Search is not available for this dataset
article stringlengths 4.36k 149k | summary stringlengths 32 3.35k | section_headings listlengths 1 91 | keywords listlengths 0 141 | year stringclasses 13
values | title stringlengths 20 281 |
|---|---|---|---|---|---|
We present the results of a model inversion algorithm for electrocorticography ( ECoG ) data recorded during epileptic seizures . The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy . These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures . Observing the dynamics of effective connectivity provides insight into mechanisms of seizures . Estimation of patients seizure dynamics revealed: 1 ) a highly stereotyped pattern of evolution for each patient , 2 ) distinct sub-groups of onset mechanisms amongst patients , and 3 ) different offset mechanisms for long and short seizures . Stereotypical dynamics suggest that , once initiated , seizures follow a deterministic path through the parameter space of a neural model . Furthermore , distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset . There were also distinct patterns between long and short duration seizures that were related to seizure offset . Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy , since specific therapies may have preferential effects on the various parameters that could potentially be individualized . Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research . This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data . Our results underscore the power of theoretical models to inform epilepsy management . It is our hope that this work guides further efforts to apply computational models to clinical data .
Understanding how and why the brain generates spontaneous seizures is an unsolved problem in neuroscience . The medical implications of seizures are profound , with over 50 million people affected by epilepsy , and at least 30% not adequately controlled by available therapies [1] . Surgical treatment does not provide complete seizure freedom for all patients [2] , and novel drugs have not greatly improved on the level of seizure freedom that can be achieved [3] . On the other hand , data-driven , computational techniques have shown early promise in obtaining a more individualized picture of a patient’s seizures , which may shed new light on mechanisms of seizures and lead to targeted treatment strategies [4 , 5] . Patient-specific , computational models can provide unique insight into seizure mechanisms , and are well accepted in the study of epilepsy [6] . In particular , lumped parameter neural mass models [7 , 8] have been extensively used to investigate cortical activity during epileptic seizures [9–11] . These models describe seizures as state transitions in the brain [12] that arise from endogenous noise perturbations or ‘pathways through the parameter space’ of a neural model [13] . Clinically , it is recognized that electrographic ( EEG ) recordings of seizures show stereotypical changes in the signal morphology that are regarded as state changes ( i . e . between interictal , peri-ictal , and ictal states ) [14] . Despite the ubiquity of neural mass models to study seizure transitions , the translation of these theoretical insights into clinical practice has not been widely realized . The validation of neural network models to aid clinical decision making has made some advances in diagnosis [15] , and surgical planning [16–19] . Ideally model-based techniques can also improve outcomes at earlier stage interventions , such as drug selection . Another area models can aid treatment may be in seizure forecasting [20 , 21] , or the design of electrical counter-stimulation ( using model predictive control ) [22–24] . A fundamental hurdle to overcome is validating theoretical models of brain dynamics in a clinical setting . This hurdle largely exists due to the difficulty of obtaining in vivo neural recordings from humans . Whilst simulation has proven valuable to generate new hypotheses regarding the mechanisms of seizures , a complete validation must unite empirical data with theoretical models and demonstrate that models have predictive value , as well as being descriptive of the data [4 , 5] . Model inversion is a powerful approach to combine patient-specific recordings with accepted principles of brain structure and function encapsulated by the parameters of a neural model [25] . Previous work has outlined a generalizable framework to estimate the most likely states and parameters of a neural model given observed data [25] . For many years this problem was intractable for non-linear neural models [26] . Previously , model inversion has relied upon simplifying assumptions , such as linearization , or sampling techniques [27] . Another approach is to re-frame the problem , so that the objective is to find the most likely model to generate the observed data . In this way , the estimation is conditioned on the model space , which is generally explored via some heuristic model selection criteria [28 , 29] . Alternatively , the inversion can be conditioned on the data , where the most likely model is identified using an assumed density ( Kalman ) filter [30] . This approach has been validated for investigation of seizure dynamics [31–33] . Recent advances have also incorporated a fast , semi-analytic solution to handle the propagation of estimates through the non-linear neural mass equations [25 , 34 , 35] . Model inversion techniques that enable time-varying estimates of key parameters provide a powerful means of inferring cortical mechanisms from functional neuroimaging data . This is particularly true for EEG/ECoG data , which has high temporal resolution . The ability to update estimates with each new data point can lead to insights into ictal dynamics that evolve over fast time scales . Statistical observations from data are also important to validate models of seizure transitions . Some studies have investigated the distributions of times spent in different seizure states [36] . Models that are predictive of higher-order statistics derived from many seizures are more convincing than models which are only descriptive of ( or fitted to ) individual seizures [4] . An important observation is that the distribution of times spent in the ictal state has a patient-specific peak [37] , rather than a uniform distribution . These peaks indicate that patients have a characteristic seizure duration , or trajectory length . Intriguingly , a subset of patients showed a distinctly bimodal distribution of seizure durations , indicating two populations of seizures ( long and short ) [37] . We hypothesize that these distributions reveal a crucial aspect of seizure dynamics , which should not be neglected in computational modeling .
Seizure mechanisms were investigated for continuously recorded ECoG from 12 patients with focal epilepsy monitored during a previous clinical trial [38] . All subjects were implanted with intracranial electrode arrays with a total of 16 platinum iridium contacts around the seizure onset zone . The ECoG was sampled at 400 Hz and wirelessly relayed to an external , portable personal advisory device . Seizure detection was automated and reviewed by expert clinicians . This study used data from 3010 clinical seizures ( average 250 per patient ) . Seizures were either associated with confirmed clinical symptoms or were electrographically similar to clinical seizures . Other epileptiform discharges without clinical symptoms were excluded . All seizures had onset and offset labelled by expert epileptologists . For further details on the data collection procedures the reader is referred to Cook et al . ( 2013 ) [38] . A similar procedure to that outlined by Cook et al . ( 2016 ) [37] was used to identify patients with bimodal seizure durations . Both k-means clustering and Gaussian mixture model fitting were used to test for bimodality . Clusters were assigned for one , two and three seizure populations ( based on the logarithm of seizure duration in seconds ) . The optimal number of clusters was determined using gap criteria [39] . The current study used patients who had at least 20 seizures that had a lead time of one hour . Recordings were used from five minutes before seizure onset , until one minute after seizure offset . Seizures with telemetry dropouts were excluded from analysis . Data were bandpass filtered ( second-order , zero-phase Butterworth filter ) from 1 Hz to 180 Hz with a notch filter at 50 Hz ( second-order , zero-phase Butterworth filter ) . The energy of the signal was computed for a 1s sliding window ( 50% overlap ) as energy = ∑ n = 1 N x 2 . The states ( mean membrane potentials ) and parameters ( synaptic connectivity strength ) of neural mass models were fitted to data recorded during epileptic seizures . The formulation of the neural mass model in the following section is derived from the model introduced by Jansen and Rit ( 1995 ) [8] ) , and has also been outlined in our previous work [33 , 34] . The neural mass model is suitable to model ECoG measured at this scale ( electrodes approximately 5mm in diameter with spacing on the order of centimeters ) , in line with similar neural models used to describe EEG/MEG activity [10 , 11 , 40] . A single , independent neural model was fitted to each ECoG channel ( 16 models in total ) . Neural models were not coupled between channels; hence , estimates primarily captured local connection strengths within a single cortical region . The input parameter , u described non-local inputs to the pyramidal population . The Jansen and Rit model consists of three neural populations ( excitatory , inhibitory and pyramidal ) . Neural populations were described by their time varying mean membrane potential , vn , which is the sum of contributing mean post-synaptic potentials , vmn ( post-synaptic and pre-synaptic neural populations are indexed by n and m , respectively ) . For the current model , the index n ( post-synaptic ) represents either pyramidal ( p ) , excitatory ( e ) or inhibitory ( i ) populations , as shown in Fig 1 . The post-synaptic potential , vmn arises from the convolution of the input firing rate , ϕ ( vn ) , with the post-synaptic response kernel , v m n ( t ) = α m n ∫ - ∞ t h m n ( t - t ′ ) ϕ ( v n ( t ′ ) ) d t ′ , ( 1 ) where αmn , which are the estimation parameters , represent lumped connectivities that incorporate average synaptic gain , number of connections , and average maximum firing rate of the presynaptic populations . ϕ ( vn ) is the sigmoid function ϕ ( v ) = 1 2 ( erf ( v - v 0 ς ) + 1 ) ( 2 ) where v0 = 6mV , and ς = 0 . 0030 ( as defined by Freestone et . al . ( 2014 ) [25] ) . The convolution in Eq 1 can be written as two coupled , first-order , ordinary differential equations , d v m n d t = z m n d z m n d t = α m n τ m n ϕ m n - 2 τ m n z m n - 1 τ m n 2 v m n . ( 3 ) where τmn is a lumped time constant . The values of τep , τpe , and τpi were fixed to 10ms and the value of τip to 20ms , as defined by Jansen & Rit ( 1995 ) [8] . External ( non-local ) inputs to the pyramidal population are modeled as an additive term affecting the pyramidal membrane potential , v p ( t ) = v p e ( t ) - v p i ( t ) + u ( t ) . ( 4 ) The recorded ECoG for each channel , i , is derived from the average pyramidal membrane potential of each independent neural mass model ( resulting 16 disconnected models in the estimation ) , y i ( t ) = v p i ( t ) ( 5 ) The neural model can be expressed in matrix notation x ˙ ( t ) = A x ( t ) + B ϕ → ( C x ( t ) ) , ( 6 ) where x ∈ R N x is a state vector representing the postsynaptic membrane potentials generated by each population synapse and their time derivatives . There are two states per synapse and Nx = 2Ns is the total number of states , where , for Ns synaptic connections in the models , the state vector is of the form , x = [ v 1 z 1 … v N s z N s ] ⊤ . The definitions of A , B , and C are provided in S1 Appendix . The observation equation is of the form y ( t ) = H x ( t ) + v ( t ) , ( 7 ) where H ∈ R N x × N y is the observation matrix , v ( t ) ∼ N ( 0 , R ) ∈ R N y is the observation noise , and Ny is the number of observations ( here Ny = 1 as each neural mass model describes a single ECoG channel ) . As our measurement function is linear , H is simply an index vector of zeros and ones that defines the average pyramidal membrane potential given by Eq 4 . A joint state ( membrane potentials ) and parameter ( external input and connectivity strengths ) estimation algorithm was implemented for every sample of the recorded ECoG . To obtain estimates it was necessary to augment the state-space representation of the neural model . To define the augmented model , we first define a vector of parameters as θ = [ u α p e α p i α i p α e p ] ⊤ . The dynamics for the parameter are modeled as a random walk θ ˙ = 0 . ( 8 ) The state vector x and the parameter vector θ are concatenated to form the augmented state vector , ξ = [ x T θ T ] ⊤ . ( 9 ) Our augmented state-space model is ξ t = A θ ξ t - 1 + B θ ϕ ( C θ ξ t - 1 ) + w t - 1 , ( 10 ) where w t ∼ N ( 0 , Q ) . The state vector ξ ∈ R N ξ × 1 and matrices Aθ , Bθ , and Cθ are ∈ R N ξ × N ξ and have the form A θ = [ A 0 0 I ] , B θ = [ B 0 0 0 ] , C θ = [ C 0 0 0 ] . ( 11 ) For simplicity we will drop the subscript θ on the system matrices , as the remainder of the equations refer to the augmented model . The estimation scheme uses an assumed density filter . This filter provides the minimum mean squared error estimates for the states and parameters , under the assumption that the underlying probability distribution is Gaussian ( the assumed density ) . Formally stated , the aim of estimation is to compute the most likely posterior distribution conditioned on previous measurements , ξ ^ t + =E [ ξ t | y 1 , y 2 , … , y t ] ( 12 ) P ^ t + =E [ ( ξ t - ξ ^ t + ) ( ξ t - ξ ^ t + ) ⊤ ] , ( 13 ) The estimator proceeds in two stages; prediction and update . In prediction , the prior distribution ( obtained from the previous estimate ) is propagated though the neural mass equations . This step provides the so called a priori estimate , which is a Gaussian distribution with mean and covariance , ξ ^ t - = E [ ξ t - 1 | y 1 , y 2 , … , y t - 1 ] ( 14 ) P ^ t - = E [ ( ξ t - 1 - ξ ^ t - 1 + ) ( ξ t - 1 - ξ ^ t - 1 + ) ⊤ ] . ( 15 ) In the second stage , a Bayesian update is performed to shift the estimated posterior based on the observed data , giving the a posteriori distribution , ξ ^ t + = ξ ^ t − + K t ( y t − H ξ ^ t − ) ︸ ECoG prediction error . P ^ t + = ( I − K t H ) P ^ t − , ( 16 ) where K is the Kalman gain ( readers are referred to [27] for a detailed description of the Kalman filter ) . After each time step , the a posteriori estimate becomes the prior distribution for the next time step , and the filter proceeds . In general , the Kalman filter equations do not have a solution for nonlinear model or measurement functions . Previous efforts to use Kalman filtering on the nonlinear neural mass model have relied on simplifying assumptions ( either linearization of the model , or sampling to estimate the posterior distribution ) . This work applied an exact , semi-analytic solution for the mean and covariance of a multivariate Gaussian distribution transformed by the nonlinear neural mass model . This solution provides the a priori estimate of the mean and covariance ( see S1 Appendix for details ) . As the observation function is linear , the updated ( a posteriori ) mean and covariance are obtained trivially using Eq 16 . The Kalman filter requires ξ ^ 0 + and P ^ 0 + to be initialized to provide the a posteriori state estimate and state estimate covariance for time t = 0 . The other parameters that must be initialized are the model and measurement noise , Q and R , respectively . Further details of filter initialization are given in the S1 Appendix .
An assumed density filter was used to track the time-varying states and parameters of neural mass models during every seizure ( as seen in Fig 1 ) . This estimation technique finds the most likely model given the observed ECoG data . Importantly , the model is updated at every time step , so there is no loss of temporal resolution . Estimated states were mean membrane potentials , and parameters ( alpha parameters in Fig 1B and 1C ) , which were the external input and average synaptic strengths between pyramidal , non-pyramidal excitatory , and inhibitory neural populations . In this way , the neural models provided an estimate of the average activity and effective connectivity within intracortical circuits [25] . We found that slow changes in the synaptic connectivity parameters led to seizure transitions in the neural models . As seen in Fig 1C , a deterministic forward simulation of neural models using time-varying connectivity estimates reproduced the beginning and end of seizures . It is important to also quantify estimation accuracy before proceeding . A full summary of the model and estimation errors is given in the Supplementary Material . However , it is worth noting briefly that errors between the estimated signal and real data were small ( see S1 Fig ) . The mean squared error ranged from 0 . 2-0 . 9 mV when averaged across all seizures ( note that the mean amplitude of the measured ECoG signal ranges from approximately 25-100mV ) . The uncertainty ( covariance ) of state variables and parameters was also small ( see S2 and S3 Figs ) , suggesting that key seizure activity was well described by the model , rather than by the residuals . Across patients , the mean covariance ranged from 2-16% for state variables , and from 0 . 1-10% for connectivity parameters ( expressed as a percentage of the estimated value ) . Numerical instability of the filter was occasionally observed ( for all patients , instability occurred for less than 1% of the data ) . Estimates that became unstable were removed from further analysis . Fig 2 shows the average energy of recorded ECoG during every seizure ( averaged across 16 electrode channels ) . Patients’ seizures showed strikingly consistent patterns of signal energy between seizures . These patterns were generally time locked to seizure onset , as demonstrated by the vertical alignment of energy changes ( note that Patients 2 , and 4 did not show a vertically aligned onset pattern ) . Long and short seizures began similarly , but evolved differently ( see Patients 3 , 8 , and 11 ) . Long seizures entered a secondary phase where energy increased . Short seizures and the early phases of long seizures were characterized by an energy reduction ( note the darker vertical band following seizure onset ) . Some patients’ seizures only showed the “long” stereotypical pattern with a high-energy phase ( see Patients 2 , 4 , 6 , 9 , and 15 ) . Patient 13 had only low energy , stereotypical “short” seizures . Patient 7 had a large majority of short seizures , with a small number evolving to have increased energy . These two patterns of seizure energy suggest that long and short seizures reflect distinct event types , each with a characteristic electrographic evolution . We hypothesized that these stereotypical signal patterns represent two alternative seizure trajectories , which could be differentiated by their onset and/or offset mechanisms . Note that although the average energy ( averaged across electrode channels ) was presented , the observed patterns were consistent across different channels . Full plots are provided in the Supplementary Material ( S8 to S19 Figs ) . Fig 3 presents the dynamic estimation results for the five connectivity parameters of a neural model during every seizure . Seizures followed a remarkably consistent trajectory through the parameter space of the neural mass models , showing similar patterns across all events for an individual . This indicates that seizure transitions follow a stereotypical pathway . Note that transitions during the seizure are locked to the onset time ( as demonstrated by the vertical banding in parameter changes ) . A higher contrast ( normalized ) version of these patterns is provided in S4 Fig , to more clearly expose connectivity patterns . For all patients , the strongest ictal changes in connectivity strength occurred for in-going connections to the pyramidal neurons ( the first three columns of Fig 3 ) . Conversely , outgoing pyramidal connections ( to inhibitory and excitatory neurons ) were more stable over the durations of the seizures , demonstrated by values which were closer to zero ( reflecting no change from baseline ) , and less vertical patterning ( reflecting no stereotypical transitions during seizures ) . Patient 6 was one possible exception , showing some decrease in outgoing pyramidal connections ( 6D and 6E ) . Note that although neural models were fitted independently to all 16 electrode channels , Fig 3 shows results for a single example channel per patient . The data associated with every channel generates 60 full page figures , which are provided in an online repository ( https://github . com/pkaroly/Data-Driven-Estimation ) . The consistency of stereotypical patterns between channels is investigated in the following sections . Fig 4 shows the mean change in connectivity strength during seizures . A consistent motif was a decrease followed by an increase in ingoing connections to the pyramidal population ( see columns A and C for Patients 1 , 3 , 6 , 8 , 9 , and 15 ) . Patient 11 showed the same motif , but with connectivity strength always above baseline . Patients 7 , 10 , and 13 showed only decreases in ingoing pyramidal connections . Patients 2 and 4 demonstrated only strengthened connections . There were three classes of ictal parameter transitions: decrease , increase , and decrease-then-increase , where connections into the pyramidal populations were on average weaker , stronger , or weakened then strengthened ( compared to a pre-ictal baseline ) , respectively . These classes aligned well with the stereotypical seizure evolution patterns that were identified based on signal energy ( Fig 2 ) . For instance , “decrease-then-increase” patients ( 1 , 3 , 6 , 8 , 9 , and 15 ) showed long seizures that began with lower energy and evolved into a higher energy state . The “increase” patients ( 2 and 4 ) showed primarily high energy seizures without an obvious alignment to seizure onset . The “decrease” patients ( 7 , 10 , and 13 ) showed short , low-energy seizures . Outgoing connections from pyramidal cells to excitatory/inhibitory populations showed little to no change . For some patients ( 2 , 4 , 9 and 10 ) , a slight increase in pyramidal to inhibitory strength was observed . Fig 5 shows the average seizure trajectory for all channels . Trajectories were qualitatively similar across channels . Most subjects showed focal patterns in which a subset of channels demonstrated connectivity changes during seizures while other channels did not have significantly increased or decreased connectivity during seizures ( compared to a pre-ictal baseline period ) . Such patterns were not surprising , given that all subjects had focal seizures , which typically appear first on a subset of EEG channels before spreading . Apart from focal connectivity changes , there was some inter-channel variability at the ends of seizures . Subject 6 showed some channels with increased connection strengths and others with decreased strengths . Subject 9 showed increased inhibitory connections across most channels but decreased inhibition on a subset of channels ( Fig 5 , subpanel 9B ) , that occurred toward the ends of seizures . Overall , significant changes in connectivity ( above or below baseline ) followed the same stereotypical , patient-specific pattern across all channels . Exceptions to this consistency were observed for a few subjects in the later stage of seizures ( significant changes are shown in Supplementary Material S6 Fig ) . In other words , there were no channels with markedly different trajectories; changes in connectivity were either in the same direction or showed no significant change from baseline . This consistency supports the finding of characteristic pathways of epileptic seizures , although these pathways were only observed on a subset of ( possibly focal ) channels , while other channels did not show altered connectivity patterns during seizures . Overall , there was no difference in the average connection strength trajectories for long compared with short seizures ( when connections were averaged across seizures in the long and short populations , respectively; see S5 Fig for details ) . Therefore , we hypothesized that short and long seizures were primarily differentiated by termination ( i . e . both types follow a similar path from onset , with short seizures terminating earlier ) . This hypothesis was tested by measuring the correlation between connection strength ( now averaged across 16 electrode channels ) and seizure duration before onset and offset ( correlation results were qualitatively similar when evaluated for individual electrode channels , and are provided in S7 Fig ) . Fig 6 shows that almost no patients showed significant correlation between seizure duration and onset dynamics . In other words , there was no relationship between average connection strength and seizure duration at the outset ( measured over a 5s window prior to seizure onset ) . However , at 5s before seizure offset , there were strong correlations for all connections . In general , longer seizures were associated with increased excitatory inputs and decreased inhibition to the pyramidal cells . Bimodal patients ( 3 , 8 , 10 , and 11 ) all showed a similar relationship between connectivity strength and seizure duration . Four other patients also showed significant correlations; therefore , correlations do not arise purely because of the two duration populations .
Patients were classified into three groups of connectivity patterns during seizures ( seen in Fig 4 ) : increased , decreased , and decreased-then-increased strength of ingoing connections to pyramidal cells . These parameter shifts may relate to distinct mechanisms of seizure onset . For the decrease , and decrease-then-increase patterns we speculate that seizures arise from either under-regulation or disinhibition of pyramidal neurons . The corresponding rebound of connection strength ( in the decrease-then-increase group ) may be linked to a regulatory mechanism that was not triggered for patients with shorter seizures ( in the decrease group ) . Previous work using the neural mass model confirms that inhibitory populations are likely to play a role in generating epileptiform activity , with the time scale of inhibitory dynamics also highly relevant [10] . There were also two patients who showed only increased connection strength to pyramidal cells ( in Fig 4 , Patient 2 showed all connections were increased and Patient 4 showed an increase of excitatory inputs ) . For these patients , seizures may have been driven by over-excitation of pyramidal neurons . There is a lack of consensus as to whether noisy fluctuations ( multi-stability ) or deterministic parameter changes ( bifurcations ) drive seizure onset/offset [4] . Other mechanisms , such as intermittency , may also be involved in seizure transitions [41 , 42] . This study demonstrated that the transitions of connectivity parameters were locked to the onset of seizures , and not the offset ( i . e . the patterns in Figs 3 and 2 arise when the seizures are aligned by start time , rather than end time ) . This finding suggests that there is a deterministic process conditioned on the start time of the seizure , whereas the lead up to seizure offset showed more stochasticity . Based on these results we speculate that seizure onset is more likely to occur through a deterministic process ( as in a bifurcation ) , where the brain state is driven across some ‘point of no return’ . Offset is more likely to result from noisy fluctuations . Other studies have hypothesized that seizures terminate as the result of a bifurcation [43 , 44] . However , the brain’s state during a seizure may merely approach a critical transition , without crossing over [45] . Therefore , it is possible to observe signs of critical slowing ( as in ( Kramer et al . , 2012 ) ) yet still have seizure termination driven by noise [4 , 46 , 47] . The presence of characteristic seizure durations should inform theoretical approaches to modeling seizure transitions . For instance , in a bistable regime , where noisy fluctuations drive the transition between a fixed point and oscillatory ( ‘seizure-like’ ) state , characteristic dwell times can emerge for the different states [4 , 46 , 47] . Dwell times provide one candidate mechanism for characteristic seizure durations . Bimodal populations in some patients suggest that the brain can support two distinct seizure trajectories ( short and long ) . It has been shown experimentally that different durations of seizures may arise as the result of distinct onset stimuli [48] . Explanations for multiple seizure types can also be derived from computational models . For instance , different background stability properties in a cortical model can result in two distinct types of seizures [49] . Multiple seizure trajectories can also arise from different onset bifurcations [43] . Similarly , multiple offset bifurcations could terminate seizures earlier or later , giving two populations of duration . The results of this work suggest that long and short seizures arise from distinct mechanisms of seizure termination . This hypothesis is supported by a recent study from Payne et al . ( 2018 ) , which found that long and short seizures were associated with different durations of post-ictal suppression [50] . Knowledge of parameter transitions within neural models can increase the information extracted from EEG , informing new hypotheses of seizure mechanisms and guiding clinical practice . There is some evidence to suggest that the clinical classification of a seizure is predictable soon after its onset [51] , in other words , the evolution of a seizure may be somewhat predetermined . Our results support the existence of predictable seizure types , and provide additional metrics ( based on the parameters of a neural model ) that may extend our understanding of traditional seizure types . The consistency of neural model parameters over many seizures suggests that , for some patients , seizure trajectories are established via repetition . The notion of ‘learned epilepsy’ [52] , is an interesting interpretation of epileptogenesis whereby the abnormal process is learning and spontaneously repeating a pathological sequence , rather than the sequence itself ( all brains can support seizures ) . For some patients , successful treatment strategies may involve disrupting or even reversing memorization of the seizure , rather than addressing an underlying cause [52] . On the other hand , the current results ( Fig 3 ) also showed that seizure pathways were highly patient-specific and not all subjects’ trajectories were conserved over time . Neural mass models have the potential to highlight the relative contributions of excitatory versus inhibitory connections during seizures . This information can guide whether GABAergic or glutamatergic drugs are required . Previous studies using neural mass models have demonstrated alterations in the balance of excitation and inhibition estimated from data recorded during seizures [40 , 53 , 54] . The current estimation technique enables previous efforts to be extended to investigate a large number of seizures . Some patients showed decreased inhibition at seizure onset , whereas others demonstrated increased excitation ( Fig 4 ) , potentially warranting different therapies . Furthermore , patients with two duration populations may require different strategies to terminate their seizures . Knowing in advance when two adjunct therapies are needed is an important clinical insight that can provide crucial benefits to patients with drug refractory epilepsy . This study found that long seizures were correlated with lower inhibition and higher excitation ( in one patient , the reverse was the case ) , which can guide electrical stimulation designed to precipitate early termination of seizures . The presented model inversion technique and results have wide-ranging applications . The parameter estimates were consistent across many seizures . Until now , it has not been possible to show consistency of models of seizure transition in ECoG due to the limited availability of long-term seizure recordings . Results also generalized across patients . Although the cohort of 12 patients was not large , prior studies have restricted model inversion of seizures to only one or two patients [13 , 55–58] . Another important aspect is that the techniques can be generalized across models . The estimation filter is not specially formulated for the Jansen and Rit model used in this study but can be generalized to any model that uses the basic matrix representation provided in the derivation ( see Supplementary Materiall S1 Appendix ) . That is , the approach can be applied to any combination of coupled neural populations or indeed any network model that can be represented by a linear component and a non-linear sigmoidal ( error function ) coupling term . Data driven modeling may provide the opportunity to identify which drug could be helpful for different classes of seizure , as different mechanisms of anti-epileptic drug action may preferentially effect the various connectivity parameters , though further validation of model predictions is needed to translate estimation results to clinical practice . Levels of AEDs have been related to features of the EEG signal [59 , 60] . Therefore , it may be possible to extend this relationship to predicting the mode of action of an AED from an individual’s EEG . The time scale of connectivity changes may also be highly relevant to suppressing epileptic activity [10] . Future work should extend estimation to include time constants and investigate the utility of the outlined neural parameters to detect and predict drug action . This study provided estimates of independent neural circuits for each channel of ECoG . Previous studies using coupled neural mass models have highlighted the importance of inter-channel interactions , particularly for seizure propagation [61] . However , this work considered local coupling as potentially more relevant to capture the onset of focal seizures . Non-local effects were described by the lumped input parameter , u , rather than explicitly by inter-model connections . It is possible that long and short seizures could be differentiated earlier based on inter-channel connectivity patterns . Future work will focus on extending the estimation algorithm for non-locally connected neural regions . An inverse solution to the time-varying , multi-scale network problem is not trivial and is likely to require additional constraints . For example , structural MRI data may inform prior probabilities of connection strength [58] . Individual neural models can also be coupled within a larger scale network [15] . The approach taken by Schmidt et . al . ( 2016 ) can be adapted to set prior probabilities , or otherwise constrain the propagation of an assumed density ( Kalman ) filter . The challenge of large-scale model inversion is relatively well understood [4 , 26] . A more recent problem in EEG analysis is the challenge of dealing with very high dimensional data . This study involved separate dimensions for model parameters , seizures , patients , electrode channels , and time . Distilling insights from such a large dataset is computationally intensive . To provide some insight into this problem , the estimation results presented were 1 . 5TB in size . Generation of each figure can take up to a week to complete for all patients . The use of “big data” techniques for EEG are becoming more relevant to the study of epilepsy [62] . It is important that tools for large scale analysis of EEG are made clinically available . The model inversion technique presented in this work is generalizable and freely available ( https://github . com/pkaroly/Data-Driven-Estimation ) . It is important to note that the presented results are only valid insofar as the connectivity parameters of a neural model capture the relevant dynamics underlying seizure transitions . The use of neural mass model to investigate seizures has gained wide acceptance among epilepsy researchers [11 , 63–65] . Tracking excitatory and inhibitory strengths within a network is considered highly relevant to understanding and treating seizures [66] . The ability to infer directional connections ( differentiate between in-going and outgoing pyramidal connections ) is also an important feature of model inversion compared with alternative graph inference measures . The estimation method was previously validated on simulated data [25] . Nevertheless , it is highly challenging to quantify the accuracy of the model reconstructions from real data , where there is no ground truth . The results showed that the difference between reconstructed and actual ECoG was small ( Supplementary S1 , S2 and S3 Figs ) . The consistency of results across many seizures provides evidence that the estimation can give overarching insight into mechanisms of patients’ seizures . It is our hope that this study provides a stepping stone towards a fully validated model inversion framework to guide the clinical management of epilepsy . Future experimental work should investigate whether modulating connectivity strengths in a stereotypical fashion does lead to different energy and/or duration of seizures , as predicted by the current analysis . This work provided a demonstration that the hidden local connectivity parameters of a neural mass model can be dynamically inferred from ECoG . Our results showed that seizures follow stereotypical pathways through parameter space . It is apparent that once a seizure has begun , a predefined sequence of states must be traversed before termination . For a subset of patients , there were two routes ( short and long ) to seizure termination . Short and long seizures began the same way but showed distinct offset mechanisms . Finally , the connectivity patterns at seizure onset showed common motifs across patients . These distinct sub-groups of onset mechanisms may suggest targeted treatment . Techniques that unify neural mass models with data provide the means to address some of the unanswered hypotheses pertaining to epileptic dynamics . For example , theoretical studies have hypothesized that seizure trajectories are “innate” , or “repeatable” [13 , 52] . The current results confirm that seizure pathways are indeed patient-specific and highly stereotyped . It has also been suggested that there are limited classes of onset mechanisms for seizures [43] . The current results show that there does appear to be a limited number of seizure onset “motifs” among patients . Finally , our group had previously hypothesized that long and short seizures reflect distinct cortical mechanisms [37] . The current results demonstrate that long and short seizures follow the same pathways but have different termination mechanisms . These results underscore the power of theoretical models to shed light on seizure mechanisms . It is our hope that these insights guide further modeling studies and may even prove to be directly translatable into clinical practice . | A fundamental question in clinical neuroscience is how and why the brain generates epileptic seizures . To address this problem it is important to unify theoretical models of seizure mechanisms with clinical data . This study investigated a large database of human epileptic seizure recordings . Model inversion was used to track seizure dynamics through the lens of a mathematical model for cortical regions . These models can reveal the relative activity and coupling between excitatory , inhibitory and pyramidal neural populations that cannot be directly measured . Measuring cortical dynamics during seizures can provide insight into epilepsy , and facilitate new treatment strategies . Our analysis of connection strengths revealed important aspects of seizure onset and seizure termination . Our findings have implications for understanding seizure mechanisms and treating epilepsy . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"medicine",
"and",
"health",
"sciences",
"mechanisms",
"of",
"signal",
"transduction",
"applied",
"mathematics",
"membrane",
"potential",
"brain",
"electrophysiology",
"electrophysiology",
"random",
"variables",
"neuroscience",
"covariance",
"epileptic",
"seizures",
"algori... | 2018 | Seizure pathways: A model-based investigation |
Sleep is critical for hippocampus-dependent memory consolidation . However , the underlying mechanisms of synaptic plasticity are poorly understood . The central controversy is on whether long-term potentiation ( LTP ) takes a role during sleep and which would be its specific effect on memory . To address this question , we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II ( pCaMKIIα ) in the rat hippocampus immediately after specific sleep-wake states were interrupted . Control animals not exposed to novel objects during waking ( WK ) showed stable pCaMKIIα levels across the sleep-wake cycle , but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep ( SWS ) followed by a rebound during rapid-eye-movement sleep ( REM ) . The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions . Based on these results , we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK , SWS and REM . Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values . In contrast , LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking . To better understand the interaction between rescaling and restructuring during sleep , we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons . Synaptic homeostasis was implemented by weakening potentiation and strengthening depression , while synaptic embossing was simulated by evoking LTP on selected synapses . We observed that synaptic homeostasis facilitates controlled synaptic restructuring . The results imply a mechanism for a cognitive synergy between SWS and REM , and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis , its presence causes synaptic restructuring .
In the hippocampus , slow-wave sleep ( SWS ) is characterized by large amplitude , low-frequency oscillations of the local field potential ( LFP ) , concomitant with a phasic regime of neuronal firing , with relatively low mean firing rates and intermittent synchronization [1–4] . In contrast , rapid-eye-movement sleep ( REM ) displays small amplitude , high-frequency oscillations that underlie a tonic firing regime , with relatively high mean firing rates and decreased synchrony [1–4] . Both sleep states play a role in the consolidation of hippocampus-dependent memories [5 , 6] , but the mechanisms remain poorly understood . Two theories are in dispute . The synaptic homeostasis hypothesis ( SHY ) proposes that SWS causes generalized synaptic weakening [7–9] , leading to the down-selection of weak synapses [10] . The notion that synaptic depression is determinant for off-line memory processing departs from the conventional Hebbian learning rule , by which connections among simultaneously firing neurons are reinforced [11] . On the other hand , the synaptic embossing hypothesis postulates the combination of non-Hebbian rescaling and Hebbian potentiation of synaptic weights in complementary circuits during REM [6 , 12 , 13] . The core of the dispute is the controversy on whether long-term potentiation ( LTP ) occurs during sleep . Empirical studies diverge considerably , with molecular , electrophysiological and morphological evidence for [14–22] and against [4 , 7 , 23–25] it . The theories also differ on the roles of the different sleep states in memory consolidation . SHY only considers SWS and does not propose any role for REM [4 , 26] , while the embossing theory encompasses both states [6 , 12 , 13] . The substantial differences in the firing and correlation regimes of SWS and REM suggest that the two states should be separately and sequentially modeled [6 , 12 , 27] . One study has suggested that SWS leads to general memory reinforcement while REM leads to forgetting of all but the strongest memories traces [28] . LTP during SWS has been proposed to amplify the synaptic changes acquired during WK [29] , with further processing of the resulting synaptic weights during REM [30 , 31] . In fact , plasticity factors such as protein kinases and transcription factors encoded by immediate-early genes are up-regulated during REM [14–16 , 20 , 32 , 33] . Therefore , it is possible that a complete sleep cycle traversing SWS and REM leads to important perturbations in the pattern of synaptic weights [6 , 12 , 27] , rather than to the simple weight convergence observed during SWS alone [4] . To address this debate , we first assessed phosphorylated Ca2+/calmodulin-dependent protein kinase II ( pCaMKIIα ) in the hippocampus of rats exposed ( or not ) to novel objects , and killed immediately after subsequent WK , SWS or REM ( S1 Fig ) . CaMKIIα phosphorylation is one of the earliest mechanisms with a critical role in LTP , memory and learning [34 , 35] . CaMKIIα undergoes conformational changes towards the active form within seconds after the beginning of synaptic stimulation [36] , triggering later events that include the up-regulation of immediate-early genes required for long-term synaptic remodeling , such as Zif-268 [37] . Given the very fast kinetics of CaMKIIα phosphorylation [36] in comparison with Zif-268 [38] , pCaMKIIα levels were hypothesized to show experience-dependent changes immediately after SWS and/or REM , while Zif-268 protein levels were expected to be invariant immediately after any given state . The protein levels of total CaMKIIα and Actin were also assessed as negative controls expected to show invariant levels across groups , given their much slower transcriptional and translational regulation . To gain insight in the state dependency of pCaMKIIα regulation , we also investigated the relationship between pCaMKIIα levels and electrophysiological markers of SWS ( delta oscillations ) , REM ( theta oscillations ) or the SWS/REM transition ( neocortical spindles ) [39] . Since both SHY and the synaptic embossing theory have empirical support , computational work may be particularly insightful . SHY has been modeled at the single neuron and network levels , but without real neurophysiological inputs [4 , 10] . A recent SHY model found deleterious effects for memory when instantaneous potentiation was switched on during sleep [10] , but the synaptic embossing hypothesis is yet to be simulated with realistic LTP onset and dynamics . To that end , we computationally investigated the network consequences of LTP triggered during sleep , by feeding simulated neurons with action potentials recorded from the rat hippocampus across the sleep-wake cycle . LTP was applied at REM onset as constrained by the empirical results . Synchronization-based LTP was calculated from coincident spiking during SWS only or SWS+REM , while an alternative model captured the notion that short-term changes in pCaMKIIα levels determine long-term increases in synaptic weights . Finally , we investigated the interaction of synaptic homeostasis and embossing mechanisms by simulating the dynamics of memory formation during a sleep cycle in a canonical hippocampo-cortical model .
For quantification of pCaMKIIα and Zif-268 levels , adult male Wistar rats ( n = 27 , 300–350 g ) were housed , surgically implanted and recorded according to National Institutes of Health ( NIH ) guidelines and Edmond and Lily Safra International Institute of Neuroscience of Natal ( ELS-IINN ) Committee for Ethics in Animal Experimentation ( permit # 05/2007 ) . Implanted animals were housed in individual home cages with food and water ad libitum , and were kept on a 12h light/dark schedule with lights on at 06:00 . At the end of the experiment , rats were anesthetized with isoflurane 5% , and decapitated . The network was implemented as a modified Boltzmann machine [44] . The total synaptic current for each neuron i is defined as Ii ( t ) =wieei ( t ) +1N−1∑j=1Nwijvj ( t ) where N is the number of neurons , ei ( t ) is an external input and vj ( t ) is the state of pre-synaptic neuron j; wie and wij are the corresponding synaptic weights . The neuron state is binary ( 0 or 1 ) and stochastically updated by the adjusted sigmoid function P ( vi ( t ) =1 ) =11+e ( Kt−KsIi ) , where Kt = 6 and Ks = 11 are constants; with these values the mean firing rate of the spontaneous network activity ( when wie = 0 ) is around 0 . 5 and 1Hz ( S9 Fig ) . During all simulations , we used wie = 0 . 5 and pre-synaptic weights wij were randomly initiated following a uniform distribution between 0 and 1 , except when i = j , in which case the synaptic weight is set to 0 throughout the simulation . The network was exposed to 2 types external inputs ( ei ( t ) ) : Real spike data and Poisson spike trains with same mean firing rates as those observed during WK , SWS and REM ( S4A Fig ) . When Poisson spike trains were used as inputs , the network was composed of 150 neurons . When real spike data were used , the network was composed by the same number of neurons as recorded in each animal ( Rat1 = 45 , Rat2 = 39 , Rat3 = 39 , Rat4 = 34 , Rat5 = 22 and Rat6 = 13 ) . Despite the lack of inhibitory neurons , this simplified network replicates the synaptic rescaling dynamics ( see below ) of a more complex network with both excitatory and inhibitory processing units [4] . We built a network of 45 leaky integrate-and-fire neurons with network feedback inhibition [50–52] ( S6 Text ) . Each neuron received synaptic connections from 200 input units and was not directly connected to other neuron . Half of the input units were assigned to memory A ( MA = [1 … 100] ) and the other half to memory B ( MB = [101 … 200] ) . Input unit i emitted actions potentials ( S[t] = 1 if there is a spike at time t , S[t] = 0 , otherwise ) following a Poisson distribution with mean frequency factive = 20 Hz when in an active state and finactive = 10 Hz when in an inactive state . Input pattern was determined in 125 ms windows by randomly selecting one memory to be in an active state . Each cell gets selective to a specific memory through the synaptic weight dynamics driven by the plasticity mechanisms described below .
Six groups were defined based on prior experience ( with or without exploration of novel objects ) , and on the state immediately before killing ( WK , SWS or REM ) ( S1 Fig ) . In unexposed control groups , no significant differences in hippocampal pCaMKIIα levels were detected across states ( Fig 1A; Kruskal-Wallis p = 0 . 2958 , Dunn’s test for consecutive states , adjusted p values: WK vs . SWS p>0 . 9999; SWS vs . REM p = 0 . 5615 ) . However , in animals previously exposed to novel objects , hippocampal CaMKIIα phosphorylation significantly increased from SWS to REM ( Fig 1A; Kruskal-Wallis p = 0 . 0396 , Dunn’s test , adjusted p values: WK vs . SWS p = 0 . 0954; SWS vs . REM p = 0 . 0473 ) . The number of cells with pCaMKIIα somatic labeling was counted to determine whether the densitometric changes observed could be attributed to a sheer increase on the total number of cells engaged in the pCaMKIIα response during REM . No significant differences were detected when labeled cells were counted . This indicates that the changes observed from SWS to REM in exposed animals did not reflect increased numbers of responsive neurons , but rather increased pCaMKIIα levels in the neuropil and soma . To control for potential a priori inter-group differences in baseline CaMKIIα phosphorylation levels , we assessed the protein levels of total CaMKIIα , Actin and Zif-268 in adjacent brain sections . The rationale for this comparison was the fact that the regulation of these proteins is downstream of CaMKIIα phosphorylation , with much slower kinetics [48 , 49 , 53] . Since killing occurred immediately after specific sleep-wake states , we did not expect the levels of total CaMKIIα , Zif-268 or Actin to be differentially modulated across groups , unless there were spurious inter-group differences unrelated to states . We found no significant differences across groups for these proteins , irrespective of previous exposure to novel objects ( Fig 1A; Kruskal-Wallis: Zif-268 Exposed p = 0 . 5387 , Zif-268 Control p = 0 . 8775; total CaMKIIα Exposed p = 0 . 8270 , total CaMKIIα Control p = 0 . 6505; Actin Exposed p = 0 . 6907; Actin Control p = 0 . 8781 ) . The even distribution of the levels of total CaMKIIα , Zif-268 or Actin across groups indicated that the increase in CaMKIIα phosphorylation from SWS to REM was not an artifact of group sorting . It is clear that the significant increase of CaMKIIα phosphorylation from SWS to REM could only occur because of a reduction from WK to SWS , which nevertheless only reached a near-significant trend ( p = 0 . 0954 ) . To grasp this effect , we calculated quadratic fits of the data across wake-sleep states ( Fig 1A , red curves , curvatures indicated by numbers in the bottom ) . The pCaMKIIα data show a “U” effect in the exposed group but not in the control group , while no such effect was observed for total CaMKIIα , Actin or Zif-268 . Schematic pCaMKIIα profiles predicted by the synaptic homeostatic hypothesis ( monotonic decrease ) , the synaptic embossing hypothesis ( “U”shape ) and the null hypothesis ( stable levels ) are shown in Fig 1B . The data fit the picture of homeostatic rescaling during SWS ( decrease in pCaMKIIα levels ) followed by experience-dependent LTP during REM ( increase in pCaMKII levels ) . To investigate the electrophysiological correlates of CaMKIIα phosphorylation during REM , we assessed the correlation of pCaMKIIα levels with LFP power in the delta ( 0 . 5 to 4 . 5 Hz ) and theta ( 4 . 5 to 12 Hz ) bands during the last 15 min before killing . Neither frequency band showed significant correlations with pCaMKIIα levels ( for theta , SWS group: R2 = 0 . 05322 and p = 0 . 5504 , REM group: R2 = 0 . 02431 and p = 0 . 6888; for delta , SWS group: R2 = 0 . 002432 and p = 0 . 8997 , REM group: R2 = 0 . 1463 and p = 0 . 3097 ) . Next we calculated the correlation of CaMKIIα phosphorilation with cortical spindle counts during the last 15 min before killing ( Fig 2A and 2B ) . Spindle occurrence in the SWS and REM groups was assessed on spectral ratio maps ( Fig 2C and 2D ) . While animals in the SWS group did not show any significant correlation ( Fig 2E left panel ) , animals allowed to enter REM showed a positive correlation between spindle counts and pCaMKIIα levels ( Fig 2E right panel ) . Interestingly , while pCaMKIIα levels were correlated to the sum of all spindles that occurred in the transition from SWS to REM ( Fig 2E right panel , black dots and regression , R2 = 0 . 484 , p = 0 . 0375 ) , no correlations were observed for spindles occurring exclusively within SWS ( Fig 2E right panels , blue dots and regression , R2 = 0 . 116 , p = 0 . 369 ) , nor for spindles sampled only from the IS state immediately before REM ( Fig 2E right panels , magenta dots and regression , R2 = 0 . 007 , p = 0 . 825 ) . S3A Fig shows that REM animals spent significantly more time in IS than SWS animals . REM animals did not have significantly more spindles than SWS animals ( S3B Fig ) , but spindles lasted longer in IS than in SWS ( S3C Fig ) . Altogether , more IS time in REM animals and longer spindles in IS explain why SWS animals displayed significantly less time with spindle occurrence ( S3D Fig ) . The results above support the notion that LTP is triggered at the SWS/REM transition . To model the consequences of this phenomenon , we first investigated how state-dependent variations in firing regimes affect the synaptic weights of a fully-connected network comprising an excitatory population of stochastic binary units ( see Materials and Methods ) . The synaptic weights were initialized with a random uniform distribution , and therefore with maximum entropy . A stable Hebbian learning rule based on pairwise synchrony was used to update synaptic weights over time [45] . Synchrony was evaluated in 4-ms bins , well within the interval of maximum STDP [47] , and lack of synchrony led to a fixed amount of synaptic weight weakening . The simulations were generated by feeding the network with 2 kinds of external inputs ( S1 Table; representative example ) : Poisson spike trains at various rates; and actual spike trains recorded from the rat hippocampal field CA1 during WK , SWS and REM [20] . The data statistics conformed to the expected state-dependent changes across the sleep-wake cycle [1–3]: SWS featured low mean firing rates ( Fig 3A; representative example ) and decreased firing synchronization ( Fig 3B; representative example ) compared with WK . REM was characterized by mean firing rates in between those of WK and SWS ( Fig 3A ) , with very low firing synchronization ( Fig 3B ) . For data from 5 other rats see S4 Fig . Fig 3C depicts the durations of intervals separating consecutive SWS/REM transitions ( left panel; n = 6 ) , and a cumulative plot showing that 91 , 6% of these intervals are shorter than 30 min ( right panel ) . Two major scenarios were implemented , with and without LTP during sleep . For statistical robustness , all simulations were independently repeated 25 times . The dynamics of the synaptic weight patterns were quantified using 2 metrics: the Similarity Index measured the sum of absolute differences between a given synaptic weight pattern and a reference pattern , while the Spearman´s correlation quantified ranking changes among synaptic weights compared to the reference pattern . Altogether , these metrics allowed us to estimate the rescaling and restructuring of the synaptic weight patterns over time . Rescaling was indicated by a reduction in the Similarity Index without major changes in Spearman´s correlation . Restructuring was indicated by a reduction in the Similarity Index accompanied by a reduction of Spearman´s correlation . We began by characterizing the rate-dependency of synaptic weight dynamics during a regime of non-correlated external inputs . The network was fed Poisson surrogated spike trains with mean rates between 5 and 10 Hz , which represent the real data range ( Figs 3A and S4A ) . The distribution of synaptic weights was rescaled over time to a narrow range of values ( S5A and S6A Figs ) , exactly as observed previously for a single cell model [45] , as well as a network model with both excitatory and inhibitory units [4] . The mean synaptic weight at the convergence time point ( S5A Fig , dashed black lines ) depended on the mean firing rate of the inputs ( S6B Fig , blue curve ) . By the same token , the time required for the synaptic pattern to converge was inversely proportional to the input rate ( S6B Fig , red curve ) . For simulations with low rate inputs typical of SWS ( Figs 3 and S4A ) , mean synaptic weights at the time of convergence were smaller than 0 . 5 , resulting in net weakening ( Mw<0 , see Material and Methods ) of the synaptic weights ( S5A and S6A Figs , distributions for 3 , 5 and 7Hz ) . However , synaptic weights that were initially below the mean at the time of convergence became strengthened ( S5A Fig , bottom panels ) . Therefore , net weakening of synaptic weights does not imply that all synaptic weights decay over time , since weak synaptic connections are potentiated . Conversely , for simulations in which inputs had higher rates typical of REM or WK ( >7Hz ) , synaptic weights at the time of convergence were larger than 0 . 5 , resulting in net potentiation of synaptic weights ( S5A Fig , for 10Hz and its corresponding gray distribution in S5C and S6A Figs , distributions for 12 , 20 and 40Hz ) . Yet , synaptic weight values initially above the convergence range were reduced over time ( S5A Fig , bottom panels ) . In summary , when the network was fed with non-correlated Poisson inputs , the wide range of synaptic weights used to initialize the network converged to a narrow and stable distribution , producing net weakening or net potentiation of the synapses for low and high firing rates , respectively . To further characterize the state-dependency of synaptic weights , we fed the network with spike data from concatenated WK , SWS or REM episodes ( S5B Fig ) . The simulations confirmed that the mean firing rates of the external inputs determine the mean value reached over time by the distribution of synaptic weights P ( w ) , as shown for Poisson data in previous work [45] and in the preceding section . Periods of increased spike rate and synchronization , such as WK , resulted in a smaller standard deviation of the synaptic weights when the network reached steady state , in comparison with periods of reduced spike rate and correlation , such as SWS or REM ( S1 Table and S5B and S5C Figs green distributions ) . Note that the standard deviations at steady state were even smaller for non-correlated Poisson data of identical mean rates , and also obeyed the relationship WK<REM<SWS ( S1 Table and S5C Fig , gray distributions ) . Next we investigated how external inputs with the real dynamics of state alternation affected the distribution of synaptic weights . Fig 4 shows results when the network inputs were real spike data recorded over 5 hours from one rat ( Rat1 ) cycling freely through the sleep-wake cycle , i . e . containing the natural alternation of WK , SWS and REM ( Fig 4A , hypnogram ) . As expected , population firing rates were markedly state-dependent throughout the recording ( Fig 4A , Pop . rate ) . The model displayed net potentiation during WK and net weakening during sleep ( Fig 4B ) . We also observed that most synaptic connections did not reach extreme values ( close to 0 or to 1 ) but converged to the middle range of possible values ( Fig 4B ) . Two alternative LTP models triggered near the SWS/REM transition were investigated . Since LTP is tightly associated with firing synchrony [11 , 47 , 54] , one model attempted to capture the notion that SWS causes LTP through firing synchronization [55–57] and enhanced calcium influx [19 , 30 , 58] , leading to calcium accumulation during SWS that would then lead to increased pCaMKIIα levels at REM onset . To simulate this scenario , LTP1 full SWS model applied a long-term bonus to each synapse starting at the SWS/REM transition , but according to the amount of pairwise synchrony observed throughout an entire SWS episode ( 87 . 06 ± 47 . 47 , mean ±SEM in seconds , n = 6 rats ) . The second model ( LTP2 ) simulated short-term changes in pCaMKIIα levels as short-term increases in synaptic weights at the SWS/REM transition , and then used these short-term increases to determine long-term increases in synaptic weights . This model is compatible with the empirical data ( Figs 1 and 2 ) , and with the evidence of REM-dependent upregulation of plasticity factors [14–16 , 20] . For each synaptic weight , LTP2 model coupled short-term changes at the SWS/REM transition to a gradual long-term bonus . The rationale for this coupling was the fact that the balance between high and low calcium influx in the millisecond scale is reflected in the balance between kinase and phosphatase activation in the seconds range , in particular CamKIIα , and determines the subsequent activation for over 30 min of Rho GTPases such as cdc42 , and consequently to changes in gene regulation and protein synthesis in the hours scale [59–63] . To simulate this scenario , short-term changes in synaptic weights assessed for 60s at the transition from SWS ( 30s ) to REM ( 30s ) determined long-term bonuses applied for 30 min to the synaptic weights ( see Material and Methods ) . Specifically , the angle formed by the synaptic weight trajectory at the transition from SWS to REM determined a long-term bonus . To comply with the notion that LTP requires positive calcium transients , LTP was applied exclusively to synaptic weights whose trajectory showed a positive slope during REM ( LTP2 permissive 60s SWS/REM ) . The long-term bonus applied in both models consisted of a Gaussian curve with onset at a reference SWS/REM boundary and peak value at 30 min , to match the duration of the spine-specific signaling cascade Ca2+–CaMKIIα–Cdc42 [64] . The evolution of synaptic weight patterns varied according to the LTP model employed . When LTP1 full SWS model was fed real data as inputs ( Fig 4C ) , about 85% of the synapses underwent potentiation , in comparison with 21% in the No-LTP control . This resulted in a marked modulation of synaptic weight values ( S7D Fig , 1st and 2nd panels ) , with a substantial net increase of the mean weight ( S7A Fig , top panel , red curve ) and increased spreading towards high synaptic weight values ( S7C Fig , red with black edge distribution ) . Only 8% of the synapses underwent weakening , in comparison with 64% in the No-LTP control ( S6A Fig , 1st and 2nd panels ) . The selection obeyed a uniform distribution across the synaptic weight range , so that even weak synaptic connections had a 16% chance of being potentiated . Overall , LTP model 1 led to a net potentiation of synaptic weights across their entire range ( S7D Fig , middle panel ) , greatly exceeding that observed without LTP ( S7B Fig , top panel , red curve ) . The distributions of synaptic weight changes ( ΔWij ) showed a marked upward shift , with a concavity change on the quadratic fit of LTP1 full SWS model , in comparison with the fit for the No-LTP model ( compare red and black curves in S7E Fig ) . When LTP2 permissive model was fed real data ( Fig 4D ) , about 48% of the synapses were recruited to undergo LTP ( S7D Fig , right panel , red bar ) , substantially less than in the case of LTP1 full SWS model . LTP2 permissive model also led to net synaptic potentiation across the entire range of weights , but many more connections were de-potentiated in comparison with LTP1 full SWS model ( compare with middle panel ) . The distributions of differences between final and initial synaptic weight values ( S7E Fig , blue ) show an upward shift without concavity change in which most changes affect weak connections , i . e . the shift was largest for the lowest initial synaptic weights , and decreased gradually for larger initial weights . The different consequences of the LTP models ( LTP1 full SWS and LTP2 permissive ) are shown in Fig 5 , which depicts the temporal evolution of a fully connected network of 16 neurons under Poisson ( Fig 5A ) or real data regimes ( Fig 5B ) . The initial synaptic weight pattern ( Fig 5A , 1st column ) was quickly erased when Poisson-distributed data were used as inputs ( Fig 5A , 2nd and 3rd columns ) . LTP1 full SWS model uniformly enhanced all synaptic weights , while LTP2 permissive model embossed a new pattern ( Fig 5A , 4th and 5th columns ) . Real data allowed for a much longer persistence of the initial pattern , which evolved in distinct ways for LTP1 full SWS and LTP2 permissive . While the pattern changed monotonically over time in LTP1 full SWS model ( Fig 5B , 2nd row ) , LTP2 restrictive model caused a major revamp of synaptic weights ( Fig 5B , 3rd row ) , so that the pattern that emerged at the end of the simulation was very different from the initial pattern . Were the differences between LTP1 and LTP2 models related to the different durations of the inputs , to the role assigned to the SWS/REM transition , or to intrinsic mechanistic differences between the models ? To address these questions , we futher simulated LTP1 using not an entire SWS episode , but either a limited 30s SWS period immediately before REM ( LTP1 - 30s SWS end ) , or that 30s SWS period plus the following 30s of REM ( LTP1 - 60s SWS/REM ) . We found that short sleep periods near the SWS/REM transition produced less net synaptic potentiation for LTP1 than full SWS episodes ( Fig 6A and 6B ) . They also produced more pattern restructuring than full SWS episodes ( i . e . decrease in Spearman´s correlations , Fig 6C ) , partially replicating the results of LTP2 permissive model . Next we compared LTP2 permissive model with an alternative in which LTP2 was applied only when the REM slope was positive and larger than the SWS slope ( LTP2 restrictive ) ( see Material and Methods ) . This more restrictive version of LTP2 led to less potentiation ( Fig 6A , last two panels; Fig 6B , right panel ) , and less pattern restructuring ( Fig 6C ) , than the original , more permissive LTP2 . Of note , the case presented so far is representative of 5 other animals , despite the high variability of the spike datasets used as inputs , which resulted from traversing quite different real sleep-wake cycles ( S8 Fig ) . Spearman´s correlations and mean synaptic weights ( Fig 7 ) , used respectively to characterize restructuring and rescaling , confirm across animals that LTP during sleep leads to pattern restructuring , especially when assessed at the SWS/REM transition . Since both SHY and the synaptic embossing hypothesis have empirical support , it is possible that the use of both mechanisms is synergistic . To clarify this point , we used a network model based on a canonical hippocampal-cortical circuit capable of developing specificity in response to concurrent inputs [50–52] ( Fig 8 ) . The model was adapted by implementing plasticity mechanisms analogous to SHY and the synaptic embossing hypothesis ( see Material and Methods ) . The effect of sleep on memory was studied by comparing the patterns of synaptic weights post-sleep with pre-sleep ( to assess the level of memory restructuring ) and with the pattern reinforced by sleep-dependent LTP ( to assess the influence of the SWS/REM transition pattern over the established memory ) . Synaptic homeostasis was implemented by modulating the STDP rule [47 , 54] during sleep through weakening of potentiation and strengthening of depression [4] . As observed in our empirical data , the rate of the input units was reduced during sleep . The effect of sleep on synaptic restructuring was measured by assessing the number of neurons whose pattern selectivity was either stable or switched . We observed that an increase in STDP modulation led to a number of stable memories , as expected by a random permutation of the pattern selectivity ( Fig 9A ) . Although this experiment demonstrates that synaptic homeostasis shuffles synaptic weights , there is no mechanism to determine the output synaptic structure , i . e . , SHY allows an acquired memory to be erased , but it cannot promote a specific pattern into the synaptic weights . Embossing was implemented by evoking LTP on selected synapses during sleep . The LTP pattern was set proportional to the synaptic weights of another neuron selected randomly at a time previous to sleep onset . LTP control over the synaptic organization could be measured by comparing the memory of the neuron after sleep with the memory of the neuron selected as reference for LTP just before sleep onset . We observed that an increase of the intensity of LTP modulation led to a full control of the memory by the evoked LTP ( Fig 9B ) . We then implemented both hypotheses and observed the development of synaptic reorganization in the model . We simulated sleep cycles for a range of values of STDP modulation and LTP intensity and observed the number of memory hits between the pattern following the sleep cycle and the reference pattern with evoked LTP ( Fig 9C ) . With an increasing level of STDP modulation , a lower LTP intensity was required to enhance LTP control , although some level of LTP was still needed to ensure LTP control over the synaptic organization . This result indicates that synaptic homeostasis facilitates controlled synaptic restructuring during synaptic embossing .
Rescaling occurred in the absence of LTP during sleep , and was most pronounced when non-correlated Poisson inputs were used ( Figs 4A and S4A ) . Synaptic weights quickly converged to a narrow range of values as previously described for different networks that reach an equilibrium state through a homeostatic process [7] , including a network with inhibitory inputs [4] . Net weakening or potentiation depended on how low or high was the external activity . Non-correlated Poisson activity with high rate caused a faster decrease in the diversity of synaptic weight values than correlated spiking at lower rates ( S4A Fig ) . Similarly , REM-only inputs were more effective in rescaling than SWS-only inputs ( S4B and S4C Fig ) , which contradicts SHY [7] but agrees with the empirical findings of decreased firing rates after REM [25] . When the network received correlated real data inputs , the resulting synaptic weight distributions were much wider but still converged to the center ( S4B Fig ) , with a tight relationship between input rates and the mean synaptic weight at the end of the simulation . These results conform to the notion that a network that goes through sleep without LTP remains stable , avoiding extremely weak or strong synaptic weights [45] . Synaptic weight convergence was orderly , preserving synaptic weight ranks as indicated by the relatively stable values of Spearman´s correlations over time ( S6A Fig , bottom panel , black curve , 10 , 000s to 14 , 500s interval ) . While synaptic weight ranks were preserved in the absence of LTP , mean synaptic weights progressively decreased ( S6A Fig , top panel , black curve ) , reaching the lowest value during sleep . The same occurred for the Similarity Index ( S6A Fig , middle panel , black curve ) . Without LTP during sleep , net synaptic weights were downscaled , and the initial pattern was rescaled . When LTP was modulated by the spike synchrony exhibited during an entire SWS episode , synapses were uniformly selected for potentiation ( LTP1 full SWS model ) . When synapses were potentiated based on the angle formed by the synaptic weight trajectories at the SWS/REM transition ( LTP2 model ) , synaptic recruitment was also quite uniform but the magnitude of potentiation was stronger for weights that were initially low ( S6E Fig , blue curve ) . In both models , synaptic weight values were scattered away from the convergence range ( Figs 4C , 4D and S6C middle , right panel , distribution with black edges ) . Real neuronal activity imposes network relations that do not exist for Poisson inputs , including inhomogeneous firing rate variability and synchronous firing among neurons . These conditions determine that specific connections undergo markedly different dynamics under LTP1 and LTP2 models . The most interesting cases are those in which synaptic weights undergo opposite changes . About 85% of the connections undergo potentiation under LTP1 full SWS model but no modulation or weakening under LTP2 permissive model ( S6E Fig , triangles ) . In contrast , only 8% of the connections undergo no modulation or weakening under LTP1 model , but show potentiation under LTP2 model ( S6E Fig , squares ) . Interestingly , the results produced by LTP1 and LTP2 models converged when the former was evaluated around the SWS/REM transition; or when the latter was more restrictive ( Fig 6 ) . The results support the notion that LTP during sleep allows weaker synaptic connections to also play a role in mnemonic processing [70] . Weight-dependent plasticity of hippocampal neurons , favoring weak over strong synapses , has been shown in vitro [45 , 47] . Here , the combination of real hippocampal inputs and LTP during sleep triggered long-term changes in the synaptic weights that were specific of the particular LTP mechanism simulated . The consequences of LTP1 model during sleep were overall synaptic potentiation , increased similarity with the initial pattern and preservation of synaptic weight ranks . For LTP1 and LTP2 models evaluated at the SWS/REM transition , sleep gave rise to new synaptic weight patterns , as indicated the low Spearman´s correlation values ( Fig 5B , last row , from 10 , 000s to 14 , 000s ) . Given that the typical period of the sleep cycle in rats is below 2 min ( Fig 3C , left panel ) , with 91 , 6% of the episodes under the 30 min LTP Gaussian peak ( Fig 3C , right panel ) , the sleep-dependent synaptic weight changes introduced by LTP models are prone to stagger from cycle to cycle , leading to progressively different patterns over time . SHY does not seem to account for all the cognitive effects of sleep , which not only protects memories passively from retroactive interference , but can actively enhance specific memories in detriment of others , and lead to novel insights [71–79] . A simplified integrate-and-fire SHY model exclusively composed of excitatory neurons was recently used to simulate gist extraction and integration of new with old memories without the need of LTP during sleep [10 , 80] . These properties derive directly from the down-selection of weak synapses and protection of strong synapses , which end up eliminating weak memories ( supposedly “spurious” information ) and therefore increasing the signal-to-noise ratio of memory retrieval . However , by the same token , such mechanisms are not bound to explain the additional information content that arises from memory restructuring during sleep [71–79] . For instance , a recent study of perceptual learning in humans found that REM rescues memories from interference , preferentially strengthening weak memories [81] . In contrast , the results presented here for Model 1 show that LTP calculated over short periods near the SWS/REM transition lead to pattern restructuring compatible with the synaptic embossing theory . Furthermore , the more realistic hippocampal-cortical architecture with both excitatory and inhibitory synapses ( Model 2 ) showed that the restructuring of synaptic patterns due to LTP was enhanced by homeostasis , which could explain the sleep-dependent enhancement of specific memories . These results highlight the importance of the SWS/REM coupling , providing strong support for the sequential hypothesis of mnemonic processing during sleep [6 , 12 , 27] . The joint occurrence of Hebbian and non-Hebbian plasticity during sleep , which leads to new synaptic patterns embossed over a background of homeostatically rescaled synaptic weights , may ultimately explain the positive role of sleep in the cognition . | Sleep is important for long lasting memories . There exists , however , a controversy regarding the mechanisms by which sleep modifies synapses to consolidate enduring memories . One theory posits that sleep weakens synapses , leading to the forgetting of all but the strongest memories . The alternative theory proposes that sleep promotes both weakening and strengthening of different connections , the latter through a process known as long-term potentiation ( LTP ) . Here we measured the levels of a phosphorylated protein related to LTP during the sleep cycle of rats and used the data to build models of sleep-dependent synaptic plasticity . By feeding one model with spikes recorded from the rat hippocampus , we observed that LTP during sleep not merely strengthens certain connections , but actually reorganizes how these connections are ranked in strength , leading to substantial changes of the overall pattern . A more detailed model of hippocampus and cortex showed that the interaction of the mechanisms predicted by the competing theories promotes a more efficient control of which memories are stored . Our results provide a step forward in the understanding of the cognitive role of sleep by indicating that the current competing theories are not mutually exclusive . Instead , each constitutes an important stage of memory consolidation . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle |
Expression from the HIV-1 LTR can be repressed in a small population of cells , which contributes to the latent reservoir . The factors mediating this repression have not been clearly elucidated . We have identified a network of nuclear RNA surveillance factors that act as effectors of HIV-1 silencing . RRP6 , MTR4 , ZCCHC8 and ZFC3H1 physically associate with the HIV-1 TAR region and repress transcriptional output and recruitment of RNAPII to the LTR . Knock-down of these factors in J-Lat cells increased the number of GFP-positive cells , with a concomitant increase in histone marks associated with transcriptional activation . Loss of these factors increased HIV-1 expression from infected PBMCs and led to reactivation of HIV-1 from latently infected PBMCs . These findings identify a network of novel transcriptional repressors that control HIV-1 expression and which could open new avenues for therapeutic intervention .
The human immunodeficiency virus-1 ( HIV-1 ) remains one of the world’s leading health threats with over 36 million people infected worldwide ( UNAIDS , Global Report , 2016 ) . Despite significant advances in antiretroviral therapy , it is not currently possible to eradicate HIV-1 infection due to the presence of a small but highly persistent reservoir of infected cells that host transcriptionally repressed proviruses . Transcriptional activation of these proviruses occurs rapidly following cessation of therapy leading to renewed high viral burden . The key to eradicating HIV-1 infection could be found in a more complete understanding of HIV-1 transcription and the mechanisms contributing to transcriptional repression . Transcription of the HIV-1 proviral DNA is initiated by the binding of RNA polymerase II ( RNAPII ) to the HIV-1 promoter located in the 5’ long terminal repeat region ( LTR ) . RNAPII recruitment is mediated by viral enhancer and promoter elements . However , HIV-1 transcription is repressed by proximal RNAPII pausing and premature termination [1–3] . Short transcripts accumulate in infected cells in culture as well as in CD4+ T cells from patients [1 , 4 , 5] . These short transcripts contain a stable stem loop structure known as the trans-activation response region ( TAR ) . Elongation-competent transcription depends on the recruitment of HIV-1 trans-activator protein , Tat which binds the bulge region of TAR RNA , and brings with it the host Super Elongation Complex ( SEC ) and histone acetyltransferases to suppress RNAPII pausing and premature termination and facilitate processive elongation of transcription [6–8] . While Tat-dependent transactivation has been widely studied , the mechanisms underlying premature termination are less well understood . We have shown that the termination factors , SETX and XRN2 are required for premature termination of HIV-1 transcription , which occurs as a consequence of cleavage of nascent TAR-containing RNA by Microprocessor [3] . This study also revealed a role for the 3′ to 5′ exoribonuclease , RRP6 , which generates short HIV-1 transcripts that repress HIV-1 transcription . However , the mechanisms by which HIV-1 is repressed remain unclear . Proviral transcription is also controlled by host epigenetic mechanisms . In the absence of Tat , proviral chromatin is marked by repressive chromatin modifications including H3K27Me3 and H3K9Me2/3 [9–13] as well as the recently described repressive mark , H4K20Me1 [14] . However , the mechanisms contributing to HIV-1 transcriptional repression remain relatively poorly understood . RRP6 is one of the two catalytic subunits of the nuclear RNA exosome , which is responsible for the quality control of transcripts in the nucleus . The 11-subunit exosome is an essential 3’-to-5’ exoribonuclease complex that degrades or processes nearly every class of cellular RNA [15] . The nuclear RNA exosome consists of a 9-subunit non-catalytic core that binds RRP44 ( DIS3 ) and RRP6 subunits to modulate their processive and distributive exoribonuclease activities , respectively . The nuclear exosome is targeted to its substrates through its co-factor , MTR4 that links it to one of at least 2 RNA-binding complexes , the nuclear exosome targeting ( NEXT ) complex or the polyA tail exosome targeting ( PAXT ) connection [16–18] . In this study , we identified a network of nuclear RNA surveillance factors including RRP6-associated factors , MTR4 , ZCCHC8 and ZFC3H1 , that function as effectors of HIV-1 silencing . RRP6 , MTR4 , ZCCHC8 and ZFC3H1 were found to interact with RNAPII , associate with the HIV-1 TAR region and repress transcriptional output from the HIV-1 LTR . In the absence of these factors , RNAPII became highly recruited to the promoter . In J-Lat cells that harbor transcriptionally silent HIV-1 , knock-down of these factors activated HIV-1 expression and led to the acquisition of H3K36Me3 and H4 pan-acetyl marks associated with the activation of transcription . In PBMCs , loss of the factors increased HIV-1 expression , as measured by p24 antigen assay and RT-qPCR , and furthermore led to reactivation of HIV-1 from latently infected cells . Our findings identify RRP6 , MTR4 , ZFC3H1 and ZCCHC8 as repressors that dampen transcriptional output from the integrated HIV-1 promoter .
To investigate mechanisms of HIV-1 transcriptional repression , we identified the interactome of a known HIV-1 repressor , RRP6 [3] , and nuclear exosome co-factor , MTR4 , by tandem-affinity purification followed by mass spectrometry ( Fig 1A and S1 Table ) . Among the 235 and 202 interactants identified for RRP6 and MTR4 , respectively , approximately 50% were found in common ( S1A Fig ) . Among those identified , 81 RRP6 and 71 MTR4 interactants were also identified in a previous study carried out in HEK293 cells [17] ( S1B and S1C Fig ) . These proteins likely represent the core interactants of RRP6 and MTR4 . All known subunits of exosome were present , as well as exosome-associated factors , C1D and MPP6 . Sub-units of the NEXT complex , ZCCHC8 and RBM7 , were found in association with both RRP6 and MTR4 , while the zinc-knuckle protein and PAXT subunit ZFC3H1 was better represented in association with MTR4 . The more loosely associated catalytic subunit DIS3 was not found among the interactants of either RRP6 or MTR4 , possibly due to the high stringency of the Dignam extraction protocol used . Interactions between MTR4 , RRP6 , ZFC3H1 and ZCCHC8 that were suggested by mass spectrometry were supported by co-immunoprecipitation analysis ( Fig 1B ) . While MTR4 interacted robustly with all factors , association between RRP6 and ZFC3H1 was less robust than with either MTR4 or ZCCHC8 , as suggested by mass spectrometry analysis . Glycerol gradient sedimentation analysis of MTR4-associated complexes revealed a complex consisting of at least MTR4 , RRP6 and ZCCHC8 , which likely corresponds to the NEXT complex ( Fig 2A , left panel , fraction 5 ) , and a higher molecular weight complex containing at least MTR4 and ZFC3H1 ( Fig 2A , left panel , fraction 7 ) . Similar analysis of RRP6-containing complexes identified the NEXT complex ( MTR4 , ZCCHC8 and RRP6 ) ( Fig 2A , right panel , fractions 5 and 6 ) and furthermore confirmed that RRP6-containing complexes are largely devoid of ZFC3H1 ( Fig 2A , right panel ) . These results were confirmed by re-IP analysis ( Fig 2B ) , which showed that MTR4/ZFC3H1 complexes contain little or no RRP6 or ZCCHC8 . Co-immunoprecipitation analysis confirmed interactions between the endogenous proteins and furthermore indicated that ZFC3H1 and ZCCHC8 likely occur in distinct complexes ( Fig 2C ) . These results corroborate previous analysis suggesting that MTR4 , ZCCHC8 and RBM7 form the NEXT complex [17] . The distinct higher molecular weight MTR4-containing complex is likely to be the recently identified ZFC3H1/MTR4/PABPN1 complex termed PAXT [18] . Interestingly , among the interactants , we found several proteins associated with roles in transcription such as HDAC2 , TAF15 and CDK9 . Additionally , since several studies including our own point to a direct role of the RNA exosome in transcription and since previous studies showed interaction between RRP6 and RNAPII in Drosophila , we wondered whether human RRP6 and its sub-complexes could interact with RNAPII . Consistent with this idea , we found that subunits of both complexes physically interact with RNAPII ( Fig 2C ) . Furthermore , both ZFC3H1 and ZCCHC8 partially co-localized with RNAPII in the nucleoplasm ( S2 Fig ) . Since we have previously shown that RRP6 is required for processing of TAR RNA that is implicated in transcriptional repression of HIV-1 [3] , we wondered whether RRP6-interacting factors might also be implicated in repression of HIV-1 . We first performed knock-down of RRP6 , MTR4 , ZFC3H1 and ZCCHC8 individually and analyzed the effect on LTR-driven luciferase activity ( Fig 3A ) . Knock-down of either RRP6 or MTR4 significantly increased LTR activity , whereas loss of either ZFC3H1 or ZCCHC8 had a very modest effect . Interestingly , immunoblot analysis of the knocked-down cells indicated cross-regulation of the abundance of the different factors ( Fig 3A , lower panel ) . Notably , knockdown of ZFC3H1 enhanced expression of ZCCHC8 , and vice versa . Given that others [18] and we show that ZFC3H1 and ZCCHC8 exist in largely distinct complexes , the results shown in Fig 3A suggest that these factors may be partially redundant and their activities may be compensatory . To test this , simultaneous knock-down of ZFC3H1 and ZCCHC8 was performed . As shown in Fig 3B , only the double knock-down successfully diminished the expression of both factors and significantly increased LTR-driven luciferase activity . We also tested the effect of RRP6 co-factor C1D , core exosome components and the second exosome-associated RNase , DIS3 . Knock-down of C1D increased luciferase activity ( S3A Fig ) . Conversely , simultaneous knock-down of 3 core exosome subunits ( EXOSC2 , EXOSC7 and EXOSC9 ) or of DIS3 had no effect on LTR-driven luciferase activity ( S3B Fig ) . Thus , RNA processing factors RRP6 , MTR4 , ZFC3H1 , ZCCHC8 and C1D are implicated in the repression of LTR-driven activity , in a manner that is independent of core exosome and DIS3 . We next analyzed the effect of knock-down of each factor on LTR-directed mRNA synthesis . As shown in Fig 4A–4C , loss of RRP6 , MTR4 or double knock-down of ZFC3H1 and ZCCHC8 increased the abundance of LTR-directed mRNA . Given that the factors are involved in RNA degradation in the nucleus , we sought to determine whether the increase in LTR activity observed following loss of these factors was due to a direct effect on HIV-1 transcription or rather through stabilization of HIV-1 RNA . We performed nuclear run-on transcription ( NRO ) analysis that specifically measures nascent transcripts to distinguish an effect on transcription from that of RNA stabilization . Fig 4D shows that RRP6 , MTR4 and ZFC3H1/ZCCHC8 increased LTR-driven nascent transcript synthesis . To confirm that these factors indeed modulate LTR-driven transcription , chromatin immunoprecipitation ( ChIP ) of RNAPII was performed in control or knock-down conditions . Significant recruitment of RNAPII was observed following loss of RRP6 , MTR4 or ZFC3H1/ZCCHC8 ( Fig 4E ) , reflecting results obtained by luciferase assay , RT-qPCR and nuclear run-on transcription ( Figs 3 and 4A–4D ) . We noted that loss of RRP6 , MTR4 or ZFC3H1/ZCCHC8 had no significant impact on the expression of several host factors that modulate HIV-1 transcription , including SupT6H , PAF1 , AF9 , HDAC1 or RNAPII ( S4 Fig ) . Taken together , these findings suggest that RRP6 , MTR4 and exosome targeting factors ZFC3H1 and ZCCH8 co-operate to control HIV-1 LTR-driven transcriptional activity . To determine whether , like RRP6 , MTR4 , ZFC3H1 and ZCCHC8 are directly implicated in the repression of HIV-1 transcription , we performed ChIP analysis since we would expect to find repressive factors associated with HIV-1 chromatin . ChIP analysis revealed that RRP6 , MTR4 , ZFC3H1 and ZCCHC8 are associated with HIV-1 chromatin , particularly with the transcription start site , TAR ( Fig 5A and 5B ) . We next performed RNA-ChIP analysis , which measures chromatin association of factors implicated in co-transcriptional RNA processing [19] . This analysis suggested that RRP6 , ZFC3H1 and ZCCHC8 are in close contact with TAR RNA , while MTR4 may be less stably associated with HIV-1 RNA molecules ( Fig 5C ) . Indeed , ZFC3H1 and ZCCHC8 belong to complexes that target nuclear exosome to its RNA substrates [16–18] . Since ZCCHC8 and ZFC3H1 showed cross-regulation of expression , we analyzed the association of these factors following loss of ZFC3H1 . Interestingly , loss of ZFC3H1 led to enhanced recruitment of ZCCHC8 consistent with the idea that ZCCHC8 functionally compensates for the loss of ZFC3H1 ( Fig 5D ) . We next wished to determine whether nuclear surveillance factors may affect expression of HIV-1 in the context of a full viral genome . We first analyzed the interactions between RRP6 , MTR4 , ZFC3H1 and ZCCHC8 in the presence of HIV-1 proteins . In order to do so , we used J-lat 10 . 6 cell line which harbors a repressed HIV genome in which Nef has been replaced by GFP [20] . To induce viral protein expression and HIV-1 particle production , the cells were activated with a combination of 20 ng/ml TNFα and 20 nM trichostatin A ( TSA ) . Under these conditions , 80% of cells were positive by flow cytometry and p24 was present in the cell culture media ( 34 ng/ml ) , confirming the production of HIV-1 proteins . Co-immunoprecipitation analysis showed that , similarly to that observed in HeLa cells , MTR4 interacted with RRP6 , ZFC3H1 and ZCCHC8 while RRP6 interacted with MTR4 and ZCCHC8 and modestly with ZFC3H1 in the presence of HIV-1 proteins ( Fig 6A ) . We next determined whether the repressive factors are associated with HIV-1 chromatin in J-Lat cells . ChIP analysis revealed that MTR4 , RRP6 , ZFC3H1 and ZCCHC8 are associated with HIV-1 DNA in J-Lat cells ( Fig 6B ) . Thus , nuclear surveillance factors appear to form molecular complexes in HIV-1 target cells in the presence of viral proteins , and to be associated with HIV-1 chromatin . We next wondered whether the repressive factors might be involved in the control of HIV-1 virus expression . Thus , J-Lat 10 . 6 cells were transduced with shRNAs targeting either MTR4 or ZFC3H1 and ZCCHC8 ( Fig 7A ) . Expression of HIV-1 was detected by flow cytometry ( Fig 7B and 7C ) . Knock-down of either MTR4 or ZFC3H1+ZCCHC8 activated HIV-1 expression to a level similar to that observed following treatment with the HDAC inhibitor , TSA . Loss of MTR4 or ZFC3H1+ZCCHC8 appeared to have an additive effect with TSA , resulting in expression of HIV-1 in nearly 10% of cells . We next investigated whether the activation of HIV-1 expression following loss of nuclear surveillance factors was accompanied by changes to HIV-1 chromatin . Thus , chromatin modifications associated with the activation of transcription , H3K36Me3 and H4 acetylation , were analyzed in cells expressing HIV-1 following loss of either MTR4 or ZFC3H1 +ZCCHC8 . ChIP analysis showed the presence of histone marks associated with the activation of transcription were significantly increased while association of histones , as measured by histone H3 , remained mostly unaffected ( Fig 7D ) . These results suggest that MTR4 , ZFC3H1/ZCCHC8 are implicated in the regulation of HIV-1 expression in J-Lat cells . We next wanted to determine whether nuclear surveillance factors are also relevant for the control of HIV-1 expression in peripheral blood mononuclear cells ( PBMCs ) . Thus , CD4+ T cells were purified from PBMCs , activated using α-CD3/CD28 and IL2 then infected with HIV-1BaL as previously described [21] . At 3 days post-infection , cells were transduced with lentiviral particles expressing shRNAs targeting MTR4 , ZFC3H1+ZCCHC8 or a negative control shRNA . Viral release in the supernatant was quantified 7 days later by p24 ELISA and HIV RNA in cells was measured by RT-qPCR of vif sequence . Loss of either MTR4 or ZFC3H1+ZCCHC8 led to an approximately 2-fold increase in HIV-1 particle production and viral RNA compared to a control shRNA ( Fig 8A–8C ) . These results indicate that nuclear surveillance factors can modulate the expression of HIV-1 in activated PBMCs . Next , to determine whether the same factors might be implicated in controlling the latent infection of HIV-1 , we exploited a PBMC model of latency using HIV-1DuoFluo virus that has been described previously [22] . Briefly , HIVDuoFluo is a full-length HIV-1 genome with GFP in place of Nef and mCherry under the control of an internal EF1α promoter . Productively infected cells that are GFP and mCherry double positive can therefore be distinguished by flow cytometry from latently infected cells , that are mCherry single positive . Thus , α-CD3/CD28 + IL-2-activated CD4+ T-cells were infected with HIV-1DuoFluo . Latently infected mCherry positive cells were isolated by FACS sorting and transduced with lentiviruses expressing shRNAs targeting MTR4 , ZFC3H1+ZCCHC8 or a control . Ten days later , latent and reactivated cell populations were quantified by flow cytometry ( Fig 8D–8F ) . Although the mCherry signal had diminished in the latently infected cells , possibly due to further silencing of provirus , as reported previously [22] , the control sample ( shControl ) still contained mCherry-expressing single positive cells ( 7 . 26% ) but did not contain mCherry/GFP-expressing double positive cells ( Fig 8D ) . Importantly , Loss of MTR4 or ZFC3H1/ZCCHC8 led to an increase in the number of double positive cells ( approximately 2% ) while α-CD3-CD28 activation , used as a positive control , contained 9 . 5% double positive cells while the population of mCherry single positive cells was diminished in shRNA-treated and α-CD3-CD28-stimulated samples ( Fig 8D ) . Of note , transduction of uninfected PBMCs with shRNAs or stimulation with α-CD3-CD28 did not induce mCherry- or GFP-positive signals ( S5 Fig ) . To confirm that loss of MTR4 or ZFC3H1/ZCCHC8 led to reactivation of latent virus , p24 was measured in the cell culture supernatants concomitantly with FACS analysis ( Fig 8E ) . The shCon sample did not express significant levels of p24 , suggesting that these cells were latently infected , while a modest increase in p24 was detectable in shMTR4 and shZFC3H1/ZCCHC8 samples relative to the α-CD3-CD28-stimulated positive control sample . These findings suggest that MTR4 , ZFC3H1 and ZCCHC8 are implicated in the silencing of HIV-1 virus .
Details on antibodies , siRNAs and PCR primers used in this study can be found in Supplemental experimental procedures . Antibodies used in this study are listed in S2 Table . Plasmids encoding pOZ-Flag-HA-RRP6 and pOZ-Flag-HA-MTR4 were cloned using pOZ-N-FH plasmid [27] . Plasmids expressing shRNAs targeting RRP6 , MTR4 , ZFC3H1 or ZCCHC8 were purchased from Sigma . A plasmid expressing a control shRNA was obtained through Addgene ( plasmid 1864 ) . Sequences of shRNAs are shown in S4 Table . HeLa cells ( ATCC ) containing a stably integrated LTR linked to a luciferase reporter gene ( HeLa LTR-luc cells [28] ) were propagated in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% FBS and antibiotics . HEK293T ( ATCC ) were grown in DMEM with Hepes ( 25 mM ) , supplemented with 10% FBS and antibiotics . All cells were grown in a humidified incubator at 37°C with 5% CO2 . HeLa LTR-luc were transfected with siRNAs ( 30 nM final concentration ) using Interferin ( PolyPlus Transfection ) according to the manufacturer’s instructions . All samples were harvested at approximately 60 hours post-transfection . For lentiviral particle production , RRP6- and MTR4-expressing lentiviruses were produced in HEK293T cells by transfecting plasmids using calcium-phosphate and HeLa cells were transduced as described previously [7] . Lentiviruses expressing shRNAs ( Sigma ) were produced in 293T cells according to manufacturer’s instructions . For HIV-1 infection , peripheral blood mononuclear cells ( PBMCs ) were isolated from buffy coats of healthy HIV negative donors using Ficoll density gradient ( Eurobio ) and CD4+ T-cells were isolated by negative selection ( Stem Cell Research ) . Cells were then activated with anti-CD3/CD28 beads ( Miltenyi ) and IL2 ( 30 U/ml ) for 3 days , then infected with HIV-1BaL as previously described [21] . Three days post-infection , cells were transferred to media containing 10 U/ml IL2 and transduced with lentiviral particles expressing shRNAs . Viral release in the supernatant was quantified 7 days later by p24 ELISA ( InGen ) . To establish latently infected primary cells , PBMCs were isolated from buffy coats ( Day 0 ) of healthy HIV negative donors using Ficoll density gradient and CD4+ T-cells were isolated by negative selection . Cells were activated by anti-CD3/CD28 + IL2 ( 30 U/ml ) at day 1 as previously described [29] and infected with HIV-1DuoFluo at day 4 . Transduction with lentiviruses expressing shRNA constructs was performed at day 7 on previously sorted latently infected cells . Cells were maintained in cell culture medium containing 10 U/ml IL2 . FACS analyses were performed at day 19 . HIV-1DuoFluo was obtained through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH: Cat# 12595 DuoFluo ( R7GEmC ) from Drs . Vincenzo Calvanez and Eric Verdin [29] . Target sequences of double stranded RNA oligonucleotides used for RNAi ( Eurofins MWG Operon or IDT ) are shown in S3 Table . Sequences of PCR primers used in this study are shown in S4 Table . RRP6 and MTR4 complexes were purified from Dignam high salt nuclear extracts ( https://dx . doi . org/10 . 17504/protocols . io . kh2ct8e ) from HeLa-S3 cells stably expressing Flag-HA-RRP6 or Flag-HA-MTR4 by two-step affinity chromatography ( 10 . 17504/protocols . io . kgrctv6 ) . Sequential Flag and HA immunoprecipitations were performed on equal amounts of proteins . Silver-staining was performed according to the manufacturer’s instruction ( Silverquest , Invitrogen ) . Mass spectrometry was performed at Taplin facility , Harvard University , Boston , MA . One ml layers of glycerol ( 35% to 15% ) were loaded in Ultraclear tubes ( Beckman ) . RRP6-associated proteins were purified by affinity chromatography using Flag-beads ( Sigma ) . Purified complexes were eluted by competition using Flag-peptides ( Sigma ) . Flag-peptide-eluted material was resolved by size-exclusion chromatography . One mL of each glycerol buffer ( final concentration 15 to 35%–20 mM Tris pH 7 . 5 , 0 . 15 M KCl , 2 . 5 mM MgCl2 , 0 . 05% NP-40 , 0 . 1% Tween ) was layered into centrifugation tubes ( 13 x 51 mm Ultra-Clear Tubes , Beckman ) . A linear gradient was obtained after 12 h of diffusion at 4°C . Flag elution from HeLa-Flag-HA-MTR4 immunoprecipitate was loaded on top of the glycerol gradient . Complexes were fractionated by ultracentrifugation in an SW 55Ti rotor ( Beckman ) at 40 , 000 rpm for 8 h at 4°C . 25 fractions of 200 μL were collected from top of the gradient . An equal volume of fractions was resolved by SDS-PAGE and immunoblotted with indicated antibodies . For immunoblot , protein extracts were obtained using RIPA buffer ( 50 mM Tris-HCl pH = 7 . 5 , 150 mM NaCl , 1% NP40 , 0 . 5% Sodium Deoxycholate , 0 . 1% SDS ) supplemented with Complete protease inhibitor ( Roche ) . Protein extracts were immunoblotted using the indicated primary antibodies ( S2 Table ) and anti-mouse or anti-rabbit IgG-linked HRP secondary antibodies ( GE Healthcare ) followed by ECL ( ThermoFisher ) . Band intensities were quantified using ImageJ . Luciferase assay was performed according to the manufacturer’s instructions ( Promega ) . P24 was quantified in cell culture supernatants using Innotest HIV Ag assay ( InGen ) according to manufacturer’s instructions . Total RNA was extracted from cells using TRIzol ( ThermoFisher Scientific ) according to the manufacturer’s instructions . Extracts were treated with DNase I ( Promega ) and reverse transcribed using SuperScript III First-Strand Synthesis System ( ThermoFisher Scientific ) . RT products were amplified by real time PCR ( Lightcycler , Roche ) using Quanti Tect SYBR Green ( Qiagen ) with the indicated oligonucleotides . Q-PCR cycling conditions are available on request . Sequences of qPCR primers used in this study are shown in S4 Table . Run-on transcription was performed as described in https://dx . doi . org/10 . 17504/protocols . io . khxct7n . Run-on transcripts were reverse transcribed and quantified by PCR using the oligonucleotide pairs indicated . Results were normalized to the amount of GAPDH run-on transcript in the same sample . HeLa LTR-luc cells were transfected as indicated in the figures . Following 64 hr incubation , cells were washed and harvested for cross-link ChIP , which was performed as described in https://dx . doi . org/10 . 17504/protocols . io . knkcvcw , or RNA-ChIP , which was performed as described previously [28] using the antibodies indicated . Samples were amplified by qPCR or RT-qPCR using the primer pairs indicated . An aliquot of chromatin was amplified in parallel and values obtained for immunoprecipitates were normalized to values for chromatin ( % input ) . For non-treated J-Lat cells , cells were harvested for cross-link ChIP using iDeal ChIP q-PCR kit ( Diagenode ) according to the manufacturer’s instructions . For J-lat cells that had been transduced with shRNAs as indicated in the figures , cells were harvested 7 days post-transduction and processed using LowCell ChIP kit ( Diagneode ) according to the manufacturer’s instructions . J-Lat cells clone 10 . 6 ( obtained from E . Verdin ) were fixed in PBS containing 1% paraformaldehyde for 10 mins , washed with PBS and resuspended in fresh PBS . GFP fluorescence was measured with a MAQS Quant machine ( Miltenyi Biotech ) . Electronic compensation was applied during analysis . Analysis was gated on live cells according to forward and side scatter . A gate ( GFP positive ) containing GFP-positive cells was drawn compared to untreated cells . For FACS analysis of HIV-1DuoFluo infected PBMCs , control cells infected with pHRET ( GFP-positive ) or NL4-3 mCherry viral vector ( Addgene#44965 ) were used to apply fluorescence compensation to the data . Data were collected on a Novocyte cytometer ( Ozyme ) and data were analyzed using FlowJo software ( TreeStar ) . | Following integration into the host genome , HIV-1 expression is silenced in a small population of cells , largely via epigenetic mechanisms that repress LTR-mediated transcription . This repression creates a reservoir of cells that prevent an effective cure . It is unclear how and why integrated HIV-1 becomes transcriptionally silenced . Here , we identify a network of nuclear RNA surveillance factors that repress HIV transcription and whose loss increases virus expression in latently infected J-Lat and PBMCs . These findings advance the understanding of transcriptional repression of HIV-1 . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"flow",
"cytometry",
"medicine",
"and",
"health",
"sciences",
"vesicles",
"pathology",
"and",
"laboratory",
"medicine",
"molecular",
"probe",
"techniques",
"gene",
"regulation",
"pathogens",
"microbiology",
"immunoblotting",
"dna",
"transcription",
"retroviruses",
"viruse... | 2018 | Nuclear RNA surveillance complexes silence HIV-1 transcription |
Vertebrate limb outgrowth is driven by a positive feedback loop that involves Sonic hedgehog ( Shh ) and Gremlin1 ( Grem1 ) in the posterior limb bud mesenchyme and Fibroblast growth factors ( Fgfs ) in the overlying epithelium . Proper spatio-temporal control of these signaling activities is required to avoid limb malformations such as polydactyly . Here we show that , in Tbx2-deficient hindlimbs , Shh/Fgf4 signaling is prolonged , resulting in increased limb bud size and duplication of digit 4 . In turn , limb-specific Tbx2 overexpression leads to premature termination of this signaling loop with smaller limbs and reduced digit number as phenotypic manifestation . We show that Tbx2 directly represses Grem1 in distal regions of the posterior limb mesenchyme allowing Bone morphogenetic protein ( Bmp ) signaling to abrogate Fgf4/9/17 expression in the overlying epithelium . Since Tbx2 itself is a target of Bmp signaling , our data identify a growth-inhibiting positive feedback loop ( Bmp/Tbx2/Grem1 ) . We propose that proliferative expansion of Tbx2-expressing cells mediates self-termination of limb bud outgrowth due to their refractoriness to Grem1 induction .
