Sequencing Core using an Illumina HiSeq2500 , with 5–30 million reads dedicated to each sample . Sequencing files have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE93782 . NGS-pipeline data ( provided by the CCHMC Bioinformatics Core ) of ~15 million reads per sample were mapped to the Drosophila genome ( Dm3 ) with >78% of the reads uniquely mapped . A pooled meta-analysis ( F-test , p-value 0 . 05 ) was performed on genes within the 10-100th percentile of the RPKM ( S1 Table ) using Avadis NGSv1 . 6 software . The trimmed mean of M-values ( TMM ) normalization method [170] was used to remove RNA compositional biases between samples with different experimental and sequencing conditions and to improve the compatibility of cross-sample analysis . All samples were normalized to whole animal transcriptome data , and pooled meta-analysis and read density thresholding was used to remove genes that fell below statistically significant levels of expression ( <2 NRPKM , p<0 . 05 ) . This allowed analysis of ~6 , 500 genes expressed at each stage of CC development ( S1 Table ) . Validation of the integrity of cell specificity and sequencing was confirmed by comparing the adult CC transcriptome with a second , independently prepared Drosophila adult CC library . Strong agreement between the two sequencing data sets was observed ( R2 = 0 . 84 ) ( S2A’ Fig ) . For intra- and inter-species analysis , enhanced gene sets for CCs , PRs , CNS ( from modEncode sample #5312 ) , and digestive system ( DS , from modEncode sample #3445 ) were calculated based on ranked relative differential ( square-root ) expression of cell-type specific transcripts as compared to adult whole fly transcriptome data ( NCBI GSM 694258–61: average of 2 males and 2 female ) ( S2 Table ) [171 , 172] . For comparison of enhanced sets across species , sets of 1000 genes that most clearly differentiated CCs from PRs ( and vice versa ) ( CC>PR and PR>CC , respectively ) were calculated based on a ranked list of divergence using the differential of square root TMM expression values between these two cell types ( S2 Table ) . Fly-to-mouse gene conversions were performed using the DRSC Integrative Ortholog Prediction tool ( version 5 . 1 . 1 ) [173] , using the criteria: only genes with score size >2 unless only match score is 1 or 2 and the ortholog is represented the best score in fly-to-mouse or mouse-to-fly direction ( S4 Table ) . Enriched gene sets from mouse neural cell types were based on previous Dropseq analysis of retinal cells [106] and microarray analysis of isolated forebrain astrocytes , oligodendrocytes and cortical neurons [107] . From the microarray studies , astrocytes , oligodendrocytes and cortical neuron-enriched genes were defined as being statistically enriched ( FDR<1% ) by at least 1 . 5 fold in each cell type [107] . Gene identifiers from all sets were converted to Entrez IDs for cross experiment analysis using the 5/1/2016 update of NCBI Gene IDs of WebGestalt [174] ( S5 Table ) . Overlap between gene sets was analyzed using Venny ( http://bioinfogp . cnb . csic . es/tools/venny/ ) [175] , with statistical significance of overlap calculated based on normal approximation of hypergeometric probability [176] . | Glia are the caretakers of the nervous system . Like their neighboring neurons , different glial subtypes exist that share many overlapping functions . Despite our recognition of glia as a key component of the brain , the genetic networks that mediate their neuroprotective functions remain relatively poorly understood . Here , using the genetic model Drosophila melanogaster , we identify a new glial cell type in one of the most active tissues in the nervous system—the retina . These cells , called ommatidial cone cells ( or Semper cells ) , were previously recognized for their role in lens formation . Using cell-specific molecular genetic approaches , we demonstrate that cone cells ( CCs ) also share molecular , functional , and genetic features with both vertebrate and invertebrate glia to prevent light-induced retinal degeneration and provide structural and physiological support for photoreceptors . Further , we demonstrate that three factors associated with gliogenesis in vertebrates—prospero/Prox1 , Pax2 , and Oli/Olig1 , 2—control genetically distinct aspects of these support functions . CCs also share molecular and functional features with the three main glial types in the mammalian visual system: Müller glia , astrocytes , and oligodendrocytes . Combined , these studies provide insight into potentially deeply conserved aspects of glial functions in the visual system and introduce a high-throughput system to genetically dissect essential neuroprotective mechanisms . | [
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"analysi... | 2017 | Multifunctional glial support by Semper cells in the Drosophila retina |
A common metaphor for describing development is a rugged “epigenetic landscape” where cell fates are represented as attracting valleys resulting from a complex regulatory network . Here , we introduce a framework for explicitly constructing epigenetic landscapes that combines genomic data with techniques from spin-glass physics . Each cell fate is a dynamic attractor , yet cells can change fate in response to external signals . Our model suggests that partially reprogrammed cells are a natural consequence of high-dimensional landscapes , and predicts that partially reprogrammed cells should be hybrids that co-express genes from multiple cell fates . We verify this prediction by reanalyzing existing datasets . Our model reproduces known reprogramming protocols and identifies candidate transcription factors for reprogramming to novel cell fates , suggesting epigenetic landscapes are a powerful paradigm for understanding cellular identity .
Understanding the molecular basis of cellular identity and differentiation is a major goal of modern biology . This is especially true in light of the work of Takahashi and Yamanaka demonstrating that the overexpression of just four transcription factors ( TFs ) is sufficient to convert somatic fibroblasts into cells resembling embryonic stem cells ( ESCs ) , dubbed induced pluripotent stem cells ( iPSCs ) [1] . The idea of using a small set of TFs to reprogram cell fate has proven to be extremely versatile and reprogramming protocols now exist for generating neurons [2] , cardiomyocytes [3] , liver cells [4] , [5] , neural progenitor cells ( NPC ) [6] , and thyroid [7] ( see reviews [8] , [9] for more details ) . Despite these revolutionary experimental advances , cell fate is still poorly understood mechanistically and theoretically . Recent experiments suggest cell fates can be viewed as high-dimensional attractor states of the gene regulatory networks underlying cellular identity [10] . In particular , cell fates are characterized by a robust gene expression and epigenetic state resulting from the complex interplay of transcriptional regulation , chromatin regulators , non-coding and microRNAs , and signal transduction pathways . These experiments have renewed interest in the idea of an ‘epigenetic landscape’ that underlies cellular identity [11]–[15] . The landscape picture requires several key features to be consistent with experimental observations ( see Figure 1 ) . All cell fates must be robust attractors , yet allow cells to change fate through rare stochastic transitions [8] , [16] as in cellular reprogramming experiments ( Figure 1A ) . A common result of reprogramming is not the desired cell fate , but partially reprogrammed cells [17] , [18] . These results suggest that the landscape is rugged and may contain additional spurious attractors corresponding to cell fates that do not naturally occur in vivo . In addition , environmental and external signals can control cell fates . Some environments stabilize particular cell fates ( Figure 1B ) . A dramatic example of this is a protocol for reprogramming to neural progenitor cells ( NPCs ) that is identical to Yamanaka's protocol for reprogramming to ESC except for the culturing media [19] . Other external signals deterministically switch cell fates , as occurs in normal development ( Figure 1C ) [20] . Together , these imply the landscape is a dynamic entity that depends on environmental signals . The recent experimental progress has inspired several different theoretical approaches to understand the epigenetic landscape and the underlying gene regulatory networks governing cell fates . One focus has been on explicit construction of landscapes for specific cell fate decisions such as the erythroid vs myeloid choice in hemopoietic development [21] , pancreatic cell fates [22] , or C . elegans vulva development [23] . Other network based approaches use experimental data to constrain the possible networks [24] , [25] . A second area of work is based on understanding the underlying gene regulatory network [26] , [27] . A recent paper [28] attempts to combine the network and landscape picture by using the network entropy to define a landscape . On a more abstract level , there has been a renewed interest in understanding Waddington's landscape mathematically using ideas from dynamical systems and nonequilibrium statistical mechanics [15] , [29] . Most of these models focus on in vivo developmental decisions and hence consider the dynamics of a few genes or proteins . Here , we present a new modeling framework to construct a global ( i . e . all cell fates and all TFs ) epigenetic landscape that combines techniques from spin glass physics with whole genome expression profiles . We were inspired by the successful application of spin glasses to model neural networks [30]–[33] and protein folding landscapes [34] . Here , we construct an epigenetic landscape model for cellular identity with 63 stable cell fates and 1337 TFs using cell-fate specific , mouse microarray gene expression data . Each cell fate is a robust attractor , yet cells can deterministically switch fates in response to external signals . Our model provides a unified framework to discuss differentiation and reprogramming . It also naturally explains the existence of partially reprogrammed cell fates as ‘spurious’ attractors resulting from the high dimensionality of the landscape . Our model predicts , and we verify , that partially reprogrammed cells are hybrids that co-express TFs of multiple naturally occurring cell fates . Finally , our model reproduces known reprogramming protocols to iPSCs , heart , liver , NPC , and thyroid , and has the potential for designing reprogramming protocols to novel cell fates . Taken together , these results suggest that epigenetic landscapes represent a powerful framework for understanding the molecular circuitry and dynamics that gives rise to cell fate . The organization of the paper is as follows . First , we explain the motivation for using an attractor neural network to model the epigenetic landscape . Second , we define the state space for the model and the actual biological data used to construct the state space . Third , we give an overview of our landscape model ( with details given in Table 1 and Materials and Methods: Landscape Model ) . Next , we show that our mathematical model captures the essential experimental features of cellular identity . We then show that our model naturally explains the existence of partially reprogrammed cells and makes predictions about their gene expression profiles . We verify this by reanalyzing experimental data . Finally , we show that our model can identify key reprogramming genes in existing reprogramming protocols , suggesting it can be used to identify candidate TF for reprogramming to novel cell fates . We conclude by discussing the implications of our mathematical model for understanding cellular identity and reprogramming .
The Takahashi and Yamanaka reprogramming experiments [1] are reminiscent of content-addressable memory and attractor neural networks . First , let us introduce a content-addressable memory with a paraphrasing of the original Hopfield paper . A content-addressable memory allows one to retrieve a full memory based on sufficient partial information . For example , suppose the complete stored memory is “John J . Hopfield , Neural networks and physical systems with emergent collective computational abilities ( 1982 ) . ” A content-addressable memory is capable of retrieving the full memory based on partial , incomplete input . Therefore , the details “Hopfield , ” “Neural networks , ” and “1982” could be enough to recall the full memory . In the Yamanaka reprogramming protocol , overexpressing only four TFs is enough for a fibroblast to “recall” the global TF expression of an ESC . A content-addressable memory is naturally represented as a basin of attraction in a dynamical system , with partial recall corresponding to entering the basin of attraction and full recall corresponding to reaching the minimum of the basin . Hopfield attractor neural networks [30] , [31] , [33] are a general method to take an input set of vectors ( “memories” ) and explicitly construct a unique , global , landscape such that each input vector is a global minimum and has a basin of attraction . In what follows , we will exploit the analogy between associative memory in attractor neural networks and cellular reprogramming to explicitly construct the epigenetic landscape underlying cellular identity . Our goal is to model the global epigenetic landscape involving all cell fates by using genome wide data . Currently , microarrays are the only technology with genome wide data for a multitude of cell fates ( although RNA-seq and other technologies will likely be useful in the future ) . Specifically , we compiled a dataset of 601 mouse whole genome microarrays ( details in Materials and Methods: Data Analysis ) resulting in the gene expression for N = 1337 transcription factors for cell fates . We restricted our considerations to TFs due to their importance in cellular reprogramming and differentiation . However , our model can be easily generalized to include other important genes . To robustly compare microarrays from multiple platforms , we converted the raw expression data into a rank ordered list . We assumed that gene expression is log-normal distributed ( the minimal-assumption model for positive-definite random numbers such as gene expression ) and assigned a z-score to each TF . The final output of this procedure is that it assigns each TF in every cell fate a z-score gene expression . This continuous gene expression could be used to construct our epigenetic landscapes . However , for mathematical convenience , we discretize the continuous gene expression data into high expression ( for z-score ) and low expression ( for z-score ) . See Text S1 for an extended discussion on continuous vs discrete TF expression in attractor neural networks . This discretization process is biologically plausible . Cellular identity and differentiation are largely controlled by epigenetics , especially histone modifications ( HMs ) [35] ( Figure 2A ) . Epigenetics primarily controls the accessibility of DNA and depending on the HM , the DNA can be stabilized in an open or closed configuration . Using global HM data [36] , [37] and comparing it to microarray data , we created a conditional probability distribution of having a HM given a TF expression level ( Figure 2B ) . We find that between a z-score of to there is a sharp threshold which distinguishes genes with the activating modification of histone 3 tri-methylation at lysine 4 ( K4 ) from genes with the inactivating modification of histone 3 tri-methylation at lysine 27 ( K27 ) and poised/bivalent genes ( both K4 and K27 ) . This provides a potential biological justification to our discretization . In summary , we take the continuous gene expression and binarize ( Figure 2C ) . These binary ( i . e . on/off ) TF data are the only biological input into our model . In order to precisely describe the landscape results , we need to define the correct way to measure distances . One possible measure is the overlap ( aka dot product or magnetization ) , defined for cell fate as: ( 1 ) where is an arbitrary expression state and is the gene expression in the natural cell fate . The overlap between cell fate and state for exactly correlated , anti-correlated , or uncorrelated states is , , or respectively . Cell fates from similar lineages ( ex . blood ) often have similar gene expression patterns . For example , B cells and T cells have a 77% overlap in their gene expression profiles . Such large correlations between cell fates makes the overlap , , a poor distance measure . In order to measure distances between highly correlated vectors , it is helpful to define the “projection” of a gene expression state on a cell fate by ( 2 ) where is the inverse correlation matrix and is the overlap on cell fate and is given by ( 3 ) The projection measures the orthogonal projection of a state onto the subspace spanned by naturally occurring cell fates , ( see Figure 2D and Text S1 ) , and a perfect projection onto state is given by . In contrast with the overlap , B cells have zero projection on T cells , and vice versa . Our landscape assigns an “energy” to every global expression state . We emphasize that this energy does not correspond to physical energy consumption of ATP; instead it is an abstract energy that corresponds to stability and developmental potential of cell fates . The complete landscape can be thought of as arising from four terms with a simple interpretation ( see Figure 1 ) : ( 4 ) The first term , , ensures that observed cell fates are valleys in our landscape ( Figure 1A ) . The second term , , describes biasing of specific TFs by experimentalists ( not shown in Figure 1 ) . The third term , , increases the radius and depth of cell fates that are favored by the environment or culturing conditions ( Figure 1B ) . Finally , in the presence of an external signal that gives rise to differentiation ( ex . growth factors associated with differentiation ) , the fourth term , , opens a low energy path between the initial and final cell fates ( Figure 1C ) . We give a complete mathematical description of the model in the Materials and Methods: Landscape Model and a summary in Table 1 . We performed self-consistency checks for our model using two in silico experiments ( see details in Materials and Methods: Simulations ) . To verify that naturally occurring cell fates are dynamic attractors , we randomly perturbed the gene expression profile of cells from the ESC state and then tracked the gene expression over time . Real biology has many potential sources of noise , and the asynchronous dynamics introduced above will likely underestimate the noise . To show that our model is still robust to other large sources of noise , in our simulations we also add in periodic bursts of noise by flipping a fixed percentage of TF states ( 2% ) to mimic the observation that cellular divisions produce HM errors [38] . Figure 2E shows the projection of the TF state on the ESC state as a function of time . For a large number of starting conditions , after an initial transient , the system relaxes back to the ESC state ( red bracket ) , explicitly demonstrating the existence of a large basin of attraction [10] . This is true even when we break detailed balance by making the interaction matrix asymmetric by randomly deleting 20% of interactions ( Figure 2E Diluted ) . Our model can also deterministically switch between cell fates in response to differentiation signals . For example , the common myeloid progenitor ( CMP ) is a blood cell fate that in vivo can differentiate into either granulo-monocytic progenitors ( GMP ) or megakaryocyte-erythroid progenitors ( MEP ) . In Figure 2F , we show in silico validation where we start the system in the CMP state and show the trajectories after applying either the GMP ( signal 1 , blue ) or MEP ( signal 2 , red ) differentiation signal , resulting in branching to two distinct cell fates . When performing a reprogramming experiment , besides the initial cell fate and the end goal cell fate , experimentalists often produce “novel cell fates” , dubbed partially reprogrammed cells [17] , [18] . These partially reprogrammed cells have the characteristics of a stable cell fate ( i . e . they can be passaged indefinitely in culture ) , but may express a mix of key markers for multiple cell fates and have a global gene expression that does not match any in vivo cell fate [18] . While the existence of partially reprogrammed cells was surprising to experimentalists , they have a natural interpretation in our model . One of the most generic properties of all attractor neural networks is that in addition to the desired attractors , , the non-linearity of the dynamical process and topology of high-dimensional ( in our case N = 1337 ) vector spaces induces additional attractors , which are termed spurious attractors [33] . In our model , since the natural cell fates are the input vectors , these spurious attractors can be interpreted as potential cell fates that do not occur in vivo . These spurious attractors are predicted to be low-dimensional combinations , or hybrids ( see Materials and Methods: Spurious Attractors and Text S1 for details ) that should also be stable attractors but with smaller basins of attraction . A priori , there are several valid hypotheses for the relationship between partially reprogrammed cells and natural cell fates . In the original experiments [17] , [18] , it was expected that partially reprogrammed cells should be a hybrid of the starting and goal cell fate only ( i . e . have a significant projection only on the starting or ending cell fate ) . Another hypothesis was that in a high-dimensional landscape , randomly chosen vectors should be orthogonal ( Figure S2 ) ( i . e . have a projection of with all cell fates ) . However , our model predicts that partially reprogrammed cells should be low-dimensional hybrids of existing cell fates , but that they do not necessarily have to be a combination of the starting and goal cell fate . Mathematically , we predict that partially reprogrammed cells should only have a projection ( 2 std above 0 , see Figure S2 ) for a small number of natural cell fates . Reanalyzing existing genome-wide datasets on partially reprogrammed cells ( Table 2 ) validates the prediction of our model that partially reprogrammed cells are low-dimensional hybrids of existing cell fates . This qualitative agreement between the predicted spurious attractors and the partially reprogrammed states is independent of details of our landscape function . Importantly , such hybrid states are a generic property of all attractor-based landscape models and hence represents an important criteria for judging whether attractor-based models are suitable for describing epigenetic landscapes . Our landscape model provides a quantitative method to identify “predictive” TFs for a given cell fate . These predictive TFs can be used as markers of a cell fate and are potential candidates for reprogramming protocols . We expect reprogramming TFs to be a subset of all predictive TFs but not all predictive TFs will lead to successful reprogramming . For example , cell-specific downstream targets of reprogramming TFs are likely to also be highly predictive for a cell type but may not lead to successful reprogramming . Most reprogramming experiments follow an experimental protocol similar to the one outlined by Takahashi and Yamanaka in their seminal paper [1] , [8] . Initially the starting cells ( usually mouse embryonic fibroblasts , MEFs ) are infected with viruses containing all the TFs of interest . The original Yamanaka experiment over-expressed 24 TFs [1] , while more recent experiments usually start with about 10 TFs [2]–[6] . Several days after infection , the cells are switched to culturing conditions that support the desired final cell fate . If an experiment is successful , cells resembling the desired cell fate will appear after a few weeks . This original list is then pruned to identify a “minimal” ( essential ) set of TFs that still allows for successful reprogramming . In many cases , the viruses are excised [39] to confirm that the the reprogramming does not depend on viral expression . Furthermore , recent experiments indicate that the same TFs can be used to reprogram to a desired cell fate from multiple initial cell fates [16] . These experiments suggest that reprogramming TFs should be based on final , not initial , cell fate . Intuitively , reprogramming candidates should be both highly expressed and highly “predictive” of the desired cell fate . The TF z-score naturally defines high and low TF expression levels . Within our landscape , the “predictivity” of the TF for a given cell fate , is measured by its contribution to the potential energy of that cell fate , and is mathematically defined as: ( 5 ) where is the cell fate correlation matrix and is the expression of TF in cell fate . We note that the projection and predictivity are directly related as can be seen by ( 6 ) where is the predictivity of TF in cell fate and is an arbitrary gene expression state . For a desired target cell fate , TFs that are high ( low ) in both predictivity and expression in that cell fate are candidates for over expression ( knock out ) in reprogramming ( see Figure 3A ) . For a simple , single measure of reprogramming efficacy of a TF , the predictivity and expression can be multiplied together to give a “reprogramming score” , where the top ( bottom ) rank order TFs are the best candidates for over expression ( knock out ) . Figure 3 shows the expression and predictivity for TFs in a variety of cell fates . In Figure 3B , we have explicitly labeled the TFs used in the original Yamanaka protocol for reprogramming to ESC . Consistent with our model , these TFs are both predictive and highly expressed . Figure 3C shows TFs that have been successfully used in any reprogramming protocol to ESCs [8] as well as the pluripotency genes ( involved in maintaining stem cell fate ) Zfp42 ( Rex1 ) [40] and Nr0b1 ( Dax1 ) [41] . Once again these genes are highly predictive for ESCs . As a further check on the biological validity of our predictions , we analyzed the GO Annotation of our top 50 candidates for ESC reprogramming ( Table S1 ) . Within these top TFs , 12 have successfully been used in reprogramming , 7 are known pluripotency TFs , 16 are involving in cell differentiation , while 15 have no known function and are intriguing reprogramming candidates . Taken together this suggest that we are capturing the essential biology despite minimal biological data for input . While ESC have been studied in the most detail , recent experiments have reprogrammed ( aka direct conversion ) to other cell fates such as cardiomyocytes [3] ( Figure 3D ) , liver [4] , [5] ( Figure 3E ) , and thyroid [7] ( Figure 3F ) . Once again we have explicitly labeled the TFs that have been successfully used for direct conversion . Notice that all of these TFs ( except Mef2c ) are highly predictive and highly expressed . Note that p19Arf [5] used in the direct conversion to liver was not differentially expressed in our microarrays and therefore was not included in our model . We also examined TFs used in direct conversion to neural lineages . As discussed in [2] , these TFs were chosen because they were known to be important in either neurons or neural progenitor cells ( NPC ) . Figure 3F and 3G show the expression and predictivity of TFs for neural progenitor cells ( NPC ) [6] ( Figure 3G ) , and neurons [2] respectively . Induced NPC were made using a four TF cocktail consisting of Pou3f2 ( Brn2 ) , Sox2 , and Foxg1 [6] . Our analysis shows that the first two of these TFs are predictive for NPCs while Foxg1 is predictive for neural stem cells ( NSC ) ( see Figure S3 ) . Induced neurons ( iN ) can be made using the TFs Myt1l , Pou3f2 , and Ascl1 [2] . Consistent with their experimental design , we find that Myt1l is highly predictive for mature neurons , while the remaining TFs ( Pou3f2 , Ascl1 ) are predictive for NPCs . While it is not possible to perform statistical tests to test our examples due to the scarcity of reprogramming protocols , we performed a simple numerical exercise to gauge the predictive power of our model . The four Yamanaka factors are all in the top 50 when ranked by their reprogramming score for ESCs ( where the reprogramming score of a TF is defined as the product of the expression and predictivity scores of a TF ) . We randomly permuted TF labels and asked how often all four Yamanaka factors remained in the top 50 . For a million independent permutations , this occurred only once , confirming that our model is capturing many essential aspects of cellular reprogramming . Our results suggest that epigenetic landscapes may be useful for rationally-designing reprogramming protocols to novel cell fates . To this end , we have used our model to identify candidate TFs for reprogramming , see File S5 for the top 50 candidates for overexpression for all cell fates and File S6 for top 50 candidates for knockouts for all cell fates .
A common biological metaphor used to describe development and cellular reprogramming is a rugged “epigenetic landscape” which emerges from a complex gene regulatory network , with cell fates corresponding to attracting valleys in the landscape . Despite decades of biological innovation , the large number of genes and their complex interactions has prevented the quantitative modeling of a global epigenetic landscape . To meet this challenge , we have developed a new quantitative framework of cellular identity to directly model the global , high-dimensional epigenetic landscape . Using whole genome expression data , we constructed an epigenetic landscape based on techniques from spin glass physics and neural networks . Our landscape only depends on the experimentally determined gene expression of natural cell fates . Yet , it explains the existence of spurious cell fates ( known as partially-reprogrammed cells ) and can reproduce known reprogramming protocols to embryonic stem cells , heart , liver , thyroid , neural progenitor cells , and neurons . More importantly , our model can be used to identify candidate transcription factors for reprogramming to novel cell fates . An interesting question is if spurious attractors are ubiquitous throughout the landscape , why does standard development not produce partially reprogrammed cells ? The key is the difference in the dynamics . In cellular reprogramming , the starting cell fate is forced to express a small number of TF and this leads to a stochastic conversion to the desired cell fate ( Figure 1A ) . During this stochastic exploration of the landscape , there is only a weak bias towards the final state , so it is easy for the cells to get trapped in a metastable state . However , during standard development , the external signals actively reshape the landscape and open up low energy valleys between cell fates ( Figure 1C ) . This strong bias towards the final cell state results in a deterministic switch during which the spurious attractors are only a small road bump on the path to the final cell state . Therefore , it is not a surprise that partially reprogrammed cells are only found during cellular reprogramming and not during standard development . Epigenetic landscapes can also be used to identify important , or predictive , TFs for cell fates . The predictivity of a TF for a cell fate generalizes the idea of specificity . A TF is specific to a cell fate if it is expressed only on in a small subset of cell fates . In contrast with specificity , predictivity weighs the global correlations amongst cell fates when assessing the importance of a TF for a cell fate . Thus , the predictivity not only picks out important specific TFs , but also TFs that are lineage markers . For example , Brachyury ( T ) [42] is a general marker of mesodermal lineages . Since it is highly expressed in large a number of cell fates , it is not specific to any given cell fate . However , it is predictive because its expression is a strong indicator that a given cell fate is a mesodermal lineage . The concept of predictivity also yields new insights into the Yamanaka protocol . When the Yamanaka factors were first published , two of the four TFs , Pou5f1 ( Oct4 ) and Sox2 were known to be important for ESCs . In contrast , the role of the other two TFs , Klf4 and Myc , was not well understood [43] . It was quickly shown that Myc was was not essential to reprogramming ( Oct4 , Sox2 , and Klf4 can reprogram alone ) , but nonetheless enhanced the efficacy of reprogramming [44] . The importance of Klf4 was surprising given that it is neither highly expressed nor specific for ESC . However , Klf4 is highly predictive of ESC ( Table S2 ) . For this reason , our model actually explains why Klf4 is a prime candidate for reprogramming to ESCs . We make several experimentally verifiable predictions . First , our model predicts the partially reprogrammed cells should be hybrids of existing natural cell fates . As more partially reprogrammed cells are studied , if they are found to either have high projection on only one cell fate ( for one ) or no projections on any cell fates ( for all ) , this would call into question whether partially reprogrammed cells are truly the spurious attractors of an attractor neural network . Second , our model can be used to identify important , or predictive , TFs for cell fates . TFs with large positive ( negative ) predictivity should be positive ( negative ) markers for a cell fate . Additionally , for cellular reprogramming we predict that TFs with large positive ( negative ) predictivity and expression could be over expressed ( knocked out ) to reprogram to a desired cell fate . Therefore , our model has several predictions that can be tested against future experimental progress in the field . Our model has several limitations . First , a generic limitation for any method relying on microarrays to define gene expression is that one cannot distinguish between direct , causal , interactions and indirect , correlative , interactions . Therefore , predictivity can establish the importance of a gene , but further experiments are needed to determine if the predictive gene is the controller of the cell type or just a passive indicator of a cell type . Second , it fails to accurately capture the dynamics of reprogramming . Simulations of reprogramming with known protocols , such as the Yamanaka protocol , lead to rates of reprogramming that are comparable to the rates from a reprogramming simulation with a randomly selected protocol . This is likely due to the fact that cell fates are extremely stable and hence reprogramming is extremely rare . Third , our model does not directly explain the importance of the non-specific transcription factor Myc . Many protocols use Myc [8] , but it can be replaced ( with no deleterious effect ) by short hairpin RNAs ( shRNAs ) [45] , or dropped completely from protocols at the expense of speed and less efficient reprogramming [44] . This suggests that Myc may have an alternative role and instead of being a biasing field , , it may instead raise the effective noise of the system ( i . e . decrease ) . Another limitation is that based on the currently available experimental data , our landscape construction cannot definitively be distinguished from alternative constructions . For example , the interaction network could be constructed by such that it does not weigh each cell fate equally ( as is currently done ) . This would have the effect of changing the relative stability of cell fates . Therefore , in the absence of more experimental data , our landscape and a weighted landscape cannot be distinguished . A popular approach to inferring landscapes from biology data are “Maximum Entropy” models . This method has been used to model firing neurons [46] , protein configurations [47] , [48] , and antibody diversity [49] . The Maximum Entropy approach takes as input large samples of biological data and a set of constraints and outputs a landscape that maximizes the entropy . While Maximum Entropy models can be used to infer landscapes with basins of attraction [50] , it can quickly become a computationally challenging problem . Our approach differs from Maximum Entropy models in the following way . Since our goal is to model a landscape with basins of attractions , we make the ansatz that the landscape can be described by a Hopfield neural network . Then we insert real biological data , , to construct the landscape exactly . Our method requires no computational inference of parameters . There are several natural extensions of the model discussed in this paper . The landscape could be constructed with additional biological input such as other genes , microRNAs , or histone modification data . This opens up possibilities of improving upon the high reprogramming rates achieved by overexpressing microRNAs [51] or synthetic mRNAs [52] . Another attractive element of the framework presented here is that it allows for a quantitative analysis of whole genome-wide expression states ( see Table 2 ) . This is likely to yield a more accurate classification of reprogrammed cells . Finally , directed differentiation protocols [53] attempt to mimic standard development in vitro and have proven to have high efficiency and fidelity . Future work will try to use our landscape to predict the necessary signaling factors for rationally designing more efficient directed differentiation protocols . Overall , epigenetic landscapes provide a unifying framework for cell identity , reprogramming , and directed differentiation , and our results suggest these landscapes can provide crucial insight into the molecular circuitry and dynamics that gives rise to cell fate .
Here we present the details of the dataset . All data used in this paper are available in the online Supplementary Information and is organized as follows: An older version of this manuscript , Arxiv v3 [54] , has additional microarrays available that are unused in this version of the text . All microarrays used in this paper were taken from the public databases ArrayExpress ( www . ebi . ac . uk/arrayexpress ) or GEO ( www . ncbi . nlm . nih . gov/geo ) . See File S1: Microarray Sources for details on where to obtain raw , pre-normalized and pre-averaged data . There are two datasets , the natural cell fates and the partially reprogrammed cells . For the natural cell fates , we only used the Affymetrix GeneChip Mouse Gene 1 . 0 ST platform due to the large number of available microarrays on ArrayExpress ( www . ebi . ac . uk/arrayexpress ) and the better technical design of the platform ( 1 . 0 ST has probe matches throughout a gene in contrast to just the 3′ UTR in Affymetrix GeneChip Mouse Genome 430 2 . 0 ) . There is limited data on partially reprogrammed cells so we used microarrays from Affymetrix GeneChip Mouse Genome 430 2 . 0 . The raw microarray data was converted to an expression level as follows . Microarray probe-to-gene map was created with Bioconductor 2 . 10 . All raw microarray files were initially processed by robust mean averaging ( RMA ) in MATLAB , and genes with multiple microarray probes were averaged . We did additional processing of this output for two reasons . First , we need to compare microarrays from multiple platforms , but the standard RMA output can vary significantly from platform to platform . Second , since gene expression is a set of positive definite numbers , the minimal assumption model of gene expression is a log-normal distribution . Therefore , to make robust comparisons across platforms , we used order statistics [55] . The RMA output was converted to a rank order . Next , we want to convert this rank order to the z-score of a log-normal distribution . We converting the rank to a percentile ( for genes , divide by ) , and then this percentile into a normal z-score . For later mathematical convenience , we used a biased estimator ( normalize by not ) since then the Euclidean norm of each microarray gene expression is . At this point , the natural dataset consisted of 601 microarrays with 20719 genes . Since we were interested in cellular identity , only transcription factors , transcription factor co-factors , or chromatin remodeling genes were kept ( for short hand , referred to as transcription factors ( TF ) throughout the text ) [56] , leaving 1715 TFs . As explained in the main text , since continuous ( sigmoidal input ) attractor neural networks and discrete attractor neural networks are known to have the same stable fixed points [57] , we used the binarized gene expression . We binarized the gene expression by setting a positive z-score to and a negative z-score to . While this was mainly done for mathematical convenience , this is potentially biologically justified . Histone modifications ( HM ) either leave chromatin in an open , accessible configuration or a closed , inaccessible state [35] . We found global HM data for embryonic stem cells ( ESC ) , mouse embryonic fibroblasts ( MEF ) , and neural progenitor cells ( NPC ) [36] , [37] . Consequently , we used the global HM data for these three cell fates and compared them to microarray TF expression levels . This allowed us to create a conditional probability distribution of each HM for a given TF expression level ( Figure 2B ) . We found a sharp cutoff ( that coincides with a z-score of ) which distinguished TFs with the activating modification of histone 3 tri-methylation at lysine 4 ( K4 ) from TFs with the inactivating modification of histone 3 tri-methylation at lysine 27 ( K27 ) , poised/bivalent TFs ( both K4 and K27 ) , and no HM ( most likely DNA methylation ) . This shows that our mathematical assumption is justified by the HM data . After the binarization of TF expression , all TFs that were not differentially expressed across cell fates ( i . e . TFs that are always on/always off in every cell fate ) were dropped , leaving 1337 TFs . The binarized TF expression for the 63 cell fates was found by first binarizing all 601 microarrays and then taking the majority vote for each cell state ( with ties broken by averaging the continuous data ) . The final result was the binary expression state for 63 cell fates . Microarrays for partially reprogrammed cells were on the Affymetrix GeneChip Mouse Genome 430 2 . 0 Array . The same procedure was used to convert raw microarray data to z-score expression . However , since different microarrays do not have the same genome coverage , the analysis comparing partially reprogrammed cells and natural cell fates used the N = 1329 TFs common to both platforms . Several self-consistency checks were performed on the data . First , the correlation matrix ( explained in main text and below ) was calculated for the original continuous data and for the binarized data ( Figure S1 ) . Both correlation matrices are consistent with each other showing binarization does not change the global correlations . Note that in the correlation matrix , cell fates have been grouped by tissue type , leading to a block diagonal form . Second , the expression state of all cell fates was constructed from multiple microarray experiments . These different experiments were compared with each other and were within 2 standard deviations ( std equal to ) for all cell fates . This demonstrates that microarrays from multiple laboratories can be directly compared . Here we give an overview of our epigenetic landscape model . The model is summarized in Table 1 , and Text S1 provides a supplementary overview of attractor neural networks . Here we include details of the simulations in this paper . For all simulations , we set and evolved the system for TF updates . In Figure 2E , we demonstrate that we have basins of attraction . The initial conditions were created by taking the ESC expression vector and randomly flipping of the TFs . After every updates of asynchronous dynamics , burst errors were introduced by randomly flipping of TFs . For the asymmetric dilution , the standard interaction matrix was created . Then of matrix entries were randomly set to . In Figure 2F , we demonstrate that the landscape can deterministically switch between basins . The initial conditions were always the CMP expression vector . For signal 1 , we set and all other . For signal 2 , we set and all other . Here we provide more details on spurious attractors and hybrid cell fates . As explained in more detail in Text S1 , for the traditional Hopfield model , these spurious attractors take the form of odd-majority vote mixtures [33] ( i . e . majority vote at each TF of of the ) . The projection method also has the additional spurious attractors of any linear combination of that spans the discrete state space ( see geometric interpretation given in Text S1 ) [32] . For convenience , we use the word hybrid as the collective term for either majority vote mixtures or linear combinations of existing cell fates . As discussed in the main text , the prediction of spurious attractors in the projection method inspired us to reexamine data on existing partially reprogrammed cells . Surprisingly , we found that partially reprogrammed cells could be thought of as hybrids of existing cell fates . However , we are currently only able to obtain qualitative agreement between partially reprogrammed cells and the predicted nature of the spurious attractors . While it is known that the projection method retains these odd-majority vote mixtures spurious attractors , the correlations between states implies these spurious attractors may no longer be symmetric mixtures . However , the exact nature of these spurious attractors is not known and will be explored in future work . | Traditionally , standard development has been viewed as a one-way process; an organism starts as a single cell ( embryonic stem cell , ESC ) that divides into a multitude of mature cell types ( skin cells , heart , liver , etc ) . But , in 2006 Takahashi and Yamanaka revolutionized this view by stochastically converting skin cells into cell types resembling ESC ( called induced pluripotent stem cells , iPSC ) . Following this groundbreaking experiment , other reprogramming protocols have been found , so now scientists can switch between a variety of cell types such as ESC , skin , liver , neurons , and heart . This has already revolutionized the understanding of biology and could change the future of medicine . A common metaphor for development is Waddington's landscape , in which an ESC is like a ball rolling down a hill which eventually ends in a valley ( mature cell type ) . In this paper , we make this analogy more precise by developing a mathematical model of cellular development . Using data on real cell types , we can provide insight into existing reprogramming protocols and potentially predict new reprogramming protocols . | [
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] | 2014 | Epigenetic Landscapes Explain Partially Reprogrammed Cells and Identify Key Reprogramming Genes |
Considering the short lifetime of IgA antibodies in serum and the key advantages of antibody detection ELISAs in terms of sensitivity and specificity , Bio-Rad has just developed a new ELISA test based on the detection of specific anti-dengue IgA . This study has been carried out to assess the performance of this Platelia Dengue IgA Capture assay for dengue infection detection . A total of 184 well-characterized samples provided by the French Guiana NRC sera collection ( Laboratory of Virology , Institut Pasteur in French Guiana ) were selected among samples collected between 2002 and 2013 from patients exhibiting a dengue-like syndrome . A first group included 134 sera from confirmed dengue-infected patients , and a second included 50 sera from non-dengue infected patients , all collected between day 3 and day 15 after the onset of fever . Dengue infection diagnoses were all confirmed using reference assays by direct virological identification using RT-PCR or virus culture on acute sera samples or on paired acute-phase sera samples of selected convalescent sera . This study revealed: i ) a good overall sensitivity and specificity of the IgA index test , i . e . , 93% and 88% respectively , indicating its good correlation to acute dengue diagnosis; and ii ) a good concordance with the Panbio IgM capture ELISA . Because of the shorter persistence of dengue virus-specific IgA than IgM , these results underlined the relevance of this new test , which could significantly improve dengue diagnosis accuracy , especially in countries where dengue virus is ( hyper- ) endemic . It would allow for additional refinement of dengue diagnostic strategy .
Caused by any of four dengue virus serotypes ( i . e . DENV-1 to DENV-4 ) , dengue infection is currently the most significant arthropod-borne viral disease [1] . Whereas the World Health Organization ( WHO ) estimated in 2009 that 50–100 million infections occur each year [1] , a recent study estimated that 390 million dengue infections occurred annually , of which more than 90 million were symptomatic [2] . World Health Organization has identified approximately 100 tropical and sub-tropical countries around the world where populations experience a high risk of dengue exposure . Because of its rapid spread and its impacts on human health , dengue epidemic has become a major and global public health concern . Without specific therapeutics or vaccines , dealing effectively with this disease emergency requires innovative and appropriate diagnostic tools [3–6] . During the acute phase of the disease , dengue diagnosis is based on direct viral detection targeting the genome , especially by RT-PCR approaches , virus isolation on culture cell , or a viral antigen , the non-structural protein 1 ( NS1 ) by ELISA or rapid tests . Indeed , the virus and soluble NS1 circulate in patients’ blood and persist for 5–7 days after fever onset . Indirect methods of dengue diagnosis are based on dengue-specific antibody detection , particularly the specific immunoglobulin M ( IgM ) by an IgM antibody-capture enzyme-linked immunoabsorbent assay ( MAC ELISA ) , but also virus-specific IgG and IgA . Different commercial kits are available to detect these specific antibodies [1 , 3 , 5 , 7] . In response to the disease , IgM could be detected in 50% of cases early after infection ( within 3–4 days after fever onset ) and the majority of infected individuals become positive for IgM by day 5–6 . IgM have been described as persisting until about 3 to 8 months post onset [7] . As for IgM , IgG are generally detectable at the end of the first week of illness ( within 7–10 days after fever onset ) , and still detectable in serum after several months , and probably even for life . Concerning anti-dengue IgA antibodies kinetics , their positive detection often occurs one day after the beginning of the IgM time frame ( on average at 5 . 5 days after the onset of fever ) reaching their highest titres around day 8 following this onset . The IgA titre decreases rapidly until it reaches undetectable levels by day 40 , indicating a shorter persistence of dengue virus-specific IgA in serum than IgM and IgG [6–11] . The dengue virus and circulating antibodies displayed well-known dynamic patterns: these patterns are influenced by the infecting serotype and patients’ clinical status , which shows significant differences following a primary or secondary dengue infection [1 , 7 , 11] . In secondary infection , IgM antibodies appear earlier or within the same time frame , but are usually at lower titres than during primary infection . They may even be undetectable . During secondary infection , the dominant antibodies are IgG , present from the previous infection and detectable at high levels , even in acute-phase serum samples . Concerning IgA dynamics in secondary infection , these antibodies in serum appeared to be slowly increasing during the first days , until reaching a higher level than in primary dengue infections [8 , 11] . Notably because both IgM and IgG antibodies persist for several months or years after infection , an IgM or IgG positive result from one serum sample is no more than suggestive: only seroconversion from paired serum samples-or a four-fold IgG titre increase- can confirm a recent dengue diagnosis [1] . But in practice , due to difficulties in obtaining blood samples taken on two occasions with an interval of no less than fifteen days apart , the serological analysis is carried out from a single acute-phase serum specimen and therefore provides only probabilistic diagnosis . The interpretation of these indirect tests is especially difficult in countries where dengue virus is hyperendemic and where other flaviviruses circulate , which could induce serological cross reactivity [1] . In this type of epidemiological context , obvious ways to overcome these diagnostic issues are to combine several diagnostic tests based on different approaches ( direct and indirect , or multiple indirect tests ) and to develop new tests targeting new infection markers . IgA thus appears to be an early , high-quality serological marker . Because dengue-specific IgA antibodies are detectable in acute-phase serum and persist for a shorter period of time than dengue-specific IgM , some studies have recognized the value of IgA detection in sera for dengue virus diagnosis using ELISA and immunofluorescence assays [7 , 9 , 12–14] . Helping to narrow the time frame of marker detection after infection , the IgA based method could be a more informative diagnostic tool and a better marker of recent dengue infection [8 , 10–13] . In keeping with these aims , a novel ELISA test for the detection of anti-dengue virus IgA from human sera , Platelia Dengue IgA Capture , was recently developed by Bio-Rad . The main objective of our study was to evaluate the performance of this serological dengue diagnostic test , based on specific IgA detection in clinical samples from patients exhibiting a dengue-like syndrome . Dengue diagnosis was confirmed using reference assays ( detection of DENV RNA by RT-PCR and virus culture ) . Accuracy was also evaluated in relation to both DENV infecting serotype and DENV immune status . The second objective was to compare the Platelia Dengue IgA Capture performance to the PanBio Capture IgM test for dengue diagnosis in serum .
A total of 184 human sera were used for this study in order to reach a precision of 5% of the performance of the index test and to conform the STARD requirements [15] . The index test , Platelia Dengue IgA Capture , was evaluated using dengue diagnostic reference assays , as described below . Sera were provided from the French Guiana NRC ( National Reference Center ) sera collection ( Laboratory of Virology , Institut Pasteur in French Guiana ) , stored at -80°C . The collection encompasses sera collected between 2002 and 2013 from patients exhibiting a dengue-like syndrome ( fever , arthralgia , headache and/or myalgia ) . These sera were collected either for diagnostic purposes or for identifying the DENV serotype from patient sera already found positive for NS1 antigen in the context of epidemiological surveillance . The 184 sera selected for this evaluation included two groups: a group of 134 sera from confirmed dengue-infected patients and a second group of 50 sera from non-dengue infected patients . Sera were classified according to the onset of fever ( day 0 was defined as sera collected within 24h after the onset of fever ) . A patient with febrile illness consistent with dengue fever was defined positive for DENV infection if an acute-phase serum sample was found positive for either RT-PCR targeting viral RNA [16] and/or viral isolation in Aedes pseudoscutellaris cell ( AP61 ) [17] . Dengue-positive samples were selected to achieve a balanced collection of sera sampled between the third and the fifteen day following the onset of fever and of sera infected by the four DENV serotypes . All dengue-positive patients constitute the “dengue group” . A patient with febrile illness consistent with dengue fever was defined negative for DENV infection if at least two of the three following analyses were obtained: ( i ) Negative RT-PCR or viral isolation from samples collected on day 0 and day 5 of the disease [16 , 17]; ( ii ) Negative NS1 detection of sera obtained prior to five days ( Platelia Dengue NS1 AG , Bio-Rad ) ; ( iii ) Negative in-house IgM capture assay on no less than 8 days sera [10] . All dengue-negative patients constitute the “non-dengue group” . Convalescent serum was included in this evaluation , provided that its own paired acute-phase serum sample was available . Finally , all sera were analyzed for the presence of anti-dengue IgM and IgG using the Dengue IgM and Dengue IgG capture assay from PanBio ( Panbio Dengue IgM Capture ELISA , Panbio Dengue IgG Capture ELISA—Australia ) , conducted according to the manufacturer’s instructions . No research-specific blood collection was performed for the study purpose . All the analyzed samples were remaining samples resulting from diagnosis procedures following blood collection required by the care for any patient presenting dengue-like symptoms in French Guiana , and kept by the NRC biobank for both health and scientific purposes . According to the French legislation ( article L . 1211–2 and related of the French Public Health Code—FPHC ) , biobanking and secondary use for scientific purpose of remaining human clinical samples are possible as long as the corresponding patients ( or their parents if less than 18 years of age ) were previously informed and had given no oral objection ( documented in the medical or laboratory files ) to them . Whenever no information are available concerning patient’s objection , a waiver from one of the 39 French Ethical Committees ( Comités de Protection des Personnes—CPP ) could be sought according to the FPHC . In the present research , those two requirements are fulfilled . Information had been given to patients through the brochure entitled “Information for patients” during the prospective blood collection , and no immediate or delayed patient’s opposition was reported by the clinicians to the Arboviruses NRC . Study ethical approval and information waiver for retrospective remaining samples were obtained from the CPP Sud-Ouest Outre-Mer III ( CPP # DC 2013/27 ) . Moreover , in application of French legislation ( article L . 1243–3 and related of the FPHC ) , the NRC biobank for research purpose had been declared to both the French Ministry for Research and the CPP Ile de France I ( declaration #2010/1223 ) . The NRC database was declared to the French Data Protection Agency ( Commission Nationale de l’Informatique et des Libertés , CNIL # 1248768 ) and provided clinical information about the age and sex of each patient , the date of serum collection and the date on which symptoms began . Platelia Dengue IgA Capture ( Bio-Rad Laboratories—Marnes La Coquette , France ) is a microplate immunoassay using immuno-capture format for detection of specific IgA against DENV in human serum or plasma . Intra- and inter-assay repeatabilities were already assessed using 4 samples , tested in the same assay in 32 replicates: coefficients of variation ranged from 2 . 3% for the medium positive samples to 26 . 8% for the low negative one ( cf . the analytical and clinical performance report from Bio-Rad ) . The test was used strictly following the instructions provided by the manufacturer . Briefly , 200 μl of 1/100 diluted sera of each 184 patients were distributed in each well then incubated for 1 h at 37°C . The plate was then washed four times and 200 μl of conjugate were added and incubated for 1 h at 37°C . After a 4-time washing step , revelation was carried out with a TMB substrate solution for 30 min at room temperature then stopped with 1N sulfuric acid . Optical densities ( OD ) were read at 450/620 nm using a plate reader within 30 minutes after stopping the reaction . Results were expressed in ratio = ( OD of tested sample ) / ( appropriate Cut-Off ) . Results were interpreted as positive , negative or equivocal using the ratio provided with the kit: positive result when ratio was greater than 1 , negative lower than 0 . 8 and equivocal between 0 . 8 and 1 . Detections of specific dengue IgM and IgG were carried out using the Panbio Dengue IgM Capture ELISA and the Panbio Dengue IgG Capture ELISA kits according to manufacturer’s recommendations . An IgM/IgG ratio was used to distinguish between the primary and the secondary dengue virus infections on serum samples showing positive IgM and positive IgG results , as recommended by the World Health Organization [1] . Using patient’s sera at 1/100 dilution , dengue infections were classified as primary if the IgM/IgG OD ratio was greater than 1 . 2 and as secondary if that ratio was lower than 1 . 2 . In addition , a host immune status was defined in the acute sample as being due to a primary infection when it was found positive for IgM and negative for IgG , and due to a secondary infection when it was found negative for IgM and positive for IgG . Others equivocal results or combinations were defined as unclassifiable . All serum analyses were tested blinded to the confirmed dengue samples ( virus isolation and/or RT-PCR positive ) . Continuous variables were expressed as median and interquartile ranges ( IQ1-IQ3 ) or mean ± SD and categorical variables as percentages . Differences among percentages were analyzed using the Fisher’s exact test and differences among continuous variables were analyzed using the Kruskal-Wallis test . In case of global significant differences between the groups , Bonferroni correction was applied . AUROCs were calculated with 95% confidence intervals for Platelia Dengue IgA Capture and Panbio Dengue IgM Capture ELISA and compared using the non parametric Delong test [19 , 20] . Kappa ( K ) coefficient was calculated to evaluate the concordance between Platelia Dengue IgA Capture and Panbio Dengue IgM Capture ELISA results , using the interpretation scale of Landis-Koch [21] . Results were considered statistically significant when p<0 . 05 . The sensitivity and the specificity for the assays were calculated based on confirmed dengue with the binomial exact 95% CIs . STATA 12 . 2 ( StataCorp , College Station , Texas ) software was used for all statistical analyses .
One hundred and eighty-four patients presenting a dengue-like syndrome were included to estimate the Platelia Dengue IgA Capture performances . Gender ( 94 females , 51 . 1%; 90 males , 48 . 9% ) was equally distributed between both groups ( p = 0 . 120 ) ( Table 1 ) . The mean patient age was 35 . 8 ± 17 . 6 years , with age ranging from 1 to 90 years old . Mean age of the dengue positive patient group ( dengue group ) was 34 . 8 ± 1 . 4 years; mean age of dengue negative patient group ( non-dengue group ) was 36 . 5 ± 2 . 9 years , with no statistical difference between the two groups ( p = 0 . 58 ) . Only one sample out the 184 tested sera was found equivocal , which represents only 0 . 54% of inconclusive results ( Table 2 ) . Out of the 134 dengue group sera , the IgA index assay detected 124 positive , indicating a sensitivity of 93% ( 95% CI , 87% to 96% ) . Six sera out of the 50 non-dengue group ones were also found IgA positive , demonstrating a specificity of 88% ( 95% CI , 75% to 95% ) . Out of the 10 IgA seronegative sera from dengue positive patients , 8 were collected 3–4 days after the onset of fever , with the other 2 collected 6 days following fever onset . Seropositivities for IgA broadly increased from 50% for sera collected 3–4 days after the onset of fever to 100% for those collected seven days after . In that study , the IgA index assay displayed a sensitivity of 93% ( 124/134; 95% CI , 87% to 96% ) , while the PanBio Dengue IgM kit positive for IgM detected 95% of the sera ( 127/134; 95% CI , 90% to 98% ) . These differences were not statistically different ( p = 0 . 25 ) . As observed for IgA , seropositivities for IgM also rose from 56% for sera collected 3–4 days after the onset of fever , to 100% for those collected five or more days after fever onset . Moreover , ROC curves were drawn for both IgA index test and Panbio IgM capture assay , both of which showed very good performances , with Area Under ROC around 0 . 95 ( Fig . 1 ) . The comparison between the two AUROC showed no difference ( p = 0 . 4135 ) . When using a qualitative approach , the results are similar with an almost perfect kappa coefficient , equal to 0 . 8632 .
The novel Platelia Dengue IgA Capture assay’s performance is very good: the overall sensitivity and specificity of the IgA index test are 93% ( 124/134 ) and 88% ( 43/49 ) , respectively , and only 0 . 54% ( 1/184 ) of the results was inconclusive . These results suggest that the Platelia Dengue IgA Capture assay is well correlated with dengue diagnosis on clinical sera from patients exhibiting a dengue-like syndrome . The development of this new assay could contribute to the improvement of dengue diagnostic performance , currently a major challenge in managing dengue disease [1 , 6] . Even if the detection of specific IgA in the serum has been evaluated as a useful diagnostic parameter [8 , 10 , 13 , 14] , there is still no single available ELISA commercial kit to measure IgA antibodies . The only available Dengue IgA kit was recently developed by MP Diagnostic ASSURE as a rapid test ( Dengue IgA RT ) , and even when evaluated under different conditions and inclusion criteria , the performances were lower , with an overall sensitivity of 61 . 0% and specificity of 85 . 1% by comparing the index test results with IgM detection by ELISA [22–24] . In concordance with theoretical IgA antibodies kinetics observed in serum , sensitivities of the Platelia Dengue IgA Capture index test vary according to the sera collection day . Sensitivity was highest when estimated on convalescent samples ( day 8 to 15 collected sera ) , compared to acute samples ( day 3 to 7 collected sera ) , displaying sensitivities of 100% for convalescent versus 84% ( 95% CI , 73% to 92% ) for acute samples . The effect of the collection day of sera data was also observed in the analysis concerning the infecting dengue virus serotype: performances obtained showed significantly lower accuracy in detecting serotype 1 . To the best of our knowledge , no existing studies have reported such differences observed in a study based on anti-dengue immunoglobulin detections . Results of serological IgM test obtained on these samples suggest the same trend of better performance observed for serotype -2 , -3 and -4 detection . Our dataset features , and particularly the small size of the groups , does not allow for more thorough statistical analysis of the specific effects of each of these variables however , this point could be fully explained by the differences observed between the collection day of sera data: matched to the median ( IQ1-IQ3 ) numbers of days after the onset of fever , DENV-1 sera displayed the lowest value , i . e . 7 ( 5–8 ) , compared with 8 ( 6–10 ) , 10 ( 7–14 ) , 7 ( 6–10 ) for DENV-2 , DENV-3 , DENV-4 sera respectively . The IgA test displayed good sensitivity in detecting immune status , with better efficiency at detecting secondary infections , when evaluated according to the IgM/IgG ratio method recommended by WHO . This test’s ability to detect IgA for primary and secondary dengue infections is a major criterion for quality and relevance , particularly in dengue ( hyper ) -endemic area where the detection of dengue secondary cases at early stage of infection is especially important , because secondary cases are more frequently associated with severe outcomes . This point should be correlated with collection day medians , which are lower for primary sera ( 7 ( 5–10 ) days ) , and higher for secondary ones ( 8 ( 6–11 ) days ) . Moreover , the results leading to a specificity of 88% ( 6 sera found positive by the Platelia Dengue IgA Capture among the non-dengue group ) could be correlated either with a previous dengue infection , preceding the recent dengue-like syndrome associated with these clinical samples , or with a cross-reaction potentially induced by another etiological agent , because of a higher risk of exposure to multiple flavivirus infections in French Guiana . A high degree of cross-reactivity is frequently observed among flavivirus infection serology , particularly where the circulation of multiple flaviviruses compromises the local specificity of such measurements . In addition , it is interesting to note that global diagnostic performance reaches 100% for both specificity and sensitivity when adding the NS1 diagnosis assay . As expected , combining the NS1 assay with an IgA assay will enhance the sensitivity of detection . The estimated accuracy of this test evaluated under our criteria could provide better diagnostic value if combined with another direct diagnostic test , as commonly illustrated and recommended [1 , 4 , 6 , 7 , 25 , 26] . Finally , the comparable overall performances estimated for both IgM and IgA serological marker measurements make this IgA index test as reliable as the IgM test . Even if the most frequently used serological test is the IgM capture ELISA format , based on the fact that around 80% of patients are IgM positive by the 5th day following the onset of symptoms [1] , its limitations from the anti-dengue IgM antibody persistence over several months make their detection can be due to infection up to several months earlier . They do not necessarily indicate an acute dengue infection . Because of longer IgM persistence in serum , IgA based method could be a more informative diagnosis tool because it is a marker of an earlier dengue infection , which narrows the time frame of infection and increases diagnostic accuracy [8 , 10–13] . To conclude , these results suggest that the Platelia Dengue IgA Capture assay is an acceptable test , with overall sensitivity and specificity of about 90% . This new test could contribute to dengue diagnosis , especially in countries where dengue virus is endemic and where many serotypes of dengue viruses are circulating . Using the IgA test assay to measure a good quality serological marker detectable in acute-phase serum and persisting for a shorter period of time than dengue specific IgM allows a more accurate dating of infection . It reduces the window of potential recent dengue infection and refines the diagnostic strategy for dengue adopted by physicians . Moreover , it would be interesting to increase the sample numbers per day after fever onset enough to minimize the effect of this variable on others . Lastly , it appears essential to design and to conduct prospective diagnostic evaluations during different phases of dengue epidemic in an endemic area [5 , 27] . | Dengue disease has become a major global public health concern , but an ideal diagnostic test that permits early and rapid diagnosis is not yet available . Improving diagnostic performance in this area is a major challenge and necessitates the development and evaluation of new efficient , accurate methods . According to the kinetics of dengue infection in serum , virus isolation and nucleic acid or antigen detection are the most specific diagnostic methods during the early acute stage of disease; serology is often used for diagnosis later in the course of infection . In order to provide an earlier and reliable dating of the dengue infection , few recent studies showed that the detection of specific IgA in the serum is a useful diagnostic parameter . Exploring that new approach , this study was carried out to assess the performance of a Platelia Dengue IgA Capture assay for dengue infection detection , newly developed by Bio-Rad , using 184 well-characterized samples provided by the French Guiana NRC sera collection of the Institut Pasteur in French Guiana . This study revealed good overall performances of this test , constituting promising assistance in dengue diagnosis , especially in hyper-endemic countries . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Evaluation of the Diagnostic Accuracy of a New Dengue IgA Capture Assay (Platelia Dengue IgA Capture, Bio-Rad) for Dengue Infection Detection |
Rhodnius ecuadoriensis is the main triatomine vector of Chagas disease , American trypanosomiasis , in Southern Ecuador and Northern Peru . Genomic approaches and next generation sequencing technologies have become powerful tools for investigating population diversity and structure which is a key consideration for vector control . Here we assess the effectiveness of three different 2b restriction site-associated DNA ( 2b-RAD ) genotyping strategies in R . ecuadoriensis to provide sufficient genomic resolution to tease apart microevolutionary processes and undertake some pilot population genomic analyses . The 2b-RAD protocol was carried out in-house at a non-specialized laboratory using 20 R . ecuadoriensis adults collected from the central coast and southern Andean region of Ecuador , from June 2006 to July 2013 . 2b-RAD sequencing data was performed on an Illumina MiSeq instrument and analyzed with the STACKS de novo pipeline for loci assembly and Single Nucleotide Polymorphism ( SNP ) discovery . Preliminary population genomic analyses ( global AMOVA and Bayesian clustering ) were implemented . Our results showed that the 2b-RAD genotyping protocol is effective for R . ecuadoriensis and likely for other triatomine species . However , only BcgI and CspCI restriction enzymes provided a number of markers suitable for population genomic analysis at the read depth we generated . Our preliminary genomic analyses detected a signal of genetic structuring across the study area . Our findings suggest that 2b-RAD genotyping is both a cost effective and methodologically simple approach for generating high resolution genomic data for Chagas disease vectors with the power to distinguish between different vector populations at epidemiologically relevant scales . As such , 2b-RAD represents a powerful tool in the hands of medical entomologists with limited access to specialized molecular biological equipment .
Vector control has been the mainstay of Chagas disease control strategies in Latin America . Several Latin American countries implemented nation-wide insecticide-spraying programs to eradicate Chagas disease vector populations in human dwellings over the last 30 years . These campaigns resulted in a dramatic reduction in vectorial transmission [1–3] . Despite this success , domicile recolonization is a constant threat due to the ability of several triatomines species to disperse from sylvatic to domestic/peridomestic environments and establish local domestic populations [4–8] . Triatomines , members of the arthropod family Reduviidae , subfamily Triatominae , commonly known as kissing bugs , are distributed from the southern United States to central Argentina [9] . Over 130 species are identified , but only a few dozen are known to transmit Chagas disease [10] . In Ecuador , Triatoma dimidiata and Rhodnius ecuadoriensis are main vectors of Chagas disease , with the latter widely distributed from coastal and southern Ecuador to northern Peru [11 , 12] . Multiple molecular genetic studies exist which attempt to explain genetic structure and gene flow in triatomine populations [8 , 13–20] . An example of those tailored to address defined epidemiological hypotheses include that of Fitzpatrick et al . [13] . Fitzpatrick et al . confirmed that gene flow ( and therefore vector dispersal ) occurs between sylvatic , domicile and peridomicile ecotopes in Venezuelan Rhodnius prolixus based on pairwise FST values derived from both cytochrome b ( cytb ) and nine microsatellite loci . R . prolixus is the major vector species in Venezuela and Colombia , as well as Andean and Central American countries . Fitzpatrick et al . ’s data suggested that colonization of domestic locales by wild triatomines is indeed possible in the region , and these findings had major implications for control . Other species have also been the subject of study . Population genetic data from Triatoma infestans based on ten microsatellite loci showed fine-scale genetic structure in domestic populations several years after the spraying of insecticides [18] . In this case , genetic data were tested under two different models of dispersal: isolation by distance and hierarchical island with stratified migration . The latter best reflected vector genetic structure among the sample sites . Finally , Almeida and colleagues [20] compared cytb and 8 microsatellite loci in Triatoma brasiliensis to investigate its genetic structure and to assess gene flow among sylvatic and domestic/peridomestic populations . As with Fitzpatrick et al . [13] , pairwise comparison of FST values obtained from microsatellite loci analysis also demonstrated connectivity between locales . Given that vector control remains the mainstay of Chagas disease intervention strategies , greater understanding of vector genetics and dispersal is urgently required . Of particular importance are genotyping approaches that provide very high resolution at local , epidemiologically relevant scales , as well as the ability to share and combine datasets across different studies and research groups . Microsatellite loci offer little flexibility in terms of shareability as data standardization guidelines for amplicon size estimation and allele nomenclature between laboratories , although possible [21] , are rarely established , time-consuming and expensive to resolve , an issue already seen in Trypanosoma cruzi typing [22] . Likely as a function of funding constraints , molecular genetic research on triatomine vectors , and on Chagas disease in general , has been relatively late to arrive on the ‘omics’ scene . The belated publication of R . prolixus genome in 2015 , as compared to other vector species , represents a step in the right direction and has revealed much about the core adaptations that underpin the biological success of triatomines [23] . A number of expressed sequence tags have been developed for T . infestans [24 , 25] . However , in general , genome sequencing efforts in triatomines so far have yielded little benefit to scientists and public health professionals attempting to map vector dispersal . In tandem with the emergence of high throughput next generation sequencing ( NGS ) approaches , several groups have pioneered the use of restriction enzymes ( REases ) on restriction site-associated DNA sequencing ( RADseq ) protocols to allow a small fraction of the genome to be sequenced across multiple samples [26–34] . Several variants of the RADseq technique currently exist [35–39]; however , protocol choice to address a specific research question must balance technical issues , budget and laboratory capacity [40] . The 2b-RAD genotyping strategy specifically uses Type IIB restriction enzymes ( IIB-REases ) for genomic DNA ( gDNA ) digestion [38] . Advantages of this protocol include simplicity and cost-efficiency , since it is carried out in 3 steps in the same 96-well plate , as compared to 4–6 steps required in other RADseq protocols [35–37 , 39] . Furthermore , library preparation can be achieved with no more than a PCR machine and a standard agarose gel . Moreover , IIB-REases capacity to generate identically sized 2b-RAD tags ( IIB-REase-dependent ) across all samples [38 , 40] and cleave at both strands of DNA removes the need for a post-digestion fragment size selection step . These characteristics also prevent fragment size [41] and strand [42] sequencing bias , which can compromise genotyping calls , as seen in other RADseq protocols . One disadvantage compared to other RADseq methods is that 2b-RAD may be inappropriate where accurate mapping against a highly duplicated/polyploid reference genome is required due to short fragment size production ( 33–36 bp ) [43] . Finally , bias from PCR duplicates , sequencing errors and allele dropout can be introduced in all RADseq protocols . In our study , we were able to rapidly and cost-effectively generate several hundred Single Nucleotide Polymorphism ( SNP ) markers for R . ecuadoriensis allowing for resolution of regional population genetic structure . Furthermore , by comparing the performance among the three IIB-REases , we were able to recommend the appropriate IIB-REase and read depth to employ in order to yield a given number of SNP markers for R . ecuadoriensis , and presumably for other members of the Rhodnius genus .
A total of 20 samples of R . ecuadoriensis were selected from the communities of La Extensa , Chaquizhca , and Coamine in Loja Province ( southern Andean region ) , and from the community of Bejuco in Manabí ( central coast ) in Ecuador ( Fig 1 ) . Triatomines were captured in previous field surveys [44–46] from June 2006 to July 2013 ( see S1 Table for further sample information ) . For each sample , head , legs and thoraxes were dissected and preserved in 100% ethanol . A salt extraction protocol modified from Aljanabi and Martinez [47] was used to extract total gDNA from R . ecuadoriensis heads , legs and thoraxes ( hindgut excluded ) . The modified protocol involved an additional overnight chitinase digestion step , as well as one overnight 75% ethanol wash to ensure purity ( Table 1 and Fig 2 ) . gDNA concentrations and purity ratios assessments were obtained by using NanoDrop ND-1000 Spectrophotometer ( NanoDrop Technologies , Inc . ) . Integrity of the extracted DNA was evaluated by agarose electrophoresis and highly fragmented samples were excluded from subsequent analysis . Initial selection of potential IIB-REases for our 2b-RAD protocol involved an in silico digestion of the R . prolixus genome , which is available from Genbank ( accession code: KQ034056 . 1 ) . For this purpose , 7 REases ( AlfI , CspCI , BsaXI , SbfI , EcoRI , BcgI and KpnI ) were screened . Three IIB-REases , namely AlfI , BcgI and CspCI were chosen based on the total number of restriction fragments produced in silico for the draft R . prolixus genome ( www . vectorbase . org ) , financial resources , known efficiency in previous studies [31–33 , 38] and authors’ previous experience working with those enzymes [48] . We expected REases with abundant in silico restriction sites to show larger coverage variability among samples , at lower read depths . On the contrary , REases with less abundant restriction sites in silico could provide more exploitable markers at lower read depths . Libraries were prepared using the 2b-RAD protocol proposed by Wang et al . [38] ( Table 1 and Fig 2 ) . Reaction mix and PCR conditions varied ( S2 Table ) depending on which IIB-REase was used . First , approximately 100–400 ng of high-quality gDNA from each sample was digested separately by each IIB-REase , producing IIB-REase-specific , uniform length fragments ( 32 bp , 35 bp and 33 bp for AlfI , BcgI and CspCI , respectively ) with random overhangs . To confirm that the restriction reaction took place appropriately , equal amounts of digested DNA ( dDNA ) and gDNA from the same sample were visualized on a 1% agarose gel . Subsequently , the dDNA of each sample was ligated to a pair of partially double-stranded adaptors with compatible and fully degenerated overhangs ( 5’NNN3’ ) . Finally , the obtained 2b-RAD tags were amplified to introduce a sample-specific 7bp barcode and the Illumina NGS annealing sites using two different pairs of sequencing primers . A 1 . 8% agarose gel electrophoresis of the PCR products was performed to verify the presence of the expected 150 bp target band ( fragment , barcodes and adaptors included ) . In order to ensure an approximately equimolar contribution of each sample to the library , the exact amount of each PCR product was measured from the intensity of the target band in a digital image of the 1 . 8% agarose gel . We prepared three libraries in total , one for each IIB-REase , according to the relative concentration of each sample . The purification of the libraries from high-molecular weight fragments and primer-dimers was achieved first by removing the target band on agarose gel from each sample among the three libraries and eluting them in water overnight , followed by DNA capture with magnetic beads ( SPRIselect Beckman Coulter ) based on the Solid-Phase Reversible Immobilization method [49] . The DNA concentration in the purified libraries was quantified with a Qubit Fluorometer ( Invitrogen ) and the libraries were assembled in one single pool according to their relative concentrations . The library pool was sequenced on MiSeq ( Illumina , San Diego , CA , USA ) with a single 1x50 bp setup using ‘Version2’ chemistry at the Science for Life Laboratory ( SciLifeLab , Stockholm , Sweden ) , which also implemented the reads demultiplexing and quality-filtering ( Table 1 and Fig 2 ) . Raw sequencing data has been uploaded to the Dryad Digital Repository ( 10 . 5061/dryad . 02bf1 ) . The quality of demultiplexed and quality-filtered raw reads was verified by using FastQC software [50] . Subsequently , custom-made Python scripts were used for trimming the adaptors and then filtering the reads on the IIB-REase-specific recognition site ( Table 1 and Fig 2 ) . For each of the three libraries ( AlfI , BcgI and CspCI ) we sought to determine the relationship between sequencing effort ( number of reads ) and the total yield of polymorphic loci ( set at up to two SNPs per locus ) . Therefore , we subsampled the total number of reads for each library in each individual using the fasta-subsample package from MEME SUITE [51] portal . This script randomly subsampled 25% , 50% , and 75% of total reads in triplicate to assess variability . This process resulted in 10 datasets per IIB-REase library: nine representing the three subsampling repetitions of the fixed percentages and only one from the total ( 100% ) reads . To estimate the polymorphic loci growth rate among the three IIB-REases , a nonlinear least square fitting ( NLS ) approach [52 , 53] was used with the R software [54] package NLS [55] . Specifically , NLS algorithm fits to the data by approximating a nonlinear function to a linear one , applying an iterative process to calculate the optimal parameter values for the growth rate [52 , 53 , 56] . Different built-in NLS models were tested in order to find the best fit to our data . These models were represented each with a different version of the Power-law equation [57]: Y=aXb ( 1 ) Here , Y is the expected number of polymorphic loci at reads yield X; a is the estimate starting amount of Y when X is close to 0; b is the estimate of the relative change of Y in relation to a unit change in X ( slope ) . A detailed description of the equations used for each dataset is provided in S3 Table . All datasets created were analyzed separately using STACKS software version 1 . 42 [58] , in which in silico assembly of loci and individual genotyping was performed by running the DENOVO_MAP . PL pipeline ( Table 1 and Fig 2 ) . STACKS algorithm , first , reconstructs stacks ( alleles ) from exactly matching reads of each sample ( -m ) . These stacks are then either merged with others to form a single polymorphic locus or kept as separate monomorphic loci depending on the number of nucleotide mismatches ( -M ) . Stacks with repetitive sequences are removed from the pipeline . Finally , each sample information is stored in a catalog ( stored in the MySQL repository ) containing the consensus ( -n ) of all loci and alleles in the entire population ( See [58] tutorials ) . Due to the failure of the protocol in one of the samples from the AlfI library ( likely as a result of low gDNA quality ) , we decided to discard this sample from the other two datasets to avoid biased results in the de novo assembly . After several parameter adjustments , we set the minimum number of identical raw reads necessary to create a stack ( -m ) to 5 . We kept the number of mismatches allowed between loci when building a locus in a single individual ( -M ) and when comparing across all individuals to build the population catalogue ( -n ) at default values . The bounded SNP calling model for identifying a SNP and estimating the sequencing error rate for calling at that SNP ( —bound ) ranged from 0 to 0 . 05 . Finally , the significance level required to call a heterozygote or homozygote ( —alpha ) was set to 0 . 01 . The EXPORT_SQL . PL utility was used to export loci shared by at least the 80% and the 90% of samples with the same polymorphism level ( loci with up to 2 SNPs ) from the MySQL database for all datasets analyzed in STACKS for each IIB-REase ( Table 1 and Fig 2 ) . Although both total number of samples ( N = 19 ) and sample size per community ( N = 4–5 ) were low , we conducted pilot explorations of the population structure of R . ecuadoriensis in the study area . We retained polymorphic loci shared by at least 90% of the samples , characterized by the presence of 1 and 2 SNPs and with a minor allele frequency of 0 . 01 . We performed preliminary genomic analysis using two different datasets: i ) one dataset contained 361 polymorphic loci obtained from 18 samples processed with the BcgI IIB-REase ( one sample was excluded from the analysis due to the high level of missing data ) and ii ) the second dataset contained 1225 polymorphic loci obtained from 19 samples processed with the CspCI IIB-REase . The number of markers obtained for the AlfI dataset derived from digestion with AlfI was too low to be used for the preliminary assessment of genomic structure of this particular sample . During the genotype calling , it is possible for more than one SNP to appear within the same region . When two SNPs were recovered at a single locus , a conservative approach was used to retain the first SNP for analysis , thereby excluding tightly linked SNP variation . ARLEQUIN version 3 . 5 [65] was used to calculate non-hierarchical analysis of molecular variance [AMOVA; 66] . To deal with missing data , the locus-by-locus option was set . Bayesian clustering implemented in STRUCTURE 2 . 3 . 4 [67] was conducted to investigate the most likely number of clusters of genetically related individuals excluding the locality origin ( model LOPRIORI ) . After several trials , a burn-in of 300 000 followed by 3 million runs for K = 1 to K = 4 and 5 iterations per each K value was set; admixture model and correlated allelic frequencies were assumed . The most probable number of clusters was identified from delta K , implemented online with STRUCTURE HARVESTER [68] . Then , in order to confirm our polymorphic loci was Rhodnius sp . -related , we also aligned the total polymorphic loci shared by at least the 90% of samples obtained from BcgI and CspCI datasets to the reference R . prolixus genome using BOWTIE 1 [69] . The highest alignment score ( —best ) was chosen and no more than 3 mismatches ( -v ) were allowed .
The extraction method allowed us to obtain RNA-free genomic DNA from all twenty samples with an average DNA concentration ( ng/μL ) of 62 . 77 ± 33 . 75 ( s . d . ) with average DNA purity ratios of 1 . 81 ± 0 . 05 ( s . d . ) and 1 . 81 ± 0 . 62 ( s . d . ) for absorbance at 280/260 and at 260/230 , respectively ( see S1 Table for detailed information ) . The in silico digestion on R . prolixus genome sequence by AlfI , BcgI and CspCI IIB-REases produced 204895 , 103268 and 69984 putative cut sites , respectively . The 2b-RAD experimental approach used in this study was effective for R . ecuadoriensis gDNA samples using any of the three IIB-REases ( Fig 3 ) , except for one sample ( ID: CQ12 , see S1 Table ) digested by AlfI ( CQ12 was thus not included in the pool for sequencing ) . A 2b-RAD pool of fifty-nine samples was established from nineteen samples digested by AlfI , twenty by BcgI , and twenty by CspCI IIB-REases . The Illumina NGS yielded a total of 14 . 8 million de-multiplexed and quality-filtered reads , approximately 3 , 6 . 2 and 5 . 6 million reads for AlfI , BcgI , and CspCI , respectively . FastQC analysis showed high per-base quality scores ( > 32 ) for the reads of all samples processed with each of the three IIB-REases . After trimming the adaptors and filtering the IIB-REase-specific recognition site , 2 . 9 , 5 . 8 and 4 . 8 million reads for AlfI , BcgI , and CspCI ( respectively ) were retained ( Fig 4 ) . The average trimmed Mreads per sample for each IIB-REase was 0 . 15 ± 0 . 06 , 0 . 30 ± 0 . 04 and 0 . 25 ± 0 . 07 . The number of reads subsampled and the total polymorphic loci for each IIB-REase are reported in Table 2 . STACKS reference genome free runs assembled and identified a catalogue of loci from each of the datasets . The EXPORT_SQL . PL script was used to extract two datasets which included all the polymorphic loci with up to 2 SNPs shared by at least 80% and 90% of samples from each of the set percentages ( 25% , 50% , 75% , 100% ) among the three replicates . We found only minor variation in the number of polymorphic loci called for each of the three subsampling replicates in all IIB-REase libraries . The average number of exported polymorphic loci obtained among replicates and from the total number of reads for each IIB-REase is reported in Table 2 . We observed growth in the number of loci recovered as we increased the read depth for all enzymes ( Fig 5 ) . However , while increasing read depth led to corresponding moderate and minor gains in locus number for BcgI and AlfI , respectively , for CspCI this number of loci is highlighted by a greater exponential growth in comparison to the other REases . Our results of best fit model analysis and estimated parameters ( S3 Table ) for each REase dataset were obtained by assessing different NLS models residual standard error , parameter significant p-values , number of iterations to convergence , the correlation between y and predicted values , and Akaike Information Criterion ( AIC ) . In the first dataset ( Fig 5A ) , we found that logarithmic ( y∼a+bln ( x ) ) , geometric ( y∼axbx ) and exponential ( y∼ae ( bx ) ) NLS equations best fit to the AlfI , BcgI and CspCI datasets , respectively , allowing the estimation of growth rate parameters α and b ( S3 Table ) . As for the second dataset ( Fig 5B ) , geometric ( y∼axbx ) and Power-law ( y∼axb ) equations converged the best fit and parameters estimation for AlfI and BcgI , and CspCI , respectively ( S3 Table ) . Detailed statistical analysis is provided in S1 Code . The non-hierarchical AMOVA carried out on all four community samples for both datasets ( BcgI and CspCI ) detected a strong signal of genetic structuring across the study area , with highly significant ( P< 0 . 0001 ) global FST values of 0 . 20452 ( BcgI ) and 0 . 39327 ( CspCI ) . The most likely number of genetic clusters ( K ) identified by STRUCTURE was 2 for both datasets: on one side , the 3 samples from Loja region ( CE , EX , CQ ) were grouped together , and on the other , the sample from Manabí ( BJ ) was considered as a distinct cluster ( Fig 6 ) . The alignment to the R . prolixus reference genome resulted in a 42% and 31% of polymorphic loci aligned for BcgI and CspCI , respectively , likely due to genomic variability between the R . ecuadoriensis and the available R . prolixus reference genome as well as the difficulty in mapping short reads .
In our study , we have gone somewhat further than a proof-of-principle by evaluating the performance of three distinct Type IIB restriction enzymes , pre-screened in silico for their performance in terms of marker density against the Rhodnius prolixus genome [23] . Our methodological development aim was to test the predictability of the in silico cutter and to provide recommendations for suitable read depths , marker numbers and sample sizes for studies involving Rhodnius sp . vectors . We expected that an abundant in silico enzyme cutter would provide less usable molecular markers at lower read depths ( Fig 4 ) . It is important to highlight that , enzyme performance in silico in terms of number of restriction sites is not necessarily the same in an actual experiment due to genome size , nucleotide distribution , depth of coverage and GC composition [27 , 40 , 71] . Thus , a pilot experiment always offers valuable information on actual restriction enzyme performance . Random re-sampling ( rarefaction ) of our datasets revealed distinct relationships between read depth and marker ( polymorphic locus ) number between the different enzymes , CspCI , BcgI and AlfI ( Fig 5 ) broadly in line with predictions of the number of usable markers ( Fig 4 ) . As such , CspCI produced the largest amount of polymorphic markers regardless of read depth , evidencing its experimental performance for R . ecuadoriensis and likely for other Rhodnius sp . vectors . AlfI and BcgI , on the other hand , showed a marked tendency of deceleration for marker recovery as read depth increases . However , AlfI does show the initially steeper growth , in line with predictions that AlfI cut sites in the R . prolixus genome are more abundant ( AlfI = 204895 sites , BcgI = 103268 sites and CspCI = 69984 sites ) . Additionally , we were able to fit nonlinear regression models to the data and estimate growth rate parameters for each enzyme ( Fig 5 ) . Although the model function varies per enzyme and dataset , all of them follow an exponential growth pattern which is more evident in CspCI datasets . The model function applied to the second CspCI dataset ( Fig 5B ) did not entirely fit the data; however , it constitutes the best fit compared to generalized linear models or more complex NLS fitting functions . Fitting NLS models to fewer data points for parameter estimation is challenging; however , based on our best-fit selection process we were confident that by substituting x for a determinate read depth we can obtain an estimate of polymorphic loci growth per restriction enzyme . Moreover , both parameters , a and b , are crucial for estimating the starting number of polymorphic loci and shape of the growth curve and understanding how the number of polymorphic loci changes as the number of reads increases . We hope this will be helpful to others planning similar studies . At the read depth we achieved on one Illumina MiSeq single-ended run across 20 R . ecuadoriensis DNA samples , we generated 1244 markers for CspCI , 367 for BcgI and 68 for AlfI . Even the lowest of these values eclipses the size of marker panels currently in use to explore Triatomine population genetics [8 , 13–20] . However , to generate read depths to exploit the higher density IIB-REase cutters ( e . g . AlfI , BcgI ) , a HiSeq approach might be more sensible . On the other hand , based on our data , CspCI can be expected to generate the best coverage and over a thousand polymorphic markers for approximately sixty vector samples on one MiSeq run . Interestingly , Graham et al . [72] assessed the impact of degraded gDNA in a modified double-digest-RAD protocol [37] on the MiSeq platform and found a significant correlation between DNA degradation , read quality reduction and loss . They also suggested that a higher throughput platform , HiSeq , and short fragment producer protocols , such as 2b-RAD , could help dealing with degraded gDNA and subsequent sequencing problems . As such , 2b-RAD might be an option for research teams with large and long-term stored triatomine bug collections , in which gDNA might already have started degradation processes . Based on our study , CspCI is the best candidate for generating enough usable markers , seconded by BcgI , and it is likely that a sequencing platform such as HiSeq can exploit a higher number of markers for both enzymes . As well as ‘range finding’ for the application of 2b-RAD sequencing to triatomine populations , our second aim was to undertake preliminary population genomic analysis to explore genetic structuring in our study region . To this end , we focused on datasets generated with BcgI and CspCI since they presented higher numbers of polymorphic loci . An AMOVA indicated a significant proportion of variation was explained by between-population differences for both datasets . Moreover , we demonstrated the feasibility of our markers to distinguish structuring among populations in both BcgI and CspCI datasets . By using a Bayesian clustering framework our markers from both data sets detected two distinct clusters without previous location information , one of them was Bejuco , the clear geographic outlier with respect to Loja populations . Morphometric and genetic studies of R . ecuadoriensis in Ecuador would also predict a similar pattern of diversification [73 , 74] . However , inter-population diversification in Loja might be happening [74] at a rate undetectable by coarse test for isolation-by-distance and other conventional population analysis techniques . Our genomic information coupled with a landscape genetics/genomics framework could test whether landscape heterogeneity and environmental variables are driving such processes [64] . Earlier in the manuscript we presented the notion that , fewer steps , simplicity , cost-effectiveness , fragment size and strand bias absence are advantages of using a 2b-RAD protocol compared to other RADseq methods . Nevertheless , researchers must be aware of potential pitfalls and sources of bias accompanying all RADseq protocols , as well as most NGS-based methods . However , development of sophisticated analysis and more powerful software tools to deal with the types of issues produced by most NGS platforms is an active and evolving field of research [75] . During the initial steps of library preparation , degraded gDNA seems to have a greater impact on read quantity and quality in all other RADseq protocols than in 2b-RAD [72] . However , guidelines [27] for assessing gDNA quality should be implemented in all protocols . Another drawback in all RADseq methods is that polymorphism can occur at the restriction site . This so-called allele dropout ( ADO ) prevents enzymes from cutting at that location and thus precludes recovery of that SNP allele ( null allele ) [40 , 76] . ADO will have a direct impact in the estimation of allele frequencies and consequently in overestimation/underestimation of F-statistics as individual heterozygote at the null allele will be recognized as homozygote . However , filtering loci successfully genotyped among a high percentage of the samples can help to remediate the problem [40] . PCR duplicates arise in all RADseq protocols with a PCR step , and only identifiable in protocols with a random shearing digestion ( original RADseq protocol [35 , 36] ) as duplicate fragments are identified by having the same length . Another promising approach described by Andrews et al . [40] to identify PCR duplicates is to use degenerated base regions within sequencing adaptors to mark parent fragments . However , Puritz et al . [43] highlighted that , though untested , skewed allele frequencies by PCR artefacts have little effect in statistical bias within loci and thereby genotype calling errors . No less important are sequencing errors introduced in all Illumina instruments . Although several genotype-calling algorithms account for sequencing errors , a high depth sequencing coverage ( ≥ 20x ) is always recommended . Finally , sequencing depth variability among loci could reduce genotyping accuracy for some less covered loci , thus allowing for fewer individuals to be multiplexed per sequencing lane , i . e . , increasing cost per sample [40 , 59] . In our study , most of the above issues encountered in RADseq have been circumvented either during the library preparation or the raw data filtering steps . Nevertheless , our main challenge is the absence of a reference genome to map short reads in order to ensure that all markers do indeed belong to R . ecuadoriensis and not to microorganisms such as bacteria and fungi . Furthermore , it may be important to differentiate between mitochondrial and autosomal loci or sex-specific chromosomes that might have an effect in population divergence analysis . To overcome this difficulty , we adopted a stringent approach during raw data trimming and genotype calling . We focused analyses to loci shared by a high proportion of individuals and removed loci and samples with high amounts of missing data . Landscape genetics/genomics is a powerful and relatively new approach to explore the underlying spatial processes that affect genetic diversity in biological organisms [63] . Next to isolation-by-distance , isolation-by-resistance is a common null hypothesis tested in landscape genetics when more complex ecological and environmental processes are thought to be at play . The landscape genetics framework and tools such as causal modelling and environmental association analysis have the potential [63 , 64 , 77–79] to uncover whether the same is true for R . ecuadoriensis genetic structuring and dispersal in Ecuador . In our study , the main limitation to carry out a wide range of conventional between and within-population analysis was the sample size per population . Low sample size required our analyses to consider an extended area to resist exploration of processes at finer geographic scales . The high-resolution genotyping approach we have developed in this study now paves the way for landscape genetic/genomics analysis in vector-parasite systems [64] , with genuine potential insights for rational disease and entomological control . For example , landscape genetics approaches expanded our understanding of the natural and human-aided dispersal dynamics of the invasive Asian tiger mosquito , Aedes albopictus [80] . Similarly , insecticide resistance gene spread in Anopheles sinensis has been tracked in China using landscape genetics approaches , demonstrating multiple origins and the importance of long term agricultural insecticide use [81] . More widely , high resolution SNP datasets are increasingly used to explore the local and international spread of important disease vectors ( e . g . Aedes aegypti [29 , 82] ) . 2b-RAD typing not only promises a potential applicability for population genetic studies but also for linkage and quantitative loci mapping given that marker density can be controlled using selective adaptors [38] . In fact , via its GENOTYPE pipeline , the STACKS package potentiates the construction of genetic maps from F2 or backcrosses of R . ecuadoriensis or other triatomine species . In conclusion , the decreasing cost and increasingly simplicity of approaches to generate high resolution SNP data puts such tools increasingly in the hands of researchers in endemic countries working on non-model organisms that act as vectors of Neglected Tropical Diseases . An analytical framework to incorporate detailed spatial and environmental variation into genetic analyses is now in place to facilitate a better understanding of the biology and dispersal of disease vectors . | Understanding Chagas disease vector ( triatomine ) population dispersal is key for the design of control measures tailored for the epidemiological situation of a particular region . In Ecuador , Rhodnius ecuadoriensis is a cause of concern for Chagas disease transmission , since it is widely distributed from the central coast to southern Ecuador . Here , a genome-wide sequencing ( 2b-RAD ) approach was performed in 20 specimens from four communities from Manabí ( central coast ) and Loja ( southern ) provinces of Ecuador , and the effectiveness of three type IIB restriction enzymes was assessed . The findings of this study show that this genotyping methodology is cost effective in R . ecuadoriensis and likely in other triatomine species . In addition , preliminary population genomic analysis results detected a signal of population structure among geographically distinct communities and genetic variability within communities . As such , 2b-RAD shows significant promise as a relatively low-tech solution for determination of vector population genomics , dynamics , and spread . | [
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"neglec... | 2017 | 2b-RAD genotyping for population genomic studies of Chagas disease vectors: Rhodnius ecuadoriensis in Ecuador |
During 2011 a large outbreak of typhoid fever affected an estimated 1430 people in Kikwit , Democratic Republic of Congo . The outbreak started in military camps in the city but then spread to the general population . This paper reports the results of an ecological analysis and a case-control study undertaken to examine water and other possible transmission pathways . Attack rates were determined for health areas and risk ratios were estimated with respect to spatial exposures . Approximately 15 months after the outbreak , demographic , environmental and exposure data were collected for 320 cases and 640 controls residing in the worst affected areas , using a structured interview questionnaire . Unadjusted and adjusted odds ratios were estimated . Complete data were available for 956 respondents . Residents of areas with water supplied via gravity on the mains network were at much greater risk of disease acquisition ( risk ratio = 6 . 20 , 95%CI 3 . 39–11 . 35 ) than residents of areas not supplied by this mains network . In the case control study , typhoid was found to be associated with ever using tap water from the municipal supply ( OR = 4 . 29 , 95% CI 2 . 20–8 . 38 ) . Visible urine or faeces in the latrine was also associated with increased risk of typhoid and having chosen a water source because it is protected was negatively associated . Knowledge that washing hands can prevent typhoid fever , and stated habit of handwashing habits before cooking or after toileting was associated with increased risk of disease . However , observed associations between handwashing or plate-sharing with disease risk could very likely be due to recall bias . This outbreak of typhoid fever was strongly associated with drinking water from the municipal drinking water supply , based on the descriptive and analytic epidemiology and the finding of high levels of faecal contamination of drinking water . Future outbreaks of potentially waterborne disease need an integrated response that includes epidemiology and environmental microbiology during early stages of the outbreak .
Typhoid fever ( TF ) is an infection caused by the bacterium Salmonella enterica serovar typhi ( S . Typhi ) . The primary symptoms are fever and related malaise , but serious complications , such as intestinal haemorrhage or perforation ( 1–4% of all cases [1] ) , encephalitis , respiratory infections and metastatic abscesses can occur . In the absence of treatment , there is a case fatality rate of 10–30% , which drops to ~1% with timely treatment [2] . Data from 2010–2013 suggested that the TF disease burden in Africa was 4 . 3 million cases per year ( 95%CI 3 . 7–5 . 1 million ) [3] . Data to estimate the total case fatality rate in sub-Saharan Africa are unreliable [1 , 3] . The mean case-fatality rate after S . Typhi caused by intestinal perforation has been reported at 19 . 5% ( 95%CI 16–22% ) in African countries [4] . Typhoid is a strictly human infection and spreads from one person to another especially through faecal oral or urine oral pathways including consumption of contaminated food or water . Spread is exacerbated by poor sanitation and hygiene . Outbreaks have regularly been reported , but those occurring in low income countries are not well researched . Instead , most studies have described outbreaks in industrialized settings [2] . Consequently , opportunities to reduce transmission in these low-resource settings may not be identified or properly understood . Repeated and sometimes severe outbreaks of typhoid have occurred in the Democratic Republic of Congo [5] . After a large outbreak of typhoid in the city of Kikwit , Bandundu Province , in the year 2006 , typhoid became endemic , with low but persistent numbers of cases reported annually until another large outbreak in November 2011 to early January 2012 . The 2011–2012 event resulted in 1430 identified cases . Seventy-one people developed peritonitis with perforation , and 17 people died in 2011–2012 . The fatality rate was 1 . 5% [6] . An initial descriptive epidemiological study recognised that contaminated water supplies were likely responsible for most cases in 2011 [6] , but did not elucidate on transmission pathways or other risk factors for infection in the subsequent phases of the outbreak . Patients in the 2006 and 2011 outbreaks appeared to come from the same areas of the city , which led to suspicions that an infrastructure problem or spatial feature might contribute to the risk of catching TF ( S1 Protocol ) . It was observed that 2011 attack rates were highest in military camps within the city , especially early in the outbreak . The descriptive evidence suggested that the 2011 outbreak originated in the camps and subsequently spread to the general population . Here , we provide a quantitative investigation of exposure factors linked to infection in 2011 , accompanied with recommendations both for prevention and during emergency response .
Kikwit is the largest city in the Bandundu province of DRC . The estimated population in 2011 was 400 , 000 ( Fig 1 ) . Kikwit is located in the south-west of DRC and is an important commercial and administrative centre . In 2011 , the city had one general referral hospital and was administratively divided into two health zones , north and south . Each health zone ( “Zone de Santé” ) was divided into health areas ( “Aires de Santé” ) . There were 19 and 22 health areas respectively in north and south Kikwit at the time of the study . At the time of the outbreak , there were three military camps in the city , which accommodated about 2400 people: staff and families . Ngubu camp and Nsinga camp were in close proximity to the general population , while Ebeya camp was relatively separate from the city . The camp residents could be considered a highly mobile population and are mostly not of local origin . They had living conditions distinct from and mostly worse than the city’s settled residents . The military staff and families originate from many regions of DRC and speak many languages . Overall , living conditions in the three camps were poor and featured relatively high population density and poor hygiene and sanitation conditions . As a result , this study treated the three camps as three additional health areas which were analysed independently of the Aire de Santé where they were situated [6] . Kikwit is a hilly city with sandy soils and large erosion gullies . The city has a long wet season with an average of about 200mm of rain per month , from early September through end May . The city has centrally collected and distributed piped water , which is extracted from artesian wells , inconsistently chlorinated ( S1 Report ) and distributed via an aging pipe network to community tap points ( standpipes , Fig 2 ) . Water pressure is usually low throughout the network . Mains water is relatively expensive and difficult to access in parts of the city ( S1 Report ) . Some homes have access to private wells . Surface water sources are widely available and generally free-of-charge . Surface sources are the river Kwilu , its tributaries , as well as protected and unprotected springs . No changes to the water infrastructure occurred in the time that elapsed between the 2011 outbreak and the survey dates . Sanitation in the city is privately managed . Latrines are typically dug out by hand , and often are open air ( no roof ) and shared among multiple households . After a latrine fills up it is typically covered with thin soil , a mango tree is planted and a replacement latrine is dug nearby . Flooding of human waste out of active latrines is not unusual following significant rainfall events . Study design and protocol for the spatial analysis is in S1 Protocol . During the 2011 typhoid outbreak , The Ministry of Public Health ( MOPH ) created a central register line list of all cases , both suspected and confirmed . The case definitions were set by the Ministry of Public Health ( MOPH ) ; “suspected case” was any person with fever ≥ 38°C for more than three days and digestive disorders , which were defined as diarrhoea , constipation or abdominal pain , as well as a negative malaria test . A “confirmed case” was a suspected case confirmed by isolation of S . Typhi from blood , bone marrow or duodenal fluid . The outbreak was confirmed by the MOPH based on the results of cultures of 50 blood and stool samples , which were tested in the University Hospital in Kinshasa . From the start of the outbreak , all TF cases had their data entered into a central electronic line list developed by the MOPH . The register included patient’s name , sex , age and address . By the end of the outbreak , the line register had 1430 cases , with illness onset dates from 19 Nov 2011 to 5 January 2012 . The attack rate for each of the 41 health areas and three military camps was determined using the central register line list and estimated 2011 population data . Environmental elements were mapped [7] in early-mid 2013 for each health area in Kikwit using ArcGIS software ( ESRI , California , USA ) . Spatial attributes were assigned to an entire health area as the predominate trait for that health area . A key environmental item of interest was the predominant water distribution network that public water taps connected to in each area . The network options were: northern pump , central pump , central gravity or off network . Other spatial attributes for each health area were population density the ( average ) distances from each health area to the nearest camp , market , school , springs , tarred road , water point or health care clinic . Distances to these locations were of interest because these places tend to be places where people cluster and thus might pass on infection . Data on socio-economic status , sanitation statistics or water quality ( such as via the WHO Joint Monitoring Programme ) were unavailable at the scale of health areas . Incident risk ratios were estimated for each health area using negative binomial regression , testing for any association between single or multiple exposures to environmental attributes in the health area and attack rate . The outcome was set to be the number of cases within each health area , the model offset was the ( natural log transformation of the ) total population in that same area . Distances were modelled continuously under a Poisson functional form . Categories were also available that described the water source in each health area ( gravity or pumped , north or central , see data in S1 Spreadsheet ) . Study design and protocol is item S2 Protocol . The case-control study focused on individual risk factors . The data were collected using structured household interviews between February and May 2013 . Descriptive results of the data collected about cases during these interviews are described elsewhere [6] . Aspects of the structured survey as reported previously will be briefly recapped here . The survey was targeted at cases and controls living in the Aires de Santé with the highest attack rates during the outbreak . The case-control study was done in only these areas primarily for logistical reasons . The attack rate for each health area was estimated using the denominator from population census data . For the case control study , we surveyed residents in the eight most affected health areas ( attack rates > 0 . 36% ) while also separating out and interviewing residents of the three military camps , because the camps all had AR > 4% ( Fig 3 ) . To identify an odds ratio of at least 1 . 5 with a power of 80% for risk factors present in 30% of the control population , a sample size was set at 320 cases ( 25% of total ) , frequency matched by age and sex to 640 controls . A structured questionnaire was developed and piloted ( S1 Questionnaire ) . Twelve interviewers and one supervisor who spoke local languages were trained to verbally administer the questionnaire . Interviewers exercised own judgement in how to translate the questionnaire from French into other languages , when required . The interviewers were trained how to record household location and its principal water source using GPS devices ( Garmin GPSMAP 76 ) . Using the recorded addresses in the line list records , typhoid cases ( from the concurrent case definition ) were chosen at random , using random numbers generated in MS Excel . Cases were traced and then interviewed in the community . Serology was not used to confirm recovered case status . Two controls of the same sex , age class of 5 years interval ( 0–5 , 6–10 , 11–15 , 16–20 , etc . ) and health area were selected per case . Selection criterion for each control was not having been suspected of typhoid during the outbreak period . After interviewing a case , interviewers decided which end of their road to treat as the starting point , and then chose two control households by rolling a die to select the nth residences ( alternating between left and right side of the street ) . Posited controls were asked if they had TF or were ill during the TF outbreak . Those who reported having TF or being ill were excluded as was their entire household ( assumed to share same latrine and water supply ) . The die was rolled again if necessary to select a different household for a potential control . A maximum of one person was interviewed per household . All respondents were requested to show their household water storage containers , available soap and latrines . For children less than 13 years old , the interview was conducted with the guardian or a family member who was living in the same household and aware of the child’s condition . During the household survey , level of awareness about the 2011–12 outbreak was very high and people claimed to be able to remember with accuracy if they had been ill during that period . Interviewers were welcome in people’s homes as representatives of MSF . No one refused to be interviewed . All GPS readings were recorded and visualized using ArcGIS software ( ESRI , California , USA ) . Conditional logistic regression ( clogit ) for risk factors were undertaken using STATA version 14 . 2 , with data grouped by health area . Odds ratios ( OR ) for most risk factors were first estimated in single predictor models . Exposures and factors were excluded from univariate analysis if < 5% of responses were different from the most popular answer to a specific question . Any individual variable p-value <0 . 20 was carried forward into a multiple predictor case-control analysis . We endeavoured to keep all categories in the model if a variable had multiple levels . However , some variables were trialled by recoding them into binary variables where exploratory analysis found a strong association ( such as having any tap water , as primary or secondary source or outside the home ) . Otherwise , risk factors were retained in iterative modelling as long as they had p-value < 0 . 05 to produce the final estimated adjusted odds ratios reported here . For purely categorical items , the reference value was set at the value with greatest frequency; for ordinal items , the reference value was set at the lowest rank answer . Using adjusted ORs and the fraction of cases receiving an exposure , the population attributable risk [8] percentage was determined for key predictors in the final model . Where few data were missing , those specific observations were excluded in the final model; where many data were missing , the variable was not used in multivariate analysis . Study design and protocol for the water quality analysis is within S2 Protocol . Water samples of the principal source of drinking water of all interviewed cases were collected by two trained water and sanitation community workers , on 18 distinct dates from 13 . 3 . 2013 to 10 . 4 . 2013 . Replicate tests were done on water samples onsite for Free Residual Chlorine ( FRC ) levels using the HANNA Photometer . Concentrations of ThermoTolerant Coliforms ( TTC ) were measured using a Delagua field kit . Tests for S . Typhi specifically were not undertaken–they seemed inappropriate so long ( 16 months ) after the outbreak . A pathogen-specific test ( such as PCR for S . Typhi ) also exceeded our research budget and required equipment or laboratory facilities ( for molecular biology ) not available locally . In contrast , tests for TTC were useful to indicate likelihood of ongoing faecal contamination problems . Further details of the testing regime and results , including verification strategies , are in Ali et al [6] . The water quality data were used to calculate what proportion of water samples from each source could be considered high or low risk for transmission of human disease , using categories adapted from UNHCR guidelines [9] . Members of the research team changed . The procedure for selecting controls as described in the protocol was not used; a different set of procedures for selecting controls was devised , as described previously . The minimum age of independent respondents was changed from ten to 13 years old . Although specified in the protocol , the final study did not assess risk factors for disease severity ( as indicated by peritonitis or intestinal perforation ) . No analysis of the 2006 outbreak was undertaken . Area-level education and income data were not suitable or available , so not used in the ecological analysis . Data collection dates were three months later than anticipated . The protocol also contains some factual errors because it was written prior to data collection , such as stating there were 33 Aires de Santé in Kikwit ( actually there were 41 , not 33 ) , while the estimated population total was misstated to be 350 , 000; actual population turned out to be higher . The number of water quality tests per source was 1–4 ( most often but not always 2 ) . In the case-control study , we decided to focus on the eight most affected Aires de Santé , not the seven most affected areas as stated in the protocol . We don’t believe that any of these deviations or factual errors undermine our results or conclusions . Ethics approval was received from the Ethical committee of the School of Public Health , University of Kinshasa ( DRC ) and Ministry of Higher Education , Academic and Scientific Research . Written informed consent was sought and obtained for all respondents or from their caretakers/guardians ( children under 18 ) .
None of the spatial attributes could be linked with statistical significance ( p < 0 . 05 for risk ratio ) to the 2011 TF attack rate at health area level , except for water source . Table 1 shows findings ( see supporting data in S1 Spreadsheet ) , which reports risk ratios and attack rates for residents dependent on given water sources ( p < 0 . 001 ) . Residents who were dependent on the central gravity system were five times more at risk compared to residents on the northern ( pumped ) network ( RR = 6 . 20 vs . 1 . 21 ) , and about three times more at risk compared to those on the central pump system ( RR = 6 . 20 vs . 2 . 25 ) . Data on occupational status of the head of household , demographic , sanitation and water quality traits identified for camp and city populations are described in greater detail in Ali et al [6] . Refer to the original survey questionnaire ( S1 Questionnaire ) and survey data , both raw and derived variables , ( S2 Spreadsheet ) for more details . Out of the 320 cases interviewed , 59 ( 18% ) lived in the camps . Although the heads of households in the camps had more secure employment ( 75% in camps vs . 25% in town had work contracts ) , the city dwellers were more affluent , as indicated by greater access to electricity or a functioning TV . Sharing latrines with other families is normal practice in Kikwit for both military and civilian families . None of the observed latrines of the cases in camps and only 3% of cases in the general population had materials to facilitate wash hands ( eg . , soap and water ) at a close distance ( < 3 metres ) from latrines . Upon request , 66% and 82% of cases in camps and general population showed the available soap in the household . This suggests that although respondents often said they washed their hands , many were in fact only rinsing their hands . Age and education profiles were similar for both military and civilian families , but households in the camps were more likely ( 77% ) to live in a house of brick or concrete construction; most non-brick homes were made from mud . Controls were 2:1 frequency matched by age and sex to the recruited 320 cases . The unadjusted odds ratios ( OR ) comparing cases and controls for individual risk factors are in Table 2 . The OR in Table 2 used the factors as coded in the original survey ( S1 Questionnaire and S2 Spreadsheet ) , although some survey elements were excluded for reasons described in the Methods , and for brevity , not all univariate results are listed in the table . Age and sex associations with case status are shown to be insignificant in Table 2 , which indicates that frequency matching was successfully implemented . Seventeen possible predictive factors had odds ratios with p-values ≥ 0 . 20 in single variate analysis . Twenty factors were taken forward to be tried in multivariate estimations of OR ( because they had p < 0 . 20 ) . Some hygiene , cooking customs , and indicators of socio-economic status were among the risk factors that qualified for trial in multivariate OR estimations . At the single variate stage , intake of any tap water ( OR 3 . 41 , 95%CI 1 . 88–6 . 19 ) , whether tap water was a primary or secondary source ( OR 2 . 80 , 95%CI 1 . 64–4 . 79 ) , knowledge to wash hands ( OR 2 . 36 , 95%CI 1 . 45–3 . 86 ) , assertions that they know how to avoid typhoid ( OR 0 . 44 , 95%CI 0 . 31–0 . 61 ) , and statement of habitual washing of hands before cooking ( OR 5 . 12 , 95%CI 3 . 11–8 . 44 ) had the strongest association with increased disease . Those who said that they regularly shared their plates of food had reduced risk . All indicators suggestive of better handwashing behaviour ( more frequent handwashing or knowledge that handwashing should reduce disease transmission ) , were positively associated with typhoid case status ( see data in Table 2 ) . Aspects of the home environment ( topography and home construction materials ) as well as habits of eating uncooked food were also significant enough to be trialled in multivariate modelling . The number of water storage containers in the household or claiming to have soap in the home did not reach the threshold to be tried in multivariate analysis . In all cases ( OR data in Table 2 ) , those who stated that they always wash hands after defecation or before cooking and before infant care had significantly increased risk of disease . Those who stated never in response to these questions , had strongly decreased risk . Knowledge about handwashing was similarly correlated; those who said they knew they should wash hands had much increased risk . Explanations for this unexpected finding are explored in the Discussion . To put multiple variables about washing hands behaviour or beliefs into the same model could create collinearity problems . Moreover , the information about washing hands before cooking or after childcare is incomplete because this question was only asked to female heads of household , and hence there were missing data for 253 respondents . Similarly , answers were missing for 264 respondents ( 56 cases and 208 controls ) , on whether they mentioned washing hands when asked about ways to avoid catching TF . However , there were no missing data about washing hands after defecation for any respondent . To minimise collinearity and for ease of interpretation , only the variable about handwashing habits after defecation was used to indicate handwashing knowledge or behaviour , when generating the final model . Table 3 shows our final predictive model with all final significant predictors with adjusted odds ratios . Complete data were available for 320 cases and 636 controls . This model adjusts for age and sex for completeness , but their coefficients are not shown because their distribution was artificially imposed by the control recruitment method and therefore cannot be interpreted as risk indicators . Regularly sharing food was also linked to less illness ( adj . OR 0 . 07 , 95%CI 0 . 03–0 . 14 ) . Contaminated mains water ( adj . OR 4 . 25 , 95%CI 2 . 18–8 . 28 ) was likely to be an important route for typhoid transmission in this population , either via direct ingestion or additional exposure ( hand washing habits ) . The population attributable risk percentage ( PAR% ) for tap water consumption was estimated at 69 . 6% . Choosing a water source for perceived protected status seemed to confer reduced risk ( adj . OR 0 . 68 , 95%CI 0 . 48–0 . 95 ) , while the indicator of visible urine/faeces in the respondent’s primary latrine area conferred increased risk of disease acquisition ( adj . OR 1 . 43 , 95%CI 1 . 05–1 . 95; PAR% = 17 . 3% ) . Other PAR values are reported in Table 3 , although not for exposures that reduced risk–the PAR was not developed for that purpose . According to the survey responses , the majority ( 90% ) of cases in the general ( not camp ) population used taps at communal distribution points as their principal source of drinking water . Water sources for camp residents were more diverse . The most common sources of drinking water for cases in each camp were ( Table 4 ) : an artesian well for Ngubu camp ( 36% ) , taps at communal distribution points for Nsinga camp ( 34% ) , and an unprotected source for Ebeya camp ( 30% ) . Overall , 34% of all camp cases used communal taps as their principal source of drinking water . Almost all the sources of tested principal drinking water were contaminated with faecal coliforms to a very high degree ( see S3 Spreadsheet for original data ) . Free residual chlorine levels measured at the public water taps were insufficient ( <0 . 2 mg . l-1 ) to zero [6] . Fig 4 indicates the main types of water source tested and the proportion of each type of each source that fell into risk categories to human health . There were 102 unique sources identified by interviewees . Protected springs were most likely to be low risk . Only one of the water taps conformed to published standards . Most respondents ( 892/960 , 92 . 9% ) who were asked about possible treatment methods did not report that they treated their water by chlorination , boiling or filtration ( or another pathogen inactivation method ) ; therefore , we did not include water treatment factors when estimating odds ratios and exposures .
This study was undertaken 13–18 months after the end of the 2011 outbreak . Assuming that responses to questions asked in early 2013 can truly describe behavioural practices in late 2011-early 2012 outbreak may be suspect . The questionnaire did not ask about individual hygiene behaviour and practices during the outbreak to avoid other types of recall bias . Local staff translated the questionnaire from French to other languages as required during interviews; we did not monitor this process and it may have led to inconsistencies in how questions were asked or answered; in the Kikwit area , French and the Kituba language predominate but there are many regional languages and dialects spoken plus interviewees could have come from anywhere in the DRC , which has over 200 recognised languages . We did not use serology to confirm that controls were negative or to confirm cases . This means likely misclassification of some controls , which will have biased the odds ratios downwards in Table 3; this means our evidence for implicating water and sanitation in the spread of TF is understated . Ecological analysis was limited to only one type of geography ( health areas ) and only in parts of the city , and only some spatial variables ( ones we could get data for ) . Water quality could have changed between 2011 and 2013 . We measured ThermoTolerant Coliforms about 16 months after the outbreak to gauge ongoing contamination of city water supplies , rather than PCR amplification that specifically looked for S . Typhi during the actual outbreak weeks . Heavy rainfall can cause latrine overflows in Kikwit and could affect local supplies , changing preferred water sources; however , the outbreak , survey and water quality testing all took place in wet months with very similar levels of monthly rainfall ( November-May period ) . We assumed that general state of sanitation facilities ( soap , latrines ) did not change since the outbreak; however , we do have considerable anecdotal information that this assumption is valid . Challenges in tracing cases were encountered due to the 13 months elapsed time since creation of the line list and survey start . Some civilian cases may have been misidentified as camp residents , due to proximity of the camps to the general population , and vice versa . Our recommendations address both prevention and emergency responses , and also draw on observations and suggestions made by water sanitation engineers who visited Kikwit in December 2011 ( S1 Report ) . Our key recommendation to prevent or minimise future outbreaks in Kikwit of typhoid and similar diseases , is improvement to the water network . Descriptive , spatial and case-control studies all identify the water network as instrumental in transmission of typhoid in 2011 . Surveys of water supplies in Kikwit in both July and November 2015 also found widespread unacceptable faecal contamination in all drinking water sources tested; 97% of the isolated bacteria in surface waters had human origin [21] . This finding was strongly linked to outbreaks of waterborne diseases thought to affect up to 30% of the city’s population annually . A full revamp of the city’s water system would clearly be very desirable . Work is arguably most urgent in those areas fed by gravity supply , which are in the central area that also had the highest attack rates . Improvements to the mains water network could include but should not be limited to: repairs to prevent inundation ( including replacing pipes and reversing soil erosion ) , consistent chlorination of tap water , regular monitoring of the chlorination levels , rehabilitation of unprotected springs , and closing latrines located uphill and in relative proximity to frequented water sources or water mains pipes . Hygienic harvesting of rainwater in public places could be implemented to make it easier to properly wash hands ( S1 Report ) . It would also be desirable to improve the overall sanitation and hygiene situation in Kikwit , especially within the places that were hotspots for TF transmission in 2011 ( military camps ) [6] . Rehabilitating latrines , provision of ongoing resources to make handwashing safer and more effective , to enable handwashing with soap and consistent household water treatment , could be beneficial . Recommendations as part of an immediate actionable response to an outbreak should include: creation of minimum perimeters from latrines to water sources and rapid drainage of runoff water around standpipes and hoses ( S1 Report ) . Rapid testing of water sources and rapid ascertainment of exposure risks during an outbreak would quickly facilitate understanding how such disease was spreading . It is undesirable that the exposure data in this study were collected as late as 14 months after the outbreak . Distribution of handwashing materials with health campaigns to promote full washing , for users of all water sources , would be desirable . Emergency distribution of chlorine , either in tablets or via buckets , with usage instructions , to ensure more water treatment could be protective , although work needs to be done to make the taste of chlorinated water more acceptable to the local populace ( S1 Report ) . Distribution of hygiene kits may well be appropriate , especially to high risk groups [16] . Vaccine-based strategies for typhoid control are recommended for school-age children in endemic countries–in this context , a targeted vaccination in the camps might be effective emergency response or short-term prevention measure [22 , 23] . Following high early transmission in military camps near the city of Kikwit , use of contaminated mains water was consistently and reliably , strongly associated with typhoid fever acquisition . A safer mains water network is the most valuable change that could prevent future disease . Effective measures to better protect water supplies , include but are not limited to: relocation of intake points , more consistent chlorination , preventing inundation to the distribution network , and more convenient access to treated water . Safe sources for the purposes of cooking and hand cleaning could reduce the size of TF and similar disease outbreaks in future . | There was a large outbreak of typhoid fever in Kikwit , DRC , in late 2011 . The outbreak started in military camps in the city but then spread to the general population . Multiple investigations were undertaken to understand how the disease spread . The worst affected areas of the city were mapped and compared to the water network . In early 2013 , demographic and exposure data were collected for 320 cases and 640 controls residing in the worst affected areas , using a structured interview questionnaire to try to better understand individual risk factors . Residents of areas with water supplied via a gravity fed network were about six times more likely to have been ill with typhoid fever than residents of areas not supplied by the mains network . The most important individual risk factor was ever using tap water . Visible urine or faeces increased risk of getting typhoid but having chosen a water source because it is protected was linked to lower risk . Not handwashing and regularly sharing plates of food were also linked to less illness , but these findings may be especially subject to recall bias . The water network was also found to be heavily contaminated , including with faecal bacteria of human origin in multiple microbiological studies . Spatial , microbiological and case-control studies all implicate the water supplies in Kikwit to be unsafe and linked to spread of typhoid fever in 2011 . Improvements to the mains water network in Kikwit are urgently needed to prevent future typhoid fever outbreaks . | [
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"symptoms",... | 2018 | Typhoid fever outbreak in the Democratic Republic of Congo: Case control and ecological study |
The genome sequencing of several Leishmania species has provided immense amounts of data and allowed the prediction of the metabolic pathways potentially operating . Subsequent genetic and proteomic studies have identified stage-specific proteins and putative virulence factors but many aspects of the metabolic adaptations of Leishmania remain to be elucidated . In this study , we have used an untargeted metabolomics approach to analyze changes in the metabolite profile as promastigotes of L . donovani develop during in vitro cultures from logarithmic to stationary phase . The results show that the metabolomes of promastigotes on days 3–6 of culture differ significantly from each other , consistent with there being distinct developmental changes . Most notable were the structural changes in glycerophospholipids and increase in the abundance of sphingolipids and glycerolipids as cells progress from logarithmic to stationary phase .