Polydactyly , the condition of having more than the normal number of toes and/or fingers is the most frequent form of limb malformation in humans with an incidence of 1∶500 . It most commonly occurs as post-axial polydactyly ( extra digit ( s ) towards 5th finger in limb ) , less common is pre-axial polydactyly ( extra digit ( s ) towards thumb or toe ) , very rare is central ( mesoaxial ) polydactyly ( with extra digits within the three middle digits ) . In all cases , the excess digits can be undeveloped and only attached by a little stalk mostly on the small finger side of the hand or fully formed and working . Polydactyly can occur by itself , or more commonly , as one feature of a syndrome of congenital anomalies as e . g . in Pallister-Hall syndrome , Smith-Lemli-Opitz syndrome or Bardet-Biedl syndrome ( OMIM 146510 , 270400 , 209900 ) . Elucidation of the genetic , molecular and cellular changes that underlie polydactyly ( as well as other limb defects ) in humans has greatly benefitted from the analysis of normal limb development , and of the consequences of altered gene functions in suitable animal models such as the chicken and the mouse [1] . All of these studies unraveled that proper establishment and elaboration of the two main limb axes during development is crucial for setting up a correct number and identity of digits . In the limb primordium two signaling centers control the morphogenesis along these limb axes . The apical ectodermal ridge ( AER ) , a distal thickening of the ectodermal jacket of the limb bud , controls proximal-distal ( from shoulder to finger tips ) , whereas the zone of polarizing activity ( ZPA ) in the posterior region of the mesenchymal core mediates anterior-posterior ( A-P , from thumb to the small finger ) development . Fibroblast growth factors ( Fgf4 , Fgf8 , Fgf9 and Fgf17 ) and Shh secreted from the AER and ZPA , respectively , specify distal and posterior positional values in the early limb bud mesenchyme ( in the mouse until E9 . 5 ) [2] , [3] . Removal of the signaling centers or the signals , results in time-dependent distal truncations [4] and loss of posterior limb positions ( ulna and digits 2–5 ) [5] , respectively . However , both AER-FGFs and ZPA-Shh do not only provide patterning functions , they also account for the massive outgrowth of the limb bud at subsequent stages ( from E9 . 5 to 11 . 5 in the mouse ) by promoting cell survival and proliferative expansion of the distal and posterior limb bud mesenchyme , respectively [6] , [7] . During this phase the three primordia of the stylopod ( the future upper arm/leg ) , the zeugopod ( lower arm/leg ) and the autopod ( hand/foot ) are laid down and expanded and digit formation is initiated . It has long been noted that AER and ZPA are mutually dependent on each other , thus linking the two signaling centers by an epithelial-mesenchymal ( e-m ) signaling loop [8] , [9] , [10] . Fgf signaling is likely to maintain Shh directly , whereas Shh signaling to the overlying AER is relayed in the posterior limb bud mesenchyme by Gremlin 1 ( Grem1 ) , a secreted antagonist of Bone morphogenetic protein ( Bmp ) signaling [11] , [12] , [13] . Inhibition of Bmp signaling allows Fgf4 expression in the posterior AER enabling the further propagation of the loop . Limb bud outgrowth comes to a halt ( around E11 . 5 to E12 . 0 in the mouse ) when Fgf4 and Shh expression is shut-off due to a rise of Bmp signaling , resulting in cell-cycle exit and initiation of chondrogenic differentiation [10] , [14] . Although the precise mechanisms are unclear , refractoriness of ZPA descendants to induce Grem1 may be of critical importance to self-terminate the e-m signaling loop [15] . Tbx2 encodes a transcriptional repressor of the T-box gene family that has recently been implicated in digit development . In the mouse , Tbx2 is expressed in the anterior and posterior mesenchymal flanks of the early limb bud . From E11 . 5 on , the posterior domain of Tbx2 extends more distally and is then found in the interdigital mesenchyme ( IDM ) , most prominently in IDM4 , and at the distal tips of the digit condensates at E12 . 5 [16] ( Figure S1 ) . Limbs of Tbx2-deficient mice exhibit a hindlimb-specific duplication of digit 4 [17] . The spatially restricted nature of this phenotype may be due to redundancy with the closely related Tbx3 gene . While both genes are coexpressed in the proximal mesenchyme on either limb margin , Tbx3 is absent from the distal mesenychme of the posterior flank [16] ( Figure S1 ) . Exclusive expression of Tbx3 in the AER may relate to the variable distal truncations and oligodactyly observed in Tbx3−/− embryos [18] . Retroviral mis- and overexpression experiments in the chick model provided phenotypic outcomes that suggested additional or alternative functions for Tbx2 ( and Tbx3 ) in regulating digit identity rather than digit number [19] and in anterior-posterior positioning of the limb bud [20] . Here , we set out to gain further insight into the role of Tbx2 in digit development by genetic loss- and gain-of-function experiments in the mouse . We show that increased digit number in Tbx2-mutant mice and oligodactyly in embryos overexpressing Tbx2 ( in the limbs ) relates to the maintenance of the e-m feedback loop in the posterior limb bud mesenchyme . Tbx2 terminates this loop by locally repressing Grem1 . Our experiments identify a Bmp/Tbx2/Grem1 loop that counteracts the Shh/Grem1/Fgf4 loop to mediate self-termination of limb bud outgrowth .
Mice homozygous for a Tbx2 null allele ( Tbx2cre ) maintained on a NMRI outbred background died shortly after birth due to craniofacial defects [21] . Mutant E18 . 5 embryos had normal forelimbs but hindlimbs displayed six instead of the normal five digits . Soft tissue webbing , i . e . the persistence of IDM tissue was not observed ( Figure 1B ) . Hindlimb polydactyly showed 70% penetrance in homozygous embryos , heterozygous embryos were not affected . Skeletal preparations of hindlimbs of E18 . 5 Tbx2−/− embryos revealed that the proximal segment of digit 4 was broadened and split distally to connect to a duplicated pair of second and third phalangeal segments ( Figure 1D ) . Analysis of chondrogenic elements at E15 . 5 and E13 . 5 showed that the duplication of skeletal elements of digit 4 was established shortly after onset of chondrogenesis in the mutant hindlimb ( Figure 1H and 1L ) . Comparative expression analysis for the ( pre- ) chondrogenic marker gene Sox9 [22] and Raldh2 , which marks the IDM [23] , showed that the occurrence of duplicated distal cartilagenous segments of digit 4 was preceded by a posterior expansion of the prechondrogenic condensate at the expense of the adjacent IDM4 at E12 . 5 ( Figure 1M–1P ) . Since Tbx2 expression in the developing limb is confined to flank and ID mesenchyme that are removed by programmed cell death [24] , we determined the distribution of apoptotic cells by Lysotracker Red staining . We noted a specific reduction of apoptotic cells in IDM4 of Tbx2−/− hindlimbs at E13 . 5 , whereas other domains of programmed cell death ( IDM1-3 ) were unchanged ( Figure 1Q and 1R ) . In E12 . 5 wild-type embryos , apoptosis was confined to the mesenchyme underlying the AER at the anterior and posterior margins of the hindlimb ( Figure 1S ) . Programmed cell death appeared unaffected in the anterior region whereas it was largely reduced in the posterior mesenchymal region of mutant hindlimbs at this stage ( Figure 1T ) . The latter may explain the increased size and the characteristic outward curvature of the posterior edge of the mutant hindlimb . Together with the strong expression of Tbx2 in the distal region of the posterior flank mesenchyme at E11 . 5 and in IDM4 at E12 . 5 ( Figure S1 ) , this suggests that Tbx2 functions after E11 . 5 in the development of the posterior autopod region to maintain IDM4 fate and to restrict the expansion of chondrogenic material at the posterior limit of the digit 4 condensation . Proliferative expansion and apoptotic removal of the limb bud mesenchyme is governed by reciprocal signaling with the overlying AER [1] . Given the increased posterior size and the decreased apoptosis of E12 . 5 Tbx2-deficient hindlimbs , we studied the expression of factors that are involved in e-m signaling before the manifestation of morphological defects . In E10 . 5 hindlimb buds expression of Fgf4 , Fgf8 , Fgf9 , Fgf17 and Shh was unaffected ( Figure 2A–2E ) . At E11 . 5 , however , we found increased expression of Fgf4 ( in 7/8 embryos ) , Fgf9 ( in 3/3 embryos ) and Fgf17 ( in 2/3 embryos ) in the posterior AER of Tbx2−/− hindlimbs ( Figure 2F–2H ) . At E12 . 5 , Fgf4 expression was no longer detected in wild-type hindlimbs , but residual AER expression of Fgf4 was observed in 3/8 mutant embryos ( Figure 2K ) . Shh was unchanged at E11 . 5 ( Figure 2I ) , but expression was aberrantly maintained at E12 . 5 ( in 5/5 embryos ) ( Figure 2N ) . Fgf8 , which is continuously expressed in the entire AER , was unaffected in Tbx2-deficient hindlimbs at all analyzed stages ( Figure 2E , 2J and 2O , and data not shown ) . Together we conclude that Tbx2 is required to assure correct termination of the Fgf4/9/17-Shh signaling loop in the posterior mesenchyme of the hindlimb bud . To explore if a gain in e-m signaling indeed causes polydactyly in Tbx2-deficient hindlimbs , we genetically reduced the level of mesenchymal Fgf-signaling . We took advantage of cre recombinase driven from our Tbx2 mutant allele to delete a floxed allele of the Fgf-receptor 1 gene in the posterior limb bud mesenchyme . Double heterozygous animals ( Tbx2cre/+;Fgfr1fl/+ ) were intercrossed and the limb skeleton of compound mutants was analyzed . Homozygous loss of Fgfr1 ( Tbx2cre/+;Fgfr1fl/fl ) caused a reduced number of 4 digits in fore- and hindlimbs ( Figure 3F ) . This result is consistent with previous experiments using the Shh-cre line that recombines in a domain very similar to that of Tbx2cre [7] , [25] ( Figure S2A–S2D ) . At E16 . 5 Tbx2cre/cre;Fgfr1fl/+ embryos were underrepresented and Tbx2cre/cre;Fgfr1fl/fl embryos were absent , most likely due to a synthetic lethal cardiac defect . Double heterozygous Tbx2cre/+;Fgfr1fl/+ embryos exhibited a normal limb skeleton ( Figure 3B ) in agreement with previous results using the Prrx1-cre line that deletes in the entire limb bud mesenchyme [25] , [26] . In Tbx2-deficient embryos , however , dose-reduction of Fgfr1 strongly reduced the penetrance of polydactyly ( 3/7 compared to 7/10 in Tbx2cre/cre;Fgfr1wt/wt embryos ) or even caused oligodactyly ( 4 digits in 3/7 embryos analyzed ) ( Figure 3D and 3E ) . In oligodactic limbs the characteristic shape of the proximal end of the posterior metacarpal indicated a digit 5 identity ( white arrows ) . Absence of Eomes expression in E12 . 5 Tbx2cre/cre;Fgfr1fl/fl hindlimbs argued for a specific loss of digit 4 ( Figure S2E , S2F ) [27] . Our results suggest that polydactyly in Tbx2-deficient mice critically depends on increased activity of the e-m signaling loop . Since Tbx2 encodes a nuclear transcriptional repressor [28] , we sought to identify targets that may help to explain the observed molecular changes . We judged it unlikely , that Shh itself is a target of Tbx2 repression since Tbx2 and Shh are broadly coexpressed in the posterior limb bud mesenchyme . Furthermore , unchanged expression of Shh in E11 . 5 mutant hindlimbs cannot explain up-regulation of Fgf4 in the AER at this stage . Because AER expression of Fgf4 is mediated by inhibition of Bmp signaling by the secreted Bmp antagonist Grem1 [12] , [13] , we explored the possibility of Grem1 regulation by Tbx2 . In E12 . 0 and E11 . 5 wild-type embryos , the expression of Grem1 in two crescent-shaped domains at the dorsal and ventral surface of the limb mesenchyme was sharply excluded from the Tbx2-positive posterior hindlimb mesenchyme ( Figure 4A and 4B , 4D and 4E ) . In Tbx2−/− hindlimbs , the expression of Grem1 was unaffected at E10 . 5 and E13 . 0 ( data not shown ) , but appeared specifically up-regulated at E12 . 0 and E11 . 5 in the posterior region ( Figure 4C and 4F; arrows ) . On adjacent sagittal sections of E11 . 5 Tbx2cre/+ hindlimbs , cre and Grem1 were expressed in neighboring , non-overlapping domains ( Figure 4G–4I ) . In Tbx2cre/cre- embryos , however , Grem1 expression was shifted posteriorly and overlapped with the domain of cre expression that itself was unchanged . Consistent with local expansion of the Bmp-antagonist Grem1 the Bmp target gene Dkk1 [29] was absent in the mesenchyme underlying the posterior AER in Tbx2−/− hindlimbs at E11 . 5 ( Figure S3A ) . Expression of Bmp targets Msx1 and Msx2 [30] was unaffected ( Figure S3B and S3C ) , suggesting that the level of Bmps was still sufficient to activate these genes . To explore if Tbx2 mediates Grem1 repression directly , we performed chromatin immunoprecipitation experiments ( ChIP ) using anti-Tbx2 IgG and E11 . 5 wild-type hindlimb tissue ( Figure 4J and 4K ) . We found that in posterior limb-bud halves Tbx2 protein specifically interacted with the known Grem1 limb bud enhancer [31] but not with control genomic regions . No binding was observed to chromatin prepared from anterior limb bud halves that lack Tbx2 expression , demonstrating specificity of this assay . Together our results strongly suggest that Tbx2 terminates Fgf4/Shh signaling in the posterior limb bud by direct repression of Grem1 . Bmp ligands are expressed in the AER and at the margins of the limb mesenchyme [13] and represent good candidates as activators of Tbx2 expression , similar to other developmental contexts [32] , [33] , [34] , [35] . To explore this possibility , we analyzed the effect of Bmp on Tbx2 expression by bead implantation experiments . We found that Bmp4-soaked beads implanted into the central mesenchyme of E10 . 5 forelimb buds caused upregulation of Tbx2 after 16 hours of culture ( Figure 5A ) . This effect was further increased after ectoderm removal ( Figure 5B ) . We established micromass cultures of E10 . 5 limb bud mesenchyme and studied Tbx2 expression by quantitative RT-PCR 2 hours after addition of Bmp4 to the medium . A dose-dependent increase of Tbx2 mRNA was observed that closely resembled induction of the known Bmp targets Msx1 and Msx2 ( Figure 5C ) . To study Bmp requirement of Tbx2 expression , we added Dorsomorphin , a selective inhibitor of bone morphogenetic protein ( BMP ) type I receptors [36] , during the last two hours of culture . We observed a twofold reduction of basal Tbx2 expression in the presence of 1 µM Dorsomorphin that again resembled the response of Msx1 and Msx2 ( Figure 5D ) . Thus , Bmp signaling is necessary and sufficient to induce Tbx2 expression in the limb . Tbx2 – via repression of Grem1 – therefore operates in a positive feedback loop with Bmp ( s ) at the posterior margin of the limb . Next we used a cre/loxP-based misexpression approach to analyze if expression of Tbx2 in the entire limb bud would interfere with e-m signaling and digit development . In HprtTBX2 mice expression of the human TBX2 cDNA ( introduced into the X-chromosomal Hypoxanthine guanine phosphoribosyl transferase ) locus , is inducible by cre-mediated recombination [37] . We employed the Prrx1-cre mouse line to drive transgene expression in the entire limb mesenchyme [38] . X-chromosome inactivation in females causes mosaicism , we therefore only analyzed hemizygous Prrx1-cre/+;HprtTBX2/Y male embryos that express the transgene in a uniform manner ( abbreviated as Prrx1-TBX2 ) . By Western blot analysis and immunostainings we confirmed ubiquitous expression of TBX2 in the E10 . 5 limb mesenchyme at levels comparable to the endogenous Tbx2 protein ( Figure S4A–S4D ) . At E18 . 5 , transgenic embryos exhibited oligodactyly with a dramatic reduction of the limb length ( Figure 6A–6D ) . Forelimbs ( 3 digits in 9; 4 digits in 1 out of 10 embryos ) were stronger affected than hindlimbs ( 4 digits in 5; 5 digits in 4 and 6 digits in 1 out of 10 embryos ) , most likely reflecting the relatively delayed onset of Prrx1-cre mediated recombination in hindlimbs [38] . We invariantly observed a single zeugopodial element and a reduction of pectoral and pelvic girdles . Forelimb stylo- and zeugopod elements were fused , in the hindlimb the femur was strongly reduced . The delayed onset of recombination in the hindlimbs led us to study the early consequences of TBX2 misexpression in forelimbs that demonstrated a complete lack of autopod outgrowth at E12 . 5 . In sharp contrast to the Tbx2 loss-of-function situation , Lysotracker Red staining showed apoptotic mesenchymal cells underneath the entire AER at this stage in Prrx1-TBX2 embryos ( Figure 6E and 6F ) . In E10 . 5 wild-type forelimbs , apoptosis was confined to the proximal mesenchyme as previously reported [6] ( Figure 6G ) . Age matched Prrx1-TBX2 embryos exhibited strongly reduced forelimb buds and widespread apoptosis throughout the mesenchyme as observed by LysoTracker Red and by TUNEL staining ( Figure 6H and Figure S4F ) . A highly similar phenotypic spectrum of limb defects including reduced limb length and autopod outgrowth , oligodactyly , fusion of zeugopodial elements , as well as early and widespread mesenchymal apoptosis was reported in Grem1−/− mutants [12] , [13] , suggesting that loss of Grem1 expression may account for the observed effects in Prrx1-TBX2 embryos . Indeed , we found strong reduction of Grem1 expression in E10 . 5 transgenic forelimb buds ( Figure 6K and 6L ) and residual expression at E12 . 5 at the base of the autopod ( Figure 6I and 6J ) . Thus , Tbx2 represses Grem1 expression distally , whereas the more proximal expression domain at E12 . 5 might be controlled by an independent mechanism . Next , we analyzed the effects of TBX2 misexpression on known downstream effectors of Grem1 . At E10 . 5 , expression of both Fgf4 and Shh was strongly reduced ( Figure 6M–6P ) , again closely resembling the situation in Grem1−/− limbs . Reduction of the target genes Spry4 and Ptch1 [39] , [40] confirmed a decrease in Fgf4 and Shh signaling ( Figure S5 ) . Fgf9 and Fgf17 expression was reduced but expression of Fgf8 and of the more broadly expressed Fgf target Etv4 ( Pea3 ) [41] was unaffected , demonstrating that Tbx2 acts specifically on posterior e-m signaling ( Figure S5C and S5D ) . To study if reduction of Grem1 is associated with increased Bmp signaling we analyzed Msx1 and Msx2 expression , and found that both genes were upregulated in the distal limb bud ( Figure 5Q–5T ) , i . e . in regions normally devoid of Msx1/2 expression due to Grem1-mediated Bmp-antagonism ( compare Figure 5K ) [12] , [13] . Notably , we did neither observe transformations of digit identity nor changes in limb positioning ( data not shown ) , as reported for Tbx2 and Tbx3 misexpression in the chick model [19] , [20] . Hence , control of digit formation by local repression of Grem1 is the primary function of Tbx2 in the mouse .
Precise termination of the e-m signaling loop involving Shh , Grem1 and Fgfs is crucial to restrict limb bud size and to assure a normal digit number . Studies in chick and mouse have indicated that downregulation of Grem1 drives termination of this loop but suggested two different molecular mechanisms: The one is based on the observation that Shh expressing cells as well as their descendants are unable to express Grem1 [15] , [42] . Proliferative expansion of ZPA-derived cells [7] would thereby displace the source of Grem1 secretion from the AER to a point where the distal range of Grem1 diffusion is eventually exceeded . As a consequence , Bmp signaling increases and suppresses AER-Fgfs , followed by termination of Shh expression and proliferative expansion . Although this model is supported by the sequence of signal terminations in the chick , the factor responsible for the cell-autonomous repression of Grem1 in the Shh lineage cells has remained enigmatic ( Scherz et al . , 2004 ) . As an alternative mechanism , recent loss-of-function experiments in the mouse have supported the existence of an inhibitory Fgf/Grem1 signaling loop that becomes progressively activated once the positive Shh/Grem1/Fgf loop has induced sufficiently high levels of Fgfs [43] . This model can elegantly rationalize the regulation of limb bud size via interconnected , self-terminating signaling loops . However , it fails to explain the selective absence of Grem1 from the posterior limb bud mesenchyme . The parallel upregulation and prolonged expression of Grem1 , Fgf4/9/17 and Shh in Tbx2-deficient hindlimbs and their coordinated downregulation upon limb-specific TBX2 overexpression identifies Tbx2 as an essential factor for the termination of the e-m signaling loop ( summarized in Figure 7 ) . Given the virtual overlap of Tbx2 and Shh cell lineages ( see Figure S2A–S2D and [7] ) , we propose that Tbx2 renders Shh-descendant cells unable to induce Grem1 . Consistently we found that the transcriptional repressor Tbx2 binds to the Grem1 limb enhancer in vivo . This 437 bp element has been identified previously by genome-wide , limb-specific ChIP analysis of Gli3 , the transcription factor that mediates Shh-dependent gene transcription in the limb bud [31] . In transgenic mice the element largely recapitulates the complex Grem1 limb expression pattern , which argues for an integration of both activating ( Gli3 ) as well as repressive modules ( Tbx2 ) . Absence of Tbx2 expression from the anterior limb margin can explain earlier observations that Shh loaded beads are sufficient to induce Grem1 in this region but not in the posterior limb mesenchyme [15] . Importantly , we noted that in Tbx2-deficient hindlimbs the posterior mesenchyme directly underneath the AER remained Grem1 negative ( Figure 4F ) . This suggests that the negative Fgf-Grem1 signaling loop [43] stays active in Tbx2−/− embryos and argues that both termination mechanisms ( see above ) operate in parallel in adjacent domains of the limb mesenchyme to achieve spatio-temporal control of Grem1 expression . The hindlimb-specific requirement for Tbx2 cannot easily be explained at this point . It may result from differences in the proliferative expansion of fore- and hindlimbs , differential Shh/Fgf signaling activities , or the existence of additional repressors that might operate redundantly with Tbx2 in forelimbs . Tbx3 is unlikely to compensate for the loss of Tbx2 given the absence of Tbx3 expression at the distal margin of forelimb buds ( Figure S1 ) . However , we can clearly exclude a function of mouse Tbx2 in specification of digit identity , as previously suggested from experiments in chicken embryos [19] . Although some of the observed differences may relate to species-specific variations in the underlying molecular programs , experimental caveats due to unphysiological levels of protein obtained after retroviral mis- and overexpression cannot be excluded at this point . We have shown that Bmp signaling activates Tbx2 expression in the limb mesenchyme . This notion is supported by reduction of Tbx2 expression in embryos with reduced levels of Bmp signaling in the limb [44] . Since Tbx2 expression is normal in Prrx1-cre;Bmp2fl/fl;Bmp4fl/fl or Prrx1-cre;Bmp2fl/fl;Bmp7−/− mice [45] , several Bmp ligands are likely to operate redundantly . Recent transgenic analysis of the Tbx2 promoter led to the identification of Smad binding sites [34] , [35] that mediate Tbx2 expression in the limb ( supporting material in [35] ) further stressing the relevance of Bmp signaling for Tbx2 expression in this organ . In chick embryos the non-AER marginal ectoderm activates Tbx2 expression , whereas transplantation of the AER or Fgf-soaked beads repress Tbx2 expression [45] . In contrast to chick embryos , however , murine Tbx2 expression is not excluded from mesenchymal regions underling the AER and extends more distally , demonstrating species-specific differences in Tbx2 regulation . Abrogation of Shh signaling by cyclopamine treatment has shown that Shh is dispensable for posterior Tbx2 expression [45] . The fact that implantation of Shh-soaked beads can induce Tbx2 expression in the anterior mesenchyme [46] may therefore be secondary to induction of Bmp expression in these regions [42] . Thus , Bmp signaling constitutes a major positive input for Tbx2 limb expression but additional factors may feed-in to fine-tune its expression . However , the mutual relationship: Bmp-induction of Tbx2 and Tbx2 repression of Grem1-mediated Bmp-antagonism , constitute a positive feedback loop that acts locally to diminish and terminate Shh/Fgf4 signaling ( Figure 7 ) . Postaxial polydactyly , expansion of Sox9 expression and reduced apoptosis in Bmp pathway mutants [44] , [47] represent phenotypic similarities that support engagement in a common pathway . While loss of Grem1 ( following TBX2 misexpression ) causes a general increase in Bmp signaling , as indicated by induction of distal Msx1/2 expression , both genes were unaffected in Tbx2−/− limbs . Here , absence of the Bmp target Dkk1 was observed in the sub-AER mesenchyme , indicating that Dkk1 expression requires higher levels of Bmp signaling . In fact , the loss of Dkk1 expression might explain reduced apoptosis in the Tbx2−/− sub-AER mesenchyme as ectopic Dkk1 induces programmed cell death [29] . Moreover , the postaxial polydactyly in Dkk1 mutants [48] is compatible with a role downstream of Tbx2 . Mesoaxial polydactyly has been suggested as a characteristic feature of Oral-Facial-Digital syndrome ( OFDS ) type IV ( OMIM 258860 ) [49] . Interestingly , a variant case of OFDS , that partially resembles OFDS type IV and type II ( also known as Mohr Syndrome , OMIM 252100 ) shows a malformation spectrum including endocardial cushion defects , cleft palate and central polydactyly with bifurcated Y-shaped metacarpals of the forth digit [50] phenocopying Tbx2 loss-of-function in the mouse [17] , [21] , [51] . Together , our data allow the integration and refinement of existing models for termination of distal limb outgrowth , and emphasizes how local differences of signaling activities are translated into the architecture of the adult skeleton , i . e . the number or digits . They show that central polydactyly like preaxial and postaxial variants arise from perturbation in components of the signaling loops including Shh , Grem , Bmp and Fgf signaling .
All animal work conducted for this study was approved by H . Hedrich , state head of the animal facility at Medizinische Hochschule Hannover and performed according to German legislation . Mice carrying a null allele of Tbx2 ( Tbx2tm1 . 1 ( cre ) Vmc , synonyms: Tbx2− , Tbx2cre ) [52] , a floxed allele of Fgfr1 [53] , the transgenic lines Tg ( Prrx1-cre ) 1Cjt/J ) ( synonym: Prx1-Cre ) [38] and the reporter lines R26lacZ ( synonym: R26R ) [54] and Tg ( CAG-Bgeo/GFP ) 21Lbe ) ( synonym: Z/EG ) [55] and mice with integration of the human TBX2 gene in the Hprt locus ( Hprttm2 ( CAG-TBX2 , -EGFP ) Akis , synonym: HprtTBX2 ) [51] were maintained on an outbred ( NMRI ) background . For timed pregnancies , vaginal plugs were checked in the morning after mating; noon was taken as embryonic day ( E ) 0 . 5 . Pregnant females were sacrificed by cervical dislocation; embryos were harvested in phosphate-buffered saline , decapitated , fixed in 4% paraformaldehyde overnight , and stored in 100% methanol at −20°C before further use . Genomic DNA prepared from yolk sacs or tail biopsies was used for genotyping by polymerase chain reaction ( PCR ) . Limb buds from E10 . 5 wild-type NMRI embryos were dissected in PBS and placed on Nucleopore filters ( Whatman , pore size 1 . 0 µm ) on top of a stainless steel mesh at the air-liquid interface in 3 . 5 cm cell culture dishes . The surgical removal of the ectoderm was performed with forceps in DMEM/10% FCS , after incubation of limb buds in 2% Trypsin/PBS ( w/v ) for 20 min at 4°C . Affi-Gel blue beads ( 100–200 µm diameter , Bio-Rad ) were rinsed in PBS and incubated at room temperature for 1 h in either recombinant human BMP4 ( 100 µg/ml , AbD Serotech ) or in 1 mg/ml BSA ( control ) . Beads were rinsed in PBS before implantation into the limb mesenchyme . The culture was performed at 37°C and 5% CO2 in organ culture medium ( DMEM/10% FCS , 1× solutions of Penicillin/Streptomycin , Glutamax , sodium pyruvate , and non-essential amino acids [Gibco] ) . Micromass cultures were established by dissociation of E10 . 5 fore- and hindlimb buds in DMEM/10% FCS , after incubation in 2% Trypsin/PBS ( w/v ) for 5 min at 37°C . A single cell suspension was obtained by gentle pipetting; clumps of ectoderm were removed after sedimentation . Cells were adjusted to 1 . 5×107 cells/ml in organ culture medium ( as above ) , before 10 µl spots were placed on 24 well plates . Cells were incubated for 1 hour at 37°C to allow adherence , before the wells were filled with medium . Recombinant BMP4 ( as above ) or Dorsomorphin ( Sigma ) were added to the medium after 16 h of culture , 2 hours before RNA isolation . Total RNA was extracted from single micromass cultures with PeqGOLD reagent ( Peqlab ) . RNA ( 500 ng ) was reverse transcribed using oligo dT primer and RevertAid M-MuLV Reverse Transcriptase ( Fermentas ) following the manufacturer's recommendations . Relative gene expression was measured using iQ−SYBR Green reagent ( Biorad ) and calculated using the DDCT method by normalization to Hprt expression . The error bars show standard deviation from 4 independent experiments . Skeletal preparations with Alcian blue and Alizarin red , β-galactosidase stainings , detection of apoptosis by the TUNEL assay , and in situ hybridization analyses on whole embryos and on 10 µm paraffin sections were performed as previously described [56] , [57] , [58] . All experiments were performed on at least three independent embryos . For experiments that showed variable results , numbers of used specimens are mentioned in the text . For detection of apoptotic cells , embryos were collected in PBS and incubated for 30 min at 37°C in prewarmed PBS containing 2 . 5 µM LysoTracker Red ( Sigma ) , followed by several washes in PBS . Tbx2 antibody ( #07-318 , Millipore ) was used 1∶100 for immunostainings on 5 µm paraffin sections . Signals were amplified by Tyramide Signal Amplification ( PerkinElmer ) . Distal pieces of E11 . 5 wild-type hindlimb buds were separated in anterior and posterior halves ( as indicated in Figure 4J ) and collected in 3 separate pools of 8 embryos each and treated with 4% paraformaldehyde overnight . ChIP experiments using an anti-Tbx2 antibody were performed essentially as previously described [59] . Primer sequences for the Grem1 enhancer were TTCCCCTCCTCTTCCACAGTAGG and GGCCAAATAACCACACAGGAAAC , corresponding to a 447 bp fragment previously tested in transgenic animals [31] . Primer pairs of control regions were TGAAAACCCCAAGGAGTCTG , CATGGGCAGGATACTACGCT ( 193 bp product , 25 . 5 kbp distal ) and AGCCTGACTCTCCCATCTCA , GGCACTGGATAAAACTCCCA ( 273 bp product , 24 . 3 kbp distal from the Grem1 limb enhancer ) . Whole-mount specimens were photographed on Leica M420 with Fujix digital camera HC-300Z . Whole-mount GFP-epifluorescence was documented on a Leica MZFLIII macroscope equipped with a Leica DFC300 camera . Sections of in situ hybridizations were photographed using a Leica DM5000 microscope with a Leica DFC300FX camera . All images were processed in Adobe Photoshop CS . | Developmental defects of the limb skeleton , such as variations from the normal number of digits , can result from an abnormal size of the early limb bud . The mechanisms that restrict limb bud growth to avoid polydactyly , i . e . the formation of extra digits , are unclear . Gremlin 1 ( Grem1 ) has been identified as a key regulator in this process via its role as secreted antagonist of Bone morphogenetic protein ( Bmp ) signaling . But it remains unknown how Grem1 expression is switched off appropriately to achieve normal limb bud size . Here we show in the mouse embryo that T-box transcription factor 2 ( Tbx2 ) directly represses Grem1 . We show that Tbx2-positive mesenchymal cells at the posterior margin of the limb bud create a Grem1-negative zone that expands concomitantly with limb bud growth . Progressive displacement of the source of Grem1 and its target region , the apical ectodermal ridge , eventually disrupts epithelial-mesenchymal signaling that is crucial for further proliferative expansion . Our data show how local control of signaling activities is translated into the architecture of the adult skeleton , i . e . the number or digits , which helps us to understand the molecular bases of human polydactyly . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"musculoskeletal",
"system",
"bone",
"birth",
"defects",
"molecular",
"development",
"gene",
"regulation",
"genetics",
"gene",
"expression",
"molecular",
"genetics",
"musculoskeletal",
"anatomy",
"biology",
"anatomy",
"and",
"physiology",
"cart... | 2013 | Tbx2 Terminates Shh/Fgf Signaling in the Developing Mouse Limb Bud by Direct Repression of Gremlin1 |
Horizontal gene transfer shapes the genomes of prokaryotes by allowing rapid acquisition of novel adaptive functions . Conjugation allows the broadest range and the highest gene transfer input per transfer event . While conjugative plasmids have been studied for decades , the number and diversity of integrative conjugative elements ( ICE ) in prokaryotes remained unknown . We defined a large set of protein profiles of the conjugation machinery to scan over 1 , 000 genomes of prokaryotes . We found 682 putative conjugative systems among all major phylogenetic clades and showed that ICEs are the most abundant conjugative elements in prokaryotes . Nearly half of the genomes contain a type IV secretion system ( T4SS ) , with larger genomes encoding more conjugative systems . Surprisingly , almost half of the chromosomal T4SS lack co-localized relaxases and , consequently , might be devoted to protein transport instead of conjugation . This class of elements is preponderant among small genomes , is less commonly associated with integrases , and is rarer in plasmids . ICEs and conjugative plasmids in proteobacteria have different preferences for each type of T4SS , but all types exist in both chromosomes and plasmids . Mobilizable elements outnumber self-conjugative elements in both ICEs and plasmids , which suggests an extensive use of T4SS in trans . Our evolutionary analysis indicates that switch of plasmids to and from ICEs were frequent and that extant elements began to differentiate only relatively recently . According to the present results , ICEs are the most abundant conjugative elements in practically all prokaryotic clades and might be far more frequently domesticated into non-conjugative protein transport systems than previously thought . While conjugative plasmids and ICEs have different means of genomic stabilization , their mechanisms of mobility by conjugation show strikingly conserved patterns , arguing for a unitary view of conjugation in shaping the genomes of prokaryotes by horizontal gene transfer .
Prokaryotes , both bacteria and archaea , have remarkably plastic genomes because they can acquire genetic information at high rates by horizontal transfer from other prokaryotes . This allows them to adapt rapidly to specific niches and results in large differences in gene repertoires among closely related strains [1]–[3] . Three major mechanisms allow gene transfer: natural transformation , transduction and conjugation . Natural transformation is controlled by the receptor cell and mostly implicated in DNA transfer within species leading to allelic recombination [4] . Both transduction and conjugation are more invasive , since the recipient has little control over both processes which change gene repertoires dramatically and allow transfer between distant lineages . Conjugation , in particular , can lead to the transfer of very large fractions of genomes and even entire chromosomes in one single event [5] , [6] . Several studies suggest that conjugation is the preponderant mechanism of horizontal gene transfer between distant lineages [7] , [8] . Such cross-clade transfer might be at the origin of the rapid spread of antibiotic resistance through most major lineages of bacterial pathogens in the last few decades [2] , [9] , [10] . Conjugative elements are also known for encoding other adaptive traits such as toxins , transporters and many secreted proteins including enzymes of industrial interest [11] , [12] . Conjugation involves a relaxase ( MOB ) , which is the key element in a multiprotein DNA-processing complex , a type IV secretion system ( T4SS ) and a type IV coupling protein ( T4CP ) ( reviewed recently in [13] ) ( Figure 1 ) . The relaxase binds and nicks the DNA at the origin of transfer . The relaxase-DNA nucleoprotein complex is then coupled to the T4SS by the T4CP . The T4SS translocates the relaxase-DNA complex through the membrane of the donor cell delivering it to the cytoplasm of the recipient cell . The T4SS is a large complex of proteins spanning from the cytoplasm to the extracellular space , including an ubiquitous ATPase ( VirB4 or TraU ) , a set of mating-pair formation ( MPF ) proteins ( from a minimum of 12 to more than 20 ) that elaborate the transport channel , as well as a pilus that allows the attachment to the recipient cell and thereby the translocation of the relaxase-DNA complex . Protein homology of MPF genes allowed the clustering of all known proteobacterial T4SS into four groups [14] , named after one model of each group , the vir system of the Ti plasmid ( MPFT ) [15] , the F plasmid ( MPFF ) [16] , the R64 IncI plasmid ( MPFI ) [17] and the integrative conjugative element ( ICE ) ICEHIN1056 ( MPFG ) [18] . For other taxonomic clades , the genes associated with the T4SS , apart from VirB4 , the T4CP and the relaxase , are poorly characterized . Once the relaxase-DNA complex is in the recipient cell , the T4CP translocates the full DNA and the relaxase ligates the two ends of the DNA into a single circular molecule . At the final stage of the conjugation process , the element exists in ssDNA state in both cells and the hosts' replication machineries are recruited to replicate them to reconstitute the original dsDNA molecules [13] . A self-transmissible conjugative element must thus comprise three components: the relaxase , the T4CP , and the T4SS . While most described conjugative systems are located in plasmids , the last decade has seen a growing interest in conjugative systems integrated in chromosomes ( ICEs ) , which include the so-called “conjugative transposons” or “integrated conjugative plasmids” [19] , [20] . The conjugation of ICEs is poorly documented but is generally assumed to resemble that of plasmids , with a preliminary step of excision with circularization and an additional final step of re-integration in the genome ( Figure 1 ) . For these steps , some ICEs encode supplementary genes resembling those of temperate phages , e . g . integrases of the lambda tyrosine-recombinase family [21] , [22] , which have led to their classification as “phage-like elements” . Other ICEs integrate in the chromosome , or excise from it , by using other tyrosine-recombinases [23] , [24] , DDE-transposases [25] , serine-recombinases [26] or by homologous recombination with chromosomal copies of transposable elements [27] , [28] . Contrary to plasmids , there is little evidence of ICEs replication in cells ( but see , for instance , [29] ) so it is often assumed that they cannot be stably maintained in an extra-chromosomal state [20] . While ICEs , by definition , are conjugative elements , many other mobile elements populate prokaryotic genomes . Integrative mobilizable elements ( IMEs ) do not code for a T4SS but can use one coded by other elements just like mobilizable plasmids [30] , [31] . Genomic islands are integrative elements that can be mobilized by conjugation when they have compatible origins of transfer [32] or by integrating in conjugative elements [33] . Yet , like for non-mobilizable plasmids , the exact mechanism of mobility of most of these elements remains obscure [34] . Finally , some chromosomes encode T4SS that are not involved in conjugation but in other processes such as protein secretion and natural transformation [35] , [36] . It has been suggested that these T4SS probably derived from ancestral conjugative systems [37] . The presence of an ICE can in principle be assessed by the observation of a conjugative T4SS within a chromosome . Since it is presently known how to class transmissible plasmids [14] , it should be possible to do the same for ICEs . There are however important difficulties in this process . First , it is not known if all ICEs conjugate like plasmids . The family of conjugative elements of ICEHin1056 was proposed to exist exclusively as ICEs [18] . Even though a few rare conjugative plasmids of this family were subsequently identified [14] , there might be other families exclusive to ICEs . Second , the presence in chromosomes of T4SS not used for conjugation may obscure the identification of conjugation systems if no relaxase is present at the locus . Third , the most conserved proteins involved in conjugation are ATPases . Finding them in genomes and distinguishing them from other ATPases is challenging . Fourth , ICEs that are non-functional because of pseudogenization might be difficult to distinguish from functional elements . In this work we present the results of a scan of prokaryotic genomes for conjugative systems in plasmids and chromosomes and the subsequent analysis to understand their functional and evolutionary relations . Previous studies provided precious insights of ICE evolution by analyzing closely related ICEs [38] , [39] . Here we take the complementary approach and aim at the bigger picture . By searching for conjugative elements in all sequenced chromosomes and plasmids , we quantify the number of ICEs , characterize their diversity in terms of mechanism and phylogenetic representation , and study their evolution at the light of that of conjugative plasmids .
If our assumption that ICEs and plasmids use similar conjugation machineries is correct we should be able to identify ICEs by using the sequence information of a large panel of proteins involved in plasmid conjugation . Previously , we carried out an analysis of plasmids by performing iterative similarity searches followed by protein clustering [14] , but this approach poses problems of lack of convergence when using chromosomal data . Profile hidden Markov models ( HMM ) can retrieve more distant similarities than BLAST and do not pose as many problems of convergence as PSI-BLAST [40] . We therefore built protein profiles of the major representatives of the conjugation machinery using the information on proteins used in plasmid conjugation: relaxases ( MOB types ) , T4CPs and VirB4s ( see Materials and Methods ) . Additionally , we built profiles for proteins characteristic of each of the 4 types of T4SS found in plasmids of proteobacteria ( see Materials and Methods ) . By using this approach , we did not need to use ad hoc methods to separate the ATPases ( VirB4 and VirD4 ) because the hits of their profiles did not cross-match significantly . HMM protein profiles do not use the information of the new hits to change the protein profiles so they can be used reproductively upon change of the databank and independently of any reference dataset . We will soon make all the protein profiles available to the community by a web server . All the results of this scan are available in Dataset S1 , including composition of all hits , accession numbers , gene names ( with synonyms ) , and location in the replicons . We scanned 3 , 489 replicons for the presence of conjugative systems , including 1 , 207 chromosomes , 891 plasmids sequenced along with chromosomes ( PSC ) and 1 , 391 plasmids that were sequenced alone , i . e . without the host chromosome ( s ) ( PSA ) . Our analysis identified over 7000 proteins with significant matches ( Figure 2 ) . Close co-occurring hits were clustered together and this allowed the identification of putative T4SS . When a MOB and a T4CP neighbored a T4SS this locus was regarded as a conjugative system ( see Materials and Methods ) . Conjugative loci in chromosomes were named ICEs . Our present results with plasmid sequences were very similar to those previously published [14] ( see Methods ) . The comparison between chromosomes and the accompanying PSC plasmids allows an unbiased quantitative comparison between plasmids and ICEs in that both sets reflect the same sampling . Hence , we will show the results on all plasmids only when explicitly mentioned , otherwise all results concern the PSC plasmids . If we are correct in assuming homology between conjugative systems in ICEs and plasmids , we should be able to detect a large fraction of ICEs in prokaryotic genomes using information on proteins involved in plasmid conjugation . Indeed , we checked previously published lists of experimentally studied ICEs [20] , [41] and were able to retrieve all for which experimental validation of mobility by self- conjugation and full sequence data were available ( Table S1 ) . Two mobilizable elements were missed in our analysis: Tn4555 [42] and NBUI1 [43] . These elements are mobilizable and have similar relaxases with no homolog in our genomic bank; as such , we did not include them in our study . We were thus able to identify all model ICEs in firmicutes ( e . g . Tn916 ) , bacteroides ( e . g . CTnBST ) and proteobacteria ( e . g . SXT , ICEHin1056 , ICEclc ) . The only exceptions were ICEs of actinobacteria that use FtsK-based transport systems within multi-cellular assemblages ( e . g . pSAM2 ) [44] , [45] . These systems transport dsDNA not ssDNA between cells within mycelia of some actinobacteria . As they don't contain relaxases neither T4SS these systems were not expected to be found in our analysis . Overall , these results indicate that using the accumulated body of knowledge on plasmid conjugation we can extensively identify and class ICEs . Within the analyzed 1 , 124 complete prokaryotic genomes , which included the 1 , 207 chromosomes and their accompanying 891 PSC plasmids , we identified 335 putative ICEs and 180 putative conjugative plasmids . Additionally , we found 402 relaxases in chromosomes lacking neighboring T4SS . If these correspond to IMEs , the estimate of the ratio of conjugative over mobilizable elements both in chromosomes ( ICE/IME = 0 . 83 ) as in PSC plasmids ( ratio = 0 . 96 ) is approximately similar and lower than 1 , suggesting that mobilization in trans is frequent in natural populations . Naturally , mobilization in trans of an IME can only occur if the host genome encodes somewhere else a T4SS with the ability to build a compatible conjugative pilus . The frequency with which conjugative systems exist in prokaryotic cells is high . Overall , almost half of the genomes contain a T4SS , either in an ICE ( 18% ) , a conjugative plasmid ( 12% ) or a T4SS without an accompanying relaxase ( 18% ) . Unfortunately , at this stage we cannot infer computationally if a given T4SS can mobilize another given mobilizable element in trans . Furthermore , we do not really know how often a T4SS is capable of mobilizing DNA in trans . Several T4SS that lack neighboring MOB and are involved in protein transport have this ability , e . g . the dot/icm system of Legionella pneumophila [46] . The Bartonella tribocorum T4SS can also complement deficiencies in the conjugative system of plasmid R388 [47] , [48] . Further experimental work is required to assess the generality of these observations . An IME or mobilizable plasmid arriving at a cell has a probability of 30% of finding a conjugative element at the time of arrival . Naturally , given the high flux of these elements , if the mobilizable element remains long enough in the cell it will likely co-reside with a conjugative element . The probability that a cell harbors a conjugative element at a given moment depends on genome size ( Figure 3 ) . Small genomes rarely contain ICEs or conjugative plasmids , whereas large genomes often do so . This fits the common assumption that prokaryotes with smaller genomes engage more rarely in horizontal transfer . Nevertheless , several small genomes contain conjugative systems , as previously described for Rickettsia [49] and tenericutes [50] . Some T4SS have been present in the genomes of rickettsiales for a long period of time and their genomic organization is scattered , i . e . conjugation-related genes are not necessarily found in one single cluster [51] . We used the available literature to annotate these cases [51] . Analysis of the genomes of other proteobacteria suggests that this situation is relatively rare and that most conjugative systems are coded at one single cluster , which is required to ensure mobility of the locus upon transfer to a new recipient cell . Using the method explained above we could make the first large-scale quantification of the abundance and diversity of ICEs among prokaryotes . We found ICEs in all bacterial clades where occurrences have been described previously , including the five major branches ( α , β , γ , ε , δ ) of proteobacteria , the bacteroidetes , and the firmicutes ( Figure 4 ) . In bacteroidetes , as well as in α- and β-proteobacteria , more than 50% of the available genomes contain at least one ICE . The other groups show relatively fewer ICEs , with these elements present in less than 30% of the genomes . We only found one ICE in archaea—in Aciduliprofundum boonei—and one conjugative PSC plasmid—plasmid pNG500 in Haloarcula marismortui . Yet , we found both in chromosomes and in plasmids many bona fide homologs of VirB4 , often associated with a T4CP . It is possible that unknown relaxases exist in archaea , since conjugative plasmids are known in this clade and were included in our dataset [52] , [53] . In actinobacteria , we found many MOB , but few T4SS or T4CP , both in plasmids and chromosomes . The rarity of T4SS in this clade could be explained by the alternative modes for DNA transfer within mycelia . Yet , elements in actinobacteria that are classed as mobilizable because they encode a relaxase presumably need a T4CP and T4SS to transfer as we know of no experimental evidence of functional interactions between relaxases and FtsK-based systems . Therefore , the number of conjugative systems in the clade still seems surprisingly low . Low sequence similarity is unlikely to be responsible for the lack of identifiable T4SS in actinobacteria since we can uncover distant homologs of VirB4 in all major clades of prokaryotes and we can even indentify by sequence similarity paralogous functionally unrelated ATPases . We found ICEs and conjugative plasmids in cyanobacteria . We had previously failed to do so [14] , but the new protein profiles we built are more sensitive and show that this clade also contains conjugative systems both in plasmids and in chromosomes ( to be published elsewhere ) . Additionally , we found ICEs in acidobacteria , in fusobacteria and one conjugative plasmid in chlorobi ( pPAES01 ) . In short , all clades with a significant number of sequenced genomes contain conjugative systems showing the ubiquity of this DNA transfer mechanism in the prokaryotic world . While few ICEs have been experimentally studied in terms of conjugation , we found large numbers of them in the genomes of prokaryotes . Importantly , we found 86% more ICEs than conjugative plasmids ( Figure 2 and Figure 3 , p<0 . 001 , binomial test ) . It should be emphasized that this is contrary to the expected if our method was biased , since we use information on plasmid conjugation systems to identify ICEs , not the other way around . Conjugative plasmids have been most thoroughly studied in proteobacteria whereas ICEs were discovered first in bacteroidetes and in firmicutes [54] . There is thus often a tendency to consider that conjugative plasmids are prevalent in proteobacteria and ICEs in the other two clades . Indeed , the preponderance of ICEs over conjugative plasmids varies between clades ( Figure 4 ) . In firmicutes and bacteroidetes ICEs do represent respectively 84% and 81% of all conjugative elements , while in proteobacteria ICEs only slightly outnumber conjugative plasmids . We identified no conjugative PSC plasmid within actinobacteria . Cyanobacteria were the only clade for which we found more conjugative plasmids ( 11 ) than ICEs ( 4 ) . While we found conjugative plasmids in several different genera of cyanobacteria ( Cyanothece , Nostoc , Anabaena , Acaryochloris ) , we only found ICEs in Cyanothece . Besides confirming the preconception that , in bacteroidetes and firmicutes , ICEs outnumber conjugative plasmids , we show that prevalence of ICEs over conjugative plasmids is almost general . ICEs might be more abundant in the analyzed genomes because of sequencing biases . First , certain sequencing projects might have ignored the sequencing of plasmids . Second , if ICEs are more stable in genomes than plasmids , bacterial culturing might induce a bias towards the over-representation of ICEs . In any case , our results clearly demonstrate that ICEs are a significant fraction of all conjugative elements in prokaryotes . We next investigated if conjugation systems in plasmids and ICEs are of similar types . For this , we divided the conjugative systems found in proteobacteria into the four different archetypes: MPFF , MPFT , MPFI and MPFG . MPFT conjugative pili are short and thick , mate essentially in solid media and include elements such as CTn4371 [55] and MlSymR7A [56] . MPFT are equally distributed , in relative terms , among conjugative plasmids and ICEs ( Figure 4 ) . Interestingly this is not the case for the other mating types that show significantly different frequencies among plasmids and ICEs ( p<0 . 001 , χ2 test ) . MPFF , which have long flexible pili , mate efficiently in solid and liquid , and include the SXT family [39] . These pili are rare among ICEs , whereas they are the second most frequent type in plasmids . On the other hand , the MPFG pili have only been described to mate in solid surfaces [18] and are found essentially among ICEs , e . g . the clc or pKLC102 elements of Pseudomonas [57] , [58] . We found few MPFI systems in plasmids and even fewer in chromosomes . The latter were essentially found in the dot/icm systems of Legionella and Coxiella , where only the latter encode a MOB close to the T4SS . As a result , MPF types known to mate in liquid are under-represented in ICEs relative to plasmids . We then analyzed the co-occurrence of ICEs in a given genome . Conjugative plasmids rarely code for two T4SS and , when they do , they tend to have multiple MPFT [14] . We found 73 chromosomes encoding multiple ICEs and 32 genomes containing multiple conjugative plasmids . We found all MPF types in multiple copies , except for MPFI in chromosomes and MPFG in plasmids , but this could result from their rarity . A striking previously described case concerns Orientia tsutsugamushi genomes , which contain a large number of conjugation-related genes in clusters that for the most part present evidence of pseudogenization [59] . It is unclear in this case how many effective conjugation systems are encoded in the chromosome , but we could identify 5 complete clusters of MPFF . In our dataset the largest number of intact ICEs ( seven ) was found in Bordetella petrii DSM 12804 ( which comprises both MPFT and MPFG elements ) and in the firmicute Clostridium difficile 630 . The genome of Agrobacterium vitis S4 contains the largest number of conjugative plasmids ( 4 , all MPFT ) . In summary , conjugative systems in chromosomes and plasmids co-occur and sometimes in large numbers . This is expected , since each ICE is an independent element . This suggests that different types of T4SS can co-exist in a functional state in the cell . Discrimination between T4SS could be achieved by the specificity of the T4CP . Alternatively , one could imagine that in some cases conjugative elements also use T4SS encoded in trans . One major surprising finding of this work was the high number of T4SS lacking nearby relaxases and thus not classed as ICEs ( Figure 2 ) . We can explain these findings in three different ways: as an artifact , as an indication of unknown relaxases or as evidence of high frequency of T4SS not involved in conjugation . Artifacts can occur in our analysis in several ways . First , one might have found many false positives in the detection of VirB4 . This is unlikely because in proteobacteria ( 33% of MOBless T4SS ) , we find MOBless virB4 genes neighboring other type-specific genes of T4SS ( 92 out of 109 clusters ) . This shows that in the vast majority of cases the virB4 assignment in MOBless T4SS is correct . In the 17 remaining cases we almost always find at least one T4SS specific gene neighboring the MOBless virB4 gene ( 16 out of 17 cases ) , but not enough to make it a valid cluster , suggesting that these loci correspond to inactive T4SS ongoing genetic degradation . Second , we might be failing to identify a large number of homologous T4CP or MOB in conjugative systems and this might lead to the misclassification of these clusters as MOBless T4SS . Yet , this does not fit the remaining observations: that MOBless T4SS are much more abundant in chromosomes than in plasmids and that we are able to identify VirB4 , T4CP and MOB in clades distant from proteobacteria . All these pieces of evidence advocate against the hypothesis that the large number of MOBless T4SS is a consequence of methodological artifacts . We showed above that the abundance of ICEs and conjugative plasmids depends strongly on genome size and that small genomes are practically devoid of conjugative systems ( Figure 3 ) . The distribution of MOBless T4SS is very different since these elements are abundant in small genomes and their frequency practically does not change with genome size ( Figure 3 ) . Small genomes tend to correspond to bacterial pathogens , and many of these are known to use T4SS to secrete proteins into the host cells for their subversion . T4SSs used for protein transport , as opposed to conjugation , have been described in strains of Bartonella , Brucella , Bordetella , the Legionellales , Helicobacter , and the Rickettsiales [46] , [60]–[64] . Out of the 109 MOBless T4SS in proteobacteria , 77 are indeed found among these clades reinforcing the speculation that MOBless T4SS do often correspond to protein secretion systems . If so , this would include MPFF elements , not known before to be recruited for that , and several clades of environmental prokaryotes , which so far were not known to carry such protein transport systems . We have not yet done the precise delimitation of ICEs in genomes . Yet , we already carried out a preliminary analysis of the integrases co-localizing with the T4SSs to check for differences between ICEs and MOBless T4SSs . As described above , most ICEs include a tyrosine or serine recombinase and only a minority of well-characterized elements integrate by other means . Therefore the conjugation systems we identify in genomes are expected to have neighboring integrases . Co-localization of MOBless T4SS with integrases is expected under a number of situations: ( i ) if the protein secretion system is in a mobile element itself , as is frequently the case for T3SS [65] , [66]; ( ii ) if it represents an element undergoing genetic degradation is which the relaxase was inactivated but not the integrase nor the T4SS genes; ( iii ) or if the genes encoding the T4SS happen to be near an unrelated mobile element . Yet , since integration is strictly necessary for ICE , we did expect to find more integrases neighboring the T4SS of ICE than those of MOBless T4SS . Using the PFAM domains ( PF00589 for the tyrosine recombinases; PF07508 and PF00239 for serine recombinases ) , we found that within proteobacteria 87% of the ICEs and 50% of the MOBless T4SSs have a neighboring integrase distant no more than 60 genes from the conjugation-related genes . The difference is highly significant ( p<0 . 001 , binomial test ) and suggests that MOBless T4SS are indeed intrinsically different from ICEs . We then analyzed the other clades to see if their MOBless T4SS were more frequently neighboring integrases since that could be the sign of the presence of unnoticed relaxases in these poorly studied genomes . We found that 90% of the ICEs and 56% of the MOBless T4SS in these other clades contain an integrase , within a distance of less than 60 genes , which is very close to the values found in proteobacteria . These results are consistent with intrinsic functional differences between the T4SS of ICEs and the MOBless T4SS . Finally , we analyzed the co-occurrence of relaxases with T4SS in ICEs ( Figure 5 ) . Many MOB/MPF combinations are found among conjugative elements . This suggests that the MOB and MPF modules can shuffle over long evolutionary distances . However , there are some expected relevant associations between MPF and MOB , e . g . MPFT with MOBP or MPFF with MOBF as suggested by their frequent association in conjugative plasmids [14] , [67] . Among less studied groups , MOBB is specific of bacteroidetes and MPFG only use one type of relaxase , MOBH ( 58 cases in chromosomes and 2 in plasmids ) . It is therefore possible that some sub-types of T4SS use yet unknown relaxases . In particular , it is tempting to suggest that this is the case in archaea where we find very few relaxases . As conjugation is an agent of horizontal transfer , and some very broad range plasmids have been described , one might expect little concordance between the phylogeny of VirB4 and that of the 16S rDNA . Yet , in plasmids it was found that large clades within bacteria corresponded to large clades in VirB4 with little apparent transfer between domains [14] . To check that similar results are still valid when using the information on ICEs and the new data on cyanobacteria and bacteroidetes , we made a phylogenetic analysis of the only ubiquitous element of T4SS: VirB4 ( see Materials and Methods ) . This tree was built using a non-redundant subset of proteins and shows several remarkable things ( Figure 6 ) . First , MPF classification within proteobacteria remains meaningful , since the four types ( F , G , I , T ) are found in four monophyletic groups that exhibit strong support values . Both cyanobacteria and bacteroidetes form monophyletic clades , suggesting lack of significant transfer of conjugative systems between these and other clades since their divergence . This is consistent with their specific relaxases: MOBV is mainly found in cyanobacteria and MOBB is only found in bacteroidetes ( Figure 5 ) . Firmicutes and actinobacteria ( FA in Figure 6 ) , on one side , and firmicutes , actinobacteria , tenericutes and archaea ( FATA in Figure 6 ) , on the other , form the two remaining clades , but inside these groups one still finds mostly monophyletic clades . Thus , while elements propagating by means of conjugation systems are the most promiscuous known agents of horizontal transfer , the evolution of these systems does not show signs of frequent transfer of mobility backbone modules between types . The existence of every type of T4SS in both chromosomes and plasmids of proteobacteria , albeit at very diverse frequencies , suggest that conjugative plasmids and ICEs have exchanged T4SS along their evolutionary history . To test this , we marked in the phylogenetic tree of VirB4 the respective genes that were encoded in chromosomes and in plasmids . An example for the MPFT is presented in Figure 6 . If ICEs were derived from conjugative plasmids , then one would expect large monophyletic clades of ICEs , indicating creation of the ICE , and clades devoid of ICEs , indicating lack of creation within the lineage . Furthermore , one would see evidence of plasmids as ancestral traits in the tree . If conjugative plasmids were derived from ICEs then the opposite picture should arise . The data presented in this work is not suggestive of any of these scenarii . Conjugative plasmids and ICEs ( or chromosomal T4SS lacking nearby MOB ) are intermingled along the whole tree ( data not shown ) . At closer phylogenetic distances , i . e . the comparisons including the 15% of the tree closest to the tips , we do observe that the most similar VirB4 of an ICE is in general a VirB4 from another ICE and the reciprocal occurs for conjugative plasmids ( Figure 7 ) . We found 5 pairs of VirB4 encoded in different types of replicons that are distant by less than 1% in the tree . In three of the cases they are in a chromosome of one species and in a plasmid of another species within enterobacteria . Hence , at short evolutionary distances , plasmids and ICEs are indeed distinguishable . Yet , at slightly larger distances this signal quickly disappears and the ICEs and conjugative plasmids are perfectly mixed . The resulting picture is that one finds ICEs resembling much more some conjugative plasmids than other ICEs . For the most part of the evolutionary history of conjugation , ICEs have probably been converted to and from plasmids . As conjugative systems of both plasmids and ICEs shared most of their evolutionary history , they should be regarded as one and the same .
In this work we present the results of a semi-automatic method to detect conjugation-associated mobility systems not only in plasmids but also in chromosomes . This analysis paves the way for a systematic quantification of conjugation systems in prokaryotic genomes and in metagenomic data . When coupled with the detection of integration junctions ( work in progress ) it will also allow to analyze the gene repertoires of ICEs , and evaluate the evolutionary interplay between ICEs , conjugative plasmids and phages . Therefore , our present results only concern the C part of ICEs and conjugative plasmids . In the case of ICEs , this only gives an indication of their position in genomes , but not of their limits . ICEs can be very large ( more than 500 kb for ICEMlSymR71 of Mesorhizobium loti [56] ) . Since the size of the C part is more or less constant , the variations in ICEs size will reveal the cargo genes they contain , much like for plasmids . The next step of this work will thus be to delimit ICEs within genomes in order to study the genes they carry . Our quantitative analysis shows that conjugative systems are more likely to be found in larger genomes . This fits the current assumption that larger genomes engage more frequently in horizontal gene transfer . The study of the cargo genes will help to quantify and qualify the role of ICEs in the functional diversification of prokaryotes . Our analysis of MOBless T4SS in proteobacteria strongly suggests that many of these are involved in protein transport and not in conjugation . First , besides the archaeal clade , the relative frequency of these elements is similar in well-studied and poorly studied clades , suggesting this is not a methodological bias . Second , small genomes show abundant MOBless T4SS but practically no conjugative systems . This is in agreement with the utilization of MOBless T4SS in small genomes of pathogenic bacteria and in disagreement with the hypothesis that MOBless T4SSs are ICEs with unknown relaxases . Third , ICEs contain a significantly larger fraction of neighbouring integrases than MOBless T4SSs , both in proteobacteria and in the other clades . Fourth , a large fraction of the MOBless T4SSs in proteobacteria indeed corresponds to experimentally verified protein secretion systems or to orthologous systems in closely related genomes . If most MOBless T4SSs are indeed protein secretion systems , our results suggest that these systems are more frequent than previously suspected . Unexpectedly , many environmental bacteria have MOBless T4SSs , e . g . Caulobacter , Thermoanaerobacter or cyanobacteria . Protein secretion systems in these bacteria might be involved in antagonistic interactions with grazing protozoa , as was proposed for T3SS [68] . They could also be involved in protein transport , not associated with conjugation , or signaling interactions with other bacteria . To the best of our knowledge these functions have not yet been proposed for MOBless T4SSs . However , since conjugation is a form of protein secretion between prokaryotic cells , the evolution of a T4SS towards protein secretion between prokaryotes seems simpler than the evolution required to some of its other known functions , such as evolution into protein secretion into eukaryotic cells , or DNA uptake in H . pylori . Interestingly , Agrobacterium tumefaciens vir system exports both proteins and DNA at the time of conjugation of T-DNA into plants [69] . Protein secretion by MOBless T4SS might therefore be simple to evolve from a conjugative system . We find that ICEs and conjugative plasmids use similar T4SS , but at different frequencies , especially concerning MPFF , which are more abundant in plasmids , and MPFG , which are present almost exclusively under the form of ICEs . The reasons for these preferences are puzzling and might be clarified by a better understanding of the conjugation mechanisms of Conjugation of ICEs is often assumed to take the same path as that of plasmids , once the element is excised and circularized . Differences in the two processes at the initial or final stages of conjugation might explain why one finds an enormous over-representation of ICEs in some clades whereas in cyanobacteria we find more conjugative plasmids . Looking at the evolutionary relationships between ICEs and conjugative plasmids , we observed a close interplay between them in that the deepest clades in the VirB4 tree contain both types of elements . This strongly suggests that plasmids often become ICEs , and/or vice-versa . A plasmid might become an ICE upon acquisition of an integrase , e . g . from a phage , a genomic island or another ICE , although this is not strictly necessary , as documented in the Introduction . In fact , many plasmids contain some type of recombinase that could mediate site-specific integration or some type of DNA repeats that might allow integration by homologous recombination . Conversely , an ICE might become a plasmid upon acquisition of a REP system . Interestingly , some ICEs do contain REP systems ( e . g . ICEBs1 [29] ) . In conclusion , our results suggest that plasmids and ICEs might be just the two faces shown by a very similar type of element . One can speculate that plasmids disseminate to bacterial species in which they can replicate and to others in which they cannot . If the selection pressure for the presence of the element is high enough , the preservation of the element might be favored by its integration in the chromosome . This process can occur forwards and backwards so that we do not observe a terminal specialization between both types of element for some time . But certainly some ICEs end up stabilizing as chromosomal structures that remain as such for evolutionary long periods of time . What are the circumstances that drive them one way or another is a relevant question that remains to be answered .
Data on complete prokaryotic chromosomes and plasmids was taken from Genbank Refseq ( ftp://ftp . ncbi . nih . gov/genomes/Bacteria/ ) . This data included 1 , 207 chromosomes , 891 plasmids associated with the chromosomes and 1 , 391 plasmids that were sequenced independently . We used the annotations of the Genbank files , removed all pseudogenes and all proteins with inner stop codons . The data on proteins of plasmid conjugation systems were taken from [14] . The following protein families were considered . Relaxases ( see [67] for a description of each family , except MOBB , and MOBT ) : MOBT ( corresponding to protein Q47728 of Enterococcus faecalis conjugative transposon Tn916 Orf20 [70] ) , MOBB ( corresponding to mobilization protein B of Bacteroides thetaiotaomicron VPI-5482 [71] ) , MOBV , MOBQ , MOBP , MOBH , MOBF , MOBC . Major ATPases: VirB4 and TraU . T4CP: VirD4 . MPFF: TraLEKVCWUcNHD . MPFT: VirB3689 . MPFI: TraIKLMNPQRWY . MPFG: p31 , p35 , p41 , p44 , p51 , p52 . We took the data published previously [14] , and for each protein family we repeated the analysis in that paper , i . e . we did PSI-BLAST of each key protein on chromosomes and plasmids and clustered the resulting proteins by MCL . This approach failed to produce good results because PSI-BLAST often did not converge in the searches made in chromosomes . For example , the searches for ATPases tend to put together many different ATPases of prokaryotes rendering their accurate separation difficult . We have thus used a different approach . For each protein family uncovered in our previous analysis of plasmids we did the following: ( i ) We carried out a multiple alignment with MUSCLE [72] and built a phylogenetic tree using PHYML [73] . With these two pieces of evidence we removed the very few cases of extreme divergence , the proteins that were too short and the proteins that were too long ( typically false positives , fusions or fissions of proteins motivated by sequencing errors or pseudogenization ) . ( ii ) We built multiple alignments with MUSCLE of the selected proteins , checked manually the alignments and trimmed them to remove poorly aligned regions at the edges , if relevant . The C-terminal regions of MOB alignments were systematically trimmed , as suggested previously [67] . The alignment of the T4CP family showed two conserved regions separated by a region that aligned poorly . As a result , we split this alignment in two and made separate profiles with the two conserved regions . In general the two profiles were found together but only the second was found to be present in all conjugative elements apart some of those of the Tn916 family . These latter T4CP showed poor matches to the general T4CP profiles and we built one specific profile for this family . ( iii ) We used HMMER 3 . 0 to build protein profiles from the manually curated multiple alignments . We scanned the plasmid and chromosome sequences using the protein profiles and hmmsearch from HMMER 3 . 0 ( http://hmmer . janelia . org/ ) . Since this version of the program only does local alignment , we filtered the hits using a criterion of alignment size . In particular , we ignored all proteins that had a hit to the protein profile covering less than half of its length . Furthermore , we only kept for further analysis the proteins with at least one hit to the profile with a c-value <0 . 01 . We then checked that the profiles matched significantly all the proteins in the protein families that originated the profile itself . Having thus obtained the hits of each gene in each replicon we analyzed them for cross-hits , i . e . proteins that matched significantly more than one profile . Some protein families with evidence of significant , albeit often weak , sequence similarity include VirB4 and TraU , VirB4 and T4CP and several of the MOB families . Proteins that hit significantly two families showed much better score to one family than to the other and we classed them using this information . A particular case concerns the hits between VirB3 and VirB4 , since we often found these proteins as a fusion in one single peptide among MPFT . In this case we matched the two profiles and accounted the VirB4 profile for the possible presence of a T4SS and the VirB3 to its classification as an MPFT . With the list of hits of each protein family we identified the putative conjugation loci . For this , we mapped the hits in replicons and clustered them together when they were encoded in the same region ( less than 60 genes apart ) . Clusters of hits were defined transitively , i . e . they are successions of hits spaced by less than 60 genes . In practice , the clusters tend to be much smaller because the T4SS genes are coded in one or a few contiguous operons and the T4CP and the MOB also tend to be close . However , since ICE integrate from a circular form in chromosomes , the integration can lead to positioning of hits in opposite ends of the element , sometimes separating the MOB from the T4SS . We therefore checked by hand all occurrences of pairs of clusters that were between 60 and 100 genes apart . In the few cases where the clusters had complementary genes and where intervening genes did not correspond to prokaryotic housekeeping functions we put the clusters together . Protein export T4SS of Rickettsiales have been conserved for some time in these genomes and their genes have been scattered on the chromosome [51] . These clusters were reconstructed manually . We finally classified proteobacteria clusters using the 4 MPF types previously described [14] . A type is attributed to a cluster if the cluster contains at least 5 , 4 , 4 , and 3 type-specific genes respectively for MPFF , MPFG , MPFI , MPFT . We made our initial analysis with HMMER 2 . 0 and then shifted to 3 . 0 because it is much faster . Yet , HMMER 3 . 0 only does local alignment and we tested if the HMMER 3 . 0 hits matching more than 50% of the domain were the same as the hits of the glocal approach in HMMER 2 . 0 ( alignment local on the protein and global on the profile ) . We found that over 95% of the hits were retrieved by both approaches independently of the protein . We then compared the results obtained on plasmids with this method and those from the previous study using PSI-BLAST+MCL [14] . Among the 250 conjugative plasmids that were previously identified , 241 have also been found by our new approach . There are some MOBs found by BLAST for which the HMMER local alignment was too short to pass our length criterion . The new procedure detected 97 conjugative plasmids that were missed by the previous one , e . g . due to the new hits among cyanobacteria that our previous approach missed . We made two types of phylogenetic analyses: ( i ) As a control for the presence of spurious elements in protein families . In this case we did maximum likelihood trees based on JTT model with PHYML [73] . ( ii ) To build the phylogenetic tree of VirB4 . In this case to obtain a more accurate phylogeny we first aligned the proteins using MUSCLE [72] with default parameters as implemented in SeaView [74] . We removed all columns of the alignment containing more than 80% of gaps , and all the sequences that were more than 90% identical with another one in the alignment . We then tested the different protein models implemented in RAxML 7 . 2 . 7 [75] and chose the GTRGAMMA model since it gave the best likelihood . We built the tree by executing 100 replicates and keeping the best; we inferred 1000 bootstrap trees to obtain the confidence values of each node . | Some mobile genetic elements spread genetic information horizontally between prokaryotes by conjugation , a mechanism by which DNA is transferred directly from one cell to the other . Among the processes allowing genetic transfer between cells , conjugation is the one allowing the simultaneous transfer of larger amounts of DNA and between the least related cells . As such , conjugative systems are key players in horizontal transfer , including the transfer of antibiotic resistance to and between many human pathogens . Conjugative systems are encoded both in plasmids and in chromosomes . The latter are called Integrative Conjugative Elements ( ICE ) ; and their number , identity , and mechanism of conjugation were poorly known . We have developed an approach to identify and characterize these elements and found more ICEs than conjugative plasmids in genomes . While both ICEs and plasmids use similar conjugative systems , there are remarkable preferences for some systems in some elements . Our evolutionary analysis shows that plasmid conjugative systems have often given rise to ICEs and vice versa . Therefore , ICEs and conjugative plasmids should be regarded as one and the same , the differences in their means of existence in cells probably the result of different requirements for stabilization and/or transmissibility of the genetic information they contain . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"bacteriology",
"genomics",
"genome",
"evolution",
"heredity",
"genetics",
"molecular",
"genetics",
"biology",
"gene",
"flow",
"microbiology",
"bacterial",
"evolution",
"genetics",
"and",
"genomics"
] | 2011 | The Repertoire of ICE in Prokaryotes Underscores the Unity, Diversity, and Ubiquity of Conjugation |
A three-stage genome-wide association study recently identified single nucleotide polymorphisms ( SNPs ) in five loci ( fibroblast growth receptor 2 ( FGFR2 ) , trinucleotide repeat containing 9 ( TNRC9 ) , mitogen-activated protein kinase 3 K1 ( MAP3K1 ) , 8q24 , and lymphocyte-specific protein 1 ( LSP1 ) ) associated with breast cancer risk . We investigated whether the associations between these SNPs and breast cancer risk varied by clinically important tumor characteristics in up to 23 , 039 invasive breast cancer cases and 26 , 273 controls from 20 studies . We also evaluated their influence on overall survival in 13 , 527 cases from 13 studies . All participants were of European or Asian origin . rs2981582 in FGFR2 was more strongly related to ER-positive ( per-allele OR ( 95%CI ) = 1 . 31 ( 1 . 27–1 . 36 ) ) than ER-negative ( 1 . 08 ( 1 . 03–1 . 14 ) ) disease ( P for heterogeneity = 10−13 ) . This SNP was also more strongly related to PR-positive , low grade and node positive tumors ( P = 10−5 , 10−8 , 0 . 013 , respectively ) . The association for rs13281615 in 8q24 was stronger for ER-positive , PR-positive , and low grade tumors ( P = 0 . 001 , 0 . 011 and 10−4 , respectively ) . The differences in the associations between SNPs in FGFR2 and 8q24 and risk by ER and grade remained significant after permutation adjustment for multiple comparisons and after adjustment for other tumor characteristics . Three SNPs ( rs2981582 , rs3803662 , and rs889312 ) showed weak but significant associations with ER-negative disease , the strongest association being for rs3803662 in TNRC9 ( 1 . 14 ( 1 . 09–1 . 21 ) ) . rs13281615 in 8q24 was associated with an improvement in survival after diagnosis ( per-allele HR = 0 . 90 ( 0 . 83–0 . 97 ) . The association was attenuated and non-significant after adjusting for known prognostic factors . Our findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct . Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention , early detection , and treatment .
Breast cancers vary greatly in clinical behavior , morphological appearance , and molecular alterations . Accumulating epidemiologic data also suggest that different types of breast cancers have different risk factor profiles and thus might result from different etiologic pathways ( which might be shared by different tumor types or be type specific ) . Notably , age-specific incidence rates [1] and the strength of the associations with known risk factors for breast cancer [2]–[4] differ by clinically important tumor characteristics . Evidence that genetic factors can also influence tumor type is provided by the fact that carriers of highly penetrant mutations in BRCA1 are more likely to be diagnosed with basal breast tumors which are estrogen receptor ( ER ) negative , progesterone receptor ( PR ) negative and HER2 negative [5] . This raises the possibility that other susceptibility loci may also be associated with specific subtypes of breast cancer . We recently performed a two-stage genome-wide association study ( GWAS ) in 4 , 398 breast cancer cases and 4 , 316 controls , followed by a third stage in 21 , 860 cases and 22 , 578 controls from 22 studies , identifying single nucleotide polymorphisms ( SNPs ) in 5 loci associated with breast cancer risk [6] . Of the five loci identified , 4 were within genes or linkage disequilibrium ( LD ) blocks containing genes , including: 1 ) rs2981582 in the FGFR2 gene coding for a receptor tyrosine kinase that plays an important role in mammary gland development [7] , has been implicated in carcinogenesis [8] , and is amplified [9]–[11] or over-expressed [12] in up to 10% of breast tumors; 2 ) rs3803662 in a LD block containing TNRC9 ( also known TOX3 ) and the hypothetical gene LOC643714; 3 ) rs889312 in a LD block containing MAP3K1 and two hypothetical genes ( MGC33648 and mesoderm induction early response 1 , family member 3 ( MIER3 ) ) ; and 4 ) rs3817198 in the LSP1 gene . The fifth SNP ( rs13281615 ) lies on a region of 8q24 that does not contain known genes , but has multiple independent variants associated with prostate [13] , [14] and colorectal [15]–[18] cancer risk . Two additional genome wide association studies also recently identified SNPs in FGFR2 [19] and TNRC9 [20] as breast cancer susceptibility loci . We used the large data resource provided by the Breast Cancer Association Consortium ( BCAC ) to evaluate the hypothesis that tumor characteristics modify the association between breast cancer risk and the low penetrant susceptibility loci recently identified [6] . Determining whether breast cancer risk factors are linked to tumors with specific clinical presentations , pathologic characteristics or mechanisms of development may provide a gateway for developing tailored prevention and early detection strategies . In addition , we evaluated whether these genetic factors affect overall survival after diagnosis of breast cancer , either independently or through their association with tumor characteristics of clinical importance .
Cases and controls were identified through 21 case-control studies in Europe , North America , South-East Asia and Australia , participating in the BCAC ( see Table S1 for description of study populations ) . All of these studies , except for two Germany studies ( Mammary Carcinoma Risk Factor Investogation ( MARIE ) , Genetic Epidemiology Study of Breast Cancer by Age 50 ( GESBC ) ) , were included in our previous publication [6] ( the ORIGO study was previously referred to as LUMCBCS ) , and provided information on disease status , age at diagnosis/enrollment , ethnic group ( European , Asian , other ) , first degree family history of breast cancer and bilaterality of breast cancer . Twenty studies with a total of 23 , 839 invasive breast cancer cases and 26 , 928 controls also provided data on tumor characteristics ( i . e . histopathologic subtype , ER and PR receptor status , tumor size , grade , nodal involvement or stage; see Table S2 for data sources ) . Of these , 800 cases and 655 controls were excluded from analyses because of failures in genotyping quality control ( see details under Genotyping ) or because they belonged to “other” ethnic groups with few subjects . Data on survival after diagnosis was available for 13 , 527 cases participating in 13 studies ( after excluding failures in genotype QC and “other” ethnicities ) , including the USRT study , which lacked data on tumor characteristics ( Table S4 ) . Overall , 95 . 6% of cases and 96 . 7% of controls were of European origin . The mean ages were 56 years for cases and 57 years for controls . The distribution of tumor characteristics by study among the 23 , 039 ( = 23839-800 ) cases from 20 studies with pathology information is shown in Table S4 . Data pertaining to the first tumor detected were used for women with bilateral disease . Data related to histological subtype was available for 86% of the cases ( 18 studies ) , ER status for 74% ( 20 studies ) , PR status for 62% ( 18 studies ) , tumor grade of differentiation for 70% ( 17 studies ) , nodal involvement for 65% ( 17 studies ) , tumor size for 35% ( 9 studies ) , and stage at diagnosis for 68% ( 11 studies ) . A total of 1 , 487 of the 23 , 039 cases were excluded because they had missing information on all tumor characteristics , leaving 21 , 552 cases and 26 , 273 controls of European or Asian origin available for analyses by tumor characteristics . The actual number of cases and controls included in each analysis , after excluding missing genotype data , is shown in the tables . Genotyping procedures have previously been described [6] . All studies genotyped for the five SNPs with the exception of rs3803662 that was not genotyped in the KConFab study , and rs13281615 that was not genotyped in KConFab and MARIE studies . Any sample that could not be scored on 20 percent of the SNPs attempted was excluded from analysis . We also removed data for any center/SNP combination for which the call rate was less than 90 percent . In any instances where the call rate was 90–95 percent , the clustering of genotype calls was re-evaluated by an independent observer to determine whether the clustering was sufficiently clear for inclusion . We also eliminated all of the data for a given SNP/center where the reproducibility in duplicate samples was <97 percent , or where there was marked deviation from Hardy-Weinberg equilibrium in the controls ( p< . 00001 ) . Polytomous logistic regression was used to estimate adjusted odds ratios ( OR ) and associated 95 percent confidence intervals ( CI ) as measures of association between genotypes and risk of breast cancer subtypes ( comparing case subtypes to all controls ) . All models included terms for study ( dummy variables ) . Further adjustment for age at diagnosis/enrollment did not substantially influence OR estimates ( data not shown ) . We estimated the association for each SNP in terms of genotype-specific ORs and per-allele ORs ( assuming a log-additive model ) . Heterogeneity between genotype odds ratios for different tumor subtypes was assessed using logistic regression analyses restricted to cases ( case-only analyses ) with the tumor characteristic as the outcome variable . For tumor subtypes with more than two levels ( i . e . grade , size , stage ) , we used a polytomous logistic regression model constraining the effect size to increase linearly across levels ( e . g . the parameter for grade 3 vs grade1 = 2*grade2 vs grade1 ) . To evaluate which of several correlated tumor features was most important in determining genotype associations , we fitted logistic regression models with one of the tumor features as the outcome and the genotype and other tumor features as explanatory variables . Survival analyses were based on 13 , 527 breast cancer cases from 13 studies with available follow-up data . Univariate analyses for each SNP were carried out by estimating Kaplan-Meier survival curves stratified by genotypes , and by fitting Cox proportional hazards regression models adjusting for study and left-truncating at date of blood draw to allow for inclusion of prevalent cases . This provides an unbiased estimate of the hazard ratio provided that the proportional hazards assumption holds . The assumption of proportional hazards was tested by visual inspection of standard log-log plots and analytically using Schoenfeld residuals . Time at risk was calculated from the date of blood sample draw to date of death or last follow-up , whichever date came first . Follow-up for all cases was censored at 10 years after the initial diagnosis because the number of cases with longer time of follow-up was relatively small , and they are likely to be a selected group of patients due to lost to follow up . A total of 1 , 584 deaths occurred during eligible follow-up . We also carried out analyses adjusting for other determinants of survival ( age at diagnosis ( continuous ) , ER and PR status ( each dichotomous ) , grade ( ordinal ) , tumor size ( continuous ) and nodal involvement ( dichotomous ) ) . Survival analyses were conducted for all cases combined , and separately for ER-positive and ER-negative cases . Data were analyzed using STATA v . 9 . for Windows ( College Station , TX ) . The main conclusions from our analyses are based on comparisons of five SNPs with seven correlated tumor characteristics ( i . e . ER , PR , grade , nodes , size , histology and stage at diagnosis ) and survival after diagnosis . We have used a permutation adjustment procedure [21] to correct P values for these 40 hypothesis tests . The tumor characteristics were permuted in a group with respect to the SNPs . In this procedure , the outcomes ( i . e . tumor characteristics ) were randomly assigned against the SNPs while retaining the correlation structure of the outcomes . We performed 1000 permutations to obtain the empirical distribution of P values under the null hypothesis of no association . Multiple-comparisons-permutation-adjusted P values for each of the 40 tests were calculated as the proportion of P values equal or smaller than the observed P value . GFR2: 2263 TNRC9 or TOX3: 27324 MAP3K1: 4214 MIER3: 166968 LSP1: 4046 v-myc myelocytomatosis viral oncogene homolog ( avian ) ( MYC ) : 4609
Minor allele frequencies and estimates for the association between the five SNPs evaluated and overall breast cancer risk are shown in Table S5 . Stratification of tumors by ER status indicated that rs2981582 in FGFR2 had a stronger association with ER-positive ( per-allele OR ( 95% CI ) = 1 . 31 ( 1 . 27–1 . 36 ) ) than ER-negative tumors ( 1 . 08 ( 1 . 03–1 . 14 ) ; P for heterogeneity of ORs = 10−13; Table 1; Figure 1 panel A; see Table S6 for estimates by ethnicity ) . Women with the homozygous variant genotype ( present in 14% of controls ) had a risk of ER-positive tumors 1 . 74 ( 95%CI = 1 . 63–1 . 85 ) times higher than those with the common homozygous genotype ( present in 39% of controls ) ( Table 1 ) . The difference in ORs between ER-positive and ER-negative tumors is consistent across studies ( Figure 1 panel A ) , and it is highly significant even after permutation adjustment for multiple comparisons ( P<0 . 001 ) . The rs2981582 association was also stronger for other tumor characteristics associated with ER status , i . e . PR expression ( P = 10−5 ) and lower grade ( P = 10−8; Table 2; Tables S7 , S8 ) . The associations of rs2981582 with ER , PR and grade were significant after permutation adjustment for multiple comparisons ( P≤0 . 001 ) . The modification by ER status remained statistically significant after adjustment for PR status and grade ( P = 0 . 002 ) based on data from those studies with information on all three tumor characteristics ( 16 studies including 10 , 951 cases ) . On the other hand , the evidence for associations with PR status became non-significant after adjustment for ER status ( P = 0 . 45 ) . The association with grade ( Table 2 ) remained statistically significant after adjustment by ER status ( P = 0 . 003 ) , and after further adjustment for PR status ( P = 0 . 030 ) . Grouping tumors as ER and PR negative versus ER and/or PR positive tumors did not result in further discrimination of risks ( data not shown ) . The association of rs2981582 with breast cancer risk tended to be stronger for patients with positive ( per-allele OR ( 95% CI ) = 1 . 33 ( 1 . 27–1 . 39 ) ) compared to negative ( 1 . 25 ( 1 . 20–1 . 29 ) ) nodal involvement ( P = 0 . 013; Table 3; see Table S9 for estimates by ethnicity ) . Although differences were small and not significant after permutation adjustment for multiple comparisons ( P = 0 . 41 ) , they were consistent across studies ( Figure 1 , panel B ) . Nodal involvement was correlated with tumor grade and size , and the association between nodal involvement and rs2981582 among cases remained significant ( P = 0 . 010 ) after adjustment for these tumor characteristics in 9 studies with 6 , 204 cases . Nodal involvement and ER status were independently associated with rs2981582 in 12 , 374 cases from 17 studies with data on these two factors ( P value for node association with rs2981582 adjusted by ER = 0 . 022; P = 0 . 75 after adjusting for multiple testing ) . rs2981582 showed the strongest association with node positive ER-positive tumors ( 29% of all tumors; per-allele OR ( 95% CI ) = 1 . 37 ( 1 . 29–1 . 44 ) ) , followed by node negative ER-positive tumors ( 48% of all tumors; 1 . 30 ( 1 . 25–1 . 36 ) ) and node positive ER-negative tumors ( 10% of all tumors; 1 . 18 ( 1 . 09–1 . 29 ) ( Table S10 ) . No increase in risk was observed for node negative ER-negative tumors ( 13% of tumors; 1 . 05 ( 0 . 97–1 . 13 ) . The association of rs13281615 in 8q24 with risk was also stronger for ER-positive compared to ER-negative tumors ( P = 0 . 001; Table 1; Figure S1 ) . This SNP also showed a stronger association with PR-positive than negative tumors ( P = 0 . 011; Table S7 ) and lower tumor grade ( P = 10−4; Table S8 ) . Only the associations of rs13281615 with ER and grade , but not with PR , were significant after permutation adjustment for multiple comparisons ( P = 0 . 037 , 0 . 016 , 0 . 35 , respectively ) . The associations with ER and grade were significant after adjustment for each other ( P = 0 . 029 for ER adjusted for grade and 0 . 035 for grade adjusted for ER in 15 studies with 11 , 419 cases with data on ER and grade ) , while the association with PR was not significant after ER adjustment ( P = 0 . 31 ) . The association of rs3803662 in TNRC9 and breast cancer was also significantly modified by ER status ( P = 0 . 015; Table 1 ) ) and grade ( P = 0 . 018; Table 2 ) . However , these differences were not significant after permutation adjustment for multiple comparisons ( P = 0 . 42 for ER , 0 . 50 for grade ) , or when adjusted for each other in 16 studies with 13 , 075 cases with data on ER and grade ( P = 0 . 11 for ER adjusted by grade , and P = 0 . 37 for grade adjusted by ER ) . Three SNPs ( rs2981582 in FGFR2 , rs3803662 in TNRC9 and rs889312 in MAP3K1 ) were associated with significant increases in risk of ER-negative tumors ( Table 1 ) , although to a lesser extent than ER-positive tumors . Of these SNPs , rs3803662 showed the strongest association with ER-negative tumors: women with the homozygous variant genotype ( present in 8% of controls ) had a 1 . 28 ( 95%CI = 1 . 13–1 . 45 ) higher risk of developing ER-negative disease than women with the common homozygous genotype ( present in 53% of controls ) ( Table 1 ) . No significant modification of the ORs was observed for stage at initial diagnosis for any of the 5 loci ( Table S13 ) . Of note , rs889312 in MAP3K1 and rs3817198 in LSP1 were not associated with any of the tumor characteristics ( Tables S6 , S7 , S8 , S9 and S11 , S12 , S13 ) . Modification of ORs by tumor characteristics generally followed similar patterns for Europeans and Asians , although the number of Asians was substantially smaller , and thus most differences by tumor type were not statistically significant . An exception was the presence of stronger associations with larger tumors for rs889312 in MAP3K1 ( P = 0 . 015; Table S11 ) in Asian but not in European populations . The average time at risk ( i . e . date of blood sample draw to date of death , last follow-up or censored time , whichever date came first ) among 13 , 527 breast cancer patients in 13 studies was 6 . 0 years with a range between <1 and 10 years in individual studies . Cases were followed-up for a total of 54 , 716 person-years with the occurrence of 1 , 515 deaths from any cause ( Table S3 ) . As expected , survival was poorer for patients with ER negative , PR negative , higher grade and larger tumors and in patients with positive nodes ( Figure S2 ) . No differences in survival by genotype were found , except for possibly better survival in patients with the variant allele in rs13281615 at 8q24 ( unadjusted per-allele HR ( 95%CI ) = 0 . 90 ( 0 . 83–0 . 97 ) , P = 0 . 009; Table 4 ) . This association was no longer significant after adjustment for ER status , grade and age at diagnosis ( adjusted HR = 0 . 92 ( 0 . 83–1 . 01 ) , Table 4 ) . Weaker evidence of poorer survival was observed in patients diagnosed with ER-negative tumors carrying the variant allele in rs3803662 ( P = 0 . 071 ) . This association was independent of grade and age at diagnosis ( adjusted per-allele HR ( 95%CI ) = 1 . 19 ( 0 . 98–1 . 44 ) ; Table 4; Figure S3 ) .
This report has demonstrated that common genetic variants that predispose to breast cancer may also be linked to clinically important characteristics of tumors , including size , grade , ER and PR status , and nodal involvement . A major strength of our study is the large sample size after pooling data from multiple studies with information on tumor characteristics , which allowed for precise estimates of relative risk by most tumor subtypes . The most notable finding was for rs2981582 located in FGFR2 , which showed a stronger association with ER-positive than ER-negative tumors ( P = 10−13 ) , with lower than higher grade tumors ( P = 10−8 ) and with node positive than negative tumors ( P = 0 . 013 ) . This SNP was significantly associated only with ER-negative tumors that involved lymph nodes . rs2981582 also showed stronger associations with PR-positive tumors but this association was not independent of ER status . The stronger association with ER-positive tumors is supported by previous observations indicating that FGFR2 is involved in estrogen-related breast carcinogenesis [22]–[25] , and that levels of expression of the receptor are higher in ER-positive than ER-negative cell lines [26] and tumors [27] . We have shown previously that the causative variant in FGFR2 is likely to be one of six variants correlated with rs2981582 in a region of intron 2 containing multiple transcription factor binding sites . This suggests that the association with breast cancer risk may be mediated through differential levels of FGFR2 expression [6] . In addition , as FGFR2 has been shown to be overexpressed or amplified only in a small percentage of breast cancers [9] , [10] , [24] , it is possible that the association with breast cancer risk could be stronger and more clinically relevant for the small subset of tumors that express high levels of the receptor . Epidemiological studies stratifying by levels of tumor expression of FGFR2 , its ligands or co-factors may clarify the role of FGFR2 variation in breast cancer risk . rs13281615 in 8q24 was also more strongly associated with ER-positive and lower grade tumors , although differences were smaller than for rs2981582 in FGFR2 . Other independent variants in the 8q24 region which does not contain known genes , have been associated with prostate cancer risk [11] , [13] , [14]; however , the mechanisms for the associations with these cancers are unknown . A recent GWAS comprising five studies with 4 , 533 cases and 17 , 513 controls ( including samples from the MEC study in this report ) showed the risk from rs3803662 in TNRC9 to be significantly greater in ER-positive tumors [20] . Our data also showed a stronger association with ER-positive than ER-negative tumors , but the difference was smaller and not statistically significant based on the analysis of 12 , 832 cases and 22 , 356 controls from 18 studies . Moreover , this SNP showed the strongest association with ER-negative disease among the five evaluated . Future studies might reveal stronger associations between these SNPs and tumor subtypes defined by different markers , or perhaps molecular subtypes previously defined by gene expression profiling [28] , [29] . It is possible that our study preferentially detected SNPs associated with ER-positive rather than ER-negative disease , since the majority of breast cancer cases in the initial GWAS were ER positive . This raises the possibility that genome-wide association studies focusing on the less common breast tumor subtypes may identify different risk loci . Of particular importance might be SNPs identified in studies of basal tumor subtypes since they are often clinically aggressive and difficult to treat effectively , and have been associated with germline mutations in BRCA1 [5] , [28] . Differences in the design , source of information on tumor characteristics and criteria to classify tumors across studies could lead to heterogeneity of findings by study , which limits the ability to detect modification of genotype associations by tumor characteristics . However , findings were generally consistent across studies ( Figure 1 and Figure S1 ) , particularly for the FGFR2 ( rs2981582 ) association by ER status , arguing for the robustness of our results . Genotype associations with risk of breast cancer were similar for subjects with and without information on tumor characteristics ( data not shown ) , indicating that missing information is unlikely to substantially affect our results . None of the five SNPs included in this report had a significant association with overall survival independent of their associations with known prognostic factors . Only rs13281615 in 8q24 was significantly associated with survival in unadjusted analyses . Adjustment for ER status and grade resulted in a weaker , non-significant association with survival , suggesting that the increased survival is partially mediated through the higher probability of developing tumors with favorable prognostic characteristics . Any SNP effect on overall survival , if mediated through known prognostic tumor characteristics , would be expected to be small because of the small magnitude of risk differences by tumor subtypes; thus the power to detect a difference in survival would be low . For instance , at a type I error rate of 0 . 01 , the power to detect alleles with minor allele frequency ( MAF ) = 0 . 3 that confer a per-allele HR of 1 . 1 is only 40% . Another limitation of the survival analyses is that relapse or disease-specific mortality data were not available for most studies and use of all cause mortality as the end point may further reduce power . Finally , any impact of SNPs on survival may interact with treatment , particularly adjuvant chemotherapy , or other determinants of survival such obesity . However , this could not be evaluated since information on treatment or other factors affecting survival was not available . We have shown that there is heterogeneity in the risk of different tumor types for common breast cancer susceptibility alleles , with the clearest difference being in the relative risk of ER-positive and ER-negative tumors for the variant in FGRF2 . Other differences were observed , however , the weight of evidence was weaker and needs further confirmation in additional studies . These findings provide further support for the notion that ER-negative and ER-positive tumors result from different etiologic pathways , rather than different stages of tumor evolution within a common carcinogenic pathway [30] . The magnitude of the observed differences is small , and by themselves these findings are unlikely to have any immediate clinical implications . However , the observed differences provide clues to the biological mechanisms that underpin tumor heterogeneity , which may ultimately lead to improved treatment and prevention . | This report from the Breast Cancer Association Consortium evaluates whether common variants in five recently identified breast cancer susceptibility loci ( FGFR2 , TNRC9 , MAP3K1 , 8q24 , and LSP1 ) influence the clinical presentation of breast cancer and survival after diagnosis . We studied these susceptibility loci in relation to clinically important tumor characteristics in up to 23 , 039 invasive breast cancer cases and 26 , 273 controls of European or Asian origin from 20 studies . The association , with overall survival , was evaluated in 13 , 527 cases from 13 studies . The most notable findings were that the genetic variants in the fibroblast growth factor receptor 2 ( FGFR2 ) gene and the 8q24 region were more strongly related to ER-positive than ER-negative disease , and to low rather than high grade tumors . The loci did not significantly influence survival after accounting for known prognostic factors . Analyses indicated that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative diseases are biologically distinct tumors . Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention , early detection , and treatment . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"oncology/breast",
"cancer"
] | 2008 | Heterogeneity of Breast Cancer Associations with Five Susceptibility Loci by Clinical and Pathological Characteristics |
The WHO yaws eradication strategy consists of one round of total community treatment ( TCT ) of single-dose azithromycin with coverage of > 90% . The efficacy of the strategy to reduce the levels on infection has been demonstrated previously in isolated island communities in the Pacific region . We aimed to determine the efficacy of a single round of TCT with azithromycin to achieve a decrease in yaws prevalence in communities that are endemic for yaws and surrounded by other yaws-endemic areas . Surveys for yaws seroprevalence and prevalence of skin lesions were conducted among schoolchildren aged 5–15 years before and one year after the TCT intervention in the Abamkrom sub-district of Ghana . We used a cluster design with the schools as the primary sampling unit . Among 20 eligible primary schools in the sub district , 10 were assigned to the baseline survey and 10 to the post-TCT survey . The field teams conducted a physical examination for skin lesions and a dual point-of-care immunoassay for non-treponemal and treponemal antibodies of all children present at the time of the visit . We also undertook surveys with non-probabilistic sampling to collect lesion swabs for etiology and macrolide resistance assessment . At baseline 14 , 548 ( 89% ) of 16 , 287 population in the sub-district received treatment during TCT . Following one round of TCT , the prevalence of dual seropositivity among all children decreased from 10 . 9% ( 103/943 ) pre-TCT to 2 . 2% ( 27/1211 ) post-TCT ( OR 0 . 19; 95%CI 0 . 09–0 . 37 ) . The prevalence of serologically confirmed skin lesions consistent with active yaws was reduced from 5 . 7% ( 54/943 ) pre-TCT to 0 . 6% ( 7/1211 ) post-TCT ( OR 0 . 10; 95% CI 0 . 25–0 . 35 ) . No evidence of resistance to macrolides against Treponema pallidum subsp . pertenue was seen . A single round of high coverage TCT with azithromycin in a yaws affected sub-district adjoining other endemic areas is effective in reducing the prevalence of seropositive children and the prevalence of early skin lesions consistent with yaws one year following the intervention . These results suggest that national yaws eradication programmes may plan the gradual expansion of mass treatment interventions without high short-term risk of reintroduction of infection from contiguous untreated endemic areas .
Yaws is a chronic , relapsing , neglected tropical disease caused by Treponema pallidum spp . pertenue , a spirochaete closely related to that which causes syphilis [1] . The disease is currently reported from 13 of the 85 countries previously considered endemic in the 1950s , with an estimated 89 million people living in the affected districts [2] . Yaws is usually acquired by children in impoverished communities in tropical and subtropical countries when traumatized skin comes into contact with another person’s early infectious lesion exudate , often during play . The disease affects mostly children aged under 15 years . The frequency of infection appears to be higher in boys than in girls [3] . Primary yaws lesions develop at the site of initial inoculation after an incubation period of 9–90 days . These lesions are initially papules , which can develop into papillomata and eventually ulcerate , and are most frequently found on the lower legs and ankles and , less frequently , on the skin of the upper limbs and elsewhere on the body . If left untreated , the disease may progress to the secondary stage , which is characterized by multiple skin lesions as well as osteitis and periostitis of the bones underlying the skin lesions . Untreated disease may spontaneously resolve clinically and enter a period of latency prior to the development of non-infectious gummas of the skin , cartilage and bone , resulting in the destructive , often disfiguring , lesions of late yaws [4] . During the 1950s and 1960s , the World Health Organization ( WHO ) and the United Nations Children’s Fund ( UNICEF ) led a global campaign to eradicate the disease by providing mass treatment to affected communities using single intramuscular injections of long-acting penicillin . The strategy was based on the need to screen at least 90% of the population , treat the entire reservoir of treponemal infection ( including those with clinical disease , latent infection and contacts ) and to perform periodic surveys at 6–12 months to identify and treat missed , new and imported cases [5] . The criteria for eradication were defined by the WHO Expert Committee on Venereal Infections and Treponematoses in 1960 as the absence of an indigenous infectious case in the population for three consecutive years and absence of any new seroreactor aged under 5 years [6] . The implementation of the global yaws eradication programme between 1952–1964 reduced the prevalence of infection by approximately 95% ( from 50 million to 2 . 5 million cases ) worldwide , indicating that the mass treatment approach using benzathine penicillin was highly successful [7] . In some countries such as Haiti and Nigeria , experience showed that one round of mass treatment with coverage of > 90% significantly reduced the prevalence of infectious cases by approximately 98% within 12 months [8 , 9] . Despite the success of the campaign , the ultimate goal of eradication could not be achieved owing to several factors including the failure of many countries to adequately integrate active surveillance activities into the local health system after the mass treatment campaigns ended , complacency , limited resources , lack of political will and many new competing health priorities including the shifting of the dedicated mobile teams for yaws eradication to deal with diseases such as smallpox and cholera [5] . This situation led to the resurgence of yaws in several countries in the late 1970s , prompting the World Health Assembly to adopt resolution WHA31 . 58 [10] which called on countries to take the necessary measures to interrupt transmission at the earliest possible time . Despite the possibility that yaws transmission has ceased in a number of countries that were endemic in the 1950s but not confirmed , only India has recently been formally certified by WHO as yaws-free [11] . The disease still remains endemic in many countries in West and Central Africa , South-East Asia and the Western Pacific [12] . In 2012 , the finding that a single oral dose of azithromycin was as effective as injectable penicillin for the treatment of yaws [13] prompted WHO to revisit the global eradication of the disease . In 2012 , WHO published a roadmap on neglected tropical diseases that targeted yaws eradication by 2020 [14] . In the same year , WHO devised a new yaws eradication strategy ( the Morges Strategy ) [15] . The strategy recommends total community treatment ( TCT , equivalent to mass treatment for other neglected tropical diseases ) of affected communities with single doses of azithromycin followed by ongoing active surveillance , case-finding and treatment of missed , new and imported cases and their contacts ( household , neighborhood and school playmates ) through a health system approach . Depending on the initial treatment coverage and accessibility to the endemic communities , the strategy recommends repeat surveys to identify and treat any new infectious cases or in response to localized outbreaks ( using total targeted treatment , TTT ) . The feasibility of global yaws eradication and the progress made in implementing the Morges Strategy have previously been reported [16 , 17] . In 2015 , the first empirical data of the impact of mass treatment with azithromycin on disease transmission became available . A study carried out in Lihir , Papua New Guinea ( PNG ) demonstrated that mass treatment with azithromycin to the population of yaws-endemic island communities resulted in a significant decrease in the prevalence of clinically early yaws lesions and a decrease in reactive serological markers for the disease [17] . Similar findings have emerged from a study in the Solomon Islands that evaluated the secondary benefits of mass treatment of trachoma using 20 mg/kg azithromycin on the prevalence of yaws [18] . However , despite the encouraging results obtained in these countries , focused mass treatment campaigns are , theoretically , more likely to succeed in isolated island communities where the risk of reintroduction of infection in the treated population from untreated adjoining communities is less likely . Since the disease is also endemic in many countries with affected communities spread over a contiguous land mass , it is also important to determine whether high coverage with azithromycin mass treatment in a defined area can result in a sustained decrease in infectious yaws up to one year after the intervention . This is also important , since the resources available to yaws eradication programmes in many countries are limited , resulting in an inevitable delay or progressive expansion of the implementation of the programme to adjoining endemic areas that could act as a source of re-infection for the initial target communities . In this study , we aimed to assess the impact of a single round of TCT with azithromycin using two markers of infection among school-going children in the target communities before and one year after the intervention . We measured the prevalence of dually seropositive for non-treponemal and treponemal serological markers , and the prevalence of active yaws-like lesions among children . A secondary objective was to establish the etiology of active yaws-like lesions among schoolchildren using sensitive molecular techniques and to assess the occurrence of mutations associated with azithromycin resistance among T . pallidum spp . pertenue positive lesions in the local population .
We conducted a prospective observational study in the Abamkrom sub-district , West Akim district of the Eastern Region of Ghana between October 2013 and December 2014 . The sub-district is highly endemic for yaws ( Fig 1 ) [20] . The total population of the sub-district in mid-2013 was estimated to be 16 , 287 people , i . e . 7 . 7% of the population of the district as a whole ( 212 282 people ) . The eligible population for mass treatment was everyone older than 6 months of age , excluding pregnant women ( as decided by the local study team ) , 15 , 310 people in total . The sub-district has 5 health centres , 24 primary schools and 36 communities . The total population of children 5–15 years registered in primary schools that were targeted for impact assessment surveys was 2 , 909 children . Each community has a village health volunteer who is responsible for monitoring and reporting health events . These volunteers are used for mass drug administration for other neglected tropical diseases . The total eligible population ( aged ≥ 6 months ) living in the 36 communities was targeted to receive mass treatment with azithromycin as part of the study . The school attendance rate in the sub-district among children aged 5–15 years has been estimated to be 47 . 6% . Prior to the implementation of the study , the health workers and village volunteers were trained on the objectives of the study , recognition of yaws-like lesions , implementation and data collection tools . The TCT programme was conducted by 13 teams of two trained volunteers drawn from affected communities who were supervised by local health-care workers or members of the national yaws eradication programme . During a five-day period in November–December 2013 , the teams offered azithromycin tablets ( purchased by WHO from Medopharm , Chennai , India ) at a single oral dose of 30 mg/kg per body weight ( maximum 2 g ) to all members of the 36 targeted communities aged 6 months and above at no cost to the participants . The tablets ( 500g strength ) were administered to the eligible population according to age as described in the WHO Morges Strategy document [15] . For children aged under 6 years , the tablets were crushed and mixed with water . The volunteers directly observed treatment of participants , maintained tally sheets; they marked the fingers of treated participants with indelible ink to document the administration of the medicines , and observed participants for approximately one hour after ingestion of the medication , reporting any adverse events that could be related to azithromycin treatment . The supervisors collected the tally sheets daily and followed up any adverse drug reactions reported by volunteers . TCT coverage rates were calculated using the number of persons treated according to the tally sheets divided by the total population . We used cluster sampling with individual schools as the cluster unit . Eligibility for inclusion was met by 20 primary schools with a population larger than 100 children among 24 schools located in the sub-district . The schools were randomly assigned to either the pre- or post- TCT evaluation surveys . Therefore , the sets of schools for pre- and post- TCT surveys were mutually exclusive and schools could not be chosen repeatedly for more than one survey . Every child present at the time of our visit to the schools selected was invited to participate in the study . The primary outcome was prevalence of sero-positivity , defined as dually non-treponemal and treponemal antibody positivity using a point-of-care test among symptomatic or asymptomatic children . Secondary outcomes , were the prevalence of suspected yaws , defined as a child with a history of residence in an endemic area who presented with clinically active ( visible ) yaws-like lesions , and the prevalence of serologically-confirmed yaws , defined as a suspected case with a dually-positive serological test result for non-treponemal and treponemal antibodies . In October 2013 , before the mass treatment campaign ( November–December 2013 ) , we conducted a baseline assessment survey in 10 randomly selected primary schools and every child present , 943 children in total , was enrolled . One year after the intervention ( November–December 2014 ) , every child from a further group of 10 randomly selected schools , 1211 children in total , were selected for an identical post-intervention assessment of impact survey . Fig 1 shows the geographical distribution of these schools within the sub-district and Fig 2 the flowchart of the study design . During both assessments , all children were examined clinically for skin lesions consistent with early infectious yaws ( i . e . skin papilloma , chronic solitary or multiple skin ulcerations ) and a specimen of capillary blood was collected from all , symptomatic and asymptomatic , participants to perform a point-of-care immunoassay for antibody to yaws infection . All children with active yaws-like lesions detected during either the initial or post-TCT assessments or those who were asymptomatic but had a dually non-treponemal and treponemal antibody-positive result on initial field screening were treated with a single dose of azithromycin ( 30 mg/kg ) and followed up for adverse events . A point-of-care immunoassay , developed for the serological diagnosis of syphilis , that can simultaneously detect both non-treponemal and treponemal antibodies ( DPP Syphilis Screen and Confirm Assay , Chembio Diagnostic Systems , Medford , NY , USA ) [21–23] was used to test 10 μl samples of capillary blood obtained by finger prick from each of the schoolchildren who were included in the selected pre- and post-TCT schools . In each case , the child’s finger was cleaned with an alcohol swab , the skin punctured with a lancet and the capillary blood collected in a pre-graduated micropipette supplied in the test kit . Thereafter the test was performed directly in the field according to the manufacturer’s instructions . We conducted clinical surveys to collect lesion swabs for PCR testing before and one year after TCT . Due to the low number of T . pallidum PCR positive cases detected in Abamkrom sub-district before the intervention , we decided to extend the baseline study to the entire West Akim district ( 8 sub-districts , 212 282 people ) . We had 8 teams , one in each sub-district , that examined the skin of schoolchildren aged less than 15 years of age . The sampling for this study was non-probabilistic; teams visited enough schools and enrolled all consecutive eligible children to achieve 150 symptomatic children sampled . If they identified any skin lesion , the child was invited for diagnosis , swab sample collection and treatment after their parents or guardians provided written informed consent . Similar procedures were used for post-TCT surveys that were conducted only in the Abamkrom sub-district . Children with suspected yaws had their lesions photographed and specimens taken directly from the largest papilloma or ulcer after cleansing with sterile saline using either a sterile plastic curette ( Ear Curette , Sklar Instruments , West Chester , PA , USA ) or sterile dacron-tipped swabs ( Medical Wire & Equipment , Corsham , UK ) for PCR testing . PCR-confirmed yaws was defined as a suspected case with a positive PCR detection of DNA of the polymerase I gene and/or the T . pertenue-specific 23S rRNA gene sequence on material collected from suspected lesions [24] . Scrapings from papillomata and swabs taken from the bases of skin ulcerations were expressed into 1 . 2 ml of Assay-Assure nucleic acid transport medium ( Thermo Fisher Scientific , Waltham , MA , USA ) . All specimens were stored frozen at −20°C before shipping , on dry ice , to the WHO Collaborating Centre for Reference & Research in Syphilis Serology at the Centers for Disease Control in Atlanta , GA , USA . Genomic DNA was extracted from 350 μl aliquots of assay-assure samples using the iPrep PureLink gDNA blood kits ( Life Technologies , Grand Island , NY , USA ) and iPrep purification instrument . The specimen DNAs were originally screened with TaqMan-based real-time 4-plex polymerase chain reaction ( PCR ) targeting tp858 and two areas of the tprl ( tp620 ) [25] . However , due to the discovery of primer binding site mutation ( i . e . some strains may be undetectable by the PCR used ) , [26] we changed to a more sensitive PCR strategy and all specimens were re-tested using a real-time PCR targeting the DNA polymerase I gene ( polA , tp0105 ) of pathogenic treponemes ( which detects all 3 T . pallidum subspecies ) [24] and a real-time 3-plex PCR that detects the two 23S rRNA point mutations ( A2058G and A2059G ) associated with macrolide resistance in T . pallidum described previously [27] . If a specimen tested positive by polA-PCR and/or 23S rRNA PCR ( wild type or mutant ) , then we used the TaqMan-based real-time 4-plex PCR to differentiate T . pallidum spp . pertenue from spp . pallidum and endemicum [25] . A nested-PCR and sequencing of a portion of tp858 were used to resolve discrepant results among those three assays and to confirm the presence of T . pertenue–specific DNA sequences . All DNA samples were tested for Haemophilus ducreyi ( which had previously been detected in yaws-like lesions in PNG , Ghana , Vanuatu and the Solomon Islands [28–31] ) and Mycobacterium ulcerans ( the causative bacterium of Buruli ulcer , known to be endemic in the region ) using specific sequences ( hemolysin gene , HdhA ) and Insertion Sequence ( IS ) 2404 respectively in a real time duplex PCR [32 , 33] . Genomic DNA samples purified from H . ducreyi or a M . ulcerans culture ( kindly provided by Dr . Anthony Ablordey , Noguchi Memorial Institute for Medical Research , University of Ghana , Legon , Ghana ) were used as the positive controls for PCR assays . At least one no-template-control ( NTC ) and one positive control were included in every test run . The analytical sensitivity of the duplex PCR assay is approximately 10–100 copies per reaction and the analytical specificity was assessed using DNA from a panel of organisms including commensal and pathogenic microbes found in the genitourinary tract and as part of the normal skin flora . All real-time multiplex PCR assays were performed on a Rotor-Gene Q real-time PCR instrument ( Qiagen Inc . , Valencia , CA , USA ) . In addition , serum samples were obtained from venous blood collected from all children with active lesions , for laboratory-based testing . These were stored frozen at −20°C and transported on dry ice to the WHO Collaborating Centre for Reference & Research in Syphilis Serology , where they were tested using a quantitative rapid plasma reagin ( RPR ) test ( Alere North America , Inc . , Orlando , FL , USA ) and a T . pallidum passive particle agglutination assay ( TPPA , Fujirebio Diagnostics Inc . , Malvern , PA , USA ) . Data were entered in Microsoft Access software , version 15 . 0 ( Microsoft , Redmond , WA , USA ) at the Ministry of Health , Ghana . The integrity of the data was verified by using a double data entry process . The primary outcome was change in prevalence of dual seropositivity following TCT , secondary outcomes were changes in rates of prevalence of suspected cases with lesions and change in prevalence of cases with lesion and seropositivity . We calculated that a sample size for the pre- and post-TCT surveys of at least 854 schoolchildren aged 5–15 years ( EPI INFO 2000 sample size calculator ) was required to detect a reduction by 45% in yaws seroprevalence among students before and after TCT intervention with a 95% confidence interval ( CI ) and 80% power . A design effect of 2 for the cluster sampling method was used to calculate the power [34 , 35] . The average number of children among the 24 schools of the subdistrict is 145 . 5 , therefore we considered that at least 10 schools had to be sampled at each survey . We calculated prevalence rates and 95% confidence intervals using the clustered sandwich estimator to control the variability of clusters . We evaluated the changes in yaws seroprevalence , and prevalence of yaws-like lesions among the schoolchildren sampled before and one year after TCT using logistic regression models controlling the variance-covariance matrix ( VCE ) corresponding to the parameter estimates . We reported the standard errors of parameter estimates as the square root of the variances of the VCE . For these , we use the option cluster in the calculus of Odds Ratios with the logistic regression models . We calculated Odds Ratios ( post- compared to pre- TCT ) for positive serology or clinical findings . The differences in the prevalence rates were considered statistically significant when two-sided p-values were less than 0 . 05 . The statistical analysis was performed with Stata StataCorp . 2017 ( Stata Statistical Software: Release 15 . College Station , TX: StataCorp LLC )
During the 5-day community-based mass treatment campaign , 14 548 ( 89% ) of 16 , 287 residents in the sub-district received a single oral dose of azithromycin . Individuals who were not eligible for treatment ( 977 , 6 . 0% ) , or absent during the mass treatment ( 762 , 4 . 7% ) accounted for 10 . 7% . of the total population ( 16 287 ) of the sub-district . There were no severe adverse events attributable to the study drug; only 45 ( 0 . 3% ) of the 14 548 participants treated reported mild to moderate self-limiting adverse events including abdominal discomfort , nausea and vomiting . Of the 943 children examined at schools before the community-based mass treatment campaign , 487 ( 51 . 6% ) were male , and a similar proportion of males was found among those examined at schools after the TCT ( 632 /1211 , 52 . 2% ) . The mean ( SD ) age of the pre-TCT schoolchildren ( 10 . 5 [2 . 5] years ) compared with that of the post-TCT children ( 9 . 4 [3 . 6] years ) was not significantly different . The prevalence rate of dual seropositivity in the DPP test decreased significantly , from 103/943 ( 10 . 9% 95% CI 6 . 5–17 . 5 ) among children in the pre-TCT survey to 27/1211 ( 2 . 2% , 95%CI 1 . 3–3 . 7; OR 0 . 19 , 95%CI 0 . 09–0 . 37 ) in the post-TCT survey ( Table 1 ) . In addition , the prevalence rate of serology confirmed yaws-like active lesions among schoolchildren was significantly reduced from a pre-TCT rate of 54/943 ( 5 . 7% 95%CI 3 . 2–9 . 9 ) to 7/1211 ( 0 . 6% 95%CI 0 . 2–1 . 6; OR 0 . 10 , 95%CI 0 . 25–0 . 35 ) in the sample of schoolchildren examined one year following the TCT . The results of PCR testing obtained from the children with active lesions in the extended intervention area pre- TCT are shown in Table 2 . Among 158 children with active skin lesions sampled before mass treatment , 29/158 ( 18 . 4% ) tested positive for T . pertenue-specific DNA sequence ( none of which contained mutations associated with azithromycin resistance ) and 45/158 were H . ducreyi positive including 7/158 lesions that were dually T . pertenue and H . ducreyi-PCR positive . M . ulcerans-specific DNA sequences were not detected in any specimen obtained from lesions , either pre- or post-TCT . In the Abamkrom sub-district 3/53 ( 5 . 7% ) sampled cases pre-TCT were T . pertenue-PCR positive ( Table 3 ) , compared with 0/49 ( 0 . 0% ) of specimens sampled one year after mass treatment while the proportion of H . ducreyi-positive ulcers remained largely unchanged . Of the 158 blood samples collected pre-TCT from children with active lesions that underwent serological testing at the CDC laboratory ( Table 2 ) , 57 ( 36 . 1% ) showed reactivity in both the non-treponemal and treponemal tests , and 8 ( 5 . 1% ) were reactive for treponemal antibody alone . Among dually reactive specimens , only 7/57 ( 12 . 3% ) had RPR titres ≤1:4 , while the remaining 50/57 ( 87 . 7% ) sera exhibited titres between 1:8 and 1:128 . The agreement between T . pertenue-PCR and serologic assays was high . However , RPR and TPPA were positive in 31 children with lesions that were T . pertenue-PCR negative ( 24 all PCR tests negative , 7 H . ducreyi-PCR positive; false positivity rate 31/63 , 49 . 2% ) which can be explained because serology remains detectable in serofast status .
Our study demonstrates that the provision of mass azithromycin administration given as a single oral dose of 30 mg/kg , up to a maximum dose of 2 g , is effective in reducing both the rates of seropositivity and the presence of serologically positive skin lesions consistent with yaws . Our results support the findings of earlier publications from PNG [17] and the Solomon Islands [18] and for the first time provides information on the efficacy of the Morges Strategy in an area that was geographically contiguous with neighboring endemic areas , unlike the previous studies . In addition , we used potentially more operationally feasible approach compared to the studies in the Pacific countries to measure the impact of one round TCT by focusing on the school-going population . However , we suggest that if this approach is used , school attendance rate should be >75% like in lymphatic filariasis surveys . One year after TCT , we recorded a reduction in the prevalence of active yaws-like skin lesions among schoolchildren living in the targeted communities which was consistent with a reduction of passively detected suspected yaws cases in the same sub-district area ( from 103 cases in 2012 to 20 in 2014 ) recorded in the routine reporting system ( DHIMS2 ) of the Ministry of Health . Although the sample size was extremely small , we were unable to detect T . pallidum spp . pertenue using PCR test in any lesions that were clinically diagnosed as yaws seen in a survey conducted one year after TCT . The population coverage that was achieved in the Ghanaian population reported here ( 89% ) was slightly higher than that achieved in the PNG study ( 84% ) [17] . The impact observed in both studies was very large consisting of a reduction by 90% of active yaws , but in Ghana we observed a lack of T . pertenue-PCR positive lesions ( albeit small number of positives detected pre-TCT in the Abamkrom subdistrict ) which could be related to the higher coverage rate achieved in Ghana or to the initial lower burden of infection in the present study or both when compared to PNG . Recent studies on yaws conducted in PNG [28] , Ghana [29] , Vanuatu [30] and the Solomon Islands [31] have identified H . ducreyi as an important cause of skin ulcers in yaws endemic communities . Isolates of H . ducreyi obtained from skin lesions from children in Ghana are fully sensitive to azithromycin in vitro and the antibiotic is frequently used to treat chancroid , a sexually transmitted infection also caused by H . ducreyi . In our study , the overall prevalence of lesions caused by H . ducreyi was greatly reduced after TCT . However , unlike yaws , the relative proportion of H . ducreyi-positive ulcers remained essentially unchanged following TCT . It seems logical to speculate that community mass treatment with azithromycin may have less impact on lesions caused by this bacterium than those caused by T . pallidum spp . pertenue . We raise two possible explanations: Firstly , H . ducreyi strains that cause skin lesions in children may be more infectious than T . pallidum spp . pertenue . Secondly , in common with sexually transmitted H . ducreyi infections , non-sexual transmission of H . ducreyi does not appear to engender protective immunity to subsequent re-infection , unlike the transient immunity that occurs following treponemal infection [36 , 37] . Our study has some limitations . First , the use of school-going children as a sampling methodology may introduce bias because the poorest children , who are at most risk of the disease , may not attend school . However , school-sampling is generally considered a good and convenient sub-population sample for other NTDs and routine surveillance data from DHIMS2 confirmed the overall decrease of yaws-like cases seen in the sub-district one year after the mass treatment . Further studies to compare the impact of interventions on school versus community-based populations are clearly indicated . Second , we selected two different groups of schools for pre- and post-TCT assessments , rather than returning to the original schools to determine the impact of TCT on the children who were seen initially . We considered that the additional survey for clinical and serological screening , and treatment of positive cases , that these schools would receive was effectively an additional public health intervention that is not a normal part of the larger interventions that the study aimed to evaluate . We therefore randomly selected a different group of schools for the post-TCT evaluation one year later to avoid measuring the potential impact of a double treatment . Third , the DPP point-of-care test that was used in this study lacks some sensitivity at low titres compared with conventional laboratory-based testing [23] . However , we believe that since the DPP test is capable of detecting non-treponemal antibody in more than 90% of cases with RPR titres ≥ 1:8 ( which have previously been more closely associated with proven infectious lesions ) [17] the benefits of field testing outweigh the logistics of providing a more reliable laboratory-based service which may not be readily available in impoverished yaws-endemic regions . Indeed , the lack of sensitivity of the DPP test at low titre could actually be a benefit since , in this study , confirmed low-titre RPR seropositivity ( ≤ 1:4 ) , which could be missed when using the DPP test , was not associated with any lesion actively shedding T . pertenue . Finally , to determine the sample size for our surveys , we used a design effect of 2 based on previous experience in the Solomon Islands and Fiji [34 , 35] , but which is lower than the design effect used in similar studies on NTDs [38] . In our study , the findings are still statistically strong after accounting for clustering; therefore , we believe that the design effect used was not a major limitation . However , a comprehensive review of sampling methods for yaws should be made to provide appropriate guidance for future intervention studies . It seems clear , from the results of this intervention study , that yaws-like lesions caused by H . ducreyi or other unknown pathogens may continue to persist after a single-round of mass treatment giving the erroneous impression to both the affected population and health authorities alike that yaws has not been eliminated from a previously endemic community . This situation is compounded by the finding that H . ducreyi-positive lesions may be associated with dually positive non-treponemal and treponemal serological results in children with latent yaws or serofast status . Azithromycin is one of the antibiotics recommended for the treatment of sexually transmitted H . ducreyi infections [39 , 40] , and it is also effective in the treatment of cutaneous ulcers in children caused by H . ducreyi [41] . In the long term , it may be necessary to devise appropriate “syndromic” management protocols for non-yaws / non-H . ducreyi lesions following the successful elimination of yaws through TCT . Further studies on the etiology of these yaws-like skin lesions in various yaws-endemic regions around the world are required . In conclusion , our findings provide additional evidence that one round of TCT with azithromycin with high coverage ~ 90% , as part of the WHO Morges Strategy , is highly effective in providing a sustained and significant decrease in the prevalence of yaws 12 months after mass treatment from endemic communities , even if they adjoin other untreated endemic areas . Because 6-monthly resurveys using field staff may be costly , perhaps , if the initial coverage is >90% , in some places , a practical approach is to use trained village volunteers for ongoing active community surveillance and health promotion activities for yaws , especially , in the post-TCT phase similar to the experience of the guinea worm eradication programme [42] . Although our study is limited in size to allow us to make any firm recommendations regarding the ideal intervals between mass treatment and number of rounds required to interrupt transmission , we have presented some evidence to support the notion that with high coverage , a single round of TCT followed by TTT may be adequate and any additional TCT rounds to interrupt transmission may be carried out at intervals longer than 6 months . Further studies are needed to address these important issues . We also believe that the point-of-care DPP test applied to a sample of school-going children is a practical and convenient alternative to laboratory-based serological testing of a sample of the whole population to evaluate the effectiveness of yaws interventions in resource-poor settings . However , in view of the first report of macrolide resistance in yaws [43 , 44] , vigilance and close monitoring of cases are required . It is important that health workers to take specimens from any active skin lesions ( papilloma and ulcer ) of dually seropositive cases that are not healed or occur after TCT for PCR testing to definitely confirm yaws and detect any azithromycin resistance that may emerge among T . pertenue strains and to warrant the treatment of such cases with benzathine benzylpenicillin . | In this study , we provided a single round of total community treatment ( TCT ) with azithromycin to the population of a sub-district in Ghana ( 16 , 287 people ) that is endemic for yaws and surrounded by other yaws-endemic communities to determine whether a sustained decrease in yaws prevalence could be achieved up to one year after the intervention . The efficacy of TCT was assessed by performing a clinical evaluation and serological testing of any yaws-like lesions found as well as serological screening of asymptomatic schoolchildren aged 5–15 years pre-TCT and at one year post-TCT . The results indicate that after a single round of high coverage TCT ( 89% ) with azithromycin , the prevalence of active and latent yaws was significantly reduced . We also found that the use of a dual point-of-care immunoassay to detect non-treponemal and treponemal antibodies among school-going children is a practical alternative to laboratory-based serological testing to evaluate the effectiveness of yaws interventions in resource-poor settings . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"schoolchildren",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"education",
"pathogens",
"sociology",
"tropical",
"diseases",
"microbiology",
"social",
"sciences",
"geographical",
"locations",
"treponematoses",
"bacterial",
"disease... | 2018 | Community-based mass treatment with azithromycin for the elimination of yaws in Ghana—Results of a pilot study |
Bushmeat represents an important source of animal protein for humans in tropical Africa . Unsustainable bushmeat hunting is a major threat to wildlife and its consumption is associated with an increased risk of acquiring zoonotic diseases , such as Ebola virus disease ( EVD ) . During the recent EVD outbreak in West Africa , it is likely that human dietary behavior and local attitudes toward bushmeat consumption changed in response to the crisis , and that the rate of change depended on prevailing socio-economic conditions , including wealth and education . In this study , we therefore investigated the effects of income , education , and literacy on changes in bushmeat consumption during the crisis , as well as complementary changes in daily meal frequency , food diversity and bushmeat preference . More specifically , we tested whether wealthier households with more educated household heads decreased their consumption of bushmeat during the EVD crisis , and whether their daily meal frequency and food diversity remained constant . We used Generalized Linear Mixed Models to analyze interview data from two nationwide household surveys across Liberia . We found an overall decrease in bushmeat consumption during the crisis across all income levels . However , the rate of bushmeat consumption in high-income households decreased less than in low-income households . Daily meal frequency decreased during the crisis , and the diversity of food items and preferences for bushmeat species remained constant . Our multidisciplinary approach to study the impact of EVD can be applied to assess how other disasters affect social-ecological systems and improve our understanding and the management of future crises .
The recent Ebola virus disease ( EVD , [1] ) epidemic that emerged in March 2014 in West Africa [2 , 3] was the largest recorded , resulting in over 28 , 600 cases and 11 , 300 deaths in Guinea , Liberia and Sierra Leone [4] . EVD is a deadly zoonotic disease that is transmitted to humans through contact with the blood and other bodily fluids of infected wildlife , such as fruit bats , forest antelopes , and nonhuman primates [5 , 6] . Although there are several contributing factors that caused the outbreak to rapidly expand , such as poverty [7 , 8 , 9] and weak medical infrastructure [8 , 10] , the harvest and butchering of bushmeat ( i . e . , wild animal meat ) have been suspected as potential sources for initial spillover events in this [2 , 11] and other EVD epidemics [12 , 13] . Despite its health risks , bushmeat consumption is widespread throughout tropical regions and common in both rural and urban areas [14 , 15] , although the reasons for its consumption tend to vary between and within areas . In remote , impoverished , rural areas , bushmeat is often an essential source of animal protein that may contribute to food security , particularly where livestock and fish are inaccessible [16 , 17 , 18] or unaffordable [19] . In contrast , urban consumers are likely to choose bushmeat from a number of interchangeable animal protein sources , and for a variety of reasons , such as its low cost , preference of taste , or perception of prestige [16 , 20] . Bushmeat also provides an important source of cash income for rural and forest dwellers who may depend on wildlife to alleviate periods of economic hardship ( e . g . crop failures ) , or supplement their primary source of income , which is often agriculture [21 , 22] . Along with the rapid growth of human populations , the extraction of wildlife for subsistence and commercial use has become a major biodiversity threat [23] . Since the 1970s , the abundance of large mammals in African protected areas has halved , largely due to over-hunting [24] . To make more profit , hunters prefer large animals , such as large antelopes , elephants , and great apes , whose low intrinsic rates of population growth make them extremely vulnerable to intensive hunting . Regardless of their scarcity , they are continued to be targeted and have already been hunted to the point of extirpation in a number of places [20 , 25] . These unsustainable hunting practices ultimately lead to the defaunation of otherwise undisturbed forests and create “empty forests” [26] . The ecological impacts of over-hunting include the direct effect on the hunted populations ( e . g . Tweh et al . [27] have documented this across Liberia ) , as well as indirect effects on ecosystem function and structure , which are more difficult to measure [20 , 28] . For instance , Effiom et al . [29] found that the seedling layer in forest sites with widespread bushmeat hunting was significantly different from that of protected sites , suggesting that the loss of seed-dispersing primates in particular might impact forest regeneration processes and ultimately forest composition . Indeed , the bushmeat trade encompasses a broad range of socio-economic and ecological issues , which highlights the need to use interdisciplinary approaches to better understand the links between the exploitation of natural resources and human socio-economic status [19 , 30] . It is therefore imperative to identify the underlying drivers of bushmeat consumption to develop more effective , targeted conservation management strategies . Such strategies must aim to reduce the unsustainable harvest of bushmeat whilst improving human livelihoods and lowering the risk of zoonotic disease transmissions , such as EVD . It is likely that due to social pressure and risk aversion , human behavior and local attitudes toward bushmeat consumption changed during the most recent and largest-ever recorded Ebola outbreak in West Africa [2] . Furthermore , the magnitude of these changes may have been influenced by different socio-economic factors . Here , we analyze household-level data on socio-economic status , wildlife consumption and eating habits , to answer the following research question: were wealthier , more educated or literate people more likely to change ( 1 ) bushmeat consumption , ( 2 ) number of meals per day , ( 3 ) food diversity , and ( 4 ) bushmeat preference during the Ebola crisis ?
Our surveys were conducted across the entire country of Liberia . Following 14 years of civil conflict , the country’s economy had been growing rapidly in recent years [31] . However , Liberia is still among the poorest countries in the world ( HDI rank 177/188 , [32] ) , and was one of the countries hardest hit by the Ebola crisis [10 , 31] . It is also found within the richest 5% of land area for threatened bird , amphibian and mammal species [33] . In addition , it is home to one of the most viable chimpanzee populations in West Africa , which is primarily threatened by hunting [27] . We used two different data sources: ( 1 ) interview data collected in Liberia during a nationwide chimpanzee and large mammal survey from 2010 to 2012 , and ( 2 ) interview data from a follow-up nationwide interview survey on socio-economic status and natural resource use of Liberian households during the Ebola crisis in 2015 . Between August 2010 and May 2012 a nationwide chimpanzee and large mammal survey was conducted on line transects that were systematically distributed across the country [27] . When travelling to these transects to record large mammal abundance , survey teams visited nearby villages to conduct interviews to collect data that served as the basis of this study ( for more details see Tweh et al . [27] ) . In each location ( i . e . , village ) , one to ten household heads were interviewed . If the head of the household was absent at the time , the person otherwise responsible for the household was interviewed instead . A total of 275 household heads from 70 locations were interviewed during this survey . A follow-up interview survey was conducted between January and June 2015 to gather information on the impact of Ebola on socio-economic status and natural resource use of Liberian households . The geographical distribution of interview locations during the follow-up survey in 2015 was based on the sampling locations from the 2010–2012 survey ( Fig 1 ) , but the respondents and households were not necessarily the same across surveys . The majority of the interview questions were paired with identical retrospective questions , which made it possible to collect information about the time period before the Ebola crisis . This was an important feature of the questionnaire because the 2010–2012 survey questionnaires were far less extensive ( see S2 Appendix for the complete questionnaires used in the surveys ) . For this reason , data from the 2015 follow-up survey were mainly used in our data analysis . Overall , there was an overlap of 60 interview locations that were sampled during both household surveys .
The full-null model comparison was significant ( likelihood ratio test [LRT]: χ2 = 11 . 138 , df = 4 , p = 0 . 025 ) . The number of meals that respondents consumed per day decreased significantly during the Ebola ( LRT: χ2 = 10 . 369 , df = 1 , p = 0 . 001 ) . Income , literacy , and the respondent’s level of education did not influence the response ( Table 3 , Fig 2 ) . The full model testing for changes in frequency of bushmeat consumption fitted the data significantly better than its corresponding null model ( LRT: χ2 = 21 . 029 , df = 4 , p <0 . 001 ) . The interaction of time period with income was a trend ( LRT: χ2 = 3 . 119 , df = 1 , p = 0 . 077; Table 4 , Fig 3 ) . In addition , the control predictor for local bushmeat prices was a trend ( LRT: χ2 = 3 . 813 , df = 1 , p = 0 . 051; Table 4 ) , indicating a decrease in bushmeat consumption frequency where local bushmeat prices were high . Only the control predictor for perceived risk of bushmeat consumption had a significant influence on the frequency of bushmeat consumption; households were more likely to decrease their consumption frequency if the household head believed that Ebola could be contracted from bushmeat ( LRT: χ2 = 8 . 731 , df = 1 , p = 0 . 003; Table 4 ) . We found a similar pattern regarding changes in the proportion of the community that preferred to eat bushmeat . There was a significant difference between the full model and its respective null model ( LRT: χ2 = 12 . 964 , df = 4 , p = 0 . 012 ) . The interaction of time period with income had a significant effect ( LRT: χ2 = 4 . 073 , df = 1 , p = 0 . 044; Table 5 , Fig 4 ) . Heads of low-income households thought that a smaller proportion of their community continued to prefer eating bushmeat during the crisis , whereas heads of high-income households perceived a smaller decrease in bushmeat consumption in their community . Both full models testing for changes in food diversity were not different from their respective null models ( change in number of food items consumed , LRT: χ2 = 3 . 841 , df = 4 , p = 0 . 428; change in number of food groups consumed , LRT: χ2 = 2 . 411 , df = 4 , p = 0 . 661 ) . As a complement to our models , we used descriptive statistics to further investigate changes in food diversity , and we found differences in the proportions of individual food items and food groups that were typically consumed in a meal before and during the crisis . Notably , the consumption of bushmeat dropped from 81% to 16 . 5% , while chicken and fish consumption increased from 11 . 3% to 46 . 3% and from 47 . 1% to 86 . 5% respectively ( Table 6 , Fig 5 ) . None of the three full models were different from their respective null models ( preference for monkey meat , LRT: χ2 = 2 . 317 , df = 1 , p = 0 . 128; duiker meat , LRT: χ2 = 0 . 525 , df = 1 , p = 0 . 469; and pangolin meat , LRT: χ2 = 0 . 106 , df = 1 , p = 0 . 74 ) . Thus , the likelihood of respondents preferring to eat these three species during the Ebola crisis did not change . The full model for changes in local bushmeat prices was not significantly different from the null model ( LRT: χ2 = 2 . 260 , df = 1 , p = 0 . 133 ) ; hence , prices for bushmeat did not significantly change during the Ebola crisis . Fish prices significantly increased during the crisis ( Table 7 ) by 19 . 5% , from an estimated 71 . 9 to 85 . 9 Liberian dollars per piece ( LRT: χ2 = 4 . 804 , df = 1 , p = 0 . 028 ) . Likewise , domestic meat prices increased during the crisis ( Table 7 ) by 34% , from 3757 . 0 to 5050 . 0 Liberian dollars per body ( LRT: χ2 = 8 . 864 , df = 1 , p = 0 . 003 ) .
The overall decrease in bushmeat consumption associated with the Ebola crisis may have had a short-term positive effect on vulnerable wildlife populations . Needless to say , however , that this should not advocate the use of fear for the disease as a medium for accomplishing conservation goals [68] . It is problematic to suggest that the epidemic presented a “silver lining” for conservation [69] because of the catastrophic impacts on human livelihoods and food security . Furthermore , spreading fear about the disease could backfire in that this may lead to attempts to eradicate the vectors of the deadly disease [68] . Conservation efforts should instead focus on developing strategies that are compatible with human livelihoods and food security [18] . Due to the complexity and variability of bushmeat consumption drivers that need to be addressed , multiple interventions may be required [51 , 70] . Based on our results , conservation strategies that aim to reduce bushmeat consumption in Liberia may be more effective by making a distinction between the consumption patterns of high-income households to those of low-income households . In addition , although we did not find an effect of literacy or education on bushmeat consumption during the Ebola crisis , environmental education should not be disregarded as an important component in conservation strategies , as it has been shown to correlate with environmental health [19 , 71] . Indeed , household heads were more likely to consume bushmeat less frequently if they thought that Ebola could be contracted from bushmeat , suggesting that knowledge about the disease had an impact on bushmeat consumption patterns . This also means that there is a difference in the effect of general education and specific knowledge on human behavioural patterns . Law enforcement is often called for as an indispensable means of mitigating the illegal bushmeat trade ( e . g . , [70 , 72 , 73] ) . The enforcement of laws that prohibit the sale and consumption of protected and endangered species in urban markets is crucial for reducing the demand for bushmeat of high-income , urban households [18] . However , a complete ban on bushmeat is unrealistic , given that poor households that rely heavily on bushmeat as a source of nutrition will be negatively impacted; especially if alternative sources of protein are not provided [49 , 70 , 74] . It is therefore important to distinguish between resilient species that may still be hunted sustainably from those that are too vulnerable to be harvested [20] . Providing alternative income and protein sources could reduce the reliance on bushmeat of low-income households [19 , 70 , 75] . Similar to other studies [17 , 19 , 76] our results indicate that fish is an important alternative protein source to bushmeat . However , if fish represents a direct replacement for bushmeat , it is necessary to improve the management of domestic fisheries to help increase the sustainability of fish stocks [76 , 77] . Similarly , meat from domestic animals may be an acceptable replacement for bushmeat; however , the negative environmental impacts associated with increased livestock production must be reduced through proper management [70 , 77] . Furthermore , it is also important to secure the availability of staple foods such as grains , roots and tubers throughout the year [70] and expand the use of plant proteins , such as dried beans and other pulses , which have a long shelf life when stored properly and could provide a readily-available source of protein in times of crisis [78] . | The consumption of wild animal meat , commonly known as bushmeat , is widespread throughout tropical regions . Bushmeat provides an essential source of protein and income for human livelihoods . However , its consumption is linked to the transmission of zoonotic diseases , such as Ebola , and its over-harvest is a major threat to many wildlife species . The bushmeat trade therefore encompasses a broad range of socio-economic and ecological issues . As such , we think it is highly important to use an interdisciplinary approach to investigate the drivers of bushmeat consumption , to improve our understanding and management of future crises . Our analysis of household interview data collected during two surveys across Liberia shows that there was an overall decrease in bushmeat consumption during the recent Ebola crisis . However , the consumption of bushmeat in wealthier households decreased less than in poorer households . In addition , we found that daily meal frequency decreased during the crisis , and the diversity of food items and preferences for bushmeat species remained constant . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"animal",
"types",
"medicine",
"and",
"health",
"sciences",
"domestic",
"animals",
"tropical",
"diseases",
"diet",
"social",
"sciences",
"ebola",
"hemorrhagic",
"fever",
"neuroscience",
"animals",
"habits",
"animal",
"products",
"physiological",
"processes",
"research",... | 2017 | The socio-economic drivers of bushmeat consumption during the West African Ebola crisis |
Many mRNAs specifically localize within the cytoplasm and are present in RNA-protein complexes . It is generally assumed that localization and complex formation of these RNAs are controlled by trans-acting proteins encoded by genes different than the RNAs themselves . Here , we analyze slow as molasses ( slam ) mRNA that prominently colocalizes with its encoded protein at the basal cortical compartment during cellularization . The functional implications of this striking colocalization have been unknown . Here , we show that slam mRNA translation is spatiotemporally controlled . We found that translation was largely restricted to the onset of cellularization when Slam protein levels at the basal domain sharply increase . slam mRNA was translated locally , at least partially , as not yet translated mRNA transiently accumulated at the basal region . Slam RNA accumulated at the basal domain only if Slam protein was present . Furthermore , a slam RNA with impaired localization but full coding capacity was only weakly translated . We detected a biochemical interaction of slam mRNA and protein as demonstrated by specific co-immunoprecipitation from embryonic lysate . The intimate relationship of slam mRNA and protein may constitute a positive feedback loop that facilitates and controls timely and rapid accumulation of Slam protein at the prospective basal region .
Subcellular RNA localization is a widespread phenomenon [1–4] . A large-scale survey of RNA localization in Drosophila embryos by RNA in situ hybridization with fluorescent probes revealed that about 70% of all tested transcripts were distributed in a specific subcellular pattern , such as apical or basal localization [5] . The physiological relevance of RNA localization is unknown for most of these transcripts , however . The function and mechanism of RNA localization have been studied in detail in numerous cases . Specific localization is usually mediated by cis-acting elements within the transcript and trans-acting factors [6–9] . Trans-acting factors are encoded by transcripts different than the localizing RNA , in most cases . We reported previously that slow as molasses ( slam ) RNA and protein strikingly colocalize during early Drosophila development [10] . However , the function and underlying mechanism of slam mRNA-protein colocalization have so far not been analyzed . slam is a key player in the change from syncytial to cellular development [11–14] . slam is specifically expressed during this stage and is required for furrow invagination and separation of cortical domains during cellularization and , later , for germ cell migration [10 , 15 , 16] . slam mRNA and protein strongly accumulate at the basal cortical domain , which forms the so-called furrow canal ( FC ) . For the specification of the basal domain , slam functions redundantly with nullo . Markers of the FC such as Dia are present in slam mutants but are uniformly distributed in slam nullo double mutants [17] . An important feature of the transition from syncytial to cellular development is the rapid and coordinated change of several processes within a few minutes only . As zygotic gene expression gradually increases during the course of many minutes , additional posttranscriptional mechanisms potentially control the temporally and spatially regulated activity of the key players such as slam to ensure a switch-like change in behavior of the cellular processes from nuclear cycle 13 to cycle 14 . Here , we investigate the functional and molecular interactions of slam mRNA and protein and address the function of slam RNA localization and RNA-protein colocalization . In addition to functional interactions , we identified a specific biochemical interaction , in that Slam protein specifically coprecipitated with slam mRNA .
slam RNA and protein ( about 4 , 000 nucleotides and 1 , 173 amino acid residues , S1 Fig ) mark the basal region of the cellularization furrow , the FC ( Fig 1A and 1B ) [10] . Morphologically visible furrows emerge within a few minutes after the last mitosis by invagination of the plasma membrane between adjacent nuclei ( Fig 1A ) . slam RNA and protein colocalization was also observed when Slam was ectopically localized ( Fig 1B ) . We achieved ectopic Slam protein localization by employing embryos from nuf females , which are impaired for the recycling endosomes [17 , 18] . In these embryos , we detected slam RNA together with Slam protein at the apical membrane . These data show that slam RNA and protein colocalize also in situations of ectopic localization and indicate that the mechanisms controlling slam RNA and protein localization are interconnected . Previous expression and histological analysis indicated an up-regulation of slam RNA and protein during the blastoderm stage [10 , 15 , 16] . Expression analysis by NanoString technology defined the window of strong up-regulation to nuclear cycle 13 and early cellularization ( cell cycle 14 ) [19] . We confirmed these data by measuring total RNA and protein levels . We found an about 10-fold up-regulation of total RNA levels during cellularization ( 2–3 h ) by quantitative PCR ( S2A Fig ) and a peak of total protein levels in extracts from embryos in cellularization ( S2B Fig ) . Slam is required for formation of the cellularization furrows [15 , 16] and identity of the basal domain [10] . As slam RNA and protein are strongly enriched at the FC , we next analyzed accumulation of slam RNA and protein specifically at the FC in fixed and stained embryos , which were staged by nuclear cycle and by the length of the furrow and nuclei ( S2C Fig ) . By quantification of the fluorescence signal at the FC , we observed a strong ( 6-fold ) up-regulation of both localized RNA and protein within a short period of a few minutes at the transition from mitosis 13 to interphase 14 ( Fig 1C ) . Following this up-regulation , protein levels remained constant , whereas RNA levels gradually decreased after 30 min during the second half of cellularization ( Fig 1C ) . Based on the colocalization of RNA and protein , we hypothesized that translation of slam might be linked to RNA localization . To address this hypothesis , we first defined Slam protein stability and the timing of translation . slam RNA may be translated with a constant rate throughout cellularization or specifically during onset of cellularization . To separate the contribution of translation and degradation to the steady state levels of Slam protein , we first measured the stability of green fluorescent protein ( GFP ) -slam protein during cellularization ( Fig 2A , S1 Fig ) . GFP-slam serves as a proxy for untagged Slam , because GFP-slam can rescue the cellularization phenotype of slam mutants . Furthermore , the dynamics of GFP-slam largely reflect the dynamics of Slam protein [17] . To measure the half-life of GFP-slam , we injected cycloheximide into embryos in cellularization to stop new translation ( S3A–S3C Fig ) and recorded the persistence of GFP-slam fluorescence . From the measured decay of GFP-slam during periods of 10 min , we extrapolated an estimated half-life of approximately 40 min ( Fig 2A and 2B ) . A half-life of 40 min is in the range of the length of cellularization and thus indicates that GFP-slam is quite a stable protein . Together with the relatively stable expression levels of GFP-slam as well as endogenous Slam protein ( Fig 1C , S2B Fig ) , the long half-life indicates a low translation rate of slam mRNA after the initial phase of cellularization . We could not measure the role of GFP-slam synthesis for the dynamics of GFP-slam during the onset of cellularization , as injection of cycloheximide would induce a mitotic arrest . Secondly , we analyzed the mobility of GFP-slam by quantifying fluorescence recovery after photobleaching ( FRAP ) . Previously , we found that the recovery of GFP-slam fluorescence dramatically changes from fast and complete during the onset of cellularization to slow and incomplete recovery during the course of cellularization [17] . The slow fluorescence recovery after the initial phase of cellularization may be due to the exchange of bleached and unbleached molecules by mobile GFP-slam molecules . Alternatively , fluorescence recovery may be due to translation of new GFP-slam molecules . We distinguished these 2 options by FRAP experiments in embryos , in which new GFP-slam synthesis was blocked by cycloheximide . We observed a comparable recovery rate with and without cycloheximide . Thus , new GFP-slam translation does not contribute to fluorescence recovery during cellularization ( Fig 2A and 2C ) . These data are consistent with the long half-life of GFP-slam during cellularization . Furthermore , the slow exchange of Slam molecules did not require vesicle budding , because a mutation in Dynamin ( shibire ) [20] had little influence on the recovery rate ( Fig 2D ) . In summary , our data suggest that Slam protein is largely synthesized during a short period of a few minutes at the transition from mitosis 13 to interphase 14 . Following this initial phase , Slam protein is subject to low turnover during the remainder of cellularization . Next , we addressed the spatial and temporal dimension of Slam protein accumulation at the basal domain . Protein accumulation may be due to recruitment of Slam protein from the cytoplasm to the basal compartment or , alternatively , to local translation of slam RNA . To distinguish these 2 options , we employed a recently developed method to fluorescently label not yet translated mRNA molecules [21 , 22] . We generated genomic transgenes with a bacteriophage PP7 ( PP7 ) hairpin loop inserted in the slam coding sequence ( Fig 3A , S1 Fig ) . We inserted the PP7 sites close to the stop codon of the mRNA in order to assay all slam transcripts , including the mRNAs , which have initiated but not completed translation . Corresponding transcripts bind a bacteriophage PP7 coat protein ( PCP ) -GFP marker protein only until a ribosome has moved over the PP7 site [21 , 22] . Thus , only not yet translated transcripts are labelled by PCP-GFP fluorescence . The slamPP7 RNA shows a spatial and temporal expression pattern similar to endogenous slam RNA , persisting until the second half of cellularization ( S2D Fig ) . In fixed embryos , we detected a dotted PCP-GFP signal ( Fig 3B ) . PCP-GFP staining largely colocalized with Slam protein at the basal region . The staining was dynamic and only visible in embryos in early cellularization . Time-lapse imaging allowed a precise timing of PCP-GFP dynamics ( Fig 3C ) . The dotted signal was largely observed during the onset of cellularization and quickly disappeared during the following minutes . These data show that a proportion of slam mRNA molecules reaches the basal domain before the first ribosomes have passed 3′-located PP7 sites and the first round of translation has been completed . Correspondingly , these data suggest that the synthesis of at least a fraction of the Slam protein molecules is completed at the FC . As the localized PCP signal quickly disappears , much fewer or no new and not yet fully translated slam RNA molecules reach the FC during cellularization . Given the peculiar temporal and spatial restrictions of slam translation , we functionally analyzed the role of RNA protein colocalization . We established a complementation assay either with injection of in vitro transcribed RNA or with zygotic expression from a transgene ( Fig 4A ) . Injected slam RNA with a fluorescent label accumulated at the basal region as revealed by time-lapse imaging ( Fig 4B , S1 Movie ) . Localization of the injected RNA and its derived protein was confirmed by staining for RNA and protein in fixed embryos . We did not observe an influence of GFP or myc tags on RNA and protein localization ( S4A Fig ) . RNA and protein localization was also reconstituted in embryos lacking any endogenous slam RNA and protein ( S4A Fig ) . slam deficient embryos were recognized by the absence of endogenous slam RNA and protein and staining at the injection site . These experiments demonstrate that the localization of slam RNA and colocalization of slam RNA and protein can be reconstituted in vivo . In a simple model , slam RNA would accumulate independently of Slam protein at the basal region . Alternatively , Slam protein may be involved in the localization of its mRNA . To distinguish these 2 options , slam RNA with an early stop codon ( GFP-stop-slam RNA ) ( Fig 4C ) was introduced into embryos that are maternally and zygotically deficient for the slam locus ( “m−z−”[17] ) . Such embryos were derived from females with slam germ line clones crossed to slam heterozygous males . Fifty percent of the embryos from such a cross are slam ( ”m−z−“ ) deficient and 50% of the embryos zygotically express slam ( “m−z+ , ” zygotic rescue ) ( Fig 4C ) . These 2 genotypes were easily distinguished by staining for endogenous Slam protein and by their morphology . The zygotically rescued embryos served as an internal reference for the injection and staining procedure . We could score for localization of the injected RNA also in slam deficient embryos , because the FC/basal domain is specified even in the absence of slam [17] . We did not detect localizing RNA in slam deficient embryos . The signal for GFP-stop-slam RNA was uniformly distributed at the apical surface . In contrast , in control embryos , the injected RNA colocalized with endogenous Slam protein ( Fig 4D ) . The GFP-stop-slam RNA localization in control embryos may be due to an interaction with Slam protein or with the endogenous slam RNA . We favor the first option , because injected slam RNA encoding functional Slam protein is sufficient for slam RNA and protein localization in embryos lacking endogenous slam RNA ( S4A Fig ) . Taken together , these experiments show that Slam protein is required for slam RNA localization at the FC . Next , we asked whether slam RNA localization is required for protein localization . For this , we mapped multiple regions within the 5′ untranslated region and the coding sequence , which are sufficient for localization at the FC ( S4 Fig ) . Given the complexity of multiple parts contributing to RNA localization , we generated a novel slam gene , slam alternative codon usage ( [ACU] ) , in which a majority of the codons were replaced by synonymous codons ( Fig 5A , S1 and S5 Figs ) . slam[ACU] RNA lost the ability to localize to the FC ( Fig 5A and 5B ) . slam[ACU] RNA was uniformly distributed in the cytoplasm in the presence or absence of endogenous slam , when expressed from a transgene ( myc-GFP-slam[ACU] ) or injected as synthetic RNA ( myc-slam[ACU] ) ( Fig 5A and 5B ) . These data indicate that our mutagenesis strategy successfully impaired the FC localization of slam RNA . Having generated a nonlocalizing slam[ACU] RNA , we could test whether Slam protein accumulation at the FC would depend on RNA localization . We stained embryos injected with the nonlocalizing slam[ACU] mRNA for Slam protein ( Fig 5B ) . slam embryos were recognized by the absence of overall slam RNA or protein signal . Staining restricted to the injected site is due to the injected construct . We clearly detected Slam protein at the FC in slam embryos ( Fig 5B ) . We conclude that Slam protein has an intrinsic RNA-independent affinity for the FC . These data also indicate that we preserved the coding capacity of slam[ACU] . We got the impression that protein levels with slam[ACU] were lower than with wild-type slam . The difference may be due to inefficient translation of slam[ACU] , to inefficient localization of Slam protein , or to the injection procedure resulting in lower mRNA levels than endogenous slam expression . In order to test translation efficiency in embryos , we generated a genomic transgene with the slam[ACU] sequence , preserving introns , 5′ untranslated regions , and 3′ untranslated regions ( Fig 5C , S1 Fig ) . A corresponding genomic transgene with the endogenous coding sequence fully complements a slam deficiency [17] . slam[ACU] RNA expressed from the genomic transgene was comparably abundant as the endogenous wild-type allele ( Fig 5D ) , confirming the integrity of the transgene and transcript . slam[ACU] RNA showed a low degree of FC localization in wild-type background and slightly more so in slam deficient embryos ( Fig 5C ) . This low degree of localization is likely due to the 5′ untranslated region , which is sufficient to localize at the FC in the injection assay ( S4B Fig ) . Next , we assayed protein levels by embryo staining and western blot with total extracts . Staining for Slam protein in slam[ACU] embryos in comparison to rescued siblings revealed strongly reduced protein levels at the FC ( Fig 5C and 5E ) . A similarly clear difference in total protein levels was detected by western blot ( Fig 5F ) . For this , we manually sorted embryos according to stage ( mid-cellularization ) and genotype . No Slam was detected in slam deficient embryos ( Fig 5F ) . slam[ACU] embryos contained much less Slam protein than the rescued siblings , which zygotically expressed Slam from the endogenous gene ( Fig 5F ) . These data indicate Slam protein derived from slam[ACU] is much less abundant and that slam[ACU] was thus much less translated than wild-type slam . The reduced translation may be due to secondary RNA structures or codon usage affecting translation efficiency . We distinguished these options by expression of slam[ACU] in comparison to slam[WT] in cultured Drosophila melanogaster Schneider 2 cells ( S2 cells ) . S2 cells do not express Slam protein in detectable levels ( S6A Fig ) . Following transient transfection , we detected comparable slam RNA and protein levels for slam[ACU] and wild-type slam ( S6B and S6C Fig ) . These data indicate that generic translation in S2 cells is comparably efficient for slam[ACU] and wild-type slam . These data do not rule out the conceivable option that slam[ACU] contains secondary RNA structures or peculiar codon usage affecting translation efficiency that are specifically present in the embryo but not in S2 cells . Consistent with the reduced protein expression from slam[ACU] in embryos , the slam[ACU] genomic transgene only partially complemented the cellularization phenotype and did not rescue the lethality of a slam deficiency . Embryos maternally and zygotically deficient for slam but with slam[ACU] only formed a short cellularization furrow and did not complete cellularization ( S7 Fig ) . This functional test showed that efficient expression of slam is physiologically important . Taken together , these data suggest that localization of slam RNA at the basal domain or its colocalization with Slam protein are important for full translation . The transient FC accumulation of not yet translated slam RNA suggests a local translation of slam , at least partially ( Model 1 , Fig 6A ) . In addition to local translation , slam RNA may recruit Slam protein to the basal region , which was synthesized by translation within the cytoplasm ( Model 2 , Fig 6A ) . To functionally assess the significance of Model 2 , we injected a translation-incompetent but localization-competent slam RNA ( GFP-stop-slam ) into the slam[ACU] embryos ( Fig 6B ) . We would expect localization of the injected RNA and in the case of Model 2 , a corresponding local increase of Slam protein levels . We detected the injected GFP-stop-slam RNA at the FC in levels comparable to slam in control embryos ( Fig 6B ) . Slam protein staining was uniform in low levels , similar to slam[ACU] embryos ( Fig 6B ) . We did not detect any increased signal at the injection site . These data suggest that localized slam RNA does not attract cytoplasmic Slam protein to the FC . A key feature of slam is , on the one side , the functional interdependence of RNA and encoded protein and , on the other side , the temporally and spatially restricted translation . We hypothesized that the functional interaction of slam RNA and protein is based on a biochemical interaction . By Slam immunoprecipitation , we found that slam mRNA was enriched in the bound fraction as compared to immunoprecipitates by Dia antibodies ( Fig 7A and 7B ) . The formin Dia nucleates and elongates F-actin and is enriched at the FC [23–25] . The enrichment of slam RNA was higher than for a series of control RNAs ( Fig 7A ) . We confirmed the specific association of slam RNA with Slam protein by conducting immunoprecipitation with single-chain GFP antibody ( GFP-binder ) and lysates of embryos expressing GFP-slam from a genomic transgene in comparison to wild-type extracts ( Fig 7C and 7D ) . We found a higher enrichment of slam mRNA than other RNAs in the bound fraction ( Fig 7C ) . This enrichment confirmed the specificity of the RNA-protein interaction . To assess whether the bound fraction contained other mRNAs beside slam RNA , we identified associated transcripts in an unbiased manner by next generation sequencing ( Fig 7E ) . RNA was isolated and sequenced from the bound fractions of the immunoprecipitation experiments with Slam antibody and GFP-binder , including the controls . In the bound fractions , we detected 102 and 16 transcripts , respectively , which were enriched more than 4-fold ( Fig 7E , S1 Table ) . The intersection of the 2 experimental approaches contained slam with the strongest enrichment and 2 more unrelated transcripts . These data indicate that slam mRNA is present in a specific complex with Slam protein in embryos . Although slam is among the most abundant transcripts during this stage , none of the other abundant transcripts were enriched in our immunoprecipitation experiments ( S1 Table ) . As the primary structure of Slam does not contain any obvious structural domains related to RNA binding , other RNA binding proteins likely mediate the biochemical association of slam RNA and protein .
The primary function of slam RNA is encoding Slam protein . In addition to coding information , slam RNA contains 2 more pieces of noncoding information: ( 1 ) information for specific subcellular localization of the RNA at the FC , which is at least partially mediated by an interaction with Slam protein , and ( 2 ) information for spatial and temporal control of translation , which is high at the FC during the onset of cellularization . By comparing wild-type RNA with a variant RNA , slam[ACU] with the same coding information , the relevance of the noncoding information was uncovered . slam[ACU] RNA is widely distributed in the cell and gives rise to much less Slam protein . Containing coding and noncoding information distinguishes slam RNA from generic mRNAs , which contain only coding information . slam RNA may be related to the growing class of mRNAs with coding and noncoding functions ( cncRNA ) [26] . Using the injection assay with synthetic RNA transcribed from truncation constructs , we identified several regions of slam RNA that are sufficient for localization to the FC in wild-type embryos . This includes the 5′ untranslated region and at least 2 large regions within the coding sequence , which we have so far not further defined . Each of these regions is able to localize to the FC on its own in wild-type embryos , which indicates redundancy in the mechanism of RNA localization . Interpretation of these data is difficult , however , given that endogenous slam RNA and protein were present in our assay , which may lead to localization by oligomerization or other indirect binding mechanisms . slam RNA is subject to spatiotemporal control of translation . Although the RNA is present in high levels during the first half of cellularization , translation is restricted to the initial minutes of cellularization . The almost constant protein levels throughout cellularization are due to the stability of Slam protein , as inhibition of new synthesis by cycloheximide leads only to a weak decrease of GFP-slam fluorescence . In contrast to these constant levels during cellularization is the sharp increase in protein levels during the onset of cellularization . This initial rise in protein levels is partly due to the transcriptional up-regulation of slam . The transcriptional regulation appears not to be sufficient , as we observed a striking difference between slam[wild-type] RNA with slam[ACU] RNA . Although both RNAs contain the same coding information and give rise to similar amounts of Slam protein in cultured cells , slam[wild-type] RNA is more efficiently translated than slam[ACU] RNA in blastoderm embryos . Based on the correlation of impaired RNA localization and reduced translational efficiency , we favor the model that efficient translation is linked to RNA localization or interaction with Slam protein at the FC . However , the embryo-specific lower efficiency of slam[ACU] translation may alternatively be due to secondary RNA structures or disadvantageous codons , which were introduced in our mutagenesis . A particular feature of slam is that the encoded protein is required for the elaboration of noncoding features . slam RNA requires Slam protein for accumulation at the FC . Not only do we observe a functional interaction of the RNA and protein but also colocalization and biochemical association , indicating molecular interactions . These molecular interactions between RNA and protein may be direct or indirect . They are likely to be indirect , as Slam protein does not contain a canonical RNA binding domain or does not share any detectable sequence homology to RNA binding proteins . Analysis of transcripts associated with Slam protein provided distinct lists depending on the experimental procedure . Importantly , slam RNA was identified by both approaches , which is consistent with our quantitative polymerase chain reaction ( qPCR ) analysis for a few selected genes . The list of associated transcripts contained transcripts with high and low abundance , indicating that the procedure was sufficiently sensitive to also detect weakly expressed genes . Screening through the gene functions , we did not detect a specific set of genes , such as genes involved in cellularization or cytoskeletal organization . The biological function of the intimate relationship of slam mRNA and its protein has been unclear . Given the observed specific spatiotemporal profile of slam translation and Slam dynamics , we propose the following model ( Fig 8A ) . Initially , Slam protein accumulates in low levels at the FC independently of its mRNA . Starting with the onset in zygotic transcription , slam mRNA exits the nucleus as part of a complex that is not competent for efficient translation . At least a fraction of slam RNA molecules accumulates at the basal domain forming the FC prior to the first round of translation . At the basal domain , slam mRNA becomes competent for efficient translation , which leads to an increase in Slam protein at the FC . The increased amounts of Slam protein further promote accumulation of slam mRNA , leading to even more Slam protein . Such a mechanism constitutes a positive feedback loop , which provides an explanation for the observed switch-like profile of Slam protein staining at the FC ( Fig 8B ) . Some minutes later , when full Slam levels have been reached , translation is turned off . Slam protein then functions in spatially restricted activation of Rho signaling , actomyosin organization , Patj recruitment , cortical compartmentalization , and furrow invagination ( Fig 8 ) [10 , 17 , 25] . As slam is a key regulator of cellularization , the rapid increase of Slam protein may be important for a coordinated and timely onset of its downstream processes . We identified slam as an mRNA with noncoding information for localization and translational control in addition to its coding information . On the molecular level , slam is special in that mRNA and protein associate in a complex as demonstrated by co-immunoprecipitation . This unconventional relationship of slam RNA and protein may be important for the tightly restricted subcellular localization and strong increase in protein levels at the FC . The functional interaction of slam RNA and protein constitutes a positive feedback loop , which contributes to the fast increase in Slam protein levels at the FC .
Fly stocks were obtained from the Bloomington stock center , if not otherwise noted . The following fly strains and mutations were used: Df ( 2L ) slam Frt2L slam5′rescue [17] , nuf1 [18] , shi [20] . The following transgenes were used: slam[wild-type] , genomic transgene with the slam locus [17] , GFP-slam , genomic transgene with GFP at N-terminus , slam[ACU] , genomic transgene with alternative coding sequence , slamPP7 , genomic transgene with PP7 sites inserted within the coding sequence , UASp-myc-GFP-slam[wild-type] , UASp-myc-GFP-slam[ACU] , transgenes with GAL4 driven expression . Genomic transgenes were generated by PhiC31 integrase-mediated site-specific insertions on the third chromosome at cytological position 68A4 [27] . slam deficient embryos were generated by slam germ line clones in progenies of the cross Df ( 2L ) slam Frt2L slam5′rescue/CyO females with hs-Flp122; ovoD2L Frt2L/CyO males and heat shock for 2× 1 h at 37°C in larvae [17] . The Df ( 2L ) slam Frt2L slam 5′rescue is also deficient for some proximal genes in addition to the slam locus . These genes do not function in early embryonic development , as the cellularization phenotype is rescued with the genomic slam transgene [17] . For microinjection experiments , females with slam germ line clones were crossed with males Df ( 2L ) slam Frt2L slam5′rescue/ CyO . In the case of transgenes , females with slam germ line clones ( hsFlp122/+; Df ( 2L ) slam Frt2L slam5′rescue/ovo[D2L] Frt2L; transgene/+ were crossed with males Df ( 2L ) slam Frt2L slam5′rescue/CyO; transgene/transgene . Rescue of viability was tested with Df ( 2L ) slam Frt2L slam5′rescue/Df ( 2L ) slam Frt2L slam3′rescue; transgene/+ [17] . All embryos from crosses with slam germ line clones are maternally deficient for slam . 50% of the progeny are also zygotically deficient for slam , whereas the other half contain a zygotic wild-type allele of slam ( zygotic rescue ) . Embryos with zygotic rescue form a furrow during cellularization and have strong and uniform slam RNA and protein expression . For imaging of not yet translated slam RNA , embryos were obtained from females expressing PCP-GFP [21 , 22] crossed to males with the slamPP7 transgene . Mapping of the RNA localization regions was performed with wild-type flies ( OregonR ) . The shibire phenotype was induced in embryos from heterozygous females by shifting the embryos to 32°C 10 min prior to the FRAP experiment [17] . Truncations and gene fusions of slam cDNA as specified in Table 1 were cloned by PCR-based cloning . The vector pCS2 or pCS2-6xmyc ( obtained from R . Rupp ) contains a short leader sequence derived from the beta globin leader sequence . Genomic constructs were cloned into a pattB plasmid , suitable for generation of transgenic flies [27] . Details of the cloning procedures and PCR are available on request . cDNA constructs were based on cDNA clone LD22808 , which served as template for PCR . In comparison to other cDNA clones available now , LD22808 lacks 69 nucleotides at position CDS 1083 , which do most likely represent an additional , rarely used intron . The slam gene with alternative codon usage ( slam[ACU] ) was designed by each 3-nucleotide codon with another suitable codon , according to the codon usage frequency in Drosophila [28] and synthesized by MWG Eurofins . Noncoding parts of the gene ( 5′ and 3′ untranslated regions and introns ) were not mutated . slam[ACU] was cloned into the pattB-slam8 . 6 genomic construct [17] by PasI and SphI restriction sites . Introns were preserved . For slamPP7 , a sequence with 12xPP7 [21 , 22] was inserted at the unique SacII site of the coding sequence within the 8 . 6-kb genomic DNA of the slam locus cloned in pBKS and transferred to the transformation vector pattB . This construct also contained a 6x MS2 site inserted at position 50 of the 3′ untranslated region . For the transgenes with GAL4 driven expression pUASp-myc-GFP-slam and pUASp-myc-GFP-slamACU , slam and slam[ACU] , cDNAs were cloned in frame into pUASp-myc-GFP . For microinjection , capped transcripts were synthesized with linearized plasmid templates and the SP6 mMESSAGE mMACHINE high yield capped RNA transcription kit ( Applied Biosystems ) . For live imaging of injected RNA , the reaction mix was complemented with 0 . 5 μl aminoallyl-UTP ( 25 mM , Jena bioscience ) . Isolated RNA ( 4 μl ) was labeled with 5 μl Rhodamine Red-X , ( succinimidyl ester in 56 μl DMSO , Invitrogen ) , 1 μl 0 . 1 M Na-Borate [pH 9] at RT overnight . The labeling dye and salts were removed using a desalting Sephadex G50 spin column . For RNA in situ hybridization , slam RNA probes were generated with T7 RNA polymerase and antisense templates as previously described [10] . Total RNA was extracted from staged embryos using Trizol Reagent ( Invitrogen ) . Reverse transcription was performed with 1–2 μg RNA and oligo-dT primers according to the instructions of the manufacturer ( Roche ) . Two μl ( 12 . 5% of the sample ) of reverse transcripts were analyzed by quantitative PCR with specific primers . qPCR reactions were performed in duplicates . Specificity was controlled by a sample in which reverse transcriptase enzyme was omitted and in reactions with defined amounts of templates . The following primer pairs were used for quantitative PCR: WT1 SY88 ( 603–622 ) + SY89 ( 885–903 ) , WT2 SY94 ( −334–−316 ) + SY95 ( −24–−1 ) , ACU SY92 ( 603–620 ) + SY93 ( 886–905 ) . ACU is specific for the slam[ACU] allele . The numbers in parentheses specify the position of the nucleotide within the cDNA , according to the coding sequence . For genome-wide analysis , RNA extracted from immunoprecipitates was subjected to next generation sequencing ( Illumina HiSeq2000 ) , according to the manufacturer’s protocols . Analysis was performed with 2–4 biological replicates . Weakly expressed genes ( N < 50 or N < 10 for the experiment with Slam/Dia antibodies or GFP binder , respectively ) were not considered in the analysis . RNA expression data were obtained from wild-type embryos with 1 . 5–2 . 5 h ( [29] , GEO Series accession number GSE97557 ) . The data from Next Generation Sequencing have been deposited in NCBI’s Gene Expression Omnibus [30] and are accessible through GEO Series accession number GSE99761 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE99761 . Embryos were fixed in 8% formaldehyde in phosphate-buffered saline ( PBS ) and fluorescent RNA in situ hybridization was performed as previously described [10] . Protein staining with a specific antibody was performed after RNA staining . The following antibodies were used: primary antibodies: rabbit/guinea pig-α-Slam [10] , mouse-α-myc-9E10 ( Roche ) ; secondary antibodies: Alexa-coupled goat-anti-mouse/rabbit/guinea pig antibodies ( Invitrogen ) , peroxidase coupled α-digoxigenin Fab-fragments ( Roche ) . RNA probes against slam cDNA sequence , against slam[ACU] , against GFP , or against PP7 were used . DNA was costained by DAPI . Embryos were mounted in Aquapolymount . Fluorescent images of fixed and immune-stained embryos and live imaging experiments were recorded with a confocal microscope ( Zeiss LSM780 ) . Images were processed with ImageJ/Fiji and Photoshop ( Adobe ) . Images from FRAP experiments were analyzed as previously described [17] . Time-lapse recordings of embryos injected with fluorescently labeled slam RNA were recorded with a Leica epifluorescence microscope . Time-lapse recordings with differential interference contrast optics were recorded with an inverted microscope ( Zeiss Observer Z . 1 , 25× NA0 . 7/oil ) with a computer-controlled stage , allowing simultaneous recordings of multiple embryos . Embryos were injected as previously described [10] . The mRNA constructs were injected at concentrations of about 2–5 μg/μl . Fixation was performed about 1–1 . 5 h after injection , when the majority of embryos were in the cellularization stage . Cycloheximide ( 1 mg/ml ) or buffer ( 0 . 1 M Na-phosphate , 5 mM KCl ) was injected from the posterior end . About 10 minutes later imaging or FRAP was started . The embryos contained a genomic GFP-slam transgene . slam wild type and slam[ACU] ( on pCS2 plasmid , driven by a CMV promoter ) were tested for protein production levels in cultured D . melanogaster Schneider line 2 ( S2 ) cells . S2 cells were grown in Schneider’s Drosophila medium ( Gibco/Invitrogen ) supplemented with 10% fetal bovine serum ( Gibco/Invitrogen ) at 25°C . Cells were reseeded 1 day before transfection at about 1 . 0 million cells/ml ( counted in Neubauer chamber ) . Transfection was performed according to the instruction of the manufacturer ( Qiagen Transfection Kit containing Effectene Transfection Reagent ) . A control reaction ( using pCS-GFP ) indicated an approximately 6% transformation efficiency . Cells were harvested after 48–72 h . Half of the cells were dissolved in SDS loading buffer ( Laemmli ) to a final concentration of 50 , 000 embryos/μl . SDS gel electrophoresis was performed with 1 million cells per sample lane . RNA was extracted from the second half of the cells using Trizol Reagent ( Invitrogen ) . Guinea pig-α-Slam antibody [10] or guinea pig-α-Dia antibody [24] was bound to Dynabeads coated with Protein A ( Invitrogen ) in PBT for 1 h at 4°C . Beads were washed 5 times with PBT . Embryonic extracts were prepared by lyzing about 100 mg of 1 . 5–3-hour-old embryos in 1 ml YSS buffer [31] ( 50 mM Tris/HCl [pH8] , 75 mM NaCl2 , 1 mM MgCl2 , 0 . 05% NP40 , 100 mM sucrose , 1 M DTT , protease inhibitors [Roche] , RNAase inhibitors ) in a Dounce homogenizer . The lysate was centrifuged for 15 min at 14 , 000 rpm to remove debris . The cleared supernatant was mixed with the beads coated with antibody and incubated on a rotator for 1 h at 4°C . A 200-μl sample was taken from the supernatant ( unbound fraction ) . Beads were then washed 4 times with cold YSS buffer . 200 μl of YSS buffer was added to the bound samples . Unbound and bound samples were complemented with 20 μl 10% SDS and 0 . 5 μl glycogen ( 20 μg/ml ) . Samples were extracted with phenol/chloroform/isoamylalcohol . Protein was precipitated from the organic phase with 1 ml acetone . RNA was precipitated by the addition of 40 μl Na-acetat ( 3 M ) and 500 μl 100% ethanol to the aqueous phase . Protein and RNA samples were analyzed by western blot , quantitative RT-PCR , and next generation sequencing , respectively . For co-immunoprecipitation with the single-chain GFP antibody ( GFP binder or GFP-TRAP , Chromotek ) , staged embryos from GFP-slam transgenic or wild-type flies were collected and lysed as described above , except for the following: 0 . 5 μl biotinylated-GFP binder ( 50 mM ) was mixed with cleared embryo lysate for 1 h at 4°C . Then , 20 μl PBS-washed streptavidin-coupled Dynabeads were added and mixed for a further 1 h at 4°C . SDS polyacrylamide gel electrophoresis ( PAGE ) and western blot were performed as previously described [10] . Primary antibodies were guinea pig or rabbit-α-Slam [10] , mouse-α-tubulin-α ( Sigma ) , and rabbit or guinea pig-α-Dia [22] . Western blots were developed with fluorescently labeled secondary antibodies ( 800CW Donkey-α-guinea pig/mouse/rabbit IgG ) and detected with a LICOR system . Sixteen-bit images were processed by Photoshop and FIJI/ImageJ . For analysis of embryos with defined genotype and stage , embryos were heat fixed as previously described [10] , stained for Slam and DAPI , and mounted in 50% glycerol . Under fluorescent microscope , embryos in cellularization were sorted by their zygotic genotype and stage . Sorted embryos ( N = 10–20 ) were lysed in Laemmli buffer with a Dounce homogenizer and analyzed by western blot . | While proteins and their encoding messenger RNAs share the same intracellular space during the translation process , thereafter they are usually spatially and biochemically separated . RNA localization follows a specific subcellular pattern—such as apical or basal—and is thought to have important physiological implications during development , which are generally independent from the protein function . Here , we investigate the potential mutual dependencies between slam mRNA and its encoded protein during cellularization in early Drosophila embryos . slam RNA and protein are known to colocalize and are essential for epithelial compartmentalization and timely invagination of the plasma membrane between adjacent nuclei . We now show that Slam protein is required for RNA localization at the basal domain and that this event is needed for efficient translation . In addition to the functional interactions , we find that slam RNA and protein are both present in a specific molecular complex . Our findings indicate that slam is locally translated and that the interaction between Slam protein and RNA constitutes a self-enhancing mechanism leading to the fast accumulation of Slam protein at the basal domain during the first minutes of cellularization . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"nucleic",
"acid",
"synthesis",
"non-coding",
"rna",
"sequences",
"messenger",
"rna",
"rna",
"extraction",
"green",
"fluorescent",
"protein",
"membrane",
"proteins",
"developmental",
"biology",
"luminescent",
"proteins",
"rna",
"synthesis",
"embryos",
"cellular",
"struc... | 2017 | Slam protein dictates subcellular localization and translation of its own mRNA |
Within sub-Saharan Africa , helminth and malaria infections cause considerable morbidity in HIV-positive pregnant women and their offspring . Helminth infections are also associated with a higher risk of mother-to-child HIV transmission . The aim of this study was to determine the prevalence of , and the protective and risk factors for helminth and malaria infections in pregnant HIV-positive Rwandan women receiving anti-retroviral therapy ( ART ) . Pregnant females ( n = 980 ) were recruited from health centres in rural and peri-urban locations in the central and eastern provinces of Rwanda . Helminth infection was diagnosed using the Kato Katz method whilst the presence of Plasmodium falciparum was identified from blood smears . The prevalence of helminth infections was 34 . 3%; of malaria 13 . 3% , and of co-infections 6 . 6% . Helminth infections were more common in rural ( 43 . 1% ) than peri-urban ( 18 . 0%; p<0 . 0005 ) sites . A CD4 count ≤350 cells/mm3 was associated with a higher risk of helminth infections ( odds ratio , 3 . 39; 95% CIs , 2 . 16–5 . 33; p<0 . 0005 ) and malaria ( 3 . 37 [2 . 11–5 . 38]; p<0 . 0005 ) whilst helminth infection was a risk factor for malaria infection and vice versa . Education and employment reduced the risk of all types of infection whilst hand washing protected against helminth infection ( 0 . 29 [0 . 19–0 . 46]; p<0 . 0005 ) ; ) . The TDF-3TC-NVP ( 3 . 47 [2 . 21–5 . 45]; p<0 . 0005 ) , D4T-3TC-NVP ( 2 . 47 [1 . 27–4 . 80]; p<0 . 05 ) and AZT-NVP ( 2 . 60 [1 . 33–5 . 08]; p<0 . 05 ) regimens each yielded higher helminth infection rates than the AZT-3TC-NVP regimen . Anti-retroviral therapy had no effect on the risk of malaria . HIV-positive pregnant women would benefit from the scaling up of de-worming programs alongside health education and hygiene interventions . The differential effect of certain ART combinations ( as observed here most strongly with AZT-3TC-NVP ) possibly protecting against helminth infection warrants further investigation .
Globally , the most common nematode species that cause soil-transmitted helminthic diseases are Ascaris lumbricoides , Trichuris trichiura , and the hookworm species Necator americanus and Ancylostoma duodenale [1] , [2] , [3] . Although morbidity due to helminths can be controlled by delivering preventive chemotherapy with antihelminthic medicines , elimination and finally eradication will not be achieved until affected populations have access to effective sanitation , sewage treatment and waste disposal; which remains a common problem in most rural African settings . In most of sub-Saharan Africa , the health burden of helminthic disease is enormous [4] . Co-infections with malaria and HIV are numerous and important causes of morbidity and mortality . Combating co-infections has been identified as an important public health goal [5] . Important areas of current research interests are the effects of helminth infections on immune regulation and their possible consequences for susceptibility to other infections and immunologically mediated conditions such as allergy and autoimmune diseases [6] . The immunological interplay between helminth infections and HIV is complex , and there are different hypotheses on the influence of the infections on each other; the most important being the Th2 bias induced by helminth infections , suppressing Th1 responses specific to HIV; thus leading to more rapid HIV progression [7] , [8] . HIV acquisition was positively correlated with female urogenital schistosomiasis [9] but in contrast , a randomized controlled trial ( RCT ) showed no benefit of deworming on prevention from mother to child transmission of HIV [10] . A few systematic reviews have been published describing the effect of anti-helminthic treatment on markers for HIV disease progression showing inconsistently beneficial effects of anti-helminthic treatment; lower increases in HIV viral loads and increases in CD4 counts have been reported [11]–[13] . However , results from a recent RCT did not suggest a beneficial role of empric deworming to delay HIV progression [14] . Risk factors for helminth infections depend on the route of transmission and the life cycles of the various helminth species; they are usually related to hygiene and sanitation [15] . The geographical distribution of helminth infections is largely influenced by several environmental factors such as climate and presence of stagnant water bodies [16] . In the absence of vaccinations , the only currently recommended public health intervention for soil transmitted helminths is regular mass de-worming , particularly for high risk groups; backed up by facilitating access to clean water , improved sanitation and health education [17] . Pregnancy may increase susceptibility to helminths , but this is uncertain . A recent study from Gabon showed increased prevalence in pregnancy [18] , but a study from Thailand found no association [19] . Susceptibility and clinical outcomes are further complicated by co-infection with HIV and malaria . Malaria in pregnancy due to Plasmodium falciparum , combined with helminthic infections in HIV-positive women is of great concern from a public health perspective [20] , [21] . Control of P . falciparum infection by intermittent preventive treatment and use of insecticide-treated bed nets is of high importance especially in primigravidae [22] , [23] . HIV-infected women tend to experience faster CD4 decline during and after pregnancy [24] , and could therefore be even more susceptible to helminth infection and malaria . This prospective cohort study was designed to assess the major risk factors for helminth and malaria co-infection in HIV-positive pregnant women who participated in an early anti-retroviral therapy ( ART ) initiation program for the prevention of mother-to-child transmission in Rwanda . We describe the baseline prevalence of malaria and helminth infection in HIV-infected pregnant women on ART , and assess the factors that may increase or decrease rates of both infections .
Ethical approval was obtained from the Rwanda National Ethics Committee and the Ethics Committee ( Human Research ) of the University of the Witwatersrand Medical School , Johannesburg , South Africa . All subjects provided written informed consent at enrolment . Subjects who could only provide oral consent were asked to give thumb marks using indelible ink on the consent form , in case they were illiterate; in accordance with the IRB oral standard ethical consent guidelines of the two ethical committees and in accordance with Helsinki declarations . The study participants were recruited amongst women accessing antenatal care and ART services at rural and peri-urban health centers in the central and eastern provinces of Rwanda , between 02 January 2010 and 29 February 2011 . After giving written informed consent , women in the second trimester of pregnancy were enrolled at their fourth , fifth or sixth month of gestation . Additionally , women were enrolled if they lived within walking distance from the study areas , and if they planned to deliver at the registered study health center . Enrolment criteria were HIV infection; pregnancy ( in the second trimester ) ; use of ART; and willingness to provide three stool samples on consecutive days . Women were excluded if they were diagnosed with tuberculosis , or if they had taken any anti-helminthic drugs at any time point prior to entry into the study . Those who had been enrolled in other research projects during the study period were also excluded . Participants provided blood and stool samples before being treated in accordance with the study protocol . On enrolment , participants were interviewed to obtain demographic and socio-economic information pertaining to the relevant environmental risk factors for helminth infections and malaria . Subjects were asked whether they had attended school , whether they were employed , their source of water ( river or piped ) , if they wore shoes , whether they washed their hands after using the toilet , and whether they used dietary supplements . Study participants were also asked about their number of previous pregnancies . Intestinal helminths were identified by the Kato Katz method [22] , [23] . Three Kato Katz slides were prepared from each stool sample , and then examined within 30 minutes for hookworm species . The same specimens were again examined the following day for ova of other soil transmitted helminths with the formol ether concentration method . Eggs per gram ( EPG ) of stool were calculated by taking the mean of the mean values obtained for each of the three stool samples . In all study women asymptomatic parasitaemia was determined . Plasmodium falciparum was identified by light microscopic examination of Giemsa stained thick and thin blood smears , and screening for malaria was also performed by detection of the P . falciparum histidine rich protein 2 ( HRP-2 ) antigen using a rapid diagnostic test kit ( Biotec Laboratories Ltd . , UK ) . In the case of discordant results , expert light microscopy results were considered as gold standard . Participants received treatment for helminth infections and malaria in accordance with the study protocol . Artemether/lumefantrine ( 120/20 mg ) was given as standard falciparum malaria treatment , administered orally in four doses for three days . Deworming was performed every 12 weeks with 400 mg albendazole given to women with helminth infections only/not to those negative in the ‘targeted treatment’arm; whereas all women received 400 mg albendazole irrespective of infection status in the ‘untargeted treatment’arm . Women received nevirapine for prevention of mother-to-child HIV transmission and subsequent combination ART , irrespective of CD4 cell levels in accordance with the latest ( 2010 ) Rwandan Ministry of Health treatment guidelines [25] . Data analysis was performed using Stata version 11 . 0 ( College Station , TX , USA ) and Statistica version 9 . 1 ( StatSoft , Tulsa , OK , USA ) . Data that was not normally distributed was log transformed to normality before analysis . The prevalence of helminth , malaria and helminth-malaria co-infections were determined in population sub-groups e . g . in employed and unemployed subjects , and differences between the groups were assessed using the χ2 test . Backward , stepwise multiple logistic regression analysis was used to determine the principal factors associated with helminth and malaria infections and helminth-malaria co-infections . Models were constructed for infection with each individual helminth species , i . e . A . lumbricoides , T . trichiura and hookworms , and a combined model for infection with any helminth species . The independent variables included in the initial logistic regression models were , for helminth infections: location , month of year , ART , gravidity , education , employment , water source , use of shoes , hand washing , dietary supplement use , HIV viral load , presence/absence of malaria , age , height , gestational age and CD4 counts . With malaria as the dependent , dichotomous variable , the same list of independent variables was used . The presence/absence of helminth infection was included as an additional independent variable whilst presence/absence of malaria was removed . With co-infection ( helminth-malaria infection ) as the dependent variable , both malaria and helminth presence/absence were removed from the model . In all the logistic regression models , the independent variable with the highest p-value was removed at iteration until only variables with a p<0 . 05 were left in the model . Backward , stepwise multiple regression analysis was used to identify the principal determinants of fecal helminth egg counts and blood hemoglobin levels . Univariate analyses were initially performed , and any variable with p<0 . 50 was included as an independent variable in the multiple regression models . The same procedure as described for the logistic regression models was then followed . A sample size calculation was not performed for this study . N was chosen based on logistical factors , taking into account future follow-up studies . However , if one performs a post-study sample size calculation based on the logistic regression model for identifying the principal determinants of helminth infection and using the equation N = ( 10*k ) /p [26] , where k is the number of co-variables ( k = 18 ) and p is the frequency of helminth infections ( p = 0 . 34 ) , we obtain a minimum N of 529 , which is far below the actual N of 980 .
The data in Table 1 show that infection with any helminth species ( in the presence or absence of malaria ) occured in 336 participants ( 34 . 3% of the population investigated ) , being significantly ( p<0 . 0005 ) more common in rural than in peri-urban communities . Infection with helminths in the absence of malaria showed a similar trend , occurring in 36 . 5% of rural , and in 11 . 3% of peri-urban subjects ( p<0 . 0005 ) . Infection with each of the three helminth species also occured more often in the rural than the peri-urban population , with A . lumbricoides being the most commonest . The presence of a malaria infection ( in the presence or absence of helminths ) was more frequent in peri-urban than in rural subjects ( p<0 . 05 ) . This trend was mirrored by malaria-only infections , with a prevalence of 4 . 39% in rural and 10 . 7% in peri-urban females ( p<0 . 0005 ) . The prevalence of helminth-malaria co-infection was similar in both environments ( Table 1 ) . The prevalence of helminth , malaria and helminth-malaria co-infections were calculated for different population sub-groups ( Table 2 ) . Helminth infections of any type , asymptomatic malaria or co-infections were all less prevalent in subjects receiving AZT-3TC-NVP when compared to those taking d4T-3TC-NVP ( p<0 . 005 ) . Treatment with AZT-3TC-NVP was also associated with a lower prevalence of malaria or co-infection when compared to AZT-NVP therapy , and a lower prevalence of co-infection compared to TDF-3TC-NVP . The latter therapy was associated with a lower prevalence of asymptomatic malaria compared to subjects receiving AZT-NVP . A number of factors had the opposite effect on helminth infection compared to malaria or co-infection rates . Thus , helminth infections were more common , but malaria and consequently co-infections less common in females who were tested in March–May compared to those tested in January or February . This same pattern was observed for females who did not wear shoes compared to those who did , and in females who did not regularly wash their hands or take dietary supplements when compared to those that did ( Table 2 ) . Pregnant females who were older than 30 years at testing had a lower prevalence of helminth infections but higher levels of asymptomatic malaria and co-infections than those females who were 30 years or younger . Primigravidae had higher prevalences for all three infection types compared to females who had more than one previous pregnancy , whilst females who presented for testing at an earlier stage of their pregnancy ( 4 months ) had higher prevalence levels of malaria and co-infections compared to those at a later stage of pregnancy ( 5–6 months ) ( Table 2 ) . If this latter group was divided into 5 and 6 months of gestation , the prevalence of malaria was not significantly different between them ( 4 . 20% vs 3 . 52% respectively ) . Study participants who were unemployed and subjects with no formal education had a higher prevalence of helminth infections , malaria and co-infections compared to subjects with employment and the uneducated , respectively . Women who used river surface rather than piped water had a higher prevalence of both helminth infection and co-infection but a lower prevalence of malaria , although this last comparison did not reach statistical significance ( p = 0 . 09 ) . A detectable viral load and a CD4 count ≤350 cells/mm3 were both associated with higher levels of all infections . Subjects with asymptomatic malaria had a higher prevalence of helminth infections , and vice versa ( see Table 2 ) . Table 3 depicts the results of multiple logistic regression analyses to identify risk and protective factors for helminth infections . With regard to ART , the d4T-3TC-NVP regimen groups exhibited higher prevalences of infection with A . lumbricoides and hookworm compared to AZT-3TC-NVP . The same applied with the AZT-NVP and TDF-3TC-NVP regimens regarding T . trichiura prevalence when compared to the AZT-3TC-NVP therapy . There were lower rates of hookworm infestation in subjects who were screened for infections during January and February compared to those screened later in the year , whilst subjects who were older than 30 years , or who were multigravid had a lower risk of any helminth infection when compared , respectively , to those 30 and younger , or primigravidae . Subjects who were residents of a peri-urban location had a lower risk of A . lumbricoides , hookworm or any helminth infection in comparison to those from a rural environment . Educated study participants and those who used piped water were at a lower risk of A . lumbricoides or any helminth infection when compared , respectively , to subjects with no formal education and who used river water . Furthermore , pregnant women who were employed or who regularly washed their hands were at a lower risk for A . lumbricoides , T . trichiura or any helminth infection in comparison to subjects who , were employed or did not wash their hands regularly , respectively . Pregnant females who had a detectable viral load when compared to those who did not , were at a higher risk for A . lumbricoides , and subjects with a CD4 count at or below 350 cells/mm3 were at a higher risk for all kinds of helminth infections when compared to those with CD4 counts above 350 cells/mm3 . The presence of malaria was associated with a higher risk of any helminth infection . The risk for malaria was higher in the months of January and February than from March to May ( Table 4 ) . Risk was also higher in older females but lower in those in the third trimester . This latter trend was also mirrored by risk for co-infection . Co-infection risk was also reduced in subjects with some formal education and in those with employment . Pregnant females who used piped rather than river surface water had a higher risk of malaria . Helminth infection was associated with a higher risk for malaria , whilst low CD4 counts were linked to a higher risk of malaria and co-infection . Interestingly , and being difficult to interpret , women who regularly washed their hands had a higher risk of both malaria and co-infections . Backward , stepwise multiple regression analysis demonstrated that fecal helminth egg counts were highest in females who were multigravid; who did not wear shoes and who had low CD4 counts ( Table 5 ) . Hemoglobin levels were lowest in females who had helminth or malaria infections , who had low CD4 counts and who had a higher ( 5 or 6 months compared to 4 months ) gestational age .