Leishmaniasis remains one of the major infectious diseases with 350 million people at risk in 88 countries worldwide and 2 million estimated new cases every year [1] . The lack of effective chemotherapy and emergence of drug resistance ( reviewed in [2] ) highlights the need for an improved knowledge of the parasite's cell biology to discover peculiarities that could potentially be explored as drug targets . The Leishmania life cycle involves several developmental stages and alternates between sand fly and mammalian hosts . A major developmental difference is the occurrence as intracellular amastigotes in mammalian macrophages and as extracellular promastigotes in the sand fly . However , multiple forms of promastigotes have been identified based on morphology , location , infectivity , growth rate , ability to divide , and specific features such as expression of surface molecules [3]–[8] . It is believed that the parasite's occurrence in different developmental forms is a mechanism whereby it adapts to survive and persist in the various environmental conditions in which it is confronted with variations in temperature , pH , nutrient and oxygen availability and exposure to reactive oxygen ( ROS ) and nitrogen species ( RNS ) [9] . Despite the extensive investigations on various features of Leishmania over many years and the recent pioneering application of metabolomics technologies to studies on the parasite [10]–[13] , particularly the elucidation of ways in which amastigotes differ from promastigotes [13]–[16] , currently relatively little is known about the detail of the metabolic variation that happens during this developmental sequence in the sand fly . The developmental sequence in the sand fly vector , which terminates in transformation to the metacyclic form infective to a mammalian host , appears to be mimicked , at least in part , during growth axenically in vitro; this comprises multiplication of procyclic promastigote forms and then differentiation to the metacyclic form , a process known as metacyclogenesis which is accompanied by morphological changes , including reduction in size of the cell body and a relatively longer flagellum , and some known biochemical changes such as lipophosphoglycan ( LPG ) and surface protein expression [5]–[7] , [17]–[20] . Thus the in vitro system provides an opportunity to investigate the metabolome changes that accompany and perhaps underpin the developmental sequence of the promastigote . In the present study , we have applied state-of-the-art metabolomics approaches to analyse the changes in the metabolome of promastigotes of Leishmania donovani during culture in vitro . The results show that there is distinct variation in the metabolome , especially in the lipid composition .
Leishmania donovani ( MHOM/NP/03/BPK206/0clone10 ) promastigotes had been cloned from an isolate from a visceral leishmaniasis patient sensitive to pentavalent antimonials in Nepal , as described by Rijal and co-workers [21] . Promastigotes were grown on modified Eagle's medium ( designated HOMEM medium , Invitrogen ) supplemented with 20% ( v/v ) heat inactivated fetal calf serum ( FCS , PAA Laboratories ) at 26°C . Cultures were set up initially at a density of 2 . 5×105 parasites/ml and sub-passaged every 6 days . L . donovani promastigote cultures were initiated at 2 . 5×105 cells/ml in 16×10 ml cultures in order to obtain cell samples from four independently growing cultures ( biological replicates ) on each day . Promastigotes from each culture were harvested at days 3 , 4 , 5 and 6 for metabolite extraction . The metabolite extraction was performed as previously described [11] . Briefly , promastigotes quenching was performed in a dry ice/ethanol bath with rapid temperature decrease to 2°C and then immediate transfer to ice . Two aliquots of 4×107 cells were taken from each culture flask ( technical replicates ) . Cell pellets were obtained by centrifugation at 12000 g for 10 min at 4°C , and washed 3 times in 1 ml of PBS . For cell disruption and metabolite extraction , cell pellets were resuspended in 200 µl cold chloroform/methanol/water ( 20/60/20 , v/v/v ) and incubated for 1 h in a Thermomixer ( 1400 rpm , 4°C ) . After centrifugation at 12000 g for 10 min at 4°C , the supernatant containing the extracted metabolites was recovered and stored at −70°C until analysed . LC-MS analysis and data processing was done as described by t'Kindt and co-workers [11] , [12] . Metabolite level comparisons between the time points analyzed ( after 3 , 4 , 5 and 6 days of in vitro growth ) were performed based on the ratio between the intensity on each day and the mean intensity level for the 4 day period , that is x/3–6 . The following criteria were applied to assign differences in metabolite levels among the time points analyzed as being potentially interesting and so worthy of inclusion in the full analysis: ( i ) there was at least a 2-fold difference between at least one of the time points when compared with the mean intensity level; and ( ii ) there was a statistically significant difference ( p<0 . 05 ) between the time points being compared . The data are expressed as intensity per 25 µg cell protein . Statistical analysis was performed using Analysis of Variance ( ANOVA ) , which allowed the simultaneous comparison of all time point analyzed: a p value smaller than 0 . 05 ( p<0 . 05 ) was considered significant; SPSS Statistics software ( IBM ) was used to perform principal component analysis; GraphPad Prism 4 was used for plotting the graphs and VisuMap software ( VisuMap Technologies Inc . ) was used to visualise the data as heatmaps .
In order to obtain detailed information about the metabolic changes that occur during the development of promastigotes under defined conditions in vitro , we have applied an untargeted metabolomics approach . Promastigotes were collected after 3 , 4 , 5 and 6 days of in vitro growth ( thus including different proportions of various promastigote forms , including procyclic promastigotes which dominate during logarithmic growth and the non-dividing metacyclic forms that start to be formed at late stages of logarithmic phase ) and analyzed by LC-MS . We used four parallel cultures ( designated biological replicates ) to obtain representative data and during the cell processing from each culture two samples were taken ( designated technical replicates ) to control for variation due to technical factors . Analysis of a parasite's metabolome needs to take into account the changes in cell volume that occur during development and growth . Measurement of the protein content of the cells showed that transformation to the metacyclic form at late logarithmic phase of growth was accompanied by a decrease in protein content , which is thought to correlate with the decrease in cell size ( Figure 1 ) . There was a great difference in protein content between the cells on days 3 and 6 ( p<0 . 0028 ) but also a significant difference between the cells on days 3 and 4 ( p<0 . 025 ) and days 5 and 6 ( p<0 . 034 ) . To assure that the metabolome analysis of the parasite would reflect the changes observed in cell size , we have expressed the data as intensity per cell protein ( rather than per cell number ) , thus facilitating meaningful comparison of the metabolite levels in cells of differing volume . This method of normalization had significant effects , for instance the general decreases in metabolite intensity when expressed per cell number that were observed between promastigotes on day 3 and day 6 were almost abolished when data were expressed as intensity/mg cell protein . Indeed the summed total of the metabolite intensities normalized to cell protein were relatively constant over the four days whereas when expressed as intensity/cell number it declined 33% on day 6 . We believe that this method of taking into account the changes in cell size during growth is currently optimal and provides a means of generating data that are meaningful and can be interpreted with confidence . In order to understand better the metabolic fluctuations as the promastigotes developed over the four days , we compared the profiles of metabolites levels ( Figures 2A and 3 ) . The analysis shown in Figure 2A ( in which the metabolite intensity level on each day is compared with the mean level over the 4 day period ) highlights that , of the total metabolites identified , the levels of the majority remained rather similar throughout although 26 . 9% differed by at least 2-fold on one of the days when compared with the mean . The day 3 levels were the most different from the others ( 22% being at least 2-fold different from the centered-mean for the period analyzed ) and with just some metabolites differing greatly at other times . This is consistent with there being a progressive change in many metabolite levels over the four day period . However , comparison between the metabolite intensities on days 3 and 6 revealed that 48 . 4% of all the metabolites identified differed by more than 2-fold ( Figure 2B ) , suggesting a significant difference in metabolic profile between promastigote populations in logarithmic ( mainly procyclic promastigotes ) and stationary phases ( containing many metacyclic promastigotes ) . The metabolic profile was also analysed by principal component analysis ( PCA ) . PCA is an unsupervised clustering technique that allows the reduction of the data into two dimensions ( principal component 1 [PC1] and principal component 2 [PC2] ) , which capture and enable visualization of data variability; this method is generally applied to large sets of data , such as those resulting from microarray or metabolomic analyses , as a way of obtaining a summary or overview of all samples , to find clusters and trends , and to identify the outliers . It is recommended as a starting point for analysis of multivariate data [22] . The PCA score plots ( Figure 4 ) of the LC-MS data show clearly the identification of four distinct clusters , each one corresponding to one of the groups of samples analyzed on a particular day of growth . PC1 and PC2 account for more than 81% of the variables which shows the clear metabolic differences between the samples . Moreover , the tight clustering within each group indicates good reproducibility . The data in Figure 4 show that promastigotes on days 4 and 5 are aligned closely with each other indicating that they have a similar metabolic profile that is clearly distinguished from those on days 3 and 6 ( which explains 59 . 0% of total variance given by the second principal component ) . These data are consistent with there being metabolic changes as the promastigotes develop from procyclic promastigotes to metacyclic promastigotes , and the relatively large number of metabolites that differ in levels significantly between days 3 and 6 . The identity of the metabolites was carried out based on the databases detailed by t'Kindt and co-workers [11] , [12] . We were able to identify 368 putative metabolites ( 267 at <1 parts per million [ppm] deviation and 101 at the 1–2 ppm deviation level ) . The compounds identified belong to a wide range of metabolic pathways and include amino acids , nucleosides , carbohydrates , fatty acyls , sterols and glycerophospholipids among others , as shown in Figure 3 . The full list of putatively identified metabolites at days 3 , 4 , 5 and 6 at below 1 ppm and between 1–2 ppm deviation are provided in Tables S1 and S2 of supplementary data , respectively . The majority of the metabolites remained at a relatively constant level . Indeed , the overall sum of intensities of the identified metabolites in the samples from the different days show that there is little apparent variation in the total metabolome identified , with the only difference being between day 3 and day 4 ( Figure S1 ) ; clearly , however , such data have to be used with caution as not all of the parasite's metabolites are included in the dataset and the method is not fully quantitative . There were , however , some apparent variations within each group of metabolites ( Figure S2 ) . Lipids , in general , increased substantially from day 3 to day 6 . Carbohydrates and nucleosides similarly apparently increase in abundance , whereas other groups of metabolites including amino acids and derivatives , organic acids and alcohols remain at relatively constant levels . All metabolites that differed from the mean for the 4-day period by at least a 2-fold on one or more days and were statistically different between the time points analyzed ( p<0 . 05 ) are represented in heatmap format to visualize the main changes that occur during transformation of promastigotes in logarithmic phase to those in stationary phase ( Figure 5 ) and the intensity levels are provided on Table S3 . This group ( 97 in total , 26% of the total number of metabolites putatively identified ) includes metabolites representative of all of the compound categories shown in Figure 3 with the exception of organic acids and alcohols . It was possible to distinguish five general patterns by which metabolites fluctuated during the 4-day period analyzed ( Figure S3 and Table S3 ) . The levels of some metabolites continually increased from day 3 to day 6 ( pattern 1 , 74% of the 97 varying metabolites ) , while the opposite happened with others ( pattern 3 , 9% ) . Other metabolites showed peak levels on days 4 or 5 which then declined ( pattern 2 , 9% ) , while others decreased from day 3 to day 4 and then increased ( pattern 4 , 2% ) . Some metabolites had a fluctuating profile showing an increase followed by a decrease and then another increase ( pattern 5 , 6% ) . A more detailed analysis of each of the categories of metabolites suggests specific variation potentially related with the cell stage . For instance , analysis of structural properties of the fatty acyl side chains of phosphatidylethanolamine ( PE ) and phosphatidylcholine ( PC ) lipids revealed that there was an increased abundance of the PC lipids with lower unsaturated fatty acyl chains as the promastigotes developed from day 3 to day 6 ( Figure 6 and S4 in supplementary data ) ; this seems also to happen in PE lipids , but less so than with PC lipids . These data suggest that there are changes in the composition of membranes with development from procyclic to metacyclic promastigotes . Another class of metabolites showing striking differences depending on the cell stage were the sphingolipids ( SLs ) . In Leishmania , SLs are not essential for growth but they are for differentiation , probably due to the high demand in vesicular trafficking required for parasite remodeling [23] . The abundance of these metabolites in general increased on day 5 and greatly on day 6 , such that for some of the SLs identified , such as N- ( eicosanoyl ) -sphinganine , N- ( hexadecanoyl ) -sphinganine and heptadecasphinganine , the day 6 intensity amounted to more than 70% of the total amount detected over the four days ( Figure 7A ) . A similar situation was seen with some of the identified glycerolipids , with the diacylglycerol putatively identified as DAG ( 42∶3 ) being especially increased on day 6 ( Figure 7B ) . The abundance of sterols , prenol lipids and fatty acyl metabolites generally increased during growth and thus were more abundant on days 5 and 6 , although there were exceptions such as N- ( 11Z-eicosaenoyl ) -ethanolamine and N- ( 11Z , 14Z-eicosaenoyl ) -ethanolamine ( Figures S5 and S6 ) . In contrast to the lipids , amino acids and derivatives in general did not differ greatly during the four day period , although some were less abundant on day 6 ( proline , glutamate-semialdehyde , homocysteine , carnitine and cystathionine among others ) while others were increased ( for example N-butyrylglycine , lysine , valerylglycine , acetyl-lysine , and N-acetyl-arginine ) ( Table S3 and Figure S7 ) . A higher abundance of carbohydrates , such as maltohexose among others , was observed as cells reach stationary phase ( Table S3 and Figure S8 ) . The intensity variation for hypoxanthine and xanthine over the 4 days had a clearly distinct pattern from the other nucleosides or nucleoside conjugates , with a large increase in the abundance of these metabolites on day 6 , while , for example , cytosine and deoxycytidine decreased in abundance on day 6 ( Table S3 and Figure S9 ) . The great increase in the abundance of hypoxanthine and xanthine at day 6 is responsible for the large change observed in the overall abundance of all metabolites included in this group ( Figure S2 ) ; the overall abundance of this group was relatively unchanged over the 4 day period of analysis if these two metabolites were not included . None of the metabolites included in the organic acids group accomplish the criteria defined , despite , for example , the statistically significant difference observed in the levels of mevalonate on day 6 ( Figure S10 ) . A marked decrease in abundance on day 6 was also observed for 5-methyl-THF , dihydrobiopterin and N-acetylputrescine ( Table S3 and Figure S11 ) .
Leishmania promastigotes development in the sand fly includes a wide range of modifications in order to prepare the parasite for transmission to a mammalian host . The number of distinct developmental stages that occur is uncertain for although many have been named and identified based on morphology [3] , [4] most have not been sufficiently characterized to be certain that they are truly distinct developmental stages . As expected , in vitro we were able to observe various morphological forms of L . donovani promastigotes but the major clear difference was the appearance of small morphs as the culture reached stationary phase , when metacyclic forms are predominant . This reflects the remodeling of cell shape during life cycle transitions and involved a decrease in protein content ( Figure 1 ) . This was taken into consideration in analyzing the metabolite dataset and indeed the data were normalized to protein content as a means of taking into account changes in cell size . Analysis of metabolome during in vitro growth of promastigotes revealed that whereas the overall metabolite abundance remained relatively constant there were variations in the levels of individual metabolites , suggesting that parasite differentiation from procyclic to metacyclic forms takes place in a progressive manner and involves changes in certain individual or groups of metabolites . This study reinforces the idea that there are multiple forms of promastigotes that are adapted differently at the metabolic level , presumably reflecting the differing challenges that they face naturally in the sand fly . It has been postulated previously from studies on morphology of Leishmania promastigotes in sand flies and in vitro cultures that the parasite undergoes similar developmental transitions in vitro as occur in the sand fly host [24] , despite the absence of the host pressure . This has been interpreted as the parasite being genetically pre-adapted to survive in the sand fly . The biochemical changes that accompany these morphological/developmental changes are not fully known , although some characteristics of the metacyclic promastigote of L . major have been reported . Differentiation to the infective metacyclic promastigote form involves modifications in LPG structure [6] , [25] , which have been shown to occur both in in vitro culture and during in vivo development in the sand fly [26] . HASPB and SHERP are stage-specific proteins present in the infective stages , with SHERP being exclusively present in the metacyclic forms; the stage-specific expression of both has been observed in vitro as well as during the development in the vector [8] . Other surface molecules , including the metallopeptidase GP63 , also undergo changes in expression pattern as the promastigotes development progresses [27] . The LPG modifications are essential for parasite infectivity and occur in vitro and in vivo demonstrating that in the absence of the host this essential processes still occurs; these findings are suggestive that other changes similarly also occur in vitro and the data of our current study show that indeed this is the case . Metacyclogenesis is marked by a great increase in membrane trafficking and remodeling [28] and previous studies have shown that the organization of Leishmania membrane differs between procyclic and metacyclic promastigotes , in part due to the distribution of LPG into lipid rafts during differentiation [29] . Phospholipids ( PLs ) account for ∼70% of total cellular lipids in Leishmania , with PC , the most abundant glycerophospholipid , predominantly present in a diacyl form [30] with unusually long and unsaturated fatty acid species [31] . These properties , the acyl length and degree of unsaturation , may play an important role in the fluidity of Leishmania membrane , thus they are likely to be regulated throughout parasite development in its hosts . Indeed , our data show that promastigote phospholipid composition changed remarkably in terms of the unsaturation levels observed in the fatty acid chains , in particular of PC lipids . Promastigotes in stationary phase ( day 6 ) presented a higher abundance of PC lipids with lower levels of unsaturation than those observed on day 3 ( Figure 6 ) , revealing a shift towards lower unsaturation of PC lipids and consequently a decrease in membrane fluidity with metacyclic promastigote generation . These observed changes in membrane fluidity may be a mechanism whereby the parasite becomes pre-adapted for survival upon infecting a mammalian host , at which time it is confronted by a dramatic increase in temperature . Thus perhaps the change in membrane composition enables the parasite to maintain an appropriate membrane fluidity even at the higher temperature encountered . It is well known that the well-being of organisms is dependent upon the maintenance of optimal level of membrane fluidity [32] . In yeast , changes in the degree of unsaturation of fatty acids has been reported as a response to changes in the environmental temperature and complements other mechanisms such as modifications in fatty acid chain length , branching and cellular fatty acid content [33] . A recent study by Turk and co-workers [34] have related membrane fluidity to the adaptation level of different yeast to environmental stresses and to their growth temperature range , demonstrating that plasma-membrane fluidity can be used as an indicator of fitness for survival in extreme environments [34] . Changes in membrane fluidity in plants was also suggested to be crucial in sensing and influencing gene expression during temperatures fluctuations [35] . Alterations in membrane fluidity have been associated with the occurrence of drug resistance in Leishmania; it is thought that membrane lipid composition may influence drug-membrane interactions and interfere with drug uptake by the amastigotes residing in the mammalian host [36]–[38] . Indeed , comparison of promastigotes derived from clinical L . donovani isolates with different antimonial sensitivity has shown a shift towards higher unsaturation of PC lipids in drug-resistant clones , suggesting an increase in membrane fluidity that may be related to the changes in uptake ability observed in the drug-resistant cell lines [12] . Another group of lipids that notably increased in abundance during L . donovani promastigotes development in vitro were the SLs ( Figure 7A ) . SLs are not required for growth of Leishmania , since parasites that completely lack SLs grew normally in logarithmic phase and were still able to make “lipid rafts” . However , deletion of spt2- , the gene that encodes the key de novo biosynthetic enzyme serine palmitoyltransferase subunit 2 , resulted in parasites deficient in de novo SLs synthesis that once in the stationary phase were not able to differentiate into metacyclic forms [23] . The increase in SLs during stationary phase we have found in this study is consistent with the requirement of these metabolites for differentiation to metacyclic froms . SLs are considered essential membrane components in all eukaryotes , mediating many signaling pathways including those key for apoptosis , growth and differentiation [39] . However , in Leishmania the primary role of SLs appears to be the provision of ethanolamine , as ethanolamine supplementation was able to overcome the phenotype observed in the SL-deficient mutant parasites [40] . Ethanolamine and choline are essential nutrients , and when available exogenously they can be salvaged by Leishmania via membrane transporters [41] , [42] . Thus the significance of SL biosynthesis is likely to be stage-specific , being important in those stages in the sand fly that cannot rely upon salvaged ethanolamine . Indeed , amastigotes deficient in de novo SLs synthesis recovered from a mammalian host showed normal levels of inositolphosphoryl ceramide ( IPC ) and thus amastigotes seems to be able to perform SLs salvage [43] . Glycerolipids , represented by diacylglycerols ( DAG ) and triacylglycerols ( TAG ) , also increased in abundance as L . donovani promastigote development progressed ( Figure 7B ) . PC and PE lipids are synthesized by conjugation of a lipid anchor such as DAG with either CDP-choline or CDP-ethanolamine , the last step of the in de novo biosynthesis of phospholipids ( the Kennedy pathway ) ( reviewed in [44] ) . Thus the observed increase in neutral lipids correlates well with the changes observed in Leishmania membrane lipid composition during promastigote development . Sterol and prenol lipids also increased with time in culture , although the changes during L . donovani promastigotes development were not so accentuated as for SLs and glycerolipids . Sterols are the target of the important antileishmanial drug amphotericin B [36] and they may also play a significant role in the activity of miltefosine against the parasite , as sterol depletion led to a decrease in susceptibility [45] . Effectiveness of these drugs is mainly dependent on their interaction with the Leishmania membrane , thus it is clear that the ability of the parasite to change its lipid membrane composition , which occurs inherent during its life cycle , should be taken into consideration when considering new drug formulations . Leishmania parasites are auxotrophic for many amino acids and must scavenge essential amino acids from their hosts . Besides the use in protein biosynthesis , some amino acids , notably proline , can be used as major energy sources [46] . Recently , Saunders and co-workers have reported that aspartate , alanine and glutamate are internalized by L . mexicana promastigotes and incorporated into the TCA cycle , revealing the importance of this pathway in glutamate , glutamine and proline synthesis and demonstrating that the TCA cycle in Leishmania is not only a catabolic pathway [47] . One notable feature in the levels of amino acids and amino acid conjugates during growth of promastigotes in vitro was a large increase in metabolites of fatty acids named acyl glycines ( valerylglycine , tiglyglycine , N-butyrylglycine ) . Increases in levels of acyl glycines in higher eukaryotes is associated with mitochondrial energy metabolism disorders , indeed the measurement of these metabolites is used as a diagnostic tool . Glycine conjugation is considered to be an important detoxification system , preventing the accumulation of acyl-CoA esters in several inherited metabolic disorders of humans [48] . Moreover , valerylglycine was found to be increased in urine of Plasmodium vivax-infected individuals , which indicates an alteration in the fatty acid metabolism during infection [49] . These changes in acylglycines abundance during promastigote development could reflect changes in mitochondrial metabolism , but caution needs to be exercised as increased levels of this group of compounds was also found in drug-resistant parasites [12] and also in genetically manipulated mutants ( A . M . Silva et al . , unpublished results ) – which indicates that the levels of these metabolites may be disturbed in a variety of situations . Leishmania also take up from their environment other essential nutrients , such as purines and growth factors . Biopterin and folate uptake has been shown to decrease when promastigote have entered stationary phase [50] , which is consistent with our findings that there was a decrease in abundance of 5-methyl-THF and dihydrobiopterin in L . donovani promastigotes at day 6 of in vitro growth . Indeed , low levels of tetrahydrobiopterin were associated with increased differentiation of L . major into the infective metacyclic form and thus postulated to be an important factor controlling this process [51] . Leishmania parasites are not able to synthesize purines de novo and need to acquire either nucleosides or nucleobases [52] . Hypoxanthine uptake by L . major promastigotes is greatly reduced in stationary phase compared with logarithmic growth phase . This was shown to correlate with down-regulation of expression of the NT3 permease as promastigotes reach stationary phase [53] and it was reasoned that these changes reflected the fact that at this stage the population is mainly composed by non-dividing cells , the metacyclic promastigotes , that do not require purines for mitosis . Our findings of an increase in the levels of hypoxanthine and xanthine at day 6 of L . donovani promastigotes in vitro growth explain why less uptake is required at this stage . Moreover , one can speculate that these higher levels in the metacyclic forms are beneficial in enabling the subsequent differentiation events after infection of a mammal , a transition phase that occurs in the parasitophorous vacuole of a macrophage where availability of some nutrients may be limiting [54] , [55] . Overall the results of this study have provided convincing data that promastigotes of Leishmania at different stages of culture in vitro differ from each other significantly in terms of the composition of their metabolome , whereas the total metabolite abundance appears to remain relatively constant as the promastigotes develop from day 3 to day 6 . The study has provided insights into the overall changes that occur , which adds to the many previous reports on changes in individual metabolites , groups of metabolites and enzymatic reactions involved in metabolite production ( see , for example , [16] , [46] , [56]–[58] ) . Our data are consistent in particular with previous findings obtained using other approaches , such as changes observed in the content of sphingolipids and other lipids that may contribute to successful parasite survival in the mammalian host [23] , [57] . These changes observed undoubtedly reflect adaptations to differing conditions that Leishmania encounters in its two hosts , but the full understanding of how these adaptations function require additional data on the environments themselves ( the detailed content of the parasitophorous vacuole in a macrophage and the intestinal tract of the sand fly , and how these change with time , are largely unknown ) as well as more complete analyses of metabolism of individual promastigote forms and if possible integration of the generated data with those arising from other –omics approaches . However , understanding the variation in metabolism of promastigotes will be informative in elucidating more fully the metabolic capabilities of Leishmania and hopefully highlight unusual features that can be exploited in novel approaches to designing therapies . | Leishmania infections are considered neglected tropical diseases as the parasites affect millions of people worldwide but there are limited research efforts aimed at obtaining vaccines and new drugs . Leishmania has a digenetic life cycle alternating between promastigote forms , which develop in the sand-fly , the vector of the disease , and an amastigote form , which grows in mammals after being bitten by an infected sand-fly . In vitro studies with the promastigote forms are routinely used to gain insights about the parasite's cell biology . Little is known about how the different promastigotes forms are metabolically adapted to their particular micro-environment in the host or how they are pre-adapted metabolically for infecting a mammal , thus we have undertaken a study of the metabolite profile of L . donovani promastigotes in order to gain an understanding of the changes that occur during promastigote development . The analysis has revealed that the changes in promastigotes' metabolome between days 3 and 6 take place in a progressive manner; however major differences were observed when comparing the promastigotes on days 3 and 6 . An increase in lipid abundance as promastigote development occurred was notable and is likely to reflect remodelling of the parasite's surface in readiness for infecting a mammal . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biochemistry",
"infectious",
"diseases",
"biology",
"microbiology",
"zoology"
] | 2011 | Metabolic Variation during Development in Culture of Leishmania donovani Promastigotes |
The recent development of genetic markers for Bacillus anthracis has made it possible to monitor the spread and distribution of this pathogen during and between anthrax outbreaks . In Namibia , anthrax outbreaks occur annually in the Etosha National Park ( ENP ) and on private game and livestock farms . We genotyped 384 B . anthracis isolates collected between 1983–2010 to identify the possible epidemiological correlations of anthrax outbreaks within and outside the ENP and to analyze genetic relationships between isolates from domestic and wild animals . The isolates came from 20 animal species and from the environment and were genotyped using a 31-marker multi-locus-VNTR-analysis ( MLVA ) and , in part , by twelve single nucleotide polymorphism ( SNP ) markers and four single nucleotide repeat ( SNR ) markers . A total of 37 genotypes ( GT ) were identified by MLVA , belonging to four SNP-groups . All GTs belonged to the A-branch in the cluster- and SNP-analyses . Thirteen GTs were found only outside the ENP , 18 only within the ENP and 6 both inside and outside . Genetic distances between isolates increased with increasing time between isolations . However , genetic distance between isolates at the beginning and end of the study period was relatively small , indicating that while the majority of GTs were only found sporadically , three genetically close GTs , accounting for more than four fifths of all the ENP isolates , appeared dominant throughout the study period . Genetic distances among isolates were significantly greater for isolates from different host species , but this effect was small , suggesting that while species-specific ecological factors may affect exposure processes , transmission cycles in different host species are still highly interrelated . The MLVA data were further used to establish a model of the probable evolution of GTs within the endemic region of the ENP . SNR-analysis was helpful in correlating an isolate with its source but did not elucidate epidemiological relationships .
Anthrax is an endemic disease in Namibia . Outbreaks occur year round in the Etosha National Park ( ENP ) with strong peaks in the late wet season ( March and April ) among zebra ( Equus quagga ) , springbok ( Antidorcas marsupialis ) and wildebeest ( Connochaetes taurinus ) and in the late dry/early wet season ( October peak ) in the case of elephants ( Loxodonta africana ) . Sporadic outbreaks also occur on private game farms and in livestock throughout the country . The earliest mortality records from the ENP are those of Ebedes [1] . Bacteriologically-based studies begun in 1983 [2] , [3] , [4] , [5] , [6] and provided insight into the temporal and spatial distribution of the disease in the Park as well as the relative roles of living and non-living vectors . Nevertheless , the lack of appropriate methods to track strains associated with particular outbreaks of anthrax and thereby resolve the overall endemic occurrence of the disease in the Park or elsewhere into a preponderance of particular strains . The development of genetic markers for Bacillus anthracis over the past decade has made possible the way to a highly informative approach for monitoring the potential spread and distribution of outbreak strains of B . anthracis . Various sets of repeat sequences with high mutability became available for fingerprinting protocols after 2000 . With respect to studies on the epidemiology of anthrax in animals , the first version of a multi loci variable number of tandem repeats analysis ( MLVA ) using 8 markers [7] was applied to the analysis of outbreak strains in the Kruger National Park , South Africa [8] . The discriminatory power of the method was later increased by the addition of further markers by Lista et al . [9] and van Ert et al . [10] . In 2004 , Keim et al . [11] described a fingerprinting system for B . anthracis called PHRANA ( Progessive Hierarchical Resolving Assays using Nucleic Acids ) that can be used to analyze the temporal and spatial parameters of anthrax outbreaks . In this system the analysis of canonical SNPs ( Single Nucleotide Polymorphisms ) is used to establish phylogenetic groups , which is followed by genotyping with MLVA and finally single nucleotide repeat ( SNR ) analysis [12] , [13] to distinguish between outbreak strains . In the study presented here a 31-marker MLVA together with the other two fingerprinting methods were used to analyze genetic relationships within a large collection of isolates from wild and domestic animals in Namibia and to identify the possible epidemiological correlations of anthrax outbreaks within and outside the ENP between 1983 and 2010 . The MLVA data were further used to establish a model of the probable evolution of GTs within the endemic region of the Etosha National Park .
Most isolates included in the study were collected after 2005 , associated with an anthrax study in the ENP that began in 2006 . Samples and cultures available from before that were included for the additional information they supply , albeit recognizing the limitations resulting from the small sample size . Most of the isolates originating from elsewhere in Namibia were collected at the Central Veterinary Laboratory ( CVL ) , Windhoek , and were recovered from diagnostic samples . The collecting period spans from 1983 until 2010 , with gaps in 1984–1986 , 1990 , 1993 , 1996–1999 , and 2001 and fewer than 5 isolates in each of the years 1994 , 1995 , 1998 , 2000 , and 2003 . Samples used for the isolation of B . anthracis consisted of either clinical swabs taken from the blood of fresh carcasses or various tissue specimens taken from older carcasses . In some cases , soil samples taken from soil underneath a carcass , heavily contaminated with blood or spilled body fluids were used . Environmental samples were either taken from 10–20 cm beneath the surface in case of dry soils by means of core samplers or from sediments of water holes using a special scoop . Swab samples with fresh blood were left exposed to the air in a biosafety cabinet for two days to ensure sporulation . Isolation of B . anthracis from the specimens using semi-selective media and confirmation of diagnosis by classical bacteriological and PCR techniques , targeting both the virulence plasmids and a chromosomal marker , were performed as described in WHO [14] , Beyer et al . [15] , and Beyer [16] . The DNA from isolates made between 1983–1987 was kindly provided by Dr Chung Marston , CDC , Atlanta , USA . DNA for PCR was prepared either by boiling in phosphate buffered saline ( PBS ) a suspension of colony material or using a commercially available kit for extraction of genomic DNA . In the first case , a 20 µl loop of a colony grown over night on blood agar was suspended in 200 µl PBS in a 0 . 5 µl eppendorf tube . The suspension was overlaid with 2 drops of paraffin and boiled for 15 min in a thermoblock , held at 100°C . After centrifugation at 13 000× g for 5 min at room temperature the supernatant was used for PCR . The DNAeasy Plant Kit ( Quiagen ) was used if a higher quality of DNA was desired . Thirty-one loci were used from previous studies [7] , [10] , with the following modifications in primers used: bams21 forward ( TGTAGTCCAGATTTGTCTTCTGTA ) , bams30 reverse ( GCATAATCACCTACAACACCTGGTA ) , and CG3 forward ( TGTCGTTTTACTTCTCTCTCCATTAC ) . All other primer sequences were as published . To perform a 31-marker MLVA , seven multiplex PCR reactions ( primer mixes ) were set up . For multiplex PCRs 1–4 the following mixtures were used: 0 . 25 µM of each dNTP , 1/10 the total volume of buffer and 5 U ( multiplex reaction 1 ) or 2 . 5 U ( multiplex reactions 2 , 3 , and 4 ) of HotMaster TM Taq DNA polymerase ( both from 5-Prime GmbH , delivered by VWR Int . GmbH , Germany ) , 20 µl of primer mix 1 ( multiplex reactions 1 , 3 , and 4 ) or 10 µl of primer mix ( multiplex reactions 2 ) , 5 µl of DNA solution , and deionized water to a final volume of 40 µl . The Multiplex-PCRs 1 , 2 , 3 , and 4 were carried out with the following profile: denaturation at 94°C , 2 min; 35 cycles with denaturation at 95°C , 20 s; annealing at 60°C , 30 s; elongation at 65°C , 2 min; a final elongation step at 65°C , 5 min; and cooling at 8°C . Following the PCR , the samples were purified with a commercial PCR product purification kit ( Roche Diagnostics , Mannheim , Germany ) . The purified PCR products were diluted at 1∶30 ( multiplex-PCR 1 and 3 ) , 1∶10 ( multiplex-PCR 2 ) , and 1 . 20 ( multiplex-PCR 4 ) . For multiplex PCRs 5–7 the following mixtures were used: Each dNTP at 0 . 25 µM , 1/10 the total volume of buffer and 1 U of HotMaster TM Taq DNA polymerase , 15 µl of primer mix 5 or 10 µl of primer mix 6 or 7 , 5 µl of DNA solution , and deionized water to a final volume of 50 µl . The Multiplex-PCRs 5 and 6 were carried out with the following profile: Denaturation at 94°C , 2 min; 29 cycles with denaturation at 94°C , 20 sec; annealing at 50°C , 20 s; elongation at 65°C , 45 s; a final elongation step at 65°C , 5 min , and cooling at 8°C . In the multiplex PCR 7 an annealing step at of 60°C for 30 s was applied . The 3 PCR reactions were diluted by 1∶20 ( multiplex PCR 5 ) , 1∶27 ( multiplex PCR 6 ) , and 1∶5 ( multiplex PCR 7 ) , 20 µl of each reaction was combined and 50 µl of this mix was purified by the purification kit mentioned above . The capillary electrophoresis was performed using an ABI PRISM 310™ Genetic Analyzer ( Applied Biosystems ) . From the multiplex reaction 1 , 3 , and 5 to 7 two microliter of the sample was mixed with 2 µl of a 1∶30 dilution of the size standard MegaBACE™ ET ( GE Healthcare , Germany ) and 18 µl of HiDi™ formamide ( Appl . Biosystems ) . From multiplex reaction 2 and 4 , a two microliter sample was mixed with 1 µl of a 1∶2 dilution of the size standard MapMarker® 1000 ( BioVentures , TN , USA ) and 19 µl HiDi™ formamide . The samples were boiled for 5 min at 95°C and then analyzed in a 45 min run in the ABI PRISM 310™ Genetic Analyzer ( Applied Biosystems ) according to the recommendations of the manufacturer . The data were analyzed with GeneMapper™ software ( Applied Biosystems ) . The raw data of fragment lengths were normalized by codes , reflecting the actual copy numbers of the repeat sequences where possible ( Table S1 ) . For the purpose of orientation the appropriate copy code numbers are added for the Ames ancestor strain as deduced from the sequence available at Genbank , accession No . : AE017334 . 2 , GI:50082967 . The observed fragment lengths for all alleles found in the 329 isolates of this analysis is provided in Table S4 . This table also shows the correlations between the observed and expected fragment lengths . The latter are taken from the values of alleles provided by Lista et al . [9] and deduced from sequences of B . anthracis available on Genbank , accession No . AE017225 . 1 ( strain Sterne ) , AE017334 . 2 ( strain Ames ancestor ) , AE017336 . 2 ( plasmid pXO1 of Ames ancestor ) , and AE017335 . 3 ( plasmid pXO2 of Ames ancestor ) . Values of alleles not published were artificially added by interpolation using a repeat length as provided in Table S2 . DNA for SNP analysis was prepared as described for the MLVA . The canonical SNP analysis with 13 markers was performed as described by van Ert et al . [10] with the following modifications . Primers and probes for SNP A . Br . 004 were changed to A . Br . 004v2 ( J . Beaudry , NAU , pers . com . ) as follows: Primer A . Br . 004v2 for ( GCATTTGCAAGAACGCTAATG ) , primer A . Br . 004v2 rev ( GGGTCTAAGCCGATTGTAGGT ) , probe 6-FAM-CCAATCATGGTACTAGAT and probe VIC-ACCAATCATTGTACTAGAT . For SNPs A . Br . 003 and B . Br . 004 the cycling parameters on the StepOne real-time PCR system of Applied Biosystems were 50°C for 2 min , 95°C for 10 min , followed by 45 cycles of 95°C , 15 s; 90°C , 30 s; and 72°C , 30 s , followed by a final step at 60°C for 30 s . DNA for SNR analysis was done as described for the MLVA . The SNR analysis with the 4 markers CL10 , CL12 , CL33 , and CL35 was performed by first amplifying a larger DNA fragment , each including the original PCR-fragment as published by Kenefic et al . [12] . For amplification the following primers were used: CL10for ( CCAAATGAGACCAGCAACAG ) , CL10rev ( AGCAGGAGTGGACAGAAAAG ) , CL12for ( CTATGGAGTTGCTCACGTTG ) , CL12rev ( TCTCTTATACCCGCATACCC ) , CL33for ( CATCGAATCCCTTTATCTAATTCAGG ) , CL33rev ( GTTATACAGAGAAAAAGCGGACAT ) , CL35for ( CGTATTGTGTTGAGAAACTTGTTG ) , and CL35rev ( GTCGAATGCAAAGTATTCATCGT ) . The cycling parameters for all amplifications were 94°C , 2 min followed by 40 cycles of 94°C , 30 s; 45°C ( CL12 ) , 50°C ( CL35 ) , or 55°C ( CL10 and CL33 ) for 30 s; and 72°C , 30 s; followed by a final elongation at 72°C , 10 min . The PCR-fragments were sequenced by MWG Biotec AG , Germany . Sequences were analyzed using Vector NTI Advanced 10 software ( Invitrogen , Germany ) . To determine the eventual fragment length , the primer sequences published by Kenefic et al . [12] were applied to the sequence of an amplicon and the correct fragment size was used for further analysis . Data from MLVA and SNR analysis were processed by means of the Bionumerics software package version 5 . 10 ( Applied Maths ) . For cluster analysis by UPGMA a categorical coefficient was used . In the maximum spanning tree ( MST ) a maximum neighbor distance of 1 and a minimum number of 3 genotypes was used to build a complex . To determine significant differences between the relative frequency of each GT amongst isolates sampled from within the ENP we calculated simultaneous multinomial credible intervals [19] for the proportion of our isolates ( pooled across all years ) that were a given GT . All GTs that occurred greater than five times ( GT4 , GT6 , GT9 , G14 , GT22 ) were considered individually and all other GTs were pooled into an “other” category . Multinomial median probabilities and 95% credible intervals were calculated in a Bayesian framework using Markov Chain Monte Carlo methods . The GT of each isolate was assumed to be drawn from a multinomial distribution with probabilities of each GT drawn from a symmetric Dirichlet prior . After a burn-in of 1000 , we simulated the posterior using 100 , 000 samples and thinning by 10 to estimate credible intervals and median estimates . Posterior distributions were sampled for data pooled across all years as well as for individual years 2005–2010 , and for years 1988–2004 grouped ( due to small sample sizes ) . To assess whether genetic differentiation occurred over time or between species we used the multiple regression on distance matrix ( MRM ) extension to the Mantel Test [20] . Briefly , genetic distances between GTs were determined using the Jaccard Index ( the proportion of markers that two genotypes share ) . We fitted generalized additive models ( GAM ) of genetic distance between each pair of isolates to the temporal distance between their time of sampling and host species similarity ( coded as a binary for same or different ) . Temporal distance and an interaction with species similarity were fitted with a smoothing function in the GAM after exploratory analysis revealed that this relationship was nonlinear . To avoid overfitting the smoothing function ( as n isolates yields n*n-1 isolate pairs ) , we chose the smoother dimension to obtain similar estimated degrees of freedom to the mean estimated degrees of freedom obtained by generalized cross validation on random subsets of data of size equal to the number of isolates . Null distributions of all statistics of interest were obtained by fitting models to 2000 randomly permuted genetic distance matrices . Significance of terms in the GAM was determined by comparing the F statistic for each term to its null distribution as calculated above . Overall model fit was assessed by comparing the deviance explained by the best model to its null distribution obtained by permutation . By using the permuted models to create null distributions , the P-values reported in the results fully account for variation in number of isolates obtained during the study period . All statistical analyses were performed in ‘R’ and using the ‘mgcv’ package [21] .
In total , 384 isolates of B . anthracis from 20 animal species and 20 isolates from environmental samples in the ENP not related to a carcass were included in this analysis . Also included were seven isolates , which proved to be the Sterne vaccine strain . Six were from dead goats with unknown histories and one was an environmental isolate from a game ranch . Figure 1 and table 1 provide a summary of the distribution of isolates by GT over time and the distribution of GTs related to the species affected , respectively . A total of 37 genotypes ( GT ) were identified by the 31-marker MLVA; of these 13 were not found in the ENP , 18 were only found within the borders of the ENP and 6 both inside and outside the ENP . All GTs found so far in Namibia belong to cluster A , as originally defined by Keim et al . [7] ( Figure S1 ) . From the 24 GTs found in the ENP , 23 belong to SNP group 8 ( A . Br . Aust94 ) as defined by van Ert et al . [10] . One GT ( GT11 ) belongs to SNP group 11 ( A . Br . 008/009 ) . This was the only GT not associated with any GT from a carcass and was represented by just two isolates from sediments of waterholes about 170 km apart . Two other SNP groups , group 6 ( A . Br . 005/006 ) and group 9 ( A . Br . 001/002 ) were only found outside the ENP . This is the first record of SNP groups 6 , 9 , and 11 in southern Africa . The temporal and spatial occurrences of the most prevalent GTs within the ENP ( GTs 4 , 6 , 9 , and 22 ) are shown in Figures 2 and 3 . Additionally , the cluster analysis of all isolates in Figure S1 provides information on the spatial and species origin of each isolate by time of sampling . The 24 GTs found in the ENP are distinguishable by the apparent part they play in the epidemiology of anthrax within the Park . The majority of the GTs were only found sporadically and , apart from GT22 , with just one or very few representative isolates ( Figure 1 ) . While many GTs were found once or a few times only at the beginning or at the end of the study period , some GTs were found both once in the early years and then again many years later on only single or a few occasions . GT22 was unusual in being the cause of a substantial outbreak affecting several species and being the most prominent GT in 2005 ( Figure 2 and Table S3 ) . The proportion of isolates sampled from 2005 that were GT22 was greater than that of any other genotype for that year , as calculated by simultaneous multinomial 95% credible intervals [19] ( Figure S2 ) . It was isolated only once more in 2006 . In contrast , GT4 , GT6 and GT9 were found more frequently . GT6 was first found from samples of dead elephants and zebras in 1983–1988 and then again most years after that in which cultures were available . This GT accounted for 167 ( 49 . 5% ) of the 337 isolates in the ENP ( Figure 1 ) and was found throughout the Park , from mountain regions near the western border to the plains in the northeast , spanning 300 km ( Figure 3 ) . GT4 and GT9 were first found in 1987 and 1992 and represent 74 ( 22% ) and 32 ( 9 . 5% ) of the 337 isolates from the ENP , respectively . GTs 4 , 6 , and 9 were each significantly more prevalent than all other isolates when all years are pooled ( Figure 4 ) . Together the three GTs account for more than 80% of all isolates from the ENP . The presence of a dominant outbreak clone in the park was further tested by evaluating the relation between genetic distances between each pair of isolates and the temporal distance between their times of isolation as well as whether they were from the same host species or not . The best fit generalized additive model ( GAM ) performed in a multiple regression on distance matrices framework ( MRM ) included both a nonlinear smoother function of temporal distance ( p<0 . 0005 ) and a term for species similarity ( p<0 . 0005 ) . The interaction between the terms was not significant ( p = 0 . 61 ) and was removed from the final model . The temporal relationship is characterized by increasing genetic distance with temporal distance to a peak at around 8 years apart and then decreasing genetic distance thereafter ( Figure 5 and Figure S3 ) . This model explained a significant proportion of the deviance ( 0 . 149 , p<0 . 0005 ) . The average proportion of markers differing between isolates was 0 . 031 for isolates from the same host species and 0 . 054 for those from different species . The distribution of cases monitored in this study supports the concept of persisting or recurring outbreaks at least in the case of zebra and springbok , with peaks in cases occurring during the late wet season . Also elephants may contract the disease any time of a year but are more likely to do so in the late dry/early wet season ( Figure 6 ) . By categorizing the isolates by GT and time of isolation ( Table S3 ) the overall endemic occurrence of anthrax in the ENP can be differentiated into long lasting or recurrent outbreaks caused by the same GT and short term sporadic outbreaks caused by numerous different GTs . Long lasting outbreaks may continue for more than one year , such as those caused by GT6 in 2007 and in 2009 . Those caused by GT6 in 1992 , 2006 , and 2008 , by GTs 4 and 9 in 2008 and by GT9 in 2009 , may exemplify recurring outbreaks . There are many examples of separate outbreaks occurring simultaneously in the same region , such as those caused by GT18 and GT22 in 2005 , by GTs 4 , 6 , 9 , 14 , and 22 in 2006 , and by GTs 4 , 6 , and 9 in 2008 and 2009 , all in the Okaukuejo region of central ENP . The close temporal and regional relationship between GT4 , GT6 , and GT9 prompted an interest in the evolutionary distances between these and the other GTs found . For this purpose all GTs , except GT30 from the vaccine strain , were subjected to minimum spanning tree ( MST ) analysis , which indicated that GT6 might be the ancestor of all other GTs found in the ENP . The MST in Figure 7 shows the GTs with the markers in which they differ . GTs 4 and 9 differ from GT6 in only one highly mutable marker located on the pXO2 plasmid . Mutations at the same locus are also responsible for the possible evolution of GTs 2 and 3 from GT6 . Differences in only one or two markers , located in both high and lower mutable markers , indicate the presence of a closely related clonal group of B . anthracis strains within the ENP . Only the GTs 11 ( from sediments of two far apart waterholes ) , 29 , and 36 are more distantly related and differ from GT6 by , respectively , 16 , 9 and 4 markers . Six GTs were found both within and outside the ENP . Among them only GT18 and GT22 were also found in livestock , though both on only one occasion . GT18 was found four times in the ENP between October and November 2005 and also that year and the year before on four game farms , A–D , all located east of Windhoek , some 400 km southeast of the ENP ( Figure 3 ) . SNR analysis was able to resolve the ten GT18 isolates into a further six SNR types . On each of farms A and B , the two isolates found were of the same SNR type but different from any other SNR type in this GT ( Figure S4 ) . GT22 was found on 13 occasions in the ENP between June 2005 and August 2006 . In October 2004 this GT was isolated from both a goat ( Capra spp . ) at a game farm near the south border of the ENP ( farm E ) and an oryx ( Oryx gazella ) at a second farm ( farm F ) located a few kilometers west of farm E . These two isolates had the same SNR profile . However , three isolates from the same farm F , from a kudu ( Tragelaphus strepsiceros ) ( June 2005 ) , a hartebeest ( Alcelaphus buselaphus ) ( July 2005 ) and an oryx ( October 2005 ) belonged to three different SNR types within GT22 ( Figure S5 ) . Other GTs occurring both inside and outside the ENP were GTs 2 , 3 , 6 , and 9 . GT2 has only been found recently , twice in the Okaukuejo region in 2007 and 2009 and once in 2008 on game farm G approximately 35 km south-west of Okaukuejo . GT3 was found twice in the ENP – once each in the northeast of the Park in 1992 and in the west of the Etosha Pan in 2009 – and again once outside the Park , some 450 km to the southeast of the ENP in 2007 . GT6 , the most prominent GT within the ENP was isolated once in 1992 from the Caprivi province and once again in 2008 from an elephant near the north-east border of the Park ( Okashana ) . GT9 was found on 31 occasions in the ENP between 2005 and 2010 . In 2006 and 2007 the same GT was found near the southern border of the ENP , killing one kudu each on nearby farms H and I and an oryx on farm G . Though being of the same GT these three isolates were of two SNR types , both different from most of the SNR types found among the ENP isolates of GT9 . In 2009 GT9 caused an outbreak on two other game farms east of Okaukuejo ( farms K and L ) where an oryx and a number of cheetahs ( Acinonyx jubatus ) died after being fed meat from the infected oryx . The route of infection was verified by these isolates having the same but unique SNR type ( Figure S6 ) . Thirteen GTs were only found outside the ENP . Among them GT28 was isolated both from a wild animal ( buffalo [Syncerus caffer] , in 1992 ) and a domestic animal ( bovine in 2004 ) , both from the Caprivi region , while GTs 12 , 13 , 20 , and 34 were found only in game animals , and only once each . Among the latter , GT13 from farm G is closely related to GTs 6 and 9 ( Figure 7 ) which are both also present at this farm . There are six GTs [1] , [24] , [26] , [31] , [32] , [ and 33] which were isolated only from domestic animals , namely cattle and sheep from different farms in the northern , eastern and southern parts of the country . All of them are genetically rather distantly related to GTs from within the ENP except GT26 , which differed from GT22 in only two markers . GT30 , representing the locally used veterinary vaccine strain was isolated from six goat carcasses , from different farms between 2002–2009 , and once from the environment ( water pan ) of a game ranch .
Of the 37 GTs found so far in Namibia , none belong to the large cluster B1 of Keim et al . [7] . GTs of the B1 cluster have so far only been isolated in southeast Africa ( northeast of South Africa , southeast of Zimbabwe and an unspecified region of Mozambique ) [8] , [10 , unpublished findings] , though cluster B1 and B2 isolates are present in historical strain collections of many European countries and the USA [10] , [22]–[24] and even among recent isolations from outbreaks in Germany and France ( unpublished data ) . Of the 24 GTs found in the ENP , 23 belong to a single SNP group ( A . Br . Aust94 ) as defined by van Ert et al . [10] , supporting the theory of a clonal expansion of an ancestor strain , introduced to the region a long time ago . The only exception are two isolates of GT11 , belonging to SNP group 11 differing from GT6 by 16 markers . Together with GT36 and GT29 , differing in 4 and 9 markers from GT6 , these three GTs are highly unrelated to all other GTs present in the ENP and may , therefore , represent separate introductions of B . anthracis to that region . Two other SNP groups , group 6 ( A . Br . 005/006 ) and group 9 ( A . Br . 001/002 ) were only found outside the ENP . This is the first record of SNP groups 9 and 11 in southern Africa . We did not find SNP groups 7 ( A . Br . 003/004 ) and 5 ( A . Br . Vollum ) in Namibia , both described by van Ert et al . [10] in South Africa , nor – in concordance with the lack of any isolates of the B-branch – any SNP group of the B-branch . While an expected increase in the number of different GTs did occur with an increase in the number of isolates collected each year in the last six years , GTs 4 , 6 , and 9 remained the only ‘dominant’ ones in terms of their temporal occurrence and the number of cases they caused . DNA from the earliest isolates available from the ENP [2] , [3] revealed the presence of GT6 and 4 in the Park for more than 25 years . Since GTs 4 and 9 differ from GT6 by only one mutation in a highly mutable marker on the pXO2 plasmid , these three GTs can be considered one strain that is likely to have been causing outbreaks in the ENP for a very long time . All other GTs , with the exception of GT22 , which was the prominent GT in 2005 have only been found sporadically . The statistical relationship between time and genetic differentiation appears counter-intuitive . The greatest genetic distance between isolate pairs occurred for isolates obtained at an intermediate distance apart in time ( Figure 5 ) . However , the decline after about 8 years apart indicates that isolates far apart in time were often closely related . This suggests that while certain GTs arose and faded away during the sample period , certain “dominant” GTs were present throughout the study period , and were particularly prevalent during the beginning and end of this period . Genetic distance was also significantly greater on average for isolates from different host species . Given that GTs were highly related in general , we do not believe that the species-GT relationship is indicative of adaptive evolution . Rather , we suppose that these results suggest that the transmission cycles of anthrax in the ENP are somewhat differentiated by host species , owing to their ecological and behavioral differences . While significant , the effect of species dissimilarity was not extreme ( Figure S3 ) . Thus , although there are transmission processes that differ among species , the transmission cycle of B . anthracis in the ENP appears to be highly connected among species . Our results raise the question of the kind of relation between the markers used for MLVA and the part a GT will play as an outbreak strain in the long term . Despite the obvious stochastic effects of sampling and genetic drift on the one hand and environmental and ecological factors affecting transmission on the other hand , the overall picture of spatial and temporal distribution of GTs presumably also depends on the genetic features of the strains present . Genes coding for superior survival in the environment , for example sporulation capability [25] and tenacity after release from an infected host and virulence features , such as lower infectious dose , higher replication rate within a host , and higher resistance against the innate immune mechanisms of a host , may give some GTs a fitness advantage over others . As far as is known , several of the VNTRs used in the 31 marker MLVA are part of coding sequences in chromosomal and plasmid genes . However , with a few exceptions , such as genes where bams13 , bams30 , bams24 , bams34 , bams44 , and vrrC1 and vrrC2 are located , they would not be expected to play a major role in the mechanisms mentioned above . Thus , it remains unknown whether the three most frequently isolated GTs dominate the genotypic landscape due to fitness advantages . Further whole genome and transcriptome analysis will allow investigation of the possible genetic and phenotypic relations indicated by the different levels of occurrence of the GTs observed in this study . Our minimum spanning tree ( MST ) suggests that GT6 is the ancestor of all other GTs found . The reliability of evolutionary trees mainly depends on the rate and stability of the mutations used to build the tree [10] , [11] , [26] , [27]–[29] . For our strain collection the mean index of diversity of all 31 markers is 6 . 06 ( from 0 . 0 to 59 . 5 ) . The number of different alleles ranges from 1 to 11 ( Table S4 ) . All VNTR-markers used in our analysis were shown to be stable during routine bacteriological diagnosis and passage in mice and rabbits [30] . Our MST may , therefore , represent a reasonable model for the evolution of B . anthracis within the ENP . If a competent vector is present , newly evolving GTs may spread rapidly throughout the Park and sometimes beyond its borders . GT22 may provide an example of a rapidly spreading outbreak strain , raising the question as to what can serve as an appropriate vector under the conditions of the ENP . Flowing water , water holes , and airborne spread of spores are rather unlikely to play a role [6] , [31] . Depending on the geographical and seasonal conditions , live vectors are probably the principal spreaders of the disease over long distances either mechanically [14] , or by infected animals incubating the agent while , at the same time , moving long distances . In this regard elephants , among other herd animals , may serve as spreaders of anthrax within the ENP and beyond its fenced borders . In 1988 , GT6 was found in elephants simultaneously in the far west of the Park and about 300 km to the east near Namutoni . GT22 was found in October 2005 from two elephants , one in the Halali region and the other approximately 70 km away in the north-east of the Park . While currently available movement data for elephants in the ENP do not indicate these cases were located along known movement corridors , the latter two cases occurred within a feasible distance of elephant movement . GT23 was found in April 1992 in elephants near Okaukuejo and some 50 km away around Halali , in this case in regions that are on known elephant movement routes . The elephant deaths outside the Park from GT6 and GT9 in October 2008 at the same time as these GTs were being isolated from cases within the Park provides support for this hypothesis . However , this theory does not exclude the possibility of casual infections outside the Park , particularly because the occurrence of GTs 6 and 9 is not restricted to the ENP . The relationship between those GTs found both in the ENP and outside the ENP can only be speculated upon . The ENP is a >22000 square kilometers territory , comprehensively described in numerous public websites ( see http://de . wikipedia . org/wiki/Etosha-Nationalpark for an overview ) . While the park perimeter is fenced , animals do occasionally break through the fence ( particularly elephants ) or go underneath it ( particularly through holes dug by warthogs ) . Farms located at close vicinity to the ENP may be part of the regional ecosystem promoting the spread of outbreak strains between the ENP and farms . For instance , GT13 , isolated from a farm abutting the ENP is as closely related to GT6 as the majority of the GTs found within the park and , therefore , probably belongs to the same group of descendents from GT6 ( Figure 6 ) . To investigate possible epidemiological relations further , SNR analysis was applied to the MLVA clusters of GT9 , 18 , and 22 , as single nucleotide repeats are known to have a very high mutation rate [36] , [37] and , therefore , are applicable to forensic and outbreak analysis [11] . While our SNR analysis clearly correlated isolates with their source of sampling , its usefulness as a tool to prove or disprove possible transfers of outbreak strains between different locations was rather limited . In case of high numbers of SNR types within a MLVA based genotype , the analysis obscures rather than clarifies the epidemiological relationships . Isolates from the same GT but differing in their SNR type can still be considered “the same outbreak strain” if other epidemiological criteria , such as regional and temporal relationships or certainty as to how the B . anthracis was spread , suggest a highly likely epidemiological connection between two outbreaks , as in the case of the four different SNR types within GT22 at the same game farm . However , the same SNR type as in the case of the cheetahs dying from contaminated meat strongly supports the source and route of infection . Thirteen of the total of 37 GTs were only found on farms in different regions of Namibia . Seven of these , including the vaccine strain , were only found in domestic animals housed at 8 different locations . Though most of the GTs isolated outside the ENP have only been found so far from single cases , the genetic distance from isolates within the Park indicates separate epidemiological anthrax cycles in livestock . There have been temporally separated but closely related cases of anthrax in livestock on nearby farms . However , assessing whether or not this also represents possible epidemiological connections between such outbreaks requires more data than are currently available . In conclusion , the molecular characterization of isolates from outbreaks of anthrax in Namibia has permitted an understanding of the divergence of B . anthracis and probable evolution of an ancestral strain introduced into the region long ago , revealing substantial genetic distances between the strains circulating within the ENP and those in livestock . It has also confirmed the value of the 31-marker MLVA for revealing single outbreak events within an otherwise endemic occurrence of anthrax in animals . The routine application of molecular characterization offers a highly valuable addition to conventional epidemiological methods for the surveillance and prevention of this neglected disease . | Anthrax , the disease caused by Bacillus anthracis , is a neglected zoonotic diseases in the context of its impact on poor rural and periurban communities in Africa and other less developed areas of the world . Several regions of Namibia , the Etosha National Park in particular , are well known as being endemic areas for anthrax and , together , provide a good model for the investigation of the genetic diversity of B . anthracis circulating in livestock , wildlife and humans , and surrounding environments . The application of modern molecular strain typing techniques to the analysis of genotypic diversity , as it relates to the spatial and temporal distribution of B . anthracis strains in Namibia , is described in this paper . In particular , we demonstrate how it is possible to distinguish outbreaks of the disease caused by different strains from those caused by the spread of a single strain , to trace an outbreak strain back to its possible origin , and to track the routes of transmission of an outbreak strain within and between animal populations . The data described are relevant to all those concerned with monitoring , surveillance and prevention of the spread of anthrax in endemic areas . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [
"bacteriology",
"veterinary",
"diseases",
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] | 2012 | Distribution and Molecular Evolution of Bacillus anthracis Genotypes in Namibia |
Anthropogenic environmental changes may lead to ecosystem destabilization and the unintentional colonization of new habitats by parasite populations . A remarkable example is the outbreak of intestinal schistosomiasis in Northwest Senegal following the construction of two dams in the ‘80s . While many studies have investigated the epidemiological , immunological and geographical patterns of Schistosoma mansoni infections in this region , little is known about its colonization history . Parasites were collected at several time points after the disease outbreak and genotyped using a 420 bp fragment of the mitochondrial cytochrome c oxidase subunit 1 gene ( cox1 ) and nine nuclear DNA microsatellite markers . Phylogeographic and population genetic analyses revealed the presence of ( i ) many genetically different haplotypes at the non-recombining mitochondrial marker and ( ii ) one homogenous S . mansoni genetic group at the recombining microsatellite markers . These results suggest that the S . mansoni population in Northwest Senegal was triggered by intraspecific hybridization ( i . e . admixture ) between parasites that were introduced from different regions . This would comply with the extensive immigration of infected seasonal agricultural workers from neighboring regions in Senegal , Mauritania and Mali . The spatial and temporal stability of the established S . mansoni population suggests a swift local adaptation of the parasite to the local intermediate snail host Biomphalaria pfeifferi at the onset of the epidemic . Our results show that S . mansoni parasites are very successful in colonizing new areas without significant loss of genetic diversity . Maintaining high levels of diversity guarantees the adaptive potential of these parasites to cope with selective pressures such as drug treatment , which might complicate efforts to control the disease .
Environmental change and increasing movements of people , plants and animals have led to species introductions and proliferations into new areas . The colonization , establishment and success of introduced species depend on various biotic and abiotic factors [1–4] . In the case of parasitic organisms , the life cycle is paramount in determining the success of colonization [5] . Parasites with a direct life cycle ( when a single host species is involved ) can readily invade new areas together with their host [6] , while parasites with a complex life cycle need the presence of one or more intermediate host species in order to establish successfully [7 , 8] . The epidemic outbreak of human intestinal schistosomiasis in Northwest Senegal represents a suitable case study to investigate the evolutionary dynamics of an invasive species . This debilitating disease is caused by the flatworm Schistosoma mansoni that per generation cycles through a human final host and a snail intermediate host of the species Biomphalaria pfeifferi [9] . As the Senegal River Basin ( SRB ) suffered from severe droughts during the 1970s and 1980s [10] , two dams were built to improve the agricultural conditions for rice production: the Diama dam near the mouth of the Senegal River in 1985 and the Manantali dam upstream in Mali on the Bafing River in 1989 [11] . Subsequent agricultural and hydrological changes were accompanied by 1 ) strong agro-industrial developments in the village of Richard Toll , resulting in a massive immigration of agricultural workers from neighboring regions in Senegal , Mali and Mauritania [12 , 13] , and 2 ) major ecological changes such as reduced salinity levels and the formation of open and permanent water bodies and irrigation canals , favoring the growth and spreading of B . pfeifferi snails [14] . These factors promoted the introduction and successful establishment of S . mansoni followed by one of the most severe outbreaks of intestinal schistosomiasis ever described [11 , 13 , 15–18] . Before the construction of the two dams , human population densities were relatively low in the Delta of the SRB and they were concentrated around Saint-Louis , Ross Béthio and Richard Toll . There were no reports of S . mansoni and the intermediate snail host B . pfeifferi was only reported at low densities ( < 1% of all collected snails ) in the city of Saint-Louis , Lake Guiers and the village Pakh [19] . The first human cases of Schistosoma mansoni were reported in 1988 in Richard Toll , the epicenter of the disease outbreak [13] . About 70% of all collected snails were identified as B . pfeifferi with 44% of them infected with S . mansoni [20] . The number of cases of intestinal schistosomiasis increased rapidly to epidemic proportions [15 , 21] , and soon after S . mansoni colonized much of the Delta and part of the Middle Valley of the SRB [18] . Many studies investigated the epidemiological , immunological and geographical patterns of S . mansoni infections in the SRB , either in single or in mixed infections with S . haematobium [13 , 15 , 16 , 18 , 22–29] . A few studies used molecular tools to investigate the distribution of S . mansoni among hosts [30] and in response to praziquantel drug treatment [31] , both on a small geographic scale . However , no study looked at the phylogeography and population genetic structure of S . mansoni in Senegal at a larger scale and over time . Here we investigated the evolutionary consequences of the anthropogenic introduction of S . mansoni in Northwest Senegal since 1988 . This is a retrospective study incorporating samples collected at several time points following parasite introduction . In order to understand the dynamics of such a rapid colonization we aimed to reconstruct the epidemic by using nine microsatellite markers and sequences of the mitochondrial cytochrome c oxidase subunit 1 gene ( cox1 ) . More specifically , we wanted to test whether the current S . mansoni population was founded by a small number of strains or by multiple introductions from disparate source populations . In the first case , we expected very low levels of genetic diversity and high genetic structure due to genetic drift ( e . g . [32 , 33] ) . In the second case , we expected average or high levels of genetic diversity , with various potential outcomes of genetic structure depending on the amount of gene flow among the introduced parasite populations ( e . g . [34 , 35] ) .
This study is part of a larger investigation on the epidemiology and control of schistosomiasis in Senegal , for which approval was obtained from ‘Le Comité National d'Ethique de la Recherche en Santé’ in Dakar ( Senegal ) , the review board of the Institute of Tropical Medicine Antwerp ( Belgium ) and the ethical committee of the Antwerp University Hospital ( Belgium ) . Before the start of the study , the respective health authorities , village leaders and school staff of the selected villages were informed about the objectives of the study . These meetings were held in the local language to ensure full comprehension . Informed consent was obtained from teachers and parents or guardians for each participating child . For the village Assoni visited in 2011 in Southwest Senegal , written informed consent was given . For the villages visited in 2006 and 2007 in Northwest Senegal , oral informed consent was given and recorded on paper by assigning ID numbers , name , age , gender and village of residence to those for whom consent was obtained . The data were anonymized prior to analyses . All schistosomiasis positive children were treated with a single dose of praziquantel at 40 mg/kg bodyweight . In schools or classes where the percentage of Schistosoma infections was more than 50% , mass treatment of all children was carried out at the end of the study according to WHO guidelines [36] . Three genetic datasets were prepared ( Table 1 ) . The first genetic dataset , hereafter referred to as DSEQ , comprised the cox1 sequences that were generated in this study from the villages Richard Toll ( 1993 and 1994 ) , Ndombo ( 1997 and 2006 ) , Assoni ( 2011 ) and the villages Wayowayanko and Farako in Southwest Mali ( 1993 ) ( Table 1 ) ( see section ‘Molecular analyses’ for details on sequencing ) . Miracidia from the village Assoni were collected within the framework of this study ( see below ) , while all other samples were adult worms provided by the Schistosomiasis Collection at the Natural History Museum in London ( SCAN ) [37] ( SCAN numbers: 2800 , 2916 , 2953 , 3108 , 3109 , 3421 , 3445 , 3464 , 3465 , 3815 , 4580 ) . Note that sequences could not be generated for miracidia sampled in 2007 in Northwest Senegal as there was insufficient DNA . Our sequence data were therefore complemented with previously published GenBank sequences from miracidia obtained from Northwest Senegal in the villages Temey and Nder in 2007 , from Southeast Senegal in the village Kolda in 2009 , and from seven other countries in Africa ( Fig 1a and Table 1 ) [38] . Sequences of cercariae and adults worms from Webster and colleagues [38] were not included here as they might be clones from each other , possibly introducing a bias in estimates of diversity . In contrast , sequences generated from worms in this study were included because the microsatellite genotyping allowed us to identify and exclude clones ( see below ) . The second genetic dataset , hereafter referred to as DMS1 , comprised parasites that were genotyped at nine microsatellite markers ( see section ‘Molecular analyses’ for details on microsatellite genotyping ) , and that were either miracidia collected within the framework of this study or adult worms provided by SCAN [37] ( see Table 1 for details on sample origin and sample type ) . These parasites originated from eight villages in Northwest Senegal and from one village in Southeast Senegal . There are three major water bodies in Northwest Senegal: the Senegal River , the Lampsar River and Lake Guiers , each with their tributaries ( Fig 1c ) . The Senegal River and Lake Guiers are connected in Richard Toll through the Canal of Taouey ( Fig 1c ) . The respective villages enrolled in this study along the Senegal River are Rhonne ( 2007 ) , Diadiam ( 2007 ) , Richard Toll ( 1993 , 1994 , 2007 ) and Ndombo ( 1997 , 2006 ) . Note that Ndombo lies near the Canal of Taouey within one-kilometer distance from Richard Toll . Hence , samples from Richard Toll and Ndombo combined ( 1993–2007 ) are representative for the epicenter of the disease outbreak . Near Lake Guiers , the following villages were enrolled: Theuss ( 2006 , 2007 ) , Nder ( 2007 ) and Ndieumeul ( 2007 ) . Near the Lampsar River only one village was enrolled , namely Mbodjene ( 2007 ) . To our knowledge , there were treatments in most of the villages around Lake Guiers in March and April 2006 . Detailed information on the treatment history for all villages enrolled in this study is however lacking . In each village , about 75 schoolchildren aged 7 to 14 were selected randomly . From each child one stool sample was collected and examined by Kato Katz ( 2 slides of 25mg ) [39] . Eggs from positive stool samples were isolated after filtration , hatched and miracidia were individually pipetted onto Whatman FTA indicator cards in a volume of 3μl of water as described in [40] . Adult worms , provided by SCAN [37] , were obtained after one laboratory passage of naturally collected miracidia and/or cercariae ( Table 1 ) . As adult worms may be genetically identical ( i . e . clones ) , microsatellite genotypes obtained from worms were visually inspected to identify identical multilocus genotypes . When such identical genotypes were found within a sample , all but one were removed from the dataset . In addition , miracidia were collected within the framework of this study from eight children aged 5–14 in 2011 in the village Assoni in the Region of Kédougou ( Southeast Senegal ) ( Fig 1b ) . Six of these children were treated eight months prior to this study . The village is situated near tributaries of the Tiokaye River , itself a tributary of the Gambia River . Assoni was enrolled as pilot village in the national control program of Senegal , namely ‘le Programme National de Lutte contre les bilharzioses’ ( PNLB ) [41] . The prevalence of S . mansoni infection in Assoni among children aged 6–14 years was initially 100% in 2006 ( i . e . before treatment ) , but decreased to 50% in 2013 after three treatment rounds ( 2008 , 2010 , 2012 ) with praziquantel to those children that were diagnosed positively for S . mansoni infection [41] . Similar to above , eggs from positive stool samples were isolated after filtration , hatched and miracidia were individually pipetted onto Whatman FTA indicator cards in a volume of 3 μl of water [40] . The third genetic dataset , hereafter referred to as DMS2 , includes the same genotypes as DMS1 , but complemented with previously published S . mansoni genotypes from miracidia that were collected in the village Kokry-Bozo in 2007 in Southwest Mali within the framework of the study of Gower and colleagues [42] . Main local watercourses are irrigation channels fed by the Niger River . The prevalence of S . mansoni infection in Kokry-Bozo at the time of sampling was 88% [42] . Samples were obtained from children that had not been treated previously and miracidia were genotyped at the Department of Infectious Disease Epidemiology ( Faculty of Medicine , Imperial College London ) [42] . The genetic data of 104 miracidia were provided as raw genotyping chromatogram files that were scored within our lab using GENEMAPPER v4 . 0 ( Applied Biosystems ) . Six microsatellite markers ( CA11-1 , S9-1 , SMD25 , SMD28 , SMD89 , SMDA28 ) were shared among the study of Gower and colleagues [42] and our study ( see section ‘Molecular analyses’ ) ; hence DMS2 only contained six loci . All genotypes were imported into ALLEOGRAM v2 . 2 [43] for binning of allele lengths . Note that only those samples that were successfully typed at all markers ( nine for DMS1 and six for DMS2 ) were included in the analyses . Genomic DNA extractions of lab-derived adult worms and naturally collected miracidia were performed with the Nucleospin Tissue kit ( Macherey Nagel ) following the manufacturer’s instructions . For miracidia , 3 mm discs containing the whole miracidium were excised from the FTA cards and for worms the whole sample was used as DNA source [40] . Sequences of the mitochondrial cox1 gene ( 450 bp ) were obtained using primers Asmit-1 and Schisto-3' [44 , 45] in 25μl PCR reactions , each containing 2 μl of DNA template , 0 . 5 units of Platinum Taq DNA polymerase ( Life Technologies ) , 1x reaction buffer ( Life Technologies ) , 2 mM MgCl2 , 0 . 2 mM dNTPs and 0 . 8 μM of each primer . PCR conditions were the following: denaturation for 3 min at 95°C , followed by 35 cycles of 45 s at 94°C , 45 s at 49°C , 45 s at 72°C with a final extension of 10 min at 72°C . PCR products were visualized on a 1% agarose gel to check for amplicons , and subsequently purified and sequenced using a Big Dye Chemistry Cycle Sequencing Kit v1 . 1 in a 3130 Genetic Analyser ( Applied Biosystems ) . The forward primer Asmit-1 was used and complemented with sequencing reverse primer Schisto-3' when sequence quality was poor . All individual S . mansoni parasites ( both naturally obtained miracidia and lab-derived worms ) were genotyped using nine microsatellite loci in a single multiplex reaction ( L46951 , SMD11 , S9-1 , CA11-1 , SMD25 , SMD28 , SMD43 , SMD89 , SMDA28 [46–48] ) as described in [40] . All PCR products were analyzed using an ABI 3130 Genetic Analyser ( Applied Biosystems ) and GeneScan 500 LIZ as Size Standard . Allele sizes were manually verified using GENEMAPPER v4 . 0 ( Applied Biosystems ) . All cox1 sequences were manually edited and aligned using MUSCLE [49] as implemented in Geneious R6 ( http://www . geneious . com/ ) . Species identity was confirmed using BLAST ( http://blast . ncbi . nlm . nih . gov/ ) . Genetic diversity at the cox1 fragment ( DSEQ ) was quantified per region , per village and per year in DNA-SP v5 . 10 . 1 [50] by estimating the number of haplotypes ( i . e . unique sequences ) , the haplotype diversity h [51] , the number of polymorphic sites and the average number of nucleotide differences per site between two randomly chosen DNA sequences ( i . e . nucleotide diversity Π ) . Based on the commonly used mutation rate of 10−8 mutations per site per year for mitochondrial DNA [52] , we assume that the time frame ( ~ 30 years ) is too short to generate new mtDNA lineages , and that mutation will therefore not have affected mtDNA diversity . Genealogical relationships between all sequences were explored by constructing two haplotype networks based on statistical parsimony [53] in the package ‘pegas’ [54] as implemented in the R software [55] . Haplotypes were first identified using the function haplotype and used to construct a network with the function haploNet . The number of sequences that represented a given haplotype was logarithmically transformed to narrow high and small values , and used to determine the size of its corresponding pie diagram . A first network included all sequences from the DSEQ dataset . A second network included only sequences from Senegal and Mali . First , the genetic diversity of parasite populations was quantified by estimating the proportion of heterozygous individuals ( i . e . observed heterozygosity , Ho ) , the expected proportion of heterozygous individuals assuming Hardy-Weinberg Equilibrium ( unbiased expected heterozygosity , Hs ) and the number of alleles corrected for sample size ( allelic richness , AR ) . Ho and Hs were estimated in GENETIX v4 . 05 [56] while AR was estimated in the R package ‘hierfstat’ [57] . The inbreeding coefficient FIS was estimated by f [58] in GENETIX; the significance of f was tested using 10 , 000 permutations and corrected for multiple testing using Bonferroni corrections . Analyses were done per village , year and region . Second , the ancestry of individual parasites was inferred using a Bayesian Markov Chain Monte Carlo ( MCMC ) clustering analysis as implemented in STRUCTURE v2 . 2 . 3 [59] . The program assigns individuals to K populations that are each characterized by a set of allele frequencies . Individuals could be assigned to two or more populations if their genotypes indicate that they are admixed . As K is unknown , the model is run multiple times , each time with a different K-value ( from 1–10 ) . Sampling locations ( i . e . village ) were included in the model as a prior ( LOCPRIOR = 1 ) , as they can assist clustering when the amount of genetic markers is low [60] . Note that the LOCPRIOR model will not falsely identify genetic structure when there is none and will ignore sampling information when the ancestry of individuals is uncorrelated with sampling locations [60] . Five replicate runs were initiated assuming the admixture model and correlated allele frequencies for datasets DMS1 and DMS2; each run consisted of 106 MCMC chains , initiated by 105 burn-in steps . All jobs were run parallel on multiple cores using the R package ‘ParallelStructure’ [61] . The optimal K value was identified by the maximum LnP ( D ) , which is the log likelihood of the observed genotype distribution in K clusters . Third , population genetic structure was visualized by exploring the distribution of genotypes of DMS1 and DMS2 in hyperspace using a Factorial Correspondence Analysis ( FCA ) as implemented in GENETIX and results were visualized using the R software . FCA visualizes genotypic ( dis ) -similarities among individual parasites . Fourth , genotypic ( dis ) -similarities were studied among groups of parasites by estimating the FST analogue θ [58] . This was done pairwise between regions and villages in GENETIX for DMS1 and DMS2 . Significant population differentiation was tested for all estimates by 1 , 000 permutations of individuals among localities , and Bonferroni correction was applied for multiple testing . Pairwise estimates of θ between villages were visualized with classical multidimensional scaling ( CMDS ) plots using the R software . Only populations with at least 10 genotypes were kept in order to minimize bias due to low sample size .
The DSEQ dataset comprised a total of 657 cox1 sequences of which 121 were generated in this study ( Table 1 ) . After alignment and trimming , sequence fragments of 420 bp long were retained for further analyses . The DMS1 dataset comprised a total of 542 S . mansoni parasites that were successfully genotyped for all nine microsatellite loci , among which 154 originated from the village Assoni in Southeast Senegal and 388 from several villages across Northwest Senegal ( Table 1 ) . Sample sizes for Northwest Senegal ranged from five genotypes in Ndombo in 2006 to 98 genotypes in Rhonne in 2007 ( Table 1 ) . The DMS2 dataset comprised a total of 758 S . mansoni parasites that were successfully genotyped for all six microsatellite loci . A total of 73 out of 104 genotypes were successfully scored for the Kokry-Bozo sample from Southwest Mali . Sample sizes for Northwest Senegal ranged from seven in Diadiam and Ndombo ( 2006 ) to 152 in Nder ( Table 1 ) . Twenty unique cox1 haplotypes were found in Northwest Senegal ( Table 2 ) , which is currently about one fifth of the total number of haplotypes observed so far in Africa ( i . e . 103 ) . Almost all of the haplotypes found in Northwest Senegal were also found within the village Nder ( i . e . 19 out of 20 ) . A total of six haplotypes were identified within a single village ( Assoni ) in Southeast Senegal , of which three haplotypes were unique to this village; the other three haplotypes were shared with Northwest Senegal . In Kolda in Southeast Senegal , three cox1 haplotypes were identified of which one was unique to that village , while the other two haplotypes were also found in Northwest Senegal . The three sequences from Wayowayanko and Farako ( Southwest Mali ) differed from each other but were in common with Northwest Senegal , of which one was also found in Assoni ( Southeast Senegal ) . Haplotype diversity of all parasites found in Northwest Senegal ( h = 0 . 847; N = 241 ) was high compared to other regions in Africa , ranging from 0 . 543 in Niger ( N = 160 ) to 0 . 927 in Tanzania ( N = 44 ) . Haplotype diversity was 1 . 000 in Mali , but the sample size was very low ( N = 3 ) . In Richard Toll , haplotype diversity in 1993 ( N = 8 ) and 1994 ( N = 30 ) was equal to 0 . 929 and 0 . 772 respectively , comparable to other regions in Africa ( Table 2 ) . Similarly , nucleotide diversity of all parasites sampled in Northwest Senegal ( Π = 0 . 0081 ) was similar to other populations in Africa . Only parasites sampled in Zambia and Coastal Kenya showed higher levels of nucleotide diversity ( Table 2 ) . The statistical parsimony network showed that sequences from Northwest Senegal clustered together with haplotypes found in Southeast Senegal , Southwest Mali and some from Niger , and the corresponding haplotype diversity was 0 . 860 ( SD = 0 . 010 ) ( Fig 2a ) . Parasites from other regions in Africa were grouped into divergent phylogeographic clades , which were separated from the ‘West-African’ clade by many unsampled or extinct haplotypes ( Fig 2a ) . Haplotype diversity was highest in the ‘East-African’ phylogeographic clade ( h = 0 . 907; SD = 0 . 015 ) containing parasites from Coastal Kenya , Tanzania and Uganda ( Fig 2a ) . The second network revealed the diversity found in Northwest Senegal , showing many divergent haplotypes that did not cluster according to village or year of sampling ( Fig 2b ) . Parasite population genetic diversity as estimated by unbiased expected heterozygosity ( Hs ) , observed heterozygosity ( Ho ) and allelic richness ( AR ) was rather uniform across all villages sampled in Northwest Senegal ( Table 3 ) , with the exception of Ndombo ( 2006 ) that showed lower values of genetic diversity . The diversity levels of S . mansoni in Northwest Senegal ( Hs = 0 . 38; Ho = 0 . 36; AR = 2 . 90 ) were similar to the diversity in Assoni in Southeast Senegal ( Hs = 0 . 35; Ho = 0 . 32; AR = 2 . 85 ) , but slightly lower compared to Kokry-Bozo in Southwest Mali ( Hs = 0 . 45; Ho = 0 . 42; AR = 3 . 31 ) ( Table 3 ) . The highest log likelihood values as estimated in STRUCTURE were found for K = 4 for DMS1 and for K = 3 for DMS2; the log likelihood decreased thereafter for larger values of K . For DMS1 , parasites from almost all villages sampled in Northwest Senegal were assigned to one genetic cluster , with the exception of parasites from Ndombo ( 1997 ) and Mbodjene ( 2007 ) ( Fig 3a ) . In addition , parasites from Assoni were assigned to a separate genetic cluster ( Fig 3a ) . For DMS2 , three genetic clusters were identified that were concordant with the three regions Northwest Senegal , Southeast Senegal and Southwest Mali ( Fig 3b ) . Factorial Correspondence analysis ( FCA ) for DMS2 revealed that most of the parasites from Northwest Senegal sampled in several villages from 1993 to 2007 always clustered together and differed strongly from parasites sampled in Assoni in 2011 ( Southeast Senegal ) and Kokry-Bozo in 2007 ( Southwest Mali ) ( Fig 4b ) . For DMS1 , parasites sampled in Mbodjene ( 2007 ) and Ndombo ( 1997 ) differed slightly from the remainder of Northwest Senegal , although the second axis explained only 9 . 84% of the total observed variation ( Fig 4a ) . Pairwise estimates of FST between regions for DMS2 were 0 . 064 ( p < 0 . 001 ) between Southwest Mali and Northwest Senegal , 0 . 056 ( p < 0 . 001 ) between Southwest Mali and Southeast Senegal and 0 . 044 ( p < 0 . 001 ) between Northwest Senegal and Southeast Senegal . Table 4 summarizes the pairwise FST between villages and shows that parasite populations from Kokry-Bozo ( 2007 ) , Assoni ( 2011 ) , Ndombo ( 1997 ) and Mbodjene ( 2007 ) were almost always significantly differentiated from the other samples when genotypes were permuted among villages . In contrast , genetic differentiation among the other samples in Northwest Senegal was mostly low and often insignificant ( Table 4 ) . Classical multidimensional scaling ( CMDS ) plots based on pairwise FST between villages visualized this pattern ( Fig 4c and 4d ) .
Our results showed that genetic diversity of S . mansoni was high , both at the mitochondrial and at the nuclear level . A total of 20 different cox1 haplotypes ( out of 103 described across Africa ) were identified in Northwest Senegal ( Fig 2a ) . Despite the fact that mitochondrial genes are particularly prone to diversity loss after colonization events due to their haploid state and uniparental inheritance [52] , a substantial level of nucleotide and haplotype diversity was detected , also for those parasite populations collected in 1993–1994 shortly after the onset of the epidemic ( Table 2 ) . This observation was confirmed by statistical parsimony analysis that showed a relatively wide range of haplotypes that were often separated by many mutations ( Fig 2b ) . These results suggest that many S . mansoni parasites were introduced from multiple source populations . Note that , based on the commonly used 2% divergence rate for mitochondrial DNA [52] , we expect none or only a few mutations in the 30 years since the beginning of the disease outbreak . At the nuclear level , levels of diversity in Northwest Senegal were relatively similar compared to Southeast Senegal and Southwest Mali ( Table 3 ) . These results suggest that there was no significant loss of genetic diversity upon introduction , and confirm that many parasites were probably introduced at the onset of the epidemic . An interesting finding in this respect is that S . mansoni genetic diversity in Richard Toll remained relatively stable since 1993 despite the many treatments during the course of the disease outbreak . Although treatment with praziquantel is expected to result in decreased population sizes and thus decreased genetic diversity [62] , field-based studies revealed only a slight [63] or no decrease in diversity at all following treatment [31 , 64] suggesting that treatment may only have little effect on the genetic composition of natural S . mansoni populations . Note however that the actual diversity in 1993/1994 may have been larger than the one observed here , as parasites were passaged through mice , which may have induced a loss in genetic diversity . These results highlight the success of this parasite in extending its geographic range without notable loss of genetic diversity . Maintaining genetic diversity allows the parasite to quickly adapt to a new environment or a new host , or to counteract selective pressures such as drug treatment [65 , 66] . Such a strong evolutionary potential could explain why Schistosoma parasites continue to ( re- ) emerge successfully into new regions [67] , and why caution is warranted in any future anthropogenic environmental changes involving creation of potential new transmission sites . Multidimensional scaling of pairwise FST , Factorial Correspondence Analysis and STRUCTURE analysis revealed the presence of three genetic populations in Northwest Senegal: one dominant S . mansoni population that is present in almost all villages and at all times , and two smaller populations in Ndombo 1997 and in Mbodjene 2007 ( Figs 3 and 4 and Table 4 ) . The presence of one widespread population suggests that most S . mansoni parasites originated from one and the same source population ( scenario 1 ) , or that they are the result of intraspecific hybridization ( admixture ) among multiple introduced parasite populations ( scenario 2 ) . Admixture among different introductions might produce recombinant genotypes that are characterized by a unique genetic profile if these genotypes were extensively shuffled , which will furthermore erode the genetic signal of the native range [68–71] . We favor the second scenario of admixture among multiple introductions because of the following reasons . ( i ) The presence of many divergent haplotypes at the non-recombining mitochondrial marker suggests that multiple introductions likely occurred ( see above ) . ( ii ) Multiple introductions could have happened due to the substantial seasonal immigration of infected agricultural workers from neighboring regions in Senegal , Mauritania and Mali [11 , 12] . ( iii ) There is evidence of high parasite gene flow in Northwest Senegal , which tends to homogenize populations , as 19 out of 20 identified cox1 haplotypes were found within a single village ( Table 2 ) and pairwise FST between most villages was low and insignificant ( Table 4 ) . ( iv ) The presence of two divergent populations in Ndombo ( 1997 ) and Mbodjene ( 2007 ) might support the hypothesis of multiple introductions . However , population bottlenecks followed by genetic drift could also produce divergent populations [72] , which could apply to the sample from Ndombo that was obtained after laboratory passage [73 , 74] . Altogether , these findings support the hypothesis that the widespread dominant population of S . mansoni in Northwest Senegal is more likely the result of an admixture between multiple introductions ( scenario 2 ) than the result of a single introduction ( scenario 1 ) . Unfortunately , our sampling in the native range is too restricted to reliably discriminate among both scenarios . Analyses of genetic structure indicated that the samples from Southeast Senegal and Southwest Mali represent separate genetic groups ( Table 4 and Figs 3 and 4 ) , suggesting that parasites were probably not introduced from these regions , or at least not from the villages that were sampled in these regions . Based on statistical parsimony networks it is clear that the putative source populations are within West Africa , as haplotypes sampled in Northwest Senegal clustered tightly with those from Southwest Senegal , Mali and some from Niger ( Fig 2a ) . Additional sampling within neighboring regions is therefore necessary , preferably from regions such as Mauritania from where agricultural workers have immigrated [12] or from villages in Mali closer to the Senegalese border . In light of the dynamic nature of a disease outbreak , it is remarkable that parasite genetic diversity remained relatively stable in space and time . Schistosoma mansoni genetic diversity in 1993–1994 from Richard Toll is of the same order of magnitude as 14 years later ( Tables 2 and 3 ) and genetic differentiation between samples from different years ( 1993–1994 and 2007 ) and different villages was low and insignificant ( Table 4 ) . These results indicate that a successful parasite population , whether the result of a single introduction or of admixture between multiple introductions , must have established and spread quickly at the start of the epidemic . Such a colonization history could be linked to the colonization history of the intermediate snail host B . pfeifferi . Microsatellite analyses revealed that B . pfeifferi snail populations were genetically homogeneous in the region around Richard Toll , suggesting a rapid expansion of one or a few fecund lines at the expense of less fecund ones [75] . Experimental infection studies revealed that B . pfeifferi from Senegal showed unusual high vectorial capacities , with higher snail longevity and higher frequency of patent infections in combination with Senegalese S . mansoni ( i . e . sympatric combination ) than in combination with Cameroonian S . mansoni ( i . e . allopatric combination ) [76] . This was also evidenced from the extremely high S . mansoni prevalence ( 44% ) in B . pfeifferi snails at the onset of the epidemic [20] . Altogether these results suggest local adaptation of S . mansoni to its intermediate snail host B . pfeifferi in Northwest Senegal . This could have led to priority effects in S . mansoni , lowering the establishment success of subsequent invasions and ensuring the temporal and spatial stability of the dominant S . mansoni population in Northwest Senegal [77 , 78] . An interesting finding is that S . mansoni parasites from Mbodjene ( 2007 ) in the Lampsar region were significantly differentiated from most other samples in the vicinity of Richard Toll . Likewise , the B . pfeifferi population sampled close to this locality was genetically different from the other two populations near the Lampsar River and the populations around Richard Toll [75] . This correspondence between host and parasite geographic structure further suggests that the genetic composition of the intermediate snail hosts could be an important factor determining establishment success of S . mansoni in a certain region . At the molecular level , this could comply with the hypothesis of a matching phenotype model where the interactions between parasite antigens and host immune receptors during the early stages of the infection determine the success or failure of the infection [79] . This study is the first to reconstruct a recent epidemic of human intestinal schistosomiasis using mitochondrial and nuclear markers , revealing the evolutionary consequences of such a rapid colonization . The occurrence of many different haplotypes as revealed by the non-recombining mitochondrial marker indicated that multiple introductions occurred , while the recombining microsatellite markers pointed to the presence of mainly one widespread homogeneous population . We argue that admixture among multiple introductions generated a homogenous parasite population with a distinct genetic signature . The spatial and temporal stability of the established S . mansoni population suggest a swift local adaptation of the parasite to its intermediate snail host B . pfeifferi at the onset of the epidemic . Further research using samples from different localities in Senegal , Mali and Mauritania will help to pinpoint the putative source population ( s ) of this disease outbreak . | Schistosoma parasites successfully colonize new regions following the construction of water schemes for power production or agricultural purposes . Here we investigated the colonization history of the human parasite Schistosoma mansoni in Northwest Senegal following the construction of two dams in the ‘80s . Parasites were collected at several time points following the disease outbreak and their genetic profile was characterized using molecular markers . Our results showed that many genetically different parasites must have been introduced at the onset of the epidemic , which complies with the extensive immigration of infected seasonal agricultural workers from neighboring regions in Senegal , Mauritania and Mali . Furthermore , we showed that parasite transmission occurred over a large geographic distance , which implies that new alleles , like resistance alleles , could spread rapidly in this system . These new insights demonstrate how colonization following anthropogenic environmental changes may lead to genetically diverse parasite populations within a short time span . High genetic diversity is often linked with a stronger potential to cope with selective pressures such as drug treatment , which may complicate efforts to control the disease . | [
"Abstract",
"Introduction",
"Material",
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"Methods",
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"Discussion"
] | [] | 2015 | Reconstructing Colonization Dynamics of the Human Parasite Schistosoma mansoni following Anthropogenic Environmental Changes in Northwest Senegal |
The epigenetic activity of transposable elements ( TEs ) can influence the regulation of genes; though , this regulation is confined to the genes , promoters , and enhancers that neighbor the TE . This local cis regulation of genes therefore limits the influence of the TE's epigenetic regulation on the genome . TE activity is suppressed by small RNAs , which also inhibit viruses and regulate the expression of genes . The production of TE heterochromatin-associated endogenous small interfering RNAs ( siRNAs ) in the reference plant Arabidopsis thaliana is mechanistically distinct from gene-regulating small RNAs , such as microRNAs or trans-acting siRNAs ( tasiRNAs ) . Previous research identified a TE small RNA that potentially regulates the UBP1b mRNA , which encodes an RNA–binding protein involved in stress granule formation . We demonstrate that this siRNA , siRNA854 , is under the same trans-generational epigenetic control as the Athila family LTR retrotransposons from which it is produced . The epigenetic activation of Athila elements results in a shift in small RNA processing pathways , and new 21–22 nucleotide versions of Athila siRNAs are produced by protein components normally not responsible for processing TE siRNAs . This processing results in siRNA854's incorporation into ARGONAUTE1 protein complexes in a similar fashion to gene-regulating tasiRNAs . We have used reporter transgenes to demonstrate that the UPB1b 3′ untranslated region directly responds to the epigenetic status of Athila TEs and the accumulation of siRNA854 . The regulation of the UPB1b 3′ untranslated region occurs both on the post-transcriptional and translational levels when Athila TEs are epigenetically activated , and this regulation results in the phenocopy of the ubp1b mutant stress-sensitive phenotype . This demonstrates that a TE's epigenetic activity can modulate the host organism's stress response . In addition , the ability of this TE siRNA to regulate a gene's expression in trans blurs the lines between TE and gene-regulating small RNAs .
Transposable elements ( TEs ) are mobile fragments of DNA that can accumulate and occupy large fractions of a genome , including over 45% of the human genome [1] . When active , TEs have the potential to create mutations by inserting into genes or generating breaks in DNA . To suppress the mutagenic potential of TEs , the eukaryotic genome has evolved defense mechanisms to inhibit TE proliferation , which are distinct from the developmental regulation of genes [2] . TEs are targeted for epigenetic repression mediated by the overlapping signals of cytosine DNA methylation , repressive histone tail modifications , and remodeling of chromatin into transcriptionally recalcitrant condensed heterochromatin ( reviewed in [3] ) . Gene regulation can be influenced by the epigenetic regulation of TEs; however , this only occurs due to the proximity of a preexisting or newly transposed TE to a gene . This regulation of genes by neighboring TEs in cis can be due to multiple mechanisms , including interruption of a regulatory element , or by local spreading of repressive chromatin modifications such as DNA or histone methylation , resulting in position-effect variegation and potentially the formation of heritable epialleles [4]–[5] . TEs are major producers of small RNAs that act to maintain the TE in an epigenetically silenced state . In plants , and perhaps in animals , heterochromatin modifications are targeted by the activity of small RNAs . For example , in the mouse TE-derived piwi-interacting RNAs ( piRNAs ) guide DNA methylation to TEs [6] . In the reference plant Arabidopsis thaliana , the cycle of RNA-directed DNA methylation ( RdDM ) is initiated by the plant-specific RNA Polymerase IV ( PolIV ) , which produces a non-protein coding transcript that is converted into double stranded RNA ( dsRNA ) by the activity of RNA-dependent RNA Polymerase 2 ( RDR2 ) ( reviewed in [7] ) . Dicer-like 3 ( DCL3 ) cleaves this TE dsRNA into small interfering RNAs ( siRNAs ) of 24 nucleotides ( nt ) in length , which are incorporated into either Argonaute 4 ( AGO4 ) , AGO6 , or potentially AGO9 [8] . These siRNA-loaded Argonaute proteins act to maintain the heterochromatic state of TEs by targeting them for DNA and histone tail methylation . Athila LTR retrotransposons are the largest family of TEs in Arabidopsis , occupying over 2 . 7% of the genome [9] . Athila elements are transcriptionally silenced , and silencing is dependent on symmetrical CG DNA methylation . When DNA methylation is removed , either in a DNMT1-homolog maintenance of DNA methylation 1 ( met1 ) mutant , or in a swi/snf family chromatin remodeling protein decrease in DNA methylation 1 ( ddm1 ) mutant , transcriptional activation occurs [10]–[11] . Athila retrotransposons are also transcriptionally activated in the vegetative nucleus of wild-type ( wt ) pollen grains [12] . In all of these examples heterochromatin modifications and condensation are lost , and global activation of TEs occurs [4] , [12]–[13] . Upon global activation of TEs , there are widespread shifts in the accumulation of small RNAs derived from TE transcripts . Transcriptional activation of most silenced TEs results in the loss of their corresponding siRNAs [12]–[14] . However , some retrotransposon families , including Athila , are unusual in the fact that they produce more siRNAs when epigenetically active than when epigenetically silenced [12] , [15]–[16] . The Athila siRNAs that increase in abundance are primarily 21–22 nt in length and are produced from the non-protein coding region downstream of the gag and pol ORFs of the consensus Athila element . The abundance and specific location of these siRNAs generates islands of 21–22 nt siRNAs in the genome when epigenetic silencing of Athila is lost [12] . In Arabidopsis , as well as in animals , the production of small RNAs and subsequent targeting of TEs is distinct from the production of gene-regulating small RNAs ( reviewed in [17] ) . The first examples of a TE piRNA or siRNA regulating a genic mRNA in trans were only recently discovered in Drosophila and mouse [18]–[19] . In addition , recently an example of a plant viral siRNA was shown to regulate a gene [20] . However , these examples represent exceptions to the general rule of separation between TE/viral and gene-regulating small RNAs [21] . For example , there is a clear distinction between the biogenesis mechanism and target of TE siRNAs and microRNAs . MicroRNAs act in plants and animals to regulate gene expression on the post-transcriptional or translational level . In Arabidopsis , DCL1 produces 21 nt microRNAs from stem-loop precursor transcripts generated by RNA polymerase II ( PolII ) , and these microRNAs are loaded primarily into AGO1 . Thus , microRNAs are not amplified by an RNA-dependent RNA polymerase , and only one or two single small RNA species accumulate from the microRNA locus . In contrast , most plant siRNAs are the products of RNA-dependent RNA polymerases , and cleavage of these long dsRNAs produces clusters of siRNAs from each locus . However , the notion that only microRNAs regulate genes , while endogenous siRNAs do not , is incorrect , as some inverted repeat-derived siRNAs act to regulate genes , [22] and other siRNAs act to regulate genes through the trans-acting siRNA ( tasiRNA ) pathway in Arabidopsis . This pathway begins with the cleavage of a non-protein coding transcript by the microRNA-loaded AGO1 or AGO7 , which initiates the DCL4- and RDR6-dependent phased production of siRNAs ( reviewed in [23] ) . These siRNAs are loaded into AGO1 and regulate gene expression similar to a microRNA . DCL4 , RDR6 and AGO1 , as well as DCL2 , also act on viral transcripts in the virus-induced gene silencing ( VIGS ) pathway to initiate and amplify the 21–22 nt siRNA signal that participates in the post-transcriptional degradation of the viral mRNAs , as well as to transport these siRNAs to unaffected regions of the plant to mount a systemic resistance to the spread of the virus [24]–[28] . Therefore , the Arabidopsis AGO1 protein is highly versatile , as it is involved in the microRNA , tasiRNA and VIGS pathways . It is currently unknown if , how or why AGO1 distinguishes between a gene-regulating tasiRNA and a VIGS siRNA involved in viral transcript processing , as both are generated using the same DCL4 and RDR6 machinery . Arteaga-Vázquez et al demonstrated that 12 elements of the Athila6 subfamily each encode a small RNA , for which they predicted and provided indirect evidence targets a genic transcript for translational repression [29] . They predicted that this small RNA was generated as a microRNA from a stem-loop precursor transcript and determined that it was processed by the microRNA machinery DCL1 , HEN1 and HYL1 . Additionally , they predicted that this microRNA , which they named microRNA854 , targets the 3′ untranslated region ( UTR ) of the UPB1b gene , a homolog of the mammalian TIA-1 that encodes an RNA binding protein involved in the formation of stress granules [30]–[31] . They observed that 21 nt microRNA854 accumulates in wt vegetative tissues and found that the microRNA854-targeted UPB1b 3′UTR inhibits translation in wt plants when the 3′UTR is added to a reporter transcript . Lastly , Arteaga-Vázquez et al provided evidence that microRNA854 is highly conserved from plants to mammals . We were unable to detect the accumulation of 21 nt microRNA854 in wt seedling , root , leaf and inflorescence tissues . Due to the failure to meet multiple criteria in order to validate this small RNA as a microRNA [32] , including the biogenesis pathway of this small RNA ( see below ) , we have renamed this small RNA siRNA854 to avoid confusion . We have directly demonstrated that the TE-derived siRNA854 regulates in trans the transcript of the UBP1b gene . We show that the accumulation of siRNA854 is under the same trans-generational epigenetic regulation and inheritance patterns to which Athila TEs are subject . Upon Athila6 epigenetic activation , siRNA854 production is shifted from a 24 nt TE siRNA dependent on PolIV and RDR2 , to 21–22 nt siRNAs that are dependent upon DCL2 , DCL4 and RDR6 and are incorporated into AGO1 . We demonstrate that UBP1b regulation is altered only when Athila6 is epigenetically activated , resulting in the phenocopy of the stress-sensitive upb1b mutant phenotype .