In this study population , we determined the prevalence and identified protective and risk factors of helminth , malaria and co-infections in HIV-infected pregnant women on ART in Rwanda . We found that helminth infection was more prevalent in rural than peri-urban settings . Poor education and unemployment were risk factors for both helminth and P . falciparum infection , whilst hand washing protected against worm infections . HIV treatment with AZT-3TC-NVP was associated with a lower prevalence of helminth infections . A CD4 count ≤350 cells/mm3 was associated with higher levels of all infections . Multiple linear regression analysis demonstrated that helminth egg counts ( EPG ) were highest in females who were multigravid and hemoglobin levels were lowest in females who had helminth or malaria infections . The prevalence of helminth infection was higher among the rural than peri-urban populations . Whilst we did not notice general differences between women recruited at the various health centers , this is best explained by variations in lifestyle between both settings . The most prevalent species were A . lumbricoides followed by T . trichiura and hook worm species ( A . duodenale and N . americanus ) . This is in agreement with previous findings from the same location [21] . Our results are further supported by findings from an earlier study in the region which indicated that A . lumbricoides and T . trichiura were more commonly found in Rwanda and Burundi than in most other East African countries [27] . Our findings show lower prevalence levels for malaria and malaria-helminth co-infection than previously reported for pregnant females in Ghana but higher rates of helminth infections [28] . A study in Uganda [29] reported that the prevalence of helminth infection among pregnant women was 68% and malaria was 11%; however , only 12% of the women were HIV infected . These results indicate ( not surprisingly ) that there are varying prevalence levels of helminth infection during pregnancy across East African populations . It should be noted that in the study in Ghana the HIV status of the participants was not known , whilst in our study all participants were HIV-infected and receiving ART . An earlier study of malaria prevalence conducted in HIV-positive pregnant females in Kigali , Rwanda , demonstrated that 8 . 0% of the study group had malaria [21] . It is well documented that pregnant women living in malaria endemic areas have an increased risk of P . falciparum infection during pregnancy but this usually remains asymptomatic . In the current study , we found seasonal fluctuation , with the prevalence of asymptomatic malaria being higher in subjects tested in the months of January–February than those tested in March–May . In the current study , we report that pregnant females who were older than 30 years at the time of testing had a lower prevalence of helminth infections but higher levels of malaria than younger females . The helminth data is supported by a previous study from Uganda [30]; however , most studies show that malaria is also more common in younger , pregnant females [31] , [32] . This difference may be related to a number of factors including lifestyle and socio-cultural differences across the population groups included in these studies . Little data exists on the relationship between gravidity and the risk of helminthiasis . One study shows no effect of gravidity on the risk of helminth infection [28] whilst a second study demonstrates a higher risk of hookworm infection but a lower risk of A . lumbricoides infection in primigravid compared to multigravid females [32] . The data from the current study shows that primigravid females have a higher prevalence and risk of helminth infection compared to multigravid females . This is an important finding and suggests that de-worming programs should target such individuals . Our data also shows a higher fecal egg count in multigravid compared to primigravid females . Thus , although multigravid females are at a lower risk of helminth infections than primigravidae , when they do acquire a helminth infection they have a higher intensity of infection than primigravid females . Females who presented for testing at an earlier stage of their pregnancy ( 4 months ) had a higher prevalence of malaria and helminth-malaria co-infection than those at a later stage of pregnancy ( 5–6 months ) . This data is supported by findings from previous African studies [31] , [33] . Education and employment acted as protective factors against both helminth infection and helminth-malaria co-infection . Previous studies have shown similar associations [28] , [30] , suggesting that socio-economic status is a strong modulator of disease risk . Helminth infection was shown to be more prevalent in subjects who did not wash their hands . Studies have shown that the risk of helminth infection is reduced in subjects who regularly wash their hands , more so in those who use soap [34] , [35] . Thus , simple changes in hygiene practices would be important for reducing the prevalence of helminth infections . In our analysis , however , hand washing was statistically significantly associated with an increased risk of malaria and – consequently - helminth-malaria co-infection . This finding is surprising and difficult to understand . Of note , the use of piped compared to river water reduced the risk of helminth infection but seemed to increase the risk for malaria . Whilst improved access to water is known to reduce the risk for helminth infection [36] the possible reasons for a greater risk of malaria associated with hand washing and piped water are not known , with little data available in the literature to confirm these associations . We believe that we are dealing here with a confounder , although it is apparently difficult to understand its nature , and neither an elevated social status nor local vector behavior or distribution offers any clue to understand this observation . However , one possible explanation is that stand pipes for the collection of water may have been situated in areas more suitable for mosquito breeding , or that puddle formation around stand pipes created favourable breeding conditions . Helminth egg counts were highest in multigravidae who did not wear shoes regularly and who had low CD4 counts ( Table 5 ) . Hemoglobin levels were lowest in females who had helminth or malaria infections , who had low CD4 counts and who had a gestational age of 5 or 6 months . Based on the distinct mechanisms by which helminth and malaria affect hemoglobin levels , it can be speculated that their combined presence might interact to enhance the risk of anemia when intensity is moderately higher than in light worm intensities . The relationship between helminth infection , intensity and anemia has been described in several settings in Africa as well as in South East Asia [37] . Although women in our study group were all on ART with some having received nutritional supplements as part of their antenatal care package , previous regional studies also reported lower hemoglobin levels to be associated with high prevalence of helminth and malaria [38] . Our findings are further supported by other studies [17] , [39] which report that pregnant women are known to exhibit fluctuating CD4 levels in pregnancy , which might expose them to higher helminth infection prevalence leading to maternal anemia . This could be explained by the fact that during pregnancy the immune system is impaired; therefore , HIV positive pregnant women who live in highly endemic areas in sub-Saharan Africa are likely to be at increased risk for helminth-malaria co-infections . In the present study the risk of helminth infection was higher in females with a reduced CD4 cell count , and in subjects with a detectable viral load . This is in agreement with previous studies conducted in pregnant females in Uganda [38] and Rwanda [21] where CD4 counts correlated negatively with the risk of helminth infection . However , another study has found the opposite [38] , although this investigation was not carried out in pregnant females . Webb et al . [40] reviewed the epidemiology and immunology of helminth–HIV interactions , and concluded that there is inconsistent data support to postulate a beneficial effect of anti-helminthic therapy on CD4 counts and viral load in HIV-1 co-infected individuals . With regard to malaria , we found that a low CD4 count was associated with an increased number of malaria episodes . This is in contrast with data from a very similar study performed in Rwanda , where no such an association was found [21] . This discrepancy may be related to the lower power of the earlier investigation . There is clear evidence from a number of studies that HIV does lead to more , and to more severe malaria episodes , particularly in pregnant women [41] . The prevalence of helminth infection was increased in subjects with malaria , and vice versa . A study conducted in Ghana on pregnant females also showed that helminth infection increased the risk of malaria [28] . It is thought that helminth infections have a number of effects on the immune system that leads to increased susceptibility to malaria [39] , [42] . In our study population all subjects were taking ART irrespective of CD4 counts , as prescribed by the new ( 2010 ) Rwandan Ministry of Health guidelines for the prevention of mother to child transmission of HIV [25] . Helminth infections of any type , malaria or co-infection , were all less prevalent in subjects receiving AZT-3TC-NVP when compared to the other three ART regimens ( Table 2 ) . These effects remained significant for helminth infections after adjusting for confounding variables in a logistic regression analysis ( Table 3 ) . However , the protective effect of AZT-3TC-NVP for malaria was not sustained in the logistic regression model ( Table 4 ) . A previous study performed in pregnant Rwanda females also demonstrated that specific ART regimens seemed to reduce helminths prevalence but had less effect on malaria [21]; in this study the AZT-3TC-NVP regimen was the least protective compared with the other therapies i . e . AZT-NVP , d4T-3TC-NVP and AZT . This may be related to a much smaller sample size ( n = 328 ) compared to the current study ( n = 980 ) , and the lack of data for confounding variables . Whilst these findings suggest a possible anti-helminthic effect of ( certain ) ART combinations , there was no non-ART control arm in both studies , as they were not designed to detect ART effects on helminth infections in the first place . Whilst there is therefore a limit to the interpretation of this finding , it warrants further investigation . Although the ART-induced reconstitution of cellular immunity would probably be the main factor for reducing helminth infections among HIV patients , previous in vitro and in vivo investigations indicated that HIV treatment , especially with protease inhibitors ( PIs ) , could have a direct effect in killing of parasites including malaria [43] . It has been described before that ART without PIs may reduce the prevalence of helminth infection [21] , [44] . Thus , ART itself might have contributed to the decline in helminth prevalence , asymptomatic malaria or co-infection seen in our study . We hypothesize that anti-mitochondrial toxicity of ART compounds may play a direct role here , a hypothesis for which support seems to accrue in a currently ongoing field trial in Gabon , designed to address this question ( MP Grobusch and S Janssen , unpublished data ) . The strengths of this study are the screening of a large number of women from eight health centers which caters for women of all socio-economic classes; the screening of three stool samples on three consecutive days for the presence of helminths; the use of a combination of two different screening techniques to increase the sensitivity of helminth diagnosis . However , our study has limitations . Methodologically , its cross-sectional design makes it impossible to determine temporal causality , including the inability of multivariate models to adjust for all confounding factors . All participants in the study were women and as hemoglobin levels differ between men and women , our findings cannot be extrapolated to men . The Kato Katz method used to determine the number of helminth eggs could have underestimated the proportion of women with light hookworm infection . The study had no control group of HIV-positive subjects not receiving ART . In conclusion , we found that the prevalence of helminth infections , malaria and co-infections is common in HIV-positive pregnant women on ART in Rwanda . Helminth and malaria infection in this population are important risk factors for low hemoglobin levels . Subjects with low CD4 counts were at higher risk of infections and helminth infection is a risk factor for malaria . Education and employment were independent protective factors for helminth infection and malaria , whilst hand washing reduced the risk only for helminth infections . The possible antihelminthic effect of some ART combinations warrants further studies . | There is an overlap in the worldwide distribution of intestinal worms ( helminths ) , malaria and HIV . Co-infections with helminth and malaria parasites cause a significant problem in the host , particularly in the presence of HIV infection . The aim of this study was to assess the prevalence of intestinal worm and malaria infection and co-infections and the associated risk factors among HIV-positive pregnant women that attended rural and peri-urban health centers in Rwanda . Our findings indicate that intestinal worms were more common among HIV-infected pregnant women in the rural than peri-urban settings . HIV-positive pregnant women who had lower CD4 cell counts were more at risk of being infected by intestinal worms and malaria . Malaria also increased the risk of being infected by intestinal worms and vice versa . Socio-economic factors such as lack of education and unemployment were among the risk factors for intestinal worm infections and malaria . Hand washing was found to reduce the risk for worm infections; whilst one particular ART combination ( AZT-3TC-NVP ) led to a reduced rate of helminth infections when compared to others . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine"
] | 2013 | Helminthic Infections Rates and Malaria in HIV-Infected Pregnant Women on Anti-Retroviral Therapy in Rwanda |
Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile . But how progenitor cells overcome the stability of their gene expression configuration ( attractor ) to exit the attractor in one direction remains elusive . Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state , such that differentiating cells undergo a critical state transition . Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point . The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor . Thus , early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems , offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition , does not require knowledge of specific pathways , and could be used to predict impending major shifts in development and disease .
A multipotent stem cell or a progenitor cell is in a state that poises it to be able to commit to one of multiple available options of predestined cell lineages and to differentiate . However , its state-characteristic gene expression profile is stably maintained because the cell is in a stable ( or meta-stable ) attractor ( potential well ) [1 , 2] generated by the gene regulatory network ( GRN ) in the high-dimensional gene expression state space . An attractor state represents a local minimum or , as sometimes referred to , a ground state [3] , the lowest point in the basin of attraction of the attractor . The high-dimensional attractor guarantees that the state-characteristic genome-wide gene expression pattern is self-stabilizing , withstanding the stochastic molecular fluctuations . Therefore , as cells differentiate and alter their gene expression pattern in a coordinated manner to ultimately implement the expression pattern of the new cell type , they must first overcome this stabilization of the progenitor ground state imposed by the GRN . Individual multipotent progenitor cells can , due to the stochastic gene expression fluctuations , temporarily and by chance , approach the border of the basin of attraction of their attractor and thereby be transiently primed to exit the progenitor state in a random direction , giving rise to the occasionally observed spontaneous , apparently stochastic differentiation into one of a set of alternative lineages . It is thought that the associated ( unlikely ) chance configurations of expression in the appropriate set of regulatory genes could place an outlier cell so as to facilitate its jump over basin boundaries into the neighboring basin of attraction of a destination lineage [4–6] . Once in the new basin , the cell will robustly establish the new gene expression pattern of the respective destination cell type as it enters the new attractor state [2] . In the qualitative parlance invoking gene regulatory circuitries , a committed cell’s robust , self-sustained , and apparently irreversible move toward a new ( differentiated ) state once it has left the old ( undifferentiated ) state is often explained by the reinforcing activity of a positive feedback control loop . By contrast , the formal approach that treats complex regulatory networks in an integrative manner as a high-dimensional dynamical system posits that stable cell states ( cell types ) are attractor states . This formalism naturally explains , without invoking specific gene regulatory circuitries , why spontaneous differentiation without instructive signal can produce the highly specific gene expression patterns of existing cell types [1 , 2] . Under physiological conditions , spontaneous differentiation is rare . But appropriate tissue signals can trigger an efficient exit from the progenitor attractor and the commitment to a specific cell lineage . Here , the fundamental question remains as to how such signals overcome the stability of the progenitor attractor state and direct the fate decision toward another attractor representing a particular cell type . One possibility is that differentiation signals operate simply by coordinating gene expression changes in a deterministic manner so as to place the cells either at a specific site on the border of the basin of attraction of the progenitor cell , thus priming the cell to be susceptible to noise-driven entry into the desired destination attractor of the new lineage , or already at a state within the basin of attraction of that destination cell type . This older view [7] , schematically intuited in Fig 1 ( right panels ) , does not involve any distortion of the attractor basins . The alternative possibility is that the differentiation signal may cause a destabilization of the ( high-dimensional ) gene expression attractor state , thereby drastically facilitating the ( noise-driven ) exit from the progenitor attractor and entry into a new attractor state . The destabilization can be imagined as a flattening of the potential well . Then , a bias ( tilt ) in the direction of destabilization ( asymmetric flattening of the attractor well ) would allow the differentiation signal to influence the fate decision toward a given lineage [8] . A destabilization that results in the disappearance of an attractor state constitutes a bifurcation event in a nonlinear dynamical system: a “sudden” qualitative shift of a system’s configuration of steady states x ( t ) ( here , from existence to nonexistence of the progenitor attractor ) while a control parameter , the bifurcation parameter μ in the systems equations that describe the dynamics of the system , ẋ ( t ) = F ( x ( t ) , μ ) , is continuously altered [9–10] . Here , however , the system equations of the high-dimensional system ( see S1 Fig ) , notably , the bifurcation parameters μ , are typically not known . In such cases , a phenomenological description of the bifurcation behavior is more appropriate , in which the bifurcation appears as a critical state transition [11–12] . Herein , a presumed stable attractor state is observed to gradually destabilize until the system ( cell ) suddenly passes a tipping point . At this critical point , the attractor basin is completely flattened ( at least with respect to one state space dimension ) , and a new neighboring attractor state that is discretely distinct from the initial one becomes accessible . The system ( the cell ) can then rapidly descend into it . Critical state transitions have been implicated in abrupt shifts in ecosystems , climates , and social systems , and also in disease transitions [13] . The preceding destabilization of the system is equivalent to a weakening of the stabilizing forces . Hence , it is manifest in increased ( noise-driven ) excursions of the system state x ( t ) away from its stable steady-state ( equilibrium ) point x* , as well as in a slowed return to it . These manifestations are plausible within the permissive image of a flattening attractor basin . Such observables of a system approaching a bifurcation event are the early warning signals of an impending critical transition and can often be quantified as an increase in the amplitude and temporal autocorrelation of the stochastic fluctuations of the systems variable x ( t ) around x* [11–12] . While critical transitions have been widely studied in systems whose system state can be described by a one-dimensional state variable x ( t ) [11–13] , cell states are defined by a high-dimensional state vector x ( t ) [14] , which , for practical purposes , can be defined by the reliably measurable transcript abundance of a set of m genes that participate in the shift of gene expression patterns associated with the cell state transition . Systems equations that describe the change in time of a GRN state x ( t ) could , in principle , be formulated to model all the regulatory interactions of the relevant regulatory genes and predict the existence of a bifurcation in the dynamics of x ( t ) . However , because the specification of the regulatory interactions required for such dynamical models are typically not available in sufficient detail despite our increasing knowledge of the topology of GRNs ( S1 Fig ) , the identity of the relevant bifurcation parameter whose gradual change would drive the differentiation process remains elusive . Nevertheless , numerous generic mathematical models of the dynamics of small GRNs driving cell differentiation for specific cell types have been proposed and successfully predict attractor states and bifurcations that map to the observed cell state behaviors [8 , 15–20] . Given the uncertainties in GRN specification for cell fate decisions , we depart from such explicit modeling of GRN dynamics and study them within the phenomenological framework of critical state transitions . This is warranted because cell lineage commitment by a multipotent cell is , in fact , characterized by a sudden , discontinuous shift of a stable cell state to another state . But instead of monitoring a single state variable x ( t ) continuously , we measure a set of m = 17 transcripts that serve as components of the high-dimensional state vector x ( t ) and are members of a core regulatory network that is involved in the commitment of a multipotent progenitor cell to either the myeloid or erythroid lineage ( S1 Fig ) . Because high-dimensional gene expression profiles can currently only be measured in a destructive manner , their changes cannot be monitored in a continuous way but only at discrete time points in replicate systems . High-dimensional critical state transitions have been studied for global shifts in microarray-based transcriptomes [21] , but this type of data on x ( t ) is an aggregate of heterogeneous mixtures of dynamical systems ( i . e . , cells ) . Here we take advantage of single-cell resolution measurements of the state vector x ( t ) representing the GRN state of an actual system ( a cell ) , defined by its abundance of the m species of transcripts but for a population of cells that represents a statistical ensemble of n systems ( cells ) . We derive a generic quantity , the index IC ( t ) , that is computed from the ( n × m ) data matrix X ( t ) at discrete time points t during the differentiation process . Thus , in essence , we make up for the lack of a continuous time series that captures the fluctuations of cell state by exploiting the availability of the individual states in a statistical ensemble of n cells , measured as time snapshots . We show formally and experimentally that a ( relative ) increase of IC ( t ) serves as an early warning signal of a critical transition that coincides with lineage commitment following a gradual destabilization of the multipotent progenitor state . Thus , we exploit high-dimensionality and the new granularity afforded by single-cell gene expression analysis of a cell population and take into consideration first principles from the theory of nonlinear dynamical systems to predict , without explicit modeling of the underlying regulatory interactions , an impending qualitative phenotype shift . Our theory also explains the observation of “rebellious cells” during binary fate decisions and , together with the findings , unites the old dichotomy between selection and instruction in cell fate determination .
To determine if differentiation goes through a tipping point in high-dimensional gene expression state space , we studied the commitment of the murine multipotent hematopoietic precursor cell line EML [22] into an erythroid or a myeloid fate when released from the progenitor state and stimulated either with EPO ( erythropoietin ) or with GM-CSF ( granulocyte macrophage colony-stimulating factor ) / IL-3 ( interleukin 3 ) , respectively [4] . In a third experiment , we treated EML cells with a combination of EPO and GM-CSF/IL-3 to separate destabilization from fate choice , because we reasoned that the latter should be neutralized by the conflicting combination treatment . To ensure that heterogeneity of the starting cell population is strictly due to dynamic fluctuations and not due to preexisting , differentially preprimed cells of unknown developmental history ( which then would merely be selectively enriched for particular fates by the respective growth factors ) [23] , we used a clonal cell line as opposed to purified primary cells . This permitted the study of the actual phenotypic diversification in maximally uniform cell populations of cells recently derived from a single common ancestor under invariant and homogenous conditions to assure common history and maximal phenotypic homogeneity . While such a progenitor cell line may not reflect biological reality because the cells are likely trapped in a ground state attractor not necessarily present in vivo [3 , 24] , as readily revealed by molecular profiling , it offers a robust model system to expose and study fundamental principles of dynamical systems that do not depend on idiosyncrasies of the specific biology . We monitored transcript expression patterns at single-cell resolution using qPCR to acquire information about the stability of a nominal cell state x ( t ) presented by the cell population , which constitutes a statistical ensemble of ( randomly distinct ) replicates of a system . For instance , increase in cell state diversity would suggest destabilization of the nominal cell state . We found that qPCR was far more sensitive than single-cell RNA-seq in the detection of low abundance transcripts ( S1 Appendix , A . 7 ) . The problem of low sensitivity is amplified by the low capture rate of transcripts in single cell–gene expression analysis ( typically 60% to 90% of transcripts of a given cell are lost ) and by technical ( sampling ) noise [25–27] . This can lead to false-positive sets of mutually exclusive expression of transcripts and thereby inflate cell-cell diversity , which is a crucial quantity in our analysis ( S1 Appendix , A . 8 ) [28] . Exit from the progenitor state was first verified by flow cytometry measurement of the downregulation of the stem-cell markers Sca1 and c-kit . The induction of a bimodal distribution with a new discrete subpopulation with lower Sca1 ( and c-kit ) surface protein expression confirmed the switch-like state transition to a committed state ( Fig 1A ) . Fig 1B shows the time course of single-cell transcript patterns of 17 selected genes known to be functionally involved in or to mark the fate commitment of EML cells , plus two “housekeeping genes” ( S1 Fig and S1 Table ) . The single-cell states were visualized by plotting each cell as a point in the Cartesian space spanned by the three principal components ( PC ) from a principal component analysis ( PCA ) of concatenated expression data across all time points to reduce the 19-dimensional state space ( 17 genes of interest + 2 reference genes; see S1 Appendix ) . As seen in Fig 1B , the “cloud” of untreated cells ( grey , depicted for reference for each time point ) spread upon treatment ( colored balls; where red and blue colors are a priori labels , indicating treatment with EPO or GM-CSF/IL-3 , respectively ) , and reached highest diversity at day 3 ( d3 ) . The cells then coalesced into two distinct dense clusters at day 6 ( d6 ) , representing the cells committed to the erythroid ( red ) and myeloid ( blue ) lineages , which were identified by the characteristic expression of erythroid or myeloid transcript levels ( S2 Fig and S1 Table ) . As shown in S3 Fig , in this single-cell qPCR , measurement noise was only a small fraction of biological cell-to-cell variability; thus , the dispersion of points in state space predominantly reflects biological diversity of cells . Loading of gene scores show that PC1 captures the erythroid–myeloid dichotomy , whereas PC2 reflects the stemness–differentiation axis ( S4 Fig ) . For visualization purposes , single-cell resolution measurement , which provides the local cell density for each position in state space , can be depicted as the elevation of an approximate ( fixed ) quasipotential landscape ( Fig 1C , legend ) [29] , which serves as visual guide and shows the three attractor states as minima ( potential wells ) compressed into one landscape . Interestingly , progenitor cells receiving a combined treatment also diverged at d3 but stayed in an intermediate , undecided region of the state space before consistently joining the myeloid cluster ( Fig 1B ) . Thus , the conflict of signals delayed the fate decision , but a uniform decision was eventually made . This decision-making , in view of ambiguous signals , corroborates the notion that gene expression change during lineage determination is not simply instructed by external growth factors but also governed by intrinsic constraints that channel cells toward predestined fates—the attractors of the GRN . Importantly , this model does not allow for stable intermediates , as Waddington first observed [30] . In this case , it appears that the attractor for the myeloid fate is more readily accessible or that , in our combined differentiation protocol , the myeloid signal somehow dominates , although the cells that resolved the conflict still remained distinguishable from the pure myeloid cells . Independent of the ( unknown ) detailed dynamics of the underlying GRN , a destabilization and disappearance even of a high-dimensional attractor state is a bifurcation event and , therefore , should display the signatures of an approach to a critical state transition [11] at which cells would undergo a discontinuous switch toward the destination state . While the bimodal distribution of Sca1 ( Fig 1A ) after d3 indeed suggests a quasi-discrete transition , it cannot reveal a destabilization of a high-dimensional state x ( t ) prior to the switch . Recently reported cases of critical transitions in stressed ecosystems and disease processes ( refs . in [13] ) pertain to low-dimensional systems in which , typically , one system variable x ( t ) was observed longitudinally over time . By contrast , here we examine time snapshots of states of a high-dimensional system ( m = 17-dimensional cell state vector ) embodied by the GRN . Based on theoretical consideration , we showed that a critical destabilization and transition to a new attractor will be manifest in two changes in the correlation statistics ( as explained and derived in S2 Appendix , B . 1–B . 3 ) of the ( n × m ) data matrix X ( t ) [31]: The first change is ( trivially ) a decrease of cell–cell correlation R ( cell k , cell l ) between all pairs of the n cell state vectors in the m = 17-dimensional gene space . This reflects the expected increase of amplitudes of random fluctuation of gene expression due to the weakening attracting force in the flattening basin of attraction prior to the bifurcation [32] . This decrease in the coefficients of correlation R between all pairs of n cell state vectors captures an increase in cell–cell diversity . The second change is the concomitant increase of gene–gene correlation R ( gene i , gene j ) between all pairs of the m gene vectors that describe the gene expression values of each gene across all the cells . Unlike the former , this change is less intuitive . The increase in the correlation coefficients between all pairs of the m gene vectors ( with n components each ) , as mathematically derived in S2 Appendix , arises because of the symmetry-breaking destabilization in a high-dimensional attractor . In short , this can be made plausible from two different perspectives: Note that here , “correlation” ( between cells or genes ) does not simply have the usual function of indicating an association between two variables but is derived from elementary principles of the constraints in data and of dynamical systems theory . The above considerations concerning the two opposite changes in the two correlation statistics then motivate an index for critical transitions , IC: IC ( t ) = 〈|R ( gi , gj ) |〉〈R ( Sk , Sl ) 〉 , where g are gene vectors , S are the cell state vectors at sampling time t , and 〈R ( … , … ) 〉 denotes the average of all Pearson’s correlation coefficients of respective pairs of vectors . We postulate that IC increases toward a maximum when cells go through the critical state transition ( S2 Appendix ) . Recently , Chen et al . proposed a similar index for full transcriptome time courses , which , for lack of single-cell resolution state vectors , has to estimate state diversification and requires a separate explicit selection of a subset of genes among which the correlation is computed [21] . Here we start from a predefined set of m = 17 selected genes that are known to change significantly ( as actuator or as marker ) during fate commitment in order to demonstrate the signature of a high-dimensional critical state transition . Fig 2A shows the n × n heat map for cell–cell correlation coefficients R ( Sk , Sl ) for all pairs of the n = 1 , 600 cells for the three treatments ( EPO , GM-CSF/IL-3 and combined ) at each time point t . The diagonal shows that correlation of cells within the populations decreases at d1 and notably at d3 , compared to d0 , and increases again at d6 , indicative of a transient diversification of cell states and a return to a more homogenous population consistent with an attractor state . Because we also recorded the cells’ position with respect to the Sca1 surface marker expression ( roughly partitioning the population into three fractions , Sca1-high ( H ) , Sca1-medium ( M ) and Sca1-low ( L ) [see Fig 1A] ) , one can see that the decrease of correlation was not due to comparing cells across subpopulations in bimodal populations ( Fig 1A ) . The higher correlation among the cells within the extreme-low Sca1 fraction ( Lʹ ) in both EPO and GM-CSF/IL-3 treatment is consistent with advanced commitment of cells that are enriched in the Sca1-low fraction toward the erythroid fate , as previously reported [4] . By contrast , the high correlation among the H cells at the end of EPO treatment reflects “rebellious” cells that became myeloid under EPO treatment ( see below ) . The second criterion of a critical state transition , the increase in gene–gene correlation 〈R ( gi , gj ) 〉 , is shown in Fig 2B . Both EPO and GM-CSF/IL-3 treatment resulted in almost a doubling of 〈R ( gi , gj ) 〉 at d3 , which returned toward baseline at d6 . The heat maps ( Fig 2C ) show that the increase of 〈R ( gi , gj ) 〉 resulted from correlated ( red ) as well as anticorrelated gene pairs ( blue ) at d1 and , more pronounced , at d3 . By contrast , genes were mostly uncorrelated in the progenitor state , consistent with the dominance of random fluctuations around the attractor state ( explained in S2 Appendix ) . Together , the cell–cell and gene–gene correlation computed from the single-cell gene expression level data matrix X ( t ) indeed gave rise to a temporal course of the index IC that increased toward ( and culminates around ) d3 after induction of both fate commitments , as predicted by theory if the cell population approaches a critical transition . The maximum of IC at d3 coincided roughly with the beginning of lineage separation in state space ( Fig 1B ) . The decrease after d3 is plausible if one considers that cells enter a new attractor hereafter . However , this decrease is not strictly predicted by the theory because the assumption of ergodicity is not necessarily met if cells in distinct attractors are considered ( S2 Appendix , B . 2 ) . We next analyzed published single-cell gene expression data to further examine the robustness of index IC . Due to the intrinsic structure of the formula for IC , which is the ratio of the result of applying the same operation ( averaging of all Pearson correlation coefficients ) to a data matrix and to its transpose , it is expected that significant deviations of the value of IC from 1 in random data is extremely improbable , as bootstrap analysis of our data confirms ( p < 10−10; see S2 Appendix , B . 1 ) . We first examined whether the increase of IC precedes discontinuous cell phenotype transitions in other systems . Few studies monitor a cell developmental process at multiple time points prior to the key phenotype transition event , but one publicly available dataset [34] used single-cell RNA-seq for whole transcriptome profiling of the differentiation of bipotent lung epithelium progenitor cells into the AT1 and the AT2 subtypes during embryonic development and appeared to be suited for our purpose . The fate commitment to AT2 cells takes place between E16 . 5 and E18 . 5 . Computing IC for all reported transcripts at the various time points showed that IC indeed increased significantly between E16 . 5 and E18 . 5 , which was indeed due to concomitant decrease of cell–cell correlation and increase of gene–gene correlation ( S3 Appendix , C . 1 ) . Because , in this case , transcriptome-wide data was available , we next asked whether the number m of genes analyzed could affect IC . Note that IC is derived under the assumption that the genes defining the cell state vector are members of the dynamical system ( the core GRN ) that drives or is affected by the critical transition at study , and that only the change of IC but not its absolute value has a biological meaning . Thus , we examined a situation similar to the problem in reference [21] , when the core set of genes is not known a priori . What can be expected when a transcriptome-wide gene set is considered ? Because the majority of genes in the transcriptome are not members of the relevant core GRN , one possibility is that including a larger number of genes for computing IC would decrease the sensitivity of IC . On the other hand , because the expression behavior of all genes in the transcriptome are already largely correlated overall , this could , due to increased possibility of the range restriction effect ( S2 Appendix , B . 2 ) [35] , boost the changes in 〈R ( gi , gj ) 〉 and increase sensitivity of IC when more genes are considered . We thus compared randomly selected subsets of 2 , 000 , 200 , and 20 genes in the lung cell differentiation data for computing IC and performed bootstrap analysis to determine the significance of change of IC . As shown in the Supporting Information ( S3 Appendix , C . 1 ) , in all cases , IC increased significantly toward the point of fate commitment ( and also decreased afterwards as cells terminally differentiated to virtually the same extent for all three cases ) ; however , the error was smaller when the number of genes considered was larger . Thus , the fact that , in our data from the EML cells , we see a drastic and significant increase of IC by more than two-fold ( Fig 2B ) suggests that considering a small number of genes as we did here—entailed by the use of the more sensitive qPCR—sets the bar higher for statistical significance . To test a case in which the range restriction effect does not hold because of large cell–cell variability ( minimal baseline cell-cell correlation ) , we analyzed the single-cell transcriptome data of the most heterogeneous natural cell population we could find: glioblastoma cells [36] . Random sampling of increasingly larger sets of genes to compute IC indeed showed that , with increasing number of genes considered , the average cell–cell correlation 〈R ( Sk , Sl ) 〉 decreased—the opposite of the above cases . This elevated the absolute value of IC as well as its statistical fluctuations . Thus , in case of low inherent cell–cell correlation , increasing the number of ( randomly chosen ) genes increases noise . However , even with 2 , 000 genes used and higher variance of IC , a change of IC by two-fold as we observe ( Fig 2B ) would still have been significant at p < 0 . 01 ( S3 Appendix , C . 2 ) . In summary , IC is robust to varying the number of genes m used for its computation and for a wide range of preexisting intrinsic correlation between the cells . But sensitivity is higher in less heterogeneous cell populations ( as are cell lines ) and is increased by the use of a selected set of genes known to participate in the phenotype transition . To exclude the possibility that the observed pattern of gene expression changes indicating a critical transition is an idiosyncrasy linked to monitoring the exit from the progenitor attractor along the particular state space direction of decreasing Sca1 expression , we also monitored and dissected differentiation along the axis of the increase of differentiation marker CD11b , a reliable indicator of myeloid differentiation ( Fig 3A ) . Following GM-CSF/IL-3 treatment , first CD11b surface expression increased and then Sca1 decreased; that is , the cells moved from the CD11bLOW/Sca1HIGH to the CD11bHIGH/Sca1LOW state . We observed that at d3 , the time around which maximal destabilization was expected , the entire cell population split into three subpopulations with respect to CD11b: Sca1HIGH/CD11bLOW ( termed α ) , Sca1HIGH/CD11bHIGH ( β ) , and , unexpectedly , Sca1LOW/CD11bVERY-LOW ( γ ) ( Fig 3A ) . Single-cell transcript analysis suggests that the α-subpopulation corresponds to the destabilized but not yet fully committed cells because it displays the highest cell–cell diversity and high correlation of the gene vectors ( Fig 3B , S5 Fig ) . The cells of subpopulation β were most advanced toward the myeloid lineage ( high expression of Gfi1 , CEBPα and cJun transcripts ) , consistent with the high CD11b expression , whereas cells of subpopulation γ correspond to rebellious cells that moved in the opposite direction from that intended by the treatment with GM-CSF/IL-3 ( see below ) and , thus , displayed erythroid gene expression patterns , including a large number of EpoR positive cells ( S5A–S5D Fig ) . The index IC is drastically increased in all three cell populations at d3 ( Fig 3B , inset ) , indicating a destabilization of the progenitor attractor . Because IC was computed separately for each subpopulation , this also suggests that its increase was not driven by the increase in gene–gene correlation as a trivial consequence of separation into distinct cell types . At d6 , the γ population disappears ( Fig 3A ) , consistent with the rebellious cells in the PCA analysis of Fig 1B . However , addition of EPO to sorted subpopulations in growth factor–free cultures rescued the γ cells ( Fig 3C ) and , to a lesser extent , the α cells , but not the myeloid committed β cells . This finding not only confirms that the γ cells have aberrantly moved toward the erythroid lineage despite instruction for commitment to the myeloid lineage but also corroborates the notion of cell selection in fate control in which cytokines act as growth factors to determine lineage by providing the survival and mitogenic signals to the early committed cells that express the cognate receptor , in this case the EpoR [37–41] . We next examined a dynamical signature ( early warning signal ) of an approach to a critical transition used in low-dimensional systems: the slowing down of relaxation back to the original attractor states due to reduced attracting force [13 , 41] . Here , critical slowing down was exposed by measuring the relaxation of sorted outlier cells , which are ( transiently ) in an extreme state with respect to projection into just one dimension , that of Sca1 [4] . We thus isolated the Sca1LOW tail of populations either treated for 1 d with GM-CSF/IL3 to destabilize the progenitor state , or in untreated populations . As previously shown , the Sca1LOW fraction re-establishes the parental distribution within 5 to 6 d [4] . By contrast , cells exposed to GM-CSF/IL-3 for just 1 d ( which had not yet visibly altered Sca1 expression ) required at least 9 d to reconstitute the parental Sca1 expression distribution ( Fig 4 ) . Although there could be many reasons for the impaired relaxation , including any nonspecific , nonphysiological perturbation of the progenitor state by the cytokines , these reasons may themselves be seen as a manifestation of attractor destabilization , and this finding is at least phenomenologically in line with a critical slowing down . Intriguingly , in both projections of monitoring differentiation along the axis of decreasing Sca1 ( Fig 1B ) as well as increasing CD11b ( Fig 3A , the γ-cells ) , at d3 in both cases some cells consistently went in the “wrong” direction , opposite to the instruction by the respective cytokines ( i . e . , some EPO-treated cells were associated with the myeloid cell cluster and vice versa ) . Consistent with previous observations [4] , the lineage of rebellious cells mirrored their Sca1 expression levels in the untreated population: EPO-treated cells moving toward the myeloid fate at d3 stemmed from the Sca1HIGH fraction in the progenitor population , whereas GM-CSF/IL3-treated cells fated toward the erythroid cells originated in the Sca1LOW fraction ( S6 Fig ) . This suggests that the priming of cells in the progenitor population toward the erythroid or myeloid fate ( as reflected in the Sca1 surface expression [4] ) , respectively , predisposes the cells to react in a rebellious way if the differentiation signal is opposed to their priming . This is consistent with an initially more or less symmetrical destabilization of the progenitor attractor state , such that wrongly primed cells are pushed toward the opposite lineage as the basin of attraction flattens and vanishes . Note that the rebellious cells disappeared at d6 , possibly by transdifferentiating to the correct lineage or by dying out ( if they are not rescued by providing the commensurate growth factor; Fig 3C ) . The existence of rebellious cells may correspond to the observation of mixed colonies in early colony assays for hematopoietic differentiation [8 , 42 , 43] . The repeated observation of rebellious cells is consistent with a bifurcation at which two new attractors become accessible , representing the dichotomy between the two sister lineages [18] . The destabilization of the progenitor attractor , unlike in the canonical saddle-node bifurcation [11] , opens up a choice of two attractors , and despite a bias toward either one imposed by the lineage-determining growth factors , this still allows cells to spill into the “wrong” attractor if molecular noise overcomes the instructive bias toward the intended lineage . Thus , the existence of rebellious cells is also a signature of a critical transition . To show that this polarized behavior is not an artifact of projection in one state space dimension ( in this case , with respect to Sca1 or CD11b surface expression ) but holds in the high-dimensional state space , we measured the transcriptomes of the subpopulations that have either responded to the growth factor or appeared to have not responded , at least with respect to change in Sca1 expression ( Fig 5 ) . As shown earlier ( Fig 1 ) , all three treatments , with either cytokines as well as combined , triggered a split of the population into two distinct subpopulations with respect to the progenitor marker Sca1 ( bimodal distribution at d3 , Fig 5A ) . Intriguingly , cells from the Sca1HIGH subpopulation that appeared to have not responded after 3 d in EPO because Sca1 stayed high ( fraction #3 or H-Sca1 in Fig 5A ) had a transcriptome that resembled that of the cells that had responded to GM-CSF/IL-3 treatment and had down-regulated Sca1 ( fraction #8 or L-Sca1 in Fig 5A ) . Conversely , Sca1HIGH cells that had apparently not responded yet at d3 to GM-CSF/IL-3 ( fraction #9 in Fig 5A ) displayed a more pronounced change of the transcriptome that was remarkably similar to that of Sca1LOW cells ( fraction #2 ) that had responded to EPO ( for quantitative analysis of transcriptome similarities , see S2 Table ) . Extraction of those 17 genes in the microarray that were used in the single-cell qPCR analysis and hierarchical cluster analysis with these genes ( Fig 5B ) recapitulated these relationships , confirming that this set of genes represented the genome-wide high-dimensional dynamics well . In the combined treatment cells exhibited a transcriptome behavior similar to that of the nominally myeloid fated ( i . e . GM-CSF/IL-3 treated ) cells , in agreement with the single-cell transcript analysis ( Fig 1 ) . Thus , transcriptome measurements of subpopulations that appear to have not responded to the differentiation signal with respect to downregulating the progenitor state marker suggest that they actually had responded but by altering gene expression in the non-observed state space dimensions , underscoring the importance of considering high-dimensional dynamics . The intriguing crosswise similarity of the transcriptome changes in the non-responders in one treatment to that of the responders in the other treatment strongly supports the model of a constrained dynamics with a finite number ( here: two ) of fate options . These are embodied by attractor states that establish the predestined developmental potentials , and they become accessible once the progenitor state is destabilized . The aberrant but highly defined behavior of rebellious cells exposes the poised instability and a stochastic , non-instructive component in fate determination . We suspect that the rebellious cells represent those cells that , following the flattening of the progenitor attractor initiated by the external differentiation signal , erroneously enter the non-intended attractor because the stochastic gene expression fluctuations may , in some cells , overcome the instructive signal that biases the destabilization toward a specific lineage attractor . Nevertheless , the rebellious cells , being in the “wrong” fate , should eventually die because the lack of survival signals provided by the presence of the respective growth factor , as their disappearance in the measurement in Fig 1 implies . The rescue of the rebellious cells by the opposite cytokines confirms this model ( Fig 3C ) . Thus , instruction ( extrinsic determination ) and selection ( driven by intrinsic stochasticity of responsiveness ) synergize in fate control in a two-step scheme: cells must both be instructed and be selected for by the differentiation signal in order to adopt a particular phenotype [38–40] . This two-step process increases the fidelity of fate determination in the tissue . The attractor destabilization concept unites these two mechanisms of lineage commitment that has historically been opposed to each other but logically are not mutually exclusive [37 , 42 , 45] .
Here we present a novel use of single-cell gene expression analysis that is phenomenological but informed by dynamical systems theory to make predictions ( Fig 6 ) . We show that exit from the multipotent progenitor state and commitment to a particular cell lineage exhibit signatures of a critical state transition that can be exposed by single-cell resolution gene expression analysis of a cell population undergoing cell fate commitment . This phenomenological approach does not require detailed modelling of the dynamics of the underlying gene regulatory pathways and a definition of a bifurcation parameter , which is currently not realistic given our insufficient knowledge of the GRN architecture . The key formal assumption is only that the GRN state change implementing the cell fate decision and commitment is due to a monotonical gradual alteration of the value of an ( unidentified ) bifurcation parameter that drives the change of the attractor landscape through a bifurcation ( without specifying which type ) . Its chief consequence , the destabilization of a high-dimensional attractor state , can be observed . To do so , we treat the cell population as a statistical ensemble that ( ergodically ) explores the structure of a high-dimensional gene expression state space . The assessment of the latter directly confirmed that the notion of a critical state transition and associated early warning signals also extends to high-dimensional dynamics , as recently suggested [20 , 22] . Single-cell resolution analysis of a statistical ensemble at discrete time points makes up for the technical challenge of measuring the temporal fluctuations in cells , and the phenomenological but formal framework of critical transitions obviates the need for explicit modeling of the stochastic dynamics of gene regulatory circuits . We show that high-dimensional critical dynamics of mammalian GRN can be captured by measuring the transcript levels of a set of 17 genes in individual cells at multiple time points t and by computing from such data the index IC ( t ) , a quantity derived from the theory of non-linear dynamical systems that measures concomitant changes in cell–cell diversity and gene–gene coordination . Ideally , the genes considered for computing IC will encompass those that undergo coordinated changes during the critical transition , as dictated by the GRN that drives the critical transition . However , we show that 20 randomly selected genes from the transcriptome may also suffice , likely because of the basal genome-wide coordination of gene expression . IC is particularly useful for single-cell resolution snapshots of molecular profiles provided by burgeoning RNA-seq and CyTOF technologies and taken in statistical ensembles of cells ( i . e . cell populations ) at multiple time points during a biological time course . IC captures the information immanent in both the m gene vectors ( the expression level of a gene across a large number n of individual cells ) and the n cell vectors . Thus , IC does not require time-continuous monitoring of fluctuations as in many studies of critical state transitions because the information needed is in the high dimensionality ( m ) and in the statistical ensemble ( n ) . Although the decrease of cell–cell correlation ( denominator of IC ) as a sign of attractor destabilization is intuitively obvious , IC is not an ad hoc statistical measure that identifies patterns in the data but is grounded in first principles of non-linear dynamical systems [46] , as is particularly evident in the numerator , the gene–gene correlation . Conceptually , it is possible to speculate on a correspondence between the increase of gene–gene correlation to the appearance of long-range correlations of state variables in time ( autocorrelation ) and/or space used in the classical phenomenological analyses of critical state transitions [11] or of stressed populations [31] , although such an equivalence has yet to be formally shown . A recent study also examined the approach to the bifurcation as a critical transition and its phenomenological manifestation in the presence of stochastic fluctuations , albeit in a continuous two-dimensional system representing a version of the canonical toggle switch with two mutually suppressing genes . The authors found that not only the cell diversity increases but also the anticorrelation of the two genes [20] . On the biology of cell fate commitment , we show that bifurcation dynamics and single-cell expression analysis naturally integrate the two opposing classical models of cell fate determination [38]: instruction by extrinsic factors that explicitly regulate the gene expression change in each cell so as they adopt a particular cell fate [42] and selection of cells that have entered an intrinsically predestined gene expression state ( attractor ) [37] by promoting their survival and proliferation . Both models are supported by our observations . The deterministic bias toward a prospective lineage in the destabilization of the progenitor state , as manifest in the early changes of the transcriptome ( Fig 5A ) , confirms that instruction plays a role in fate determination . Conversely , the survival of the rebellious cells when the appropriate growth factors are later provided ( to promote the non-intended fate ) exposes a role for selection in cell lineage determination ( Fig 3C ) . In the case of selection , the promotion of one differentiation fate over the other is straightforward . But how does instruction steer a cell toward the desired state if leaving the multipotent state is driven by attractor destabilization ? What causes the asymmetry of outcome that ensures that cells in the desired lineage dominate over the rebellious cells ? The term “biased destabilization” used herein is more than a metaphoric explanation . It is grounded in the underlying bifurcation dynamics and could be elaborated in formal ways if one were able to explicitly model the dynamics of the relevant gene circuit for which many generic theoretical models have been proposed [8 , 15–20] . In the simplest case , binary lineage branching has often been modeled as a symmetrical supracritical pitchfork bifurcation [47]; here , the instructive signal could alter the values of other parameters in addition to the bifurcation parameter , such that one of the two post-bifurcation stable steady states ( depending on the signal ) is more stable than the other . In an alternative class of models , the bifurcation is inherently asymmetric , e . g . , modelled as an imperfect pitchfork bifurcation [48] . Such models are more realistic because these nonsymmetrical bifurcations are structurally robust . Here , one of the two post-bifurcation stable states is detached from the destabilizing progenitor attractor state and , thus , is less accessible ( requiring stronger gene expression noise ) than the other , which can be directly accessed from the vanishing progenitor attractor , as depicted in landscape diagrams , as in Fig 6 and references [11–13] . The instructive signal could introduce the imperfection , such that either one of the new attractors becomes separated . Future detailed analysis using measurements of single cell states at much higher time-resolution could address these questions and distinguish between these possibilities . In this study , we did not consider cell-to-cell communication mediated by local and soluble signals , which can modulate the internal GRN dynamics and , hence , affect phenotype transitions . This is an important aspect because the nonlinear , high-dimensional dynamics of cell states driven by a GRN poises cell populations at metastable states , making them akin to an excitable medium that , upon external perturbation , can respond in nonintuitive ways , as most lucidly epitomized by the rebellious cells . This response introduces cell population heterogeneity , underscoring the importance of cell population dynamics in which subpopulations coexist and shift their relative abundances in many ways that are affected by cell–cell communication between cells of distinct types . Indeed , a transition from cell type A to cell type B often does not follow first-order kinetics , suggesting non-cell autonomous effects ( S . H . , unpublished observation ) . For instance , cells that have reached an attractor state may secrete signals that can either promote or suppress the transition into it . A cell–cell interaction network would add an additional layer of dynamics to that of the intracellular GRN . The resulting ( multiscale ) cell population dynamics would be manifest as an accentuated modulation of the attractor landscape ( Fig 6 ) but could still be captured using the phenomenological framework of critical transitions because attractor destabilization preceding the phenotype switch does not depend on the specific ( physical ) implementation of the bifurcation . As single-cell resolution molecular profiling of cell states become routine , it will be important to analyze this new type of data , embodied by the matrix X ( t ) , in a fashion that is hypothesis-driven or is informed by the underlying first principles of dynamical systems ( Fig 6 ) , as opposed to solely using the current palette of ad hoc computational data analytics tools to find cell clusters and reduce dimensionality [46] . Although still phenomenological , invoking concepts of critical transitions provide a formal link to the fundamental principles . It remains to be seen whether critical transitions can be as readily detected in other cell systems as in the few examples examined in this study . Moreover , the significance for diseases and response to drugs of cell population dynamics with instabilities and critical behaviors could be further elucidated as single-cell analysis becomes commonplace and we move beyond descriptive data analysis . The notion of critical behaviors could be of practical utility for predicting major shifts in cell populations and tissues relevant in development and disease using data from single-resolution measurements at multiple discrete time points . NOTE ADDED IN PROOF: During the submission of this manuscript we became aware of the work of Richard , et al . , 2016 ( doi: 10 . 1371/journal . pbio . 1002585 ) which was motivated by a different conceptual framework and used different analysis methods but arrived at a similar conclusion .
Blood progenitor EML cells ( ATCC CRL-11691 ) were cultured and maintained as described previously [26] . Multipotent EML cell population was stimulated with either EPO ( to differentiate into erythroid cells ) , GM-CSF/IL-3 and ATRA ( to obtain myeloid cells ) , or a mixture of all cytokines for the “combined” treatment as previously reported [4 , 26] . Wright-Giemsa staining was performed with some modification following a reported protocol [27] . In brief , 60 , 000 cells in 250 μl of PBS + 1% FBS buffer were cytospun at 350 rpm for 5 min per slide and allowed to air dry for 10 min . Slides were subjected to five 1-second dips in methanol , followed by Wright-Giemsa staining solution ( 0 . 4% [w/v] , Sigma ) . After a final rinse with water , slides were allowed to air dry for 30 min . Colored phase contrast images were obtained using a Zeiss Axiovert 200M microscope . Cell surface protein immunostaining and flow cytometry measurements were performed using established methods [4] . Briefly , the antibodies Sca1-PE ( BD Pharmingen #553335 ) , ckit-FITC ( BD Pharmingen #553355 ) , and CD11b-FITC ( BD Pharmingen #557396 ) were used at 1:1 , 000 dilutions in ice-cold PBS containing 1% fetal calf serum with ( flow cytometry ) or without ( FACS ) 0 . 01% NaN3 . Appropriate unstained and single-color controls were used for gate definition and compensation setup . Isotype control antibodies ( BD Pharmingen #553988 for FITC and #553930 for PE isotype ) were used to establish the background signal caused by nonspecific antibody binding . Propidium iodide ( Roche #11348639001 ) staining was used to identify dead cells that were removed from analyses . Flow cytometry analysis was performed on a BD FACSCalibur cell cytometer with 10 , 000 viable events for each sample . Data were acquired using CellQuest Pro ( BD ) software and analyzed in FlowJo . For FACS sorting , the Sca1 protein distribution was measured and the expression distribution was gated into three regions according to the Sca1 expression level as Sca1-Low , Mid , and High on day 0 , 1 , and 6 or four regions on day 3 after differentiation initiation ( Fig 1A ) . Single cell sorting was conducted on a BD Biosciences FACSAria III in lysis buffer ( see below ) . For myeloid differentiation , cells were stained with antibodies for both Sca1 and CD11b protein markers , and cell subpopulations were gated as illustrated in Fig 3A . For studies involving the dynamics of sorted subpopulations , antibodies were removed after sorting using brief incubation in a low-pH buffer [4] . Single cells were directly sorted into 5 . 0 μl of lysis buffer ( CellsDirect kit , Invitrogen ) containing 4 . 25 μl Resuspension Buffer and 0 . 25 μl Lysis Enhancer using a FACSAria III ( BD Biosciences ) . 0 . 5 μl RNaseOut ( Invitrogen ) was added to the lysis solution to protect the RNA from degradation . To ensure that liquid droplets containing single cells were deposited at the center of the well and not at the wall , the position was checked on the plastic film covering the PCR plate . To reduce the possibility of cells sticking to the wall of the PCR well plate , we used low-binding PCR plates ( Axygen , #6509 ) . As control sample , a small population of 100 cells were sorted into a single well for qPCR analysis . To test for contamination of sorted cells with mRNA from lysed dead cells , 5 . 5 μl liquid from the FACS instrument was collected and analyzed . After sorting , the samples were heated to 75°C for 10 min to accelerate the lysis process , and samples were stored at -80°C . From these single-cell lysate samples , cDNA was directly synthesized as described previously [26] . The obtained cDNA was preamplified by 18 cycles [26] and subsequently diluted with Tris-EDTA buffer at a ratio of 1:10 , resulting in templates for the real-time PCR analysis . This protocol led to fewer than 30 quantification cycles ( Cq ) during the single-cell qPCR analysis on an OpenArray system ( Life Technologies ) . On this system , each qPCR plate consists of 12×4 subarrays and each subarray contains 8×8 reaction chambers of 33 nl volume ( S7A Fig ) [28] . Each sample was divided into a subarray with 64 reaction chambers prior to qPCR quantification . No-template ( water ) control was also run on each plate to check for nonspecific products and/or presence of contaminants in the master mix . Following the amplification , the corresponding curves and Cq values were processed using the OpenArray Real-Time qPCR Analysis software ( version 1 . 0 . 4 ) with a quantification threshold of 100 ( +/-5 ) . Specific PCR primers were preimmobilized in the chambers ( S7B Fig ) and released in the first cycle by heat . For each subarray , 2 μl of target sample was loaded into each well of a 384-well plate ( Applied Biosystems ) ; subsequently , 3 μl of the master mix reaction consisting of TaqMan OpenArray Real-time PCR Master Mix ( Applied Biosystems ) was added to each well . Target and master mix were combined and centrifuged , and the 384-well plate was processed in the OpenArray AccuFill system ( Applied Biosystems ) . During processing , 2 . 1 μl of the reaction solution was transferred automatically from each well into the corresponding subarrays of a qPCR plate , where the reaction solution retains into the reaction wells due to the differential hydrophilic–hydrophobic coating between wells and surface of the qPCR array [28] . The qPCR step was performed using thermocycling conditions of 50°C for 2 min , 95°C for 10 min , 40 cycles of 95°C for 15 sec , and 60°C for 1 min . We used off-the-shelf primers designed by Applied BioSystems ( Life Technologies ) for the analysis . The primers are usually designed to span exon–exon junction to target multiple splice variants of one transcript and to target only and specifically the gene of interest , avoiding amplification of genomic DNA . S3 Table lists all genes of interest , the inventoried TaqMan assay IDs ( Applied Biosystems ) , and further relevant information when the manufacturer does not provide primer and probe sequences . To evaluate qPCR assay performance , calibration ( standard ) curves were generated by performing qPCR on a serial dilution of a prepared template . Each of these dilutions was dispensed into two subarrays of OpenArray plate , leading to six technical qPCR replicates for each single cell sample . To minimize the effect of sampling errors on quantification precision , only sample/assay combinations with at least three quantifiable replicates were considered for preparing the standard curves . The GAPDH assay was not preimmobilized on OpenArray plate but was independently tested on BioRad qPCR platform . Data analysis is described in more details in S1 Appendix . Single-cell expression data were initially analysed with OpenArray qPCR analysis software . For quality control , amplification curves were quality filtered and Ct thresholds were set for each assay with the same thresholds used across all experiments and cell populations . Data were subsequently exported to Excel as csv files . All of Cq values are available in S1 Data . Samples not expressing any gene were excluded from the analysis . Experimentally determined LODs were used as cutoff Cqs ( S3 Table ) . Each assay was performed in triplicates , and the median of the triplicates was used for subsequent analysis . After this preprocessing , ΔCq was calculated as previously described [29] . Higher level of analysis such as correlation , clustering , and PCA was performed on log2-transfromed expression data . Microarray analyses were performed by the Vancouver Prostate Centre . EML progenitor cell population was stimulated with EPO alone , IL-3/GM-CSF alone , or a combination of all cytokines . On d3 and d6 after stimulation with different cytokines , the main “peaks” in the Sca1 distribution were gated and cell subpopulations were sorted using FACSAria III . Fig 5A illustrates the experimental design for the microarray experiments . Total RNA was extracted from 1×106 of sorted subpopulations using mirVana miRNA Isolation Kit ( Ambion ) following the manufacturer’s instructions . Genomic DNA was removed from the isolated and purified RNA using DNase I . Total RNA quality was assessed with the Agilent 2100 Bioanalyzer prior to microarray analysis . Samples with a RIN value equal to or greater than 8 . 0 were deemed acceptable for microarray analysis . Samples were prepared following Agilent’s One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labeling v6 . 0 . An input of 100 ng of total RNA was used to generate Cyanine-3 labeled cRNA . Samples were hybridized on Agilent SurePrint G3 Mouse GE 8x60K Microarray ( Design ID 028005 ) . Arrays were scanned with the Agilent DNA Microarray Scanner at a 3 μm scan resolution , and data was processed with Agilent Feature Extraction 11 . 0 . 1 . 1 . To filter out genes that were not expressed above the background noise , a raw intensity cutoff value of 25 was applied because the correlation between the technical replicates decreases for higher levels . Green processed signal was quantile-normalized using the “normalize . quantiles” function in R that takes care of inter-chip variability . To filter out genes which did not change between the samples , the distribution of each gene across all samples was analyzed . Therefore , the standard deviation ( STD ) distribution was calculated and only genes with STD > 10% were selected . As a result , 6 , 297 genes passed the criteria and were selected as the top 10% of genes among the samples . Self-organising maps ( SOM ) of the top 10% of most varied genes ( 6 , 297 genes ) were generated using the Gene Expression Dynamics Inspector program ( GEDI ) [44] . Cluster analysis was performed using the “clustergram” function in Matlab R2012a Bioinformatics toolbox using hierarchical clustering with Euclidean distance metric and average linkage to generate the dendrogram . Input data was log2-tranformed values of normalized fluorescent intensity signals for genes of interest extracted from the samples and plotted as a heatmap . Data represented the average of n = 2 independent biological replicates . The normalized fluorescent intensity values of 17 genes of interest in the curated network were extracted from each sample . | A certain type of multipotent progenitor cell of the blood can commit to either the white ( myeloid ) or the red ( erythroid ) blood cell lineage , thus making a discrete binary cell fate decision . To test a theory on fundamental principles of cell fate dynamics ( as opposed to the usually studied molecular mechanisms ) , we monitored such a fate decision in vitro using single-cell resolution gene expression analysis . We found that blood progenitor cells undergoing a fate decision to commit to either lineage after treatment with fate-determining cytokines , according to theory , first destabilized their original state . Cell states hereby diversified , manifesting the predicted flattening of an attractor’s potential well , which allows the increasingly vacillating progenitor cells to “spill” into adjacent potential wells corresponding to either lineage—myeloid or erythroid . This destabilization of an old stable state until suddenly opening access to new stable states is consistent with a critical transition ( tipping point ) . We propose and demonstrate a new type of early warning signal that precedes critical transitions: an index IC based on a change in the high-dimensional cell population structure obtained from single-cell resolution measurements . This index may be used to predict imminent tipping point–like transitions in multicell systems , e . g . , before pathological changes in tissues . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"flow",
"cytometry",
"innate",
"immune",
"system",
"medicine",
"and",
"health",
"sciences",
"immune",
"physiology",
"cytokines",
"immunology",
"cell",
"differentiation",
"developmental",
"biology",
"systems",
"science",
"mathematics",
"stem",
"cells",
"genome",
"analysi... | 2016 | Cell Fate Decision as High-Dimensional Critical State Transition |
“Use it and improve it , or lose it” is one of the axioms of motor therapy after stroke . There is , however , little understanding of the interactions between arm function and use in humans post-stroke . Here , we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy ( CIMT ) in the EXCITE clinical trial . Using a Bayesian regression framework , we systematically compared this model with competitive models that included , or not , interactions between function and use . Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model . Furthermore , by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization , we found that therapy increased the parameter that controls the effect of arm function on arm use . Increase in this parameter , which can be thought of as the confidence to use the arm for a given level of function , lead to increase in spontaneous use after therapy compared to before therapy .
Stroke often leaves patients with predominantly unilateral motor impairments . Although the affected upper extremity is often not completely paralyzed , the recovery of upper extremity function is often achieved solely by compensatory use , i . e . , choice of the less-affected arm [1] . Improving use of the more affected arm is important however , because difficulty in using this arm in daily tasks has been associated with reduced quality of life [2] . There is now definitive evidence that intensive task-specific practice is effective for improving upper extremity function and use after stroke [3] , [4] , [5] , [6] . Such training reverses , at least partially , the loss of cortical representation due to lesion through recruitment of adjacent brain areas in animals [7] , [8] and in humans [9] . This reorganization lasts several years [10] , and has been linked to improved performance [11] and increased use of the affected limb [12] . On the contrary , lack of training has been associated with further loss of cortical representation [7] , [13] . Thus , the axiom “Use it and improve it , or lose it” [14] , seems appropriately applicable to the training period , when the individual is “forced” to use the affected upper extremity . But , what happens outside of therapy , when the individual is free to use , or not use , the affected limb ? In some individuals , function and use further improve in the years following therapy [15] , [16] , [17] ( see Figure 1A ) . For other individuals , function and use decrease in the years following therapy ( see Figure 1B ) . We previously hypothesized that the repeated decisions to use the affected limb in daily activities may be a form of motor practice that can lead to further improvements [15] . Similarly , repeated , failed , attempts to use the affected limb have been hypothesized to underlie worsening of the impairment in a process termed “learned non-use” [18] . In our previous neuro-computational model of stroke recovery , we attempted to shed light on the interactions between function and use in general and learned non-use in particular [19] . Our model contained two independent motor cortices , each controlling the contralateral arm , with one being affected by stroke . Before each movement , one motor cortex was selected by an adaptive decision-making system , tentatively located in cortico-striatal networks . Arm performance improved via neural reorganization in the motor cortex , which learned both to minimize directional errors ( via supervised learning ) and to maximize neuronal activity for desired movement directions ( via Hebbian learning ) . Furthermore , the decision to use one limb or the other was made by comparing the “action value” of each limb in the adaptive decision-making system . The values for each arm were updated based on reward prediction errors ( via reinforcement learning ) . If performance-based rewards were greater than expected , the arm was chosen more often for this particular movement . Thus , the model predicted that function of the affected arm depends on prior use and that , in turn , arm use depends on non-linear competition between prior functions of the affected and the non-affected arm . The model also predicted that if spontaneous recovery , or motor training , or both , brings performance above a certain threshold , the repeated spontaneous arm use provides a form of motor learning that further bootstraps performance and spontaneous use . Below this threshold , spontaneous arm use after training decreases ( thus the model exhibits “learned non-use” ) , and compensatory movements with the less affected hand are reinforced . We previously provided clinical evidence for such a threshold at the group level [20] . Here , our principal aim was to test the hypothesis that , in individuals in the chronic phase post-stroke , function of the affected arm depends on prior use of that arm and arm use , in turn , depends non-linearly on function , as predicted from our previous model . For this purpose we developed a new data-driven quantitative first-order dynamical model of stroke recovery that links arm function and use with a small number of parameters , which can be directly adjusted from actual data . We obtained data on upper extremity function and use for a two-year period starting from 3 months or more after stroke from the database of the Phase III randomized controlled clinical EXtremity Constraint Induced Therapy Evaluation ( EXCITE ) trial [3] , which aimed at demonstrating the efficacy of a rehabilitative intervention for upper extremity . Arm function was derived from the time score of the Wolf Motor Function Test ( WMFT ) [21] , [22] and arm use data was derived from the Motor Activity Log Amount of Use ( MAL AOU ) [23] , [24] . Because of the sparsity of the data , we used Bayesian regression to fit the model . In addition , Bayesian regression allowed us to systematically compare our model with alternative models to test our hypothesis . We validated the model by computing the prediction errors of the model with a leave-one-out method . Our secondary aim was to investigate whether motor therapy can change the hypothesized relationship between arm function and use by examining the model parameters before and after therapy . Besides improving both function and use , therapy may increase the confidence to use the arm [25] , [26] . We thus predicted , that , the relationship that links arm function to arm use can be modified by therapy , and that controlling for the level of function , arm use can increases after therapy compared to before therapy .
In EXCITE , two groups of participants 3 months or more post-stroke were randomly assigned to either an immediate or a delayed Constraint Induced Movement Therapy ( CIMT ) group [3] , [27] , [28] . After 3 months , changes in function can be attributed more to learning and adaptation rather than to significant physiological modifiers that dominate the initial recovery period . The immediate group received two weeks of therapy from time Pre1 ( t = 0 ) to Post1; the delayed group received two weeks of therapy after a one-year delay , from Pre2 ( t = 1 year ) to Post2 . The measure of function that we used to develop our model was the negative of the logarithm of the WMFT time score , normalized between 0 and 1 . The WMFT time score [21] , [22] has been used as either a primary or a secondary outcome in more than 70 published studies including the EXCITE trial . The test determines the time required for patients with stroke to perform 15 everyday tasks with each upper extremity . Tasks are sequenced so that the first six tasks involve simple limb movements , primarily of the proximal musculature; the next nine tasks require manipulation and distal control . The time score is computed by adding the times of the tasks that the subject can perform within 120 seconds . For each task that the subject cannot perform , 120 sec are added . The WMFT time score has good reliability , validity , and no learning effect [22] . Note that because the more simple tasks can normally be performed quickly , the distribution of the WMFT time score has a long-tail . The natural logarithm of the WMFT time score is therefore taken to transform the distribution into a normal distribution [3] . To readily incorporate the time score of the WMFT ( after logarithm transformation ) into our model , we negated the logarithm transformed WMFT score such that a good ( low ) WMFT time score corresponds to good ( high ) arm function . We then normalized the range by dividing by the difference between the highest score and the lowest score in the data set , and subtracting the lowest score in the data set from each point . Thus , a normalized score of 1 corresponds to excellent function and 0 to very poor function . The measure of arm use that we incorporated to develop our models was the average MAL AOU score , normalized between 0 and 1 . In the MAL AOU [24] , [29] , the participants ( or their caregivers ) rate how much the paretic arm is used spontaneously to accomplish 30 activities of daily living outside of the laboratory . Each item on the MAL AOU is ranked from 0 ( no use ) to 5 ( normal ) via increments of 0 . 5 . Validity and reliability of the MAL AOU has been established [24] . The MAL AOU has been used extensively in studies with a few repeated measurements , including in the EXCITE trial . Participants were tested with the WMFT and the MAL AOU at Pre1 ( t = 0 week ) , Post1 ( t = 2 weeks ) , Pre2 ( t = 1 year ) , and Post 2 ( t = 1 year+2 weeks ) . All participants were also tested at 4 months , 8 months , 16 months , 20 months , and 24 months . In the immediate group , because we only studied the participants' behavior after therapy , we excluded data at Pre1 . Furthermore because little change in function or use is likely to happen within a 2-week-period one year after CIMT for the immediate group [16] , we averaged the data at between Pre2 and Post2 for this group . Thus , for each subject of the immediate group , a total of 7 data points were available , each spaced by 4 months ( at Post1 , 4 , 8 , 12 , 16 , 20 , and 24 months ) , as shown in Figure 1 . In the delayed group , we compared the participants' behavior after therapy to the behavior before therapy . Because little change in function or use is likely to happen in two weeks between Pre1 and Post1 for this group [3] , we averaged the function and use data at these two data points . Thus , for each subject of the delayed group , 4 data points were available before therapy ( at 0 , 4 , 8 months , and Pre2 ) and 4 data points available after therapy ( at Post2 , and 16 , 20 , and 24 months ) . Because of the very limited number of time points in our study , we only analyzed the data of participants with full data sets , that is , each participant had a full complement of WMFT and MAL AOU data . In the immediate group , 48 participants had a full data set . In the delayed group , 45 participants had a full dataset . We investigated the simplest possible model that best accounted for four essential characteristics of our previous neuro-computational model [19]: 1 ) Time varying changes in arm function and use reflecting the dynamic of stroke recovery . 2 ) Effect of use on function , with high use leading to higher future function , and low use leading to lower future function . 3 ) Effect of function on decision to use the arm , with higher function leading to higher future use , and lower function leading to lower future use . 4 ) Decision to use the affected arm or the non-affected arm based on competition between prior function of the affected and function of the non-affected arm . We specifically hypothesized that a first order non-linear dynamical system , with two equations , can account for the interactions between arm function and spontaneous use in individuals post-stroke . The first ( state-space ) equation updates the function of the affected arm; the second equation updates the use of that arm . Characteristics ( 1 ) and ( 2 ) above can be encapsulated by the evolution of arm function at time step t in terms of arm function and use at the previous time step as:where arm function at t , , is updated based on arm function and use at the previous time step t−1 , and , is a decay rate , a ‘use effect’ rate , and a constant input . Given the very few data points at our disposal ( 7 points in the immediate group ) , it is unlikely however that such a complex model with 3 free parameters would provide both good fit and good generalization ( See sub-section “Model fit” below ) . Although we consider the 3-parameter model above and a simpler 2-parameter model with = 0 for model comparison ( see below ) , we take as our reference model the simplest model , the 1-parameter model given by: ( 1 ) where is a free parameter . Equation ( 1 ) represents a condensed version of “Use it and improve it , or lose it” , in the condition that 0≤≤1: if is zero or small , decreases . If is large , then increases . The parameter can be considered as a ‘use effect’ rate; the larger this rate , the greater the effect of spontaneous arm use on function . The term ( 1− ) is a decay rate of arm function: with zero use , arm function would decay exponentially with time constant D/ , where D is the time step of 4 months . Characteristics ( 1 ) , ( 3 ) and ( 4 ) above can be encapsulated by the update of arm use at time step t , , in terms of arm function in the previous time step , as: ( 2 ) where and are free parameters . Equation ( 2 ) is a sigmoidal equation that arises from common decision-making models in the reinforcement-learning framework [30] , in which the probability to take an action is computed by comparisons of the values of each actions , with the action with the highest “value” being the most probable . Here , we assumed that the “action value” of each limb is proportional to the function of each limb at the previous time step . The slope parameter thus controls the sensitivity of arm function on arm use and can tentatively be considered as a “confidence parameter”: for equal function , greater or smaller leads to more or less use , respectively . The parameter encapsulates the function of the non-affected arm together with any non-modeled bias for preferred use of one arm versus the other , such as arm dominance or side of stroke . We did not include in the model because the average changes in function of the unaffected arm following therapy are relatively small compared to the average changes observed in the affected arm . Among participants of the immediate group the average log time WMFT scores is 8 . 62±9 . 78 ( SD ) for the affected arm , and 1 . 99±0 . 82 for the unaffected arm . The median percentage change in the score for the unaffected arm from just after therapy ( Post1 , t = 2 weeks ) to 24 months , normalized by the score of the affected arm just after therapy , is −3 . 6% and the interquartile range 7 . 1% . By comparison , the median percentage change of the score for the affected arm from just after therapy to 24 months , normalized by the score just after therapy ( Post1 , t = 2 weeks ) is −23 . 2% and the interquartile range 55 . 5% . We thus considered the function of the non-affected arm constant over the two years following therapy; only the function of the affected arm enters Equation ( 2 ) ( henceforth , we drop the subscript “affected” ) . Note that because of the simple 1-parameter model of function , arm function converges to the same value as use in the steady state ( although after transformations to the original WMFT and AOU score , the values would be of course different ) . This is simply due to our choice of a single parameter function model , and there is no reason why this should happen in actual individuals post-stroke . Nevertheless , our model may still be adequate given 1 ) that the variables may not converge to their asymptotic values within two years because of long-time constants , and 2 ) the trade-off between fit and complexity that favors simpler models . We estimated , , and from function and use data of the EXCITE trial participants in both the immediate and the delayed group . We also aimed at testing our hypothesis of interactions between arm function and use as encapsulated in Equation ( 1 ) and ( 2 ) , against a number of alternative hypotheses , as we now describe . Because we have only 7 data points ( immediate group ) and 4 data points ( delayed group for each before and after-therapy ) for each arm function and use , we must ensure that the model does not overfit the data , that is , the model should describe the underlying relationship , not the random error or noise . Overfitting generally occurs when a model is excessively complex , such as having too many parameters relative to the number of data points . For instance , in frequentist ( maximum likelihood ) linear regression , a minimum of 10 or 15 points per predictor is usually considered necessary . In contrast , Bayesian regression is the method of choice in our case , as it does not overfit the data for very small data sets ( see [31] and below for rationale ) . The Bayesian regression framework has the additional advantage of allowing principled model comparison based on the training data alone , that is , without the need for cross-validation , which “wastes” training data . In light of these qualities , we used Bayesian regression to determine the parameters of all the candidate models based on ( normalized ) WMFT and MAL AOU data in the immediate group following therapy for each individual participant ( N = 48 ) . Here we illustrate Bayesian regression for our reference model of Equation ( 1 ) and ( 2 ) . Similar methods are used for the alternative models . We first reformulated Equation ( 1 ) and ( 2 ) to form equations linear in model parameters: ( 3 ) ( 4 ) We then transformed in a linear regression form: ( 5 ) ( 6 ) where ( 5 ) and ( 6 ) correspond to ( 3 ) and ( 4 ) respectively . and are the dependent ( target ) variables , representing the left-hand side of ( 3 ) and ( 4 ) respectively , and , , and are basis functions ( , = , and ) . Note that we can decouple and for the purpose of model parameter estimation; hence , we use as an example in the following discussion . Using a vector form , Equation ( 6 ) gives the regression model given model parameters: ( 7 ) where , where L is the number of measurements , , and the design matrixWe need two consecutive measurements to estimate our regression model Therefore L = 7−1 = 6 for the immediate group , and L = 4−1 for the delayed group . Hence L×M matrix , where M is the number of model parameters ( i . e . , M = 2 for arm use model ( 2 ) ) . Measurements from the EXCITE clinical data contain noise , and we assume that this noise is Gaussian added to the linear regression model y . The data distribution is thus assumed to be drawn independently from Gaussian distribution with mean and variance : ( 8 ) where is a data accuracy hyper-parameter ( inverse of variance ) . In Bayesian regression , we treat model parameters as a probability distribution . We assume that the prior distribution of model parameters is also independent identically distributed Gaussian . ( 9 ) where is the mean of model parameter , and the model accuracy hyper-parameter . The goal of Bayesian regression is to maximize the Bayesian model evidence , which is the probability of data distribution , given the model parameters: . Using the sum rule and product rule of probability from ( 8 ) and ( 9 ) , and taking the logarithm , we obtain the log of the model evidence ( see [31] page 167 for derivation for 0 centered priors , and Supplementary material: Text S1 ) : ( 10 ) ( 11 ) ( 12 ) where || is the determinant of , and is the Euclidean norm . As shown from Equation ( S24 ) to ( S26 ) in Supplementary Material Text S1 , is actually the mean of the posterior distribution of the model parameters , and is the accuracy ( inverse of covariance ) of the distribution . Note how reduces to the frequentist regression solution for . Equation ( 10 ) illustrates how Bayesian regression implements a trade-off between data fitting and model complexity . With larger ( more model parameters ) , can better approximate the data distribution , and the error between and , decreases . On the other hand , because the size of vector also scales up with larger number of parameters , the regularization term may increase . Similar trade-offs are found in ( 11 ) and ( 12 ) in the form of weighted average between prior knowledge and data . Note that , since the design matrix utilizes all data points , we do not need to spare testing data points for evaluating model fit , unlike cross-validation ( e . g . , leave-one-out ) . We maximized the model evidence in terms of the two hyper-parameters , which controls model parameter distribution ( 9 ) , and , which controls data distribution ( 8 ) . Note that ( 11 ) and ( 12 ) are also functions of and . We used an iterative method [31] , where we fixed and in the first step and optimized and , and update them with the new and in the second step . We provide here a summary of the algorithm to compute the model evidence ( see Supplementary material: Text S1 for details ) . Our hypothesis of “Use it and improve it , or lose it” is encapsulated in our reference model of arm function , which assumes that current arm F ( t ) function depends on a weighted sum of previous arm function F ( t−1 ) and previous arm use U ( t−1 ) . We compared this model with alternative hypotheses in which F ( t ) does not depend on use , but only on previous arm function F ( t−1 ) ( i . e . use has no effect on function ) , or conversely , in which F ( t ) depends solely on previous use U ( t−1 ) , not on previous function . As noted above , our hypothesis is not specific to the exact model given in Equation ( 1 ) , but other models containing a linear combination of F ( t−1 ) and U ( t−1 ) also fall under “Use it and improve it or lose it” . Thus , we also considered more complex linear stable models with 2 and 3 parameters . Table 1 shows the 7 possible models of function that we considered , with the bold model our “reference model” . Our reference model of arm use assumes that current use of the affected arm U ( t ) depends via a sigmoidal function on previous function of the affected arm F ( t−1 ) and a constant representing the function of the non-affected arm . We compared this model with alternative models in which U ( t ) depends linearly on previous arm function F ( t−1 ) . In simulations of our previous neuro-computational model , the values for each arm were updated based on reward prediction errors at a much higher rate than the update of performance . Since our time step in the current model is 4 months , it is thus possible that the decisions to use the arm are updated much faster than performance . We therefore also compared the model of Equation ( 2 ) to models in which the current arm use U ( t ) depends on current arm function F ( t ) , either via a sigmoid or linearly . Table 2 shows the 4 possible models of use that we considered , with the bold model our “reference model” . Initial means of the parameter distributions were taken as the values found with maximum likelihood regression of all entries of the immediate group , except the weighted average model ( bold in Table 1 ) with initial mean value of at 1 . We reflected our emphasis on data and lack of prior knowledge by setting the ratio of the initial values of the prior accuracy and the data accuracy to = 10−3 and choosing almost flat priors with = 10−11 , for both the function and use models . Note that these initial parameter values were taken equal for all subjects . We verified in simulations that when <10−11 , the results of model comparison are qualitatively the same as that presented . We set = 10−8 for model fitting . We also performed a sensitivity analysis ( i . e . a systematic variation ) on the initial data accuracy ( See Supplementary Figure S1 ) . The Bayesian model evidence for each model was used to compare models by computing the Bayes factor ( BF ) , which is the ratio of model evidence probability of competitive models to the reference model [32] . Thus , given the model evidence probability for our reference model and the model evidence probability for a competitive model , the Bayes factor is given by BF = / . The Bayes factor has a role similar to the p-value in frequentist statistics and is used to accept or reject the hypothesis [33] . If BF<1 , there is negative evidence for the hypothesis , and the hypothesis should be rejected . If 1≤BF<3 , the evidence is “barely worth mentioning” . If 3≤BF<10 , there is then substantial evidence for the hypothesis , and BF = 3 is a threshold for accepting the hypothesis similar to p = 0 . 05 in classical statistics . Then for BF>10 , 30 , and 100 there is strong , very strong , and decisive evidence for the hypothesis , respectively . To compare the models over groups of subjects , a “group Bayes factor” can be computed by multiplying the individual Bayes factors [34] . However , such group Bayes factor is misleading in the presence of the strong outliers , which are present in our analysis due to poor convergence of the models for a number of individuals ( as a result of our very limited data set ) . Therefore , we evaluated the number of comparisons for which BF>3 for either of the compared models to compute the “positive evidence ratio” , which serves as a measure of which model is optimal at the group level [34] . Positive evidence ratios read as x∶y , where x is the number of subject for which the Bayes factor of the reference model is greater than 3 , and y the number of subjects for which the Bayes factor of the alternative model is greater than 3 . For N− ( x+y ) subjects , no conclusion can be drawn . For this analysis , we hypothesized that motor training in the EXCITE trial , besides improving function and use , also had a “meta-learning” effect ( e . g . , [35] , [36] ) . According to this hypothesis , CIMT has an effect not only on arm function and use , but also on the relationships between function and use . In our model , such meta-learning would translate to different values of the parameters , , and before and after therapy . In particular , we hypothesized that training increases the confidence to use the arm for a specific level of function , in which the model translates in an increase in the parameter . Using data from the delayed group in the EXCITE trial ( N = 45 ) , we used Bayesian regression for our reference model of Equation ( 1 ) and ( 2 ) , and we compared the means of the parameters for each subject before and after therapy . The initial values of the hyper-parameters were the same as of the immediate group analysis .
We first computed the Bayes factors to test the two hypotheses encapsulated in Equations ( 1 ) and ( 2 ) . Then we computed the positive evidence ratio for each model from the individual Bayes factors . Table 3 shows that our reference arm function model weighting previous arm function and previous use with a single parameter is strongly preferred over all other models with 2 or 3 parameters . This is presumably because of the sparsity of data in our database . Our reference arm function model is preferred over the model that depends only on previous arm function for 27 subjects out of 48 subjects , For 1 subject this alternative model is preferred , and for 20 subjects , no conclusion can be drawn . Similarly , our reference arm function model is preferred over the model that depends only on previous arm use for 25 subjects . For 5 subjects this alternative model is preferred , and for 18 subjects , no conclusion can be drawn . Table 4 shows that our reference use model with sigmoidal model of arm is strongly preferred over the two linear models . However , our reference model is not preferred over an alternative model in which arm use depends on current function; there is indeed a small advantage to the model that computes use based on current function . Figure 2 shows examples of fits with our model for both arm function and use , using the mean parameters for three subjects in the immediate group . In Figure 2A , both function and use continue to increase after therapy ( mean model parameters = 0 . 76 , = 2 . 98 and = 0 . 42 ) . In Figure 2B , arm use initially largely decreases post-therapy despite relatively high function . This subject thus exhibits “learned non-use” ( Mean model parameters = 0 . 14 , = 3 . 36 and = 3 . 03 ) . In Figure 2C , conversely , arm use increases after therapy , while function is relatively high . Because arm function slightly decreases in the months following therapy , so does arm use , which reaches immediately post-therapy levels after 2 years ( mean model parameters = 0 . 19 , = 3 . 48 and = 1 . 89 ) . These figures illustrates the dynamic , nonlinear nature of arm function and use post-therapy , and how our model adequately captures these dynamical interactions and provide a reasonably good fit to the data , although the use model appears to better fit the data than the function model , and with better fit soon after therapy . To systematically evaluate the goodness of fit , we trained the model on 6 of the 7 data points available in the immediate group and compared the prediction of the model to the actual data point for testing ( thus performing a leave-one-out model fit ) . Note that we kept the first and the 7th point , since we used them as an initial and final value of our model . Table 5A shows the average absolute errors of prediction among subjects of the immediate group . The average absolute errors of all 2nd to 6th leave-one-out prediction errors were 0 . 16 for arm function and 0 . 091 for arm use in the range between 0 and 1 . The models thus reasonably fit the data , especially in the first year after therapy , although the prediction errors of the use model are lower than those of the function model overall ( p<0 . 0001 , t-test ) . As a comparison , the average absolute errors of randomized models were 0 . 22 for arm function and 0 . 26 for arm use ( Table 5B ) . Here , the randomized model generates predictions points from randomly selected subject at the corresponding time step . A repeated measure ANOVA confirmed that mean prediction errors of the proposed arm function model are smaller overall than those of the randomized arm function model ( p = 0 . 01 ) , although the prediction errors in the proposed model increase with time ( model×time interactions: p<0 . 0001 . One way repeated ANOVAs , effect of time , proposed function model: p<0 . 0001 , randomized function model p>0 . 1 ) . Similarly , the prediction errors of the proposed arm use model are smaller overall than those of the randomized arm use model overall ( p<0 . 001 , no model×time interactions; p>0 . 5 ) . Histograms of the mean parameters , , and for the models of Equation ( 1 ) and ( 2 ) are shown in Figure 3 . Because of the very few and noisy data points , Bayesian regression did not exhibit adequate convergence of the model parameter distributions for all subjects; that is , the parameter distributions were relatively flat for some subjects . In a first approximation , we defined good convergence as follows: the standard deviation of the final parameter distributions after convergence should be less than one standard deviation of the distributions of the parameters means . This criterion resulted in the following cut-off standard deviations: 0 . 316 for ; 6 . 38 for , and 3 . 67 for . As shown in Figure 3 , all negative mean parameters were removed after applying this criterion . Thus , for all 27 subjects with good convergence of the Bayesian regression for the function model , the mean parameter was positive and in the range [0 , 1] , with median 0 . 64 . This indicates a positive effect of arm use on the previous time step upon arm function at the next time step . Similarly , mean parameters and with large absolute values were removed by the cut-off procedure . The median of the mean of for the 32 subjects with good convergence was 2 . 20 . The median of the mean of for the 33 subjects with good convergence was 1 . 40 . Positive parameters indicate that arm function has a positive effect on arm use , as hypothesized . Positive parameter indicates competition between function of the affected limb and ( constant ) function of the non-affected limb , as predicted by models of decision-making based on comparisons of “values” . Note that we verified with surrogate data derived from the model that our Bayesian regression method can indeed retrieve the parameters of the original model ( see Supplementary material: Text S2 and Figure S2 ) . We then examined whether CIMT had an effect on the model parameters in the delayed group by comparing before and after therapy models . Before-therapy model parameters were trained with arm function and use in the year before therapy . After-therapy model parameters were trained with arm function and use in the year after the therapy period . The standard deviation cut-off values were the same as above , and only parameters with good convergence before and after therapy were analyzed . Among the three model parameter means , only the means of was significantly different between before and after and therapy ( Figure 4B , mean of before therapy 2 . 95±0 . 32; after-therapy 4 . 58±0 . 49; p = 0 . 041; N = 22; 2-tailed pair t-test ) . There was no difference in ( Figure 4A , before-therapy 0 . 759±0 . 044; after-therapy 0 . 825±0 . 036; p = 0 . 55; N = 27; 2-tailed pair t-test ) and in ( Figure 4C , before-therapy 2 . 21±0 . 16; after-therapy 2 . 10±0 . 20; with p = 0 . 54 , N = 28 , 2-tailed pair t-test ) . Our previous neuro-computational model of stroke recovery [19] exhibited non-linear and bi-stable behavior of stroke recovery: the model predicted that if natural recovery , motor training or both , brings performance above a certain threshold , training can be stopped , as the repeated spontaneous arm use provides a form of motor learning that further bootstraps performance and spontaneous use . Here , we simulated our model made of Equation ( 1 ) and ( 2 ) to study whether the simplified model of the present study also contained such threshold and bi-stable behavior , and to study the effect of the increase of the “confidence” parameter from before to after therapy , with the simplifying assumptions that therapy does not increase function and use . For this purposes we performed a parameter sensitivity analysis using the continuation and bifurcation toolbox Matcont ( http://sourceforge . net/projects/matcont/ ) . The sensitivity analysis of Figure 5B shows that for ≤3 and low values of , asymptotic function and use are low . However , by increasing , therapy can “move” the participants from one low attractor to a high attractor region , exhibiting convergence to different arm function values , as shown in simulation results of Figure 5 A and B . Thus , if therapy increases the confidence to use the arm , the greater spontaneous arm use will lead to greater function , in a virtuous cycle ( Figure 5A , = 4 or = 5 ) . In contrast , for a low value of the parameter , the simulated patient is in a vicious cycle and use decreases ( as in Figure 5A for = 3 ) . Because of competition between function or each arm in computing use , high values of lead to greater non-use compared to smaller values of ( See left side of Figure 5B ) . This is illustrated by comparing arm use for the two subjects in Figure 2B and 2C . The main difference in parameters between the subjects of Figure 2B and 2C is the value of . Because is relatively large in 2B , arm use decreases to low level; in contrast use stays relatively high in 2C . However , for >3 , a sufficient increase in the parameter will bring the system in a truly bi-stable mode . Depending on the initial condition ( i . e . values of F ( t ) and U ( t ) just after therapy ) , function and use can either remain near low values or near high values delimited by the low Limit Point ( LP ) and high LP in the Figure 5B . Thus , the model exhibits a “threshold” in function , as we proposed in our previous work [19] , [20] .
Stroke recovery is , by definition , a time-varying process . Although our dynamical “state-space” model naturally accounts for the time-varying nature of stroke recovery , this paper represents , to our knowledge , the first effort to use approach to quantitatively model recovery of individuals post-stroke . The stroke recovery model proposed here depicts a time-evolving process with interactions between arm function and use . The model , which is composed of two sub-models , one that updates arm function ( Equation ( 1 ) ) and the other that updates arm use ( Equation ( 2 ) ) , has only three free parameters , which were estimated with repeated measurements of upper extremity function and use obtained in a phase III randomized controlled clinical trial , the EXCITE trial . For a majority of the participants in the immediate group of the EXCITE trial that we studied , arm function depends both on prior function and prior use . Presumably because of the very limited amount of data that penalizes models with more parameters , the preferred arm function model performs a weighted average of previous arm function and use with a single parameter . This model is preferred for 27 subjects out of 48 over a competitive model in which arm function is not dependent on previous use , and is preferred for 25 subjects out of 48 over a model in which function is solely based on use . The alternative models are preferred for 1 and 5 subjects respectively; for the remainder of the subjects , no conclusion can be drawn . Furthermore , parameter analysis showed a positive effect of arm use at the previous time step upon arm function at the current time step , thus truly capturing the phenomenon of “Use it and improve it , or lose it” for a majority of the participants we studied . Although this phenomenon may be taken for granted by stroke rehabilitation specialists , this is , to our knowledge , the first systematic demonstration of the effect of the upper extremity use on changes in function and vice-versa in stroke recovery in individual subjects ( in our previous study [20] we only study this effect of function immediately following therapy upon future use at the group level ) . We further showed that for the large majority of the participants we studied , models of spontaneous arm use based on a sigmoidal dependency of arm function are preferred over linear models . This result indicates that the non-linear dependency of use on function has a strong effect on the fit of the use data . Furthermore , parameter analysis showed that arm function has a positive effect on arm use with competition between function of the affected limb and ( constant ) function of the non-affected limb , as predicted by models of decision-making based on comparisons of “values” e . g . [37] . Time has a lesser effect: Our reference model of Equation ( 2 ) in which use depends on previous function F ( t−1 ) ) is only preferred for 14 subjects over a model in which use depends on current function F ( t ) ) . Contrarily this alternative model is preferred for 19 subjects over the reference model ( no conclusion can be drawn for the remainder 15 subjects ) . This inconclusive effect of time on arm use suggests that update of the arm choice ( presumably via the learning of “values” ) is fast compared to the update of arm function . Our previous neuro-computational model of stroke recovery [19] exhibited bi-stable behavior of stroke recovery . Here , our simpler data-driven model also exhibits a bi-stable behavior , although for relatively large values of the parameters and of the use model ( see Figure 5B ) . However , even for lower value of the parameters ( around the mean of the estimated parameters ) therapy can , by increasing the parameter , “move” the participants from one low attractor to a high attractor region shown in simulation results of Figure 5A . This simulation of the model made of Equation ( 1 ) and ( 2 ) illustrates the effect of the increase of the “confidence” parameter from before to after therapy , with the simplifying assumptions that therapy does not increase function and use . Simulations show that if therapy increases confidence to use the arm , the greater spontaneous arm use will lead to greater performance , in a virtuous cycle ( Figure 5 , = 4 or = 5 ) . In contrast , for a low value of the parameter , the patient is in a vicious cycle and use decreases ( as in Figure 5 for = 3 ) . Unfortunately , because of the limited data set , the sustainability of this increase in confidence in participants of the EXCITE trial is unclear . Since the median post-therapy in the immediate group ( 2 . 20 ) is inferior to the median post-therapy in the delayed group ( 3 . 90 ) such increase may be relatively short-lasting post-therapy . In sum , our results suggest that learned non-use results , at least in part , from three non-mutually exclusive factors: 1 ) a decrease in function of the affected arm; 2 ) a relative increase in function of the non-affected arm ( if for instance stroke affects the right arm and the right-hand dominant subject is learning how to use her left arm ) ; 3 ) reduced “confidence levels” in using the arm for a given function ( as a result of spilling a hot coffee on someone else for instance ) . Since our study is only a model of changes in behavior , we can only speculate on the causes of non-use at the neural level . Reduced use may lead to contraction of motor cortical maps leading to decreased performance and further reduced use [19]; contrarily forced use ( i . e . practice ) may lead to map expansion and increase performance [7] . If such improvements in function together with confidence levels are sufficient , then use of the affected arm in daily activities may increase sufficiently such that function will improve spontaneously , effectively reversing non-use , as shown by our simulations in Figure 5 . The median of across subjects with good convergence was 0 . 64; given a time step of 4 months , this is equivalent to a median time constant of forgetting of 1/0 . 64*4 months = 6 . 25 months . This appears reasonable in light of the long-lasting cortical reorganization after training , e . g . [10] . Our model assumes the existence of independent measures of arm function and use across individuals at specific times . So does the MAL AOU reflect arm use that does not depend on arm function ? We found a moderate but significant correlation ( r = 0 . 58 , p<0 . 0001 ) between the normalized MAL AOU vs . WMFT at t = 0 for all 93 patients ( 48 in immediate and 43 in delayed group ) . However , there is no correlation between arm function and use for those 54 patients with medium to low function ( normalized WMFT<0 . 5 , r = 0 . 10 , p = 0 . 45 ) . For this sub-group , normalized MAL AOU ranges between 0 and 0 . 64 . This indicates that , within this sub-group , some patients have relatively high use with low function , and vice-versa , and that function and use are independent variables across subjects . Model comparisons for this sub-group of subjects with medium to low function still largely favor our hypothesized models over competitive models ( See Supplementary material: Text S3 ) . The results of the present study need to be replicated with to-be-developed databases that contain dozens of repeated measurements of upper extremity function and use before , during , and after therapy . In particular , our model provides only “substantial” ( in a Bayesian model comparison terminology ) evidence for the “use and improve it or lose it” hypothesis for a majority but not for all the EXCITE participants we studied . Because of the sparcity of the data , the models did not fit the data in a satisfactory manner for large subgroups of subjects , and no conclusion can be drawn for these subjects . Furthermore , the predictions from our model , quite accurate in the first year , became worse with time across subjects ( See Table 5 ) . A possible interpretation of this result is that the influence of function on use and vice versa is stronger soon after therapy , but that this influence is reduced due to the myriad of other un-modeled factors that influence use after stroke ( the patient could for instance go back to work , start to exercise , hire a caregiver , etc . , all of which could affect the rate of recovery ) . Thus , our model is currently best viewed as a prototype against which one can develop further time dependent models of stroke recovery . Future models , based on a richer longitudinal data set of arm function and use , including measurements just after the stroke , and that include neural measurement variables such as lesion size , location , excitability of the corticospinal tract etc . , might better characterize the time course of stroke recovery . Our assumptions of two independent cortices , equal roles of each arm , and pure uni-manual actions are also clear oversimplifications . Also , while motor ( re- ) learning after stroke can be understood at least in part as practice-dependent reduction of kinematic and dynamic performance errors [38] , no such error data were available in our data set , and we therefore did not include a corresponding error-based ( supervised ) learning term in this simplified model ( unlike in our previous model [19] ) . Instead , the present model only includes a trivial form of unsupervised learning in the update of arm function , and a degenerated form of reinforcement learning , with “values” simply equal to functions . Finally , our model cannot predict the time course of spontaneous recovery in the acute phase post-stroke . Here again , more longitudinal data points , including early after stroke , are needed for viable extensions of the model . Nonetheless , our model , although preliminary and despite its important limitations , is a first step in the direction of the development of an accurate recovery model that can predict the time course of recovery post-stroke . Our long-term goal is to validate and test a method based on such dynamical models to compute the dose of arm and hand motor therapy for individual patients and provide treating therapists with such a method to be used in the clinic . A well-validated model of upper extremity recovery that generates accurate predictions of long-term use and performance , and the confidence intervals of the predictions , could be highly valuable because the clinician , patient , or provider ( if applicable ) will be able to make informed decisions about treatment and potentially determine the critical dose of motor therapy for an individual patient . If for instance the model predicts that no amount of recovery can increase use , rehabilitation may be in “vain” , and compensatory strategy should be emphasized . On the other hand , if therapy is predicted to be effective , a well-validated and accurate model could be used to determine minimally effective dose of therapy to maximize the benefit/cost ratio of therapy . | Although , there is now definitive evidence that intensive task-specific practice is effective for improving upper extremity function and use after stroke , it is unclear how individual patients recover from stroke , and how they respond to therapy . Here , we propose a novel computational model of stroke recovery to study the time-varying dynamics of recovery of individuals at least 3 months post-stroke with mild to moderate impairments . Our model gives support to one of the axiom of neuro-rehabilitation “use it or lose it” . Furthermore , analysis of the model parameters showed that increase in confidence to use the affected arm during therapy may affect the dynamics of recovery . Our long-term goal is to develop and validate a method based on such dynamical models , to allow clinicians and patients to make informed decisions about treatment and potentially determine the critical dose of motor therapy for an individual patient . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"computer",
"science",
"computer",
"modeling",
"biology",
"neuroscience",
"physiotherapy",
"and",
"rehabilitation"
] | 2012 | Use It and Improve It or Lose It: Interactions between Arm Function and Use in Humans Post-stroke |
Interferon-γ ( IFN-γ ) represents one of the most important innate immunity responses in a host to combat infections of many human viruses including human herpesviruses . Human N-myc interactor ( Nmi ) protein , which has been shown to interact with signal transducer and activator of transcription ( STAT ) proteins including STAT1 , is important for the activation of IFN-γ induced STAT1-dependent transcription of many genes responsible for IFN-γ immune responses . However , no proteins encoded by herpesviruses have been reported to interact with Nmi and inhibit Nmi-mediated activation of IFN-γ immune responses to achieve immune evasion from IFN-γ responses . In this study , we show strong evidence that the UL23 protein of human cytomegalovirus ( HCMV ) , a human herpesvirus , specifically interacts with Nmi . This interaction was identified through a yeast two-hybrid screen and co-immunoprecipitation in human cells . We observed that Nmi , when bound to UL23 , was not associated with STAT1 , suggesting that UL23 binding of Nmi disrupts the interaction of Nmi with STAT1 . In cells overexpressing UL23 , we observed ( a ) significantly reduced levels of Nmi and STAT1 in the nuclei , the sites where these proteins act to induce transcription of IFN-γ stimulated genes , and ( b ) decreased levels of the induction of the transcription of IFN-γ stimulated genes . UL23-deficient HCMV mutants induced higher transcription of IFN-γ stimulated genes and exhibited lower titers than parental and control revertant viruses expressing functional UL23 in IFN-γ treated cells . Thus , UL23 appears to interact directly with Nmi and inhibit nuclear translocation of Nmi and its associated protein STAT1 , leading to a decrease of IFN-γ induced responses and an increase of viral resistance to IFN-γ . Our results further highlight the roles of UL23-Nmi interactions in facilitating viral immune escape from IFN-γ responses and enhancing viral resistance to IFN antiviral effects .
Human cytomegalovirus ( CMV ) , a member of the human herpesvirus family , is a common opportunistic virus causing severe ailments and deaths in people with immature or compromised immune systems [1–4] . HCMV ability to evade the host immune system can significantly impact the course of illness . For example , predisposition to bacterial and fungal infections is common for HCMV positive patients undergoing hematopoietic stem cell transplant procedure [5 , 6] . HCMV expresses viral proteins to modulate the host immune responses at every step of its life cycle , which play a crucial role in viral pathogenesis [7–9] . Interferons ( IFNs ) such as IFN-γ are part of the innate immune response to viral infections and confer potent antiviral effects . These cytokines also have important roles in immune-surveillance for malignant cells [10 , 11] . IFN-γ , a type II IFN , binds to a cell-surface receptor , which is known as the type II IFN receptor [12–14] . Upon viral infection , IFN-γ [15 , 16] activates cellular signaling networks , such as the JAK–STAT pathway [17 , 18] . The transcription of type II IFN ( IFN-γ ) -dependent genes is regulated by gamma-activated sequence ( GAS ) elements , and the signal transducer and activator of transcription ( STAT ) protein , STAT1 is the most important transcription factor for the regulation of these transcriptional responses . After JAK1 and JAK2 are activated via the binding of IFN-γ to its receptor , they then regulate the downstream phosphorylation of STAT1 to form STAT1–STAT1 homodimers . The homodimers are transported to the nucleus and bind to GAS elements to induce transcription of various interferon stimulated genes [19–22] . The STAT family of transcription factors regulates many cellular processes including cell growth , apoptosis , immune responses , and oncogenesis [20 , 23 , 24] . STATs have been found to interact with various proteins , which help to modulate STAT signaling and mediate crosstalk with other cellular signaling pathways [25 , 26] . Nmi ( N-Myc interactor ) is one of these proteins , which was first identified through a yeast two-hybrid screen [27] . Nmi interacts with all STATs except STAT2 [27–29] , and has been shown to augment STAT-mediated transcription in response to IL-2 and IFN-γ by recruiting transcription factors CBP/p300 and potentiating their interactions with STAT proteins [30] . Because of the vital role of Nmi in IFN-γ response , it is reasonable to suggest that a virus may encode a protein to modulate Nmi function in order to escape IFN-γ-induced antiviral responses . However , no proteins encoded by any herpesviruses have been reported to modulate Nmi function and diminish IFN-γ responses , leading to increased viral resistance to IFN-γ and escape from IFN-γ responses . In order to establish persistent and latent infection in healthy individuals , HCMV encodes a large array of proteins that can modulate different components and pathways of the immune responses including MHC antigen presentation and NK cell activation [31–33] . For example , HCMV IE1 protein has been shown to interact with STAT proteins ( e . g . STAT2 ) and elicit a type II IFN-like host cell response that depends on activated STAT1 but not interferon-γ [31 , 34–37] . In this report , we provide the first direct evidence that HCMV UL23 interacts with Nmi and disrupts its interaction with STAT1 . UL23 is a virion protein found in the tegument and is expressed in the cytoplasm in HCMV infected cells [38] . However , UL23 is dispensable for viral replication in cultured cells and little is currently known about its function [39] . Binding of UL23 to Nmi resulted in the cytoplasmic localization of Nmi and STAT1 and led to a decreased level of transcription of the IFN-γ stimulated genes . In IFN-γ treated cells , HCMV mutants lacking UL23 expression induced much higher levels of transcription of IFN-stimulated genes and were much more sensitive to the antiviral effect of IFN-γ than the parental virus with UL23 expression . Our results suggest that UL23 represents the first herpesvirus-encoded protein that interacts with Nmi and inhibits the transcription of IFN-γ stimulated genes , leading to HCMV resistance to the antiviral effect of IFN-γ . Modulating the function of Nmi may represent a novel mechanism for a herpesvirus to escape from IFN-γ response and increase its resistance to IFN-γ .
We used a yeast two-hybrid system to screen for cellular partners that potentially interact with HCMV UL23 by transforming S . cerevisiae AH109 containing pGBKT7-UL23 with a cDNA library derived from human embryonic kidney . We consistently observed in our yeast two-hybrid screens a positive interaction between pGBKT7-UL23 and pACT2-Nmi , which contained the full-length coding sequence of Nmi ( S1 Fig ) . Of approximately 1 x 107 independent cDNA clones tested , 44 yeast colonies yielded positive results . Of these 44 positive clones , 12 contained plasmid constructs with the full-length and partial coding sequence of Nmi , while each of the remaining 32 clones contained the sequence of a unique human gene ( S1 Table ) . Nmi has been shown to bind to several STAT proteins including STAT1 and induce IFN-γ induced STAT1-dependent transcription by recruiting transcription factors CBP/p300 and potentiating their interactions with STAT proteins [40–42] . Co-IP experiments were performed to determine whether the interaction between UL23 and Nmi occurs in human cells . In these experiments , UL23 HA and Nmi FLAG epitope tag proteins were generated by constructing pCMV-HA-UL23 and pCMV-FLAG-Nmi mammalian expression vectors . The mammalian expression constructs were transfected into human U251 cells and proteins were extracted at 48 hours . Protein samples were separated by SDS-PAGE , transferred to membranes , and probed with anti-HA and anti-FLAG antibodies . HCMV UL23 and Nmi were detected as proteins of about 33 and 38 kDa , respectively ( Fig 1A and 1B ) , consistent with their coding sequences of 284 and 307 amino acids , as predicted from the HCMV TowneBAC and human genomic sequences [39 , 43] . The co-IP experiments with HA-tagged UL23 and the FLAG-tagged Nmi proteins in U251 cells confirmed that UL23 was associated with Nmi ( Fig 1A and 1B ) . Protein lysates from the transfected cells were first either immunoprecipitated with anti-HA or anti-FLAG antibodies , and immunoblotted with antibodies against the HA and FLAG epitope tags . The HA-tagged UL23 was co-precipitated with the FLAG-tagged Nmi ( Fig 1A and 1B ) . In contrast , when using the FLAG-tagged UL70 protein of HCMV that is not known to interact with UL23 as negative control , we observed no significant binding or co-precipitation with the HA-tagged UL23 ( Fig 1A and 1B , lanes 1–3 and 7–9 ) [44] . These results confirmed the specificity of the UL23-Nmi interaction in the co-IP assay and suggested that it may occur in human cells . Similar interaction between HA-tagged UL23 and FLAG-tagged Nmi proteins were also observed in HeLa cells and HCMV-infected U251 cells . It is possible that UL23 or Nmi may bind non-specifically to tagged or over expressed proteins . Meanwhile , it is important to study if native ( untagged UL23 ) can interact with endogenous Nmi in human cells during HCMV infection . To determine if this is the case , we used UL23 proteins expressed in E . coli as the antigen to generate an anti-UL23 monoclonal antibody in collaboration with Promab , Inc ( Albany , CA ) . We screened a large number of anti-UL23 antibody-producing clones and selected the clones that generated anti-UL23 antibodies with excellent reactivity and specificity . To determine whether Nmi also interacts with other viral proteins in addition to UL23 , we investigated if Nmi is associated with the HCMV polymerase processivity factor , UL44 protein , which is essential for viral DNA replication [4] . Protein lysates from HCMV-infected U251 cells were first either immunoprecipitated with anti-UL23 , anti-UL44 , or anti-Nmi antibodies , and then immunoblotted with antibodies against UL23 , UL44 , and Nmi . UL23 was found to be co-precipitated with the endogenous Nmi ( Fig 1C and 1D , lanes 19 and 23 ) while we observed no significant binding or co-precipitation between UL44 and Nmi ( Fig 1C and 1D , lanes 13–18 ) . These results suggest that Nmi may specifically interact with UL23 but not UL44 during HCMV infection . As a protein known to interact with STAT proteins , Nmi has three distinct domains: ( i ) a highly conserved coiled-coil domain at the amino terminus ( amino acid 1–105 ) ; ( ii ) a NID1 domain ( amino acid 105–199 ) ; and ( iii ) a NID2 domain ( amino acid 199–307 ) , with its COOH-terminal region showing homology to interferon-induced leucine zipper protein IP35 ( Fig 2 ) [27 , 45 , 46] . The STAT binding domains of Nmi are in amino acids 57–99 and 143–202 [30] . To further map the domains of association between UL23 and Nmi , a series of truncation mutants of UL23 and Nmi were constructed ( Fig 2 ) and yeast two-hybrid analyses were carried out to investigate the binary interactions of mutants and full-length proteins ( S1 Fig ) . We also constructed a series of UL23 and FLAG-tagged Nmi truncation mutants ( Fig 2 , Table 1 ) . Co-IP experiments were carried out to examine the interactions between UL23 and different FLAG-tagged Nmi mutants after the FLAG tagged constructs were transfected into U251 cells . The results , summarized in Fig 2A , indicate that the minimal Nmi mutant that binds to UL23 contains amino acid 199 to 292 and covers the NID2 domain . These results suggest that amino acids 199–292 of Nmi bearing the NID2 domain , a critical domain for Nmi homo-and hetero-dimerization [40 , 47] , is necessary for its association with UL23 . Similarly , experiments were performed to study the association of Nmi and a series of truncated FLAG-tagged UL23 ( Fig 2B , S1 Fig ) . The results showed that the minimal UL23 mutant containing amino acids 129 to 284 was capable of binding to Nmi and mutant with carboxyl-terminal deletion from amino acid 179 to 284 failed to bind to UL23 ( Fig 2B ) . Thus , the carboxyl-terminal sequence of UL23 is essential for its interaction with Nmi . To determine if the binding of UL23 affects the interaction of Nmi with STAT1 protein , cell lines U251-FLAG and U251-FLAG-UL23 that contained empty vector pCMV-FLAG and pCMV-FLAG-UL23 , respectively , were constructed , and the expression of FLAG-tagged UL23 protein was confirmed in U251-FLAG-UL23 cells . Cell lines U251-C and U251-UL23 that contained empty control vector pCDNA and construct pCDNA-UL23 , respectively , were also constructed from U251 cells , and the expression of UL23 in U251-UL23 cells was confirmed ( Fig 3 , lane 2 ) . The interactions of Nmi with UL23 and STAT1 were investigated by co-immunoprecipitation experiments with anti-UL23 , anti-STAT1 , and anti-Nmi antibodies . STAT1 appeared to be associated with Nmi in U251 and U251-C cells but not in U251-UL23 cells when cells were treated with IFN-γ ( Fig 3 , compare lanes 5 and 6 , 7 and 8 ) . Furthermore , Nmi was in a complex containing either UL23 or STAT1 but not in a complex containing both UL23 and STAT1 ( Fig 3 , lanes 3–8 ) . Similar results were also observed in cells containing empty control vector pCMV-FLAG and construct pCMV-FLAG-UL23 . These results suggest that binding of UL23 inhibits the ability of Nmi to interact with STAT1 protein . To further investigate this issue in the context of HCMV infection , we constructed two mutants , ΔUL23 and UL23stop , which were derived from TowneBAC by deleting the entire ORF sequence of UL23 and introducing a stop codon immediately downstream from its translation initiation codon , respectively . Furthermore , rescued viral mutants , R-ΔUL23 and R-stop , were generated from ΔUL23 and UL23stop , by restoring the UL23 expression , respectively , following the procedures as described previously [39] . U251 cells were treated with IFN-γ and then infected with these viruses . Coimmunoprecipitation experiments confirmed the interactions of Nmi with UL23 in TowneBAC infected cells treated with IFN-γ ( Fig 3 , lanes 12 and 14 ) . Our results also showed that STAT1 was associated with Nmi in cells infected with ΔUL23 and UL23stop mutants but not with TowneBAC and rescued viruses R-ΔUL23 and R-stop ( Fig 3 , compared lanes 13 and 14 , 15 and 16 ) . Similar results were also observed in IFN-γ treated human foreskin fibroblasts infected with these viruses . These results suggest that binding to Nmi by UL23 abolishes the association of Nmi with STAT1 . UL23 and Nmi are expected to co-localize if they are associated with each other in the cell . UL23 , a tegument protein , has been shown to be localized in the cytoplasm [38] . Under certain conditions ( e . g . under cellular stress and IFN treatment ) , Nmi , which binds to multiple STAT proteins and facilitates interactions of these STAT proteins with nuclear transcription factors such as CBP/p300 [48] , is localized in the nuclei [25 , 30 , 49] . To determine whether UL23 is co-localized with Nmi , the cellular localization of these expressed proteins in U251 and U251-FLAG-UL23 cells treated with IFN-γ was studied using immunofluorescence microscopy . Consistent with previous observations of Nmi as a nuclear protein [25 , 30 , 49] , the endogenous Nmi protein was found to localize primarily in the nuclei in U251 cells or U251-FLAG cells ( Fig 4 , S2 Table ) . In contrast , FLAG-tagged UL23 and Nmi were primarily localized in the cytoplasm in U251-FLAG-UL23 cells ( Fig 4 ) . No obvious fluorescence was observed in control cells not treated with anti-Nmi or anti-FLAG antibody , indicating that the staining observed is specific and not due to secondary antibody binding to viral or cellular proteins . Furthermore , these results suggest that the cellular distribution of Nmi is affected by the presence of UL23 , which is primarily localized in the cytoplasm . It is reasonable to suggest that the cellular distribution of STAT1 protein is affected by UL23 because UL23 affects the cellular localization of Nmi , a STAT1 binding protein . To determine if this is the case , cell lines U251 and U251-FLAG-UL23 were treated with IFN-γ and studied using immunofluorescence microscopy . STAT1 and Nmi appeared to be predominantly in the nuclei of U251 and U251-FLAG cells but were found to be primarily localized in the cytoplasm of U251-FLAG-UL23 cells that expressed FLAG-UL23 ( Fig 4 , S2 Table ) . These observations indicate that UL23 expression affects the cellular localization of Nmi protein as well as STAT1 protein . To confirm these results and investigate cellular localization of untagged UL23 , Nmi , and STAT1 proteins , cell lines U251-C and U251-UL23 , which contained empty vector pCDNA and pCDNA-UL23 respectively , were treated with IFN-γ and stained with antibodies . Similar to the results with the tagged proteins ( Fig 4 ) , Nmi and STAT1 proteins were found primarily in the nuclei of U251-C cells ( S2 Table ) . In contrast , these proteins , along with UL23 , were found to be primarily expressed in the cytoplasm of U251-UL23 cells ( S2 Table ) . Combined with the results using tagged-UL23 constructs , these results indicate that the FLAG tag sequence in FLAG-UL23 does not affect the interaction and co-localization of UL23 with Nmi , and that UL23 specifically interacts with and affects the cellular distribution of Nmi . Furthermore , our results suggest that UL23 may bind and retain Nmi to the cytoplasm and affect the interaction of Nmi with STAT1 , leading to the modulation of the cellular distribution of STAT1 in the cytoplasm . To further determine the effects of UL23 expression on the localization of Nmi and STAT1 , we performed cell fractionation and Western blot analysis to look at the distribution of these proteins ( Fig 5 ) . Different cells ( e . g . U251 , U251-C , and U251-UL23 cells ) were treated with IFN-γ and then harvested . Furthermore , in order to study the effects of UL23 expression on the localization of Nmi and STAT1 in the context of HCMV infection , U251 cells were treated with IFN-γ , infected with TowneBAC , ΔUL23 , or R-ΔUL23 , and the cell extracts were separated into nuclear and cytoplasmic fractions by centrifugation . The purity of the fractions was confirmed by immunoblotting for histone H1 ( nuclear marker ) and actin ( cytoplasmic marker ) ( Fig 5A ) . In uninfected parental U251 cells , more than 80% of Nmi and STAT1 was found in the nuclei while less than 20% of these proteins was in the cytoplasm ( Fig 5A , lanes 2 and 4 ) . Similar results were also found in control U251-C cells that contained the empty expression vector ( Fig 5B and 5C ) . In contrast , less than 20% of these two proteins ( i . e . Nmi and STAT1 ) along with UL23 was found in the nuclear fractions while more than 80% of these three proteins was in cytoplasmic fractions in uninfected U251-UL23 cells ( Fig 5A , lanes 1 and 3 , Fig 5B and 5C ) . In U251 cells infected with mutant ΔUL23 , more than 80% of Nmi and STAT1 was found in the nuclei while less than 20% of these proteins was in the cytoplasm ( Fig 5B , lanes 6 and 8 , Fig 5B and 5C ) . In contrast , less than 20% of Nmi and STAT1 along with UL23 was found in the nuclear fractions while more than 80% of these three proteins was in cytoplasmic fractions in U251 cells infected with parental virus TowneBAC and rescued virus R-ΔUL23 ( Fig 5A , lanes 5 and 7 , Fig 5B and 5C ) . These findings validate our immunofluorescence microscopy experiments results ( Fig 4 , S2 Table ) and suggest that UL23 plays an important role in the cellular localization of Nmi and STAT1 . STAT1 protein is among the most important transcription factors responsible for the induction of transcription of IFN-γ stimulated genes [20 , 23 , 24] . To activate the transcription of IFN-γ stimulated genes , STAT1 needs to be localized in the nuclei and recognizes the gamma-activated sites ( GAS ) of these genes [30] . If UL23 interaction of Nmi leads to the cytoplasmic accumulation of STAT1 , it is expected that expression of UL23 may inhibit the STAT1-dependent transcription of IFN-γ stimulated genes . Three sets of experiments were carried out to determine if this is the case . In the first set of experiments , we used a reporter system to study the effect of UL23 on the IFN-γ-induced transcription responses . The luciferase reporter plasmid pGL3-Promoter-3×GAS and the internal control reporter plasmid pRL-TK were transfected into different cells . At 24 hours post transfection , cells were treated with IFN-γ and cultured for additional 24 hours . As expected , treatment of IFN-γ resulted in the augment of IFN-γ-dependent transcription from the reporter construct in U251 and U251-C cells , which contained the control empty vector pCDNA ( Fig 6A ) . However , much less increase of IFN-γ-dependent transcription was observed from the reporter construct in U251-UL23 cells that expressed UL23 ( Fig 6A ) . In the second set of experiments , we examined the effect of UL23 on the expression of HLA-B , IRF1 , and IFIT3 genes , which are regulated by IFN-γ [10 , 24] . As shown in Fig 6B and 6D , the mRNA levels of these genes induced by IFN-γ were much less in U251-UL23 cells than those of parental U251 cells and control U251-C cells . These results suggest that UL23 inhibits the induction of the transcription of IFN-γ stimulated genes mediated by the interaction between Nmi and STAT1 , possibly by modulating cellular localization of Nmi and STAT1 . In the third set of experiments , we investigated the role of Nmi in the UL23-mediated inhibition of IFN-γ-induced transcription response . Different cells ( e . g . U251 , U251-C , and U251-UL23 ) were transfected with siRNA molecules that target the Nmi mRNA ( anti-Nmi siRNA ) or do not recognize any viral or cellular transcripts ( control siRNA ) , respectively . Treatment with anti-Nmi siRNA substantially reduced the expression of Nmi in these cells ( Fig 7 , compare lanes 5–8 with lanes 1–4 ) . At 24 hours after siRNA transfection , cells were further transfected with pGL3-Promoter-3×GAS and pRL-TK and treated with IFN-γ . Downregulation of Nmi expression mediated by siRNA decreased the IFN-γ-induced transcription in U251 and U251-C cells ( Fig 7 ) . However , anti-Nmi siRNA did not further decrease the IFN-γ-induced transcription in U251-UL23 cells , which expressed UL23 ( Fig 7 ) . These results are consistent with our results that UL23 modulates the level of IFN-γ-induced transcription by interacting with Nmi and disrupting its interaction with STAT1 . Our results suggest that UL23 inhibits IFN-γ induced transcription responses by interacting with Nmi . Hence , it is reasonable to suggest that viral mutants lacking UL23 expression may induce higher IFN-γ-dependent transcription and exhibit less resistance to IFN-γ than the parental virus and rescued viruses that express UL23 . To determine if this is the case , cells were transfected with the luciferase reporter plasmid pGL3-Promoter-3×GAS and the internal control reporter plasmid pRL-TK , treated with IFN-γ , and then infected with different viral mutants . At 12 hours postinfection , treatment of IFN-γ led to significant induction of luciferase activity from the reporter construct in mock-infected cells as well as in cells infected with UL23-deficient mutants , ΔUL23 and UL23stop ( Fig 8A ) . In contrast , infection of parental TowneBAC and rescued viruses R-ΔUL23 and R-Stop , which expressed UL23 protein , suppressed the IFN-γ-mediated induction of the luciferase activity from the reporter construct ( Fig 8A ) . The IFN-γ-mediated induction of the luciferase activity as observed in mock-infected U251 cells and cells infected ΔUL23 and UL23stop was not found in mock-infected U251-UL23 cells and U251-UL23 cells infected with these viral mutants , suggesting that UL23 expression inhibited the IFN-γ-mediated induction ( Fig 8A ) . Similar results were also observed when the transcription levels of IFN-γ stimulated genes IRF1 , IFP35 , and IFI44 were assayed in cells infected with these viruses ( Fig 8B and 8D ) . These results suggest that IFN-γ induced transcription can be inhibited by UL23 in the context of HCMV infection . In our experiments , a reduction of at least 50 fold in viral titers was found in IFN-γ-treated Towne-infected cells compared to those untreated cells ( compare Towne-infected U251 cells in Fig 9A and 9B ) , consistent with previous observations that HCMV replication and infection is inhibited in IFN-γ-treated cells [4 , 10 , 50] . If UL23 blocks IFN-γ induced transcription responses during HCMV infection , it is conceivable that HCMV mutants lacking UL23 expression may be more susceptible to IFN-γ treatment and exhibit less growth than the parental and rescued viruses that express UL23 . To determine if this is the case , different cells were either pretreated with IFN-γ or not , and then infected with viruses . Growth of different viral mutants in these cells was studied . In the absence of IFN-γ treatment , mutants ΔUL23 and UL23stop exhibited similar titers as the parental strain TowneBAC and the control revertant viruses R-ΔUL23 and R-stop ( Fig 9A ) , consistent with previous observations that UL23 is dispensable for viral lytic infection [39] . However , upon treatment of IFN-γ , UL23-minus mutants , ΔUL23 and UL23stop , exhibited a peak titer of at least 5 , 000 fold lower than TowneBAC and revertant viruses R-ΔUL23 and R-stop in U251 and U251-C cells ( Fig 9B ) . To further determine the role of UL23 , we repeated the experiments using U251-UL23 cells . We observed no difference in the titers of TowneBAC , ΔUL23 , UL23-stop , R-ΔUL23 , and R-UL23-stop in U251-UL23 cells pre-treated with IFN-γ ( Fig 9B ) . These results suggest that UL23 blocks IFN-γ induced response and increases viral resistance to IFN-γ during viral infection , possibly by interacting with Nmi and modulating the cellular localization of STAT1 protein .
IFN represents one of the most important innate immunity responses in a host to combat infections of many human viruses [11 , 24] . Understanding the mechanism of how human viruses modulate IFN responses to achieve successful infection will facilitate the development of novel strategies for the treatment and prevention of human viral infections . In this study , we provide the first direct evidence that HCMV UL23 protein specifically interacts with human N-myc interactor ( Nmi ) protein . We identified this interaction through a yeast two-hybrid screen and co-immunoprecipitation in human cells . We also showed that Nmi , when bound to UL23 , was not associated with STAT1 , suggesting that UL23 binding of Nmi disrupts the interactions of Nmi with STAT1 . In IFN-γ treated cells overexpressing UL23 , we observed ( a ) significant reduced levels of Nmi and STAT1 in the nuclei , the sites where these proteins act to induce transcription of IFN-γ stimulated genes , and ( b ) decreased levels of the induction of the transcription of IFN-γ stimulated genes . UL23-deficient HCMV mutants induced higher transcription of IFN-γ stimulated genes and exhibited lower titers than parental and control revertant viruses expressing a functional UL23 in IFN-γ treated cells . These results suggest that UL23 functions to bind to Nmi and reduce the nuclear import and the availability of Nmi and its associated protein STAT1 for the activation of IFN-γ stimulated gene transcription , leading to a decrease of IFN-γ dependent responses and an increase of viral resistance to IFN-γ . It is possible that Nmi may bind non-specifically to tagged or over expressed proteins but not the native viral protein . However , several experimental results presented in our study indicate that this is not the case and that the observations from using the tagged UL23 protein may be identical to those using the native untagged protein . First , in co-immunoprecipitation experiments , Nmi interacted with the HA-tagged UL23 but not the negative control , UL70 , suggesting that Nmi does not bind to the tag sequence ( Fig 1A and 1B ) . Second , Nmi co-immunoprecipitated with untagged native UL23 but not UL44 in HCMV-infected cells , demonstrating that the interaction with UL23 during HCMV infection is specific ( Fig 1C and 1D ) . Third , both tagged and untagged UL23 protein co-localized with Nmi ( Figs 4 and 5 , S2 Table ) . These results suggest that ( 1 ) the tag sequence does not enhance or interfere with the interaction of Nmi and UL23 , ( 2 ) the interaction is specific , and ( 3 ) the observations from experiments with the tagged UL23 protein may be representative of those obtained with the native untagged protein during HCMV infection . IFN-γ response is one of the most important components in the immune responses against human viruses including HCMV [11 , 24] . IFN-γ induces transcription of many IFN-stimulated genes such as those involved in antigen presentation and immunomodulation , conferring increased antiviral effects of the stimulated cells [10] . Nonprofessional antigen presenting cells have been shown to express MHC II molecules upon IFN-γ stimulation and subsequently present viral antigens including HCMV IE1 peptides [51] . IFN-γ produced by CD4+ T cells can block HCMV replication in vitro , highlighting the important role of IFN-γ in controlling HCMV infection [50] . It has been further shown that IFN-γ is more potent than IFN-α or -β against HCMV and murine cytomegalovirus ( MCMV ) , and type I and II IFNs act synergistically when administered in combination [10] . Consistent with these previous observations , our experiments showed that a reduction of more than 50 fold in viral titers was observed in HCMV infected cells treated with IFN-γ compared to those untreated cells ( Fig 9 ) . In order to escape from and counteract against IFN responses , many human viruses including herpesviruses such as HCMV and MCMV have been shown to modulate various steps of IFN induced signaling pathways by encoding immunomodulatory proteins [11 , 24] . By targeting different steps during the IFN response at different time points of the infection , viruses can counteract IFN responses by interfering with the IFN downstream signaling and become resistant against existing IFN . Viruses can also express evasins targeting IFN induction within the infected cells to avoid the priming of bystander cells . For example , HCMV IE1 proteins have been shown to bind with STAT proteins ( e . g . STAT2 ) and these interactions diminish DNA binding of the ternary ISGF3 complex to promoters of type I ISGs , leading to inhibition of transcription activation of human IFN stimulated genes including ISG54 , PKR , and CXCL10 [31 , 34–37] . Recent studies have also shown that IE1 can elicit a Type II interferon-like host cell response that depends on activated STAT1 but not IFN-γ [35] . Moreover , it has been recently reported that IE1 can rewire upstream IL6-type to downstream IFN-γ-like signaling , two pathways linked to opposing effects , resulting in repressed STAT3- and activated STAT1-responsive genes [52] . MCMV also encodes a viral protein , M27 , which modulates IFN signaling and confers IFN resistance [53] . M27 can bind to STAT2 protein and block STAT2-mediated IFN-γ signaling and antiviral responses by recruiting DNA-damage DNA-binding protein ( DDB ) 1 , a host ubiquitin ligase complex adaptor protein , for targeting STAT2 for proteasome degradation [53 , 54] . However , no proteins encoded by herpesviruses including CMV have been reported to interact with Nmi and modulate Nmi-mediated immune responses . Our study provides the first direct evidence that a herpesvirus encodes a viral protein that specifically binds to Nmi and modulates signaling steps of IFN-γ responses mediated by Nmi . In response to interferon and cytokine signaling , Nmi is a transcription cofactor that can modulate the activity of members of the STAT protein family [55] and has been shown to play a central role in IFN-γ induced STAT1-dependent transcription [30] . During Sendai virus infection , Nmi can bind to IRF7 , a master regulator for Type I IFN-dependent immune responses , and target IRF7 for proteasome-mediated degradation , leading to inhibition of Type I IFN responses induced by viral infections [56] . In recent studies , a miRNA encoded by torque teno virus ( TTV ) targets Nmi expression to inhibit type II interferon signaling and promote immune evasion [57] while protein 6 of severe acute respiratory syndrome coronavirus ( SARS-CoV ) can bind to Nmi and promote its degradation [58] . These results suggest the critical roles of Nmi in controlling infections of these viruses and possibly , other human viruses . Consistent with these observations , we showed ( a ) that over-expression of UL23 modulated the cellular localization of Nmi and its associated protein STAT1 and inhibited IFN-γ signaling mediated by Nmi and STAT1 and ( b ) that UL23-deficient mutants induced much higher IFN-γ responses and were more susceptible to IFN-γ antiviral effects . These results highlight the important roles of Nmi in human anti-HCMV immune responses and roles of UL23-Nmi interactions in conferring resistance to IFN-γ responses . Little is currently known about the effects UL23 may have on the resistance of HCMV to type I compared to type II IFNs . IFN-γ has been shown to be more potent than IFN-α or -β against HCMV and MCMV , and type I and II IFNs act synergistically when administered in combination [10 , 31 , 34–37] . As Nmi interacts with multiple STAT proteins , it is conceivable that UL23 may affect the expression of IFN-α/β responsive genes in addition to the expression of IFN-γ responsive genes as shown in this study ( Figs 6 and 8 ) . Our preliminary studies suggest that UL23 modulates the expression of several IFN-α/β responsive genes . Further studies on these issues will elucidate the roles of UL23 in interfering with host type I and type II IFN responses and facilitating HCMV infection and replication . It is possible that Nmi interacts with HCMV proteins other than UL23 , leading to a modulation of IFN-γ stimulated gene transcription and IFN-mediated responses as observed in our experiments . However , we observed no positive interaction between Nmi and more than 100 HCMV ORFs other than UL23 in our yeast two hybrid screen experiments [44] . UL23-mediated inhibition of IFN-γ induced gene transcription can be mimicked by suppressing Nmi expression using anti-Nmi siRNAs ( Fig 7 ) , and UL23-deficient viral mutants ( i . e . ΔUL23 and UL23stop ) exhibited significantly more IFN-γ responses than parental virus TowneBAC . While we cannot completely exclude the possibility that Nmi may affect the IFN-γ responses by interacting with HCMV proteins other than UL23 , our results in experiments expressing UL23 alone in the absence of HCMV infection and in experiments using anti-Nmi siRNA and UL23-deficient mutants suggest that UL23 may interact with Nmi and affect its cellular localization and interactions with STAT1 in the absence of other HCMV proteins , possibly leading to the inhibition of IFN-γ induced STAT1-dependent transcription . The coding sequences of more than 30 human proteins other than Nmi were also found to interact potentially with UL23 in our yeast two hybrid assays ( S1 Table ) . To confirm the interactions of these proteins with UL23 , we will perform additional experiments ( e . g . co-immunoprecipitation ) and these results will elucidate the effects of other human proteins on UL23 function during HCMV infection . We showed that UL23 interacted with Nmi in co-IP experiments in various cell types , including U251 , human foreskin fibroblasts , and HeLa cells . Furthermore , U251 cells were used to investigate the roles of UL23 in Nmi localization and HCMV infection . It may be important to investigate the function of UL23 in fibroblasts , which are fully permissive and commonly used to study HCMV infection [4] . However , due to poor transfection efficiency in fibroblasts , it is technically difficult to carry out similar experiments in these cells . Moreover , it is not feasible to constitutively express high level of UL23 over many passages in fibroblasts because they are primary cells . In contrast , the neuronal origin U251 cells are permissive to HCMV infection and have been used to study HCMV previously [4 , 59 , 60] . As these cells are immortalized , U251 cell lines that constitutively express UL23 can be easily generated . Additional investigation in other cells such as fibroblasts will facilitate the elucidation of UL23 function . Our results indicate that overexpression of UL23 impedes the nuclear translocation of Nmi and retains it in the cytoplasm . The mechanism of cytoplasmic retention of Nmi by UL23 is currently unknown . Nmi has been shown to interact with several transcription factors such as the Myc family members and STATs , and is primarily localized in the nuclei under certain conditions ( e . g . under cellular stress and IFN treatment ) [25 , 30 , 49] . These observations are consistent with our results that Nmi is predominantly localized in the nuclei of IFN-γ treated cells ( Fig 4 ) . Meanwhile , Nmi has been reported to localize in the cytoplasm in untreated cells and the domains of Nmi that facilitate nuclear import and cytoplasmic localization have been studied [45] . However , little is known about how Nmi is imported into the nuclei and the protein carriers for its nuclear transport have not been reported . It is conceivable that UL23 sequesters Nmi in specific cytoplasmic compartments so that Nmi is not available for interaction with other viral or cellular protein carriers for nuclear transport . It is currently not known how binding of Nmi by UL23 inhibits the association of Nmi with STAT1 . Previous studies have shown that the STAT binding domains of Nmi are in amino acids 57–99 and 143–202 [30] . The minimal Nmi mutant that binds to UL23 contains amino acid 199 to 292 and covers the NID2 domain critical for Nmi homo-and hetero-dimerization [40 , 47] , which partially overlaps with the binding domains to STAT proteins ( Fig 2 ) . Perhaps UL23 may compete for binding to the same Nmi regions as STAT1 . It is also possible that binding of UL23 may affect the conformation of Nmi , leading to inhibition of its binding to STAT1 . HCMV IE1 proteins have been shown to bind with STAT proteins ( e . g . STAT2 ) and these interactions diminish DNA binding of the ternary ISGF3 complex to promoters of type I ISGs , leading to inhibition of transcription activation of human IFN stimulated genes including PKR , CXCL10 , and ISG54 [31 , 34–37] . Recent studies have also shown that IE1 can elicit a Type II interferon-like host cell response that depends on activated STAT1 but not IFN-γ [35] . Moreover , it has been recently reported that IE1 can rewire upstream IL6-type to downstream IFN-γ-like signaling , two pathways linked to opposing effects , resulting in repressed STAT3- and activated STAT1-responsive genes [52] . The common impacts on STAT1 by IE1 and UL23 via different pathways , represent an interesting and potentially important regulatory mechanism of host responses during HCMV infection . UL23 encodes a tegument protein and IE1 is an immediately early protein [31 , 34–38] . HCMV IE1 is dispensable for replication of a UL23 competent virus under high multiplicity of infection ( MOI ) conditions while UL23 is not essential for replication of an IE1 competent virus in cultured cells [39 , 61] . It is possible that UL23-mediated modulation of STAT1 signaling is enhanced or repressed by IE1-mediated effect on STAT1 function . Further studies on these issues may elucidate the potential roles of the impact on the STAT1 signaling by these two HCMV proteins in supporting HCMV infection . How STAT1 protein is retained in the cytoplasm in the presence of UL23 is currently unknown , given the fact that UL23 binds to Nmi and disrupts Nmi interaction with STAT1 . While phosphorylation of STAT1 is required for its nuclear localization , the mechanism of the nuclear import of STAT1 is not completely understood [20 , 23 , 24] . Our results showed that Nmi and STAT1 interacted with each other and localized in the nuclei in IFN-γ treated cells . However , in the presence of UL23 , these two proteins failed to interact with each other and were localized in the cytoplasm ( Figs 4 and 5 ) . It is conceivable that Nmi or Nmi-associated proteins facilitate STAT1 nuclear import by recruiting STAT1 to the Nmi-containing complex that may contain the protein carriers for STAT1 nuclear import . Binding of Nmi by UL23 disrupts the interactions of STAT1 with Nmi , and may block the association of STAT1 with the Nmi-containing complex that contains the protein carriers for STAT1 nuclear import , leading to its accumulation in the cytoplasm . Detailed characterization of the nuclear import processes of Nmi and STAT1 proteins should clarify these issues . Further studies on the interactions of UL23 with Nmi and the effects of these interactions on Nmi-associated proteins will provide insight into the mechanism of how Nmi and its mediated immune responses play a role in combatting viral infections in a host and how viruses such as HCMV develop novel strategies to escape from or counteract against these immune responses including those Nmi-mediated IFN-γ responses .
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 ( 8th Edition ) . The protocol for all animal experiments was either approved by the Animal Care and Use Committees of the University of California-Berkeley ( Protocol #R240 ) or Jinan University ( Guangzhou , China ) . All efforts were made to minimize suffering . Human glioblastoma U251 cells , HEK 293T cells , and Hela cells were purchased from American Type Culture Collection ( ATCC ) ( Manassas , VA ) while human foreskin fibroblasts ( HFFs ) were obtained from Lonza Inc ( Allendale , NJ ) [62] . HCMV ( TowneBAC , ΔUL23 , UL23stop , R-ΔUL23 , and R-stop ) was grown in human cells [39] . Rabbit anti-FLAG ( Santa Cruz Biotech , Santa Cruz , CA ) , mouse anti-β-actin ( Proteintech , Manchester , United Kingdom ) , mouse anti-HA ( Proteintech ) , mouse anti-Nmi ( Santa Cruz Biotech ) , and mouse anti-STAT1 ( BD Biosciences , San Jose , CA ) antibodies were used in the experiments . Mouse monoclonal anti-FLAG antibody was purchased from Cell Signaling Technology , Inc ( Boston , MA ) . Cy3- or FITC-labeled secondary antibodies were purchased from Beyotime ( Shanghai , China ) and Invitrogen ( Carlsbad , CA ) , respectively . The anti-UL44 ( Virusyn , Taneytown , MD ) and anti-IE1 HCMV monoclonal antibodies used in this study have been described previously [60] . UL44 was detected as a predominant species in HCMV-infected cells using the antibody [60 , 63] . The anti-UL23 monoclonal antibody was generated by cloning the UL23 sequence into plasmid pET-28a ( + ) ( Novogen , Madison , WI ) . His-tagged UL23 protein isolated from E . coli BL21 ( DE3 ) was used to immunize BALB/C mouse . The spleen cells were subsequently isolated and fused with myeloma cells . ELISA screening and HAT culture medium were used to select for positive fused cells . Positive hybridoma cell cultures were propagated to generate UL23 monoclonal antibody . Western blot analyses , co-IP , and indirect immunofluorescence assays were subsequently used to characterize and confirm the reactivity of the antibody to UL23 protein . Special efforts were made to screen many clones to identify those clones that generated anti-UL23 antibodies with excellent reactivity and specificity . For example , to assess their specificity , antibodies were serially diluted and screened for their reactivity with lysates of uninfected cells and cells infected with HCMV Towne and ΔUL23 . Only those anti-UL23 antibodies that reacted with lysates of cells infected with Towne but not with those of uninfected cells or cells infected with ΔUL23 were used in our study . To generate U251-FLAG , U251-FLAG-UL23 , U251-C , and U251-UL23 cell lines , DNAs of constructs pCMV-FLAG , pCMV-FLAG-UL23 , pCDNA , and pCDNA-UL23 were co-transfected into U251 cells with DNA of LXSN vector [64] . Neomycin with a final concentration of 600 μg/ml was added to the culture medium at 48–72 hour postinfection and neomycin resistant cells were selected and cloned after two weeks [65 , 66] . The levels of UL23 in individual cell clones were determined by Western blot analysis . The constructs generated in this study are listed in Table 1 . The construct containing the DNA sequence of Nmi was purchased from Origene Technologies , Inc ( Montgomery , MD ) . Constructs pGBKT7-UL23 and pCMV-HA-UL23 that were used for the yeast two hybrid screen and for the expression in human cells have been previously described [44] . To generate pGL3-Promoter-3×GAS , the coding sequence of GAS was constructed by using oligonucleotide GASsense ( 5’-CAATTCTGTGAAGAAAGAATTCTGTGAAGAAAGAATTCTGTGAAGAAAG-3’ ) and oligonucleotide GASantisense ( 5’-GATCTCTTTCTTCACAGAATTCTTTCTTCACAGAATTCTTTCTTCACAGAATTGGTAC- 3’ ) . To generate pGADT7-Nmi , the coding sequence of Nmi was amplified by PCR using the primers Nmi-F1 ( 5’-AAG- CATATGGAAGCTGATAAAGATGAC-3’ ) and Nmi-R ( 5’-GAACTCGAGTT- CTTCAAAGTATGCTATGTG-3’ ) from a human embryo kidney cDNA library of Matchmaker two-hybrid system 3 ( Clontech , South San Francisco , CA ) and then cloned into 5’NdeI/3’Xho I-digested pGADT7 . To generate pCMV-FLAG-Nmi , the coding sequence of Nmi was amplified by PCR using the primers Nmi-F1 ( 5’-AAGGAATTCGAAGCTGATAAAGATGAC-3’ ) and Nmi-R ( 5’-GAACTCGAGTTCTTCAAAGTATGCTATGTG-3’ ) and then cloned into 5’EcoRI/3’XhoI-digested pCMV-FLAG . Various designed UL23 deletion mutant DNA sequences were generated by PCR using pGBKT7-UL23 as the template . The PCR products were cloned into NdeI/BamHI-digested pGBKT7 for yeast two hybrid analysis and SalI/KpnI-digested pCMV-FLAG for expression in human cells . The constructs for various Nmi deletion mutants were generated by PCR using pGADT7-Nmi as the template . The PCR products were cloned into NdeI/BamHI-digested pGADT7 for yeast two hybrid analysis and SalI/KpnI-digested pCMV-FLAG for expression in human cells . All constructs were subjected to restriction digestion profile and sequencing analysis for confirmation . A cDNA library derived from human embryonal kidney ( Clontech ) was screened using the Matchmaker two-hybrid system 3 ( Clontech , South San Francisco ) , following the procedures as described previously [44 , 62] . Cell lysates were obtained after incubating cells with RIPA lysis buffer ( Sigma Aldrich , St . Louis , MO ) supplemented with protease inhibitor cocktail ( Roche , Basel , Switzerland ) . The protein content was determined by Bradford assay ( Bio-Rad , Hercules , CA ) . Equivalent amounts of protein were separated by SDS-PAGE , transferred onto membranes , reacted with antibodies , stained using a Western chemiluminescent substrate kit ( Thermo Fisher , Waltham , MA ) and quantitated with a STORM840 PhosphorImager or a Gel Documentation Station ( BioRad , Hercules , CA ) [60 , 64] . The samples were serially diluted and analyzed in order to accurately determine the protein levels in the cytoplasm or nuclei . The percentages of STAT1 and Nmi were calculated using the levels of actin or histone H1 in cytoplasm and in nuclei as the internal controls , respectively . The experiments were repeated three times . Co-IP experiments were performed using the protein A/G immunoprecipitation kits ( Cell Signaling Technology , Inc , Boston , MA ) [44] . To perform Western blot staining of immunoprecipitated protein samples ( e . g . in Figs 1 , 3 , and 5 ) , the primary antibodies were directly conjugated to alkaline phosphatase ( AP ) with an AP conjugation kit ( Abcam , Cambridge , MA ) and then purified , following the manufacturer’s recommendation . The immunoprecipitated protein samples were separated by SDS-PAGE and transferred onto membranes , reacted with the AP-conjugated primary antibodies in the absence of secondary antibodies , and stained using a Western chemiluminescent substrate kit ( Thermo Fisher , Waltham , MA ) and quantitated with a STORM840 PhosphorImager or a Gel Documentation Station ( BioRad , Hercules , CA ) [60 , 64] . A luciferase reporter assay ( Promega , Madison , WI ) was used to evaluate the activation of the IFN-γ signal pathway . Luciferase activity was quantified using a luminometer ( TD-20/20; Turner Designs , Sunnyvale , CA ) according to the manufacturer’s instructions . Briefly , cells were seeded into 6-well plates and transiently transfected or electroporated with reporter plasmid pGL3-Promoter-3×GAS , Renilla luciferase expression plasmid pRL-TK ( Promega , Madison , WI ) , and the indicated expression plasmids using the PolyFect Transfection system ( Qiagen ) . At 24 hours posttransfection , cells were mock-treated or treated with IFN-γ ( 1000 U/ml ) ( R&D , Minneapolis , MN ) for 24 hours , lysed in passive lysis buffer of Dual-Luciferase Reporter Assay System ( Promega , Madison , WI ) , and the luciferase readings of each sample were normalized against the rLuc levels . Experiments were repeated three times . All of the data shown in this study were obtained from at three independent experiments . Cells were seeded in 6 well plates and transfected with the corresponding siRNA using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) , according to the manufacturer’s instructions . Small interfering RNAs ( siRNAs ) targeting human Nmi mRNA and control siRNAs were designed and synthesized by Thermo Fisher Scientific ( Waltham , MA ) . Cells were transfected with siRNAs as described previously [59] , and then mock-treated or treated with IFN-γ ( 1000 U/ml ) ( R&D , Minneapolis , MN ) for the indicated time . The cell lysates were further assayed for luciferase activities or western blotting . Cells grown on glass coverslips , which were treated with IFN-γ and mock-infected or infected with different viruses for different periods of time , were washed with washing buffer ( 0 . 1% BSA in phosphate-buffered saline ( PBS ) ) , fixed and permeabilized with PBS containing 4% paraformaldehyde and 0 . 2% Triton X-100 . Cells were then incubated with appropriate primary antibodies and secondary antibody Cy3 IgG or FITC IgG ( Beyotime , Shanghai , China ) . Nuclear staining was performed with 4’ , 6-diamidino-2-phenylindole ( DAPI ) ( Beyotime , Shanghai , China ) . The cells were visualized with a Nikon Eclipse TE2000-S microscope . Confocal images were collected using a ZEISS LSM700 microscope in separate channels and the digital images were subsequently merged by using the ZEN software ( ZEISS , Oberkochen , Germany ) . In some of the experiments , images were taken with a Nikon Eclipse TE300 microscope using a SPOT RT Slider camera and imaging software ( Diagnostic Instruments , Inc . , Sterling Heights , MI ) [62] . We manually counted cells showing UL23 , Nmi , and STAT1 localization and carried out three independent experiments to assay the percentages of the numbers of cells . Cells were harvested and suspended in buffer A ( 10 mM HEPES ( pH 7 . 4 ) , 10 mM KCl , 1 mM dithiothreitol , 0 . 6% NP-40 ) ( TransGen Biotech , Shanghai , China ) [59] . We collected the supernatant as the cytoplasmic fraction after centrifugation . We suspended the remaining pellet in nuclear extraction buffer ( 20 mM HEPES ( pH 7 . 4 ) , 150 mM NaCl , 1 mM dithiothreitol ) ( TransGen Biotech , Manchester , United Kingdom ) , and collected the supernatant as the nuclear fraction after centrifugation [59] . We used equal amounts of cytoplasmic and nuclear extracts for immunoblotting of STAT1 , Nmi , actin , and histone H1 ( Proteintech , Manchester , United Kingdom ) . Quantitative reverse transcription-PCR ( qPCR ) analysis of viral mRNA was carried out as described elsewhere [67] . Total RNA was extracted from cells with TRIzol ( Invitrogen , Carlsbad , CA ) according to the manufacturer’s manual . RNA ( 500 ng ) was reverse transcribed using a cDNA synthesis kit ( TAKARA , Shanghai , China ) and PCR reactions were performed using SYBR Green supermix ( Applied Biosystems , Foster City , CA ) . Oligonucleotide primers used are as follows: HLA-B forward ( 5′-CTACCCTGCGGAGATCAC-3′ ) , HLA-B reverse ( 5′-TAGGACAGCCAGGCCAGCAACA-3′ ) ; IRF1 forward ( 5’-CGATACAAAGCAGGGGAAAA-3’ ) , IRF1 reverse ( 5’-GTGGAAGCATCCGGTACACT-3’ ) ; IFIT3 forward ( 5’-TTCTCCTCTGGACTGGCAAT-3’ ) , IFIT3 reverse ( 5’-AGGACATCTGTTTGGCAAGG-3’ ) ; IFP35 forward ( 5’-CTAGGGATGGAGTGGCTCAG-3’ ) , IFP35 reverse ( 5’-TCAGGAATGTTGAGCACCAG-3’ ) ; IFI44 forward ( 5’-AGCCGTAGTGGGGTCTGATA-3’ ) , IFI44 reverse ( 5’-ATGTGGGGAATGTCATCCAT-3’ ) ; β-actin forward ( 5’-TCGTCCACCGCAAATGCTTCTAG-3’ ) , β-actin reverse ( 5’-ACTGCTGTCACCTTCACCGTTCC-3’ ) . Thermal cycling conditions were as follows: 50°C for 2 min , 95°C for 15 min , and 45 cycles of 95°C for 30 sec and 60°C for 1 min [59] . Relative quantitation was determined using the comparative CT method with data normalized to β-actin . The PCR results were derived from three independent experiments . Mutant ΔUL23 , which contained a deletion of the entire coding sequence of UL23 , was derived from TowneBAC and has been previously described [39] . A two-step mutagenesis protocol was used to construct mutant UL23stop , which contained a stop codon immediately downstream from the UL23 translation initiation site . In the first step , we inserted at the UL23 translation initiation codon with a cassette ( tet/str ) containing the tetracycline resistance gene tetA and rpsL gene conferring streptomycin susceptibility , following the mutagenesis procedures as described previously [39] . The bacteria harboring the mutant BAC constructs were electroporated with the PCR-amplified tet/str cassette . Successful insertion of the tet/str cassette was screened by selecting for bacterial colonies resistant to tetracycline . In the second step , the tet/str cassette was targeted for deletion and replacement by the mutated UL23 sequence that contained a stop codon immediately downstream from the translation initiation site . The resulting mutant , which only contained the mutated UL23 sequence and would not contain the cassette , was streptomycin-resistant and , therefore , was easily selected in the presence of the antibiotics [39] . Rescued mutants R-ΔUL23 and R-stop were generated from ΔUL23 and UL23stop , respectively , following the experimental procedures for construction of rescued viruses as described previously [39] . The UL23 region in the mutants and the rescued viruses was analyzed by restriction digestion profile and sequencing analyses . The expression of UL23 in cells infected with these mutants was studied by Western blot analysis using the anti-UL23 antibody . Cells ( n = 1x106 ) were either mock-infected or infected with HCMV ( MOI = 1–3 ) as described previously [39] . For quantification of viral growth , the cells and medium were harvested at 5 days postinfection and viral stocks were prepared . Viral stocks were serial diluted and used to infect 1 x 105 human foreskin fibroblasts , followed by agar overlay . Viral titer was determined by counting the number of plaques 10–14 days after infection [39] . Susceptibility to IFN-γ was assayed by virus growth in the presence of human recombinant 1000 U/mL IFN-γ ( R&D , Minneapolis , MN ) after preincubation with IFN-γ for 12 hours before infection . The values obtained were averages from three independent experiments . | Interferon-γ ( IFN-γ ) responses are vital for a host to combat infections of many human viruses including human herpesviruses . Upon treatment of IFN-γ , transcription of many genes responsible for IFN-γ immune responses is activated primarily by the signal transducer and activator of transcription ( STAT ) proteins such as STAT1 protein . Human N-myc interactor ( Nmi ) protein has been shown to interact with STAT proteins including STAT1 and activate IFN-γ induced STAT-dependent transcription . However , no proteins encoded by herpesviruses have been reported to interact with Nmi and inhibit Nmi-mediated activation of IFN-γ immune responses to achieve immune evasion from IFN-γ responses . In this study , we show strong evidence that the UL23 protein of human cytomegalovirus ( HCMV ) , a human herpesvirus , specifically interacts with Nmi protein . UL23 appears to interact directly with Nmi and inhibit nuclear translocation of Nmi and its associated protein STAT1 , leading to a decrease of IFN-γ responses and an increase of viral resistance to IFN-γ . Blocking UL23 expression led to higher transcription of IFN-γ stimulated genes and significant inhibition of viral growth in infected cells . These results suggest that interfering with Nmi function may represent an effective mechanism for a herpesvirus to block Nmi-mediated IFN-γ responses and increase viral resistance to IFN-γ . This also provides a potentially new therapeutic strategy to treat HCMV infection by modulating Nmi activity with blocking the expression of a viral protein . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"luciferase",
"protein",
"interactions",
"pathology",
"and",
"laboratory",
"medicine",
"enzymes",
"pathogens",
"immunology",
"microbiology",
"enzymology",
"dna",
"transcription",
"viruses",
"dna",
"viruses",
"molecular",
"biology",... | 2018 | Human cytomegalovirus UL23 inhibits transcription of interferon-γ stimulated genes and blocks antiviral interferon-γ responses by interacting with human N-myc interactor protein |
BREVIPEDICELLUS ( BP or KNAT1 ) , a class-I KNOTTED1-like homeobox ( KNOX ) transcription factor in Arabidopsis thaliana , contributes to shaping the normal inflorescence architecture through negatively regulating other two class-I KNOX genes , KNAT2 and KNAT6 . However , the molecular mechanism of BP-mediated transcription regulation remains unclear . In this study , we showed that BP directly interacts with the SWI2/SNF2 chromatin remodeling ATPase BRAHMA ( BRM ) both in vitro and in vivo . Loss-of-function BRM mutants displayed inflorescence architecture defects , with clustered inflorescences , horizontally orientated pedicels , and short pedicels and internodes , a phenotype similar to the bp mutants . Furthermore , the transcript levels of KNAT2 and KNAT6 were elevated in brm-3 , bp-9 and brm-3 bp-9 double mutants . Increased histone H3 lysine 4 tri-methylation ( H3K4me3 ) levels were detected in brm-3 , bp-9 and brm-3 bp-9 double mutants . Moreover , BRM and BP co-target to KNAT2 and KNAT6 genes , and BP is required for the binding of BRM to KNAT2 and KNAT6 . Taken together , our results indicate that BP interacts with the chromatin remodeling factor BRM to regulate the expression of KNAT2 and KNAT6 in control of inflorescence architecture .
In flowering plants , internode patterning and pedicel characteristics are two important determinants of inflorescence architecture , which is highly diversified among flowering plant species [1 , 2] . Inflorescence architecture results from the activity of the shoot apical meristem ( SAM ) , a cluster of pleuripotent stem cells located at the apex of the primary shoot . In Arabidopsis , determining the SAM function is mainly controlled by overlapping activities of two protein family members , the class-I KNOTTED1-like homeobox ( KNOX ) transcription factor subfamily and the BELL1-like ( BELL ) transcription factor subfamily . Both KNOX and BELL proteins belong to the three-amino-acid loop extension ( TALE ) homeodomain superfamily and are able to form heterodimers in determining meristem maintenance [1 , 2 , 3 , 4 , 5 , 6 , 7] . The class-I KNOX family contains four members , SHOOT MERISTEMLESS ( STM ) , BREVIPEDICELLUS ( BP , also called KNAT1 ) , KNAT2 , and KNAT6 [8] . STM is required for the initiation of SAM during embryogenesis and maintenance of proliferation of the cells in SAM [2 , 9] . BP , together with STM , contributes to SAM maintenance as loss of function of BP reduces the residual meristematic activity of the weak allele stm-2 [10] . Furthermore , mutations of BP in Arabidopsis cause severe inflorescence architecture defects , with downward-pointing pedicels , short and abnormal internodes with pronounced node bending [1 , 2] , suggesting that BP may play crucial roles in inflorescence architecture development . Further studies showed that PENNYWISE ( PNY ) , a member of the BELL subfamily , could physically interact with BP [4 , 6 , 11] . bp pny double mutants showed a synergistic phenotype of extremely short internodes interspersed with long internodes and increased branching , suggesting that BP-PNY complex is essential for proper inflorescence architecture development . Moreover , a genetic study showed that inactivation of both KNAT2 and KNAT6 could rescue inflorescence architecture defects caused by the bp or pny single mutation [12] . Increased expression of KNAT2 and KNAT6 was detected in bp and pny mutants , indicating that BP and PNY may restrict KNAT2 and KNAT6 expression to promote correct inflorescence architecture development . Taken together , these studies revealed that the BP-PNY complex regulates inflorescence architecture development mainly by repressing the expression of KNAT2 and KNAT6 . However , the molecular mechanism of BP-mediated transcription regulation remains largely unknown . In eukaryotic cells , gene activity is controlled not only by DNA but also by epigenetic marks . Epigenetic changes involve the modification of DNA activity by methylation , histone modification , and chromatin remodeling [13 , 14 , 15 , 16] . ATP-dependent chromatin remodeling factors use the energy derived from ATP hydrolysis to change the interaction between histone octamer and DNA , and alter the accessibility of genomic regions to transcription factors or the general transcriptional machinery in the context of chromatin [17 , 18] . BRAHMA ( BRM ) , a member of SWI/SNF ATPases , plays an essential role in reprogramming of transcription in vegetative , embryonic and reproductive plant development in Arabidopsis [19 , 20 , 21 , 22] . Mutation 27of BRM in Arabidopsis causes many morphological defects , such as reduced plant sizes with short roots and small leaves , floral homeotic defects , and earlier flowering [23 , 24 , 25] . More recently , BRM was shown to interact with LEAFY and SEPALLATA3 , two key transcription factors involved in controlling floral organ identity by regulating APETALA3 ( AP3 ) and AGAMOUS ( AG ) expression [21] . Furthermore , BRM associates with the transcription factor TCP4 in regulation of leaf maturation by modulating the cytokine responsive gene expression [26] . In addition , an interactome screen revealed that BRM interacts with a larger subset of transcription factors , including MYB , bHLH and zinc finger proteins [26] . Collectively , these findings suggest that the SWI/SNF ATPase BRM may act together with different transcription factors in modulating gene expression in plant development processes . In present work , we demonstrated a direct protein-protein interaction between BRM and BP both in vitro and in vivo . Furthermore , BRM and BP co-repressed KNAT2 and KNAT6 expression in control of inflorescence architecture development .
To identify the interaction proteins of BRM , we performed a yeast two-hybrid library screening . BP was identified as a candidate BRM-interacting partner . Yeast cells co-transformed with AD-BRM ( full-length of BRM fused to pGAKT7 ) and BD-BP ( full-length of BP fused to pGBKT7 ) could grow on selective medium QDO ( synthetic medium lacking tryptophan , leucine , histidine and adenine ) ( Fig . 1A-C ) , indicating that BRM could directly interact with BP in yeast . Further deletion analysis showed that the DII domain of BRM ( amino acids 689–952 ) and the MEINOX domain ( amino acids 130–240 ) of BP ( Fig . 1A-C ) were responsible for their interaction . We further detected the interaction between BRM and BP by pull-down assays . Purified BRM ( amino acids 689–952 ) -His was pulled down by GST-BP proteins ( Fig . 1D ) , confirming that BRM physically interacts with BP in vitro . The interaction of BRM and BP was further examined in vivo by bimolecular fluorescence complementation ( BiFC ) and co-immunoprecipitation ( Co-IP ) assays . For the BiFC assay , BRM and BP were fused to the YN vector pUC-pSPYNE or the YC vector pUC-pSPYCE [27] . The constructs were co-delivered into tobacco Bright Yellow 2 ( BY-2 ) suspension cells by polyethylene glycol ( PEG ) mediated transformation . As shown in Fig . 2A , BRM interacted with BP in BiFC assays . Among the cells observed , about 10% cells showed positive signals and similar results were obtained in four different experiments . For the Co-IP assay , we transiently expressed BRM and BP proteins in tobacco ( Nicotiana benthamiana ) [14] . As the full length BRM protein could not be well expressed in tobacco cells , we made a construct with the DII domain ( amino acids 689–952 ) of BRM fused with three FLAG tags ( BRM-Δ-FLAG ) . The full length of BP was fused with a GFP tag ( BP-GFP ) . These constructs were co-transformed into tobacco epidermal cells by Agrobacterium-mediated infiltration assays . We showed that BRM-Δ-FLAG protein was co-immunoprecipitated by BP-GFP ( Fig . 2B ) . Taken together , these data indicate that BRM interacts with BP both in vitro and in vivo . Previous studies indicated that BP is strongly expressed in inflorescences including pedicels and internodes [1] . GUS-staining analyses with pBRM:GUS plants showed that BRM is also expressed in the florescence in Arabidopsis ( S1 Fig . ) . Furthermore , expression patterns from the public Arabidopsis microarray databases ( http://www . bar . utoronto . ca/efp/cgi-bin/efpWeb . cgi ) revealed that both BRM and BP are expressed in shoot apex , stems and internodes in Arabidopsis ( S2 Fig . ) . These findings suggested an overlapping expression pattern of BP and BRM in the inflorescences . To study the genetic interaction of BRM and BP , several brm alleles , brm-1 [19] , brm-3 [28] , brm-4 and brm-5 [29] , and the null bp allele , bp-9 [1 , 2] , were analyzed . bp-9 contains a dSpm transposon insertion in the 1st intron of BP [6] . The transcript of BP was not detected in the bp-9 mutant ( S3 Fig . ) , confirming that bp-9 is a null allele . Furthermore , the expression level of BP was not significantly altered in brm-3 compared with wild-type ( S3 Fig . ) , suggesting that BRM may not affect BP expression in inflorescence . We observed that brm-3 and bp-9 plants displayed similar inflorescence architecture defects , with horizontally orientated pedicels ( Fig . 3A and 3B ) , clustered inflorescences ( Fig . 3C ) , shorter internodes and pedicels ( Fig . 3D-F ) compared to wild-type plants . Similar inflorescence architecture defects were also observed in brm-1 , brm-4 and brm1-5 mutant alleles ( S4A–S4D Fig . ) . Interestingly , loss-of-function mutants of SWITCH/SUCROSE NONFERMENTING 3C ( SWI3C ) encoding an interaction partner of BRM [25] also showed inflorescence architecture defects as bp-9 ( Fig . 3A ) . The null allele brm-1 was completely sterile [19] . Therefore , we generated the double mutant by crossing the weak allele brm-3 with bp-9 . The brm-3 bp-9 double mutants displayed more severe inflorescence architecture defects compared with brm-3 and bp-9 single mutants , with more compacted inflorescences , shorter internodes and pedicels , downward-oriented siliques ( Figs . 3A-F and S5 ) . The brm-3 bp-9 double mutant showed synergistic interaction in inflorescence architecture development , suggesting that additional factors other than BP likely interact with BRM to regulate the same processes . Previous studies indicated that the BELL subfamily transcription factor PNY interacts with BP and is involved in repression of KNAT2 and KNAT6 [12] . It is possible that PNY may also interact with BRM in regulating inflorescence architecture development . In addition , we also showed that the internodes of brm-3 bp-9 plants were severely bent ( Fig . 4A and 4B ) . Chlorenchyma are the specialized parenchyma cells , which contain chloroplasts and are distributed in the outer cortex of stems . Bends in stems correlate with a loss of chlorenchyma tissue at the node adjacent to lateral organs [1] . The chlorenchyma density was dramatically reduced in the internodes of brm-3 bp-9 plants compared with the bp-9 single mutant ( Fig . 4C and 4D ) , suggesting an involvement of BRM in control of internode patterns . Taken together , our findings indicate that BRM is required for the inflorescence architecture development in Arabidopsis . Previous studies indicated that inflorescence architecture defects of bp mutants are caused by increased expression of two class-I KNOX genes , KNAT2 and KNAT6 [12] . We further examined the expression levels of KNAT2 and KNAT6 in brm-3 , bp-9 and brm-3 bp-9 plants . The expression levels of KNAT2 and KNAT6 in inflorescences of Col , brm-3 , bp-9 , brm-3 bp-9 were analyzed . Compared with wild-type , the expression of KNAT2 and KNAT6 was increased in brm-3 , bp-9 and brm-3 bp-9 mutants ( Fig . 5 ) . Furthermore , the transcription of KNAT2 and KNAT6 was up-regulated in brm-1 and brm-4 mutants compared to wild-type plants ( S6 Fig . ) . Much higher expression levels of KNAT2 and KNAT6 were detected in the brm-3 bp-9 double mutant compared to brm-3 and bp-9 single mutants ( Fig . 5 ) , indicating that BRM may function synergistically with BP in repression of KNAT2 and KNAT6 expression . We further determined the levels of the activation marker H3K4me3 and the repression marker H3K27me3 of KNAT2 and KNAT6 in brm-3 , bp-9 and brm-3 bp-9 mutants by chromatin immunoprecipitation ( ChIP ) assays . The relative enrichment of H3K4me3 and H3K27me3 levels was determined by real-time PCR using gene specific primers ( Fig . 6A ) . Increased H3K4me3 levels were detected in both proximal promoter regions ( region P of KNAT2 and region Y of KNAT6 ) and transcription starting sites ( region S of KNAT2 and region Z of KNAT6 ) of KNAT2 and KNAT6 in brm-3 , bp-9 and brm-3 bp-9 plants . Elevated H3K4me3 levels were also detected in the intron of KNAT6 ( region E ) in brm-3 and brm-3 bp-9 mutants compared with wild-type ( Fig . 6B and 6C ) . Increased H3K4me3 levels of KNAT2 and KNAT6 observed in brm-3 , bp-9 and brm-3 bp-9 plants are consistent with the up-regulation of these genes in these mutants . Increased expression and H3K4me3 levels of KNAT2 and KNAT6 in brm-3 bp-9 plants were observed compared to bp-9 plants . The enhanced brm-3 bp-9 phenotype relative to bp-9 suggests that additional factors other than BP likely interact with BRM to regulate KNAT2 and KNAT6 . By contrast , the H3K27me3 levels of KNAT2 and KNAT6 were not significantly altered in brm-3 , bp-9 and brm-3 bp-9 mutants ( S7 Fig . ) . To examine whether BP protein could directly bind to KNAT2 and KNAT6 in vitro , we performed electrophoretic mobility shift assays ( EMSA ) . The target sequences of KNOX proteins have been identified previously with a core motif of TGAC [30 , 31] . In maize , the KNOX protein KN1 binds to an intron of GA2ox1 through a cis-regulatory element containing two adjacent TGAC motifs [32] . We identified two TAGC motifs in the promoter of KNAT2 ( -1039 to -991 bp , the Y region as indicated in Fig . 6A ) and two TAGC motifs in the third intron of KNAT6 ( 4269 to 4319 bp between M and N regions as indicated in Fig . 6A ) ( Fig . 7A ) . EMSA assays showed that BP bound strongly to the TAGC motifs of KNAT2 and KNAT6 ( Fig . 7B ) . We further showed that the mutated competitor probes could not affect the binding of BP to the TAGC motifs of KNAT2 and KNAT6 ( S8 Fig . ) , indicating that BP specifically binds to the TAGC motifs of KNAT2 and KNAT6 in vitro ( S8 Fig . ) . To determine whether BRM proteins can also directly bind to KNAT2 and KNAT6 , purified BRM ( 689-952aa ) -GST protein was incubated with the KNAT2 and KNAT6 probes . BRM ( 689-952aa ) -GST alone could not directly bind to KNAT2 and KNAT6 ( Fig . 7C ) . When BRM ( 689-952aa ) -GST , BP-His proteins and the KNAT2 and KNAT6 probes were incubated together in EMSA assays , two slower shifted bands were detected ( Fig . 7C ) , indicating that BRM may form a complex with BP thus bind to KNAT2 and KNAT6 in vitro . To study whether KNAT2 and KNAT6 are direct targets of BP in vivo , ChIP assays were performed using transgenic plants expressing green fluorescent protein ( GFP ) -Tagged BP driven by the native BP promoter ( ProBP:BP-GFP ) . Expression of ProBP:BP-GFP in bp-9 background fully rescued the inflorescence architecture defects of bp-9 ( Fig . 8A-C ) , suggesting that BP-GFP is functional in vivo . BP strongly bound to the proximal promoter region ( Y ) of KNAT2 and the third intron ( M and N ) of KNAT6 ( Fig . 8D and 8E ) , indicating that KNAT2 and KNAT6 are direct target genes of BP . We further analyzed whether KNAT2 and KNAT6 are also direct targets of BRM in vivo . The transgenic plants expressing GFP-tagged BRM driven by the BRM native promoter ( ProBRM:BRM-GFP ) [33] was used to perform the ChIP assay . ProBRM:BRM-GFP brm-1 and ProBRM:BRM-GFP brm-3 plants were generated by crossing ProBRM:BRM-GFP plants with brm-1 and brm-3 plants , respectively . The growth defects of brm-1 and brm-3 were rescued by ProBRM:BRM-GFP , indicating that BRM-GFP is functional in vivo ( S9 Fig . ) . In addition , ProBRM:BRM-GFP bp-9 plants were also generated by crossing ProBRM:BRM-GFP plants with bp-9 . Similar to the previous studies [20] , we showed that BRM bound to the promoter region of ABI5 , but not to the control genes , TA3 and TUB2 ( S10 Fig . ) . Similar to BP , BRM also bound to the proximal promoter region ( Y ) of KNAT2 and the third intron ( N ) of KNAT6 ( Fig . 8F and 8G ) , suggesting that BRM and BP co-target to KNAT2 and KNAT6 in vivo . Compared to ProBRM:BRM-GFP plants , a decrease of binding of BRM to KNAT2 and KNAT6 was observed in ProBRM:BRM-GFP bp-9 plants ( Fig . 8F and 8G ) . Taken together , these analyses suggest that BP is required for the binding of BRM to KNAT2 and KNAT6 . We further analyzed the genetic interaction of BRM with KNAT2 and KNAT6 in inflorescence architecture development . We generated brm-3 knat2-5 and brm-3 knat6-1 double mutants as well as brm-3 knat2-5 knat6-1 triple mutants by genetic crossing brm-3 with knat2-5 and knant6-1 alleles [34] . The pedicel angel , internode and pedicel length were determined in brm-3 , brm-3 knat2-5 , brm-3 knat6-1 and brm-3 knat2-5 knat6-1 plants . Similar to a previous report [12] , no difference was found in the pedicel and internode length of the knat2-5 , knat6-1 and knat2 knat6 mutants compared to wild-type . Compared to brm-3 plants , a significant decrease of average pedicel angel was found in brm-3 knat2-5 knat6-1 but not in brm-3 knat2-5 and brm-3 knat6-1 plants ( Fig . 9A and 9B ) . Quantitative phenotype analysis showed that knat2 and knat6 mutations could fully rescue the pedicel orientation defect of brm-3 , indicating a requirement of both KNAT2 and KNAT6 in control of pedicel orientation . The distribution of internodes along the main inflorescence was also determined . The brm-3 knat2-5 , brm-3 knat6-1 and brm-3 knat2-5 knat6-1 mutants showed longer internodes compared to brm-3 plants ( Fig . 9C ) . However , removal of both KNAT2 and KNAT6 activity could not rescue the pedicel length of brm-3 , since brm-3 , brm-3 knat2-5 , brm-3 knat6-1 and brm-3 knat2-5 knat6-1mutants displayed a similar pedicel length ( Fig . 9D ) . Taken together , our findings suggest that inactivation of KNAT2 and KNAT6 partially rescues the brm-3 phenotype .
In eukaryotes , the ATP dependent SWI/SNF chromatin remodeling complexes use energy from ATP hydrolysis to alter the interaction between histones and DNA and control accessibility of cis-regulatory DNA regions to transcription machinery [35] . BRM , a member of SWI/SNF ATPases , plays an essential role in reprogramming of transcription in vegetative , embryonic and reproductive development in Arabidopsis [19 , 20 , 21 , 22 , 29] . In present work , we showed that BRM is required for inflorescence architecture development . Loss of function BRM mutants display inflorescence architecture defects , with clustered inflorescences and horizontally orientated pedicels . Mutations of SWI3C , another SWI2/SNF2 chromatin remodeling ATPase gene in Arabidopsis , also cause a horizontally-pointing pedicel phenotype . BRM was shown to interact with SWI3C and they function in the same protein complex [25] . The similar pedicel orientation defect of brm and swi3c mutants supports an involvement of the SWI/SNF ATPases chromatin remodeling complex in inflorescence architecture development . The SWI/SNF complex has a co-activator function , catalyzing chromatin remodeling and recruiting activator determinants to gene sequences [36] . Furthermore , SWI/SNF can remodel chromatin resulting in either activation or repression of gene expression [37] . In present work , increased expression of two KNOX genes , KNAT2 and KNAT6 , was detected in brm-3 , brm-1 and brm-4 plants . EMSA and ChIP experiments showed that KNAT2 and KNAT6 are the direct target genes of BRM both in vitro and in vivo . These findings suggest that BRM may act as a repressor in regulation of KNAT2 and KNAT6 expression in Arabidopsis . The human BRM was shown to associate with Methyl CpG Binding Protein 2 ( MeCP2 ) in vivo and is functionally linked with gene repression [38] . Moreover , a direct association of BRM with the histone demethylase UTX was also reported in Drosophila melanogaster [39] . Increasing levels of H3K4me3 in KNAT2 and KNAT6 in brm-3 indicate that BRM may associate with a histone H3K4 demethylase in repression of gene expression . Previous studies showed that SWI/SNF ATPases act antagonistically with Polycomb-group ( PcG ) proteins in gene expression in mammalian [40] . PcG proteins are subunits of two multi-protein complexes , Polycomb Repressive Complex 1 ( PRC1 ) and PRC2 [41 , 42] . PRC2 catalyses the trimethylation of lysine 27 of histone H3 ( H3K27me3 ) [43 , 44] . More recently , it was reported that KNAT2 is repressed by ASYMMETRIC LEAVES 1 ( AS1 ) and AS2 via recruitment of PRC2 [45] . However , the H3K27me3 levels of KNAT2 and KNAT6 were not changed in brm mutants . Further research is required to investigate the interaction between BRM and PcG proteins in repression of KNAT2 and KNAT6 . A previous study showed that knat2 knat6 bp mutants rescue the pedicel orientation and internode length defects of the bp mutant [12] . Similarly , we found that introduction of knat2-5 and knat6-1 into brm-3 can also rescue the pedicel orientation and internode length phenotypes of brm-3 . Increased expression of KNAT2 and KNAT6 was found in both brm and bp mutants . These findings indicate that BRM and BP act upstream of KNAT2 and KNAT6 in regulation of inflorescence architecture . ChIP analysis indicated that BRM and BP co-target to KNAT2 and KNAT6 genes , suggesting that BRM and BP directly regulate KNAT2 and KNAT6 expression in the inflorescences . Furthermore , brm-3 bp-9 double mutants displayed more severe inflorescence architecture defects compared with brm-3 and bp-9 single mutants , supporting that BRM acts synergistically with BP in regulation of inflorescence development . knat2 and knat6 mutations did not rescue the shorter pedicel phenotype of brm-3 and bp mutants [12] , suggesting that other genes are also involved in the control of pedicel growth . KNOX proteins promote shoot apical meristem activity by coordinately regulating cytokinin ( CK ) and gibberellin ( GA ) biosynthesis genes [46] . Furthermore , BRM could directly regulate GA and CK responsive genes to promote leaf growth and shoot apical meristem activity [26 , 47] . Further research is required to identify additional target genes regulated by BRM and BP in promotion of cell proliferation and elongation in Arabidopsis . Accurate initiation of gene transcription requires multiple factors , including transcription cofactors ( coactivator or corepressors ) and chromatin remodeling factors [13] . In vitro studies have shown that transcription factors recruit chromatin remodeling factors and histone modification factors to affect the chromatin status of specific loci [48] . For example , two Jumonji N/C ( JmjN/C ) domain-containing proteins , ELF6 and REF6 , are recruited by their interacted transcription factor BES1 to regulate their co-target genes and coordinate BR responses [49] . Histone deacetylase HDA15 is recruited by its interacted partner PIF3 to repress chlorophyll biosynthetic and photosynthetic genes in etiolated seedlings [50] . In addition , HDA6 and HDA19 are recruited by AS1 and HSL2 , respectively , to regulate gene expression involved in leaf and seed development [51 , 52 , 53] . More recently , the chromatin remolding factor BBM was shown to interact with transcription factors in yeast two-hybrid assays [26] , indicating that BRM may be associated with different transcription factors involved in regulation of gene expression . In present work , we showed that BRM physically interacted with BP both in vitro and in vivo , suggesting that BP may associate with BRM to regulate gene expression . Furthermore , the binding of BRM to the target genes depended on the presence of BP , indicating that BRM may be recruited by BP through the protein-protein interaction . PNY , a member of the BELL subfamily protein , has been shown to interact with BP physically [6] . In addition , PNY was also shown to play a role in repressing of KNAT2 and KNAT6 expression . It remains to be determined whether PNY is also associated with BRM in regulating inflorescence patterning by epigenetic regulation of KNAT2 and KNAT6 expression .
brm-1 , brm-3 ( SALK_088462 ) , brm-4 ( WiscDsLox436E9 ) , brm-5 and swi3c-3 ( SAIL_224_B10 ) were obtained from the Arabidopsis Biological Resource Center ( http://www . arabidopsis . org/ ) . knat6-1 ( SALK_047931 ) and knat2-5 ( SALK_099837 ) were obtained from Nottingham Arabidopsis Stock Centre ( NASC ) . bp-9 was kindly provided by Prof . Lin Xu ( Shanghai Institute of Plant Physiology and Ecology , Chinese Academy of Sciences ) . ProBP:BP-GFP bp-9 transgenic plants were generated by transforming the ProBP:BP-GFP construct into bp-9 plants using the floral dip method [54] . The ProBRM:BRM-GFP bp-9 plants were generated by crossing ProBRM:BRM-GFP plants [33] with bp-9 plants . All Arabidopsis plants were grown in 22°C under long-day ( 16 h light/8 h dark ) conditions . The pedicel orientation , pedicel length and internode ( the stem between two nodes ) length between siliques were measured in 35-day-old plants . 10 individual plants were used for quantitative analysis , and 8–10 pedicels were measured for each plant . The minimum age of pedicel selected for analysis is 15 days after flowering . A protractor was used to determine the angle of pedicels . Bend at node was imaged using a stereoscope ( ZEISS , SV11 ) . The thin sections of chlorenchyma tissue were prepared with a razor blade and observed under a microscope . Total RNA was isolated from inflorescences ( 0 . 15 g ) of 35-day-old plants using 1 mL Trizol reagent ( Invitrogen ) . The first strand cDNA synthesis was generated using 2 μg total RNA according to the manufacturer’s instructions of TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix Kit ( TransGen , Beijing ) . 100 ng synthesized cDNA was used as a template to perform quantitative RT-PCR analysis . PCR reactions were performed in the total volume of 20 μL , with 0 . 5 μL for each primer ( 10 mm , final concentration 100 nm ) and 10 μL for SYBR Green PCR Supermix ( Bio-Rad Laboratories ) on a ABI7500 Real-Time PCR System ( Applied Biosystems ) . The PCR program included an initial denaturation step at 94°C for 3 min , followed by 40 cycles of 5 s at 94°C and 1 min at 60°C . Each sample was quantified at least triplicate and normalized using Ubiquitin 10 ( UBQ ) as an internal control . The gene-specific primer pairs for quantitative Real-Time PCR are listed in S1 Table . All PCR reactions were normalized using Ct value corresponding to the reference gene UBQ . The relative expression levels of target gene were calculated with formula 2-ddCt [55] . Values represented the average of three biological replicates . Yeast two-hybrid assays were performed as described in the manual of Matchmaker Gold Yeast Two-Hybrid Systems ( Clontech ) . Full length and different deletion coding regions of BRM and BP were subcloned into pGBKT7 and pGADT7 vectors to construct different bait and prey constructs ( primers are listed in S1 Table ) . Then , different pairs of bait and prey constructs were co-transformed into yeast strain Gold Y2H by PEG , and yeast cells were grown on DDO medium ( minimal media double dropouts , SD medium with-Leu/-Trp ) for 3 days . Transformed colonies were dropped onto QDO medium ( minimal media quadruple dropouts , SD medium with-Leu/-Trp/-Ade/-His ) containing 4 mg mL-1 X-a-Gal ( QDO/ X ) to test for possible interactions between BRM and BP according to their growth status . In vitro pull-down assays were performed as described [50] . His-BP recombinant protein was incubated with 30 mL His resin ( QIAGEN ) in a phosphate buffer ( 10 mM Na2HPO4 , 10 mM NaH2PO4 , 500 mM NaCl , and 10 mM imidazole ) for 2 h at 4°C , the binding reaction was washed three times with the phosphate buffer , and then BRM ( 689-952aa ) -GST or GST was added and incubated for an additional 2 h at 4°C . After washing three times with the phosphate buffer , the pulled-down proteins were eluted by boiling , separated by 10% SDS-PAGE , and detected by western blotting using an anti-His antibody . For BiFC assays , full length coding regions of BRM and BP were subcloned into YN vector pUC-pSPYNE and the YC vector pUC-pSPYCE , respectively [27] . Then fused YN and YC constructs were transformed into tobacco cells by polyethylene glycol for transient expression [56] . Transfected protoplast cells were imaged using a TCS SP5 confocal spectral microscope imaging system ( Leica ) . Co-IP assays were performed as described previously [14] . Two days after infiltration , tobacco ( Nicotiana benthamiana ) leaves were harvested and ground in liquid nitrogen . Proteins were extracted in an extraction buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 2 mM MgCl2 , 1 mM DTT , 20% glycerol , and 1% NP-40 ) containing protease inhibitor cocktail ( Roche ) . Cell debris was pelleted by centrifugation at 14 , 000g for 20 min . The supernatant was incubated with 30 μL of GFP-Trap A beads ( Chromo Tek ) at 4°C for 4 h , then the beads were centrifuged and washed six times with a washing buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 2 mM MgCl2 , 1mM DTT , 10% glycerol , and 1% NP-40 ) . Proteins were eluted with 40 μL of 2×loading buffer and analyzed by western blotting using anti-GFP ( Roche ) and anti-Flag antibodies ( Life Tein ) . ChIP assays were performed as previously described [57] . Chromatin was extracted from the inflorescence tissues ( 0 . 3 g ) bearing the first 10 siliques of 35-d-old flowering plants , after fixation with formaldehyde , the chromatin was extracted and then sheared to an average length of 500 bp by sonication . The chromatin was immunoprecipitated with specific antibodies including anti-H3K27me3 ( Millipore , 07–449 ) , anti-H3K4me3 ( Millipore , 07–473 ) , and anti-GFP ( Abcam , ab290 ) . The histone H3 occupancy at specific gene loci was analyzed by using an anti-H3 antibody ( Millipore 06–775 ) . Equal amount of the sonicated chromatin solution was set aside as the input sample . After cross-linking reversed , the amount of precipitated DNA fragments and input DNA was detected by quantitative Real-Time PCR using specific primers listed in S1 Table . The relative enrichments of various regions of KNAT2 and KNAT6 in brm-3 , bp-9 and brm-3 bp-9 over Col were calculated after normalization to TUB2 . The percentage of input was calculated by determining 2-ΔCt ( = 2-[Ct ( ChIP ) -Ct ( Input ) ] ) . The exon region of retrotransposon TA3 [58] was used as negative control . In EMSAs , purified recombinant BP-His and BRM ( 689-952aa ) -GST proteins are used . Oligonucleotide probes of KNAT2 ( -1039 to -991 bp ) and KNAT6 ( 4269 to 4319 bp ) sequences were commercially synthesized with 5'-end biotin-labeled as single-stranded DNA ( Invitrogen ) . To generate double-stranded oligonucleotides , equal amounts of complementary single-stranded oligonucleotides were mixed , heated to 95°C for 5 min , and slowly cooled down to 25°C . For a binding reaction , the Light Shift Chemiluminescent EMSA kit ( Pierce ) was used . For BP-His or BRM ( 689-952aa ) -GST binding , the purified protein is incubated with binding buffer ( 2 . 5% glycerol , 5 mM MgCl2 , 50 ng/μL poly [dI . dC] , 0 . 05% Nonidet P-40 ) mixed with the labeled probe for 1 h at 4°C in 20 μL reaction volume . For cold competition , the non-labeled probe is added first for 1 h at 4°C followed by the labeled probe added . For BP-His and BRM ( 689-952aa ) -GST interaction complex binding , first purified BP-His and BRM ( 689-952aa ) -GST proteins were incubated together as the GST-pull down assay , then the mixed proteins were used for the EMSA assay . After the binding incubation , the reaction mixture is loaded on a 5% polyacrylamide gel ( acrylamide:bisacrylamide , 29:1; Bio-Rad ) and run in 0 . 5×Tris-borate-EDTA buffer at 4°C . The DNA-protein complex was transferred to a Hybond-N+ membrane , and the membrane was cross-linked . Detection was performed according to the manufacturer’s instructions ( Pierce ) . Sequence data from this article can be found in the Arabidopsis Genome initiative or GenBank/EMBL databases under the following accession numbers: BRM ( AT2G46020 ) , BP ( AT4G08150 ) , KNAT2 ( AT1G70510 ) , KNAT6 ( AT1G23380 ) , SWI3C ( AT1G21700 ) , TUB2 ( AT5G62690 ) , TA3 ( AT1G37110 ) and PNY ( AT5G02030 ) . | BP is a class-I KNOX transcription factor that controls normal inflorescence architecture development by repressing the expression of two KNOX genes , KNAT2 and KNAT6 . In this study , we showed that Arabidopsis BP directly interacts with the SWI2/SNF2 chromatin remodeling ATPase BRM . brm and bp mutants displayed similar inflorescence architecture defects , with clustered inflorescences , horizontally orientated pedicels , and short pedicels and internodes . Furthermore , BP and BRM co-target to KNAT2 and KNAT6 genes and repress their expression . This work reveals a new regulatory mechanism that BP associates with BRM in control of inflorescence architecture development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Arabidopsis BREVIPEDICELLUS Interacts with the SWI2/SNF2 Chromatin Remodeling ATPase BRAHMA to Regulate KNAT2 and KNAT6 Expression in Control of Inflorescence Architecture |
The phytochrome ( phy ) family of photoreceptors is of crucial importance throughout the life cycle of higher plants . Light-induced nuclear import is required for most phytochrome responses . Nuclear accumulation of phyA is dependent on two related proteins called FHY1 ( Far-red elongated HYpocotyl 1 ) and FHL ( FHY1 Like ) , with FHY1 playing the predominant function . The transcription of FHY1 and FHL are controlled by FHY3 ( Far-red elongated HYpocotyl 3 ) and FAR1 ( FAr-red impaired Response 1 ) , a related pair of transcription factors , which thus indirectly control phyA nuclear accumulation . FHY1 and FHL preferentially interact with the light-activated form of phyA , but the mechanism by which they enable photoreceptor accumulation in the nucleus remains unsolved . Sequence comparison of numerous FHY1-related proteins indicates that only the NLS located at the N-terminus and the phyA-interaction domain located at the C-terminus are conserved . We demonstrate that these two parts of FHY1 are sufficient for FHY1 function . phyA nuclear accumulation is inhibited in the presence of high levels of FHY1 variants unable to enter the nucleus . Furthermore , nuclear accumulation of phyA becomes light- and FHY1-independent when an NLS sequence is fused to phyA , strongly suggesting that FHY1 mediates nuclear import of light-activated phyA . In accordance with this idea , FHY1 and FHY3 become functionally dispensable in seedlings expressing a constitutively nuclear version of phyA . Our data suggest that the mechanism uncovered in Arabidopsis is conserved in higher plants . Moreover , this mechanism allows us to propose a model explaining why phyA needs a specific nuclear import pathway .