To determine when siRNA854 accumulates , we interrogated publicly available deep sequencing small RNA libraries [33] and found that in the plant body of wild-type Columbia ecotype plants ( wt Col ) , 21 nt siRNA854 does not accumulate . Only one read of 21 nt siRNA854 was detected in over 6 . 6 million genome-matched reads of wt Col leaf and inflorescence small RNAs combined ( Table 1 ) . However , when epigenetic repression of Athila6 is lost , 21 nt siRNA854 levels increase . Table 1 shows that , compared to the extremely low levels in wt Col inflorescence and leaf tissue , siRNA854 accumulates in met1 and ddm1 mutant inflorescences . Increased siRNA854 levels were also detected in pollen of wt Col plants , albeit to a lower level of 21 nt siRNA854 reads per million than in met1 or ddm1 mutants . In the plant body , retrotransposons such as Athila6 are tightly epigenetically suppressed by heritable symmetric DNA methylation and RdDM [3] . In each case of 21 nt siRNA854 accumulation ( met1 , ddm1 and pollen ) loss of TE epigenetic silencing is known to occur [11]–[12] , [34]–[35] . To determine if the Athila6 retrotransposon is specifically activated in these mutants and pollen , we performed real-time quantitative RT-PCR ( qRT-PCR ) and found that in met1 and ddm1 mutants and wt pollen , Athila6 transcript accumulation is significantly increased compared to wt Col whole seedlings , leaf and inflorescence tissue ( Figure 1A ) . We used qRT-PCR to measure Athila6 expression ( Figure 1 ) , as well as a separate Athila6 primer set specific to the microRNA stem-loop structure previously proposed ( Figure S1 ) [29] . Both primer sets provided similar data , showing that neither the proposed stem-loop nor flanking Athila6 region transcripts accumulate in wt Col seedlings , leaves or inflorescences , while both regions are expressed in ddm1and met1mutants . In addition , Athila6 transcripts accumulate in wt Col pollen ( Figure 1A ) . Compared relatively , pollen has considerably less Athila6 transcript accumulation than either ddm1 or met1 mutants , perhaps due to the fact that pollen TE reactivation only occurs in the pollen vegetative nucleus , one of three nuclei expressing mRNA in mature pollen [12] . To examine siRNA854 accumulation in more detail , we performed small RNA Northern blots and found in wt Col and ddm1 and met1 inflorescences , a 24 nt version containing the siRNA854 sequence accumulates , while 21–22 nt versions of this sequence accumulate only in ddm1 and met1 ( Figure 1B ) , as well as in pollen ( Table 1 ) . We then probed this Northern blot with a 300 bp siRNA854-flanking region of Athila6 ( Athila6 3′ probe ) and found that this region also produces other 24 and 21–22 nt siRNAs at levels comparable to those of siRNA854 . These results demonstrate that the production of 21–22 nt siRNAs from this entire region is under the same epigenetic regulation as siRNA854 , and combined with the results of deep sequencing of small RNAs from ddm1 , met1 and pollen [12]–[13] , demonstrates that siRNA854 is one member of a larger region of siRNA production . Our data refutes previous data that characterized a specific microRNA produced from this region of the Athila6 retrotransposon [29] . The phenotype of ddm1 mutant plants becomes more severe in progressive generations . Second generation homozygotes for the recessive ddm1-2 allele ( ddm1 F2 ) display little to no morphological phenotype , while after propagation as a homozygote for four additional generations ( ddm1 F6 ) , leaf size and infertility phenotypes are severe [36] . Figure 1C shows that increasing transcript accumulation of the Athila6 retrotransposon is associated with the progression of ddm1 from the F2 to F6 generation . To determine if the different transcript levels of Athila6 directly correlate with the accumulation of 21–22 nt siRNA854 and flanking 21–22 nt Athila6 3′ siRNAs , we examined siRNA854 accumulation by Northern blot in ddm1 F2 and F6 individuals . F6 ddm1 individuals produce increased levels of siRNA854 and other Athila6 3′ siRNAs compared to F2 generation ddm1 individuals ( Figure 1D ) . These data , together with the transcript accumulation and siRNA accumulation in met1 and pollen ( Figure 1A and 1B , Table 1 ) , suggests that the epigenetic activation and level of Athila6 steady-state transcripts directly and positively correlates with the accumulation level of Athila6 21–22 nt siRNAs , including siRNA854 . To determine the biogenesis mechanism responsible for producing the 21–22 nt versions of Athila siRNAs and siRNA854 , we first screened four tissues of wt Col and ddm1 mutant plants and determined that Athila6 21–22 nt siRNAs are not detected in wt Col seedlings , roots , leaves or inflorescences ( Figure 2A ) . Athila6 21–22 nt siRNAs are most easily detectable in ddm1 inflorescence tissue , while leaf and seedling tissues have lower relative levels , and the siRNAs are undetectable in roots ( Figure 2A ) . In wt Col , 24 nt Athila6 siRNAs weakly accumulate in the root and inflorescence ( Figure 2A ) , and these inflorescence 24 nt siRNAs are dependent on the PolIV component NRPD1A , RDR2 , and the small RNA-modifying protein HEN1 ( Figure 2B ) . HEN1 is responsible for the accumulation of both microRNAs and siRNAs [37] , while the requirement of NRPD1A and RDR2 demonstrates that , like other known 24 nt TE siRNAs , Athila6 24 nt siRNAs are generated by the RdDM pathway which is responsible for maintaining epigenetically silenced regions of the genome [21] , [38] . The 24 nt siRNA854 accumulation in wt Col inflorescences is not dependent on RDR6 , the PolV component NRDE1 , or the microRNA processing DCL1 ( Figure 2B ) . Contrary to previously published results , these data demonstrate that there is no siRNA854 version in wt Col inflorescences that is dependent on the microRNA machinery . To determine the small RNA biogenesis pathway responsible for producing 21–22 nt siRNA854 and Athila6 siRNAs when Athila is epigenetically activated , we generated 12 double mutant combinations with ddm1 , using mutants for genes with known roles in the various Arabidopsis small RNA biogenesis pathways , such as different AGO , DCL and RDR genes . Double mutant inflorescences were used to assay the accumulation of siRNA854 ( Figure 2C ) . As some of these double mutants are in Col x Landsberg erecta ( Ler ) mixed genetic backgrounds , as a control we confirmed that Ler ddm1 mutants also accumulate Athila6 siRNAs , while wt Ler does not . We found that the RdDM pathway responsible for producing Athila6 24 nt siRNAs involving NRPD1A and RDR2 does not generate the 21 nt or 22 nt siRNA854 . This represents a change in siRNA biogenesis pathways for Athila siRNAs , as their epigenetic reactivation results in a new set of proteins responsible for the 21–22 nt siRNA production . We determined that RDR6 function is required for both 21 nt and 22 nt siRNA854 accumulation , as in ddm1;rdr6 double mutants neither of these siRNAs accumulate ( Figure 2C ) , while RDR6 is not responsible for the 24 nt version of these siRNAs ( Figure 2B ) . RDR6's involvement suggests that an Athila6 transcript is copied into dsRNA , which is required for 21–22 nt siRNA production . We also found that in ddm1;dcl4 double mutants , the 21 nt siRNA854 is absent , while increased levels of the 22 nt and 24 nt versions are detected . There are well-described hierarchical relationships among DCL2 , DCL3 , and DCL4 . When DCL4 is not present to generate 21 nt siRNAs , DCL2 primarily substitutes for this function and generates 22 nt siRNAs , while DCL3 substitutes for DCL4 to a lesser degree and produces 24 nt siRNAs [39] . Conversely , ddm1;dcl2 double mutants lose the 22 nt version of Athila6 siRNAs , including siRNA854 ( Figure 2D ) , demonstrating that the 21 nt and 22 nt versions of siRNA854 that accumulate in ddm1 mutants are generated by the activity of DCL4 and DCL2 , respectively . The production of 21 nt or 22 nt siRNA854 in either ddm1;dcl2 or ddm1;dcl4 double mutants suggests that the processing by DCL4 and DCL2 proteins occurs after RDR6 converts the Athila6 transcript into dsRNA . In addition , the proteins responsible for the biogenesis of the 24 nt version of siRNA854 and , separately , for the 21–22 nt version are identical to those responsible for generating the corresponding sizes of the flanking Athila6 siRNAs , further indicating that siRNA854 is not solely or specifically cleaved from this region . To determine which Argonaute protein ( s ) are responsible for siRNA854 accumulation , we tested ddm1 double mutants with ago1 , ago4 , ago5 , ago6 and ago10 . ddm1 double mutants with ago4 , ago5 , and ago6 did not result in loss of siRNA854 , and ago10 double mutants displayed only reduced accumulation ( Figure 2C ) . In the ddm1;ago1 double mutant both the 21 nt and 22 nt versions of siRNA854 fail to accumulate ( Figure 2E ) , demonstrating that AGO1 is essential for 21–22 nt siRNA854 accumulation . The requirement of RDR6 , DCL2 , DCL4 and AGO1 suggests that either the known VIGS pathway of post-transcriptional degradation of viral RNAs , or the tasiRNA pathway is responsible for Athila6 21–22 nt siRNA biogenesis . While generating the ddm1 double mutant plants , we encountered an unusual pattern of inheritance of Athila6 siRNAs , which stems from the atypical genetic inheritance of ddm1 mutants . For example , both the ddm1 mutant phenotype and Athila6 expression become more severe over increasing generations ( Figure 1C ) [36] . In addition , ddm1/+ heterozygote plants produced by crossing a plant homozygous for the ddm1-2 recessive allele to wt Col inherit epigenetically decondensed and transcriptionally uncontrolled chromatin from the ddm1 parent , which is not fully restored in the ddm1/+ heterozygote [40]–[42] . This mutant chromatin in a ddm1/+ heterozygous individual continues to express TEs [42] . In Figure 2F we demonstrate that a ddm1/+ heterozygote produced from a ddm1 homozygous parent ( Col x ddm1 in Figure 2F ) still produces 21–22 nt siRNA854 and Athila6 3′ siRNAs , although to a considerably lower level than the ddm1 homozygote . This is in contrast to a ddm1/+ heterozygote that was not the progeny of a homozygous parent , but has been backcrossed to wt Col for six generations while being maintained as a heterozygote . In this ddm1/+ heterozygote ( ddm1/+ in segregating family , Figure 2F ) the amount of mutant chromatin inherited from the ddm1 homozygous parent has been diluted away by segregation in each generation of crossing to wt Col , demonstrating that there is an effect of the parent's genotype on the production of Athila6 siRNAs in ddm1/+ heterozygous plants . The requirement of AGO1 for the production of 21–22 nt siRNA854 in ddm1/+ heterozygotes was demonstrated using an F2 family segregating for ago1 and ddm1 , which was produced from a ddm1 homozygous P1 individual ( Figure 2E ) . In ago1 mutants that are ddm1/+ heterozygotes ( ago1;ddm1/+ ) , neither 21 nor 22 nt versions of siRNA854 accumulate , while they do in the corresponding ago1/+;ddm1/+ double heterozygote siblings . These data demonstrate that AGO1 is necessary for siRNA854 accumulation in ddm1 mutants and in the progeny of ddm1 homozygotes . The 21 nt version of siRNA854 was previously predicted to target the 3′UTR of the UBP1b gene in four locations using modified criteria that allows for non-canonical or ‘wobble’ G:U base pairing [29] . G:U base pairing has been demonstrated to be tolerated in microRNA target sites , even within the critical first 7 base pairs ( bp ) or ‘seed’ pairing region [43] . However , the targeting of the 3′UTR by small RNAs was not previously shown directly , and complementarity of siRNA854 to the UBP1b 3′UTR relies heavily on non-canonical base pairing and lacks a strong 7 bp seed-pairing region ( shown in Figure S2 ) . To directly test if the 21 nt siRNA854 sequence has the ability to target the UBP1b 3′UTR , we took advantage of the fact that wt Col inflorescences do not accumulate 21–22 nt siRNA854 ( Table 1 , Figure 1 , Figure 2 ) . To directly examine the role of the siRNA854 sequence , we constructed plants constitutively expressing a GUS reporter gene with the GUS mRNA fused to the UBP1b 3′UTR and transformed these plants with artificial microRNA ( amiRNA ) constructs expressing the siRNA854 sequence , or an unrelated sequence as a control , from the Arabidopsis microRNA319a stem-loop transcript [44] . Quantitative assays to detect GUS activity , as well as qualitative plant staining , demonstrate that the plants with the control amiRNA have high levels of GUS activity , while plants expressing the siRNA854 sequence from a microRNA stem-loop display significantly lower levels of GUS activity ( Figure 3A ) . These data demonstrate that although the alignment of siRNA854 to the UBP1b 3′UTR lacks a strong seed pairing region and relies on G:U base pairing , the 21 and/or 22 nt siRNA854 sequence can target the UBP1b 3′UTR resulting in decreased reporter protein accumulation . The developmental expression profiles of UBP1b and the six Athila6 elements on the Affymetrix ATH1 gene expression microarray are negatively correlated , with UPB1b expressed highly in all wt tissues except mature pollen , specifically where Athila6 activation occurs ( Figure S3A ) . To determine if the increased levels of endogenous 21–22 nt siRNA854 observed when Athila6 is epigenetically activated can regulate the UBP1b 3′UTR , we transformed both wt Col and ddm1 plants with either the GUS-UBP1b 3′UTR transgene from Figure 3A , or a control transgene without the UBP1b 3′UTR . We assayed GUS activity in plants homozygous for the transgenes and found that in wt Col , the presence of the UBP1b 3′UTR did not affect GUS activity ( Figure 3B ) . In contrast , when this same analysis was previously published , the same GUS-UPB1b 3′UTR transgene in a wt Col plant resulted in little to no GUS protein production in leaves and inflorescences [29] . Our data , which demonstrate no inhibition of the UBP1b 3′UTR in wt Col leaves and inflorescences , are supported by the fact that there is no 21/22 nt siRNA854 detected in leaves or inflorescence by either Northern blot ( Figure 1 , Figure 2A ) or small RNA deep sequencing ( 1 read in a combined 6 . 6 million ) ( Table 1 ) . In ddm1 mutants , the GUS-UBP1b 3′UTR and GUS control ( no 3′UTR ) transgenes both display reduced GUS activity ( Figure 3B ) . It remains enigmatic why the constitutively expressed GUS transgene without a 3′UTR has reduced expression in ddm1 compared to wt Col . However , the presence of the UBP1b 3′UTR resulted in a significant reduction of GUS activity compared to the no 3′UTR control in ddm1 ( Figure 3B ) . To make sure that position effects of these transgene insertions were not the cause of this differential regulation , we crossed a wt Col plant with the UBP1b 3′UTR transgene that displayed high GUS activity to a ddm1 homozygote , as the resulting heterozygote will have siRNA854 accumulation ( Figure 2F ) . The GUS activity in this ddm1 heterozygote is significantly reduced compared to both the wt Col homozygous GUS transgene parent and to wt Col plants heterozygous for the same GUS transgene ( Figure 3C ) . Therefore , the regulation of the UBP1b 3′UTR is sensitive to the levels of siRNA854 , with either the production of this sequence as an amiRNA , or accumulation of this sequence as an siRNA in ddm1 resulting in repression of GUS activity . We determined that all of the transgenes in either wt Col or ddm1 from Figure 3B have GUS transcripts that accumulate to similar levels ( Figure 3D ) , indicating that the regulation of these transgene transcripts is not due to post-transcriptional degradation of the GUS RNA , but is likely rather due to the inhibition of translation of these mRNAs . In addition to the increased levels of siRNA854 in ddm1 mutants , siRNA854 also accumulates in wt Col pollen ( Table 1 ) . To determine if endogenous siRNA854 in pollen is able to regulate the UBP1b 3′UTR , we performed similar transgene reporter assays as above in wt Col pollen . We used a pollen vegetative cell promoter to drive GFP and added one of three different 3′UTRs to these reporter transgenes . GFP fluorescence was quantitatively measured by subtracting the fluorescence of segregating pollen grains that did not inherit the transgene from the fluorescence of pollen grains that did inherit the transgene . With no 3′UTR , transgene protein accumulates , and a moderate level of fluorescence is observed ( Figure 4 ) . When the wt UBP1b 3′UTR is added to this transgene , significantly less fluorescence is observed , likely due to the accumulation of siRNA854 in wt Col pollen . To test if the binding of siRNA854 was specifically responsible for this regulation , we generated a version of the UBP1b 3′UTR that lacks all four of the siRNA854 predicted target sites , resulting in a shorter 3′UTR ( shown in Figure S2 ) . This deleted 3′UTR transgene ( DEL transgene ) resulted in significantly increased fluorescence compared to the wt UBP1b 3′UTR ( Figure 4 ) . We also produced a UBP1b 3′UTR variation of the same length as the wt UBP1b 3′UTR , in which each of the perfectly complementary base pairs in all four of the siRNA854 predicted target sites have been switched to bases that do not show complementarity ( or G:U pairing ) with siRNA854 ( Figure S2 ) . Pollen grains with the base-modified 3′UTR ( MOD transgene ) on the GFP transcript display significantly increased fluorescence compared to the wt UBP1b 3′UTR ( Figure 4 ) , demonstrating that these bases are necessary for the regulation of the UBP1b 3′UTR . Pollen from both the MOD and DEL 3′UTR transgenes display fluorescence levels even higher than the control lacking a 3′UTR , likely due to the ability of the UBP1b 3′UTR , when not targeted by small RNAs , to stabilize transcripts or promote their translation . Lastly , we transformed the GFP transgene with and without the wt UBP1b 3′UTR into rdr6 mutants . We observed that the expression of Lat52:GFP ( no 3′UTR ) in rdr6 mutant pollen is higher than that of the same transgene in wt Col pollen ( Figure 4 ) . This difference is likely due to the role of RDR6 in post-transcriptional silencing of transgenes [45] . We speculate that the wt Col Lat52:GFP transgene is subject to a certain low amount of post-transcriptional regulation mediated by RDR6 . When this transgene is present in rdr6 mutant plants , this post-transcriptional regulation does not occur , resulting in higher expression of the transgene compared to wt Col . Interestingly , we did not observe a reduction in pollen fluorescence for the Lat52:GFP-UBP1b 3′UTR transgene in rdr6 compared to the no-3′UTR control transgene in rdr6 ( Figure 4 ) , demonstrating that RDR6 is necessary for the targeting of the UBP1b 3′UTR in pollen . The combined data from Figure 3 and Figure 4 demonstrate that the RDR6-dependent accumulation of siRNA854 and base pairing with the UBP1b 3′UTR target sites are required for the inhibitory regulation of UBP1b 3′UTR reporter genes . We aimed to determine if siRNA854 has a regulatory effect on the endogenous UBP1b gene or transcript . To aid our characterization of UBP1b we isolated two mutant upb1b alleles , which are in the Ws background . UBP1b insertion alleles result in a lack of polyadenylated transcripts , although un-spliced and non-polyadenylated transcripts are still produced ( Figure S4 ) . First , we wondered if the sequence similarity between the 21 , 22 or 24 nt versions of siRNA854 was directing DNA methylation to the UBP1b 3′UTR through the RdDM pathway , as a possible mechanism of epiallele production . We have determined that the DNA methylation status of the UBP1b 3′UTR is not altered in ddm1 inflorescences relative to wt Col ( Figure S5A ) . Next , we utilized microarray data and RT-PCR to analyze UPB1b transcript levels , and we found they accumulate to the highest levels in inflorescence tissue , intermediate levels in seedling and leaf tissues , and either to extremely low levels or not at all in wt Col pollen ( Figure S3 ) . We measured UBP1b transcript accumulation in ddm1 mutants at two developmental time points: inflorescence buds and mature pollen . In inflorescence tissue , the transcript accumulation of UPB1b is not significantly altered in ddm1 F2 or ddm1 F6 inflorescences ( Figure 5A ) . Therefore , qRT-PCR expression analysis and DNA methylation analysis both demonstrate that in inflorescence tissue there is no transcriptional or post-transcriptional effect of siRNA854 on UBP1b transcript accumulation . We continued to assay UBP1b in inflorescence tissue of wt Col and ddm1 supposing that the regulation may be on the translational level , as was observed for the inflorescences of the GUS-UBP1b 3′UTR transgene transcript in Figure 3 . We assayed two known microRNA-induced alterations to transcripts associated with translational regulation ( reviewed in [46] ) . We determined that in ddm1 inflorescence tissue the polyA tail length of UBP1b is unaffected , and uncapped transcript does not accumulate ( Figure S5B and S5C ) . Without the availability of a specific antibody to assay endogenous UBP1b protein accumulation , we can provide no direct evidence that endogenous UPB1b transcripts are regulated by the elevated siRNA854 levels that accumulate in ddm1 inflorescences . In contrast to inflorescence tissue , the transcript accumulation of UBP1b in pollen is regulated at the post-transcriptional level by siRNA854 . In wt Col pollen , the UBP1b transcript does not accumulate ( Figure S3 ) . To determine if this is a consequence of the increased levels of siRNA854 in pollen , or if the UBP1b promoter is simply not active in mature pollen , we performed qRT-PCR in rdr6 mutant plants as well as from plants heterozygous for ago1 . Plants homozygous for the recessive ago1-11 allele do not produce pollen , so we used an ago1-11/+ heterozygote that produces pollen segregating 1∶1 wt and mutant for ago1 . In both rdr6 pollen and ago1 segregating pollen there is a significant increase in UBP1b transcript accumulation , with rdr6 having a >16-fold increase ( Figure 5A ) . This demonstrates that the UBP1b promoter is active in pollen , but the transcripts are subject to post-transcriptional degradation . Attempts at identifying siRNA854-induced cleavage sites in the UPB1b 3′UTR from inflorescence and pollen were inconclusive ( data not shown ) , likely due to the high rate of non-small RNA-induced processing and degradation of the endogenous UBP1b 3′UTR detected in whole genome degradome analysis [47]–[48] . To determine if the UBP1b 3′UTR is specifically responsible for the differential UBP1b accumulation in inflorescence and pollen , we generated two transgenes with the native UBP1b promoter and coding region , with and without the 3′UTR . This transgene also has a 5′ FLAG epitope tag to distinguish it from the endogenous UBP1b . We found that the presence of the UBP1b 3′UTR in inflorescence tissue increases the transcript accumulation levels >11-fold , presumably by stabilizing this transcript ( Figure 5B ) . In wt Col pollen the UBP1b promoter is active , and without the 3′UTR this transcript accumulates to levels 4-fold less compared to inflorescence tissue . However , in contrast to inflorescence tissue , the addition of the UBP1b 3′UTR resulted in >73-fold reduced transcript accumulation in wt pollen . Together , these data demonstrate that in wt Col pollen the presence of the UBP1b 3′UTR causes a decrease in UBP1b transcript accumulation . The mature pollen grain contains two sperm cells with highly condensed chromatin imbedded into the larger vegetative cell , which displays a chromatin-decondensed vegetative nucleus . Communication between the vegetative cell and imbedded sperm cells has been previously hypothesized to occur ( reviewed in [49] ) . To determine in which cell post-transcriptional silencing in the pollen grain is taking place , we aimed to decipher where in the mature pollen grain the repression of the UBP1b 3′UTR is occurring . Since we demonstrated that both RDR6 and AGO1 are necessary for the repression of the endogenous UBP1b transcript in the mature pollen grain ( Figure 5A ) , we examined the localization of the RDR6 and AGO1 proteins by fusing them to GFP and expressing them from their native promoters . We found that both of these proteins localize to the nucleus and cytoplasm of the pollen vegetative cell and are not detectable in sperm cells ( Figure S6 ) , in agreement with mined microarray data from purified sperm cells [50] . The pollen vegetative cell is also the location of epigenetic TE reactivation [12] , suggesting that the activation of Athila6 , cleavage into siRNAs and potentially the repression of the UBP1b 3′UTR are all occurring in this cell . Since a functional AGO1 protein is required for the accumulation of siRNA854 ( Figure 2E ) , and UBP1b transcript levels increase in segregating mutant ago1 pollen ( Figure 5A ) , we aimed to determine if siRNA854 is incorporated into AGO1 protein complexes . We performed an immunoprecipitation ( IP ) of AGO1 complexes using a commercially available AGO1 antibody and purified the incorporated small RNA . To verify the specificity of the AGO1 antibody , we demonstrated that this antibody does not detect any other proteins by first performing a Western blot on protein extracts from Col , Ler , and ago1-11 inflorescences . We found that the AGO1 antibody yields no cross-reactive bands ( Figure S7A ) . Additionally , we used Western blot analysis to confirm the success of the IP by both detecting the presence of AGO1 protein in the IP Input extract and AGO1 IP samples , and observing the absence of AGO1 protein in the no antibody IP control ( Figure S7B ) . After the IP , we purified the AGO1-bound small RNAs and used qRT-PCR to assay levels of siRNA854 , a known AGO1-incorporated microRNA ( miR161 ) , a known AGO1-incorporated tasiRNA ( TAS3a-D8 ) , and two 24 nt heterochromatic siRNAs not present in AGO1 complexes ( siRNA02 and siRNA1003 ) [51]–[52] . We found no AGO1-IP enrichment of siRNA854 or the control siRNAs , siRNA02 and siRNA1003 , in the wt Col plant body , while we did detect enrichment of the control microRNA miR161 and control tasiRNA TAS3a-D8 ( Figure 5C ) . In contrast , in ddm1 F2 and F6 plants we found enrichment of siRNA854 in AGO1 . Analysis of the melting curves generated from the products of the qRT-PCR demonstrate that the background levels of siRNA854 from wt Col plants are the larger 24 nt version ( which have higher melting temperatures ) compared to the AGO1-enriched 21–22 nt siRNA854 from ddm1 plants ( lower melting temperatures ) ( Figure 5C ) . The level of enrichment of siRNA854 in AGO1 complexes in ddm1 mutants is not as high as miR161 , but is more similar to the level of enrichment of the tasiRNA TAS3a-D8 in wt Col ( Figure 5C ) , likely due the fact that both siRNA854 and TAS3a-D8 are single siRNAs from transcripts that generate multiple siRNAs through the activity of RDR6 and DCL4 . In addition , we detected no difference in enrichment levels between ddm1 F2 and ddm1 F6 plants . However , since there are higher levels of siRNA854 in the ddm1 F6 plants ( Figure 1D ) and input ( non-IP ) sample that was used for normalization , there are likely more AGO1 protein complexes interacting with siRNA854 in F6 ddm1 plants compared to the F2 generation . These data demonstrate that only when epigenetically activated , the Athila6-generated 21–22 nt siRNA854 is incorporated into AGO1 , and this complex is responsible for the regulation of the UBP1b transcript . TIA-1 has a known role in the sensing and response to cellular stress , and mutant cells unable to form stress granules display increased sensitivity to stress [53]–[55] . We have experimentally determined that the germination and growth of ubp1b mutant plants are also sensitive to both ionic ( +NaCl ) and osmotic ( +mannitol ) stress conditions compared to its corresponding wt background Ws , particularly when grown on 100 mM NaCl or 300 mM mannitol ( Figure 6A ) . In addition , wt Ws itself is more sensitive to these stresses than wt Col , as at higher NaCl or mannitol concentrations , wt Col survives but wt Ws does not . Since ddm1 plants have increased levels of siRNA854 ( Table 1 , Figure 1 ) , and siRNA854 can target the UBP1b 3′UTR ( Figure 3 , Figure 4 ) , we tested ddm1 seedlings to determine if they display a similar stress sensitivity as upb1b mutant plants . Plants that have been homozygous ddm1 for six generations ( ddm1 F6 ) are significantly more sensitive than the corresponding wt Col for both ionic and osmotic stress , while ddm1 F2 is only sensitive to ionic stress ( Figure 6A ) . Similar to ubp1b mutants , ddm1 mutant plants are sensitive to ionic and osmotic stress conditions . These data , combined with our demonstrated regulation of UBP1b levels by siRNA854 ( Figure 5 ) , suggest that the ddm1 stress sensitivity acts directly through epigenetic activation of Athila6 and production of siRNA854 , which results in the post-transcriptional and/or translational repression of UBP1b . The mammalian homolog of UBP1b is TIA-1 , an RNA binding protein localized to the nucleus that moves into the cytoplasm and aggregates into stress granules upon induction of stresses such as treatment with arsenite , glucose deprivation , and viral infection [56]–[58] . To visualize the sub-cellular localization of the UBP1b protein in response to cellular stress in Arabidopsis , as well as to determine the influence of ddm1 on this process , we expressed an siRNA854-resistant version of UBP1b ( without its native 3′UTR ) fused to GFP , under constitutive expression . In wt Col seedling roots this protein is localized to the nucleus , with distinct bright peri-nuclear foci observed ( Figure 6B ) . However , when transformed into ddm1 plants , this same transgene displayed cytosolic fluorescence ( Figure 6B ) . We aimed to induce stress in both the wt Col and ddm1 UBP1b-GFP lines; however , the ionic and osmotic conditions from Figure 6A inhibited growth in the ddm1 plants . Therefore , we experimentally determined that germination and growth in the dark ( etiolation ) would generate a UPB1b protein stress response , without killing the plant . We germinated and grew the wt Col UBP1b-GFP plants under etiolation conditions and observed a shift in the sub-cellular localization of UBP1b-GFP , as fluorescence accumulated around the periphery of the nucleus and in the cytoplasm ( Figure 6B ) . This change in sub-cellular distribution of UBP1b-GFP under a condition of stress ( etiolation ) is analogous to the movement of TIA-1 out of the nucleus under stress conditions [59] . In etiolated ddm1 plants , the UBP1b-GFP fluorescence pattern is the same as in non-stressed ddm1 plants ( data not shown ) . We quantified these fluorescence patterns by measuring the amount of nuclear vs . cytoplasmic fluorescence and found a statistically significant difference between the localization of UBP1b-GFP in unstressed wt Col compared to unstressed ddm1 ( Figure 6C ) . The unstressed ddm1 fluorescence pattern resembles the stressed wt Col roots ( Figure 6B and 6C ) . Additionally , in a very small number of ddm1 cells ( roughly 1/1000 ) , we observe a second accumulation pattern that displays distinct cytoplasmic foci reminiscent of mammalian stress granules ( red arrows , Figure 6B ) , as well as fluorescence at the periphery of the nucleus . Together , these data suggest that the translocation of the UBP1b protein in ddm1 mutant cells is a response to an intracellular stress as a result of the ddm1 mutation , perhaps due to altered gene expression in ddm1 mutants , or due to the loss of DNA methylation , loss of repressive histone modifications , and activation of TEs [4] , [60] .
SiRNA854 is a gene-regulating endogenous siRNA that is produced from the Athila6 family of LTR retrotransposons , and its accumulation is strictly dependent upon the transcriptional epigenetic activation of the Athila6 element . Athila elements , as well as nearly all other types of TEs in wt Col Arabidopsis , are normally transcriptionally silenced and are associated with DNA methylation and 24 nt siRNAs involved in maintaining the transcriptionally silenced state [7] , [13] . The 21–22 nt versions of siRNA854 are only produced upon Athila6 transcriptional activation , such as in ddm1 and met1 mutants , or in the vegetative cell of wt pollen . Like Athila6 activity itself , siRNA854 production displays two unusual epigenetic trans-generational inheritance patterns . First , siRNA854 is produced in a ddm1/+ heterozygote that was generated from a ddm1 homozygote , and the pathway of this accumulation remains dependent on AGO1 in the ddm1/+ heterozygote . Second , there is a positive correlation between the increasing levels of Athila6 mRNA and accumulation of Athila6 21–22 nt siRNAs ( including siRNA854 ) as ddm1 mutants are propagated from the F2 to the F6 generation . The progressively increasing transcript levels in ddm1 F2 to F6 generations could be due to increased rates of transcription , perhaps due to progressive loss of heterochromatin control , such as Athila DNA methylation , each generation . The positive correlation in Athila mRNA and siRNA levels suggests that the Athila6 transcript is the limiting factor in siRNA854 production , and that at least some Athila6 mRNA transcripts can accumulate although the 3′ region of Athila6 is degraded into siRNAs and amplified using a RNA-dependent RNA polymerase . This correlation could potentially be due to two different subsets of elements that increase in expression from the ddm1 F2 to F6 generation , one subset that is cleaved into siRNAs and one subset that is not . Alternatively , Athila6 transcripts may be converted into siRNAs at a very slow rate , allowing time for the transcripts to accumulate before degradation . Most Arabidopsis TEs lose siRNA production when epigenetically activated . There is an unknown factor that causes some TEs , such as Athila , to produce siRNAs even when transcriptionally active . In contrast to some TE families such as ATGP1 , which simply retains 24 nt siRNAs when activated , Athila is one of only very few element families identified to date that produces 21–22 nt siRNAs when epigenetically activated and expressed [12] , [15]–[16] . The production of 21–22 nt siRNAs represents a shift in small RNA biogenesis pathways from PolIV-dependent 24 nt siRNAs processed by the RdDM pathway , to a post-transcriptional silencing pathway that presumably uses PolII-derived transcripts and involves DCL2 , DCL4 , RDR6 and AGO1 , with at least siRNA854 eventually incorporated into AGO1 . These DCL , RDR and AGO proteins act in both the tasiRNA and VIGS pathways , and Athila processing shows hallmarks of both . For example , the VIGS pathway likely acts on Athila6 transcripts , as Athila has evolved from an LTR retrovirus , and , due to the conservation of the envelope protein coding domain , the Athila4 subfamily has even been classified as an Arabidopsis endogenous retrovirus [61] . Athila6 siRNAs may be produced via the VIGS pathway; however , siRNA854's ability to regulate UBP1b in trans is functionally more similar to the tasiRNA pathway . Therefore , we defer classifying Athila6 21–22 nt siRNAs as either tasiRNAs or VIGS siRNAs . The classification of Athila6 21–22 nt siRNAs as either tasiRNAs or VIGS siRNAs perhaps can be resolved once the initiation of their production is understood . We currently have three models for how the initiation of Athila6 21–22 nt siRNAs may occur . First , the secondary structure of the Athila6 transcript , particularly in the region of siRNA production , may fold back into hairpin-like structures , producing a substrate for DCL4 cleavage . Second , overlapping sense and antisense Athila6 transcripts may result in the formation of a dsRNA trigger , as Athila elements accumulate in nested arrays of elements near the centromere that favor the production of read-through transcripts ( reviewed in [62] ) . A pathway of natural antisense transcript siRNA ( nat-siRNA ) production exists in Arabidopsis; however , PolIV and RDR2 are required for this pathway [63]–[64] , and we have experimentally determined that these proteins are not required for Athila6 21–22 nt siRNA accumulation . A third proposed mechanism of 21–22 nt Athila6 siRNA initiation may be similar to tasiRNA initiation and the initiation of islands of 21–22 nt siRNA accumulation in maize and rice . MicroRNA ( s ) may initiate the cleavage of an Athila6 transcript , causing the production of RDR6- and DCL4-dependent siRNAs . In rice , the production of 21 nt phased siRNAs is dependent first on microRNA cleavage , and then on OsDCL4 for production , and these siRNAs preferentially accumulate in male reproductive organs [65] . One microRNA that shows potential seed region complementarity to Athila6 is the 22 nt microRNA845b; however , Athila siRNAs are produced on either side of the predicted Athila6 cleavage site , and our experiments to date provide no evidence that microRNA845b is required for Athila6 21–22 nt siRNA biogenesis ( data not shown ) . In addition to acting downstream of siRNA854 production , it is currently unknown if AGO1 acts upstream of DCL4 and RDR6 to initiate Athila6 transcript cleavage , but it is likely that a 22 nt siRNA initiates the RDR6-dependent amplification of Athila6 siRNAs [66] . If initiated by a tasiRNA-like mechanism , tasiRNA-like phasing should be detected in the Athila6 siRNA production . We have not detected any such phasing of Athila6 siRNAs ( data not shown ) , but this analysis is complicated by the 12 nearly identical Athila6 elements that carry siRNA854 , and dozens more Athila elements that are cleaved into siRNAs at the same time . If each element that produces 21–22 nt siRNAs is correctly phased , but not in the same register as each other , our analysis would detect no phasing . Therefore , although we have identified AGO1 , RDR6 and DCL4 as necessary for the accumulation of siRNA854 , the trigger for Athila6 siRNA initiation remains to be elucidated . We used the UBP1b 3′UTR in reporter assays to demonstrate that whenever we observe the accumulation of the 21–22 nt siRNA854 sequence ( in ddm1 mutants , wt pollen , or expressed as an amiRNA ) , we observe decreased reporter protein accumulation . Expression of the siRNA854 sequence as an amiRNA in the vegetative tissue of wt plants demonstrates that the potentially complicating secondary effects occurring from loss of heterochromatin control in ddm1 mutant plants and wt pollen are not responsible for repression of the UPB1b 3′UTR . Both the siRNA854-amiRNA and endogenous siRNA854 are able to inhibit protein production from reporter transcripts bearing the UBP1b 3′UTR , and in pollen this regulation is dependent on the siRNA854 target sites in the 3′UTR , as well as on RDR6 . We have also demonstrated that the endogenous UBP1b transcript is regulated by siRNA854 . In inflorescence tissue , this regulation is likely on the translational level , and this result is supported by the translational regulation of the GUS-UBP1b 3′UTR transcript in inflorescences . In contrast , in mature pollen we detect post-transcriptional regulation of the endogenous UBP1b transcript by siRNA854 . This pollen post-transcriptional regulation of the endogenous UPB1b transcript is under the control of RDR6 and AGO1 , suggesting that the accumulation of siRNA854 is necessary for this regulation . The basis of the switch from translational control in inflorescence tissue to post-transcriptional control in pollen remains puzzling . One possibility is that the four predicted target sites for siRNA854 in the UBP1b 3′UTR are not equally available to base pair in inflorescence tissue compared to pollen . Therefore , in pollen the interaction of siRNA854 with one target site may cause transcript cleavage , while in inflorescence tissue the interaction with a different target site may lead to translational inhibition . Lastly , the observation of 21–22 nt siRNA854 still present in ddm1 heterozygotes produced from ddm1 homozygous parents suggests that there may be a trans-generational epigenetic component to the regulation of UBP1b , as UBP1b may continue to respond to Athila activity even when the plant is no longer homozygous for ddm1 . This trans-generational regulation was observed with the GUS-UBP1b 3′UTR transgene in an individual heterozygous for the recessive ddm1-2 allele , due to the inheritance of mutant chromatin from the parental plant . Under the stress condition of etiolation , the UBP1b-GFP protein traffics from the nucleus and accumulates in the cytoplasm . In unstressed ddm1 mutants , this siRNA854-resistant form of UBP1b is also located in the cytoplasm , suggesting that some aspect of the ddm1 mutation triggers this stress-sensing change in protein location , independent of siRNA854 . It is currently unknown which characteristic ( s ) of the ddm1 mutation triggers this response , as ddm1 mutants display aberrant control of genic epialleles , global TE activation , TE mobilization , and general chromatin decondensation [4] , [35] , [60] , [67] . Conversely , ddm1 mutant seedlings show a phenotype similarly sensitive to ionic and osmotic stress as upb1b mutants . Several studies have shown that TEs are reactivated during stress conditions [68]–[69]; however , in this case TEs are regulating the stress-responsive pathway . Taken together , these data suggest that an antagonism exists between the UBP1b-induced stress response , which is activated in ddm1 mutants , and Athila6 , which inhibits this response by targeting UBP1b through siRNA854 . This antagonism may also exist in animal cells , as some DNA viruses generate microRNAs that specifically target the UBP1b homologue TIA-1 mRNA [70] , while other RNA viruses specifically target stress granule proteins for proteolysis [71] , presumably for the same reason that Athila targets UBP1b . Since TIA-1 is known to repress the activity of some animal viruses and retrotransposons through the formation of stress granules [72]–[73] , we speculate that the same is true for Athila . Therefore , we envision a three-layered host repression of Athila activity . First , transcriptional regulation dependent on DNA methylation epigenetically silences Athila . Second , when transcriptionally active , Athila mRNA accumulation is inhibited by the post-transcriptional regulation mediated by the tasiRNA/VIGS siRNA pathway components DCL2 , DCL4 , RDR6 and AGO1 . Third , we speculate that Athila transcripts may be translationally inhibited due to their sequestration in stress granules , targeted by the UBP1b protein . Transcripts in stress granules are not degraded , but are not translated due to their separation from active ribosome complexes ( reviewed in [54] ) . Akin to a virus encoding a suppressor of gene silencing [20] , Athila may encode siRNA854 to inhibit UBP1b protein formation and interfere with the function of this translational-level repression . AGO1 is known to mediate gene regulation via siRNAs in the tasiRNA pathway [74]–[75] . We have demonstrated that an siRNA which is not part of one of the four known tasiRNA producing loci ( TAS1-4 ) , but rather part of an epigenetically regulated TE , is able to act on genic transcripts in trans in a similar fashion to a tasiRNA . We think the key aspect of this regulation is the incorporation of siRNA854 into AGO1 . AGO1 is the main Argonaute protein responsible for gene regulation in Arabidopsis ( reviewed in [76] ) . This protein is likely unable to distinguish between an siRNA generated from a transcriptionally reactivated TE and one generated from a tasiRNA precursor transcript , at least in the case of siRNA854 . Sequencing from AGO1 immunoprecipitations has demonstrated a higher than expected level of siRNAs [52] , [77] , providing evidence that AGO1 is likely regulating both genic transcripts using microRNAs , as well as viral , TE or other repeat transcripts via siRNAs and post-transcriptional silencing . Further investigation is required to understand if and how AGO1 protein complexes determine which siRNAs should target genic mRNAs in trans and which should not . Therefore , the possibility currently exists that siRNA854 does not act alone , and the genome-wide regulation of many transcripts is altered by TE or viral siRNAs loaded into AGO1 . It is an intriguing possibility , since both TE epigenetic activation and viral infection lead to a series of still unexplained changes in gene regulation and phenotype . In fact , one longstanding enigmatic viral symptom of the Cucumber mosaic virus was recently found to be caused by a viral satellite siRNA targeting a host gene in trans [20] . In animals , many viruses encode microRNAs that target cellular genes to generate a favorable cellular environment [78] . In order to gain this same advantage , plant TEs may carry sequences that do not require a microRNA stem-loop structure , but utilize a different mechanism by co-opting the tasiRNA/VIGS siRNA biogenesis machinery to regulate a diverse set of cellular transcripts .
The mutant alleles used in this study are listed in Table S1 . All mutants are in the Col background except ago1 ( Ler ) , ago10 ( Ler ) , hen1-1 ( Ler ) , ubp1b ( FLAG_298B04 ) ( Ws ) , upb1b ( FLAG_071F09 ) ( Ws ) , and ddm1 Ler . Plants were grown under standard long-day growth chamber conditions . Etiolated and stress-test plants were stratified for 3 days at 4°C and grown for 11 days on 1/2X MS media+Gamborg's vitamins with supplemented sucrose in 16 hours of light per day , with the exception of etiolated seedlings , which were grown without light . For the stress-test analysis , the number of plants surviving after 11 days was counted . Fifty seedlings of each genotype for each condition were grown , and the analysis was replicated three or more times . Total RNA was extracted using TRIzol reagent ( Invitrogen ) or the RNeasy Plant Kit ( Qiagen ) . Total RNA was DNAseI treated and reverse transcribed using an oligo-dT primer and SuperScript III Reverse Transcriptase ( Invitrogen ) . qRT-PCR was performed with iQ SYBR Green Supermix ( BioRad Laboratories ) using 3 technical replicates each of 3 or more biological replicates . qRT-PCR primers are shown in Table S1 . qPCR reactions were annealed at 60°C unless otherwise noted . Since most standard qRT-PCR control genes are not highly expressed in pollen , the relative expression values for all experiments were calculated based on the expression of the experimentally validated control gene At1g08200 . qPCR was performed on a CFX96 thermocycler and the results analyzed on the CFX Manager Software package ( BioRad Laboratories ) . Relative expression was calculated using the ‘delta-delta method’ formula 2−[ΔCP sample−ΔCP control] , where 2 represents perfect PCR efficiency . Statistical significance was calculated using unpaired T-tests . Total RNA was extracted using TRIzol reagent ( Invitrogen ) , and small RNA was enriched by polyethylene glycol precipitation . The quantity of small RNA loaded in each lane ranged from 16–60 µg between blots , though the same amount of RNA was loaded per lane on each blot for comparison between samples . We accounted for the equal loading and sizes of small RNAs by re-probing our Northern blots with a known 21 nt microRNA ( miR161 ) and/or a known 24 nt siRNA ( siRNA02 ) . In addition , our small RNA Northern blot analysis is supported by independent small RNA deep sequencing data [33] . Gel electrophoresis , blotting and cross-linking were performed as in Pall et al . [79] . Probes for siRNA854 , miR161 , and siRNA02 were generated by 5′ labeling DNA oligonucleotides with P32-ATP , whereas the probe for Athila 3′ was generated by randomly degrading a P32-UTP labeled in vitro transcribed RNA as in [80] . Sequences of DNA oligonucleotides and primers for generating the in vitro transcription template are listed in Table S1 . The 35S:amiRNA-siRNA854 transgene was generated by cloning the sequence 5′GATGAGGATAGGGAGGAGGAG into the microRNA319a stem-loop transcript as in [44] . This transcript was sub-cloned into the 35S promoter binary plasmid pB2GW7 . The wt version of the UBP1b 3′UTR was amplified from the wt Col genome , and the 35S:GUS-UBP1b 3′UTR transgene was produced as in [29] . GUS staining was performed as in [81] . For GUS protein activity quantification , protein was quantified using the DC assay ( BioRad Laboratories ) , and 1 mg was used to assay the cleavage of MUG into fluorescent 4-MU as in [82]–[83] . Fluorescence was measured in 96-well format with a Tecan-SpectraFluor Plus microplate reader , and the specific activity of GUS in each sample was calculated as nmol of 4-MU formed per hour per mg of protein ( nmol 4-MU/h/mg ) . RT-PCR of these lines was performed with oligo-dT primed cDNA for 28 cycles of PCR using primers listed in Table S1 . The modified and deleted versions of the UBP1b 3′UTR were synthesized by IDT . The Lat52 promoter-driven GFP-UPB1b 3′UTR transgene was constructed by cloning the either the wt UBP1b 3′UTR , Modified UBP1b 3′UTR or Deleted 3′UTR version into the SacI site at the end of the mGFP coding sequence of the binary plasmid pMDC107 , and then by adding the Lat52 promoter to the KpnI site upstream of mGFP in these clones . GFP fluorescence quantification was performed on a Nikon C2 confocal microscope with the NIS-Elements software suite ( Nikon Corporation ) . GFP quantification was performed with the same microscope settings ( exposure time , laser power ) on the same day . Subtraction of the fluorescence of pollen grains that did not inherit the GFP transgene from the same hemizygous plant negated the background pollen auto-fluorescence . The FLAG-UBP1b transgene was constructed by adding the FLAG epitope sequence to the 5′ end of the UBP1b CDS as in [51] . This FLAG-UBP1b fragment was amplified and cloned into pENTR/D-TOPO ( Invitrogen ) . The UBP1b promoter and 5′UTR were inserted 5′ to the FLAG tag , and the UBP1b 3′UTR was inserted 3′ of the UBP1b coding region by In-Fusion Recombination ( Clontech ) . Subsequent constructs were recombined into pBGW by Gateway LR Recombination ( Invitrogen ) . Specific qRT-PCR primer sets detecting the FLAG-UBP1b transgene are shown in Table S1 . Attempts at identifying the FLAG-UBP1b protein using a FLAG-epitope antibody were repeatedly unsuccessful . The 35S:UBP1b-GFP transgene was generated by cloning the UBP1b coding region into the binary plasmid pK7FWG2 . Seedlings were grown on 1/2X MS media for 11 days before their roots were analyzed by confocal microscopy . The ratio of nuclear to cytosolic fluorescence was calculated by using the NIS-Elements software ( Nikon Corporation ) by manually defining the average fluorescence touching an analysis line transecting the nucleus and cytosol of an individual cell . The ratios of 25 cells were examined per condition . All Arabidopsis transformations were performed using Agrobacterium strain GV3101 and standard laboratory techniques . Statistical significance was calculated using unpaired T-tests . The AGO1 protein was immunoprecipitated as follows using a commercially available polyclonal AGO1 antibody ( Agrisera AB ) specific to the unique N-terminal peptide of AGO1 , which has been demonstrated to lack cross-reactivity with various over-expressed AGO proteins [84] . Inflorescence tissue was ground with liquid nitrogen and homogenized in 2 ml extraction buffer ( 100 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM EDTA , and 5 mM DTT ) containing 1 tablet/10 mL protease inhibitor cocktail ( Roche ) per gram of tissue . In a standard immunoprecipitation reaction , Arabidopsis protein extract was pre-cleared by incubation with 10 µl of goat anti-rabbit magnetic beads ( NEB ) . Pre-cleared extracts were then incubated overnight with goat anti-rabbit magnetic beads pre-incubated with 1 µg α-AGO1 . All washes were performed with extraction buffer . Immunoprecipitated , mock-immunoprecipitated and input sample RNA was isolated using TRIzol ( Invitrogen ) . 125 ng of each RNA sample was subjected to polyA tailing , cDNA synthesis , and qRT-PCR according to the QuantiMir product specifications ( System Biosciences Incorporated ) . The PCR was annealed at 61 . 5°C and performed on 2–3 biological replicate immunoprecipitations for each genotype tested , each one having 3 technical qPCR replicates . Each qRT-PCR IP C ( t ) value was normalized to the amplification of its own input sample , using the ‘delta-delta method’ formula 2−[ΔCP IP−ΔCP Input] , where 2 represents perfect PCR efficiency . | The portion of the genome that does not encode for genes is often overlooked as a source of cellular regulatory information . Here , we demonstrate that regulatory information controlling expression and protein production from a gene called UBP1b is coming from a distant non-gene transposable element ( TE ) . TEs are fragments of DNA that , unlike genes , are capable of duplicating themselves from one location in the genome to another , and occupy nearly half of the human genome . TEs are often referred to as “junk DNA , ” as the study of cellular regulation and function is focused on genes . The regulation of TEs is distinct from genes , as a process termed epigenetic silencing heritably represses TE expression and activity . We have demonstrated that the epigenetic status ( active versus silenced ) of the Athila TE family regulates the UBP1b gene through the activity of a TE small RNA . The function of the UPB1b gene is to respond to and regulate cellular stress , and the epigenetic regulatory status of the Athila TE therefore modulates this stress response . This demonstrates that the epigenetic regulation of TEs can be a source of gene regulatory information , influencing a basic cellular function such as the stress response . | [
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] | 2012 | Gene Expression and Stress Response Mediated by the Epigenetic Regulation of a Transposable Element Small RNA |
The Mitochondrial Carrier Family ( MCF ) is a signature group of integral membrane proteins that transport metabolites across the mitochondrial inner membrane in eukaryotes . MCF proteins are characterized by six transmembrane segments that assemble to form a highly-selective channel for metabolite transport . We discovered a novel MCF member , termed Legionella nucleotide carrier Protein ( LncP ) , encoded in the genome of Legionella pneumophila , the causative agent of Legionnaire's disease . LncP was secreted via the bacterial Dot/Icm type IV secretion system into macrophages and assembled in the mitochondrial inner membrane . In a yeast cellular system , LncP induced a dominant-negative phenotype that was rescued by deleting an endogenous ATP carrier . Substrate transport studies on purified LncP reconstituted in liposomes revealed that it catalyzes unidirectional transport and exchange of ATP transport across membranes , thereby supporting a role for LncP as an ATP transporter . A hidden Markov model revealed further MCF proteins in the intracellular pathogens , Legionella longbeachae and Neorickettsia sennetsu , thereby challenging the notion that MCF proteins exist exclusively in eukaryotic organisms .
Legionella pneumophila is an intracellular pathogen and the major causative agent of Legionnaire's disease , an acute form of pneumonia . The ability of the bacteria to replicate in environmental protozoa such as amoebae has equipped the bacteria with the capacity to replicate in human alveolar macrophages , leading to lung inflammation and disease [1] , [2] . Within macrophages and amoebae , the bacteria replicate within a membrane bound vacuole , block phagolysosome fusion and intercept vesicles trafficking in the secretory pathway [3] , [4] . Mitochondria are also transiently recruited to the L . pneumophila intracellular compartment [5] . The membrane of the mature Legionella-containing vacuole ( LCV ) shares many characteristics with membrane of the rough endoplasmic reticulum , reviewed in [6] , [7] but interactions with the endocytic pathway are also evident [8] . Therefore formation of the intracellular replicative niche of L . pneumophila results from extensive remodelling of the intracellular vacuole and multiple interactions with vesicle trafficking pathways within the host cell [8] , [9] . The formation of the LCV relies on a functional bacterial Dot/Icm Type IVB secretion system , which delivers at least 275 effectors into the host cell cytosol [10]–[13] . The effectors target multiple host cell functions including GTPase activity , ubiquitination , phosphoinositide metabolism , eukaryotic protein translation , autophagy and apoptosis , reviewed in [6] , [14]–[17] . Many groups of effectors have overlapping and somewhat redundant activities making the use of reverse bacterial genetics to identify gene function difficult . Instead , many investigators have applied cell biology and protein biochemistry techniques to understand the biochemical activity of Dot/Icm effectors and their possible role during LCV formation and L . pneumophila intracellular replication [18]–[21] . Genomics has revealed that a substantial number of Dot/Icm effectors share similarity with eukaryotic proteins [22] . For example , a large group of effectors contain multiple ankyrin repeat domains [23] and another group share similarity with F-box and U-box proteins involved in protein ubiquitination [24]–[26] . One effector termed LegS2 shares amino acid sequence similarity with eukaryotic sphingosine-1-phosphate lyases and is targeted to mitochondria during infection [27] , although the importance of this targeting to LegS2 function is unknown . In this study , we discovered that the genome of L . pneumophila strain 130b encodes a putative member of the Mitochondrial Carrier Family ( MCF ) , termed LncP for Legionella nucleotide carrier Protein . MCF proteins are a signature family of eukaryotic proteins that evolved in the course of endosymbiosis , ultimately giving rise to mitochondria [28] . MCF proteins are found in the broadest distribution of eukaryotes , including humans , yeast , plants and parasites such as trypanosomes and amoebae [29]–[32] . In humans , yeast and other eukaryotes , MCF proteins are synthesized in the cytoplasm and enter mitochondria via a defined “carrier pathway” . The proteins are chaperoned through the cytosol by Hsp70/Hsp90 and delivered to the Tom70 receptor on the mitochondrial surface [33] . After threading through the channel in the outer mitochondrial membrane , unfolded MCFs are bound by the TIM9:10 chaperone in the intermembrane space and then assembled into the mitochondrial inner membrane by the TIM22 complex ( reviewed in [34]–[37] ) . Here we found that LncP was translocated into host cells by the Dot/Icm type IV secretion system and transported into the mitochondrial inner membrane by the mitochondrial TIM9:10 chaperones and the TIM22 complex . A yeast model system and biochemical transport assays suggested that LncP mediated the unidirectional transport of ATP . In this otherwise exclusively eukaryotic group of proteins , LncP is the first example of a MCF member from bacteria that may contribute to the persistence of L . pneumophila within eukaryotic cells .
When the UniProt data set of protein sequences was screened with a hidden Markov model for mitochondrial carrier family ( MCF ) proteins , an expected number of MCF proteins were detected in mammals , plants and fungi [38]–[42] and a smaller number in protists such as Entamoeba histolytica [32] . Unexpectedly , a handful of protein sequences was also retrieved from bacteria . Two of these were encoded in the genome of the intracellular pathogen Neorickettsia sennetsu , the causative agent of Sennetsu fever [43] , [44] . Three other carriers were encoded in the genome of L . longbeachae ( Llo1924 , Llo3082 and Llo1358 ) , with Llo1924 having a homolog ( sequence identity of 57%; Figure 1A ) , encoded in the genome of the related pathogen L . pneumophila strain 130b ( open-reading frame LPW_31961 ) [45] , [46] . The putative MCF protein from L . pneumophila was subsequently termed LncP . The crystal structure of the prototypical MCF , the adenine nucleotide transporter from mammals , shows that the protein has six transmembrane segments that are embedded in the mitochondrial inner membrane [47] . Bioinformatic analysis indicated that the amino acid sequences of Llo1924 and LncP had six predicted transmembrane segments and a three-fold repeated signature motif ( Figure 1A ) which are the essential characteristics of members of the MCF ( Figure 1B ) [38]–[42] . MCF proteins differ to nucleotide carriers in the inner membranes of the Chlamydiales and the Rickettsiales , which represent different family of proteins , referred to as TLC ATP/ADP transporters ( PF03219 ) [48] , [49] . This latter group is of bacterial origin , and has spread from chlamydial ancestors to other classes of bacteria and to chloroplasts via lateral gene transfer events . TLC ATP/ADP transporters contain twelve transmembrane segments and their nucleotide exchange properties do not require membrane potential [50] . Many eukaryotic-type proteins from L . pneumophila are translocated into infected cells via the Dot/Icm type IV secretion system . To determine if LncP was a Dot/Icm effector , we generated a translational fusion of the calmodulin-dependent adenylate cyclase from Bordetella pertussis ( CyaA ) , with the N-terminus of LncP ( Cya-LncP ) . The Cya-LncP fusion construct was introduced into wild type L . pneumophila 130b or a dot/icm ( dotA ) mutant [51] . Upon infection of THP-1 macrophages , Cya-LncP translocation was detected by increased cyclic AMP ( cAMP ) production at levels similar to the positive control ( Cya-RalF ) ( Figure 2A ) . This translocation was dependent on dotA indicating that LncP is a Dot/Icm effector . Compared to eukaryotic MCF members , LncP carries a short amino acid extension at the C-terminus ( Figure 1A ) . As the secretion signal for many Dot/Icm effectors lies in the C-terminus of the protein [52] , [53] , we tested whether this region contained a Dot/Icm secretion signal , however deletion of the C-terminal amino acid residues PTRKR had no effect on Dot/Icm dependent translocation ( Figure 2A ) . To determine if LncP localized to mitochondria during infection of macrophages , we generated a 4HA epitope-tagged version of LncP for expression in L . pneumophila . The resulting expression plasmid , p4HA-LncP , was transformed into wild type L . pneumophila 130b and the dotA mutant . Upon infection of macrophages for 5 h with 130b carrying p4HA-LncP , anti-HA staining co-localized extensively with Mitotracker red in infected cells ( Figure 2B ) . Anti-HA staining was not observed in macrophages infected with L . pneumophila 130b carrying the empty vector , pICC562 , or in macrophages infected with the dotA mutant carrying p4HA-LncP ( Figure 2B ) . Similar results were observed upon L . pneumophila infection of HeLa cells ( Figure S1 ) . We detected increasing amounts of LncP associated with mitochondria over time ( Figure 2C ) and at earlier time points , we frequently observed LncP staining at the poles of the bacterial cell where the Dot/Icm secretion system is believed to be located ( Figure 2C ) . Altogether , this demonstrated that LncP was localized to mitochondria during L . pneumophila infection and this event relied upon a functional dot/icm system . Many genes encoding Dot/Icm effectors are dispensable for intracellular replication due to functional redundancy [9] , reviewed in [6] . Likewise here , the gene encoding LncP was not required for L . pneumophila 130b intracellular replication in THP-1 macrophages ( Figure 3A ) or in the model amoeba , Acanthamoeba castellanii ( Figure 3B ) . However , PCR screening of 37 distinct L . pneumophila isolates detected the gene encoding LncP in 28 of these strains ( Table S1 ) . The high carriage rate ( ∼75% ) of the lncP gene among L . pneumophila strains strongly suggests LncP provides a competitive advantage during interactions with host cells . Fluorescence microscopy confirmed that GFP-LncP was targeted to mitochondria when expressed ectopically in HeLa cells ( Figure 4A ) . This substantiates a model whereby cytosolic LncP can access the mitochondrial import machinery in mammalian cells . To test whether LncP was imported by mitochondria , the putative MCF protein was translated in vitro and incubated with mitochondria isolated from yeast . This represents the best experimental system to characterize the pathway by which LncP is imported into mitochondria . LncP was imported into mitochondria and protected from Proteinase K treatment showing that it is not imported into the mitochondrial outer membrane ( Figure 4B ) . Import of mitochondrial carrier proteins is reliant on a membrane potential across the inner membrane . Here pretreatment of mitochondria with CCCP , that dissipates the transmembrane potential ( Δψm ) , also inhibited LncP import ( Figure 4B , “-Δψ” ) . Imported LncP behaved as an integral inner membrane protein similar to Tim23 , being largely resistant to alkali extraction , unlike the non-membrane embedded , matrix targeted protein , F1β ( Figure 4C ) . The TIM9:10 chaperone characteristically binds carrier proteins during the initial phase of their assembly in the inner mitochondrial membrane . Blue-native ( BN ) -PAGE analysis of imported phosphate carrier PiC ( Figure 4D ) and Aac1 ( data not shown ) showed intermediate forms of the carrier during its import pathway and final assembly as a mature dimer complex . Folded PiC mostly existed as the dimeric ( Stage V ) form with only a small amount of folded monomer detected . LncP was also assembled in mitochondria efficiently but much of the folded protein accumulated as monomeric protein , possibly because there was no pre-existing LncP in mitochondria with which imported LncP could oligomerise . The folding of carrier proteins is dependent on the TIM9:10 chaperone [54]–[56] . Mitochondria from a tim10 “shut-down” strain were not able to assemble LncP or PiC into complexes detectable by BN-PAGE ( Figure 4D ) . Consistent with this finding , mitochondria from a tim10 “shut-down” strain , imported both PiC and LncP to a protease protected location at a greatly reduced efficiency ( Figure 4E ) . When ImageQuant was used to compare the band intensities in lanes from wild-type and tim10 mutant mitochondria , the percentage decrease of import for both PiC and LncP was between 20% and 35% of wild-type ( data not shown ) . In order to show the localization of mitochondrial proteins unambiguously it is possible to sequentially rupture the mitochondrial outer membrane ( mitoplasting ) or both membranes and test for sensitivity to protease digestion . Since these protease treatments are sensitive to rough handling , the digestion was performed in duplicate . LncP was degraded by Proteinase K after rupture of the outer membrane ( Figure 4G ) . This characteristic is consistent with that of Tim23 , an integral inner membrane protein with domains exposed to the intermembrane space . The matrix targeted protein , F1β was not degraded by Proteinase K unless the inner membrane was also ruptured by the addition of detergent ( Figure 4G ) . Slight changes in band intensity from lane to lane were not significant upon repetition , rather protease treatment drastically altered the levels of susceptible proteins such as after mitoplasting or treatment with detergent ( Figure 4G ) . Saccharomyces cerevisiae encodes 35 mitochondrial carrier proteins , including four proteins that can transport ATP: Aac1 , Aac2 , Aac3 and Sal1 [57] ( Figure 5A ) . Yeast is a powerful model system to study cellular phenotypes , and fluorescence microscopy showed that ectopically expressed LncP is targeted to mitochondria in yeast ( Figure 5B ) . Mutant yeast strains , each lacking one of these 35 carriers were transformed with a plasmid-based LncP expression construct and the transformed cells tested for growth complementation . The mutants were scored under conditions where characteristic growth defects were known . However no complementation was observed upon LncP expression in any of the mutants tested . For example , Δagc1 mutant cells lacking the amino acid transporter Agc1 form only microcolonies on rich medium with glycerol as a carbon source; expression of LncP did not complement this growth defect ( Figure 5C ) . However , we noted a dominant-negative phenotype from expression of LncP in wild-type cells which represented a 5-fold loss in viability on rich growth medium , exacerbated to ∼500-fold loss of viability on minimal medium ( Figure 5D ) . We therefore screened the carrier mutant collection for mutants resistant to this LncP-induced inhibition of cell viability . Only the Δaac1 mutant was resistant to the dominant-negative effect of LncP expression ( Figure 5E ) . In yeast , Sal1 is a Ca2+-dependent ATP-import carrier that co-transports ATP and Mg2+ into the matrix during growth on glucose [58] , [59] , and the Aac1 transporter balances this effect by ATP export . The most likely explanation for the Aac1-dependent dominant-negative effect of LncP expression is that combined export of ATP from the matrix by LncP and Aac1 leads to a growth defect . Thus , yeast can tolerate the expression of Aac1 or LncP , but not both of these carrier proteins . In order to measure directly substrate transport catalyzed by LncP , purified recombinant protein was reconstituted into liposomes . LncP transported nucleotides , phosphate and pyrophosphate , with a strong preference for ATP and GTP ( Figure 6A ) . The kinetic constants of purified reconstituted LncP were determined by measuring the initial transport rate at various external [3H]ATP or [3H]GTP concentrations in the presence of a fixed saturating internal concentration of ATP or GTP , respectively . The transport affinities ( Km ) of LncP for ATP and GTP were 190±37 and 183±32 µM , respectively . The average Vmax values for ATP and GTP were 926±216 and 688±213 µmol/min x g of protein , respectively ( mean values of 4 experiments ) . Powerful inhibitors of the well-characterized ADP/ATP carrier , which transports only ADP and ATP , fix the transporter in a specific state: atractylosides ( such as carboxyatractyloside; CAT ) fixes the transporter in the “cytosolic” c-state thereby inducing swelling of mitochondria and apoptosis , and bongkrekic acid ( BKA ) fixes the transporter in the “matrix” m-state thereby suppressing induction of apoptosis [60] . LncP was not inhibited by CAT or BKA ( Figure 6B ) . It was also not inhibited by the SH alkylating reagent N-ethylmaleimide ( NEM; inhibitor of the phosphate , glutamate and ornithine carriers ) . In contrast , ATP transport catalyzed by LncP was effectively prevented by other reagents such as mersalyl ( MER ) , p-hydroxymercurybenzoate ( p-HMB ) and HgCl2 , which are organic mercurials , and by pyridoxal-5′-phosphate ( PLP ) and bathophenanthroline ( BAT ) , which alone or in combination inhibit the activity of several mitochondrial carriers , although their mechanism of action is not known . Therefore , both the substrate specificity ( Figure 6A ) and the inhibitor sensitivity ( Figure 6B ) of LncP distinguish it biochemically from the ADP/ATP carrier . To characterize further the transport properties of LncP , the kinetics of [3H]ATP and [3H]GTP uptake into proteoliposomes were compared either as uniport ( in the absence of internal substrate ) or as exchange ( in the presence of internal ATP or GTP , respectively ) ( Figure 6C ) . Both the exchange and the uniport reactions of ATP and GTP uptake followed first-order kinetics , isotopic equilibrium being approached exponentially . The ratio of maximal substrate uptake by both reactions was 9 . 8 for ATP and 13 . 0 for GTP , in good agreement with the expected ratio of 10 from the intraliposomal concentrations at equilibrium ( 1 mM and 10 mM for uniport and exchange , respectively ) . The uniport mode of transport of reconstituted LncP was also investigated by measuring the efflux of [3H]ATP from pre-labeled proteoliposomes ( Figure 6D ) because this approach provides a more sensitive assay for unidirectional transport [61] . A significant efflux of ATP was observed in the absence of external substrate ( filled circle ) and a more rapid and extensive efflux occurred upon addition of ATP ( open square ) or phosphate ( open triangle ) . Moreover , the ATP-induced efflux of radioactivity was prevented by the presence of the carrier inhibitors PLP and BAT ( filled square ) . Similar results were obtained using GTP as substrate ( data not shown ) . Thus , LncP was able to catalyze unidirectional transport of ATP and GTP and a fast exchange reaction of substrates .
Recently , 275 effectors of the Dot/Icm secretion system were described in the Philadelphia-1 strain of L . pneumophila [13] . This represents almost 10% of all open reading frames encoded in the L . pneumophila genome . Given that there is also diversity in the presence and range of effector genes among the different sequenced L . pneumophila genomes and even greater differences between Legionella species [22] , the total Dot/Icm effector repertoire is likely to be much larger . Here we describe a new Dot/Icm effector from L . pneumophila , LncP , with sequence and functional similarity to eukaryotic mitochondrial carrier proteins . LncP was predicted to have six transmembrane domains , similar to eukaryotic MCF members . Remarkably , this highly hydrophobic protein crosses five biological membranes to reach its final destination in the mitochondrial inner membrane . Generally bacterial membrane proteins are assembled into the cytoplasmic membrane by YidC and SecYEG [62] , reviewed in [63] . Chaperones for the Dot/Icm machinery , such as IcmS , IcmW and LvgA [64]–[66] , must be in active competition with the bacterial YidC/SecYEG machinery to dictate which integral membrane proteins will be assembled into the bacterial inner membrane and which will be evacuated via the Dot/Icm T4SS . Therefore recognition of LncP by the Dot/Icm machinery presumably allows this hydrophobic protein to avoid assembly into the bacterial inner membrane by YidC/SecYEG ( Figure 7 ) . The mechanism by which this recognition occurs is unknown but probably involves detection of a C-terminal Dot/Icm secretion signal . Here we removed the C-terminal amino acids PTRKR from LncP but found that this had no effect on LncP translocation . Bioinformatic analysis of known Dot/Icm substrates has revealed a preference for short acidic or negatively charged amino acids in the C-terminal secretion signal [26] , [53] . Recently , a glutamate rich region ( E Block ) was associated with the translocation signal of many Dot/Icm effectors [67] . The E Block motif was located in the C-terminal 30 amino acids of the effectors . LncP also contains a putative E Block motif in the C-terminus that may contain the signal for translocation ( Figure 1A ) . However , the motif is predicted to lie within the most distal transmembrane domain of the carrier protein and likely contributes to correct protein folding and function . Hence , further investigation of the LncP secretion signal will require careful mutational analysis by amino acid substitution rather than deletion to dissect the bona fide secretion signal from the transmembrane domain . The mechanism by which hydrophobic membrane proteins such as LncP can be accommodated in the translocase channel and assisted on the host cytoplasmic side of the Legionella-containing vacuole membrane without aggregating is unknown . When we analyzed the Dot/Icm effector repertoire of L . pneumophila 130b using two independent hidden Markov model approaches , HMMtop [68] and TMHMM v 2 . 0 [69] , 71 effectors were predicted by both methods to have one or more transmembrane segments ( Table S2 ) . Thus the Dot/Icm T4SS has evolved to handle the export of proteins with significant hydrophobicity across at least three biological membranes . Currently , mitochondrial localization of only one other Dot/Icm effector , LegS2 , has been reported , although the precise mitochondrial compartment was not described . LegS2 has sphingosine-1-phosphate lyase activity and it is not yet clear if mitochondrial targeting plays any role in effector function [27] . Here we found that LncP was also targeted to mitochondria during infection of eukaryotic cells with L . pneumophila and assembled into the mitochondrial inner membrane , where the effector appeared to act as a unidirectional nucleotide transporter . Mitochondrial import required the TIM9:10 chaperones and hence the TIM22 machinery , according to classical mitochondrial protein transport mechanisms . In the yeast model system , expression of LncP led to a dominant-negative phenotype . Although not lethal , the expression of LncP greatly slowed growth , particularly growth on minimal media . This dominant-negative phenotype depended on the activity of the endogenous MCF protein , Aac1 . Whereas the yeast MCFs Aac2 and Aac3 are classic ADP/ATP carriers that regenerate cytoplasmic ATP levels ( because ATP export can only be achieved with a concomitant import of ADP ) , a distinguishing feature of Aac1 is its propensity to export ATP from the mitochondrial matrix [57] . Thus , the dominant-negative effect seen in yeast is likely a cellular consequence of an imbalance of ADP/ATP transport across the mitochondrial inner membrane . We also observed ATP transport activity for LncP in reconstituted liposomes . The kinetic parameters of ATP transport by LncP were comparable to those of genuine ATP carriers . There are two classes of transporters for ATP in the mitochondrial inner membrane: the carboxyatractyloside-inhibitable ADP/ATP carriers ( Aac ) and the ATP-Mg/Pi carriers ( in humans named APC and in yeast Sal1 ) . Studies in which the Vmax of Aac has been measured in reconstituted liposomes ( either as ATP/ATP or ADP/ADP exchange ) using protein purified from mitochondria or after heterologous expression , obtained Vmax values ranging from 360 and 1300 mmol/min/g protein [70]–[74] . Here we measured the Vmax of ATP transport in LncP-reconstituted liposomes ( measured as ATP/ATP exchange ) as 926 mmol/min/g protein . This means that the ratio between the activity of LncP and the activity of genuine ADP/ATP carriers varied from 2 . 6 to 0 . 7 . The Km for ATP of genuine mitochondrial ADP/ATP carriers , measured in reconstituted liposomes , ranges between 9 and 120 µM , lower than the Km of LncP for ATP ( 190 µM ) . However , the internal concentration of ATP in respiring mitochondria is sufficiently high to saturate both LncP and Aac . To date there is no other data available about the kinetic parameters of carboxyatractyloside-sensitive ADP/ATP carriers either purified from mitochondria or after heterologous expression . For the ATP-Mg/Pi carrier , only the human orthologs encoded by the SLC25A23 and SLC25A24 genes have been reconstituted into liposomes [75] . The Vmax values of the ATP-Mg/Pi carriers ( measured as ATP/ATP exchange ) ranged from 65 to 523 mmol/min/g protein , lower than the Vmax of LncP . The Km values of human ATP-Mg/Pi carriers for ATP ( 0 . 3 mM ) are 1 . 5-fold higher than the Km of LncP for ATP . In conclusion , the ATP transport activity of reconstituted LncP is at least as high as that of the known mitochondrial ATP transporters and is therefore compatible with the conclusion that LncP catalyzes ATP efflux from the mitochondria of infected cells . Our reconstitution studies suggested LncP could evacuate ATP from the membrane lumen ( matrix ) by either uniport or an exchange reaction with substrates ( e . g . , phosphate ) . It is not yet clear how this assists L . pneumophila infection , however the high carriage of lncP in strains of L . pneumophila and L . longbeachae suggests that control over mitochondrial adenine nucleotide levels favours Legionella replication and survival . While fundamental studies show that elevated levels of cytosolic ATP primes cells to respond to apoptosis-inducing stimuli [76] , [77] , our preliminary experiments indicated that over-expression of LncP alone was insufficient to change the rate or extent of HeLa cell death induced by the exogenous trigger , staurosporine ( Figure S2 ) . Thus the contribution of LncP activity to L . pneumophila intracellular replication and persistence remains to be determined . L . longbeachae and L . pneumophila share only some aspects of their life-cycle , and genome sequence analysis suggests that while these bacteria have a highly conserved Dot/Icm T4SS , they secrete quite different pools of effectors [46] . Despite this , L . longbeachae also harbors a strong homolog of LncP and two other putative MCF proteins . Further prokaryotic MCF sequences were found in another intracellular macrophage pathogen , N . sennetsu , which causes an infectious mononucleosis-like disease called sennetsu ehrlichiosis [44] . The presence of these exclusively eukaryotic proteins in bacteria is curious and suggests that the genes encoding the MCF proteins were acquired at some stage by lateral gene transfer from a eukaryotic host . MCF proteins are found in almost all species of eukaryotes [78] , including protists that support the growth of L . pneumophila . Based on previous analysis and our own HMM search we found MCF proteins in all of Acanthamoeba ( unpublished ) , Dictyostelium discoideum [31] , [79] and Naegleria gruberi [80] . The association of bacterial MCF proteins with intracellular pathogens suggests the proteins could play similar roles in the pathogenesis of all these organisms . Further work on the biochemical function of the bacterial MCF members will aid our understanding of how bacteria modulate mitochondrial function during infection .
The methodology for hidden Markov model analysis has been described previously [81] . A hidden Markov model tailored from 34 manually compiled mitochondrial carrier protein sequences was built and used to scan UniProt ( Release 12 . 4 , containing Swiss-Prot Release 54 . 4 and TrEMBL Release 37 . 4 ) . The program HMMER 2 . 3 . 2 was used in all calculations [82] , and the search results were extracted with programs prepared in-house . Homology modeling of the mitochondria carrier protein was performed with SwissModel [83] using the structure of bovine ANT ( PDB ID 2C3E ) as the template [47] . Sequences were aligned using ClustalX [84] and further edited in BioEdit ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) . L . pneumophila strain 130b and derivatives were grown on buffered charcoal-yeast extract ( BCYE ) agar or in ACES [N- ( 2-acetamido ) -2-aminoethanesulfonic acid]-buffered yeast extract broth at 37°C . E . coli strains were cultured aerobically in Luria broth ( LB ) or on LB agar . When required , antibiotics were used at the following final concentrations: ampicillin at 100 µg/ml; kanamycin at 100 µg/ml for E . coli , at 25 µg/ml for L . pneumophila; chloramphenicol at 12 . 5 µg/ml for E . coli , at 6 µg/ml for L . pneumophila . Saccharomyces cerevisiae strain W303a was grown in rich medium or selective medium as previously described [85] . For ectopic expression of LncP in yeast , the complete lncP open reading frame was amplified by PCR from L . pneumophila 130b genomic DNA and cloned into p425MET25 and p416MET25 for complementation or GFP-LncP localization respectively . The individual carrier deletionmutants ( in a BY4741 background ) were purchased from Open Biosystems . For the preparation of mitochondria yeast cultures were grown in rich medium containing lactate as a carbon source ( YPlac media ) at 25°C . Mitochondria were isolated by differential centrifugation as described previously [85] , [86] . For the growth assays the cells were grown to a mid-logarithmic phase in a complete medium , diluted to OD600 = 0 . 2 , spotted in a series of five-fold dilutions on the plates and incubated at 30°C for 3–6 days . For the isolation of wildtype and Tim10 depleted mitochondria the Saccharomyces cerevisiae strains W303 , PMET3Tim10 [87] were grown in synthetic glucose media [0 . 67% ( w/v ) yeast nitrogen , 2% ( w/v ) glucose , 0 . 01% ( w/v ) leucine , tryptophan , uracil , adenine and histidine at 30°C for 10 hours as a pre-culture . The pre-culture was diluted to A600 nm = 0 . 2units/mL in media supplemented with 0 . 2 mM methionine then grown for 2 days to reach A600 nm = 1 . 0 before harvesting and mitochondrial isolation by previously described methods [86] . DNA encoding LncP was amplified and cloned into pSP73 ( Promega ) from genomic DNA isolated Legionella pneumophila ( strain 130b ) . The oligonucleotides LncP-FW BamHI ( GCGCGGATCCATGAAAGACAAAACAATA ) , and LncP-REV XhoI ( GATCCTCGAGCTACCTGTTCCTTGTTGG ) were used to amplify full length LncP DNA In vitro transcription was carried out as previously described [88] . Rabbit reticulocyte lysate was purchased from Promega and in vitro translation reactions were carried out for 30–60 minutes in the presence of [35S]- methionine/cysteine ( MP Biomedicals ) [88] . [35S]-Methionine/cysteine-labeled LncP or PiC were synthesized in vitro and were incubated with mitochondria ( 50 µg per lane ) for the indicated time periods at 25°C in import buffer ( 0 . 6 M sorbitol , 50 mM Hepes ( pH 7 . 4 ) , 2 mM KPi ( pH 7 . 4 ) , 25 mM KCl , 10 mM MgCl2 , 0 . 5 mM EDTA , 1 mM dithiothreitol , 4 mM ATP , and 2 mM NADH ) . Samples were treated with Proteinase K ( 40 µg/mL ) in import buffer for 15 minutes on ice to remove un-imported material before addition of protease inhibitor ( 1 mM PMSF ) . The mitochondria were re-isolated by centrifugation at 10 , 000 g and this was followed by either protein separation under denaturing gel electrophoresis ( SDS ) or protein complexes separated by Blue Native electrophoresis [89] . For Proteinase K shaving or mitochondrial membrane potential dissipation conditions , mitochondria were treated with 40 µg/ml Proteinase K or AVO mix ( 8 µM antimycin A , 1 µM valinomycin , and 20 µM oligomycin ) respectively . For preparation of mitoplasts ( mitochondria with ruptured outer mitochondrial membrane ) , post-import mitochondria were subjected to osmotic shock by resuspension in 20 mM Hepes/KOH , pH 7 . 4 with and without Proteinase K where indicated . After the completion of in vitro import reactions for 8 minutes at 25°C , mitochondria were re-isolated by centrifugation at 10 , 000 g and resuspended in 200 µL of 100 mM sodium carbonate which was adjusted to pH 11 . 5 and left on ice for 30 minutes with gentle mixing every 5 to 10 minutes . A membrane pellet was then separated from a supernatant by ultra-centrifugation at 100 , 000g for 30 minutes at 4°C . The pellet was resuspended in 200 µL of 100 mM sodium carbonate and both the pellet and supernatant were then subjected to Trichloroacetic acid precipitation . Each experiment was conducted in duplicate with one set of pellet and supernatant samples recombined to make the “total” sample . This was repeated 5 times in order to assess statistical significance . LncP protein level was quantified by densitometry of phosphorimages using the Image Quant software . Expression of recombinant LncP is detailed in the supporting methods ( Protocol S1 ) . The recombinant , purified LncP was reconstituted into liposomes by cyclic removal of the detergent with a hydrophobic column of Amberlite beads ( Fluka ) [61] . The composition of the initial mixture used for reconstitution was 35 µl of purified LncP ( 15 µg of protein ) , 70 µl of 10% Triton X-114 , 100 µl of 10% phospholipids in the form of sonicated liposomes , 10 mM ATP ( except where otherwise indicated ) , 10 mM PIPES ( pH 7 . 0 ) , 0 . 42 mg of the mitochondrial lipid cardiolipin ( Sigma ) and water to a final volume of 700 µl . After vortexing , this mixture was recycled 13 times through the Amberlite column ( 3 . 5×0 . 5 cm ) pre-equilibrated with a buffer containing 10 mM PIPES pH 7 . 0 . All steps were performed at 4°C , except for the passages through Amberlite , which were carried out at room temperature . External substrate was removed from proteoliposomes on Sephadex G-75 columns pre-equilibrated with 50 mM NaCl and 10 mM PIPES at pH 7 . 0 ( buffer A ) and 4°C . The eluted proteoliposomes were distributed in reaction vessels and used for transport measurements by the inhibitor-stop method [61] . Transport at 25°C was started by adding [3H]ATP ( Perkin Elmer ) or [3H]GTP ( American Radiolabeled Chemicals ) to proteoliposomes and terminated by addition of 20 mM pyridoxal-5′-phosphate and 20 mM bathophenanthroline . In controls , the inhibitors were added at the beginning together with the radioactive substrate . Finally , the external radioactivity was removed from each sample of proteoliposomes by a Sephadex G-75 column; the proteoliposomes were eluted with buffer A and their radioactivity was measured . The experimental values were corrected by subtracting control values . The initial transport rate was calculated from the radioactivity taken up by proteoliposomes after 2 min ( in the initial linear range of substrate uptake ) . For efflux measurements , proteoliposomes containing 2 mM ATP or GTP were labeled with 10 µM [3H]ATP or [3H]GTP , respectively , by carrier-mediated exchange equilibration [61] . After 50 min , the external radioactivity was removed by passing the proteoliposomes through Sephadex G-75 pre-equilibrated with buffer A . Efflux was started by adding unlabeled external substrate or buffer A alone to aliquots of proteoliposomes and terminated by adding the inhibitors indicated above . An insertional mutation in LncP was created via homologous recombination . A ∼1 kb fragment encompassing LncP was amplified by PCR from L . pneumophila 130b genomic DNA using the oligonucleotide primers , 5′- caacggatcctatttcatttgtagtcccttg -3′ and 5′- tcctgtcgacctgaaatattttcatggaaac -3′ . The resulting product was cloned into the BamHI and SalI sites of pPCRScript and a kanamycin resistance gene from Tn5 was introduced into the native PstI site of LncP . The construct was introduced into L . pneumophila 130b via natural transformation , as described previously [90] . Kanamycin resistant clones were assessed by PCR analysis and ampicillin sensitivity to detect replacement of lncP with lncP::km and the loss of pCR-Script . Two independent L . pneumophila lncP::km clones , LncP-3 and LncP-4 , were chosen for further analysis in host cell replication assays . The human monocytic cell line , THP-1 was maintained in RPMI 1640 supplemented with 10% fetal bovine serum in 5% CO2 at 37°C . The cells were prepared for infection with stationary-phase L . pneumophila as previously described [91] . THP-1 cells were infected at a multiplicity of infection ( MOI ) of 5 cells for 2 h in 5% CO2 at 37°C . Cells were then treated with 100 µg/ml gentamicin for 1 h to kill extracellular bacteria and washed with PBS before being lysed with 0 . 01% digitonin . Serial dilutions of the inoculum and bacteria recovered from lysed cells were plated on BCYE agar and results were expressed as the percentage of the inoculum that resisted killing by gentamicin ( mean ± standard deviation of at least 3 independent experiments ) . Immortalized macrophages from wild type C57BL/6 mice [92] were seeded at 2×105 per coverslip 16 h prior infection . The B6 macrophages were a gift from Dr Ashley Mansell ( Monash Institute of Medical Research ) . Cells were maintained in DMEM supplemented with 10% FCS , 2 mM glutamine , 100 U penicillin/ml and 100 µg streptomycin/ml . Immediately prior to infection , macrophages were washed and the medium replaced with DMEM supplemented with 1 mM IPTG and lacking antibiotics . Macrophages were infected for 30 min , 1 h , 2 h , 3 h or 5 h with derivatives of L . pneumophila 130b at a multiplicity of infection of 50 . Bacterial strains for infection were grown overnight in ACES broth supplemented with antibiotics where appropriate and 1 mM IPTG . HeLa cells were infected using an identical protocol . Following the infection period , cells were washed once with fresh tissue culture medium and incubated with 500 nM Mitotracker® Red FM ( Invitrogen ) for 30 min at 37°C and 5% CO2 . Labelled cells were then fixed in 4% paraformaldehyde-PBS for 20 min and permeabilized with 0 . 1% TritonX-100-PBS for 20 min . Cells were incubated for 60 min in staining solution containing 0 . 2% BSA , 1∶50 dilution of anti-HA . 11 monoclonal antibody ( Covance ) and 1∶75 dilution of rabbit raised anti-Legionella pneumophila antibody ( Acris ) . The bound primary antibodies were detected using 1∶1000 dilution of Alexa Fluor 405-conjugated anti-rabbit antibody and Alexa Fluor 488-conjugated anti-mouse antibody ( Invitrogen ) respectively . Coverslips were mounted onto glass slides with Dako Fluorescent Mounting Medium ( Dako ) . Immunofluorescence images were acquired using a confocal laser scanning microscope ( Leica TCS SP2 confocal imaging system ) with a 100x/1 . 4 NA HCX PL APO CS oil immersion objective . Adenylate cyclase ( Cya ) fusions with RalF and LncP were generated as described previously [51] . Details are provided in the supporting methods ( Protocol S1 ) . Hemagluttinin ( HA ) fusions with LncP were generated as described in the supporting methods ( Protocol S1 ) . A . castellanii ATCC 50739 was cultured in PYG 712 medium at 20°C for 72 h prior to harvesting for L . pneumophila infection . A . castellanii cells were washed once with A . c . buffer ( 0 . 1% trisodium citrate , 0 . 4 mM CaCl2 , 2 , 5 mM KH2PO4 , 4 mM MgSO4 , 2 . 5 mM Na2HPO4 , 0 . 005 mM ferric pyrophosphate ) and seeded into 24-well tissue culture trays ( Sarstedt , Leicestershire , United Kingdom ) at a density of 105 cells/well . Stationary-phase L . pneumophila was added at an MOI of 0 . 01 and incubated at 37°C . At set time points , entire co-culture volumes were collected and plated onto BCYE agar to count colony-forming units of L . pneumophila . Proteins were analyzed by SDS-PAGE or by Blue Native ( BN ) -PAGE ( Figure S3 ) as previously described [85] . N-terminal sequencing was carried out as described previously [93] . Purified LncP was quantified by laser densitometry of stained samples , using carbonic anhydrase as the protein standard [93] . Protein incorporation into liposomes was measured as described [93] and varied between 20-30% of the protein added to the reconstitution mixture . | Mitochondrial carrier proteins evolved during endosymbiosis to transport substrates across the mitochondrial inner membrane . As such the proteins are associated exclusively with eukaryotic organisms . Despite this , we identified putative mitochondrial carrier proteins in the genomes of different intracellular bacterial pathogens , including Legionella pneumophila , the causative agent of Legionnaire's disease . We named the mitochondrial carrier protein from L . pneumophila LncP and determined that the protein is translocated into host cells during infection by the bacterial Dot/Icm type IV secretion system . From there , LncP accesses the classical mitochondrial import pathway and is incorporated into the mitochondrial inner membrane as an integral membrane protein . Remarkably , LncP crosses five biological membranes to reach its final location . Biochemically , LncP is a unidirectional nucleotide transporter similar to Aac1 in yeast . Although not essential for intracellular replication , the high carriage rate of lncP among isolates of L . pneumophila suggests that the ability of the pathogen to manipulate mitochondrial ATP transport assists survival of the bacteria in an intracellular environment . | [
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] | 2012 | Legionella pneumophila Secretes a Mitochondrial Carrier Protein during Infection |
Self-motion , steering , and obstacle avoidance during navigation in the real world require humans to travel along curved paths . Many perceptual models have been proposed that focus on heading , which specifies the direction of travel along straight paths , but not on path curvature , which humans accurately perceive and is critical to everyday locomotion . In primates , including humans , dorsal medial superior temporal area ( MSTd ) has been implicated in heading perception . However , the majority of MSTd neurons respond optimally to spiral patterns , rather than to the radial expansion patterns associated with heading . No existing theory of curved path perception explains the neural mechanisms by which humans accurately assess path and no functional role for spiral-tuned cells has yet been proposed . Here we present a computational model that demonstrates how the continuum of observed cells ( radial to circular ) in MSTd can simultaneously code curvature and heading across the neural population . Curvature is encoded through the spirality of the most active cell , and heading is encoded through the visuotopic location of the center of the most active cell's receptive field . Model curvature and heading errors fit those made by humans . Our model challenges the view that the function of MSTd is heading estimation , based on our analysis we claim that it is primarily concerned with trajectory estimation and the simultaneous representation of both curvature and heading . In our model , temporal dynamics afford time-history in the neural representation of optic flow , which may modulate its structure . This has far-reaching implications for the interpretation of studies that assume that optic flow is , and should be , represented as an instantaneous vector field . Our results suggest that spiral motion patterns that emerge in spatio-temporal optic flow are essential for guiding self-motion along complex trajectories , and that cells in MSTd are specifically tuned to extract complex trajectory estimation from flow .
Existing theories of path perception are a set of heuristics that do not specify the mechanisms by which the path is perceived in the brain . Some theories depend on the active tracking of ‘features’ in the visual scene [27] , [35] , while others implicate an extensive cognitive component , such as updating estimates with respect to external reference objects [31] , [36] . We first summarize the former class of path perception theories . The passing flow line hypothesis observes that optic flow integrated over an extended period of time yields a streamline that passes underneath the observer and coincides with the path of travel [35] . This hypothesis assumes that the observer gaze is in the direction of heading and requires the environment to have a textured ground plane passing directly underneath the observer . A related hypothesis proposed by Wann and Swapp , which we call the vertical vector hypothesis , notes that if the observer maintains gaze on the destination of travel , the path can be recovered from retinal flow by integrating first-order flow vectors that are vertically aligned [37] . This strategy does not require knowledge of heading . The vertical flow line hypothesis posits that the visual system tracks the constellation of vertical optic flow streamlines that exist when the observer fixates a point on the future path . This strategy assumes that humans fixate on their destination while traveling along curvilinear paths . The reversal boundary hypothesis notes that the future path of travel coincides with direction reversals or “zero-crossings” in the horizontal motion component once the optic flow has been projected onto the retina [27]; the horizontal motion component of texture inside ( outside ) the path will be rightward ( leftward ) , or vice versa depending on whether the circle is traversed CW or CCW . While psychophysical evidence suggests that humans are most accurate in judging path curvature when the gaze direction is aligned with the heading , it is not clear how this strategy could tolerate momentary fluctuations in gaze . Warren and colleagues have proposed a vector normal hypothesis whereby the center of the circular path can be determined by computing the intersection of the vector normals of two points in the environment [27] . Using the vector normals , knowledge of the circular path center , and the observer's current position , the radius and therefore the curvature of the path of travel can be recovered . The hypotheses reviewed above suffer from rigid constraints on the environment or observer gaze , and are unlikely to represent general theories of human path perception . The strategies proposed by the passing flow line , vector normal , and vertical vector hypotheses only hold when observers look where they are going—i . e . gaze is along the heading direction . These hypotheses cannot account for activities that presumably depend on the perception of path , such as steering a vehicle [38] , for which successful control of navigation accompanies natural changes in gaze [37] . Humans perceive their path of travel in sparse environments composed of small quantities of dots . The boundary reversal hypothesis , however , requires dense optic flow to ascertain the horizontal motion zero-crossing . From a neural computation point of view , it is unclear how the brain could track the context-specific local features proposed by any of the above hypotheses over time . The following path estimation theories rely on external landmarks in the environment . The reference object hypothesis posits that observers either update their position or integrate the change in heading over time with respect to an object embedded in the environment [36] . Subjects in the experiments of Li and Cheng were able to judge their future path of travel in the absence of persistent objects in the environment , rendering the reference object hypothesis an incomplete strategy [31] . Li and Cheng tested whether humans can integrate the change in heading without a reference object by tracking the “drift” in the FoE over time with gaze remained fixed along a particular axis when the observer travels along the circle without any rotation ( Z-axis condition ) . Subject responses were consistent with the percept of moving along a straight rather than a circular path , making the FoE drift hypothesis unlikely [31] . Finally , Li and Cheng proposed that observers first estimate heading to established a reference , then estimate the path curvature , which is mathematically defined for a circular path as the ratio between the rotation and translation rates [31] . It is not clear if , or how , mechanisms in the brain could perform these operations . In summary , theories of path perception either treat path perception as independent of heading or depend on its prior estimation . In the present article , we propose a neural model of the primate visual system in which representations of heading and path are determined simultaneously and dynamically interact in the same population of neurons . Neurons in the primate medial superior temporal area ( MST ) of the superior temporal sulcus ( STS ) exhibit tuning in the laboratory to radially expanding optic flow patterns , similar to those experienced by an observer moving forward on a straight path . MSTd cells have therefore been the focus of neurophysiological investigations of the mechanisms underlying visually-guided navigation . MST is the earliest visual area , the fewest synapses away from the retina in the primate dorsal stream , that responds to large field pattern motion . Evidence suggests that MST in monkey is composed of functionally distinct dorsal ( MSTd ) and ventral ( MSTv ) regions . Whereas neurons in MSTd exhibit sensitivity to optic flow patterns that occupy areas of the visual field as large as , MSTv neurons have smaller receptive field sizes and are suspected to be involved in the perception of object motion [39] . MSTd neurons demonstrate sensitivity to dot speed [40] and spatial shifts in FoE position [41] , and therefore are thought to be involved in heading perception [42] . Froehler and Duffy have conducted the only neurophysiological study to date that reports the existence of “path selective” neurons in cortex [43] . Monkeys were placed on a sled in a dark room that contained bright dots on the three walls that were within view . The sled moved CW or CCW along a circular path ( Figure 2 ) . The sled was configured not to rotate the body as it traversed the circular path . The monkeys maintained gaze , throughout the trial , on a target that was projected from the sled onto the distal wall . Because the projector was attached to the sled and the monkey was trained to maintain gaze on the target , the fixation point occupied the same position within the monkeys' visual field over time . The optic flow experienced by the monkeys contained no rotation and appeared to radially expand or contract at each instant , with a FoE or focus of contraction ( FoC ) that ‘drifted’ horizontally during the trial . A monkey traveling once around the circle on the sled therefore viewed a sequence of headings and each had an equivalent at antipodal positions in both the CW and CCW trials . Froehler and Duffy recorded from single neurons in MSTd and elicited differential activity at antipodal positions on the track , where expansion/contraction optic flow patterns were identical . The neurons' response depended on whether the circle was traversed CW or CCW , and as a result the authors claimed these cells demonstrated path selectivity . The authors also found heading selective cells , which fired when the optic flow contained their preferred heading irrespective of the CW or CCW traversal direction , and place selective cells , which responded when the monkey moved to a particular location of the room irrespective of the visual motion pattern . The selectivity of neurons in the sample was distributed along a continuum , ranging from those demonstrating high ( path selective ) to those demonstrating low ( heading selective ) CW v . s . CCW differential activity . The mechanisms that underlie how these cells in MSTd respond along a continuum to heading and path were not evaluated by the study . In summary , neurons in MSTd demonstrate tuning to optic patterns , similar to those that would be viewed by an observer traveling on a straight path , and may exhibit sensitivity to path in the absence of rotation [43] . Locomotion along curved paths typically involves rotation , so , if the neurons discovered by Froehler and Duffy are in fact path-selective , it remains unclear how their response patterns would generalize to more natural movement conditions . Our model proposes mechanisms by which the MSTd neurons identified by Froehler and Duffy elicit differential firing rates when the instantaneous visual motion appears the same , yet the monkey moves CW or CCW around the circle . Our analysis integrates the findings with other known properties of MSTd neurons . If the primary role of MSTd were to determine heading , most MSTd neurons would be expected to preferentially respond to radial expansion and contraction . While many neurons in MSTd are tuned to such patterns , many others exhibit preferential responses to patterns in a spiral space spanned by radial and center templates ( Figure 3 ) . Moreover , neurons in MSTd would be expected to discount retinal rotation , as many appear to do with extra-retinal rotation [16] . However , Orban and colleagues demonstrated that MSTd neurons tuned to radial expansion did not respond to expansion displays when adding simulated retinal rotation [44] . Therefore , rotation does not appear to be discounted in MSTd neurons when the source is retinal rather than extra-retinal . Graziano and colleagues found that more neurons preferentially responded to CW and CCW spirals than to rotation or contraction , and the tuning curve width and selectivity did not differ across the MSTd population for radial , spiral , and center patterns [45] . That is , neurons tuned to radial expansion did not exhibit sharper tuning curves than those tuned to spirals . Spiral tuning also appears in neurons in the ventral parietal area ( VIP ) [46] and area 7a [47] , two of the brain regions to which MSTd projects [48] . Despite the diversity of tuning in MSTd , no well-defined hypothesis has been proposed for the functional role of MSTd neurons tuned to spiral patterns . Graziano and colleagues speculated that spiral tuning may allow MSTd to detect a rotating moving object or perceive the pattern of motion experienced by walking forward while tracking a point on the ground , however , these hypotheses are unproven . We claim that selectivity to optic flow across a spiral space continuum simultaneously affords MSTd with sensitivity to the curvature of the path and to the heading direction . When an observer travels along a curvilinear path on a ground plane with a fixed direction of gaze , a spiral-like pattern is experienced and optic flow contains rotation that specifies the path curvature [31] . Theoretically , spiral selective neurons should be sensitive to the curvature of their preferred spiral pattern and would therefore be capable of extracting information about the future path . Although the actual representation in the brain is unlikely to resemble an abstractly-defined mathematical spiral space [49] , we assume that the spiral space selectivity in MSTd spans the continuum between radial and center patterns that has been electrophysiologically tested in numerous studies [44]–[47] . Because we developed a neurophysiological model , it is important to constrain the model to constructs that can be verified by data . Since no neurophysiological data exists with more ecological stimuli we would be unable to verify a model design constructed using such templates designed to reflect our intuitive understanding of the ecologically valid space . Figure 4 shows a visualization of the proposed functional organization of MSTd . Each cylindrical volume represents a functional MSTd hypercolumn with respect to spiral selectivity . A hypercolumn contains a subpopulation of MSTd neurons that are sensitive to a spectrum of optic flow patterns in spiral space that have receptive fields centered at the same location of visuotopic space . The horizontal ( ) and vertical ( ) axes specify the spatial dimensions of the MSTd visuotopic map . Each point in this two-dimensional space indicates tuning to a FoE , FoC , or more generally a center of motion ( CoM ) in that particular visuotopic location—irrespective of the pattern selectivity . For example , the top-right hypercolumn in Figure 4 contains subpopulations of MSTd neurons tuned to motion patterns ( e . g . radial expansion , radial contraction , spiral , center ) that have the CoM located on the top-right region of the visual field . The axis than spans the depth of the hypercolumn represents the degree of spiral tuning for the subpopulation of neurons that have receptive fields centered at a particular location of the visual field . MSTd neurons may exhibit tuning to CW or CCW spiral patterns that either expand or contract . Spiral patterns smoothly vary in ‘spirality’ between patterns that are radial with no curvature ( top and bottom ) , and those that are centers ( left and right ) . We propose that the ‘spirality’ of the most active subpopulation of neurons in MSTd encodes the curvature of the path , and the two-dimensional visuotopic position of that maximally active subpopulation represents the heading . In the simple case of travel along a straight path , we expect neurons tuned to radial expansion to be most active , indicating no path curvature , and we anticipate the peak to spatially coincide with the FoE to indicate the heading . Therefore , the population MSTd response in this example is the same as if there were only neurons selective to radial patterns . In the case of a circular path , we expect the spiral-selective neurons with spiral arms that best match the path curvature to be most active . As reported in several psychophysical studies [11] , [31] , different gaze patterns modulate the rotation present in the optic flow . In the present paper , we test whether the maximal activity of neurons tuned in spiral space maps onto human judgments of path curvature as gaze varies . We present a dynamical model of primate MSTd that builds on electrophysiological findings and explains a range of human psychophysical data on path and heading perception with and without eye movements . The main goal is to present a mechanistic hypothesis of path perception that provides a unified framework to interpret psychophysical and neurophysiological data on heading and path perception . Our model goes beyond existing heuristics by providing a mathematical description and biologically-plausible implementation that is readily testable . Our analysis and simulations show that the model yields performance similar to humans under different gaze conditions , circular path radii , and eye movement patterns . The model predicts that the neurons reported by Froehler and Duffy obtain their path selectivity through spiral pattern tuning [43] .
The proposed neural model contains three stages that correspond to primate primary visual cortex ( V1 ) , medial temporal area ( MT ) , and the dorsal medial superior temporal area ( MSTd ) ( Figure 5 ) . In this paper , we do not model retinal input , but rather use analytical equations to model the vector-based optic flow representation in V1 [51] . A prior version of the model demonstrates how retinal inputs are processed through neural circuits to generate those representations [52] , [53] Unless otherwise noted , all simulation parameters matched those used in the following psychophysical experiment descriptions .
Figure 7a depicts the path error obtained in each experimental condition , averaged across the three path curvatures . The random dot displays in model simulations and the human experiments contained 200 dots . In the Z-axis condition , an observer was simulated to travel on a circular path and gaze remained parallel to the Z-axis ( Figure 6a ) . The instantaneous vector field contained no rotation , the field at any time appeared to radially expand , and over time the FoE laterally ‘drifted’ . In the outside path condition , the simulated gaze was on a target outside the circular path ( Figure 6b ) . In the on path condition , the simulated gaze was on a target away from the initial heading ( Figure 6c ) . In the inside path condition , the simulated gaze was on a target located inside the path ( Figure 6d ) . The gaze along heading condition is the natural case whereby the observer's gaze was simulated to be aligned and rotate with the body and the observer's heading was always tangent to the path ( Figure 6e ) . Positive and negative path errors correspond to overestimations and underestimations of the path curvature , respectively . Zero path error signifies an accurate assessment of path curvature . Mean model path errors agree well with those produced by humans subjects in the experiments of Li and Cheng [31] . The model and human subjects on average underestimated the path curvature in the Z-axis , outside path , and on path conditions , overestimated the path curvature in the inside path condition , and accurately judged the path curvature when gaze was aligned with the heading direction . When optic flow experienced by an observer moving along a curvilinear path is presented to the model , a subpopulation of units in a particular model MSTd hypercolumn becomes most active ( Figure 4 ) . Path curvature is coded by the spiral tuning of these most active units in MSTd . The visuotopic tuning of this maximally active subpopulation does not impact the encoding of path curvature . Figure 7b plots the peak magnitude of each MSTd unit tuned to a different template in spiral space , irrespective of the unit's tuning in visuotopic space , in the five gaze conditions when the path curvature was . The axis corresponds to the pattern tuning across the spiral space continuum , and the axis shows the maximal activity elicited by units sensitive to a particular optic flow pattern in spiral space , irrespective of its visuotopic tuning . A spirality of 0 signifies a MSTd neuron that is preferentially tuned to radial expansion , a spirality of 1 indicates a tuning to CCW center motion patterns , and intermediate values correspond to preferential responses to CCW spiral patterns . In the Z-axis and outside path conditions , the maximally active MSTd unit was the one that was sensitive to radial expansion ( ) . The positions of MSTd activity peaks in the Z-axis ( black ) and outside path ( red ) conditions were to the far left of the spiral space continuum . Radially expansion patterns contain no curvature , therefore , the model signals , similar to humans , in the Z-axis and outside path conditions that the path is straight . To compute path error from representations of path curvature in the model , we have to ground the spiral continuum into perceptual space . When humans look where they are going , judgments of path curvature are accurate . This is most often the case during normal locomotion [26] , so we calibrate the model around the distribution of activity in model MSTd yielded in the natural gaze along heading condition ( Figure 7b , blue ) . We subtracted the spirality of the peak obtained in each condition ( ) from that obtained in the gaze along heading condition to yield the model path error . We were able to configure the model such that no transformation of the subtraction in spiral space was required to yield the results shown in Figure 7 . The ordinal positions of peaks shown in Figure 7b correspond to path errors made by humans in the experiments of Li and Cheng [31] . As mentioned above , the MSTd activity peaks in the Z-axis and outside path conditions are produced by units tuned to radial expansion . These peaks are positioned far to the left compared to the activity peak in the gaze along heading condition , indicated by the blue * . Subtraction of the abscissae of the peaks yields large magnitude negative path errors , consistent with the large underestimations of path curvature made by human subjects . The position of the activity peak in the on path condition ( pink ) is closer to that in the gaze along heading condition ( blue * ) . This yields a negative path error , albeit lower in magnitude than those produced in the Z-axis and outside path conditions . Therefore , the model signals an underestimation of path curvature , consistent with the judgments of human subjects . The bimodality observed in the MSTd activity distributions shown in Figure 7b arise due to an interaction between the input optic flow , temporal dynamics , and competition in the model . Retinal flow that contains a large amount of rotation ( e . g . inside path condition ) yields activity peaks in units tuned to spiralities around 1 . Conversely , flow that contains a small amount of rotation ( e . g . Z-axis condition ) yields activity peaks in units tuned to low spiralities around 0 . Due to observer gaze , the rotational component in the optic flow changes over time during travel along the circular path . As a result , the activity peaks in model spiral space , such as those shown in Figure 7b , “move” over time . Subpeaks arise , such as the “ripples” in the green curve , because at one point in the time history , a unit with the corresponding spirality of the subpeaks was most active . Competitive dynamics in the network suppress subpeaks over time . Although peaks in spiral space stabilized in the network , subpeaks were not always completely suppressed by the end of the trial . Because the projected CoM location and rotation in the optic flow vary nonlinearly with time ( e . g . Eq . 11 ) , peaks are not always displaced to continuous locations in spiral space . This yields a bimodal distribution . Note that the “valley” between the peaks in the inside path and gaze along heading conditions arose due to the spatio-temporal characteristics of the input optic flow and the MSTd network . Peaks emerged within this region of spiral space when simulating travel along paths with different radii . Figure 7c compares the average path errors produced by the model ( solid lines ) with those yielded by human subjects ( dashed lines ) in the five gaze conditions of Li and Cheng [31] . Model path error is assessed on , , and radii circular paths with curvatures of , , and , respectively . Error bars in Figure 7c correspond to the standard error of the mean ( SEM ) yielded over 100 simulations of the model . Our model is deterministic , but the random dot positions in the input introduced variance into the results . Model path estimates produced a good fit to those yielded by human subjects in the Z-axis ( ) , outside path ( ) , on path ( ) , inside path ( ) , and gaze along heading ( ) conditions . Similar to human subjects , the model overestimated path curvature when gaze was inside of the path ( green ) that had the least curvature ( ) . As the path curvature increased , path curvature estimates in the model converged to those obtained in the gaze along heading condition . In the highest path curvature condition , the model path curvature estimates followed the tendency for humans to largely underestimate the path curvature in the on path , outside path , and Z-axis conditions . Across all conditions , the decrease in path error varied as a linear function of increasing path radius ( ) . The dynamics in model MSTd explain why humans overestimate path curvature in the gaze inside path condition along paths with larger radii , but yield more accurate estimates when the path radius is small . In the gaze inside path condition ( green ) , a bimodal distribution emerged in model MSTd . The activity peak occurred to the CCW center side of the spectrum , indicated by the green * , and a subpeak occurred closer to the middle of the spiral continuum , indicated by the green ( Figure 7b ) . Recall that path error is computed in the model by considering the distance between the peaks obtained in a particular condition and in the gaze along heading condition , indicated by the blue * . As shown in Figure 7b , a subpeak exists in the gaze along heading condition , indicated by the blue , which is close to the peak in the gaze inside path condition ( green * ) . When path curvature increases , more rotation is introduced into the optic flow , which changes the distribution of activity in MSTd spiral space . The subpeak in the gaze along heading condition ( blue ) , becomes dominant and its proximity to the peak in the gaze inside path condition ( green * ) results in small path errors . Therefore , high path rotation brings the MSTd peaks ( blue and green * ) closer together in the gaze inside path and gaze along heading conditions , yielding close to zero path error . The opposite occurs when the path radius increases—the peak in the gaze along heading condition ( blue * ) shifts leftward in Figure 7b , yielding larger path error . Figure 8 plots the results of model simulations of the two experimental conditions of Cheng and Li , in which human subjects performed smooth pursuit eye movements to track a moving target [25] . Path errors produced by our model fit the human data well in both the orientation along heading ( ) and orientation along Z axis ( ) conditions . Model gain fields modulate the optic flow signal proportional to the mean eye tracking speeds of human subjects , which increase with path curvature . Model gain fields modulate the optic flow signal proportional to the mean eye tracking speeds of human subjects , which increase with path curvature . Subjects tracked a target moving in the direction of the drift in FoE position in the orientation along Z-axis condition . The only source of rotation in the optic flow field is that due to pursuit eye-movements , the gain field adds rotation in the opposite direction with a pursuit-speed proportional magnitude , effectively nulling the rotation and producing a translation-only optic flow field . The model increasingly underestimates the path curvature as the curvature increases because the combined effect of rotation due to path curvature , rotation due to eye-movements , and rotation added by the gain-fields is a rotation component of the flow field that is less than would be observed due to path curvature alone . As a result , model path errors are small . In Figure 9a , model heading bias in the outside path , on path , and inside path conditions is compared to that of human subjects in the experiments of Li and Cheng [31] . Heading is represented in the model as the preferred 2D visuotopic position of the maximally active MSTd neurons ( see Materials and Methods ) . Positive and negative heading errors correspond to heading judgments biased in the direction of and opposite to the path curvature , respectively . Human heading judgments were slightly biased outside the path in the outside path and on path conditions , and more greatly biased toward the inside of the path in the inside path condition ( Figure 9a , red ) [31] . The model produced similar heading errors , but unlike the human data , model heading estimates were veridical in the outside path condition . This occurred because the model was not sensitive enough to detect differences between the MSTd activity peaks in the Z-axis and outside path conditions ( Figure 7b ) , so the model signals the veridical heading . Neither heading errors produced by the human subjects nor by the model were influenced by the path radius . Figures 9b–c depict the temporal evolution of the spatial distribution in MSTd among units sensitive to radial expansion without competition ( Figures 9b ) and with competition ( Figures 9c ) . The x-axis corresponds to MSTd unit FoE selectivity to particular horizontal locations within the visual field . The simulation is of the Z-axis condition , wherein the instantaneous optic flow is always expanding radially without rotation , and Figures 9c shows the activity of model neurons tuned to radial expansion . The visuotopic positions of the activity peaks in MSTd do not change due to the competition , but the model competitive interactions sharpen the spatial distribution . Any heading bias therefore is preserved in the model through the competition in MSTd . In human psychophysical studies that employ a simulated eye rotation condition , the observer moves on a straight path with an added amount of rotation [11] . However , human subjects report the perception of moving along a curved path [15] . We tested whether our model produces similar heading bias to human subjects in the simulated rotation condition , which would offer an mechanistic explanation of the curved path percepts . To compute heading bias , we compared the heading garnered by the model in the gaze along heading condition with that obtained when simulating observer travel along a straight path with added rotation rates between . We simulated travel toward two fronto-parallel planes and otherwise mimicked experiment 2 of Royden et al . [11] . Figure 10 depicts model heading bias ( blue ) for different amounts of simulated rotation fitted by a hyperbolic tangent function ( , where and , ) . The red curve in Figure 10 shows the hyperbolic tangent function fit ( ) to mean human data from [11] . The sigmoidal functions fit the human data and model well , and the two were well correlated with one another ( ) . Figure 10 shows that heading was biased in the direction of the simulated rotation , which is the same sign of error observed in Figure 9a . Therefore , both the model and humans data exhibit heading bias in the simulated rotation condition , which may explain the curved path percepts in humans . Our model incorporates competitive dynamics across a spiral space . To determine whether competition across spirality , spiral orientation ( CW v . s . CCW ) , and visuotopic space in model MSTd was necessary to produce path errors comparable to humans , we selectively lesioned certain competitive interactions between model neurons . Path errors were computed by comparing the peak activity in spiral space to that obtained in the gaze along heading condition , as in the unlesioned case . Figure 11 compares human and intact model mean path errors with those produced when the three types of competition in the model were lesioned . In all cases , omitting a particular type of competitive interaction in the model resulted in changes in path errors . For instance , lesioning the horizontal spatial interactions between model MSTd neurons resulted in a shift and compression in path error across all path radii: the path errors for the inside path , on path , and gaze along heading conditions converged to the same value for each path radius , and path errors in the Z-axis and outside path conditions converged to a different value . Introducing lesions into model MSTd connections garnered results that did not exhibit the same pattern as human judgments . Human behavioral performance is compatible with the use of competitive interactions between subpopulations of cells in MSTd . We tested the model stability and path curvature estimation performance as a function of the number of dots in the scene . The path curvature judgments made by human subjects in the experiments of Li and Cheng [31] and the model results shown in Figure 7 were derived from environments with 200 dots . Figure 12 shows model performance across the path curvature conditions as function of scene dot count . The y axis plots the path error deviation , which indicates the relative path error compared to that obtained with 200 dots . Independent of the path radius , the model yields reliable results , with modest mean path error deviations ( ) even with only 25 dots . Human path curvature judgments have been tested in environments containing different dot densities in conditions that most closely resemble those in the gaze along heading condition , and model produces similar errors to these human data [27] . Path errors in scenes with greater numbers of dots than 200 also yielded low magnitude path error deviations , which indicates that the model results shown in Figure 7 are stable and model parameters did not overfit the human data . Figure 13a shows a model simulation of first-order optic flow experienced by the monkey in the experiments of Froehler and Duffy [43] . The gaze of the monkey traveling along the circular track was tantamount to that of the Z-axis condition . Therefore , according to traditional theory , and the assumptions of the experimenters , the radial subpopulation of cells in MSTd was expected to be maximally active due to the absence of rotation in the instantaneous optic flow field . However , in our simulations the maximally active model MSTd subpopulation was tuned to spiral patterns rather than those that are radial ( dark orange ) . When the angular rotation rate exceeded that used by Froehler and Duffy [43] ( ) , MSTd neurons in the model tuned to spiral patterns remained the most active . When the angular rotation rate was comparable to that used in the Z-axis condition of Li and Cheng ( ) , the model neurons selective to radial patterns were most active . Our analysis indicates that temporal accumulation due to the dynamical properties of the MSTd model ( Eq . 6 ) and the distance-dependent weighting ( , see Eq . 5 ) induced a peak shift in spiral space , from neurons sensitive to radial patterns to those sensitive to spirals . As shown in Figure 13b , the temporal accumulation and spatial weightings transform the sequence of radial patterns with a ‘drifting’ FoE into a spiral pattern with a fixed FoE . When the speed around the circle is slower than that of the monkey in the experiments of Froehler and Duffy , the activity in MSTd spiral space is distributed so that the subpopulation of units tuned to radial expansion is most active . At higher speeds around the circle , the position of the MSTd peak shifts so that units sensitive to spiral patterns are most active ( Figure 13 ) . The peak shift occurs at higher speeds because the temporal dynamics ‘blur’ the flow fields and the spatial weighting distorts the flow near the FoE . Our analysis suggests that the path selective neurons identified by Froehler and Duffy in MSTd are in fact preferentially tuned to spiral patterns , and the spiral space competition employed in our model can explain the mechanism underlying their path selective properties . We predict that if Froehler and Duffy performed their experiment at slower rotation rates , decreased spatio-temporal accumulation would occur and fewer MSTd neurons would yield a differential CCW versus CW path selectivity . In addition , we predict that if Li and Cheng simulate travel along circular paths at faster rotation rates in the Z-axis condition , subjects would underestimate path curvature to a lesser degree .
In the experiments of Li and Cheng , human path errors were not modulated by the structure of the visual scene [31] . Our simulations demonstrated that model performance was only modestly impacted by the dot density of the ground plane ( Figure 12 ) . This is consistent with the findings of Li and Cheng that denser textured environments did not modulate human path judgments . The robustness of the model results to dot density is also consistent with findings that indicate that path perception does not depend on local features in the environment [31] . The stability of path errors across different types of scenes in humans and the model suggests that mechanisms underlying path perception depend on areas such as MSTd that prefer stimulation by large field pattern motion . Existing studies of path perception have explored path perception during travel along ground planes [24] , [31] . We are unaware of investigations that explored other environmental structures , such as 3D dot clouds and fronto-parallel planes . Model simulations mainly contained ground plane environments due to their natural relevance to human locomotion and the availability of psychophysical data . To investigate the simulated rotation condition with our model , we mimicked the conditions of experiment 2 of Royden et al . , which contained two dot fronto-parallel planes [11] . More work needs to be done to assess human path perception in different types of environments . The simulated rotation condition of Royden presents an interesting test for the model [11] . We hypothesize that humans perceive that they are traveling along a curved path in the simulated rotation condition due to the activation of spiral-selective neurons in MSTd . Our hypothesis is supported by simulations that demonstrate that spiral-selective units , not those tuned to radial expansion , are maximally active in the simulated rotation condition . Conversely , in the Z-axis condition of Li and Cheng , human subjects responded as if they were traversing a straight path despite actually traveling along a curved path . In this case , model neurons tuned to radial expansion produced the most activity , which signals a lack of path curvature and is consistent with human path errors . In the simulated rotation and Z-axis conditions , the spiral space mechanisms in the model correctly predict the perceived path curvature . This suggests that humans rely on retinal rotation ( i . e . rotation not due to extra-retinal sources ) to perceive the curvilinear path and that MSTd neuronal tuning to spirals extracts information about path curvature . The large heading biases produced by humans in the presence of retinal rotation [11] is consistent with the finding of Orban and colleagues that MSTd neurons tuned to expansion do not appear to compensate for rotational components in the optic flow field , except when accompanied by an extra-retinal signal [44] . Interestingly , the environments and rotation rates tested by Li and Cheng and Royden et al . are remarkably similar , yet our model yields different heading bias in each of these experimental conditions reflecting the differences found in humans ( compare Figure 9a and 10 ) . In particular , experiments 4 and 5 in the Royden et al . study both use ground planes defined by random dot patterns , with rotation rates in the range of . Despite these similarities in the instantaneous optic flow fields , human heading bias reached in the Royden et al . study and it did not exceed in the experiment of Li and Cheng . The difference in heading bias may be attributed to spatio-temporal differences in the optic flow displays . As a dynamical system , our model responds differently to different spatio-temporal optic flow evolution [15] . Rather than using a ground plane with a uniform dot density similar to that of Royden and colleagues , Li and Cheng distributed dots to maintain a constant density at different depths within the observer's field of view . This manipulation increased motion parallax in the displays , which has been shown to improve the accuracy of heading judgments [36] . There were only 220 dots visible at the trial outset in the experiments of Royden and colleagues compared to 300 in the displays of Li and Cheng . Differences in motion parallax and dot density may account for the disparity in heading bias between the two studies . It is also possible that subjects in the experiments of Royden et al . reported perceived path rather than heading . Our model provides a good quantitative fit to the heading bias in both studies . Only the spatio-temporal structure of the displays used to simulate the studies differed . If we assume that human subjects followed the experimental instructions , then our fit of the data is consistent with the reporting of heading . However , if we assume that subjects attempted to indicate the curvature of their path , then the interpretation of our data fit is incorrect . The activity curves in model MSTd spiral space ( Figure 7b ) exhibit different widths and sharpnesses . Because model MSTd was configured as a soft winner-take-all network ( Eq . 6 ) , given sufficient time , the network will select a single MSTd unit to be active and all other model neurons will be suppressed through competition . The winning unit signals the path curvature through its pattern selectivity in spiral space . As noted in other computational studies [51]–[53] , [57] , broad activation in the network could implicate a greater degree of uncertainty about the path curvature and the dynamical competitive interactions require longer to resolve a high confidence solution . We configured model MSTd with a single set of parameters , but it is possible in vivo that different subpopulations exhibit differential response latencies [63] . As depicted in Figure 7b , simulations of travel along curved paths gives rise to complex distributions of activity across MSTd that are important to how the model encodes heading and path . It is unclear how the brain decodes this information that is distributed across the MSTd population . We believe the population activity is important to heading and path perception , and taking the argmax just provides a simple and straightforward method to assess model performance and properties about the MSTd population . Because competitive dynamics occur while input optic flow signals remain present , a total suppression of the activity of the non-winning units is not guaranteed to occur [51] , [63] . The argmax operation allows us to read out information about the most active model unit to understand model performance , and is not part of the model's operation . The winner-take-all mechanism is part to the model's operation , and as indicated by our results ( Figure 11 ) , represents an important characteristic of the model that allows it to fit the human data . We selected spiral templates in the model to resemble the optic flow patterns used in a number of electrophysiological studies [45] , [47] , [64] to investigate large motion pattern selectivity in neurons located in MSTd and other areas of the STS . Although electrophysiological studies report tuning in the spiral space that spans radial expansion , contraction , and center fields , actual MSTd neuron receptive fields may exhibit far greater complexity . Pack and colleagues modeled the feedforward subunit structure of MSTd neurons based on single-cell recordings and discovered complicated subunit configurations that deviated from characteristic radial , spiral , and center motion patterns [49] . Feedback and other types of horizontal connectivity was not modeled , and only of the MSTd response variance was accounted for , so the actual receptive fields of MSTd units are likely even more complex . MSTd receptive fields may follow the motion statistics experienced by primates during ecological locomotion along a ground surface . For instance , model templates spanned the entire visual field , but ‘ecological templates’ may be biased toward the lower portion of the visual field . The statistics of videos collected from head-mounted cameras on human mothers carrying infants show that the optic flow during locomotion is fairly evenly distributed across expanding , contracting , upward , downward , CW , and CCW motion patterns , with a bias for expansion [60] . The selectivity of MSTd neurons in the sample of Graziano and colleagues also are biased toward expansive motion patterns . Humans accurately judge heading in environments with many different structures , even with dynamic occlusion , unless the textures become unstructured [30] . Therefore , ecological statistics may be important for guiding the development of MSTd receptive fields . In simulating monkey movement along a circular path , we found that the location of the MSTd activity peak in spiral space depended on the speed at which the circular path is traversed . At speeds slower around the circular track than that used by Froehler and Duffy , the optic flow more closely mimicked the Z-axis condition of Li and Cheng [31] , and the subpopulation of MSTd neurons tuned to radial expansion was most active—thereby signaling travel along a straight path . However , when the path traversal speed equaled or exceeded that used in the study of Froehler and Duffy [43] , the activity peak shifted rightward , signaling navigation along a curved path . Our analysis indicates that at a sufficiently fast speed around the track , the motion signal MSTd neurons receive in the experiment of Froehler and Duffy is temporally ‘blurred’ and actually resembles a spiral pattern ( Figure 13 ) . Froehler and Duffy did not report testing selectivity to spirals in their sample . Our analysis and simulation results predict that the path selective neurons discovered by Froehler and Duffy were tuned to spirals rather than expansion patterns . We predict that human subjects would produce path curvature judgments consistent with the percept of traveling along a curved path in a psychophysical experiment with the Z-axis gaze condition when the rotation rate along the circle is increased . In this proposed experiment , the model makes the prediction that humans would produce different path errors in the Z-axis condition , depending on how much of and the speed at which the circular path is traversed . Our model results suggest information about future path may be processed in areas as early as MSTd . Path estimation may more fundamentally indicate the functional role of area MSTd in primates . | Much human and primate psychological and electrophysiological research on visually-guided navigation has focused on heading perception , defined as the instantaneous direction of travel . However , the perception of path of travel , or trajectory , is arguably more important , because it informs in a more general sense whether the observer is on a collision course with moving objects or will intercept a target . In the present article , we describe a theory based on physiological evidence of how primate visual area MSTd may simultaneously and dynamically encode heading and path . The model connects many different sources of data , including psychophysics on human perception of heading and path with and without eye movements , and primate electrophysiological data on path-selective cells in MSTd . We propose neural mechanisms explaining why humans report traveling along curved paths when the display represents a straight path with simulated eye rotations . We predict that perceptual sensitivity to heading and path emerges in primate MSTd through the dynamical and competitive interactions between neurons tuned to the continuum of spiral-radial patterns . | [
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"mech... | 2014 | A Unified Model of Heading and Path Perception in Primate MSTd |
Apicomplexan parasites can change fundamental features of cell division during their life cycles , suspending cytokinesis when needed and changing proliferative scale in different hosts and tissues . The structural and molecular basis for this remarkable cell cycle flexibility is not fully understood , although the centrosome serves a key role in determining when and how much replication will occur . Here we describe the discovery of multiple replicating core complexes with distinct protein composition and function in the centrosome of Toxoplasma gondii . An outer core complex distal from the nucleus contains the TgCentrin1/TgSfi1 protein pair , along with the cartwheel protein TgSas-6 and a novel Aurora-related kinase , while an inner core closely aligned with the unique spindle pole ( centrocone ) holds distant orthologs of the CEP250/C-Nap protein family . This outer/inner spatial relationship of centrosome cores is maintained throughout the cell cycle . When in metaphase , the duplicated cores align to opposite sides of the kinetochores in a linear array . As parasites transition into S phase , the cores sequentially duplicate , outer core first and inner core second , ensuring that each daughter parasite inherits one copy of each type of centrosome core . A key serine/threonine kinase distantly related to the MAPK family is localized to the centrosome , where it restricts core duplication to once per cycle and ensures the proper formation of new daughter parasites . Genetic analysis of the outer core in a temperature-sensitive mutant demonstrated this core functions primarily in cytokinesis . An inhibition of ts-TgSfi1 function at high temperature caused the loss of outer cores and a severe block to budding , while at the same time the inner core amplified along with the unique spindle pole , indicating the inner core and spindle pole are independent and co-regulated . The discovery of a novel bipartite organization in the parasite centrosome that segregates the functions of karyokinesis and cytokinesis provides an explanation for how cell cycle flexibility is achieved in apicomplexan life cycles .