Plants are sessile organisms and therefore have to adapt growth and development to the environmental conditions at their site of germination . Light is one of the most important factors directing such adaptive responses and it is involved in many developmental steps throughout the life of plants [1] , [2] . To detect intensity , quality ( wavelength ) and direction of incident light plants have evolved a set of photoreceptors monitoring red/far-red ( R/FR ) , blue/UV-A and UV-B [3]–[7] . The phytochrome family of red/far-red photoreceptors plays a key role in seed germination , leaf and stem development , circadian rhythms , shade avoidance and induction of flowering [8] . Although in higher plants phytochromes are not the primary photoreceptors controlling phototropism and chloroplast movements , the phytochromes modulate these responses [9]–[11] . Phytochromes are homodimeric chromoproteins containing the linear tetrapyrole phytochromobilin as chromophore . They photoconvert between two spectrally distinct forms: the red-light-absorbing Pr and the biologically active far-red light-absorbing Pfr form [3] , [12] . As the absorption spectra of the two forms overlap the photoconversion is not complete in either direction . Irradiation with light therefore results in a wavelength-specific equilibrium between the Pr and Pfr forms , with only ∼2% Pfr in far-red light and ∼85% Pfr in red light [13] . Under natural conditions the Pfr/Pr ratio differs dramatically depending on the position of the plant within the community ( canopy shade versus open environment ) [14] , [15] . In Arabidopsis the phytochrome gene family consists of five members ( PHYA–E ) , among which PHYA and PHYB play the most prominent functions [16] . phyB is the major red light receptor and mediates the red/far-red reversible low fluence response ( LFR ) . Other members of the phytochrome family contribute to responses primarily controlled by phyB . In contrast , responses to continuous far-red light ( high irradiance response , HIR ) and to single light pulse of very low fluence light ( VLFR ) depend exclusively on phyA [1] , [3] , [12] . Photoreceptor mutants have reduced fitness but only the phyA mutant is conditionally lethal , highlighting the importance of this photoreceptor [17] , [18] . Its functional importance is further revealed by the high degree of sequence conservation among all angiosperms [19] . phyA is also crucial for the modulation of phototropin responses such as the enhancement of phototropism [10] , [11] . The subcellular localization of phytochromes is tightly regulated by light . They localize to the cytosol in the dark but translocate into the nucleus upon light activation , where they interact with several transcription factors ( e . g . PIFs , phytochrome interacting factors ) [20]–[24] . Given that light-activated phytochromes localize to the nucleus and interact with transcription factors , it is not surprising that 10–20% of the genes in Arabidopsis are subject to regulation by red and/or far-red light [25] . Consequently , nuclear accumulation of the photoreceptor is a key step in both phyA and phyB signaling [26]–[29] . The C-terminal half of phyB presumably contains an Nuclear Localization Signal ( NLS ) , which is masked in the dark by the N-terminal half of the photoreceptor . Light triggers a conformational change , potentially unmasking the NLS and allowing nuclear transport of phyB [30] . This model predicts that the general nuclear import machinery is sufficient for phyB nuclear transport . In contrast , it has recently been shown that nuclear accumulation of phyA depends on two plant specific proteins called FHY1 and FHL [11] , [26] , [27] . Importantly , these proteins are not required for nuclear accumulation of phyB and for phyB signaling [26] , [27] . FHY3 and FAR1 , two transposase-related transcription factors , directly control FHY1 and FHL transcription and thus indirectly affect phyA nuclear accumulation [31] . FHY1 and FHL are small proteins ( 202 and 181 aa , respectively ) containing an NLS and a Nuclear Export Sequence ( NES ) [32] , [33] . High similarity between FHY1 and FHL is confined to the 36 most C-terminal amino acids . This small domain is necessary and sufficient for the light-regulated interaction with phyA in vitro and it is essential for function in vivo [26] , [32] . Our previous work has shown that FHY1 and FHL are essential for phyA nuclear accumulation but the molecular mechanism involved remains elusive [26] , [27] . Three models can explain the requirement of FHY1/FHL for light-regulated nuclear accumulation of phyA . i ) FHY1/FHL may be essential for nuclear import of phyA and work as adapter proteins using their NLS and phyA binding-site to link phyA to the general nuclear import machinery . Alternatively , phyA would enter the nucleus independently of FHY1/FHL but ii ) FHY1/FHL action may be required to stabilize phyA and protect it from degradation or iii ) to trap it in the nucleus and prevent it from being exported back into the cytosol . In this report we provide strong evidence for a model , in which FHY1 and FHL work as adaptor proteins facilitating nuclear transport of phyA . Our data reveal an intriguing system for regulated nuclear transport of a cargo protein that does not contain an NLS of its own .
The high degree of sequence conservation among phyA in angiosperms suggests that the same might be true for phyA signaling components , such as FHY1 and FHL [19] . Yet , the amino acid identity between them is below 30% although they are functional homologs [33] . The only motifs conserved in FHY1 and FHL are the NLS ( and to a minor degree the NES ) in their N-terminal region and the phyA binding-site at the C-terminus . A database search for FHY1/FHL homologs revealed the presence of FHY1-like proteins in numerous plant species . This is interesting given the key function of FHY1/FHL in phyA signaling in Arabidopsis . The only motifs conserved between all the FHY1-like proteins found in the database and Arabidopsis FHY1/FHL are the NLS and the C-terminal phyA binding-site ( Figure 1A ) . In contrast , the ∼150 aa linking the NLS and the motif essential for interaction with phyA are too diverse to be aligned . Together with the finding that the FHY1/FHL homologs from both rice and dandelion complement the fhy1 mutant phenotype ( data not shown ) this suggests that FHY1-like proteins may be defined as proteins containing an NLS and an “FHY1 type” phyA binding-site separated by a ∼150 aa spacer . To test whether this definition holds true we generated an artificial FHY1 consisting of an SV40 NLS and the C-terminal 36 aa of Arabidopsis FHY1 ( FHY1 167–202 = FHY1 CT ) with Yellow Fluorescent Protein ( YFP ) as a spacer in between . fhy1 mutant seedlings expressing this artificial FHY1 under the control of the CaMV 35S promoter were hypersensitive to FR , similar to fhy1 seedlings complemented with P35S∶YFP-FHY1 ( Figure 1B ) . Furthermore , the artificial FHY1 accumulated in the nucleus and colocalized with phyA in light-induced nuclear speckles ( Figure 1C , D ) thus behaving like Arabidopsis FHY1/FHL [26] , [27] . We therefore conclude that the NLS and the phyA binding-site of FHY1/FHL are necessary and sufficient for phyA nuclear accumulation . Given that both the NLS and the phyA-interaction domain of FHY1 are sufficient for FHY1 activity we tested whether adding the NLS to phyA directly would be enough to promote nuclear localization of phyA fused to the Green Fluorescent Protein ( GFP ) . phyA null mutants transformed with either PHYA-GFP ( Figure 2A , B ) or PHYA-NLS-GFP ( Figure 2C–2F ) driven by the PHYA promoter were analyzed microscopically . As previously described [23] nuclear accumulation of phyA-GFP was light-dependent ( Figure 2A , B ) . In contrast , in lines expressing phyA-NLS-GFP nuclear localization was constitutive ( Figure 2C , D ) . Nuclear bodies appeared extremely rapidly upon light excitation in phyA-NLS-GFP plants . When nuclei of etiolated phyA-NLS-GFP seedlings were imaged without a light treatment or immediately after a 5 sec red light pulse a smooth nucleoplasmic staining was observed ( Figure 2E , data not shown ) . However as little as 1 minute after a 5 sec red light pulse nuclear bodies appeared in those nuclei ( Figure 2F ) . The phenotypic consequences of expressing a constitutively nuclear version of phyA was evaluated by comparing wild type , phyA and phyA transformed either with a construct encoding PHYA-GFP , PHYA-NLS-GFP or PHYA-NLS . Western blot analysis of dozens of independent transgenics showed that while we obtained lines expressing wild-type levels of phyA-GFP at a reasonable frequency ( 10–20% ) we never found lines expressing high levels of either phyA-NLS or phyA-NLS-GFP ( data not shown ) . For our phenotypic analysis we used two homozygous single insertion lines for each construct . phyA-GFP line 1 expressed wild-type levels of phyA while phyA-GFP line 2 expressed phyA levels comparable to the highest expressing phyA-NLS-GFP lines we obtained ( Figure S1 ) . Despite the relatively low levels of phyA , the phyA-NLS-GFP lines rescued the FR-HIR phenotype of phyA mutants very efficiently for hypocotyl elongation and anthocyanin accumulation ( Figure 3A , B ) . Moreover , the phyA-NLS and phyA-NLS-GFP lines also showed a normal phyA-mediated VLFR response for inhibition of hypocotyl elongation in response to pulses of FR light ( Figure 3C ) . It should also be noted that , despite having constitutively nuclear phyA , phyA-NLS ( -GFP ) lines did not show a cop ( constitutively photomorphogenic ) phenotype , indicating that nuclear import of phyA is not sufficient to trigger a light response ( Figure 3 and data not shown ) . The nuclear localization of phyA-NLS-GFP in darkness ( Figure 2C ) , a condition , where there is much reduced phyA-FHY1 interaction , suggested that phyA-NLS-GFP nuclear accumulation did not require FHY1 . In order to test this hypothesis genetically we crossed phyA phyA-NLS-GFP with fhy1 mutants and selected siblings in the F2 that were homozygous for phyA , fhy1 and the transgene . Microscopic analysis of such seedlings demonstrated that neither nuclear accumulation nor light induced formation of nuclear bodies of phyA-NLS-GFP required FHY1 ( Figure 4C , D , G , H ) . In control experiments we confirmed that for phyA-GFP plants light-dependent nuclear import was strongly dependent on FHY1 ( Figure 4A , B , E , F and data not shown ) [26] , [27] . We concentrated our analysis on fhy1 mutants because fhy1 has a much stronger phenotype than fhl [33] . Given that nuclear accumulation of phyA-NLS-GFP did no longer require FHY1 , we tested whether fhy1 mutants expressing phyA-NLS-GFP had a normal light response to continuous FR light . Interestingly , both the hypocotyl elongation and anthocyanin accumulation phenotypes of fhy1 mutants were efficiently rescued by phyA-NLS-GFP but not by phyA-GFP ( Figure 5 ) . Our data thus indicate that FHY1 becomes dispensable in seedlings expressing phyA-NLS-GFP , suggesting that during the FR-HIR FHY1 is only necessary to control nuclear accumulation of phyA . It was recently shown that FHY3 and FAR1 , two closely related transcription factors , directly regulate the expression of FHY1 and FHL [31] . Given that phyA-NLS-GFP could rescue the fhy1 phenotype , we hypothesized that this construct may also be capable of rescuing fhy3 mutants , in which the major defect appears to be reduced FHY1 and FHL levels . We restricted our analysis to fhy3 mutants because FHY3 plays a significantly more important role for this response than FAR1 [31] , [34] . We thus crossed fhy3 with phyA-NLS-GFP plants and analyzed homozygous wild type and mutant fhy3 siblings . Our phenotypic characterization of the response to far-red light showed that while phyA-NLS-GFP rescued the fhy3 mutant phenotype phyA-GFP could not ( Figure 6 ) . Our results are thus consistent with the notion that the major function of FHY1 and FHY3 is to respectively operate a directly and indirectly control of phyA nuclear accumulation . The only functionally important and widely conserved parts of FHY1 are the NLS and the phyA-interaction domain ( Figure 1 ) [26] , [32] , [33] . Moreover , nuclear accumulation of phyA-NLS-GFP occurred independently of light and FHY1 ( Figures 2 and 4 ) . Taken together these data support the notion that FHY1 mediates light-dependent nuclear import of phyA upon interaction in the cytoplasm . A prediction of this model is that over-expression of either native or artificial FHY1 lacking the NLS should sequester phyA in the cytoplasm and thus result in a dominant negative phenotype . To test this hypothesis we omitted the SV40 NLS in the artificial FHY1 or replaced it by an NES and transformed the constructs ( i . e . ( NES- ) YFP-FHY1 CT ) into wild-type plants . As the fusion proteins encoded by the constructs are below the size exclusion limit of the nuclear pore [35] they can enter the nucleus by diffusion but do not accumulate there due to the absence of an NLS . The NES containing version , which is predicted to be actively exported from the nucleus , localized mainly to the cytosol ( Figure 7B ) . As predicted by the nuclear import model , seedlings expressing these constructs were strongly hyposensitive to FR ( Figure 7A ) . This phenotype is consistent with the previous finding that FHY1 containing a disrupted NLS does not complement the fhy1 phenotype but rather results in an almost complete loss of FR sensitivity [32] . Western blot analysis confirmed that the phyA levels were normal in seedlings expressing ( NES- ) YFP-FHY1 CT thus excluding the possibility that reduced amounts of phyA were responsible for the dominant negative phenotype ( Figure S2 ) . However , NES-YFP-FHY1 CT strongly inhibited phyA nuclear accumulation when crossed into plants expressing Cyan Fluorescent Protein ( CFP ) tagged phyA ( Figure 7B ) . This suggests that in the cytosol NES-YFP-FHY1 CT competes with endogenous FHY1/FHL for binding to phyA ( -CFP ) and thereby interferes with phyA ( -CFP ) nuclear transport .
It was previously shown that FHY1 and its paralogue FHL are required for nuclear accumulation of phyA [26] , [27] . The analysis of mutants clearly demonstrates that FHY1 plays the predominant function for both phyA nuclear accumulation and phyA-mediated light responses [27] , [33] . This is presumably due to the roughly 15-fold higher level of FHY1 mRNA compared to FHL [33] . We therefore restricted our analysis to the fhy1 single mutant background , i . e . in the presence of functional FHL . Both FHY1 and FHL interact with light-activated phyA through a conserved C-terminal domain [26] . However , the mechanism , by which these proteins enable nuclear localization of phyA , remains to be established . Our phylogenetic analysis shows that , similarly to phyA , FHY1-related proteins are widely distributed among angiosperms ( Figure 1A ) , suggesting conservation of this aspect of phyA signaling . Moreover , this analysis shows that among FHY1-like proteins only the amino-terminal NLS , which is essential for the interaction with importin alpha ( Figure S3 ) , and the carboxy-terminal phyA-interaction domain are conserved . It has previously been shown that both these domains of FHY1 are necessary for function [32] . Our analyses now show that they are also sufficient for FHY1 activity and that the ∼150 aa in between do not perform an essential function . The simplest model ( hereafter termed “import” model ) accounting for those results is that upon light excitation phyA interacts with FHY1 in the cytoplasm and that this complex enters the nucleus using the general nuclear import machinery ( Figure S6 ) . According to this model adding a strong ( and exposed ) NLS to phyA should render phyA nuclear accumulation both light- and FHY1-independent . Our experiments show that these predictions are fulfilled in plants expressing phyA-NLS-GFP ( Figures 2 and 4 ) . In addition , when an FHY1 variant lacking the NLS sequence is over-expressed in wild-type plants this construct sequesters phyA in the cytoplasm and results in a dominant-negative de-etiolation phenotype ( Figure 7 ) . These observations are fully consistent with the notion that FHY1 mediates light-regulated phyA nuclear import by binding selectively to the active Pfr form of phyA in the cytosol and , thereby , linking phyA in a regulated manner to the nuclear import machinery ( Figure S6 ) . Our findings indicate that during de-etiolation in far-red light the system essential for nuclear localization of phyA , i . e . FHY3 and FHY1 , can be replaced by simply attaching an NLS to phyA . It is however highly unlikely that such plants do not show a decrease in fitness under more natural conditions . The complex system relying on FHY3/FAR1 and FHY1/FHL is highly conserved in evolution ( Figure 1A ) [31] and FHY1-like proteins from dandelion and rice can compensate for the absence of FHY1 in Arabidopsis ( data not shown ) . The strict conservation of FHY1-like proteins in angiosperms ( in the sense of proteins containing a phyA binding-site linked to an NLS ) points to a common molecular mechanism of phyA nuclear import and underlines the importance for regulated subcellular localization of phyA . An obvious advantage of the FHY1/phyA system over targeting phyA to the nucleus using an NLS is that it allows for co-existence of nuclear and cytosolic phyA pools and that the pool sizes can be regulated . This may be especially important with regard to possible cytosolic functions of phytochromes as recently described [11] . Nuclear import of phyB does not rely on the FHY1/FHY3 pathway but is light regulated nevertheless [23] , [26] , [27] , [36] , [37] . The FHY1-mediated nuclear import described here may explain how phyA can be imported so rapidly in response to light and how this import is possible under light conditions where the pool of Pfr is extremely small [13] . Such conditions are typically encountered for phyA-controlled light responses , such as the VLFR and the FR-HIR [1] . Two alternative scenarios for FHY1 function have been proposed , in which nuclear transport of phyA would not depend on FHY1-like proteins and may even be light-independent ( i . e . both Pr and Pfr are transported ) [26] . In these models ( hereafter referred to as the “FHY1 nuclear anchor” and “protection” models ) phyA could either be trapped in the nucleus or protected from degradation by binding to FHY1 . As the phyA/FHY1 interaction is light dependent , these models would explain the light regulated nuclear accumulation of phyA as well . Yet , these hypotheses are inconsistent with our data for several reasons . In etiolated seedlings phyA protein levels are much higher than FHY1 ( data not shown ) . This renders both the “FHY1 nuclear anchor” and the “protection” models difficult to envisage unless one FHY1 molecule would bind to multiple phyA proteins . In the “import” model one FHY1 molecule would transport one phyA dimer per cycle resulting in nuclear accumulation of large numbers of phyA molecules after multiple transport cycles . In addition , the subcellular localization of phyA-NLS-GFP was not affected in the fhy1 mutant background ( Figure 4 ) , which is only compatible with the “nuclear import” model . The normal localization of phyA-NLS-GFP in fhy1 mutants is also supported functionally , given that this construct complements fhy1 ( Figure 5 ) . Moreover , western blot analyses show that FHY1 does not affect phyA protein levels in far-red light irrespective of whether phyA enters the nucleus using FHY1 [11] . Moreover the abundance of constitutively nuclear phyA-GFP was also unaffected in the fhy1 background ( Figure S4 ) . These data indicate that FHY1 does not act by protecting phyA from degradation once the photoreceptor entered the nucleus . Although phyA strongly accumulates in the nucleus in response to irradiation with FR in vivo spectroscopic measurements indicate that not significantly more than ∼2% of the total phyA is in the Pfr form under such conditions [38] . This strongly suggests that in FRc the major fraction of nuclear phyA is in the Pr and not the Pfr form [12] . Furthermore , yeast two hybrid experiments show that the light-induced interaction of FHY1 and phyA is R/FR reversible , suggesting that the phyA/FHY1 complex rapidly dissociates upon conversion of Pfr to Pr ( Figure S5 ) . It is , however , inherent to the “FHY1 nuclear anchor” and “protection” models that FHY1 has to be bound to phyA to inhibit its export into the cytosol or protect it from degradation . Again , the only model compatible with our findings is the “import” model , where an interaction for a limited time period would be sufficient to allow accumulation of phyA in the nucleus . A constitutive interaction of phyA and FHY1 may even interfere with phyA nuclear accumulation as it may block recycling of FHY1 . Once in the nucleus phyA would be trapped in the “import” model – irrespective of whether it is in the Pr or Pfr form – because it is too big to exit the nucleus by diffusion . Taken together our findings strongly support the import model ( Figure S6 ) . After accumulation in the nucleus phyA interacts with various transcription factors ( e . g . PIFs ) [20] , [21] , [24] . It is noteworthy that nuclear body formation is still light dependent for phyA-NLS-GFP ( Figure 2 ) . Moreover , formation of these subnuclear structures does not require FHY1 ( Figure 4 ) although FHY1 and phyA have been found in light-induced nuclear bodies ( Figure 1 ) [11] , [26] , [27] . The light-induced nuclear bodies may thus represent sites of phyA-PIF interaction as has previously been reported [39] , [40] . Complementation of the fhy1 mutant by phyA-NLS ( -GFP ) shows that the interaction of phyA and downstream signaling components does not require FHY1 . Rather , binding of FHY1 may prevent the interaction of phyA and effectors . If dissociation of the phyA/FHY1 complex were a prerequisite to initiate downstream signaling this would be an additional argument against the “FHY1 nuclear anchor” and “protection” models . Answering these questions will provide a “molecular” link between phyA nuclear accumulation and initiation of the signaling cascade ( s ) leading to transcriptional regulation of 10–20% of the genes in the Arabidopsis genome [25] , [41] , [42] . Adding a strong NLS to phyA results in light- and FHY1-independent nuclear accumulation of the protein . Nevertheless , dark-grown seedlings expressing such constitutively nuclear localized phyA display a normal morphology in darkness and still show normal light responses ( phyA-mediated VLFR and HIR ) ( Figure 3 ) . The fluence-rate dependency and the need for sustained excitation are hallmarks of the HIR [1] and it is well established that nuclear accumulation per se is an HIR [23] , [43] . Yet , maximal hypocotyl growth inhibition and anthocyanin accumulation in seedlings expressing constitutively nuclear localized phyA are still fluence-rate dependent and require continuous irradiation ( Figure 3 ) . Thus , the “physiological HIR” does not derive exclusively from the HIR characteristics of phyA nuclear accumulation , indicating that in wild-type plants more than only one step in phyA signaling is an HIR . The phenotype of plants expressing constitutively nuclear phyA is thus clearly distinct to the partial det/cop phenotype of a mutant expressing a constitutively Pfr-like phyA [44] . Thus , control of phyA nuclear accumulation does not seem to play an essential role to prevent initiation of downstream signaling in the absence of light , which is crucial for the highly sensitive VLFR . The different affinities of phyA in the Pr and Pfr forms for downstream signaling components such as PIF1 and PIF3 may be sufficient to inhibit the induction of a VLFR in the dark . Despite having a low total level of phyA ( only around 25% of wild-type levels ) inhibition of hypocotyl elongation and promotion of anthocyanin accumulation is very efficiently complemented in the phyA-NLS and phyA-NLS-GFP lines ( Figure 3 and S1 ) . These results suggest that nuclear phyA abundance ( rather than total phyA levels ) primarily controls these light responses . The strong phenotype of the fhy1 fhl and fhy3 far1 double mutants , which do not contain detectable levels of phyA in the nucleus , further supports this view [26] , [31] , [33] . Thus , nuclear accumulation of both phyA and phyB has been shown to be functionally important in Arabidopsis [26] , [27] , [29] . While these studies show that this is an important step of the signal transduction cascade for numerous phytochrome responses , they by no means exclude the possibility for cytoplasmic activities of the phytochromes . Cytoplasmic phytochrome responses are widely described in cryptogam species [45]–[47] and a recent paper indicates that cytoplasmic phyA may be required for the modulation of the phototropic response in Arabidopsis [11] . The vast majority of proteins enters the nucleus either passively or by active , importin-mediated transport [35] . However , there are nuclear localized proteins , which are too big to pass through the nuclear pore by diffusion but still do not contain an NLS . Similar to phyA many of these proteins use a piggyback mechanism and rely on the NLS of an interacting protein for nuclear transport [48]–[56] . Yet , in contrast to phyA , most of these proteins seem to interact with the NLS containing protein constitutively [48] , [49] , [51] , [52] , [56] or they are even part of a stable oligomeric complex with one of its components providing an NLS [53] , [55] . Often the NLS containing protein also performs an essential function besides nuclear transport [49]–[52] , [54] . Compared to the piggyback systems described above , the FHY1/phyA system is unique inasmuch as i ) nuclear transport of the cargo protein is regulated by a conformational change of phyA [27] and ii ) the NLS containing protein is dedicated exclusively to nuclear transport of the cargo protein given that FHY1 becomes dispensable in a strain where phyA possesses it own NLS ( Figure 5 ) .
To obtain the PPHYA∶PHYA-NLS-GFP5 construct ( CF461 ) , we inserted the following sequence AALQKKKRKVGGAAA between phyA and GFP5 of CF161 [27] using standard molecular biology techniques ( NLS is underlined ) . PPHYA∶PHYA-NLS ( CF460 ) is the same construct except that there is a stop codon directly after the last codon of the NLS sequence ( i . e . does not contain GFP5 ) . Transgenic plants expressing phyA-NLS ( CF460 ) and phyA-NLS-GFP ( CF461 ) under the control of the PHYA promoter were obtained by transforming the constructs ( CF460 , CF461 ) into phyA-211 mutants by Agrobacterium-mediated transformation [57] . Transgenic plants were selected on 0 . 5× Murashige & Skoog ( MS ) medium ( Duchefa ) , 0 . 7% agar ( Sigma ) with 30 µg/ml kanamycin . Single insertion lines were selected by determining the kanr/kans ratio in T2 . Homozygous progeny of two representative single insertion lines for each construct were used for further studies . pphyA40-phyA ( contains PPHYA∶PHYA-CFP∶TerRbcS ) is a T-DNA vector derived from pCHF40-phyA ( contains P35S∶PHYA-CFP∶TerRbcS ) and was used to generate plants expressing PHYA promoter driven phyA-CFP . pphyA40-phyA and pCHF40-phyA were obtained as described for pphyA30-phyA and pCHF30-phyA but contain ECFP ( Clontech ) instead of EYFP [26] . pCHF70- , pCHF72- and pCHF73-FHY1 167–202 are T-DNA vectors used to generate plants expressing CaMV 35S promoter driven YFP-FHY1 CT , NLS-YFP-FHY1 CT ( artificial FHY1 ) and NES-YFP-FHY1 CT . Details regarding cloning of these constructs can be found in Text S1 . pCHF70- , pCHF72- and pCHF73-FHY1 167–202 were used for Agrobacterium-mediated transformation of Ler and fhy1-1 ( only pCHF72-FHY1 167–202 ) , pphyA40-phyA for transformation of phyA-201 [57] . Transgenic plants were selected on soil using BASTA ( AgrEvo ) . Unless indicated otherwise , homozygous progeny of single insertion lines ( 1∶3 segregation of the selection marker ) were used for the experiments . Lines co-expressing either NLS- or NES-YFP-FHY1 CT and phyA-CFP were obtained by genetic crossing of Ler P35S∶NLS/NES-YFP-FHY1 CT and phyA-201 PPHYA∶PHYA-CFP ( Ler ecotype ) . The F1 generation was used for microscopic analysis . The phyA-211 fhy1-1 plants expressing phyA-NLS-GFP were obtained by crossing phyA-211 PPHYA∶PHYA-NLS-GFP5 ( Col ecotype ) into fhy1-1 ( Ler ecotype ) background . In F2 siblings were selected that were homozygous for the transgene and phyA-211 and either wild-type ( i . e . phyA-211 FHY1 PPHYA∶PHYA-NLS-GFP5 , in Col×Ler background ) or mutant ( i . e . phyA-211 fhy1-1 PPHYA∶PHYA-NLS-GFP5 , in Col×Ler background ) for FHY1 . In all experiments with phyA-211 fhy1-1 PPHYA∶PHYA-GFP5 the phyA-211 FHY1 PPHYA∶PHYA-NLS-GFP5 in Col×Ler background was used as wild-type control . phyA-211 fhy3-1 PPHYA∶PHYA-NLS-GFP plants were obtained by crossing phyA-211 PPHYA∶PHYA-NLS-GFP ( Col ecotype ) into fhy3-1 ( Col ecotype ) background . In F2 seedlings homozygous for phyA-211 , fhy3-1 and the transgene were selected . The Columbia ( Col-0 ) and Landsberg erecta ( Ler ) ecotype of A . thaliana were used as wild type . phyA-211 [58] , cop1-4 [59] and fhy3-1 [60] , [61] are in Col while fhy1-1 [61] , [62] and phyA-201 [58] are in Ler . phyA-211 PPHYA∶PHYA-GFP5 ( A-GFP1 ) , phyA-211 fhy1-1 PPHYA∶PHYA-GFP5 , phyA-211 fhy3-1 PPHYA∶PHYA-GFP5 and fhy1-1 P35S∶YFP-FHY1 were previously described [27] . A second phyA-211 PPHYA∶PHYA-GFP5 line ( A-GFP2 ) which was obtained during the screen described previously [27] was used because its phyA-GFP protein level is close to the phyA-NLS-GFP protein level in the lines we obtained . Measurements of hypocotyl length in continuous FR light and anthocyanin accumulation were performed as described [63] . For hypocotyl length seedlings were grown on half-strength MS , 0 . 7% agar while for anthocyanin accumulation seedlings were grown on half-strength MS , 0 . 7% agar supplemented with 1 . 5% sucrose . The VLFR of hypocotyl elongation and its transition to the HIR was investigated essentially as described [64] . Briefly , chilled seeds were exposed to red light for 6 h followed by 18 h of incubation in darkness before transfer to pulses of FR ( 3 min ) given at different dark intervals ( 117 min , 57 min , 27 min or 0 min = continuous FR ) . Hypocotyl length was measured to the nearest 0 . 5 mm after 3 d of treatment and is expressed relative to dark controls . Data are means and SE of at least 11 replicate boxes ( 10 seedlings per box ) . Microscopic analyses in Figures 2A–D and 4A–D were performed with a Leica DM 600B equipped with Leica LTR6000 laser ( software LAS , Leica Application Suite ) using GFP and DAPI filter sets and a 20× air objective . 3-day-old dark-grown seedlings were directly observed under the microscope ( dark condition ) . For light conditions , 3 day-old-dark-grown seedlings were pretreated for 10 min with white light before they were observed under the microscope . For microscopic analyses in Figures 1C and 1D , 2E and 2F , 4E–H and 7B a Zeiss Axioscope 2 equipped with a 63× oil-immersion objective and GFP , YFP and CFP specific filter sets was used . The seedlings used for microscopy were grown as described in the figure legends . Materials and Methods for Figures S1–S6 can be found in Text S1 . | In response to changes in the environment , animals can take shelter while the sessile plants must adapt to the prevalent conditions . Great plasticity in growth and development are striking examples of how plants cope with a changing environment . In plants , light is both a source of energy and an essential informational cue perceived by several classes of photoreceptors . Phytochrome-mediated light signaling is particularly well studied , because these photoreceptors control all aspects of the plant life cycle . The phytochromes are cytoplasmic in the dark and must enter the nucleus upon light activation to initiate signal transduction . How this important light-regulated event is achieved is poorly understood . Here we describe the function of an evolutionary conserved protein called FHY1 for Far-red elongated HYpocotyl 1 . We demonstrate that FHY1 interacts with a light-activated phytochrome in the cytoplasm , allowing the complex to be transported into the nucleus . Interestingly , if this phytochrome can enter the nucleus by another mechanism , FHY1 is no longer required for seedling development , indicating that a major function of FHY1 is to chaperone an activated phytochrome into the nucleus . Our experiments suggest that this mechanism uncovered in Arabidopsis is widely conserved among flowering plants . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"cell",
"biology/cell",
"signaling",
"developmental",
"biology/plant",
"growth",
"and",
"development",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"plant",
"biology/plant-environment",
"interactions",
"cell",
"biology/plant",
"cell",
"biology",
"genetics",
"an... | 2008 | FHY1 Mediates Nuclear Import of the Light-Activated Phytochrome A Photoreceptor |
The incidence of zoonotic cutaneous leishmaniasis ( ZCL ) makes it the most widespread parasitic disease in Tunisia and the Arab world . Yet , few studies have addressed its psychological and psychosocial effects . The purpose of this study was to examine the psychosocial impact of ZCL scars among Tunisian women . We conducted an exploratory study , we administered Revised Illness Perception Questionnaire ( IPQ-R ) , World Health Organization Quality Of Life-26 ( WHOQOL-26 ) and Psoriasis Life Stress Inventory ( PLSI ) to a group of girls and women with ZCL scar in the region of Sidi Bouzid . This group was randomly selected from volunteers who came to primary health care facilities to seek for treatment for any pathology . Descriptive statistics showed that the collected scores from the three scales exhibit heterogeneous distributions: IPQ-R ( M = 63 . 6 , SD = 15 . 6 ) , PSLI ( M = 9 . 5 , SD = 6 . 7 ) , WHOQOL-Physical ( M = 63 , SD = 12 . 9 ) , WHOQOL-Psychological ( M = 52 . 6 , SD = 11 . 1 ) , WHOQOL-Social ( M = 61 . 8 , SD = 17 . 5 ) , and WHOQOL-Environmental ( M = 47 . 8 , SD = 13 . 3 ) . The correlation analyses performed on Inter and intra-subscales showed that the emotional representations associated with ZCL were correlated with the loss of self-esteem and feelings of inferiority ( r = 0 . 77 , p<0 . 05 ) . In addition , high education level and the knowledge about ZCL are positively correlated with cognitive and emotional representation in the IPQ-R ( r = 0 . 33 , p<0 . 05 ) . "Rejection experiences" and the "anticipation and avoidance of stress" were respectively negatively correlated with age ( r = -0 . 33 , p<0 . 05 and r = -0 . 31 , p<0 . 05 ) . Correlations between the scores on IPQ-R domains and PLSI factors were significant . The results showed that anticipation of rejection and avoidance of stress are strongly correlated with a negative perception of ZCL . Quality of life scores were not correlated with either age , education level , time of illness , or the number of facial or body scars . However , the correlations between quality of life scores and the multiple IPQ-R domains were all insignificant . Finally , there was a negative correlation between the scores on the perceived quality of social life and the knowledge about ZCL ( r = -0 . 34 , p<0 . 05 ) . This makes it vital to strengthen preventive health education . Conducting studies on ways to establish a holistic support system for managing ZCL , a system that covers the psychological challenges and the barriers it causes to women’s social and professional integration , is a vital first step .
Cutaneous leishmaniasis is the most common form of leishmaniasis . It is a skin infection caused by a unicellular parasite transmitted by infected sandflies that feed on the blood of rodents bred in caves or burrows in degraded environments . It is known also as Oriental Sore , Biskra Button , Aleppo boil or Tabaa Sidi Bouzid , depending on the geographic region concerned . Cutaneous leishmaniasis is endemic in many parts of the world . There are about twenty different species of Leishmania capable of infecting humans . The distribution of cutaneous leishmaniasis is very closely related to the geographical characteristics and the ecological specificities of the endemic areas . Cutaneous leishmaniasis always heals spontaneously , but leaves permanent scars . New treatment options and new molecules are still in the process of getting validated . ZCL is the most widespread parasitic disease in Tunisia . It mostly affects people who live in the central and south-western parts of the country . ZCL is not a fatal disease , but it causes significant changes in victims that affect their psychosocial condition and quality of life . Work done in Pakistan and Afghanistan [1] suggests that the level of stigma and social exclusion suffered by ZCL victims is tied to the number and visibility of their ZCL scars . Affected girls and women have fewer chances of getting a job , getting married and leading a fulfilling social life [2] . This shows that ZCL can have severe consequences in many respects , for it seriously restricts the subject’s social , economic and cultural life . The theoretical framework for this study is the self-regulatory theory framework by which Leventhal , Meyer & Nerenz [3] explain adaptive behaviour during a health problem . In the self-regulatory model [4] , illness perception and coping style are directly related . A person’s perception of illness contributes considerably to guide their choice of coping strategies . According to Leventhal , Nerenz and Steele [4] , there are five domains of illness perception . These are identity: what the illness is called and the symptoms that affect the patient and are associated with the illness; the cause: the patient’s opinion on the causes of the illness . An illness with no identifiable cause is a devastating experience that causes impotence and difficult adjustment; the timeline: chronic , acute or cyclical; the consequences: for the patient and his entourage; and cure and control: this domain refers to personal perceptions of one’s potential power to control the illness and its spontaneous development or treatment . Apart from these five domains of illness perception , there are other domains such as emotional representation , relating to all the negative feelings caused by the illness or the coherence of the illness , a metacognitive domain relating to the general understanding of the illness and to the degree of integration of its various dimensions , symptoms and causes . Our study intended to evaluate the psychological and social impact of ZCL on women with facial scars in the region of Sidi Bouzid . Understanding perceptions of ZCL and quality of life among patients with ZCL and ZCL scars can help us develop psychosocial intervention strategies to fight against stigma and self-deprecation , and to develop strategies for preventing harmful behaviour patterns on ZCL .
The study was conducted in Sidi Bouzid , a predominantly rural or semi-rural region with scattered groups of extended families attached to their farmland . Sidi Bouzid is a region that provides over 20% of the country's agricultural production . There is no psychological services available for this population . For the facility and the feasibility of this first study and the disponibility of the population , the sample was made up of 41 girls and women from El Hichria ( n = 31 ) and Ouled Mhamed ( n = 10 ) in the Sidi Bouzid Governorate ( Fig 1 ) . We conducted an exploratory study among a volunteer group of girls and women selected randomly in primary health care facilities during their medical visits for any pathology or for kids routine vaccination . This study focused on the impact of ZCL scar among females because they are most vulnerable to the psychosocial consequences than males . They had facial and non facial scars due to ZCL and were relatively young , the age ranged from 12 to 53 years old ( 85% are under 30 years ) . Ninety three per cent of the respondents had at least one facial scar and 54% had scars on other parts of the body . The questionnaires were administered in the following order: the IPQ-R , the PLSI and WHOQOL Bref . The information gathered was processed using Statistica 6 software after verifying that it was entered properly . We performed statistical analysis using parametric tests after checking for the normal distribution of variables . The correlation analysis were done using the Pearson correlation coefficient . For comparison , we used Student’s t-test or variance analysis . We didn’t perform multivariate analysis because of the small sample size . The questionnaires were administered by a psychologist and/or a health educator on ZCL patients in their homes or at the local clinic . All persons who participated in this study did so willingly . The study was approved by the "Ethic committee of Pasteur Institute of Tunis ( IPT ) " . All women provided informed consent , and parents of any child participant provided also informed consent on their behalf . The informed consent given by all subjects was oral since the majority of them were illiterate , the psychologist and the health educator explained the aim and the procedure of the study and asked them for consent . The "Ethic committee" approved the study since we didn't need any clinic or biological investigation from all subjects with the guarantee from the research team about confidentiality and the respect of individual information . The response on the questionnaire attest the oral consent of participants .
For the respondents in the survey , this disease , "leishmaniasis" , is linked with sand flies . All the respondents stated that they consider ZCL scars as marks of the disease , and that the number of scars varies from person to person . Seventy-eight per cent considered that ZCL is sex-specific . Seventy-eight per cent declared that it is seasonal and spares no age group . Seventy per cent believed the insect bites eventually become scars . The respondents had different views on whether this disease was found recently ( 56% ) or a long time ago and on the possible disappearance of the scars over time ( 51% ) . Further , 68% stated that ZCL is dangerous . Concerning perceptions on the causes of ZCL , the respondents mentioned the human responsibility for the environmental changes . The majority considered that ZCL is not hereditary ( 80% ) . It was not related to the quality of food ( 92% ) , or stress in everyday life ( 78% ) . However , 73% linked the environment ( stagnant water ) and physical activity ( working alongside animals ) with the incidence of ZCL . About 66% thought extreme temperatures contribute to the incidence of ZCL . Close to three quarters ( 73% ) saw a correlation between stagnant water , sand flies and the incidence of ZCL . About fifty percent made a link between ZCL and abundant rainfall that frequently ends by forming stagnant water . This may be because these events rarely occur in Sidi Bouzid . The summer is usually very hot and associated easily with sand flies and increased risk of sand fly bites and illness . Almost 71% of the respondents mentioned the notion of vectors and reservoirs . Close to two thirds linked rat’s sand flies to ZCL transmission . Yet , barely 43% said that killing rats and sand flies vectors is relevant to preventing risk of exposure to ZCL . Eighty five per cent of the respondents thought that sand flies are responsible for the disease . Eighty seven per cent admitted there is a correlation between working with animals and exposure to ZCL . None of the subjects believed that the sandfly that are related to ZCL are small and almost invisible . Seventy per cent said that the sand fly that are linked to ZCL are the same size as other mosquitoes . We found that the respondents’ perceptions on the time it takes for scars to develop ranged from an uncertain period ( some scars are permanent while others appear and then fade away ) to chronicity , sometimes with an increase in the number and appearance of the scars . Almost half the number of respondents stated that the scars do not fade quickly , but they were carefully optimistic that the scars would fade gradually with time . The respondents said unanimously that ZCL has had no positive effects on their lives . The consequences have rather been negative . About 73% suffered social exclusion and stigmatization . Their relationships have been broken and they face more interpersonal conflicts in society , regardless of the context ( family , social or professional ) . The consequences were seen also in their chances of getting employment . ZCL affected women who stay at home more than those who study or are already working . It reduced marriage prospects for men ( 75% ) and women ( 59% ) . The consequences were felt also on aesthetic features , for the scars alter women’s beauty ( 58% ) . With respect to individual ability to control ZCL , the most common perception was an absolute lack of power to control and cure the disease . The respondents thought there is nothing useful they can do to control the disease , its symptoms or its progress . Drugs were seen to have little or no effect , and the risk of getting the disease again is still likely . When it comes to understanding the problem , we noted that patients still cannot explain how they contracted ZCL . There is little understanding of the problem , and respondents keep asking themselves "why am I the one who is infected" . We found that the emotional representations associated with ZCL were associated with the loss of self-esteem , feelings of inferiority and the idea that the disease is equal to an obvious social disadvantage . There was also a strong sense of shame . Table 2 summarizes the relationship between illness perception and sociodemographic and clinical factors . The correlation analysis shows that high education level and high knowledge levels are associated with greater cognitive sensitivity ( perception of consequences ) and emotional sensitivity ( emotional representations ) . Hence , variables such as "age" and "education level" have to be monitored closely , for they may partly explain why there is a variance in the IPQ-R results . The inter-correlation matrix for multiple IPQ-R domains ( Table 3 ) showed that the domains do not all measure the same constructs , because not all of them are inter-correlated . The matrix showed further that the "identity" domain has a negative correlation with the "curability" domain ( r = -0 . 35 , p<0 . 05 ) and a positive correlation with the "coherence" domain ( r = 0 . 41 , p<0 . 01 ) . These results suggest that the more patients can clearly identity ZCL , the better they will perceive it coherently and the more they will know it is incurable . We noted also the presence of a correlation ( r = -0 . 30 , p = 0 . 048 ) between the "cause" domain and the "curability" domain: it seems that the more patients know about CL , the more pessimistic they get about the prospects of recovering from the disease . This correlation analysis suggested also that patients who have a more coherent perception of ZCL do have stronger emotional reactions ( r = 0 . 48 , p<0 . 001 ) and face more severe consequences ( r = 0 . 53 , p<0 . 001 ) . Finally , those who are more inclined to perceive ZCL as an illness with negative consequences were more likely to claim that they experience severe emotional difficulties ( r = 0 . 77 , p<0 . 001 ) . All these significant correlations are maintained even after testing them on the "age" variable and the "education level" variable , using partial correlations . The descriptive data focused more on anticipation of a negative reaction from others and the efforts made to display anticipatory avoidance behaviour meant to deny such reactions , than on convictions from the presence of negative experiences that patients have had through social interactions on the state of their skin ( Table 4 ) . "Rejection experiences" and the "anticipation and avoidance of stress" were respectively , significantly and negatively correlated with age ( r = -0 . 33 , p<0 . 05 and r = -0 . 31 , p<0 . 05 ) . The younger the subject , the higher the number of experiences tied to stigma; and the older the subject , the more such feelings are dismissed . Thus , the correlation analysis showed that stigma assessment indices were not correlated with level of education , "total knowledge" score , or time of illness . They were correlated only with age: the younger the subject , the more they tend to experience stigma . Furthermore , there are moderate correlations and just around the significant level between the total number of body scars and "rejection experiences" ( r = 0 . 31 , p<0 . 05 ) , and between the number of facial scars and the scores on "Anticipation and avoidance of stress" ( r = 0 . 29 , p = 0 . 06 ) . It is important to note that this correlation between the number of scars and stigma indices was at the same threshold of significance when checked against age-related variations . Hence , the number of body scars had a strong link to experience with stigma , regardless of a patient’s age . We also noted correlations between the scores on multiple IPQ-R domains ( identity , chronicity , causes , consequences , curability , controllability , coherence and emotional representations ) and PLSI factors ( rejection experience and the anticipation and avoidance of stress . The results showed that anticipation and avoidance of stress is strongly correlated with the scores on "Consequences" , "Coherence" and "Emotional representation . " These results suggest that the more a person looks at the world anticipating segregation and rejection , the more they perceive ZCL as a disease with harmful effects on them , the more they see it as a mysterious and incomprehensible illness , and the more they talk about suffering and emotional difficulties . Emotional representation also appeared to be significantly correlated with the total score on stress , as seen in the PLSI . The scores on quality of life of women with ZCL facial scars showed that various domains of quality of life are perceived in a very different manner ( p<0 . 001 ) . Environmental quality of life is seen as significantly less fulfilling ( p<0 . 001 ) than quality of social life . According to the respondents , it appeared to be far less fulfilling than ( p<0 . 001 ) the physical quality of life . Quality of mental life did not differ much from environmental quality of life . However , it is said to be significantly more impaired than quality of social life ( p<0 . 001 ) and the physical quality of life ( p<0 . 001 ) . The physical quality of life was not described as significantly different from quality of social life ( Fig 2 ) . Quality of life scores were not significantly correlated with either age , education level , time of illness , or the number of facial or body scars ( Table 5 ) . In addition , the correlations between quality of life scores and the multiple IPQ-R domains were all insignificant . On the other hand , quality of life scores had interesting correlations with PLSI scores on stigma ( Figs 3 and 4 ) . The quality of social life score was significantly and negatively correlated to the "Anticipation and avoidance of stress" score ( r = -0 . 36 , p<0 . 05 ) suggesting that the more a person anticipates segregation and psychosocial stress , the lower their quality of social life was seen to be . Equivalent correlations were observed with the "total stress" score ( r = -0 . 32 , p<0 . 05 ) : the more a person experiences high stress levels in society , the lower the quality of their social life was seen to be . Finally , there was a significant and negative correlation between the score on quality of social life and that on "Total Knowledge" . It seems that the higher a person’s knowledge of ZCL , the less likely they are to view their quality of social life in a negative manner ( r = -0 . 34 , p<0 . 05 ) . The quality of knowledge seems therefore to be tied closely to the quality of social life ( Table 6 ) . It was important also to note that the correlation between scores on 'Rejection experience" and on mental quality of life index was on the verge of the threshold of significance ( r = -0 . 27 , p<0 . 092 ) . Mental quality of life tends to be perceived negatively , if the subject claims to have suffered many rejection experiences . The mental quality of life index tended to be associated with the total stress and stigma score ( r = -0 . 27 , p<0 . 087 ) .
The objective of this study is to explore the perceptions of ZCL , the stigma associated with the disease , and the quality of life of people with ZCL in Sidi Bouzid . We studied the perceptions of CL based on the theoretical model by Leventhal et al . [4] , which identifies the factors involved in patients’ formation of cognitive representations of their illness . Research on the observation of treatment showed that the reasons for adopting health-enhancing behaviours depend on the representational and cognitive factors related to their state of health [8] . It is the first time this type of model , and the assessment tool associated with it ( IPQ-R ) , is applied to study CL . For example , on the 18th of February , 2016 , we found that ScienceDirect contained 1 758 articles on "IPQ-R" and 21 831 articles on "leishmania" , but no entry on "IPQ-R" and "leishmania" . It is the first time also that the psychological and psychosocial aspects of CL are studied in the Arab world , which is one of the areas with the highest number of cases of CL in the world . All works dealt basically with the epidemiological and/or biological aspects of the issue . Despite the originality of this work , our results should be taken with caution and have to be confirmed because we only did an explanatory study on a small sample size . Based on the results from the IPQ-R , we see that patients tend to consider ZCL as a disease that affects entire generations and is more rampant in some regions more than others . The disease mostly affects women and spreads in cyclical trends , caused by "mosquito" bites associated with the presence of rats , and related to climate change and the favourable ecological context in Sidi Bouzid . The patients were incline to think ZCL has negative consequences on their personal , family , social and professional life . They generally believe that ZCL considerably alters their natural beauty and reduces their prospects of getting employment , getting married and being valued in society . They see ZCL as a mysterious , incurable and uncontrollable disease . After analysing the responses we had gathered under the “causes” domain in the survey questionnaire , we were able to identify patients’ beliefs on the underlying causes of ZCL . The contents relate basically to biological and/or ecological causes . The patients did not talk about irrational , supernatural and magical causes , as was the case with some patients in Colombia [9] . ZCL patients think that living with the disease is a steady source of stress that forces them to develop anticipatory avoidance behaviour against rejection . They try to anticipate stigma primarily out of feelings of inferiority in society . These problems bring about a huge sense of injustice and anger to the point where there is little sympathy with other girls or women with ZCL . These difficulties can lead to depression and anxiety . The patients think their scars are degrading and this increases their feelings of shame . They describe their feeling of isolation , that is , they think subjectively that they are inferior to other members of society , that they are not fully fledged individuals , or that they have an "impaired identity" and suffer discrimination , which includes rejection and isolation from others . Anticipated stigma is a subjective experience that could negatively affect psychosocial and professional development and adjustment among patients . These results largely confirm the findings from similar studies conducted on patients with vitiligo [10] . The psychological effects of ZCL tend to reduce as patients get older . The more educated a patient is , the more his knowledge of the disease develops , and the more he tends to analyse the consequences of the disease and display dramatic emotional reactions . A high level of education and high levels of knowledge are associated with greater cognitive sensitivity ( perception of consequences ) and emotional sensitivity ( emotional representations ) . After calculating our correlation analysis , the results we obtained were consistent with the theoretical construct on illness perception in Leventhal’s model [4] . We noted a positive and significant correlation between the "consequences" , "emotional representation" and "coherence" domains . The more the patient with CL considers that the disease is mysterious and incomprehensible , the more severe would be its consequences and effects on their emotional life are seen to be . The ability to identify the disease has a significant and negative correlation with the ability to cure it . It suggests that the more a patient is able to identify CL , the lower their hope that it can be cured . These correlations are strong and are not attenuated by controlling the level of education . Moreover , there is a high level of consistency between perceptions of CL in the IPQ-R and stigma indices in the PLSI . Anticipation and avoidance of stress have a strong correlation with a patient’s perceptions of the "consequences" , "coherence" and "emotional representation" of the disease . The more patients see ZCL as a disease with severe , unambiguous consequences that have harmful emotional effects , the more they tend to expect to be rejected by others , and the more they would be inclined to form avoidance attitudes towards others . These conclusions do not concern the relationships between these variables and the patient’s age , level of education , or knowledge of the disease . The correlation between the level of stigma and the number of lesions is not related to the location of these lesions on the face or elsewhere on the body . This rather surprising result confirms the work of Papadopoulos et al . [10] who report that the location of vitiligo does not play a critical role in the way patients experience stigma . Our results also suggest that the nature of stigma that patients experience are associated with certain general fears and anticipation of rejection , rather than the possible rejection that patient truly experience from society . However , this is not the case in some countries like Pakistan , where Afghan refugees with CL face stigma and are excluded from social groups [1] , or in Afghanistan where women with CL are separated from their children because of fears that the mothers will contaminate the children [2] . Stigma that results from the disfiguring scars of CL apparently depends on how patients think others perceive them . This means that the disability associated eventually with the disfigurement depends on how patients see themselves in their own environment ( personal , social and professional ) . The respondents believe that ZCL has a generally negative impact on quality of life . But our results show that ZCL has differential impacts on the four domains of quality of life in the WHOQOL-26 . The quality of life of ZCL patients is largely unsatisfactory in two domains: environmental quality of life and quality of mental life . The respondents are inclined to think that the environmental quality of life is the more degraded of the two . It includes availability of financial resources , freedom , security , access to and quality of housing , opportunities for acquiring new information and skills , opportunities for recreation and leisure , as well as the availability and quality of transport facilities . We are assuming that ZCL patients are not the only ones dissatisfied with this domain . The entire population shares this dissatisfaction with the environmental quality of life in the region of Sidi Bouzid . For the respondents , the quality of mental life is lower than physical quality of life and the quality of social life . It suggests that they are dissatisfied with their self-image and appearance , with the negative sentiment displayed towards them , with their self-esteem and their cognitive abilities ( the ability to think , learn , memorize and concentrate ) . This latter result is frequently described in literature on skin infections [10] . None of the domains on quality of life is closely correlated with the scores that reflect patients’ perception of ZCL in the IPQ-R . However , the quality of social life domain is significantly and negatively correlated to the "Anticipation and avoidance of stress" factor in the Psoriasis Life Stress Inventory ( PLSI ) questionnaire . The more a person expects to face rejection and stigma , the lower the quality of social life they have when it comes to personal relations and social support . These dynamics have been observed already in patients with psoriasis [11] . ZCL got little attention for long because , apart from the aesthetic effects it has , the disease is neither contagious nor fatal , and causes no major health concerns . However , it can have adverse socio-economic effects . ZCL is a health problem in developing countries because of the social and economic challenges it causes [12] . The disease leaves disfiguring scars on the face or any other part of the body bitten by mosquitoes . It undermines the social roles and social identity of patients , reduces their chances of being accepted by others or getting employment in institutions , and induces psychological distress . These results show that public health officials , in planning control programmes , should pay attention to the gaps in knowledge perceptions , the vulnerability of women to cutaneous leishmaniasis , the ways of preventing their exposure and the psychological support to reduce the psychosocial effects [13] . With the Leventhal model [4] , we have assessed the mental perceptions of ZCL in patients in Sidi Bouzid and what they make of this problem . Understanding these personal perceptions of the disease is a key first step in efforts to seek help , design an adaptive strategy or adopt a management regime for the disease [14] . The studies that are going to be done on these therapeutic aspects should pay attention to these perceptions of CL , so that they put forward ideas on how to improve quality of life for patients . The work of Nilforoushzadeh et al . [15] shows that providing psychological care contributes effectively to improve the psychological and social well-being of these patients , and enhances their commitment to follow courses of treatment . Lusli et al [16 , 17] showed the benefit of psychosocial support like counselling on the leprosy-related stigma . Moreover , Peters et al [18 , 19] illustrated the success of a contact intervention in Indonesia to reduce leprosy-related stigma in the community . These interventions are likely to work for CL as well . It is crucial also to take preventive measures , given the importance of behavioural aspects in the onset of the disease . Byrne et al . [20] have described cognitive and behavioural therapy that was done to change patients’ illness perceptions after a heart attack , and how this improved the quality of life for the patients . Ensuring that the causal model on ZCL places more emphasis on environmental factors may be the first step in behavioural therapy for this epidemic . This would mean creating opportunities for people concerned to personally control future trends of the disease and the risk of contamination through other insect bites in the future . Combating real and/or perceived stigmatization is another decisive step in the delivery of care , because stigma has negative impacts on the quality of life , mental health and participation in social and professional life . | Zoonotic cutaneous leishmaniasis ( ZCL ) is the most common form of leishmaniasis in Tunisia . The disease is not severe and heal spontaneously with a definitive scar causing a social impact mainly when the lesion occurs in the face . Yet , few studies have addressed these psychological and psychosocial effects . To examine these issues among Tunisian women suffering from ZCL , we administered Revised Illness Perception Questionnaire ( IPQ-R ) , World Health Organization Quality Of Life-26 ( WHOQOL-26 ) and Psoriasis Life Stress Inventory ( PLSI ) to a group of girls and women with ZCL scars in the region of Sidi Bouzid . This study demonstrated the wide range of psychological effects ( anxiety , psychological distress , lack of self-confidence and self-esteem , frustration , etc… ) and psychosocial impacts ( stigma , rejection , discrimination in the social and professional setting , etc… ) of ZCL permanent scars; mainly when it is located in the face; among women and girls . This study suggests the need of social and psychological support and professional integration for women and girls in rural remote areas first and second conducts studies on ways to establish control measures for ZCL , mainly in vulnerable exposed population . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"dermatology",
"medicine",
"and",
"health",
"sciences",
"tropical",
"diseases",
"social",
"sciences",
"sand",
"flies",
"parasitic",
"diseases",
"neuroscience",
"psychological",
"and",
"psychosocial",
"issues",
"health",
"care",
"neglected",
"tropical",
"diseases",
"inse... | 2016 | Psychological and Psychosocial Consequences of Zoonotic Cutaneous Leishmaniasis among Women in Tunisia: Preliminary Findings from an Exploratory Study |
Ovules are fundamental for plant reproduction and crop yield as they are the precursors of seeds . Therefore , ovule specification is a critical developmental program . In Arabidopsis thaliana , ovule identity is redundantly conferred by the homeotic D-class genes SHATTERPROOF1 ( SHP1 ) , SHP2 and SEEDSTICK ( STK ) , phylogenetically related to the MADS-domain regulatory gene AGAMOUS ( AG ) , essential in floral organ specification . Previous studies have shown that the HUA-PEP activity , comprised of a suite of RNA-binding protein ( RBP ) encoding genes , regulates AG pre-mRNA processing and thus flower patterning and organ identity . Here , we report that the HUA-PEP activity additionally governs ovule morphogenesis . Accordingly , in severe hua-pep backgrounds ovules transform into flower organ-like structures . These homeotic transformations are most likely due to the dramatic reduction in SHP1 , SHP2 and STK activity . Our molecular and genome-wide profiling strategies revealed the accumulation of prematurely terminated transcripts of D-class genes in hua-pep mutants and reduced amounts of their respective functional messengers , which points to pre-mRNA processing misregulation as the origin of the ovule developmental defects in such backgrounds . RNA processing and transcription are coordinated by the RNA polymerase II ( RNAPII ) carboxyl-terminal domain ( CTD ) . Our results show that HUA-PEP activity members can interact with the CTD regulator C-TERMINAL DOMAIN PHOSPHATASE-LIKE1 ( CPL1 ) , supporting a co-transcriptional mode of action for the HUA-PEP activity . Our findings expand the portfolio of reproductive developmental programs in which HUA-PEP activity participates , and further substantiates the importance of RNA regulatory mechanisms ( pre-mRNA co-transcriptional regulation ) for correct gene expression during plant morphogenesis .
Ovules are fundamental for plant reproductive success and food production . Ovule development predetermines the female gametophyte , proper fertilization , and ultimately fruit growth , as well as embryo and seed development [1] . Importantly , seeds constitute the primary basis for human sustenance and in recent years they have had an increasing role in biofuel production [2] . In flowering plants , ovules arise as lateral organs from meristematic placental tissue that differentiates inside the carpels , the female flower organs that constitute the pistil or gynoecium [3 , 4] . It is therefore that organ specification is a key aspect of ovule development . Ovule identity largely depends on the concerted action of MADS-box transcription factors collectively defined as the floral D-activity [5 , 6] . In the reference plant Arabidopsis thaliana ( Arabidopsis hereafter ) the D-class comprises three genes , SHATTERPROOF1 ( SHP1 ) , SHP2 and SEEDSTICK ( STK ) , that redundantly confer ovule identity [7 , 8] , in addition to playing crucial roles during fruit patterning and dehiscence ( SHP1 , SHP2 ) and fertilization or in seed coat development ( STK ) [9–11] . Thus , in shp1 shp2 stk plants , ovules lose identity and convert into flower organ-like structures whereas single or double mutants develop essentially normal ovules [7 , 8] . SHP1 , SHP2 and STK are the closest paralogs of the floral C-function gene AGAMOUS ( AG ) [12 , 13] , a selector homeotic gene pivotal for flower patterning . As stated by the iconic ABCE model , the combinatorial activity of four classes of transcription factors specify the identity of flower organs in a stereotypical pattern of concentric whorls of sepals ( A+E ) , petals ( A+B+E ) , stamens ( B+C+E ) and carpels ( C+E ) , respectively [14–16] . For example , in Arabidopsis AG specifies carpel identity in the fourth whorl in concert with E-class SEPALLATA ( SEP1 to SEP4 ) genes [17 , 18] . Genetic and molecular evidence indicated that SEP and AG proteins are also required for ovule identity in addition to the D-factors ( Reviewed in [19] ) . According to the floral quartet hypothesis , MADS domain proteins assemble into diverse organ-specific ( including ovules ) tetrameric complexes [20–22] . Therefore , disrupting the MADS-box monomer balance may alter the stoichiometry of the corresponding tetramer and , thus , change specificity . For example , in shp1 shp2 stk triple mutant plants , ovules develop as flower organs that show carpellar features [7 , 8] . This transformation was interpreted as a reconfiguration of the MADS-box complexes from E ( SEP ) and D proteins ( specifying ovule fate ) , to those including only AG and SEP proteins , thus conferring carpel identity as in the fourth whorl [7 , 8 , 21] . Temporal and spatial regulation of floral homeotic gene expression has been studied in great detail at the transcriptional level ( reviewed in [16 , 23] ) . However , and although its importance is becoming increasingly more relevant , post-transcriptional control of floral homeotic genes has not been studied in detail [24–26] . In eukaryotes , superimposed layers of post-transcriptional regulation are major determinants of gene expression [27 , 28] . Producing functional RNA involves a complex interplay between transcription and RNA processing activities in which numerous RNA-binding proteins ( RBP ) participate , assembling into multifunctional ribonucleoprotein ( RNP ) complexes that coat nascent transcripts [27 , 29] . In this regard , the carboxyl-terminal domain ( CTD ) of RNA polymerase II ( RNAP II ) plays a pivotal role coordinating transcription and transcript maturation , thus increasing the fidelity of the process [27 , 29 , 30] . Modulation of the CTD , mainly via phosphorylation , is key to pre-mRNA co-transcriptional modifications , thereby affecting the final output of gene expression [29 , 31 , 32] . Processing of pre-mRNA via splicing and 3’ cleavage/polyadenylation expands the transcriptome and the proteome by generating multiple isoforms that increase developmental flexibility and adaptive responses of organisms [33–35] . This is particularly relevant for sessile organisms such as plants . Studies on plant differential RNA processing have been focused mainly on floral timing ( recently reviewed in [28] ) and plant-environment interactions [36 , 37] . However , the understanding of how pre-mRNA maturation impacts plant morphogenesis is still at its infancy . In Arabidopsis , the HUA-PEP activity [26] comprises a suite of RBP-encoding genes that genetically and physically interact to maintain the floral C-function by securing the correct processing of the AG pre-mRNA [24 , 26] . The HUA-PEP activity includes HUA1 , which encodes a nuclear CCCH-type zinc-finger [38] , the RPR-domain ( Regulation of nuclear pre-mRNA ) gene HUA2 [39] , and three KH ( K-homology ) domain genes: HUA ENHANCER 4 ( HEN4 ) [24] , FLOWERING LOCUS K ( FLK ) [40 , 41] , and PEPPER ( PEP ) [42] . Single loss-of-function mutants in HUA-PEP activity genes are essentially indistinguishable from wild-type plants . Conversely , higher-order hua-pep mutant combinations exhibit defects in floral organ identity and meristem determinacy that closely resemble those of ag mutants [12 , 24 , 26] . In line with this , hua-pep mutants accumulate aberrant and non-functional AG transcripts that are prematurely polyadenylated in the large second intron at the expense of the functional AG mRNA [26] . Here , we report that , in addition , the HUA-PEP activity controls ovule development by regulating the expression of D-class floral homeotic identity genes . Strong hua-pep mutants exhibit ovules transformed into flower organ-like structures and reduced levels of SHP1 , SHP2 and STK functional messengers , concomitant with the accumulation of aberrant transcripts prematurely terminated at intronic sequences . We also provide compelling evidence that the HUA-PEP activity can regulate their target genes even when they are mis-expressed outside the flower , supporting the fidelity and specificity of this regulation . Our data support a model in which HUA-PEP factors regulate RNA processing co-transcriptionally , a view reinforced by the ability of PEP and HUA1 proteins to interact with the CTD regulator C-TERMINAL DOMAIN PHOSPHATASE-LIKE1 ( CPL1 ) /FIERY2 ( FRY2 ) [43] . This study expands the functional scope of the HUA-PEP activity , and provides new insights into ovule development , illustrating the importance of co-transcriptional processing as a major gene regulatory mechanism in reproductive plant morphogenesis .
Previous studies have shown that mutations affecting the HUA-PEP activity lead to dramatic morphological alterations in flowers [24 , 26] . In addition , sterility ( or reduced fertility ) was a recurring phenotype in hua-pep combinations , including genetic backgrounds in which flowers show minor or no obvious defects but yet producing fewer seeds than the wild type . For example , the hua1-1 pep-4 double mutants are very weak when compared to stronger higher order hua-pep mutant combinations [26] . Nevertheless , they showed a significant loss of fertility due to reduction in seed set ( S1 Table and Fig 1C ) , suggesting additional roles for the HUA-PEP gene activity besides flower morphogenesis . We examined hua1-1 pep-4 fruit and detected many empty spaces in the ovary corresponding to ovule abortions ( Fig 1 ) . Most interestingly , we observed that some ovules adopted floral organ identity ( Fig 2 and S1 Fig ) . These ovule homeotic transformations occurred at a moderate frequency ( ~20% of flowers examined ) , being absent in single and most hua-pep double mutants , or with very low penetrance in flk-2 hua2-4 pep-4 plants ( 5% of flowers; S2 Fig ) . However , in stronger mutant combinations such as hua1-1 hua2-7 ( ~40% ) , flk-2 hua1-1 hua2-7 ( ~80% ) , hua1-1 hua2-7 pep-4/+ ( 93% ) and hua1-1 hua2-7 35S::PEP ( 100% ) their abundance was more conspicuous ( Fig 2 and S1 Fig ) . The ectopic organs occurring in place of ovules showed very similar characteristics in the different hua-pep mutant combinations examined ( see below ) . As noted above , in the hua1 hua2 background , either reduction ( hua1 hua2 pep/+ ) or gain ( hua1 hua2 35S::PEP ) in PEP dosage lead to high profusion of ovule homeotic transformations and the same array of flower phenotypes [26] . Therefore , and unless indicated otherwise , hua1-1 hua2-7 and hua1-1 hua2-7 35S::PEP plants ( for simplicity , h1h2 and h1h2P hereafter , respectively ) were used as the reference genotypes to evaluate the effects of the HUA-PEP gene activity upon ovule identity . Wild-type ovule primordia emerge from the placenta as finger-like outgrowths that later develop outer and inner integuments from the flanks of the chalaza to cover the distal nucella which contains the gametophyte ( Fig 2A–2C ) . At maturity , full integument development leaves only a small opening , the micropyle , through which pollen sperm cells are discharged during fertilization . The ovule is connected to the placenta by a short stalk or funiculus ( Fig 2B and 2C ) [1 , 44] . By contrast , in hua pep backgrounds , transformed ovules often showed long funiculi and appeared as leaf-like organs with white or pale-green pointed tips ( Fig 2D and 2E ) , reminiscent of the white fringe of tissue in sepals ( S1E Fig ) . Close inspection by scanning electron microscopy ( SEM ) allowed us to verify that , rather than the typical smooth surface of wild-type ovule cells , in strong HUA-PEP activity mutant backgrounds the ovule surface contained wax-crenulated cells , irregular in size and shape , with interspersed stomata , which never form on ovules ( Fig 2B , 2F and 2I ) . These morphological features are typical of sepal and carpel tissues , strongly suggesting that ovule integuments adopted sepaloid/carpeloid identity . In addition , ovules were sometimes replaced by finger-like protrusions that showed proximal funicular histology and distal cells with cuticular ridges ( Fig 2F–2H ) . Altogether , these results evidence the importance of the HUA-PEP gene activity in ovule morphogenesis . Members of the HUA-PEP gene activity regulate flowering time and flower morphogenesis by influencing the expression of the master regulatory MADS-box genes FLC and AG [24 , 26 , 40 , 41 , 45–48] . On the other hand , ovule identity is largely dependent on the MADS-box D-class genes SHP1 , SHP2 and STK and their closest paralog AG [7 , 8 , 13] . In this context , we decided to examine the effect of hua-pep mutations on the expression levels of the D-class genes using real-time quantitative PCR ( qPCR ) . As shown in Fig 2J , transcript abundance of the three genes diminished , this reduction being more conspicuous as the severity of the hua-pep mutant phenotype increased . In line with this , h1h2P plants displayed the most dramatic ovule defects together with very reduced D-class gene transcript abundance ( Fig 2J ) . This may explain the formation of long funiculi in these plants as a result of the reduced levels in STK expression , known to restrict funicular growth [8] . Pinyopich et al . [8] also described that in shp1 shp2 stk plants a fraction of ovules transformed into finger-like structures with radial symmetry; a defect that was also detected in hua-pep mutants ( Fig 2F–2H ) . In addition to their role during ovule morphogenesis , SHP1 and SHP2 are best known for their redundant role in valve margin differentiation and dehiscence ( fruit opening ) so that shp1 shp2 fruit fail to dehisce and seeds get trapped inside the silique [9] . Interestingly , valve margin development in h1h2P fruit is blocked ( S2 Fig ) , which explains the h1h2P indehiscent phenotype . We previously reported that PEP is expressed in developing ovules [42] . This is not surprising as genetic backgrounds with compromised PEP expression , in combination with mutations in other members of the HUA-PEP activity lead to dramatic ovule defects ( Fig 2 and S1 Fig ) . On the other hand , publicly available transcriptomic data show that the HUA-PEP activity genes ( PEP , FLK , HEN4 , HUA1 and HUA2 ) are expressed in ovules [49] . To gain further insight , we decided to use the GUS-reporter line PEP::GUS [42] as an expression “proxy” for the HUA-PEP activity , and compared its stage-by-stage ovule pattern to that of SHP2 and STK marker lines . It is worth mentioning that the activity of these reporter lines essentially mirrors their respective mRNA in situ hybridization patterns [8 , 9 , 42 , 50] . SHP1 was not analyzed as its expression pattern is virtually identical to that of SHP2 [51 , 52] . The GUS signals for PEP and SHP2 reporters were largely coincident during ovule development . At stage 2-I/II ( all stages according to [44] ) , both reporters were broadly expressed in placental tissue and developing ovule primordia ( Fig 3A , 3B , 3E and 3F ) . Later , at stage 2-III/IV GUS activity was higher in the growing inner integument ( Fig 3C and 3G ) . At maturity , the signal became weaker in both cases ( Fig 3D and 3H ) . In STK::GUS plants , reporter expression in funiculi was intense at stage 2-III/IV ( Fig 3K and 3L ) , being the GUS activity more persistent than in the case of PEP and SHP2 ( Fig 3M ) . These results recapitulate previous reports for SHP2 and STK expression during ovule development [8 , 52] . Our genetic and molecular data together support a model in which HUA-PEP function is active in ovules and targets the D-function genes for correct ovule morphogenesis . To generate a comprehensive view of the gene expression landscape influenced by the HUA-PEP activity , we performed RNA sequencing ( RNA-Seq ) experiments using the Illumina HiSeq2500 platform ( see Materials and Methods ) . RNA was isolated from wild-type , h1h2 and h1h2P flower buds . Our RNA-Seq analysis pipeline ( false discovery rate or FDR threshold of 5% ) uncovered 72 and 210 genes expressed at significantly higher levels in h1h2 and h1h2P , respectively , relative to the wild-type ( Col-0 , S1 Dataset ) . Of these genes , 35 were common to h1h2 and h1h2P . At this FDR level , 676 and 993 additional genes ( including 502 common genes ) were expressed at lower levels in h1h2 and h1h2P , respectively , than in Col-0 . The higher number of differentially expressed genes in h1h2P mutants when compared to h1h2 strongly suggests that in the former the HUA-PEP activity is further compromised , which may explain the more dramatic phenotype of those plants . As expected from their genotypes , both h1h2 and h1h2P had significantly reduced levels of At3g12680 ( HUA1 ) and At5g23150 ( HUA2 ) , and h1h2P had significantly increased levels of At4g26000 ( PEP ) ( S1 Dataset ) . To generate a better view of the processes affected in h1h2 and h1h2P plants , we searched for overrepresented gene ontology ( GO ) terms within differentially expressed genes , and performed Singular Enrichment Analysis ( SEA ) as implemented in the agriGO website ( see Materials and Methods ) . We detected 24 and 48 GO terms significantly overrepresented in the sets of genes differentially expressed in h1h2 ( S3 Fig and S2 Dataset ) and h1h2P ( S4 Fig and S2 Dataset ) , respectively . Interestingly , some enriched GO terms were shared between h1h2 and h1h2P sets , including terms such as ‘lipid localization’ ( GO:0010876 ) , ‘gametophyte development’ ( GO:0048229 ) , the related term ‘pollen development’ ( GO:0009555 ) , ‘floral whorl development’ ( GO:0048438 ) and other terms specifically related to reproductive organ development . Some differentially expressed genes known to be required for gametophyte and/or floral whorl development included ARGONAUTE 9 ( AGO9; At5g21150 ) , AGAMOUS-LIKE 18 ( AGL18; At3g57390 ) , ASYMMETRIC LEAVES 2 ( AS2; At1g65620 ) , JAGGED ( JAG; At1g68480 ) , CRABS CLAW ( CRC; At1g69180 ) , and AG ( At4g18960 ) , all downregulated in both h1h2 and h1h2P ( S1 Dataset ) . Other genes , including NOZZLE/SPOROCYTELESS ( NZZ/SPL; At4g27330 ) , WUSCHEL ( WUS; At2g17950 ) , AUXIN RESPONSE FACTOR 17 ( ARF17; At1g77850 ) , INNER NO OUTER ( INO; At1g23420 ) , SHP2 ( At2g42830 ) , and STK ( At4g09960 ) , were significantly downregulated only in h1h2P although their expression was also clearly reduced in h1h2 ( S1 Dataset ) . To confirm the accuracy of the transcriptomic profiling , we validated the expression of some of these genes using qPCR . In these studies , we also included genes participating in flower and/or ovule development whose variation was barely above the FDR threshold such as SUPERMAN ( SUP , At3g23130 ) or VERDANDI ( VDD; At5g18000 ) . As shown in Fig 4 and S5 Fig , qPCR results largely mirrored RNA abundance inferred from RNA-Seq experiments . When compared to the wild type , RNA levels for these genes decreased in h1h2 plants , being even lower in h1h2P , in agreement with the higher strength of this mutant background ( Fig 4 and S5 Fig ) . Similarly , genes upregulated in the mutant backgrounds were more highly expressed in h1h2P than in h1h2 plants ( S5 Fig , S1 Dataset ) . CRC provides carpel identity and contributes to style and stigma formation , and carpel fusion [23 , 53 , 54] . In addition to its role specifying boundary between whorls 3 and 4 , SUP has been recently shown to be involved in keeping female identity and flower determinacy [55] . Thus , the reduced activity of these genes is consistent with developmental defects previously observed in hua-pep flowers [24 , 26] . Our results also indicated misregulation of genes critical for ovule development . NZZ/SPL , a direct downstream target of AG [51 , 56 , 57] , was strongly downregulated in hua-pep mutants ( Fig 4 ) , providing further support and validation for our previous investigations [26] . Similarly , transcript abundance for INO , VDD and AGL18 was also reduced in hua-pep mutants ( Fig 4 ) . NZZ/SPL promotes INO activity , which participates in integument development [19] . NZZ/SPL is also required for gametophyte development and , like VDD , known to work downstream of D-class genes and necessary for proper antipodal and synergid cell development [21 , 49] . AGL18 is also expressed in developing gametophytes [58] . Taken together , these results support the notion that altering the HUA-PEP gene activity perturbs AG and D-class gene functions , and thus their downstream gene expression programs . We have previously shown that HUA-PEP proteins maintain the floral C-function via accurate processing of the AG large second intron . Otherwise , non-functional prematurely terminated transcripts , including intronic sequences , accumulate [24 , 26] . The genomic configuration of the D-class genes is similar to AG , containing long introns located near the 5’ end of the gene [59] . To test whether HUA-PEP factors affect precise processing of such intronic sequences , we examined the normalized read coverage for SHP1 , SHP2 and STK genes ( including introns ) . For robustness , we incorporated AG into the analysis as a positive control . For AG , SHP1 , SHP2 and STK , the relative transcript abundance for exonic regions decreased in h1h2 and h1h2P mutants when compared to wild-type ( Fig 5 ) , in line with the qPCR assays shown above ( Fig 2J ) and previous data on AG expression in the same mutant backgrounds [24 , 26] . It is worth noting that the RNA levels of SHP1 ( At3g58780 ) varied barely above the FDR threshold in our RNA-Seq experiments ( S1 Dataset ) , even though our qPCR experiments firmly validated such changes ( Fig 2J and see below ) . Interestingly , we also observed reads mapping to the long second introns of SHP1 and SHP2 loci , as well as to the long introns 1 and 2 of STK . Intronic reads also appeared , although very scarcely , in the wild type and increased abruptly in the mutants ( Fig 5 ) . Such reads identified RNA products that extend beyond the exon-intron borders and terminate within the large introns , generating truncated and aberrant transcripts that exclude downstream exons and , thus , are not functional ( Fig 5 and S6 Fig ) . Some of these transcripts were identified by 3’ rapid amplification of cDNA ends ( 3’ RACE ) for the SHP2 gene ( S7 Fig ) . Indeed , in h1h2 and h1h2P , transcript abundance for exons located after the long introns was lower than that of initial exons , indicating that the truncated transcripts account for most of the gene expression decrease observed in the mutants ( Fig 5 and S6 Fig ) . This situation was most evident in AG , further confirming our previous observations ( Fig 5; [26] ) . To validate these findings we performed qPCR assays using RNA from wild-type , h1h2 and h1h2P flower buds , and intronic primers located near the exon2/intron2 junction within the SHP1 and SHP2 loci , and exon1/intron1 junction within STK ( see S2 Table for a list of primers ) . AG was again included as a positive control [26] . In all four cases , relative abundance of qPCR products incorporating intron sequences ( corresponding to aberrant transcripts ) increased significantly in the mutants when compared to wild-type , with the exception of STK intron 1 in the h1h2P mutant ( Fig 5 ) . This was also observed in our RNA-Seq results ( Fig 5 ) , in stark contrast with the dramatic reduction of the corresponding processed transcripts ( see Fig 2J above ) . The levels of D-class gene transcripts were also estimated by measuring correctly spliced products corresponding to exons situated at the 3’ regions , downstream of their respective large introns ( S8D Fig ) . Again , the three genes showed reduced expression in the mutant backgrounds ( S8A–S8C Fig ) conforming to upstream premature transcript termination . Altogether , these results support a role for HUA-PEP activity in intron processing , and further suggests that proper removal of long proximal introns seems to be a key regulatory aspect for AG and the D-class genes during development , critical for functional mRNA formation . It is worth noting that we detected a similar behavior ( abnormally high number of reads in initial introns ) in a few non-MADS-box genes of unknown function in the hua-pep mutant backgrounds ( S9 Fig ) . However , the significance of this result is currently unclear . Our current and previous studies have shown that the HUA-PEP activity targets AG and the D-class genes for correct transcript processing during reproductive growth ( [26]; this work ) . In this scenario , we wanted to independently test the ability and robustness of the HUA-PEP activity to control these genes in developmental contexts in which AG and the D-class genes are not usually expressed . To this end , we made use of the curly leaf ( clf ) mutant . CLF encodes a component of the Polycomb repressive complex PRC2 that prevents the ectopic expression of floral homeotic genes ( such as those forming part of the ABCDE model ) outside the flower . Consequently , floral homeotic genes are ectopically expressed in clf rosette leaves , tissues in which normally floral homeotic genes are not expressed [60 , 61] . We therefore introduced the null clf-29 Col-0 allele [62] into h1h2 . We decided to use this background because , unlike h1h2P plants , h1h2 plants are not fully sterile [26] , thus facilitating the analysis . In addition to the ectopic expression of floral homeotic genes [60] , CLF negatively regulates the transcription of the flowering integrator FLOWERING LOCUS T ( FT ) , and thus , clf plants bolt precociously due in part to the high levels of FT expression [61 , 63] as we observed in clf-29 . FT levels were not significantly different when comparing clf-29 to clf-29 h1h2 triple mutants ( Fig 6A ) . Likewise , the up-regulation in AP1 expression was very similar between clf-29 and clf-29 h1h2 plants ( Fig 6B ) . This aligns with our previous observations showing that the HUA-PEP function has little or no effect on AP1 control [26] . As expected and previously shown , clf-29 plants also showed extremely high levels of AG ectopic expression [60] ( Fig 6C ) . Interestingly , AG transcript abundance was much lower in clf-29 h1h2 leaves ( Fig 6C ) , reinforcing the idea that the HUA-PEP activity acts as a positive regulator of AG function ( [24 , 26]; this work ) . Similarly , we observed that whereas SHP2 transcripts were not detectable in wild-type leaves , they were highly abundant in clf-29 samples and , interestingly , sharply attenuated in clf-29 h1h2 rosette leaves ( Fig 6D ) . We next monitored the relative abundance of SHP2 transcripts retaining intron 2 sequences ( see Fig 5 above ) . This category of transcripts was barely detectable in the wild-type leaves , whereas they accumulated in clf-29 plants ( Fig 6E ) . Remarkably , the relative amount of these aberrant transcripts further increased dramatically in the rosette leaves of clf-29 h1h2 seedlings ( Fig 6E ) . Altogether , these data reinforce our hypothesis that HUA-PEP activity targets AG and the D-class genes for regulation , and it does so regardless of the developmental context . Coordination of transcription and RNA processing is accomplished by the RNAP II CTD , whose phosphorylation status is critical in determining its activity [30 , 31] . In Arabidopsis , the CTD phosphatase FRY2/CPL1 plays a prominent role modulating co-transcriptional pre-mRNA processing thus affecting growth and stress responses [43] . Recently , a paralog of PEP , the KH-domain protein REGULATOR OF GENE EXPRESSION 3 ( RCF3 ) , aka HIGH OSMOTIC STRESS GENE EXPRESSION 5 ( HOS5 ) /SHINY1 ( SHI1 ) / ENHANCED STRESS RESPONSE 1 ( ESR1 ) , has been identified as a CPL1 direct interactor [64–67] . On the other hand , the results shown above argue that the HUA-PEP activity affects D-class genes pre-mRNA processing co-transcriptionally . Therefore , we decided to test whether members of the HUA-PEP activity were capable of associating with CPL1 . To this aim , we carried out in vivo and in planta protein-protein interaction assays . Both bimolecular fluorescence complementation ( BiFC ) assays and yeast-two-hybrid ( Y2H ) assays showed that PEP and CPL1 interact ( Fig 7 and S10 Fig ) . We additionally challenged HUA1 , a non-KH member of the HUA-PEP complex , against CPL1 and found interaction ( Fig 7 and S10 Fig ) . Taken together , these results are consistent with the physical association of CPL1 with PEP and HUA1 proteins , strongly suggesting a functional interplay between HUA-PEP and RNAP II ( likely via its CTD ) activities , which is consistent with HUA-PEP proteins participating in pre-mRNA processing co-transcriptionally , probably influencing the phosphorylation status of the RNAP II CTD .
Development relies on precise mechanisms of gene regulation among which mRNA processing plays a critical role . We previously defined the HUA-PEP activity [26] as a post-transcriptional regulatory module composed by different RBPs that function in vital developmental programs for plant reproduction such as flowering time control and flower morphogenesis , by regulating the expression of FLC and AG , respectively [24 , 26 , 40 , 41 , 45 , 47 , 48] . This study uncovers an additional key contribution of the HUA-PEP activity in plant morphogenesis: the control of ovule development and identity by regulating the expression of the D-class homeotic genes [7 , 8 , 21] . We provide several lines of evidence based on molecular , genetic and genome-wide profiling analyses to support our model . Mutant combinations affecting the HUA-PEP activity displayed homeotic transformations of ovules into floral organ-like structures similar to those described for D-class mutants [7 , 8] . Accordingly , loss of ovule identity was accompanied by a reduction in SHP1 , SHP2 and STK functional mRNAs . This was most obvious in the strongest h1h2P background , in which extremely reduced expression of the three genes nicely correlated with the high penetrance of the ovule homeotic conversions . The overlapping expression patterns between the D-class identity genes and the HUA-PEP activity genes is consistent with this regulation [42 , 49] ( this study ) . Furthermore , by using the clf mutant background we have shown that the HUA-PEP activity retains its ability to regulate its target genes even when they are expressed ectopically in leaves . The absence of valve margin in h1h2P gynoecia ( S2 Fig ) nicely fits with the down-regulation of SHP1 and SHP2 in our RNA expression assays ( Fig 2J ) [9] . The SHP genes also function in style formation and apical carpel fusion in concert with CRC [54] . Hence it is tempting to speculate that loss of SHP activity , together with the abatement of CRC expression ( this study ) , contributes to the distorted open gynoecia previously described in hua-pep mutants [26] . Despite not being ovule-specific , our RNA-Seq study reveals that important genes critical for ovule patterning and function are misregulated in hua-pep mutant backgrounds . For instance , downregulation of NZZ/SPL and VDD is consistent with our model since they are both directly activated by AG and the D-class genes [49 , 56] . Moreover , NZZ/SPL promotes INO and PHABULOSA ( PHB ) expression in the ovule , and lack of NZZ/SPL perturbs the coordination between proximal-distal and adaxial-abaxial growth , also contributing to the appearance of longer funiculi ( reviewed in [19] ) . In addition to homeotic transformations , low levels of NZZ/SPL may be related to ovule abortion in hua-pep mutants ( Fig 8 and see below ) since ovule formation is arrested in nzz/spl mutants early in development in Arabidopsis and tomato [68] . Likewise , VDD is required for proper female gametophyte development [49] like other genes that appeared down-regulated in our mutants , such as AGL91 , AGL87 and AGL77 [69] . Finally , our qPCR and RNA-Seq assays allowed us to verify mRNA processing defects of D-class genes in hua-pep mutants , very similar to those previously described for AG ( [24 , 26] and see below ) . Aside from their idiosyncratic protein modules , several MADS-box genes , including AG , STK , FLC or its close relative FLOWERING LOCUS M ( FLM ) , contain large introns that house critical regulatory cis-elements conserved across species [70–77] . Lengthy introns , however , may increase the risk of aberrant mRNA processing due to cryptic signals . In yeasts , splicing of nascent transcripts was found to coincide with intron exit from RNAP II [78] , and a recent study in Arabidopsis revealed the presence of numerous processing factors in the RNAP II elongation complex [79] . Given the structural similarity of SHP1 , SHP2 , and STK to AG , we conceived a role for the HUA-PEP activity in the maintenance of D-function by affecting processing of their corresponding pre-mRNAs , as previously shown for AG [26] . This idea gained support from our genome-wide data . Indeed , our RNA-Seq studies in strong hua-pep mutants corroborated the reduction of presumptive functional mRNAs for AG and D-class genes and the concomitant accumulation of aberrant transcripts ( prematurely terminated within the large introns ) . These observations were validated by our PCR assays and , remarkably , were also substantiated by the analysis of ectopic expression in clf leaves . Moreover , we detected genes that , in the mutant backgrounds , also accumulated transcripts prematurely terminated inside large introns , which suggests that the regulatory action of the HUA-PEP activity might include functions other than securing the correct expression of the AG-clade members ( S9 Fig ) . This is an issue worth to be explored in future studies . The results discussed above fit our previous model in which the HUA-PEP factors facilitate transcription elongation by preventing accessibility of the processing machinery to intronic cryptic signals in the nascent RNA , thus avoiding the production of non-functional transcripts [26] . In eukaryotes , coordinating transcription and RNA processing is an efficient mechanism to optimize gene expression during development . Co-transcriptional RNA modifications are surveilled by the CTD of the RNAP II large subunit , whose activity largely depends on its phosphorylation status [30 , 32] . We have shown that HUA1 and PEP proteins are binding partners of the phosphatase CPL1 , a critical CTD regulator [43] . These results might support the participation of the HUA-PEP activity during co-transcriptional regulation of gene expression . A possible mechanism by which the HUA-PEP proteins protect nascent transcripts might involve their interaction with particular RNA sequence and/or structural motifs . Alternatively , but not mutually exclusive , the HUA-PEP proteins might modulate the activity of CPL1 , and perhaps other phosphatases , critically affecting CTD phosphorylation and mRNA co-transcriptional modifications . Pre-mRNA splicing and 3’ end maturation occur co-transcriptionally , and the phosphorylated CTD bridges these processes by binding components of both processing machineries [29 , 30] . This is important because CTD phosphorylation increases protein accessibility to the elongation complex , and compactness may prevent imprecise spatiotemporal recruitment of processing factors [80] . Another possibility is that the HUA-PEP proteins might interact directly with the CTD to regulate proper incorporation of such processing factors . In our protein-protein assays PEP showed a strong affinity for CPL1 . PEP belongs to the group of KH proteins defined by mammal hnRNP K and PolyC binding protein ( PCBP ) family [42 , 81 , 82] . The KH domain can bind DNA and RNA , and can serve as a platform for protein-protein interactions [81] . RCF3 , a previously identified CPL1 interactor , also contains KH domains similar to those of PEP [42 , 65] . Interestingly , the rcf3 mutant displays altered polyadenylation site selection and intron retention [64 , 66] . Thus , it might be worth exploring the connection between RCF3 and the HUA-PEP gene activity . However , those studies go beyond the scope of the current work . Alternative splicing and polyadenylation are widely accepted as basic mechanisms that add complexity , regulatory robustness and flexibility to animal and plant genomes [28 , 35] . Additionally , in animal systems , regulation of prematurely processed transcripts is emerging as an important checkpoint to modulate developmental and adaptive decisions in a tight cross-talk with splicing [83–87] . KH-domain proteins seem to play prominent roles in these two regulatory processes . Thus , knock-down of mammalian PCBPs , KH-domain proteins structurally related to PEP , favors usage of cryptic intronic processing sites and the accumulation of non-effective transcripts for pre-mRNAs and long non-coding RNAs ( lncRNA ) [81 , 88 , 89] . Remarkably , binding of the KH-domain splicing factor Sam68 to an intronic polyadenylation site of the Aldehyde Dehydrogenase 1A3 ( Aldh1a3 ) gene prevents its recognition and premature transcript termination , thus promoting self-renewal of mouse neural progenitor cells rather than differentiation [90] . In plants , flowering time regulation provides examples of developmental switches based on the use of intronic polyadenylation sites . Thus , the levels of FCA and FPA functional proteins , two FLC regulators , are controlled through a negative feedback by premature polyadenylation in their long third and first intron , respectively [91 , 92] . Intronic polyadenylation in the large first intron of the floral repressor FLM was also detected in the wild type , being suggested as a mechanism for modulating FLM transcript levels [93] , thus contributing to adapt floral timing to optimal conditions . In fact , this type of regulation has been recently demonstrated to modulate ambient temperature-dependent flowering in natural Arabidopsis accessions [76 , 77] . Although at very low levels , prematurely terminated AG and D-class gene transcripts are also present in the wild type ( [24 , 26]; this study ) and , beyond the floral transition , reproductive development can be perturbed by adverse circumstances that reduce fertility . For instance , ovule abortion increases under stress [94 , 95] . The hua-pep mutants also show ovule abortions , indicating that compromising the HUA-PEP activity affects ovule viability without altering identity ( Fig 8 ) . Indeed , shp1 shp2 stk triple mutant plants are virtually sterile . Although not all of their ovules are homeotically transformed , they show instead numerous abortions [8] . AG and D-class genes are fundamental to ovule identity acquisition but they are also necessary to activate gene functions required for further development of maternal and gametophytic tissues [49 , 96–98] . Stress conditions might impinge upon the HUA-PEP activity , altering RNA processing of AG and D-class genes , thus affecting flower and ovule development ( Fig 8 ) . This could contribute to fine-tune the allocation of resources for reproduction and stress tolerance . Exploring this scenario surely deserves further investigation .
This work was carried out with the Arabidopsis thaliana Columbia ( Col-0 ) accession as the wild type . Strains previously obtained in Ler , hen4-2 [24] , hua1-1 and hua2-1 [39] , were backcrossed at least five times into Col-0 before any further experiment . Other lines used in this study were pep-4 and PEP::GUS [42] , flk-2 [41] , hua2-4 [45]; hua2-7 [47] , 35S::PEP [46] , SHP2::GUS [50] , and STK::GUS [8] . clf-29 ( SALK_021003 ) was obtained from the NASC . Information about all primers used in this work and molecular genotyping can be found in S2 Table . Plants were grown in MS plates or soil as previously described [42] . Light microscopy and scanning electron microscopy ( SEM ) were performed as previously described [42] . Samples were also cleared with Hoyer solution [99] for 30 min and observed under differential interference contrast ( DIC ) optics . All GUS staining assays were performed in homozygous lines , essentially as described [42 , 100 , 101] . Light microscopy samples were photographed in a Nikon E800 microscope equipped with a Nikon Digital Camera DXM1200F ( operated by the ACT-1 2 . 70 program ) . For quantitative reverse transcriptase-polymerase chain reaction ( qPCR ) , 5 μg of total RNA was extracted from young flower buds ( until stage 9 ) or 10-day-old rosettes , treated with DNase I , and used for cDNA synthesis with an oligo ( dT ) primer and RevertAid Reverse Transcriptase ( Thermo Fisher ) following the manufacturer’s instructions . Subsequently , for each qPCR reaction , 0 . 5 μl of the cDNA was used as template . Relative changes in gene expression levels were determined using the LightCycler 1 . 5 system with the LightCycler FastStart DNA amplification kit according to the manufacturer ( Roche Diagnostics ) . RNA levels were normalized to the constitutively expressed gene OTC ( ORNITHINE TRANSCARBAMYLASE ) , and the corresponding wild-type levels , as previously reported [26] . Each experiment was undertaken using three biological replicates with three technical replicates each . Statistical significance was estimated by the Student’s t-test according to [102] ( * P < 0 . 05 , ** P < 0 . 01 , *** P < 0 . 001 ) . 3’ rapid amplification of cDNA ends ( 3’ RACE ) was conducted as previously reported [26 , 103] . 5 μg of young flower bud total RNA was reverse transcribed using Maxima Reverse Transcriptase and the adaptor oligo d ( T ) -anchor ( kit 5’/3’ RACE , Roche Diagnostics ) as a primer . Then , SHP2 cDNAs were amplified with Phusion High-Fidelity DNA Polymerase ( Thermo Scientific ) using forward primers situated in the exon 2 ( S2 Table ) and the PCR anchor ( Roche Diagnostics ) as a reverse primer hybridizing with the adaptor sequence , thus ensuring that only polyA-containing sequences were amplified . Amplified products were cloned into pSC-A plasmids and sequenced with M13F and M13R primers . Sequences were analyzed using CLUSTAL-W aligning [104] . Library construction was performed using the TruSeq Stranded mRNA Library Preparation Kit ( Illumina ) and the resulting fragments were sequenced in the lllumina Hiseq 2500 platform , using 100 bp paired-end reads , at StabVida ( Caparica , Portugal ) . The bioinformatic analysis was performed as described in [105] . Paired-end reads were aligned to the TAIR10 version of the Arabidopsis thaliana genome sequence and annotation ( https://www . arabidopsis . org/ ) using Tophat version 2 . 2 . 1 [106] and Bowtie 2 version 2 . 2 . 4 . 0 [107] , feeding the program with the coordinates of TAIR10 gene models in a GFF ( General Feature Format ) file ( using option -G ) and discarding all discordant read mappings ( with options—no-discordant and—no-mixed ) . Transcript levels were quantified for these gene models using the cuffdiff program of the Cufflinks version 2 . 2 . 1 package [106] after filtering out all reads mapping to rRNA , tRNA , snRNA and snoRNA genes , whose coordinates were supplied in a separate GFF file ( using option -M ) . Two biological replicates were used for each genotype . The resulting read alignments , supplied as files in BAM format , were visualized using Integrative Genomics Viewer ( IGV ) [108] and Tablet software [109] . For the identification of overrepresented GO terms , we used the agriGO online tools ( http://bioinfo . cau . edu . cn/agriGO/; [110] ) using a selected set of genes ( including those marked “OK” by Cufflinks ) as the customized annotated reference , as previously described [111] . BiFC and Y2H were performed as previously described in [26] and [112] . For BiFC , the corresponding coding sequences were amplified from their respective cDNAs using the proof-reading Phusion ( New England Biolabs , Inc . ) polymerase ( see S2 Table for primers ) and cloned into pBJ36-SPYNE and/or pBJ36-SPYCE plasmids , containing N-terminal ( nt ) and C-terminal ( ct ) halves of the yellow fluorescent protein ( YFP ) , respectively ( YFPnt and YFPct ) [113] . The resulting 35S::SPYNE and 35S::SPYCE cassettes were sequenced and then cloned into the T-DNA binary vectors pGreen0229 and pGreen0179 [114] , respectively . Transformed AGL-0 Agrobacterium tumefaciens cells were used to infect Nicotiana benthamiana leaves . YFP reconstituted fluorescence was visualized 72 h after inoculation under a Nikon Eclipse TE2000-U epifluorescence microscope . The reciprocal BiFC assays were also performed obtaining the same results as shown in Fig 7 and S10 Fig , thus endorsing specificity of the interactions . As a positive control we used CPL1-RCF3 assays , previously shown to associate [64–66] . As negative controls , Nicotiana leaves were co-infiltrated with the corresponding recombinant YFPct construct and the empty YFPnt version . As additional negative interactions we assayed PEP , HUA1 and RCF3 against a non-related ARF transcription factor ( see Fig 7 and S10 Fig , and reference [115] . For yeast two-hybrid assays , the cDNA PCR amplicons for PEP , HUA1 , CPL1 and RCF3 genes were generated using the corresponding primers ( S2 Table ) and cloned into the pB42AD ( +Trp ) and pGilda ( +His ) vectors via Gibson DNA assembly procedure [116] . The integrity of the resulting pGilda and pB42AD constructs was checked by sequencing . The yeast strain EGY48 ( -Ura ) was cotransformed with the corresponding combinations of pGilda and pB42AD constructs . Empty vectors were used as negative controls . Positive colonies were selected on solid media ( -Ura , -His , -Trp +glucose ) . Induction for testing protein-protein association was assayed growing the resulting yeast strains on plates in the presence of galactose and raffinose ( DB Falcon ) . X-gal was used for colorimetric assays on plates ( SIGMA ) , and ONPG ( 2-Nitrophenyl β-D-galactopyranoside , SIGMA ) for β-galactosidase liquid experiments . The Clontech protocol book was followed for all these procedures . | Plant ovules are crucial reproductive structures in which the female gametophyte develops , giving rise to seeds after fertilization . Global food supply depends mainly on seed production , thus understanding the underlying regulatory mechanisms that orchestrate ovule development is vitally important . The establishment of ovule identity is a key process that largely relies on the MADS-box transcription factors that define the floral D-function . In Arabidopsis the D-class is represented by SHP1 , SHP2 and STK , the closest paralogs of AG , the master regulator of floral morphogenesis . Previous studies indicated that the post-transcriptional regulatory module termed “HUA-PEP gene activity” facilitates AG pre-mRNA maturation to secure AG function . Here we show that the HUA-PEP activity also targets D-class gene expression for the correct specification of ovule identity . Homeotic transformations of ovules into flower organ-like structures occur in plants in which the HUA-PEP function is compromised . In such backgrounds , prematurely terminated transcripts of SHP1 , SHP2 and STK accumulate at the expense of their respective functional transcripts . The data presented here provide compelling evidence for considering the HUA-PEP proteins as part of the co-transcriptional regulatory machinery involved in coordinating transcription and pre-mRNA processing , and further highlights the importance of precise RNA regulation for correct plant reproductive morphogenesis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"plant",
"anatomy",
"gene",
"regulation",
"ovules",
"brassica",
"plant",
"science",
"model",
"organisms",
"experimental",
"organism",
"systems",
"plants",
"flowering",
"plants",
"research",
"and",
"analysis",
"methods",
"arabidopsis",
"thaliana",
"genome",
"complexity",... | 2018 | Ovule identity mediated by pre-mRNA processing in Arabidopsis |
Phenotypical variability in the absence of genetic variation often reflects complex energetic landscapes associated with underlying gene regulatory networks ( GRNs ) . In this view , different phenotypes are associated with alternative states of complex nonlinear systems: stable attractors in deterministic models or modes of stationary distributions in stochastic descriptions . We provide theoretical and practical characterizations of these landscapes , specifically focusing on stochastic Slow Promoter Kinetics ( SPK ) , a time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes like DNA methylation and chromatin remodeling . In this case , largely unexplored except for numerical simulations , adiabatic approximations of promoter kinetics are not appropriate . In contrast to the existing literature , we provide rigorous analytic characterizations of multiple modes . A general formal approach gives insight into the influence of parameters and the prediction of how changes in GRN wiring , for example through mutations or artificial interventions , impact the possible number , location , and likelihood of alternative states . We adapt tools from the mathematical field of singular perturbation theory to represent stationary distributions of Chemical Master Equations for GRNs as mixtures of Poisson distributions and obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system . As illustrations , the theory is used to tease out the role of cooperative binding in stochastic models in comparison to deterministic models , and applications are given to various model systems , such as toggle switches in isolation or in communicating populations , a synthetic oscillator , and a trans-differentiation network .