Infection with apicomplexan parasites is the cause of numerous important human diseases , including malaria , cryptosporidiosis , and toxoplasmosis . Pathogenesis of these diseases is closely tied to parasite replication [1] and the destruction of host cells , leading to tissue and organ damage . This fundamental relationship between parasite growth and disease is evident by the action of drugs used to combat these infections since the best treatments all reduce or block parasite proliferation . Existing therapies , in particular for malaria , are under constant pressure from acquired parasite drug resistance , a situation that requires a broad portfolio of antiparasitic compounds with different parasite-specific targets . The peculiar proliferative cycles of Apicomplexa parasites differ substantially from the hosts they inhabit and should offer fertile ground to supply an active pipeline of new treatments . To fulfill this promise , we need a better understanding of the unique structural and molecular features of parasite proliferation . Modern Apicomplexa are the result of millions of years of evolution [2] , involving successful encounters with many invertebrate and vertebrate hosts that have led to an extraordinary worldwide distribution . The development of specialized invasion and replication strategies [3–5] has permitted these parasites to surmount a variety of host-defensive barriers and achieve sufficient expansion in many different host tissues . Apicomplexan replication has adapted to different host cells , most commonly using a sequence of two chromosome replication cycles uniquely regulated in different parasite genetic lineages [4] . A single G1 phase that varies in length with the scale of parasite production precedes a first chromosome cycle ( S/Mn ) , the biosynthetic focus of which is genome replication ( nuclear cycle ) , followed by a unconventional chromosome cycle ( S/Mn+1 ) that produces infectious parasites ( budding cycle ) . The budding cycle is restricted to a single round of chromosome replication , and therefore , the amplification of the genome in the nuclear cycle determines the scale of biotic expansion . That scale can range depending on the species from a few to thousands of parasites produced from a single infected cell . Through simple variation in the nuclear to budding cycle sequence , apicomplexan parasites have solved the problem of adjusting proliferation to a wide variety of host cells . What is not understood are the mechanistic details that afford this tremendous cell division flexibility , while also preserving the fidelity of chromosome replication . Viewed from the restricted principles of model eukaryotic cell cycles , successful Apicomplexa replication often appears chaotic and in violation of some basic cell cycle restrictions ( e . g . “copy once only once” in the nuclear cycle ) . This paradox is one of the major mysteries of the phylum Apicomplexa . During their life cycle , Toxoplasma gondii parasites switch between multi- ( merozoite stage ) and binate-nuclear replication ( tachyzoite stage ) [6] , with the binary division cycle of the tachyzoite ( called endodyogeny , i . e . “inside two are borne” ) now a major experimental model for understanding basic principles of apicomplexan proliferation . The cell cycle periods of the tachyzoite [4 , 7–10] are reasonably defined and have provided evidence for major checkpoint control that was exploited to synchronize parasite growth [9 , 11 , 12] . The most unusual feature of Apicomplexa cell division is budding , which occurs by the assembly of daughter cells within or from the mother cell using a highly ordered process [3] that is accompanied by the de novo synthesis and packaging of invasion organelles [13] . In T . gondii tachyzoites , as in Plasmodium falciparum merozoites , assembly of new parasites ( budding ) is guided by a unique cell cycle transcriptome that delivers proteins in a "just-in-time" order [7 , 14] . A major conclusion from these studies is that the tachyzoite centrosome has a vital role in coordinating budding and mitotic events . The centrosome is one of two sites of microtubule nucleation in the tachyzoite with direct responsibility for assembly of the intranuclear spindle . A second microtubule-organizing center ( MTOC ) is the polar ring of the apical complex . This MTOC organizes the subpellicular microtubules ( MTs ) scaffold supporting the pellicle that gives the parasite its shape [3] . Importantly , the centrosome also governs the position and activity of this second MTOC through a physical tether , the striated fiber [15] . During mitosis in tachyzoites , a polar striated fiber emerges from each centrosome and at its other end gives rise to the MTOC that defines the daughter cells . Both centers are thus aligned and physically connected [15] . Across the Apicomplexa phylum , similar structural and mitotic principles are observed to govern parasite division . The centrosome ( also called a centriolar plaque ) has a central role in regulating the Apicomplexa cell cycle [4] . Ultrastructural studies of different coccidian parasites have demonstrated that the duplication of the centrosome occurs prior to budding , and this involves an unusual parallel configuration of the internal centriole structures [4 , 16–18] . Assembly of new daughters initiates in close proximity to these structures [8 , 19 , 20] , and in Toxoplasma mutants that fail to duplicate the centrosome parasite , budding is inhibited [21–23] . Perinuclear centrosome structures also duplicate prior to each nuclear division during the intraerythocytic cycle of P . falciparum merozoites , with coordination of these structures in the last nuclear division that precedes budding [1] . A fixed spatial orientation of the centrosome to a unique protein complex embedded in the nuclear envelope , called the centrocone , is also a common structural feature of the apicomplexan mitosis [24] . Importantly , these mitotic structures are positioned near where centromeres tether chromosomes during interphase [3 , 4 , 25] , and it is through the centrocone structure that spindle fibers pass into the nucleus to segregate chromosomes during mitosis [4] . The centrosome also has a master function in limiting chromosome replication in the tachyzoite that appears to require physical contact . Evidence from tachyzoite growth mutants indicates that "copy once" restrictions in the budding cycle require a connection between the centrosome and new daughter cytoskeleton [26 , 27] . Breaking this contact by drug treatment [28] or by genetic ablation of centrosome factors [15 , 22 , 26] leads to unregulated nuclear re-duplication and abnormal budding . To help explain the master functions associated with this mitotic organelle , we describe here the discovery of a complex internal structure of the T . gondii centrosome that is comprised of two independent and replicating core structures . The centrosome cores have unique structural and regulatory protein composition , and each core has a fixed spatial orientation to the budding and mitotic machineries . Genetic analysis of specific centrosome proteins indicates that each type of core serves distinct roles in regulating either cytokinesis or karyokinesis . Together , these results support a model whereby differential modulation of the centrosome cores could provide the flexibility required to achieve different modes of apicomplexan parasite replication .
To proliferate , T . gondii tachyzoites build two daughter parasites , internally enclosing an intact nucleus and a full complement of metabolic and invasion organelles . The daughter buds then consume the residual mother cell to become infectious parasites . The centrosome must be duplicated for this process to unfold , indicating a central organizing function ( shown as red dots in diagram , Fig . 1A ) [3 , 4 , 8] . Budding structures emerge close to the centrosome , and the centrosome is oriented to the unique centrocone compartment embedded in the nuclear membrane that is required for chromosome segregation [25 , 29] . The molecular basis for the complex coordinating functions of the centrosome in these parasites is not well understood . To decipher the structural features of the T . gondii centrosome , we mined Apicomplexa genome sequences for conserved centrosomal proteins ( see Table 1 and S1 Fig . for gene lists ) and examined how these factors are expressed during tachyzoite division . One of the key centrosome structural proteins present in T . gondii is an ortholog of the cartwheel protein Sas-6 [30]; note that this protein is distinct from the recently described TgSas-6L , which is associated with the apical MTOC [31] . Other canonical centrosomal proteins identified in T . gondii are centriole elongation factor Sas-4 [32 , 33] , centrin-binding protein Sfi1 [34] , gamma-tubulin , and a large protein that contains a single Aurora-like kinase domain ( Table 1 ) . The T . gondii Sas-6 ortholog retains the three-domain structure of the human protein known to be responsible for the 9-fold symmetry of the centriolar barrel ( S1A Fig . ) [35 , 36] . The organization of the PISA ( Present in Sas-6 ) motif and Sas-6 conserved domain [30 , 37] relative to the central coiled-coil domain is also preserved . The major difference between these proteins is the extended and unstructured N- and C-tails of TgSas-6 ( S1A Fig . ) . Epitope tagging the TgSas-6 protein in T . gondii tachyzoites by genetic knock-in ( TgSas-6HA , C-terminal 3xHA fusion ) demonstrated co-localization with centriolar TgCentrin1myc that was also epitope tagged in the endogenous gene locus in this clone ( Fig . 1B and see S2 Table for a full list of transgenic strains used in this study ) . DAPI ( 4’ , 6–diamidino-2-phenylindole ) co-staining was included in this analysis in order to follow the distribution of genomic DNA and to localize the nucleus in the parasites studied . This immunofluorescent microscopy analysis ( IFA ) validates the centrosome assignment of the TgSas-6 ortholog . We identified an ortholog of the centrin-binding protein Sfi1 ( Table 1 and Fig . 1C and S1B and S2 Figs . ) first discovered in budding yeast and also present in human cells [34 , 38] . The TgSfi1 ortholog is a highly disordered protein comprised of multiple short domains and a large disordered C-terminus ( 1 , 145 residues , http://www . disprot . org ) [39] . A total of 31 divergent centrin binding sites were identified that are evenly distributed over two-thirds of the protein length with none in the disordered C-terminal tail ( S1B Fig . and S2 Fig . ) . Consistent with its known role in pairing with centrin , we found endogenously tagged TgSfi1myc closely co-localized with TgCentrin1HA in the tachyzoite centrosome ( Fig . 1C ) . As expected , T . gondii γ-Tubulin ( Table 1 ) localized to the centrosome and duplicated along with TgCentrin1-associated structures ( Fig . 1D ) . Studies of the centrosome in higher eukaryotes identified a number of large coiled-coil proteins with important functions associated with centrosome and are , therefore , named the CEP ( CEntrosomal Proteins ) family of proteins ( for review see [40] ) . Using a combination of BLAST and protein structural analysis , we found several T . gondii genes encoding proteins similar to the CEP protein family of higher eukaryotes . Apicomplexan orthologs for CEP76 , CEP97 , CEP120 , and CEP250 are listed in Table 1 ( structural features shown in S1D Fig . and Fig . 2A ) . The CEP250/c-Nap1 factor is known to be required for centrosome separation in animal cells [41] , and it was of particular interest to determine if this factor was present in tachyzoite centrosomes . The T . gondii gene encoding a protein with highest similarity to human CEP250/C-Nap is TGME49_212880 ( Table 1 and S1 Table ) . Human CEP250 has coiled-coil domains running across the entirety of the protein length ( Fig . 2A , indicated by the red plot line ) . The T . gondii TGME49_212880 protein also has numerous coiled-coil domains and appears to preserve an overall architecture of the two central extended helical domains surrounded by short coiled-coil regions ( Fig . 2A , blue line and conservation block ) as well as having long extended N- and C-terminal tails . To verify centrosomal localization , we tagged TGME49_212880 by genetic knock-in with a 3xmyc epitope . TGME49_212880myc was associated with a discrete perinuclear structure consistent with the position of the centrosome ( Fig . 2B ) . We have designated TGME49_212880 ( TgCEP250 ) as the ortholog of hCEP250 . Curiously , the T . gondii genome also encodes an abundant complement of coiled-coil factors with significant homology to human CEP250 . The ten CEP250-related T . gondii proteins with highest similarity scores predicted to have extended coiled-coil domains ( S1 Table ) comprise a group of novel proteins with variable length ( from 1 , 232 to 6 , 668 amino acid residues ) . Two of the TgCEP250-like factors on this list were recently described as proteins containing the CRMP domain , were localized to the apical cone ( TGME49_244470 ) or conoid ( TGME49_252880 ) [42] , and were not studied here . Two other CEP250-like proteins were endogenously tagged , and immunofluorescence microscopy analysis revealed differential subcellular localizations . Protein encoded by TGME49_242790 gene localized to the peripheral annuli of the parasite cytoskeleton ( S3A Fig . ) , a novel compartment that was previously shown to house TgCentrin2 [43] , and therefore , was given the name TgPAP1 ( Peripheral Annuli Protein 1 ) . A non-periodic CEP250-related protein encoded by gene TGME49_265840 formed a peculiar fibrous net enclosing nucleus ( S3B Fig . ) and was named TgNMP1 ( Nuclear Mesh Protein 1 ) . The only other factor in the group of CEP250-like proteins that showed centrosomal localization similar to TgCEP250 was a novel member of the family encoded by the TGME49_290620 gene ( Fig . 2C , co-localization with TgCentrin1 ) . The distinguishing structural feature of this CEP250-related factor was a single coiled-coil domain that spanned a central 800 residues within the large 2663 amino acid protein ( Fig . 2D ) . We designated this protein TgCEP250-L1 ( TgCEP250-like protein 1 ) . To determine co-localization of this novel centrosomal protein with the related factor TgCEP250 , we created a dual-tagged transgenic strain expressing TgCEP250-L1HA and TgCEP250myc ( S2 Table ) . Surprisingly , in tachyzoites undergoing mitosis we observed four TgCEP250-positive perinuclear foci , while TgCEP250-L1HA protein co-localized with only two of the TgCEP250myc positive foci that were always proximal to the nucleus ( Fig . 2E; TgCEP250-L1HA , red; TgCEP250myc , green ) . The discovery of multiple structures in the perinuclear region occupied by centrosomal factors TgCEP250 and TgCEP250L-1 indicated the centrosome of tachyzoites has a complex organization . To investigate this further , we produced several single or dual epitope-tagged transgenic strains ( see S2 Table ) and in combination with antibodies to TgCentrin1 , TgMORN1 ( centrocone ) , or TgCenH3 ( centromere/inner kinetochore ) examined in detail the structural organization of the parasite centrosome and associated mitotic nuclear structures . An unexpected result of these studies was finding that the TgCentrin1 marker commonly used to identify centrosomes in these parasites was contained in only one of two internal protein core structures of the tachyzoite centrosome . The TgCentrin1-associated protein core was named the outer core because it was always distal to the tachyzoite nucleus ( Fig . 3A ) , while an inner core ( protein complex with immediate perinuclear orientation ) lacking TgCentrin1 harbored proteins TgCEP250myc and TgCEP250-L1HA ( Fig . 3A and B , and Fig . 2E ) . Paired with TgCentrin1HA , the TgSfi1myc protein was exclusively localized to the outer core ( Fig . 3C ) as was the centriole cartwheel protein TgSas-6 ( dual tagged strain , TgSas-6HA/TgCentrin1myc , Fig . 1B ) . The TgCEP250-L1HA-associated inner core structure was closely aligned , but resolved from the centrocone in late mitosis ( Fig . 3D , anti-TgMORN1 co-staining ) [29] . Co-staining with putative nuclear envelope factor TgNMP1myc placed the inner core on the outside of the nucleus ( Fig . 3E , TgCEP250-L1HA/TgNMPmyc ) . TgCEP250myc protein was associated with both types of core structures in mitotic tachyzoites ( Fig . 3A ) , while TgCEP250-L1HA showed more exclusive association to the inner core ( Fig . 3B ) with transient translocation to the outer core providing possible linkage between two structures ( Fig . 3D ) . T . gondii γ-Tubulinmyc showed preferential localization to the TgCentrin1-containing outer core and not to the inner core ( Fig . 3F , CEP250-L1HA/γ-Tubulinmyc ) . Finally , the visualization of the two cores ( TgSas-6HA and TgCEP250-L1myc ) with the centromere/inner kinetochore complex ( TgCenH3 ) by structured illumination microscopy ( Fig . 3G , Sas-6HA/CEP250-L1myc/anti-CenH3 ) confirmed that the parasite centrosome contained two novel cores that aligned to the kinetochore constituting a multi-layered mitotic machinery spanning the nuclear membrane of the dividing tachyzoite . A diagram ( Fig . 3H ) summarizes the protein composition and alignment of the two centrosomal cores in one-half of a dividing nucleus . To understand the duplication of the unusual tachyzoite centrosome , we followed the development of the dual centrosome cores and the centrocone in all phases of the parasite cell cycle ( Fig . 4A: TgSas-6HA , blue; TgCEP250-L1myc , red; anti-TgMORN1 , green ) . Newly formed G1 parasites inherit a single , condensed centrosome as demonstrated in the first series of images ( Fig . 4A , vertical series 1 ) . The compact G1-centrosome was tightly associated with the nuclear envelope and enlarged during G1 progression . In late G1 , the outer core containing TgSas-6HA expanded and duplicated ( Fig . 4A , series 2 ) . Simultaneously , the inner core containing TgCEP250-L1myc separated from the outer core , although it retained close association with the nuclear centrocone ( Fig . 4A , series 2 green stain ) . The expanded inner core duplicated immediately after the outer core had replicated , also retaining the distinctive spatial orientation with respect to the nucleus , inner core proximal , and the outer core distal ( Fig . 4A , series 3 ) . The duplication of the cores prepared the tachyzoite for mitosis and analysis by super-resolution microscopy demonstrated that the cores aligned with the TgCenH3 containing inner kinetochore [25] in a typical metaphase relationship ( Fig . 4B ) . The two outer cores ( TgSas-6HA , red stain ) , two inner cores ( TgCEP250-L1myc , green stain ) , and centromeres ( anti-TgCenH3 , blue stain ) were aligned in a linear array extended 1 , 300 nm from one outer core to the other . At this mitotic stage the distance between each pair of outer and inner centrosome cores measured 300 nm , reaching a maximum of 400 nm in anaphase and telophase ( Fig . 4B ) . The nuclear centrocone containing TgMORN1 protein split in two last in the observed mitotic sequence ( Fig . 4A , series 4 ) . The distinct centrosome core structures segregate into each daughter parasite ( Fig . 4A , series 5 ) indicating there is a physical mechanism that ensures each new parasite receives one copy of each type of centrosome core . The discovery of two distinct centrosome core complexes and the association of TgCentrin1 to only one core [44] indicates the Toxoplasma centrosome is more complex than previously appreciated . We turned to the large collection of conditional cell cycle mutants generated by earlier studies [21 , 27] for insight into their interdependence and regulation . Among the group of mutants with defects in mitosis , the mutant 9–86E4 possessed a temperature-sensitive allele of the outer core protein TgSfi1 ( see characterization of the protein in Figs . 1C , 3C , S1B , S1C , and S2 ) . Mutant 9–86E4 parasites grew normally at the permissive temperature of 34°C , while at 40°C they quickly growth arrested ( Fig . 5A ) . At 40°C mutant 9–86E4 parasites were typically larger than parasites grown at 34°C , although this was not systematically quantified . At high temperature , there was absence of budding . Parasites were able to duplicate the nucleus ( see circled parasite in Fig . 5B , 40°C panel ) , but with the primary budding defect this led to unequal DNA and nuclei distribution with rare DNA-free zoites present . Genetic complementation , followed by marker rescue ( Fig . 5C ) [23 , 26] and whole genome sequencing of mutant 9–86E4 ( see “Identification of Temperature-Sensitive Mutations” in Materials and Methods ) [45 , 46] , pinpointed a E1759K mutation in the TgSfi1 gene as responsible for these growth defects ( Fig . 5D ) . To monitor the ts-allele of TgSfi1 protein , we introduced 3xHA-epitope into the ts-TgSfi1 locus using modified CRISPR technology [47] . Western blot analysis of the mutant parasites expressing ts-TgSfi1HA showed high instability of the tagged protein after 24 h incubation at 40°C ( Fig . 5E ) , indicating that parasites grown at 40°C were phenotypically null for this factor . Consistent with a centrosomal function , and as a known partner with Centrin [48] , the loss of ts-TgSfi1 led to dramatic reduction in the TgCentrin1-associated cores in the 9–86E4 mutant at 40°C ( Fig . 5B and F ) . To determine how the inner core containing TgCEP250-L1 was affected by the loss of ts-TgSfi1 at high temperature , we introduced into the 9–86E4 mutant a C-terminal tagged version of the TgCEP250-L1 protein regulated by its own promoter ( see fosmid construction in “Generation of Transgenic Tachyzoite Strains” of Materials and Methods ) . The resulting transgenic strain carrying the ts-TgSfi1 ( E1759K ) mutation was used to visualize both the outer ( anti-Centrin , green ) and inner core ( TgCEP250-L1HA , red ) . At the permissive temperature , parasites maintained the expected 1:1 ratio of outer/inner cores ( Fig . 5F , average 1 . 37 at 34°C in a population average from an asynchronous culture comprised of 60% G1 and 40% S and M/C parasites ) . However , a shift to 40°C reduced the number of the TgCentrin1-outer cores below the minimal one per nucleus ( Fig . 5F , dot plot: 0 . 8 at 40°C versus 1 . 4 at 34°C ) , while the number of TgCEP250-L1HA-inner cores dramatically increased beyond the proper 1:1 nuclear stoichiometry ( Fig . 5F , red ) . While the TgCentrin1 outer cores were reduced , the few outer cores that remained were maintained in the normal distal perinuclear position observed in normal replicating parasites . By contrast , over-amplified inner TgCEP250-L1 cores decorated the nucleus in a pattern that was independent of the remaining outer TgCentrin1 cores , indicating the position and replication of the inner core structures was uncoupled from the outer core . Intriguingly , the amplification of inner TgCEP250-L1 cores was always paired with and orientated to replicated TgMORN1-associated centrocone structures , suggesting a common mechanism was directing the amplification of both structures ( Fig . 5G ) . Therefore , the loss of ts-TgSfi1 limited function of the outer core at the restricted temperature , which caused blocking of the outer core duplication while loosening control of the single duplication of the inner core . The other consequences of the defect were physical separation of two cores and severe restriction of daughter budding . The uncoupling of the replication of outer and inner cores in the ts-TgSfi1 mutant above indicates that complex regulatory mechanisms operate in the Toxoplasma centrosome to control duplication of the centrosome cores . To investigate the replication of these cores further , we studied a serine/threonine protein kinase related to mammalian ERK1 , which previous studies determined is required for tachyzoite growth , although the underlying mechanism responsible was not reported [45] . The gene TgME49_312570 is one of three protein kinase genes in T . gondii possessing a MAPK-like kinase domain [49] . The similarity of TGME49_312570 to eukaryotic MAPK factors lies almost exclusively in the ATP binding pocket , which corresponds to approximately 270 amino acids ( aa ) of an otherwise 1 , 298 aa novel protein . Given the lack of the MEKK-MEK-MAPK signal transduction module in the Apicomplexa [50] , a similarity limited to part of the kinase domain in TGME49_312570 and no established mechanistic function for this protein , we have designated this gene as TgMAPK-like 1 ( TgMAPK-L1 ) . Epitope tagging of this protein by genetic knock-in demonstrated a prominent pericentrosomal pattern surrounding the TgCentrin1 outer core in the tachyzoite S-phase and early in mitosis ( TgMAPK-L1HA; Fig . 6A and S4A Fig . ) . During budding , expression of the TgMAPK-L1HA rapidly decreased and dropped below detection level in the newly emerged G1 parasites , consistent with the cyclical pattern of the encoded mRNA ( protein cell cycle properties; S4A Fig . ; for mRNA profile see Toxodb ) . Similar staining is often seen for proteins localized in the pericentriolar matrix ( PCM ) [51] , which , because of low conservation of PCM markers , have not been identified in T . gondii . The pericentrosomal localization of TgMAPK-L1 makes this protein a first candidate for the PCM compartment . There are few reports in animal cells of MAPKs exclusively localized to the centrosome as we observed for TgMAPK-L1HA in tachyzoites , nor is direct mitotic control the mechanism typically associated with MAPKs of higher eukaryotes , in which signal transduction in response to external growth factors is the more common function [52] . Insight into the function of TgMAPK-L1 was provided by a temperature-sensitive mutant , 11–31G12 , recently identified in the large collection of tachyzoite cell cycle mutants [21] . Mutant 11–31G12 parasites carrying a L534Q mutation immediately C-terminal of the TgMAPK-L1 kinase domain ( S4 Fig . ) rapidly growth arrest at 40°C ( Fig . 6B ) with defects in the coordination of daughter budding with mitosis leading to abnormal numbers of internal daughters and nuclei ( Fig . 6C , 40°C panel ) . The unlinking of parasite budding and nuclear duplication in the mutant led to an elevated ratio of centrosome to bud numbers , as evident from anti-Centrin staining ( Fig . 6D , dot plot: 1 . 53 at 34°C versus 4 . 6 at 40°C ) . Genetic complementation followed by marker rescue and ts-allele sequencing identified ts-TgMAPK-L1 as responsible for the high temperature defects ( S4B Fig . and S4C Fig . ) . To verify that the mutation in ts-TgMAPK-L1 was solely responsible for the cell cycle defects , we transferred the L534Q mutation into parent RHΔku80 parasites and simultaneously tagged the ts-TgMAPK-L1 protein with three copies of the HA epitope ( S4D Fig . ) . The introduction of the L534Q mutation recapitulated the temperature and cell cycle defects ( S4E Fig . ) of the 11–31G12 mutant . After 20 h at 40°C , tachyzoites carrying the ts-TgMAPK-L1 mutation were yet to produce a mature daughter parasite with multiple buds forming in a single mother cell ( S4E Fig . , anti-IMC1 panel ) , indicating there is a loss of the critical controls that restrict binary division in these parasites . Western blot and IFA analysis of the ts-TgMAPK-L1HA protein ( S4E , F Fig . ) indicated that instability of this protein at high temperature creating a null phenotype is the major cause of conditional growth arrest in ts-TgMAPK-L1 mutant parasites . To further investigate the mechanism of irreversible and lethal growth arrest caused by ts-TgMAPK-L1 , we introduced 3xHA-tagged TgCEP250-L1 into the original ts-TgMAPK-L1 mutant , 11–31G12 , and with the use of anti-Centrin antibody analyzed the outer and inner centrosome cores in this mutant . Upon shift to high temperature , we observed rapid amplification of both centrosomal cores that roughly maintained the internal alignment ( Fig . 6E; anti-Centrin , green; TgCEP250-L1HA , red ) . Replication of the inner core was often delayed in the mutant , leading to accumulation of the distinctive “dumbbell” forms ( Fig . 6E , inset 40°C panel ) . We next examined the relationship between the centrosome and developing daughter structures and how the loss of ts-TgMAPK-L1 in the centrosome affected critical features of daughter budding in mutant parasites ( Fig . 7 ) . In these experiments we monitored TgMORN1 , which is present in the distinctive spindle-associated nuclear centrocone and early ( daughter ) basal ring [29] . Both of the basal ring and centrocone structures are tightly associated in a single complex in the parasite S phase and early mitosis ( Fig . 7A and 7B , 34°C ) and are in close proximity to the PCM localized TgMAPK-L1HA in wild-type tachyzoites ( Fig . 7A , magnified merge images on the right ) . Although deficiency in ts-TgMAPK-L1 in mutant parasites did not affect alignment of the nuclear centrocone with centrosome ( Fig . 7B; 40°C; lower panel; anti-TgMORN1 , green; inner core visualized with TgCEP250-L1HA , red ) , it severed stable associations between the centrocone and the early forming daughter basal rings ( Fig . 7B , 40°C panel , loose basal rings ) . Disconnection of the basal ring and centrocone compartments was observed in 50% of the ts-TgMAPK-L1 parasites at high temperature ( Fig . 7C ) . Further analysis of the ts-TgMAPK-L1 mutant revealed that a delay in basal ring development accompanied by subsequent karyokinetic counting defects was the prevalent phenotype of ts-TgMAPK-L1 parasites at 40°C . The images of co-markers TgMORN1 and TgCEP250-L1HA in the Fig . 7D illustrate three consecutive stages of the centrocone/basal ring uncoupling defect in ts-TgMAPK-L1 parasites . Note that under normal conditions , the cell cycle length of T . gondii tachyzoite used in this study is 8 h at 37°C [7 , 10] , and by 20 h post-infection , replicating parasites typically complete three cell cycles in a single infected host ( four to eight parasites per vacuole ) . By contrast , mutant 11–31G12 parasites at 40°C do not divide following invasion , forming large cells that retain the original mother cell IMC1 and basal complex ( Fig . 7D; panels 1 and 2; anti-IMC1 , red; anti-TgMORN1 , green ) . The majority of ts-TgMAPK-L1 deficient parasites had over-amplified centrocones , with a distinct subpopulation also failing to form daughter basal rings ( Fig . 7D , panel 1 ) . The lack of basal rings appeared to induce another round of nuclear duplication in the absence of budding . In other parasites , we observed the formation of daughter basal rings that retained the proximity to the centrocone ( Fig . 7D , panel 2 ) leading to the assembly of multiple abnormal daughter buds ( Fig . 7D , panel 3 ) . At high temperature , all daughter formation in these vacuoles was nonviable due to numerous mitotic defects ( Fig . 6C , retention of the mother DNA in the lower panel ) . Together , these results indicate TgMAPK-L1 has a specific role in restricting tachyzoite nuclear replication that likely involves the preservation of the physical connection between the karyokinetic and cytokinetic centers . It had been shown that several mitotic kinases , including cyclin-dependent , Polo-like , Aurora , and NIMA-related kinases , coordinate timing and fidelity of the centrosome function in higher eukaryotes [53] . Similarly , a recently identified T . gondii ortholog of the NIMA-related kinase , TgNek1–2 , showed dynamic association with the centrosome in which it positively regulated the TgCentrin1-core complex duplication [54] . Another group of the conserved eukaryotic kinases is Aurora family , which plays a central role in the establishment of the bipolar spindle [55] . A single gene in the Toxoplasma genome encodes a protein possessing a kinase domain similar to Aurora kinases of higher eukaryotes [56 , 57] . The single exon gene ( Table 1 ) encodes a large , 2 , 812 amino acid protein that is dynamically regulated over the tachyzoite cell cycle at the mRNA and protein level ( Fig . 8A and B ) . We epitope tagged TgAurora-related kinase 1 ( TgArk1 ) with 3xHA and discovered by IFA analysis that this regulatory factor provides another example of the heterologous composition of the centrosome core structures in this parasite . As predicted by the mRNA profile , TgArk1HA was not detected in G1 parasites but quickly reached maximum expression in S phase and gradually disappeared from parasites containing mature daughter buds ( Fig . 8B , red ) . In S phase and early mitosis , the TgArk1HA factor was localized exclusively to the TgCentrin1-associated outer core of the parasite centrosome ( Fig . 8B , TgArk1-head morphology ) and not in the centrocone compartment that is visualized using anti-TgMORN1 antibody ( Fig . 8C ) [24 , 29 , 58] . Later in mitosis , TgArk1HA became associated with a linear structure ( Fig . 8B and C , TgArk1-tail morphology ) lying along one side of the growing daughter bud . To determine whether TgArk1-tail associated with nuclear or microtubule structures , we treated TgArk1HA transgenic parasites with microtubule disrupting agent oryzalin . In the absence of the assembled subpellicular microtubules , the TgArk1-tail associated with the inner membrane disrupted material ( Fig . 8D , anti-IMC1 co-staining ) and not with the parasite nucleus ( Fig . 8D , DAPI ) .
The T . gondii tachyzoite stage studied here divides using a budding cycle that is limited to one round of genome duplication ( endodyogeny ) , which is a restriction lifted in the merozoite stage of the cat life cycle that replicates by a sequence of nuclear and budding cycles ( Fig . 9 ) . This ability of Toxoplasma and other parasites of this family to adapt their cell cycles to different hosts and tissues is one of the unsolved mysteries of Apicomplexa molecular biology . The key event that initiates the tachyzoite budding cycle is the duplication of the centrosome at the G1/S boundary [10 , 23] . Once duplicated , a complex process unfolds in and around these central structures , including assembly of the striated fiber required for budding initiation [15] , formation of the spindle needed for mitosis , and organelle segregation [4] . How the centrosome performs the myriad of coordinating functions may be explained in part by our discovery of a unique binary internal organization . In this study , we show that the tachyzoite centrosome has two replicating core complexes . These cores have a distinct protein composition and a stereotypical geometry that defines their orientation with respect to the nucleus and forming daughter cells ( Fig . 9 , budding cycle ) . Interestingly , protein kinases , including TgArk1 , TgMAPK-L1 , and TgNek1–2 [54] appear to decorate specific structures in and around the centrosome indicating a complex and specialized regulatory machinery likely operates from the centrosome . The inner core complex ( closest to the nucleus ) of the tachyzoite centrosome contains factors TgCEP250 and TgCEP250-L1 and is aligned with the centrocone ( Fig . 9 ) , which in turn is oriented to the intranuclear kinetochore/centromeres throughout the cell cycle [25] . This arrangement suggests the inner core and centrocone may work in concert to control the adjacent nuclear environment . The unusual co-amplification of the centrocone and the inner centrosome core in the ts-TgSfi1 mutant shown here is consistent with a shared regulatory relationship . By contrast , the principle role for the centrosome outer core complex , which contains factors TgCentrin1/TgSfi1 , TgSas-6 , and the large TgArk1 protein kinase , is in regulating the initiation and assembly of daughter buds ( Fig . 9 , budding versus nuclear cycles ) . Genetic experiments strongly support this spatial segregation of function . The loss of the outer core in mutant 9–86E4 , which we show in this study to be defective in ts-TgSfi1 , leads to a primary block in budding ( Fig . 6 ) . Conversely , the Gubbels laboratory has recently produced a mutant of TgCEP250 leading to a loss of the inner core and a primary disruption of mitosis , while duplication of the outer core and budding initiation was not lost ( Chen and Gubbels , personal communication ) . These consequences are reciprocal to the loss of budding and outer cores in the ts-TgSfi1 mutant; here the TgCEP250 inner core amplifies and the nucleus divides ( Fig . 6 ) . Altogether , these results indicate that the principle roles of the outer and inner centrosome cores in cytokinesis and karyokinesis , respectively , can be uncoupled . Notwithstanding the distinct protein composition and unusual independent function of the tachyzoite centrosome cores , there are other mechanisms that ensure each type of core is inherited by daughter parasites . Both cores duplicate at the G1/S phase transition , segregate by pairs into each daughter , and are co-regulated by factors residing in the PCM , which surrounds the internal cores . The loss of ts-TgMAPK-L1 in the temperature-sensitive mutant 11–31G12 leads to abnormal amplification of both centrosome cores and disrupts the normal linkage between the new daughter cytoskeleton and the centrocone structure ( Fig . 7 and Fig . 8 ) . Consistent with previous studies in which spindle and subpellicular microtubules were disrupted with oryzalin [28] , breaking the physical connection between mitotic and budding machinery in the ts-TgMAPK-L1 mutant also causes a loss of normal restrictions over nuclear and daughter bud duplication . These results indicate TgMAPK-L1 has a key role in determining the scale of parasite counting in Toxoplasma replication ( Fig . 9 ) . Proteomic analysis of human centrosomes reveals a more extensive asymmetrical protein distribution in centrosome structures than previously considered [59] , and examples of this core diversity are elegantly demonstrated by recent high-resolution microscopy of human centriole structures [51] . Structural heterogeneity of the centrosome is thought to pave the way for specialized development [60] and also represents stages of procentriole maturation , although the diversity of novel protein complexes in centrosomes from different eukaryotes [60] suggests we are only beginning to understand the functions involved . The family history of the centrosome cores in the T . gondii tachyzoite is not fully understood; however , the spatiotemporal behavior of these structures is very distinct from animal cells . Coccidian parasites , which include T . gondii , possess recognizable centrioles in their centrosomes that are arranged not orthogonal , but in distinct parallel configuration [17 , 61] . They are also shaped differently ( 200 nm x 200 nm ) and have a nine plus one all singlet organization [17 , 61] . There does not appear to be an equivalent procentriole maturation process as seen in animal cells; rather , we have shown here that the outer TgCentrin1/TgSfi-1 core duplicates first in early S phase , followed in minutes by the duplication of the TgCEP250/TgCEP250-L1 inner core . These core structures progressively resolve from each other over the next hour , reaching > 400 nm of separation in the post-metaphase of tachyzoite mitosis . Our results also demonstrate the TgCEP250/TgCEP250-L1 inner core never acquires outer core proteins TgCentrin1/TgSfi1 , TgSas-6 , or TgArk1 kinase . This would be expected as cells progress through successive cell cycles , if the inner core was a procentriole destined to become a mature mother centriole . These findings raise questions about the biogenesis of the tachyzoite centrosome core structures . The association of TgSas-6 cartwheel protein only with the outer TgCentrin1/TgSfi1 core provides a known template capable of seeding the duplication of this structure , as this factor templates the 9-fold symmetry of MT assembly of centrioles in other eukaryotes [30 , 35] . How the inner core replicates is more of a mystery because we did not detect TgSas-6 in this structure . It is possible we missed a transient association of TgSas-6 with the inner core , or de novo synthesis of the inner core centriole is responsible for duplication , which is known to occur in rare instances in eukaryotic cells [60] . Higher resolution studies of centrosome biogenesis in these parasites should help resolve these questions and test if both outer and inner centrosome cores in Toxoplasma tachyzoites have centrioles , although it remains a possibility that the inner core is part of the unique spindle pole complex , and therefore lacks a centriole . Comprehensive phylogenetic analysis of several centriolar factors defined the inheritance of an ancestral module that regulates the 9-fold symmetry and the assembly of the centriole microtubules that precedes bikont and unikont divergence [37] . We have shown here that several of these core centrosome proteins are conserved in T . gondii , and they are also found encoded in the genomes of most other apicomplexans . It is therefore surprising that experimental evidence of the MT centriole barrels in the centrosome exists only for the coccidian branch , while the cartwheel protein Sas-6 that templates this structure is present in all apicomplexan branches ( Table 1 ) [30 , 37] . Centrioles of Toxoplasma are small ( 200 x 200 nm ) compared to animal cells ( 700 x 250 nm ) and experimentally challenging to recognize in ultrastructure preparations [4] . Intriguingly , P . falciparum orthologs of several T . gondii outer core proteins have peak mRNA expression quite late in the intraerythrocytic cycle . Importantly , the mRNA encoding PfSas-6 cartwheel protein ( PF3D7_0607600 ) is maximum at >35 h in growth-synchronized merozoites . This profile indicates that peak PfSas6 expression occurs in late schizogony , which is several hours after the initiation of nuclear replication in nuclear cycle [14] . PfSas-4 ( PF3D7_1458500 ) and the ortholog of the large T . gondii aurora-related kinase ( PF3D7_0309200 ) as well as one of two centrins ( PfCEN2 ) that associate with the P . falciparum centrosome are also exclusively expressed in the late schizont ( PfCEN2 and 3 ) [62] . The late expression of PfSas-6 , PfCEN2 , and the aurora-related kinase may indicate they function in the final cell cycle as part of the global control machinery that coordinates budding . Such a switch from local to global control is required to allow for synchronous budding and to complete P . falciparum merozoite replication [1] . Many of the molecular details of centrosome architecture and function in the Apicomplexa remain to be explored . However , the critical problem of how cytokinesis ( budding ) might be suspended during nuclear reduplication in the Apicomplexa may be solved by the fundamental independence of a two-compartment centrosome , in which one compartment controls budding while the other rules mitosis that we describe here for the T . gondii tachyzoite . This model provides a new framework to understand how multi-nuclear schizogony replication of P . falciparum and other important apicomplexan parasites is achieved . As noted previously [1] , the unusually complex mitotic structures in these parasites appears to be a mix of strategic elements reminiscent of the mammalian extranuclear centriolar centrosome and the nuclear embedded yeast spindle pole body . This complex architecture may have evolved in these parasites to achieve regulatory diversity . Importantly , these distinct structural elements are connected and often remain physically tethered in a fixed linear array during the tachyzoite budding cycle . Spindle microtubules from the chromosomal centromeres hitch the genome to the inner core of the centrosome ( note that centromeres remain sequestered in this region through interphase ) . Emanating from the centriolar region of the centrosome , the striated fiber connects to the new apical microtubule-organizing center of the daughter bud [15] . This fiber extends during the growth of the bud pellicle and then disappears as the daughter cytoskeleton reaches maturity . Given the elegant demonstration of active cyclin-CDK protein complexes tethered to the mitotic spindle in Hela cells [63] , it is not a stretch to suggest that cell cycle checkpoints in the Apicomplexa likely exploit the remarkable physical connections from chromosome to daughter bud in order to coordinate cytokinesis and karyokinesis in the budding cycle . There are few examples of eukaryotic cells with specialized centrosome cores , and we would propose that this arrangement could achieve the regulatory flexibility required for these parasites to adapt to multiple host life cycles ( Fig . 9 ) . The implication of this model is that differential regulation of centrosome core composition and/or activity could provide the switch between the local control of chromosome replication in the nuclear cycle to the globally controlled "copy once" regimen of the budding cycle ( Fig . 9 ) . It is conceivable that post-translational regulation of the outer core could regulate activation or suspension of the budding cycle ( Fig . 9 , nuclear cycle ) , and the presence of TgArk1 exclusively in this core structure highlights a possible candidate regulator . Further studies to define how multiple organizational hubs ( i . e . , centrosome and centrocone ) segregate responsibilities within the apicomplexan cell cycle will be important to understand this critical and truly fascinating aspect of the parasites’ life cycles .
Parasites were grown in human foreskin fibroblasts ( HFF ) as described [64] . All transgenic and mutant parasite lines are derivatives of the RHΔhxgprt parasite strain [65] . Temperature-sensitive clones 9–86E4 and 11–31G12 were obtained by chemical mutagenesis of the RHΔhxgprt strain [21] . Growth measurements were performed using parasites pre-synchronized by limited invasion , as previously described [12 , 26] . Parasite vacuoles in the infected cultures were evaluated over various time periods with average vacuole sizes determined at each time point from 50–100 randomly selected vacuoles . Endogenous tagging by genetic knock-in technique . Selected T . gondii proteins were tagged with a triple copy of the HA or myc tag by genetic knock-in ( See S2 Table for full list of genes , primers and transgenic strains created in the current study ) . PCR DNA fragments encompassing the 3′-end of the gene of interest ( GOI ) were used to construct the plasmids pLIC-GOI-HA3X/dhfr-DHFR-TS , pLIC-GOI-HA3X/dhfr-HXGPRT or pLIC-GOI-myc3X/dhfr-DHFR-TS and the constructs were electroporated into RHΔku80 strain deficient in non-homologous recombination [66] . The double-tagged transgenic lines were established by sequential selection under alternative selection markers with cloning . Expression of the epitope tagged fusion proteins was verified by IFA . A new strain expressing the ts-TgMAPK-L1 mutation was generated in the RHΔku80 strain . To introduce the L534Q mutation into a new genomic background , we PCR amplified a 3 , 354 bp DNA fragment from mutant 11–31G12 that includes the 3′ end of the TGGT1_312570 ( ts-TgMAPK-L1 ) using primers LIC-TgMAPK-L1_FOR and LIC-TgMAPK-L1_REV ( S2 Table ) . In parallel , we also amplified a genomic fragment from the wild-type TGGT1_312570 locus in the RHΔku80 to generate 3xHA tagged native TgMAPK-L1HA . The PCR products were cloned into pLIC-HA3X/dhfr-HXGPRT vector , and the resulting construct was introduced in RHΔku80 strain [66] . Strains were tested for growth at 40°C and analyzed by IFA and western blot analysis . Endogenous tagging using CRISPR/Cas9 technology . To introduce 3xHA-epitope to the C-terminus of ts-TgSfi1 , we constructed gsTgSfi1 CRISPR/Cas9 plasmid by modifying sgUPRT-CRISPR/Cas9 plasmid generously provided by Dr . David Sibley ( Washington University , MO , United States ) , as previously described [47] . Replacement was driven by Q5 DNA polymerase mutagenesis ( New England Biolabs , Ipswich , MA , US ) using primers specific for TgSfi1 gsRNA ( S2 Table ) . To obtain the insertion cassette that includes C-terminus of TgSfi1 gene fused to 3xHA-epitope and HXGPRT selection marker we introduced synonymous mutation into the PAM site of the corresponding gsRNA in the pLIC-tgSfi1-HA3X/dhfr-HXGPRT plasmid using Q5 DNA polymerase mutagenesis ( see primers design in the S2 Table ) . The amplified insertion cassette and sgTgSfi1 CRISPR/Cas9 plasmid were mixed in 1:1 molar ratio and electoporated into the mutant 9–86E4 parasites . Selection for growth in the mycophenolic acid/xanthine media was performed at 34°C . Ectopic expression of epitope-tagged TgCEP250-L1 . A large insert fosmid clone containing a fragment of chromosome IX ( 3757055–3790435 ) that includes the TgCEP250-L1 gene ( RHfos10J10 ) [67] was modified by recombination with a cassette containing a 3xHA epitope tag , a chloramphenicol selection cassette ( T . gondii selection ) , and a gentamycin selection cassette ( for selection in bacteria ) downstream of the TgCEP250-L1 gene . Primers contained appropriate overhangs to provide homology for recombination into the TgCEP250-L1 gene 3′end in the fosmid were used ( S2 Table ) . Recombined fosmid was introduced in the mutants 9–86E4 and 11–31G12 and selected in the medium with 20 μM chloramphenicol . Established clones were analyzed by IFA and tested for growth at the permissive ( 34°C ) and non-permissive ( 40°C ) temperatures . Ectopic expression of epitope-tagged TgCentrin2 . The coding sequence of TgCentrin2 ( TGME49_250340 ) was amplified from RHΔhxgprt cDNA library ( see primers design in the S2 Table ) and cloned into the pDEST_tub-YFP_CAT plasmid by recombination ( Gateway , Life Technologies ) , which resulted in the C-terminal fusion of TgCentrin2 with YFP-protein . Recombinant plasmid was introduced into RHΔku80 parasites expressing endogenously tagged TgPAPHA protein . Temperature-sensitive mutants 9–86E4 ( ts-TgSfi1 ) and 11–31G12 ( ts-TgMAPK-L1 ) were complemented using the ToxoSuperCos cosmid genomic library as previously described [21 , 23 , 26] . Mutant parasites were transfected with cosmid library DNA ( 50 μg DNA/5 x 107 parasites/transfection ) in 20 independent electroporations . After two consecutive selections at 40°C , parasites were selected by the combination of high temperature and 1 μM pyrimethamine . Double-resistant ( temperature and drug ) populations were passed four times before genomic DNA was isolated for marker-rescue [21] . To identify the complementing locus in T . gondii chromosomes , rescued genomic inserts were sequenced using a T3 primer and the sequences mapped to the T . gondii genome ( Toxodb ) . To resolve the contribution of individual genes in the recovered locus , we transformed the mutants with individual cosmids from a cosmid collection mapped to the T . gondii genome ( toxomap . wustl . edu/cosmid . html ) . For direct complementation of the mutant 11–31G12 with DNA fragments , the TGGT1_312570 gene locus ( including 550 bp 5′UTR and 367 bp of 3′UTR ) was amplified from genomic DNA isolated from the parental strain RHΔhxgprt or the mutant ( S2 Table ) . Specific cosmids or PCR fragments were transfected into 1 x 107 parasites using 6–10 μg of purified DNA . To quantify genetic rescue , established drug-resistant populations were tested for growth at the high temperature by standard plaque assay performed in triplicate [21 , 23 , 26] To validate the cosmid genetic complementation we used next generation sequencing to verify the mutation in mutant 9–86E4 ( ts-TgSfi1 ) . Whole genome DNA libraries were prepared , sequenced , and analyzed for single nucleotide variation ( SNV ) according to published methods [68] . In brief , genomic DNA from the mutant and parent strains was fragmented and then , following end-repair , ligated with Illumina paired-end adaptors ( Illumina , CA , US ) . Purified library fragments were enriched via PCR amplification using Illumina paired-end PCR primers ( Illumina , CA , US ) , the fragments normalized to 2 nM and denatured using 0 . 1 N NaOH . Denatured libraries were cluster amplified on V2 flowcells using V2 chemistry according to manufacturer’s protocol ( Illumina , CA , US ) . Flowcells were sequenced on Genome Analyzer II’s , using V3 Sequencing-by-Synthesis kits and analyzed with the Illumina’s v1 . 3 . 4 pipeline following manufacturer’s protocol ( Illumina , CA , US ) . The resulting FASTQ sequence traces were aligned to T . gondii GT1 genome reference v7 . 3 and the Human genome reference build 37 . MOSAIK was used to perform the alignments using the standard parameters described in the documentation V1 . 0 ( available at https://github . com/wanpinglee/MOSAIK/wiki/QuickStart ) . SNVs were called using the SNV caller FreeBayes , using standard parameters as described in the documentation , software version 0 . 7 . 2 . SNVs were filtered to remove SNVs whose calls had less than 5x coverage in the mutant and 3x in the parent , a p-value less than 0 . 8 , and did not have a single allele that comprised 70% or more of the sequence reads . Purified parasites were washed in PBS and collected by centrifugation . Total lysates were obtained by resuspending the parasite pellets with Leammli loading dye , heated at 95°C for 10 min , and briefly sonicated . After separation on the SDS-PAGE gels , proteins were transferred onto nitrocellulose membrane and probed with monoclonal antibodies against HA- ( rat 3F10 , Roche Applied Sciences ) , myc-epitope ( mouse , Cell Signaling Technology ) , and α-Tubulin ( mouse 12G10 , kindly provided by Dr . Jacek Gaertig , University of Georgia , GA , US ) . After incubation with secondary HRP-conjugated anti-mouse or anti-rat antibodies , proteins were visualized by enhanced chemiluminescence detection ( PerkinElmer ) . Confluent HFF cultures on glass coverslips were infected with parasites for the indicated times . Infected monolayers were fixed , permeabilized , and incubated with antibody as previously described [26] . The following primary antibodies were used: mouse monoclonal αMyc ( Santa Cruz Biotechnology , Santa Cruz , CA , US ) , αTgCenH3 [25] , rat monoclonal αHA ( Roche Applied Sciences ) , rabbit polyclonal αMyc ( Cell Signaling Technology ) , αHuman Centrin 2 [23] , αMORN1 ( centrocone and basal complex stains , kindly provided by Dr . Marc-Jan Gubbels , Boston College , MA , US ) , and αIMC1 ( parasite shape and internal daughter bud stains , kindly provided by Dr . Gary Ward , University of Vermont , VT , US ) . All Alexa-conjugated secondary antibodies ( Molecular Probes , Life Technologies ) were used at dilution 1:500 . Coverslips were mounted with Aquamount ( Thermo Scientific ) , dried overnight at 4°C , and viewed on Zeiss Axiovert Microscope equipped with 100x objective . Images were processed in Adobe Photoshop CS v4 . 0 using linear adjustment when needed . Super-Resolution images were acquired using the Zeiss ELYRA S1 ( SR-SIM ) microscope using a 63x lens . Images were collected and processed using Zeiss Zen software . | Apicomplexan parasites infect many different hosts and tissues , causing numerous human diseases , including malaria . These important pathogens have a peculiar cell cycle in which chromosomes sometimes amplify to remarkable levels , followed by concerted cell division—providing an unusual proliferative capacity . This capacity for proliferation , combined with an ability to change the scale of replication when needed , are hallmarks of the cell cycles of these parasites . Yet the molecular mechanism responsible for these peculiar cell cycles remains one of the unsolved mysteries of Apicomplexa biology . Here we show that the centrosome—an organelle that orchestrates several aspects of the cell cycle—of the apicomplexan parasite Toxoplasma gondii contains specialized structures that coordinate parasite cell division . Our findings demonstrate that a two-part centrosomal architecture , comprising an inner and an outer core with distinct protein compositions , segregates the processes of mitosis from the assembly of new daughter parasites . The modular organization of the centrosome offers an explanation for how cell division can be suspended while the parasites amplify their genome to the biotic scale required for their life cycles . It is unknown whether these distinct centrosome core complexes evolved independently in Apicompexa . Another possibility is that the foundations for these mechanisms were present in the original eukaryote , which could explain how the distinct extranuclear centrosome of animal cells and the novel yeast spindle pole body of the nuclear envelope may have evolved from a common ancestor . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Novel Bipartite Centrosome Coordinates the Apicomplexan Cell Cycle |
Burkholderia pseudomallei ( B . pseudomallei ) , the causative agent of melioidosis , is a deadly pathogen endemic across parts of tropical South East Asia and Northern Australia . B . pseudomallei can remain latent within the intracellular compartment of the host cell over prolonged periods of time , and cause persistent disease leading to treatment difficulties . Understanding the immunological mechanisms behind persistent infection can result in improved treatment strategies in clinical melioidosis . Ten-day LD50 was determined for the small-colony variant ( SCV ) and its parental wild-type ( WT ) via intranasal route in experimental BALB/c mice . Persistent B . pseudomallei infection was generated by administrating sub-lethal dose of the two strains based on previously determined LD50 . After two months , peripheral blood mononuclear cells ( PBMCs ) and plasma were obtained to investigate host immune responses against persistent B . pseudomallei infection . Lungs , livers , and spleens were harvested and bacterial loads in these organs were determined . Based on the ten-day LD50 , the SCV was ~20-fold less virulent than the WT . The SCV caused higher bacterial loads in spleens compared to its WT counterparts with persistent B . pseudomallei infection . We found that the CD4+ T-cell frequencies were decreased , and the expressions of PD-1 , but not CTLA-4 were significantly increased on the CD4+ and CD8+ T cells of these mice . Notably , persistent infection with the SCV led to significantly higher levels of PD-1 than the WT B . pseudomallei . Plasma IFN-γ , IL-6 , and IL-17A levels were elevated only in SCV-infected mice . In addition , skewed plasma Th1 and Th17 responses were observed in SCV-infected mice relative to WT-infected and uninfected mice . B . pseudomallei appears to upregulate the expression of PD-1 on T cells to evade host immune responses , which likely facilitates bacterial persistence in the host . SCVs cause distinct pathology and immune responses in the host as compared to WT B . pseudomallei .
Burkholderia pseudomallei ( B . pseudomallei ) is a Gram-negative , aerobic , rod-shaped bacillus that causes melioidosis , a potentially fatal disease endemic across parts of tropical South East Asia and Northern Australia afflicting human and animals[1 , 2] . B . pseudomallei has been classified as a category B biothreat agent by the Center for Disease Control and Prevention[3] . The most common routes of infections include percutaneous inoculation , inhalation , and ingestion of contaminated soil , dust , or aerosol[3] . Besides acute infection , it has also been well documented that B . pseudomallei can cause persistent disease , where the host normally shows little to no signs of infection during the prolonged period of latency[4–6] . Eighty percent of the individuals diagnosed with melioidosis have one or more underlying conditions , suggesting the possibility of persistence of B . pseudomallei in the host during a prior exposure or infection , and only relapse when the host immunity wanes[3] . Evidence suggests that persistent infections could be associated with small-colony variants ( SCVs ) [7–11] . SCVs are reportedly defective in growth and form pin-point colonies on agar medium after 24–72 hours [12–14] . Although described in various bacteria , SCVs of Staphylococcus aureus are the most extensively studied[14] . SCVs are relatively less susceptible to antibiotics and difficult to treat , causing recurrent diseases [7 , 8 , 14 , 15] . More importantly , SCVs of B . pseudomallei reportedly have higher degree of antibiotic resistance and distinct virulence-associated proteins [16–18] . Thus , SCVs might be important in melioidosis as relapse after treatment is common [19] . To date , there is only limited in vivo information regarding interactions between SCVs and WT and the host . The adaptive immune response attributes to melioidosis in the host still remains poorly understood . A recent study showed that strong CD4+ and CD8+ T-cell responses were required for patients to survive acute melioidosis[20] . CD4+ T cells were also reported to protect mouse in the late stage of B . pseudomallei infection [21] . Therefore , robust T-cell functions are paramount to protection against B . pseudomallei infection in the host . Nevertheless , T-cell responses could likely undergo attrition following increased expression of co-inhibitory molecules on T cells . For instance , the expressions of programmed death-1 ( PD-1 ) and cytotoxic T-lymphocyte-associated protein 4 ( CTLA-4 ) can cause T-cell exhaustion following engagement with their cognate ligands expressed on host cells [22–25] . Mounting evidence suggests that pathogens , such as Mycobacterium spp . and human immunodeficiency virus ( HIV ) , can upregulate PD-1 and CTLA-4 expressions during chronic infections to evade adequate host immune responses[26–31] . A recent study demonstrated that PD-L1 on B . pseudomallei-infected human polymononuclear neutrophils inhibit T cell activities in vitro [32] . However , direct effects of persistent B . pseudomallei infection on PD-1 expression on T cells using an in vivo model have yet to be investigated . Here , we hypothesized that B . pseudomallei can employ the strategy of upregulating co-inhibitory molecules to evade host immune surveillance , potentially rendering its persistence in the host . To elucidate the role of PD-1 and CTLA-4 , we generated a persistent murine model of B . pseudomallei infection and compared between wild-type and SCVs in regard to the host immune responses . Besides , we also determined Th1 , Th2 , and Th17 cytokine levels in peripheral blood to understand the immunoregulatory system during persistent B . pseudomallei infection .
All mouse experiments were performed following the guidelines of the University of Malaya Animal Care and Use Policy ( UM ACUP ) and the protocols were reviewed and approved by the Animal Experimental Unit of University of Malaya , Kuala Lumpur , Malaysia ( Ref . No . : 2014-08-05/MMB/R/JSV ) . The University of Malaya Animal Care and Use Policy ( UM ACUP ) is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) , and conforms to all government laws and regulations . It provides for approved research and teaching activities , and safeguards the health and welfare of staff and students involved in scholarly activities using animals or animal parts derived from animals . Animals were maintained with controlled temperature , 12h light /dark cycles and given water and feed ad libitum . All efforts were made in order to minimize animal suffering . B . pseudomallei strains OB ( WT , INSDC: APLK00000000 . 1 ) and OS ( SCV , INSDC: APLL00000000 . 1 ) were originally isolated as previously described [33] from a clinical melioidosis case admitted to the University Malaya Medical Center ( UMMC ) . OS was differentiated from OB by its slow growth rate on nutrient agar under aerobic conditions . OB produced clear visible colonies within 24 hours , while OS produced small visible colonies within 48 hours . In addition , OB and OS were characterized with commercial analytical profile index API 20NE ( Biomeriux ) test and in house PCR assay specific to B . pseudomallei [34] . A single colony was chosen from OB and OS streaked on nutrient agar , and inoculated into LB broth at 37°C in an incubator with 200rpm . Overnight cultures were diluted with Luria-Bertani ( LB ) broth . Fifty milliliters of initial cultures with an OD of 600nm ( OD600 ) of 0 . 05 were incubated at 37°C with 200rpm for 48 hours in 250mL conical flasks . OD600 of the cultures were measured from time to time . Appropriate dilutions were conducted with LB broth when necessary to obtain readable range of a spectrophotometer . A single colony from nutrient agar was cultured in Luria-Bertani ( LB ) broth and incubated overnight with the same conditions as in growth curve . Later , cultures were adjusted to an OD600 of 0 . 05 and incubated again . Cultures that reached the mid-logarithmic phase ( OD600 0 . 5–0 . 7 ) were harvested , washed , and re-suspended in phosphate-buffered saline ( PBS ) . Subsequently , the bacterial suspensions were serially diluted ten-fold with PBS until the desired of inoculum was obtained . Inoculum was plated on nutrient agar to enumerate colony- forming unit ( CFU ) . All procedures involving animal infections were performed according to ethics code approved by the Animal Experimental Unit ( AAALAC accredited ) of University of Malaya . ( Ref . No . : 2014-08-05/MMB/R/JSV ) . Seven to eight-week-old female BALB/c mice were used for infection . They were obtained from University Putra Malaysia , and acclimatized for two weeks prior to infection . Mice were under ad libitum feeding conditions in all experiments . Mice were anaesthetized with isoflurane ( Piramal Healthcare Ltd ) , and 10μL of bacterial inoculum was administered via intranasal route . Groups of six mice were infected with different doses , ranging from 102–107 CFU/mouse . Mice were observed daily . Ten days LD50 was determined using Reed and Muench method[35] . Persistent infection was generated in mice as described before[36] with slight modifications . Sublethal bacterial dose ( ~2–8% of LD50 ) was optimized based on previously determined LD50 of each strain . Mice were infected with OB and OS strains , respectively . Only mice that survived for ≥60 days after infection were sacrificed for use in the downstream experiments . Mice inoculated with PBS were used as controls , and will be referred as uninfected mice for simplicity . Mice with persistent B . pseudomallei infection were anaesthetized with isoflurane , and blood was drawn via terminal cardiac puncture . Heparinized blood samples were centrifuged , and plasma samples were collected and stored at -80°C until use . PBMCs were isolated as described before [37 , 38] . Briefly , PBMCs were prepared by density-gradient centrifugation over Ficoll-Paque ( Sigma Aldrich ) , and washed twice with PBS . Cell viability was determined by 0 . 4% trypan blue ( Life Technologies ) staining . Lungs , livers , and spleens of mice with persistent B . pseudomallei infection were aseptically harvested and homogenized in PBS . Later , the suspension was ten-fold serially diluted and inoculated on selective Ashdown’s agar[39] at 37°C to examine the presence of B . pseudomallei . CFU in each organ was enumerated after 48 hours of incubation to determine the bacterial burden in each organ . To confirm if B . pseudomallei grew on Ashdown’s agar , DNA was extracted from the colonies by boiling method , and PCR targeting B . pseudomallei-specific 16S rRNA gene was performed as described before[40] with slight modifications . The total volume for PCR was 25μL , including 1μL of DNA , 0 . 2μM of BS3L and BS4R primers each , 5X PCR Master Mix ( containing 7 . 5mM MgCl2 , 1mM deoxynucloside triphosphates , 10% glycerol , 0 , 1 unit/μL Taq Polymerase ) ( i-DNA Biotechnology , Malaysia ) , and dH2O . PCR products were resolved on 1 . 5% ( w/v ) agarose gel . In each tube , 1x10^6 PBMCs were stained with Fixable Viability Stain 510 ( BD Biosciences , cloneR35-95 ) , Alexa Fluor 488 hamster anti-mouse CD3e ( BD Biosciences , clone 145-2C11 ) , Pe-Cy7 rat anti-mouse CD4 ( BD Biosciences , clone GK1 . 5 ) , APC-H7 rat anti-mouse CD8a ( BD Biosciences , clone 53–6 . 7 ) , APC hamster anti-mouse PD-1 ( BD Biosciences , clone J43 ) , and PE hamster anti-mouse CTLA ( BD Biosciences , clone UC10-4F10-11 ) . Respective isotype control for each antibody was used to place proper gate for backgrounds . All antibodies were pre-titrated for optimal working concentration . Data were acquired on 8-color FACS Canto II ( BD Biosciences ) immunocytometry system with FACS Diva software ( BD Bioscience ) . Data were analyzed using Flowjo software version 10 ( Tree Star , Oregon , USA ) . Plasma samples collected were used for Cytometric Bead Array ( CBA ) Mouse Th1/Th2/Th17 Cytokine Kit ( BD Biosciences ) to measure the levels of IL-2 , IL-4 , IL-6 , IL-10 , IL-17A , TNF-α , and IFN-γ according to the manufacturer’s instructions . All results were analyzed using FCAP Array 1 . 0 ( Softflow , USA ) . Two-tailed Mann Whitney-U test was used to determine statistical significance among different groups , due to the assumption that samples might not follow Gaussian distribution . Results were illustrated using Box-Whisker Plots . Multiple correlation analysis was done using a two-tailed Spearman’s Rank Order Correlation . All statistical analysis was done using GraphPad Prism 6 software ( La Jolla , USA ) . Level of significance was first set at *P<0 . 05 , ** P<0 . 01 , ***P<0 . 001 , and adjusted with appropriate Bonferroni correction .
We first compared the growth kinetics of OB ( WT ) and OS ( SCV ) to characterize phenotypic differences observed after aerobic incubation on nutrient agar at 37°C ( Fig 1A and 1B ) . Our growth kinetics results revealed distinct growth profiles between the two morphotypes . We found that the growth of OB was relatively rapid as compared to the OS morphotype . OB showed a very short lag phase , and it doubled its OD600 reading within the first hour ( Fig 1C ) of growth . On the other hand , OS showed a lag phase of ~2 hours , and it started to double its OD600 reading from the third hour onwards . After entering the log phase , OB reached its stationary phase after 32 hours , with the OD600 reading reaching 14 . OS reached its stationary phase after 14 hours , which was much earlier than OB , with the OD600 reading only reaching 5 . 5 . OB did not reach its death phase within 48 hours , while OS reached its death phase after 28 hours . Our results indicated a defect in SCV to grow in vitro , compared to the WT morphotype of B . pseudomallei . Next , we seek to understand the virulence of WT and SCV in vivo , in the interest of determining appropriate sub-lethal dose for persistent B . pseudomallei infection . We compared the virulence of OB and OS using a BALB/c mouse model . Ten days survival rate of BALB/c mice in each group infected with OS and OB via intranasal route was observed ( Fig 2A and 2B ) . We noticed that 1 . 77x105 CFU of OB was able to kill all animals within 7 days , while a 10-fold lower dose of OB was only capable of killing two in 10 days . We observed that OS was less virulent than OB , with 1 . 53x105 CFU of OS only killed two in 10 days and 1 . 53x106 CFU of OS killed four in 8 days . In addition , we performed Log-Rank test to investigate the survival curves between same log CFU of OB and OS , and confirmed that OS was significantly less virulent than OB ( P<0 . 001; Fig 2C ) . Ten days LD50 was calculated using Reed and Muench method[35] . The ten days LD50 for OB and OS in BALB/c mice with intranasal infections were 3 . 15x104 CFU and 6 . 45x105CFU , respectively . Based on the determined LD50 , OS was ~20 times less virulent than OB . We generated a persistent murine model to study host immune responses against persistent B . pseudomallei infection . A sublethal ( ~2–8% of the LD50 ) dose was used to inoculate groups of six mice via intranasal route . Persistent infection was defined as survival of mice despite B . pseudomallei infection for ≥60 days , and presence of the bacteria in lungs , livers or spleens[36] . Homogenates of lungs , livers or spleens harvested from mice with persistent B . pseudomallei infection after 60 days showed growth on selective Ashdown’s agar , with a detection limit of 20CFU/organ . Bacterial loads in lungs , livers , and spleens from each mouse were compared . We found that OS caused significantly higher bacterial burden in spleens than OB , but not in lungs and livers ( Fig 3A–3C ) . In addition , we noted that OS was more likely to cause liver and splenic abscesses in mice compared to OB ( Fig 3D and 3E ) . These data suggested that WT and SCV could have different pathogenesis in vivo . DNA was also extracted from colonies on Ashdown’s agar , and PCR which amplified the 397bp region corresponding to a conserved 16S rRNA sequence of B . pseudomallei was done to exclude the possibility of the presence of other bacteria species on Ashdown’s agar ( Fig 3F ) . We sought to compare the frequencies of CD4+ and CD8+ T cells between mice without and with persistent B . pseudomallei infection by using flow cytometry ( Fig 4A ) . Our data revealed that the CD4+ T-cell frequencies were significantly decreased in both OB- and OS-infected than uninfected mice ( Fig 4B ) . No statistical significance was seen between the two strains in their CD4+ T-cell frequencies . On the other hand , the CD8+ T-cell frequencies were remarkably increased in both OB- and OS-infected than uninfected mice . No statistical significance was observed for CD8+ T-cell frequencies between the two strains ( Fig 4C ) . We also found that the CD4+/CD8+ T-cell ratios were remarkably reduced in the OB- and OS-infected than uninfected mice ( Fig 4D ) . No statistical significance was found with CD4+/CD8+ ratios between the two strains . Together , we concluded that B . pseudomallei can alter both CD4+ and CD8+ T-cell frequencies during persistent infection , possibly suggesting that the ability of the bacteria to modulate host immune responses . PD-1 and CTLA-4 belong to the CD28-B7 superfamily , and their expressions on T cells are reportedly upregulated in persistent infections such as tuberculosis and HIV [26–31] . Thus , we investigated if persistent B . pseudomallei infection led to any signs of immune exhaustion on T cells in mice . We found that PD-1 expression ( MFI ) was higher on CD4+ T cells of both OB- ( median , 257; IQR , 75 . 8 ) and OS-infected ( median , 686 . 0; IQR , 586 . 0 ) , as compared to uninfected mice ( median , 204 . 5; IQR , 32 . 7; Fig 5A and 5B ) . PD-1 expression was strain-dependent as we observed higher PD-1 MFI on CD4+ T cells of OS , as compared to OB-infected mice ( P<0 . 005 ) . At the same time , PD-1 MFI was significantly increased on both CD8+ T cells of OB- ( median , 88 . 1; IQR , 49 . 3 ) and OS-infected ( median , 233 . 5; IQR , 232 ) relative to uninfected mice ( median , 28 . 9; IQR , 56 . 5; Fig 5C ) . We also found higher PD-1 expression on CD8+ T cells of OS , as compared to OB-infected mice ( P<0 . 005 ) . Given the indispensable role of CD4+ T cells in mice with persistent B . pseudomallei infection[21] , and that reduced CD4+ T-cell frequency and increased PD-1 expression on CD4+ and CD8+ T cells were observed in our experiments , it would be interesting to see if there exists any correlations between these parameters . We found significant negative correlations between CD4+ T-cell frequency and PD-1 expression on CD4+ T cells and also between CD4+ T-cell frequency and PD-1 expression on CD8+ T cells ( Fig 5F ) . We also observed a strong positive correlation between PD-1 expression on CD4+ T cells and CD8+ T cells ( Fig 5F ) . Besides PD-1 , we also investigated CTLA-4 expressions on CD4+ and CD8+ T cells . Interestingly , we did not observe any statistical significance in CTLA-4 expression ( MFI ) on CD4+ T cells among OB-infected ( median , 174 . 5; IQR , 349 . 3 ) , OS-infected ( median , 550 . 5; IQR , 1629 . 5 ) and uninfected mice ( median , 222 . 5; IQR , 297 . 7; Fig 5A and 5D ) . We also found no significant differences in CTLA-4 expression on CD8+ T cells among OB-infected ( median , 46 . 9; IQR , 16 . 1 ) , OS-infected ( median , 52 . 0; IQR , 10 . 3 ) and uninfected mice ( median , 42 . 4; IQR , 19 . 3; Fig 5E ) . Together , our results suggest that PD-1 could be one of the inhibitory molecules that play an important role in persistent B . pseudomallei infection . Since PD-1 negatively regulates T-cell functions , we asked whether PD-1 expressions on T cells in PBMCs from mice with persistent B . pseudomallei infection could alter plasma Th1 , Th2 , and Th17 cytokine levels . Cytokine levels were measured using a CBA mouse Th1/Th2/Th17 ( BD Biosciences ) assay by flow cytometry . Indeed , we found that several cytokines , especially pro-inflammatory cytokine levels in OS-infected mice were significantly higher than OB-infected and uninfected mice . IFN-γ and IL-6 levels in OS-infected mice were significantly higher than OB-infected and uninfected mice ( Fig 6C and 6E ) . In addition , we observed higher IL-17A level in OS-infected as compared to uninfected mice , but not compared to OB-infected mice ( Fig 6G ) . Interestingly , no significant difference in any cytokine level was found between OB-infected and uninfected mice . These results demonstrated that the SCV can trigger significantly the release of more pro-inflammatory IFN-γ , IL-6 , and IL-17 , possibly due to a higher bacterial load in spleens compared to WT ( Fig 5C ) . Last but not the least , we asked if plasma cytokines were skewed toward Th1 , Th2 , or Th17 profiles in mice with persistent B . pseudomallei infection . We measured IFN-γ/IL-4 , IFN-γ/IL-17A , and IL-4/IL-17A as an indication of Th1/Th2 , Th1/Th17 , and Th2/Th17 balance , respectively . We observed skewed Th1 and Th17 responses in OS-infected , as compared to OB-infected and uninfected mice ( Fig 7A–7C ) . We found that Th1/Th17 ratio was higher , and Th2/Th17 ratio was lower in OS-infected relative to the OB-infected and uninfected mice . We further performed correlation analysis between the levels of plasma cytokines to better understand relationships between each cytokine ( Table 1 ) . Our investigations showed that IFN-γ positively correlated with IL-6 , IL-10 , and IL-17A . IL-6 correlated positively with IL-10 and IL-17A . IL-4 had a positive correlation with IL-10 and IL-17A . Lastly , IL-10 correlated positively with IL-17A . These results suggested a complex regulation between Th1 , Th2 , and Th17 cytokines in mice with persistent B . pseudomallei infection .
Two common mutants , hemB and menD , exhibit electron transport-defective SCVs phenotype [41 , 42] . There are mixed opinions in regard to the virulence of SCVs . Some have reported that the virulence of SCVs decreased in rabbit and Caenorhabditis elegans models [43–45] . A more recent data showed that clinical SCVs , hemB , and menD mutants of S . aureus were significantly less virulent than their genetically complemented isogenic strains [46] . Here , we showed that the SCV of B . pseudomallei is ~20-fold less virulent than its parental WT strain by determining the LD50 in a murine model . In contrast , Johnsson et al . have reported that NMRI mice infected with hemB mutant of S . aureus had a higher frequency and severity of arthritis than its isogenic parental strain[47] . The most important aspect to notice is Johnsson et al . have used severity of arthritis as the parameter to determine virulence of SCVs . We and others have used survival rate as the parameter [43–46] . The discrepancy in the measuring parameter for virulence might have produced different results . We propose that survival rate is a more accurate and objective parameter for determining the virulence of an organism , as severity of a disease could be subjective based on researchers and does not necessarily correlate to death . Thus , we believe that SCVs are less virulent , but more persistent in the host than their parental strains , which is congruent with the fact that SCVs have always been recovered from patients with chronic or relapsing persistent infections . Persistent infections could possibly alter CD4+ and CD8+ T-cell frequencies , since these T cells are actively involved in cellular immune responses against infections . For instance , the hallmark of chronic HIV infections is CD4+ T-cell depletion , which occurs partly because HIV preferentially infects CD4+ T cells and triggers their apoptosis[48] . Our results revealed that CD4+ T-cell frequencies were lower in mice with persistent B . pseudomallei infection . These results might suggest the unique role of CD4+ T cells in conferring protection to mice with persistent B . pseudomallei infection , which was also demonstrated by the other study[21] . The mechanism behind the decrease in CD4+ T cells requires further investigation . PD-1 causes T-cell exhaustions , and is reportedly upregulated in Mycobacterium tuberculosis ( MTB ) and HIV infections [26 , 28] . Nevertheless , the role of PD-1 in persistent B . pseudomallei infection has rarely been studied . Hence , we characterized the levels of PD-1+ T cells in persistent B . pseudomallei infection using a murine model , and compared between the SCV and its isogenic parental strain . We demonstrated for the first time that PD-1 expressions were increased on both CD4+ and CD8+ T cells in mice with persistent B . pseudomallei infection . Interestingly , mice infected with the SCV showed higher PD-1 expressions on both T cell subsets , as compared to its wild-type counterpart . There could be two possible explanations for this observation . The first explanation would be higher bacterial loads in spleens of SCV-infected mice led to higher inflammatory responses , thus a higher PD-1 expression on T-cells was required to limit inflammatory-induced pathology . Another explanation would be the distinct feature of SCVs in persistent infections . It is plausible that SCVs cause more persistent infections due to their relatively better abilities in impairing protective T-cell responses , rendering them better potential to persist in the host . Although PD-1 and CTLA-4 are closely related , our results showed that PD-1 , but not CTLA-4 , was one of the co-inhibitory molecules exploited by B . pseudomallei to possibly evade host immune responses , with SCVs having greater abilities in elevating PD-1 expression on T cells . Hence , it poses the possibility of using PD-1 pathway inhibitors against persistent B . pseudomallei infection; this also because PD-1 antagonists have shown remarkable results against different diseases particularly cancer [49] . Treatment with anti-PD-1/PD-L1 antibodies can reverse T-cell exhaustion and boost T-cell effector functions , which could bolster effective eradication of cancer cells and intracellular pathogens [49–51] . Several anti-PD-1/PD-L1 antibodies are commercially available , and many are currently in the pipe-line of evaluation in clinical trials [52] . Furthermore , an alternative treatment potentially with anti-PD-1/PD-L1 antibodies may be warranted in clinical melioidosis , as relapse can be as high as 10% despite appropriate antibiotic treatment[19] . In addition , immunotherapy with anti-PD-1/PD-L1 antibodies might also assist in eradicating the recalcitrant bacteria , thus preventing relapse of the disease . Moreover , our data revealed that CD4+ T-cell frequency correlated inversely with PD-1 expression on CD4+ T cells and CD8+ T cells . Our study provides a clear association between PD-1 expression on CD4+ and CD8+ T cells and CD4+ T-cell frequency in persistent B . pseudomallei infection . We also observed ≥3-fold higher PD-1 expression on CD4+ than CD8+ T cells , suggesting distinct regulations in these T-cell subsets . We also found a strong positive correlation between PD-1 expressions on both CD4+ and CD8+ T cells , which indicate that persistent B . pseudomallei infection concertedly governs their expression T cell subsets . Our correlation results were in line with other HIV studies [28 , 53] , which suggest that persistent B . pseudomallei infection can deplete the functional immune system . Besides , we noted differential cytokine responses in only SCV-infected mice . Peripheral blood IFN-γ and IL-6 levels were significantly elevated in SCV-infected , compared to WT-infected and uninfected mice . Previous study on mice with chronic B . pseudomallei infection showed that IFN-γ and IL-6 levels in sera were only increased in bacteremic mice , compared to non-bacteremic mice[54] . Indeed , we observed that survival mice infected with the SCV after two months were more likely to develop liver or splenic abscesses compared to the WT . We did not observe significant changes in lungs , despite using intranasal route for infection . Our observation can be supported by previous persistent model study that demonstrated higher bacterial recovery percentage from livers and spleens compared to lungs after intranasal challenge[36] . We speculate that the SCV is the one that is more likely to cause severe persistent disease , which was indicated by the higher bacterial loads in spleens compared to the WT . This possibly contributed to IFN-γ and IL-6 upregulation . We think that mice infected with the WT would either succumb to the disease within two months due to higher virulence of the WT , or their immunity are able to control the bacterial burden to very minimal amount , which will not trigger massive cytokine responses . We also found a positive correlation between IFN-γ and IL-6 levels , which shows that they were synergistically released in response to the disease . Interestingly , we also observed higher plasma IL-17A level in SCV-infected mice compared to uninfected , but not compared to WT-infected mice . IL-17A is a pro-inflammatory Th17 signature cytokine that controls bacterial infections by recruiting neutrophils [55] . IL-17A could have been released more in the SCV-infected mice as part of the inflammatory process , which however needs to be investigated further . Our results showed a skewed Th1 and Th17 responses in the SCV-infected mice , as compared to the WT-infected and uninfected mice . SCVs are known to persist intracellularly better than the WT , leading to persistent infections [56] . This explains the skewed Th1 responses in the SCV-infected mice , and that Th1 cells are mainly responsible for eradicating intracellular pathogens [57] . The role of Th17 responses against intracellular pathogens are seldom well understood , but few pieces of evidence have demonstrated that Th17 responses promote Th1 immunity to pulmonary intracellular bacterial infections [58 , 59] . Our results suggest the possibility of skewed Th17 responses that are required for mediating Th1 responses against intracellular SCVs of B . pseudomallei , which warrants further studies . We acknowledge our study limitation in addressing changes of bacterial loads , cytokine levels , and PD-1 expression over a time course , which can provide more useful insights to understand immunopathogenesis caused by the SCV and the WT . Despite that , we demonstrated for the first time that the SCV was different from the WT in the aspect of virulence , bacterial loads , PD-1 expression , and cytokine level in mice with persistent B . pseudomallei infection . Future studies are warranted preferably using blocking antibodies or gene-knock-out murine models to examine the direct effects of eliminating co-inhibitory or cytokine genes in regulating persistent SCV and WT B . pseudomallei infection over a time course . | Melioidosis is an endemic tropical disease in South East Asia and Northern Australia , which is caused by Burkholderia pseudomallei , an environmental bacterium found in the soils of paddy fields and muddy waters across these regions . The bacterium is known to reside within the host cell for prolonged periods of time and is capable of causing long-lasting disease . Recurrent disease is common even with appropriate antibiotic treatments . The mechanisms behind the persistence of B . pseudomallei in the host are still unclear . We investigated the host cell-mediated immune responses against persistent B . pseudomallei infection in BALB/c mice . We found a reduced CD4+ T-cell frequency in mice with persistent B . pseudomallei infection , suggestive of the key role of these cells in experimental melioidosis . Moreover , we also observed significant upregulation of PD-1 on both CD4+ and CD8+ T cells in mice with persistent B . pseudomallei infection , possibly indicating that the T cells were undergoing exhaustion . Based on our results , we postulated that B . pseudomallei is able to impair host immune responses , likely by facilitating the depletion of CD4+ T cells and upregulation of PD-1 on T cells , which potentially facilitates bacterial persistence in the host . Targeting T-cell responses could be an approach to develop vaccines or therapeutics against persistent B . pseudomallei infection . | [
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"bacterial"... | 2016 | Experimental Persistent Infection of BALB/c Mice with Small-Colony Variants of Burkholderia pseudomallei Leads to Concurrent Upregulation of PD-1 on T Cells and Skewed Th1 and Th17 Responses |
Regulatory changes have long been hypothesized to play an important role in primate evolution . To identify adaptive regulatory changes in humans , we performed a genome-wide survey for genes in which regulation has likely evolved under natural selection . To do so , we used a multi-species microarray to measure gene expression levels in livers , kidneys , and hearts from six humans , chimpanzees , and rhesus macaques . This comparative gene expression data allowed us to identify a large number of genes , as well as specific pathways , whose inter-species expression profiles are consistent with the action of stabilizing or directional selection on gene regulation . Among the latter set , we found an enrichment of genes involved in metabolic pathways , consistent with the hypothesis that shifts in diet underlie many regulatory adaptations in humans . In addition , we found evidence for tissue-specific selection pressures , as well as lower rates of protein evolution for genes in which regulation evolves under natural selection . These observations are consistent with the notion that adaptive circumscribed changes in gene regulation have fewer deleterious pleiotropic effects compared with changes at the protein sequence level .
A central goal of evolutionary biology is to elucidate the genetic architecture of adaptation . In humans , in particular , this question is of interest both for what it will reveal about human specific traits [1]–[4] and because of the emerging links between adaptation and disease susceptibility [5] , [6] . A long standing hypothesis is that changes in regulation play an important role in adaptive evolution , notably in primates [7]–[11] . Consistent with this theory , the past decade of research has yielded an increasing number of cases where regulatory changes contribute to species-specific adaptations and to reproductive isolation [8] , . Nonetheless , to date , there are still only a handful of examples of regulatory adaptations in primates . A better understanding of the evolutionary forces influencing gene regulation in primates is not only of interest in an evolutionary context but also promises to shed light on the contribution of regulatory variation to human diseases [18] . Indeed , while the main focus of disease susceptibility studies has been on coding regions [19] , a number of recent association studies of complex human diseases identified candidate loci in regulatory regions , or in intergenic regions , which are thought to have a function in gene regulation ( e . g . , references [20]–[23] ) . More generally , mutations in putative regulatory regions have been associated with well over 100 human phenotypes including diverse aspects of behavior , physiology and disease ( reviewed in references [24] and [25] ) . One approach to study the evolution of gene regulation is by studying variation in gene expression levels within and between populations or species . The challenge is then to use comparisons of variation within and between populations to distinguish between neutral changes in gene expression and patterns that are consistent with natural selection [26] . Ideally , one would want to partition the observed variation in gene expression into its genetic and non-genetic ( e . g . , environmental and genetic by environment interaction ) components in order to study the genetic basis for variation in gene expression without the confounding effects of environmental variation . In model organisms , minimizing the difference in environment between samples helps to reduce the environmental variance , and mutation accumulation studies provide estimates of the neutral mutational variance in gene expression [27]–[29] . However , studying phenotypic evolution in primates is more difficult in this respect , because key experiments often cannot be performed to distinguish between competing hypotheses or to estimate important parameters ( such as the neutral mutational variance ) . Moreover , material is often scarce , leading to largely unknown and uncontrolled environmental variance between samples . These limitations are particularly problematic for dynamic , environmentally sensitive traits like gene expression . In addition , until recently , most inter-primate studies of gene expression used microarrays that were designed based on the genomic sequences of only one species ( “single-species arrays” ) , and as a result , their inter-species expression estimates were confounded by the effect of sequence mismatches on hybridization intensity [26] , [30] , [31] . Perhaps due to the difficulties discussed above , the first few studies that have examined the selection pressures that shape gene expression profiles in humans and close evolutionary relatives resulted in somewhat conflicting conclusions [32]–[38] . To address this , we previously developed a pilot multi-primate cDNA microarray , containing probes for 1056 genes expressed in human liver , which allows one to accurately estimate expression differences between species [31] . Using this pilot array , we estimated gene expression differences between liver samples from humans , chimpanzees , orangutans and rhesus macaques , and found that , consistent with observations in model organisms [27]–[29] , [39] , there was little evidence for change in expression levels across species for most genes , suggesting widespread stabilizing selection . Nonetheless , the regulation of a subset of genes appeared to have evolved under positive ( directional ) selection in the human or chimpanzee lineages [11] . Here , we used a second generation of the multi-species array , with probes for 18 , 109 orthologous genes from human , chimpanzee , and rhesus macaque , to estimate variation in gene expression within and between species in livers , kidneys , and hearts . Using this comparative expression data , we identified genes whose regulation likely evolves under natural selection , including a large number of transcription factors . We also identified specific regulatory pathways , notably metabolic pathways , which have likely been remodeled exclusively in the human lineage .
We used a linear mixed-effects model to analyze the background-corrected , normalized probe-level data for each tissue . Our gene-wise model was designed with fixed effects for species , sequence mismatches , and probes , and a random effect for individuals ( see Methods ) . As a first step of our analysis , we used estimates from the linear model to identify genes that are differentially expressed between tissues ( z-statistic , FDR<0 . 01 ) . We observed a consistent pattern whereby , in all species , the number of differentially expressed genes is lowest in the comparison between kidney and heart ( Table 1 ) . Next , we used likelihood ratio tests within the framework of the linear model to identify differentially expressed genes between pairs of species , choosing a cutoff of 10 for the χ2-distributed likelihood ratio test statistic ( which corresponds to global FDR<0 . 006 across all comparisons in Table 1 ) . As expected , in all tissues , the number of differentially expressed genes is ( roughly two-fold ) smaller between human and chimpanzee than between human ( or chimpanzee ) and rhesus macaque . Interestingly , in liver and heart , we find more differentially expressed genes between human and rhesus macaque than between chimpanzee and rhesus macaque ( whereas in kidney the numbers are comparable ) . Also , while the number of differentially expressed genes between species is smaller in liver compared with the other two tissues ( regardless of the species ) , the magnitude of expression change is slightly larger in the liver: for example , while 15% ( 421 ) of the genes differentially expressed between the human and chimpanzee livers are different by more than 1 . 5-fold , this is the case for only 9% ( 303 ) and 13% ( 415 ) of the differentially expressed genes in kidney and heart , respectively . ( We observed similar patterns for genes differentially expressed between the other pairs of species . ) Thus , a first overview of the inter-species gene expression pattern across the three tissues suggests a marginally higher rate of regulatory evolution in the liver , notably in humans . This observation is consistent with previous results [40] . In order to infer lineage-specific expression changes , we used the expression level in rhesus macaque as an estimate of the gene expression level in the common ancestor of human and chimpanzee . Based on this estimate , we calculated lineage-specific changes in gene expression in the human and chimpanzee lineages ( see Text S1 ) . Contrary to previous reports , we do not find evidence for ‘accelerated’ gene expression evolution in either lineage . Indeed , the magnitude of lineage-specific change is higher in human compared to chimpanzee in 47 . 9% , 50 . 7% and 51 . 7% of genes in liver , kidney , and heart , respectively ( Figure 1A ) . Similarly , we find no evidence for bias towards elevated expression levels in either lineage: the proportion of genes with elevated expression level compared to the estimate of the ancestral gene expression level is 0 . 46 and 0 . 47 , for human and chimpanzee , respectively , in liver , 0 . 51 and 0 . 50 in kidney , and 0 . 49 and 0 . 48 in heart ( Figure 1B ) . As our estimate of the ancestral expression level relies on the unrealistic assumption that there has been no change of expression level in rhesus macaque or in the common ancestor of human and chimpanzee , we confirmed that similar patterns of lineage-specific expression changes are seen when we retain only genes for which the rhesus macaque expression level is an intermediate between the human and chimpanzee expression levels ( i . e . , when deviations from this assumption will have a smaller effect; see Figures S9 and S10 ) . We examined whether inter-species differentially expressed genes are more likely to be located in proximity to known chromosomal rearrangements , as has been observed previously in a smaller dataset [35] . The largest chromosomal rearrangement that occurred in the human lineage is the fusion of two independent great ape chromosomes that created the human chromosome 2 [41] . On chromosome 2 , we find a slight enrichment of genes that are differentially expressed between human and chimpanzee in heart ( by FET; one tailed P = 0 . 02; assuming that differentially expressed genes are randomly distributed in the genome ) . Moreover , using the estimated position of the fusion point [42] and by considering only genes located on chromosome 2 , we find that genes that are differentially expressed between human and chimpanzee heart samples are enriched within a region of 10 Mb on either side of the fusion point ( one tailed P = 0 . 03 ) . To study this further , we focused on seven other known large-scale chromosomal rearrangements between humans and chimpanzees [43] ( Table S6 ) , and tested whether genes that are differentially expressed between the species are enriched in the area flanking the breakpoints ( within 10 Mb ) . We found similar patterns in two of the seven rearrangements ( on chromosome 16: genes differentially expressed between the species in liver , P = 0 . 002; and on chromosome 18: genes differentially expressed between the species in heart , P = 0 . 04 ) . Although the patterns are weak , taken together , these results are consistent with previous observations [35] and suggest a role for large-scale chromosomal rearrangements in the evolution of gene regulation . Our next analysis aimed at finding genes whose expression profiles are consistent with the action of natural selection on gene regulation . As discussed in the introduction , we are unable to explicitly test a null model of no selection due to uncertainty about salient parameters in primates . Instead , we identified genes whose regulation has likely evolved under evolutionary constraint by using a heuristic approach based on the expectation that gene expression levels under constraint will vary little within and between species . As a first step , we ranked genes by their estimated between-individual variance for each tissue . Based on the ranked distribution of the estimated variance across genes , we classified genes as having high or low between-individual variance ( Figures S12 and S13 , see Methods ) . We excluded 17–26% of genes ( depending on the tissue ) with very low absolute intensity values , as these genes may have low expression variance between individuals simply because they are not expressed , or because their probes do not hybridize effectively ( see Methods and Figure S14 ) . Of the remaining genes , low between-individual variance in gene expression ( i . e . , low within-species variance ) may reflect constraint on gene regulation . Indeed , among genes with low between-individual variance - regardless of the tissue - we find enrichments ( unadjusted FET P<0 . 05; see Text S1 ) of genes that are traditionally defined as ‘housekeeping’ , genes involved in metabolic pathways , and transcription factors . Among genes with high between-individual variance , we find enrichments of genes associated with different human diseases ( Table S3 ) . The next step was to identify genes whose expression patterns between as well as within species are consistent with evolutionary constraint on gene regulation . To do so , we used an approach similar to the one used by Gilad et al . , ( 2006 ) [11] , namely , we ranked genes by the summary of the evidence for stabilizing selection within and between species . Our approach relies on the expectation that genes whose expression levels remained constant within and between species will be enriched with genes whose regulation evolves under stabilizing selection ( see Methods for more details as well as Figure 2A for examples of such patterns ) . Using this approach , we identified 3613 , 3354 , and 3198 genes with constrained expression patterns within and between species in liver , kidney , and heart , respectively ( Figure 3; Table S1 ) . The overlap of such genes across all three tissues is highly significant ( 529 genes , compared with an expected overlap of 118 genes if results across the three tissues were independent ) , consistent with our intuition that a large number of genes have important functions in multiple tissues . As expected , among genes with constrained expression patterns within and between species , we find enrichments of ‘housekeeping’ genes , metabolic genes , and transcription factors , regardless of the tissue ( Table 2 and Table S7 ) . We also find enrichments for genes in which somatic or germline mutations have been causally implicated in cancer ( Table 2 ) . When we looked for specific pathways that might be enriched for genes whose regulation is constrained ( see our discussion regarding multiple testing in Text S1 ) , we found a number of pathways that are associated with complex human diseases in all tissues ( Table 2 ) as well as the adherens junction pathway , methionine metabolism and genes involved in cell cycle in liver; reductive carboxylate cycle ( CO2 fixation ) and ribosomal genes in kidney; and TGF-beta signaling pathway and proteasome genes in heart . Using a similar approach , we also looked for expression patterns that are consistent with directional selection on gene regulation , namely , a significant lineage-specific shift in gene expression level combined with low within-species variance [31] . For example , we expect an enrichment of genes whose regulation evolves under directional selection in humans among the group of genes whose expression levels are constant within and between the non-human primates , but whose expression levels were significantly elevated or reduced exclusively in the human lineage ( see Figure 2B and Figure S15 for examples of such patterns ) . Using this approach , we found 928 , 856 , and 1053 genes with constant expression levels in the non-human primates and a significantly different expression level exclusively in humans , in liver , kidney , and heart , respectively ( Figure 3; Table S1 ) . The overlap of such genes across tissues is relatively small , an observation that may reflect the flexibility of adaptation through changes in gene regulation ( see Discussion ) . In agreement with our previous observations for only 907 genes [11] , we find an enrichment of transcription factors among genes whose regulation likely evolved under directional selection in humans , regardless of the tissue ( Table 3 and Table S7 ) . We find similar enrichments for genes that belong to the focal adhesion , adherens junction , and tight junction pathways . In addition , we find tissue-specific enrichments of genes associated with different metabolic pathways in the human liver; glycerolipid metabolism , inositol phosphate metabolism , and riboflavin metabolism in human kidney; and fatty acid metabolism as well as genes associated with metabolic syndromes and dyslipidemia in the human heart ( Table 3 and Table S7 ) . In order to gain further insight into the phenotypes that might be affected by directional selection on gene regulation in humans , we used the Ingenuity pathway analysis tool ( http://www . ingenuity . com/ ) to explore known interactions between genes . Figure 4 illustrates the top interaction network generated using genes whose regulation is under directional selection in liver . As can be seen , this network is enriched with transcription factors and genes with metabolic functions . The phenotypes that may be affected by the regulatory perturbation of this network include carbohydrate metabolism , lipid metabolism , and calcium signaling . Indeed , selection on metabolic related pathways , and in particular on calcium signaling , is particularly intriguing given the marked shift in diet that occurred during human evolution . When we performed a similar analysis to identify genes with constant expression levels in rhesus macaques and humans and a significantly different expression level exclusively in chimpanzees , we found 686 , 774 , and 761 such genes in liver , kidney , and heart , respectively ( Figure S16 ) . Thus , our observations suggest that , regardless of the tissue , fewer genes underwent directional selection at the regulatory level in chimpanzee compared to human ( 74% , 90% , and 72% in liver , kidney , and heart , respectively ) . Furthermore , in contrast to our observations in humans , we did not find an enrichment of transcription factors among genes whose regulation has likely evolved under directional selection in the chimpanzee ( Table S4; in chimpanzee liver , we found a slight under-representation of transcription factors among such genes; by FET , P = 0 . 04 ) . The comparison of gene expression patterns within and between tissues and species also allowed us to examine tissue-specific selection pressures on gene regulation . In principle , one might argue that there is reasonable evidence for tissue-specific selection pressure in every case where a pattern that is consistent with the action of natural selection is inferred based on the expression data from one tissue but not others . However , since our inference is based on ranking genes by a summary of their expression level variation within and between species , lack of evidence for natural selection using our approach cannot be taken as good evidence for no selection . We therefore applied more stringent criteria , using the approaches described above to identify genes for which we have evidence for distinct types of selection on gene regulation in different tissues . Examples of such patterns are given in Figure 5 for genes whose regulation appears to evolve under directional selection in one human tissue yet whose regulation seems to be under stabilizing selection in the two other tissues . By combining such information across tissues and species , we found 48 , 65 , and 74 genes whose regulation evolves under stabilizing selection in two tissues , and under directional selection in the human liver , kidney , and heart , respectively ( Table S1 ) . Similarly , we found 35 , 45 , and 43 genes whose regulation evolves under stabilizing selection in two tissues , and under directional selection in the chimpanzee liver , kidney , and heart , respectively . Thus , even though we imposed highly stringent criteria , we found a clear signature of tissue-specific natural selection on gene regulation for an appreciable number of genes . Finally , we examined the relationship between selection on gene regulation and selection at the protein coding level . To address this question , we used dN/dS ratios as a measure of protein evolution , i . e . , the ratio of the rates of non-synonymous to synonymous substitutions ( see Text S1 for more details ) . Regardless of the tissue , we observed significantly lower dN/dS values for genes whose regulation evolves under natural selection ( either stabilizing or directional ) compared with genes for which we did not find evidence for selection at the gene expression level ( by permutation , all P<0 . 022; see Figures 6A and S17 for liver gene expression data and Table S5 for all tissue-specific comparisons ) . Moreover , we observed significantly lower dN/dS values for genes whose regulation evolves under stabilizing selection in all three tissues compared with genes for which we have evidence for stabilizing selection on gene expression levels only in one tissue ( by permutation; P = 0 . 024; see Figure 6B ) .
An important caveat of studies of primate tissues , including the current study , is that we cannot stage the primate tissues that we work with , or control for environmental effects on gene expression . In addition , due to the difficulty of obtaining tissue samples from chimpanzees , we could not perfectly balance the study design with respect to sex ( see Table S2 ) , and yet our sex-specific sample sizes are too small to explicitly take into account the effects of sex and sex-by-species interaction on gene expression levels . Similarly , while all our samples were obtained from adult individuals , we could not match the ages across species . Thus , although it is well known that gene expression levels are affected by age , sex , and different environments , in our analysis , we could not account for these effects . We note , however , that variation in age , environment , and sex should generally result in an increase in gene expression variance between individuals . In our different analyses , we focused on genes with low between individual gene expression variance . In other words , we focused on genes that have highly constrained expression levels between individuals , even though the individuals were not controlled for age , sex , and environment . Our findings are therefore unlikely to be affected by the effects of environmental variation between individuals – although we may miss additional genes whose expression levels were perturbed by non-genetic effects . In contrast , our findings may be affected by environmental variation between species , as it is likely that individuals from the same species share a more common environment than individuals from different species . For example , all non-human primate individuals may share certain aspects of their diet , which may be lacking from the diet shared by humans . Such species-specific environmental effects may contribute to the observed inter-species differences in gene expression and , in our study , would be indistinguishable from genetic effects . Changes in regulatory elements may be more likely to underlie adaptive phenotypes if mutations in these elements produce circumscribed expression pattern changes . The rationale is that changes in gene regulation that are affecting limited number of cell types or tissues may result in fewer deleterious pleiotropic effects than might be expected when protein sequences are changed [49]–[51] . Several of our findings support this conjecture . First , we observed a much smaller overlap across tissues of genes whose regulation evolved under directional compared with stabilizing selection . Second , we found evidence for tissue-specific selection pressures , whereby a gene's expression pattern may be consistent with directional selection in one tissue and stabilizing selection in the other tissues . Both of these observations are consistent with adaptive changes in regulatory elements that affect the expression patterns of individual genes in one tissue , without affecting gene functions and regulations in other tissues . Third , we found evidence for a correlation between both stabilizing and directional selection on gene regulation and evolutionary constraint at the protein sequence level ( note that this result is inconsistent with our previous observation , which was based on a much smaller number of genes [11] ) . This observation suggests that adaptation at the regulatory level occurs disproportionably in genes that are widely constrained at the protein sequence level . In other words , our results support the hypothesis that adaptation through changes in evolutionary constrained genes can occur by altering their regulatory patterns . Moreover , we observed the lowest rates of protein evolution for genes whose regulation evolves under stabilizing selection in multiple tissues . These results support and refine previous observations of a correlation between gene expression breadth and rates of protein evolution [34] , [52] . Indeed , while previous studies used gene expression as indication of function ( i . e . , when a gene is expressed in a given tissue it was concluded that it has a function in that tissue ) , here , we use tissue-specific stabilizing selection on gene regulation to indicate that a gene is functionally important in that tissue . A curious observation is that transcription factors appear to be enriched among genes whose expression profiles are consistent with the action of directional selection on gene regulation in humans , but not in chimpanzees . This result is consistent with our previous observation based on a much smaller number of genes , using a different array platform and using tissue samples from different human and chimpanzee individuals [11] . Evolution of gene regulation through transcription factors is an intuitively appealing mechanism , as a small change in a transcription factor expression level can affect the regulation of a large number of genes and result in a significant phenotypic effect [53] . While we cannot explain why this pattern is specific to humans , we note that the number of genes whose regulation evolves under directional selection is significantly larger in the human lineage compared with the chimpanzee lineage ( in all tissues ) . This is in contrast to the pattern observed when we considered lineage-specific estimate of expression change for all genes ( Figure 1 ) , for which we find similar lineage-specific changes in gene expression for both human and chimpanzee . Thus , the difference in the overall number of genes whose regulation evolves under directional selection in humans and chimpanzees does not seem to have a technical explanation ( i . e . , it is unlikely an artifact ) . Instead , this difference between the patterns in human and chimpanzee may reflect a signature of regulatory propagation of the effects of directional selection on transcription factor regulation in humans . Our results provide some of the first examples of pathways that have likely been remodeled specifically in the human lineage . In particular , we find a signature consistent with the action of directional selection on gene regulation in genes involved in metabolic pathways in both humans and chimpanzees , with different pathways undergoing selection in each lineage . This result is intriguing because , in addition to the obvious cognitive and linguistic differences between humans and non-human apes , a clear life-style shift between us and other primates can be found in our diet . For example , we are the only primate to regularly consume cooked food , with the earliest unequivocal evidence for controlled use of fire dating to ∼400 , 000 years ago [54] . The digestion of cooked food , among other shifts in nutrition such as increased calcium intake and greater meat consumption , has led to a human diet that differs sharply from that of our close relatives [55] . Such changes are likely to have been accompanied by molecular adaptations [56]–[58] , in particular , in relevant tissues such as liver and kidney . While we cannot directly study selection on gene regulation in primates , our comparative genomics expression data allowed us to identify a large number of genes and specific pathways with expression patterns within and between species that are consistent with the action of natural selection on gene regulation . Our observations raise interesting hypotheses regarding functional differences between humans and other primates , which may be subjected to further tests using cell line systems or model organisms . Finally , our results support the long standing hypothesis that changes in gene regulation have an important role in human evolution , and suggest that many adaptive regulatory changes in humans may be mediated through directional selection on transcription factor gene expression levels .
All known human mRNA sequences were downloaded from the RefSeq database ( www . ncbi . nih . gov/RefSeq ) in August 2006 ( RefSeq release 18 ) . When multiple variants existed for the same gene , we considered only the longest available transcript . To find the non-human primate orthologous sequences for the human mRNAs , we downloaded the full genome sequences of chimpanzee ( Pan troglodytes , March 2006 draft , panTro2 ) and rhesus macaque ( Macaca mulatta , January 2006 draft , rheMac2 ) from the UCSC Genome Browser database ( www . genome . ucsc . edu ) . We then used BLAT [59] to align the human mRNA sequences to the chimpanzee and rhesus macaque genomes . The BLAT algorithm allows one to align mRNA in blocks ( corresponding to exons in this case ) , skipping the introns in the target genome . After filtering the matches by aligned sequence length ( the numbers of “matching” aligned bases ) , we found chimpanzee and rhesus macaque orthologs for 18 , 109 human genes ( complete 3-way alignments are available by request ) . We performed several quality controls to examine this alignment that are detailed in Text S1 . Based on our alignments , probes for the microarray were designed by NimbleGen ( www . nimblegen . com ) . Within each gene , a set of up to 7 non-overlapping 60mer genic regions were chosen as probes from the human gene sequence ( hereafter: a probe-set ) . The corresponding orthologous sequences in the other two genomes defined species-specific probes for chimpanzee and rhesus macaque . Hence , each probe-set consists of up to 7 species-specific probes that are aligned to different locations in the gene , and there are 3 species-specific versions for each individual probe ( and therefore each gene is represented by 3 species-specific probe-sets ) . The array includes a total of 368 , 678 probes , with 126 , 763 probes from human , 122 , 387 from chimpanzee , and 119 , 528 from rhesus macaque . The percentage of genes having exactly 7 probes is 99 . 9% , 91 . 5% , and 85 . 3% for human , chimpanzee , and rhesus macaque , respectively . In addition , a set of random sequence probes was included on the array as controls . As expected , these probes generally showed low intensity values in all hybridizations . Complete details about the study design , samples used , hybridization procedures , and quality control analyses are given in Text S1 and Tables S1 and S2 . Briefly , using the multi-species microarray , we compared gene expression levels within and between species in three tissues: Livers , Kidneys ( cortex ) and Heart muscle . For each tissue , we hybridized RNA samples from 6 individuals from each of the three species , and preformed two technical replicates for each sample . The total number of arrays analyzed is therefore 108 ( = 3 species×3 tissues×6 individuals×2 technical replicates ) . Gel pictures of all RNA samples are available in Figure S18 . Following hybridization , washing , and scanning , raw data was extracted from the images using the NimbleScan software ( version 2 . 4 ) . We performed background correction using the normexp function in limma with an offset of 32 [60] , and normalization using an adaptation of the quantile normalization approach . We used the following gene specific linear mixed model to analyze the background corrected normalized data for each tissue ( 1 ) where ysroij is the normalized log2 intensity for species s ( s = human , chimpanzee or rhesus macaque ) , from individual i in replicate j from a specific probe within a probe-set r which was derived from species o . The term μs is the expected log expression level of species s . The term πro represents the probe effect for each individual probe within a probe-set and the effect of species-specific orthologous probes [61] . The κsro represent the attenuation on hybridization intensities due to sequence mismatches between species of RNA ( s ) and a species-specific derived probe ( o ) , which are different for each individual probe within a probe-set ( r ) . We assumed that κsro = 0 when s is the same species as o , and that the attenuation is symmetric for combinations of RNA species and probe ortholog species ( i . e . , κsro = κors ) . The term αi is a random effect representing the effect for individuals i assumed to be normal with mean zero and variance σα2 , and εsroij is the residual error assumed to be normal with mean zero and variance σε2 . The model was fitted to each gene by residual maximum likelihood using the lme function ( in the nlme package ) . We used likelihood ratio ( LR ) tests within the framework of the linear model in order to identify genes that are differentially expressed ( DE ) between species ( see Text S1 for more details ) . The reported P-values were adjusted for multiple testing using the false discovery rate approach ( FDR; [62] ) . To identify genes whose regulation likely evolves under stabilizing selection in the three primate species , we used two criteria . First , we wanted to exclude genes with evidence for differential expression between species ( as such a pattern is not consistent with stabilizing selection on gene expression levels ) . To so do , we used a likelihood ratio test to test the null hypothesis that there are no expression differences between species ( i . e . μH = μR = μC ) . Under the null hypothesis , −2× ( log-likelihood ratio ) of the fits of the reduced and full model has an approximate χ2 distribution on 2 degrees of freedom . Since our goal at this step is to exclude genes that are DE between species , we retained genes where this statistic was less than 6 ( corresponding to P>0 . 05 ) . Among the genes that are not DE between species , those whose regulation evolves under stabilizing selection are expected to have low between-individuals variance . Figures 2 and S13 illustrate examples of expression patterns that are consistent with such expectation . Thus , we ranked the remaining genes by their between individuals variance ( see Text S1 for more details ) . Finally , we excluded genes that had very low expression levels , as these might have low variance within and between species simply because they are not expressed . To do so , we calculated the average normalized log-expression level for each gene across all probes , plotted this intensity against the between-individual variance , and selected a cutoff that excluded genes within the obvious cluster of small absolute intensity values ( Figure S14 ) . Using this approach , we excluded genes with log absolute intensity values smaller than 7 for liver ( 23% of genes excluded ) , 6 . 7 for kidney ( 17% of genes excluded ) , and 7 for heart ( 26% of genes excluded ) . To find genes whose regulation likely evolved under directional selection in humans , we focused on genes whose expression level has changed exclusively in either the human or the chimpanzee lineage , as well as maintained low within-species variance . Figure S15 illustrates examples of expression patterns that are consistent with such expectation . To identify these patterns , we used three criteria: first , we excluded genes that are DE between the non-human primates . To do so , we constructed a reduced model to test if the chimpanzee and rhesus macaque expression levels are similar ( i . e . , μC = μR ) ; the maximum likelihood estimate was compared to the full model in [1] . Genes that are differentially expressed between chimpanzee and rhesus macaque will have a high likelihood ratio; therefore we excluded them from further analyses ( using a LR test cutoff of 2 ) . Among genes with consistent expression level in the non-human primates , we selected those that have a significantly different expression levels in humans , by using a second LR test . Here , we tested a model that reflects the assumption of similar expression levels in chimpanzee and rhesus macaque ( μC = μR ) against a null model that reflects the assumption of similar expression for all species ( μH = μR = μC ) , this time retaining genes for which we could reject the null ( using an LR test cutoff of 10 ) . Finally , we ranked these genes by their between individuals variance . In order to identify functional categories and pathways that are enriched among genes with either high or low between individual variance in gene expression , we applied either a Fisher Exact Test ( FET ) , using 2×2 contingency tables , or a Mann-Whitney test , using ranks ( e . g . , the rank of the between individual variance ) . We excluded from this analysis , and the following ‘enrichment’ analyses genes that do not have a record in GO , in order to avoid biasing the results with enriched functional categories that simply have more genes with studied/known functions . To identify functional categories and pathways that are enriched among genes whose regulation has likely evolved under natural selection , we defined ( for each tissue ) the following three mutually exclusive gene groups: ( i ) genes whose regulation has likely evolved under directional selection , ( ii ) genes whose regulation has likely evolved under stabilizing selection , and ( iii ) all other genes not in groups ( i ) or ( ii ) – referred to as “others” in Table S7 . Genes with high between-individual variance were excluded from group ( iii ) because they can never be included in groups ( i ) or ( ii ) ( including these genes in group ( iii ) may bias the results ) . To test for enrichment we performed a two-tailed FET ( using the fisher . test function ) . For the GO analysis , we initially only asked for enrichment of transcription factors ( GO:0030528 ) and/or metabolic genes ( GO:0008152 ) , where we have a strong prior given previous studies , including our own . We did not ask about any other GO functional category and therefore did not correct the P-values reported in Tables 2 and 3 for multiple tests . Thus , our first step represents a test of explicit hypothesis . As a second step , we performed a global analysis of enrichment in all GO categories under ‘biological processes’ and ‘molecular function’ using DAVID ( http://david . abcc . ncifcrf . gov/ ) . The results of this analysis are provided in Table S7 . We note that a global analysis of all GO terms is somewhat difficult to interpret , because many of the functional annotations in GO are not mutually exclusive at any level of the hierarchy , and are often not very informative . That said , it can be seen in Table S7 that many of the top results are GO terms related to gene regulation and metabolic pathways , and in particular when we put together all genes whose regulation is inferred to have likely evolved under directional selection , the two top enriched GO terms are ‘transcription factor binding’ ( GO:0008134 ) and ‘metabolic processes’ ( GO:0008152 ) . Thus , the results of the global GO analysis are consistent with our hypothesis that transcription factors and genes in metabolic pathways are enriched among genes whose expression profiles have changed exclusively in the human lineage . All expression data files were submitted to the GEO database ( http://www . ncbi . nlm . nih . gov/geo/ ) under provisional series accession number GSE11560 . | It has long been hypothesized that in addition to structural changes to proteins , changes in gene regulation might underlie many of the anatomic and behavioral differences between humans and other primates . However , to date , there are only a handful of examples of regulatory adaptations in humans . In this work , we present a genome-wide study of gene expression levels in livers , kidneys , and hearts from three species: humans , chimpanzees , and rhesus macaques . These data allowed us to identify genes and entire pathways in which regulation evolved under natural selection and therefore are likely to be functionally important . Our results provide some of the first examples of pathways that have been remodeled specifically in the human lineage . In particular , we find that the regulation of a large number of genes involved in metabolic pathways evolved under lineage-specific directional selection . This result is intriguing , because , in addition to the obvious cognitive and linguistic differences between humans and non-human apes , a clear lifestyle shift between us and other primates can be found in our diet . We also found evidence for tissue-specific selection pressures on gene regulation , an observation that provides strong support to the notion that adaptive circumscribed changes in gene regulation have fewer deleterious pleiotropic effects compared with changes at the protein sequence level . | [
"Abstract",
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] | 2008 | Gene Regulation in Primates Evolves under Tissue-Specific Selection Pressures |
Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes , metabolic pathways , and other functional modules . Although epistasis between adaptive mutations is also common in laboratory evolution experiments , the functional basis for these interactions is less well characterized . Here , we quantify the extent to which gene function ( as determined by a genome-wide screen for epistasis among deletion mutants ) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules . We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly , on average , than those with deletions in different modules . At the same time , initial fitness also plays a role: independent of the specific functional modules involved , adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds , consistent with a general pattern of diminishing returns epistasis . We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis ( i . e . mutations tend to be more beneficial in the background in which they arose ) . In two functional modules , epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment , indicating an intimate functional relationship . Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations , meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics .
Epistasis has often been thought of as a signature of functional interactions . For example , if two residues in a protein are in physical contact , we might expect that mutations in these sites will have different effects in combination than they do independently . Similarly , if two different proteins interact , either physically or as components in some biochemical pathway , we might expect the physiological effect of knocking out both proteins together to be different from the effects of knocking out either individually . Based on this intuition , screens for epistatic interactions have long been used as a way to identify previously unknown functional interactions . More recently , genome-wide screens of epistatic interactions among large numbers of mutations ( e . g . all combinations of single-gene deletion mutations in budding yeast ) have been used to characterize the global functional landscape of the cell , identifying functional modules and their interactions based on common patterns of epistasis [1–4] . Many recent studies of adaptation in laboratory microbial evolution experiments have shown that epistasis among adaptive mutations is relatively common and widespread . For example , several studies reconstructed all possible combinations of mutations that accumulate along the line of descent in adapting populations , finding that epistasis is pervasive [5–7] . Other studies have used patterns of evolution across multiple replicate lines to infer patterns of epistasis [8–14] . For example , Tenaillon et al . [15] found that epistasis within specific functional modules leaves clear signatures in the patterns of parallel evolution across 114 replicate Escherichia coli populations adapting to high temperature . However , many of these laboratory evolution studies have not identified a clear connection between the patterns of epistasis among adaptive mutations and functional interactions . For example , numerous studies have found that epistasis among beneficial mutations is generally negative ( i . e . double-mutants are less fit than the product of the single-mutant fitnesses ) [6 , 7 , 10 , 14 , 16] , though there are a few exceptions [17] . This trend towards negative epistasis is one explanation for the fact that these experiments tend to show a general pattern of declining adaptability: initially more-fit populations tend to adapt more slowly than less-fit populations [18] . By reconstructing sets of adaptive mutations in a variety of genetic backgrounds , several studies have argued that this generally negative epistasis reflects an overall pattern of diminishing returns epistasis , where the fitness effect of any individual beneficial mutation tends to decline systematically with the fitness of the genetic background on which it occurs [6 , 7 , 10] . This picture of a global pattern of diminishing returns epistasis stands in contrast to the typical view of epistasis as a marker of functional interactions . However , diminishing returns has primarily been observed among small sets of mutations that arise in laboratory evolution experiments , which each start with a specific initial strain that adapts to a specific environmental condition . To the extent that this initial strain is poorly adapted to that environment due to a particular type of defect , all subsequent adaptation may reflect ways to correct this defect . This could lead to sets of adaptive mutations that reflect one or a few functional pathways and hence interact in a way that does not reflect the overall functional landscape of the cell . In other words , if the environment and initial genotypes select for adaptive mutations that are all within one or a few specific modules , the patterns of epistasis among these mutations will reflect only these modules and not the overall organization of the cell . To find more general signatures of functional epistasis , it might thus be useful to compare the evolutionary fates of initial strains that have a variety of different types of defects . With this in mind , a few recent studies have investigated adaptation among diverse initial genotypes . For example , Jerison et al . [12] studied how initial genotype affects the rate and genetic basis of adaptation among 230 offspring of a cross of two distantly related yeast strains . Similarly , Szamecz et al . [11] analyzed compensatory adaptation in 187 yeast strains , each with a different single gene knockout . Remarkably , these studies both found that the rule of declining adaptability still applies across these diverse initial genotypes . It is possible that , despite being more diverse , these initial genotypes still share a common defect that drives adaptation . However , since neither study reconstructed individual mutations in different genetic backgrounds , it is unclear whether the rule of declining adaptability in these experiments arises due to general diminishing returns epistasis . Instead , there are hints of other factors involved in both cases . For example , Szamecz et al . [11] found that the mutations acquired in each strain are enriched in genes that share functional annotations with the deletion in that initial strain . Here , we sought to investigate the relationship between the functional landscape of yeast ( based on genome-wide screens for epistasis among deletion mutants ) and the patterns of epistasis among mutations that arise during laboratory adaptation . To do so , we analyze compensatory adaptation in 37 strains , each founded by a single gene deletion mutant nested in one of 16 functional modules ( plus a negative and positive control ) defined using the yeast genetic interaction map [3] . We evolved 20 replicate lines founded by each of these 37 deletion mutants , finding that the overall rule of declining adaptability still applies—initially less-fit strains tend to adapt more rapidly than more-fit strains . However , we also find signatures of the functional landscape: strains with deletions in the same module tend to adapt more similarly than those with deletions in different modules , even after accounting for the effects of declining adaptability . To investigate the patterns of epistasis underlying these results more directly , in a subset of lines we reconstructed evolved mutations on a wild-type background lacking the initial gene deletion . We find that many mutations do not reflect compensation for the specific functional defects introduced by the initial deletion . Instead , they are adaptive in both wild-type and deletion backgrounds . However , this is not universal: some evolved mutations do compensate for defects that are specific to the initial deletion in that line . This compensatory adaptation results in patterns of epistasis that can lead to evolutionary entrenchment of the original deletion .
We founded 20 replicate populations from each of the 37 gene deletion mutants , and evolved the resulting 740 populations in batch culture in rich laboratory media ( YPD ) at an effective population size of about 5 × 104 using our standard methods for laboratory evolution experiments ( Fig 1 , Methods ) . After 500 generations , we measured the fitness of each evolved population . In Fig 2A we show how the fitness of each evolved line depends on the initial fitness of the gene deletion mutant from which it descends ( the “Founder fitness” ) . We see that most evolved lines increased in fitness , though there is substantial variation in the extent of this adaptation even among lines descended from the same Founder . In Fig 2B we show how the average fitness gain in lines founded from each Founder depends on that Founder fitness . We see that there is a general trend of declining adaptability: lines descended from less-fit Founders tend to adapt more rapidly than those descended from more-fit Founders . However , there is also substantial variation between Founders , with some Founders adapting systematically more or less rapidly than others at similar initial fitness . There are several sources of variation in the rate of adaptation across our 740 evolved populations . We are primarily interested in the effect of the Founder genotype: that is , how the initial gene deletion influences the rate and genetic basis of adaptation in its descendant lines . In addition to the effect of Founder genotype , the evolutionary process is inherently random , so we expect some inherent evolutionary stochasticity in how rapidly any evolved population evolves . This inherent stochasticity leads to variation between lines descended from the same Founder genotype . Further , measurement error in our fitness assays leads to additional variation , which we can quantify by comparing replicate measurements of the same evolved populations . To quantify the relative importance of these different factors , we conducted a hierarchical analysis of variance to partition the variation in the rate of adaptation between our evolved lines into the components that can be attributed to Founder identity , evolutionary stochasticity , and measurement error ( Fig 2C , Methods ) . We find that Founder identity plays a dominant role , explaining 88 percent of the variance in rate of adaptation , while inherent evolutionary stochasticity explains 9 percent and measurement error explains 3 percent . These latter two effects lead to similar absolute amounts of variance as reported by Kryazhimskiy et al . [10] in a study analyzing the rate of adaptation in lines descended from a set of very closely related Founder genotypes . However , in the present study this corresponds to a much lower fraction of variance , because we find much greater overall variation in evolutionary outcomes and this additional variance is almost entirely explained by Founder genotype . We can further subdivide the effect of Founder genotype into several components . We find that the initial Founder fitness can explain almost half ( 46 percent ) of the total variance in the rate of adaptation . As we will describe below , this fitness effect is consistent with a rule of declining adaptability , where initially less-fit strains adapt more rapidly than initially more-fit strains . Above and beyond this effect of fitness , the module identity in which the initial gene deletion is categorized ( “Founder module” ) explains 26 percent of the variance . Finally , the idiosyncratic effect of the specific gene deletion , above and beyond the effect of module and fitness , contributes an additional 16 percent of the variance ( Fig 2C ) . We next sought to more directly investigate the underlying patterns of epistasis that lead to the effects of Founder genotype on the rate of adaptation . To do so , we attempted to revert the initial Founder gene deletion in each of 270 evolved lines ( about 7 lines descended from each of the 37 Founder genotypes ) . Specifically , we used standard transformation methods on whole-population samples from each of the 270 evolved lines , and selected three independent transformants from each ( Methods ) . This procedure led to three revertant clones from each of 100 independently evolved populations . In the remaining 170 populations , we were unable to obtain revertant transformants . The populations in which we were able to obtain reversions were distributed in a highly nonrandom way across Founder genes and Founder modules ( Fisher’s exact test , p ≪ 0 . 01 in both cases , S2 Table ) ; in six cases all evolved descendants of a given Founder genotype produced no transformants . In addition to experimental errors , there are two potential explanations for this phenomenon . The first possibility is that these evolved populations share mutations that make them less transformable or change the copy number of the deletion cassette . Either of these changes would disrupt our method for making the reversions ( see Methods ) . One potential candidate for such a mutation would be a spontaneous autodiploidization event , which has been observed to occur frequently in other yeast evolution experiments [19–21] . The other possibility is strong epistasis: compensatory mutations in evolved clones are lethal or very strongly deleterious in the absence of the initial deletion . To be conservative in inferring effects of functional module , we excluded from further analysis nine Founder genotypes from which we were not able to generate revertant transformants in at least two independently evolved descendant populations ( specifically , we excluded 2 populations descended from founders that only yielded a single revertant , leaving a total of 98 populations for further analysis ) . The remaining 28 founders have a uniform rate of reversibility ( about 54 percent ) . We measured the fitness of each of the 294 revertant clones ( 3 clones derived from each of 98 evolved populations descended from 28 different Founder genotypes ) . In almost all cases , the three revertant clones descended from the same evolved population had very similar fitness , with a few outliers likely caused by mutations either segregating in the evolved populations or introduced due to transformation artifacts ( S1 Fig ) . We use the median fitness of the three clones derived from a given evolved population as a measure of the fitness effect of the mutations that accumulate during adaptation of that line ( the “evolved mutations” ) on the wild-type ancestral background . In contrast , the fitness of the evolved line minus the fitness of the initial Founder gene deletion it descends from reflects the fitness effect of the evolved mutations on the background of the initial Founder gene deletion . The difference between these two fitness effects , denoted by ϵ , indicates epistasis between the evolved mutations and the initial gene deletion . In Fig 3A we show how the fitness effects of the evolved mutations on the ancestral background depend on their fitness effects on the background of the initial gene deletion in which they evolved . In the majority of cases ( see Fig 3B ) , we see that the evolved mutations are moderately beneficial in both backgrounds , with an enrichment for cases where the evolved mutations are more strongly beneficial in the deletion background than the wild-type background . In principle , since the initial deletion backgrounds are typically less fit than the wild-type ancestor , this could reflect diminishing returns epistasis . However , in Fig 3C , we show that ( with a few exceptions we return to below ) , the fitness of the initial gene deletion mutant does not strongly affect ϵ , the difference between the fitness of the evolved mutations in the deletion versus wild-type background . Thus , the enrichment of positive epistasis between evolved mutations and initial gene deletions may reflect functional coupling where some of these evolved mutations provide compensatory adaptation to the defects introduced by the initial gene deletions . We further sought to quantify the extent to which these epistatic differences ( as measured by ϵ ) depend on the Founder identity . To do so , we conducted a hierarchical analysis of variance to partition the variance in ϵ into contributions from measurement error , evolutionary stochasticity , and Founder identity ( including fitness , module , and gene ) . We find that while evolutionary stochasticity plays a large role , there is some effect of Founder identity ( Fig 3D ) . Thus , some initial founding gene deletions are more likely to lead to specific compensatory adaptation . As described above , in the majority of cases , evolved mutations were beneficial in both the ancestral and evolved backgrounds . However , there were a few exceptions . Most strikingly , in all evolved lines descended from Founder genotypes with deletions in one module ( hxk2Δ and tps1Δ in the Central metabolism 1 module ( Table 1 ) ) and in ade2Δ ( our positive control for functionally dependant adaptation ( see below ) ) , the evolved mutations were strongly beneficial in the background of the initial gene deletion in which they evolved but strongly deleterious in the ancestral background . These cases can lead to evolutionary entrenchment of a deleterious gene deletion: although it would have initially been favorable to revert the deletion , after compensatory evolution this is no longer beneficial ( Fig 4 ) . In other cases , despite the fact that the evolved mutations are deleterious in the ancestral wild-type background , reverting the initial deletion is still beneficial ( though less so than before compensatory adaptation ) . For lines descended from the ade2Δ Founder genotype , the mechanism for evolutionary entrenchment is straightforward . Briefly , ADE2 codes for an enzyme in the adenine biosynthesis pathway immediately downstream of a toxic metabolic intermediate ( S2 Fig ) . Thus the ade2Δ mutation is strongly deleterious because it leads to accumulation of the toxic intermediary . In this genetic background , loss-of-function mutations in genes that code for enzymes upstream of ADE2 in the adenine biosynthesis pathway will eliminate the precursors to this toxic intermediary and hence compensate for the deleterious effect of the initial ade2Δ mutation . However , these upstream loss-of-function mutations are deleterious in the ancestral background because they eliminate adenine biosynthesis . We sequenced five populations descended from the ade2Δ Founder ( Methods ) , and found that , consistent with this expectation , all five populations had a loss-of-function mutation in a gene upstream of ADE2 ( either ADE4 , ADE6 , or ADE8; one population acquired mutations in both ADE4 and ADE8 ) . In contrast to the case of ade2Δ , the mechanism of compensation and entrenchment in lines descended from the hxk2Δ and tps1Δ Founders is unclear . We sequenced three populations descended from the hxk2Δ Founder , finding that two had mutations in ATP2 and one had a mutation in ATP1 . These genes code for components of the mitochondrial ATP synthase complex . While both HXK2 and the ATP synthase are crucial components of central carbon metabolism in yeast , we lack a mechanistic explanation of how perturbations to the ATP synthase could compensate for the deletion of HXK2 . One possibility is that because our hxk2Δ founder has impaired mitochondrial function ( as indicated by its reduced ability to grow on the glycerol media ) , mutations in ATP1 and ATP2 may compensate for a loss of membrane potential in the mitochondria . van Leeuwen et al . [22] found mutations in ATP1 and ATP2 in strains lacking mitochondrial DNA ( including one specific mutation , ATP2-Q412E , also found in our evolved lines ) and suggested that these mutations may reverse ATP synthase activity to generate ADP3− instead of ATP4− and thereby allow the mitochondria to rebuild a membrane potential , which is thought to be required for protein import into the mitochondria . We also sequenced two populations descended from the tps1Δ Founder but were unable to identify any of the compensatory mutations ( Methods ) . This may simply reflect the limited coverage of our sequencing , though given the above discussion it is also possible that compensation in this case involves mitochondrial mutations . We next sequenced one clone from each of the 100 evolved populations in which we were able to successfully revert the initial gene deletion . We used the breseq software package [23] to identify a total of 153 coding mutations across the 100 sequenced clones ( Methods ) ; a list of all coding mutations called in each clone is given in Supplementary S2 Data . We note that because our sequencing method is unable to identify certain types of mutations ( e . g . mitochondrial mutations and large indels and structural variants , see Methods ) , this represents only a subset of all mutations present in these clones . Keeping this caveat in mind , we do see a weak relationship between the number of mutations in each evolved line and the initial fitness of the founding genotype as well as with the fitness gain during compensatory adaptation ( S7 Fig ) . Thus larger fitness increases during compensatory adaptation may result from more compensatory mutations ( though it may also be true that the compensatory mutations that do occur have larger fitness benefits ) . To analyze how Founder identity and other factors influence the genetic basis of adaptation , we analyzed the patterns of parallelism in acquired mutations . We first investigated parallelism at the gene level , focusing on genes that were independently mutated in at least two populations ( Fig 5 ) because genes mutated only a single time provide no additional power in the gene-level analysis . These “multi-hit” genes are likely to be enriched for beneficial mutations that were drivers of adaptation in that population . Excluding the descendants of the ade2Δ and hxk2Δ founders , we found that all multi-hit genes belong to pathways that are common targets of laboratory adaptation , the Ras/cAMP and the mating pathways [10 , 12 , 20 , 24] . This is unsurprising , because highly specific compensatory mutations for particular initial Founder gene deletions would be beneficial in only a small number of evolved lines , and hence much less likely to appear as multi-hit genes . Instead , it is likely that these multi-hit genes represent mutations that are generally adaptive to the laboratory conditions in our system . Nevertheless , it is possible that initial gene deletions in different modules could enrich for particular sets of adaptive mutations . This is clearly true in the descendants of the ade2Δ and hxk2Δ Founders . To measure the potential influence of other Founder genotypes on the identity of the multi-hit genes accumulated in its descendants , we calculated the mutual information between mutated genes and Founder modules ( excluding ade2Δ and hxk2Δ ) and compared it to a null distribution obtained by permuting the data while keeping the number of mutations per clone fixed ( Methods ) . Intuitively , the mutual information measures the amount of information ( in bits ) that we gain about the identities of mutated genes by knowing the identity of the Founder . Higher values of mutual information indicate that the Founder identity more strongly predicts the genes that are mutated . We found a significant relationship between acquired mutations and functional module of the initial Founder gene deletion ( Table 2 , top left ) . To measure the additional information provided by the specific Founder gene deletion ( after controlling for the effect of the functional module corresponding to this gene ) , we performed an analogous analysis to compute the mutual information between Founder gene and acquired mutations , conditioning on Founder module . We found a significant association between acquired mutations and the Founder gene even after conditioning on Founder module ( Table 2 , bottom left ) . These results indicate that even among apparently generally adaptive multi-hit genes , the Founder genotype does influence the genetic basis of adaptation in a module- and gene-specific way ( i . e . descendants of Founders with deletions in the same module are more likely to acquire mutations in the same multi-hit genes ) . We next analyzed patterns of parallelism at the level of functional modules , focusing now on clusters defined by genetic interactions , “interaction clusters” , ( see Methods ) that were independently mutated in at least two populations ( S3 Table ) . We repeated our mutual information analysis at this level , finding statistically significant associations between the Founder genotype ( both module and the specific gene deletion ) and the interaction cluster in which mutations arose in its descendants ( Table 2 , right column ) .
Large-scale surveys of epistatic interactions have long been used to investigate the functional organization of cellular processes . This approach has been used to reveal the binding structure of members in protein complexes , the biochemical order of enzymes in metabolic pathways , the interactions between different complexes and pathways , and the relationships between biological process at the highest levels of cellular organization [3 , 25 , 26] . We expect these patterns of epistasis to also have important consequences for the rate and molecular basis of adaptation , and to influence the degree of parallelism and contingency in evolving populations [27–30] . For example , we might expect functional epistasis to lead to historical contingency that decreases the degree of parallelism in evolution , as different lines stochastically accumulate mutations in different functional modules and then tend to accumulate different compensatory mutations in future adaptation . However , the connection between the epistatic signatures of functional modules and the patterns of epistasis important to evolutionary dynamics have not been extensively studied . It is thus unclear how observations of the effects of epistasis in evolutionary dynamics can be predicted from ( or be used to infer ) functional organization . In many earlier laboratory evolution experiments , the most striking pattern of epistasis between adaptive mutations is a general tendency towards negative interactions ( e . g . global diminishing returns epistasis [10] ) . These studies suggest that , with a few exceptions , the bulk of the interactions between mutations that are relevant for adaptation in these systems can be explained without any functional information using a simple model of diminishing returns , in which the fitness effect of a beneficial mutation is systematically smaller in higher-fitness genetic backgrounds . However , other laboratory evolution studies have found some signatures of epistatic interactions that reflect functional organization [11 , 12 , 15] . Here , we describe an experiment designed to test the degree to which functional relationships , as defined by a genome-wide screen of epistatic interactions , influence evolutionary dynamics . Our hierarchical design , in which we evolved 20 replicate lines descended from each of 37 gene deletion mutants representing 16 functional modules ( plus two controls ) , allows us to quantify the effects of gene and module identity on the rate and genetic basis of adaptation . We find that the rule of declining adaptability still applies in this system , and initial fitness can explain almost half of the variation in the rate of adaptation of different strains . The mechanistic basis of this effect of initial fitness remains unclear . However , in addition to this effect , we find that functional epistasis does indeed have predictive power: populations descended from Founders with gene deletions in the same functional module adapted more similarly than average , even after controlling for the effects of initial fitness . In a few cases , the functional basis for this pattern was straightforward: strong epistatic interactions between the initial gene deletions ade2Δ and hxk2Δ and the mutations in their descendant lines indicated a clear functional interaction . In other cases , while some form of epistatic interactions between acquired mutations and the initial deletion leads to a signature of similarity between lines descended from the same Founder ( and Founders with deletions in the same module ) the functional basis of these effects is less clear . It is important to note that our experimental approach has several important limitations . One key limitation is the time scale of our experiment: our analysis of evolutionary outcomes after 500 generations can only give a snapshot of a phenomenon that is likely to be much richer . Second , our study was carried out in a single strain background , BY4741 ( very similar to the strain background used in Costanzo et al . [3]; the only differences are that our strains are uracil and methionine prototrophs ) , and a single environment , rich laboratory yeast media . Since the function and typical effect size of generally adaptive mutations is different in different strains and environments , it would be surprising if the effect of functional module on compensatory adaptation did not depend on the strain background or environment used for evolution . However , we note that an earlier study by Szamecz et al . [11] measured the adaptation of 4 replicates of each of 187 yeast deletion strains in a closely related genetic background . While these authors did not employ the same type of hierarchical design , we can apply the same analysis framework that we have used here , and find that consistent with our results , compensatory adaptation in this earlier study is affected both by initial fitness and by functional module ( S8 Fig ) . Despite these limitations , our results show that the functional information revealed by genome scale maps of epistasis between gene deletion mutations is indeed predictive of evolutionary dynamics , at least in our system . Thus , genotypes with similar epistatic interactions with gene deletion mutations also seem to have similar epistatic interactions with adaptive mutations . These interactions can have important long-term evolutionary impacts , affecting patterns of parallelism and repeatability . For example , we found several cases where evolution leads to the entrenchment of initially deleterious gene deletions . This entrenchment can lead to extensive historical contingency in adaptive trajectories , potentially driving irreversible divergence between populations [27 , 30 , 31] , though we note that weaker forms of epistasis can also lead to similar contingency , particularly when clonal interference is important [32] . While we have identified only a few cases of entrenchment here , it is important to note that we were unable to generate reversions of the initial Founder gene deletions in a number of cases . We therefore cannot rule out the possibility that these cases may reflect even more extreme forms of entrenchment , where reverting the initial deletion becomes lethal after compensatory adaptation . Our results also highlight how laboratory evolution experiments could be useful as a way to investigate the functional organization of the cell . Large-scale hierarchically organized experiments of the type we describe can in principle be used as a type of screen for epistatic interactions that might have more subtle or undetectable effects using other methods ( e . g . in direct genome-wide gene deletion screens [1–4 , 33] or suppressor screens [22 , 34–36] ) , or might involve types of mutations that are difficult to screen via other methods . The patterns of parallelism between replicate lines could then be used to create a type of evolutionary similarity metric which could be the basis for an alternative functional clustering .
Our ability to measure the effect of functional epistasis on the rate of adaptation depends on assigning genes to functional modules . To do so , we relied both on curated functional annotations and on the yeast genetic interaction map . Annotations can be used directly to group genes into protein complexes , metabolic or signaling pathways , and broader biological process . On the other hand , the genetic interaction map [3] does does not assign genes to distinct groups . Instead , the interaction map , which consists of measurements of epistatic interactions between about 23 million pairs of gene deletion mutations in yeast , provides a genetic interaction profile for each gene , which shows how it interacts with other genes in the genome . Costanzo et al . [3] argued that correlated interaction profiles imply close functional relationships and used this insight to infer the modular organization of the yeast genome . We reasoned that groups of genes with both functionally related annotations and correlated genetic interaction profiles would be most likely to exhibit signatures of functional epistasis . We first clustered all genes using correlations in their genetic interaction profiles as a similarity metric . Specifically , we performed hierarchical clustering using Ward’s clustering criterion ( implemented in the R function hclust with option ward . D2 [37] ) on a matrix of gene-gene distances defined for each pair of genes as 1 − |ρ| , where ρ is the Pearson correlation in genetic interaction profiles of the two genes . We then filtered out genes without significant fitness effects , genes of unknown function , and genes known to increase mutation rate or cause other genetic instabilities . From the remaining set of clustered genes , we compared cluster membership to gene annotations and hand selected 43 genes from 16 different clusters which shared functional annotations . Finally , we added the genes HO and ADE2 as negative and positive controls , respectively , for functionally dependent adaptation . We also attempted to sort genes hit by newly acquired mutations into functionally related groups . To do this we developed an automated clustering criterion based only on correlations in genetic interaction profiles . Specifically , we used a genetic interaction profile correlation threshold of 0 . 2 to connect functionally similar genes into groups we call “interaction clusters” . The strains used in this study are derived from yAN184 , a haploid MATa strain of the BY background with the genotype: his3Δ1 , leu2Δ0 , met17Δ0 , ura3Δ0 , trp1Δ0 . We replaced the HML locus in yAN184 with MET17 and inserted a fluorescently labeled mating type selection “Magic marker” RPL39pr-ymCherry-tADH1-Ste2pr-SpHIS5-tSkHIS3-Ste3pr-LEU2 at the CAN1 locus [38] to create yJIR4 . We next constructed our set of Founder gene deletion mutants from yJIR4 by replacing the gene to be deleted with a doubly counter-selectable cassette , UWMX ( pTEF-CaURA3-tADH1-pCgTRP1-CgTRP1-tTEF ) . This cassette contains URA3 from Candida albicans and TRP1 from Candida glabrata flanked by the TEF1 promoter and terminator sequences that are homologous to the KanMX cassette . To create the mutants , we amplified a KanMX cassette ( along with 400 bp of both upstream and downstream DNA for homology ) from the appropriate yeast deletion collection haploid strain [39] and co-transformed it with a NotI digest of pFA6a-UWMX ( S3 Fig ) . The resulting transformants were selected on uracil and tryptophan dropout media , and replica plated to YPD G418 200 mg L−1 ( GoldBio #G-418-25 ) to ensure the desired product of recombination ( between the KanMX amplicon and the genome and between the UWMX and KanMX , S4 Fig ) . For each gene of interest , we screened three transformants for correct cassette integration by PCR following the yeast deletion collection protocol [39] . We were able to construct and verify 37 of the 45 deletion mutants we attempted . To create a reference strain for competitive fitness assays , we replaced ymCherry with ymEGFP and inserted UWMX at the inactive HO locus in yJIR4 to create yJIR9 . Except for the fluorescent marker , this strain has the same genotype as the hoΔ founder; it was used as a reference in all fitness assays . From each of the 37 gene deletion strains , we picked 20 independent colonies to found replicate populations . We propagated the resulting 740 populations in batch culture for 500 generations , using the experimental evolution protocol previously described by Lang et al . [40] . Briefly , we randomly arrayed the 740 populations across eight flat-bottom polypropylene 96-well microplates ( Greiner , VWR catalog #29445-154 ) . We randomly interspersed 28 blank wells to allow us to monitor potential cross-contamination events ( no such events were observed ) . Each population was maintained in one well in 128 μL of rich laboratory media YPD ( 1% yeast extract ( BD , VWR catalog #90000-722 ) , 2% peptone ( BD , VWR catalog #90000-368 ) , and 2% dextrose ( BD , VWR catalog #90000-904 ) ) , at 30°C . Each day , we resuspended populations by shaking at 1000 rpm for 2 min on a Titramax 100 plate shaker , and diluted them 1: 25 twice using a BiomekFX liquid handling robot ( Beckman Coulter ) . Every 100 generations , we added glycerol to each plate to a final concentration of 10% w/v , sealed the plates with an aluminum seal , and stored them at -80°C . This protocol results in approximately 10 generations per day at an effective population size of approximately 105 . Over the course of the experiment , 4 populations were lost due to pipetting error . We measured the fitness of strains and evolved populations by direct competition to the fluorescently labeled reference strain yJIR9 , using the protocol described previously by Jerison et al . [12] . Briefly , we revived frozen strains , populations , and references from frozen stocks by diluting them 1: 25 into fresh YPD media . After 24 hours , the test strain or population was mixed 1:1 by volume with the reference and thereafter maintained using the same protocol used for evolution . 10 and 30 generations after mixing , the mixed populations were analyzed on the Fortessa or LSRII flow cytometers ( BD Biosciences ) to measure the ratio of reference to non-reference cells r . From these ratios , we calculated the fitness difference between the test and reference strains , s , given by s = 1 t log ( r f r i ) , where rf and ri are the final and initial measurements of the ratio of reference to non-reference , respectively , and t is the number of generations between those measurements . To revert the founding gene deletion mutations , we transformed an intact copy of the deleted gene ( PCR amplified from BY4741 genomic DNA ) and counter selected both the URA3 and TRP1 genes that were used to delete the original gene using standard protocols . The double counter selection dramatically increases the probability that the resulting transformants have the intended reversion , but these transformants also differ from their parents in being uracil and tryptophan auxotrophs . To correct for this discrepancy , we isolated three clones from each successful reversion and inserted our doubly counter selectable cassette UWMX at the neutral HO locus using a PmeI digest of the plasmid pHO-UWMX ( S5 Fig ) , which has the UWMX cassette flanked by homology to HO . S6 Fig shows that the UWMX cassette had consistently positive effect across clones , though not always of the same magnitude . Some clones proved impossible or nearly impossible to revert . In these clones , our double counter selection yielded no viable transformants . It is possible that some of these clones acquired mutations that affected the ability of clones to be transformed . Alternatively , it is also possible that a mutation changing the copy number of the counter selectable cassette has made reversion effectively impossible , as a successful reversion would require multiple integrations of the wild-type gene . Thus , tandem duplications , aneuploidy , or auto-diploidization ( all common types of mutations observed in yeast laboratory evolution experiments [20 , 21 , 41–43] ) would make reversions using our method impossible . We note that auto-diploidization is a particularly likely possibility since it has a beneficial fitness effect and can arise at high rates during laboratory evolution [20] . Since transformation may induce new mutations , we analyzed the fitnesses of replicate transformants for evidence of new mutations . All but 7 of the 100 clones produced 3 independent transformants with less than 0 . 5% standard deviation in fitness . The 7 clones which produced transformants of significantly different fitnesses all showed a clear pattern where only one of the three was significantly different from the median fitness ( S1 Fig ) , as we would expect if transformation had induced a new mutation in that clone . Thus by using the median fitness of the three transformants in our analysis , we largely correct for these instances when transformation induced new mutations . We sequenced a clonal isolate from each of 100 populations whose founding deletion was successfully reverted , as well as the 37 gene deletion founders and yJIR4 , the ancestor of the gene deletion founders . Indexed genomic DNA libraries were prepared as previously described [44] and sequenced on an Illumina NextSeq 500 . We first trimmed reads using trimmomatic v0 . 35 function ‘Illuminaclip’ , with options: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 [45] . We then used breseq v0 . 27 . 1 [23] to align the trimmed reads to the reference genome of the strain BY4741 from the University of Toronto [46] . All further analyses were conducted using the conservative list of mutation calls output by breseq . We note that while this approach does detect many small indels ( on the order of 30 bps or less ) , it tends to miss larger indels and rearrangements . In principle , we could attempt to detect these events using a junction-calling approach , but given our limited sequencing depth in this study , we cannot accurately confirm calls based on coverage variation , so the potential for false positives is high . Similarly , due to limited coverage we cannot confidently call mitochondrial mutations . Thus to avoid excessive false positives , we have not attempted to call these types of events . To understand the different factors that contribute to the rate of adaptation , we inferred the parameters to a model analogous to model 3B in Kryazhimskiy et al . [10] . Briefly , each fitness measurement is one of 3 replicate measurements , l , of one of 20 replicate evolved populations , k , descended from one of 37 gene deletion founders , j , which belongs to one of 16 modules , i . In our model , we assume that each measurement of the change in fitness of each population after 500 generations , yijkl , is the sum of an average fitness across all populations , α , a linear effect of initial fitness , βxij , a Founder module specific random effect , mi , a Founder gene specific random effect , gij , a population specific random effect ( which accounts for evolutionary stochasticty ) , pijk , and a term to account for measurement error , τijkl: y i j k l = α + β x i j + m i + g i j + p i j k + τ i j k l m i ∼ N ( 0 , σ m 2 ) g i j ∼ N ( 0 , σ g 2 ) p i j k ∼ N ( 0 , σ p 2 ) τ i j k l ∼ N ( 0 , σ τ 2 ) . The maximum likelihood parameter values for this model are summarized in the last of row Table 3 and the corresponding variance components are summarized in Fig 2C . Finally , to investigate the significance of the different factors , we compared the model above to the nested set of simpler models using the likelihood ratio test . The maximum likelihood fits for all models are shown in Table 3 . In all comparisons , the likelihood ratio test rejects the less complex model . To understand the different factors that contribute to epistasis between founder gene deletion and evolved mutations , we inferred the parameters of an analogous hierarchical model . In this model , epistasis between evolved mutations and the Founder deletion mutations is a function of the fitness effects of the founder gene deletions on the wild-type background along with the random effects described above . The maximum likelihood fits and analysis of nested models is summarized in Table 4 . To measure the association between evolved mutations and founder genotypes , we used a test statistic based on mutual information similar to the one described in Jerison et al . [12] . Briefly , we define the mutual information between the possible modules or genotypes of a founder , W ∈ {W1 , … , Wm} , and the possible multi-hit genes or multi-hit modules of evolved mutations g ∈ {g1 , … , gn} , as: M ( W , g ) = ∑ g = ( g 1 , … , g n ) ∑ W = ( W 1 , … , W n ) p ( W ) ∑ m g = ( 0 , 1 ) p ( m g | W ) log 2 p ( m g | W ) p ( m g ) , where mg is an indicator variable with value 1 when an evolved mutation belongs to g and 0 otherwise . We estimate probabilities from observed counts: p ( W = Wi ) is the frequency of populations with property Wi , p ( mg = 1 ) is the frequency of populations with a mutation in g across all populations , and p ( m = 0|W = Wi ) is the frequency of populations without a mutation in g among populations with property Wi . To measure the additional mutual information provided by a property of the founders after accounting for a second property , Z , we use conditional mutual information defined as: M ( W , g | Z ) = ∑ g ∑ Z p ( Z ) ∑ W p ( W | Z ) ∑ m g p ( m g | W , Z ) log 2 p ( m g | W , Z ) p ( m g | Z ) . We calculate this statistic for founder modules with evolved genes and founder modules with evolved modules , as well as for founder genes with evolved genes and evolved modules , conditioning on founder module . Then , we compare these statistics to null distributions generated by permuting mutations across populations , keeping the number of mutations per population fixed . We report the mutual information in excess of null , M ( · ) - M ¯ p ( · ) , with 95% confidence intervals calculated from the null distribution ( Table 2 ) . | The effects of mutations often depend on the presence or absence of other mutations . This phenomenon , known as epistasis , has been used extensively to infer functional associations between genes . For example , genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes . In the context of adaptation , epistasis can cause the outcomes of evolution to depend strongly on the initial genotype . Although studies have found that epistasis is common in laboratory populations , we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution . Here , by comparing evolution in strains with gene deletions in different functional modules , we quantify the effect of functional epistasis on evolutionary outcomes . We find that mutants with deletions in the same module have more similar evolutionary outcomes , on average , than mutants with deletions in different modules . This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics . | [
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"evolution... | 2019 | Modular epistasis and the compensatory evolution of gene deletion mutants |
As a major diarrheagenic human pathogen , enterohemorrhagic Escherichia coli ( EHEC ) produce attaching and effacing ( A/E ) lesions , characterized by the formation of actin pedestals , on mammalian cells . A bacterial T3SS effector NleL from EHEC O157:H7 was recently shown to be a HECT-like E3 ligase in vitro , but its biological functions and host targets remain elusive . Here , we report that NleL is required to effectively promote EHEC-induced A/E lesions and bacterial infection . Furthermore , human c-Jun NH2-terminal kinases ( JNKs ) were identified as primary substrates of NleL . NleL-induced JNK ubiquitylation , particularly mono-ubiquitylation at the Lys 68 residue of JNK , impairs JNK’s interaction with an upstream kinase MKK7 , thus disrupting JNK phosphorylation and activation . This subsequently suppresses the transcriptional activity of activator protein-1 ( AP-1 ) , which modulates the formation of the EHEC-induced actin pedestals . Moreover , JNK knockdown or inhibition in host cells complements NleL deficiency in EHEC infection . Thus , we demonstrate that the effector protein NleL enhances the ability of EHEC to infect host cells by targeting host JNK , and elucidate an inhibitory role of ubiquitylation in regulating JNK phosphorylation .
EHEC is a globally spread , pathogenic Escherichia coli that infects animals and humans [1 , 2] . Particularly , O157:H7 , as the most prominent serotype in the EHEC group , is a leading cause of diarrhea or hemorrhagic colitis in humans [3] . These pathogens belong to a distinct family of enteric bacteria that cause marked cytoskeletal changes and form unique attaching and effacing ( A/E ) lesions on intestinal epithelium [4 , 5] . A/E lesions are characterized by effacement of microvilli , intimate adherence between the bacterium and the host cell membrane , and the generation of actin pedestals , polymerized actin structures beneath the adherent bacteria [2] . Although the specific functions of actin pedestals are currently unclear [6] , many studies suggest that the capability of A/E pathogens to form actin pedestals correlates with their ability to cause disease in hosts [7–9] . The type III protein secretion system ( T3SS ) , as well as additional EHEC effector proteins , were reported to be involved in actin pedestal formation [10–12] , but it remains incompletely understood how pedestal formation can be modulated . Although the ubiquitin ( Ub ) system is exclusive to eukaryotes , prokaryotic bacteria have produced many E3 ligase-like effectors [13 , 14] . Recently , a bacterial T3SS effector NleL ( Non-Lee-encoded effector ligase; also named EspX7 ) from EHEC O157:H7 was shown to be a HECT-like E3 ligase in vitro , with Cys753 as the catalytic site ( Fig 1A ) [15] . Later biochemical work revealed that NleL interacts with human E2 UbcH7 and is capable of assembling heterotypic Ub chains in vitro [16–18] . While NleL has been proposed to modulate EHEC-induced actin-pedestal formation [19] , NleL’s specific host targets and functions in EHEC infection remain elusive . In this study , we have identified human JNK as the first substrate of the bacterial E3 ligase NleL . The JNK ( also known as stress-activated protein kinase , SAPK ) family includes three highly homologous isoforms: ubiquitously expressed JNK1 and JNK2 , and tissue-specific JNK3 [20] . JNKs are phosphorylated and activated by upstream kinases and regulate a wide range of cellular functions [21] . However , little is known about post-translational modifications other than phosphorylation regulating JNK functions . Here , we report that JNK proteins are ubiquitylated and inactivated by a bacterial effector NleL in EHEC infection , which promotes EHEC-induced A/E lesion formation and infection .
To evaluate the effect of NleL on EHEC infection , a nleL-deletion mutant ( ΔnleL ) was first constructed by chromosomally inactivating the nleL gene in the parental EHEC O157:H7 Sakai strain ( RIMD 0509952 ) as described previously [22] . Deletion of nleL has no effect on the growth of EHEC in culture medium ( S1A Fig ) . We then assessed the ability of EHEC strains to infect mammalian cells . As a hallmark of EHEC infection , bacteria closely attach to cultured mammalian cells [23] . As shown , deletion of nleL from EHEC significantly reduced bacterial attachment to mammalian cells . More importantly , complementation of the nleL-deletion strain with wild-type NleL ( ΔnleL + pNleL ) , but not the enzymatically-dead NleL mutant C753A ( where the active site Cys at position 753 is replaced with Ala ) ( ΔnleL + pC753A ) , effectively restored the strong adherence of EHEC to host cells ( Fig 1B and 1C ) . These data indicate that the bacterial effector NleL enhances the ability of EHEC to attach to mammalian cells in a manner dependent on its E3 ligase activity . To identify the host targets of NleL , a human ORFs library was screened by a yeast two-hybrid ( Y2H ) system with full-length NleL as the bait . A cDNA encoding human JNK1 , Mapk8 , was identified in the Y2H screen ( S1B Fig ) . A series of assays were then carried out to confirm the interaction of NleL with JNK1 . Both wild-type NleL and the mutant C753A ( also NleL-CA ) readily co-immunoprecipitated with JNK1 , suggesting NleL could form a complex with JNK1 independent of its E3 activity ( Fig 1D and 1E and S1C Fig ) . A GST pull-down assay with the recombinant proteins confirmed that JNK1 can directly interact with NleL or its C753A mutant in vitro ( Fig 1F ) . Compared to NleL170–782 , a truncation mutant of NleL frequently used in structural or in vitro biochemical analyses [15 , 17] , full-length NleL was shown to interact with JNK1 with significantly higher affinity ( Fig 1G and S1D Fig ) , suggesting that the N-terminal unordered region of NleL might be involved in the interaction with JNK1 . Moreover , we further demonstrated that endogenous JNKs interact with secreted NleL from EHEC in the infected mammalian cells ( Fig 1H ) . Altogether , NleL interacts with host protein JNK1 , providing a physical basis for their potential functional interplay . We next asked whether the interaction between NleL and host JNK might cause JNK ubiquitylation . As shown in Fig 2A , infection with wild-type EHEC O157:H7 increased the ubiquitylation of JNK , but infection with the ΔnleL strain had little or no impact on JNK ubiquitylation . Moreover , complementation of ΔnleL with wild-type NleL ( but not the C753A mutant ) effectively promoted JNK ubiquitylation in the infected cells . Thus , NleL could induce JNK ubiquitylation in the EHEC-infected host cells . Although NleL170-782 was sufficient to mediate the assembly of poly-ubiquitin ( poly-Ub ) chains in vitro [15 , 16] , the full-length form of NleL was used for all subsequent ubiquitylation assays and functional assays because of its stronger interaction with JNK1 and its intact E3 activity in vitro and in vivo ( S2A and S2B Fig ) . Wild-type NleL , but not C753A , was found to efficiently promote mono- and poly-ubiquitylation of JNK1 in vivo and in vitro , depending on its E3 activity ( Fig 2B–2D ) . Thus , these results established human JNK1 as the substrate for NleL . NleL was previously shown to assemble Lys 6 and/or Lys 48 linked poly-Ub chains in vitro , while auto-ubiquitylation of NleL occurred preferentially via other Ub linkages [15 , 16] . Here , we found that NleL-catalyzed ubiquitin chains on JNK1 were primarily linked via Lys 27 , Lys 29 and Lys 33 ( K27 , K29 and K33 ) linkages , especially the K29 linkage ( S2C and S2D Fig ) . As shown in Fig 2E , NleL readily modified JNK1 with mono-Ub and K29-linked Ub chains , which can be completely removed by Usp2cc , the catalytic core of human ubiquitin-specific protease 2 ( USP2 ) . Our data also indicated that several E2s , particularly UbcH7 , could support NleL-mediated JNK1 ubiquitylation in cells ( S3A Fig ) . We next mapped potential ubiquitylation sites in JNK1 . JNK1α1 , as the canonical isoform of human JNK1 , contains 29 lysine residues . Trypsinolysis of ubiquitin conjugation yields signature “diGly remnants” , which could be enriched with anti-diGly monoclonal antibody for mass spectrometry ( MS ) analysis [24 , 25] . MS analysis of JNK1 purified from NleL-expressing 293T cells identified 6 diGly-containing peptides , which corresponded to ubiquitylation at Lys residues 68 , 166 , 250 , 251 , 265 , and 308 ( Fig 2F ) . The other five ubiquitylation sites in JNK1 ( K140 , K153 , K203 , K222 , and K236 ) were identified by protein-protein docking analysis . Therefore , 11 Lys residues of JNK1 were the putative ubiquitylation sites for NleL ( Fig 2G ) . To further pinpoint the major Lys residues of JNK1 for NleL-induced ubiquitylation , in vivo ubiquitylation assays were performed with JNK1 mutants bearing Lys-to-Arg substitutions at each potential ubiquitylation site . Single Lys-to-Arg substitution on each of three sites ( K153R , K222R or K265R ) markedly attenuated NleL-induced JNK1 poly-ubiquitylation ( Fig 2H and S3B and S3C Fig ) . Moreover , combined mutation of these three sites ( K153/222/265R , 3KR ) significantly abolished NleL-induced poly-ubiquitylation of JNK1 ( Fig 2I ) . On the other hand , the K68R substitution alone completely abolished mono-ubiquitylation of JNK1 by NleL in vivo and in vitro , while the 3KR mutant was still clearly mono-ubiquitylated ( Fig 2J–2L ) . Thus , four Lys residues ( K68 , K153 , K222 and K265 ) of JNK1 were established as the major NleL-associated ubiquitylation sites , with the K68 residue predominantly responsible for mono-ubiquitylation . It has been established that activation of JNK signaling constitutes an early cellular response to bacterial infection [26 , 27] . We next investigated whether NleL functionally regulates this JNK role . As shown in Fig 3A and 3B , while wild-type EHEC induced slight phosphorylation of endogenous JNK in mammalian cells , infection by ΔnleL strain elicited much stronger JNK phosphorylation . Complementation of the ΔnleL strain with wild-type NleL restored the EHEC inhibitory effect on JNK phosphorylation , but the C753A-mutant–complemented ΔnleL strain did not ( Fig 3A ) . Additionally , TNFα stimulation did not induce JNK phosphorylation when the mammalian cells were infected by the EHEC overexpressing wild-type NleL ( but not C753A ) ( Fig 3B ) . These results prompted us to further investigate whether NleL alone is sufficient to suppress JNK phosphorylation . Indeed , overexpression of wild-type NleL , but not NleL C753A , efficiently reduced the basal phosphorylation level of JNK ( Fig 3C and 3E ) . Even when the cells were stimulated by TNFα at either low ( 1 . 0 ng/ml ) or high ( 10 . 0 ng/ml ) concentration , wild-type NleL significantly suppressed JNK phosphorylation ( Fig 3C and 3D ) . Thus , the E3 ligase activity of NleL is sufficient to suppress JNK phosphorylation in host cells . We further investigated the relevance of NleL-induced ubiquitylation to JNK1 phosphorylation . In the presence of wild-type NleL ( but not C753A ) , JNK1 was ubiquitylated efficiently but poorly phosphorylated , suggesting that NleL-mediated JNK1 ubiquitylation might adversely impact JNK1 phosphorylation ( Fig 3F ) . Moreover , a JNK kinase assay with recombinant c-Jun as the substrate revealed that the E3 activity of NleL markedly impaired the total kinase activity of JNK1 ( Fig 3G ) . We then tried to explore the roles of JNK1 mono-ubiquitylation at the K68 residue and poly-ubiquitylation at other sites ( K153 , K222 and K265 ) in suppressing JNK1 phosphorylation . As shown in Fig 3H , NleL almost completely abolished TNFα-induced phosphorylation of wild-type JNK1 as well as the 3KR mutant; however , NleL had only negligible effects on the phosphorylation of the JNK1 K68R mutant . In addition , NleL had no effect on the phosphorylation of p38 or Erk , two other members of the MAPK superfamily ( Fig 3I ) . Therefore , NleL-induced JNK1 ubiquitylation , particularly mono-ubiquitylation of K68 , specifically inhibited JNK1 phosphorylation . JNK family proteins share over 90% sequence homology to each other ( Fig 4A and S4 Fig ) , including a conserved Lys 68 residue . We next asked whether NleL might also target JNK2 or JNK3 , the other two members of the JNK family . Binding assays confirmed that NleL indeed interacted with JNK2 and JNK3 ( Fig 4B and 4C ) . NleL , but not C753A , effectively catalyzed ubiquitylation of JNK2 and JNK3 ( Fig 4D and S5A–S5C Fig ) , and inhibited their phosphorylation as well ( Fig 4E and S5D Fig ) . Furthermore , similar to JNK1 , ubiquitylation of JNK2/3 by NleL was negatively correlated with JNK2/3 phosphorylation and activation ( Fig 4F and 4G ) . Thus , NleL appeared to target all the members of JNK family . Currently , two MAP2Ks , MKK4 and MMK7 , are known to phosphorylate and activate JNK proteins , with MKK7 being more specific to JNK [28] . It is natural to speculate that NleL-induced ubiquitylation might suppress JNK phosphorylation by either inhibiting the kinase activity of MKK7 or disrupting the interaction between MKK7 and JNK . Since NleL had little or no effect on the phosphorylation of MKK7 ( Fig 5A ) , we proceeded to explore the latter possibility . Usually , JNK1 or JNK2 interacts with MKK7 in host cells . NleL readily reduced MKK7 association with JNKs , but the C753A mutant had little effect ( Fig 5B and 5C and S6A and S6B Fig ) , suggesting that NleL impairs the MKK7-JNK interaction independent of direct competition against MKK7 . We also ruled out the possibility that NleL might obstruct MKK4 phosphorylation or MKK4-JNK association ( Fig 5D and S6C Fig ) . Moreover , NleL disrupted the recruitment of wild-type JNK1 to MKK7 , but had no effect on the interaction of K68R mutant with MKK7 , although wild-type JNK1 and its K68R mutant had the same ability to interact with MKK7 ( Fig 5E and 5F ) . Thus , the K68 residue of JNK1 is required for NleL to suppress the MKK7-JNK interaction . Since E . coli do not have the ubiquitin system , NleL should not conjugate Ub to JNK1 in bacterial cells . If NleL could mediate modifications in addition to ubiquitylation , it could still potentially modify and inactivate JNK1 when co-expressed with JNK1 in E . coli . However , we found that MKK7 readily phosphorylated purified JNK1 that was co-expressed with either GST-tagged NleL or GST alone in the E . coli BL21 ( DE3 ) strain ( Fig 5G ) . These data suggested that NleL did not inactivate JNK1 through other post-translational modifications . Altogether , we conclude that NleL-induced ubiquitylation at the K68 residue of JNK1 suppresses the phosphorylation of JNK1 , through disrupting the JNK1-MKK7 interaction . We found that NleL-mediated JNK inactivation was independent of NF-κB signaling ( S7A Fig ) . Next , we examined the possible effects of NleL on downstream of JNK . As expected , wild-type NleL suppressed the basal-level phosphorylation of endogenous c-Jun , a bona fide physiological substrate of JNK , while the C753A mutant did not ( Fig 6A ) . Immunofluorescence microscopy analysis also revealed that EGFP-tagged NleL , but not EGFP , markedly reduced c-Jun phosphorylation ( Fig 6B and S7B Fig ) . Consistently , overexpression of NleL ( but not C753A ) impaired TNFα-stimulated phosphorylation of c-Jun ( Fig 6C ) . However , depletion of Jnk1/2 using short hairpin RNAs in mammalian cells almost completely abolished the inhibitory effect of NleL on c-Jun phosphorylation ( Fig 6C ) . Therefore , NleL inhibits c-Jun phosphorylation by targeting JNK proteins . As c-Jun is a major component of the AP-1 transcription factor [29] , we next investigated the regulation of AP-1 activity by NleL . An AP-1 luciferase reporter assay showed that wild-type NleL , but not C753A , suppressed AP-1 activity in cells ( Fig 6D and S7C and S7D Fig ) . AP-1 is known to regulate the expression of a large number of genes , e . g . cyclin D1 ( CCND1 ) [30] . As expected , the basal expression of CCND1 in mammalian cells was down-regulated by NleL ( but not C753A ) ( Fig 6E ) . Consistently , NleL also diminished JNK phosphorylation and CCND1 expression induced by different stimulators ( Fig 6F and 6G and S7E and S7F Fig ) . These results suggest that the E3 activity of NleL is required to suppress AP-1 activity and the expression of AP-1 target genes . Based on previous work by multiple groups [4 , 5 , 31] , some AP-1 targets ( e . g . CD44 , TPM1 , ARPC1B and EZR ) are known to be important for the formation of EHEC-induced pedestals . A protein-protein interaction ( PPI ) network analysis was performed to characterize the potential interplay among the actin-associated proteins targeted by AP-1 and the host proteins identified in EHEC actin pedestals ( Fig 6H ) . The data strongly suggested an emerging role of AP-1 signaling in regulating the formation of EHEC actin pedestals . Furthermore , we found that ectopically expressed NleL suppressed the phosphorylation of VSAP ( Fig 6I ) , one of the critical components in actin pedestals that was recently reported to be modulated by JNK/AP-1 signaling [32] . Meanwhile , CCND1 , another AP-1 target protein shown above to be down-regulated by NleL , also interacts with AP-1 actin-associated targets and EHEC pedestal proteins ( Fig 6H ) . Thus , NleL might promote the formation of EHEC actin pedestals by modulating the host JNK/AP-1 pathway . We next explored the role of NleL on the formation of EHEC actin pedestals . Compared to the wild-type EHEC strain , deletion of nleL from EHEC reduced actin-pedestal formation on mammalian cells ( Fig 7A ) . Complementation of ΔnleL strain with NleL , but not C753A , significantly restored the EHEC pedestal-forming abilities ( Fig 7A and 7B ) . On the other hand , Jnk1/2 depletion in HeLa cells promoted actin-pedestal formation by each of the EHEC strains . Additionally , ΔnleL and wild-type EHEC had similar pedestal-forming abilities on Jnk1/2-silenced host cells ( Fig 7A and 7B ) . Thus , NleL promoted the formation of EHEC actin pedestals through targeting host JNKs . C . rodentium has been used as an alternative approach to study EHEC . However , differing from our findings on the role of NleL in EHEC infection , NleL-deficiency was found to have no effect on the ability of C . rodentium to form actin pedestals and attach to HeLa cells or mice colon ( S8 and S9 Fig ) . Instead , overexpression of EHEC NleL in the ΔnleL C . rodentium suppressed the ability of C . rodentium to attach to HeLa cells and form actin pedestals . Thus , C . rodentium was not a suitable model system to study NleL . As an A/E pathogen , the pedestal-forming ability of EHEC is considered to correlate with its capability to colonize host [4] . Caco-2 is a human colorectal epithelial cell line that can form an epithelial cell monolayer when cultured for 6 days; continued Caco-2 monolayers growth for 21 days can become differentiated with a brush border that more closely resembles human intestinal epithelium [33–35] . We next performed EHEC infection assays on 6-day-old and 21-day-old Caco-2 monolayers . NleL increased the ability of EHEC to colonize Caco-2 monolayers in a manner dependent on NleL’s E3 ligase activity . Moreover , treatment of Caco-2 with the JNK inhibitor SP600125 effectively rescued the capability of the ΔnleL strain to attach to the Caco-2 monolayer ( Fig 7C and S10A and S10B Fig ) . Similar results were also observed in HeLa cells ( S11 Fig ) . We additionally observed EHEC-induced A/E lesions on 21-day-old Caco-2 monolayers ( Fig 7D ) . Wild-type EHEC ( but not ΔnleL ) caused A/E lesions on 21-day-old Caco-2 monolayers , marked by microvillus damage . Complementation of the ΔnleL strain with wild-type NleL , but not the C753A mutant , restored the EHEC A/E lesion-forming ability; SP600125 treatment also rescued the ability of ΔnleL to form A/E lesions on the Caco-2 monolayer . These data suggested that the suppression of JNK1/2 functions in host cells could compensate for nleL-deletion-caused loss of the EHEC ability to colonize the host and form A/E lesions . JNK proteins are thus identified as the critical host targets for NleL to promote EHEC O157:H7 infection ( Fig 7E ) .
In this work , we have demonstrated that NleL , a bacterial effector and HECT-like E3 ubiquitin ligase from EHEC O157:H7 , is critically involved in promoting actin-pedestal formation during EHEC infection . Notably , this finding is different from a previous report by Piscatelli et al . , in which NleL was shown to down-regulate the EHEC-induced formation of actin pedestals [19] . If NleL down-regulates actin-pedestal formation in infection as suggested by Piscatelli et al . , the presence of NleL should have disrupted bacterial attachment to the host cells . However , results from the same study by Piscatelli et al . indicated that EHEC NleL , reintroduced into the nleL-deleted C . rodentium , was actually required for efficient infection in vivo . Further work is needed to clearly understand the discrepancy between our findings and the findings of Piscatelli et al . As described above , C . rodentium cannot be a suitable surrogate for EHEC O157:H7 to study NleL . Multiple studies have indicated that EHEC can trigger actin-pedestal formation in host cells in ways differing from C . rodentium [36–38] . While EHEC O157 uses the bacterial effector Tir and TccP adaptor protein to trigger actin polymerization , C . rodentium relies on the phosphorylation of Tir Y471 and the host protein Nck . This may partially account for why the NleLs from these two pathogens have very different effects on actin-pedestal formation , despite their high homology . Given that EHEC NleL targets and suppresses the JNK/AP-1 pathway , it is likely that NleL plays roles in the later stages , rather than the triggering stage , of actin polymerization . Further work is warranted to elucidate the mechanisms underlying why NleL has different roles in these two bacteria . On the other hand , we found that the differentiated Caco-2 monolayers ( grown for 21 days ) could be used as an in vitro infection model to study A/E lesions . Caco-2 monolayers can mimic human colonic epithelium for EHEC infection , providing an alternative approach to in vivo infection . Different Ub chain linkages have different impacts on targeted proteins ( e . g . K48- or K63-linked Ub chains usually cause protein degradation ) [39–41] . Here , NleL primarily assembled mono-Ub and K29-linked Ub chains on JNK , suggesting a non-proteolytic function of NleL-induced JNK ubiquitylation . Instead , NleL-induced mono-ubiquitylation at K68 of JNK , but not the poly-ubiquitylation at other residues , disrupted the interaction between JNK and MKK7 . Although a recent report showed that JNK1 binds MKK7 using multiple binding sites [42] , K68 of JNK1 is not in the MKK7-JNK1 binding interface . Other mechanisms , such as those involving allostery , may underlie how JNK1 mono-ubiquitylation disrupts the MKK7-JNK1 interaction . NleL-mediated JNK mono-ubiquitination appeared to drastically suppress JNK activity , although the mono-ubiquitylated sub-population of JNK1 seemed limited ( 5% ~ 10% of all cellular JNK1 ) . Currently there are two accepted explanations: 1 ) cellular proteins can exist in different subcellular populations , so targeted ubiquitylation of a particular subpopulation can be sufficient to generate significant impacts on a specific pathway; 2 ) NleL-induced JNK1 ubiquitylation should be a dynamic process balanced by removal of ubiquitin by deubiquitylating enzymes ( DUBs ) , as there are plenty of DUBs in mammalian cells [43–45] . In other words , although only part of JNK were observed to be modified by NleL , it is highly possible that most of the JNK molecules undergo NleL-mediated ubiquitylation and then deubiquitylation by DUBs . The JNKs are master regulators in mammalian cells [46] . It is well established that JNKs are phosphorylated by upstream kinases and then activate downstream targets . However , little was known about the posttranslational modifications other than phosphorylation that might occur on JNKs until recently , when several ubiquitylation and acetylation sites of endogenous JNKs were uncovered by proteomic analyses [24 , 47 , 48] . Whether these potential modifications might impact the functions of JNKs remains poorly understood . We thus demonstrate for the first time that ubiquitylation of JNKs by the bacterial effector NleL negatively regulates the function of JNKs . It will be intriguing to investigate whether an unknown endogenous E3 ligase might exist to catalyze the ubiquitylation of JNKs and regulate JNK signaling in mammalian cells . Worldwide , outbreaks of EHEC O157:H7 infection constitute constant and serious threats to human population and live stocks , without effective treatments [49] . Antibiotic therapy is generally contraindicated as it may promote expression of Stx toxin protein and increase the risk of the hemolytic–uremic syndrome ( HUS ) [50–52] . Only supportive care can be provided for the infected patients who have developed HUS . Thus , a diversity of treatment and prevention strategies should be developed to protect against EHEC . As NleL promotes EHEC infection by suppressing host JNK , disrupting the NleL-JNK interaction may represent a novel strategy against EHEC O157:H7 infections .
All animal use procedures were in strict accordance with the Guide for the Care and Use of Laboratory Animals ( 8th edition , National Research Council , 2011 ) , approved by the Institutional Animal Care and Use Committee ( Protocol number SIBCB-S330-1512 ) of Shanghai Institute of Biochemistry and Cell Biology , Chinese Academy of Sciences . DNAs for NleL amplified from the genomic DNA of E . coli O157:H7 Sakai strain was inserted into pCDNA3 . 0 and pEGFP-C1 for mammalian expression , and pGEX-4T-1 for recombinant expression in E . coli . NleL DNA was also ligated into the pTRC99A vector for complementation in EHEC ( under the trc promoter; pTRC99A is kindly provided by Dr . Xueli Zhang from Tianjin institute of industrial biotechnology , Chinese academy of sciences ) . Genes for encoding JNK1α1 , JNK2α2 ( kindly provided by Dr . Jinzhang Zeng from Xiamen University , China ) , JNK3α2 ( Addgene #13759 ) and MKK7 ( Addgene #14623 ) were cloned to pCDNA3 . 0 vector with a C-terminal Flag tag ( or HA tag ) , and pET28a vector with 6× His tag . AP-1 luciferase reporter plasmid was a gift from Dr . Jine Yang ( Sun Yat-sen University , Guangzhou , China ) . Plasmids expressing HA-tagged Ub and its mutants were described previously [53] . All the point mutations were generated by using the QuickChange Site-Directed Mutagenesis Kit ( Stratagene ) according to manufacturer’s protocol . All constructs were verified by DNA sequencing . Antibodies for JNK1 ( 2C6 ) ( #3708 ) , phospho-SAPK/JNK ( 81E11 ) ( #4668 ) , phospho-SAPK/JNK ( G9 ) ( #9255 ) , phospho-c-Jun ( 54B3 ) ( #2361 ) , phospho-c-Jun ( #9261 ) , caspase-3 ( #9662 ) , VASP ( 9A2 ) ( #3132 ) and phospho-VASP ( Ser157 ) were obtained from Cell Signaling Technology . Anti-JNK2 antibody ( EP1595Y ) ( ab76125 ) , anti-MEK7 antibody ( EP1455Y ) ( ab52618 ) , anti-JNK1/2/3 antibody ( ab179461 ) and anti-MEK7 ( phospho S271 + T275 ) ( ab4762 ) were purchased from Abcam . Rabbit anti-HA antibody ( H6908 ) , mouse monoclonal anti-Flag antibody ( F1804 ) , and Anti-Flag M2 affinity gel ( A2220 ) were from Sigma . Other antibodies were purchased from BD pharmingen for anti-JNK1 ( 551197 ) , Santa Cruz Biotechnology for anti-ubiquitin ( P4D1 ) , Bioword for anti-c-Jun ( G237 ) , Absci for anti-MKK4 ( #AB21132 ) and anti-phospho-MKK4 ( Ser80 ) ( #AB11177 ) , HangZhou HuaAn Biotechnology for anti-His6 tag ( M0812-3 ) , Proteintech for mouse anti-Cyclin D1 ( 60186-1-Ig ) , rabbit anti-Flag tag ( 20543-1-AP ) and mouse anti-GAPDH ( 60004-1-Ig ) . Peroxidase-conjugated goat anti-rabbit and goat anti-mouse IgG secondary antibodies were purchased from Jackson ImmunoResearch Laboratories , Inc . Chemicals were purchased from Sigma if not otherwise indicated: PS-1145 ( Santa Cruz Biotechnology ) , ATP ( Thermo Scientific Fermentas ) . JNK inhibitor SP600125 ( S1460 ) was from Selleck . HeLa , Caco-2 and HEK293T cells were obtained from the American Type Culture Collection ( ATCC ) . All cell culture products were from Corning . HEK293T , HeLa , Caco-2 cells were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) ( Hyclone ) supplemented with 10% fetal bovine serum ( FBS ) , 2 . 0 mM L-glutamine , 100 units/ml penicillin and 100 mg/ml streptomycin . Cells were maintained in 5 . 0% CO2 at 37°C . Transfections were carried out with Lipofectamine 2000 ( Invitrogen ) according to the manufacturer’s instruction . Enterohemorrhagic Escherichia coli ( EHEC ) O157:H7 Sakai strain ( RIMD 0509952 ) and C . rodentium ( CR ) strain DBS100 ( ATCC 51459 ) were used as wild-type strains . These bacteria were commonly cultured at 37°C LB broth . Deletion of nleL from EHEC O157:H7 genome was achieved through standard homologous recombination , as reported previously [22] . For CR , the nleL-deleted mutant and escR-deleted mutant had been constructed through a homologous recombination method “Gene doctoring” [54 , 55] . The mutants were verified by PCR and DNA sequencing . For rescue assay , the ΔnleL strain was transformed with plasmid encoding wild-type Flag-tagged NleL or its C753A mutant . The infection was performed as described before with slight modifications [56 , 57] . Briefly , EHEC or CR strains were cultured overnight in 2 × YT ( 16 . 0 g/L tryptone , 10 . 0 g/L yeast extract , 5 . 0 g/L NaCl ) medium without shaking at 37°C . Bacterial cultures were then diluted by 1:40 with serum-free DMEM medium , and cultured for an additional 3 ~ 4 h at 37°C in the presence of 5% CO2 to induce the expression of type III secretion system before infection . For the complementation assay in EHEC or CR , the medium was added with 1 . 0 mM Isopropyl-B-D-thiogalactopyranoside ( IPTG ) . Bacterial cells were then collected and suspended in PBS . After measuring the O . D . of cultured bacteria , infections were performed with mammalian cells at a multiplicity of infection ( MOI ) of 100:1 or 20:1 , if not indicated otherwise , with a centrifugation at 800 g for 10 min , and then proceeded with incubation at 37°C in 5% CO2 for 2 ~ 3 h . After that , cells were washed three times and further cultured in fresh DMEM medium for another 2 . 5 ~ 5 h . Then the infected cells were subjected to immunofluorescence assay or IB analyses with indicated antibodies . E . coli BL21 ( DE3 ) strains harboring the corresponding recombinant plasmids were grown in LB medium supplemented with appropriate antibiotics . Protein expression was induced overnight at 16°C with 0 . 3 mM IPTG when OD600 reached 0 . 6 ~ 0 . 8 . To purify His6-tagged proteins , bacteria were harvested and lysed in lysis buffer containing 50mM Tris-HCl ( pH 7 . 6 ) , 300 mM NaCl , 40 mM imidazole and 5 . 0 mM beta-mercaptoethanol , and then proteins were purified with affinity chromatography using Ni-NTA beads ( Qiagen ) according to the manufacturer’s instruction . For GST-fusion proteins , purifications were performed by affinity chromatography using Glutathione Sepharose Fast Flow beads ( GE Healthcare ) . Eluted proteins were further dialyzed overnight at 4°C . Recombinant proteins were concentrated and then frozen-stored in a buffer containing 50 mM Tris-HCl ( pH 7 . 4 ) , 300 mM NaCl and 15% Glycerol ( V/V ) . Protein concentrations were determined using Bradford colorimetric assays , with their protein purities examined with SDS-PAGE followed by Coomassie Blue staining . Nucleotide sequences for the human Jnk1/2-specific shRNAs used were described before [58] . For stable knockdown of Jnk1/2 , lentiviral particles harboring specific shRNA expression vector ( pLKO . 1; Sigma-Aldrich ) were produced by transfection of HEK293FT cells with the shRNA expression plasmid and lentiviral packaging mix . Target cells ( HEK293T and HeLa ) were incubated with the viral supernatant in the presence of 8 μg/ml polybrene ( Sigma ) and selected with 2 μg/ml puromycin ( Clontech ) . As previously described with minor modifications [59] , in vitro ubiquitylation assays were carried out in a 30 μl reaction system containing E1 ( 100 ng ) , His6-tagged UbcH7 ( 200 ng ) , GST-NleL or C753A ( 500 ng ) , Flag-JNK1 ( 500 ng ) and ubiquitin ( 1 . 0 μg ) in ubiquitylation buffer ( 50 mM Tris–HCl , pH 7 . 5 , 5 . 0 mM MgCl2 , 2 . 0 mM ATP , 1 . 0 mM DTT ) at 37°C for 60 min . After the reaction was terminated by adding one-tenth volume of 10 × SDS sample buffer , the resulted mixtures were boiled and then subjected to SDS-PAGE , followed by IB analysis with indicated antibodies . In vivo ubiquitylation were also carried out as described previously [60] . Cells were collected and lysed in denaturing RIPA buffer containing 50 mM Tris-HCl ( pH 7 . 6 ) , 150 mM NaCl , 1 . 0% Triton X-100 , 1 . 0% sodium deoxycholate and 0 . 1% SDS supplemented with 1 . 0% protease inhibitor cocktail ( Roche ) . Cell lysates were then centrifuged at 15 , 000 g for 10 min , 4°C . Following overnight incubation of anti-Flag beads with cell lysate at 4°C , the beads was washed five times with RIPA buffer . Finally the beads were boiled in SDS sample buffer and subjected to IB analysis with indicated antibodies . HEK293T cells in 6 cm dish were transfected with 5 . 0 μg pCDNA3-Flag-JNK1 and cultured for 24 h . Cells were then lysed in IP buffer ( containing 50 mM Tris-HCl , pH 7 . 6 , 150 mM NaCl , 1 . 0% Triton X-100 ) , and centrifuged ( 15 , 000g ) for 15 min at 4°C . The supernatants were then subjected to IP with anti-Flag M2 beads to enrich Flag-JNK1 . Then Flag-JNK1 was eluted by using 3 × Flag peptides ( 150 μg/ml in PBS ) at 4°C for 30min . The eluted protein was verified by Coomassie Blue staining of SDS-PAGE gels . For Co-IP assay , 24 h after transfection with indicated plasmids , HEK293T cells were lysed in Triton X-100 buffer ( 50 mM Tris-HCl at pH 7 . 4 , 150 mM NaCl , 1% Triton X-100 ) plus 1% protease inhibitor cocktail at 4°C for 1 h . The corresponding antibody-conjugated Sepharose beads were added into the lysates supernatant . Following incubation overnight , the beads were washed five times ( 10 min each ) . For GST pull-down assay , purified GST-NleL , His-JNKs or cell lysate ( appropriate volume ) were mixed in total 400 μL reaction system ( 50 mM Tris-Cl at pH 7 . 5 , 150 mM NaCl , 1% ( v/v ) Triton X-100 , 1 mM EDTA ) at 4°C for 6 h . The beads were then pelleted and washed for 5 times ( 10 min incubation at 4°C for each washing ) . Then the recovered beads were boiled in 1× SDS-PAGE loading buffer and subjected to SDS-PAGE , followed by Coomassie Blue staining or IB with indicated antibodies . For immunofluorescence staining , cells were fixed with 4% paraformaldehyde for 20 min at room temperature , permeabilized for 10 min with 0 . 2% Triton X-100 in PBS , and then blocked for 60 min with 1 . 0% BSA , followed by incubation with indicated antibodies . All the cell nuclei were counterstained with DAPI before imaging . In vitro kinase assays were performed as described before [61] . Cells were transfected with plasmids encoding NleL and Flag-JNK1 or JNK2 . 24 h after transfection , cells were treated with or without TNFα ( 10 ng/m1 ) for 10 min . Cell were then lysed and subjected to IP with anti-Flag M2 beads in M2 buffer ( containing 20 mM Tris-HCl , pH 7 . 6 , 250 mM NaCl , 0 . 5% NP-40 , 3 . 0 mM EDTA , 3 . 0 mM EGTA ) . Then the beads with the precipitated JNK proteins were washed and subjected to in vitro kinase reaction ( 30 μl total ) containing 1 . 0 μg of GST-c-Jun ( 1-79aa ) in the kinase buffer ( 30 mM HEPES , pH 7 . 4 , 3 . 0 mM DTT , 30 mM PNPP , 0 . 2 mM NaVO3 , 30 mM MgCl2 ) supplemented with 2 . 0 mM ATP . The in vitro kinase assays involving human JNKs were carried out at 30°C for 60 min . Reactions were stopped by adding 1 × SDS-PAGE loading buffer and subjected to IB analysis with anti-p-c-Jun antibody . Luciferase reporter assays were performed as described previously [62] . HEK293T cells seeded in 24-well plates were transiently co-transfected with 50 ng of pAP-1-Luc and 10 ng of pRL-tk-Luc together with or without 500 ng of pCDNA3-NleL , using Lipofectamine 2000 reagent . The total amounts of DNA were kept constant by supplementing empty vector ( pCNDA3 . 0 ) . 24 h after transfection , cells were subjected to TNFα ( R&D Systems Inc . , 10 ng/ml ) or PMA ( Sigma , 20 nM ) for indicated time . Cells were then lysed in 1 × Passive Lysis Buffer ( 5 × concentrate diluted in ddH2O , Promega ) for 15 min at room temperature with vigorous shaking . AP-1 activities were finally determined using the dual luciferase assay kit ( Promega ) . Results were independently replicated for at least three experiments . Total RNAs from indicated cells were extracted using TRIzol ( Invitrogen ) . RNA concentrations were determined on Nanodrop ND-1000 sepectrophotometer . 0 . 1 μg total RNAs were used to cDNA synthesis with the ReverTra Ace qPCR RT kit ( FSQ-201 , TOYOBO ) , according to the manufacturer’s instructions . The relative levels of genes expression ( normalized to those of Gapdh ) were assessed in triplicate wells of a 96-well reaction plate by subjecting 10 ng cDNAs per well to a Bio-Rad CFX96 Touch Detection System with SYBR Green chemistry using the following primers: human Ccnd1-forward: 5’-CCGTCCATGCGGAAGATC-3’; human Ccnd1-reverse: 5’ -GAAGACCTCCTCCTCGCACT-3’ [63]; human Gapdh-forward: 5’-TGCCCTCAACGACCACTTTG-3’; human Gapdh-reverse: 5’-TTCCTCTTGTGCTCTTGCTGGG-3’ [64] . qRT-PCR data were analyzed on Bio-Rad CFX Manager 3 . 0 . Protein sequences were retrieved from the RefSeq database , aligned with ClustalW2 , and further processed on GeneDoc . Caco-2 cells were fixed in 2 . 5% glutaraldehyde and then processed for scanning electron microscopy ( SEM ) analysis as previously described [65] . SEM samples were examined at 25 kV using a FEI Tecnai G2 Spirit TEM ( SIBCB , China ) . Y2H screening with full-length NleL as the bait was performed as described previously [53] . Statistical significance of the data was determined using the Student’s t-test . In all experiments , only P value of < 0 . 05 was considered to be statistically significant . | Enterohemorrhagic Escherichia coli ( EHEC ) can cause attaching and effacing ( A/E ) lesions to form in the colons of animals and humans , contributing to severe bacterial infection . NleL , an E3 ubiquitin ligase from EHEC O157:H7 is one of the bacterial type III secretion effectors that may be involved in the regulation of A/E lesions . However , NleL’s exact host targets and the detailed mechanistic actions are still unclear . Here , we report that the effector protein NleL effectively promotes EHEC-induced A/E lesions and bacterial infection by targeting the host JNK protein . Specifically , we find that NleL-mediated JNK ubiquitylation abolishes phosphorylation and activation of host JNK , subsequently suppressing the host JNK/AP-1 signaling pathway to favor the formation of EHEC-mediated actin pedestals on the surface of mammalian cells . Collectively , our work has not only discovered the A/E lesion-promoting function of NleL during EHEC infection , but also revealed a novel regulatory mechanism of host JNK protein . | [
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"... | 2017 | Bacterial effector NleL promotes enterohemorrhagic E. coli-induced attaching and effacing lesions by ubiquitylating and inactivating JNK |
Campylobacter jejuni is currently the leading cause of bacterial gastroenteritis in humans . Comparison of multiple Campylobacter strains revealed a high genetic and phenotypic diversity . However , little is known about differences in transcriptome organization , gene expression , and small RNA ( sRNA ) repertoires . Here we present the first comparative primary transcriptome analysis based on the differential RNA–seq ( dRNA–seq ) of four C . jejuni isolates . Our approach includes a novel , generic method for the automated annotation of transcriptional start sites ( TSS ) , which allowed us to provide genome-wide promoter maps in the analyzed strains . These global TSS maps are refined through the integration of a SuperGenome approach that allows for a comparative TSS annotation by mapping RNA–seq data of multiple strains into a common coordinate system derived from a whole-genome alignment . Considering the steadily increasing amount of RNA–seq studies , our automated TSS annotation will not only facilitate transcriptome annotation for a wider range of pro- and eukaryotes but can also be adapted for the analysis among different growth or stress conditions . Our comparative dRNA–seq analysis revealed conservation of most TSS , but also single-nucleotide-polymorphisms ( SNP ) in promoter regions , which lead to strain-specific transcriptional output . Furthermore , we identified strain-specific sRNA repertoires that could contribute to differential gene regulation among strains . In addition , we identified a novel minimal CRISPR-system in Campylobacter of the type-II CRISPR subtype , which relies on the host factor RNase III and a trans-encoded sRNA for maturation of crRNAs . This minimal system of Campylobacter , which seems active in only some strains , employs a unique maturation pathway , since the crRNAs are transcribed from individual promoters in the upstream repeats and thereby minimize the requirements for the maturation machinery . Overall , our study provides new insights into strain-specific transcriptome organization and sRNAs , and reveals genes that could modulate phenotypic variation among strains despite high conservation at the DNA level .
Deep RNA–sequencing ( RNA–seq ) has been revolutionizing transcriptome analyses of both pro- and eukaryotes [1] , [2] . Several recent RNA–seq studies have revealed an unexpectedly complex transcriptional output from bacterial genomes and have been successfully used for the global identification of small RNA ( sRNA ) genes as well transcriptional start sites ( TSS ) [2] , [3] . For example , our recently developed differential RNA–seq ( dRNA–seq ) approach , which is selective for the analysis of primary transcriptomes , allowed us to provide a genome-wide map of TSS in Helicobacter pylori [4] . In addition , a steadily growing number of studies report cis-encoded antisense RNAs as a widespread layer of gene-expression control in bacteria [5] , [6] . Despite this rapid accumulation of transcriptome data , the bioinformatics-based data mining is still lagging behind . Thus , in most cases transcriptome features such as TSS and novel non-coding RNAs still have to be manually annotated , which is laborious and time-consuming . The problem is compounded for comparative transcriptomics of several species within a genus . Therefore , most RNA–seq studies have been limited to single bacterial strains so far ( reviewed in [2] , [3] ) . However , a comparative approach would not only allow for refining the transcriptome annotation of the individual species by integrating the information from multiple strains but can also reveal differences in transcriptome organization or gene expression among strains for which different phenotypes cannot be explained by the genome sequences alone . Epsilonproteobacteria , including several important human pathogens such as H . pylori and Campylobacter jejuni , show a significant strain-to-strain variability on the phenotypic and genomic level and , thus , represent good model organisms for such a comparative approach . For example , multilocus sequence typing ( MLST ) analysis revealed that C . jejuni , the most prevalent food-borne bacterial pathogen in the industrialized world to date [7] , [8] , is genetically diverse , with a weakly clonal population structure and high rates of intraspecies recombination [9] , [10] . It has been suggested that this extensive genetic diversity could lead to different clinical outcomes and facilitate adaptation to and persistence in the host [11] , [12] , [13] . Since comparison among multiple species or strains has mainly been examined at the genomic level , the differences in transcriptome structure that have an impact on phenotypic flexibility remain currently unknown [14] , [15] . C . jejuni is a commensal of chicken but leads to gastroenteritis in humans , where it has also been associated with the development of secondary autoimmune disorders such as the Guillain-Barré or Miller-Fisher syndromes [16] . Except for a cytolethal distending toxin ( CDT ) [17] and homologs of a type-IV secretion system on the pVir plasmid of strain 81–176 [18] , C . jejuni lacks most classical virulence factors of other gastrointestinal pathogens . Therefore , it has been suggested that mainly the motility and metabolic capabilities of Campylobacter are required for virulence and colonization of the host [16] , [19] . Besides the lack of classical virulence determinants , little is known about the transcriptome structure of Campylobacter . The 1 . 6 megabase A/T-rich ( ∼31% G+C content ) genome of C . jejuni encodes only three sigma factors and a few transcriptional regulators [20] , indicating additional layers of gene regulation . Bacterial sRNAs are an emerging class of post-transcriptional gene expression regulators which have been implicated in bacterial stress response and virulence regulation [21] . As do 50% of all bacteria , Campylobacter lacks a homolog of the RNA chaperone Hfq , which is a key player in sRNA–mediated regulation in enterobacteria [22] , [23] . Despite the prediction of five sRNA candidates in C . jejuni NCTC11168 using conventional , strand-insensitive RNA–seq combined with predictions of conserved RNA structures [24] , knowledge about post-transcriptional regulation in this pathogen is still very limited . Furthermore , a global TSS map including sRNA and antisense RNA promoters is still missing . However , such sRNA regulators could also contribute to phenotypic diversity by mediating strain-specific gene regulation . To gain insight into the sRNA repertoires as well as differences in primary transcriptomes among multiple strains of one bacterial species , we applied the dRNA–seq approach in a comparative manner to four C . jejuni isolates . In addition , we introduce a novel automated , comparative TSS annotation method for a fast and accurate generation of genome-wide TSS maps . Using this method we identified conserved and strain-specific TSS , some of which carry SNPs in promoter regions . Moreover , we detected 15 conserved and 24 strain-specific sRNA candidates , highlighting differential sRNA expression among strains . Furthermore , expression and conservation analysis reveals the presence of a minimal CRISPR system , an RNA–based immune system , in C . jejuni . Overall , defining the Campylobacter transcriptome structure including genome-wide promoter maps provides new insights into gene regulation and transcriptome evolution not only in this species but also in other pathogens .
The determination of exact transcript boundaries and identification of novel transcripts facilitates genome annotation and also the discovery of regulatory elements which control gene expression . Our recently developed dRNA–seq approach allows for an efficient global TSS annotation by differential sequencing of two cDNA libraries which discriminate primary and processed 5′ ends: one library ( − ) is generated from untreated total RNA , whereas the second library ( + ) is generated after treatment with terminator exonuclease ( TEX ) which specifically degrades processed RNAs with a 5′-mono-phosphate [4] . To identify conserved and strain-specific transcriptome features , we have applied the dRNA–seq approach to three human and one chicken isolate of C . jejuni ( see Table 1 and Figure S1 in Text S1 ) . Strain NCTC11168 [20] , originally isolated from a case of human enteritis in the UK , was the first Campylobacter strain for which the genome was sequenced but displays only poor motility and virulence . C . jejuni 81–176 [25] , isolated from a diarrheal outbreak in the U . S . , is highly pathogenic and carries two large plasmids , pVir [18] and pTet [26] . Strain 81116 [27] , a human isolate from a waterborne outbreak in the UK in 1983 , is a genetically stable and lab-adapted strain which is still infective for chicken [28] . The virulence potential of the chicken isolate RM1221 [14] is unknown . Upon construction of two sets of dRNA–seq libraries from biological replicates of mid-log growth RNA samples for each of the four strains , we sequenced between 2 . 3 to 5 . 5 Mio cDNA reads per library which were subsequently mapped to the individual genome sequences ( Table S1 ) . Sequencing of dRNA–Seq libraries leads to a characteristic enrichment of cDNA reads at TSS in the TEX-treated sample [4] . These enrichment patterns allowed us to determine TSS in the four strains , and in many cases we observed an enrichment of cDNA reads at a given TSS in all four strains ( for an example see Figure S2 in Text S1 , rpsL ) . Moreover , our comparative dRNA–seq data showed that homologous genes might share the same TSS , even if the promoter regions are not conserved . Thus , based on sequence information alone it can be unclear whether a gene is expressed in the individual strains , whereas the comparative dRNA–seq data can provide this information . However , for several homologous genes , a clear TSS enrichment pattern ( more than twofold in TEX+ vs . TEX− ) was observed in only some of the strains despite highly conserved promoter regions ( Figure S2 in Text S1 , Cj1380 ) . Nevertheless , a sharp flank in the cDNA distribution for all four strains indicates that transcription starts at exactly the same position . Without the comparative information from the other strains such cases could not unambiguously be defined as a TSS . Overall , a combination of sequence conservation and comparative TSS enrichment pattern analyses can be used to refine global TSS maps . Manual annotation of TSS is labor- and time-consuming for a single strain and impractical for the analysis of multiple strains or larger genomes . To facilitate TSS comparisons , we mapped the four dRNA–seq data sets to a common coordinate system , the so-called SuperGenome [29] , derived from a multiple whole genome alignment of the four strains ( Figure 1A ) . Overall , 65% of the 2 , 115 , 275 SuperGenome positions were identical among the four strains ( Table S2 ) . The SuperGenome allows for a parallel visualization of the individual TEX+ and TEX- cDNA distributions of the four strains in a genome browser and thereby for a direct comparison of TSS enrichment patterns . The comparative dRNA–seq data show that the majority of TSS are enriched and detected in multiple strains ( for an example locus see Figure 1B , black arrows ) but that there are also differences among strains . For example , we observed a TSS within kpsM , which encodes for one of the capsule export genes , in only two of the strains and a TSS upstream of homologs of Cj1456c only in strain 81116 ( Figure 1B , red arrow and blue arrows , respectively ) , indicating that there is strain-specific transcriptional output . To automatically annotate TSS in a comparative manner , a two-step algorithm was employed: 1 ) TSS were detected independently for each strain based on dRNA–seq enrichment patterns and 2 ) TSS were mapped to the SuperGenome to allow for comparison and assignment of TSS among strains . Subsequently , all TSS of the individual strains were automatically classified as primary TSS ( pTSS; main promoter of a gene ) or secondary TSS ( sTSS; alternative promoter upstream of pTSS of a gene ) , internal TSS ( iTSS; promoter within gene ) , antisense TSS ( asTSS; promoter antisense to a gene +/−100 nts ) or orphan TSS ( no association with annotation ) according to their location relative to annotated genes ( Figure S3A in Text S1 and Materials and Methods section ) . Our comparative approach enabled the annotation of a total of 3 , 377 TSS positions in the SuperGenome . 1 , 035 of the TSS were detected ( but not necessarily enriched ) in all four strains ( Figure 2A , Table 2 , and Table S3 ) . Between 1 , 905 and 2 , 167 TSS were detected in the individual strains , including ∼300 strain-specific TSS ( Table S4 ) . The overall higher number of 2 , 167 TSS and 450 strain-specific TSS in RM1221 are mainly derived from TSS within its prophage elements ( CMLP1 , CJIE2-4 ) to which only cDNA reads from RM1221 were mapped in the SuperGenome ( Table 1 and Figure 1A ) . On the two large plasmids , pVir ( ∼35 kB ) and pTet ( ∼45 kB ) , of C . jejuni 81–176 we detected 70 and 58 TSS , respectively ( Table S5 ) . Approximately 60% of the conserved TSS are classified as pTSS or sTSS and in most cases drive transcription of mRNA genes . In contrast , only 21–31% of the strain-specific TSS are pTSS or sTSS and the majority of the strain-specific TSS ( 47–54% ) are classified as antisense TSS ( Table 2 ) . Using MEME [30] we analyzed the promoter regions ( −50 to +1 of all TSS ) upstream of the total number of 8 , 019 TSS in all four strains for sequence motifs of at least 45 nt . This revealed a periodic A/T-rich pattern instead of a clear −35 box followed by an extended −10 box ( TGxTATAAT ) for ∼89% of the promoter regions in the four strains as a consensus motif for the housekeeping sigma factor , σ70 ( Figure 2B ) . This motif fits with a previously predicted consensus sequence for a smaller number of Campylobacter promoters and for σ70 in H . pylori based on dRNA–seq , indicating that transcription predominantly initiates at an extended −10 box in Epsilonproteobacteria [4] , [31] . Moreover , we identified motifs which resemble the consensus binding sites for the alternative sigma factors , σ28 ( FliA ) and σ54 ( RpoN ) , for 141/8 , 019 ( 1 . 8% ) and 36/8 , 019 ( 0 . 4% ) TSS , respectively ( Figure 2B ) . Separate analyses of the four strains indicated that there is no strong variation in their general promoter patterns among strains ( Figure S4 in Text S1 ) . Since some genes that are known to be regulated by the alternative sigma factors were missed in our MEME searches , we defined a consensus motif based on nine and eight TSS from strain NCTC11168 with a previously described FliA- and RpoN-dependent promoter , respectively ( Table S6 ) . Pattern searches with this consensus motif revealed additional 138 and 26 TSS fitting the FliA and RpoN motifs , respectively ( Table S7 ) . Approximately 35% of all TSS in each strain are pTSS and around 10% are classified as sTSS ( Table 2 and Figure S3 in Text S1 ) . The majority of the 3 , 241 5′UTRs defined by pTSS and sTSS of mRNA genes have a length between 20 and 50 nt ( Figure S5A in Text S1 ) . MEME searches detected an “AAGGA”-motif with an upstream A/T-rich sequence as a consensus for the ribosome binding site in 2 , 990 out of the 3 , 158 5′UTRs with a length ≥8 nt . Pairwise comparison of the 5′UTR lengths of genes with at least one ortholog in one of the other strains showed that the majority of the conserved genes have the same 5′UTR length but revealed also several length variations among strains ( Figure S5B in Text S1 ) . Despite a correction - where possible - for differences in start codon annotations ( Tables S8 and S9 ) , many of the 5′UTR length differences are due to different 5′ ends of the CDS of the respective genes ( Table S10 ) . In some cases , different 5′UTR lengths result from insertion of new genes upstream of the start codon and the acquisition of new promoters in certain strains ( Figure 2C ) . For example , in three of the strains , a gene encoding for a restriction modification ( RM ) enzyme ( CJE1195 , C8J_0992 , cjeI ) is inserted upstream of npdA , encoding a NAD-dependent deacetylase , which is transcribed from a pTSS that was probably acquired together with cjeI . In contrast , strain 81–176 lacks the RM enzyme as well as the downstream promoter and npdA is co-transcribed as a polycistronic mRNA from a TSS upstream of murC . Besides insertions of new upstream genes with a novel promoter , different promoters can lead to 5′UTR length variation among strains . For example , different TSS sets were detected in the four strains for homologs of a putative transporter , Cj0339 ( Figure S6A in Text S1 ) . Moreover , it has previously been reported that the asnA gene of strain 81–176 has acquired a sec-dependent secretion signal to the otherwise cytoplasmic asparaginase found in NCTC11168 and facilitates asparagine utilization in this strain [19] . Our dRNA–seq data indicate that the different asnA forms are transcribed from strain-specific promoters with ∼100-fold higher cDNA read counts for the secreted asparaginase compared to the cytoplasmic form ( Figure S6B in Text S1 ) . Comparison of promoter motifs for conserved and strain-specific TSS revealed no difference in the general promoter patterns ( Figure S7 in Text S1 ) . However , our comparative TSS detection allowed us to identify regions with strain-specific promoter usage , e . g . , regions for which we detected a TSS in only some of the strains although the region is present in the SuperGenome in all strains ( Table S3 , compare columns “mapCount” and “detCount” ) . We observed that in many cases single nucleotide polymorphisms ( SNP ) lead to disruption of promoters in a subset of strains ( Figure 3 ) . For example , the pTSS of pnk , encoding an inorganic polyphosphate/ATP-NAD kinase , is enriched and conserved in all four strains ( Figure 3A ) . In contrast an iTSS within pnk is only detected in RM1221 and NCTC11168 . In strains 81–176 and 81116 a C to T exchange in the “CGATTT” σ28 consensus seems to be sufficient to abolish transcription initiation from this TSS . Additional examples for mutations within conserved promoter elements are shown in Figure S8 in Text S1 . Moreover , we noted that mutations within the periodic A/T-rich pattern upstream of the −10 box could also affect transcription ( Figure 3B and Figure S9 in Text S1 ) . For example , in strain NCTC11168 , the Cj0004c and Cj0005c genes encode a monoheme cytochrome c and molybdopterin oxidoreductase , respectively , which allows C . jejuni to use sulphite as a respiratory electron donor [32] . This bi-cistronic operon along with the σ70 −10 box is conserved in all four strains ( Figure 3B ) . However , mutations in the A/T rich upstream pattern of its pTSS in strain 81116 coincide with a loss of transcription either due to disturbing transcription initiation or binding of some regulatory factor . In line with this , we observed cytochrome c reduction as a measure for sulphite oxidation in only three of the strains ( Figure 3C ) . This indicates , that although genes are conserved among strains and show high conservation in promoter regions , they may not necessarily be transcribed in all of them . In addition to mRNA TSS , we identified many candidates for non-coding RNAs by our comparative dRNA–seq . For example , ∼45% of the TSS for each strain were classified as antisense TSS ( asTSS ) and 445 of the asTSS were detected in all four strains , indicating a large fraction of antisense transcription ( Table 2 , Figures S3 and S10 in Text S1 ) . Furthermore , we detected several candidates for trans-encoded sRNAs in the chromosomes of the four strains and on the pVir and pTet plasmids ( Figure 4 , Figure S11 in Text S1 and Tables S11 and S12 ) . Northern blot profiling under different growth phases confirmed expression of most of these sRNA candidates . Some sRNAs , such as CJnc60 or CJnc140 , are highly conserved and show similar expression patterns in all strains . In contrast , some of the other conserved sRNAs , such as CJnc180 and CJnc190 , which are encoded antisense to each other , show differential expression patterns among strains . Moreover , our Northern blots confirmed strain-specific sRNAs , such as CJnc30 , which is only present in NCTC11168 , or CJnc20 , which is missing in RM1221 . Most of our candidate sRNAs are transcribed from their own TSS , but we also found examples of sRNA candidates generated by processing , e . g . , from 3′ ends of mRNAs ( Figure S11 in Text S1 and Table S11 ) . Furthermore , the majority of our sRNA candidates accumulated in exponential phase or during stationary phase growth . Conservation analysis of our sRNA candidates showed that the majority of them are restricted to Campylobacter jejuni ( Figure 5 ) , indicating that they either have a specific regulatory function in this species or that their sequence conservation is not high enough to detect homologs by BLAST searches . Even the housekeeping RNAs ( SRP RNA , tmRNA , RNase P RNA , and 6S RNA ) are not conserved at the sequence level outside Campylobacter species . Several of the C . jejuni sRNA candidates , e . g . , CJnc170 and CJnc190 , are highly conserved in diverse strains , and could have a more general regulatory role within C . jejuni . In contrast , some sRNA candidates such as CJnc30 and CJnc80 or the plasmid-encoded sRNAs are found in only some of the strains , indicating strain-specific sRNA repertoires which might contribute to strain-specific regulation of gene expression . One of the regions with highest numbers of cDNA reads in NCTC11168 and 811116 corresponded to the CRISPR ( clustered regularly interspaced short palindromic repeats ) locus . CRISPR loci are transcribed as precursors that are processed into mature crRNAs that together with Cas proteins silence invading foreign nucleic acids such as plasmids or phages [33] , [34] , [35] . Three of the four C . jejuni strains harbor a so-called type-II CRISPR/Cas system ( Figure 6 and Figure S12 in Text S1 ) , which requires a trans-encoded sRNA , TracrRNA , and the host factor RNase III for crRNA maturation [36] , [37] . Our dRNA–seq analysis shows that the crRNAs and TracrRNA are actively transcribed and share a stretch of perfect complementarity in C . jejuni which allows for RNase III-dependent processing of the crRNAs . In line with this , we observe an accumulation of processed ∼38-nt spacer-repeat units and processed ∼62 nt TracrRNA ( Figure 6B ) . Only weak CRISPR expression was detected in strain RM1221 . Conservation analysis showed that the cas9 gene , which is required for the stability of crRNAs and cleavage of target DNA [36] , [38] , carries a stop mutation in strain RM1221 leading to a truncated protein ( Figure 6C ) . In strain 81–176 the CRISPR region is replaced by two genes with very low G/C content compared to the flanking genomic regions . The RNase III/TracrRNA–dependent processing leads to a cleavage event within the repeat region of the crRNAs [36] . Surprisingly , the mature spacer-repeat units are enriched in our TEX-treated libraries , indicative of primary transcripts starting at position five within each spacer . Moreover , the 3′ end of each repeat ends with “GGTAAAAT” resembling an extended −10 box . Thus , C . jejuni apparently employs promoter sequences within each repeat to initiate transcription of the associated spacer unit , so that only one processing event mediated by RNase III and TracrRNA in the repeat sequence is required to generate the 3′ end of the mature crRNA . In line with this model , we observed accumulation of longer transcripts upon deletion of RNase III ( Figure S13 in Text S1 ) . Primer extension analysis confirmed that these longer transcripts start at the proposed TSS within each spacer and that most of the TracrRNA species are transcribed from these upstream promoters . Thus , in contrast to other type-II systems , where the crRNAs are transcribed together with a leader sequence from one upstream promoter , each repeat in the C . jejuni CRISPR carries its own promoter ( Figure 6D ) .
Here we present the first comparative primary transcriptome analysis of four different C . jejuni strains including a novel automated TSS annotation method , which revealed strain-specific promoter usage and sRNA repertoires . The observed strain-specific transcriptional output reveals candidate genes which could contribute to phenotypic variation among strains or facilitate adaptation to different hosts or niches . Previously , the generation of global TSS maps from RNA–seq data has mainly been performed on a manual or semi-automated basis [4] , [39] , [40] , [41] , [42] , [43] , [44] , [45] . These strategies are typically very laborious and time-consuming with limited reproducibility and require even more effort for the comparison of transcriptomes from multiple conditions or strains . Therefore , we have developed a model-based TSS prediction method based on criteria used for manual TSS annotations . It provides detailed values ( such as number of read starts , enrichment factor etc . ) as well as classifications ( pTSS , sTSS , asTSS , iTSS ) for each TSS candidate which allows the user to review the underlying thresholds for TSS detection . Benchmarking of our TSS prediction method using the manually annotated TSS from H . pylori [4] showed that our approach achieved a sensitivity of 82% and a precision rate of 75% . Since our comparative approach allows for the integration of data sets from different strains or conditions , its performance can generally be increased by the inclusion and comparison of multiple data sets and replicates . Moreover , the TSS annotation will be further improved by adjusting the parameters to additional training sets based on experimental validation . Recently , Schmidtke et al . presented a fully automated approach for TSS annotation of a single genome based on read count data and a sophisticated statistical model , which calculates p-values for TSS candidates and thereby provides a confidence estimation for observing a TSS [46] . However , the specific properties of the TSS candidates , such as expression height and dRNA–seq enrichment factor , cannot be directly inferred from the resulting p-values . In future , a combination of both approaches would be promising especially for very weak TSS candidates . Furthermore , our automated TSS method also allows for a comparative TSS annotation among dRNA–seq libraries from multiple strains by the integration of the SuperGenome approach . Considering the rapidly increasing number of genome sequences as well as RNA–seq studies , our approach will facilitate a systematic TSS annotation among different strains or growth conditions and can also be adapted to the analyses of eukaryotic transcriptomes . Comparative genomics of multiple Campylobacter strains or species provided the core genome of the genus and also revealed differences in genome content and structure which could support adaptation to different hosts [14] , [15] , [25] , [47] , [48] . Furthermore , comparison of genetically closely related C . jejuni strains isolated from different hosts indicated that Campylobacter uses high phenotypic flexibility and genetic microdiversity to reversibly adapt to changing environments [49] . In addition , variation in contingency loci contributes to rapid adaptation to novel hosts [50] . This genetic flexibility among strains might be supported by differences in transcription among strains and post-transcriptional regulation which we observed in our comparative dRNA–seq data . For this first comparative analysis we have selected widely used laboratory strains with available genome sequence . However , our comparative approach can be easily adapted to the analysis of multiple isolates from different hosts or isolation sources . For example , the use of our single-nucleotide resolution approach could help to understand the transcriptomic and phenotypic differences that were previously observed during the analysis of the genome-sequenced and original isolate of C . jejuni NCTC11168 [51] . Our comparative dRNA–seq approach allowed us to annotate between 1 , 905 and 2 , 167 TSS in the four C . jejuni strains . We observed that the majority of TSS are conserved among multiple strains , but we also found many examples where SNPs in promoter regions apparently resulted in strain-specific promoter usage . Thus , although some promoters are highly conserved and show almost perfect overlap to promoter consensus motifs - which would be indicative for active transcription - the respective genes are not necessarily expressed at the same level among strains . Therefore , comparative transcriptomics facilitates the identification of differences in the functional output from genomes which cannot be directly inferred from closely related DNA sequences . SNPs can be adaptive and , thereby , lead to niche expansion [52] . In addition , point mutations in ORFs or rRNA genes can mediate antibiotics resistances in bacteria including Campylobacter [53] . In bacterial pathogens , a variety of pathoadaptive mutations have been described which can affect cell binding , host tissue tropism or virulence regulation [54] , [55] , [56] , [57] . Furthermore , point mutations in ORFs could lead to truncated or non-functional proteins or changes in protein activity [58] , [59] , [60] and have also been shown to discriminate mRNA targets of bacterial sRNAs [61] . Even single regulatory genes or promoter inversions can be sufficient to alter bacterial host specificity [62] , [63] and promoter SNPs have been associated with overproduction of virulence genes [64] , [65] . Based on our global TSS maps , we identified several examples where SNPs disrupt conserved positions in promoter motifs recognized by the three sigma factors . Furthermore , we found several examples for disruptive SNPs in the A/T-rich upstream regions , indicating that this region is also required for transcription initiation in Epsilonproteobacteria . These promoters with SNPs might be good hints to genes which contribute to adaptation to different environments or hosts . For example , in strain 81116 we observed mutations in the promoter for monoheme cytochrome c and a molybdopterin oxidoreductase , which allow C . jejuni to use sulphite as a respiratory electron donor [32] . This indicates that strain 81116 has lost the ability to transcribe this operon and perhaps even lost the ability to respire sulphite ( Figure 3 ) . C . jejuni is unable to metabolize glucose since it lacks the enzyme phosphofructokinase . Instead , it has a complex branched respiratory chain and can utilize a variety of electron donors like formate , lactate , or sulphite . Sulphite respiration might also help C . jejuni to survive in sulphite-rich niches and foods , and might also be used for detoxification . Furthermore , a strain lacking the molybdopterin oxidoreductase has been shown to have a reduced ability to infect Caco-2 cells [66] . Since the SNPs in promoter regions could also interfere with binding of a transcriptional regulator and thereby affect transcription , such promoters with strain-specific expression patterns represent good candidates to fish for novel DNA-binding proteins . For example , a SNP in the Fur-binding site of the sodB promoter of certain Helicobacter pylori strains has been shown to affect direct binding of apo-Fur [67] . The global map of TSS and promoter SNPs may give hints as to how C . jejuni and other microbes with compact genomes and few transcription factors could adapt gene expression according to different environmental conditions . Our comparative approach allowed us to annotate TSS by combining the dRNA–seq data from multiple strains . This approach improves the annotation accuracy of the individual strains by integrating the information from multiple transcriptomes and also reveals differences among strains . Previously , bacterial RNA–seq studies mainly focused on the analyses of single strains ( reviewed in [2] ) or were not strand-specific [68] . Two recent comparative transcriptome analyses using strand-specific cDNA sequencing mainly focused on the divergence of sRNA expression as well as long antisense RNAs [45] , [69] . Apart from these studies , expression profiling of sRNAs in multiple strains has been reported only for a limited number of bacteria and was mainly based on Northern blot analysis [70] , [71] . A global RNA–seq based comparison of regulatory elements including sRNAs was recently carried out between the two closely related species , Escherichia coli and Klebsiella pneumoniae , and revealed that the majority of orthologous operons were transcribed from different promoters [72] . Our global transcriptome maps revealed several candidates for conserved and strain-specific sRNAs . Small regulatory RNAs have been implicated as key regulators in metabolic pathways and during pathogenesis [73] , [74] . The newly identified sRNAs could contribute to virulence gene regulation and host adaptation by modulating metabolic pathways which are important for host colonization in C . jejuni [19] . A study based on conventional , non strand-specific RNA–seq predicted five sRNAs in C . jejuni [24] . In our strand-specific dRNA–seq data , we observed expression for four of them ( Figure S11 in Text S1 ) . Moreover , a dRNA–seq study of C . jejuni NCTC11168 compared the transcriptome organization between C . jejuni and H . pylori [4] and identified around 20 trans-encoded sRNA candidates in C . jejuni , most of which are also detected in our study ( I . Porcelli and A . van Vliet , personal communication ) . The majority of our sRNA candidates are expressed as independent transcripts . However , we also detected examples of processed sRNA species , which can be generated from the 3′ end of mRNAs . Recently , such 3′-end derived transcripts have been shown to stably associate with the RNA chaperone Hfq in Salmonella and to act as regulatory RNAs on trans-encoded mRNAs [75] . Since Campylobacter lacks Hfq , it will be interesting to see whether its sRNAs require a different RNA chaperone for their activity and stability or whether they act independently of an auxiliary protein . Moreover , future studies will be required to uncover the target genes and physiological roles of sRNAs in Campylobacter . The most abundant sRNA in strain NCTC11168 corresponds to TracrRNA , which is required together with RNase III for maturation of CRISPR RNAs [36] . Due to the high variability of the spacer sequences , CRISPR loci have been used for strain genotyping including Campylobacter species [76] . Surprisingly , we found that the crRNAs in C . jejuni are transcribed from individual promoters within each repeat unit ( Figure 6 ) . In other prokaryotes , the crRNAs are transcribed from a leader sequence and several processing steps are required to generate the mature crRNAs . Therefore , the CRISPR locus of Campylobacter with its individual crRNA promoters and only three cas proteins represents a “minimal” system of the type-II subtype ( Figure 6D ) which requires only one processing event by RNase III within the repeats to generate the mature crRNAs . A similar CRISPR organization was also identified in Neisseria menigitidis ( N . Heidrich and J . Vogel , personal communication ) . Furthermore , in strain RM1221 , the crRNAs and TracrRNA were only weakly expressed probably due to a stop-mutation in cas9 , while strain 81–176 completely lacks the CRISPR locus . Interestingly , these two strains carry prophages or plasmids , indicating that these horizontally acquired genetic elements could be mutually exclusive with an active CRISPR system . Conservation analysis in additional strains showed that strains with plasmids or integrated elements very often carry degenerated CRISPR loci ( Figure S14 in Text S1 ) . Moreover , it has recently been shown that ganglioside-like LOS structures of GBS-associated C . jejuni strains can confer efficient bacteriophage resistance and that the presence of sialyltransferases correlates significantly with an apparently non-functional CRISPR system [77] . Further studies of the influence of the CRISPR system on pathogenicity of Campylobacter will be required . Moreover , since components of type-II CRISPR systems have recently been adapted for genome editing in humans [78] , [79] , the minimal type-II systems might be useful for further improvements of such genome editing tools . Overall our high-resolution transcriptome map revealed regulatory elements and their conservation in multiple Campylobacter jejuni strains on a genome-wide scale . The comparison of multiple strains improves annotation of transcriptome features such as TSS maps and reveals strain-specific TSS usage as well as sRNA repertoires . These strain-specific transcription patterns will provide new insights into genes which could promote phenotypic differences despite high conservation at the genome level . Our novel automated TSS annotation can easily be applied to a wider range of strains or conditions and may also be used for to the annotation of eukaryotic transcriptomes .
Campylobacter jejuni strains and DNA oligonucleotides used for cloning , as hybridization probes or for primer extension are listed in Tables S13 and S14 , respectively . Bacteria were grown on Müller-Hinton agar plates supplemented with 10 µg/ml vancomycin at 37°C under microaerobic conditions ( 10% CO2 , 5% O2 ) . For liquid cultures , a starter culture was inoculated with bacteria grown on plates to a final OD600 of 0 . 04 in 20 ml of Brucella broth ( BB ) medium including 10 µg/ml vancomycin and incubated overnight at 37°C under microaerobic atmosphere and an agitation of 140 rpm . The next day , 50 ml BB including 10 µg/ml vancomycin were inoculated to a final OD600 of 0 . 04 using the starter culture and incubated as described above . When the cultures reached mid-exponential ( 6 . 5 hrs; OD600 between ∼0 . 3–0 . 4 ) , stationary ( 13 hrs; OD600 between ∼0 . 5–1 ) and overnight phase ( 29 hrs; OD600 between ∼0 . 5–1 ) , culture volumes of cells corresponding to a total amount of 2 , 4 and 8 OD600 , respectively , were mixed with 0 . 2 volumes of stop-mix ( 95% EtOH and 5% phenol , V/V ) , frozen in liquid N2 and stored at −80°C until RNA extraction . The four strains varied in the final OD600 values that they reached at these growth phases , e . g . , strain 81–176 showed highest OD600 values of up to ∼1 . 0 in stationary and overnight phase , whereas strain 81116 reached only OD600 values of ∼0 . 5 . However , despite these differences in OD600 the harvested samples corresponded to similar growth phases along the growth curves of the four strains . Frozen cell pellets were thawed on ice and resuspended in lysis solution containing 600 µl of 0 . 5 mg/ml lysozyme in TE buffer ( pH 8 . 0 ) and 60 µl 10% SDS . Bacterial cells were lysed by incubating the samples for 1–2 minutes at 65°C . Afterwards , total RNA was extracted using the hot-phenol method described previously [80] . For Northern Blot analysis , 10 to 15 µg RNA was loaded per sample . After separation on 6% polyacrylamide ( PAA ) gels containing 7 M urea , RNA was transferred to Hybond-XL membranes , which were hybridized with γ32P-ATP end-labeled oligodeoxyribonucleotide probes indicated in Table S14 . Candidate sRNAs with more than 50 reads ( relative score ) in untreated dRNA–seq libraries were probed on Northern blots . For each of the four selected C . jejuni strains , NCTC1116 , 81–176 , 81116 , and RM1221 , dRNA–seq libraries were constructed from biological duplicates of RNA samples harvested at mid-log growth in BB . Residual genomic DNA was removed from the total RNA isolated by DNase I treatment . For depletion of processed transcripts , equal amounts of Campylobacter RNA were incubated with Terminator™ 5′-phosphate-dependent exonuclease ( TEX ) ( Epicentre #TER51020 ) as previously described [4] . Libraries for Solexa sequencing ( HiSeq ) of cDNA were constructed by vertis Biotechnology AG , Germany ( http://www . vertis-biotech . com/ ) , as described previously for eukaryotic microRNAs [81] but omitting the RNA size-fractionation step prior to cDNA synthesis . For details see Supplementary Methods in Text S1 . The resulting cDNA libraries were sequenced using a HiSeq 2000 machine ( Illumina ) in single-read mode . The raw , de-multiplexed reads as well as coverage files ( see Supplementary Methods in Text S1 ) have been deposited in the National Center for Biotechnology Information's Gene Expression Omnibus [82] under the accession GSE38883 . For a detailed description of the read mapping , expression graph construction and normalization of expression graphs see Supplementary Methods in Text S1 . In total , we sequenced between 2 . 3 to 5 . 5 Mio cDNA reads for each of the cDNA libraries which were subsequently mapped to the individual genome sequences ( Table S1 ) . A whole genome alignment of the four C . jejuni strains was computed with Mauve [83] . Based on this global alignment a common genomic coordinate system , the SuperGenome was defined into which all positional information can be projected that relates to the single genomes [29] . This resulted in a consensus sequence with the coordinates of the complete alignment and a mapping of each position of each single genome to a position in the alignment . Next , all genome-specific data ( expression height graphs derived from mapped read data , genomic annotations , and sequences ) were mapped to the common coordinate system . Our automated TSS prediction approach , which uses this SuperGenome mapping for comparative analyses , consists of several steps: The initial detection of TSS in the single strains is based on the localization of positions , where a significant number of reads start . Thus , for each position i in the RNA–seq graph corresponding to the TEX+ library the algorithm calculates e ( i ) -e ( i-1 ) , where e ( i ) is the expression height at position i ( Figure S15 in Text S1 ) . In addition , the factor of height change is calculated , i . e . e ( i ) /e ( i-1 ) . To evaluate if the reads starting at this position are originating from primary transcripts , the enrichment factor is calculated as eTEX+ ( i ) /eTEX− ( i ) . For all positions where these values exceed the threshold ( see Supplementary Material ) a TSS candidate is annotated . The TSS prediction procedure is applied to both replicates of each strain . TSS candidates , which are not detected in both replicates with a maximal positional difference of one nucleotide , are discarded . Afterwards , TSS candidates that are in close vicinity are grouped into a cluster and only the TSS candidate with the highest expression is kept . In the next step , the TSS candidates of each strain are mapped to the SuperGenome to assign each TSS to the corresponding TSS in the other strains . The final TSS annotations are then characterized on the SuperGenome level with respect to their occurrence in the different strains and in which strains they appear to be enriched . In the context of the individual strains the TSS are further classified according to their location relative to annotated genes . For this we used a similar classification scheme as previously described [4] . Thus , for each TSS it is decided if it is the primary or secondary TSS of a gene , if it is an internal TSS , an antisense TSS or if it cannot be assigned to one of these classes ( orphan ) . A TSS is classified as primary or secondary if it is located ≤300 bp upstream of a gene . The TSS with the strongest expression considering all strains is classified as primary . All other TSS that are assigned to the same gene are classified as secondary . Internal TSS are located within an annotated gene on the sense strand and antisense TSS are located inside a gene or within ≤100 bp on the antisense strand . These assignments are indicated by a 1 in the respective column of Tables S4 , S5 , S6 , S7 , S8 , S9 . Orphan TSS , which are not in the vicinity of an annotated gene , are indicated by “0” in all four columns . To validate our automated TSS detection we applied it to the previously generated dRNA–seq data of Helicobacter pylori grown under five different conditions [4] . In this study , we had manually annotated the TSS based on enrichment patterns in the TEX+ compared to TEX- libraries . We used these hand-curated TSS positions as benchmark and compared it to the results of the automated detection . We allowed a difference of up to one nucleotide when comparing an automatically detected TSS to a manually annotated TSS . With this threshold , the automated approach achieves a sensitivity of 82% and a precision rate of 75% . The parameters used for the TSS annotation in C . jejuni were selected according to this benchmarking with the manual TSS set of H . pylori ( see also Supplementary Methods in Text S1 ) . To detect potential promoter motifs , sequence regions corresponding to 50 nt upstream of the TSS positions and the TSS position itself were scanned by MEME version 4 . 8 . 1 [84] with width parameters ( fixed width of 45 nt as well as flexible widths ) . For the detection of ribosome binding site ( RBS ) motifs , the 5′UTR sequences of mRNAs were inspected by MEME . The 5′UTR length distributions were visualized using R and the ggplot2 package . The assay was adapted from Kappler et al [85] . The activity of the oxidoreductase enzyme was measured as the increase in cytochrome c absorbance at 550 nm when converted from the oxidized to the reduced form . Briefly , 2 µl cells ( corresponding to 0 . 01 OD600 nm in 10 mM Tris/HCl pH 8 ) were added to a freshly prepared mix containing the following: 45 µl 10 mM Tris/HCl pH 8 ( with or without 2 mM sodium sulphite ) , 5 µl horse heart cytochrome c ( 10 mg/ml; #C2506 , Sigma-Aldrich ) . Absorbance was measured after 15 minutes at 550 nm using an absorption coefficient of 20 mM−1cm−1 ( spectrophotometer ND-1000; Peqlab ) . In the control sample , 2 µl of 10 mM Tris/HCl pH 8 was added instead of cells . To study the conservation of sRNAs in Epsilonproteobacteria , homologous sequences were searched with blastn ( part of the BLAST+ package version 2 . 2 . 26 [86]; the word-size parameter was set to 10 nt ) . The number of identical nucleotides of the best hits of each sRNA candidate was divided by the total number of nucleotides of the query sRNA and multiplied by 100 to calculate the percentage value of conserved nucleotides . For visualization , conservation values ≥40% identity were translated into a gray scale and values below 40% were depicted as white boxes . | Many species have evolved into diverse strains with phenotypic and genotypic variations that facilitate adaptation to different ecological niches and , in the case of pathogens , to different hosts . Whereas comparison of genome sequences reveals differences and similarities among strains , the consequences of genomic variations can be tracked by studying the functional output from the genome . RNA sequencing has been revolutionizing transcriptome analyses of both pro- and eukaryotes . However , the bioinformatics-based analysis is still lagging behind , and transcriptome features are often manually annotated , which is laborious and time-consuming . This is even more compounded for the analyses of multiple strains . Here we compared the primary transcriptomes of four isolates of Campylobacter jejuni , the leading cause of bacterial gastroenteritis in humans , and provide genome-wide transcriptional start site ( TSS ) maps using a novel automated annotation method . Our comparative RNA–seq showed that most TSS are conserved in multiple strains , but we also observed SNP–dependent promoter usage . Furthermore , we identified a novel minimal RNA–based CRISPR immune system as well as strain-specific small RNA repertoires . Our automated , comparative TSS annotation will facilitate and improve transcriptome annotation for a wider range of organisms and provides insights into the contribution of transcriptome differences to phenotypic variation among closely related species . | [
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"bacterial... | 2013 | High-Resolution Transcriptome Maps Reveal Strain-Specific Regulatory Features of Multiple Campylobacter jejuni Isolates |
Interferon ( IFN ) responses are critical for controlling herpes simplex virus 1 ( HSV-1 ) . The importance of neuronal IFN signaling in controlling acute and latent HSV-1 infection remains unclear . Compartmentalized neuron cultures revealed that mature sensory neurons respond to IFNβ at both the axon and cell body through distinct mechanisms , resulting in control of HSV-1 . Mice specifically lacking neural IFN signaling succumbed rapidly to HSV-1 corneal infection , demonstrating that IFN responses of the immune system and non-neuronal tissues are insufficient to confer survival following virus challenge . Furthermore , neurovirulence was restored to an HSV strain lacking the IFN-modulating gene , γ34 . 5 , despite its expected attenuation in peripheral tissues . These studies define a crucial role for neuronal IFN signaling for protection against HSV-1 pathogenesis and replication , and they provide a novel framework to enhance our understanding of the interface between host innate immunity and neurotropic pathogens .