In this paper , a GRN is formally defined as a set of nodes ( genes ) that are connected with each other through regulatory interactions via the proteins that the genes express . The regulatory proteins are called transcription factors ( TFs ) . A TF regulates the expression of a gene by reversibly binding to the gene’s promoter and by either enhancing expression or repressing it . The formalism we employ in order to describe GRNs at the elementary level is that of Chemical Reaction Networks ( CRNs ) [53] . A CRN consists of species and reactions , which we describe below . The dynamics of the network refers to the manner in which the state evolves in time , where the state Z ( t ) ∈ Z ⊂ Z ≥ 0 | S | is the vector of copy numbers of the species of the network at time t . The standard stochastic model for a CRN is that of a continuous-time MC . Let the state be Z ( t ) = z ∈ Z , where Z is the state-space . The relevant background is reviewed in S1 Text §1 . 1 . Let pz ( t ) = Pr[Z ( t ) = z|Z ( 0 ) = z0] be the stationary PMF for any given initial condition z0 . Its time evolution is given by the CME . Since our species are either gene species or protein species , we split the stochastic process Z ( t ) into two subprocesses: the gene process D ( t ) and the protein process X ( t ) , as explained below . For each gene we define one process Di such that Di ( t ) ∈ Bi . Di ( t ) = j if and only the promoter configuration is encoded by j ∈ Bi . Collecting these into a vector , define the gene process D ( t ) ≔ [D1 ( t ) , … , DN ( t ) ]T where D ( t ) ∈ ∏ i = 1 N B i . The ith gene can be represented by |Bi| states , so L ≔ ∏ i = 1 N | B i | is the total number of promoter configurations in the GRN . With abuse of notation , we write also D ( t ) ∈ {0 , ‥ , L − 1} in the sense of the bijection between {0 , ‥ , L − 1} and ∏ i = 1 N B i defined by interpreting D1…DN as a binary representation of an integer . Hence , d ∈ {0 , ‥ , L − 1} corresponds to ( d1 , … , dN ) ∈ B1 × ‥ × BN and we write d = ( d1 , ‥ , dN ) . Since each gene expresses a corresponding protein , we define X i 1 ( t ) ∈ Z ≥ 0 , i = 1 , ‥ , N protein processes . If the multimerized version of the ith protein participates in the network as an activator or repressor then we define Xic ( t ) as the corresponding multimerized protein process , and we denote Xi ( t ) ≔ [Xi1 ( t ) , Xic ( t ) ]T . If there is no multimerization reaction then we define Xi ( t ) ≔ Xi1 ( t ) . Since not all proteins are necessarily multimerized , the total number of protein processes is N ≤ M ≤ 2N . Hence , the protein process is X ( t ) = [ X 1 T ( t ) , ‥ , X N T ( t ) ] T ∈ Z ≥ 0 M and Z = Z ≥ 0 M × ∏ i = 1 N B i .
It is crucial to our analysis to represent the linear system of differential equations given by the CME as an interconnection of weakly coupled linear systems . To this end , we present the appropriate notation in this subsection . Consider the joint PMF: pd , x ( t ) = Pr[X ( t ) = x , D ( t ) = d] , which represents the probability at time t that the protein process X takes the value x ∈ Z + M and the gene process D takes the value d ∈ {0 , ‥ , L − 1} . Recall that x is a vector of copy numbers for the protein processes while d encodes the configuration of each promoter in the network . Then , we can define for each fixed d: p d ( t ) ≔ [ p d x 0 ( t ) , p d x 1 ( t ) , … . ] T , representing the vector enumerating the joint probabilities for all values of x and for a fixed d , where x0 , x1 , ‥ is an indexing of Z ≥ 0 M . Note that pd ( t ) can be thought of as an infinite vector with respect to the aforementioned indexing . Finally , let p ( t ) ≔ [ p 0 ( t ) T , … , p L − 1 T ( t ) ] T , ( 1 ) representing a concatenation of the vectors pd , x ( t ) for d = 0 , ‥ , L − 1 . Note that p ( t ) is a finite concatenation of infinite vectors . The joint stationary PMF π ¯ is defined as the following limit , which we assume to exist and is independent of the initial PMF: π ¯ = lim t → ∞ p ( t ) . Note that π ¯ depends on ε . Consider a given GRN . The CME is defined over a countable state space Z . Hence , the CME can be interpreted as an infinite system of differential equations with an infinite infinitesimal generator matrix Λ which contains the reaction rates ( see S1 Text §1 . 1 ) . Consider partitioning the PMF vector as in ( 1 ) . Recall that reactions have been divided into two sets: slow gene reactions and fast protein reactions . This allows us to write Λ as a sum of a slow matrix ε Λ ^ and a fast matrix Λ ˜ , which we call a fast-slow decomposition . Furthermore , Λ ˜ can be written as a block diagonal matrix with L diagonal blocks which correspond to conditioning the MC on a specific gene state d . This is stated in the following basic proposition ( see S1 Text §2 . 1 for the proof ) : Proposition 1 . Given a GRN . Its CME can be written as p ˙ ( t ) = Λ ε p ( t ) = ( Λ ˜ + ε Λ ^ ) p ( t ) , ( 2 ) where p ( t ) = [ p 0 T ( t ) , ‥ , p L − 1 T ( t ) ] T , and Λ ˜ = d i a g [ Λ 0 , ‥ , Λ L − 1 ] ( 3 ) where Λ ˜ is the fast matrix , Λ ^ is the slow matrix , and Λ0 , ‥ , ΛL−1 are stochastic matrices . For each d , consider modifying the MC Z ( t ) defined in the previous section by replacing the stochastic process D ( t ) by a deterministic constant process D ( t ) = d . This means that the resulting MC does not describe the gene process dynamics , it only describes the protein process dynamics conditioned on d . Henceforth , we refer to the resulting MC as the MC conditioned on d . The infinitesimal generator of a MC conditioned on d is denoted by Λd , and is identical to the corresponding block on the diagonal of Λ ˜ as given in ( 3 ) . In other words , fixing D ( t ) = d ∈ {0 , ‥ , L − 1} , the dynamics of the network can be described by a CME: p˙X|d=ΛdpX|d , ( 4 ) where pX|d is a vector that enumerates the conditional probabilities px|d = Pr[X ( t ) = x|D ( t ) = d] for a given d . The conditional stationary PMF is denoted by: π X | d ( J ) = lim t → ∞ p X | d ( t ) , where ( J ) refers to the fact that it is joint in the protein and multimerized protein processes . Note that π X | d ( J ) is independent of ε . This notion of a conditional MC is useful since , at the SPK limit , D ( t ) stays constant . It can be noted from ( 3 ) that when ε = 0 the dynamics of pd decouples and becomes independent of p d ˜ , d ˜ = 0 , ‥ , L − 1 , d ˜ ≠ d . We show below that each conditional MC has a simple structure . Fixing the promoter configuration D ( t ) = d = ( d1 , ‥ , dN ) , the network consists of uncoupled birth-death processes . So for each di , the protein reactions of production and dimerization corresponding to the ith promoter can be written as follows without multimerization: ∅ ⇌ k − i k i d i X i , where the subscript idi refers to the production kinetic constant corresponding to the configuration species D d i i , or , if there is a multimerization reaction , it takes the form: ∅ ⇌ k − i k i d i X i , n i X i ⇌ β − i β i X i c . Note that the stochastic processes Xi ( t ) , i = 1 , ‥ , N conditioned on D ( t ) = d are independent of each other . Hence , the conditional stationary PMF π X | d ( J ) can be written as a product of stationary PMFs and the individual stationary PMFs have Poisson expressions . The following proposition gives the analytic expression of the conditional stationary PMFs: ( see S1 Text §2 . 2 for proof ) . Proposition 2 . Fix d ∈ {0 , ‥ , L − 1} . Consider ( 4 ) , then there exists a conditional stationary PMF π X | d ( J ) and it is given by π X | d ( J ) ( x ) = ∏ i = 1 N π X | d i ( x i ) , ( 5 ) where π X | d i ( J ) ( x i ) = { P ( x i 1 , x i 2 ; k i d i k − i , k i d i n i β i n i ! k − i n i β − i ) : n i > 1 P ( x i ; k i d i k − i ) : n i = 1 , ( 6 ) where ( J ) refers to the joint PMF in multimerized and non-multimerized processes , xi1 refers to the copy number of Xi , while xi2 refers to the copy number of Xic , P ( x ; a ) ≔ a x x ! e − a , P ( x 1 , x 2 ; a 1 , a 2 ) ≔ a 1 x 1 x 1 ! a 2 x 2 x 2 ! e − a 1 − a 2 . Remark 1 . The conditional PMF in ( 5 ) is a joint PMF in the protein and multimerized protein processes . If we want to compute a marginal stationary PMF for the protein process only , then we average over the multimerized protein processes Xic , i = 1 , ‥ , N to get a joint Poisson in N variables . Hence , the formulae ( 5 ) - ( 6 ) can be replaced by: π X | d ( x ) ≔ ∑ i = 1 M − N ∑ x i 2 = 0 ∞ π X | d ( J ) ( x ) = ∏ i = 1 N P ( x i ; k i d i k − i ) , ( 7 ) where M − N is the number of n-merized protein processes , and πX|d is the marginal stationary PMF for the protein process . Recall the slow-fast decomposition of the CME in ( 2 ) and the joint stationary PMF π ¯ . In order to emphasize the dependence on ε we denote π ¯ ε ≔ π ¯ ( ε ) . Hence , π ¯ ε is the unique stationary PMF that satisfies Λ ε π ¯ ε = 0 , πε > 0 , and ∑ z π z ε = 1 , where the subscript denotes the value of the stationary PMF at z . Our aim is to characterize the stationary PMF as ε → 0 . Writing π ¯ ε as an asymptotic expansion to first order in terms of ε , we have π ¯ ε = π ¯ ( 0 ) + π ¯ ( 1 ) ε + o ( ε ) . ( 8 ) Our aim is to find π ¯ ( 0 ) . We use singular perturbations techniques to derive the following theorem ( see S1 Text §2 . 3 ) : Theorem 3 . Consider a given GRN with L promoter states with the CME ( 2 ) . Writing ( 8 ) , then the joint stationary PMF π ¯ ≔ lim ε → 0 + π ¯ ε can be written as: π ¯ ( x , d ) = ∑ d = 0 L − 1 λ d π ¯ X | d ( x , d ) , where λ = [λ0 , ‥ , λL−1]T is the principal normalized eigenvector of: Λ r ≔ d i a g [ 1 T , ‥ , 1 T ] Λ ^ [ π ¯ X | 0 π ¯ X | 1 … π ¯ X | L − 1 ] , ( 9 ) where π ¯ X | 0 , ‥ , π ¯ X | L − 1 are the extended conditional stationary PMFs defined as: π ¯ X | d ( x , d ) = π X | d ( x ) , π ¯ X | d ( x , d ′ ) = 0 when d′ ≠ d . The result characterizes the stationary solution of ( 2 ) which is a joint PMF in X and D . However , we are particularly interested in the marginal stationary PMF of the protein process X and the marginal stationary PMF of the non-multimerized protein process , since these PMFs are typically experimentally observable . Therefore , we can use Remark 1 to write the stationary PMF as mixture of L Poisson distributions with weights { λ d } d = 0 L − 1: Corollary 4 . Consider a given GRN with L genes with the CME ( 2 ) . Writing ( 8 ) , let πX|0 , … , πX|L−1 be the conditional stationary PMFs of Λ0 , … , ΛL−1 , where explicit expressions are given in ( 5 ) . Then , we can write the following π ( J ) ( x ) ≔ lim ε → 0 + lim t → ∞ Pr [ X ( t ) = x ] = ∑ d = 0 L − 1 λ d π X | d ( J ) ( x ) , where λ = [λ0 , ‥ , λL−1]T is as given Theorem 3 . Furthermore , the marginal stationary PMF of the non-multimerized protein process can be written as: π ( x ) ≔ ∑ d = 0 L − 1 λ d π X | d ( x ) = ∑ d = 0 L − 1 λ d ∏ i = 1 N P ( x i ; k i d i k − i ) . ( 10 ) Remark 2 . In the remainder of the Results section , when we refer to the “stationary PMF” we mean the marginal stationary PMF of the non-multimerized protein process given in ( 10 ) . Remark 3 . If a mode is defined as a local maximum of a stationary PMF , then this does not necessarily imply that the stationary PMF has L modes since the peak values of two Poisson distributions can be very close to each other . In the remainder of the paper we will call each Poisson distribution in the mixture as a “mode” in the sense that it represents a component in the mixture PMF . The number of local maxima of a PMF can be found easily given the expression ( 10 ) . The computation of the weighting vector λ in Theorem 3 requires computing the L × L matrix Λr in ( 9 ) which can be interpreted as the infinitesimal generator of an L-dimensional MC . The expression in ( 9 ) involves evaluating the product of infinite dimensional matrices . Since the structure of the GRN and the form of the conditional PMF in ( 5 ) are known , an easier algorithm to compute Λr for our GRNs is given in Proposition SI-2 in S1 Text . The algorithm provides an intuitive way to interpret Theorem 3 and can be informally described as follows . Let D ( t ) = d , the algorithm implies that each binding reaction of the form: TF + D d i i → α D d i ′ i , gives the rate α E [ T F | D = d ] , where E denotes mathematical expectation . Hence it corresponds to a reaction of the following form in the reduced-order MC: D d i i → α E [ TF | D = d ] D d i ′ i . ( 11 ) Using Proposition 2 , we can write: ( See S1 Text §2 . 4 ) E [ TF | D = d ] = α n i ! β i β − i ( k i d i k − i ) n i . ( 12 ) Theorem 3 and Corollary 4 have been stated for GRNs that have gene expression blocks of the form given in Fig 2 . Nevertheless , the same results can also be stated for a larger class of networks . The generalized class consists of GRNs with weakly reversible deficiency zero conditional Markov chains . The stationary PMF for networks in this class can also be expressed as a mixture of Poisson PMFs . This enables us to include networks with hetero-dimerization , diffusion and multi-step multi-merization in our study . The full details are in S1 Text §4 , and a diffusion-based interconnection of toggle switches will be studied as an example . The simplest network is the unregulated gene which is used for transcriptional bursting [17] and studied using time-scale separation in [32 , 63] . Consider: D 0 ⇌ ε α − ε α D 1 D 1 → k X + D 1 , X → k − 0 . ( 13 ) Referring to Fig 2 , we identify a single gene block with two states . Using ( 5 ) , the conditional stationary PMFs are two Poissons at 0 and k/k− , and the stationary PMF is a bimodal mixture of them with weights α−/ ( α + α− ) , α/ ( α + α− ) , respectively ( See S1 Text §3 . 1 ) . In the case of fast promoter kinetics , the resulting stationary PMF is a Poisson with mean α α + α − k k − which coincides with the deterministic equilibrium . Although both stochastic models share the mean , the stationary PMFs and their variances differ drastically . Fig 3 shows the transition from fast to slow promoter kinetics using the exact solution [64] and compares it to the predicted mixture of two Poissons . A TF is said to be cooperative if it acts only after it forms a dimer or a higher-order n-mer that binds to the gene’s promoter [54] . In standard deterministic modelling , a cooperative activation changes the form of the quasi-steady state activation rate from a Michaelis-Menten function into a Hill function . Cooperativity is often necessary for a network to have multiple equilibria in some kinetic parameter ranges . For example , a non-cooperative self-activating gene can only be mono-stable , while its cooperative counterpart can be multi-stable for some parameters . Corollary 4 and ( 12 ) show that cooperativity plays in the context of SPK a role that is very different from the deterministic setting . This is since the stationary PMF is a mixture of L Poisson processes ( 7 ) which are independent of the TFs’ cooperativity indices and ratios . In the non-cooperative case , a certain mode can be made more probable only by changing either the location of the mode or the dissociation ratio ( the ratio of the binding to unbinding kinetic constants ) . On the other hand , a multimerized TF gives extra tuning parameters , namely the multimerization ratio and the cooperativity index . Hence , a certain mode can be made more or less probable by modifying either of them without changing the location of the peaks or the dissociation ratio . In order to illustrate the above idea , we analyze a self-regulating gene with SPK with and without cooperativity . Consider a non-cooperative self-regulating gene . The unbound and bound gene states are D0 , D1 with k0 , k1 production rates , respectively . The network is activating if k1 > k0 , and repressing otherwise . Similar to the previous example , the stationary PMF is a mixture of two Poissons centered at k0/k− , k1/k− with weights α−/ ( αρ + α− ) , αρ/ ( αρ + α− ) , respectively , where ρ = E [ X | D = 0 ] = k 0 / k − . ( Refer to ( 12 ) and S1 Text §3 . 2 ) . Next , consider the cooperative counterpart with dimerization rates β , β− . The stationary PMF stays the same except for ρ = E [ X 2 | D = 0 ] = k 0 2 β / ( 2 k − 2 β − ) . Hence , in both cases , the PMF has modes at k1/k− , k0/k− , where the weight of the first mode is proportional to ρ which can be used in order to tune the weights freely while keeping the modes and the dissociation ratio unchanged . For instance , the PMF can be made effectively unimodal with a sufficiently high ρ . A toggle switch is a basic GRN that exhibits deterministic multi-stability . It has two stable steady states and can switch between them with an external input or via noise . The basic design is a pair of two mutually repressing genes as in Fig 5a . The ideal behavior is that only one gene is “on” at any moment in time . The network consists of two identical genes whose expressed proteins X , Y act as TFs for each other ( The general toggle switch is discussed in S1 Text §3 . 3 ) . Each gene has dissociation ratio α/α_ , production ratio k0/k_ for the unbound state only , multimerization ratio β/β_ , and cooperativity index n . Using the algorithm given in Proposition SI-2 in S1 Text , the reduced-order Markov chain infinitesimal generator is: Λ r = [ − α ρ − α ρ α − α − 0 α ρ − α − 0 α − α ρ 2 0 − α − α − 0 0 0 − α − − α − ] , ( 14 ) where ρ = k 0 n k − n β n ! β − . Notice immediately that the transition rates towards the configuration ( 1 , 1 ) are zero , which implies that the weight of the mode corresponding to ( 1 , 1 ) is zero . Hence , we have three modes only . The weights corresponding to the modes can be found as the principal eigenvector of Λr as given in Corollary 4 . Hence , the stationary distribution for X , Y is: π ( x , y ) = 1 2 α α − ρ + 1 ( P ( y ; k 0 k − ) P ( x ; k 0 k − ) + α α − ρ P ( y ; k 0 k − ) δ ( x ) + α α − ρ P ( x ; k 0 k − ) δ ( y ) ) . Hence , we get that the PMF has three modes only at ( 0 , k 0 k − ) , ( k 0 k − , 0 ) , k 0 k − , ( k 0 k − ) with relative weights α α − ρ , α α − ρ , 1 , respectively . Since the stationary PMF has three modes , it deviates from the ideal behavior of a switch where at most two stable steady states , under appropriate parameter conditions , are possible . Nevertheless , a bimodal PMF can be achieved by minimizing the weight of the first mode at ( k 0 k − , k 0 k − ) . If we fix α/α_ , then this can be satisfied by tuning n , β/β_ to maximize ρ . Choosing higher cooperativity indices , subject to n < k0/k_ , achieves this also . For instance , a standard asymmetric design [65] uses cooperativity indices 2 , 3 . Fig 5 depicts the effect of cooperativity on achieving the desired behavior with the same dissociation constant and production ratios . Notice that cooperativity allows us to minimize or maximize the weight of the ( high , high ) mode by tuning the dimerization ratio . The toggle switch has three modes regardless of the cooperativity index . This is unlike the deterministic model where only one positive stable state is realizable with non-cooperative binding , and two stable steady states are realizable with cooperative binding . S1 Text §3 . 3 contains further Monte-Carlo simulations that show that the predicted third mode appears with a two-to-one time scale separation between the slow gene reactions and the fast protein reactions . Experimentally , a recent study has reported that the CRI-Cro toggle switch exhibits the third ( high , high ) mode and the authors proposed SPK as a contributing mechanism [66] , a behavior predicted by our results . Consider N copies of the toggle switch defined in the previous section ( we consider switches with identical genes for simplicity ) . They express proteins Xi , Yi , i = 1 , ‥ , N . Let us assume that the switches are interconnected via the diffusion of the proteins among cells , modeled with a diffusion coefficient Ω as: X i ⇌ Ω Ω X j , Y i ⇌ Ω Ω Y j , i ≠ j , i , j = 1 , ‥ , N . We view this model as a simplification of a more complex quorum sensing communication mechanism , in which orthogonal AHL molecules are produced by cells and act as activators of TFs in receiving cells , as analyzed for example in [46] . Fig 6a depicts a block diagram of such a network . For a deterministic model , there exists a parameter range for which all toggle switches synchronize into bistability for sufficiently high diffusion coefficient [46] . This implies each switch in the network behaves as a bistable switch , and it converges with all the other switches to the same steady-states . Our aim is to analyze the stochastic model at the limit of SPK and compare it to the deterministic model . This network is not in the form of the class of networks in Fig 1 . Nevertheless , we show in S1 Text §4 that our results can be generalized to networks with weakly reversible deficiency zero conditional MCs . There are 4N conditional MCs , and using Theorem 3 , the stationary PMF is a mixture of 4N − 1 Poissons . Consider now the case of a high diffusion coefficient . We show ( see S1 Text §3 . 5 ) that as Ω → ∞ , X1 , ‥ , XN synchronize in the sense that the joint PMF of X1 , ‥ , XN is symmetric with respect to all permutations of the random variables . This implies that the marginal stationary PMFs p X i , i = 1 , ‥ , N are identical . Hence , for sufficiently large Ω , the probability mass is concentrated around the region for which X1 , ‥ , XN are close to each other . Consequently , for large Ω we can replace the population of toggle switches with a single toggle switch with the synchronized protein processes X ( t ) , Y ( t ) , which are defined , for the sake of convenience , as X ( t ) ≔ X1 ( t ) , Y ( t ) ≔ Y1 ( t ) . Next , we describe the stationary PMF of X ( t ) , Y ( t ) . The state of synchronized toggle switches does not depend on individual promoter configurations , and it depends only on the total number of unbound promoter sites in the network . Hence , the number of modes will drop from 4N − 1 to ( N + 1 ) 2 − 1 . Note that similar to the single toggle switch , there are modes which have both X , Y with non-zero copy number . On the other hand , there are many additional modes . Recall that in the case of a single toggle switch , we have tuned the cooperativity ratios such that the modes in which both genes are ON are suppressed . Similarly , the undesired modes can be suppressed by tuning the cooperativity ratio which can be achieved by choosing ρ d i X , ρ d i Y , d = 0 , ‥ , 4 N − 1 sufficiently large . In particular , letting the multi-merization ratio β/β_ → ∞ , the weights of modes in the interior of the positive orthant R + 2 approach zero . In conclusion , for sufficiently high Ω and sufficiently high β/β_ the population behaves as a multimodal switch , which means that the whole network can have either the gene X ON , or the gene Y ON . And every gene can take 2N modes which are: { ( ik0/ ( Nk_ ) , 0 ) , ( 0 , ik0/ ( Nk_ ) ) : i = 1 , ‥ , N} . Comparing to the low diffusion case , the network will have up to 2N − 1 modes with sufficiently high multimerization ratio . In order to illustrate the previous results , consider a population of three toggle switches ( N = 3 ) and cooperativity n = 2 . For large Ω , the deterministic system bifurcates into bistability This means that all toggle switches converge to the same exact equilibria if Ω is greater than a threshold . In contrast , the modes in the stochastic model of the toggle switches converge asymptotically to each other . Hence , we need to choose a threshold for Ω that constitutes “sufficient” synchronization . We define this as the protein processes synchronizing within one copy number . In other words , we require the maximum distance between the modes to be less than 1 . It can be shown ( see S1 Text §3 . 5 ) that Ω has to satisfy: Ω ≥ 1 N ( k − k − ) . In this example , the minimal Ω is 75 . The stationary PMF is depicted in Fig 6d . The network has 15 modes , nine of which are in the interior and are suppressed due to cooperativity . In contrast , the deterministic model bifurcates into synchronization for Ω > 0 . 5 . The stable synchronized equilibria are ( 149 . 98 , 0 . 02 ) , ( 0 . 02 , 149 . 98 ) . The stochastic model with SPK adds four additional modes at ( 0 , 100 ) , ( 100 , 0 ) , ( 50 , 0 ) , ( 0 , 50 ) . To interpret this , note that the protein processes synchronize while the promoter configurations do not . The high states ( 150 , 0 ) , ( 0 , 150 ) correspond to the case when all the binding sites are empty . In the case when one binding site is empty , the first gene is active while the second and the third are not . Due to diffusion , the first gene “shares” its expressed protein with the other two genes , which implies that each gene will receive a third of the total protein copy numbers produced in the network . A similar situation arises when two binding sites are empty . A very different example is provided by a well-studied synthetic oscillator , the repressilator . The repressilator is a synthetic biological circuit that implements a ring oscillator [67] , and it has been simulated with slow-promoter kinetics [21] . It is a canonical example of a GRN that exhibits a limit cycle , i . e . sustained oscillation . For simplicity , we study a network consisting of three identical genes whose expressed proteins are X , Y , Z . The protein X represses Y , Y represses Z , and Z represses X as shown in Fig 7 . Each gene has dissociation ratio α/α_ , production ratio k/k_ for the unbound state only , multimerization ratio β/β_ , and cooperativity index n . Deterministic analysis of the repressilator [3] reveals that it does not oscillate with non-cooperative binding . black Applying our techniques for the stochastic case with slow promoter kinetics , we are able to find the values of the parameters so that the probability is concentrated in three modes ( K , 0 , 0 ) , ( 0 , K , 0 ) , ( 0 , 0 , K ) if w ≔ 2 ( α/α_ ) ( k/k_ ) n ( β/ ( n ! β_ ) ) ≫ 1 , where K = k/k_ ( see S1 Text §3 . 4 ) . The obtained tri-modal stationary distribution is consistent with the classical oscillations of the repressilator , and this is independent of the cooperativity index . Note that this condition is analogous to the oscillation condition in the deterministic model [3] ( but with cooperativity only ) which also requires “large” production ratios . In order to study whether the network oscillates , we need to define a notion of limit cycle for a stochastic system . Due to randomness , the time-series can not be periodic . Nevertheless , since the stationary distribution is tri-modal , we say that the network oscillates if sample paths ( time histories of trajectories ) typically jump between the modes in the same order . Assume w ≫ 1 . Let dx , dy , dz be the three dominant modes . We show that if the reduced-order Markov chain is at mode dx then it is much more likely to transition to dy rather than to dz . Similar arguments apply if we start from dy , dz . In particular , let Q ( t ) = e t Λ r be the probability transition matrix . We are interested in comparing the probabilities of transiting from rx to ry , rz . Hence , we study small t ≪ 1 . We give expressions for Q d x d x ( t ) , Q d y d x ( t ) , Q d z d x ( t ) in S1 Text §3 . 4 which show that if the Markov chain is at dx then it is most likely to stay there . The transition is much more likely to happen to dy rather than dz . Hence , we expect to see “long” periods of protein X being expressed , and then it jumps to express protein Y , and then protein Z . Since the finite Markov chain is ergodic , the pattern repeats . Note that the analysis above predicts that both the cooperative and the noncooperative repressilator are capable of oscillation with slow-promoter kinetics when w ≫ 1 . The average “period” increases with the production ratio and the transient behavior of the network follows the analysis in S1 Text §5 . We performed Monte-Carlo simulations via the Gillespie algorithm for both fast and slow kinetics . The results are shown in Fig 8 . We observe that the network always oscillates with cooperative binding . With non-cooperative binding , only the network with slow kinetics oscillates , as predicted . The network with fast kinetics does not oscillate . Recall that the deterministic model with non-cooperative binding does not oscillate [3] . We consider two networks for TF cross-antagonism in cell fate decision in this section . Both networks consist of two self-activating genes repressing each other as depicted in Fig 9a [5] . The first network [39] has independent cooperative binding of the TFs to the promoters . The genes states are D 00 X , D 01 X , D 10 X , D 11 X for gene X , and vice versa for gene Y . In order for the genes to be cross-inhibiting and self-activating we let: D 01 X , D 10 Y have zero production rates . Also , the maximal production rates for genes X , Y occur at gene states D 10 X , D 01 Y , respectively . ( See S1 Text §6 . 5 ) The network can be analyzed with the proposed framework , as it consists of two genes each with two binding sites . Hence it can theoretically admit up to 16 modes according to ( 10 ) . The PMF is depicted in Fig 9b for an example parameter set . Note that despite the fact that we have 16 modes , only eight of them contribute to most of the stationary PMF . This is to be contrasted with a deterministic model , which cannot produce more than 4 stable equilibria [52] . The second network that we study is a model of the PU . 1/GATA . 1 network , which is a lineage determinant in hematopoietic stem cells [68] . Diagrammatically , it can also be presented by Fig 9-a . However , it differs from the first network presented above in several ways . First , PU . 1 needs GATA . 1 to bind to the promoter of GATA . 1 [69] , and vice versa [70] . In our modelling framework this means that the promoter configurations D 01 X , D 10 Y do not exist , where X stands for PU . 1 and Y stands for GATA . 1 . Hence , the network has 9 gene states . Second , there is no evidence that PU . 1 and GATA . 1 form dimers to activate their own promoters cooperatively . In fact , it has been shown that self-activation for GATA-1 occurs primarily through monomeric binding [71] . Further discussion of the model is included in S1 Text §6 . 5 , and is further discussed in [72] . With lack of cooperativity , the deterministic model is only monostable and cannot explain the emergence of bistability for the above network . However , using our framework , up to nine modes can be realized . In order to simplify the landscape , we group the nine into four modes . This is possible since the states D 11 X , D 00 X , D 11 Y , D 00 Y have very low production rates . This gives a total of four modes which are ( low , low ) , ( high , low ) , ( low , high ) , ( high , high ) . Using our model , we choose the parameters to realize bistability and tristability . Fig 9c depicts the stationary PMF for a set of parameters that satisfies the assumptions and give rise to a tristable PMF .
Numerical Simulation Software Calculations were performed using MATLAB 9 . Bertini 1 . 5 was used for the computation of deterministic solutions of the “quorum sensing” numerical example .
Phenotypical variability in the absence of genetic variation is a phenomenon of great interest in current biological and translational research , as it plays an important role in processes as diverse as embryonic development [73] , hematopoietic cell differentiation [74] , and cancer heterogeneity [75] . A conceptual , and often proposed , unifying framework to explain non-genetic variability is to think of distinct phenotypes as multiple “metastable states” or “modes” in the complex energetic landscape associated to an underlying GRN . Following this point of view , we studied a general but simplified mathematical model of gene regulation . Our focus has been on stochastic SPK , the time scale relevant when transcription factor binding and unbinding are affected by epigenetic processes such as DNA methylation and chromatin remodeling . In that regime , adiabatic approximations of promoter kinetics are not appropriate . In contrast to the existing literature , which largely confines itself to numerical simulations , in this work we provided a rigorous analytic characterization of multiple modes . The general formal approach that we developed provides insight into the relative influence of model parameters on system behavior . It also allows making theoretical predictions of how changes in wiring of a GRN , be it through natural mutations or through artificial interventions , impact the possible number , location , and likelihood , of alternative states . We were able to tease out the role of cooperative binding in stochastic models in comparison to deterministic models , which is a question of great interest in both the analysis of natural systems and in synthetic biology engineering . Specifically , we found that , unlike deterministic systems , the number of modes is independent of whether the TF-promoter binding is cooperative or not; on the other hand , cooperative binding gives extra degrees of freedom for assigning weights to the different modes . Emergence of bimodality in noncooperative single gene networks in different contexts has been reported in [76] , which studies exogenous TF and fast promoter kinetics , and [77] , which studies the effect of temperature fluctuations . The intermediate promoter kinetic domain has been studied in [78] . Switching behavior in a single gene driven by a bursty exogenous input has been studied in [79] . More generally , we characterized the stationary PMFs of CMEs for our GRNs as mixtures of Poisson PMFs , which enabled us to obtain explicit formulas for the locations and probabilities of metastable states as a function of the parameters describing the system . Although we formulate our study in terms of stationary PMFs , one may equally well view our results as describing the typical dynamic behavior of realizations of the stochastic process . These recapitulate the form of the stationary PMFs: modes are reflected in metastable states along sample paths , states in which the system will stay for prolonged periods until switching to other states corresponding to alternative modes . In the SI , we provide Monte-Carlo simulations showing such metastable behavior along sample paths . We do so for the toggle switch as well as for a version of a well-studied genetic circuit [67] which exhibits oscillatory behavior along sample paths even though the corresponding deterministic model cannot admit oscillations . One application of our mathematical results was to models of single or communicating “toggle switches” in bacteria , where we showed that , for suitable parameters , there are a very large number of metastable attractors . This work was in fact motivated by our interest in hematopoietic cell differentiation , and in this paper we discussed two possible models of trans-differentiation networks in mammalian cells . In a first model , based on previous publications , we uncovered more modes than had been predicted with different analyses of the same model . This implies that in practice there could be unknown “intermediate” phenotypes that result from the network’s dynamics , which may be acquired by cells during the natural differentiation process or which one might be able to induce through artificial stimulation . The second model included only binding reactions that have been experimentally documented , and as such might be more biologically realistic than the first model . For this second model , a deterministic analysis predicts monostability , which is inconsistent with the fact that the network should control a switch between two stable phenotypes ( erythroid and myeloid ) . This suggests that stochasticity , likely due to low copy numbers and/or SPK , might be responsible for the multiple attractors ( phenotypes ) that are possible in cell differentiation GRNs . Our mathematical results , being quite generic , should also be useful in the analysis of networks that have been proposed for understanding aspects of cancer biology . For example , non-genetic heterogeneity has been recently recognized as an important factor in cancer development and resistance to therapy , with stochastic multistability in gene expression dynamics acting as a generator of phenotype heterogeneity , setting a balance between mesenchymal , epithelial , and cancer stem-cell-like states [80] [81] [82] [83] , and nongenetic variability due to multistability arising from mutually repressing gene networks has been proposed to explain metastatic progression [84] . Application of the results to practical problems entails deciding whether ε is small enough . Since singular perturbations rely on a first-order approximation of the stationary PMF ( 8 ) , the exact determination of the range of ε requires determining coefficients of higher-order terms , which can be estimated by computing the asymptotic expansion for a finite state projection of the specific problem at hand . Nevertheless , we provide a simple intuitive rule . The predicted behavior is expected to emerge when the largest rate in the reduced matrix εΛr is slower than the decay rate of proteins . Recall that the elements of Λr depend on the association and dissociation constants and the conditional expectation of protein copy numbers given in ( 12 ) as seen in ( 11 ) . Our numerical examples depicted in Figs 1 , 3 and 4 , SI1 examine how the approximation fares with multiple levels of time scale separation and agree with the rule . In particular , the latter figure provides Monte-Carlo simulations depicting the third mode of the toggle switch with a two-to-one scale separation per the definition above . Furthermore , since ( 12 ) can be tuned by the multimerization ratio , we note that the ratio can make the network more “robust” or vulnerable with respect to the emergence of modes predicted in the SPK regime . In practice , it may be difficult to estimate experimentally the average time that a TF of interest takes to find its binding targets . Hence , we suggest that our results should be considered if there is a very low number of gene copies ( i . e . , 1-5 ) and it is suspected that TF-gene binding kinetics are slower than protein kinetics , which may happen particularly in Eukaryotic cells as discussed in the introduction . Our approach can be seen as an addition to the toolbox for analysis of the spectrum of possible behaviors in GRNs , and it can explain apparent multi-modality when the deterministic model can’t . As an example , the recent experimental work on the toggle switch [66] which validated the observation of a third mode and proposed slow promoter kinetics as a mechanism , is consistent with our results . | Regulatory mechanisms of slow gene activation and deactivation play a role in triggering and sustaining phenotypically heterogeneous , yet genetically identical ( clonal ) , cellular populations in a wide variety of biological processes . These range from embryonic development and hematopoietic cell differentiation to the emergence of tumor heterogeneity and consequent resistance to therapy . In contrast to previously reported numerical simulations , we introduce in this paper a theoretical and computational approach to the characterization of the multi-attractor dynamic landscape of gene networks with slow promoter kinetics . We obtain precise formulas that are then illustrated through applications to several systems biology models including a trans-differentiation network and a communicating population of synthetic toggle switches . | [
"Abstract",
"Introduction",
"Results",
"Methods",
"Discussion"
] | [
"synthetic",
"genetic",
"systems",
"genetic",
"networks",
"engineering",
"and",
"technology",
"synthetic",
"biology",
"cell",
"differentiation",
"dna",
"transcription",
"developmental",
"biology",
"protein",
"expression",
"network",
"analysis",
"molecular",
"biology",
"te... | 2019 | Multi-modality in gene regulatory networks with slow promoter kinetics |
Parkinson's disease ( PD ) is the most common neurodegenerative movement disorder characterized by the progressive loss of dopaminergic ( DA ) neurons . Both environmental and genetic factors are thought to contribute to the pathogenesis of PD . Although several genes linked to rare familial PD have been identified , endogenous risk factors for sporadic PD , which account for the majority of PD cases , remain largely unknown . Genome-wide association studies have identified many single nucleotide polymorphisms associated with sporadic PD in neurodevelopmental genes including the transcription factor p48/ptf1a . Here we investigate whether p48 plays a role in the survival of DA neurons in Drosophila melanogaster and Caenorhabditis elegans . We show that a Drosophila p48 homolog , 48-related-2 ( Fer2 ) , is expressed in and required for the development and survival of DA neurons in the protocerebral anterior medial ( PAM ) cluster . Loss of Fer2 expression in adulthood causes progressive PAM neuron degeneration in aging flies along with mitochondrial dysfunction and elevated reactive oxygen species ( ROS ) production , leading to the progressive locomotor deficits . The oxidative stress challenge upregulates Fer2 expression and exacerbates the PAM neuron degeneration in Fer2 loss-of-function mutants . hlh-13 , the worm homolog of p48 , is also expressed in DA neurons . Unlike the fly counterpart , hlh-13 loss-of-function does not impair development or survival of DA neurons under normal growth conditions . Yet , similar to Fer2 , hlh-13 expression is upregulated upon an acute oxidative challenge and is required for the survival of DA neurons under oxidative stress in adult worms . Taken together , our results indicate that p48 homologs share a role in protecting DA neurons from oxidative stress and degeneration , and suggest that loss-of-function of p48 homologs in flies and worms provides novel tools to study gene-environmental interactions affecting DA neuron survival .
Dopaminergic ( DA ) neurons play critical roles in motor control , cognition and motivation and are affected in many neurological and psychiatric disorders [1] , [2] , [3] , [4] . The progressive degeneration of DA neurons in the substantia nigra pars compacta ( SNc ) is a principal pathological feature of Parkinson's disease ( PD ) . PD is the most prevalent neurodegenerative movement disorder , for which no preventive or restorative therapies are available [5] , [6] . The discovery of the genes associated with the rare familial forms of PD has led to the development of many animal models and advanced the understanding of PD pathogenesis . However , the majority of PD cases are sporadic and likely caused by a combination of environmental factors , such as pesticide exposure , and endogenous risk factors . These endogenous risk factors remain largely unknown . A recent meta-analysis on genome-wide association studies ( GWAS ) for PD showed that SNPs in the genes involved in multiple aspects of neural development are highly represented in sporadic PD patients [7] , suggesting that genetic variations in these pathways may contribute to PD susceptibility . Indeed , several studies in mammals have shown the critical roles of developmental genes , such as Engrailed1 , foxa2 and Nurr1 , in the survival of DA neurons in old age [8] , [9] , [10] , [11] . The identification and characterization of such genes may yield a better molecular understanding of adult-onset neurodegeneration in PD . The nervous system in invertebrate model organisms such as Drosophila and C . elegans shares many features with its mammalian counterpart and offers a powerful tool to study neural development and neurodegeneration . Drosophila DA neurons comprise multiple subclasses , some of which play roles similar to those played by the DA neurons in mammals , such as reward signaling and sleep regulation [12] , [13] . The nematode C . elegans has 8 DA neurons , which are thought to have mechanosensory functions and have been shown to play a role in the modulation of locomotion [14] . Despite advances in anatomical and functional characterization , the mechanisms underlying the development and maintenance of DA neurons in flies and worms are poorly understood . Drosophila Fer2 , a homolog of mammalian p48/ptf1a , belongs to the bHLH-transcription factor family , which is often involved in neurogenesis and neural subtype specification . The mammalian p48 gene is a critical regulator for neural tube development [15] , in which a candidate causal SNP for PD has been detected [7 , 16 , The database of Genotypes and Phenotypes ( dbGaP; NCBI ) ] . Previously , we showed that Fer2 is required for the development of a subclass of circadian clock neurons , ventral Lateral Neurons ( LNvs ) [17] . Here we characterized additional roles of Fer2 to better understand the genetic mechanisms of neuronal subtype development and maintenance . We unexpectedly found that Fer2 is required for the development and maintenance of a subclass of DA neurons important for locomotion . Fer2 exerts its neuroprotective role in adulthood in the oxidative stress response , and loss of Fer2 expression in adulthood causes adult-onset progressive degeneration of these DA neurons . We further demonstrated that the C . elegans homolog of p48 , hlh-13 , is also required for the survival of DA neurons in adult worms under oxidative stress . Collectively , our results established a conserved role of p48 homologs in protecting DA neurons from oxidative stress and degeneration .
Drosophila Fer2e03248 ( henceforth referred to as Fer21 ) mutation was induced by the insertion of PBac{RB} into the Fer2 5′UTR [18] . The Fer2MB09480 ( henceforth called Fer22 ) allele has a Mi{ET1} transposon insertion in the 3′ end of the second exon [19] ( Fig . 1A ) . To molecularly characterize the Fer2 mutant alleles , we determined the Fer2 mRNA levels using quantitative real-time PCR ( qPCR ) . Consistent with our previous results , Fer2 mRNA expression of the Fer21 homozygotes was approximately 5% of the wild-type level [17] . In the Fer22 homozygous flies , Fer2 mRNA expression was reduced to about 40% of the wild-type level . Thus , Fer21 is an extreme hypomorphic allele , whereas Fer22 is a milder hypomorph . Fer2 mRNA expression of both Fer21/+ and Fer22/+ flies was only slightly reduced relative to the wild-type level , suggesting that the loss of one copy of a Fer2 gene is compensated at the mRNA level by transcriptional or post-transcriptional mechanisms ( Fig . 1B ) . Compensation of gene dose has been observed widely in both Drosophila and mammals [20] , [21] , [22] . Therefore , Fer2 is a haplosufficient gene and Fer2 heterozygous mutants are expected to be phenotypically wild-type . We noticed that Fer2 mutant flies tend to climb up the walls poorly when tapped down to the bottom of the vials . We quantified this behavior using a startle-induced climbing assay . Wild-type and heterozygous Fer2 mutants showed similar climbing abilities at least during the first 3 weeks of the adult life . In contrast , all Fer2 homozygous or hemizygous mutants displayed severely impaired climbing abilities throughout adulthood ( Fig . 1C ) . We generated a driver fly line expressing GAL4 under the control of the Fer2 promoter ( Fer2-GAL4 ) and a UAS line expressing FLAG-tagged Fer2 cDNA ( UAS-Fer2-FLAG ) . The expression of Fer2-FLAG with Fer2-GAL4 partially but significantly rescued the decreased climbing ability of the Fer21 flies and restored the climbing ability of the Fer22 flies to the control level ( Fig . 1D ) . These data indicate that Fer2 is necessary for the startle-induced climbing ability . We have previously shown that Fer21 mutation impairs the development of LNvs , which express the neuropeptide pigment-dispersing factor ( PDF ) [17] . Although it has been shown that PDF is necessary for the normal negative geotaxis behavior [23] , whether PDF or LNvs are necessary for startle-induced climbing has not been documented . We found that the expression of UAS-hid with Pdf-GAL4 , which selectively ablates LNvs [24] , does not impair the startle-induced climbing ability ( Fig . 1E , F ) . Thus , the decrease in startle-induced climbing ability in Fer2 mutant flies is due to the deficits other than the lack of LNvs . Because loss of climbing ability is often associated with impaired CNS integrity [25] and the available transcriptome data indicate that Fer2 is almost exclusively expressed in the brain ( modENCODE Tissue Expression Data , FlyAtlas [26] ) , we examined the integrity of major neuron types in the brains of adult Fer21 mutants . We did not find any obvious differences in the overall morphology of the cholinergic , glutamatergic and serotonergic neurons between Fer21 and controls , although we cannot exclude the possibility that there are subtle differences in the number of these neurons ( Fig . S1 ) . Interestingly , we found an evident reduction of dopaminergic ( DA ) neurons in Fer21 mutants . Seven DA neuron clusters were detected by anti-tyrosine hydroxylase ( TH ) staining in the Fer21 heterozygouse flies , which were very similar in number and morphology to those in wild-type flies [27] . In contrast , there were markedly fewer DA neurons in the PAM and PAL clusters in homozygous Fer21 flies on the first day after eclosion ( day 0 ) ; there were even fewer of them in 7-day-old flies ( Fig . 2A ) . In addition , we expressed UAS-GFP under the control of the HL9-GAL4 driver to label several clusters of DA neurons [28] and found a similar dramatic reduction of PAM and PAL neurons in the homozygous Fer21 flies ( Fig . S2A ) . This indicates that PAM and PAL neurons were reduced in number in Fer21 mutants , rather than merely having reduced TH expression . Quantification of the HL9 > GFP-positive neurons revealed a 75% reduction in PAM neuron counts already at day 0 and a 90% reduction at day 7 in Fer21 compared to the heterozygous controls . Four out of 5 PAL neurons were undetectable at day 0 in the Fer21 flies , and most brains had no PAL neurons at day 7 . The numbers of other DA neuron clusters were not different between Fer21 and controls at both ages ( Fig . 2B ) . To further verify the loss of DA neurons in the Fer21 flies , we expressed GFP using the R58E02-GAL4 driver , which is derived from the promoter of the dopamine transporter gene and expressed almost exclusively in PAM neurons [29] . There were significantly fewer R58E02-GAL4-labeled PAM neurons in the Fer21 flies compared to the control , supporting the finding that a large fraction of PAM neurons were absent in Fer21 ( Fig . 2D ) . The expression of Fer2-FLAG by Fer2-GAL4 restored the loss of PAM and PAL neurons in the Fer21 flies to quasi wild-type levels ( Fig . 2C ) . To examine if Fer2 is expressed in PAM and PAL neurons , we monitored the expression of a GFP-tagged Fer2 genomic transgene in the brain by GFP/TH double staining . FER2::GFP expression was observed in all the PAM and PAL neurons and in a few other clusters of cells . As expected , GFP/PDF double staining confirmed the expression of FER2::GFP in the LNvs , consistent with the previous RNA analysis results [17] ( Fig . 3A , B ) . Fer2-GAL4 showed a more widespread expression pattern than FER2::GFP , as is often the case with promoter-GAL4s . Nonetheless , Fer2-GAL4 was also expressed in all PAM neurons and 4 out of 5 PAL neurons ( Fig . S2B ) . Having validated the expression of Fer2-GAL4 in PAM and PAL neurons , we next used it to express UAS-TH in the Fer21 flies and immunostained the brains with anti-TH antibodies . Fer2 > TH slightly increased the number of neurons detected by TH-staining in the PAM and PAL clusters but not to the control level , which demonstrates again the absence of these cells ( Fig . S2C ) . These results suggest that Fer2 is expressed in PAM and PAL neurons and further support that Fer21 mutation selectively reduces the number of these neurons . To examine the possibility that the dopaminergic neurotransmitter identity of PAM and PAL neurons is changed in Fer21 and thus they are undetectable , we analyzed the cell lineage derived from the Fer2-GAL4-expressing cells . By combining Fer2-GAL4 , UAS-FLP and UbiP63 > stop> EGFP , the Fer2-GAL4-expressing lineage was marked with GFP in the control and Fer21 flies ( Fig . S2D ) [30] . While most of the PAM and 4 PAL neurons were GFP-positive in the heterozygous control flies , the majority of these neurons were not present and no ectopic GFP-positive cells were observed in Fer21 ( Fig . S2E , F ) . Therefore , the reduction of PAM and PAL neurons in Fer21 is not due to a cell-fate switch . To learn more about the developmental impairments of PAM and PAL neurons in Fer21 mutants , we next examined DA neurons in the pupal brains with anti-TH staining , because these neurons are not present in the larval brain and some of the PAM neurons are known to be born during pupal stages [27] , [31] . In the Fer21 heterozygous controls , approximately 80% of PAM and PAL neurons were clearly detectable within 5 days after puparium formation ( APF ) . Whereas in Fer21 homozygotes , PAM and PAL neurons gradually increased in number but were significantly fewer than in the controls throughout pupal development . These observations indicate that the majority of PAM and PAL neurons were not formed or died before maturation into DA neurons in Fer21 mutants ( Fig . S3A , B ) . Taken together with the observation that the loss of PAM/PAL neurons progresses at least up to 7 days into adulthood ( Fig . 2B ) , these results indicate that Fer21 mutation impairs the development of the DA neurons in the PAM and PAL clusters and also causes their rapid degeneration in adulthood . We next asked whether the integrity of DA neurons is affected in the milder hypomorphic mutant Fer22 as well . We focused our analysis on the PAM cluster DA neurons and monitored their number in the Fer22 flies by anti-TH staining . At day 0 , the number of PAM neurons was slightly reduced in Fer22 compared to the control , but to a much lesser extent as in Fer21 . Remarkably , the loss of PAM neurons continued progressively at least up to 28 days in Fer22 ( Fig . 4A ) . Lineage tracing using HL9-GAL4 , UAS-FLP and UbiP63 > stop > EGFP flies verified the loss of PAM neurons in Fer22 ( Fig . S4A ) . The loss of PAM neurons was rescued by expressing Fer2-FLAG with Fer2-GAL4 ( Fig . S4B ) . The contrasting results between Fer21 and Fer22 mutants suggest that a moderate reduction of Fer2 expression has only a minor effect on the development of DA neurons but is sufficient to deteriorate PAM neurons in aged flies . To test this , we generated UAS-transgenic lines to express 2 independent microRNAs ( miRNAs ) that target Fer2 ( miR Fer2-4 and -5 ) and one negative control miRNA ( miR Fer2-N ) that contains a sequence of 19 random nucleotides unrelated to any Drosophila gene ( see Fig . 1A ) . When expressed with Fer2-GAL4 , both miR Fer2-4 and -5 reduced the Fer2 mRNA levels to approximately 35% of the wild-type level , whereas miR Fer2-N had no effect on the Fer2 mRNA ( Fig . S5A ) . As expected , constitutive expression of miR Fer2-N by Fer2-GAL4 at 25°C did not alter the number of PAM neurons . In the flies expressing miR Fer2-4 or miR Fer2-5 , the number of PAM neurons remained stable until 35 days of age . However , at 49 days , many flies in either of the knockdowns had a reduced number of PAM neurons , and the reduction was significant in Fer2 > miR Fer2-5 flies ( Fig . 4B ) . The constitutive knockdown by miR Fer2-4 or miR Fer2-5 at 25°C with R58E02-GAL4 or HL9-GAL4 resulted in a similar trend , with a significant reduction of PAM neurons in the flies aged over several weeks old ( Fig . 4C , S5B ) . The PAL and other DA neuron clusters were not affected by any of the Fer2 knockdowns , although Fer2-GAL4 and HL9-GAL4 are expressed in most of the DA clusters including PAL neurons . While there were subtle differences in the onset of degeneration that were likely due to the differences in GAL4 expression levels , the data nevertheless illustrate that Fer2 knockdown causes adult-onset PAM neuron degeneration . The foregoing observations indicate that a moderate reduction of Fer2 expression either by a hypomorphic mutation or by knockdown has little effect on DA neuron development but mainly affects the survival of PAM neurons in adults . This further suggests that the role of Fer2 in the survival of adult PAM neurons is independent of its role in development . To test this more directly , we knocked-down Fer2 only during adulthood using a combination of UAS-miR Fer2s , Fer2-GAL4 and temperature-sensitive GAL80 expressed under the tubulin promoter ( tub-GAL80ts ) . These flies were reared at 18°C ( a permissive temperature for GAL80ts ) until eclosion , and then the temperature was shifted to 29°C ( a restrictive temperature for GAL80ts ) to allow for transcriptional activation by GAL4 throughout adulthood [32] . We found that the adult-specific knockdown of Fer2 induced the adult-onset progressive degeneration of PAM neurons without affecting PAL neurons ( Fig . 4D ) . Notably , the loss of PAM neurons was more evident in these flies than in flies with constitutive knockdown ( Fig . 4B ) , which is consistent with the greater GAL4 activity at 29°C than at 25°C [33] . These results clearly distinguish the role of Fer2 in developing and adult DA neurons and demonstrate that Fer2 expression is required for the survival of adult PAM neurons in aging flies . We next asked whether the loss of PAM neurons , which is the most prominent cellular phenotype in Fer2 mutants , is the cause of their locomotor impairment . To test the role of PAM neurons in the startle-induced climbing ability more directly , we knocked-down Fer2 in PAM neurons by HL9-GAL4 and performed a climbing assay . Knocking-down Fer2 with either of the miRNAs resulted in significant declines in climbing ability after 49 days ( Fig . 5A ) . This is consistent with the observation that HL9 > miR Fer2s induce the adult-onset degeneration of PAM neurons only after several weeks ( Fig . S5B ) . To further assess the contribution of PAM neurons in the climbing ability , we sought to rescue the loss of PAM neurons using a PAM neuron-specific driver in Fer2 mutant flies . Expression of UAS-Fer2-FLAG using HL9-GAL4 or R58E02-GAL4 did not rescue the loss of PAM neurons in Fer21 . This is most likely because the majority of PAM neurons fail to form or die in Fer21 before these DA neuron-specific drivers start to be expressed , consistent with the fact that R58E02-GAL4 has little expression in the larval brain ( H . Tanimoto and A . Thum , personal communication ) . Thus , we reasoned that PAM-neuron specific rescue might be possible in Fer22 flies , which show little impairments in DA neuron development but display progressive PAM neuron degeneration . Indeed , R58E02 > Fer2-FLAG suppressed the degeneration of PAM neurons in the Fer22 mutants in adulthood ( Fig . 5B ) . A climbing assay revealed that R58E02 > Fer2-FLAG significantly improves the climbing impairments in the Fer22 flies ( Fig . 5C ) . The rescue of the climbing ability was partial . This may be because some of the PAM neurons that failed to develop in Fer22 were not rescued , or because other unknown cell types affected in Fer22 contribute to the climbing ability . Nevertheless , these observations together with the results of the PAM neuron targeted-knockdown indicate that PAM neurons are necessary , although may not be sufficient , for the normal climbing ability of the flies . The dopaminergic system is critically involved in the control of locomotion in both vertebrates and invertebrates [34] , [35] . The motor symptoms of PD arise mainly from the loss of DA neurons in the SNc [5] . L-dopa , a dopamine biosynthesis precursor , remains the gold standard for treatments of PD motor symptoms . We found that the locomotor deficit of the Fer21 flies was partially but significantly rescued by feeding with L-dopa ( Fig . 5D ) . By anti-TH staining , we observed no significant rescue of the number of DA neurons by L-dopa , which is consistent with a previous study [36] . Since L-dopa has to be converted to dopamine in the DA neuron terminals to exert its therapeutic effect , the partial rescue of the locomotion by L-dopa is also consistent with the marked loss of PAM neurons observed in Fer21 mutants . Accumulating evidence suggests that dysfunctions in multiple aspects of mitochondrial biology are associated with the DA neurodegeneration in PD and pathogenesis of other neurodegenerative disorders [37] , [38] . To examine whether mitochondrial dysfunction is involved in the loss of DA neurons caused by the loss of Fer2 expression , we visualized mitochondria in the adult PAM neurons by expressing mitochondria-targeted GFP ( mitoGFP ) [39] with HL9-GAL4 . The majority of visible mitochondria in the cell bodies of the remaining PAM neurons in Fer21 mutants was in enlarged aggregations and did not form tubular networks as in the control flies ( Fig . 6A ) . Similarly , DA neuron-selective Fer2 knockdown by HL9-GAL4 and Fer22 mutation lead to the accumulation of abnormally enlarged mitochondria in PAM neurons ( Fig . 6B , S6A ) . Mitochondrial morphology in some of the TRH-GAL4-positive serotonergic neurons was indistinguishable between Fer21 homozygotes and heterozygotes , suggesting that mitochondria in PAM neurons are particularly vulnerable to loss of Fer2 expression ( Fig . S6B ) . Since mitochondria are the major source of ROS , mitochondrial dysfunction leads to an excessive ROS production and oxidative damages to various macromolecules . Oxidative stress causes rapid depolarization of mitochondrial inner membrane and inhibits complex I activity , exacerbating ROS production . Thus , mitochondrial defects and elevated ROS levels are interdependent and are thought to have prominent roles in PD pathogenesis [40] . We therefore asked whether ROS levels are increased in the Fer2 mutant brains and if it has a causative role on PAM neuron degeneration . We monitored intracellular ROS levels in the brains of Fer22 mutant and the heterozygous control flies using 2′ , 7′-dichlorofluorescein ( H2DCF ) , which produces green fluorescence upon reacting with ROS . Interestingly , ROS levels were significantly elevated throughout the brain in 5-day-old Fer22 flies compared with the age-matched controls . Although there was no regional specificity of ROS accumulation , ROS levels within the PAM neurons were also significantly elevated in Fer22 ( Fig . 6C ) . These suggest that loss of Fer2 expression leads to a systemic increase in oxidative stress in the brain . The surprisingly dramatic increase of ROS levels in Fer22 mutants prompted us to further examine if Fer2 is involved in oxidative stress response . We first tested if Fer2 expression levels can be altered upon oxidative stress-challenge by feeding flies with non-lethal dose of hydrogen peroxide ( H2O2 , 5% ) for 24 hr . We found that H2O2 treatment significantly increases Fer2 mRNA levels ( Fig . 6D ) . We next examined whether oxidative stress-challenge aggravates the degeneration of PAM neurons in Fer2 mutants by anti-TH staining . The H2O2 treatment did not affect PAM neurons in Fer22 heterozygous flies , whereas the number of PAM neurons was significantly decreased in Fer22 homozygotes after the treatment ( Fig . 6E ) . DA neuron counts in other clusters were unchanged by the same H2O2 treatment in Fer22 mutants ( Fig , S6C ) , indicating that loss of Fer2 expression renders PAM neurons selectively more vulnerable to increased oxidative stress . Taken together , these results point toward a role for Fer2 in oxidative stress response and suggest that Fer2 contributes to the protection of PAM neurons against oxidative stress . Fer2 homologs are found from nematodes to vertebrates [41] . hlh-13 is predicted to be the single homolog of Drosophila Fer2 and mammalian p48/ptf1a in C . elegans [42] . Consistent with a previous study [42] , a GFP::hlh-13 genomic transgene was expressed in all DA neurons ( named CEP ( 4 cells ) , ADE ( 2 cells ) and PDE ( 2 cells ) ) and in a tail neuron in developing and adult worms . GFP::hlh-13 expression was also observed in several unidentified ventral nerve cells from L2 to L4 stages ( Fig . 7A ) . Since both fly Fer2 and worm hlh-13 are expressed in DA neurons , we next asked whether hlh-13 has a comparable function as Fer2 in DA neuron development or survival . We used the hlh-13 knockout mutant hlh-13 ( tm2279 ) to test the effect of hlh-13 loss-of-function on the number of DA neurons and on a dopamine-dependent behavior , the basal slowing response . The basal slowing response is a slowing of locomotion rate when worms encounter bacteria and has been shown to require dopamine signaling [43] ( Text S1 ) . The knockout mutant showed no differences in the number of DA neurons and basal slowing response compared to wild-type worms ( Fig . 7D control , Fig . S7A ) . Therefore , unlike Fer2 , hlh-13 is not required for the development , survival or function of DA neurons under normal growth conditions . Next , to test if hlh-13 is involved in the survival of DA neurons under oxidative stress , we treated wild-type and hlh-13 ( tm2279 ) mutant adult worms with 1 mM H2O2 for 30 min and analyzed the hlh-13 mRNA levels and DA neuron integrity at subsequent time points . hlh-13 mRNA levels were upregulated by approximately 3-fold immediately after the H2O2 treatment and returned to the non-treated levels after 2 hrs ( Fig . 7B ) . To monitor DA neurons in the wild-type or hlh-13 ( tm2279 ) background , we used the dat-1::gfp reporter driving GFP expression in DA neurons . Since it was difficult to reliably detect PDE neurons , we focused our analysis on CEP and ADE neurons located in the head . In wild-type animals , at least until 7 days after the H2O2 treatment , there were no significant differences in the number or morphology of the CEP and ADE neurons between treated and untreated groups . By contrast , H2O2- treated hlh-13 ( tm2279 ) mutants showed fragmentation of the CEP neuron projections starting from day 4 after treatment , followed by the loss of cell bodies . Similarly , the number of ADE neurons was also reduced in the H2O2-treated mutants ( Fig . 7C , D ) . Despite the apparent change in DA neuron numbers , the basal slowing response was not different between the control and stressed worms in either genotype ( Fig . S7B ) , which is consistent with the previous observation that basal slowing response is defective only when all 4 CEPs are ablated [43] . These results indicate that , similar to fly Fer2 , hlh-13 is likely to be involved in the oxidative stress response and required for the protection of DA neurons under oxidative stress in adult worms . To examine the extent to which hlh-13 shares the function with Fer2 , we next sought to test cross-species complementation of the Fer2 loss-of-function mutation in flies by the hlh-13 gene . We generated a UAS-hlh-13 construct and expressed it with Fer2-GAL4 in the Fer21 mutant background . We found that the loss of PAM and PAL neurons in Fer21 was partially but significantly rescued by the expression of hlh-13 ( Fig . 7E ) . Collectively , these results confirm that hlh-13 is the C . elegans ortholog of Fer2 and suggest that the protection of DA neurons against oxidative insults is a conserved role between these orthologs .
Many neurodegenerative disorders are multi-factorial , in which interactions between environmental and genetic factors play important causal roles . Oxidative stress has emerged as a major pathogenic factor for common neurodegenerative diseases , yet how such a ubiquitous phenomenon leads to the loss of selective neuronal populations remains unclear [44] . Here we presented evidence that loss-of-function in p48 homologs in Drosophila and C . elegans renders DA neurons susceptible to degeneration under oxidative stress in adult animals . Interestingly , genome-wide association studies for PD have identified candidate causal SNPs in p48/ptf1a [7] , [16] , suggesting the possibility that p48 loss-of-function may represent an as-yet-unknown genetic risk factor that increases susceptibility of DA neurons to environmental toxins also in mammals . Many familial PD-associated genes are widely expressed; nevertheless , mutations in these genes result in a selective loss of SNc DA neurons , suggesting that cell-type-specific factors , those similar to Fer2 and hlh-13 , might contribute to the DA neuron vulnerability even in the familial PD cases . The identification of Fer2 and hlh-13 upstream and downstream pathways may thus shed light on the common mechanisms underlying the selective loss of DA neurons in diverse PD cases . The major cellular phenotype in Fer21 mutants was the developmental defects in 2 subsets of DA neurons , in addition to the developmental loss of LNvs [17] , although we cannot exclude the possibility that other neuronal types are also affected ( Fig 2 , S1 ) . Judging from the results of the lineage-tracing experiments and the observation of DA neurons in the pupal brain , Fer2 is not a selector gene for dopaminergic phenotype in PAM/PAL neurons but is required for neurogenesis or survival of postmitotic neurons before phenotypic maturation ( Fig . S2E , F and S3 ) . The notion that genes required for the development of DA neurons confer important roles in adult DA neuron survival has been postulated by several studies in mammals [8] , [9] , [10] , [11] . Although the molecular mechanisms underlying their roles in adult neurons remain elusive , these developmental genes may actively control the genetic programs required for the maintenance of cell identity in adults [45] . Our findings on the Fer2's dual roles extend this notion to invertebrate nervous systems and underscore its significance . PAM neuron-targeted Fer2 knockdown induces PAM neuron degeneration ( Fig . 4C ) and mitochondrial dysfunction within PAM neurons ( Fig . 6B , S6A ) . These results indicate that mitochondrial dysfunction and cell death can be induced by a cell-autonomous reduction of Fer2 expression within the PAM cluster . On the other hand , ROS levels are increased brain-wide in the Fer22 flies , despite the fact that Fer2 expression is restricted to several clusters of cells in the brain ( Fig . 3 , 6C ) . Thus , loss of Fer2 expression leads to both cell-autonomous and non-cell-autonomous consequences to the animal's well-being . How does the brain-wide ROS increase occur by Fer2 mutation although Fer2 is not expressed ubiquitously ? An intriguing recent study in C . elegans demonstrated that mitochondrial perturbation in neuronal cells modulates mitochondrial stress response in distal tissues non-cell-autonomously [46] . Flies might exhibit similar non-cell-autonomous mitochondrial stress response that causes systemic ROS production . Systemic increase in oxidative stress is a clinical feature common to many aging-related neurological diseases including PD [47] . Studies in mammals have documented that inflammation is a major factor mediating excessive ROS production and PD pathology . Activated microglia produces ROS and mediates DA neuron death . Dying DA neurons stimulate microglia , exacerbating the ROS production and DA neurodegeneration [48] . As CNS glia in Drosophila are thought to possess immune-like function [49] , similar mechanisms via inflammatory responses might mediate global elevation of ROS production in Fer2 mutants . Are the abnormal mitochondria in PAM neurons a cause or a consequence of the ROS upregulation ? Because mitochondrial defects and excessive ROS production are inter-dependent , it is not possible to clarify the causality in the current study . However , because Fer2 expression is upregulated upon H2O2 treatment and the same acute H2O2 treatment triggers PAM neuron death in the absence of Fer2 ( Fig . 6D , E ) , we favor the hypothesis that Fer2 provides protection against oxidative stress rather than directly acting on mitochondria ( Fig . 8 ) . These phenomena are remarkably similar in C . elegans; an acute H2O2 treatment upregulates hlh-13 expression and triggers DA neuron degeneration in hlh-13 null mutants ( Fig . 7B–D ) . These data suggest that the oxidative stress response is an ancestral role of p48 homologs . Alternatively , hlh-13's roles in neural development in worms might have been taken over by other genes . Either way , these findings suggest that loss-of-function in Fer2 and hlh-13 can be used to study pathophysiology of DA neuron degeneration under oxidative stress . Interestingly , Fer2 mRNA levels remain upregulated at least up to 12 hr after the 24-hr H2O2 treatment , whereas hlh-13 mRNA levels return to the non-treated levels 2 hr after a brief H2O2 treatment ( Fig . 6D , 7B ) . This difference in gene expression kinetics may reflect the duration of the H2O2 treatment , RNA stability , or difference in signal transduction mechanisms . Various stress response genes show highly restricted temporal expression upon stress , as the continuous activation of these genes are often detrimental to the cell [50] . Initial upregulation of hlh-13 immediately after an acute oxidative stress might be necessary and sufficient to trigger the downstream genetic programs that continue to scavenge ROS and repair the cellular damages during the following days . Identification of the downstream genetic programs controlled by Fer2 and hlh-13 will be a key toward understanding the evolutionarily conserved mechanisms of neuroprotection . Mild loss of Fer2 expression by Fer22 mutation or knockdown leads to a progressive loss of PAM neurons associated with mitochondrial dysfunction , increase in ROS production and progressive locomotor deficits , all of which are reminiscent of the pathological characteristics of PD . Unlike other fly PD models that are derived from genetic modifications of human PD-associated genes or their homologs , Fer2 is not an ortholog of known familial PD-associated genes . Yet , the magnitude of the DA neuron degeneration caused by the loss of Fer2 expression is markedly greater than in existing fly PD models [51] . We demonstrated that the loss of PAM neurons is at least partly responsible for the impaired climbing ability caused by Fer2 loss-of-function ( Fig 5A–C ) . Because rescue of the PAM neuron counts in Fer21 mutants to quasi wild-type level does not restore the climbing ability to the control level ( compare Fig . 1D and 2C ) , it is likely that some cells other than PAM and PAL neurons are somehow affected in Fer21 and contribute to the locomotor deficits . Nonetheless , our results are in agreement with the recent study by S . Birman and colleagues , which demonstrates that the progressive motor deficits in the flies expressing human α-synuclein , a transgenic model of PD , derives from the dysfunction of a subset of PAM neurons [52] . Because the selective degeneration of DA neurons within the SNc is the principal cause of the motor manifestations of PD , the Drosophila PAM neurons parallel the DA neurons of the human SNc with regard to function and vulnerability . Thus , Fer2 loss-of-function may serve as a model to better understand the mechanisms by which the loss of specific subsets of DA neurons leads to locomotor deficits in PD .
PBac {RB} Fer2e03248 ( Fer21 ) has been previously characterized [17] . The following lines were obtained from the Bloomington Stock Center: Df ( 3R ) Exel7328 ( referred to herein as Df ) , Mi{ET1}Fer2MB09480 ( Fer22 ) , UAS-mitoGFP and the strain used for Fer2-GAL4 flip-out assay ( w; P{UAS-RedStinger}4 , P{UAS-FLP1 . D}JD1 , P{Ubi-p63E ( FRT . STOP ) Stinger}9F6/CyO ) [30] . HL9-GAL4 [28] was a gift from G . Miesenböck . dvglut CNSIII-GAL4 and Cha-GAL4 [53] were gifts from A . DiAntonio . TRH-GAL4 [54] was from O . Alekseyenko . DILP2-GAL4 [55] was a gift from P . Léopold . R58E02-GAL4 [29] was from H . Tanimoto and UAS-TH [56] was from J . True . Fer2::GFP genomic transgene FlyFos022529 was derived from a fosmid clone including approximately 5 kb upstream and 20 kb downstream of the Fer2 gene . FlyFos022529 ( pRedFlp-Hgr ) ( Fer2[16092]::2XTY1-SGFP-V5-preTEV-BLRP-3XFLAG ) dFRT ) was generated and generously provided by the project “A reverse genetic toolkit for systematic study of gene function and protein localization in Drosophila” ( M . IF . A . MOZG8070 ) . To generate Fer2-GAL4 , 2359 bp upstream of Fer2 ATG were amplified using the following primers: EagFer2upF , 5′- TTTCGGCCGTGGATTTGCTCTGGTTTGGATGC -3′ and XhoFer2upR , 5′- TTTCTCGAGTTTTACGCACTTCCGCTGTCC -3′ . The amplified fragment was cloned into a pENTR 3c gateway vector ( Invitrogen ) , verified by sequencing and cloned into to the transformation vector containing GAL4 ( pBPGal4 . 2 Uw2; Addgene ) using the gateway system ( Invitrogen ) . The Fer2-GAL4 transgene was inserted into the attP16 landing site [57] on the second chromosome by PhiC31-mediated recombination by a commercial transformation service ( BestGene , Inc . ) . UAS-miR Fer2 constructs were generated as described in [58] . Four different miRNA coding sequences were generated along with a negative control , which does not target any sequence in the Drosophila melanogaster genome . From these 4 , 2 miRNAs ( lines 4 and 5 ) were used for further experiments . The miRNA sequence of line 4 was 5′-TGAGCAAGATCGACACTCTGC-3′ , the miRNA sequence of line 5 was 5′-TCAAAGCGGATAGGGCTAATT-3′ and the sequence of the negative control miRNA was 5′- TACCCGTATCGGGTTAATCGA -3′ . The stem loop structure was predicted using the RNAfold Webserver of the institute of Theoretical Chemistry at the University of Vienna ( http://rna . tbi . univie . ac . at/cgi-bin/RNAfold . cgi ) . UAS-miR Fer2 constructs were inserted into the attP40 landing site on the second chromosome ( BestGene , Inc . ) . To generate the UAS-Fer2-FLAG construct , Fer2 cDNA with a 3xFLAG tag coding sequence at its 3′ end was amplified and cloned into pCR II Topo vector ( Invitrogen ) and verified by sequencing . A DNA fragment containing Fer2-FLAG coding sequence was then cloned into a UAS-containing transformation vector ( pUAST-UAS-Stringer attB ) . The UAS-Fer2-FLAG construct was integrated into the attP40 landing site . The UAS-hlh-13 construct was generated by cloning a PCR-amplified hlh-13 full-length cDNA into the pBid-UASC-G vector [59] by Gateway cloning , and integrated into the attP40 landing site . Throughout the text and in the figures , genotype “X>Y” indicates a combination of GAL4 ( X ) and UAS-effector ( Y ) . C . elegans were cultured using standard protocol unless otherwise indicated . The following strains were obtained from the Caenorhabditis Genetic Center: wild-type ( N2 ) , BZ555 egIs1[dat-1::gfp] and IU189 rwls1[hlh-13p::GFP::hlh-13 , mec-7::RFP] . IU129 hlh-13 ( tm2279 ) was a gift from S . Lee [42] . To assay the startle-induced locomotion of the flies , we used a negative geotaxis assay with modifications [60] . Twenty flies were anesthetized with CO2 and placed in a vertical glass column ( 25 cm length , 1 . 5 cm diameter ) with a conical bottom . The columns were divided into 5 equally spaced zones and graded from 1 to 5 from the bottom to the top . After a 1-hr recovery period from CO2 exposure , the flies were gently tapped to the bottom . The flies were then allowed to climb the wall for the subsequent 20 seconds . The experiments were video recorded and the videos were manually analyzed using VLC software . The numbers of flies that climbed up to each zone within 20 seconds were counted . Flies that remained at the bottom were defined to be in zone 0 . A climbing index ( CI ) was calculated using the following formula: CI = ( 0×n0 + 1×n1 + 2×n2 + 3×n3 + 4×n4 + 5×n5 ) / ntotal , where ntotal is the total number of flies and nx is the number of flies that reached zone X . One experiment consisted of 3 trials performed at 5 min intervals . Two or three independent experiments were performed for each condition , and the mean climbing indexes of independent experiments are shown . All climbing assays were performed 2 hr after lights on ( ZT2 ) to avoid any circadian variation in locomotor activities . The feeding of the flies with L-dopa feeding was performed as described previously with minor modifications [61] . Flies were raised on fresh medium made from instant food ( formula 4–24 ) containing the antioxidant ascorbic acid ( 25 mg/100 ml ) , the antifungal agent Nipagin and L-dopa ( 1 mM ) ( Sigma Aldrich , D9628 ) . Control vials contained only ascorbic acid and Nipagin . For the co-immunostaining of fly brains with anti-GFP and nc82 antibodies , flies were decapitated and the heads were fixed with 4% paraformaldehyde +0 . 3% Triton X-100 for 1 hour on ice and washed twice with PBST-0 . 5 ( PBS , 0 . 5% Triton X-100 ) . Subsequently , the head cuticle was partly removed and the heads were washed twice more and blocked in blocking solution ( 5% normal goat serum , PBS , 0 . 5% Triton X-100 ) for 1 hr at room temperature and incubated with the primary antibodies overnight at 4°C . After 2 washes , the heads were incubated with secondary antibodies ( Alexa-conjugated ) for 2 hours at room temperature . Cuticles and tracheas were removed , and the brains were mounted in Vectashield mounting medium . For staining with anti-TH antibodies with or without other antibodies , 0 . 3% Triton X-100 was added instead of 0 . 5% in PBST and in blocking solution . Primary antibodies were incubated over 2 nights at 4°C , and secondary antibodies were incubated at 4°C overnight . The primary antibodies and concentrations used in this study were as follows: rat monoclonal anti-GFP ( GF090R ) ( Nacalai Tesque , Inc . ) 1∶500 , rabbit polyclonal anti-tyrosine hydroxylase ( ab152 ) ( Millipore ) 1∶100 , mouse monoclonal antibody nc82 ( Developmental Studies Hybridoma Bank ) 1∶100 , mouse monoclonal anti-RFP ( AKR-021 ) ( Cell BioLabs , INC . ) 1∶500 , mouse monoclonal anti-tyrosine hydroxylase ( 22941 ) ( Immunostar ) 1∶50 and polyclonal rabbit anti-GFP ( A6455 ) ( Invitrogen ) 1∶200 . Leica TCS SP5 confocal microscope was used to image fly brains . Quantification was performed using ImageJ software ( NIH ) . To count the number of DA neurons , anti-TH-positive neurons or anti-TH , anti-GFP double-positive neurons were counted manually through confocal Z-stacks . To image the expression of hlh-13-GFP a Leica DM5500 B microscope was used . TILL Phototonics iMIC digital microscope was used to image DA neurons in worms and DA neurons were counted manually on each Z-stack using ImageJ software . Total RNA isolation from fly heads , cDNA synthesis and quantitative-PCR ( qPCR ) analysis were performed as described previously [17] . mRNA levels of Fer2 were normalized to those of the housekeeping gene elongation factor 1β ( Ef1β ) . For qPCR analysis of worm mRNAs , worms were collected from the plates with M9 buffer , placed into the Falcon tubes and left to settle for 15 min to remove bacteria in their guts . Worms were then washed twice by spinning at 3000 rpm for 1 min . Total RNAs were isolated using TRIZOL ( Life Technologies ) , and mRNAs were reverse-transcribed and used as templates for qPCR . hlh-13 mRNA levels were normalized to the its-1 levels . Statistical analyses were performed using StatPlus ( AnalystSoft ) and SPSS software ( IBM ) . For normally distributed data sets , two-tailed Student's t-tests were used to compare the means of two groups . The data that were not normally distributed were analyzed with non-parametric statistics ( Mann–Whitney U test ) . For all experiments , the level of significance was set at p<0 . 05 . The numbers of brain hemispheres examined in Figure 4A-D are as follows . ( A ) Control ( Fer22/+ ) : day 0 , n = 6; day 1 , n = 9; day 21 , n = 8 . Fer22: day0 , n = 6; day1 , n = 6; day 21 , n = 9 . ( B ) Fer2 > miR Fer2-N: day 0 , n = 6; day 35 , n = 9; day 49 , n = 17 . Fer2 > miR Fer2-4: day 0 , n = 8; day 35 , n = 7; day 49 , n = 19 . Fer2 > miR Fer2-5: day 0 , n = 8; day 35 , n = 7; day 49 , n = 21 . ( C ) R58E02 > miR Fer2-N: day 0 , n = 10; day 21 , n = 8; day 35 , n = 12; day 63 , n = 14 . R58E02 > miR Fer2-4: day 0 , n = 8; day 21 , n = 12; day 35 , n = 16; day 63 , n = 16 . R58E02 > miR Fer2-5: day 0 , n = 10; day 21 , n = 8; day 35 , n = 14; day 63 , n = 17 . ( D ) Control without UAS-transgene ( Fer2-GAL4 , tub80 ) : day 0 , n = 15; day 28 , n = 7; day 35 , n = 18 . Control miRNA ( Fer2 > miR Fer2-N ) : day 0 , n = 15; day 28 , n = 13; day 35 , n = 24 . Fer2 > miR Fer2-4 , tub80: day 0 , n = 19; day 28 , n = 14; day 35 , n = 18 . Fer2 > miR Fer2-5 , tub80: day 0 , n = 13; day 28 , n = 20; day 35 , n = 13 . Sample sizes in Figure 5B are as follows . Positive control ( Fer22/+ ) : day 0 , n = 8; day 14 , n = 14 . Negative control ( Fer22 , R58E02-gal4 ) : day 0 , n = 15; day 14 , n = 16 . Rescue ( Fer22 , R58E02 > Fer2 ) : day 0 , n = 17; day 14 , n = 13 . 5-day-old flies were transferred into the empty vials for 6 hrs , then placed onto the food prepared from instant food ( Formula 4–24 Instant Drosophila Medium , Carolina ( R ) ) containing 5% H2O2 for 24 hrs . Control food contained only dH2O . The vials were placed in a humid box and kept at 25°C . Flies were subsequently collected for RNA analysis or placed on the normal food for 24 hrs prior to the dissection and TH staining . Worms were synchronized by bleaching and stressed as young adults 2 days later ( 22 . 5°C ) . After washing worms off the plates with M9 medium , they were spun down gently at 130 g and washed once with M9 to remove bacteria in the solution . The worms were transferred in 5 ml M9 to an empty petri dish and H2O2-containing M9 ( 5 ml ) was added to the final concentration of 1 mM . The worms were kept shaking for 30 min at room temperature . Worms were then washed 3 times with M9 to remove H2O2 and were placed back on the normal NGM plates for recovery . In vivo detection of ROS production in fly brains was performed using 2′7′-dichlorofluorescein ( H2DCF ) as detailed in Owusu-Ansah et al . ( Protocol Exchange , 2008 , doi:10 . 1038/nprot . 2008 . 23 ) . Brains of the R58E02-GAL4 , UAS-mCherry flies in the Fer22 heterozygous or homozygous background were imaged by confocal microscopy and the fluorescence intensity was measured using the FIJI software [62] . Since there was no regional specificity in the H2DCF signal , the central brain area ( entire brain except for the optic lobe ) was manually defined and the signal intensity in the defined region across Z-stacks was measured by performing a Z-SUM projection . To quantify the H2DCF signal within PAM neurons , PAM neurons were defined by thresholding the RFP signal and the total H2DCF signal within the defined volume was measured by a Z-SUM projection . | Parkinson's disease is a common movement disorder with no known cure . Its characteristic motor symptoms are primarily caused by the progressive loss of midbrain dopaminergic neurons . Although studies have shown that various environmental and genetic factors both contribute to the development of the disease , the underlying mechanisms remain unknown . Here we use powerful invertebrate model organisms , fruit flies and nematode worms , and identify a new gene required for the survival of dopaminergic neurons . We show that homologs of the p48/ptf1-a gene in both flies and worms are expressed in dopaminergic neurons and mutations in p48 increase the susceptibility of dopaminergic neuron death when animals are under oxidative stress . Importantly , genetic variations in p48 in humans have been detected in the sporadic Parkinson's disease patients , indicating the possibility that similar mechanism might play a role in the death of dopaminergic neurons in humans . Oxidative stress has been regarded as a major pathogenic factor for Parkinson's disease . Our results add evidence to the link between oxidative stress and neurodegeneration , and suggest that p48 mutant flies and worms can be used to study mechanisms of neurodegeneration in Parkinson's disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"molecular",
"neuroscience",
"animal",
"genetics",
"nervous",
"system",
"neuronal",
"differentiation",
"neuroscience",
"cell",
"differentiation",
"gene",
"function",
"developmental",
"biology",
"behavioral",
"neuroscience",
"genetic",
"predisposition",
"animal",
"cells",
"g... | 2014 | A Conserved Role for p48 Homologs in Protecting Dopaminergic Neurons from Oxidative Stress |
Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge . Most current approaches rely on likelihood functions , which raise specific issues that range from computing these functions to finding their maxima numerically . Here , we present a new regression-based Approximate Bayesian Computation ( ABC ) approach , which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot . The regression step involves the Least Absolute Shrinkage and Selection Operator ( LASSO ) method , which is a robust machine learning technique . It allows us to readily deal with the large number of summary statistics , while avoiding resorting to Markov Chain Monte Carlo ( MCMC ) techniques . To compare our approach to existing ones , we simulated target trees under a variety of epidemiological models and settings , and inferred parameters of interest using the same priors . We found that , for large phylogenies , the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2 . Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model . It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types . Lastly , by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone , we showed that regression-ABC provides more realistic estimates for the duration parameters ( latency and infectiousness ) than the likelihood-based method . Overall , ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies .
To control epidemics , we must understand their dynamics . Classical analyses typically rely on prevalence or incidence data [1 , 2] , which correspond to the total number of reported cases , and the number of newly reported cases through time , respectively . By combining such data with epidemiological models , one can estimate key parameters , such as the basic reproduction number ( R0 ) , which is the number of secondary cases generated by an infectious individual in a fully susceptible host population . A robust and rapid estimation of epidemiological parameters is essential to establish appropriate public health measures [1 , 3] . As a result , inference methods in epidemiology are under rapid development [4–7] . With the advent of affordable sequencing techniques , infected individuals can now be sampled in order to sequence genes ( or even the complete genome ) of the pathogen causing their infection . In the case of outbreaks , this sampling can represent a significant proportion of infected hosts [8 , 9] . A time-scaled phylogeny can readily be inferred from virus sequences with known sampling dates . Such a “genealogy” of infections bears many similarities with a transmission chain and potentially contains information about the spread of the epidemic . This idea was popularised by Grenfell et al . [10] , who coined the term “phylodynamics” to describe the hypothesis that the way rapidly evolving parasites spread leaves marks in their genomes and in the resulting phylogeny . Obtaining quantitative estimates from phylogenies of sampled epidemics remains a major challenge in the field [11 , 12] . In most studies , epidemiological parameters are inferred using a Bayesian framework based on a likelihood function that describes the probability of observing a phylogeny given a demographic model for a set of parameter values . This model is sometimes referred to as the “tree prior” [13] . Epidemiological dynamics were first captured in the tree prior by using coalescent theory and assuming an exponential growth rate of the epidemic [14] , or more flexible variations in the effective population size over time ( i . e . effective prevalence ) [15–17] . More recently , progress has been made in deriving tree priors relevant to epidemiological models ( see [18] for a review ) . In 2009 , Volz et al . [19] managed to express the likelihood function of SIS ( for “Susceptible-Infected-Susceptible” ) and SIR ( for “Susceptible-Infected-Removed” ) epidemiological models using coalescent theory , thus allowing for the estimation of R0 . One year later , Stadler [20] derived the likelihood function of a phylogeny using the birth-death process with incomplete sampling . The method was then extended to other epidemiological models and allows for the inference of both R0 and the duration of the infection [21 , 22] . It is now possible to compute the likelihood of a tree under most SIR type models using the coalescent approach [23 , 24] . Other developments have combined state-of-the-art techniques in epidemiological modelling , for instance particle filtering , with the coalescent model for phylodynamics inference [23–25] . The success of these tree priors was made possible by advances in computing power , and the generalisation of computationally intensive techniques to explore the parameter space , such as Markov Chain Monte Carlo ( MCMC ) procedures [26] . Many of the tree priors and procedures described above , are implemented in the software packages BEAST [13] and BEAST2 [27] . Very recently , the Phylogenetics And Networks for Generalized HIV Epidemics in Africa ( PANGEA-HIV ) consortium reported on the ability of several phylodynamics methods to infer the parameters of a detailed individual-based model of HIV transmission in Sub-Saharan Africa , using only sampled sequences or phylogenies [28] . Of the five methods they compared , four were likelihood-based . The accuracy achieved by some of the methods , especially that involving the structured coalescent , was impressive , with some correlations between estimates and true values that were greater than 90% . However , this accuracy came at cost in terms of computing power ( “roughly 1 week of computation time on a 64-core machine of 2 . 5Ghz processors per analysis” for the structured coalescent on the PANGEA-HIV data [28] ) , because they rely on MCMC techniques . One of the five PANGEA-HIV methods was based on Approximate Bayesian Computation ( ABC ) . ABC is a likelihood-free method that proposes to bypass the difficulty in computing ( and even sometimes formulating ) the likelihood function , by performing simulations and comparing the simulated and “target” data , usually via distances computed on summary statistics [29–32] . The basic ABC algorithm , called rejection [33] , consists in retaining a small fraction of simulations that are close to the target in view of the computed distance . These constitute the final posterior distribution of the parameters . Over the last decade , several improvements of the rejection algorithm have been proposed . ABC-MCMC consists in searching in the prior parameter space more efficiently by using MCMC-like approaches [34] . Sequential Monte Carlo ( ABC-SMC ) methods adjust the posterior distribution obtained by rejection by re-sampling parameters from the posterior and thus iterating the rejection process until convergence [35 , 36] . Regression-ABC uses the simulations selected by rejection to learn a regression model ( linear or not ) , which is then used to adjust the posterior distribution initially obtained by rejection [33 , 37] . Importantly , regression-ABC has the advantage of being potentially less computationally intensive and also less sensitive to the curse of dimensionality of the set of summary statistics than the ABC-MCMC or ABC-SMC methods [37] . In epidemiology , ABC has been shown to infer parameters from genetic data as accurately as and more efficiently than a likelihood-based method implemented in BEAST [38] . This study did not involve phylogenies and , to our knowledge , ABC has only been applied to phylodynamics in two studies [39 , 40] . As shown in the first of these studies , this lack of enthusiasm for ABC could be due to the fact that the approach can be sensitive with respect to the choice of summary statistics and requires careful calibration of the tolerance parameter [39] . More recently , an ABC-MCMC algorithm using a tree shape kernel distance was developed [40] . This was the only likelihood-free method in PANGEA-HIV , but it produced the results with the widest confidence intervals [28] . In this article , we introduce a new ABC phylodynamics approach with two essential features . First , since phylogenies are complex objects , we use a large number of summary statistics to describe them , whereas existing ABC phylodynamics studies either use only a few of these [39] or a functional distance [40] . Second , we use regression-ABC with built-in variable selection , whereas existing methods in phylodynamics rely on MCMC-like techniques [39 , 40] . The article is structured as follows . We first present the methodology ( epidemiological models , tree simulation methods , computed summary statistics , and the data sets and inference methods used for the comparisons ) . We then analyze the location of the epidemiological information in the phylogeny . Lastly , we show that the accuracy of the estimates obtained using our regression-ABC with the LASSO approach is comparable to that based on the likelihood function . Our regression-ABC even outperforms these methods when estimating the host population size in the SIR model from large phylogenies . The accuracy of regression-ABC also increases with phylogeny size , suggesting that this method becomes more valuable for larger datasets .