Herpes simplex virus type I ( HSV-1 ) is a highly prevalent neurotropic virus that persists for the lifetime of the host . Upon initial infection , HSV-1 undergoes rounds of lytic replication in the peripheral orofacial mucosa . The virus then enters axon terminals of innervating sensory neurons and travels in a retrograde manner to the neuronal cell bodies of the trigeminal ganglia ( TG ) . While the virus may subsequently undergo round-trip zosteriform spread from the infected TG back to the periphery [1] , it is ultimately within sensory neurons that HSV-1 establishes latency , producing little to no infectious virus . Reactivation from latency can occur and HSV-1 travels in an anterograde direction down the axon of sensory neurons to the periphery where it undergoes subsequent rounds of lytic replication , enabling viral shedding and host-to-host spread [2] . The ability of HSV to establish latency enables persistence in the host , resulting in 65–90% seroprevalence [3] . In most cases , HSV-1 infection results in oral lesions or is largely asymptomatic . A minority of infected individuals , however , can develop herpes stromal keratitis ( HSK ) , which can lead to blindness . In rare cases , herpes simplex encephalitis ( HSE ) can occur , which often results in death or long-term cognitive deficits . While HSE can result from primary infection , mostly in newborns , both disease pathologies can result from reactivation of latent HSV which then travels to the eye or CNS [4 , 5] . Nucleoside analogs such as acyclovir ( ACV ) reduce HSE mortality significantly , but survivors are often left with long-term neurological sequelae , and ACV cannot eliminate the latent virus reservoir [5] . The interferon ( IFN ) -driven antiviral response is critical for controlling HSV infection [6 , 7] and this response is initiated when infected cells detect the presence of virus through pattern recognition receptors ( PRRs ) . PRRs signal through adaptor molecules , which go on to phosphorylate key transcription factors , namely IRF3 and IRF7 , resulting in an up-regulation of type I interferon ( IFN α and β ) . Type I IFN is then secreted from the cell and can signal IFN receptors on both infected and uninfected cells . This activates a JAK/STAT pathway through the transcription factor STAT1 , leading to the establishment of an antiviral state through transcriptional repression , cytokine upregulation , and apoptosis [8] . Mice lacking components of antiviral signaling , such as IFN receptors or STAT1 , have increased susceptibility to HSV infection [7 , 9] . This is mirrored in humans with genetic impairments in antiviral signaling who suffer increased frequency of recurrent HSE [6 , 10] . HSV counteracts the antiviral response through several proteins , underscoring the importance of antiviral signaling to both host and pathogen [11] . A key HSV protein that can counteract antiviral signaling is ICP34 . 5 , encoded by the gene γ34 . 5 . ICP34 . 5 prevents the phosphorylation of IRF3 and reverses the phosphorylation of eIF2α thereby relieving translational arrest [12–14] . ICP34 . 5 also inhibits autophagy , a process which can degrade intracellular virions , and potentiate antigen presentation [15 , 16] . Consistent with this , viruses lacking ICP34 . 5 are significantly attenuated in both humans [17] and animal models [18–20] . While IFN-driven antiviral signaling controls HSV infection in general , its specific role in neurons remains unclear . It is thought that neurons may lack robust innate immune signaling in order to avoid damage to a largely irreplaceable cell type [21] . Supporting this , work from Yordy and colleagues suggest that autophagy , not IFN signaling , is the dominant antiviral strategy employed by neurons to control HSV infection [21] . Consistent with this , we have shown that the intrinsic IFN-driven antiviral response of adult sensory neurons is impaired . We have also , however , demonstrated that paracrine IFN signaling can drive an effective antiviral response in neurons which is strongly countered by HSV ICP34 . 5 [22] . Consistent with these data there is mounting evidence for effective neuronal antiviral responses to several viruses [23–26] . Of relevance , IFN treatment of cultured neurons restricts HSV replication and promotes a quiescent state resembling latency [27 , 28] . Additionally , neurons derived from humans who suffer from recurrent HSE due to genetic defects in TLR3 signaling , are more permissive to HSV infection . These studies provide further evidence for a key role for neuronal antiviral signaling in controlling HSV [29] . A confounding aspect when interpreting these data , however , is that recent studies have highlighted the importance of differentiation state and neuronal subtype on antiviral signaling [23 , 30 , 31] . Taken together , this body of work led us to investigate the role of IFN signaling in mature sensory neurons during HSV-1 infection . We therefore established a culture system of purified TG neurons from adult mice grown in compartmentalized chambers [32] . This model allowed us to mimic the in vivo axonal route of HSV-1 infection while enabling independent manipulation of the soma and axon of a relevant neuronal population . Using this in vitro system , we showed that the administration of IFNβ at either the soma or axon is capable of restricting HSV-1 . To address the importance of neural IFN signaling in vivo , we employed a Cre-lox system to yield progeny mice with defects in STAT1-driven signaling specifically in neural tissue . Using these mice , we show that neural IFN signaling alone is necessary to control HSV-1 replication , disease and survival , and demonstrate restoration of virulence to a virus lacking IXΠ34 . 5 . Correspondingly , non-neuronal IFN signaling is insufficient to control HSV-1 dissemination and mortality . Together , these results demonstrate that neuronal IFN signaling is required for controlling HSV-1 replication and disease and establish a new animal model for studying the role of neuronal innate immunity in the pathogenesis of neurotropic infections .
To address neuronal IFN signaling in an in vitro system which models the in vivo route of HSV infection , we utilized Campenot chambers to allow for directional growth of neurons , separating cell body ( soma ) from axon terminals [33 , 34] . We modified this system by removing one of the two central barriers to allow for growth of adult TG neurons , which failed to robustly extend neurites through a standard double barrier ( Fig 1A ) . An average of 30 . 3% ( SD = 2 . 25 ) of the TG neurons extended a network of axons through the single barrier , as judged by the addition of DiI ( lipophilic dye ) to the axonal compartment . Further characterization of the neuronal subtypes previously shown to be important during HSV infection revealed expected percentages of KH10 and A5 neurons extending axons across the barrier ( S1 Fig ) [32] . To confirm the barrier integrity of modified Campenot chambers , we added a low molecular weight dextran-conjugated fluorescent protein to the axon compartment of neuron cultures . The low mean fluorescent intensity in the soma compartment indicated that these modified chambers provided a sufficiently tight barrier to diffusion ( S1 Fig ) . While sensory neurons are capable of signaling IFN , it is unknown whether this can occur specifically at axon terminals to generate an antiviral response . It is likely that IFN is synthesized from an infected mucosal surface , and this secreted IFN has the potential to signal axon terminals of innervating neurons , rendering them resistant to subsequent infection . To address this , IFNβ was added to the axon compartment of wild-type ( 129SVEV ) cultured neurons prior to axonal infection with WT ( strain 17 ) virus , and then viral titers were measured in the soma compartment . Surprisingly , we saw a modest , but significant 4-fold reduction in viral titers in axonal IFNβ-treated compared to untreated neurons . This demonstrated that IFNβ can signal adult sensory neurons via axon terminals ( Fig 1B ) . The HSV protein , ICP34 . 5 , is critical for inhibiting the neuronal antiviral response [19 , 22] and we therefore hypothesized that ICP34 . 5 is important for countering the effects of axonal IFN signaling . To test this we treated cultures with IFNβ in the axon compartment and then infected these neurons axonally with a virus lacking ICP34 . 5 ( Δγ34 . 5 ) . We observed a significant ( 10-fold ) reduction in Δγ34 . 5 titers recovered at the soma compared to untreated cultures , and compared to WT infected IFNβ treated cultures ( p<0 . 0001 ) . This suggests that ICP34 . 5 may play a role in countering axonal IFNβ signaling . To verify that these effects were dependent upon IFN receptor signaling , we used neurons isolated from isogenic STAT1-/- mice . As expected , the titers of WT and Δγ34 . 5 viruses were comparable in the presence or absence of IFN , demonstrating that the reductions in titers previously seen were completely STAT1-dependent ( Fig 1B ) . Having shown that IFNβ can signal via axon terminals , we wished to assess whether IFN treatment of the soma can restrict HSV-1 following infection via the axon . This invokes the in vivo scenario whereby IFN produced by a variety of infected cells acts on the soma of TG neurons prior to retrograde transport of HSV-1 from the mucosal surface . In 129SVEV neurons , addition of IFNβ to the soma compartment resulted in a 6-fold reduction of WT and a 65-fold reduction of Δγ34 . 5 titers compared to untreated cells ( Fig 1B ) . These reductions were completely reversed in neurons isolated from STAT1-/- mice ( Fig 1B ) . Together , these data demonstrate that IFNβ can signal the length of the sensory neuron at both the soma and axon to restrict HSV-1 infection , and that ICP34 . 5 may counteract this host cell response . We wished to address whether establishment of an antiviral state was responsible for restriction of viral titers following axonal IFNβ treatment of chamber cultures . To test this , we added IFNβ to the axon compartment , and the soma compartment as a control . We then infected the soma compartment with WT , or the IFN-sensitive Δγ34 . 5 virus . Therefore , if axonal IFNβ signaling induces an antiviral state at the soma , we would expect reduced Δγ34 . 5 viral titers after soma infection . Consistent with our previously published results , IFNβ added to the soma significantly ( 700-fold ) reduced titers of Δγ34 . 5 virus ( Fig 2A ) . Unexpectedly , there was also a small , but significant ( 9-fold ) reduction in WT virus titers , likely reflecting differences between coverslip and Campenot chamber cultures [22] . Most notably , however , addition of axonal IFNβ did not change soma-derived titers of either WT or Δγ34 . 5 , demonstrating that axonal IFNβ signaling may not lead to establishment of a conventional antiviral state at the soma ( Fig 2A ) . HSV encodes for several proteins besides ICP34 . 5 that counteract an IFN response [11] . This raises the caveat that the lack of an antiviral state at the soma is due to IFN disruption by viral proteins other than ICP34 . 5 . We therefore utilized VSV , a highly IFN-sensitive virus , and consistent with the data for Δγ34 . 5 , we observed no significant decrease in VSV replication upon axonal IFNβ treatment at 24hpi ( Fig 2B ) . This observation held true when higher IFNβ concentrations were employed ( 100U/mL , S2 Fig ) . In contrast , and as expected , we observed a dramatic reduction in VSV replication upon IFNβ treatment of the soma at 24hpi ( Fig 2B ) . To further investigate axonal IFN signaling , STAT1 nuclear relocalization in neurons exposed to axonal IFN was examined by immunofluorescence . DiO was added to the axonal chamber to label neurons with axons that extended through the central barrier . STAT1 localization remained cytoplasmic at all timepoints tested following axonal IFN treatment ( Fig 2C ) . In contrast , we observed robust nuclear STAT1 relocalization in neurons exposed to soma IFN , consistent with previous data [22] . We next measured transcript levels of two interferon stimulated genes ( ISGs ) , IFIT1 and ISG15 , after soma or axonal IFN treatment . Consistent with our titer and immunofluorescence data , we observed minimal upregulation of either IFIT1 or ISG15 at both 6 and 12 hours post-axonal IFN treatment ( Fig 2D ) . In contrast , we observed a large upregulation of both ISGs after soma IFN treatment . Together these data demonstrate that axonal IFNβ signaling restricts yields of WT and Δγ34 . 5 virus after axonal infection by a mechanism independent of antiviral signaling at the soma . A previous study demonstrated that IFN signaling can restrict trafficking of poliovirus to the CNS [35] . Our data are consistent with this idea in that axonal IFNβ signaling may affect retrograde viral transport , which in turn results in reduced HSV titers . To address this , we infected neurons via the axons and then measured the number of incoming viral genomes at the soma in the presence or absence of axonal IFNβ . Unexpectedly , we found no significant change in the number of viral genomes accumulating in the soma compartment of untreated or IFNβ treated cultures ( Fig 2E ) . Furthermore , treatment of neurons with capsaicin , previously shown to reduce retrograde transport [36] , resulted in decreased accumulation of viral genomes , validating this viral capsid trafficking assay . We additionally observed no difference in genome copy number when a higher concentration of IFN ( 100U/mL ) and lower inoculum of virus ( 106 PFU ) was employed ( S3 Fig ) . Interestingly , we observed a significant reduction in the number of Δγ34 . 5 genomes relative to WT , regardless of IFNβ treatment , suggesting that ICP34 . 5 affects retrograde transport . It is therefore possible that the additional restriction of Δγ34 . 5 titers upon axonal infection ( Fig 1B ) is due to an inherent defect in retrograde transport of Δγ34 . 5 mutants . Together , these data demonstrate that IFNβ acts on neurons at both the cell body and the axon to control HSV-1 through distinct STAT1-dependent mechanisms ( S4 Fig ) . Having shown that IFNβ signaling in TG neurons is important for restricting HSV-1 replication in vitro , we wished to address its role in vivo . Previous work infecting IFN-signaling null mice resulted in generalized lethal disease with viral spread to multiple organs [7 , 9 , 37] . Also , conditional knockout mice lacking IFNα responses in neural tissue are more susceptible to VSV and Rabies virus , suggesting a role for neuronal IFN signaling in controlling these viral infections [38 , 39] . To address neural IFN signaling more generally in the context of HSV-1 infection , mice with Cre recombinase under the neural-specific Nestin promoter were crossed with STAT1 floxed mice . This thereby generated a new mouse model ( Stat1N-/- ) with intact IFN signaling in all tissues except neuroectoderm-derived cells , ( e . g . PNS and CNS neurons , PNS satellite glial cells , and astrocytes ) . Littermate control mice ( Stat1fl/fl ) were used for all experiments , and Stat1N-/- and Stat1fl/fl mice were equally viable . While the background strain of the conditional knockout mice ( C57/Bl6 ) differs from that of the neurons used for in vitro studies ( 129SVEV ) , we have shown that neurons derived from these strains support equivalent rates of HSV replication [22] . To verify the IFN signaling status of neural and non-neural tissues , we cultured TG neurons , fibroblasts , bone marrow-derived dendritic cells ( BMDCs ) , satellite glial cells ( SGCs ) and astrocytes isolated from naive Stat1N-/- and Stat1fl/fl mice . Cells were treated with IFNβ and replication of VSV was measured . As expected , fibroblasts and BMDCs isolated from both Stat1N-/- and Stat1fl/fl mice restricted VSV replication when treated with IFNβ ( Fig 3A ) . In contrast , TG neurons , SGCs and astrocytes isolated from Stat1N-/- mice were significantly less able to control VSV replication compared to Stat1fl/fl neurons ( Fig 3B ) . These results are consistent with previously published reports of Nestin expression [40] . Through crossing the Nestin-Cre mouse with a reporter mouse expressing TdTomato following Cre-mediated recombination , we additionally verified that microglia of the CNS do not express TdTomato and are thus STAT1 sufficient in our system ( Fig 3C ) . These results demonstrated that IFN signaling was intact in non-neural tissues of Stat1N-/- mice , and predicted that IFN signaling should control HSV replication in non-neuronal tissues such as the cornea . To examine this , we infected mice via the cornea with WT or Δγ34 . 5 virus and measured corneal swab titers ( Fig 3D ) . As expected , we found no difference in WT viral titers , and Δγ34 . 5 was equally and highly attenuated in corneas of Stat1fl/fl and Stat1N-/- mice ( Fig 3D ) . To examine virus replication in the nervous system , we next infected Stat1N-/- and Stat1fl/fl mice corneally with WT or Δγ34 . 5 viruses then measured titers in the TG , brain stem and brain . There was a significant increase in WT titers in the TGs of infected Stat1N-/- compared to Stat1fl/fl mice ( Fig 4A and 4B ) . Additionally , Δγ34 . 5 titers in the TGs of Stat1N-/- mice were significantly increased by ~100-fold on both days almost achieving the titers of WT virus . On day 3 , low levels of virus were observed in the brain stem and notably there were no differences in the titers between Stat1N-/- and Stat1fl/fl mice ( Fig 4A ) . No virus was detected in the brain at this timepoint . On day 5 , however , significant increases in WT viral titers were observed in brain stems and brains of Stat1N-/- mice compared to Stat1fl/fl ( Fig 4B ) . Moreover , we saw large increases in Δγ34 . 5 titers in the brain stems and brains of Stat1N-/- mice , with low titers in littermate controls ( Fig 4B ) . Together , these data show that neuronal STAT1 expression is critical for controlling HSV-1 replication in nervous tissue and that ICP34 . 5 counters this STAT1-driven response . IFN signaling is important for restricting tropism of neurotropic viruses [25 , 37] . We therefore wished to address whether neuronal IFN signaling restricts HSV-1 infection to sensory neurons thereby affecting cellular tropism within the TG . To test this , we examined the colocalization of neurons ( green ) , and HSV antigen ( red ) in the TG ( Fig 5A ) . Quantification of virus antigen-positive cells showed a close correlation with the titer data ( Fig 4 ) with significantly more cells infected in Stat1N-/- mice compared to Stat1fl/fl in both WT and Δγ34 . 5 infections ( Fig 5B ) . Similarly , more neurons were infected in Stat1N-/- mice compared to Stat1fl/fl in both WT and Δγ34 . 5 infections ( Fig 5C ) . As a measure of virus tropism we next quantified the number of infected neurons from each group and expressed this as a percentage of the total number of infected cells . In Δγ34 . 5-infected Stat1fl/fl mice , >80% of infected cells were neurons , with relatively few infected non-neuronal cells ( Fig 5D ) . Notably , however , the percentage of Δγ34 . 5 infected cells that were neurons in Stat1N-/- mice was significantly lower than that seen in Stat1fl/fl mice . This suggests that IFN signaling in the projecting infected neuron prevents spread of incoming Δγ34 . 5 in the TG ( Fig 5D ) . Additionally , there were significantly more infected non-neuronal cells in WT compared to Δγ34 . 5 infected Stat1fl/fl mice . Upon closer examination , we further observed that SGCs , which surround the neuronal cell body , become infected in Stat1N-/- and st17 infected Stat1fl/fl mice ( S5 Fig ) . In contrast , this was not observed in Δγ34 . 5 infected Stat1fl/fl mice ( S5 Fig ) . These data suggest that ICP34 . 5 promotes spread to non-neuronal cells in the TG , even in the presence of intact IFN signaling ( Fig 5D ) . Zosteriform spread involves the retrograde transport of virus from infected mucosae via the peripheral nerves to the TG , followed by anterograde transport to innervated tissue distal to the site of initial infection [1] . Following corneal infection in the mouse and in humans , periocular skin infection and disease are likely a consequence of zosteriform spread of the virus rather than direct spread from the cornea , which is dependent on robust replication in the innervating TG [41] . Based on this model , our observed pattern of viral titers in the TG predicts that WT virus would cause significantly more periocular infection and disease than Δγ34 . 5 in Stat1fl/fl mice , and this should be normalized in Stat1N-/- mice , despite the presence of STAT1-dependent responses in the skin . Consistent with this hypothesis , we observed significantly more periocular disease in Stat1fl/fl mice infected with WT virus compared to Δγ34 . 5 ( Fig 6A ) . Furthermore , there was significantly more disease in Δγ34 . 5 infected Stat1N-/- mice compared to Stat1fl/fl mice , with disease levels approaching those seen in WT virus-infected mice . While this significantly increased and overt disease was in contrast to the low levels of Δγ34 . 5 virus ( <10pfu ) in corneal swabs of the Stat1N-/- mice ( Fig 3B ) , it correlated with a significant increase in skin titers on day 5 ( Fig 6B ) . These data therefore suggest that the lack of neural IFN-signaling causing increased replication in the TG of Stat1N-/- mice promotes periocular disease due to zosteriform spread of HSV-1 . These data therefore further validate the zosteriform spread model and the phenotype of Stat1N-/- mice [41] . We next tested how neuronal expression of STAT1 impacts host survival following peripheral ( corneal ) challenge . Approximately 50% of Stat1fl/fl mice infected with WT virus succumb to infection over a 21-day timecourse ( Fig 7 ) . In contrast , 100% of Stat1N-/- mice infected with WT virus succumbed rapidly to infection by day 9 ( Fig 7 ) . As expected , 100% of Stat1fl/fl mice survived infection with Δγ34 . 5 , but 100% of Stat1N-/- mice infected with Δγ34 . 5 died , demonstrating that neural STAT1 deletion restores virulence to Δγ34 . 5 ( Fig 7 ) . While these mice died within same 9-day window seen with WT virus , the timecourse was slower ( p<0 . 01 ) . Importantly , these data show that non-neural STAT1 expression alone is insufficient to control virulence of WT and Δγ34 . 5 virus .
Loss of IFN signaling results in significantly increased HSV-1 pathogenesis and mortality in humans and mice but the specific role of IFN signaling in neurons is unclear [6 , 7 , 9] . Here , we demonstrate the importance of a functional neuronal IFN response in resistance to HSV-1 replication and pathogenesis in mice with a neural-specific deletion of STAT1 . STAT1 is a key transcription factor that is downstream of multiple interferon receptors that include type I ( α and β ) , II ( γ ) and III ( λ ) [8] . We demonstrated in vitro that IFNβ is able to restrict neuronal HSV-1 replication , but it is possible that multiple IFNs are acting upon neurons in vivo . Studies examining the capacity of cultured TG neurons to upregulate an effective antiviral response revealed that IFNβ plays a predominant role in neuronal antiviral signaling . That said , TG neuron cultures also have the ability to respond moderately to IFNγ , and modestly to IFNλ , as demonstrated by STAT1 nuclear localization and inhibition of VSV replication ( S6 Fig ) . Consistent with a potential role for IFNγ signaling , functional IFNγ receptors are present at the axon terminals of peripheral neurons [42] and IFNγ can reduce reactivation of HSV-1 from explanted TGs [43] . Additionally , neuronal IFNλ restricts HSV-1 replication in vitro [44] , and IFNλ treatment can exert protective effects in vivo against HSV-2 disease [45] . It will thus be important to further investigate the roles of type II and III IFN both in vitro and in vivo on neuronal HSV replication and viral pathogenesis . Type I IFN signaling is a determinant of tissue tropism of neuroinvasive viruses such as West Nile virus , poliovirus , and HSV-1 [25 , 37 , 46] . It is unclear , however , how IFN signaling impacts this tropism on a cellular level . The proportion of non-neurons that are infected was significantly higher in the TGs of Δγ34 . 5 infected mice lacking neural STAT1 expression , revealing a role for neural IFN signaling in restricting HSV tropism to sensory neurons and counteraction of this by HSV . Furthermore , we demonstrated that there is a host-pathogen balance in determining cell tropism , as ICP34 . 5 effectively counteracts this IFN signaling , thereby promoting viral spread within the TG . These data are consistent with previous work showing that the absence of TLR3 signaling changes the tropism of HSV-2 such that it infects astrocytes rather than neurons of dorsal root ganglia [47] . Together these data show that neuronal innate responses control viral dissemination and cell-type predilection in the sensory ganglia . Altered tropism in the TG and subsequent anterograde viral spread may impact dissemination to the periocular skin . Murine models of HSV-1 infection , and clinical studies in humans have demonstrated zosteriform spread whereby the virus infects sites distal from the initial site of infection through anterograde trafficking from the sensory ganglia [1 , 41] . Given the significant attenuation of Δγ34 . 5 in the presence of IFN responses , and restoration of its replication in the absence of IFN responses , infection of Stat1N-/- mice with Δγ34 . 5 provided a unique insight into zosteriform spread and periocular disease . While corneal titers of Δγ34 . 5 were comparably low in both Stat1N-/- and control mice , overt periocular disease was only apparent in Stat1N-/- mice . The significant increase in periocular disease correlated with the high viral load in the TG and periocular skin of Δγ34 . 5 infected Stat1N-/- mice compared to littermate controls . These data validate previous findings that zosteriform spread from the TG to the periocular skin is the cause of periocular disease in corneal HSV infection models [41] . This is also consistent with previous studies of corneal and alternate routes of HSV-1 infection in mice , and clinical findings of periocular lesions during herpes keratitis [41 , 48–50] Our in vitro data support a role for neuronal STAT1-dependent IFNβ signaling at both the soma and axon in controlling HSV-1 following axonal infection . In contrast , there was no evidence that this STAT1-dependent signaling via the axon could control HSV-1 following infection of the soma or lead to a significant upregulation of ISGs . We were also unable to detect STAT1 in the nucleus of neurons post-axonal IFN treatment . This was an unexpected finding given that , canonically , STAT1 mediates signaling via translocation from the cytoplasm to the nucleus . These data suggest that STAT1 has a non-canonical function that is disrupting a process important to the viral life cycle prior to replication at the soma . An example of such non-canonical STAT signaling has been described for STAT3 which binds to stathmin , thereby stabilizing microtubules [51 , 52] . While we did not detect a change in DNA-containing capsid trafficking , STAT1 signaling may interfere with retrograde transport of tegument proteins which would , in turn , result in restricted viral replication [2] . These data also suggest that use of topical IFN may be an effective therapy for ocular or oral HSV lesions by stimulating neuronal antiviral signaling at axon terminals innervating the site of infection . Indeed clinical studies on use of topical IFN in conjunction with antivirals were promising [53] . Another unexpected finding was the significant reduction in the number of Δγ34 . 5 genomes reaching the soma after axonal infection , independent of IFN treatment , suggesting a novel role for ICP34 . 5 in regulating retrograde trafficking or entry . Some studies have suggested the presence of ICP34 . 5 in the viral tegument , although at low abundance relative to other proteins [54] . It is formally possible , therefore , that ICP34 . 5 derived from entering virions could be directly exerting an effect upon neurons . It is more probable , however , that in the absence of ICP34 . 5 , expression of structural proteins that are important in trafficking or entry are reduced in the ICP34 . 5 mutant [55] . This reduced trafficking or entry of ICP34 . 5-deficient viruses in neurons may be particularly important and useful in enhancing safety during their potential use for oncolytic therapy of glioblastoma [17] . Our studies demonstrate an important role for ICP34 . 5 in combating neuronal IFN signaling . While ICP34 . 5 has multiple functional domains that can counteract IFN signaling [12–14] , it also contains a Beclin binding domain ( bbd ) which can interfere with neuronal autophagy , an important response in combating HSV-1 [16 , 21] . We add to these findings and demonstrate that neuronal IFN signaling is also critical for controlling HSV-1 infection . It is likely that autophagic and IFN-signaling pathways synergize in sensory neurons . Indeed , there is evidence supporting a role for IFN signaling in the upregulation of autophagy [22 , 56] . Given this , the absence of neuronal IFN signaling combined with a dysregulation of autophagy may account for the dramatic increase in Δγ34 . 5 virulence in Stat1N-/- mice . As such , it will be important to examine autophagy in IFN signaling deficient neurons in vitro and in Stat1N-/- mice . The degree to which neuronal IFN signaling is critical for host survival is particularly striking . Stat1N-/- mice were markedly susceptible to infection , and the normally attenuated Δγ34 . 5 virus was nearly restored to full virulence in these mice . While it is possible that the effects seen in vivo were exacerbated by the additional loss of IFN signaling in SGCs and astrocytes , these data importantly showed that IFN signaling of the immune system and of peripheral tissues is less critical than IFN-driven innate responses in neural tissues . The data additionally demonstrated that ICP34 . 5 is critical for viral resistance to neural IFN responses , affirming the role of ICP34 . 5 as a specific neurovirulence factor . Ultimately , our in vitro and in vivo results demonstrate a requirement for neuronal STAT1 signaling in controlling HSV-1 pathogenesis , and the new models generated herein will prove useful for subsequent studies on the pathogenesis of HSV and other clinically important neuroinvasive pathogens .
Trigeminal ganglia ( TG ) neurons were isolated as described previously with some modifications [22 , 32] . Briefly , 6–10 week old mice were transcardially perfused , TGs harvested and digested in papain ( Worthington ) followed by collagenase type II ( Invitrogen ) and neutral protease ( Worthington ) . TGs were then triturated and the resulting homogenate was spun over a four-layer Optiprep ( Sigma ) density gradient and two bands of lower density were collected and washed three times . Neurons were cultured in NB-A complete media for ≥ 3 days prior to use . NB-A complete media consisted of Neurobasal-A , 2% SM1 , 1% GlutaMAX ( Invitrogen ) , 1% penn/strep , 50ng/mL Neurturin ( R&D Systems ) , 50ng/mL neuronal growth factor ( NGF , Invitrogen ) , 50ng/mL glial derived neurotrophic factor ( GDNF , R&D Systems ) , and 60μM FUDR . For satellite glial cell culture , cells resulting from density gradient spin were plated in 24-well tissue culture plates in DMEM ( HyClone ) with 10% FBS , 1% GlutaMAX ( Invitrogen ) and 1% penn/strep . Cells were trpysinized once prior to use , removing any contaminating neurons . Cells were infected at an MOI of 20 . 20mm CAMP320 chambers ( Tyler Research ) were modified by removing one internal barrier . Chambers were assembled as described previously [34] . Briefly , vacuum grease was applied to one side of the modified chamber and mounted onto PDL/laminin-coated dishes with 16 parallel grooves etched into the bottom spanning the central barrier and overlayed with 1% methylcellulose in NB-A complete media ( described above ) . Neurons were cultured for 2 weeks and assessed prior use for axonal growth . They were then infected either via the axonal compartment with 108 plaque-forming units ( PFU ) HSV ( MOI 8 , 300 ) or via the soma compartment with 24 , 000 PFU HSV ( MOI 2 ) or 600 PFU VSV ( MOI 0 . 5 ) . The high MOI used for axonal infection was empirically determined to deliver approximately equivalent genome copies as soma infection and is consistent with the literature utilizing such compartmentalized chambers [57] . When indicated , cells were treated with 12 . 5 units/mL IFNβ ( PBL Interferon source ) 18 hours prior to infection and maintained throughout . Viral titers were assessed via plaque assay on Vero cells as described previously [58] . For selective labeling of neurons extending axons through the central barrier , the lipophilic dye DiI or DiO ( Invitrogen ) was added to the axonal compartment at 5μl/mL . Barrier integrity was assessed in chambers with 2-week old neuron cultures . Dextran-fluorescein conjugated dye ( MW = 10 , 000 , Invitrogen ) was added to the axonal compartment at 0 . 2mg/mL and the mean fluorescent intensity ( MFI ) of supernatant from both compartments was measured after 72 hours on a Zeiss Axio Observer Z1 inverted microscope using Zen software . BMDCs were isolated and cultured as described previously [59] . Briefly , femurs were removed from mice that had been lightly perfused for TG neuronal isolation , flushed and filtered through a 100μM mesh . Cells were seeded at a density of 3 million cells per well and differentiated through culture with RPMI-1640 ( HyClone ) 1% sodium pyruvate ( HyClone ) , 10% fetal bovine serum ( FBS- Atlanta Biologicals ) , 0 . 5% penn/strep , 1% L-glutamine ( HyClone ) and 15% granulocyte-macrophage colony stimulating factor ( GM-CSF ) . Cells were infected at an MOI of 0 . 1 . Fibroblasts from adult mice were obtained through ear clippings and subsequently minced and digested in 1000U/mL collagenase Type II ( Invitrogen ) followed by 0 . 05% trypsin ( Cellgro ) . Resulting cell lysate was triturated and plated in 6 well plates in DMEM ( HyClone ) with 10% FBS , 1% non-essential amino acids , 1% GlutaMAX ( Invitrogen ) and 1% penn/strep . Cells were infected at an MOI of 0 . 5 . Astrocytes were isolated as previously described [60] . Briefly , cortical hemispheres of p3 mice were obtained and the meninges were removed . Tissue was minced and incubated with 0 . 1% trypsin for 30 mins . Resulting cell lysate was triturated and plated in T25 flasks in DMEM ( HyClone ) with 10% FBS , 1% GlutaMAX ( Invitrogen ) and 1% penn/strep . After 2 weeks of culture , flasks were mechanically shaken to remove microglia . Remaining cells were trypsinized and seeded in 24 well plates . Cells were infected at an MOI of 0 . 5 . Strains 17 syn+ , and the ICP34 . 5-null mutant on the strain 17 ( WT ) background , Δγ34 . 5 were made as previously described [61 , 62] . Viral stocks were grown on Vero cells as described previously [58] . STAT1-/- mice were backcrossed onto the 129SVEV background as previously published [63] . Proper genetic background of STAT1-/- mice was additionally assessed at the DartMouse Speed Congenic Core Facility as previously published [64] . 129SVEV ( 129S6/SvEvTac ) mice were purchased from Taconic labs and bred in house . Mice expressing TdTomato following Cre-mediated recombination ( Ai14 mice; B6 . Cg-Ct ( ROSA ) 26Sortm14 ( CAG-tdTomato ) Hze/J ) were purchased from Jackson Laboratories and generously provided by Hermes Yeh ( Geisel School of Medicine ) . Stat1-floxed ( Stat1fl/fl ) mice were generously provided by Floyd Wormley ( UT San Antonio ) , and generated by Mathias Müller [65] . Nestin-cre ( B6 . Cg-Tg ( Nes-cre ) 1Kln/J ) mice were purchased from Jackson Laboratories and described previously [66] . Nestin-cre mice were maintained as a hemizygous line . Progeny from Nestin-cre Stat1-flox crosses were genotyped prior to use . Primary antibodies used were rabbit anti-HSV-1 ( B0114 , Dako ) , chicken anti-NeuN ( ab134014 , Abcam ) , mouse anti-NeuN ( clone A60 , Millipore ) , and rabbit anti-beta III Tubulin ( ab18207 , Abcam ) , rabbit anti-Iba1 ( Wako ) , rabbit anti-STAT1α91 ( M-23 , Santa Cruz Biotechnology ) . The mouse anti-A5 antibody ( FE-A5 ) , developed by Bruce A . Fenderson at Thomas Jefferson University , was obtained from the Developmental Studies Hybridoma Bank , created by the NICHD of the NIH and maintained at The University of Iowa , Department of Biology , Iowa City , IA . Secondary antibodies used were goat-anti-mouse/rabbit Alexa 555 , goat-anti-mouse/rabbit Alexa 488 ( Invitrogen ) , and donkey-anti-chicken Alexa488 ( Jackson ImmunoResearch Laboratories ) . Isolectin B4 conjugated to FITC ( Sigma ) was added to cultures at a concentration of 10μg/mL to stain KH10 neurons in chambers [67] . Counterstaining was done by incubation with DAPI ( Invitrogen ) . Samples were mounted using FluorSave Reagent ( Calbiochem ) . When indicated , cells were treated with 12 . 5U/mL ( unless noted ) IFNβ ( PBL Interferon Source ) , 100ng/mL IFNγ ( Miltenyi Biotec ) or 100ng/mL IFNλ2 ( PeproTech ) for 18 hours prior to infection or for the specified amount of time prior to staining . Neurons grown in chambers were pretreated with 100μM Acyclovir ( ACV , Spectrum ) for 18 hours , and infected axonally in the presence of ACV . The DNA from the soma compartment was harvest at indicated times post infection in TRIzol ( Ambion ) per manufacturer’s instructions through use of back extraction buffer ( 4M guanidine thiocyanate , 50mM sodium citrate , 1M Tris ) . Copy number was determined through qPCR for the viral thymidine kinase ( tk ) normalized to the single-copy mouse adipsin as described previously [68] . A standard curve for tk was prepared using HSV bacterial artificial chromosome ( BAC ) 17–49 [69] . Mouse genomic material was used for adipsin standard curves . As a control , the genomic copy number of strain 17 versus Δγ34 . 5 viral stocks was empirically determined to be equivalent as judged by the above PCR protocol . Samples were harvested in TRIzol ( Ambion ) and RNA extracted per manufacturer instructions . RNA was treated with DNA-free kit ( Ambion ) and cDNA synthesized using the SuperScriptIII Reverse transcriptase kit ( Invitrogen ) with random primers ( Promgea ) . For qPCR , SYBR Select Master Mix ( Life Technologies ) was used with primers for IFIT1 ( Fw: TGC TTT GCG AAG GCT CTG AAA GTG , Rv: TGG ATT TAA CCG GAC AGC CTT CCT ) , ISG15 , ( Fw: TGA GCA TCC TGG TGA GGA ACG AAA , Rv: AGC CAG AAC TGG TCT TCG TGA CTT ) and 18s ( Fw: TCA AGA ACG AAA GTC GGA GG , Rv: GGA CAT CTA AGG GCA TCA CA ) . IFIT1 and ISG15 values were calculated by the 2-ΔΔCT method [70] normalized to 18s , and values for IFN treated cells were normalized to mock . Mice were perfused with PBS followed by 4% formaldehyde . Brain or trigeminal ganglia were embedded in OCT ( Tissue-Tek ) and 15μm sections taken on a Leica CM1860 cryostat . Tissue sections or fixed neuron cultures were incubated in 0 . 1% Triton-X100 ( Sigma ) in 5% normal goat serum ( NGS- Vector Laboratories ) in PBS for 1 hour . Primary and secondary antibody incubations were done in 2% NGS/0 . 1% Triton overnight at 4°C and for 1 hour at room temperature , respectively . Staining with A5 primary antibody was done at 4°C for 48 hours . Fixed cultures/tissue were imaged on Zeiss Axio Observer inverted microscope and montages created using motorized stage and ZEN software . Images were analyzed using ImageJ/Fiji with a minimum of 4 sections per TG and a minimum of 7 TGs per group . Quantification of neuronal subtype in compartmentalized chambers was done for a minimum of 3 , 000 neurons total per chamber over 2 experiments with 6 chambers total . Mice were anesthetized intraperitoneally with ketamine ( 90 mg/kg ) and xylazine ( 10 mg/kg ) . Corneas were bilaterally scarified and mice were inoculated by adding 2 × 106 PFU per eye in a 5μl volume . Periocular disease was monitored over time as previously described [71] . For survival studies , mice were monitored over time and euthanized upon reaching endpoint criteria consisting of loss of more than 25% body weight and/or a drop in temperature by 3°C from baseline [72] . At indicated times following corneal infection , the following tissues were harvested and titers determined as previously described [58]; corneal swabs , periocular skin , trigeminal ganglia , brain , and brain stem . For periocular skin , two 6mm biopsy punches per eye were harvested and titers were averaged . All tissues were harvested and stored at −80°C until processing . Tissues were mechanically disrupted and sonicated , and titers were determined via standard plaque assay on Vero cells as described previously [58] . This study was 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 . The protocol was approved by the Dartmouth IACUC Committee ( 06/05/12 , Permit Number: leib . da . 1 ) . No surgery was performed , and all efforts were made to minimize suffering . | Herpes simplex virus type 1 ( HSV-1 ) is a ubiquitous virus that can cause cold sores , blindness , and even death from encephalitis . There is no vaccine against HSV , and although antiviral drugs can control HSV-1 , it persists because it establishes lifelong latent infections in neurons . Humans with deficiencies in innate immunity have significant problems controlling HSV infections . In this study we therefore sought to elucidate the role of neuronal innate immunity in the control of viral infection . Sensory neurons , in which HSV resides , have projection which that extend long distances to innervate the skin , the initial site of HSV infection . We found that neurons can respond to interferon beta , a molecule that strongly stimulates innate immunity and inhibits virus growth , at both the cell body and at the end of these long projections . Moreover , we found that this interferon response of neurons is critical for controlling HSV infection in vivo and that the interferon responses of non-neuronal cells are insufficient to provide protection . Our results have important implications for understanding how the nervous system defends itself against virus infections . | [
"Abstract",
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"Methods"
] | [] | 2015 | Neuronal Interferon Signaling Is Required for Protection against Herpes Simplex Virus Replication and Pathogenesis |
Invasive non-typhoidal Salmonella ( iNTS ) serovars S . Typhimurium and S . Enteritidis are major etiologic agents of invasive bacterial disease among infants and young children in sub-Saharan Africa , including in Mali . Early studies of iNTS serovars in several countries indicated that S . Typhimurium was more prevalent than S . Enteritidis , including in Mali before 2008 . We investigated genomic and associated phenotypic changes associated with an increase in the relative proportion of iNTS caused by S . Enteritidis versus S . Typhimurium in Bamako , Mali , during the period 2002–2012 . Comparative genomics studies identified homologs of tetracycline resistance and arsenic utilization genes that were associated with the temporal shift of serovars causing iNTS shift , along with several hypothetical proteins . These findings , validated through PCR screening and phenotypic assays , provide initial steps towards characterizing the genomic changes consequent to unknown evolutionary pressures associated with the shift in serovar prevalence . This work identified a shift to S . Enteritidis from the more classic S . Typhimurium , associated with iNTS in Bamako , Mali , during the period 2002–2012 . This type of shift in underlying iNTS pathogens are of great importance to pediatric public health in endemic regions of sub-Saharan Africa . Additionally , this work demonstrates the utility of combining epidemiologic data , whole genome sequencing , and functional characterization in the laboratory to identify and characterize genomic changes in the isolates that may be involved with the observed shift in circulating iNTS agents .
Salmonella enterica is a bacterial pathogen that includes over 2500 serological variants ( serovars ) and is estimated to cause more than 1 . 3 billion cases of clinical illness annually worldwide [1] . Non-typhoidal Salmonella ( NTS ) serovars such as S . Enteritidis and S . Typhimurium , while mainly limited to gastrointestinal illness in industrialized nations [2 , 3] , have been found to cause severe invasive bacterial disease in sub-Saharan Africa [4–6] typically unaccompanied by or preceded by diarrhea . Children from 6–12 months of age suffer the greatest incidence of invasive disease , and case fatality rates for S . Enteritidis disease are high ( 20–28% ) in infants and young children in Africa [7] . In part , the differences in clinical presentation of NTS disease in pediatric populations in sub-Saharan Africa versus in industrialized countries may be due to immunosuppression , malnutrition , intensity of previous antigenic exposure , or co-infections , particularly malaria [5 , 8–10] . However , there are also differences in the inherent pathogenicity of strains of non-typhoidal Salmonella , in addition to the host and infection-related factors [11–16] . For example , ~95% of the invasive S . Typhimurium strains in Africa are multi-locus sequence type 313 ( ST313 ) , a genotype unique to that continent that has undergone extensive genomic degradation associated with host-adaptation [11 , 13] and which is different from the ST19 strains that predominate outside Africa [11 , 17] . S . Typhimurium has been identified as the leading cause of invasive NTS ( iNTS ) disease in many countries in sub-Saharan Africa [14] , although in some countries S . Enteritidis was the predominant serovar [18] . Systematic surveillance undertaken by the Center for Vaccine Development of Mali ( CVD-Mali ) and by the Center for Vaccine Development at the Hôpital Gabriel Touré ( HGT ) in Mali revealed a sizable burden of iNTS disease [19] . This surveillance was initiated in 2002 to quantify the burden of invasive bacterial disease caused by Haemophilus influenzae type b ( Hib ) and Streptococcus pneumoniae ( pneumococcus ) and to assess the need for introduction of vaccines to prevent disease caused by those pathogens , which were responsible for most cases of pediatric bacteremia . Although not originally designed to do so , review of the surveillance data identified NTS serovars Typhimurium and Enteritidis as common causes of severe invasive disease . Moreover , after routine infant immunization with Hib and pneumococcal conjugate vaccines was implemented and those two infections were controlled , NTS emerged as the predominant cause of invasive bacterial disease in infants and young children in Mali [19] . The quality and duration of the systematic surveillance at HGT facilitated identification of a shift in iNTS epidemiology around 2008 [12] . An increased relative proportion of iNTS disease due to S . Enteritidis was detected along with a concurrent decrease in the prevalence of S . Typhimurium after 2008 [19] . From the iNTS isolates included in the epidemiologic investigation by Tapia et al . [19] , a subset of 42 S . Enteritidis isolates , selected as a stratified sample by year of isolation , were included in a phylogenetic analysis examining global lineages of S . Enteritidis conducted by Feasey et al . [15] . Examination of the genomic phylogeny suggested that the newly circulating S . Enteritidis isolates collected from Mali ( 2008–2012 ) are phylogenomically distinct from the 2002–2008 isolates . While periodic Salmonella serovar shifts have been observed elsewhere in the world [20] , the genotypic characteristics and environmental pressures that lead to such serovar shifts are not well understood and have not been investigated in detail , especially using whole genome sequencing . Although data on the characterization of major genomic elements and evolutionary pressures on non-typhoidal Salmonella including S . Enteritidis are limited , studies by Nuccio and Baumler [21 , 22] have identified genomic signatures associated with increased growth within the host gastrointestinal tract and increased gut inflammation . These signatures are identified with NTS strains associated with gastroenteritis , while the genome of NTS strains associated with invasive disease is thought to be degraded [12] . Feasey et al . [15] examined global lineages of S . Enteritidis , including the isolates in this study , and identified two clades of S . Enteritidis that have become prominent in Africa . Feasey et al . [15] also established that the S . Enteritidis clades exhibit similar host-adapted genomic degradation as seen with S . Typhi , S . Paratyphi A , and S . Typhimurium . None of the previous studies investigated genomic changes in S . Enteritidis within a discrete population , such as the pediatric population of Bamako . Our current study was undertaken to identify and characterize genomic markers in S . Enteritidis isolates that are representative of the clade that emerged as the predominant serovar of iNTS disease in the pediatric population in Bamako , Mali during the time period since systematic surveillance was established . This work demonstrates the utility of combining epidemiological principles , whole genome sequencing , and functional characterization in the laboratory to identify and characterize genomic changes in the isolates that may be responsible for the observed shift in the iNTS serovars .
Isolates included in this study were collected by local hospital staff from the blood or spinal fluid of patients during hospital admission at the HGT , as reported by Tapia et al . [19] . The serovar of these isolates was confirmed through multiplex polymerase chain reaction ( PCR ) assay and agglutination with Salmonella antiserum . The 42 isolates submitted for whole-genome sequencing ( S1 Table ) , as described by Feasey et al . [15] and utilized for the in silico analysis in our study , had their taxonomy verified by culture on selective Salmonella-Shigella agar , positive O ( D1 ) agglutination from cultures grown on Trypticase Soy Agar , and positive H agglutination ( with antisera against g and m Phase 1 H antigens ) from cultures grown on 0 . 6% agar swarm medium . We examined a phylogenetic tree based on the core genome of the same 42 S . Enteritidis isolates examined by Feasey et al . [15] . The proportion of misclassified isolates was determined for each year and at each branch of the phylogenetic tree , with year of isolation determined by the seasonal trends in weather ( June-July ) [19] . Isolates were considered misclassified when they were collected from one side of the temporal breakpoint , but appeared in a branch that consisted mainly of isolates from the other side of the breakpoint . The branches of the tree were manually rotated at each node to determine the potential breakpoint year to minimize the number of misclassifications across the entire tree . Each year was tested as a potential breakpoint and the year which resulted in the lowest proportion of misclassifications across the major branches of the tree was ultimately selected . This temporal breakpoint was then compared to the epidemiologic data reported from the HGT [19] and the global phylogenetic analysis performed by Feasey et al . [15] . The whole genome sequences of the 42 S . Enteritidis isolates ( sequenced by the Wellcome Trust Sanger Institute ) were assembled and annotated using the Velvet assembly through the CloVR pipeline , release version 1 . 0-RC5 [23] at the Institute for Genome Sciences ( IGS ) at the School of Medicine , University of Maryland , Baltimore . The sequence of each isolate was analyzed for protein encoding genes using a protein-coding gene prediction program , Prodigal ( Prokaryotic Dynamic Programming Genefinding Algorithm ) [24] . Once all the putative genes had been identified , BLAST Score Ratios ( BSRs ) [25] were calculated for each putative gene in each examined isolate using Large Scale BLAST Ratio Analysis ( LS-BSR ) [26] . The protein-coding genes in all of the genomes were predicted using Prodigal and were clustered by >90% similarity using uclust . BSR is calculated as the bit score of a gene detected in a genome divided by the bit score of the gene compared to itself , resulting in a scale normalized from 0 to 1 . A BSR of ≥ 0 . 8 indicated a gene was present in a genome with significant similarity , a BSR ≤ 0 . 4 indicated a gene was absent , and genes identified with BSR values <0 . 8 but ≥ 0 . 4 were considered present with divergent similarity . The predicted protein-coding genes that were present ( BSR≥ 0 . 8 ) in all isolates were removed from further analysis , as they constituted the conserved core genome , resulting in a list of 1 , 220 putative genes with variable presence among the included isolates ( S1 Table ) . To identify genes associated with the genetic shift in S . Enteritidis , methods similar to those described by Sahl et al . [26] were used . Briefly , significant differences in the average BSRs before and after the 2008 breakpoint for each gene were identified using Student’s t-tests resulting in a p<0 . 01 ( Welch’s method was used to account for unequal variances ) . Genes significantly associated with either the pre- or post-2008 time periods were then verified by manual BLAST searches in each genome . This verification was performed to ensure that any differences in BSR were due to a true association with the 2008 cut point , and were not missing from the draft genome assemblies as an artifact of the sequencing and assembly . The genomes of the isolates in this study were compared by whole-genome phylogenomic analysis as previously described [15] . The genomes were aligned using Mugsy [27] and homologous blocks were concatenated using the bx-python toolkit [28] . The columns that contained one or more gaps were removed using Mothur [29] . The concatenated regions from each genome were used to construct a maximum-likelihood phylogeny with 100 bootstrap replicates using RAxML v7 . 2 . 8 [30] that was visualized using FigTree v1 . 4 . 2 [31] . Isolates that were isolated before or during 2008 are colored black and those post-2008 are colored blue . From surveillance through June 30th , 2012 , the isolates from 299 individuals identified to have laboratory-confirmed invasive infections caused by S . Enteritidis were collected . Of the 103 available isolates obtained before or during 2008 , 23 were sequenced using the Illumina platform . Nine of these 23 ( 39% ) exhibited one of the putative genes identified as significantly associated with the shift in S . Enteritidis . This gene ( centroid_210035_1 ) possessed the weakest level of statistical significance ( p = 0 . 0009 ) among the putative genes associated with the serovar shift . Of the 196 isolates obtained from after 2008 , 19 were sequenced , with 16 exhibiting the present allele of the same gene ( 84% ) . By focusing on this least significant associated gene , we used these prevalence rates to estimate conservative sample size calculations for examining the distribution of this , and more significantly associated genes across other available isolates based on a binomial distribution across two samples . A two-sided sample size analysis indicated that screening 72 additional samples [36 from each group , pre- and post-2008 ) would provide 80% power by PCR to identify genetic changes across all available isolates . Simple random sampling of all isolates collected before and after the breakpoint was used to select the 36 isolates from each group . This resulted in 12 isolates selected from those collected in 2002–2003 , none from 2003–2004 , 1 from 2004–2005 , 1 from 2005–2006 , 4 from 2006–2007 , 8 from 2007–2008 , 10 from 2008–2009 , 9 from 2009–2010 , 11 from 2010–2011 , and 16 from 2011–2012 . The manual BLAST searches were performed on each significant putative gene against the pre- and post-2008 isolates to determine where the genes exist on the chromosome in reference to each other . Geneious version 7 [32] was used to visualize the location of significant putative genes in relationship to each other within each isolate and comparatively across other sequenced isolates . Genes that were identified to have homologs with biologically relevant function via BLAST searches , or were co-located with other genes of interest , suggesting these genes may be in a functional unit or operon , were selected as PCR targets . Geneious software was used to confirm that primer target regions were identical across the previously sequenced S . Enteritidis isolates containing the genes of interest . NORGEN Biotek Corp . bacterial genomic DNA isolation kits were used to extract the genomic DNA of the 72 non-sequenced isolates selected by simple random sampling and 2 μl of template from each isolate was included in the PCR to screen for each of the six putative genes under their optimized conditions . PCR conditions for each gene were optimized in regard to magnesium concentration and annealing temperature on isolates that had previously been sequenced [15] and verified in silico to contain the genes of interest . Primer mix included 2 μl 10x PCR buffer , 0 . 4 μl of 10 mM dNTPs , and 0 . 2 μl of Invitrogen Taq DNA polymerase without Mg , and 1 μl of 10 mM forward and reverse primer relevant to each targeted centroid ( final volume , 18 μl ) . PCR was performed in an Eppendorf Mastercycler® . The cycling parameters for screening each centroid involved denaturation at 94°C for 2 min , followed by 25 cycles of heating to 94°C for 30 sec , the relevant optimized annealing temperature for 30 sec , 72°C for extension time of relevant to the product length ( 1 min/kbp ) , and a final step of 72°C for 5 min . Primer sequences and PCR conditions for each centroid are shown in Table 1 . PCR products were separated on a 1% agarose gel stained with SYBR Safe and visualized using a UV transilluminator . The proportion of isolates in the pre- and post-2008 groups that contained the selected PCR target were compared by chi-square analysis ( with a p-value adjusted for multiple comparisons by Bonferroni correction ) to test for significant differences in their distribution . Initial annotation of the putative genes novel to the S . Enteritidis isolates collected after 2008 were found to be homologs of tetracycline-family resistance genes and arsenic utilization genes . Kirby-Bauer tests with 30 μg tetracycline were performed on the isolates which had been screened by whole genome sequencing or PCR assay to verify the predicted phenotypic differences between these strains . Colonies grown from overnight incubations were resuspended in PBS at a turbidity of 0 . 5 MacFarland and plated on Mueller-Hinton agar with discs impregnated with 30 μg tetracycline and concentrations of 0 . 04 , 0 . 16 , and 0 . 64 mg/ml sodium arsenate [33] . Zones of inhibition were measured after 12–18 hours of incubation at 37°C . For tetracycline antibiograms , zones with a diameter of ≥19 mm were considered susceptible , and ≤14 mm were resistant per CLSI guidelines [34] . Significant differences in the proportion of tetracycline resistant isolates from pre- and post-2008 samples were identified by chi-square analysis and differences in zone diameters of arsenic resistance were compared using Student’s t-test ( p<0 . 05 ) .
While the epidemiologic analysis of iNTS by Tapia et al . [19] suggested that the increase in the relative proportion of iNTS caused by S . Enteritidis compared to S . Typhimurium occurred in 2008 , the change appeared to occur gradually over time from July 2006 to June 2010 ( Fig 1 ) . Phylogenomic analysis ( Fig 2 ) was used to determine where in this time period the breakpoint occurred , at which the novel clade was established as the predominant S . Enteritidis genotype in the region . The branches of the tree were manually rotated at each node to determine the potential breakpoint year to minimize the number of misclassifications ( isolates collected from one side of the temporal breakpoint appear on a branch with isolates collected from the other side of the breakpoint ) across the entire tree . A breakpoint of 2008 minimized the difference in misclassifications between the pre and post groups across several major branches of the phylogenetic tree ( S2 Table ) , and therefore was accepted as the year at which the novel S . Enteritidis genotype was established as the dominant serovar . Among the 42 whole genome sequences acquired from isolates obtained from the HGT from July 2002 through July 2012 , as examined by Feasey et al . [15] , LS-BSR analysis identified a total of 5 , 883 putative genes , including 1 , 220 putative genes that were not completely conserved in all samples ( S1 Table ) . After BLAST score ratios ( BSRs ) were calculated for each of these putative genes , 12 genes were found to be significantly more prevalent in the pre-2008 sequences and 15 significantly more prevalent in the post-2008 sequences ( p<0 . 01 by chi-square test ) ( Table 2 ) . After further manual BLAST verification , two putative genes associated with pre-2008 were identified to also be conserved in the post-2008 isolates , and were therefore removed from our analysis . These genes bridged across two contigs of the genetic sequences of post-2008 sequences and therefore were identified as false-negatives during the preliminary in silico analysis . A heatmap visualizing the LS-BSRs of each associated putative gene for each isolate is presented in Fig 3 . Targets for PCR screening were selected based on genes with functional annotation which could be phenotypically validated and based on the co-location of the genes of interest . Among the isolates collected before or during 2008 , S . Enteritidis genes encoding oxidoreductases , a multidrug transcription regulator ( marR ) and efflux proteins , and putative transport and membrane export proteins were identified ( Table 2 ) . Most of the genes associated with the post-2008 time period were not identified among previously sequenced S . Enteritidis available in the NCBI database . However , homologs of insertional transposases , arsR family arsenic-dependent transcriptional regulators , sodium-dependent glutamate permeases , and tetracycline resistance/efflux proteins ( tetA ) were identified ( Table 1 ) . These homologs shared greater than 95% coverage and nucleotide identity with the putative genes of interest in S . Enteritidis isolates ( with the exception of centroid_21646_1 , for which no homolog was identified through BLAST ) . Of the 25 investigated genes , seven ( 7/25 , 30% ) have no functional homolog and are considered hypothetical proteins . Mapping of the pre- and post-2008 genes to isolates sequenced for this study suggested that many of the putative genes of interest appear to be located in close proximity , or on the same genomic contig with consistent arrangement and orientation ( Fig 4 ) . This suggests that these isolates may have acquired a genomic island or other mobile element that allowed expansion into this niche . Definitive identification of these mobile elements will require further study . Primers were designed to target each centroid of interest ( Table 2 and Fig 4 ) . The results of PCR screening the six selected genes across the 72 non-sequenced isolates exhibited a similar distribution of genes as was identified through the bioinformatic analysis ( Table 3 ) . Each of the three genes associated with the pre-2008 time period were identified in significantly more of the isolates collected during or before 2008 , compared to those collected after ( p = 0 . 0008 , 0 . 0078 , and 0 . 0047 , respectively ) , suggesting that these genes were lost in the post-2008 isolates . Similarly , each of the three genes associated with the post-2008 time period were identified in significantly more of the isolates collected during that time , compared to isolates collected in the earlier years ( p<0 . 0001 , 0 . 0008 , and <0 . 0001 , respectively ) . This represents an acquisition of these genes in the post-2008 isolates . To validate the in silico results , phenotypes for tetracycline and arsenic resistance were assessed by resistance assays ( Table 4 ) . No statistically significant difference was observed in the arsenic resistance among the isolates at any of the three concentrations tested . Tetracycline resistance was identified to be highly associated with the 2008 threshold , with 53% more isolates resistant to tetracycline after 2008 than during or prior to 2008 ( p = 0 . 0008 ) . In comparing these results , the presence of the tetracycline resistance gene screened by PCR ( post1 ) is strongly associated with phenotypic tetracycline resistance as observed by antibiogram assay ( p = 0 . 0016 ) .
S . Typhimurium was initially identified as the predominant serovar of iNTS in most , albeit not all , regions in sub-Saharan Africa [12–14] . We identified that 2008 marked a breakpoint in the relative proportions of iNTS caused by S . Enteritidis versus S . Typhimurium in Bamako , Mali , collected from 2002 to 2012 . Additionally , we identified that the newly circulating clade of S . Enteritidis exhibits greater levels of tetracycline resistance than previous strains of S . Enteritidis . Our temporal breakpoint agrees with the epidemiologic data from Tapia et al . [19] and the phylogenomic analysis by Feasey et al . [15] . Our finding that S . Enteritidis represents an emerging threat of invasive bacterial disease in Bamako , Mali is mirrored by similar findings from other parts of sub-Saharan Africa [14] . Additionally , surveillance data from 2014 through 2017 with complete serovar determination support this continued change in prevalence as there were 28 , 15 , 16 and 14 cases of invasive Salmonella disease that occurred , respectively during 2014 , 2015 , 2016 and 2017 . During this four-year period S . Enteritidis was the most commonly identified organsim ( S . Enteritidis ( N = 28 ) , S . Typhimurium ( N = 12 ) , S . Typhi [11] and S . Dublin ( N = 9 ) accounted for 63 of the isolates from the 73 cases of invasive disease ) . Importantly S . Enteritidis is associated with a significantly greater case fatality rate than S . Typhimurium in all pediatric age groups [12] . The current study markedly expands the number of genome sequences of S . Enteritidis from iNTS cases available in public databases . Furthermore , our study describes an approach to examining these genomes , combining whole genome sequencing and large-scale BLAST score ratios ( LS-BSRs ) [26] . The use of the LS-BSR method allows investigation of major genetic changes across the genome by identifying and comparing putative gene content among a collection of genome sequences . In an under researched clade of a Salmonella serovar , such a broad sweeping approach was integral to identifying phylogenomic changes over time . The underlying cause of the shift in serovar predominance resulting in the emergence of S . Enteritidis as the leading cause of iNTS disease , has yet to be fully explained , and we anticipate that the bacterial component of this observation is only part of the larger infection picture . We identified that the recently emerged circulating clade of S . Enteritidis exhibits greater levels of tetracycline resistance than previous strains of S . Enteritidis . This phenotype may provide the serovar with an evolutionary advantage over S . Typhimurium , contributing to the shift wherein S . Enteritidis became the predominant serovar causing iNTS disease in Bamako [19] . Host-mediated hemolysis caused by malaria impairs resistance against iNTS infections [35 , 36] . Resistance to doxycycline , a long-acting member of the tetracycline family of drugs that can be used for prophylaxis and treatment of malaria [37 , 38] , could theoretically convey an evolutionary advantage to S . Enteritidis by allowing it to survive during malaria prophylaxis or treatment . However , in the Mali context this is implausible because doxycycline and other tetracyclines are rarely used for chemoprophylaxis or treatment of malaria , particularly in infants and young children . However , tetracyclines and other antibiotics are sometimes inserted into potions used to treat fever by traditional healers , but the relative frequency of this practice is not easily quantified . Thus , another explanation must be sought for acquisition of tetracycline genes conveying an increased fitness . Expression of the tetA resistance gene has also been associated with increased influx and accumulation of toxic heavy metal salts in E . coli [33] , which may help explain the simultaneous acquisition of this gene with the arsR regulator in the post-2008 isolates . The arsenic resistance assay performed in this study was selected as a screening method to verify the in silico findings , but the inconclusive results do not negate the bioinformatic analysis which clearly suggested that the acquisition of an arsR regulator gene is associated with the 2008 shift in prevalence of S . Enteritidis over S . Typhimurium . It is possible that our measure of arsenic resistance does not adequately assess the potential phenotypic and global transcriptomic changes generated by the arsR gene product . It is also possible that the arsR homolog gene product regulates other phenotypes that have not been examined in this study . Future studies should investigate alternative metabolism assays , arsenic utilization assays , and genetic knockouts to more thoroughly investigate the possible evolutionary advantages granted by the acquisition of this gene by S . Enteritidis . By identifying putative genes at a 90% nucleotide identity level , major genetic changes were identified that appear to be novel to the S . Enteritidis serovar . Future studies should further investigate the difference identified to determine the precise nature of the genetic acquisitions and search for additional significant changes at the single nucleotide level . Additional research involving genetic knock outs and complementation will be necessary to fully assess the impact of each potential gene on the phenotype of the organism and to formally identify which phenotypic and genotypic features should be examined through GWAS . Additionally , the shift in NTS serovars was identified to be associated with a specific year , but it is possible that the evolution of infectious disease may occur as a more gradual phenomenon that cannot truly be dichotomized to a single year . As such , our 2008 cut point to identify the novel phylogenomic clade could be subject to misclassification bias . We have attempted to minimize this misclassification by minimizing the amount of differential misclassification between pre- and post-2008 groups , thus biasing our results towards the null . The concurrent acquisition of the putative novel genes , and the increase in relative importance of the serovar in causing invasive bacterial disease suggests that the genetic changes may provide an evolutionary advantage to S . Enteritidis over S . Typhimurium . This appears to have resulted in S . Enteritidis losing minor enzymes and a putative multidrug transporter in order to gain resistance to tetracycline family antibiotics and an arsenic responsive transcriptional regulator . These data provide a potentially important clue that can be pursued through future research to characterize the competitive advantages and to confirm that no other change in S . Typhimurium is responsible for the decline of that serovar relative to S . Enteritidis . These findings will help illuminate the public health effects of these evolutionary changes to the serovar and shed more light on the evolutionary pressures on S . Enteritidis . Further analyses of the functional effects of this documented change in antibiotic resistance or other phenotypes in the natural environment can help generate novel hypotheses of natural reservoirs of NTS and help elucidate how the S . Enteritidis serovar competes with S . Typhimurium within the human host or in an environmental reservoir or vehicle of transmission . | Much remains unknown about the mode of transmission of iNTS or the reservoirs of infection . As such , insight into potential selective pressures on underlying serovars bears importance to public health . Longitudinal studies over the years 2002–2012 identified a shift in the proportion of invasive non-typhoidal Salmonella ( iNTS ) disease caused by S . Typhimurium or S . Enteritidis in Bamako , Mali . Since S . Enteritidis exhibits a higher rate of fatal cases among the pediatric population than S . Typhimurium , it is important to understand what led to the increased proportion of cases from this serovar . This study examined the genetic changes in S . Enteritidis associated with this serovar shift through comparative genomics and laboratory findings . Through these methods , genes related to tetracycline resistance and arsenic catabolism were associated with the shift in serovars . These findings represent preliminary steps in investigating this underlying shift and determining the long-term repercussions of these changes to the epidemiologic profile of iNTS disease . | [
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"... | 2019 | Genetic changes associated with the temporal shift in invasive non-typhoidal Salmonella serovars in Bamako Mali |
In animal development , secreted signaling molecules evoke all-or-none threshold responses of target gene transcription to specify cell fates . In the chordate Ciona intestinalis , the neural markers Otx and Nodal are induced at early embryonic stages by Fgf9/16/20 signaling . Here we show that three additional signaling molecules act negatively to generate a sharp expression boundary for neural genes . EphrinA signaling antagonizes FGF signaling by inhibiting ERK phosphorylation more strongly in epidermal cells than in neural cells , which accentuates differences in the strength of ERK activation . However , even weakly activated ERK activates Otx and Nodal transcription occasionally , probably because of the inherently stochastic nature of signal transduction processes and binding of transcription factors to target sequences . This occasional and undesirable activation of neural genes by weak residual ERK activity is directly repressed by Smad transcription factors activated by Admp and Gdf1/3-like signaling , further sharpening the differential responses of cells to FGF signaling . Thus , these signaling pathways coordinate to evoke a threshold response that delineates a sharp expression boundary .
In animal development , secreted signaling molecules often elicit the production of multiple cellular identities by controlling the activity of transcription factors . Molecular gradients can produce differential responses in identical cells [1] , [2] . For example , in Drosophila syncytium embryos , a concentration gradient of the transcription factor Bicoid specifies the anterior-posterior axis [3] , [4] . In the vertebrate neural tube , a gradient of the secreted signaling molecule Sonic Hedgehog is responsible for defining five distinct neural progenitor domains [5]–[7] . Translation of a graded distribution of molecules into sharp gene expression boundaries is central to many developmental processes , but apart from a few cases , the molecular mechanisms underlying this process are not yet fully understood . Especially , even a weak signal can potentially activate transcription of target genes due to the inherently stochastic nature of signal transduction processes and binding of transcription factors to target sequences [8] . How is such weak undesirable activation blocked in animal embryos ? Cells in the animal hemisphere of ascidian embryos ( Ciona intestinalis ) give rise to both epidermal and neural cells ( Figure 1 ) . At the 32-cell stage , an earliest neural marker gene , Otx , begins to be expressed in a pair of anterior animal cells ( a6 . 5 ) and a pair of posterior animal cells ( b6 . 5 ) , and Nodal expression also begins in b6 . 5 ( Figure S1A and S1B ) [9]–[11] . Some embryos also express Otx in a6 . 7 [12] , indicating that Otx expression in a6 . 7 is not tightly regulated . In the present study , we disregarded this cryptic expression unless otherwise noted . The remaining animal cells are all restricted to epidermal fate . In addition , Otx and Nodal are expressed in vegetal cells ( Figure S1A and S1B ) . Otx is required for subsequent expression of neural genes [13] , and ectopic Nodal expression in non-neural ectodermal cells results in embryonic patterning defects [14] . At the 16-cell stage , all ectodermal cells express the same set of regulatory genes , except for FoxA-a , which is expressed in anterior but not posterior cells [10] ( Figure S1C ) . Even though FoxA-a activates the anterior fate , some other instructive mechanism likely functions to induce neural fate . However , no asymmetric localization of maternal mRNA has been detected in the animal hemisphere in spite of extensive efforts to identify such a molecule . In addition , a cell dissociation experiment indicated that cell-cell interactions are required for specification of the neural fate [9] . Therefore , it is likely that neural fate is specified primarily by cell-cell interactions . It is possible that maternally provided signaling molecules and mRNAs encoding signaling molecules play a role in the specification of neural fate , even if they are distributed evenly within the embryo . However , it is more likely that signaling molecules expressed from the zygotic genome of specific cells play a more important role . Our previous study [10] showed that only five signaling ligand genes are zygotically expressed at the 16-cell stage , one stage earlier than the 32-cell stage when Otx and Nodal expression begins ( Figure S1D–H ) . Fgf9/16/20 is expressed in all of the vegetal cells except for the most posterior ones [15] , [16] , EphrinA-d is expressed in the entire animal hemisphere , Wnt-NAe ( a Wnt ligand gene whose phylogenetic position is unclear ) and Admp are expressed in posterior vegetal cells ( B5 . 1 ) , and Gdf1/3-like ( or orphan Tgfβ-1 ) , is expressed in the entire animal hemisphere . Among the ectodermal cells of the 32-cell embryo , cells with neural fate have a larger area of surface contact with FGF-expressing vegetal cells and are accordingly expected to be exposed to stronger FGF signaling [12] . This results in activation of maternal GATA and Ets transcription factors , which in turn directly activate Otx expression [16] . Nodal is similarly activated [17] , but only in b6 . 5 . However , because non-neural ectodermal cells also contact vegetal cells expressing Fgf9/16/20 , it is very likely that these cells are exposed to weak FGF signaling . Due to the inherently stochastic nature [8] , even weak FGF signaling might activate Otx and Nodal enhancers . In the present study , we show that weak FGF signaling indeed activates Otx and Nodal expression , and that EphrinA signaling amplifies the difference in ERK phosphorylation levels induced by differing strength of FGF signaling . Moreover , the occasional activation of Otx and Nodal by residual weak ERK activity is repressed by Admp/Gdf1/3-like signaling . Thus , FGF , Ephrin , and Admp/Gdf1/3-like signaling cooperate to evoke a threshold response to establish neural fate .
Our previous comprehensive screen [10] showed that FoxA-a is the only regulatory gene that are expressed differently between the a- and b-line cells . FoxA-a directly activates ectodermal genes in anterior cells at later stages [18] and represses Nodal at the early gastrula stage [13] . Therefore , we first examined whether FoxA-a similarly represses Nodal at the 32-cell stage . In embryos injected with an antisense morpholino oligonucleotide ( MO ) for FoxA-a , Nodal expression was indeed expressed ectopically in a6 . 5 at the 32-cell stage ( Figure 2 ) , indicating that FoxA-a normally suppresses Nodal expression in anterior cells . As we described in the Introduction section , neural fate is probably specified primarily by cell-cell interactions . To understand the mechanisms that activate Otx and Nodal specifically in the neural lineage , we examined the functions of the five signaling ligand genes that are expressed at the 16-cell stage . We first confirmed the effect of FGF signaling on neural marker expression . As previously shown [9] , [16] , [17] , [19] , responsiveness to FGF signaling , as indicated by activated ERK ( dpERK ) , was observed in a6 . 5 and b6 . 5 in normal 32-cell embryos ( Figure 3A ) , and expression of Otx and Nodal was absent from the animal hemisphere in Fgf9/16/20 morphants ( Figure 3B–D; Tables S1 and S2 ) . On the other hand , overexpression of Fgf9/16/20 by synthetic RNA microinjection into fertilized eggs and one posterior animal cell of 8-cell embryos resulted in ectopic expression of Otx ( Figure S2A and S2B ) [16] . Thus , FGF signaling activates Otx and Nodal expression via ERK activation . As previously shown in later stage embryos [20]–[22] and in vertebrates [23] , EphrinA-d attenuated ERK phosphorylation in 32-cell embryos , as indicated by the fact that dpERK immunostaining was observed ectopically in all of the animal blastomeres of EphrinA-d morphants ( Figure 3E ) . Otx was expressed ectopically in animal cells in EphrinA-d morphants , and Nodal was expressed ectopically in posterior animal cells in these morphants ( Figure 3F and 3G; Tables S1 and S2 ) . Conversely , overexpression of EphrinA-d resulted in complete loss of Otx expression ( Figure 3H ) . Thus , all of the animal cells indeed receive FGF signaling , and EphrinA-d appears to modulate FGF signaling by inhibiting ERK phosphorylation , generating clear differences in the strength of ERK activation . In a previous study [12] , “3D-virtual embryos” were reconstructed and the surface contacts of cells with their surrounding cells were calculated . This work showed that a6 . 5 and b6 . 5 have the greatest surface contacts with Fgf9/16/20-expressing cells and suggested that differences in the contact area of competent cells are important for Otx expression in neural cells [12] . Our calculation using this tool indicated that a6 . 5 and b6 . 5 have the least surface contact with EphrinA-d-expressing cells ( Figure S3 ) . Therefore , a6 . 5 and b6 . 5 are likely subject to the lowest levels of inhibitory signals repressing ERK activation , if cell contact areas represent the degree of EphrinA-d signaling as they do in the case of FGF9/16/20 signaling . Thus , inductive FGF signaling and inhibitory EphrinA signaling likely accentuate differences in the strength of ERK activation in animal cells . In Wnt-NAe morphants , Otx and Nodal were expressed in both of the b5 . 3 daughter cells ( b6 . 5 and b6 . 6 ) ( Figure 3I and 3J ) , whereas overexpression of Wnt-NAe did not affect Otx expression ( Figure 3K ) . This ectopic expression was likely due to the abnormal position of the b6 . 5 and b6 . 6 sister cells . In normal embryos , the b6 . 5 and b6 . 6 cells were both found in the periphery of the animal hemisphere ( Figure 3L ) , while in the morphants one of them was located at a more interior position ( Figure 3M ) . The boundary between these sister cells is significantly more oblique in the morphants . Because the positions of the rest of the blastomeres of the morphant embryos did not appear to be altered , we could identify these two cells as the daughter cells of b5 . 3 . The mispositioning of the daughter cells of b5 . 3 likely changed the balance between FGF and EphrinA signaling , because dpERK signal was detected in both of the daughter cells of b5 . 3 in Wnt-NAe morphants ( Figure 3N ) . Thus , this Wnt signaling was not directly involved in transcriptional regulation of Otx and Nodal . In Admp or Gdf1/3-like morphants , the expression of Otx and Nodal was normal ( Figure S4; Tables S1 and S2 ) . Since these two molecules are both members of the TGFβ superfamily and might therefore work together , we knocked down these two genes simultaneously . In Admp and Gdf1/3-like double morphants ( Admp/Gdf morphants hereafter ) , Otx and Nodal were ectopically expressed ( Figure 4A and 4B; Tables S1 and S2 ) , suggesting redundancy between Admp and Gdf1/3-like . Admp signaling is transmitted through the BMP pathway , while GDF1 and GDF3 act through the Activin pathway [24] . A pharmacological inhibitor of BMP signaling , dorsomorphin , resulted in ectopic expression of Otx and Nodal ( Figure 4C and 4D ) , but an inhibitor of Activin signaling , SB431542 , did not ( n = 70 , 99% for Otx; n = 77 , 99% for Nodal ) . Knockdown of Smad1/5 , which encodes an effector transcription factor of the BMP pathway , resulted in ectopic expression of Otx and Nodal ( Figure 4E and 4F ) . Knockdown of Smad2/3b , which encodes an effector of the Activin pathway , also resulted in ectopic expression of Otx and Nodal ( Figure 4G and 4H ) , although the effect was weaker . These data indicate that the BMP and Activin pathways suppress Otx and Nodal expression , although the BMP signaling may contribute to this suppression more than Activin signaling . The ectopic expression of Otx and Nodal in Admp/Gdf morphants was not due to elevated FGF/ERK signaling , as indicated by the facts that expression of Fgf9/16/20 and EphrinA-d was unaffected at the 16-cell stage ( Figure 5A and 5B ) , and that no ectopic ERK activation was observed in Admp/Gdf morphants at the 32-cell stage ( Figure 5C ) . Nevertheless , FGF signaling was required for the ectopic expression of Otx and Nodal in Admp/Gdf morphants , because Otx and Nodal were not expressed in triple Fgf9/16/20/Admp/Gdf morphants ( Figure 6A and 6B; Tables S1 and S2 ) , Admp/Gdf morphants treated with an MEK inhibitor U0126 ( Figure 6C and 6D ) , or triple Ets1/2/Admp/Gdf morphants ( Figure 6E and 6F ) . These data suggest that even weak ERK activation that cannot be detected experimentally activates Otx and Nodal expression in non-neural ectodermal cells , if Admp/Gdf signaling is absent . However , this suppressing activity of Admp/Gdf signaling is limited and the distributions of these signaling molecules are probably unimportant , because overexpression of Admp and/or Gdf1/3-like rarely suppresses the endogenous expression of Otx ( Figure S5 ) . As shown in Table S1 , the ectopic expression of Otx was observed more frequently in a6 . 6 than in a6 . 8 , and ectopic expression in a6 . 6 was observed in all embryos that ectopically expressed Otx in a6 . 8 . In addition , expression in a6 . 7 was also observed in all embryos that ectopically expressed Otx in a6 . 6 and a6 . 8 . Similarly , ectopic expression of Otx and Nodal in b6 . 7 was observed in all embryos that expressed these genes in b6 . 8 ( Tables S1 and S2 ) . The expression in b6 . 6 was observed in all embryos that expressed them in b6 . 7 and b6 . 8 . These hierarchies within the a- and b-lines ( a6 . 5<a6 . 7<a6 . 6<a6 . 8 , b6 . 5<b6 . 6<b6 . 7<b6 . 8 ) closely accord with the order of the estimated strength of the EphrinA-d activity ( a6 . 5<a6 . 7<a6 . 6<a6 . 8 , b6 . 5<b6 . 7<b6 . 6<b6 . 8; Figure S3 ) . The only exception is b6 . 6 and b6 . 7 , and notably the contact area with FGF-expressing vegetal cells is estimated to be larger in b6 . 6 than in b6 . 7 [12] . Therefore , the above observation supports the estimation of EphrinA-d signaling strength by the 3D-virtual embryos . Our data suggested that Admp/Gdf morphants are more sensitive to FGF signaling than normal embryos . Indeed , we found that Fgf9/16/20/Admp/Gdf morphants responded more sensitively to human bFGF added to the sea water than Fgf9/16/20 morphants; namely , Fgf9/16/20/Admp/Gdf morphants expressed Otx more frequently with increasing concentrations of bFGF ( Figure 7A ) . On the other hand , there was no significant difference in the proportion of cells stained with the dpERK antibody ( Figure 7B ) . At an intermediate concentration ( 5 ng/mL ) , 76% of the animal cells in Fgf9/16/20/Admp/Gdf morphants and 37% in Fgf9/16/20 morphants expressed Otx ( Figure 7A ) , while dpERK signal was detected in 31% and 38% of cells in these morphants ( Figure 7B ) . Thus , weak FGF signaling that is experimentally undetectable by dpERK immunostaining can activate Otx expression , and this weak signaling is inhibited by Admp/Gdf signaling . At the same time , the dose-dependent response of Otx activation indicates that differential FGF/ERK signaling strength alone cannot explain the threshold response . Previous studies [16] , [25] showed that an upstream region ( a-element ) of Otx is responsible for Otx expression in a6 . 5 blastomeres at the 32-cell stage . GATA and Ets transcription factors activated by the ERK signaling pathway bind to the a-element [16] ( Figure S6A ) . Thus , we used a previously characterized reporter construct , in which the a-element and the minimal promoter region of the Brachyury gene were fused to the LacZ coding sequence [16] ( Otx[a]>LacZ ) . This reporter construct was electroporated into fertilized eggs , and expression of LacZ mRNA was examined at the 32-cell stage . In addition to strong signal in a6 . 5 and b6 . 5 [16] , we found weak signals in non-neural ectodermal cells in 10% of the experimental embryos ( Figure 8A and 8D ) . By examining the genomic sequence around the a-element of Otx , we identified two putative Smad-binding elements and one binding element for Smad4 , a co-factor of regulatory Smad proteins [26] , [27] , within the 100-bp upstream region of the a-element ( Figure S6A ) . When the region containing these Smad-binding elements ( collectively called SBEs ) was placed upstream of the a-element ( Otx[SBE-a]>LacZ ) , LacZ was expressed specifically in the neural lineage , although the number of embryos expressing the reporter was reduced ( Figure 8B and 8D ) . Treatment with dorsomorphin again induced ectopic expression of LacZ and enhanced overall expression , indicating that the SBEs work downstream of BMP signaling to weaken the activity of the enhancer ( Figure 8C and 8D ) . A Nodal cis-regulatory element responsible for expression in the neural lineage of cells ( Nodal-a-element ) was also identified previously [17] ( Figure S6B ) . The Nodal-a-element induced the reporter gene expression in the anterior and posterior animal cells ( Nodal[a]>LacZ ) , probably because it lacks FoxA-a binding sites . As in the case of Otx , this Nodal-a-element also induced non-neural expression ( Figure S7A ) . We found one regulatory Smad binding site and one Smad4 binding site downstream of this enhancer . These SBEs suppressed LacZ reporter expression , when connected to the Nodal-a-element , and this suppression was abolished by dorsomorphin treatment ( Figure S7B–D ) . Thus , Admp/Gdf signaling directly suppresses the activity of Otx and Nodal enhancers to evoke a robust threshold reaction .
Previous studies showed that differential FGF signaling from vegetal cells to animal cells plays a primary role in specific expression of Otx and Nodal [9] , [12] , [16] , [17] , [19] . However , it was unclear why non-neural ectodermal cells , which still receive FGF signals but at lower levels , fail to activate Otx and Nodal at all . Here , we showed that EphrinA-d , which antagonizes FGF signaling [20]–[22] , amplifies the difference in ERK activity between ectodermal cells , as shown by dpERK immunostaining . Even below the detection limit , weak ERK activation occasionally activates Otx and Nodal expression , probably due to the inherently stochastic nature of signaling pathways and transcriptional activation [8] . The activity of Otx and Nodal transcriptional enhancers is weakened by Admp/Gdf signaling through the SBEs within the enhancers . The silencing activity of the SBEs is relatively weak and never overcomes fully activated enhancing activity . Thus , cooperation of multiple signaling pathways evokes a robust threshold reaction . However , this cooperation cannot perfectly evoke a threshold response , because some embryos express Otx in a6 . 7 ( 6% in a previous assay [12] and 35% in our assay ) . As previously shown [12] , FGF signaling is expected to be stronger in a6 . 7 than in a6 . 6 and a6 . 8 . EphrinA-d signaling is expected to be stronger in a6 . 7 than in a6 . 5 , and weaker in a6 . 7 than in a6 . 6 and a6 . 8 , if cell contact areas with EphrinA-d-expressing cells simply represent the degree of EphrinA-d signaling . It is very likely that the sum of the positive and negative signaling activities in a6 . 7 is near the threshold , and consequently a6 . 7 occasionally activates Otx . Admp is expressed in the posterior vegetal cells , and Gdf1/3-like is expressed in all of the cells in the animal hemisphere . Although these two factors are probably two major factors activating the BMP and Activin pathways , several members of the TGFβ-superfamily , including BMP2/4 and BMP3 are also expressed maternally [10] . In addition , TGFβ-superfamily molecules must be processed to become functional . Therefore , it is difficult to measure how these signaling molecules are distributed . However , because Admp and Gdf1/3-like cannot repress endogenous Otx and Nodal expression when overexpressed , the distributions of these two signaling molecules are probably unimportant for controlling Otx and Nodal expression . Intriguingly , we found that knockdown of either of Smad1/5 or Smad2/3b causes ectopic expression of Otx and Nodal , while knockdown of either Admp or Gdf1/3-like does not produce an obvious phenotype . There are several possible explanations for this observation . Admp and Gdf1/3-like might not be fully knocked-down by the MOs we used , or other maternally expressed TGFβ-superfamily members might function redundantly . Additionally , there might be crosstalk between the BMP-signaling and Activin signaling pathways [28] . Nonetheless , the role of BMP/Activin-signaling we demonstrated in the regulation of Otx and Nodal expression is clear . Transcriptional repressors play an important role in delineating sharp boundaries of gene expression [29] . For example , in Drosophila embryos , repressors that antagonize Bicoid activity are responsible for converting gradients into threshold responses [4] . Although reverse gradients can make a steep gradient , transcriptional repressors are often also required to repress residual activities , as in the case of neural cells of the Ciona embryo . A similar mechanism might function in a variety of developmental processes in which multiple signaling pathways are involved . Similar to neural fate specification in the Ciona embryo , neural induction in Xenopus embryos involves BMP and FGF signaling . According to the most widely accepted “default model” , BMP inhibition is both necessary and sufficient for neural induction of vertebrate embryos [30] , while FGF has an instructive role [31]–[34] . In addition , FGF signaling inhibits BMP signaling by phosphorylating Smad1 , leading to the degradation of Smad1 [35] , [36] . Inhibition of BMP signaling also induces FGF expression [33] . These mechanisms do not seem to be the principal mechanism of neural induction in Ciona embryos . However , it would be interesting to investigate whether the mechanism we describe here in Ciona embryos also functions in the vertebrate organizers . Although it is not involved in evoking a threshold reaction , Wnt signaling is required for the proper spatial expression of Otx and Nodal . Our finding that Admp , Gdf1/3-like , and Wnt signaling regulate Otx and Nodal expression in the neural lineage is based on an unbiased and systematic analysis of signaling molecule genes expressed at the 16-cell stage in Ciona embryos . Because Admp/Gdf signaling and Wnt signaling do not play an instructive role in Otx and Nodal expression in the neural lineage , their involvement might have been difficult to uncover apart from such a comprehensive and unbiased approach .
C . intestinalis adults were obtained from the National Bio-Resource Project for Ciona . cDNA clones were obtained from our EST clone collection [37] . Inhibitors of BMP signaling ( dorsomorphin; Wako ) , Activin signaling ( SB431542 , Sigma ) , and MEK signaling ( U0126 , Promega ) were used at concentrations of 100 µM , 5 µM , and 10 µM , respectively . To examine responses to FGF , we used human recombinant bFGF ( Sigma ) . SB431542 and U0126 were shown to work properly in the Ciona embryo in previous studies [11] , [19] . As shown in Figure S8A , dorsomorphin treatment inhibits phosphorylation of Smad1/5; Western blotting with polyclonal antibodies against phosphorylated Smad5 ( Abcam , ab92698 ) showed that treatment with human BMP4 ( 100 ng/mL; humanzyme ) evoked hyper-phosphorylation of Smad1/5 in the 32-cell embryo and dorsomorphin ( 50 µM ) inhibited this phosphorylation . After stripping the membrane , we performed Western blotting with antibodies for β-tubulin for a loading control ( Sigma , T5293 ) . The morpholino oligonucleotides ( MOs ) ( Gene Tools , LLC ) for FoxA-a , Admp , Gdf1/3-like , Fgf9/16/20 , Wnt-NAe , EphrinA-d and Ets1/2 were the same ones that we used in a previous study [13] . We designed an additional MO for Wnt-NAe ( 5′-TGTAAATGAAGACAACAGTTTAGAG-3′ ) , which produced the same phenotype ( ectopic Otx expression ) as the original one , so only the results obtained with the second MO are shown . We also designed MOs for Smad1/5 ( 5′-AACAACTTCTCCACACAACAACCTG-3′ ) and Smad2/3b ( 5′-CATATTTACTCTCAATGTTCGATGT-3′ ) in the present study . All of these MOs were designed for blocking translation . The specificity of the Smad1/5 MO was confirmed by Western blotting . As described above , in embryos treated with human BMP4 , phosphorylated Smad1/5 was detectable . When embryos injected with the Smad1/5 MO were treated with human BMP4 , phosphorylated Smad1/5 was rarely detected ( Figure S8B ) . The specificity of the Smad2/3b MO was confirmed by a rescue experiment: when we injected the Smad2/3b MO with a synthetic Smad2/3b mRNA that the MO cannot bind , ectopic expression of Otx , which is a phenotype of Smad2/3b morphants , was not observed ( Figure S8C ) . Synthetic overexpression transcripts were prepared from cDNA cloned into pBluescript RN3 vector [38] by in vitro transcription using a commercially available kit ( mMESSAGE mMACHINE T3 , Ambion ) , and injected into fertilized eggs at a concentration of 1 mg/mL . DIG-RNA probes for whole-mount in situ hybridization were synthesized by in vitro transcription with T7 RNA polymerase . The detailed procedure has been described previously [10] . To detect activation of the receptor-tyrosine kinase cascade , embryos were fixed with 3 . 7% formaldehyde and were treated with 3% H2O2 for 30 minutes to quench endogenous peroxidase activity , and then incubated overnight with mouse anti-dpERK ( 1∶1000 , Sigma , M9692 ) in Can-Get-Signal-Immunostain Solution B ( TOYOBO ) . The signal was visualized with a TSA Kit ( Invitrogen ) using HRP-conjugated goat anti-mouse IgG and Alexa Fluor 488 tyramide . To visualize cell morphology , F-actin was stained with Alexa Fluor 555–conjugated Phalloidin ( Invitrogen ) . The contact surfaces of individual animal blastomeres of the 32-cell embryo with cells expressing EphrinA-d were calculated using the 3D-virtual embryo [12] . Given the delay between gene expression and protein translation , we assumed that cells descended from EphrinA-d-expressing cells at the 16-cell stage express EphrinA-d protein at the 32-cell stage . Because EphrinA-d is GPI-anchored , we ruled out autoregulatory effects . The contact surfaces of individual animal blastomeres of the 32-cell embryo with anterior vegetal cells expressing Fgf9/16/20 were previously calculated [12] . However , because Fgf9/16/20 is also expressed in posterior vegetal cells , we included the posterior vegetal cells in our present calculations using the 3D-virtual embryo [12] . DNA constructs for examining regulatory elements were introduced by electroporation [39] . Cis-regulatory elements of Otx and Nodal were fused to the Brachyury and Fog basal promoters [17] , [40] , [41] , respectively . LacZ was used as a reporter gene . The expression of LacZ was examined by in situ hybridization . | Graded signals often provide positional information to organize gene expression in animal embryos . In the simplest cases , graded signals are translated into all-or-none threshold responses . However , recent studies have shown that signal transduction processes and binding of transcription factors to target sequences are inherently stochastic . This means that even weak activating signaling might activate target genes stochastically . However , the precise mechanism , by which this stochastic undesirable activation is avoided , is still largely unknown . In the embryo of a simple chordate , Ciona intestinalis , FGF signaling is known to induce neural fate . In the present study , we demonstrate that three additional signaling molecules cooperate to evoke a threshold response for specification of neural fate . First , EphrinA signaling inhibits FGF signaling by attenuating ERK phosphorylation , accentuating differences in the strength of ERK activation . However , even weak ERK activity occasionally turns on the neural genes . This occasional undesirable activation of the neural genes is turned off by Admp and Gdf1/3 signaling through Smad transcription factors . Thus , these two qualitatively different negative regulatory mechanisms evoke an all-or-none threshold response to specify neural fate . | [
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] | [] | 2013 | Multiple Signaling Pathways Coordinate to Induce a Threshold Response in a Chordate Embryo |
Recent studies demonstrate that rabies post-exposure prophylaxis ( RPEP ) in international travelers is suboptimal , with only 5–20% of travelers receiving rabies immune globulin ( RIG ) in the country of exposure when indicated . We hypothesized that travelers may not be receiving RIG appropriately , and practices may vary between countries . We aim to describe the characteristics of travelers who received RIG and/or RPEP during travel . We conducted a multi-center review of international travelers exposed to potentially rabid animals , collecting information on RPEP administration . Travelers who started RPEP before ( Group A ) and at ( Group B ) presentation to a GeoSentinel clinic during September 2014–July 2017 were included . We included 920 travelers who started RPEP . About two-thirds of Group A travelers with an indication for rabies immunoglobulin ( RIG ) did not receive it . Travelers exposed in Indonesia were less likely to receive RIG in the country of exposure ( relative risk: 0 . 30; 95% confidence interval: 0 . 12–0 . 73; P = 0 . 01 ) . Travelers exposed in Thailand [Relative risk ( RR ) 1 . 38 , 95% Confidence Interval ( 95% CI ) : 1 . 0–1 . 8; P = 0 . 02] , Sri Lanka ( RR 3 . 99 , 95% CI: 3 . 99–11 . 9; P = 0 . 013 ) , and the Philippines ( RR 19 . 95 , 95% CI: 2 . 5–157 . 2; P = 0 . 01 ) , were more likely to receive RIG in the country of exposure . This analysis highlights gaps in early delivery of RIG to travelers and identifies specific countries where travelers may be more or less likely to receive RIG . More detailed country-level information helps inform risk education of international travelers regarding appropriate rabies prevention .
International travelers may be exposed to rabid animals while traveling abroad . The estimated incidence of potential rabies exposures requiring post-exposure prophylaxis ( RPEP ) among international travelers is 0 . 4 per 1 , 000 per month of stay [1] . The proportion of international travelers requiring RPEP at GeoSentinel clinics among other patients increased from <0 . 5% of visits in 2003 to >2% in 2012 . The increase may be due to greater diversity of travel destinations and number of international travelers [2] . Limited data are available on the proportion of international travelers that receive pre-travel rabies vaccination , but vaccine provision is usually guided by individual risk assessment and cost [1] . At the time of writing , World Health Organization ( WHO ) guidelines recommended that any traveler who had not received a three-dose pre-exposure prophylaxis ( PrEP ) and sustained a category III exposure—transdermal bite ( s ) or scratch ( es ) , licks to broken skin , mucus membrane contamination , or contact with a bat—required rabies immunoglobulin ( RIG ) administration in addition to rabies vaccine [3] . Pre-exposure immunization obviates the need for RIG after exposure [3] . Recent studies demonstrate that only 5–20% of travelers received RIG in the country of exposure when indicated [4–12] ( Table 1 ) . The objective of this analysis was to conduct a multi-center review of international travelers exposed to potentially rabid animals . We describe the characteristics of travelers who received RIG and/or RPEP during travel in the region of exposure , to determine whether receipt of RIG varied by country or region .
GeoSentinel is a global clinician-based sentinel surveillance system that monitors travel-related illness and other conditions among international travelers . It was established in 1995 as a collaboration between the Centers for Disease Control and Prevention ( CDC ) and the International Society of Travel Medicine . GeoSentinel currently consists of 70 specialized travel and tropical medicine clinical sites in 31 countries [13] . GeoSentinel’s data collection protocol has been reviewed by the CDC’s National Center for Emerging and Zoonotic Infectious Diseases and is classified as public health surveillance and not human subjects research . Additional ethics clearance was obtained by participating sites , as required by their respective institutions . We analyzed records submitted to GeoSentinel of international travelers with exposure to a potentially rabid animal . Demographics and travel history , place of exposure , and animal involved in the exposure were recorded . Travelers who started RPEP before ( Group A ) and at ( Group B ) presentation to a GeoSentinel clinic during September 2014–July 2017 were included . Among travelers in the former group , data were collected and assessed for type of exposure [3] , rabies PrEP , occurrences where RPEP with or without RIG should have been administered , and differences between international travelers who received RIG in the country of exposure and those who did not ( relative risk ) . We excluded records if they were not travel-related or the exposure country was unascertainable . Data were managed using Microsoft Access ( Redmond , Washington , USA ) . Demographic analyses were descriptive; for the binary outcome variable of receiving RIG in the country of exposure , relative risks ( RR ) and 95% confidence intervals ( 95% CI ) were determined . Statistical significance was defined as P<0 . 05 . All analyses were performed using SAS version 9 . 4 ( Cary , NC , USA ) .
We examined 958 records; 38 were excluded ( Fig 1 ) . The analysis included 920 international travelers; 517 ( 56 . 2% ) started RPEP before presenting to a GeoSentinel clinic ( Group A ) , and 403 ( 43 . 8% ) started RPEP at a GeoSentinel clinic ( Group B ) . Travelers were assessed at 33 GeoSentinel clinics in 21 countries . Median age was 30 years ( range 0–87 ) , 52 . 3% were female , and 39 . 8% did not seek pre-travel advice ( Table 2 ) . The most frequent purpose of travel was tourism ( 731 travelers; 79 . 5% ) , followed by travelers visiting friends and relatives ( 103 travelers; 11 . 2% ) . Over 98% of travelers were seen as outpatients . Most travelers were exposed in Asia ( 697 travelers; 75 . 9% ) ; 550 travelers ( 59 . 8% ) were exposed in Southeast Asia; 115 travelers ( 12 . 5% ) were exposed in South Central Asia; and 32 ( 3 . 5% ) were exposed in North East Asia . Thailand , Indonesia , and Nepal accounted for over 50% of all exposures; the most frequent location of exposure was Bali , Indonesia [64 of 325 ( 19 . 7% ) travelers with information available] . A greater proportion of travelers from Group A ( versus Group B ) , were visiting friends and relatives ( VFRs ) ( 16 . 1% versus 6 . 0% ) or were exposed in Southeast Asia ( 68 . 5% versus 48 . 6% ) . Detailed data will be made available on request . Nine ( 3% ) travelers in Group A had received a complete 3-dose PrEP regimen; five received two-dose and five single-dose PrEP regimens . Most sought health care on the day of animal exposure ( median 0 days; range 0–366 days; interquartile range: 0–1 days ) . Travelers were exposed most frequently to dogs ( 260; 50 . 3% ) , non-human primates ( NHPs ) ( 182; 35 . 2% ) , cats ( 59; 11 . 4% ) , or bats ( 4; 2 . 1% ) . Travelers to Thailand , Indonesia , and Cambodia were exposed more frequently to NHPs than to other animals , and tourists were exposed more frequently to NHPs than were VFRs ( 42 . 6% and 3 . 6% , respectively ) . Among Group A exposures , 362 ( 70 . 0% ) were classified as WHO category III , 112 ( 21 . 7% ) were category II , and 17 ( 3 . 3% ) were category I [3] . Only 133 ( 25 . 7% ) exposures occurred in rural areas , while 210 ( 40 . 6% ) were in cities [the remaining 174 ( 33 . 7% ) were unknown] . A total of 125 ( 24 . 2% ) travelers reported being exposed through an unprovoked bite , and 112 ( 21 . 7% ) reported visiting an animal reserve . In Group A , 353 ( 68 . 3% ) had an indication for RIG according to WHO criteria ( Fig 1 ) . Of these , 126 ( 35 . 7% ) received RIG; 87 ( 24 . 7% ) travelers received RIG in the country of exposure . The remaining 227 travelers ( 64 . 3% ) did not receive RIG ( Fig 1 ) . Among the 227 travelers who did not receive RIG although indicated , 144 ( 63 . 4% ) presented to a GeoSentinel clinic when RIG administration was no longer indicated ( >7 days after the first dose of vaccine ) [3] . Travelers exposed in Indonesia were less likely to receive RIG in the country of exposure ( RR: 0 . 30; 95% CI: 0 . 12–0 . 73; P = 0 . 01 ) ( Table 3 ) . Travelers exposed in Thailand ( RR 1 . 38 , 95% CI: 1 . 0–1 . 8; P = 0 . 02] , Sri Lanka ( RR 3 . 99 , 95% CI: 3 . 99–11 . 9; P = 0 . 013 ) , and the Philippines ( RR 19 . 95 , 95% CI: 2 . 5–157 . 2; P = 0 . 01 ) , were more likely to receive RIG in the country of exposure . There were no significant differences in demographic , travel , or exposure characteristics between those who received RIG and those who did not . None of the travelers included in this analysis were known to have developed rabies .
This analysis supports the findings of previous studies that reported rabies exposures among international travelers occur most frequently in Asia and that tourists sustain more rabies exposures than other types of travelers [2] . Of major concern is the finding that almost 65% of travelers in Group A with an indication for RIG did not receive it . This finding is likely multifactorial and may be due to limited availability of RIG at both the national and primary care levels of the health system , which may be due to procurement difficulties , the need to store RIG at 2–8°C [14] , and its high cost . In addition , there may be insufficient awareness of indications for the use of RIG . Information about RIG availability at travel destinations can be difficult to find , and supply may be inconsistent . One survey , although biased toward international travel medicine organizations , demonstrated that less than half of clinics surveyed in Asia had access to RIG [15] . Similarly , a survey of US Embassy medical staff who provided health advice , conducted by the same group of investigators , found that possible rabies exposures accounted for about 2% of health inquiries . About two-thirds of the respondents in the latter survey reported that RIG was available for travelers in the country where they were based . Notably in Southeast and East Asia , human RIG was often not available [16] . In our analysis , Bali , Indonesia , was the most common location to have an exposure to a potentially rabid animal , but very few travelers received RIG in Indonesia when indicated . Since more than 90% of travelers were not completely vaccinated before traveling , and over 70% of travelers sustained a category III exposure , it is imperative to identify high-risk areas where RIG may not be widely available , such as Bali , so clinicians can encourage PrEP for travelers to these destinations and educate travelers about steps to take if they sustain animal bites . Following the Strategic Advisory Group of Experts on immunization ( SAGE ) meeting in October 2017 , WHO updated its recommendation for PrEP to two doses ( days 0 and 7 ) instead of three for immunocompetent individuals [17] . The rationale for this decision is that several studies have demonstrated similar immunogenicity after one week compared to the classical 3–4-week regimens [17] . Reducing the time frame and number of doses required for PrEP would make it simpler and more cost-effective to implement in travelers as the classic 3-dose PrEP may be difficult to complete with the short average interval to departure ( <21 days ) of many travelers [18 , 19] . High vaccine cost is another reason for very low PrEP coverage in travelers [20] . The new WHO recommendations will hopefully allow increasing rabies pre-travel vaccination coverage in travelers to destinations where RIG administration is unlikely to be provided despite being indicated . Although a single dose of PrEP should confer some protection , in the event of a potential rabies exposure , WHO recommends full RPEP including RIG if indicated [17] . Given the travel and tropical medicine specialization of GeoSentinel sites , these data may not be representative of all international travelers who receive RPEP or of travelers to a particular country or region . GeoSentinel data are not generalizable , so risk estimates of a particular illness cannot be calculated . Additional information ( i . e . , urban or rural exposure , activity during exposure , type of animal exposure , and pre-travel rabies immunization receipt ) was collected only on those travelers in Group A; data on whether RIG was provided or offered at GeoSentinel clinic visits were not collected . Information collected from travelers who started RPEP outside a GeoSentinel clinic was based upon patient report and clinical description of the WHO exposure category and is subject to observer and recall bias . We did not collect data on reasons for delay in treatment . Despite these limitations , this analysis identifies specific countries where travelers may be more or less likely to receive RIG and helps inform education of international travelers regarding appropriate rabies prevention . Travelers should seek pre-travel advice before traveling abroad to ensure they receive proper education on avoiding contact with animals and what to do after an animal exposure , notably regarding indications for RIG . Since rabies exposures are often unpredictable , travelers must be reminded that exposures often come from animals they perceive as unthreatening ( e . g . , NHPs at tourist locations ) . When performing a risk assessment at a pre-travel consultation , health-care practitioners must assess if travelers’ destinations are at high risk for rabies ( and/or with unavailable RIG ) and the possibility for additional or repeated exposures when considering offering PrEP [21] . | International travelers may be exposed to rabid animals while traveling abroad . Current guidelines recommended that any traveler who was not been vaccinated against rabies before travel and sustained an animal exposure putting him at risk for rabies required rabies post-exposure prophylaxis including rabies immunoglobulin administration in addition to rabies vaccine as soon as possible . Available data indicate that only a small proportion of travelers received rabies immunoglobulin in the country of exposure when indicated . In this multi-center survey , we collected information on rabies post-exposure prophylaxis administration in international travelers exposed to potentially rabid animals . We observed that about two-thirds of travelers with an indication for rabies immunoglobulin who started their post-exposure prophylaxis during travel did not receive it . This analysis identified specific countries where travelers may be more or less likely to receive rabies immunoglobulin and helps inform risk education of international travelers regarding appropriate rabies prevention . In our analysis , Bali , Indonesia , was the most common location to have an exposure to a potentially rabid animal , but very few travelers received rabies immunoglobulin in Indonesia when indicated . By contrast , travelers exposed in Thailand , Sri Lanka and the Philippines were more likely to receive rabies immunoglobulin in the country of exposure . | [
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"infectious"... | 2018 | Rabies post-exposure prophylaxis started during or after travel: A GeoSentinel analysis |
CCDC39 and CCDC40 were first identified as causative mutations in primary ciliary dyskinesia patients; cilia from patients show disorganized microtubules , and they are missing both N-DRC and inner dynein arms proteins . In Chlamydomonas , we used immunoblots and microtubule sliding assays to show that mutants in CCDC40 ( PF7 ) and CCDC39 ( PF8 ) fail to assemble N-DRC , several inner dynein arms , tektin , and CCDC39 . Enrichment screens for suppression of pf7; pf8 cells led to the isolation of five independent extragenic suppressors defined by four different mutations in a NIMA-related kinase , CNK11 . These alleles partially rescue the flagellar length defect , but not the motility defect . The suppressor does not restore the missing N-DRC and inner dynein arm proteins . In addition , the cnk11 mutations partially suppress the short flagella phenotype of N-DRC and axonemal dynein mutants , but do not suppress the motility defects . The tpg1 mutation in TTLL9 , a tubulin polyglutamylase , partially suppresses the length phenotype in the same axonemal dynein mutants . In contrast to cnk11 , tpg1 does not suppress the short flagella phenotype of pf7 . The polyglutamylated tubulin in the proximal region that remains in the tpg1 mutant is reduced further in the pf7; tpg1 double mutant by immunofluorescence . CCDC40 , which is needed for docking multiple other axonemal complexes , is needed for tubulin polyglutamylation in the proximal end of the flagella . The CCDC39 and CCDC40 proteins are likely to be involved in recruiting another tubulin glutamylase ( s ) to the flagella . Another difference between cnk11-1 and tpg1 mutants is that cnk11-1 cells show a faster turnover rate of tubulin at the flagellar tip than in wild-type flagella and tpg1 flagella show a slower rate . The double mutant shows a turnover rate similar to tpg1 , which suggests the faster turnover rate in cnk11-1 flagella requires polyglutamylation . Thus , we hypothesize that many short flagella mutants in Chlamydomonas have increased instability of axonemal microtubules . Both CNK11 and tubulin polyglutamylation play roles in regulating the stability of axonemal microtubules .
Defects in ciliary assembly and function cause a wide range of human diseases and syndromes called ciliopathies . Primary ciliary dyskinesia ( PCD ) is diagnosed by defects in ciliary motility , and is associated with a genetically heterogeneous group of recessive disorders [1] . Mutations causing PCD have been identified in genes encoding axonemal dynein subunits [2 , 3] , dynein assembly factors [4–6] , and dynein docking/adaptor factors [7 , 8] . The nexin-dynein regulatory complex ( N-DRC ) is an axonemal structure critical for the regulation of dynein motors and for connecting doublet microtubules to each other . Loss-of-function mutations in DRC1 ( CCDC164/PF3 ) and DRC3 ( CCDC65 ) cause severe defects in assembly of the N-DRC structure and result in defective ciliary movement in humans and Chlamydomonas reinhardtii [6 , 9 , 10] . PF2 , which encodes DRC4 , was used to identify 11 proteins in the N-DRC [10] . Mutations in CCDC39 and CCDC40 cause altered ciliary beating with the disorganization of the axoneme that includes the displacement of the peripheral outer doublets , as well as central pair microtubules , radial spokes and inner dynein arm defects [11–15] . Loss-of-function mutations in CCDC39 and CCDC40 in Chlamydomonas lead to short flagella , irregularly spaced radial spokes , absence or reduction of N-DRC components and inner dynein arm proteins [16 , 17] . CCDC39 and CCDC40 mutations in children lead to earlier and more severe lung disease than in PCD patients with outer dynein arm mutations [18] . In Chlamydomonas , there are many mutations that can lead to short flagella . Partial reduction in IFT proteins ( IFT144 ( FLA15 ) and IFT139 ( FLA17 ) ) or motors such as the kinesin-2 motor FLA10 or cytoplasmic dynein result in short flagella [19–21] . Changes in the cytoplasmic pool of tubulins and flagellar precursor proteins also affect flagellar length [22 , 23] . In addition , the simultaneous loss of multiple substructures , such as the dynein arms , radial spokes , and the N-DRC , result in short flagella [24–26] . LeDizet and Piperno isolated a suppressor ( ssh1 ) that increased the flagellar length in double mutant strains that lacked outer and inner dynein arms without restoring the missing structures [26] . A recent study identified a deletion in the TPG2 gene as the causative mutation in the ssh1 strain [27] . TPG2 encodes FAP234 , a flagellar protein that forms a complex with a tubulin polyglutamylase TTLL9/TPG1 [28 , 29] . Tubulin polyglutamylation adds multiple glutamates to both α- and β-tubulin subunits along microtubules in cilia/flagella , basal bodies , and neuron axons [30–32] . Several tubulin tyrosine ligase-like ( TTLL ) proteins carry out the polyglutamylation process . Tubulin polyglutamylation can affect microtubule assembly , stability , and motility [32] . In Chlamydomonas , tpg1 affects polyglutamylation of α-tubulin specifically and shows a flagellar motility defect [29] . Both tpg1 and tpg2 mutations suppress the short flagella phenotype found in mutants that lack multiple axonemal dynein species [27] . NIMA-related protein kinases have been found in eukaryotes and their functions are related to regulation of cell cycle , cilia length , and microtubule stability [33–38] . Currently , there are 11 NIMA-related protein kinases identified in Chlamydomonas [33] and only two of them have been functionally characterized [35 , 36] . A null mutant of the NIMA-like protein kinase CNK2 in Chlamydomonas has slightly longer flagella and defective flagellar disassembly . The cnk2-1 mutant has decreased tubulin turnover at the flagellar tip , which suggests that a reduced rate of flagellar disassembly is compensated by reduced rate of assembly [36] . The CNK2 protein , together with a MAP kinase ( LF4 ) , respond to flagellar length signals and block assembly and promote disassembly , respectively [36] . Thus , they provide input to the balance between assembly and disassembly of axonemal microtubules and flagellar length . In this study , we identify a novel NIMA-related protein kinase CNK11 that rescues the short flagella phenotype found in several N-DRC mutants , as well as mutants lacking dynein arms . In addition , we discovered that the polyglutamylation defect caused by tpg1 could not rescue the CCDC40 mutant . Instead , the CCDC40 mutation in the tpg1 background has narrower distribution of polyglutamylated tubulin at the proximal end of flagella . The microtubule stabilizing drug paclitaxel is able to rescue the short flagella phenotype in CCDC39/CCDC40 mutants but this rescue fails in the presence of cnk11 or tpg1 .
The pf7 and pf8 mutants were first isolated as mutants with no flagella or short flagella with a motility defect [17 , 39] ( Fig 1 ) , and mapped to chromosome 17 [40 , 41] . Using whole genome sequencing in combination with our SNP and short insertion/deletion library , we identified the causative mutations in both pf7 and pf8 mutant strains [42] ( Table 1 ) . A nonsense mutation in Cre17 . g698365 ( CCDC40 ) is responsible for the pf7 mutant phenotype; a nonsense mutation in Cre17 . g701250 ( CCDC39 ) leads to the pf8 mutation ( Table 2 ) . We performed BAC rescue to confirm they are the causative mutations ( Fig 1A ) . Forty-one independent transformants that contain BAC DNA 17F4 , which carries the CCDC40 gene , showed restoration of both flagellar length and motility in pf7; 20 independent transformants that contain BAC DNA 31N18 , which carries the CCDC39 gene , restored flagellar length and motility in pf8 . For each rescue , we analyzed 16 independent transformants and the transformed BAC DNA cosegregates with rescue in all transformants . Independently , Oda et al . showed that pf7 and pf8 encode CCDC40 and CCDC39 [16] . The fla12 mutant was isolated as a temperature-sensitive flagellar assembly mutant [43] that was previously mapped to chromosome 17 [40] . The fla12 cells shorten their flagella gradually and become immotile after the temperature is raised from 21°C to 32°C ( Fig 1B ) . We used the same whole genome sequencing approach to identify a L845P change in CCDC39 in fla12 ( Tables 1 and 2 ) . The transgene that rescued the pf8 mutant was introduced into fla12 by meiotic crosses . In 12 independent progeny , the transgene restores normal flagellar length and motility in all strains at 32°C . Chlamydomonas offers the ability to use suppressor analysis to find genes that restore function to motility mutants [44 , 45] . After UV mutagenesis of the pf7 mutant , we screened for swimming cells and recovered 31 independent strains . PCR/enzyme digestion and Sanger sequencing revealed that all 31 strains are intragenic revertants ( Table 3 ) . Using the same strategy , we isolated 34 revertants of pf8 and 4 revertants of fla12 ( Table 3 ) , all are intragenic events . Subsequently , we performed two independent UV mutagenesis screens on pf7; pf8 double mutants to isolate extragenic suppressors . In contrast to nitrogen-starved , autolysin-treated cells that assemble ~ 2 μm flagella ( Fig 1A ) , the pf7; pf8 mutant cells in nitrogen-containing medium are mostly aflagellate . The first UV mutagenesis screen led to isolation of three independent strains ( pf7; pf8; cnk11-1 , pf7; pf8; cnk11-2 , and pf7; pf8; cnk11-3 ) and the second UV mutagenesis screen identified three additional strains ( pf7; pf8; cnk11-4 , pf7; pf8; cnk11-5 , and pf7; pf8; sup2D ) . All six strains show partial suppression of the aflagellate phenotype of pf7; pf8 , but do not suppress the motility defect ( Fig 1A ) . None of them is linked to either pf7 or pf8 . They each contain one suppressor mutation based on crosses to the pf7; pf8 parent; the aflagellate phenotype segregates 2:2 . The suppressor mutations in five of the strains ( cnk11-1 to cnk11-5 ) are tightly linked to one another ( S1 Table ) . Whole genome sequencing ( Table 1 ) revealed that the five strains each carry a mutation in Cre07 . g339100 . The causative mutation in the sixth suppressor , sup2D , is currently under analysis . In the five strains carrying mutations in Cre07 . g339100 , two nonsense mutations ( cnk11-1 and cnk11-2 ) , a frame shift ( cnk11-3 and cnk11-5 ) , and a missense mutation ( cnk11-4 , Table 2 ) were identified ( Fig 2 ) . Using dCAPs markers designed to each mutation ( S2 Table ) , we observed linkage between suppression and the mutant allele in each strain . Cre07 . g339100 encodes a 2903 aa ( amino acid ) protein with a NIMA-like protein kinase ( NEK ) domain ( aa 582–921 ) . This protein is different from the 11 NEKs ( CNK1—CNK10 , and FA2 ) that have been previously annotated in Chlamydomonas [33] . Thus , we name it CNK11 ( Chlamydomonas NIMA-like protein kinase 11 ) . Using the conserved protein kinase domain , we constructed a phylogenetic tree with using the kinase domains found in 77 NEKs from Arabidopsis , Aspergillus , C . elegans , Chlamydomonas , Dictyostelium , Drosophila , human , mouse , Trypanosoma , rice , Xenopus and zebrafish ( S1 Fig and S3 Table ) . The tree reveals that CNK11 is phylogenetically different from any of the known NEK classes . In a previous report [16] , Oda et al . showed that the pf7 and pf8 single mutants assemble reduced amounts of two N-DRC proteins; DRC2 and DRC4 . In our analysis of isolated axonemes , we found that the single mutants assemble reduced amount of DRC1 , DRC4 , DRC5 , DRC7 , and DRC11 and completely lack DRC2 , DRC3 , and CCDC39 ( Fig 3A ) . In addition , the amount of each N-DRC proteins in a pf7; pf8 double mutant and a pf2; pf7; pf8 triple mutant is comparable to that found in the single mutants , with the exception that no DRC4 protein is found in the triple mutant . This is expected given that the pf2 mutant lacks DRC4 ( Fig 3A ) [10] . The similarity of the single and double mutants is also expected given the co-assembly of CCDC39 and CCDC40 [16] . Transformation of wild-type PF7 or PF8 into the corresponding mutant restores the N-DRC proteins ( Fig 3A ) . In axonemes from the pf7; pf8; cnk11-1 mutant , the N-DRC proteins are not restored ( Fig 3A ) . This suggests that cnk11 mutants do not suppress the flagellar length defect of pf7; pf8 via assembly of N-DRC proteins ( Fig 3A ) . In addition to the N-DRC proteins , we asked if the pf7 and pf8 mutants affect other axonemal proteins ( Fig 3A ) . Tektin , which is a microtubule binding protein , is diminished in the ida6 ( DRC1 ) and pf3 ( DRC2 ) mutants , which lack the inner dynein arms species e [46] . Tektin is missing in the single , double , and triple mutants ( Fig 3A ) . Because loss of tektin is associated with the loss of dynein e , we suggest that these mutants are likely to lack dynein e as well . The proximally localized minor dynein heavy chain , DHC11 [47] , is missing in the single , double , and triple mutants . DIC3/ IC140 , which is the intermediate chain for the I1/f inner dynein arm [48 , 49] , is reduced in pf8 , the double and triple mutants , but not in the pf7 mutant . This is one of the few difference found between pf7 and pf8 , and was independently validated [16] . DLE2/centrin , which is part of the b , e , and g inner dynein arm complex [50 , 51] , is slightly reduced in the single mutants . There is no reduction of RSP16 , which is one of the radial spoke proteins [52]; or DIC2/IC69 , an intermediate chain in the outer dynein arm [53 , 54] . DII1/p28 is only slightly reduced based on 2 independent preparations . Similar results were observed by Oda and colleagues [16] . The ribbon proteins , Rib72 and Rib43a , were first identified by their insolubility in various extraction protocols [55 , 56] . There is no loss of these two proteins in the pf7 or pf8 mutant compared to wild-type or other N-DRC mutants . LF5 , which is a CDKL5 homolog involved in length control that localizes to the proximal 1 μm of the flagella [57] , is increased in the single , double , and triple mutants . Since we load equal amounts of protein in each sample , the increase is likely to be due to increased representation of proteins at the proximal end where LF5 localizes . The pf7 and pf8 rescued strains resemble wild-type axonemes and restore all proteins to wild-type levels . The triple mutant pf7; pf8; cnk11-1 shows similar losses to the pf7; pf8 preparations . It indicates that the cnk11 mutant suppresses the pf7; pf8 length phenotype by means other than restoration of axonemal proteins . In N-DRC mutants such as pf2 and pf3 , the presence of 0 . 1 mM ATP leads to splaying of individual outer doublet microtubules in the medial and distal regions of the isolated full-length axonemes [10] . The proximal end of these axonemes remained intact . In contrast , wild-type axonemes remain intact throughout the whole length . Bower and colleagues concluded that the N-DRC provides some but not all of the resistance to microtubule sliding between doublets . This helps to maintain optimal alignment of doublets for productive flagellar motility [10] . Given pf7 and pf8 axonemes either lack or have reduced amounts of most N-DRC proteins tested , we examined isolated axonemes exposed to 0 . 1 mM ATP ( Fig 4 ) . To verify the proximal end showed splaying , we used antibodies to LF5 that localizes to the proximal end [57] . We observed little or no splaying of the doublet microtubules in wild-type axonemes . In the mutants , we observed splaying in the medial and distal regions of the single , double , and triple mutants that was similar to the splaying observed in N-DRC mutants [10] . The doublets in the proximal 1 μm end remain intact , similar to the observation found in N-DRC mutants . Thus , we conclude that CCDC39 and CCDC40 are not required for holding the microtubules together in the proximal region . Given the temperature-sensitive fla12 mutant carries a missense mutation in CCDC39 , we asked whether the temperature shift affects axonemal proteins and flagellar length in this mutant . Four hours after temperature shift from 21°C to 32°C , fla12 cells contain less CCDC39 , DRC1 , DRC3 , DRC4 , DRC7 , and tektin while maintaining normal levels of DIC2 and DIC3 ( Fig 3B ) . This suggests that the missense mutation affects the thermal stability of CCDC39 , which in turn leads to reduction of N-DRC and axonemal proteins as observed in the null CCDC39 mutant ( Fig 3A ) . At 21°C , the fla12 mutant assembles slightly shorter flagella ( ~6 . 4 μm ) when compared to wild-type ( ~8 . 5 μm ) . Eight hours after the temperature shift from 21°C to 32°C , the average flagellar length of fla12 is ~1 . 6 μm . The flagellar length of the fla12; cnk11-3 double mutant ( ~6 . 5 μm ) is similar to the single fla12 mutant at 21°C . However after temperature shift , the flagellar length is ~3 . 9 μm , which is significantly longer than fla12 cells at the same time point ( Fig 1B ) . This indicates that similar to the partial suppression of the short flagella phenotype of pf7; pf8 double mutant , cnk11 can partially suppress the temperature-sensitive short flagella phenotype of fla12 . Intraflagellar transport ( IFT ) was monitored previously in numerous flagellar assembly mutants [58] . Analysis of the fla12; pf15 double mutant suggested that the velocity of anterograde and retrograde IFT was increased over control velocities , and the frequency of IFT particles was also higher . We reanalyzed IFT by TIRF ( Total Internal Reflection Fluorescence ) microscopy using GFP-tagged IFT20 [59] in the fla12 mutant . Data obtained from 4 FLA12 and 5 fla12 cells indicate that anterograde ( Fig 5C ) and retrograde ( Fig 5D ) IFT velocities in fla12; IFT20-GFP cells are identical to those in FLA12; IFT20-GFP cells . Thus , we conclude that there is no IFT velocity defect in the fla12 mutant . There are at least two possible explanations for the disagreement between these two studies . In the fla12; pf15 study , the pf15 mutation disrupts the p80 subunit of katanin [60] . It is possible that there is some synthetic interaction between katanin and CCDC39 that affects IFT velocity and number . Alternatively , the multiple backcrosses of fla12 before the TIRF study could have removed another mutation that affected IFT . To ask about the specificity of the cnk11 suppressor , we introduced the cnk11-1 mutation into the pf2 ( DRC4 ) [10] and pf3 ( DRC1 ) [61] mutants through meiotic crosses . Mutants in DRC4 are missing N-DRC proteins as well as dyneins a and c [10] . The pf2 mutant has an average flagellar length of ~5 . 2 μm ( Fig 6A ) . In comparison , the pf2; cnk11-1 double mutant has an average flagellar length of ~8 . 6 μm ( Fig 6A ) , which is comparable to the average flagellar length found in wild-type CC-125 cells ( ~8 . 9 μm; Fig 1A ) and significantly longer than pf2 flagella . The pf3 mutant obtained from the Chlamydomonas Resource Center ( CC-1026 ) has an average flagellar length of 7 . 4 μm ( Fig 6A ) , slightly shorter than in wild-type cells . Mutants in DRC1 are missing N-DRC proteins , tektin , and RSP13 [10] . PCR on progeny from a meiotic cross between CC-1026 and cnk11-1 revealed that over 8 kb of genomic DNA on chromosome 7 is deleted . The deleted region includes most of the CNK11 gene and at least half of the adjacent gene Cre07 . g339104 ( Fig 2 ) . Therefore , the strain CC-1026 should be annotated as pf3; cnk11-6 . A meiotic cross between pf3; cnk11-6 to wild-type CC-124 cells allowed the isolation of a pf3; CNK11 strain . The average flagellar length of pf3; CNK11 cells is ~4 . 8 μm ( Fig 6A ) , which is comparable to the length observed in pf2 cells . In addition , the pf3; CNK11 cells have flagellar lengths that are more variable ( ranging from <1 μm to >8 μm ) than the pf2 ( mostly 3~7 μm ) , pf7 , and pf8 ( both mostly 1~3 μm ) cells ( S2 Fig ) . In conclusion , the cnk11 mutant rescues the short flagella phenotype of CCDC39 and CCDC40 mutants as well as two N-DRC mutants . The pf22 and pf23 mutants were first isolated as paralyzed flagella mutants and both have short flagella [62] . The PF22 gene encodes a conserved cytoplasmic protein ( DNAAF3 ) that is essential for the assembly of both outer and several inner dynein arms [4] . The pf23 mutant lacks inner dyneins a , c , d , and f [25 , 63] . The outer dynein arm mutant , oda2 , and inner dynein arm mutant , ida3 , both display slow motility with normal flagellar lengths . The oda2; ida3 double mutant is paralyzed with very short flagella ( Fig 6B ) as has been observed for many oda; ida double mutants [26] . The N-DRC is not affected in any of these mutants . To ask whether the cnk11 suppressor can restore normal flagellar length in these mutants , we introduced cnk11 mutations into pf22 , pf23 , and oda2; ida3 mutants . The average flagellar length of pf22 is ~4 . 0 μm and ~6 . 4 μm for pf22; cnk11-5 ( Fig 6A ) . The average flagellar length of pf23 is ~3 . 5 μm and ~7 . 2 μm for pf23; cnk11-5 ( Fig 6A ) . The oda2; ida3 cells have an average flagellar length of ~0 . 5 μm; and , the oda2; ida3; cnk11-1 triple mutant has an average flagellar length of ~5 . 9 μm ( Fig 6B ) . Thus , cnk11 mutations rescue the short flagella mutant phenotype of dynein arm deficient mutants , which lack multiple axonemal dynein species and presumably have unstable axonemal microtubules . Similar to the effect of cnk11 on pf7 and pf8 , the cnk11 mutations do not rescue the motility defects found in the dynein arm deficient mutants . In addition , the cnk11 mutations do not rescue the temperature-sensitivity flagellar assembly of the kinesin-2 motor mutant , fla10 , or the IFT mutants , fla15 and fla17 , after 8 hrs at 32°C . In human cell lines , knockdown of NEK4 , a NIMA-like kinase , confers paclitaxel resistance and show defects in repolymerizing microtubules after nocadozole treatment [37] . In Arabidopsis thaliana , nek4 , nek5 , and nek6 all show hypersensitivity to paclitaxel [38] . Various NEK proteins play a role in microtubule stability . We tested the cnk11-1 allele on paclitaxel media with concentrations from 5 to 60 μM . The mutant strain behaved identically to the wild-type controls and we did not observe resistance or hypersensitivity as judged by cell division and cell size [64] . We then asked whether the addition of 10 μM paclitaxel for 30 minutes , a dosage that does not causes arrest of cell division in wild-type cells [64] , would have any effect on flagellar length . In wild-type and cnk11-1 cells , which have normal flagellar lengths , there is no change ( Fig 6B ) . We examined pf2 , and the temperature-sensitive kinesin mutant fla10-1 which has about half-length flagella when grown at 28°C [19] . The addition of paclitaxel has no effect on either mutant ( Fig 6B ) . In the short flagella mutants pf7 , pf7; pf8 , and oda2; ida3 , paclitaxel conferred increased flagellar length . In contrast , paclitaxel does not lead to further elongation of flagella of these mutants when the cnk11 mutation is present ( Fig 6B ) . This suggests that CNK11 and paclitaxel could act via the same mechanism to stabilize axonemal microtubules in these short flagella mutants . Kubo et al . showed that both tpg1 and tpg2 can rescue the short flagella phenotype found in pf23 and pf28; pf30 [27] . PF28 is an allele of ODA2 ( the gamma dynein heavy chain ) and PF30 is an allele of IDA1 ( 1-alpha dynein heavy chain , I1/f complex ) . This result is similar to the effect of cnk11 on pf22 , pf23 , and oda2; ida3 ( Fig 6 ) . Therefore , we asked whether the tpg1 mutation can rescue the short flagella phenotype found in the pf7; pf8 double mutant . The TPG1 gene maps to chromosome 17 at 0 . 51 Mb , between the PF7 ( chromosome 17 at 0 . 33 Mb ) and PF8 ( chromosome 17 at 0 . 74 Mb ) genes . The short distance among these three genes makes it extremely hard to generate the pf7; pf8; tpg1 triple mutant by meiotic recombination since it would require two crossovers in an interval of only 4 map units . Given the pf7 mutant behaves similarly to the pf7; pf8 double mutant , we analyzed pf7 and pf7; tpg1 instead of pf7; pf8 and pf7; pf8; tpg1 . To our surprise , the tpg1 mutation does not rescue the short flagella phenotype found in pf7 . Instead , the pf7; tpg1 mutant has a more severe flagella phenotype than the pf7 mutant . In nitrogen-free medium , ~85% of pf7 cells have flagella . In contrast , only ~48% of pf7; tpg1 cells have flagella . Measurement of flagellated cells in both strains showed no significant difference in the flagellar lengths between pf7 and pf7; tpg1 ( Fig 6B ) . We asked whether polyglutamylation of tubulin is altered in the pf7; tpg1 mutant by both immunoblots and immunofluorescence . In wild-type cells , tubulin in axonemal microtubules is polyglutamylated . A polyclonal antibody ( Poly E ) that specifically recognizes tubulin with three or more glutamates reveals much stronger signal intensity in α-tubulin than in β-tubulin in wild-type axonemes [29] . The signal intensity of polyglutamylated α-tubulin relative to polyglutamylated β-tubulin is significantly reduced in pf7 and pf8 ( Fig 7A ) . Similar to the findings by Kubo et al . [29] , we noticed significant reduction of α-tubulin polyglutamylation but not β-tubulin polyglutamylation in tpg1 . A significant reduction of α-tubulin polyglutamylation was observed in both pf23; tpg1 and cnk11-1; tpg1 mutants , but not in the pf23 or cnk11-1 mutants . However in the pf7; tpg1 mutant , polyglutamylated α-tubulin remains ( Fig 7A ) . The change of relative signal intensities between polyglutamylated α- and β-tubulins found in pf7 , pf8 , and pf7; tpg1 is not due to their short flagellar lengths , since the short flagellar length mutant oda2; ida3 has stronger signal intensity in α-tubulin than in β-tubulin , as found in wild-type axonemes ( Fig 7A ) . Immunoblots with an anti-TPG2/FAP234 antibody [28] show that no TPG2 protein is detected in the axonemes of any strain carrying the tpg1 mutant ( Fig 7A ) . It suggests that the presence of small amount of polyglutamylated α-tubulin in pf7; tpg1 is not due to the recruitment or recovery of the TPG1-TPG2 complex in the axoneme . The abundance of TPG2/FAP234 is not significantly affected by flagellar length or the pf7 and pf8 mutations . By immunofluorescence , the polyglutamylated tubulin detected by the polyE antibody shows signal along the entire length of the axoneme in wild-type and cnk11-1 cells ( Fig 7B ) . As observed previously , the signal in tpg1 is concentrated at the proximal end [29] , and we observe that the polyglutamylated tubulin signal is only ~1 . 5 μm in length ( Fig 7C ) . The cnk11-1; tpg1 and pf23; tpg1 double mutants have a similar stretch of polyglutamylated tubulin signal regardless of their flagellar lengths ( Fig 7B ) . The pf7; tpg1 cells are strikingly different , the polyglutamylated tubulin signal is reduced to ~0 . 5 μm , which is significantly shorter than in the single or other double mutants ( Fig 7B & 7C ) . This result is different from what we observed in the immunoblots , in which the polyE signal of α-tubulin is more abundant in pf7; tpg1 than in tpg1 . It is likely that the difference is due to using isolated flagella that include both the microtubule axoneme and the flagellar membrane/matrix for the immunoblot and using axonemes that have the membrane/matrix fraction removed by detergent for immunofluorescence . Polyglutamylation of α-tubulin but not β-tubulin is associated with soluble tubulin heterodimers [65] . Thus the difference in polyE abundance between the two techniques is likely due to the removal of the soluble polyglutamylated α-tubulin in the immunofluorescence experiments . Combining the immunoblot and immunofluorescence results suggests that PF7/CCDC40 is needed for polyglutamylation at the proximal end of the microtubule axonemes . Next we asked whether paclitaxel has any effect on tpg1 . As might be expected for flagella with normal length , neither tpg1 nor cnk11-1; tpg1 is affected by treatment with paclitaxel for 30 minutes ( Fig 6B ) . However , no increase in flagellar length is observed after paclitaxel treatment of the pf7; tpg1 mutant . We suggest that paclitaxel does not increase flagellar length in strains with the cnk11 or tpg1 mutations . Given that the NIMA-related kinase cnk2-1 mutant affects the disassembly rate of flagella , we asked whether cnk11-1 affects the rates of assembly and/or disassembly . We first compared the rates of flagellar assembly after flagella amputation by pH shock in wild-type ( CC-125 ) and cnk11-1 cells ( Fig 8A ) . Within 30 minutes following flagellar amputation , the assembly rate of cnk11-1 cells was ~0 . 23 μm/min , which is not significantly faster than the rate of wild-type cells ( ~0 . 20 μm/min ) . This is very similar to rates observed in the cnk2-1 cells by Hilton et al . [36] . However , the assembly rate in cnk11-1 cells reduced significantly within the next 90 minutes , and resulted in slightly shorter flagella than in wild-type cells ( Fig 8A ) . We conclude that the overall assembly rate during flagellar regeneration is not affected in cnk11-1 cells . Another way to measure the dynamic of flagellar assembly is to test the incorporation rate of new tubulin subunits at the flagellar tip . When a pair of Chlamydomonas cells mate , they form a quadriflagellate cell ( QFC ) , which has two pairs of flagella . Tubulin subunits are added at the tip of the flagella , using subunits from the cytoplasm [19] . The two pairs of flagella can be distinguished by using one parent that carries an epitope-tagged HA-tubulin gene ( Fig 8B insert , green ) , while the other parent lacks this gene . Both pairs of flagella are visualized with an antibody to acetylated α-tubulin ( Fig 8B insert , magenta ) . The flagella from the parent with the tagged α-tubulin are stained with an antibody to the HA tag . Newly incorporated tubulin on the unlabeled flagella is visualized with the antibody to the HA tag . We mated two wild-type strains and tracked the incorporation of HA-tubulin subunits at 30 , 60 , 90 minutes after mating ( Fig 8B , magenta ) . The length of incorporated HA-tubulin at the tip of flagella gradually increased along time and reached ~0 . 48 μm at 90 minutes . We mated two parents with the cnk11-1 mutation and observed more incorporation of HA-tubulin subunits at 60 and 90 minutes ( Fig 8B , blue ) . The length of incorporated HA-tubulin at the tip was ~0 . 80 μm at 90 minutes , which suggests a rate that is nearly twice as fast as in wild-type QFCs . Since the length of the flagella did not increase , we suggest that the cnk11-1 mutation increases tubulin turnover at the flagellar tip . Kubo et al . showed that the tpg2 mutant has slow tubulin turnover at the flagellar tip [27] . We performed the same assay on the tpg1 mutant . The incorporation length of HA-tubulin in tpg1 cells was ~0 . 16 μm at 90 minutes , significantly lower than that in wild-type or cnk11-1 cells ( Fig 8B , purple ) . The incorporation length of HA-tubulin in cnk11-1; tpg1 cells was ~0 . 17 μm at 60 minutes but dropped to ~0 . 05 μm at 90 minutes ( Fig 8B , black ) . Therefore , the faster turnover rate of HA-tubulin observed in cnk11-1 flagella is suppressed by the tpg1 mutation . The addition of 1-isobutyl-3-methylxanthine ( IBMX ) causes gradual disassembly of flagella in wild-type cells . To ask whether flagellar disassembly is affected by cnk11 , we compared the flagellar shortening rates in wild-type ( CC-125 ) and cnk11-1 cells ( Fig 8C ) . The disassembly rates of CC-125 and cnk11-1 cells within the first 30 minutes were both ~0 . 10 μm/min , similar to the rate Hilton et al . reported [36] . The disassembly rate of tpg1 ( ~0 . 11 μm/min ) was similar to wild-type and cnk11-1 . It was slightly reduced in cnk11-1; tpg1 ( ~0 . 09 μm/min , Fig 8C ) . Thus , neither cnk11-1 nor tpg1 mutation affects the flagellar disassembly rate .
In mutant screens performed by McVittie and others , three pf7 and five pf8 alleles were identified [17 , 70] . The mutants show abnormalities in the organization of the axoneme and radial spokes [17] . Oda and colleagues localized CCDC39 and CCDC40 using tagged genes together with cryo-EM tomography to show that these proteins serve as docking sites along the doublet microtubules for axonemal structures , which include the radial spokes , the N-DRC and all of the inner dynein arms [16] . Our immunoblots with DLE2/centrin suggest that not all of the inner arms are missing in pf7 and pf8 since centrin associates with three inner dynein heavy chains ( b , e , and g ) and is only slightly reduced . Although the pf7 and pf8 mutants have paralyzed flagella , their dyneins are functional based on our sliding/splaying assays . Both single mutants and the double mutant show splaying of the microtubules ( Fig 4 ) that is similar to the splaying observed in the N-DRC mutants [10] . Thus , the paralysis is likely to be due to the microtubule and radial spoke disorganization that regulate the coordinated behavior of the dynein arms , as hypothesized by both McVittie and Oda et al . [16 , 17] . The splaying experiments also suggest that the link in the proximal 2 μm does not rely on CCDC39/40 , DRC1 , DRC4 , or the inner dynein arm I1/f ( Fig 4 and [10] ) . Bui and colleagues [71] identified rod-like circumferential interdoublet linkers in the proximal axoneme that are clearly structural different from the N-DRC structure . We assume that these structures are retained in the pf7 and pf8 mutants , but they have not been examined . In all patients diagnosed with PCD with CCDC39 or CCDC40 mutations , the changes result in premature truncation of the protein , which suggests that null alleles are associated with the phenotype [11] . Unexpectedly , the long-term prognosis of children with CCDC39 or CCDC40 mutations is worse than for other PCD patients , and similar to patients with cystic fibrosis [18] . These alleles would be similar to the mutations in pf7 and pf8 that have premature termination alleles . We also identified a conditional allele , fla12 , in the PF8/CCDC39 locus ( Table 1 ) . The leucine to proline change occurs in an unstructured region of the C-terminus of the protein and the leucine is not conserved in other organisms . At the permissive temperature ( 21°C ) the flagella are slightly shorter . This missense mutation leads to reduced CCDC39 and other DRC proteins at the restrictive temperature ( Fig 3B ) . After 8 hours at the restrictive temperature , the phenotype of fla12 cells resembles the phenotype of pf8 cells . The flagella are immotile and short . It is possible that missense alleles in CCDC39 in humans may have a less severe phenotype that only slightly alters the motility and would not have been grouped together with the more severe null alleles associated with PCD [11 , 12 , 14] . Our fla12 revertants ( Table 3 ) indicate that the leucine can be replaced by a variety of amino acids . This suggests that the change to a proline undermines the protein function and leads to the short and paralyzed flagella at 32°C . Given that the speeds of anterograde and retrograde IFT in fla12 shows no difference compared to those found in wild-type cells , it suggests that IFT is unlikely to play a role in flagellar length control in this CCDC39 mutant . Post-translational modification of tubulin , which includes polyglutamylation and polyglycination , affects axonemal microtubule stability . Suryavanshi et al . and Kubo et al . showed in Tetrahymena and Chlamydomonas that loss of polyglutamylation on the B-tubule is likely to affect the activity of inner arm dyneins [72 , 73] . A decrease in tubulin polyglutamylation in mouse airway cilia changes the curvature of the cilia as well as the asymmetry of the beating [74] . Overexpression of the polyglutamylation enzyme ( TTLL6 ) in Tetrahymena destabilizes axonemal microtubules [75] . Knockdown of the glycination enzymes ( TTLL3 and TTLL8 ) causes instability and results in short or absent mouse ependymal cilia . Polyglutamylation changes the binding affinities of a number of microtubule associated proteins and motors [76] , and promotes microtubule severing [77] . Thus , the presence of polyglutamylation may affect microtubule stability in a variety of ways . Polyglutamylation like acetylation of tubulin is associated with long-lived microtubules [76 , 78] . Because the loss of CCDC39 and CCDC40 affects the level of polyglutamylation , we examined the tpg1 mutation in TTLL9 . Loss of α-tubulin polyglutamylation in tpg1 causes a motility defect due to the loss of tektin but no change in flagellar length ( [29] and Fig 6B ) . Thus , reduction in tubulin polyglutamylation in the pf7 and pf8 mutants cannot be solely responsible for the short flagella in the CCDC39/40 mutants . The tpg1 mutation in combination with either pf7 or inner dynein arm deficient mutant pf23 has very different consequences . The tpg1 mutant partially rescues the flagellar length defect in pf23 ( Fig 6A ) but leads to more aflagellate cells with pf7 and no change in the length of the remaining flagella . By immunoblots , the level of polyglutamylated α-tubulin in the flagella of pf23; tpg1 is significantly less than in the flagella of pf7; tpg1 ( Fig 7A ) . By immunofluorescence , we show that localization of polyglutamylated tubulin at the proximal end of axoneme is reduced in pf7; tpg1 , but not in pf23; tpg1 , when compared to tpg1 ( Fig 7B & 7C ) . One possibility is that CCDC39 and CCDC40 are required for the activity of one or more tubulin glutamylases at the proximal end of the flagella while the TPG1 is responsible to polyglutamylation of tubulin along the rest of the flagella . There are 10 TTLL proteins found in Chlamydomonas [29] and the flagellar proteome includes only the TTLL9/TPG1 protein [79] . However , a proteomic analysis of flagellar phosphoproteins indicates that at least 3 additional TTLL proteins are found in the flagella [80] . They include TTLL13 , a homolog of human tubulin polyglutamylase TTLL6; Cre09 . g403108 , an ortholog of human tubulin polyglutamylases TTLL4 and TTLL5; and Cre03 . g145447 , a homolog of human monoglycylase TTLL3 . The former two are good candidates to be involved in tubulin glutamylation in the flagella . Multiple protein kinases affect flagellar length . In Chlamydomonas , three CDK-like kinases ( LF2 , LF5 , and FLS1 ) , one MAP kinase ( LF4 ) , and one NIMA-related kinase ( CNK2 ) , have been characterized [36 , 57 , 81–83] . Loss of LF2 , LF4 , LF5 , and CNK2 increase flagellar length . Loss of FLS1 , CNK2 , and LF4 block flagellar disassembly and loss of CNK2 decreases incorporation of new tubulin at the flagellar tip . The direct targets of these kinases remain to be identified . A recent global phosphoproteomic study revealed that over 180 Chlamydomonas flagellar proteins are phosphorylated [80] . This set includes N-DRC proteins , IFT proteins , outer and inner dynein arm proteins , central pair proteins , radial spoke proteins , and CCDC39 . Our screen for suppressors of the pf7; pf8 double mutant was designed to find swimming cells . However , no restoration of motility was found . The cnk11 mutant alleles led to short stumpy flagella , which still have a motility defect . In addition , we found that the cnk11 mutant alleles rescue the flagellar length defect but not the motility defect of N-DRC mutants as well as the dynein arm deficient mutants ( Fig 6A ) . These results , along with the fact that multiple DRC proteins and axonemal proteins are not restored in pf7; pf8; cnk11-1 and pf3; cnk11-6 mutants ( Fig 3A ) , suggest that the cnk11 mutations partially increase flagellar length via a N-DRC- and dynein protein-independent mechanism . Thus , even though CCDC39 is found to be phosphorylated in the flagella [80] , it is unlikely that it is the direct target of CNK11 . During flagellar assembly , a cytoplasmic pool of tubulin subunits are constantly transported to the tip of flagella via IFT [59] . Different from flagellar assembly , flagellar disassembly is not dependent on flagellar length [19] . It is affected by the rates of IFT , disassembly of axonemal microtubules , and disassembly of axoneme-associated protein [81] . Unlike the lf2 , lf4 , lf5 , and cnk2 mutants , both cnk11-1 and tpg1 mutants have normal flagellar length ( Figs 1A and 6B and [29] ) , flagellar assembly ( Fig 8A and [29] ) , and flagellar disassembly ( Fig 8C ) . One difference between cnk11-1 and tpg1 mutants is that cnk11-1 shows a faster than normal tubulin turnover rate at the flagellar tip and tpg1 has a slower than normal rate ( Fig 8B ) . The double mutant shows a turnover rate similar to tpg1 ( Fig 8B ) and proximal end localized polyglutamylated tubulin similar to tpg1 ( Fig 7B & 7C ) . It is unlikely that TPG1 and TPG2 are the direct targets of CNK11 , given that they are not found in the flagellar phosphoproteome [80] . In conclusion , we found that CCDC39 and CCDC40 mutants that have short flagella and fail to assemble the N-DRC and several inner dynein arms . Post-translational modification such as polyglutamylation and phosphorylation can affect flagellar length via IFT-independent and structural protein-independent pathways . These modification , may function similarly to the microtubule stabilizing drug paclitaxel and stabilize the unstable axonemal microtubules found in short flagella mutants . Further analysis of other short flagella mutants , such as shf1 , shf2 , and shf3 [84] , or pf21 [85] , are likely to identify more genes involved in flagellar assembly and length control .
The pf7 and pf8 mutant strains were obtained from the Chlamydomonas Resource Center as CC- 568 and CC-560 . The strains from the stock center were aflagellate . Different media conditions were tried , but less than 20% of the cells assembled flagella . Both mutants were backcrossed to CC-124 three successive times to determine if reassorting the genome would increase the fraction of flagellated cells . After three backcrosses , strain pf8 2–4 was chosen . After 4 hours in nitrogen-free HSM medium , greater than 10% of cells had ~7 μm flagella . These were used for additional matings and for flagella preparations . After two backcrosses , strain pf7 2–2 was chosen . After 4 hours nitrogen-free medium , greater than 80% of cells had short ( <4μm ) flagella . Other strains and culture conditions are as reported previously [86] . Treatment of cells with paclitaxel was performed in yellow Lucite boxes to prevent breakdown of the paclitaxel by white light [64 , 87] . Chlamydomonas genomic DNA preparation for whole genome sequencing was prepared as described previously [86] . Three micrograms of DNA was submitted to Genome Technology Access Core ( Washington University School of Medicine ) for library construction , Illumina sequencing , and initial data analysis . For multiplex Illumina sequencing , 7-nucleotide indexes were added to individual DNAs during the library construction before the samples were subjected to sequencing . Illumina whole genome sequencing reads were aligned onto the Chlamydomonas version 5 . 3 . 1 genome assembly , and then aligned to JGI predicted exomes ( [42] for details ) . SAMTools [88] were used for calling of SNPs/short indels from each strain . The SNPs/short indels from individual strains were compared to a SNP/short indel library ( http://stormo . wustl . edu/dgranas/form . php ) generated from 16 previously sequenced strains ( 15 in the cases of pf7 and pf8 because they were included as two of 16 original strains used to construct the library ) [20 , 42] . The unique SNPs/short indels in each strain were analyzed and filtered by SnpEff [89] . Only changes that have Phred quality scores of over 100 and rest within the coding region and splicing sites were retained . Whole genome sequencing reads of pf7 and pf8 can be found under NCBI BioProject Accession Number PRJNA245202 . Whole genome sequencing reads of fla12 , pf7; pf8; cnk11-1 , pf7; pf8; cnk11-2 , pf7; pf8; cnk11-3 , pf7; pf8; cnk11-4 , and pf7; pf8; cnk11-5 can be found under NCBI BioProject Accession Number PRJNA293107 . Revertant analysis was performed as previously reported [86] . Most of the mutant cells fail to oppose gravity and fall to the bottom of the tube . Swimming cells rise to the top of the tube and the upper 10 mL was transferred to fresh medium five times over the course of 13 days . Cultures were plated for individual colonies and one colony with swimming cells was kept from each tube . For the isolation of suppressors of the pf7; pf8 strains , we failed to recover any swimming cells . However , the nature of the pellet changed following the rounds of enrichment . Instead of large clumps of cells , the pellets were smooth and there were single cells . This was used to identify the suppressors . Two day-old cells were resuspended in nitrogen-free medium for 4 hours before treated with freshly made autolysin and fixed in cold methanol . Cells were stained with anti-acetylated α-tubulin antibody followed by Alexa 594-conjugated goat anti-mouse secondary antibody . ImageJ was used to measure the flagellar length . Protocols are as described previously [20] . Antibodies used in this study are listed in S4 Table . Cells were deflagellated by pH shock and the isolated flagella were resuspended in demembranating buffer as described [10] . Half of the resultant axonemes were treated with 0 . 1 mM ATP at room temperature for 4 minutes . Both ATP-treated and non-treated axonemes were fixed with 2% paraformaldehyde at room temperature for 10 minutes on poly-lysine-coated multi-well slides ( Thermo Scientific ) . The slide was then immersed in cold methanol for 10 minutes at -20°C . The samples were allowed to air dry on the slide before the addition of blocking buffer ( 5% BSA , 1% fish gelatin ) . The primary antibodies used were LF5 ( 1:200 dilution ) and acetylated α-tubulin ( 1:250 dilution ) diluted in 20% blocking buffer . The secondary antibodies were Alexa 488-conjugated goat-anti-rabbit IgG ( 1:500 dilution ) and Alexa 594-conjugated goat-anti-mouse IgG ( 1:500 dilution ) diluted in 20% blocking buffer . Cells were imaged on manufacturer pre-cleaned fused silica chips ( 6W675-575 20C , Hoya Corporation USA , San Jose , CA ) , and sandwiched between the fused silica surface and a coverslip ( 1 . 8 x 1 . 8 cm2 ) , resulting in a 25 μm thick water layer that allowed the 10 μm diameter Chlamydomonas cell body to move freely in solution . We used total internal reflection fluorescence ( TIRF ) microscopy to image the cells . The details of the imaging methods were reported previously [90] . Videos of individual surface-attached flagella were processed into kymographs . For visible IFT tracks in a kymograph , a minimum of 3 consecutive and clearly distinguishable IFT20::GFP intensity profiles were required for a track to be used . For each selected IFT track , the slope of the line through the centroid of the first and last IFT20::GFP intensity profiles in the track was used to determine the IFT velocity . | Cilia are specialized projections found on the surface of eukaryotic cells . They play crucial sensory functions , as well as motile functions needed for clearing airways or propelling cells . Ciliary motility is perturbed in the inherited disease , Primary Ciliary Dyskinesia ( PCD ) . Two coiled coil domain-containing ( CCDC39 and CCDC40 ) proteins are needed for the assembly of multiple key structures/complexes that are required for generating ciliary motility . Using the unicellular green alga , Chlamydomonas , we have identified a kinase ( CNK11 ) that when mutated is able to partially rescue the short flagella phenotype of the ccdc39 and ccdc40 mutants as well as mutants lacking axonemal dyneins or the N-DRC complex . In addition , CCDC40 is required for tubulin polyglutamylation at the proximal end of flagella . We suggest that substructures like dynein arms and the N-DRC , which are needed for motility , play a second role in stabilizing the axonemal microtubules and are needed for proper length control . The polyglutamylase , TTLL9 , and the kinase , CNK11 , play roles in stabilizing the axonemal microtubules based on their ability to partially rescue the short flagella phenotypes of multiple mutants . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [] | 2015 | A NIMA-Related Kinase Suppresses the Flagellar Instability Associated with the Loss of Multiple Axonemal Structures |
Mycobacterium ulcerans causes Buruli ulcer ( BU ) , a debilitating infection of subcutaneous tissue . There is a WHO-recommended antibiotic treatment requiring an 8-week course of streptomycin and rifampicin . This regime has revolutionized the treatment of BU but there are problems that include reliance on daily streptomycin injections and side effects such as ototoxicity . Trials of all-oral treatments for BU show promise but additional drug combinations that make BU treatment safer and shorter would be welcome . Following on from reports that avermectins have activity against Mycobacterium tuberculosis , we tested the in-vitro efficacy of ivermectin and moxidectin on M . ulcerans . We observed minimum inhibitory concentrations of 4–8 μg/ml and time-kill assays using wild type and bioluminescent M . ulcerans showed a significant dose-dependent reduction in M . ulcerans viability over 8-weeks . A synergistic killing-effect with rifampicin was also observed . Avermectins are well tolerated , widely available and inexpensive . Based on our in vitro findings we suggest that avermectins should be further evaluated for the treatment of BU .
Buruli ulcer ( BU ) is a neglected tropical disease that presents as skin nodules , plaques or oedematous lesions that can progress to open ulcers [1] . BU is caused by infection with Mycobacterium ulcerans , a mycobacterium that is related to the causative agents of tuberculosis and leprosy [2] . Most BU patients are children under the age of 15 [3] . The mode of transmission of the disease is not well understood . Superstitious beliefs predominate in rural West Africa , resulting in delayed treatment seeking and stigmatization of patients [4 , 5] . Mortality associated with BU is low nevertheless morbidity is high . Extensive ulcers frequently lead to lifelong physical disability [6 , 7] . No vaccine is available against Buruli ulcer and management focuses on early case detection and treatment with surgery and antibiotics [8] . Previously , Buruli ulcer was treated with surgical excision only , but since 2004 , an 8-week course of rifampicin and streptomycin is the standard treatment [9–11] . In case patients report early with limited lesions , the 8-week course of rifampicin and streptomycin delivers a good quality of life at long-term follow up [12] . However , the median time to heal is still 18 to 30 weeks , depending on the size of the lesion [10] . Patients presenting late to health care facilities have a much poorer prognosis and many suffer from functional limitations due to the disease [6 , 7] . There is also the issue that daily injections with streptomycin are impractical and can have serious side effects , such as ototoxicity [13] . It has been shown that all-oral treatment with rifampicin and a macrolide or quinolone results in high cure rates [14] . Shorter duration of antibiotic courses and more safe treatment regimes that reduce the time to healing are desirable for Buruli ulcer . Avermectins are macrolides that are used to treat helminth-infection ( such as strongyloidiasis or onchocerciasis ) and parasitic infection ( scabies ) in humans and in animals . These orally administered drugs are well tolerated and available worldwide . Ivermectin is on the essential drugs list of the World Health Organization . A recent report showed that avermectins , including ivermectin , moxidectin and selamectin , inhibit the growth of different M . tuberculosis strains in-vitro at concentrations of 2 to 8 μg/ml [15] . Motivated by these findings , we tested if avermectins also inhibit and kill M . ulcerans .
Two different M . ulcerans clinical isolates were used , JKD8049 isolated from a patient in Victoria , Australia in 2004 and 1117–13 , a 2013 clinical isolate from Benin . For time-kill assays ( see below ) , M . ulcerans JKD8049 containing a bioluminescent reporter plasmid pMV306 hsp16+luxG13 [16] was employed . The Mycobacterium marinum ‘M’ strain was also used . Mycobacteria were grown at 30°C in 7H9 Middlebrook broth supplemented with OADC ( Becton Dickinson , Sparks , MD , USA ) . M . ulcerans JKD8049 harbouring pMV306 hsp16+luxG13 was grown in the presence of 25 μg/ml kanamycin . Bacteria in mid-exponential growth phase were used for MIC testing . They were prepared to 0 . 5 Macfarlane standard and diluted 1:5 in PBS . A 500 μl volume of this preparation was used to inoculate duplicate BBL™ Mycobacteria Growth Indicator Tubes ( MGITs ) supplemented with 0 . 5 ml OADC ( Becton Dickinson , Sparks , MD , USA ) that contained doubling dilutions of moxidectin , ivermectin or rifampicin ( Sigma-Aldrich , St . Louis , MO , USA . ) . The tubes were incubated at 30°C and assessed daily for fluorescence , with a long-wave UV-A lamp ( Wood’s lamp ) for 21 days . The tube with the lowest drug-concentration displaying no growth after this period was considered as containing the inhibitory concentration . Solvent only and rifampicin 0 . 1 μg/ml were used controls . Ten millilitre aliquots of mid-exponential growth phase M . ulcerans JKD8049 were transferred in duplicate to sterile 25cm2 tissue culture flasks containing 0 , 8 and 20 μg/ml ivermectin . The aim was to obtain a M . ulcerans concentration of at least 10^6 CFU/ml in each flask . Each week , 50 μl of culture was sampled from each flask to assess viable bacteria remaining by CFU counting . Ten-fold dilutions of the sub-samples were prepared in PBS and a 3 μl aliquot of each dilution was spot plated in quintuplicate onto Middlebrook 7H10 agar containing 10% OADC . After 8 weeks of incubation at 30°C plates were examined for growth . The growth/no growth scores of the five technical replicates were used to calculate the most probable number estimate of CFU per ml . Data was analyzed using GraphPad Prism v5 . 0d . Three to five replicates of 200 μl aliquots of bioluminescent M . ulcerans JKD8049 in mid-exponential growth were transferred into a white 96-well plate and antibiotics were added . The plate was placed into a FLUOstar Omega plate reader ( BMG LABTECH GmBH , Ortenberg Germany ) . Light emission was read every 300 s via the top optic with the gain set at 3600 and plate temperature at 30°C . Before each reading , plates were shaken at 100 rpm for 10 s in double orbital mode . The results were recorded using Omega v3 . 00 R2 and analyzed using Mars v3 . 01 R2 and GraphPad Prism v5 . 0d .
M . ulcerans JKD8049 , M . ulcerans 1117–13 and M . marinum were grown in MGIT tubes in the presence of increasing concentrations of the ivermectin and moxidectin . At three weeks , no fluorescence was observed at 8 μg/ml of ivermectin for M . ulcerans JKD8049 and at 4 μg/ml for M . ulcerans 1117–13 ( Table 1 ) . Moxidectin inhibited the growth of M . ulcerans JKD8049 at 4 μg/ml . The MIC for M . marinum was 32 μg/ml for ivermectin and above 64 μg/ml for moxidectin ( Table 1 ) . These data show that M . ulcerans but not M . marinum is susceptible to avermectins . Growth of all bacteria was observed in the control tubes containing only the solvent and no growth was observed in the tubes containing 0 . 1 μl/ml rifampicin . Time-kill assays were then performed to assess if avermectins not only inhibit M . ulcerans growth but also kill the bacterium . Based on the MIC results above , a low and high dose of ivermectin was tested and M . ulcerans JKD8049 was exposed to either 8 μg/ml or 20 μg/ml ivermectin for eight weeks . A dose-dependent killing effect was observed with no CFU detected from week-4 onwards at 20 μg/ml and week-5 onwards for 8 μg/ml ( Fig 1 ) . Bioluminescence is an ATP-dependent process and it is therefore an excellent dynamic reporter of cellular metabolic activity [17] . A time-kill experiment was thus performed using bioluminescent M . ulcerans . Although the time frame of this experiment was quite short ( 21 hours ) , continuous monitoring over that period again showed a dose-dependent impact of ivermectin on bacterial viability ( Fig 2 ) . The reduction in bioluminescence at 21 hours was higher in ivermectin at all concentrations tested ( 8 , 16 and 32 μg/ml ) compared to the rifampicin positive control ( Fig 2 ) . Interestingly , the greatest impact on bacterial viability was observed in the presence of a combined dose of 8 μg/ml ivermectin and 0 . 1 μg/ml rifampicin ( Fig 2 ) .
The avermectins ivermectin and moxidectin inhibited growth of M . ulcerans at 4–8μg/ml and showed dose-dependent killing in culture-based and bioluminescence assays . The avermectins are inexpensive and already widely distributed in West Africa through their use to treat river blindness . Thus , there may be a chance to repurpose a well-tolerated drug for the treatment of mycobacterial infections , bypassing the long and expensive pipeline for discovery of new antimicrobials . We suggest that avermectins should be further investigated for the treatment of M . ulcerans , possibly in combination with other antibiotics , such fluoroquinolones . | Neglected tropical diseases such as Buruli ulcer predominantly afflict the poorest populations in the world and reduce quality of life . Buruli ulcer is a necrotising infection that destroys the skin and soft tissue , frequently presenting as nodules or open ulcers . Buruli ulcer is treated with antibiotics and sometimes surgery . Unfortunately the antibiotic treatment can have toxic side effects , such as hearing loss . Also , patients must either be hospitalized or report daily to a treatment centre to get their medicine as the treatment is delivered by injection . In laboratory experiments we tested the susceptibility of Mycobacterium ulcerans , which causes Buruli ulcer , to avermectins . Avermectins are drugs that are used to treat common parasite and worm infections , such as river blindness . These drugs are inexpensive , have few side effects and are widely available . Our findings show that two avermectins called ivermectin and moxidectin inhibit the growth and also kill Mycobacterium ulcerans strains from both Africa and Australia . If their efficacy and safety also can be proven in animal and human studies , these drugs will provide an inexpensive addition to the current treatment of Buruli ulcer . | [
"Abstract",
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"Materials",
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] | [] | 2015 | In-vitro Activity of Avermectins against Mycobacterium ulcerans |
Paramount to the success of persistent viral infection is the ability of viruses to navigate hostile environments en route to future targets . In response to such obstacles , many viruses have developed the ability of establishing actin rich-membrane bridges to aid in future infections . Herein through dynamic imaging of HIV infected dendritic cells , we have observed how viral high-jacking of the actin/membrane network facilitates one of the most efficient forms of HIV spread . Within infected DC , viral egress is coupled to viral filopodia formation , with more than 90% of filopodia bearing immature HIV on their tips at extensions of 10 to 20 µm . Live imaging showed HIV filopodia routinely pivoting at their base , and projecting HIV virions at µm . sec−1 along repetitive arc trajectories . HIV filopodial dynamics lead to up to 800 DC to CD4 T cell contacts per hour , with selection of T cells culminating in multiple filopodia tethering and converging to envelope the CD4 T-cell membrane with budding HIV particles . Long viral filopodial formation was dependent on the formin diaphanous 2 ( Diaph2 ) , and not a dominant Arp2/3 filopodial pathway often associated with pathogenic actin polymerization . Manipulation of HIV Nef reduced HIV transfer 25-fold by reducing viral filopodia frequency , supporting the potency of DC HIV transfer was dependent on viral filopodia abundance . Thus our observations show HIV corrupts DC to CD4 T cell interactions by physically embedding at the leading edge contacts of long DC filopodial networks .
For HIV infections to persist , efficient spread to the next permissive target is paramount . HIV as a cell free entity is readily a target to both innate and acquired immune defenses , and by random diffusion alone , it must travel distances up to a thousand fold its diameter to make contact with a potential target . Even after this journey , permissive cells can be often equipped with various antiretroviral restriction factors , which would simply result in one of many dead ends for the virus . The above , described concepts are the underlying reasons cell to cell contact via a molecular structure termed a viral synapse plays a major role in maintaining viral persistence [1] , [2] , [3] . Viral synapses not only deliver the virus directly to the target , but also at high multiplicity , ensuring greater probability of a productive infection . In the context of cell-cell transmission , infected DC have long represented one of the most potent forms of cell associated HIV inocula for CD4 T cells [4] , [5] , [6] , [7] and it is hypothesized that HIV subverts the normal immunological communication pathways between DC and CD4 T cells for viral broadcasting . The physiological importance of DC- HIV transfer is a function of their sentinel activities at the genital mucosa . This sentinel activity places DC as the first line of cells that come into contact with the virus and thus attention has been focused for some time on how DC can disseminate HIV infection to the major targets , CD4 T cells . Given DCs physiological location and the potent ability with which infected DC transfer virus , our main aim was to determine the mechanistic prerequisites of viral transfer between infected DC and CD4 T cells . Our focus on the de novo HIV pool in infected DC must not be confused with the extensive literature of DC HIV infection in trans [4] , [8] , [9] , [10] . The in trans phenotype is defined as the binding/uptake of virus from the surrounding inoculum , which can be then transferred from DC to CD4 -T cells in the short-term ( effectively between 4 to 6 hours ) independent of DC infection [4] , [8] , [11] , [12] . In contrast the de novo viral pool/phenotype , we define as the expression of viral proteins within an infected cell type that leads to particle assembly and transfer . The importance of the latter de novo pool in infected DC , as opposed to the viral pool in exposed immature DCs ( in trans pool ) , is reasoned three-fold . Firstly , transfer from infected DCs from the de novo pool is greater in efficiency and duration than in trans pool in immature DC [4] , [11] , [12] . Secondly , the majority of DC subsets in vivo , in particular cutaneous and mucosal DC [7] , [13] , have been observed to mobilize viral transfer from the de novo pool , whereas the survival of HIV within the in trans pool is variable and in most cases limiting [4] , [6] , [7] , [12] , [14] , [15] , [16] . Finally , whilst recent studies do not support high frequencies of DC infection , due to the direct consequence of DC expressing the HIV restriction factor SAMHD1 [17] , the majority of data to date observe only limiting numbers of infected DCs being readily capable of rapidly seeding CD4 T cell infections [5] , [6] , [7] , [15] . Thus as limiting numbers of DCs can communicate the immune response , limiting numbers of infected DCs are equally capable of communicating HIV through CD4 T cell populations . Whilst close viral synapses have been observed to be key to HIV transfer and infection , little is known what leads to their formation . For instance , an isolated infected cell needs to find a future permissive target , otherwise viral transfer through a synapse would never proceed . Recently HIV has been observed on filopodial like structures , termed viral cytonemes [18] and HIV T cell nanotubes [19] . Whilst both structures result in HIV transfer , their formation are dependent on previous cell-cell contacts and thus may not be not perceived as coordinating structures prior to cell-cell contact . In looking at potential mechanisms that may occur prior to cell-cell viral transfer , we initially hypothesized that co-ordination of closer and/or tethered cell-cell contacts proceeds via an actin based pathway . The support for an actin-based initiation of viral spread is in the mechanistic studies on many other other pathogens , including the bacteria Listeria , Shigella , Rickettsia and Burkolderia and viruses of the pox-virus family , where each pathogen has been observed to independently evolved to manipulate actin polymerization to aid in its own spread through the formation of filopodial-like structures termed actin tails ( reviewed by [20] ) . Herein , by focusing on the potent and important de novo HIV reservoir in infected DCs , we observe for the first time HIV utilizing cortical actin by embedding at the tips of networks of long myeloid enriched filopodia . Budding HIV and not mature HIV particles are observed on the ends of largely freely moving untethered filopodia and thus are not structures consistent with HIV cytonemes/nanotubes . As HIV caps the end of viral filopodia ( VF ) , initially these novel structures were supported to be similar in nature to pathogenic actin tails . However , unlike actins tails , we observed VF formation to be not dependent on the Arp2/3 pathway but rather relying on expression on the formin Diaphanous 2 ( Diaph2 ) for formation . In addition the accumulative evidence is VF are pre-existing filopodial networks that are high-jacked at the leading edge by newly forming virions , as opposed to pathogenic actin tails which are actively formed by the respective pathogen . The dynamic nature of the newly described VF , imparts three important advantages for the virus . Firstly , the virus can be positioned as close as possible to the target membrane and limit exposure of the newly forming virion to innate or acquired immune defenses . Secondly , the inherent probing and fast µm . sec−1 scanning motion of VF permit the virus to engage in a multitude of potential target contacts in a manner analogous to immune repertoire scanning by professional antigen presenting cell in vivo . Finally , as the virus is at the terminal stages of budding , it is physically tethered but primed for subsequent plasma membrane fission to be released to infect a CD4 T cell bound to an infected DC .
To investigate the dynamics of HIV transfer from infected DC to CD4 T cells , we attempted to map this process in real-time . To do so we needed to undertake imaging of the de novo viral pool present in infected primary DC . To date imaging of the de novo pool has remained limited as markers amenable to imaging need to be integrated within the size sensitive HIV genome . The generation of HIV constructs by others and ourselves have addressed this short-coming by using small genetic tags that work in conjunction with cell permeable fluorescent dyes ( termed hereon as HIV-T , Fig . 1A ) [21] , [22] , or by overcoming the complications that large fluorescent proteins create when inserted into HIV structural proteins ( termed HIV-iGFP , Fig . 1B ) [23] . To persist with the use of traditional fluorescent proteins , we needed to rescue viral infectivity at two levels . Firstly we flanked the large fluorescent proteins with HIV protease cleavage sites ( Fig . 1B ) as previously described [23] , thus giving the virus the ability to untether from the fluorescent protein when maturing . Secondly , we rescued this virus further by supplying wild type ( WT ) Gag and Gag-Pol in trans ( Fig . 1C & D ) . Initially attempts of supplying WT Gag and Gag-Pol by co-transfecting WT HIV with HIV-iGFP did increase infectivity to levels comparable to WT HIV ( Fig . 1C ) . However subsequent analysis of infected DC populations , highlight the dominance of the WT genome within the de novo pool ( Fig . 1E HIV+HIV-iGFP ) . Thus to avoid a secondary WT competing genome in future de novo virus , we supplied WT HIV Gag and Gag-Pol in trans only at the protein level using the 2nd generation lentiviral vector psPAX2 ( Fig . 1D & E ) . When using this latter approach , we could not only rescue HIV-iGFP ( Fig . 1C & D ) , but also readily infect primary DC ( Fig . 1E; HIV+psPAX2 & Fig . S1A ) . More importantly , as there was no competing WT HIV genome , we could detect every cell that was infected by high levels of Gag-iGFP expression . Using live microscopy , infected DC in CD4 T cell co-cultures were identified either by the presence of FlAsH staining for HIV-T or high eGFP fluorescence for HIV-iGFP . In both cases , punctate staining , presumably representing viral particles , was readily observed in the periphery of the DC plasma membrane and neighboring CD4 T cells ( Fig . 1F ) . As these viral particles were highly motile , we further investigated whether they were still connected to the DC membrane . Peripheral viral particles remained connected by thin and often curved membrane extensions of the DC plasma membrane ( Fig . 1F ) and often provided bridge-like connections to neighboring CD4 T cells . Subsequent staining of fixed samples for filamentous actin using fluorescent phalloidin revealed HIV particles at the tips of F-actin rich filopodial like structures of variable length and curvature ( Fig . 1G & H ) and we now define these novel structures as HIV viral filopodia ( VF ) . In live imaging of infected DC , the majority of VF ( 92 . 5% ( n = 267 ( D = 4 ) ) were terminally capped with HIV Gag as shown by focal accumulation of eGFP and/or FlAsH at the filopodial tip ( Fig . 1G . Video S1 ) . Initially there were concerns that viral filopodial structures maybe a common HIV budding defect that was a result of HIV Gag fusion proteins both in HIV iGFP and HIV-T . To exclude this possibility , we determined the ability of HIV iGFP , HIV T and HIV iGFP+HIV WT ( inocula identical to that use in Fig . 1E middle panel ) infected DCs to transfer to autologous primary CD4 T cells . Virus transfer from HIV iGFP infected DCs was 1/3 that of wild type HIV infected DCs ( Fig . S1B ) , although there there was no significant attenuation of HIV transfer from HIV T and HIV iGFP+HIV WT infected DCs to CD4 T cells compared to HIV WT infected DCs ( Fig . S1B ) . As VF are also present in DCs infected with the latter viruses , the lack of viral attenuation in these important controls does not support the concept that VF are the product of a budding defect . Rather the attenuation that is observed in HIV iGFP is consistent with that of HIV viral entry , as cell free virus that is terminally mature [23] is also approximately 1/3 less infectious than HIV WT ( Fig . 1C ) . VF were variable in length from 1 µm to as long as 32 µm ( 10 . 11±5 . 42 µm , Fig . 1H , n = 267 ( D = 5 ) ) . Given the complexity of DC membranes in the absence of infection , it was important to observe the frequency and phenotype of filopodia on uninfected DCs . Live DC cultures without CD4 T cells had polarized lamellipodia opposing a leading edge uropod ( Video S2 with lamellipodia on the left of the uninfected DC ) . The latter structure expressed short filopodia ( 3 . 59+/−0 . 69 µm , n = 102 ) and were significantly shorter to VF ( p<0 . 0001 ) . However , as VF were imaged in the context of CD4-T cell co-cultures , we repeated filopodial analyses after DC that were co-cultured with autologous CD4 T cells for 30 minutes . The addition of CD4 T cells triggered filopodial formation to equivalent lengths of VF ( Fig . 1H; Video S3; p = 0 . 387 , n = 170 ) . Thus the above observations initially supports HIV hijacking of existing filopodial networks , triggered in the presence of CD4 T cells . To determine whether filopodia could be capped on other cell types , we infected primary CD4 T cells . VF were expressed on primary activated HIV infected CD4 T cells , yet the VF frequency per cell was significantly lower compared to infected DC ( 1 . 26±0 . 72 VF . cell−1 , n = 59 ( D = 4 ) versus 8 . 89±5 . 17 VF . cell−1 , n = 49 ( D = 4 ) ) . In addition the average length of VF on CD4 T cell was shorter than on infected DC ( 4 . 69±2 . 124 µm , n = 138 ( D = 4 ) ; p<0 . 0001 ) . Uninfected T cells expressed similar filopodia , yet we could not identify co-culture conditions that triggered long filopodia analogous to uninfected DCs co-cultured with CD4 T cells . We then sought to identify a cell line that could express VF at similar lengths and frequencies to infected DC , thus permitting the use of experimental designs that may be limited in primary cells . Two decades ago , Phillips and colleagues observed a structure resembling VF using transmission electron microscopy in HIV chronically infected U937 cells engaging a neighboring epithelial cell [24] , [25] , we thus investigated whether the U937 cell line was expressing equivalent VF to infected DC . Indeed , we observed VF of equivalent length ( 8 . 71±3 . 99 , n = 149 , p = 0 . 206 ) and frequency per cell ( 13 . 27±4 . 59 , n = 78 , versus 8 . 89±5 . 17 VF . cell−1 n = 49 ( D = 4 ) ) . As observed in infected DC , VF on U937s were terminally capped with HIV and consistent with that previously observed by Phillips and colleagues , yet in the later case was in the context of polarized filopodia engaged at a synaptic junction . Whilst VF on the U937 cell line were unpolarised , as was the case in infected DCs , we did observe filopodial polarization consistent with the image by Phillips et al ( Video S4 ) . Whilst we support VF on U937 and DCs to be similar , it must be noted U937 constitutively express long filopodia in both infected and uninfected U937 cultures ( Fig . 1H ) in the absence of CD4 T cell co-culture ( unfortunately co-culture with autologous CD4 T cells was obviously not possible for this cell line ) . From hereon we use HIV iGFP as the primary tool in live imaging , as they are significantly brighter and photo bleach tolerant , which are key criteria for lower exposure times for fast acquisition of events during live imaging and the recording of longer trajectories for single particle analysis . Also at this point it is important to clarify the images in live cell acquisition versus the higher resolution images in fixed samples . In live cell acquisition there is readily detectable diffuse eGFP staining throughout the cell body , with low-level expression of eGFP at the filopodial tips . In contrast , fixed cell imaging there is resolution of HIV particles across the plasma membrane and at filopodial tips . This discrepancy is reasoned two-fold . Firstly , higher resolution fixed imaging is through the acquisition of entire infected cell volumes and subsequent 3- dimensional deconvolution [26] . In contrast in live cell imaging there was only acquisition of one Z-plane over time , thus lower resolution images due to the lack of entire Z-stack acquisition and subsequent deconvolution . The acquisition of only one Z-plane was a factor of VF dynamics , as acquisition of trajectories at velocities in excess of 1 µm . sec−1 limited the time needed for acquisition of a significant Z-stack . Secondly , the majority of fluorescence is within the cell body and was obviously limited at filopodial tips due to the relative small size of the virion . Thus exposure times that allow detection of virions on VF results in image acquisition that appear to overexpose the fluorescence in the cell body . Whilst the abovementioned fixed imaging conditions can remove the majority of out of focus light at the cell body , the restricted conditions of live imaging cannot . That said even with lower resolution images in live cell imaging , HIV particle detection at filopodial tips can be readily achieved , as the signal is of sufficient distance from the cell body to allow resolution . As we could readily resolve HIV particles on filopodia , we next characterized VF velocities and trajectories and the capacity of VF to be involved in HIV spread . We infected DCs and four days post infection we co-cultured them with autologous CD4 T cells . VF , when DC were not in immediate contact with CD4 T cells , displayed trajectories in arc-like movements , termed arc velocities , which included movements towards or away from CD4 T cells . Live acquisition of uninfected DCs observed similar arc trajectories , but only when DCs were co-cultured with CD4 T cells ( Video S3 ) . The trajectory of Arc velocities followed a single sweep , where the Arc velocity would slow or stop at the end of the trajectory and then often reversed and took the same path ( Fig . 2A; Video S5 ) . Analysis of velocity of both VF and uninfected DC filopodia observed trajectories with both acceleration to speeds >6 µm . sec−1 and deceleration with brief interludes of stationary pauses ( Fig . 2B & C ) with an average of 1 . 11±0 . 75 µm . sec−1 for VF ( Fig . 2D; n = 124 trajectories ( D = 4 ) ) and 1 . 156±0 . 75 µm . sec−1 for uninfected DC filopodia ( Fig . 2D; n = 113 trajectories ( D = 4 ) ; p = 0 . 766 versus VF ) . VF Arc velocities terminated once in contact with the CD4 T cell membrane ( Fig . 2E–F; Video S6 & S7 ) . At that time , we have observed a movement significantly slower used by VF to scan CD4 membrane surface that we have termed Scan velocity ( Fig . 2D–E; p<0 . 0001 for Arc versus Scan velocities; n = 52 ( D = 4 ) ; Video S7 ) . VF Scan velocity was 0 . 3422±0 . 196 µm . sec−1 for an average duration of 13 . 717±10 . 577 seconds ( n = 52 ) ( D = 4 ) ) . Unfortunately the tips of filopodia expressed on uninfected DCs could not be resolved when in the vicinity of CD4 T cells ( given they lacked an equivalent tip marker to HIV on VF ) and thus the equivalent dynamics of CD4 T cell contact could not be observed . To determine if VF Arc and Scan trajectories were simply a function of random Brownian motion , we calculated their respective Hurst Exponent as previously described [27] , [28] . The Hurst Exponent ( H ) mathematically classifies trajectories as random ( H = 0 . 5 ) , directional ( H>0 . 5 ) , or confined movements ( H<0 . 5 ) . Whereas Arc velocities had limited variation in H and were significantly directional ( H = 0 . 740±0 . 134; p<0 . 0001; n = 52 ) , scan velocities ranged from confined to directional movements ( H = 0 . 428±0 . 234; n = 52 ) . VF contacts were either isolated to one to two filopodia engaging in Arc movement between CD4 T cells ( Fig . 3A; Video S7 ) , or several VF interacting simultaneously towards CD4 T cell targets ( Fig . 3B & C ) . Overall estimated contacts between infected DCs and CD4 T cells ranged from 260 to 800 per hour; Average contacts = 460±167 . hour−1; n = 45 ( D = 3 ) ) . Given the speeds of VF movement and the limitations of fluorescence imaging , we must note this imaging represents a contact estimate . Furthermore the restriction of imaging to a limited Z-stack we predict to result in a somewhat conservative contact estimate . Continual and multiple VF activity preceded tethering and subsequent physical movement of CD4 T cells closer to the DC membrane ( Fig . 3D–F . Video S8 , S9 , S10 ) . For initial phenotyping of viral particles at the tips of filopodia , we first determined whether HIV envelope was present at the filopodial tip . Unfortunately staining of HIV envelope using the 2G12 antibody observed significant signal on the plasma membrane of HIV iGFP infected DCs and U937 cells . Unlike the HIV Gag signal , where there theoretically would be approximately 5000 copies [29] , HIV envelope copies have recently been estimated as 14+/−7 trimers per virion [30] . Whilst amplification of the signal using a combination of increased exposure time and CCD/EMCCD camera electronic amplification can permit detection , this was counter balanced by the significant signal of the infected cell membrane . To overcome this , we focused on filopodia that were in excess of 20 µm in length , thereby focusing on a HIV particle that was sufficiently distanced from fluorescence of the cell membrane . Using this approach we could readily detect the presence of HIV Env on the HIV particle capping the filopodial extension ( Fig . 4A ) . To further phenotype HIV filopodia , we initially determined whether HIV particles were actively forming at filopodial tips . This knowledge would address whether HIV was a part of the filopodia or simply extracellular virus that was bound to the filopodial tip . To determine if HIV Gag had recently originated from the infected cell cytosol ( as would be the case for actively budding virions ) , we utilized HIV-T . Briefly HIV-T relies on the fusion of the FLNCCPGCCMEP peptide to the C-terminus of HIV Matrix within the Gag polyprotein . When the polyprotein is within the cell ( during the budding process ) , the four-cysteine residues are reduced and can bind the cell permeable fluorescent biarsenical dye FlAsH . Once HIV buds from the plasma membrane , the cysteines are no longer reduced and this renders the peptide unable to bind the FlAsH dye [22] . As HIV Gag can be stained at filopodial tips using FlAsH ( Fig . 4B ) , this provided initial support that the particle consists of reduced HIV Gag and thus a HIV particle that was had recently formed . We also confirmed this using HIV iGFP in conjunction with antibody staining using the HIV capsid mAb clone 183 . Whilst HIV iGFP detects all HIV ( in contrast to HIV-T ) , we have previously observed the HIV mAb 183 to only detect mature HIV ( HIV cleaved Gag ) [22] . Using this latter approach , we detect all HIV iGFP positive particles at the tips of filopodia to be HIV mAb negative ( Fig . 4C ) . Both results at the fluorescent level were further confirmed after use of transmission electron microscopy that can resolve immature budding particles and extracellular mature particles by presence or absence of the electron dense capsid core . In the case of VF HIV Gag cleavage had not occurred as the HIV capsid core was not present , thus supporting particles to be budding immature virions that may or may not be in the final stages of fission ( Fig . 4D ) . To further confirm HIV was an immature bud and membrane-associated at the filopodia tip , we generated HIV iGFP with a mutation in the PTAP domain of HIV Gag , as removal of this motif results in lack of viral fission by failure to recruit Tsg101 [31] and thus synchronizes HIV budding particles at the plasma membrane . After DC infection with HIV PTAP mutants , VF length and velocities were lower compared to WT HIV yet comparable VF were observed ( Fig . 4E & F; Video S11 ) . Further analysis of total fluorescence of the particles at the tips of filopodia observes a normalized distribution of +/−19 . 7% ( n = 110 particles acquired under identical conditions ) . Therefore given the sum total of all of the abovementioned phenotypic analysis of HIV particles at the tips of filopodia , we conclude HIV particles are incorporated into filopodial tips as immature HIV buds and are not formed by the simple tethering of extracellular mature viral particles . As particles were virions originating from the cytosol and consisting of immature Gag polyprotein , this immediately differentiated VF from Vaccinia actin tails , as the latter viral particle triggers actin tails from the outside as a cell free viral particle [32] . In addition , as similar filopodia form in uninfected DCs it was important to determine whether HIV was actively involved in filopodial formation or simply embedding at the leading edge of an existing network of filopodia . Thus to further differentiate pathogenic actin tails from VF we sought to detect various antigenic footprints at the tip of filopodia that were characteristic of each pathogen , such as phosphotyrosine epitopes ( pTyr ) for Vaccinia and Arp2/3 proteins and regulators ( Arp2 , Wasp and Cortactin ) for other Arp2/3 complex dependent pathogenic tails [33] . Therefore , we phenotyped VF on infected DC for pTyr , Arp2/3 , Wasp ( a leukocyte specific Arp2/3 regulator ) , and cortactin epitopes . Whilst the majority of each stain was within the cell body , VF were positive for each antigen at focal regions along their length ( Fig . 4G & H ) . However , unlike other pathogenic actin tails , the majority of HIV particles capping VF were not routinely associated with Arp2 , Wasp , pTyr , or Cortactin ( 19% , 16% , 15% and 24% , respectively; n = 94 ( D = 3 ) ; representative images presented in Supplementary Fig . S2 ) . Taken together these data initially support VF to be structures that are divergent from Arp2/3 mediated actin tails , in that there is consistent lack of key cellular antigens in association with HIV particles . However we must note the same cellular antigens were present along the length of VF and thus may indeed be important for the dynamic nature of these structures . To explore how VF were formed , we investigated two major pathways of filopodial nucleation/elongation; the F-actin regulators Arp2/Wasp-dependent pathway , and the formin Diaph2 pathway . Whilst initial immunophenotyping observed both Arp2 and Wasp along filopodia , it was difficult to determine in this setting whether they were in fact the dominant regulators of VF . We also attempted immunostaining Diaph2 in a similar manner to Arp2 and Wasp , yet with currently commercially available antibodies , we were unable to specifically detect Diaph2 in fixed samples . Given the limitations of immunophenotyping of filopodia , we generated stable U937 clones expressing shRNA targeting Wasp and Diaph2 , and we characterized VF after successful knockdown at the protein level ( Fig . S2 ) . When Wasp was knocked-down , VF lengths and velocities remained unchanged compared to the scrambled shRNA controls ( Fig . 5A ) . We obtained similar results when we treated the infected cells with the Bcr/Abl and Src family tyrosine kinase inhibitor Dasatinib under concentrations and conditions that readily prevent Arp2/3 complex activation by Vaccinia ( Fig . 5B [34] , confirming that Arp2/Wasp was not involved in VF formation/elongation . In contrast , when shRNA knockdown of the formin Diaph2 were used , significantly reduced VF lengths and velocities were observed ( Length: 1 . 95 µm+/−1 . 683; n = 181 , versus control p<0 . 0001 . Velocity: 0 . 198 µm . sec−1; n = 164 , versus control p<0 . 0001 ) ( Fig . 5A , C & D; Video S12 ) . Using the same shRNA lentiviral pool , we could not significantly knockdown Diaph2 in primary DCs ( Fig . S2F ) , even after priming DCs with SIV Vpx as previously described [35] . Thus we utilized the TRIPZ lentiviral vector encoding shRNA towards Diaph2 , as transduced cells could be identified with the aid of red fluorescent protein . Whilst transduction rates in primary DCs using lentiviral vectors were low , we could identify TRIPZ transduced and HIV infected DC populations . Using this approach , we also observed primary DCs to express significantly shorter VF in comparison to non-transduced and scramble shRNA transduced cultures ( 2 . 86 µm+/−1 . 83 & 9 . 37+/−3 . 98 for Diaph2 versus Scrambled shRNA respectively n = 79; p<0 . 0001; Video S13 ) . Although the low primary DC transduction rates using the TRIPZ lentiviral vector limited further study of Diaph2 depleted cells . As the U937 cell line expressed equivalent VF and was homogenously depleted of Diaph2 , we utilized this cell line model to dissect the role of VF in cell-cell transfer . Diaph2-dependent VF formation was important for cell-cell transfer as subsequent co-culture of Diaph2 shRNA U937 cells with permissive CD4 T cells line showed significant attenuation in cell-cell transfer compared to control shRNA conditions ( control U937 versus diaph2; p = 0 . 00278 , n = 3 Fig . 5E ) , supporting a direct functional role of long VF in HIV spread . Of note , both stable knockdown of Wasp and Diaph2 in the context of the U937 cell line had no effect on their overall viability , as viability assays using either Alamar Blue or trypan blue exclusion were not significantly different to scrambled shRNA controls . To further control for defects in HIV assembly and release we infected Diaph2 and Sr transduced cells , normalized their infection 3 days post infection to 10% and then harvested viral supernatant 3 days post normalization . Using both detection of reverse transcription ( to detect particles in the supernatant ) versus infectivity by titering the supernatant on the TZMbl indicator cell line , we did not observe any significant difference in HIV release or infectivity ( Supplementary Fig . S2G ) . To further characterize HIV involved in VF we investigated HIV envelope ( Env ) , Gag and Nef . Initially we focused on Env , given HIV viral cytonemes are exclusively dependent on the presence of Env . To infect DCs with a virus that does not encode Env , we VSVg pseudotyped a HIV iGFP that is genetically devoid of Env . This enabled DC infection and the subsequent appearance of HIV particles in the absence of Env expression . Using this approach , we observed VF lengths and trajectory velocities to be not significantly different with HIV expression Env ( Fig . 5F ) . As we observed capped HIV at VF tip , we initially hypothesized there was a common link between HIV budding and VF formation . This hypothesis is readily supported by observations that HIV Gag membrane targeting ( that proceeds budding ) and filopodial formation occur at the same phosphoinositol 4 , 5-bisphosphate ( PIP2 ) plasma membrane domain [36] , [37] . Alternatively filopodial formation requires outward membrane curvature generated by F-Bar proteins ( eg . Toca-1 ) [38] and the formation of the HIV bud may provide the equivalent “membrane curve” substitute leading to the seeding of filopodia at the membrane during HIV budding . To test this potential “common lipid domain” hypothesis , we reasoned that HIV Gag expression alone should result in VF capping . We generated a plasmid that would allow expression of the same HIV Gag -iGFP polyprotein that is expressed in the HIV iGFP virus , and we generated stable HeLa cell clones that express Lifeact-mcherry [39] , can be used to readily visualize F-actin and VF dynamics in real-time . The choice of HeLa cell was motivated by its ease of transfection within our Gag -iGFP construct and the fact filopodia can form using this adherent cell type . Through live imaging of F-actin and HIV Gag expression alone , we readily observed the formation of VF ( Video S14 ) . Thus localization to the filopodial tip , is a function of HIV Gag alone , and is entirely consistent with Gag embedding at the same lipid site , where VF are nucleated . Finally we investigated the role of the HIV accessory gene Nef as it has recently been observed to influence non-viral filopodial formation on CD4 T cells [40] . We generated deleted HIV Nef in HIV iGFP . Using live cell imaging , we observed significantly greater numbers of VF in HIVWT-iGFP versus their Nef deleted counterparts ( termed HIVNEF-ve-iGFP ) ( Fig . 5G; HIVWT-iGFP = 8 . 89±5 . 17 VF . cell−1 versus HIVNEF-veiGFP = 2 . 49±2 . 01 VF . cell−1 , p<0 . 0001 ) . Although uninfected DCs expressed similar filopodia to infected DCs , their frequency compared to VF in HIV infected cells , was significantly lower ( Fig . 5G ) . Therefore whilst HIV Gag localizes the virus to the tip , expression of HIV Nef positively influences VF frequency . To further determine whether HIV Nef alone can increase filopodial numbers , we inserted the HIV nef gene into the lentiviral vector pRRLSIN . cPPT to create Nef-eGFP fusion equivalent to that used by Nobile and colleagues [40] . Using this approach we could readily transduce primary DCs with the Nef-eGFP fusion protein and its matched eGFP control . Expression of Nef-eGFP alone did not significantly increase the frequency of filopodial expression compared to eGFP only transduced DC co-cultured with autologous CD4 T cells ( 2 . 43+/−1 . 31 versus 2 . 875+/−1 . 67 filopodia . cell−1 for Nef-eGFP versus eGFP respectively; n = 49 , p = 0 . 4165 ) . The capacity of VF to mediate tethering and co-ordination of neighboring CD4 T cell contacts supports their role as viral synapse ( VS ) precursors , with VS formation triggered by HIV envelope and eventually mediating CD4 T cell infection . To image closer synapse formation , we identified infected DC with VF in close proximity to CD4 T cells and imaged the potential for synapse formation . Using both and HIV-iGFP ( Fig . 6A ) and HIV-T ( Fig . 6B ) , we observed viral transfer across to the CD4 T cell membrane . We observed dynamic movement of newly produced HIV virions ( HIV-T and HIV iGFP positive ) over the entire CD4 T cell membrane ( Fig . 6A & 6B; Video S15 ) . Viral movement was over and around the CD4 T cell membrane ( Video S15 ) , with continual seeding and repositioning of the virus for up to 3 hours ( the duration of the video acquisition ) . In addition , this phenomena could proceed from one infected DC over multiple CD4 T cells simultaneously ( Fig . 6B & C ) . Of note co-culture of CD4 T cells with DCs infected with VSVg HIVENV-ve ( HIV envelope negative ) did not result in seeding of virus over the CD4 T cell surface . Whilst HIV iGFP would detect all virions , the detection of HIV-T on the CD4 T cell membrane supports either covering of the CD4 T cell membrane by DC in a form of cytophagocytosis or assembly , release and dissemination of HIV from the DC cytosol ( as HIV-T only stains reduced cytosolic HIV Gag ) . To determine the extent of DC membrane that cover CD4 T cells at the viral synapse , we fixed and stained HIV infected DC-CD4 T cell co-cultures with the abundant DC membrane antigen CD209 . Whilst the DC membrane covered substantial surface areas of the CD4 T cells engaged in viral synapses , the distal areas of the CD4 T cell membrane were not CD209 positive and thus we conclude although CD4 T cells were engulfed , they were not cytophagocytosed ( Fig . 6C ) . Although our results confirmed that VF eventually mature into a close stabilized contact that extensively covered the CD4 T cell membrane , the actual point of viral fission , maturation and CD4 T cell infection was still not known . Thus utilizing antibodies specific for mature HIV [22] , we observed distal , yet frequent , VF contacts with CD4 T cells only bearing immature HIV buds . However , when analyzing closer DC- CD4 T cell contacts we observed the appearance of mature HIV particles not only at the proximal contact of the synaptic cleft , but also at the distal sections of the CD4 T cell plasma membrane ( Fig . 6D ) . We further analyzed the proportion of mature HIV particles by scoring mature HIV particles by antibody staining using the anti-capsid mAb 183 versus total HIV using HIV iGFP . Using this approach we observed the proportion of mature HIV virions at the synapse to be 0 . 4282+/−0 . 272 ( for n = 56 synapses ) . In contrast , at the distal CD4 T cell membrane , the mature particle proportion was 1 . 6+/−1 . 462 ( for n = 56 synapses ) . Taking the accumulative observations from live cell imaging of DC-T cell viral synapses and analysis of mature HIV particles , we initially conclude at the synaptic cleft the majority of particles appearing consist of immature HIV buds that eventually undergo fission , viral maturation and trafficking to the distal side of the CD4 T cell membrane . Further ultrastructural analysis of virus at the DC-T cell synaptic junction confirmed this observation with the majority of HIV particles appearing at the synapse consisting of HIV buds ( 58% ) , followed by mature HIV particles ( 35% ) and on rare occasions immature particles that have undergone fission ( 7% ) ( Fig . 6E ) ( n = 120 particles counted at DC-T cell synaptic junctions ) . The significantly lower VF number in HIVNEF-ve-iGFP infected DCs gave the unique opportunity to determine the potential role of VF in absolute viral transfer efficiency in primary DC , whilst not significantly influencing their viability and/or phenotype . Thus we tested the ability of immature DC infected with HIVNEF-ve ( low VF frequency ) versus HIVWT ( high VF frequency ) to transfer virus to a permissive ( activated ) autologous CD4 T cell population . Given CD4 T cells are VF low/absent expressing cell types , we also tested CD4 T cell to CD4 T cell transfer as a control . We stringently normalized both HIVNEF-ve and HIVWT infected populations ( Fig . 7A schematic ) prior to dilution and addition to target CD4 T cell populations . After 5 days co-culture , we utilized flow cytometry to not only resolved infected T cell population , but also determine the proportion infected . To enumerate the latter we take advantage of HIV capsid staining using the directly conjugated mAb clone KC57 . In infected populations ( both DCs and CD4 T cells ) , HIV capsid accumulates to significantly high levels to enable resolution of uninfected and infected populations as previously described [16] . Resolution of productive infection ( versus only carriage of the virus ) is further supported by the down-regulation of CD4 only observed in the capsid high population [11] . To unequivocally rule out this population is a result of CD4 T cell Gag acquisition independent of infection , we incubated DC-T cell co-cultures with 10 µM of AZT and confirmed the appearance of the p24/Capsid high population to be a result of productive infection ( Fig . S2H ) . At limiting dilutions ( the equivalent of adding 1 to 25 infected cells per 100 , 000 CD4 T cells; Fig . 5D ) , HIVWT -infected DC were significantly more efficient at virus dissemination than both DCs infected with HIVNEF-ve and CD4 T cells infected with HIVWT ( Fig . 7B ) . In contrast there was no significant difference in efficiency of dissemination when HIVWT or HIVNEF-ve HIV infected CD4 T cells were the viral donors ( Fig . 7C ) . These data coupled with lower HIV transfer in Diaph2 knockdown U937 transfer , suggest that efficiency of viral spread is correlated with high frequency of long dynamic VF .
The key to HIV survival is its ability to find new targets and to do so within an increasingly hostile environment , due to the progressive activation of the acquired immune response . Limiting DC numbers can seed CD4 T cell populations in a manner analogous to how they stimulate the immune response , thus infected DC represent a point of viral amplification within a CD4 T cell population . Herein we have mapped key mechanisms of how HIV spreads from this important cell type . In DC , HIV has hard-wired itself into the tip of promiscuous contacting filopodia . Initially VF abundance , nature of movement , and velocity allow HIV to partake in hundreds of fleeting CD4 T cell contacts . Once CD4 T cells are tethered by VF , they are then subsequently repositioned and converge to become the DC-T cell viral synapse . The mechanistic transfer of virus via long viral filopodia is generated by the formin Diaph2 , and not via the Arp2/3 complex pathway commonly observed with other pathogens in the generation of actin tails . In addition HIV Nef positively influences VF frequency and also coincides with the ability for DC to spread virus to CD4 T cells . Thus herein we hypothesize the potency of viral dissemination by DC to be the combination of CD4 T cell target selection through a mechanism of repertoire scanning by long Diaph2 dependent filopodia , followed by the maturation of this contact to ensure the virus not only has access to the maximum surface area of the CD4 T cell membrane , but is also released when the virus is least exposed to the extracellular environment . VF are not variants of nanotubes or viral cytonemes and there is significant evidence herein that set them as unique structures in their own right . Their ability to form in the absence of HIV envelope , their formation on infected cells , lack of continual cell-cell tethering and terminal restriction of the virion to the VF tip and no evidence of movement along the filopodia are in immediate opposition to the formation and function of viral cytonemes/nanotubes . Furthermore , VF formation is dependent on the formin Diaph2 , a key regulator of long filopodia [41] and an actin regulator enriched in cells of myeloid lineage [42] . We must also clarify that the structures described herein on immature DC are not equivalent to that recently described by Nikolic and colleagues [43] . Briefly , Nikolic et al , observed outside in signaling by HIV envelope by engagement of the C-Type lectin CD209 , leading to activation of the Src-CDC42-Wasp pathway . This culminated in membrane extensions originating from DC being bound with mature HIV extending towards target CD4 T cells . VF on infected DC differ significantly from such membrane extensions . Firstly , HIV DC membrane extensions represent early transfer of HIV in trans and not via the de novo reservoir of infected DCs as observed for VF . Secondly , mature virions appear along the length of membrane extensions as opposed to newly forming virions exclusively located at the tips of VF . Finally , membrane extensions are dependent on HIV envelope signaling through a Src-CDC42-Wasp pathway , whereas VF appear both independent of HIV envelope , in cells depleted of Wasp and in the presence of saturating levels of the Src inhibitor Dasatinib . As aforementioned , the Arp2/3 complex has been implicated in the formation of pathogenic actin tails for Vaccinia , enteropathogenic Escherichia coli , Listeria , and Shigella [33] , [44] , [45] , [46] . We could not conclude that the Arp2/3 complex was the dominant cellular regulator involved in HIV VF . This was supported by the lack of Arp2 and phosphotyrosine association with HIV at filopodial tips , and the inability of the Abl/Src kinase inhibitor Dasatinib and Wasp knockdowns to disrupt VF length and trajectories . Although Diaph2 is essential for elongation of long filopodia in our study and elsewhere [41] , the persistence of often numerous short VF ( similar in nature to that recently described filopodia in cryo-electron microscopy studies of HIV Gag expressing U87mg cell line [47] ) does not support initial nucleation of VF at the plasma membrane to be Diaph2 dependent . Thus , the initial nucleation of shorter VF , that we would predict to be the foundation of longer VF driven by Diaph2 , remain currently unknown . As HIV Gag alone locates to the tip of VF , there may be key variables intrinsic for HIV budding that are also important in VF formation . The use of a common plasma membrane lipid by HIV and filopodia may be one explanation of VF capping [36] , [37] and the appearance of similar filopodia in uninfected DCs when co-culture with autologous CD4 T cells is consistent with this hypothesis ( HIV budding at filopodial birth sites ) . Although the high frequency of HIV positive filopodia in infected DC would mean HIV buds near completion would need to occupy greater than 90% of lipid sites where filopodia arise . The alternative explanation would be HIV can influence filopodial formation by either physically changing the membrane or via the recruitment of a protein involved in actin regulation , analogous but obviously divergent to other pathogens . For the former hypothesis , the formation of the HIV bud may substitute for F-Bar containing proteins ( eg . Toca-1 ) to generate the membrane curvature needed for seeding F-actin polymerization that leads to filopodia . For the latter hypothesis , the common theme of all pathogens that cap filopodial-like structures is the use of a pathogen encoded protein to regulate actin ( eg . A36R , ActA and Sca2; Reviewed in [20] ) and thus this would support a viral protein positively influencing the formation of filopodia . HIV Gag has been previously observed to recruit cellular proteins involved in the endosomal sorting pathway , such as Tsg101 . However HIV Gag PTAP mutants did not support VF to be dependent on this cellular pathway . Whilst HIV Gag alone may influence VF formation , we also observed a positive influence of HIV Nef in their formation . Initial studies of SIV transfer from exposed DC to CD4 T cells [48] and latter studies using HIV [49] showed HIV transfer phenotypes from immature DC that are consistent with our current observations . Whilst other mechanisms have been suggested for the ability of Nef to manipulate immature DC [50] , [51] , at present we favor the hypothesis of Nef expression leading to greater VF numbers and in turn greater viral spread from DC . Further support for this hypothesis is observed in Diaph2 knockdown experiments , in which the appearance of shorter static filopodia also results in significant attenuation of cell-cell viral transfer . However Nef expression alone does not mediate the high frequency VF phenotype . Rather this phenotype only appears when Nef is expressed in the context of the viral genome . Thus we presently support the mechanism of increase in filopodial numbers to be a synergy between HIV Gag and Nef within the cytosol of infected cells that positively influences VF frequency . Prior to contacting CD4 T cells , we observed VF to engage in non-random fast and broad overlapping sweep targeted trajectories . Multiple VF in concert effectively increase the contact zone and thus the probability of contacting a neighboring cell type . For instance we currently estimate the number of T cells that fit within a contact zone maintained by several filopodia at 10 µm to be 104 T cells per infected DC ( accounting for the void volume between spheres as outlined by Hale [52] ) . In contrast , the DC plasma membrane can only maintain upwards of 14 T cells bound directly . Thus increasing the contact zone for the virus is one advantage , but moving the viral particle away from the plasma membrane may be additionally advantageous . For instance , this would prevent localized viral budding that would otherwise lead to re-infection ( superinfection ) of the viral producing cell ( DC ) or , in the context of an antigen presenting cell , antigenic processing and viral degradation . Whilst recent studies have observed cell-cell contact and mass viral release to result in evasion of HIV antiretrovirals [3] , the dynamics of DC to T cell transfer may provide further obstacles in blocking cell-cell transfer . For instance , the initial contacts and tethering of CD4 T cells by VF can proceed in the absence of HIV envelope expression and potentially may negatively influence the potency of HIV attachment/fusion inhibitors and/or neutralizing antibodies . The eventual synaptic contact between infected DC and CD4 T cells is intriguing as a significant proportion of the DC membrane is involved in the formation of the viral synapse . As a consequence , rather than a synaptic-like button forming , as has been observed for CD4 T cells [53] , the viral synapse resembles a “cup’ where a large surface area of the CD4 T cell membrane is engaged . Shortly following DC-T cell engagement dynamic seeding of virus proceeded over the membrane of the CD4 T cell , where a significant proportion of HIV buds appeared at the DC-T cell contact point followed by the appearance of mature virus accumulation at the distal face of the CD4 T cell membrane . The extensive sheet-like contacts and the envelope dependent dynamic viral seeding of CD4 T cells are reminiscent of recent work by Yu et al and Felts et al , [10] , [54] , who characterized similar sheet-like contacts occurring between mature HIV pulsed DCs transferring virus in the process of in trans CD4 T-cell infection . However the important difference to note in the latter studies was the fact the mature DCs were not infected , but rather transferring a finite pool of mature virions to a contacting CD4 T cell population . Infected DCs on the other hand engage CD4 T cells in a similar manner but continually supply new virions through HIV budding at the at the DC-T cell synapse . However it is important to note that both mature DCs and infected immature DCs are efficient at seeding CD4 T cell populations via the above mentioned mechanistically distinct transfer pathways . To conclude , in observing the real-time dynamics of de novo HIV spread between infected DC and CD4 T cells , we have identified a novel pathogenic structure , that combines the biogenesis of newly budding HIV virions with that of long Diaph2 dependent filopodia . Whilst projection of virus away from infected DC has the immediate benefit of preventing superinfection and viral degradation , VF dynamics support a structure that has corrupted a pre-existing and ubiquitous cell–cell contact pathway needed for the communication of the primary immune response . Fast and numerous contacts by VF ensures neighboring cells are filtered , leading to target selection and sychronized delivery of virus en masse and at a time where cell-cell membrane contacts are not only maximal , but provide limited access to the hostile external environment .
Plasmid constructs were all based on the CCR5 using HIV pNL43AD8ENV clone ( Courtesy of E . Freed , through the AIDSreagent Repository ) . As all viruses are derivatives of NL43AD8ENV , they are referred to hereon as “HIV” . HIVENV-ve was generated from the pNL43 clone as previously described [55] . HIVNEF-ve clones were generated by sub-cloning the BamHI-NcoI fragment of HIV NL43 into pLitmus29 ( New England Biolabs , Beverly , MA ) with mutagenesis of the Nef start codon to a stop codon carried out using the Quickchange mutagenesis method ( Strategene , La Jolla , CA ) . To insert imaging labels into the HIV genome ( for either HIV-T or HIV iGFP , the SpeI-BssHII restriction fragment from pNL43 was sub-cloned into pLitmus29 and all subsequent mutations were carried out using the Quickchange mutagenesis . All details regarding HIV-T and related biarsenical staining have been previously described in detail [22] , [56] . The introduction of the eGFP sequence into HIV Gag open reading frame has been previously described [23] and all viral variants listed as HIV iGFP are equivalent in Gag sequence to that previously described by Hubner and colleagues as HIV iGFP . After insertions into the GAG polyprotein were made in the pLitmus vector , clones were subsequently verified by sequencing and re-introduced into pNL43AD8ENV , pNL43ENV-ve , pNL43AD8ENV , NEF-ve and pNL43AD8ENV , PTAP-ve ( HIV PTAP mutant courtesy of Dr Eric Freed , NIH , USA ) using the SpeI-BssHII specific restriction sites . All transfections to generate HIV stocks used polyethylenimine ( at 1 mg . ml−1 and neutralized to pH 7 ( PEI Max , Polysciences , PA ) ) transfection of the F293T cell line ( Invitrogen , Carlsbad , CA ) as previously described [22] . Briefly , 20 mg of total plasmid DNA is diluted into a final volume of 1 ml in tissue culture grade 0 . 9%w/v NaCl ( Sigma ) . 80 ml of PEI Max , as prepared above , is then added drop-wise to the diluted plasmid DNA & vortexed for 10 seconds . The subsequent Plasmid-DNA mix is then left to stand at room temperature for 10 minutes and then added drop-wise to 30×106 trypsinised F293T cells resuspended in a final volume of 15 mls DMEM supplemented with 10% Fetal calf serum . Cells were cultured overnight in a T-150 tissue culture flask after which cells were gently washed and new DMEM media was replaced . For rescue experiments of HIV iGFP with either HIV or psPAX2 , molar ratios of HIV iGFP to the respective plasmid were generated whilst maintaining the total DNA content . To ensure PEI∶DNA ratios did not change ( for psPAX2 rescue experiments ) , all reactions were normalized to 20 mg using molecular grade salmon sperm DNA ( Sigma ) . For instance a ratio of 1∶1 HIV iGFP to HIV was typically 10 mg of HIV iGFP to 10 mg of HIV , whereas a 1∶2 ratio was 6 . 7 mg of HIV iGFP to 13 . 3 mg of HIV . Unless otherwise indicated , to generate “rescued” HIV iGFP viral stocks , HIV iGFP was co-transfected with psPAX2 ( From Didier Trono through the AIDSreagent , NIH . NIAID ) at a molar ratio of 2∶1 HIV iGFP to psPAX2 . DC infections with HIVENV-ve , utilized VSVg-envelope pseudotyping as previously described [22] . Routine production , purification and titering of viral stocks are outlined as previously described [22] . To image F-actin dynamics the optimized LifeAct sequence [39] was fused to the N-terminus of mCherry with the pLVX-mCherry lentiviral vector ( Clontech , Mountain View , CA ) . Lentivirus was the generated as previously described [57] using polyethylenimine ( Polysciences ) and the lentiviral helper plasmids pHEF-VSVg ( Courtesy of Dr . Lung-Ji Chang through the AIDsreagent repository , NIH ) and psPAX2 . The HIV permissive HeLa cell TZMbl ( Courtesy of Dr . John C . Kappes , Dr . Xiaoyun Wu and Tranzyme Inc , was then infected at an MOI of 1 and 7 days post transduction , high Lifeact mCherry single cells clones were sorted into 96 well plates using a FACSAria ( Becton Dickinson , San Jose , CA ) . TZMbl stably expressing LifeAct-mCherry we now refer to as TLC cells . To image HIV Gag alone with iGFP equivalent to the HIV iGFP construct , we generated the TI3 plasmid by placing the HIV GAG-iGFP open reading frame over the existing eGFP open reading frame in the pEGFP-NI vector using the Kpn1 and Not1 restriction sites . To image the formation of virus like particles using the TI3 plasmid , we co-transfected 1×106 TLC cells with TI3 and psPAX2 at a ratio of 0 . 5 mg∶0 . 5 mg with 9 ul of polyethylenimine ( Polysciences PEI Max prepared as described prior ) . One day post transfection , cells were trypsinized and replated in of a 35-mm imaging dishes with #1 . 5 coverslips ( MatTek , Ashland , MA ) , and cultured for a further 24 hours prior to imaging . To express HIV Nef in isolation , we PCR amplified the HIV nef gene from the pNL43 plasmid ( AIDSreagent ) using oligonucleotides AATTCTAGATGGGTGGCAAGTGGTCAA & CGGAGTACTTCAAGAACTGCTGGGATCCTAT . Amplicons were then purified and restriction digested with XbaI and BamHI and directionally ligated into the XbaI/BamHI cut pRRLSIN . cPPT lentiviral vector ( courtesy of Didier Trono via the Addgene plasmid repository ) . Note the above lentiviral vector was modified to encode the XbaI restriction site 6 base pairs upstream from the BamHI site . Vectors were then sequenced to ensure no PCR driven base pairs were changed in the cloning process . Lentiviral particles were then generated using the helper plasmids pHEF-VSVg and psPAX2 as described for LifeAct-mCherry lentiviral particles . To transduce DCs were generated SIV 3+ virus like particles ( SIV 3+ is courtesy of Andrea Cimarelli ) as previously described [35] and exposed DCs 24 hours prior to lentiviral transduction . Control experiments transduced primary DCs with eGFP only pRRLSIN . cPPT lentiviral particles . DCs were imaged after 4 days post-transduction under live imaging conditions outlined for HIV iGFP infected DCs . All transfections to generate viral stocks use the HEK F239T cell line ( Invitrogen ) are described in detail above and else where [22] . Initial titering and analysis of HIV viral stocks utilized the TZM-bl indicator cell line . Briefly , this cell line expresses HIV LTR-b-galactosidase and infection is detected 4 days post infection using X-gal staining and enumeration of infected cells using an Elispot reader ( AID Diagnostika ) as previously described [58] . Purified monocytes were isolated from peripheral blood mononuclear cells ( PBMC ) using CD14 positive selection as outlined by the manufacturer ( Miltenyi Biotech , Gladbach , Germany ) with the exception of using 4°C PBS supplemented with 1% ( v/v ) human serum ( Sigma ) and 1 mM EDTA ( Sigma , St Louis , MO ) . Post isolation , monocytes were cultured with IL-4/GM-CSF ( Biosource , Invitrogen ) , 400 U/1000 U . ml−1 ) supplemented RPMI-1640 media ( Invitrogen ) containing 10% fetal calf serum ( Invitrogen ) media . Unless otherwise stated , all DC infections were at 1 . 00×105 TCID50 . 10−6 cells as previously described [22] . Four days post infection % frequency of DC infection was determined by flow cytometry detection of HIV Gag ( p24/capsid ) staining of cells with clone KC57 ( Beckman Coulter , Miami , FL ) and gating on the resolved p24hi population . To confirm the appearance of de novo HIV Gag in the p24hi population , the nucleoside reverse transcriptase inhibitor AZT was also included as a control . To ensure in vitro derived DC were not exposed to any maturation stimuli ( eg . Endotoxin contamination ) during culture , surface phenotyping of CD206 , CD209 , CD83 , CD25 was routinely carried out as previously described [59] . Autologous CD4 T cells were isolated from the CD14 depleted fraction of PBMC by depletion using the CD4 T cell Isolation Kit II as outlined by the manufacturer ( Miltenyi Biotech ) . CD4 T cells were subsequently cultured at a density of 2×106 cells . ml−1 in RPMI 1640 supplemented with 10% fetal calf serum and 20 U . ml−1 IL-2 for four days , after which they were counted and co-cultured with infected DC populations at a ratio of 3 CD4 T cells to 1 DC for live imaging . For quantitative co-culture assays , purified CD4 T cells were activated with T Cell Activation/Expansion Kit as described by the manufacturer ( Miltenyi Biotech ) . Purity and activation status of CD4 T cells were determined by CD4 , CD3 , CD69 and CD25 surface staining and flow cytometric analysis . Live-cell imaging was carried out using the 60× 1 . 42 NA oil immersion lens with an inverted Olympus IX-70 microscope ( DeltaVision ELITE Image Restoration Microscope , Applied Precision/Olympus ) and unless otherwise stated a photometrics CoolSnap QE camera . For DC-CD4 T cell co-culture movies , 1×105 . 200 ml−1 cells were cultured in the centre of a 35-mm imaging dishes with #1 . 5 coverslips ( MatTek ) prior to adding 2 mls of warm GM-CSF/IL-4 supplemented media ( as above ) . Imaging was carried out 30 minutes after co-culture , once cells has settled above the coverslip within the Matek dish . For time-lapse movies , eGFP and DIC channels were imaged at approximate 3 frames . sec−1 , with time-lapse movies presented as overlays . Manual single-particle tracking was performed using ImageJ ( NIH , Bethesda , MA ) using the MtrackJ plugin ( courtesy of Erik Meijering , at Erasmus MC - University Medical Center Rotterdam ) . VF tip Velocities were calculated for each particle based on movement across the XY focal plane ( ie . If VF tips moved out of the focal plan by movement in Z direction , they were excluded for velocity calculations ) . Arc velocities were measured when VF were not in contact with CD4 T cells , whilst scan speeds were calculated when VF tips were in association with neighboring CD4 T cells . Hurst Exponents were calculated from trajectories that were in excess of 20 seconds of duration ( that is , the VF that remained within the focal plane during the trajectory period ) . To calculate approximate contacts over time , fields of view with equivalent cellular densities were selected for analysis and VF contacts with neighboring CD4 T cells were enumerated over a 10 minutes period , with results presented as approximate contacts per hour . For fixed cell imaging , cells were resuspended at 2×106 cells . ml−1 in warm RPMI 1640 , cytospun onto 22×60 mm 1 . 5 coverslips ( VWR international , Batavia , IL ) pre-coated with Celltak ( BD Bioscience ) , and then fixed in 4% paraformaldehyde ( Sigma ) for 20 minutes at room temperature and then neutralized with 50 mM NH4Cl ( Sigma ) for 3 minutes . Cells were permeabilized with 0 . 05% Triton-X ( Sigma ) for 1 minute at room temperature , stained with the indicated mAbs in presence of 5% appropriate species serum , followed by the appropriate secondary reagent . Murine mAb were used at 5 mg . ml−1 unless otherwise stated . Arp2 mAb ( clone FMS96 ) was from Abcam ( Cambridge , United Kingdom ) , Cortactin mAb ( clone 4F11 ) and anti-phosphotyrosine ( Clone 4G10 ) were from Millipore ( Billerica , MA ) and WASP mAb ( Clone D1 ) was from Santa Cruz Biotech ( Santa Cruz , CA ) . HIV p24 mAb was clone 183 ( Courtesy of Dr . Bruce Chesebro and Kathy Wehrly via the NIH AIDSreagent program ) . CD209 mAb DCN46 was from Becton Dickinson ( Franklin Lakes , NJ ) . The human monoclonal antibody to HIV-1 gp120 , 2G12 was courtesy of Dr . Hermann Katinger through the NIH AIDSReagent program . Secondary antibodies were goat anti mouse or goat anti human Alexa Fluor 555 ( IgG H+L; Highly cross-absorbed; Invitrogen ) . All washes were performed in PBS supplemented with 1% fish skin gelatin ( Sigma ) and 0 . 02% saponin ( Sigma ) . After staining , coverslips were counterstained and mounted with Prolong gold anti-fade reagent with DAPI ( Invitrogen ) onto glass slides . Cells were visualized through a 100× 1 . 4 NA oil immersion lens with an inverted Olympus IX-70 microscope ( DeltaVision Core ) and a Photometrics CoolSnap QE camera . Images were acquired as 50 to 60 serial optical sections of 0 . 15 µm to 0 . 2 µm then deconvoluted and volume projections of the entire Z-series were generated using DeltaVision SoftWoRx software , version 5 . 0 . 0 . Unless otherwise stated , all fixed cell data presented herein are entire Z-series volume projections . Samples were processed and acquired for transmission electron microscopy as previously described [59] . Wasp , Diaph2 and scrambled shRNA plasmids ( a pool of three shRNA plasmids per target ) were obtained from Santa Cruz ( Santa Cruz , CA ) . Lentivirus was produced as described above for Lifact-mCherry lentiparticles . For knockdown in the U937 cell line , cells were infected with respective shRNA containing lentivirus at a MOI of 10 . Five days post infection , transduced cells were selected by the addition of 2 mg . ml−1 puromycin and subsequently passaged in this selective media for 2 weeks . Knockdown at the protein level was verified by western blotting of U937 lysates using the Wasp mAb clone D1 ( Santa Cruz ) and goat-polyclonal sera ( C-12 ) raised against the C-terminal of Diaph2 ( Santa Cruz ) . Scrambled shRNA ( Santa Cruz ) control transduced cells were used as the control for shRNA knockdown studies . For knockdown of Diaph2 in primary DCs , we utilized the TRIPZ lentiviral vector ( Open Biosystems , Lafayette , CO ) that encodes the shRNA sequences in the context of the mir30 scaffold . For DC Transduction , immature DC were transduced with TRIPZ shRNA at a MOI of 1 and cells were subsequently cultured with 1 mg/ml doxycycline to drive TET based shRNA expression . TurboRFP is also under the control of TET in the TRIPZ vector and was used to identify cells that were expressing shRNA . To ensure Nef deleted and WT HIV-AD8ENV seeded an equivalent proportion of donor cells , virus was pseudotyped with VSVg envelope . Briefly , VSVg viral stocks were produced by co-transfecting HIV plasmids with the pHEF-VSVG ( courtesy of Dr Lung-Ji Chang , through the AIDsreagent respository ) at a molar ratio of 1∶1 . To further ensure equivalent proportions of infectivity in HIVNEF versus HIVWT in donor cells , their frequency was enumerated by flow cytometry four days post infection by fixation and permeabilization using Fix/Perm and Perm buffers ( Becton Dickinson ) as described by the manufacturer , in conjunction with the KC57-RD1 ( Beckman Coulter , Miami , FL ) anti-HIV p24 mAb staining . Following enumeration of the HIV p24 high population in infected DC or CD4 T cells , cells were further normalized using uninfected DC or CD4 T cells from the same donor . Given VSVg pseudotyped stocks were equipotent in viral titers , the latter normalization to 5% was similar between HIVNEF-veand HIVWT . The universal recipient for the transfer assay utilized 2×105 activated pure autologous CD4 T cells per well of a tissue culture treated “U” bottom 96 well plate , per 200 ml of RPMI-1640 supplemented with 10% fetal calf serum . After normalization of donor cells , 5×104 cells . 50 ml−1 were serially diluted at steps of 1/5 dilutions and then added to recipients to give a final co-culture volume of 250 ml . Four days post-co-culture , cells were resolved by CD209-APC ( clone DCN46 , Becton Dickinson , Franklin Lakes , NJ ) staining for DC donor cells , CD3 Alexa Fluor 488 ( clone UCHT1 , Becton Dickinson ) for CD4 T cell recipients and HIV p24 ( KC57-RD1 clone , Beckman Coulter , Miami , FL ) for enumeration of recipient CD4 T cell infectivity via flow cytometry using a 4 colour FACSCalibur ( Becton Dickinson ) . For each co-culture condition , the sample was acquired in its entirety and represented greater than 30 , 000 events in the CD3 recipient population in flow cytometric analyses . For U937 transfer assays , indicated cells were infected at an MOI of 0 . 1 of HIV and cultured for 48 hours . 48 hours post infection , cells were enumerated as above for DC and subsequently normalized using uninfected control of Diaph2 shRNA transduced U937 . After washing and normalization , serial dilutions of 5000 cells were co-cultured with the 20 , 000 Jurkat indicator cells - JLTRG-R5 ( Courtesy of Dr . Olaf Kutsch via the NIH AIDSreagent repository ) . Four days post infection , infection was enumerated by fluorescence microscopy , as opposed to flow cytometry , as the latter resulted in the loss of detection of GFP positive CD4 -T cell syncytia . Prior to parametric or non-parametric statistical tests , data herein was tested for normal distribution with the aid of Origin software ( Shapiro-Wilk test ) ( Originlab corporation , Northhampton , MA ) . Results of experiments have been graphed to exhibit the arithmetic means and standard deviations as indicated in figure legends . When two groups were compared , the probabilities of differences were evaluated by using the nonparametric Mann-Whitney test or , in the case of paired samples , the Wilcoxon matched pairs test . Exact p values are stated , unless highly significant different where they are listed as p<0 . 0001 . Given the data presented herein utilizes primary cells from different donors , we initially tested each data set for statistical significance between donors to determine the possibility of donor specific outliers . In the latter case where multiple donors are pooled for overall means we list each observations as ( n = ( D = ) ) where n is equally drawn events from “D” number of donors . Random trajectory analysis was carried out by acquiring XY co-ordinates of trajectories after particle tracking using the MtrackJ plugin in ImageJ . Submission of time and XY co-ordinates as a ASCII text file to the website https://weeman . inf . ethz . ch/hurst_estimator/ , allows calculation of Hurst Exponents , through previously described algorithms designed by Sbalzerini et al [28] at the Mosaic Group . | Dendritic cells represent a unique cell type with respect to HIV , as they are the first point of contact for the virus in the genital mucosa and have the ability to spread virus efficiently in very low numbers to the primary HIV target , CD4 T cells . During the primary immune response , dendritic cells work in small numbers to make numerous and repetitive contacts , in order to filter and communicate with appropriate CD4 T cells . Thus HIV is hypothesized to be hijacking the same DC-CD4 T cell communication . Attempts to observe how HIV would achieve this have largely been limited , as introduction of imaging markers in the virus has often led to significant viral attenuation . Herein by using novel HIV constructs that permit imaging of HIV in infected dendritic cells , we observed newly forming HIV virions on the tips of long finger-like projections known as filopodia . In real-time imaging filopodia pivoted at their base and moved virions along trajectories that led to numerous CD4 T cell contacts . By manipulating filopodial formation we conclude the location of the virus on long filopodial tips allows the virus to corrupt the promiscuous dendritic cell to CD4 T cell contacts for efficient viral spread . | [
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] | 2012 | Mobilization of HIV Spread by Diaphanous 2 Dependent Filopodia in Infected Dendritic Cells |
Fungi contain a remarkable range of metabolic pathways , sometimes encoded by gene clusters , enabling them to digest most organic matter and synthesize an array of potent small molecules . Although metabolism is fundamental to the fungal lifestyle , we still know little about how major evolutionary processes , such as gene duplication ( GD ) and horizontal gene transfer ( HGT ) , have interacted with clustered and non-clustered fungal metabolic pathways to give rise to this metabolic versatility . We examined the synteny and evolutionary history of 247 , 202 fungal genes encoding enzymes that catalyze 875 distinct metabolic reactions from 130 pathways in 208 diverse genomes . We found that gene clustering varied greatly with respect to metabolic category and lineage; for example , clustered genes in Saccharomycotina yeasts were overrepresented in nucleotide metabolism , whereas clustered genes in Pezizomycotina were more common in lipid and amino acid metabolism . The effects of both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts and were differentially distributed across fungal lineages; specifically , GD , which was an order of magnitude more abundant than HGT , was most frequently observed in Agaricomycetes , whereas HGT was much more prevalent in Pezizomycotina . The effect of HGT in some Pezizomycotina was particularly strong; for example , we identified 111 HGT events associated with the 15 Aspergillus genomes , which sharply contrasts with the 60 HGT events detected for the 48 genomes from the entire Saccharomycotina subphylum . Finally , the impact of GD within a metabolic category was typically consistent across all fungal lineages , whereas the impact of HGT was variable . These results indicate that GD is the dominant process underlying fungal metabolic diversity , whereas HGT is episodic and acts in a category- or lineage-specific manner . Both processes have a greater impact on clustered genes , suggesting that metabolic gene clusters represent hotspots for the generation of fungal metabolic diversity .
As one of the primary decomposers of organic material in nature , fungal species catabolize a wide diversity of substrates [1] , including cellulose and lignin , the two most abundant biopolymers on earth [2] . Fungi are also superb chemical engineers , capable of synthesizing a wide variety of metabolites , including amino acids , small peptides , pigments and other natural products with potent toxic activities , such as antibiotics and mycotoxins [3]–[6] . Fungal metabolites have historically been divided into primary , that is metabolites essential for growth and reproduction , and secondary , which include ecologically important metabolites not essential to cellular life [7] , [8] . However , this distinction is arbitrary when applied to metabolic pathways rather than their products not only because the essentiality of a given pathway is species-specific [9] but also because the pathways that generate primary and secondary metabolites are not mutually exclusive [10] , [11] . Perhaps more informatively , pathways can be divided into those shared by most organisms , which can be considered as belonging to general metabolism , and those specialized pathways that have evolved in response to the specific ecologies of certain lineages and , as a result , are more narrowly taxonomically distributed . An intriguing feature of specialized metabolic pathways in fungi is that constituent genes are often physically linked on chromosomes forming what are known as gene clusters [12] , [13] . Fungal metabolic gene clusters are distinct from the developmental gene clusters typically found in animal genomes , such as the Hox gene clusters; whereas animal gene clusters are composed of tandemly duplicated genes [14] , [15] , fungal metabolic gene clusters comprise genes that are evolutionarily unrelated . Fungal metabolic gene clusters participate in diverse activities including nitrogen [16] , [17] , carbohydrate [18] , amino acid [19] , and vitamin [12] metabolism as well as in xenobiotic catabolism [11] , [20] and the biosynthesis of secondary metabolites [e . g . ] , [ 21]–[28] . Although this extraordinary metabolic diversity , whether in the form of clustered or non-clustered pathways , is integral to the entire spectrum of fungal ecological strategies ( e . g . , saprotrophic , pathogenic and symbiotic ) , we still know little about the evolutionary processes involved in its generation . Gene duplication ( GD ) , a major source of gene innovation , is often implicated in the evolution of fungal metabolism [e . g . ] , [ 29]–[31] , especially in the context of whole genome duplication ( WGD ) [32]–[34] and gene family expansion [35] , [36] . Notable examples include the GD of enzymes involved in organic decay [30] , starch catabolism [37] , degradation of host tissues [31] , [38] , [39] and toxin production [36] . Repeated rounds of GD , followed by divergence and differential gene loss , have also been invoked to explain the evolution of the gene clusters that generate the diverse alkaloids produced by plant symbiotic fungi [4] . A second key source of metabolic gene innovation in fungi is horizontal gene transfer ( HGT ) [40]–[44]; significant cases include the transfer of genes involved in xenobiotic catabolism [45] , [46] , toxin production [45] , [47] , degradation of plant cell walls [48] , [49] , and wine fermentation [50] . More recently , HGT has been shown to be responsible for the transfer of entire metabolic gene clusters between unrelated fungi [11] , [51]–[58] . Although both GD and HGT have been extensively studied in fungal genomes , how these two major sources of gene innovation have interacted with clustered and non-clustered metabolic pathways and sculpted their evolution is largely unknown . To address this question , we analyzed 247 , 202 enzyme-encoding genes from 208 diverse fungal genomes whose protein products participate in hundreds of metabolic reactions . We found that both GD and HGT were more pronounced in clustered genes than in their non-clustered counterparts . On average , 90 . 0% of clustered metabolic genes underwent GD and 4 . 8% underwent HGT , whereas 88 . 1% and 2 . 9% of non-clustered metabolic genes experienced GD and HGT , respectively . Remarkably , some genera appear to have undergone a larger number of HGT events than entire subphyla . While the effect of GD was largely stable across metabolic categories , HGT varied extensively . These results suggest that GD is the dominant and stable process underlying fungal metabolic diversity , whereas HGT's impact is more pronounced in specific lineages and metabolic categories . The disproportionate effect of GD and HGT on clustered genes renders metabolic gene clusters into hotspots of metabolic innovation and diversification in fungi .
Analysis of 208 fungal genomes identified 247 , 202 Enzyme Commission ( EC ) -annotated metabolic genes ( ECgenes for short ) , which encoded proteins catalyzing 875 distinct enzymatic reactions in 130 metabolic pathways ( Figure 1; Table S1; Table S2 ) . The percentage of the fungal proteome dedicated to metabolism was 15 . 4% in Saccharomycotina , 12 . 6% in Pezizomycotina and 8 . 9% in Agaricomycetes ( Table S3; Figure S1 ) . Examination of fungal metabolism for the presence of metabolic gene clusters revealed that 3 . 0% ( 7 , 409 ) of ECgenes belonged to 3 , 408 distinct gene clusters , with the average genome containing 16 . 7 metabolic gene clusters and 36 . 3 clustered ECgenes ( Table S3 ) . The percentage of clustered ECgenes was highly variable across the major lineages , being more than two-fold greater in the two Ascomycota lineages , namely Pezizomycotina ( 3 . 6% of ECgenes ) and Saccharomycotina ( 3 . 7% ) , than in Agaricomycetes ( 1 . 6% ) ( Figure 1 , Table S3 ) . For example , the plant pathogen Fusarium solani species complex species 11 ( a . k . a . , Nectria haematococca , Sordariomycetes ) had 152 clustered ECgenes ( representing 6 . 2% of its ECgenes ) , the most of any genome analyzed , the yeast Torulaspora delbrueckii ( Saccharomycotina ) had 59 clustered ECgenes ( 7 . 3% ) , whereas the ectomycorrhizal fungus Laccaria bicolor ( Agaricomycetes ) had only 14 clustered ECgenes ( 1 . 1% ) . To test whether clustering was variable across fungal metabolism , we used the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) metabolism hierarchy [10] to assign all ECgenes to 12 overlapping , higher-order metabolic categories ( carbohydrate , energy , lipid , nucleotide , amino acid , glycan , cofactor/vitamin , terpenoid/polyketide , other secondary metabolite , xenobiotics , biosynthesis of secondary metabolites , and microbial metabolism in diverse environments ) . We found that the proportion of clustered ECgenes varied significantly across metabolic categories ( Figure 2 , Table S4 ) . For example , clustered ECgenes from all lineages were significantly overrepresented in the KEGG categories carbohydrate and terpenoid/polyketide and underrepresented in the glycan category . In addition , the proportion of clustered ECgenes in a given category often varied significantly between lineages . For example , clustered ECgenes in the nucleotide and xenobiotic categories were only significantly overrepresented in Saccharomycotina and Agaricomycetes; clustered ECgenes in the same categories were underrepresented in Pezizomycotina ( Figure 2 ) . Similarly , clustered ECgenes in the amino acid and lipid categories were underrepresented in Saccharomycotina , whereas clustered ECgenes in these same categories were overrepresented in Pezizomycotina and Agaricomycetes ( Figure 2 ) . To evaluate the impact of GD and HGT on fungal metabolism , we inferred GD and HGT events by reconciling the gene tree of each ECgene to the fungal species phylogeny [59]–[61] . Specifically , we assigned costs to GD , HGT , gene loss , and incomplete lineage sorting ( ILS ) and determined the most parsimonious combination of these four events to explain the ECgene tree topology given the consensus species phylogeny . Therefore , HGT events were inferred only when an ECgene tree topology was contradictory to the species phylogeny and could not be more parsimoniously reconciled using a combination of differential GD and gene loss . We evaluated multiple HGT costs and ultimately implemented a cost four times greater than the GD cost because it was the lowest HGT cost that recovered three published cases of HGT without any additional ( e . g . , potentially spurious ) cases of HGT in the corresponding ECs ( Table S5 ) . On average , 88 . 7% of ECgenes per genome were inferred to have undergone one or more GD events ( Table S3 ) . This percentage was lower in early diverging lineages; this was the case for both taxa with typical gene densities ( e . g . , Chytridiomycetes ) as well as for the extremely reduced microsporidians , which displayed the lowest percentages of duplicated metabolic genes ( 49 . 0% and 49 . 5% of ECgenes in E . cuniculi and E . intestinalis , respectively ) . While the low percentages of GD in microsporidians are likely explained by genome streamlining , the low percentages observed in other early diverging lineages are harder to explain , although we note that their current sparse representation in the set of sequenced fungal genomes increases the uncertainty associated with estimating GD and HGT . In contrast , 93 . 7% of ECgenes underwent GD in the Agaricomycetes ( Figure 1 ) , with the button mushroom , Agaricus bisporus , having 97 . 0% of its ECgenes affected by GD ( 704 to 722 ECgenes depending on the strain ) . GD percentage was also high in the Saccharomycotina ( 91 . 4%; Figure 1 ) , including in species belonging to the Saccharomyces sensu stricto group , where the average increased to 95 . 3% , most likely as a consequence of an ancient whole genome duplication [33] , [62] . Our analysis also identified that on average 2 . 8% of ECgenes per genome had undergone one or more HGT events ( Table S3 ) , which could be traced back to 823 unique HGT events . The Pezizomycotina showed the highest percentage of HGT of all the major lineages , with an average 4 . 1% of ECgenes transferred per genome , and Saccharomycotina the lowest , with an average 1 . 8% of ECgenes transferred ( Table S3; Figure 1 ) . Remarkably , some Pezizomycotina genera showed nearly as many or more HGT events than the entire Saccharomycotina subphylum ( Figure 3; Figure S2 ) . For example , we identified 111 HGT events since the last common ancestor of the 15 Aspergillus species , the largest for any genus included in our analysis , but only 60 HGT events since the last common ancestor of the 48 Saccharomycotina genomes . Notwithstanding the fact that genome coverage and age are not the same across fungal genera , several other Pezizomycotina genera showed an abundance of HGT events including Cochliobolus ( 53 HGTs; 8 genomes ) , Fusarium ( 52 HGTs; 4 genomes ) , and Trichoderma ( 50 HGTs; 6 genomes ) . Within the Agaricomycetes , the highest concentration of HGT events was observed in the two Agaricus bisporus genomes ( 23 HGTs ) . Examination of the degree to which GD and HGT have differentially impacted clustered and non-clustered metabolic genes revealed significant differences ( Figure 4; Table S6 ) . On average , 90 . 0% of clustered ECgenes and 88 . 1% of non-clustered ECgenes underwent GD ( P = 4 . 58×10−4 ) . Similarly , 4 . 8% of clustered ECgenes underwent HGT compared to 2 . 9% of non-clustered ECgenes ( P = 4 . 02×10−12 ) . Examination of the impact of GD and HGT in the three major lineages shows that only in the Pezizomycotina was the percentage of GD and HGT significantly higher for clustered ECgenes than for non-clustered ECgenes ( GD: 93 . 3% for clustered ECgenes versus 89 . 5% for non-clustered , P = 1 . 74×10−11; HGT: 6 . 6% for clustered ECgenes versus 4 . 0% for non-clustered , P = 2 . 77×10−10 ) , suggesting that the trend is largely driven by Pezizomycotina . In fact , in both Saccharomycotina and Agaricomycetes GD was more common in non-clustered ECgenes than in clustered ECgenes ( P = 0 . 02 and P = 0 . 01 , respectively; Figure 4 ) . HGT was more common in Saccharomycotina non-clustered ECgenes than in clustered ones , whereas in Agaricomycetes a higher incidence of HGT events was observed in clustered ECgenes , although neither of these associations was statistically significant ( P = 0 . 54 and P = 0 . 16 , respectively; Table S6 ) . To test whether GD and HGT prevalence varied across fungal metabolism , we examined the rates of the two processes in each of the 12 KEGG metabolic categories across our three major lineages . We found that the effect of GD was generally consistent across metabolic categories , with 9/12 categories showing the same pattern of under/overrepresentation of duplicated ECgenes across the three lineages ( Figure 2 , Table S4 ) . Specifically , the categories carbohydrate , glycan , and biosynthesis of secondary metabolites were overrepresented , the categories lipid , nucleotide , cofactor/vitamin , other secondary metabolites , and xenobiotics were underrepresented , whereas energy was not differentially represented in duplicated and non-duplicated ECgenes in all three lineages . Unlike GD , HGT differentially affected metabolic categories in a lineage-specific fashion , with 10/12 categories differing in the pattern of under/overrepresentation of duplicated ECgenes across lineages ( Figure 2 , Table S4 ) . For example , ECgenes in biosynthesis of secondary metabolites were overrepresented for HGT events in Pezizomycotina and Saccharomycotina , but not in Agaricomycetes . In contrast , ECgenes were overrepresented for HGT in lipid and terpenoid/polyketide in Agaricomycetes but underrepresented in the Pezizomycotina . Only 2 categories , amino acid and microbial metabolism in diverse environments , were overrepresented in transferred ECgenes across all three lineages .
On average 88 . 7% of fungal ECgenes retain the signature of one or more GD events in their ancestry compared to only 2 . 8% for HGT ( Table S3 ) . Even though these percentages are not directly comparable because reconciliation of ECgene histories with the species phylogeny requires that costs are assigned for every inferred GD or HGT event [60] , our finding that nearly nine out of every ten metabolic genes have undergone GD suggests that this is the dominant source of gene innovation underlying fungal metabolism . These results are consistent with the hypothesis that specialized metabolic pathways evolve via GD from general metabolic precursors . Support for this hypothesis has come from phylogenetic analysis of single gene families [63] , [64] such as the polykeytide synthases , which share a common evolutionary origin with the fatty acid synthases of general metabolism [65] . Further diversification of genes involved in specialized pathways may occur through additional duplication , functional divergence and differential loss in response to variable ecological pressures as has been proposed for polyketide , nonribosomal peptide and alkaloid biosynthesis genes [4] , [66]–[68] . Our analysis showed that certain lineages in the Pezizomycotina and Agaricomycetes have increased HGT rates . Interestingly , bacteria-to-fungi HGT events are also elevated within Pezizomycotina , particularly in Fusarium and Aspergillus genomes [43] . HGT of entire chromosomes has been reported in Fusarium [69] , [70] , a genus in our analysis , which in addition to Aspergillus , Cochliobolus and Magnaporthe , appears not only receptive to HGT but also includes highly virulent plant and animal pathogens , ecological lifestyles associated with many known cases of HGT [11] , [45] , [47] , [51] , [69]–[71] . Similarly , mycoparasitism in the genus Trichoderma may also provide ecological opportunities for fungal-to-fungal HGT . GD alone or in combination with HGT affected nearly every reaction in fungal metabolism ( 727 , 95 . 7% of ECs that passed the phylogenomic analysis; Figure 5 ) . The effect of both GD and HGT varied between metabolic categories , suggesting that some pathways may tolerate the introduction of new genes better than others . One possible explanation for this variation is that the metabolic networks associated with the different functional categories have different degrees of connectivity . Genes whose products make up large protein complexes or that have many interacting partners exhibit less variation in copy number [35] , perhaps because unbalanced increases in gene dosage can lead to malformed protein complexes and a buildup of toxic intermediates in metabolic pathways [72]–[74] , and might be less likely to undergo GD [75] , [76] as well as HGT [77] . In addition to gene dosage effects , deleterious interactions between native and horizontally acquired proteins that function as parts of multi-protein complexes , and as a consequence have distinct co-evolutionary histories , are likely also important barriers to HGT [77] , [78] . Another possible explanation is that the source of the variation of GD and HGT lies in the differing functions encoded by these metabolic categories . Gene innovation is often correlated with molecular function , with informational genes such as those involved in DNA replication , transcription and translation duplicated and transferred less often than metabolic genes [35] , [76] , [78] . Within metabolism , one might expect that widely distributed pathways involved in universal metabolic functions , such as oxidative phosphorylation and the citric acid cycle , are more likely to be functionally constrained and , as a consequence , less likely to tolerate GD or HGT of their constituent genes . In contrast , GD and HGT might be more advantageous for specialized metabolic pathways that are under strong selection in fluctuating environments [11] . 33 EC reactions are associated with 332 ECgenes that are never duplicated or transferred in our analysis; 31 of these 33 reactions ( 93 . 9% ) are also never clustered ( Table S7a ) . For the majority of these ECs , the reason for the apparent lack of GD or HGT is because they are represented by only a few ECgenes in our analysis; therefore , their ECgene trees consist of few taxa with topologies in agreement with the consensus species phylogeny . For other EC reactions in this set , strong selection pressure to maintain a single , native gene copy could explain the lack of GD and HGT . Only three genes annotated with EC reaction numbers and which were never duplicated or transferred in our analysis were present in the Saccharomyces cerevisiae genome ( YNL219C [2 . 4 . 1 . 259] , YBR003W [2 . 5 . 1 . 83] , and YPR184W [3 . 2 . 1 . 33] ) . When examined against the yeast phenotype and interaction data from the Saccharomyces Genome Database ( http://www . yeastgenome . org ) , these three genes displayed a variety of phenotypes and all their null mutants were viable ( Table S7b ) . Interestingly , overexpression of two of the ECgenes ( YNL219C [2 . 4 . 1 . 259] and YBR003W [2 . 5 . 1 . 83] ) resulted in reduced rate of vegetative growth in S . cerevisiae ( Table S7b ) , suggesting that the acquisition of additional gene copies through GD or HGT could be disadvantageous . Furthermore , one S . cerevisiae ECgene , a glycosyltransferase ( YNL219C [2 . 4 . 1 . 259] ) involved in the biosynthesis of asparagine-linked glycans , has a very complex interaction network of 315 described physical and genetic interactions ( Table S6a ) , which could serve as an additional barrier to GD and HGT . 3 . 0% of fungal genes examined in our study lie within gene clusters . This is likely a conservative estimate because ECgene annotation is better for general rather than specialized metabolism . Although our analysis includes many specialized pathways ( Table S2 ) , such as biotin production ( KEGG map00780 ) , nitrate assimilation ( map00910 ) and terpenoid backbone biosynthesis ( map00900 ) , and the fraction of enzymatic reactions encoded by clustered ECgenes is extensive ( 441 reactions , 50 . 4% of ECs; Figure 5 ) , lineage-specific genes involved in specialized metabolic pathways are less likely to be included . In addition , fungal metabolic gene clusters are often identified through the presence of one or more conserved synthesis genes ( e . g . , genes encoding polyketide synthase or nonribosomal peptide synthase enzymes ) ; proper demarcation of associated genes encoding modifying enzymes ( e . g . , oxidases and transferases ) is challenging because they often lack functional annotation and are lineage-specific , leading to underestimates of gene cluster size . Gene clustering in fungi is positively associated with both GD and HGT , but this pattern appears to be driven by Pezizomycotina ECgenes ( Figure 4 ) . Saccharomycotina ECgenes cluster more often than the global fungal average but are less often affected by HGT , whereas Agaricomycetes display the opposite trend; they experience more HGT but less gene clustering ( Figure S3 ) . GD affects nearly all ECgenes , and this large sample size undoubtedly contributes to the statistical significance of its association with gene clustering , even though the fold increase in the percentage of GD events observed in clustered versus non-clustered ECgenes is only 1 . 02 . In contrast , the effect of HGT on clustered genes is 1 . 66 fold greater than its effect on non-clustered genes . The uniqueness and wide distribution of fungal metabolic gene clusters has given rise to many models that attempt to explain their formation and maintenance [53] , [79]–[83] . For example , the selfish gene cluster model proposes that HGT allows gene clusters to avoid being lost by facilitating colonization of new genomes [84] , [85] . Although several instances of HGT of fungal gene clusters have been discovered in recent years [11] , [51]–[58] , clustered pathways are also more likely to be lost than non-clustered ones [53] . The small percentage of clustered genes affected by HGT in our analysis ( 4 . 8% ) , albeit larger than the background percentage of transferred un-clustered genes ( 2 . 9% ) , suggests that selfishness is unlikely to be the predominant mechanism driving gene cluster formation and maintenance in fungi . Nevertheless , the association between metabolic gene clusters and GD/HGT suggests that gene clustering can facilitate the duplication and transfer of entire metabolic pathways . This is consistent with the view that the barriers to gene innovation acting on gene clusters may be lower than those acting on single genes because the latter undergo GD or HGT in the absence of their functional partners .
A custom enzyme classification pipeline assigned EC numbers to protein-coding genes from the genomes of 208 fungi and 9 stramenopiles ( five oomycetes and four algal relatives ) , which were included in this analysis because of published reports of HGT between oomycetes and fungi [44] . Each gene was queried against a database of KEGG orthology ( KO ) -annotated proteins from 53 KEGG Organisms ( Table S8 ) using ublast ( http://drive5 . com/usearch ) with an accel setting of 0 . 7 and minimum identity cutoff of 0 . 3 . A KO term was assigned to the query for ublast hits with greater than 80% sequence identity and no more than 10% difference in length . In cases where highly similar matches were not recovered , KO terms were assigned to query sequences with respect to the ublast hits showing the lowest e-values; all ublast hits that followed the first e-value increase of 10−50 or greater were excluded . EC numbers were assigned according to KO term ( http://www . genome . jp/kegg-bin/get_htext ? ko00001 . keg ) . Fungal proteomes were screened for metabolic gene clusters as described [81] . Briefly , two ECgenes were considered clustered if they were separated by no more than 6 intervening genes according to published annotation and their EC numbers were nearest neighbors in one or more KEGG pathways . Gene clusters were inferred by joining overlapping metabolic gene pair ranges that were separated by no more than 6 intervening genes; the cutoff of 6 intervening genes was determined empirically with reference to previous analyses of both primary [52] , [53] and secondary [54] metabolism clusters . We constructed a draft fungal species phylogeny using protein sequences of the widely used DNA-directed RNA polymerase II subunit RPB2 marker , which were aligned with mafft using the E-INS-i strategy [86] . The resulting alignment was trimmed with trimal using the automated1 strategy [87] , and the topology was inferred using maximum likelihood ( ML ) as implemented in raxml version 7 . 2 . 8 [88] using a PROTGAMMALGF substitution model and rapid bootstrapping ( 100 replications ) . Branches with bootstrap support less than 50 were collapsed using the Consense module in the phylip program [89] . The final bifurcating and consensus ( multifurcating ) species phylogenies ( File S1 ) were constructed by making targeted corrections to the RPB2 topology based on published literature ( Table S9 ) . ECgene trees were constructed using a custom phylogenomic pipeline ( Figure S4 ) . Guide trees were first constructed for each ECgene family with mafft using the scores of pairwise global alignments [86] and rooted with the notung rooting optimization algorithm using event parsimony . This distance-based guide tree and the consensus species phylogeny were used to delineate groups of homologs by aiming to maximize taxonomic diversity while minimizing the number of paralogs in each gene tree . The ECgene sequences from each one of these groups of homologs were then extracted in FASTA format for phylogenomic analysis . FASTA files of ECgenes with less than 4 or more than 1000 sequences were excluded . Sequences were aligned in mafft using the auto strategy selection [86] . Alignments were trimmed in trimal using the automated1 trimming strategy [87] , and trimmed alignments shorter than 150 amino acid residues were discarded . Phylogenetic trees were constructed using fasttree [90] with a WAG+CAT amino acid model of substitution , 1000 resamples , four rounds of minimum-evolution subtree-prune-regraft moves ( -spr 4 ) , and the more exhaustive ML nearest-neighbor interchange option enabled ( -mlacc 2 –slownni ) . Gene tree-species phylogeny reconciliation was performed in notung using its duplication , transfer , loss and ILS aware parsimony-based algorithm [59]–[61] , [91] . Ambiguity in the fungal species phylogeny and low branch support in ECgene trees were handled through a multi-step approach . First , ECgene tree branches with less than 0 . 90 SH-like local support were collapsed using treecollapsercl v4 ( http://emmahodcroft . com/TreeCollapseCL . html ) . This collapsed ECgene tree was rooted and its polytomies resolved against the bifurcating species phylogeny . This resolved ECgene tree was then reconciled to the multifurcating , consensus species phylogeny using a duplication cost of 1 . 5 , loss cost of 1 and ILS cost of 0 . Transfer costs of 2 , 4 , 6 , 8 , 10 and 12 as well as the option to prune taxa not present in the gene tree from the species phylogeny were evaluated . A transfer cost of 6 with the prune option enabled best recovered published cases of HGT between fungi ( Table S5 ) . Percent GD and HGT were expressed over the 152 , 835 fungal ECgenes that passed this reconciliation pipeline . Because a single ancestral HGT event could be recorded in multiple ECgene trees , we defined unique HGT events as all cases where ECgenes assigned to the same EC number were inferred to have undergone HGT to/from the same recipient/donor nodes in the species phylogeny . Fisher's exact tests were performed using the R function fisher . test with a two-sided alternative hypothesis [92] . P values were adjusted for multiple comparisons using the R function p . adjust with the Benjamini & Hochberg ( BH ) method [93] . Box-and-whisker plots were created using the R plotting system ggplot2 [94] . | Fungi are important primary decomposers of organic material as well as amazing chemical engineers , synthesizing a wide variety of natural products , some with potent toxic activities , including antibiotics and mycotoxins . In fungal genomes , the genes involved in these metabolic pathways can be physically linked on chromosomes , forming gene clusters . This extraordinary metabolic diversity is integral to the variety of ecological strategies that fungi employ , but we still know little about the evolutionary processes involved in its generation . To address this question , we analyzed 247 , 202 enzyme-encoding genes participating in hundreds of metabolic reactions from 208 diverse fungal genomes to examine how two major sources of gene innovation , namely gene duplication and horizontal gene transfer , have contributed to the evolution of clustered and non-clustered metabolic pathways . We discovered that gene duplication is the dominant and consistent driver of metabolic innovation across fungal lineages and metabolic categories; in contrast , horizontal gene transfer appears highly variable both across organisms and functions . The effects of both gene duplication and horizontal gene transfer were more pronounced in clustered genes than in their non-clustered counterparts suggesting that metabolic gene clusters are hotspots for the generation of fungal metabolic diversity . | [
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] | 2014 | The Evolution of Fungal Metabolic Pathways |
Female reproductive cessation is one of the earliest age-related declines humans experience , occurring in mid-adulthood . Similarly , Caenorhabditis elegans' reproductive span is short relative to its total life span , with reproduction ceasing about a third into its 15–20 day adulthood . All of the known mutations and treatments that extend C . elegans' reproductive period also regulate longevity , suggesting that reproductive span is normally linked to life span . C . elegans has two canonical TGF-ß signaling pathways . We recently found that the TGF-ß Dauer pathway regulates longevity through the Insulin/IGF-1 Signaling ( IIS ) pathway; here we show that this pathway has a moderate effect on reproductive span . By contrast , TGF-ß Sma/Mab signaling mutants exhibit a substantially extended reproductive period , more than doubling reproductive span in some cases . Sma/Mab mutations extend reproductive span disproportionately to life span and act independently of known regulators of somatic aging , such as Insulin/IGF-1 Signaling and Dietary Restriction . This is the first discovery of a pathway that regulates reproductive span independently of longevity and the first identification of the TGF-ß Sma/Mab pathway as a regulator of reproductive aging . Our results suggest that longevity and reproductive span regulation can be uncoupled , although they appear to normally be linked through regulatory pathways .
Among human age-related declines , female reproductive cessation is one of the earliest to occur , with infertility and maternal age-related birth defects arising during the fourth decade of life [1] . While artificial reproductive technologies have improved late conception success [2]–[5] , the underlying molecular regulators of reproductive cessation remain largely unknown . Like longevity , the ability to produce progeny with advanced age is likely to be genetically regulated . Thus , understanding the processes that regulate reproductive aging may allow us to address the problems of maternal age-related infertility and birth defects . Although C . elegans produces large broods of progeny and does not care for its young , its reproductive schedule is similar to human females' in that its fertility and reproduction sharply decline in early/mid-adulthood , followed by a long post-reproductive period [6] , [7] . The similarities between C . elegans and human reproductive schedules suggest the intriguing possibility that studies in this model organism may reveal mechanisms regulating reproductive cessation across species . While many C . elegans studies have focused on reproductive fitness , measuring total numbers of offspring produced and average generation time , we are instead interested in identifying regulators of late-life reproduction ( i . e . , the ability of individual mothers to continue to reproduce viable progeny as they age ) . The standard assessments of fitness and fertility do not ascertain the length of time that an individual is capable of successful reproduction . However , we and others [6]–[8] have recently become interested in the latter aspect of reproduction , because of the obvious possible parallels with human reproductive aging , in particular , Advanced Maternal Age ( AMA ) and its related clinical problems . In other words , these model organism studies of reproductive aging are focused on determining the capacity to reproduce successfully late in life , rather than on total progeny production or evolutionary fitness . Hughes , et al . recently showed that worms undergo reproductive aging , a process that is dependent neither on tissue wear ( as manipulation of early progeny number had no influence ) nor on sperm availability [7] . Thus , the reproductive system of C . elegans ages significantly during the first week of adulthood , which is also reflected in the degree of germ line degeneration and oocyte quality decline [8] , [9] . This germ line aging results in reproductive cessation days to weeks prior to death and a relatively long post-reproductive life span , similar to human females' long post-reproductive life span . The C . elegans mutants currently known to delay reproductive aging were originally identified through their longevity phenotypes [7] . These longevity mutants include the insulin/IGF-1 receptor mutant daf-2 [7] , [10] , [11] , and a model of Dietary Restriction ( DR ) , eat-2 [6] , [12] . Insulin/IGF-1 signaling ( IIS ) and FOXO transcription factor activity have been implicated in the regulation of reproduction in several other organisms , including Drosophila [13] , mice [14] , [15] , and humans [16] . Life span extension and slowing of reproductive activity are also hallmarks of Dietary Restriction . Dietary Restriction reduces progeny number and lengthens the reproductive period of C . elegans hermaphrodites [7] , female Drosophila [16] , and female rodents [18] , [19] . C . elegans shifts its reproductive strategy after starvation , modifying its production of males and its outcrossing frequency [17] , and many animals adjust their reproductive life span in response to predation levels [18] . Together , these data indicate that the reproductive schedule is flexible , poised to respond to environmental and molecular perturbations , and that the mechanisms regulating reproductive aging , like longevity , are likely to be evolutionarily conserved . C . elegans has two highly conserved Transforming Growth Factor-ß ( TGF-ß ) signaling pathways , the Dauer ( daf-7 ) and Sma/Mab ( dbl-1 ) pathways . We recently found that TGF-ß Dauer signaling regulates lifespan through its interactions with the Insulin/IGF-1 Signaling ( IIS ) pathway [19] . Members of the TGF-ß Dauer pathway include the ligand DAF-7 , receptor heterodimers DAF-1 and DAF-4 , the R-Smads ( receptor-regulated Smad signal transducer ) DAF-8 and DAF-14 , the Co-Smad ( common-mediator Smad ) DAF-3 , and the transcription factor DAF-5 ( Figure 1A; mammalian homologs are shown in Figure S1A ) [20] , [21] . DAF-4 is a type II receptor that is shared between the Dauer pathway and a second TGF-ß pathway , the Sma/Mab pathway ( Small body/Male tail abnormal ) . The Sma/Mab pathway includes the ligand DBL-1 , the type I receptor SMA-6 , SMA-2 and SMA-3 R-Smads , the Co-Smad SMA-4 , and the SMA-9 transcription co-factor ( Figure 1E; mammalian homologs shown in Figure S2A ) [22]–[25] . Here we show that the Dauer pathway has a moderate effect on reproductive span , mediated at least in part by insulin/FOXO activity . More importantly , we have found that its shared member with the TGF-ß Sma/Mab pathway , daf-4 , and the entire TGF-ß Sma/Mab pathway , strongly influence reproductive aging . Reduced TGF-ß Sma/Mab signaling extends reproductive span disproportionately to life span , and is genetically independent of known longevity regulators . The TGF-ß Sma/Mab pathway is a novel regulator of reproductive aging , and the first regulator of reproductive aging to be identified independently of somatic aging regulation . Our results demonstrate that somatic aging and reproductive aging can be uncoupled , suggesting that different molecular mechanisms underlie the two processes , but may normally be linked .
In addition to its regulation of dauer formation [26] , we recently found that the TGF-ß Dauer pathway ( Figure 1A , Figure S1A ) regulates longevity [19] . However , whether this pathway also plays a role in the regulation of reproductive aging is unknown . To analyze the effect of TGF-ß Dauer mutants on reproduction , we determined the proportion of adults capable of progeny production as a function of age . The “reproductive span” calculated from such assays ( see Materials and Methods ) reflects the period of time animals produce viable progeny , as described previously [6] , [7] . We found that members of the TGF-ß Dauer pathway moderately extended reproduction ( Figure 1B; Figure S1B , S1C ) : while wild type's mean reproductive span was ∼3 . 5 days , the means of daf-7 , daf-1 , daf-8 , and daf-14 mutants were 4–5 days , extensions of 25–50% ( Table S2; Figure S1D ) . In addition , their maximum reproductive spans were ∼1 day longer than wild type's . At least part of the moderate reproductive span extension is likely a result of delayed onset of reproduction due to an egg-laying ( Egl ) defect ( Figure 1D ) [19] , [27] , [28]; by the end of daf-7's reproductive span , many progeny hatched into L1 larvae immediately upon being laid , as opposed to the typical 12–16 hour hatching time of wild-type eggs . daf-7 progeny do not develop into adults faster than wild type , so the advanced developmental stage of the progeny is likely due to egg retention in the mother . Unlike the moderate reproductive span extensions of other TGF-ß Dauer pathway mutants , daf-4's ∼8 day mean is more than double the reproductive span of wild-type animals ( Figure 1C; Figure S1D; Table S2 ) . daf-4 mutants continued to steadily produce progeny for several days after reproductive cessation in wild-type animals , and its maximum reproductive span was 4–5 days longer than wild type ( Figure S1G ) . This dramatic difference cannot be explained by the egg-laying defect typical of the TGF-ß Dauer pathway mutants , which extends reproductive span a maximum of one day . daf-4 encodes C . elegans' sole ortholog of the type II TGF-ß co-receptor , and is utilized by both the Dauer pathway and a second TGF-ß pathway , the Sma/Mab pathway ( Figure 1E , Figure S2A ) [22]–[25] . The large reproductive span extension that we observed in daf-4 animals , but not other TGF-ß Dauer mutants , suggested the possibility that the Sma/Mab pathway might be important in the regulation of reproductive aging . We measured the reproductive spans of seven alleles of TGF-ß Sma/Mab pathway mutants ( Figure 1E , Figure S2A ) , and found that decreased TGF-ß Sma/Mab signaling indeed increased reproductive span significantly: similar to daf-4 , the mean reproductive spans of sma-2 and dbl-1 were over 7 days , compared to ∼3 . 5 days in wild type; the rest of the mutants in this pathway ( sma-3 , sma-4 , sma-6 , and sma-9 ) also increased reproductive span substantially ( Figure 1F and 1G; Table S1; Figure S2B , S2C ) . The hatching rates of Sma/Mab mutants were comparable to wild type ( Figure 1D ) , the onset of progeny production was not delayed , and progeny were steadily produced beyond the age when wild-type reproduction ceased ( Figure S3A , S3B , S3C ) . Similar to daf-2 and eat-2 , mutants that also extend reproductive span [7] , Sma/Mab mutants produce fewer total progeny over a longer period of time ( Table S3; Figure S3 ) . The reproductive span extensions and progeny production profiles of the Sma/Mab mutants contrast with the delayed onset and sharp decline in the number of progeny produced after peak reproduction by the TGF-ß Dauer mutants ( Figure S1E , S1F ) , suggesting that the Dauer and Sma/Mab mutants are distinct in their reproductive aging phenotypes . Sma/Mab mutants exhibited a highly penetrant late egg-laying defect and internal hatching ( matricide , or “bagging” ) at the end of their reproductive period , in contrast to the Dauer mutants' very early egg-laying ( Egl ) and bagging phenotypes ( Figure 1H , Figure S2E ) . In fact , several assays were terminated when a large fraction of the worms were still reproductive , due to the Sma/Mab mutants' late matricide phenotype ( see asterisked sma-4 in Figure 1F and sma-3 in Figure 1G ) . It is likely that the full late reproductive capacity of the Sma/Mab mutants is masked by this late matricide defect . Thus , while the TGF-ß Dauer mutants' delayed onset of reproduction and Egl phenotypes may account for part of their moderate reproductive span increases ( a maximum of one day ) , these factors are not likely the cause of TGF-ß Sma/Mab mutants' dramatic reproductive span extensions . C . elegans hermaphrodite sperm number limits wild-type self-fertilized reproduction , but mating with young ( day 1 adult ) males , whose sperm are not limited and outcompete those of the hermaphrodite , increases and prolongs progeny production [29]–[31] . To rule out the possibility that the reproductive span extensions we observed in TGF-ß Sma/Mab mutants are due to increased or extended sperm production or utilization , we mated Sma/Mab mutant hermaphrodites with young wild-type males . We found that Sma/Mab mutants significantly and consistently increased mated reproductive span , from wild type's mean mated reproductive span of ∼6 . 0 days to a mean of 10–11 days ( Figure 2A–2D; Table S1 ) . In fact , Sma/Mab mutants were usually still fertile through Day 12–13 of adulthood , compared to the complete cessation of reproduction by Day 8–9 in wild-type animals . ( Interestingly , when mated with wild-type males at an older age ( day 4 ) , the Sma/Mab mutants still had significant reproductive span extensions ( Figure S2D ) , further supporting the notion that sperm quality and number are not the limiting factor , as shown previously [7] . ) To further eliminate the possibility of sperm contribution , we also tested feminized ( fem-1 ) mutant hermaphrodites , which fail to make sperm when raised at restrictive temperatures . fem-1 mutants mated with wild-type males have a mean reproductive span of 6 . 3 days , while more than 90% of the mated sma-2;fem-1 double mutants were still fertile at day 12 ( Figure 2E ) . Notably , the self-fertilized reproductive spans of Sma/Mab mutants are even longer than wild type's mated reproductive span , highlighting the extreme extensions shown by Sma/Mab mutants ( compare Figure 1F and Figure 2; Table S1 ) . Additionally , neither the self-fertilized nor the mated Sma/Mab mutants delay the onset of progeny production , and both continue to produce progeny steadily beyond the age of wild-type reproductive cessation ( Figure S3 ) . In self-fertilized worms , sperm is only made prior to oogenesis [24] , and in mated worms sperm is in excess , thus the extended reproductive span we observed cannot be due to extended spermatogenesis . Our results , together with the Hughes , et al . data , suggest that significant reproductive aging already occurs prior to the cessation of sperm availability in self-fertilized animals , and that Sma/Mab mutants , like IIS ( daf-2 ) and DR ( eat-2 ) mutants [7] , slow the rate of aging of the reproductive system . We noticed that the Sma/Mab mutants produced fewer progeny than wild type each day in the early phase of reproduction ( Figure S3; Figure S4D , S4E , S4F ) , and have smaller broods ( Table S3 , Table S4 ) . This reduction in progeny number reflects slower ovulation rate of the mutants in early reproduction ( Figure S4C ) , likely due to their small body size ( Figure S4A , S4B ) . In fact , it has been suggested that reduction of C . elegans progeny number is linked to small body size via physical constraint of the maternal gonad and/or body size [32]–[35] . The downstream transcription co-factor of the Sma/Mab pathway , sma-9 , is required in early larval development for the regulation of body size before gametogenesis [25] , [36]; however , we find that reduction of sma-9 only in adulthood is sufficient to extend late reproduction ( Figure S5 ) , suggesting that the growth and reproductive aging functions of the Sma/Mab pathway are independent . In mated assays , sperm number is not limiting; therefore , one possible explanation for extended reproductive span of the Sma/Mab mutants is that oocyte number is limiting , and thus slower ovulation allows the mutants to use up their oocyte supply more slowly . To test this hypothesis as well as the body size effect on reproduction , we examined five small but non-TGF-ß mutants ( dpy-6 ( e2762 ) , sma-1 ( ru18 ) , sma-1 ( e30 ) , dpy-1 ( e1 ) and dpy-9 ( e12 ) ) whose body sizes are similar to the TGF-ß Sma/Mab mutants ( compare Figure S4A and S4B and Figure S6A and S6B ) . Importantly , none of these strains have been reported to have egg-laying or embryonic developmental abnormalities or effects on longevity , and therefore serve as a fair set of samples for comparison . As expected , the five small mutants have slower ovulation rates ( Figure 3A and 3B ) , and as a result produce fewer early progeny and fewer total progeny ( Figure 3C and 3D ) . However , unlike the TGF-ß mutants , none of the small , non-TGF-ß mutants extended mated reproductive span ( Figure 3E and 3F ) . In fact , all of the mutants had shorter reproductive spans . These data suggest that small body size and reductions in ovulation rate and progeny number do not increase reproductive span , but rather are usually associated with shorter reproductive spans . Therefore the TGF-ß Sma/Mab mutants are special in their extension of reproductive span . We also addressed whether oocyte number becomes the limiting factor when sperm is no longer limiting by mating animals with young wild-type males , which is one basis for the assumption that slow ovulation extends reproductive span . On day 3 , all wild-type animals ( n = 12 ) produced only eggs that were able to hatch and develop to viable progeny ( Figure 3G ) , but on day 7 , 59% ( n = 17 ) of the animals laid oocytes that failed to be fertilized ( Figure 3H ) and/or eggs that were unable to hatch ( Figure 3H and 3I ) , resulting in cessation of viable progeny production . Therefore , the limiting factor is not number of oocytes , which are clearly still in excess , but the quality of the oocytes . By contrast , the majority of sma-2 mutants still produced exclusively viable eggs on day 7 ( Figure 3J ) , with only 19% ( n = 16 , p = 0 . 03 compared with wild type ) of the animals starting to lay unfertilized oocytes or unhatchable eggs . Our data , together with the observation that late reproduction is independent of early reproduction [7] , suggest that Sma/Mab mutants extend reproductive span independently of body size , ovulation rate , early progeny number , and brood size , but instead by improving oocyte quality . While daf-2 and eat-2 regulate reproductive aging [6] , [7] , [11] , they are known foremost for their roles in life span extension [10] , [12] . Thus far , all of the known mutations and treatments that extend C . elegans reproductive period also regulate longevity [6] , [7] . In addition , the link between longevity and reproduction has been suggested and/or reported in multiple organisms [37]–[43] . These data suggest the possibility that the regulation of life span and reproductive span are coupled , or even regulated by the same mechanisms . Our TGF-ß Dauer pathway data further support this notion , as the mutants increase both life span and reproductive span ( Figure 4E ) [19] . However , we found that Sma/Mab pathway mutants only mildly affect longevity , despite their dramatic effects on reproductive span . Some Sma/Mab mutants increased life span moderately ( dbl-1 , sma-6 ) or inconsistently ( sma-2 , sma-4 ) , while others appeared to have no effect on longevity ( sma-3 , sma-9 ) ( Figure 4A–4D; Table S6 , S7 , S8 , S9 , S10 ) . Because these alleles are not nulls , the inconsistencies in longevity between mutants in the pathway could be due to varying hypomorphic effects . Therefore we compared each mutant allele's effect on reproductive span and life span ( Figure 4E ) . While daf-4 mutants doubled both reproductive span and life span , and daf-7 mutations had a moderate effect on each , exclusive members of the Sma/Mab pathway disproportionately extended reproductive span compared to life span ( Figure 4E ) . This effect was maintained when the mated reproductive spans were considered ( compare sma-2 and daf-7 in Figures 4E , Figure 5G and 5H ) . daf-4's effect on the two processes is likely due to its dual roles in TGF-ß Dauer regulation of life span [19] and in TGF-ß Sma/Mab regulation of reproductive span . Matricide is a common event in the late reproductive period , but we noticed that the TGF-ß mutants are different from longevity mutants in this regard . In mated wild-type animals , the matricide frequency increased with age within the reproductive period ( Figure 5A ) , perhaps reflecting aging of the musculature required for egg-laying . After day 9 , reproduction stopped completely , therefore no matricide was observed . The matricide frequency of sma-2 mutants also increased with age at a similar rate as wild type ( Figure 5A ) . Because sma-2 mutants continued to reproduce , however , the matricide rate continued to rise further , and therefore more mutant animals suffered from matricide than wild type . The matricide frequency of daf-2 and eat-2 mutants , however , increased at a much slower rate ( Figure 5B ) . For example , on day 8 about 70% of worms died from matricide in both wild type and sma-2 animals , whereas only 40% of daf-2 and 30% of eat-2 animals died of matricide ( Figure 5A and 5B ) . ( daf-7 mutants exhibited high matricide rate from very early age ( Figure 5B ) , due to their Egl defects ( Figure 1D ) , therefore are different from the other strains . ) The matricide frequency data suggest that late-reproduction matricide may be a somatically-controlled event , separate from reproductive aging . Together with the life span data , the matricide data suggest that sma-2's soma ages at a rate that is similar to wild type , unlike the daf-2 and eat-2 longevity mutants . To further investigate sma-2's role in somatic aging , we compared the effects of mutations in sma-2 , daf-2 , eat-2 , and daf-7 on life span and reproductive span . The other mutants have longer life spans than sma-2 ( Figure 5C and 5D; Table S7 and Table S8 ) , but their reproductive spans are either shorter or comparable to sma-2 ( Figure 5E and 5F; Table S7 , S8 ) . In fact , daf-2 animals increase life span to a greater degree than reproductive span ( Figure 5G and 5H ) , while sma-2 and eat-2 have greater effects on reproductive span than life span . sma-2's effect on reproductive span is the most disproportionate among all the mutants . For example , when comparing the increases in mated reproductive spans and life spans of all the mutants ( Figure 5H ) , sma-2's increase in reproductive span is 10-fold its increase in lifespan , whereas eat-2's effect on reproductive span is only 4-fold , and the daf-7's and daf-2's are both less than one fold . Together , our data show that TGF-ß Sma/Mab signaling affects reproductive aging disproportionately to its effect on longevity compared to other reproductive span and life span mutants . The FOXO transcription factor DAF-16 is required for longevity of the IIS pathway mutant daf-2 [10] and is also required for daf-2's effects on reproduction [7] ( W . Shaw & C . T . Murphy , unpublished data ) . Previously , we showed that TGF-ß Dauer signaling regulates longevity through its interactions with the IIS pathway , as the lifespan extension of daf-7 mutants is suppressed by loss of DAF-16/FOXO transcription factor activity [19] ( Figure 6A ) . To test the role of daf-16 in TGF-ß Dauer pathway regulation of reproduction , we compared the reproductive spans of daf-7 ( e1372 ) , daf-16 ( mu86 ) , and daf-16 ( mu86 ) ;daf-7 ( e1372 ) double mutants . We found that daf-7's reproductive span extension was significantly suppressed by loss of daf-16 activity ( Figure 6B and 6C; Table S9 ) , suggesting that DAF-16/FOXO activity is required for both the life span and reproductive span extensions of daf-7 mutants . Interestingly , loss of daf-16 activity also suppressed sma-2's small life span extension ( Figure 6D , Figure S7A; Table S9 ) ; sma-2's occasional moderate effect on longevity may be due to cross-talk between the TGF-ß and IIS pathways rather than a primary output of TGF-ß Sma/Mab signaling , reminiscent of the cross-talk between the two TGF-ß pathways in dauer regulation [44] , [45] , and TGF-ß Dauer/IIS cross-talk in longevity regulation [19] . By contrast with its effect on sma-2 life span , loss of daf-16 activity failed to suppress sma-2 and dbl-1 self-fertilized and mated reproductive span extensions ( Figure 6E and 6F; Figure S7C and S7D; Table S9 ) . However , the double mutant's peak matricide frequency shifted earlier ( Figure S7B ) , consistent with daf-16;sma-2 and daf-16's shorter life spans . These results suggest that the TGF-ß Sma/Mab pathway's regulation of reproductive span is not mediated by DAF-16/FOXO activity . Together with its disproportionate effect on the two processes , the TGF-ß Sma/Mab pathway appears to have genetically uncoupled regulation of reproduction and longevity . The FoxA transcription factor PHA-4 is required for the life span extension of the Dietary Restriction model eat-2 ( Figure 7A ) [46] . We found that eat-2's reproductive span extension was also significantly suppressed by loss of PHA-4 activity ( Figure 7C and 7D; Table S10 ) when treated from L4 onward . ( To check the efficacy of pha-4 RNAi , we determined the fraction of arrested L1 progeny from L4-onward-fed mothers , and found that sma-2 and eat-2 animals are similarly sensitive to pha-4 RNAi ( Figure 7B ) . ) To test whether TGF-ß Sma/Mab signaling utilizes the Dietary Restriction pathway , we tested sma-2's requirement for PHA-4 activity . In contrast to eat-2's requirement for pha-4 , we found that pha-4 RNAi treatment did not suppress the reproductive span extension of sma-2 mutants ( Figure 7E and 7F; Table S10 ) . Thus , while pha-4 is required for the reproductive span changes associated with Dietary Restriction , it is not required for sma-2's reproductive span extension . If the reproductive span extension of Dietary Restriction animals required TGF-ß Sma/Mab signaling , we might expect the DBL-1 ligand to act downstream of the eat-2-induced DR effect , which is caused by eat-2 mutants' inability to pump their pharynx to properly ingest food . However , we observed that over-expression of the DBL-1 ligand is not sufficient to suppress eat-2's reproductive span extension ( Figure 7G; Table S11 ) , consistent with the hypothesis that Dietary Restriction and TGF-ß Sma/Mab regulate reproductive span independently . ( Interestingly , the eat-2;dbl-1 OE animals , like dbl-1 OE single mutants , are larger than wild type ( Figure 7H ) yet have a long reproductive span , further supporting the notion that there is no direct correlation between body size and reproductive span . ) Our results show that in addition to its role in body size regulation , the TGF-ß Sma/Mab pathway is a novel regulator of reproductive aging . Sma/Mab signaling regulates reproductive aging independently of at least two known regulators of somatic aging , Dietary Restriction and IIS/FOXO signaling , and uncouples reproductive aging from somatic aging .
Here we have shown that loss of function of a canonical TGF-ß pathway significantly delays C . elegans reproductive aging . Intriguingly , TGF-ß Sma/Mab mutants extend reproductive span without a proportional extension of life span . We have also found that TGF-ß Sma/Mab reproductive span extension is genetically independent of regulation by Dietary Restriction and Insulin/IGF-1 Signaling . The uncoupling of reproductive and somatic aging in TGF-ß Sma/Mab signaling mutants suggests that the molecular mechanisms underlying the maintenance of somatic [47] , [48] and reproductive tissues are distinct . This is the first identification of a pathway that regulates reproductive aging independently of somatic aging , and may lead to insights into mechanisms that specifically govern age-related reproductive cessation . Signaling from the reproductive system to the soma to regulate aging has already been shown through germ line and somatic gonad ablation experiments in worms and flies [49] , [50] . Because germ line and somatic gonad ablation both result in sterility , but have opposite effects on life span , direct resource allocation from the germ line to the soma cannot be the cause of this longevity . Instead , signals from the germ line and somatic gonad normally communicate with the rest of the soma to regulate life span , acting through the insulin/FOXO and daf-12 nuclear hormone pathways [49]–[51] . Our results suggest that the reproductive system may also normally receive signals through these regulatory pathways and the TGF-ß Sma/Mab pathway to regulate its rate of aging , allowing the animal to adjust its reproductive rate to its environment . The prevalence of matricide by the TGF-ß Sma/Mab mutants illustrates the importance of signaling that normally links reproductive and somatic aging . Many animals slow their reproductive rates in response to environmental factors , such as high predation and food shortages , in order to optimize their reproductive fitness [7] , [16] , [18] , [52] . In some cases , such as under environmental stress , late reproduction can increase fitness , allowing increased genetic diversity through mating ( “facultative outcrossing” ) [17] . Like daf-2 and eat-2 mutants , reduced TGF-ß Sma/Mab signaling slows the rate of reproductive aging and thus extends reproductive span . However , TGF-ß Sma/Mab mutants do not concomitantly slow somatic aging , and as a result they often suffer from high reproductive-age mortality induced by the physical stresses of reproduction , whereas same-aged longevity mutants daf-2 and eat-2 experience less age-related matricide ( see Figures 2D and 2E and Figure 5A and 5B ) . From these results we infer that the slowing of somatic aging is required for successful delayed reproduction , which may be necessary under certain environmental conditions . Signals through the TGF-ß Sma/Mab pathway may allow the animal to adjust its reproductive rate to its environment . In turn , signals from the germ line and somatic gonad coordinate somatic aging rate with reproductive aging rate to allow successful reproduction . If reproductive and somatic aging are coupled , why do worms and humans live so long after reproduction has ceased ? A popular but controversial theory specific to humans postulates that investment in grand-progeny by grandmothers increases fitness to a greater degree than would continuing reproduction , and thus downregulation of reproductive ability in mid-life is evolutionarily beneficial ( the “Grandmother Hypothesis” [53] , [54] ) . However , as C . elegans does not care for its young , such an investment in its grand-progeny cannot explain its similarly early decline in reproduction and long post-reproductive life span . Instead , we propose that reproduction itself requires the soma to function at its highest level , but the body can survive well below this threshold level of function . That is , if one were to plot every parameter of function ( reproduction , motility , pathogen resistance , survival , etc . ) in both worms and humans , these functions would all peak during the reproductive period , but begin to decline post-reproductively , each at a different rate . Successful reproduction requires peak physical condition of the soma; the increased matricide rates in older , reproductive Sma/Mab mutants shows that increased oocyte quality in the absence of healthy somatic tissues can be catastrophic , at least to the mother and any unproduced late progeny . By contrast , “survival” is the lowest measurable function ( assayed as live vs . dead ) and thus persists much longer than reproductive activity . Humans survive well past the age of peak physical function , a period that overlaps with female reproduction . Improvements in medicine , nutrition , hygiene , and environment have extended human life span significantly [55] , [56] , extending the post-reproductive life span but having little effect on maximum reproductive span . Analogously , the worm's life span in the low-predation and low-pathogen conditions of the laboratory is likely longer than in the wild , but likely largely affects post-reproductive life span . Therefore , the long post-reproductive life span of both worms and human females could be attributed to the high level of somatic function required for successful reproduction , essentially a side effect of the requirements for successful reproduction earlier in life . Longevity is regulated by insulin/IGF-1/FOXO signaling and by Dietary Restriction in worms through mammals [10] , [12] , [57]–[59] , despite large differences in chronological life spans of these organisms . Additionally , Insulin/IGF-1 Signaling and Dietary Restriction have been implicated in regulation of mammalian reproductive aging [14] , [52] , [60] , [61] . Intriguingly , TGF-ß levels are upregulated in aged mouse oocytes [62] , and TGF-ß activity regulates mammalian follicle cell activity [63] . Thus , it is possible that regulation of reproductive aging , like the regulation of somatic aging by IIS and DR pathways , is evolutionarily conserved , and that TGF-ß signaling may regulate human reproductive cessation . Despite the vast differences in their life histories and chronological time frames , our work suggests that the regulation of worms and humans' longevity and reproductive spans may be conserved . Future studies will determine whether Sma/Mab mutants use a conserved mechanism to slow reproductive aging in C . elegans . If so , modulation of TGF-ß signaling may offer new avenues to improve fertility and offspring health in mothers of advanced age .
All strains were cultured using standard methods [64] . In all experiments , N2 is the wild type . LG I: daf-16 ( mu86 ) , daf-8 ( e1393 ) . LG II: eat-2 ( ad465 ) , rrf-3 ( pk1426 ) , sma-6 ( wk7 ) . LG III: daf-2 ( e1370 ) , daf-7 ( e1372 ) , daf-4 ( e1364 ) , sma-2 ( e502 ) , sma-3 ( wk28 ) , sma-3 ( wk20 ) , sma-4 ( e729 ) . LG IV: daf-1 ( m40 ) , daf-14 ( m77 ) , fem-1 ( hc17 ) . LG V: dbl-1 ( nk3 ) , dbl-1 ( wk70 ) . LG X: sma-9 ( qc3 ) , sma-9 ( wk55 ) . Strains: BW1940: ctIs40 X [ZC421 ( + ) containing dbl-1;sur-5::gfp] . CQ33: eat-2 ( ad465 ) II; ctIs40 X [ZC421 ( + ) containing dbl-1;sur-5::gfp] . CQ17: daf-1 ( m40 ) IV outcrossed to N2 3× . CQ16: daf-7 ( e1372 ) III outcrossed to N2 3× . CQ14: daf-14 ( m77 ) IV outcrossed to N2 3× . CQ19: sma-2 ( e502 ) III outcrossed to N2 3× . CQ18: sma-9 ( wk55 ) X outcrossed to N2 3× . CQ53: sma-2 ( e502 ) ;fem-1 ( hc17 ) . CQ49: daf-16 ( mu86 ) ;sma-2 ( e502 ) . CQ25: daf-16 ( mu86 ) ;daf-7 ( e1372 ) . CF1041: daf-2 ( e1370 ) III . Individual synchronized L4 hermaphrodites were moved to fresh plates daily until reproduction ceased for at least two days . The last day of viable progeny production was noted as the day of reproduction cessation for each individual . When matricide occurred , the animal was censored from the experiment on that day . All experiments were performed at 20°C , except that sma-2 ( e502 ) ;fem-1 ( hc17 ) and fem-1 ( hc17 ) worms were shifted to 25°C from L3 and back to 20°C after L4 . All experiments were performed with at least 10 individuals per strain ( most experiments included >25 individuals , as indicated in Supplementary Tables ) . The log-rank ( Mantel-Cox ) method was used to test the null hypothesis . In mating reproductive span assays , L4 hermaphrodites were mated to young wild-type males at a 1∶3 ratio for 24 hours before being separated onto individual plates . Successful mating was ascertained by the fraction of male progeny each day . For the pha-4 RNAi reproductive span experiments , mothers were moved onto RNAi bacteria starting at L4 . Individual synchronized L4 hermaphrodites were moved to fresh plates and the number of progeny produced by each individual was counted daily until reproduction ceased for at least two days . When matricide occurred , the animal was censored from the experiment on that day . All experiments were performed at 20°C with at least 6 individuals per strain ( most experiments included 20–40 individuals ) . The assay was performed as described in Reproductive Span analysis , except the cumulative percentage of hermaphrodites that underwent matricide was calculated daily . The matricide frequency was determined as the frequency of reproductive worms that die of matricide; as matricide is caused by internal progeny hatching , non-reproductive worms by definition never die of matricide , and thus are not included in calculation . This number reflects the likelihood of matricide . Eggs were synchronized by hypochlorite treatment and allowed to develop at 20°C until day 3 of adulthood . ∼100 synchronized hermaphrodites were transferred to a new plate and allowed to lay progeny for 1 hour; eggs and L1 progeny were counted at 3-hour intervals . The first day of adulthood was defined as t = 0 , and the log-rank ( Mantel-Cox ) method was used to test the null hypothesis in Kaplan-Meier survival analysis , as previously described [65] . All experiments were carried out at 20°C with 50 µM FUdR starting at L4; n>60 in each experiment . Bacterial feeding RNAi experiments were carried out as previously described [66] with IPTG at 1 mM . Each clone was verified by PCR and sequence analysis . pha-4 RNAi efficacy was determined by counting the arrested L1s produced by mothers fed from L4 onward compared with control vector . | Female reproductive cessation is the earliest aging phenotype humans experience , and its importance as a clinical issue is growing as more women opt to have children later in life . While much work has been done to understand the general aging process , little is currently known about the regulation of reproductive aging . Like longevity , the ability to produce progeny with advanced age is likely to be genetically regulated . Thus , understanding the processes that regulate reproductive aging may allow us to address the problems of maternal age-related infertility and birth defects . C . elegans and humans both have long post-reproductive life spans , leaving open the possibility that their reproductive spans might be extendable . C . elegans has been used previously to discover conserved regulators of aging , and here we use worms to identify a new regulator of reproductive aging , a highly conserved TGF-ß signaling pathway . We find that TGF-ß signaling regulates reproductive aging independently of somatic aging . This is the first identification of a pathway that breaks the coupling that normally links the two processes . Our work will provide new insights into the improvement of human fertility and prevention of age-related birth defects , and it has implications for the evolutionary relationship between reproduction and longevity regulation . | [
"Abstract",
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] | [
"genetics",
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] | 2009 | TGF-ß Sma/Mab Signaling Mutations Uncouple Reproductive Aging from Somatic Aging |
Schistosoma haematobium is the etiologic agent for urogenital schistosomiasis , a major source of morbidity and mortality for more than 112 million people worldwide . Infection with S . haematobium results in a variety of immunopathologic sequelae caused by parasite oviposition within the urinary tract , which drives inflammation , hematuria , fibrosis , bladder dysfunction , and increased susceptibility to urothelial carcinoma . While humans readily develop urogenital schistosomiasis , the lack of an experimentally-tractable model has greatly impaired our understanding of the mechanisms that underlie this important disease . We have developed an improved mouse model of S . haematobium urinary tract infection that recapitulates several aspects of human urogenital schistosomiasis . Following microinjection of purified S . haematobium eggs into the bladder wall , mice consistently develop macrophage-rich granulomata that persist for at least 3 months and pass eggs in their urine . Importantly , egg-injected mice also develop urinary tract fibrosis , bladder dysfunction , and various urothelial changes morphologically reminiscent of human urogenital schistosomiasis . As expected , S . haematobium egg-induced immune responses in the immediate microenvironment , draining lymph nodes , and systemic circulation are associated with a Type 2-dominant inflammatory response , characterized by high levels of interleukin-4 , eosinophils , and IgE . Taken together , our novel mouse model may help facilitate a better understanding of the unique pathophysiological mechanisms of epithelial dysfunction , tissue fibrosis , and oncogenesis associated with urogenital schistosomiasis .
Schistosomal infections plague more than 240 million people worldwide . The most prevalent anthropophilic schistosome species globally , Schistosoma haematobium , accounts for nearly half of that number , primarily in sub-Saharan Africa and the Middle East [1] . S . haematobium infects humans through direct skin penetration by aquatic cercariae that emerge from Bulinus truncatus , the intermediate snail host . After entering the human host , the parasite rapidly migrates into the circulation as a schistosomulae , matures , and subsequently lodges in the venous plexus of the bladder where male-female worm pairs mate and produce eggs for years to decades . While in rare cases ectopic S . haematobium oviposition causes pathology outside of the urogenital tract , the vast majority of infections result in urogenital schistosomiasis . Although the symptoms are varied , the bulk of the morbidity and mortality of urogenital schistosomiasis can be ultimately attributed to the host immune response against Schistosoma eggs deposited within the walls of the urinary tract . This inflammation leads to: 1 ) compromise of urothelial integrity promoting urinary tract infections [2]–[7] , hematuria , and protein-wasting [2]; 2 ) urothelial changes leading to carcinogenesis [8] , [9]; and 3 ) urinary tract fibrosis causing bladder dysfunction , obstruction , infection , and renal failure [10] , [11] . In fact , the annual death toll of 150 , 000 due to urogenital schistosomiasis-induced obstructive renal failure makes S . haematobium one of the most lethal worms worldwide [12] . Despite the global burden of urogenital schistosomiasis , there remains little known about the basic mechanisms underlying the pathophysiology of this disease [13] . This is primarily due to the lack of an experimentally tractable animal model . Indeed , the majority of research in schistosomiasis has focused on S . mansoni infections in mice , wherein the entire life cycle can be recapitulated . In contrast , the development of a mouse model of urogenital schistosomiasis , long pursued by investigators in the field , has historically failed due to the inability of S . haematobium cercariae to efficiently mature and migrate to the bladder venous plexus in the mouse [14] , [15] . Thus , S . haematobium research is largely limited to primate [16] and non-murine rodent models [17] , [18] . Primate models , while capable of faithful recapitulation of urogenital schistosomiasis , are prohibitively expensive and difficult to manipulate . Extant non-murine rodent models ( e . g . hamster ) , in contrast , develop clinical outcomes which can differ dramatically from the human disease . These models also suffer from a paucity of species-specific tools . Herein we report the development of a robust , highly manipulable mouse model of urogenital schistosomiasis achieved by the microinjection of viable S . haematobium eggs directly into the bladder wall . This model faithfully and reproducibly recapitulates some of the salient features of the human disease including inflammatory cell activation and infiltration , urinary tract granuloma formation and fibrosis , urinary dysfunction , systemic Type 2 immune activation , and egg excretion in urine . To our knowledge , this is the first experimentally tractable mouse model of urogenital schistosomiasis . Moreover , we provide direct evidence that egg deposition alone is sufficient to reproduce several important aspects of urogenital schistosomiasis , even in the absence of the other life stages of this important human pathogen .
Although the morbidity associated with chronic S . haematobium infection is considered to result from egg deposition into the bladder wall , it is currently unclear whether oviposition alone , in the absence of adult worms , is necessary and sufficient for the bladder pathology associated with urogenital schistosomiasis . To address this issue , we directly microinjected viable S . haematobium eggs into the anterior bladder walls of female BALB/c mice . Serial transabdominal micro-ultrasonography paired with histologic verification demonstrated the development of injection site granulomata over time ( Figure 1A–H and Video S1 ) . The initial injection site response resolved entirely by day 4 in animals injected with egg-free control vehicle ( Figure 1E ) ; whereas egg-injected animals demonstrated a persistent mixed inflammatory infiltrate ( Figure 1F ) with a hypoechoic appearance on micro-ultrasonography ( i . e . , low density , grey mural nodules , Figure 1B ) . Over the following 4 weeks , the egg-associated mixed inflammatory infiltrate expanded and organized into a well-defined , egg-centered granuloma surrounded by peripheral eosinophils and neutrophils and containing distinct lymphoid follicles ( Figure 1G ) . Robust granulomata were still present 99 days after egg injection ( Figure 1H ) . These organized , dense lesions were correspondingly hyperechogenic on micro-ultrasonography ( bright nodules , Figure 1D ) . Granuloma formation was neither sex- nor strain-specific , since male and C57BL/6 and C3H/He mice also developed granulomata after egg injection ( unpublished data ) . The granulomatous character of the egg-associated lesions was confirmed by immunohistochemistry for CD68 ( Figure 1I–K ) , which demonstrated complete encapsulation of the eggs by CD68-positive epithelioid cells ( i . e . , syncytial macrophages , Figure 1K ) . Similar to human disease , granuloma development is accompanied by eosinophiluria [19]–[24] and hematuria [25] ( Figure S1 ) . Additionally , by post-injection day 4 the urothelium demonstrated pronounced egg-dependent hyperplasia and squamous metaplasia that persisted throughout the experimental time course ( Figure 1L–O and data not shown ) . These changes were present predominantly in the urothelium overlying the egg granuloma ( Figure 1F–H ) , suggesting a highly localized microenvironmental effect . Importantly , these urothelial features closely parallel those observed in urogenital schistosomiasis [26] . The observed pathology was not likely confounded by surgical complications . In more than 100 consecutive injections performed by four independent surgeons , no bladder perforation , extravesical egg deposition , or significant post-injection sequelae were observed . Micro-ultrasonographic and histologic analysis confirmed reproducible egg delivery to the same submucosal tissue plane . Moreover , 20% of mice shed eggs in their urine within one week of egg injection , which recapitulates egg shedding in infected humans ( data not shown ) . The host response to S . haematobium eggs in the human urogenital tract involves a fibroproliferative response which is thought to drive ureterovesical obstruction and bladder dysfunction , two major sources of morbidity associated with infection [27] . To determine whether our approach resulted in the development of bladder fibrosis , we evaluated egg-injected bladders through Masson's Trichrome staining and total extractable collagen assays . By day 7 , loose immature collagen was observed within nascent granuloma ( Figure 2A ) . At day 28 post-injection , dense mature collagen was found throughout the granuloma with variable extension into the surrounding bladder tissue ( Figure 2B ) . Control vehicle-injected bladders demonstrated little or no collagen staining ( data not shown ) . In addition , total bladder soluble collagen content was markedly increased 3–5 weeks post-egg injection ( Figure 2C ) . Finally , S . haematobium egg-injected mice exhibited increased voiding frequency relative to control animals ( Figure 2D ) , which is consistent with reports of urinary frequency observed in parasitized humans [28] , [29] . The defined and synchronous nature of our egg injection model allowed us to investigate the initial innate immune response to S . haematobium egg deposition . Importantly , there was an immediate , egg-independent upregulation of many cytokines in response to the injection itself; however , this rapidly resolved over time ( Figures 3 and S2 ) . Given that the foremost histologic hallmark of human parasitic infection is eosinophil infiltration , it was expected that eotaxin , a potent eosinophil chemoattractant , was significantly upregulated relative to control-injected bladders ( Figure 3A ) . Consistent with the upregulation of eotaxin , we detected a large number of eosinophils ( Siglec-F+ CCR3+ ) that rapidly infiltrated the injection site and persisted , whereas egg-free control vehicle injections did not produce a significant response ( Figure 4A , Figure S1 ) . In addition , there was a marked infiltration of neutrophils ( CD11b+Gr-1+ ) into the injected bladder wall with kinetics that were similar to eosinophils ( Figure 4B ) . This is consistent with the egg-injected bladder upregulation of neutrophil-associated chemokines such as KC ( CXCL1 ) [30] and MIP-1α ( CCL3 ) [31] ( Figure 3 ) . While B-cells ( B220+ ) also accumulated at the injection site over time ( Figure 4C ) , we noted a paucity of T cells ( CD3+ ) , though moderately elevated relative to egg-free controls ( Figure 4D ) . These data are consistent with histologic observations ( Figure 1F–H ) , and suggest that development of egg granulomata in our model features B cell and chemokine-driven innate immune cell infiltration with a relative dearth of T cells . S . mansoni and S . japonicum eggs elicit a dominant Type 2 immune response within mouse hosts . Despite the paucity of T cells in our model , we sought to determine whether S . haematobium eggs elicited the production of the canonical Type 2 cytokines IL-4 and IL-13 . The Luminex multiplexed liquid microbead platform was used to assay total cytokine expression within egg-injected bladders at early time points that corresponded to the initial immune response and nascent granuloma development ( Figure 3 and Figure S2 ) . IL-4 and IL-13 were upregulated by day 4 and remained elevated throughout all time points examined ( Figure 3A ) . IL-5 levels were also markedly increased , which is consistent with the role of this Type 2-associated cytokine in eosinophil differentiation , activation , and recruitment [32] ( Figure S2 ) . In contrast , TH1- and TH17-associated cytokines such as IFN-γ and IL-17 remained unaffected by the egg-induced inflammatory response ( Figure 3B–C ) . Consistent with the marked neutrophil and macrophage infiltration of the egg-injected bladder wall ( Figures 1 and 3 ) , the innate immunity-associated cytokines TNF-α , KC , MCP-3 , and MIP-1α demonstrated early and sustained increases relative to controls ( Figure 3A and 3D ) . Interestingly , egg injection had no effect on IL-10 and TGF-β—immunosuppressive cytokines associated with regulation of tissue fibrosis in other diseases ( Figure 3E ) . Despite the relative paucity of T cells within the egg-injected bladder ( Figure 4D ) , the strikingly Type 2 cytokine-biased microenvironment ( Figure 3 ) supported a hypothesis that T helper 2 cells were likely to be involved in the immune response to S . haematobium eggs . Indeed , pelvic lymph nodes draining egg-injected bladders demonstrated marked upregulation of the TH2-associated cytokine IL-4 throughout the experimental time course , while expression of the TH1-associated cytokines IFN-γ and IL-12 was moderately altered due to the injection procedure ( Figure 5 ) . Interestingly , at later time points the Treg-associated marker FoxP3 was markedly suppressed relative to controls while expression of the immunosuppressive cytokines IL-10 and TGF-β was unchanged . Expression of IL-17 was not detected . In our model , egg-injected mice demonstrated a reproducible , systemic Type 2-biased immune response similar to that observed in human infection . Serial multiplex serum cytokine profiling demonstrated persistently elevated levels of the Type 2-associated cytokine IL-5 , while the TH1- and TH17-associated cytokines IFN-γ and IL-17 , respectively , evinced no such increase ( Figure 6A ) . Congruent with chronic inflammation , the innate immunity-associated cytokine IL-1α was also persistently elevated ( Figure 6A ) . Interestingly , serum levels of VEGF were increased in egg-injected mice , which may have promoted aberrant vasculogenesis and hematuria analogous to human disease [33] . Finally , serum levels of IgE , a quintessential Type 2-associated antibody isotype , were increased beginning at 14 days post egg injection relative to controls , and remain elevated through day 28 post-injection ( Figure 6B ) .
S . haematobium infection , i . e . urogenital schistosomiasis , lacks a reliable mouse model despite being the most prevalent form of schistosomiasis and one of the deadliest worm infections worldwide . To address the lack of experimentally amenable tools for investigation of this medically important pathogen , we have developed an improved mouse model of urogenital schistosomiasis . Microinjection of viable S . haematobium eggs into the submucosa of the bladder wall elicits pathology similar to certain aspects of the human disease , including inflammatory cell infiltration , granuloma formation , urinary tract fibrosis and dysfunction , and systemic Type 2 immune activation . The focal deposition of eggs and resulting composite granulomata observed in this model recapitulates certain aspects of the immunopathology observed in human disease [34]–[36] . The persistent granulomatous inflammation in our model ( at least 99 days after egg injection ) parallels the chronicity of human infection . Moreover , the microinjection method features several advantages , including the induction of anatomically precise , synchronous , and reproducible pathology . Although microinjection of the bladder wall of a 20 gram mouse may appear daunting , proper magnification , clean egg preparations , sharp injection needles , and careful surgical technique render it feasible [37] . This is in contrast to existing mouse models of percutaneous or intravenous S . haematobium infection , which are prone to ectopic oviposition , variant kinetics , and unreliable disease burden [14] , [15] . Current , non-mouse animal models for urogenital schistosomiasis mostly rely on non-human primates [16] and hamsters [17] , [18] . Non-human primate models , while capable of high fidelity recapitulation of human disease , are costly and difficult to use . In hamsters transdermally infected with S . haematobium cercariae , schistosomula reach the lung by 3 days post-infection , followed by pairing of worms at approximately day 28–29 . Oviposition in hamster tissues , primarily lung , liver , intestine , spleen , kidney , and uterus , begins to occur between weeks 7–11 [38]–[42] . Clustered egg deposition ( often >20 eggs ) results in giant , composite granulomata in the hamster liver . In comparison , S . mansoni infections of hamsters result in single egg-based liver granulomata containing more eosinophils , fewer polymorphonuclear leukocytes and histiocytes [43] , [44] . Rates of hamster bladder involvement after exposure to S . haematobium cercaria are low and inconsistent , ranging from 0 to approximately 60% . Even when bladder oviposition occurs , egg burdens tend to be much lighter than that found in other organs , and less than two-thirds of hamsters with bladder eggs feature urothelial hyperplasia or squamous metaplasia [45] . Thus , hamsters develop clinical pathology which can differ dramatically from human disease , restricting their biological relevance . Hamsters also feature fewer species-specific reagents than mice . Hence , our improved mouse model of urogenital schistosomiasis may prove to be a useful alternative to existing animal models of this disease . In the course of characterizing our model we noted that significant Type 2 inflammation occurs after egg injection , but this is not accompanied by large numbers of granuloma-associated T cells . Instead , we noted a rapid and pronounced chemokine response ensued following egg inoculation . The rapidity of the eotaxin response may indicate that eosinophils were recruited in the absence of adaptive immunity , perhaps by the secretion of eotaxin from urothelial , endothelial , smooth muscle , or other resident cells that were likely in direct contact with the inoculum . Certainly , urothelial cells can serve as a rapid source of chemokines and other cytokines in response to exposure to microbial antigens [46] . While the precise mechanism by which eosinophils are initially recruited to the sites of Schistosoma infection is not known , their later recruitment and accumulation is driven by a local , robust Type 2-biased immune response [47] . Indeed many of the aspects of parasitic morbidity , including those associated with urogenital schistosomiasis , are driven by this immune program . In humans , the parasite microenvironment has been well-characterized in its later , chronic stages; however , the early development and etiologic determinants of this immunologic milieu are poorly understood , most especially in urogenital schistosomiasis . The few T cells present in our model's bladder granulomata may be amplifying and organizing the local immune response , given the development of lymphoid follicles late after egg injection ( Figure 1H ) . Despite the paucity of bladder-infiltrating T cells , the observed increase in IL-4 gene expression in draining lymph nodes ( Figure 5 ) argues in favor of regional activation of TH2 cells . Alternatively , it is possible that basophils stimulated by S . haematobium egg-derived IL-4 inducing principle from S . mansoni eggs [48] ( IPSE , originally known as S . mansoni chemokine binding protein [smCKBP] [49] ) subsequently transit through lymph nodes and secrete IL-4 in these sites [50] . Other potential sources of IL-4 include mast cells and natural killer T ( NKT ) cells , both of which have been reported to localize to the lymph nodes [51] , [52] . The latter cellular subset has been specifically implicated in anti-schistosomal immune responses [53] . Regardless of the source of IL-4 , IgE titers increased beginning two weeks after egg injection ( Figure 6B ) , providing consistent evidence for IL-4-dependent B cell isotype switching [54] . Another important observation was the lack of differential regulation of Th1 and Th17 cytokines ( Figure 3 ) . Immunologic aspects of natural S . mansoni infections of mice feature early Th0 or Th1 responses [55]–[57] , with certain inbred mouse strains also exhibiting a propensity for Th17-associated activity [58] . We speculate that the differences between our model and this body of work are due in part to the synchronous granuloma nature of our approach , which does not include the cercarial- , schistosomular- , and worm-triggered immune response . It is also possible that the bladder immune microenvironment differs from other sites of schistosomal infection , namely the lung , liver , and intestinal tract . Further refinements to our model , and combination of our model with natural infection models , will be necessary to dissect out these important questions . Besides differential cytokine expression , the systemic upregulation of the growth factor VEGF in response to egg injection was particularly striking ( Figure 6A ) . Hematuria is a hallmark of urogenital schistosomiasis , and by definition results when bladder blood vessels and the urothelium break open and communicate with the bladder lumen . We theorize that VEGF triggers disorganized vasculogenesis and results in friable , easily disrupted bladder neovasculature . Interestingly , cervicovaginal lesions associated with urogenital schistosomiasis exhibit increased amounts of sprouting blood vessels and granulation tissue , indicating a possible role for VEGF and/or other vasculogenic influences [59] . The successful mimicry of several pathophysiologic facets of urogenital schistosomiasis by direct egg microinjection suggests that egg deposition alone may be sufficient to recapitulate some of the salient aspects of human disease . We have preliminary evidence that soluble S . haematobium egg antigens alone are also capable of generating bladder inflammation ( manuscript in preparation ) . Use of genetically modified eggs in our model will further define the molecular basis of bladder immunopathology [60] . The observed long-term inflammation does not appear to be caused by bacterial or endotoxin contamination of injection solutions , since: 1 ) solutions are sterile; 2 ) endotoxin levels are <0 . 06 EU/dose; and 3 ) injection of eggs or control vehicle does not result in more TNF production than injection with low endotoxin saline ( data not shown ) . The availability of an improved animal model of urogenital schistosomiasis is of importance to multiple avenues of study . Firstly , the ability to monitor and manipulate the disease in a host ( Mus musculus ) for which numerous species-specific tools are available enables experimental approaches which were previously inaccessible . Secondly , the reliable reproduction of systemic and urogenital stigmata in our model may allow identification and evaluation of novel diagnostic and therapeutic strategies . Our use of micro-ultrasound is , to our knowledge , the first reported application of this technology for in vivo imaging of experimental schistosomiasis . We have successfully employed mouse-specific mass spectrometry and microarray analyses to profile S . haematobium-induced host protein and gene expression signatures , respectively ( manuscripts in preparation ) . Egg-specific biomarker studies are ongoing . These approaches have only been possible through use of a mouse model featuring precisely controlled and reproducible S . haematobium egg-induced immunopathology . Our model may alleviate the bottleneck on urogenital schistosomiasis research imposed by the scarcity and heterogeneity of infected human samples , particularly bladder and lymphoid tissue . When combined with other routes of egg injection or transdermal infection with cercariae , our model of schistosome egg-induced , Type 2-associated fibrosis is capable of synchronous fibrosis in multiple anatomic sites including the bladder , liver , and subcutis within the same animal ( unpublished data ) . Cheever et al . have demonstrated that sensitization of mice by adult S . mansoni worm antigens enhances the egg-specific , lung immune response [61] . Our model is amenable to testing analogous questions using S . haematobium and the bladder . Through this strategy fibrosis in different organ systems may be compared to identify shared and organ-specific mechanisms and potential therapeutic targets . Prior work by others has definitively demonstrated that schistosome egg-induced lung , liver , and intestinal granuloma development is greatly schistosome species-dependent , with differences among S . haematobium , S . japonicum , S . mekongi , and S . mansoni [62] , [63] . In addition , others have reported that hepatic- and lung-associated , S . mansoni egg granulomata develop in a highly organ-specific fashion [64] . S . japonicum granulomata also evolve in a tissue-specific manner in the liver , lung , and intestinal tract [65] . These reports highlight the critical need to develop in vivo models which properly match schistosome species with their tropism for specific host organs . Additionally , our model of urogenital schistosomiasis presents a unique opportunity to study schistosomiasis-associated carcinogenesis [3] , [9] , and potentially inflammatory carcinogenesis in general ( reviewed by Kuraishy et al . [66] ) . Urothelial carcinoma associated with S . haematobium infection arises in a Type 2-biased inflammatory environment . Approaches combining egg-induced Type 2 immunopathology and inducible models of bladder carcinogenesis represent new methods to study the role of inflammatory bias in carcinogenesis . Like all experimental models , our approach has limitations . The injection procedure itself induces non-specific upregulation of a number of cytokines at day 1 post-injection ( Figures 4 , 6 , S2 , and S3 ) . Use of uninfected hamster tissue homogenates as a control “vehicle” injection only partially mitigates this confounding issue . Accordingly , the cytokine expression observed at day 1 post-injection is likely the result of both parasite-specific and non-specific stimuli . While local immune responses to eggs are directly responsible for much of the observed pathology in S . haematobium infection , this activity is part of a larger , systemic immune response elicited in response to multiple life stages of the schistosome . In human disease , S . haematobium infection proceeds through cercarial skin invasion , systemic schistosomular circulation and maturation , adult worm mating within the bladder venous plexus , and egg deposition/excretion [67] . This complex natural history provides potential exposure to a broad range of antigens; however , the relative brevity of cercarial persistence and schistosomular circulation ( hours to days [68] ) and the relative lack of antigenicity of adult worms [69] may limit their contribution to long-term immunopathology . The unexcreted schistosome egg , in contrast , may persist for many years in host tissues and is a well-established , potent immunogen ( e . g . , soluble egg antigen or SEA [69] ) . Indeed , the systemic and granulomatous immune response to urogenital schistosomiasis is primarily driven by egg-associated antigens [70] , [71] . Our model has methodologic similarities to synchronous granuloma formation induced by bolus injection of eggs into the mouse tail , cecal , or portal vein [64] , [72] , [73] . Like other synchronous granuloma models , our approach by definition is unsuitable for the study of the cercarial , schistosomular , and worm stages of S . haematobium . In summary , we report the development of an improved mouse model of urogenital schistosomiasis . The ease and robustness of this model make it attractive for potential application to the elucidation of disease mechanisms , discovery of novel diagnostic biomarkers , and evaluation of candidate therapeutics . Additionally , this model is a prospective platform for the study of basic mechanisms of disease such as epithelial dysfunction , fibrosis , and inflammatory carcinogenesis .
All animal work has been conducted according to relevant U . S . and international guidelines . Specifically , all experimental procedures were carried out in accordance with the Administrative Panel on Laboratory Animal Care ( APLAC ) protocol and the institutional guidelines set by the Veterinary Service Center at Stanford University ( Animal Welfare Assurance A3213-01 and USDA License 93-4R-00 ) . Stanford APLAC and institutional guidelines are in compliance with the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals . The Stanford APLAC approved the animal protocol associated with the work described in this publication . 7 to 8 week-old female BALB/c mice were purchased from Jackson Laboratories . All experimental procedures were carried out in accordance with the APLAC protocol and the institutional guidelines set by the Veterinary Service Center at Stanford University . S . haematobium-infected LVG hamsters were obtained from the National Institute of Allergy and Infectious Diseases Schistosomiasis Resource Center of the National Institutes of Health . The hamsters were sacrificed at the point of maximal liver and intestinal Schistosoma egg levels ( 18 weeks post-infection [74] ) , at which time livers and intestines were minced , homogenized in a Waring blender , resuspended in 1 . 2% NaCl containing antibiotic-antimycotic solution ( 100 units Penicillin , 100 µg/mL Streptomycin and 0 . 25 µg/mL Amphotericin B , Sigma-Aldrich ) , passed through a series of stainless steel sieves with sequentially decreasing pore sizes ( 450 µm , 180 µm , and 100 µm ) , and finally retained on a 45 µm sieve . Control injections were performed using similarly prepared liver and intestine lysates from age-matched , uninfected LVG hamsters ( Charles River Laboratories ) . 7 to 8 week-old female BALB/c mice were anesthetized with isoflurane , a midline lower abdominal incision was made , and the bladder exteriorized . Freshly prepared S . haematobium eggs ( 3 , 000 eggs in 50 µl of phosphate-buffered saline , experimental group ) or uninfected hamster liver and intestinal extract ( in 50 µl of phosphate-buffered saline , control group ) was injected submucosally into the anterior aspect of the bladder dome [37] . Abdominal incisions were then closed with 4-0 Vicryl suture , and the surgical site was treated once with topical antibiotic ointment . At various time points after bladder wall injection , mice were anesthetized using vaporized isoflurane and their abdominal walls were depilated . Transabdominal images of the bladder were then obtained using a VisualSonics Vevo 770 high-resolution ultrasound micro-imaging system with an RMV 704 scanhead [40 MHz] ( Small Animal Imaging Facility , Stanford Center for Innovation in In-Vivo Imaging ) . Mice were sacrificed at serial time points 4 to 99 days after bladder wall injection , and bladders processed for routine histology . Morphologic and morphometric analyses were conducted on H&E- and Masson's Trichrome-stained sections . Total collagen content was determined from fresh-frozen ( −70°C ) bladder homogenates using the Sircol Soluble Collagen Assay Kit ( Biocolor , Carrickfergus , United Kingdom ) according to the manufacturer's instructions . Collagen concentrations were determined using standard curve analysis . Statistical comparisons were conducted using Student's t-test . ELISA measurement of serum IgE was performed using manufacturer's instructions ( Bethyl Laboratories Mouse IgE ELISA Quantitation Kit ) . In brief , coating antibody was aliquoted into and allowed to bind to microtiter plate wells . Excess antibody was washed away . Next , blocking solution was added to the wells , allowed to bind , and excess was washed away . IgE standards and experimental samples were added to wells , incubated for an hour at ambient temperature , and washed . HRP detection antibody was added to each well , incubated , and washed . TMB substrate solution was then added to each well , developed for 15 minutes at ambient temperature , and the reactions stopped using Stop Solution . Absorbance of each well was then read on a plate reader at 450 nm . Five µm sections from OCT-embedded frozen bladders were fixed in 10% buffered formalin phosphate , blocked with 10% horse serum , and incubated overnight at 4°C with a mouse-specific anti-CD68 antibody ( BioLegend , San Diego , CA ) . Sections were then processed and developed using an anti-rat IgG staining kit ( Biocare Medical , Concord , CA ) according to the manufacturer's instructions and counterstained with hematoxylin . Freshly-excised bladders from egg-injected mice were minced and incubated with agitation in 0 . 5% heat-inactivated FBS ( Thermo Scientific Hyclone , IL ) , 20 mM HEPES pH 7 , 0 . 057 Kunitz U/ml DNase I ( Sigma-Aldrich ) , and 1 mg/ml collagenase B ( Roche ) in RPMI 1640 medium for 1 hr at 37°C [75] . The tissue was then passed through a 70 µm nylon cell strainer to remove undigested tissue and macrocellular debris . After erythrocyte lysis ( 8 . 02 mg/ml NH4Cl , 0 . 84 mg/ml NaHCO3 , and 0 . 37 mg/ml EDTA in distilled water ) , 106 cells were treated with mouse anti-CD16/CD32 ( clone 2 . 4G2 , BioLegend , San Diego , CA ) for 10 min and stained with mouse anti–CD3-PE/Cy7 ( clone 145-2C11 , BioLegend ) , anti–CD45 ( B220 ) -FITC ( clone RA3-6B2 , BioLegend ) , anti-F4/80-FITC ( clone BM8 , eBioscience , San Diego , CA ) , anti–CD11b-PE ( clone M1/70 , BioLegend ) , anti–Ly-6G ( Gr-1 ) -PECy7 ( clone RB6-8C5 , eBioscience ) , anti-CCR3-FITC ( clone RB6-8C5 , R&D System , Minneapolis , MN ) , and/or anti-Siglec-F-PE ( clone E50-2440 , BD Pharmingen , San Diego , CA ) for 30 minutes at 4°C . Cells were analyzed using a BD LSRII flow cytometer and BD FACSDiva software . Data were analyzed using FlowJo v7 . 2 . 4 ( Tree Star , Ashland , OR ) . Assayed proteins are listed in Table 1 . Rapidly-excised bladders were placed immediately on ice , minced in RNAlater solution ( Qiagen ) , and stored at −80°C . For protein analysis , 50 mg of tissue was sonicated to homogeneity in 1 ml of ice-cold tissue extraction reagent ( Biosource , San Diego , CA ) supplemented with 1 mM phenylmethanesulfonyl fluoride . Clarified bladder extracts and serum samples were assayed using a mouse 26-plex cytokine kit ( Affymetrix , Santa Clara , CA ) according to the manufacturer's instructions . Samples were read using a Luminex 200 ( Luminex , Austin , TX ) with a lower cut off of 100 beads per sample ( Human Immune Monitoring Core , Stanford University ) . Assayed proteins are listed in Table 1 . Regional lymph nodes were harvested and placed in RNAlater solution ( Ambion , Austin , TX ) , and stored overnight at 4°C , then at −80°C for long-term storage . RNA was isolated and purified using RNAqueous -Micro kits ( Ambion , Austin , TX ) according to the manufacturer's instructions . The concentration of RNA was determined by Quant-iT RNA assay kit ( Invitrogen , Eugene , OR ) with the Qubit fluorimeter . The ribosomal RNA band integrity of each RNA sample was run on an Agilent Bioanalyzer using an RNA 6000 Nano Labchip . RNA samples with RNA Integrity Numbers ( RIN ) of 6 or higher were used for cDNA synthesis and real-time PCR arrays . cDNA synthesis was performed using the RT2 First Strand cDNA Kit ( SABiosciences , Frederick , MD ) . Real-time PCR was performed in the Mx3005p thermal cycler ( Stratagene ) using an RT2 custom PCR array ( SABiosciences ) with RT2 SYBR Green qPCR Master Mixes ( SABiosciences ) . Cycle thresholds ( Ct ) were calculated for each reaction . Using the comparative Ct method relative gene expression was calculated as 2 ( −ΔΔCt ) , where ΔCt = Ct ( gene of interest ) - ΔCt ( normalizer = β-actin ) . ΔΔCt was calculated as ΔCt ( egg-injected ) - ΔCt ( calibrator ) . Data are expressed as mean ± SD . P values are ΔCt of egg- versus control-injected mice . * , P<0 . 05; ** , P<0 . 01; *** , P<0 . 005 . Proteins corresponding to assayed genes are listed in Table 1 . Voided spot on paper analysis was performed as previously described [76] , [77] . In brief , mice underwent bladder wall injection with either eggs or control vehicle . One week later , mice were housed singly and acclimated for one hour in cages lined with filter paper laid underneath a wire floor bottom . Animals were given ad libitum access to food and water-soaked sponges placed on wire cage covers . After 8 hours , each piece of filter paper was photographed under ultraviolet light to localize voided urine spots . Total spots were counted for each mouse and the average number of voids was compared between the egg- and vehicle-injected mice using two-tailed T-tests . * , P<0 . 05; ** , P<0 . 01; *** , P<0 . 005 . Unpaired t tests with Welch's correction were used for comparisons between control- and egg-injected groups , and data were expressed as mean ± standard deviation . P<0 . 05 was considered statistically significant . | Urogenital schistosomiasis ( infection with parasitic Schistosoma haematobium worms , the most common human-specific Schistosoma species globally ) affects over 112 million people worldwide . S . haematobium worms primarily lay eggs in the bladder , upper urinary and genital tracts , and the host immune response to these eggs is considered to cause almost all associated disease in these organs . Resulting conditions include hematuria ( bloody urine ) , urinary frequency , fibrosis ( internal scarring ) of the urinary tract , increased risk of bladder cancer , and enhanced susceptibility to contracting HIV . Approximately 150 , 000 people die annually from S . haematobium-induced obstructive kidney failure alone , making this species one of the deadliest worms worldwide . Despite the importance of S . haematobium , a lack of an experimentally manipulable model has contributed to the paucity of research focusing on this parasite . We have circumvented the barriers to natural S . haematobium oviposition in the mouse bladder by directly microinjecting parasite eggs into the bladder wall . This triggers inflammation , hematuria , urinary frequency , fibrosis , egg shedding , and epithelial changes that are similar to that seen in clinical S . haematobium infections . Our model may provide new opportunities to better understand the basic molecular and cellular immunology of urogenital schistosomiasis and thereby contribute to the development of new diagnostics and therapeutics . | [
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] | 2012 | A Novel Mouse Model of Schistosoma haematobium Egg-Induced Immunopathology |
Various common genetic susceptibility loci have been identified for breast cancer; however , it is unclear how they combine with lifestyle/environmental risk factors to influence risk . We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer . Data from 24 studies of the Breast Cancer Association Consortium were pooled . Using up to 34 , 793 invasive breast cancers and 41 , 099 controls , we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors ( age at menarche , parity , breastfeeding , body mass index , height , oral contraceptive use , menopausal hormone therapy use , alcohol consumption , cigarette smoking , physical activity ) in women of European ancestry . We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene–environment interactions . All statistical tests were two-sided . We replicated previously reported potential interactions between LSP1-rs3817198 and parity ( Pinteraction = 2 . 4×10−6 ) and between CASP8-rs17468277 and alcohol consumption ( Pinteraction = 3 . 1×10−4 ) . Overall , the per-allele odds ratio ( 95% confidence interval ) for LSP1-rs3817198 was 1 . 08 ( 1 . 01–1 . 16 ) in nulliparous women and ranged from 1 . 03 ( 0 . 96–1 . 10 ) in parous women with one birth to 1 . 26 ( 1 . 16–1 . 37 ) in women with at least four births . For CASP8-rs17468277 , the per-allele OR was 0 . 91 ( 0 . 85–0 . 98 ) in those with an alcohol intake of <20 g/day and 1 . 45 ( 1 . 14–1 . 85 ) in those who drank ≥20 g/day . Additionally , interaction was found between 1p11 . 2-rs11249433 and ever being parous ( Pinteraction = 5 . 3×10−5 ) , with a per-allele OR of 1 . 14 ( 1 . 11–1 . 17 ) in parous women and 0 . 98 ( 0 . 92–1 . 05 ) in nulliparous women . These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors .
Both genetic and non-genetic factors are involved in the etiology of breast cancer . Known susceptibility variants include rare high-risk mutations , principally in BRCA1 and BRCA2 , more moderate susceptibility variants in genes such as PALB2 , CHEK2 and ATM , and more than 20 common genetic susceptibility variants conferring modest increased risks , principally identified through genome-wide association studies . Taken together , the known susceptibility variants have been estimated to explain about 20–25% of the observed familial breast cancer risk [1] . There is still limited knowledge about how the relative risks of common susceptibility loci might be modified by the established reproductive and lifestyle risk factors ( referred to as environmental risk factors ) for breast cancer . Such knowledge could provide insights into common biological pathways for cancer development and further our understanding of breast cancer etiology for specific tumor subtypes . Previous reports of a possible interaction between variants in FGFR2 and use of menopausal hormone therapy ( MHT ) were not confirmed [2]–[6] . All recent large studies found no statistically significant evidence of multiplicative gene-environment interaction between several common susceptibility loci and established risk factors for breast cancer after allowing for multiple comparisons [2] , [6] , [7] . The strongest previously reported findings were for an interaction between LSP1-rs3817198 and number of births ( P-value = 0 . 002 ) , between CASP8-rs104585 and alcohol consumption ( P-value = 0 . 003 ) , and between 5p12-rs10941679 and use of estrogen-only MHT ( P-value = 0 . 007 ) [2] , [6] , [7] . This lack of statistical evidence of interaction beyond that expected by chance may be partly due to limited power to detect weak gene-environment interactions and not having considered specific subtypes of breast cancer . We used pooled data from 24 studies participating in the Breast Cancer Association Consortium ( BCAC ) to evaluate whether the relative risks of single nucleotide polymorphisms ( SNPs ) at 23 published loci vary according to levels of 10 established environmental risk factors [8] . Since there is etiologic heterogeneity by subtypes of breast cancer , we also carried out these assessments for breast cancer with positive and negative estrogen receptor ( ER ) status [9] .
Up to 34 , 793 invasive cases and 41 , 099 controls of self-reported European ancestry were included in these analyses ( Table 1 ) . Based on 18 , 532 cases and 25 , 341 controls from 16 population-based studies , we found the expected associations between the environmental risk factors and breast cancer risk ( Table 2 ) . As expected , significant effect heterogeneity by age ( as a surrogate for menopausal status ) was observed only for body mass index ( BMI ) ( P-value = 0 . 007 ) . Except for TGFB1-rs1982073 , all SNPs showed highly significant associations with breast cancer overall ( Table 3 ) . Eleven SNPs showed evidence of heterogeneity in the OR by ER status at p<0 . 01 . The per-allele OR overall and for subsets of women with information available for the risk factors considered were very similar to those previously published and provided no evidence of bias in OR estimates related to data availability ( data not shown ) . The strongest evidence was found for modification of the association with LSP1-rs3817198 by number of births in parous women ( Pinteraction per birth increase in parous women = 2 . 4×10−6 ) ( Table 4; Figure 1 showing individual study results ) . Since this interaction was previously assessed in BCAC , we reassessed the interaction in 6266 cases and 3899 controls not included in the previous report [7] . The SNP association still varied significantly with number of births in parous women ( Pinteraction = 1 . 6×10−3 ) , thus independently replicating the previous finding . The results were consistent across studies ( Pheterogeneity = 0 . 37 ) ( Figure 1B ) . In the overall dataset , the per-allele OR ( 95% confidence interval ) for rs3817198 ranged from 1 . 03 ( 0 . 96–1 . 10 ) in parous women with one birth to 1 . 26 ( 1 . 16–1 . 37 ) in women with four or more births ( Figure 2 ) and in comparison was 1 . 08 ( 1 . 01–1 . 16 ) in nulliparous women ( Table S4 ) . The polymorphism 1p11 . 2-rs11249433 was associated with breast cancer in parous ( 1 . 14 , 1 . 11–1 . 17 ) but not nulliparous women ( 0 . 98 , 0 . 92–1 . 05 ) ( Pinteraction = 5 . 3×10−5 ) . The interaction was non-significantly stronger for risk of ER-positive than ER-negative tumours ( Pheterogeneity = 0 . 13 , Table S5 , Table S6 ) , corresponding to this SNP being more strongly associated with ER-positive disease ( Table 3 ) . When restricted to ER-positive breast cancer , the per-allele OR for rs11249433 was 1 . 16 ( 1 . 13–1 . 20 ) in parous women and 0 . 97 ( 0 . 90–1 . 04 ) in nulliparous women ( Pinteraction = 1 . 6×10−5 ) ( Table 4 ) . There was no significant heterogeneity in the interaction ORs by study ( Figure 1C ) . For the previously reported potential interaction between CASP8-rs1045485 ( in complete LD with rs17468277 ) and alcohol consumption ( <1 versus ≥1 drink/day ) [6] , we found moderate evidence when assessing effect modification by alcohol intake per 10 g/day increase ( Pinteraction per 10 g/day = 3 . 0×10−3 ) ( Table S4 ) . However , when alcohol intake was dichotomized at 20 g/day ( approximately 2 drinks/day ) , the estimated per-allele OR for CASP8-rs17468277 was 0 . 91 ( 0 . 84–0 . 98 ) in those who drank <20 g/day and 1 . 45 ( 1 . 14–1 . 85 ) in those who drank ≥20 g/day ( Pinteraction = 3 . 1×10−4 ) ( Table 4 , Figure 1D ) . We observed weaker evidence of differences in the associations with breast cancer for three further SNPs according to use of MHT and for one SNP according to age at first birth . These included rs13387042 and current use of combined estrogen/progestagen MHT ( yes/no ) ( Pinteraction = 2 . 4×10−3 ) , rs2823093 and current use of estrogen only MHT ( yes/no ) ( Pinteraction = 6 . 6×10−3 ) , rs999737 and duration of estrogen only MHT among current users ( Pinteraction = 4 . 0×10−3 ) , and rs614367 and age at first birth among parous women ( Pinteraction = 9 . 1×10−3 ) ( Table S4 ) . The observed SNP-environmental interaction ORs were not altered substantially ( less than 8% change in the interaction ORs ) when adjusting for additional covariates . These additional covariates included ( when not the potentially effect-modifying variable of interest ) ever parous ( yes/no ) , number of births , BMI , age surrogate for postmenopausal status ( ≥54 years ) , interaction of BMI and postmenopausal status ( ≥54 years ) , current use of MHT , past use of MHT , duration of oral contraceptives ( OC ) use , lifetime alcohol intake , smoking ( pack-years ) ( Table S7 ) . Subjects with missing covariable information were excluded from these analyses , leading to considerably reduced sample sizes . Restricting the analyses to only 16 population-based studies did not change the results substantially ( i . e . , less than 3% ) ( Table S8 ) . The false-positive report probability ( FPRP ) was below 0 . 2 at a prior probability greater than 0 . 001 for the replicated effect modification of LSP1-rs3817198 by number of births and 1p11 . 2-rs11249433 and being ever parous . For the effect modification of CASP8-rs17468277 by alcohol intake ≥20 g/day , the FPRP was below 0 . 2 at a prior probability greater than 0 . 01 . For the four potential interactions reported above , the FPRP was only below 0 . 2 at a prior probability greater than 0 . 05 . ( Table S9 ) .
We carried out a comprehensive evaluation of potential gene-environment interactions between 23 established common susceptibility variants for breast cancer and 10 established environmental risk factors , using 18 variables . Compared to the previous analysis , the present dataset from BCAC included 5 new population-based studies as well as additional study participants from some studies [7] . We examined additional environmental risk factors ( 14 variables ) , and 11 additional recently identified common susceptibility loci . In our previous report , the strongest evidence of effect modification ( P-value = 0 . 002 ) was observed for LSP1-rs3817198 by number of births [7] . The highly consistent and significant finding based on the present analysis of only additional cases and controls provided clear independent replication . We also show that the interaction holds for both ER-positive and ER-negative disease . This lack of heterogeneity is biologically plausible since neither the SNP nor number of births show heterogeneity by ER status in association with breast cancer risk [9] , [10] . Only ever parous versus nulliparous but not the number of births in parous women was assessed for gene-environment interaction in two previous studies [2] , [6] . Consistent with our data indicating no differential effects by ever parous compared to never parous , they did not find evidence of interaction between LSP1-rs3817198 and ever being parous . The rs3817198 SNP is located on the short arm of chromosome 11 and lies within LSP1 , encoding lymphocyte-specific protein 1 , an intracellular F-actin binding protein , although the gene underlying the association has not been definitively identified . The SNP lies close to the H19/IGF2 imprinted region , and the association of breast cancer with rs3817198 has been reported to be restricted to the paternally inherited allele [11] . The effect heterogeneity of LSP1-rs3817198 by number of births appears to be partly due to a significant negative correlation between number of rs3817198 C alleles and number of births in parous women ( P-value = 0 . 002 ) , which was found both in the data of our previous report as well as the additional data for the present analysis . Although not statistically significant , the mean number of children was also reported to be lower in women carrying the CC genotype in the Million Women Study [6] . Also of interest is that LSP1-rs3817198 has been associated with mammographic density , consistent with the direction of the breast cancer association [12] . Mammographic density has also been found to be reduced after a full-term pregnancy , particularly with greater number of births [13] , [14] . We also replicated the strongest finding reported in the Million Women Study based on 7 , 610 cases and 10 , 196 controls [6] . In that study , the per-allele OR of CASP8-rs1045485 ( or rs17468277 in our dataset ) was 0 . 99 ( 0 . 92–1 . 07 ) in those who reported <1 drink/day and 1 . 23 ( 1 . 09–1 . 38 ) in those who reported ≥1 drink/day ( P-value = 0 . 003 ) . Our observation of an increased per-allele OR of 1 . 45 ( 1 . 14–1 . 85 ) for those who reported high alcohol intake ≥20 g/day and 0 . 91 ( 0 . 84–0 . 98 ) for those who consume less provides independent replication of this SNP-environmental interaction . Although one drink corresponds to an intake of approximately 10 g alcohol , the Million Women study reported the strongest risk increase in breast cancer for women consuming at least 15 drinks per week ( RR 1 . 29 ( 1 . 23–1 . 35 ) ) [15] , corresponding to approximately to 2 drinks per day ( 20 g alcohol ) . There is no known functional effect of CASP8-rs1045485 , however , it is associated with a risk haplotype in CASP8 , which is more strongly associated with breast cancer risk [16] , [17] . Caspase 8 is an important initiator of apoptosis and is activated in response to DNA damage that can be caused by alcohol consumption through ethanol-related oxidative stress [18] . Ever being parous , but not number of births , was found to modify the effect of a different SNP , 1p11 . 2-rs11249433 , in particular for ER-positive breast cancer . This SNP shows significantly stronger association with risk of ER-positive tumors than of ER-negative tumors [19] . In nulliparous women , rs11249433 was not associated with risk of ER-positive disease , whereas in parous women , the per-allele OR of 1 . 14 was slightly greater than the overall OR of 1 . 12 . The Breast and Prostate Cancer Cohort Consortium evaluated interactions between 13 of the 23 genetic loci and 9 risk factors , including 1p11 . 2-rs11249433 and ever parous . They found no evidence for this interaction ( P-value = 0 . 79 ) , with per-allele OR of 1 . 09 ( 1 . 04–1 . 14 ) in parous and 1 . 11 ( 0 . 99–1 . 24 ) in nulliparous women [2] . These ORs are not in the same relative direction as our finding with respect to ever being parous . This may be in part due to misclassification of parity if information on parity for participants of the cohort studies was only available at time of recruitment and therefore incomplete with reference to the diagnosis of breast cancer . Their analysis was based on 8 , 576 cases and 11 , 892 controls , which had considerably lower statistical power than the present study . The SNP rs11249433 is located on the short arm of chromosome 1 close to the centromere , which makes it hard to map . The nearest known genes are FCGR1B ( low-affinity Fc gamma receptor family ) and NOTCH2 ( coding a transmembrane receptor protein ) . Recently , a study reported a positive association of NOTCH2 mRNA expression with the breast cancer risk allele of rs11249433 [20] . This association was strongest with the subgroup of ER-positive breast tumors without TP53 mutation , providing some evidence that the increased risk of ER-positive breast cancer might be due to differences in NOTCH2 expression [20] . The evidence for the other four potential interactions mentioned in the results was considerably weaker and confirmation of these findings in further studies is therefore required . Three of these involved effect modification by use of MHT . The effect modification of RAD51L1-rs999737 by duration of estrogen only MHT in current users is particularly interesting because this polymorphism has been associated with mammographic density in the same direction as the breast cancer association [12] . Mammographic density has also been found to be increased in postmenopausal women among users of MHT [21] . RAD51L1 is a member of the Rad51-like proteins that play a crucial role in homologous recombinational repair [22] . Rare deleterious mutations in other genes of this pathway , including BRCA1 and BRCA2 , confer a high risk of breast cancer [1] , [23] . Menopausal hormone therapy has been suggested to alter breast cancer risk in BRCA1 mutation carriers although the evidence is still limited [24] . It is thus plausible that estrogen only MHT modifies the relative risk for genetic variants in RAD51L1 on breast cancer risk . NRIP1 ( nuclear receptor–interacting protein 1 ) , also called RIP140 ( receptor-interacting protein 140 ) , is known to interact with ERα , repress ER signaling and inhibit its mitogenic effects [25] . Exposure to exogenous estrogens through MHT , which stimulate ER signalling , could therefore alter the association of NRIP1 rs2823093 with breast cancer . It is less clear how 2q35-rs13387042 might be modified by current combined estrogen/progestagen MHT use since the gene involved at this locus is still unknown . The SNP is located on the short arm of chromosome 2 and lies in a linkage disequilibrium ( LD ) block containing no known gene ( s ) or non-coding RNAs . The closest known genes are TNP1 ( transition protein 1 ) , IGFBP5 ( insulin-like growth factor binding protein 5 ) , IGFBP2 ( insulin-like growth factor binding protein 2 ) and TNS1 ( tensin 1/matrix-remodelling-associated protein 6 ) [26] . The observed effect modification would suggest that the gene involved may be responsive to steroid hormones . Both Campa et al . and the Million Women Study investigated potential interactions with MHT ( overall use ) [2] , [6] . Neither study reported evidence for interaction between 2q35-rs13387042 or RAD51L1-rs999737 with MHT and breast cancer risk . However , neither study considered current use of MHT even though elevated risks for breast cancer have been clearly established for current use and not for past use [6] , [27] , [28] . Yet Campa et al . found differences in OR estimates for 2q35-rs13387042 by ever use of combined estrogen/progestagen MHT in the same direction as our results for current combined estrogen/progestagen MHT use , with a per-allele OR of 0 . 83 ( 0 . 78–0 . 89 ) in non-users and 0 . 77 ( 0 . 69–0 . 86 ) in ever combined estrogen/progestagen MHT users ( P-value = 0 . 26 ) ( in their Supplementary Table 5 ) . We were not able to confirm the previously suggested possible interaction of 5p12-rs10941679 or FGFR2 variants with MHT and other factors [2]–[5] . Our data suggest that age at first birth in parous women may modify the effect of 11q13-rs614367 , which is located in a region containing no known genes [29] . This newly identified SNP has not been previously assessed for interaction with environmental risk factors . One of the strengths of our study is the large sample size , required for assessing weak to moderate gene-environment interactions , particularly when marker SNPs instead of causal variants are used [30] . We assessed gene-environment interaction separately for ER-positive and ER-negative disease , thereby accounting for heterogeneity by ER status in risk associated with both genetic and environmental factors . However , statistical power was still limited to detect interactions in susceptibility to ER-negative disease . Although selection bias is likely to affect estimates of environmental main effects , under reasonable assumptions , it should not influence the assessment of multiplicative gene-environment interactions or estimates of SNP relative risks [31] . Furthermore , both non-differential and differential misclassification of exposure tend to underestimate the multiplicative interaction parameter rather than yield spurious evidence of interaction [32] . To reduce potential bias due to population stratification , we restricted our analyses to subjects of European ancestry and stratified by study in all analyses . The robustness of our findings to differences in study design was supported by sensitivity analyses considering only data from population-based studies . The interaction estimates also did not change substantially when adjusting for further covariates: the p-values were however higher due to the considerably reduced sample sizes . The absence of study heterogeneity in the estimates of gene-environment interactions provides further reassurance of the robustness of the findings . The effect modifications identified in our study are relatively weak and should result in small differences in risk estimates of joint effects compared to those based on models assuming multiplicative effects . However , most of the SNPs investigated are only markers of the underlying causal variants and underestimate the effects of the causal variants if linkage disequilibrium is incomplete [33] . Thus , gene-environment interactions with the underlying causal variant could have a greater modifying effect on the relative risk [30] . These findings also underline the importance of investigating interactions separately for causally distinct subtypes of breast cancer in future assessments of gene-environment interaction . In summary , we provide strong evidence of effect modification of LSP1-rs3817198 by number of births and of CASP8-rs1045485 by alcohol consumption . For some additional common genetic variants , the associations with breast cancer risk may vary with environmental factors . However , there is little evidence for multiplicative gene-environment interactions for most susceptibility loci and environmental risk factors . Understanding the biological implications of the observed interactions could provide further insight into the etiology of breast cancer . The potential impact of these results on risk prediction for breast cancer needs to be considered in future studies .
We used primary data from the studies in BCAC . All studies had approval from the relevant ethics committees and all participants gave informed consent . A centralized BCAC database of information about common risk factors and tumor characteristics was constructed to facilitate studies of potential modifications of SNP associations by other risk factors . A multi-step data harmonization procedure was used to reconcile differences in individual study questionnaires . The reference age for cohort studies was calculated at time of enrollment and for case-control studies at date of diagnosis for cases and at date of interview for controls . All time-dependent variables were assessed at reference age . This analysis included only subjects of European ancestry that had genotype data for at least 3 SNPs and provided information on at least one of the established risk factors . Relevant data were available from 24 studies , including 16 population-based studies ( 14 case-control and 2 prospective cohort studies ) and 8 non-population-based studies ( Table 1 , Table S1 , Table S2 ) . Subsets of data from 19 studies ( with 11 population-based ) were included in a previous report that assessed interactions between 12 susceptibility variants , reproductive history , BMI and breast cancer risk [7] . We included 21 SNPs found to be associated with overall breast cancer risk at genome-wide statistical significance ( p<5×10−7 ) [10] , [25] , [34] and SNPs for TGFB1 and CASP8 from candidate gene studies [17] ( Table S3 ) . For three loci , 14q24 . 1/RAD51L1 , 12p11 , CASP8 , a surrogate SNP in high linkage disequilibrium ( LD ) ( r2 = 1 in HapMap CEU ) was genotyped in a subset ( Table 3 footnote ) [19] , [25] , [35] . Genotyping was performed in the framework of BCAC by Taqman and iPlex assays and underwent quality control as described previously [10] , [19] , [25] , [34] , [36] , [37] . Genotype data were excluded from analysis on a study-by-study basis according to the following BCAC quality control ( QC ) guidelines: 1 ) any sample that consistently failed for >20% of the SNPs within a genotyping round , 2 ) all samples on any one plate that had a call rate <90% for any one SNP , 3 ) all genotype data for any SNP where overall call rate was <95% , 4 ) all genotype data for any SNP where duplicate concordance was <98% . In addition , for any SNP where the P-value for departure from Hardy-Weinberg proportions for controls was <0 . 005 , clustering of the intensity plots was reviewed manually and the data excluded if clustering was judged to be poor . We used logistic regression to assess the main effects of the SNP and environmental risk factors on invasive breast cancer risk . Analyses were adjusted for study as a categorical variable and reference age as a continuous variable . Odds ratios ( OR ) and their 95% confidence intervals ( CI ) were calculated for the SNP associations assuming a log-additive model and tested for association with a one degree of freedom trend test . All statistical tests were two-sided . The assessment of associations with the environmental risk factors was based on data only from the 16 population-based studies to ensure unbiased estimates for comparison with established effect sizes . The variables considered were analyzed as continuous ( age at menarche , number of births in parous women , age at first birth , usual BMI , height , duration of oral contraceptive use , duration of current use of estrogen-progestagen combined therapy , duration of current use of estrogen-only therapy , pack-years of cigarette smoking , mean lifetime daily grams of alcohol intake , recent physical activity in hours per week ) , or as dichotomous ( ever parous , ever breastfed , ever OC use , ever smoked , current EPT use , current ET use ) ( Table 2 ) . Analyses were performed for all women as well as separately for women aged <54 years and ≥54 years , considering the age groups as surrogates of pre- and postmenopausal status , Differential effects by menopausal status were assessed by adding an interaction term . For all categorical variables , the lowest level of exposure ( or no use ) was used as the reference . For evaluating current use of MHT by type , we used never use of MHT as the reference category and additionally adjusted for former use of MHT and other MHT type , as appropriate . To test for interactions between SNPs and environmental risk factors , we fitted for each SNP two logistic models , a model with terms for the SNP and the risk factor of interest and another model with additionally an interaction term for the product between the SNP ( number of risk alleles ) and the risk factor variable . We modeled the interaction based on the risk factor variable definitions employed for the main effects . All analyses were stratified by study and adjusted for age as a continuous variable . The likelihood ratio test was used to compare the difference between the two models and departure from independent multiplicative effects of the SNP and the risk factor . BMI was the only variable found to show differential effects by menopausal status , which is consistent with the literature [38] . Therefore , interaction between SNPs and BMI was assessed separately for pre- and postmenopausal women whereas all other risk factors were evaluated regardless of menopausal status . To assess study heterogeneity , we calculated odds ratios for interaction for each individual study , adjusting for age , and reported P-values for heterogeneity using a Q-test . Subjects with missing data for a particular SNP or environmental factor were excluded from the respective analysis . We also calculated stratum specific per-allele ORs for each SNP: age at menarche ( ≤11 , 12–13 , ≥14 years ) , number of births ( 1 , 2 , 3 , ≥4 ) , age at first birth ( <20 , 20–24 , 25–29 , ≥30 years ) , usual BMI ( <25 , 25–29 , ≥30 ) , height ( <160 , 160–164 , 165–169 , ≥170 cm ) , duration of oral contraceptive use and of menopausal hormone use ( 0 , >0–<5 , 5–<10 , ≥10 years ) , mean lifetime alcohol intake ( 0 , 0–<10 , 10–<20 , ≥20 g/day ) , pack-years of smoking ( 0 , 1–<10 , 10–<20 , ≥20 ) , and physical activity ( 0 , >0–<3 . 5 , ≥3 . 5–<7 , ≥7 h/week ) . For SNP-environment interactions with associated P-value<10−3 , we also compared results after adjusting for additional covariates . We performed a total of 414 ( 23 SNPs x 18 risk variables ) tests . To account for chance findings due to multiple comparisons , we calculated the false positive report probability ( FPRP ) for SNP-environment interactions with associated P-value<10−3 [39] . The FPRP depends on the prior probability that the association between the SNP and breast cancer is modified by the environmental risk factor , the power of the present study , and the observed P-value . Since the prior probability of the assessed multiplicative interactions varies depending on subjective evaluation of existing evidence , we calculated the FPRPs for prior probabilities ranging from 0 . 05 to 0 . 0001 . We considered findings with FPRP below 0 . 2 to be noteworthy results , as previously proposed [39] . In secondary analyses , we examined associations and effect modifications separately for women with ER-positive tumors and ER-negative tumors , each compared to all controls . Effect heterogeneity by ER status was tested using case-case analysis . Data harmonization was performed using an ACCESS database and transformation of the data elements was performed using SAS ( Release 9 . 2 ) . All other data analyses were conducted using SAS ( Release 9 . 2 ) and the R programming language [40] . | Breast cancer involves combined effects of numerous genetic , environmental , and behavioral risk factors that are unique to each individual . High risk genes , such as BRCA1 and BRCA2 , account for only a small proportion of disease occurrence . Recent genome-wide research has identified more than 20 common genetic variants , which individually alter breast cancer risk very moderately . We undertook an international collaborative study to determine whether the effect of these genetic variants vary with environmental factors , such as parity , body mass index ( BMI ) , height , oral contraceptive use , menopausal hormone therapy use , alcohol consumption , cigarette smoking , and physical activity , which are known to affect risk of developing breast cancer . Using pooled data from 24 studies of the Breast Cancer Association Consortium ( BCAC ) , we provide first convincing evidence that the breast cancer risk associated with a genetic variant in LSP1 differs with the number of births and that the risk associated with a CASP8 variant is altered by high alcohol consumption . The effect of an additional genetic variant might also be modified by reproductive factors . This knowledge will stimulate new research towards a better understanding of breast cancer development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"cancer",
"epidemiology",
"genetic",
"epidemiology"
] | 2013 | Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors |
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