We considered four epidemiological models: a Birth-Death ( BD ) model ( Fig 1a ) , a Susceptible-Infected-Removed ( SIR ) model without demography ( i . e . with a constant host population size , Fig 1b ) , a Susceptible-Infected with Differential-Risk ( SI-DR ) model and a Birth-Death model with an Exposed class ( BDEI , Fig 1c ) . These compartmental models are defined by ordinary differential equation ( ODE ) systems ( see [40] for the SI-DR model and S1 Text for the three other models ) . In these models , individuals susceptible to the pathogen become infected after contact with infectious individuals and successful transmission , which occurs at an overall transmission rate β [2] , except for the SI-DR model [40] where the transmission rate is equal to β ci hij depending on the risk groups of the “infectee” ( i = {1; 2} ) and the “infector” ( j = {1; 2} ) . In the latter model , ci is the contact rate of the individuals belonging to risk group i and the hij are the elements of an assortativity matrix ( which [40] refers to as an “homophily” matrix ) that describes the propensity of individuals from risk group i to have contact with individuals from risk group j ( see [40] for more details on the computation of this matrix ) . Following infection , individuals either become infectious immediately ( BD , SIR and SI-DR models ) or at a rate σ after a latency period in the Exposed class ( BDEI model ) . They are then “removed” ( i . e . recover with a lifelong immunity or die ) at a rate γ . Lastly , they can be sampled , at a rate ε . By sampling , we mean that the pathogen is sequenced from the patient . Because sampling generally leads to treatment or at least to behavioral changes , we assumed that infected individuals are also “removed” after sampling . This assumption is commonly made in phylodynamics [21 , 41 , 42] and we kept it here to facilitate comparisons . However , it could easily be relaxed . The sampling proportion p is defined as the ratio of the sampling rate ( ε ) over the total removal rate ( γ + ε ) . The critical difference between BD models and the SIR model , lies in the transmission rate per infected individual λ ( t ) : this rate is constant in BD models ( λ ( t ) = β ) , but it depends on the susceptible population size in the SIR model ( λ ( t ) = β S ( t ) N , where S ( t ) is the number of susceptible individuals at time t and N is the effective population size ) . In other words , the SIR model assumes that the effective host population has a fixed size N and is initially fully susceptible ( S ( t = 0 ) = N ) . The susceptible population is depleted as the epidemic spreads ( S ( t > 0 ) < N ) and this depletion decreases the speed of the spread of the epidemic ( λ ( t > 0 ) < λ ( t = 0 ) ) . In the SI-DR model used in [40] , the number of new infections also depends on the susceptible population size , but there is no sampling because the model assumes that the sampling dates are known . The SI-DR model also accounts for demography since all individuals die at a rate μ and susceptible newborns of risk group i appear at a rate Λi . Our overall goal was to infer a vector of epidemiological parameters θ , from time-scaled phylogenies . For reasons related to method comparison , the composition of θ depends on the model: Contrary to the likelihood-based phylodynamics methods [8 , 41 , 42] , we did not attempt to infer the sampling proportion using ABC , since only two out of the three parameters ( β , γ and ε ) are identifiable in the epidemiological models that account for sampling ( see S1 Text ) [43] . The compartmental models described above are deterministic continuous-time models . However , whatever method is used ( likelihood-based or not ) , epidemiological parameter inference requires taking the stochasticity of events at the individual level into account . A time-scaled phylogeny of an epidemic can be viewed as a sampled transmission tree in which each branching represents a transmission and each leaf represents a sampled infected individual . There are several ways to simulate sampled transmission trees from epidemiological models . They all involve two processes: the simulation of the trajectory of the epidemic ( i . e . the chronology of epidemiological events ) and the construction of the sampled transmission tree based on this trajectory . In this study , we used two tree simulation approaches that can be applied to a wide variety of epidemiological models . The first approach is implemented in the software MASTER [44] and is based on Gillespie’s direct method [2 , 45] also known as the Stochastic Simulation Algorithm ( SSA ) . This algorithm enables epidemiological models to be converted into event-driven models . A great advantage of the SSA is that there is an exact correspondence between the stochastic simulations and the deterministic ODE-based model . With this approach , trees are generally simulated alongside the trajectory , that is , through a forward-in-time birth-death process , where each birth in the tree corresponds to a transmission and each death corresponds to an end of infection with or without sampling . Unless the epidemiological model includes sampling as an event , MASTER produces full transmission trees . The computational complexity of this method is linear with respect to the total event count ( C ) with an additional time penalty associated with the tree update [44] . For the BD and the SIR models , C is the sum of the numbers of birth and death events . To obtain a sampled transmission tree of n leaves simulated assuming a sampling proportion p with either model , we need to simulate a full transmission tree composed of n p leaves ( and n p - 1 internal nodes ) . Thus we need C = ( 2 n p - 1 ) events ( births and deaths ) to be performed . Gillespie’s SSA complexity is then in O ( C ) , where C is at most proportional to n p with large n , for both models . The second approach has been implemented in the rcolgem R package [23 , 24] . In this approach , epidemiological models are translated into continuous-time stochastic models to simulate trajectories . Trees are simulated afterwards based on trajectories , through a backward-in-time coalescent-like process . The coalescent approach assumes that sampling dates are known , which means the epidemiological models do not require assumptions about the sampling process . With careful implementation and reasonable approximation , the trajectory can be generated in a time that is proportional to the simulated epidemic duration ( tend − t0 ) over the chosen time step ( δt ) , and the tree can be built in a time that is proportional to its size ( n ) . This approach becomes valuable when C > ( t e n d - t 0 δ t + n ) , n representing the number of leaves . This can be the case , for instance , when simulating large trees with very sparse sampling or for epidemiological models more complex than the SIR model , where the number of events does not depend only on the tree size and sampling proportion . We used the MASTER-like approach for the BD , SIR and BDEI models , which all include sampling , and the rcolgem R package for the SI-DR model . Note that we implemented our own SSA instead of using MASTER to facilitate the addition of constraints on the simulations ( see below ) . Sampled transmission trees are complex objects . Therefore , we used summary statistics to compare them and capture the epidemiological information they may contain . We decided to compute many summary statistics to capture as much information as possible . This was motivated by the fact that there is no consensus in the field regarding which summary statistics to use . Importantly , our decision was made possible by the existence of efficient regression models that perform variable selection and can be combined to ABC ( see below ) . Overall , we used 83 summary statistics , which we grouped into three “families” to better identify where the epidemiological information is in the phylogeny: branch lengths ( Table 1 ) , tree topology ( Table 2 ) and Lineage-Through-Time ( LTT ) plot ( Table 3 ) [46] . Since branching occurs throughout the phylogeny at a rate that varies over time ( the number of infected and susceptible hosts vary in the SIR model ) , we designed all the summary statistics related to branching and internal branches ( linking two internal nodes ) in a piecewise manner ( Table 1 ) . We temporally cut the tree into three equal parts: internal branches belong respectively to the first , second or third part of the tree , if they end before the first ( 1 3 m a x _ H ) , second ( 2 3 m a x _ H ) or third ( max_H ) delimitation , respectively , where max_H represents the height of the farthest leaf . We only computed global summary statistics ( on the whole tree ) to describe sampling events and external branches ( linking internal nodes to the leaves ) . It is known that the topology of a phylogeny can be driven by processes such as immune escape [10] . Moreover , it has been shown recently that different transmission patterns can result in quantitatively different phylogenetic tree topologies . In particular , heterogeneity in host contact can influence the tree balance [49] . That is why we also used phylogenetic topological indexes as summary statistics ( Table 2 ) . The Lineage-Through-Time ( LTT ) plot provides a graphical summary of a phylogeny [46] . It represents the number of lineages along the phylogeny as a piecewise constant function of time ( Fig 2 ) . Each step up in the LTT plot corresponds to a branching in the phylogeny , and each step down to a leaf . If all the infected individuals of an epidemics are sampled , the phylogeny corresponds to the full transmission tree and the LTT plot is identical to the prevalence curve . Therefore , as noted in earlier studies [22 , 51–53] , it is reasonable to presume that this plot could contain relevant information about epidemiological parameters . We summarized the LTT plot with two sets of summary statistics: one that captures particular metrics of the plot ( Table 3 ) and another that simply uses the coordinates of its points as “summary” statistics . For this latter set of summary statistics , because the LTT plot contains as many points as there are nodes in the phylogeny ( a phylogeny of n leaves has 2n − 1 nodes ) , and because here we consider phylogenies with more than 100 leaves , we averaged the points into 20 equally-sized bins , thus generating 40 summary statistics ( 20 x-axis coordinates and 20 y-axis coordinates ) . To summarize , we used two main sets of summary statistics , the: Each summary statistic and all coordinates are computed recursively in O ( n ) , where n is the number of leaves in the tree . This was a key criterion for the choice of the 83 statistics and is an important reason for the efficiency of our regression-ABC . We wanted to assess the potential of regression-ABC methods to infer epidemiological parameters from phylogenies . To this end , we first compared these methods to likelihood-based methods . We simulated “target” trees under several scenarios . In particular , we used the BD and the SIR epidemiological models to perform exhaustive comparisons . We expected our method to perform less well than likelihood-based methods since ABC , by definition , only approximates the likelihood function . However , practically speaking , the implementation of likelihood-based approach often requires simplifying assumptions to allow for efficient computation , which sometimes affects the results , as we show here . We then compared a regression-ABC method to the kernel-ABC method presented by Poon [40] , assuming the SI-DR model . Stadler et al . inferred epidemiological parameters using Ebola full-genome sequences from the 2014–2015 epidemic using BEAST2 and assuming the BDEI model ( BEAST2-BDEI ) [8] . Even though many more sequences have been released since then , this dataset remains interesting and relevant for comparing our regression-ABC to another likelihood-based approach . From an epidemiological standpoint , it remains one of the most densely sampled outbreaks in their early phase . For this data analysis , Stadler et al . used 72 sequences obtained from patients in Sierra-Leone by Gire et al . [60] . We therefore used the RaxML phylogeny inferred by Gire et al . [60] , which was computed on 81 sequences: 3 from Guinea patients and 78 from Sierra-Leone patients . We pruned all non-Sierra-Leone leaves . To compare our estimates with theirs , we followed their protocol by also pruning 6 leaves of the phylogeny corresponding to a sub-epidemics in Sierra-Leone . The remaining 72 sequences were sampled from late May to mid-June 2014 . Using the known sampling dates , we scaled the phylogeny over time using the Least-Squares Dating ( LSD ) software , which uses fast algorithms and achieves accuracy comparable to more sophisticated methods [61] . We assumed a BDEI model and therefore estimated R0 , dI and the mean duration of latency dE , as in Stadler et al . [8] . As for previous models , the sampling proportion could not be estimated together with the other parameters due to identifiability problems [43] . The Ebola epidemic in Sierra Leone is thought to have started 6 months before it was officially identified and the first sample collected [8 , 60] . Since our simulations start assuming the insertion of an infectious individual in a fully susceptible population of hosts , we therefore need to consider an additional simulation parameter , origin , which , in our simulations , corresponds to the time ( in days ) between the beginning of the epidemic in Sierra Leone and the beginning of sampling . Over this time period , the sampling rate was assumed to be ε = 0 . We simulated a set of 10 , 000 “training” trees assuming a BDEI model . For comparison purposes , we first used priors identical to those used in Stadler et al . for their BEAST2-BDEI inferences ( see column p ≈ 0 . 7 in Table 5 ) . We then used a different interval for the prior on the sampling proportion ( p ≈ 0 . 4 ) , because another study suggested that the sampling proportion lies between 0 . 2 and 0 . 7 [9] . Moreover , to simulate only biologically realistic epidemiological scenarios [62] , we discarded all simulations where the total number of cases rose above 50 , 000 individuals . As in the simulation study , we computed Spearman’s correlation coefficients between each parameter of the set of simulated trees and the summary statistics . Rejection is a determinant step in regression-ABC with adjustment because it selects the simulated data that will be used for learning . Even if the chosen regression model is robust , it can collapse if the rejection step fails to retain a relevant training set . The goodness-of-fit test implemented in the gfit function of the abc R package [54 , 63] is an important preliminary test to be made in data analysis because it indicates whether the summary statistics are informative regarding target parameters . This test uses rejection based on the Euclidean distance on normalized entries , as defined by Beaumont et al . [33] . As dating of the Ebola phylogeny seemed poorly estimated ( S1 Fig ) , we performed an upstream test of summary statistics goodness-of-fit of the “training” set against the phylogeny . We inferred the posterior distributions of dE , dI and R0 for the Ebola phylogeny using our ABC-LASSO regression model with Pδ = 0 . 5 . We then compared our own estimates for the epidemiological parameters of the early spread of the Ebola epidemic in Sierra Leone with those obtained using the likelihood-based methods of Stadler et al [8] . Lastly , we analyzed the variables selected by the LASSO .
Fig 3 shows that the summary statistics computed on the Lineage-Through-Time plot ( ltt set ) are those that most correlate to the epidemiological parameters of the SIR model . The summary statistics describing the branch lengths ( bl set ) are less correlated and the topological summary statistics ( topo set ) are , in general , poorly correlated to the parameters . However , the topo set becomes more informative when the tree size increases , most likely because topological patterns become more distinguishable . There is little difference in the summary statistics histograms for trees of 100 leaves and trees of 1 , 000 leaves , the latter being more heavy tailed . bl set summary statistics are correlated positively to the duration of infection ( dI ) and correlated negatively to R0 ( S1 and S2 Tables ) . None of the topological summary statistics are correlated to dI , even though they are correlated with R0 . The coordinates of the LTT plot that are the most correlated to the epidemiological parameters are those of the x-axis , which are correlated positively to dI and negatively to R0 ( S3 and S4 Tables ) . Y-axis coordinates of the LTT plot strongly correlate positively with the R0 and weakly with the effective population size N . Overall , R0 is the epidemiological parameter that is the most correlated to all summary statistics , which suggests that ABC approaches should be able to infer this parameter reliably . On the opposite , Fig 3 raises doubts about the ability of ABC approaches to infer the effective population size from phylogenies , because this parameter is poorly correlated to all of the summary statistics . The correlations found for the BD model are very similar to those of the SIR model ( S2 Fig and S5 , S6 , S7 and S8 Tables ) . For the SI-DR model , which introduces host heterogeneity , the LTT plot summary statistics ( ltt set ) are correlated less strongly to the epidemiological parameters , whereas the y-axis coordinates of the LTT plot are correlated more strongly ( S3 Fig , and S9 , S10 , S11 and S12 Tables ) . These y-axis coordinates are mostly correlated positively to c1 ( contact rate associated with risk-group 1 ) , β ( transmission rate ) and N , and negatively to γ ( virulence ) . The summary statistics of the topo set are more correlated to the SI-DR parameters when trees are non-ultrametric than when they are ultrametric . However , even for this model , correlation remains low . In this sub-section , we study the influence of the tolerance parameter used in the rejection step , on the inference error of our four ABC methods: standard rejection ( ABC ) , rejection using the function distance between two LTT plots ( ABC-D ) , rejection and adjustment using regularized neural networks ( ABC-FFNN ) , and rejection and adjustment using LASSO ( ABC-LASSO ) . We expected the errors of inference of ABC and ABC-D to increase with tolerance . Indeed , higher tolerance values should cause the rejection step to retain trees that are increasingly dissimilar to the target tree , that is , which have been generated by parameter values that are increasingly distant from the target values . Globally , this is what we observe in Fig 4 . With large tolerance values , the error seems to converge towards that of the prior ( the horizontal gray line ) , suggesting that there is not sufficient signal in the summary statistics to infer dI by ABC and ABC-D . Regarding the ABC-FFNN method , when the tolerance value increases , we expected the error to decrease at first ( because the adjustment method used here requires a certain amount of training data ) and finally to reach a plateau ( when we have enough data and regularization can control for overfitting effects ) . This is the case for the inference of epidemiological parameters on small trees . For large trees , the error increases at the end for high tolerance values , which could be due to a poorly controlled regularization or to the limited size of the neural-network in the abc R function . Concerning the ABC-LASSO method , we expected an increase in the tolerance value to decrease the inference error at first for the same reason as for the FFNN . However , in Fig 4 , we only observe this effect for the SIR model with large trees . We then expected the error to reach a plateau and finally to increase because increasing the size of the training data increases the probability of non-linearity , which is problematic for the LASSO ( linear ) regression model . ABC-LASSO does not seem to reach the non-linearity zone in the tolerance range we considered here . The relative errors of the ABC-LASSO method remain below the threshold represented by the error induced by the prior ( S5 Fig ) . Overall , the error with this approach is quite stable , likely due to well-controlled regularization . We also analyzed the influence of the tolerance parameter on the 95% Highest Posterior Density ( HPD ) width ( width95% ) . As expected , the posterior distributions obtained using regression-ABC methods are more adjusted than those obtained using the ABC-D or standard ABC method ( S6 Fig ) . The width95% of the posteriors obtained using ABC , ABC-D or ABC-FFNN increases with the tolerance , whereas that of the ABC-LASSO posteriors seems to be insensitive to tolerance parameter . Overall , 0 . 01 is the best tolerance value for rejections without adjustment , and 0 . 5 is the best value with adjustment . Since this result was observed for both the BD and the SIR models , we adopted these values as default values for the remainder of the study . Globally , BEAST2 achieved good convergence toward the epidemiological parameters posterior distributions , except for the large target trees simulated assuming the SIR model with p = 0 . 05 and R0 = 2 . For those target trees , less than 20% of the N parameter posterior distributions had an ESS above 100 . Fig 5 shows that , for the SIR model and for large trees ( 1 , 000 leaves ) , regression-ABC methods can approach the accuracy of the likelihood-based approach ( BEAST2-BDSIR , in black ) and even outperform it for the inference of the effective population size . This can be explained by the fact that the BEAST2-BDSIR assumes an approximation of the true SIR model to speed up MCMC computations . Moreover , in the BDSIR model , the approximation of the number of susceptible individuals through time , S ( t ) , potentially makes the effective population size N hard to estimate [42] . The standard ABC method ( in blue ) already provides good estimations of R0 , consistently with Spearman’s correlations ( Fig 3 ) . We also find that the Euclidean distance between LTT plot coordinates ( coords set , in blue ) yields more accurate estimates than the functional distance between two LTT plots ( ABC-D , in turquoise ) . This can be explained by the fact that in the functional distance we only consider the differences on the y-axis of the LTT plots , while in the standard ABC using the coords set we also consider the differences on the x-axis , which represents the time variable and are the most correlated to epidemiological parameters ( Fig 3 ) . The performance of both regression-ABC methods is comparable when we consider small trees , and the accuracy of epidemiological parameter inference is always better for large trees . Note that , ABC-FFNN provides highly variable results for large trees , suggesting that regularization is poorly controlled in the algorithm we used . ABC-LASSO always gives better estimations than the standard ABC on large trees . It also gives reliable results regardless of the set of summary statistics used . This suggests that our LASSO implementation is robust concerning the high number of explanatory variables . We analyzed which variables were selected in the LASSO regression models but we did not identify any strong selection pattern . This might be explained by the fact that many variables are highly correlated . It is also a known fact that variable selection using LASSO can be unstable [64] . Results concerning the BD model are presented in S7 Fig and are globally similar to observations for the SIR model , except that none of the ABC methods outperforms BEAST2-BD . This is consistent with the fact that BEAST2-BD is based on the exact likelihood function of the BD model . Nevertheless , the accuracy of ABC-LASSO on large trees is close to that of BEAST2-BD . Fig 6 gives the example of a particular SIR scenario ( dense sampling , high R0 , and high dI ) , where for large time-scaled phylogenies ( Fig 6B ) , the majority of the replicates of ABC-LASSO converge towards a posterior distribution , which is adjusted and centered approximately on the target value . This is also true for the BD model ( S8 Fig ) . We find similar posterior distributions for the likelihood-based approach except for the N parameter , where the posterior clearly reveals a lack of convergence . We only ran ABC-LASSO using the sumstats and coords sets of summary statistics together , and set Pδ to 0 . 5 . As shown in Table 6 , for non-ultrametric target trees simulated with c1 = 2 , ABC-LASSO infers c1 very accurately ( MRE = 0 . 065 ) . Inferring β with this method is slightly more difficult ( MRE = 0 . 24 ) , but the target value of β always falls into the 95% Highest Posterior Density ( accuracy95% = 100 ) . Unfortunately , we fail to infer γ and N . However both parameters are easier to infer when c1 = 2 than when c1 = 0 . 5 . As shown in Table 6 , with ABC-LASSO , all four parameters of the SI-DR model , especially N and γ , are better inferred from large trees ( MRE ¯ 1000 = 1 . 14 whereas MRE ¯ 300 = 8 . 09 ) . We also observe an effect of the ultrametric nature or not of the target trees . Unlike other parameters , the inference error on β is lower with non-ultrametric trees than with ultrametric trees . Despite these contrasted results , ABC-LASSO outperforms the kernel-ABC method from [40] for all parameters . This is not affected by increasing the length of the MCMC chain to 50 , 000 steps for kernel-ABC . We ran additional analyses to compare the kernel distance with the our summary statistics using a simple rejection ( S4 Text ) . Results indicated that the kernel distance is less correlated to the inference task than the Euclidean distance computed from all of our summary statistics together ( S9 Fig ) . We analyzed the correlation between the epidemiological parameters of the BDEI model and the summary statistics or coordinates of the LTT plot for trees of 72 leaves ( S4 Fig ) . As previously observed for the SIR model , we see that the summary statistics computed on the Lineage-Through-Time plot ( ltt set ) and those computed on the branch lengths ( bl set ) are the most correlated to the epidemiological parameters . Conversely , the topological indexes ( topo set ) contain very little information about the parameters . The bl summary statistics are correlated positively to both the duration of infectiousness dI and the duration of latency dE , except the ie_BL_median_[1] statistics , which is correlated negatively to dE and correlated positively to dI ( S13 Table ) . The coordinates of the LTT plot ( coords set ) are correlated poorly to dE ( S14 Table ) . As for any data analysis , it is important to assess the fitness of the summary statistics to infer the epidemiological parameters from the “target” phylogeny . We did this for the sumstats and coords sets together and separately . The goodness-of-fit test revealed that the coords set of summary statistics was not fit to infer the epidemiological parameters of the Ebola phylogeny ( p-value < 0 . 05 ) . Therefore we only used the sumstats set of summary statistics . Fig 7 shows that the median of the posterior distribution of R0 , inferred by Stadler et al . using BEAST2-BDEI , is close to the median of their prior distribution ( in gray ) . The duration of latency seems very difficult to infer using the BEAST2 approach , as dE HPD 95% is almost as large as that of the prior . Our parameter estimates differ slightly from those of Stadler et al . We find a longer incubation period ( 11 . 7 [HPD95%: 6 . 77–17 . 74] ) and a longer duration of infectiousness ( 4 . 5 [HPD95%: 1 . 41–10 . 79] ) than Stadler et al ( 4 . 92 [HPD95%: 2 . 11–23 . 20] and 2 . 58 [HPD95%: 1 . 24–6 . 98] respectively ) . Both of these are more in line with the estimations from the WHO Ebola Response Team [65] , which found that the fitted incubation period was 9 . 9 ± 5 . 6 days and the mean duration of infectiousness in the community was about 4 . 6 ± 5 . 1 days . We also infer a greater value for R0 than Stadler et al ( 5 . 92 [HPD95%: 2 . 97–11 . 12] instead of 2 . 18 [HPD95%: 1 . 24–3 . 55] ) , which is probably driven by the longer duration of latency . Indeed , even if the duration of latency does not appear in the deterministic formulation of R0 for the BDEI model , it may have an effect in the stochastic setting . Put differently , we have more infected individuals in our simulations , but a high proportion of these individuals are still latent and do not propagate the disease . Our R0 estimation is more in line with [9] , which used the same dataset but fixed the duration of latency , and found that R0 = 2 . 40 [HPD95%: 1 . 54–3 . 87] if dE = 5 . 3 days and R0 = 3 . 81 [HPD95%: 2 . 47–6 . 3] if dE = 12 . 7 days . As the phylogeny from [60] that we used in this study is poorly supported ( average bootstrap support = 0 . 23 ) , we performed a supplementary analysis to assess the robustness of our method in the presence of phylogenetic uncertainty ( S5 Text ) . We used 10 additional trees with nearly optimal likelihood scores , and showed that , despite the presence of substantial topological differences ( average normalized RF distance among trees equal to 0 . 23 [66] ) , the posterior distributions inferred by ABC-LASSO are very similar ( S10 Fig ) .
For the BD and the SIR models , we found that the shape of the phylogeny contained less information about the epidemiological parameters than the LTT plot and the branch lengths . We also did not find any strong correlation between topological statistics and epidemiological parameters for the SI-DR model , which captures host structure and therefore could be expected to make these statistics more relevant [39 , 40 , 67 , 68] . However , we found the lineage component ( y-axis ) of the LTT plot , which is related to the topology , to be more correlated to the epidemiological parameters in the SI-DR model than in all the other models we studied . Our current set of summary statistics seems to be sufficient to infer the epidemiological parameters of the BD and the SIR models , but not those of the SI-DR model . In fact , our results on this model show that our summary statistics are quite poorly correlated to the two epidemiological parameters that we have difficulties to infer ( infection duration and population size ) . This suggests that there is no universal set of summary statistics and that there is room for additional ones , to be used to analyze the SI-DR model and likely other complex models . Summary statistics are sometimes viewed as the Achilles’ heel of ABC , because “summarizing” suggests a loss of information . Furthermore , complex objects such as phylogenies can contain information unrelated to epidemiological parameters , which may dilute the desired signal . Selecting the “relevant” summary statistics could improve the method’s accuracy , but this is notoriously difficult [39 , 69–71] . Here , we show that current machine learning techniques are efficient at performing variable selection on a large number of summary statistics . One potential limitation of the rejection approach we used is that it relies on the simple Euclidean distance between unweighted summary statistics . One option could be to use adaptive methods of distance weighting , but these are time consuming and tend to be replaced by machine learning techniques . The comparison between the LASSO and FFNN regression methods revealed that ABC-LASSO was more robust to the choice of summary statistics than ABC-FFNN . This was likely due to the R packages we used , and we expect that re-implementating an FFNN model with regularization tuning would remove this difference . The non-linearity of FFNN could then become an advantage . In theory , an advantage of LASSO compared to FFNN is that it provides us with an output on the selected summary statistics . However , we were unable to identify sets of summary statistics that were always selected or always discarded . This is likely due to the high degree of correlation between our summary statistics . A random forest approach could possibly circumvent these difficulties [72] . We compared regression-ABC methods to the kernel-ABC method [40] and to likelihood-based approaches based on birth-death-sampling ( BDS ) processes [21 , 22 , 42] . Our choice was motivated by the fact that the former relies on ABC and that the latter is widely used thanks to BEAST2 . Another powerful method , which is also likelihood-based , involves coalescent processes [19 , 23] . We did not use this method for parameter inference because , to the best of our knowledge , it is currently only implemented in R and we anticipated issues with computing time . However , we did use the tree simulator ( rcolgem ) associated with this method for comparison with kernel-ABC . In short , when comparing our ABC-LASSO method to the BDS methods , we obtained comparable ( but slightly lower ) accuracy when estimating R0 and infection duration . We also found that the accuracy of our ABC method always increases with phylogeny size . When assuming an SIR model , we obtained more accurate estimates of host population size than the BEAST2-BDSIR approach . The SI-DR epidemiological model is the model where the accuracy of the estimates using ABC-LASSO was globally the most disappointing ( even though it was still better than with kernel-ABC ) . This could be due to the fact that we made several assumptions in order to compare our results to [40] . For instance , the tree size was relatively small ( 300 leaves ) and our results showed that accuracy is better on larger trees ( 1000 leaves ) . It could also be that the target values chosen for the contact rates of the two host classes were too close ( c1 = 0 . 5 or 2 , and c2 = 1 ) to be well differentiated . The SI-DR model is a complex epidemiological model with many parameters and the four chosen by [40] are perhaps not all identifiable , at least when using our current set of summary statistics . It may be that developing additional summary statistics or using larger training sets to learn the regression model could improve the approach’s accuracy . When comparing methods , we saw that posterior distributions can be much more valuable than statistics such as the relative error . Indeed , if the prior distribution is centered approximately on the targeted value , without any selection on parameter values the posterior will not deviate from it . This is illustrated , for instance , by the population size parameter in the SIR model , where some models have reasonable relative error even though the posterior is often identical to the prior ( Fig 6 ) . Our results are consistent with those reported recently by the PANGEA-HIV consortium [28] . One aspect that deserves more investigation is related to computing time . Indeed , the most successful method in PANGEA-HIV required “considerable resources” in terms of CPU . The most time-consuming part in our ABC-LASSO is the simulation and the computation of the training set summary statistics . Rejection in itself is very fast , and LASSO is a fast machine-learning technique even if it is combined with cross-validation to avoid over-fitting . The computational complexity of simulation is generally linear with respect to the number of samples and the number of time-steps ( or events ) considered during the simulation . Moreover , the approach’s complexity also depends on the number and type of summary statistics . We chose to use a large number of summary statistics , but each of these is computed quickly in time at most linear in the tree size . Furthermore , the simulations and computation of summary statistics can both be run easily in parallel . In the likelihood-based methods we used , computing time depends on calculation of the likelihood function ( which can be easy for the simple BD model and most coalescent models , but can be complicated due to the necessity to integrate over time for some others [22] ) and on the convergence towards a posterior distribution ( which is generally led by an MCMC search ) . Lastly , for the kernel-ABC approach , the computational complexity depends on that of the simulation procedure , the functional distance ( which is much longer to compute than our simple Euclidean distance ) and the MCMC search ( which depends on the length of the MCMC chain and on the number of epidemiological parameters ) . This list suggests that regression-ABC may become advantageous when the number of training trees to learn the regression model becomes smaller than the length of the MCMC chain required to obtain convergence . Further investigation is warranted on this topic since both of these mehtods depend on the number of parameters to estimate , the size of the phylogenies , and also the relevance and information content of summary statistics . Our goal was to compare existing methods to determine whether regression-ABC can be an alternative to MCMC-based methods . We showed that this approach can reach an accuracy comparable to state-of-the-art techniques , which allows us to envisage several paths for future studies . A direct extension of our approach could be to investigate more complex models , since the major requirement of our approach is to be able to rapidly simulate data assuming such models . Additional efforts will likely be needed to design new relevant summary statistics . Another possibility would be to modify the method in order to take into account surveillance data [73] or to directly analyze sequence data . This latter modification would be valuable when the inference of a time-scaled phylogeny is difficult or impossible [12] . We could also include natural selection in the model to allow pathogen strains to spread at different speeds . On the technical side , a promising extension would be to explore random forest algorithms , which are powerful tools for clustering and non-linear regression with high explanatory power [72] . These algorithms have already led to promising results in the ABC framework [74] . Lastly , we focused here on phylogenies of epidemics but this method could be extended to infer parameters from phylogenies generated using ecological or evolutionary models [75 , 76] . | Given the rapid evolution of many pathogens , analysing their genomes by means of phylogenies can inform us about how they spread . This is the focus of the field known as “phylodynamics” . Most existing methods inferring epidemiological parameters from virus phylogenies are limited by the difficulty of handling complex likelihood functions , which commonly incorporate latent variables . Here , we use an alternative method known as regression-based Approximate Bayesian Computation ( ABC ) , which circumvents this problem by using simulations and dataset comparisons . Since phylogenies are difficult to compare to one another , we introduce many summary statistics to describe them and take advantage of current machine learning techniques able to perform variable selection . We show that the accuracy we reach is comparable to that of existing methods . This accuracy increases with phylogeny size and can even be higher than that of existing methods for some parameters . Overall , regression-based ABC opens new perspectives to infer epidemiological parameters from large phylogenies . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"taxonomy",
"plant",
"anatomy",
"medicine",
"and",
"health",
"sciences",
"applied",
"mathematics",
"epidemiological",
"methods",
"and",
"statistics",
"simulation",
"and",
"modeling",
"algorithms",
"phylogenetics",
"data",
"management",
"plant",
"science",
"mathematics",
... | 2017 | Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study |
DRM is a conserved transcription factor complex that includes E2F/DP and pRB family proteins and plays important roles in development and cancer . Here we describe new aspects of DRM binding and function revealed through genome-wide analyses of the Caenorhabditis elegans DRM subunit LIN-54 . We show that LIN-54 DNA-binding activity recruits DRM to promoters enriched for adjacent putative E2F/DP and LIN-54 binding sites , suggesting that these two DNA–binding moieties together direct DRM to its target genes . Chromatin immunoprecipitation and gene expression profiling reveals conserved roles for DRM in regulating genes involved in cell division , development , and reproduction . We find that LIN-54 promotes expression of reproduction genes in the germline , but prevents ectopic activation of germline-specific genes in embryonic soma . Strikingly , C . elegans DRM does not act uniformly throughout the genome: the DRM recruitment motif , DRM binding , and DRM-regulated embryonic genes are all under-represented on the X chromosome . However , germline genes down-regulated in lin-54 mutants are over-represented on the X chromosome . We discuss models for how loss of autosome-bound DRM may enhance germline X chromosome silencing . We propose that autosome-enriched binding of DRM arose in C . elegans as a consequence of germline X chromosome silencing and the evolutionary redistribution of germline-expressed and essential target genes to autosomes . Sex chromosome gene regulation may thus have profound evolutionary effects on genome organization and transcriptional regulatory networks .
The development of multi-cellular organisms is orchestrated by transcription factors that coordinate the spatiotemporal expression of sets of target genes . Transcription factors often act together in the context of multi-protein complexes . For instance , DREAM is a multi-protein complex conserved among Caenorhabditis elegans ( DRM ) , Drosophila melanogaster ( dREAM/Myb-MuvB ) and Homo sapiens ( hDREAM or LINC ) , and includes a retinoblastoma tumor suppressor pRb-family protein and the DNA binding heterodimer E2F/DP [1]–[7] . DREAM coordinates the expression of cell division and differentiation genes during development , and its subunit activities are altered in many human tumors [8] . In C . elegans , the genes that encode DRM subunits were originally identified in genetic screens for mutations causing defects in vulva development . Specifically , DRM subunits are encoded by synMuvB ( synthetic multivulva class B ) genes , which act “synthetically” with synMuvA genes to antagonize Ras signaling during vulva development [9]–[12] . Most synMuvB genes are broadly expressed chromatin-associated transcriptional regulators , and when mutated affect a range of biological processes including embryo polarity [13] , apoptosis [14] , [15] , sex determination [16] , and RNA interference [17] , [18] . Despite their important roles in disparate developmental contexts , a genome-wide analysis of genes bound and regulated by synMuvB proteins is lacking . Biochemical studies of D . melanogaster identified the dREAM/Myb-Muv-B complex and a partially overlapping testes-specific complex called tMAC [1]–[3] , [19] , [20] . These complexes contain homologs of C . elegans synMuvB proteins . dREAM-like protein complexes were subsequently identified from C . elegans ( DRM , [4] ) and human cells ( hDREAM/LINC , [5] , [7] ) . DRM includes LIN-35 ( Rb ) , EFL-1 ( E2F ) , DPL-1 ( DP ) , LIN-54 ( Mip120 ) , LIN-9 ( Mip130 ) , LIN-37 , LIN-52 , and LIN-53 ( Caf1 ) . The human and fly complexes share these subunits and additionally contain a Myb subunit that is not apparent in C . elegans ( Figure S1A ) . Several DREAM subunits contribute to its sequence-specific DNA binding , including E2F and DP , which together bind DNA as a heterodimer , and Myb . In flies and humans , E2F/DP and Myb act in a mutually exclusive manner to direct DREAM to its target genes [5]–[7] , [21] . Human DREAM is targeted to different sets of promoters by subunit switching [5]–[7] . During the G0 phase of the cell cycle , the DREAM complex incorporates the Rb-family protein p130 and E2F4 , but not Myb , to repress S phase genes . At cell cycle entry , p130 and E2F4 dissociate from the complex , and Myb is incorporated to promote activation of M phase genes . LIN-54 is another DREAM component that has been reported to bind DNA: D . melanogaster Mip120 ( Lin54 ) binds specific sequence elements within the chorion gene cluster [1] , C . elegans LIN-54 binds promoters in yeast one-hybrid ( Y1H ) assays [22] , and human Lin54 interacts with the human cdc2 promoter in vitro [23] . However , the overall contribution of LIN-54 DNA binding to DREAM complex function has not yet been explored . Genome-wide binding and expression profiling studies of DREAM in mammalian cell culture primarily identified cell cycle genes as targets for the complex [5] , while D . melanogaster cultured cell studies additionally revealed targets with sex- and development-specific expression [2] , [21] , [24] . Thus , it is not clear whether developmental gene regulation is a conserved DRM function . With the exception of gene expression profiling of the C . elegans germline [25] , genome-scale studies of the DREAM complex were performed in cultured differentiated cells . It is important to extend genome-wide analyses of DREAM to multiple cell types and tissues derived from intact organisms , to enable assessment of DREAM function through development . A key developmental function of D . melanogaster and C . elegans DRM subunits is the regulation of gene expression in the germline [19] , [20] , [25] , which must occur within the context of specialized germline gene expression features . The first such feature is a germline-specific form of X chromosome silencing . In male germlines of many species the single X is transcriptionally inactive and in C . elegans hermaphrodite germlines the two X chromosomes are partially silenced [26] , [27] . Whether transcription factors like DREAM act equally on X-linked and autosomal genes , which exist in different chromatin regulatory environments , is not known . The second property special to germline-expressed genes is that they primarily reside on autosomes , possibly because of an evolutionary adaptation to X silencing [28]–[31] . It has not been explored whether the chromosome-biased location of germline differentiation genes is related to chromosome-biased binding sites and chromosome-biased regulation by distinct transcription regulatory networks . Here we analyze genome-wide binding and function of C . elegans LIN-54 . We demonstrate that LIN-54 DNA-binding activity is required for the DRM complex to efficiently bind and regulate target genes containing adjacent putative E2F/DP and LIN-54 binding sites . We show that LIN-54 binds to the promoters of genes involved in cell division , development , and reproduction , and acts differently in the germline versus the soma . The E2F/DP-LIN-54 binding motif , individual target genes , and overall DRM function are conserved among worms , flies , and humans . Despite this conservation , we discovered one striking feature of C . elegans DRM not shared in flies or humans: it is depleted from X chromosomes . We show that DRM binding , the E2F-LIN-54 hybrid motif , and LIN-54-regulated genes are all autosome-enriched . One paradoxical exception occurs in the germline , where DRM binds autosomes but genes down-regulated in DRM mutants are enriched on X chromosomes . Evolutionary pressures imposed by germline X chromosome silencing in C . elegans are thought to have resulted in the autosome-biased location of germline-expressed and essential genes , major targets of DRM-mediated regulation . We propose that the autosome bias of C . elegans DRM co-evolved with the redistribution of its target genes . This example illustrates how sex chromosome gene regulation may create a biased genomic location of gene sets and their transcriptional regulatory networks .
The lin-54 gene encodes two proteins , LIN-54a and LIN-54b , both of which contain two tandem cysteine-rich repeats known as the tesmin/CXC domain ( Figure 1A ) . Genetic screens for synMuv vulva development phenotypes identified the lin-54 ( n2990 ) and lin-54 ( n2231 ) missense alleles which confer similar loss-of-function phenotypes as a lin-54 ( n3423 ) deletion mutant [4] , [11] . These missense alleles were independently isolated and contain the same single-base substitution in the second tesmin domain ( tesmin domain 2 ) , which changes glycine 252 to a glutamic acid ( G252E ) . The phenotypic effect of this mutation suggests that altering the tesmin domain compromises LIN-54 function and control experiments indicated that LIN-54 protein levels are normal in lin-54 ( n2990 ) mutant animals ( see below ) . The lin-54 ( n2231 ) allele encodes a protein that contains an additional change in the C-terminus ( A442T ) ( Figure 1A ) . We reasoned that these mutant alleles might result in loss of lin-54 function because the corresponding protein fails to interact with other DRM complex components , because it fails to bind DNA , or because of a combination of these effects . Previously , we found that LIN-54 can bind multiple C . elegans gene promoters in Y1H assays [22] . To ask whether the tesmin domains mediate DNA binding , we tested wild-type LIN-54 , and mutant versions of LIN-54 carrying lesions in a single tesmin domain ( G252E and G252E/A442T ) , or lesions in both tesmin domains ( K186E/G252E ) in Y1H assays . We found that the mutant proteins exhibited much weaker DNA binding compared to the wild-type protein ( Figure 1A and 1B ) . To examine the function of the tesmin domains in DNA binding in vivo , we performed chromatin immunoprecipitation ( ChIP ) experiments with wild-type and lin-54 ( n2990 ) mutant animals . Because we had noticed that LIN-54 binds its own promoter ( Figure 1B ) , as well as promoters of genes encoding other DRM subunits ( Figure S1B ) , we assayed binding at the lin-9 and lin-54 promoters . We observed a 4- and 2-fold decrease in LIN-54 binding in the lin-54 ( n2990 ) mutant relative to wild-type animals at promoters of lin-9 and lin-54 , respectively ( Figure 1C , Figure S1C , p-value<0 . 01 ) . Furthermore , the binding of other DRM complex proteins was also greatly reduced in lin-54 ( n2990 ) mutant animals ( Figure 1C , p-value<0 . 01 ) . These findings were supported by immunofluorescence analysis , which showed reduced chromosome localization of several DRM complex proteins in lin-54 ( n2990 ) mutant germlines ( Figure S1D ) . Control experiments showed that wild-type and lin-54 ( n2990 ) mutant animals produce a comparable amount of full-length , nuclear-localized LIN-54 protein ( Figure 2A and 2B ) , unlike lin-54 ( n3423 ) null animals which produce no detectable LIN-54 protein and reduced amounts of other DRM subunits ( Figure 2B and [4] ) . Together , these results indicate that LIN-54 , in addition to EFL-1/DPL-1 ( E2F/DP ) , is a DNA binding protein involved in recruiting the DRM complex to its target genes . We next tested whether LIN-54 tesmin mutations affect DRM complex formation in addition to compromising DNA binding . Using yeast two-hybrid assays , we found that both wild-type and mutant LIN-54 proteins can interact with the DRM subunit LIN-9 ( Figure 2C ) . In addition , other DRM complex members co-precipitated in lin-54 ( n2231 ) mutant animals ( Figure 2D ) . These observations demonstrate that the tesmin mutation does not result in an unstable protein and does not compromise the integrity of the DRM complex . We conclude that the lin-54 tesmin mutant phenotypes are most likely caused by a defect in DNA binding . We used ChIP-on-chip to identify genomic regions bound by LIN-54 in mixed-stage wild-type animals . Reproducible peaks of LIN-54 binding were detected in two biological replicas by the program MA2C ( model-based analysis of two-color arrays , Figure 3A ) [32] . Using the MA2C criteria described in Materials and Methods , we identified 1992 LIN-54 binding peaks ( Table S1 ) . We used the mode of each peak as a measure for the location of LIN-54 association and found that 69% of the regions bound by LIN-54 occur within intergenic regions ( Figure 3B ) . We next determined the relative position of intergenic LIN-54 peaks with respect to surrounding genes . We found that 60% of intergenic LIN-54 peaks occur within 1 kb upstream of protein-coding genes , and that the occurrence of a LIN-54 peak dramatically declined with distance from the translational start site ( Figure 3B and 3C ) . When transcription factors bind between divergently transcribed genes it is difficult to determine whether they regulate one or both genes , so in these cases we considered the binding to be associated with both adjacent genes . Overall , LIN-54 bound to 1572 protein-coding gene promoters ( Table S1 ) . These genes are highly enriched for three major gene ontology ( GO ) branches: developmental process ( p-value<10−100 ) , reproduction ( p-value<10−100 ) , and cell division ( p-value<10−30 ) ( Table S1 ) . These results agree with and extend observations of DREAM function in Drosophila and human tissue culture cells [5] , [21] and show that DRM has conserved roles in development . We discovered a significant degree of overlap among the individual genes bound by LIN-54 in worms , flies and humans ( Figure 3D ) . The HomoloGene program has compared D . melanogaster and C . elegans genomes and defined a total of 3015 orthologous gene pairs ( see Materials and Methods ) . Restricting our analysis to these defined fly-worm ortholog pairs , we note that 1267 are bound by LIN-54/Mip120 in flies [21] , 647 are bound by LIN-54 in worms ( this study ) , and 327 are bound in both species ( p-value<10−6 ) . Commonly bound genes are enriched for developmental GO terms such as sex differentiation as well as cell division terms such as cytokinesis and cell cycle ( Table S1 ) . Commonly bound orthologs are involved in multiple aspects of cell division ( smc-3 , zyg-9 , air-2 , plk-1 , cye-1 ) , DNA replication and repair ( cdc-6 , mcm-2 , pri-1 , mre-11 , rad-51 ) and transcription and chromatin regulation ( rbp-6 , taf-4 , mys-1 , ash-2 , mrg-1 ) . We also found significant overlap of genes bound by worm and human LIN-54: 62 orthologous gene pairs are bound in both species ( p-value<10−4 , Figure 3D ) [5] . Further , in all three species , DREAM binds immediately upstream of genes in proximal gene promoters ( this study; [5] , [21] ) . Thus , LIN-54 targets the DREAM complex to genes involved in similar overall biological processes in three different phyla by binding to the proximal promoters of multiple orthologous genes . In all three species DREAM bound the promoters of genes encoding its own subunits . ( Figure 1B and 1C , Figure 3A , Table S1 , Figure S1B ) [5] , [21] . C elegans LIN-54 also bound the promoters of other synMuvB class genes , including LIN-61/L ( 3 ) MBT , LIN-15B , LIN-13 , and LET-418 ( Table S1 ) . This may suggest conserved transcriptional feedback between DRM subunits and perhaps other synMuvB class genes . However , genes encoding DREAM subunits show little change in expression upon LIN-54 depletion in D . melanogaster or C . elegans ( [21] , Table S2 , data not shown ) . Perhaps the effects of DREAM autoregulation are small and required only to buffer DREAM levels and function . We identified two DNA motifs that are over-represented in LIN-54-bound promoters in C . elegans ( Figure 3E , Figure S2 ) . Motif 1 appears to be a hybrid E2F/DP and LIN-54 motif ( Figure 3E , top ) and is usually found near the center of LIN-54 ChIP peaks ( Figure 3F and Figure S2 ) . The 5′ end of this motif is similar to previously reported E2F/DP binding sites in C . elegans and other organisms ( [25] , [33] , [34] , http://jaspar . genereg . net ) . The 3′ end of Motif 1 resembles a cis-regulatory element in the human cdc2 promoter ( called CHR , or cell cycle homology region ) , which can be directly bound by hLin54 in vitro [23] . E2F/DP binding sites co-occur with CHRs in the promoters of some human genes , with a similar orientation and spacing as the motif we identified here ( [34] , Figure 3E “human” ) . Moreover , a related motif was identified from Drosophila DREAM-regulated genes ( [21] , Figure 3E “fly” ) . These results suggest conserved recruitment of the DREAM complex to its target genes by two DNA binding moieties: EFL-1/DPL-1 ( E2F/DP ) and LIN-54 . LIN-54 bound promoters were also enriched for a periodic T-rich motif that resembles a related motif in Drosophila DREAM-bound genes ( Motif 2 , Figure S2 , [21] ) . Other examples of periodic T-rich promoter motifs include sequences that function as nucleosome positioning signals [35] and elements with unknown function that are enriched in C . elegans germline-expressed promoters [36] . Mutations in lin-54 confer both germline and somatic abnormalities ( [4] , [11] , Figure S3 ) . To identify genes regulated by LIN-54 in vivo , we performed microarray expression profiling analysis of wild-type and lin-54 mutant C . elegans embryos and of isolated germlines . We chose embryos because they consist primarily of somatic cells , at a developmental stage with both active cell divisions and dynamic developmental gene expression programs . Since lin-54 null animals are sterile [4] , embryos were obtained from the lin-54 ( n2990 ) strain . lin-54 ( n2990 ) is a partial loss-of-function allele that causes the same spectrum of phenotypes as a null allele , albeit weaker , making it an appropriate strain in which to examine partial loss of lin-54 function ( [4] , Figure S3A ) . Germlines were dissected from lin-54 null adults that lack detectable lin-54 transcript and protein ( [4] , Figure 2 , and data not shown ) , exhibit reduced levels of other DRM complex proteins [4] , and exhibit reduced germline chromosome association of DRM complex proteins tested ( Figure S1D ) . We isolated the germline region from the tip until late pachytene stage of meiosis , because nuclei in this region are morphologically similar between wild-type and mutant ( Figure S3B ) and are undergoing X chromosome silencing [26] . While embryos contain a few primordial germ cells and dissected germlines contain some cells of the somatic gonad , the two samples predominantly represent somatic and germline tissue , respectively . We identified 678 genes whose transcripts increased at least 1 . 5-fold in mutant embryos ( Figure 4A , Table S2 ) . Of these , 119 ( 18% ) were also bound by LIN-54 ( Figure 4A ) . We note that ChIP was performed on mixed-stage animals to survey binding sites , while microarray was performed on a single stage , which may make it more difficult to identify all genes that are both bound and regulated . Nevertheless , this degree of overlap is similar to that observed in other ChIP and microarray studies [21] , [37] , and suggests that this gene set includes direct targets bound and regulated by LIN-54 . GO analysis of up-regulated genes or of bound and up-regulated genes revealed over-represented terms related to development ( Table S2 ) , terms that were also enriched among genes bound by LIN-54 ( Table S1 ) . Fewer genes showed reduced expression in mutant embryos ( 299 , Figure 4A ) . These genes showed no GO term overlap with LIN-54 bound genes , and only 2% ( 7/299 ) contained LIN-54 ChIP peaks at their promoters . This observation suggests that most of these genes are regulated indirectly . We conclude that LIN-54 predominantly functions as a transcriptional repressor in embryos ( Figure 4B ) . We noted that many up-regulated genes fell into discrete functional sub-categories related to development . Some of these gene sets might explain abnormalities of synMuvB mutant animals . For instance in lin-54 mutant embryos , 18 up-regulated genes are involved in meiosis ( GO term GO0001726 ) and overall , 11% of the up-regulated genes normally show germline-specific or enriched expression [38] . Previously , mutations in synMuvB genes were shown to cause ectopic expression of certain germline P granule components in the soma , proposed to reflect soma to germline transformation [18] , [39] . Our genome-wide study strengthens this model by indicating that LIN-54 represses transcription of a variety of germline genes in embryo soma , including the P granule protein glh-1 , the meiotic recombination protein spo-11 , and the eggshell protein cpg-2 . We also observed up-regulation of many RNA interference pathway genes in lin-54 mutant embryos , including ego-1 , rde-4 , and sago-2 . If these factors are normally limiting for a full RNAi response , their up-regulation might account for the enhanced RNAi phenotype that has been observed in synMuvB mutants [17] , [18] . In the germline , 78 genes showed increased and 251 genes showed decreased expression in mutant relative to wild-type animals ( Figure 4A , Table S2 ) . Both sets of genes exhibit overlap with LIN-54 ChIP peaks ( 18% and 12% , respectively ) ( Figure 4A ) . Further , both up-regulated and down-regulated germline genes are enriched for development GO terms , which again overlaps with the terms found in the ChIP data ( Figure 4B , Table S2 ) . These observations suggest that both up- and down-regulated germline genes could include targets directly regulated by LIN-54 . While the development GO term is associated with both embryonic and germline LIN-54 target genes , reproduction and growth terms were only enriched in genes with decreased expression in the lin-54 mutant germline . These reproduction genes that we presume are normally activated by LIN-54 include germline-produced transcripts required for meiosis , oogenesis and early embryogenesis , as observed previously for EFL-1/DPL-1 [25] . Thus in contrast to embryos , in the germline LIN-54 appears to both activate and repress gene expression , and activates a distinct set of reproduction and growth genes required for germline function . We discovered a striking non-uniform distribution of LIN-54 binding across the C . elegans genome: X chromosomes had significantly fewer LIN-54 ChIP peaks than autosomes ( p-value<10−15 , Figure 5A ) . Each autosome had on average 369 LIN-54 ChIP peaks ( 23 peaks per Mb ) , whereas the X chromosome contained only 145 ( 8 peaks per Mb ) ( Figure 5B , Table S3A ) . On average , 8% of autosomal gene promoters , but only 2% of X chromosome promoters , were bound by LIN-54 ( Figure 5C , Table S3A , p-value<10−41 ) . This analysis shows that LIN-54-bound promoters are significantly under-represented on the X chromosome , independent of chromosome size and gene density . We also found that the hybrid motif ( Motif 1 , Figure 3E ) , as well as the T-rich motif ( Motif 2 , Figure S2A ) , were under-represented on X compared to autosome promoters ( Figure 5D , Figure S2B , Table S3B , p-value<10−13 for Motif 1 ) . However , a published EFL-1 consensus site alone shows no bias against X chromosomes ( Figure 5D , [33] ) . A uniform distribution was also observed for three additional transcription factors for which a consensus DNA binding motif has previously been determined ( HLH-27 , FLH-1 , and NFI-1 , Figure 5D ) [40]–[42] . These results imply that the DRM complex is recruited more frequently to autosomes than to the X chromosome through the combined DNA binding activities of LIN-54 and EFL-1 . We addressed whether the non-uniform binding of LIN-54 in the genome results in differential regulation of autosomal versus X-linked genes . LIN-54-responsive genes are distributed across all six C . elegans chromosomes ( Table S3 ) , and we analyzed chromosome bias in two ways . First , to normalize for the variable number of genes on each chromosome , the percentage of LIN-54 responsive genes out of all genes per chromosome was calculated ( Figure 5E ) . Second , to compare expected to observed distributions , we calculated the percent of all genes in the genome located on autosomes and compared that to the percent of LIN-54 responsive genes on autosomes ( Figure 5F “all genes” versus “genes up in mut” or “genes down in mut” ) . Additionally , because the germline has an inherent autosomal bias in its expressed genes , we also calculated the percent of autosomal genes typically expressed in embryo or in germline as “expected” and compare that to the “observed” percent of LIN-54 responsive genes that reside on autosomes in each sample ( Figure 5F “expressed genes” versus “genes up in mut” or “genes down in mut . ” Embryonic genes that were up-regulated in lin-54 mutants are over-represented on autosomes ( 633/678 , 93% observed versus 86% expected by chance , p-value<10−8 , Figure 5E and Figure 5F “embryo up” ) . This finding is consistent with the idea that LIN-54 is preferentially recruited to autosomes , and primarily acts as a repressor in the embryo . Embryonic genes down-regulated in lin-54 mutants showed no significant chromosomal bias , consistent with our interpretation that these genes are mostly indirectly regulated ( 244/299 , 82% versus 86% expected by chance , p-value = 0 . 03 , Figure 5E and Figure 5F embryo down ) . To our surprise , LIN-54 exhibited two different patterns of chromosome-biased gene regulation in the germline . Genes up-regulated in lin-54 mutants were over-represented on autosomes , to a degree that is significantly different from all genes ( 77/78 , 99% versus 86% expected by chance for all genes , p-value<10−3 , Figure 5E and Figure 5F ) , and comparable to the inherent bias of the germline ( 99% versus 93% expected by chance for germline-expressed genes , p-value = 0 . 06 ) . This is consistent with the autosome-biased localization of LIN-54 . LIN-54 is likely a direct repressor of at least some of these genes , since 18% overlap with LIN-54 ChIP peaks ( Figure 5F ) . In striking contrast , germline genes that were down-regulated in lin-54 mutants were located more frequently on the X chromosome than expected ( 64/251 , 25% versus 14% expected by chance for all genes , p-value<10−5 , or versus 7% expected by chance for all germline-expressed X-linked genes , p-value<10−40 , Figure 5E and Figure 5F , “germline down” ) . It appears paradoxical that LIN-54 and its binding motif are preferentially located within autosomal gene promoters , yet in the absence of LIN-54 more genes on the X chromosome than on an average autosome decrease expression in the germline . One possibility is that LIN-54 affects these X-linked genes indirectly , which would predict less correlation between binding ( ChIP peaks ) and gene expression changes . Indeed , down-regulated X-linked genes overlap less frequently with LIN-54 ChIP peaks than down-regulated autosomal genes ( 6% versus 13% overlap , Figure 5E ) . Our interpretation of this observation is that LIN-54 is normally a direct activator of at least some autosomal genes that are down-regulated in the mutant , but that LIN-54 more indirectly regulates X-linked genes . Perhaps LIN-54 regulates an autosomal gene involved in X chromosome gene regulation , or prevents inappropriate spread of a repressor to the X chromosome ( see Discussion ) . Another apparent paradox is that LIN-54 loss leads to down-regulation of X-linked genes , when X chromosomes already undergo chromosome-wide silencing in the hermaphrodite germline . However , when we examined transcripts normally expressed in our wild-type germline samples using “present” calls from microarrays , we found that 15% of all X-linked genes are in fact expressed ( 376/2491 on array ) , consistent with published estimates from SAGE analysis ( Materials and Methods , [38] ) . Of the 376 total germline-expressed X-linked genes , 17% are down-regulated in the lin-54 mutant ( 64/376 ) while only 4% of all germline-expressed autosomal genes are down-regulated ( 187/5097 ) . The large percentage of total X-linked genes affected in the mutant may support models in which LIN-54 has chromosome-wide effects on X chromosome transcription ( see Discussion ) . Thus on the X chromosome , the loss of LIN-54 function causes further silencing of X-linked genes . We wondered whether the chromosome-biased localization and function of LIN-54 are features shared by other members of the DRM complex . We first compared germline expression profiles of lin-54 ( n3423 ) with published germline expression profiles for efl-1 ( n3639 ) , dpl-1 ( n3316 ) , and lin-35 ( n745 ) mutant animals ( [25] , Figure S4 ) . Genes commonly down-regulated in all four DRM mutants were more frequently located on X chromosomes than autosomes , consistent with observations in the lin-54 mutant ( Figure 5E , “commonly down” and Figure S4 , group B ) . Also consistent was the finding that commonly down-regulated X-linked genes overlapped less frequently with LIN-54 ChIP peaks than commonly down-regulated autosomal genes , again suggesting that more X-linked genes are regulated indirectly ( 6% versus 16% overlap , Figure 5E ) . Up-regulated genes common to all four mutants were more difficult to define . However , we did note that a commonly up-regulated group of genes primarily regulated in lin-54 ( n3423 ) ( Figure 5E “commonly up” , Figure S4 group E ) and another cluster primarily up-regulated in lin-35 ( n745 ) ( Figure S4 group I ) were each autosome-enriched , as observed for the lin-54 mutant alone . These results show that similar patterns of chromosome-biased gene regulation are exhibited by multiple DRM subunits . Next , we examined the chromosomal localization of DRM complex members in the germline by immunofluorescence . Figure 6 shows nuclei in the pachytene stage of meiotic prophase , when homologous chromosomes are paired and beginning to condense . LIN-54 ( red ) co-localized with DNA ( green ) , with the exception of one prominent region ( Figure 6A , arrowheads ) . We demonstrated that this region corresponds to the X chromosome in two different ways . First , LIN-54 colocalized with H4K12Ac ( blue ) , a histone modification associated with actively transcribed regions , which is under-represented on the partially silenced X chromosome ( [26] , Figure 6B ) . Second , LIN-54 did not co-localize with the H3K9me2-stained X chromosome in him-8 ( e1489 ) mutants ( Figure 6C ) . In these mutants the X chromosomes do not pair during meiosis and therefore acquire this heterochromatic histone mark [43] . The DRM complex members LIN-9 , LIN-35 , LIN-37 , LIN-52 , and DPL-1 were also under-represented on the X chromosome in the germline ( Figure 6D and 6E ) . Thus , most DRM complex members localize on autosomes . Only one DRM subunit was not autosome-enriched . The CAF1 homolog LIN-53 , which participates in multiple complexes [4] , showed little localization to DNA during this stage of meiotic prophase ( Figure 6E ) . It is interesting to note that despite the uniform genomic distribution of the EFL-1/DPL-1 motif , DPL-1 was enriched on the autosomes in the germline and co-localized with LIN-54 ( Figure 6D ) . These results support the hypothesis derived from our motif analysis that when EFL-1/DPL-1 and LIN-54 jointly bind Motif 1 , this complex disfavors the X chromosome . These results are also consistent with the finding that germline genes co-regulated by EFL-1/DPL-1 and LIN-54 share similar biases in chromosome location . We conclude that LIN-54 acts with other DRM complex members to govern chromosome-biased gene regulation in C . elegans .
Several members of the DREAM transcription factor complex have known or presumed DNA binding activity , but how they act in concert to direct promoter recognition was not well understood . Here we show that the DRM component LIN-54 binds DNA directly , helps recruit DRM to promoters in vivo , and likely recognizes a hybrid E2F/DP and LIN-54 consensus motif . In Drosophila and humans , Myb is a DNA-binding component of the DREAM complex and it has been shown that Myb and E2F/DP function in a mutually exclusive manner [5]–[7] , [21] . We show that LIN-54 is another key DRM recruitment subunit and may function coordinately with E2F/DP: the E2F/DP and LIN-54 motifs co-occur in LIN-54 target genes and both components regulate a common set of genes . Our recognition that the C . elegans hybrid Motif 1 , the CDE/CHR element of human cell cycle genes , and a motif identified in Drosophila DRM-bound genes are related elements suggests that coordinate binding by E2F/DP and LIN-54 is a conserved means of recruiting DRM to promoters ( this study , [5] , [21] , [34] ) . It has been observed that the E2F binding motif is more widely distributed than E2F family protein binding in vivo , and E2F family members often rely on cooperating transcription factors bound to neighboring sites for specificity [44] . Simultaneous binding of adjacent sequence motifs by E2F/DP and LIN-54 might increase the affinity of DREAM for target sites and might provide increased selectivity for target gene recognition . Future studies will reveal if there is a Myb-like component in the C . elegans DRM complex , and whether other subunits contribute to DRM targeting to the genome . Genes bound and regulated by C . elegans LIN-54 predominantly function in development and differentiation , cell cycle and cell division , and in reproduction . Similar categories of regulated genes have been reported in genome-wide studies of Drosophila DREAM [21] . In human tissue culture cells , however , only cell cycle genes were enriched [5] , [7] . The similarities between C . elegans and Drosophila suggest broad conservation of DREAM function in both cell cycle and developmental gene regulation . Within the common GO term categories targeted by the DREAM complex , interesting functional subcategories were conserved . In all three organisms DREAM binds groups of genes involved in cell division processes such as sister chromatid cohesion , spindle assembly , and cytokinesis , as well as DNA replication and DNA repair . Both worm and fly DREAM bind and regulate genes involved in sex differentiation such as those required for genitalia formation , and genes required for germline functions including gametogenesis , fertilization , and meiosis . It seems likely that DREAM also regulates transcription of developmental and reproduction genes in mammalian systems , given known developmental roles of its individual subunits and the overall conservation of DREAM function . Perhaps developmental genes were not observed in mammalian studies because of the use of cultured cells derived from differentiated tissues . We find that similarities of DREAM function across species lie not only at the overall level of biological processes: a remarkable degree of overlap exists among individual target genes . Further , the genes targeted by DREAM in all these organisms possess highly similar over-represented E2F/DP-LIN-54 motifs . Altogether , our results unveil an evolutionarily conserved mode of DNA binding that targets the DREAM complex to similar sets of functionally coherent target genes . We demonstrate that DRM acts differently in the soma versus the germline . In embryos , LIN-54 appears to primarily repress genes ( a majority of genes are up-regulated in the mutant , and up-regulated genes overlap with LIN-54 ChIP peaks and ChIP GO terms ) . In the germline , LIN-54 appears to primarily activate genes , yet may also serve as a repressor ( a majority of genes are down-regulated in the mutant , and both up- and down-regulated genes overlap with ChIP peaks and ChIP GO terms ) . The target genes regulated in embryo versus germline are largely distinct , and fall into different enriched functional pathways ( Figure 4 , Table S2 ) . For example , in the germline LIN-54 promotes expression of genes required for germline functions like oogenesis , meiosis , and fertilization , as observed previously for EFL-1 and DPL-1 [25] . In the embryo , however , LIN-54 does just the opposite: it represses germline-specific genes to prevent their ectopic activation in the soma . Even patterns of chromosome-biased gene regulation mediated by LIN-54 showed differences between soma and germline , as discussed below . Our results highlight how DRM may serve as either an activator or repressor . The mechanisms by which DRM may either activate or repress gene expression are at present not well understood , but may involve sub-complexes with different subunit composition or interactions with transcriptional co-factors such as chromatin modifiers . Importantly , our results provide the first genome-wide comparison of DRM function in two cell types isolated from whole animals , and indicate that DRM function differs depending on developmental context . Continued genome-wide analyses of DREAM binding and regulation in a variety of organisms , particularly using specific tissues isolated from animals , will further our understanding of how this key transcriptional complex functions during development and reproduction . We discovered that C . elegans LIN-54 binding and gene regulation are autosome-enriched . This bias is likely a feature of the worm DRM complex as a whole , since the localization patterns of all but one DRM subunit are autosome-enriched , as are a class of germline genes co-regulated by multiple DRM subunits . Biased binding appears to be directed by a biased recruitment element , since the hybrid E2F/DP-LIN-54 recognition motif is also autosome-enriched in C . elegans . However , when we examined the related hybrid motif in Drosophila ( Figure 3E “fly” ) , and the published Drosophila and human DREAM ChIP profiles we found that they are evenly distributed between autosome and X chromosome promoters ( data not shown , [5] , [21] ) . What evolutionary pressures might have driven the C . elegans DRM complex to disfavor the X chromosome ? X chromosomes differ from autosomes in many aspects including histone variants and modifications , gene regulation , and rates of gene divergence and movement [45] . One possibility is that DRM targets are under-represented on the X chromosome because some aspect of this chromosomal environment is incompatible with DRM-mediated transcription regulation . A second possibility is that DRM localization and its differential regulation of autosomal and X-linked genes reflects some role in balancing autosome and X chromosome gene expression . Only a limited number of non-histone proteins have been shown to exhibit X chromosome- or autosome-biased localization , and these are involved in somatic dosage compensation or germline X chromosome silencing [46]–[48] . A third possibility is that the biased localization of DRM arose as a consequence of X chromosome silencing in the germline . The X chromosome is silenced in the germline by mechanisms that are distinct from somatic X chromosome silencing [27] . Germline-expressed genes and genes with essential functions are autosome enriched , and thought to have “fled” the X chromosome to avoid being silenced [28]–[30] , [49] . One hypothesis is that the DNA-binding properties of the C . elegans DRM complex co-evolved with the redistribution of its germline-expressed and essential target genes across the genome , resulting in an autosomal bias . Silencing of the X chromosome has not been reported in Drosophila or mammalian female germlines , perhaps explaining why autosome bias is specific to C . elegans DRM . The regulation of sex chromosome gene expression , by processes that evolve rapidly and vary widely among organisms , may therefore have consequences on the genomic distribution of gene sets and , as shown here , their transcriptional regulatory networks . In embryos , the biases in DRM localization and DRM-mediated regulation correspond , but in the germline they do not . In lin-54 mutant embryos , up-regulated genes likely include direct targets based on their overlap with LIN-54 ChIP peaks , and were autosome-enriched like DRM binding . The down-regulated genes , on the other hand , are more likely indirect targets and showed no chromosome bias . In lin-54 mutant germlines , both up- and down-regulated genes included direct DRM targets . As in embryos , the up-regulated genes in the germline were primarily autosomal . Interestingly , down-regulated germline genes were X-enriched . How can we explain the paradox that the DRM complex predominantly binds to autosomes , but that its loss results in a decrease in expression of X-linked genes ? First , some LIN-54 does bind the X chromosome and might directly activate gene expression . However , fewer LIN-54-responsive genes on the X chromosome than on an average autosome are bound by LIN-54 , suggesting that many X-linked genes are indirectly regulated . Second , loss of LIN-54 might induce ectopic soma-specific pathways that include X-linked genes . However , we found no evidence for enrichment of particular pathways among the affected X-linked genes and none are soma-specific . Other models invoke chromosome-wide alterations in X chromosome gene expression . A third model is that DRM regulates expression along the X chromosome indirectly either by activating a gene involved in X chromosome activation or by repressing a gene involved in X chromosome silencing , so that in mutants the X becomes more silenced . We did not find any obvious candidate for such a factor among mis-regulated genes . Finally , a fourth model proposes that a repressor that is normally concentrated on autosomes , perhaps anchored there by DRM , spreads inappropriately to X chromosomes when DRM function is compromised . If that repressor is limiting , autosomal genes will increase in expression while X-linked genes become repressed , which is in agreement with our observations ( Figure 5E ) . Indeed , such reciprocal gene expression changes have been observed when a limiting domain-specific repressor such as the S . cerevisiae SIR proteins spread inappropriately , thereby increasing repression at ectopic locations and diluting repression at their normal site of action [50]–[52] . Related models have been invoked to explain why loss of the autosome bound MES-4 product de-silences germline X-linked genes and to explain why loss of the X chromosome bound Dosage Compensation Complex de-silences somatic X-linked genes and represses some autosomal genes in C . elegans [53] , [54] . MES-4 is an autosome-enriched histone methyltransferase that confers the “active mark” H3K36me [53] . In many biological contexts , mes-4 and synMuvB genes have opposing functions . For example , mutations in mes-4 can suppress the defects in vulva development , the increased RNAi and transgene silencing , and the ectopic expression of germline genes in the soma caused by mutations in synMuvB genes [18] , [39] , [55] , [56] . Here we define another process in which mes-4 and synMuvB mutations have opposite effects . We show that in the hermaphrodite germline LIN-54 is autosome-enriched as is MES-4 , but lin-54 mutants down-regulate while mes-4 mutants up-regulate X-linked genes . Bender et al . ( 2006 ) proposed that MES-4 indirectly regulates X-linked genes , by repelling a “global repressor” from autosomes and keeping it concentrated on the X chromosome . A possibility is that LIN-54 and MES-4 affect the X chromosome versus autosome distribution of the same repressor , in an opposite manner . A candidate for such a repressor is the C . elegans Polycomb Repressive Complex 2 ( PRC2 ) , which is composed of MES-2 , MES-3 and MES-6 . MES-2 is an E ( z ) homolog that concentrates the H3K27me3 “repressive mark” on the X chromosome in the germline [53] , [57] . MES-2/-3/-6 also keeps MES-4 and other active marks restricted to autosomes . Interestingly , it was recently shown that a class of genes repressed by the Drosophila DREAM complex is enriched for H3K27me2 and requires E ( z ) for repression [58] . However , the cytological distribution of H3K27me3 appears unaffected in mes-4 and lin-54 mutants ( [53] , data not shown ) . An important future direction is to explore potential links between DRM , MES-4 , and Polycomb Group mediated gene repression , and to shed light on how these factors might interact to govern gene regulation .
All strains were cultured at 20°C unless otherwise noted , using standard methods . The following strains were used: N2 ( Bristol ) as wild-type , lin-54 ( n3423 ) /nT1 [qIS51] , lin-54 ( n2990 ) , lin-54 ( n2231 ) [4] , [11] , and him-8 ( e1489 ) [59] . Note: Previously , lin-54 ( n2231 ) was reported to have a single mutation ( A442T ) [4]; however , sequencing revealed an additional missense mutation ( G252E ) . Embryos ( Figure 2 ) were fixed with methanol/acetone [60] . Germlines ( Figure 6 and Figure S1D ) were fixed essentially as described [61] , with the addition of 5 ul of 2% Triton-X before fixation in 4% paraformaldehyde . DNA was visualized either with DAPI or OllieGreen ( added at 1∶1000 with 10 ug/ml RNAseA with the secondary antibody ) . Whole worms ( Figure S3 ) were prepared in Carnoy's fixative as described by [62] . Primary antibodies to DRM subunits were described and validated in [4] , [10] , [13] . Another second anti-LIN-54 antibody was generated in rabbits against amino acids 207–306 ( Strategic Diagnostics Inc . ) , validated by western blot in wild-type and mutants , and showed the same localization patterns . Primary antibodies were used at 1∶100 dilutions , and detected with secondary antibodies conjugated to Alexa Fluor 568 ( Invitrogen ) at a 1∶500 dilution , except DPL-1 was performed as described [4] , [10] . Antibodies against H4K12Ac ( Serotec ) , and H3K9me2 ( Cell Signaling ) were used at 1∶1000 ( primary ) and seconday antibodies at 1∶1000 . Images for Figure 6 were captured by a Solamere Technology Group modified Yokogawa CSU10 Spinning Disk Confocal scan head attached to a Nikon TE-2000E2 inverted microscope and a 100× Plan Apo objective , using MetaMorph software ( Molecular Devices ) . The images for Figure 6A and 6B were deconvolved using the constrained iterative deconvolution algorithm developed by the UMass Medical School Biomedical Imaging Group [63] . Y1H and Y2H assays were performed as described [22] , [64] . Representative images for Figure 1B were obtained for Ppos-1 at 10 mM 3AT 5 days , Plin-54 at 20 mM 3AT 9 days , and Pvha-15 at 60 mM 3AT 9days . For western blot ( Figure 2B ) , whole worm lysates were created from 200 hand-picked synchronized young adults boiled in 2× loading buffer ( National Diagnostics EC-886 ) for 30′ with intermittent vortexing . Lysates equivalent to 25 , 50 , and 100 animals were loaded per lane and probed with anti-LIN-54 , actin ( Abcam #ab3280 , 1∶400 ) and Histone H3 ( Abcam #ab1791 , 1∶1000 ) . Immunoprecipitation , western blotting , and probing with DRM antibodies were performed as described [4] , ChIP was performed as described [65] . Briefly , mixed stage wild-type worms were cultured in S-basal at 20°C . Lysates were cross-linked in 1% formaldehyde , sonicated , and immunoprecipitated with anti-LIN-54 antibody or pre-bleed antibody control . ChIP samples including the input were subjected to two rounds of linear amplification , using the genomePlex complete whole genome amplification kit ( Sigma ) , and minimum difference between original precipitates and amplified precipitate confirmed by qPCR ( data not shown ) . Both experimental and input were processed at NimbleGen , hybridized on 385K C . elegans Whole Genome 3-Array Set ( Roche NimbleGen ) . To assay DRM subunit binding at the promoters of the lin-9 and lin-54 genes , ChIP was performed with antibodies against LIN-54 , LIN-9 , LIN-37 , or pre-bleed control from wild-type or lin-54 ( n2990 ) mixed-stage extracts . qPCR was used to calculate the amount of lin-54 or lin-9 promoter DNA in ChIP samples relative to the total input DNA . The ratio in wild-type was set at 1 . 0 . lin-9 promoter primers: 5′-cgactgtcaaacagcagctc-3′ and 5′-ttgaaatggcggttcttttc-3′ . lin-54 promoter primers: 5′-atgatgagtgacgtctacc-3′ and 5′-attgtttcgcgcgccgaaatttg-3′ . Raw ChIP-chip data were analyzed using three independent programs: MA2C [32] , ChIPOTle [66] and NimbleScan ( Roche NimbleGen ) . While ChIPOTle called fewer and NimbleScan called greater numbers of peaks than MA2C , each identified a similar set of core peaks . MA2C analysis was performed with the following settings: # MA2C Score Method ( median ) , Band Width ( 300 ) , p-value cut off ( −6 ) , and other parameters were set as default . WS180 was used to annotate gene names . LIN-54 ChIP peaks ( Figure 3 and Figure 5A ) were visualized using Affymetrix Integrated Genome browser . Modes of LIN-54 peaks were used to determine peak location for Figure 3 , and each intergenic peak was considered to associate with both neighboring genes . HomoloGene ( Ce . 01-08-2009 ) defines 3015 orthologous pairs between C . elegans and D . melanogaster , and 3488 pairs between C . elegans and human . 647 of 1572 genes bound by C . elegans LIN-54 have annotated fly orthologs; 730 genes have annotated human orthologs . 1267 of 3147 fly genes bound by Mip120 have worm orthologs ( data from Table S3 in [21] , using genes bound by Mip120 within 1 kb of 5′ end , lr peak = 2 . ) . Of 975 human genes bound by hLIN54 ( data from Table S4 in [5] , using genes bound by hLIN54 within 1 kb of 5′ end , during G0 and/or S phase ) , 186 have annotated worm orthologs . To predict motifs enriched in LIN-54 bound promoters , we defined significant peaks using ChIPOTle version 1 . 11 [66] with window size 300 bp , step size 38 bp . We selected the top 50 promoter peaks from each chromosome , based on p-value , for a total of 300 peaks , and analyzed the 1 kb sequence surrounding their centers with MEME [67] . We searched for 7–11 mer DNA motifs with parameters “-dna -mod zoops -minsites 20 -revcomp -minw 7 -maxw 11” and 5th markov model of all C . elegans promoter sequences as a background nucleotide distribution , and then searched for 12–18 mer DNA motifs with parameters “-dna -mod zoops -minsites 20 -revcomp -minw 12 -maxw 18” and the same background markov model . We confirmed that predicted motifs lie within ChIP peaks ( Figure S2 ) . We determined the genomic distributions of promoter-associated TF motifs by searching promoter regions ( 1 kb upstream from TSS ) of all 20158 C . elegans genes ( WS200 ) using MAST ( Figure 5D ) or FIMO ( Figure S2 ) in the MEME suite [67] . Although the absolute values of motif occurrence varied depending on the p-value cutoff , the under-representation of Motifs 1 and 2 on the X chromosome was observed at multiple cutoffs . p-value cutoff used to search motifs in Figure 5D and Table S3: 10−5 ( EFL-1 , HLH-27 ) , 10−6 ( Motif 1 , FLH-1 ) , and 10−7 ( NFI-1 ) . GO analysis was performed using GO-TermFinder [68] , with p-value cut off of 0 . 01 ( for LIN-54 bound genes ) or 0 . 05 ( for LIN-54 responsive genes ) with Bonferroni correction for multiple hypothesis testing . The evidence code Inferred from Electronic Annotation ( IEA ) was excluded from the analysis . Statistical analyses were performed using R , a system for statistical computation and graphics ( [69]; http://www . r-project . org ) . The rma method in the affy package from Bioconductor was used in R to summarize probe level data and to normalize the dataset to remove across array variation [70] , [71] . Log transformed data were used in subsequent analysis and plotting . WormBase version WS190 was used . To determine differentially expressed genes between wild-type and mutants , moderated T Statistics in limma [72] was used with p-value≤0 . 01 , fold change ≥1 . 5 . When multiple probes sets correspond to one gene , the average fold change was determined . Raw data from [25] was re-analyzed with the same criteria described above , and genes responsive to efl-1 ( n3639 ) , dpl-1 ( n3316 ) , lin-35 ( n745 ) , and lin-54 ( n3423 ) were clustered by the centroid-linkage hierarchical analysis ( Cluster 3 . 0 , [73] ) . Clusters were visualized with Java Treeview [74] . To calculate the percent of genes per chromosome responsive to DRM members , we used the number of genes common between the custom arrays of [25] and those represented on GeneChip C . elegans genome arrays ( Affymetrix ) . To estimate genes normally expressed in wild-type embryos or germlines , we utilized the detection ( present/absent ) call generated by the Affymetrix microarray suite . Each probe set received numeric score based on the detection calls ( present = 1 , marginal = 0 , and absent = −1 ) , and the sum of the score for three biological replicas were calculated for each probe set ( i . e . present in all three replicas = 3 ) . A gene was considered expressed if the average score was more than 1 . 5 , and absent if less than −1 . 5 . Our lists of expressed genes were comparable with those determined by SAGE analysis [38] . The microarray and ChIP data in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE28494 . http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE28494 | X chromosomes differ in number between the sexes and differ from autosomes in their associated proteins and gene regulatory properties . In C . elegans both X chromosomes are partially silenced in hermaphrodite germlines . Germline-expressed and essential genes are autosome-enriched and are thought to have fled the X chromosome during evolution because silencing these genes would result in sterility or lethality . We discovered that the C . elegans DRM complex , which controls transcription of genes implicated in development and cancer , avoids the X chromosome . We first describe how DNA–binding components of the DRM complex together recognize DNA sequences upstream of its target genes , and we describe that DRM controls different target genes in the germline versus the soma . We show that the DRM binding motif , the genes bound by DRM , and the embryonic genes regulated by DRM are all under-represented on the X chromosome . Interestingly , compromising DRM function in the germline enhances X chromosome silencing , and we discuss how autosome-bound DRM might regulate X-linked genes in trans . We propose that autosome-enriched binding of DRM co-evolved with the redistribution of its germline-expressed and essential target genes to autosomes . Our data highlight how X chromosome gene regulation may impact both the genomic distribution of gene sets and their transcriptional regulators . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genome",
"expression",
"analysis",
"x",
"chromosome",
"inactivation",
"genome",
"evolution",
"gene",
"regulation",
"dna",
"transcription",
"gene",
"function",
"animal",
"models",
"caenorhabditis",
"elegans",
"model",
"organisms",
"molecular",
"genetics",
"chromosome",
... | 2011 | Chromosome-Biased Binding and Gene Regulation by the
Caenorhabditis elegans DRM Complex |
The Spumaretrovirinae , or foamy viruses ( FVs ) are complex retroviruses that infect many species of monkey and ape . Despite little sequence homology , FV and orthoretroviral Gag proteins perform equivalent functions , including genome packaging , virion assembly , trafficking and membrane targeting . However , there is a paucity of structural information for FVs and it is unclear how disparate FV and orthoretroviral Gag molecules share the same function . To probe the functional overlap of FV and orthoretroviral Gag we have determined the structure of a central region of Gag from the Prototype FV ( PFV ) . The structure comprises two all α-helical domains NtDCEN and CtDCEN that although they have no sequence similarity , we show they share the same core fold as the N- ( NtDCA ) and C-terminal domains ( CtDCA ) of archetypal orthoretroviral capsid protein ( CA ) . Moreover , structural comparisons with orthoretroviral CA align PFV NtDCEN and CtDCEN with NtDCA and CtDCA respectively . Further in vitro and functional virological assays reveal that residues making inter-domain NtDCEN—CtDCEN interactions are required for PFV capsid assembly and that intact capsid is required for PFV reverse transcription . These data provide the first information that relates the Gag proteins of Spuma and Orthoretrovirinae and suggests a common ancestor for both lineages containing an ancient CA fold .
Spuma- or foamy viruses ( FVs ) are complex retroviruses that constitute the only members of the Spumaretrovirinae subfamily within the Retroviridae family [1] . They have been isolated from a variety of primate hosts [2–5] as well as from cats [6–8] , cattle [9] , horses [10] and sheep [11] . Endogenous FVs have also been described in sloth [12] , aye-aye [13] and coelacanth [14] . Prototype foamyvirus ( PFV ) is a FV isolated from human sources [15 , 16] . The PFV genome is highly similar to that of simian foamy virus isolates from chimpanzee ( SFVcpz ) and so infection in humans is believed to have arisen through zoonotic transmission [17–19] . Nevertheless , even though FVs are endemic within non-human primates and display a broad host range , human-to-human transmission of PFV has never been detected . Moreover , although in cell culture FV infection causes pronounced cytopathic effects [20] , infection in humans and natural hosts is apparently asymptomatic [21–23] making their usage as vectors for gene therapy an attractive proposition [24] . FVs share many similarities with other retroviruses in respect of their genome organisation and life cycle . However , they vary from the Orthoretrovirinae in a number of important ways . These include the timing of reverse transcription that occurs in virus producer cells rather than newly infected cells [25 , 26] and the absence of a Gag-Pol fusion protein [27 , 28] . In addition , the Gag protein remains largely unprocessed in FVs [29] whereas within the Orthoretrovirinae processing of the Gag polyprotein represents a critical step in viral maturation , producing the internal structural proteins Matrix ( MA ) , Capsid ( CA ) and Nucleocapsid ( NC ) found in mature virions . Furthermore , FV Gag lacks the Major Homology Region ( MHR ) and Cys-His boxes found in orthoretroviral CA and NC , respectively . Despite these profound dissimilarities , the Gag proteins of the two retroviral subfamilies carries out the same functional roles including viral assembly , nucleic acid packaging , transport to and budding through the cytoplasmic membrane of the producer cell as well as trafficking through the cytoplasm of the target cell and uncoating . In addition , FV Gag also contains the determinants for restriction by Trim5α [30 , 31] that in orthoretroviruses comprises the assembled CA lattice [32] . To date , high-resolution X-ray and/or NMR structures have been reported for MA , CA and NC components of Gag from numerous retroviruses [33–42] but among FVs only the structure of the Env-binding N-terminal domain of PFV-Gag has been reported [43] . Further structural information with regard to other Gag domains of FVs has remained elusive but is vital for any detailed understanding of how FV Gag fulfils its many functions . Here we report the structure and present structure/function studies of a di-domain from the central region of PFV-Gag . Our data reveal that although unrelated at the level of primary sequence , FV central domains are structurally related to the N- and C-terminal domains of orthoretroviral CA . Moreover , they share the capacity for self-association and are required for virion capsid assembly and viral infectivity . Further phylogenetic and combined comparative structural analysis reveals FV central domains also have the same organisational arrangement as orthoretroviral CA and we propose that both arose through genetic divergence from a common , double domain ancestor .
Alignment of the primary sequences of FV Gag proteins from primate and other mammalian hosts reveals two regions of strong conservation , an N-terminal region corresponding to the Env-binding domain [43–45] containing the cytoplasmic targeting and retention sequence ( CTRS ) [46 , 47] and the other located centrally containing highly conserved PGQA and YxxLGL sequences [48] and just N-terminal to the chromatin binding sequence ( CBS ) [49]and GR boxes [50] ( Fig 1A ) . Within this central region , large sections of highly conserved sequence are present ( Fig 1B ) . Therefore , to understand more about the nature of the PFV-Gag central conserved region , the structure of PFV-Gag ( 300–477 ) was determined in solution using multidimensional heteronuclear NMR spectroscopy . Details of data collection , structure determination and model quality are presented in Table 1 . The structure comprises two all helical domains , connected by a short 5-residue linker ( Fig 1C ) . Residues P300-H383 make up the N-terminal domain ( PFV-NtDCEN ) containing four helices ( α1-α4 ) and the C-terminal domain ( PFV-CtDCEN ) , residues H389-R477 , contains the remaining five helices ( α5-α9 ) . Superposition of the 20 conformers in the family of structures results in a backbone atom rmsd of 0 . 3 Å for ordered residues 304–355 , 358–477 showing that the structure is well defined except for the N- and C-termini and loop regions ( S1A Fig ) . In PFV-NtDCEN , helices α1-α3 form an antiparallel 3-helix bundle connected to α4 by a long loop that closely tracks one face of α3 . In PFV-CtDCEN helices α5-α9 are arranged as a five-helix antiparallel bundle . In both domains , the inner faces of the helices pack to form an extensive hydrophobic core through interaction of apolar sidechains . Examination of the protein backbone dynamics using 15N NMR relaxation measurements ( S1B Fig ) , show that residues within helices α1- α4 and α5-α9 of the PFV-NtDCEN and PFV-CtDCEN exhibit large and positive heteronuclear NOE ( HetNOE ) values and have uniform 15N-T1 and -T2 values indicating a rigid backbone . Additionally , the presence of inter-domain NOEs , together with little variation in the T1/T2 values , suggests the PFV-NtDCEN and PFV-CtDCEN are structurally and dynamically dependent and have a coupled movement . Based on these relaxation rates and assuming an isotropic model , a rotational correlation time ( tc ) of 14 . 1 ns for the NtDCEN-CtDCEN di-domain was determined , consistent with a ~ 20 kD globular protein . The residues at the N- and C-termini outside of this core region have lower T1 and higher T2 values , reduced or negative HetNOEs , close to zero 1DNH residual dipolar couplings ( RDC ) and mainly random coil chemical shifts indicating rapid ( psec ) internal motion in these terminal regions . In addition , the relaxation data also reveals internal regions of high mobility , including residues G356 to G366 located in the long loop connecting α3-α4 , residues G384 to P388 in the NtDCEN-CtDCEN interdomain linker and G432 part of a stretch of highly conserved residues ( -P431-G-Q-A434- ) located in the loop connecting α7-α8 of CtDCEN and in close spatial proximity to the conserved Y/F464-x-x-L-G-L469 motif ( Fig 1A and 1B ) , at the C-terminus of α9 that is required for Gag assembly [48] . Together with these relaxation data a number of interdomain NOEs ( S1C Fig ) define a largely hydrophobic NtDCEN-CtDCEN interface comprising 550Å2 of buried surface area ( Fig 1D ) . Although not extensive in area , there is substantial packing of apolar sidechains from NtDCEN residues on helices α2 and α4 ( I326 , V375 and F379 ) with CtDCEN residues ( V394 , I398 , L410 , M413 and L414 ) on helices α5 and α6 ( Fig 1D ) that contribute to the stability of the interface . Initial structural similarity searches of the PDB with PFV-Gag ( 300–477 ) , PFV-NtDCEN and PFV-CtDCEN were conducted using the SSM server [51] . Application of this approach , produced only very weak matches based on the quality of alignment Q-scores ( 0 . 1–0 . 3 ) . Nevertheless , 11 of the top 15 alignments for individual NtDCEN and CtDCEN domains were with either amino- ( NtDCA ) or carboxyl-terminal domains ( CtDCA ) from orthoretroviral CAs ( S1 Table ) . However , although matches were found for NtDCEN with orthoretroviral NtDCA domains and for CtDCEN with orthoretroviral CtDCA domains and the helical connectivity and topological arrangement of secondary structures were largely conserved ( S2 Fig ) , notably some top alignments were between NtDCEN and CtDCA domains and by CtDCEN with NtDCA domains , Fig 2 . Inspection of these alignments reveals a closest match for PFV-Gag NtDCEN with the CtDCA of the alpha-retrovirus RSV ( 3G1G ) based upon rmsd over all aligned α-carbons . However , in all these alignments the orthoretroviral CtDCA structures contain an additional α-helix that inserts between α3 and α4 of NtDCEN ( Fig 2D and 2E ) . Structural alignments with orthoretroviral NtDCA , reveal the closest match is between PFV-Gag CtDCEN and the NtDCA of the gamma-retrovirus MLV ( 3BP9 ) ( Fig 2H ) . Again , however , although the core fold aligns well , the interspersing loops that connect the secondary structure elements in the orthoretroviral NtDCA are absent or much shorter in PFV-Gag CtDCEN . These data provide evidence for a structural conservation between orthoretroviral CA and spumaretroviral Gag but these very weak alignments do not discriminate well between NtDCEN−NtDCA , CtDCEN−CtDCA ( forward; NN , CC ) and NtDCEN−CtDCA , CtDCEN−NtDCA ( reverse; NC , CN ) pairings . Therefore , to assess the significance and quantify the degree of similarity for forward and reverse pairings we applied a structural alignment method based on the generation of a population of 'decoy' models to provide a background distribution of scores [52] combined with structural superposition using the SAP program [53] . This method has the advantage that it uses a local structural environment-based alignment and that each comparison in the random pool is between two models of the same size and secondary structure composition as the pair of native structures being investigated . For this analysis five orthoretroviral CA proteins were chosen where both NtDCA and CtDCA structures were available . Individual CA domains were then compared with both PFV-Gag NtDCEN and CtDCEN and the associated decoy models . The degree of similarity between the domains with respect to the bulk alignments with decoy models ranged from < 2σ to > 5σ ( Z-score ) . However , as with the SSM searches significant 4σ results were obtained for both reverse as well as forward alignments , Table 2 . Of the top five Z-scores in Table 2 , four are associated with N-N and C-C pairings . Although this does suggest conventional forward linear domain equivalence , in order to obtain a more quantitative consensus for forward versus the reverse domain pairings , the Z-scores for each domain pairing were combined using a T-test statistic over all five viruses . Employing this analysis , all four possible domain pairings were significant with probabilities ( Tprob ) ranging from 10−6 to > 10−18 . However , the two reversed pairings ( NC and CN ) have lower probabilities than the forward pairings ( NN and CC ) Table 2 and by combining the probabilities log10 ( TprobNN . TprobCC ) –log10 ( TprobNC . TprobCN ) a 12-log difference-probability ( ΔTprob ) is now apparent for the forward pairing with respect to the reverse . Both the T and Z statistics support an ancestral relationship between the central domains of PFV-Gag and the NtDCA and CtDCA of orthoretroviral CA . This suggested forward pairing ( NN and CC ) would support the notion that the orthoretroviral CA and PFV-Gag NtDCEN-CtDCEN arose through genetic divergence from a common , double domain ancestor without a requirement for transposition . Given the requirement for CA oligomerisation in orthoretroviral Gag assembly and maturation , the self-association and assembly properties of PFV-Gag ( 300–477 ) , PFV-NtDCEN and PFV-CtDCEN were analysed by sedimentation velocity ( SV ) and equilibrium ( SE ) analytical ultracentrifugation ( AUC ) . The experimental parameters , molecular weights derived from the data and statistics relating to the quality of fits are shown in Table 3 . SV-AUC analysis of the whole of the conserved region , PFV-Gag ( 300–477 ) , revealed a sedimentation coefficient ( S20 , w ) of 1 . 87 ( Fig 3A ) and derived molar mass of 20 . 6 kDa demonstrating that PFV-Gag ( 300–477 ) is a stable monomer in solution . These observations were confirmed by multispeed SE-AUC at varying protein concentration . The equilibrium distribution from an individual multispeed experiment is presented in Fig 3B . The individual gradient profiles showed no concentration dependency of the molecular weight and fit globally with a single ideal molecular species model , producing weight averaged molecular weight of 20 . 3 kDa demonstrating the monomeric nature of this PFV central region . SV-AUC analysis of PFV-Gag NtDCEN measured at high protein concentration ( 188 μM ) also revealed this domain to be monomeric in solution with a only a single species , ( S20 , w ) of 1 . 25 ( Fig 3A ) with derived molar mass of 10 . 3 kDa present ( Table 3 ) . By contrast SV-AUC data recorded on PFV-Gag CtDCEN produced a sedimentation coefficient continuous distribution function , C ( S ) , that contained two species with S20 , w of 1 . 65 and 2 . 07 with derived molecular weights of 14 . 7 kD and 20 . 7 kD ( Table 3 and Fig 3A ) . Notably , the proportion of the fast 2 . 07 S , component increased with increasing concentration ( S3 Fig ) consistent with monomer-dimer equilibrium . Therefore , in order to quantify the affinity and stoichiometry of self-association , multispeed SE-AUC recorded at varying protein concentration was employed . These data ( Fig 3B ) are best fit by a monomer-dimer self-association model where the 11 . 9 kDa PFV-Gag CtDCEN monomers dimerise with an equilibrium association constant of 1 . 1x106 M-1 ( 0 . 9 μM KD ) . These data are consistent with the distribution of peaks in the C ( S ) functions derived from SV-AUC data . Moreover , they reveal that whilst the entire PFV-Gag central region is monomeric PFV-Gag CtDCEN has the propensity for self-association . Given the dimerisation properties of PFV-Gag CtDCEN and the structural homology with self-associating orthoretroviral CA-domains we determined the solution structure of the PFV-Gag CtDCEN homodimer . Details of data collection and structure determination are presented in Table 1 . Superposition of the 20 conformers in the family of structures ( S4A Fig ) results in a backbone atom rmsd of 0 . 3 Å for ordered residues 381–477 revealing a well-defined structure except for residues close to the N- and C-termini . In the structure , Fig 4A , each monomer comprises five-antiparallel α-helices ( residues N393-E402 , V404-L414 , Q420-Y429 , Q433-Q445 and Q450-L467 ) and is virtually identical to the equivalent helices , α5 to α9 , in PFV-Gag ( 300–477 ) with the exception that α5 is ~2 turns shorter . Analysis of NMR relaxation data ( S4B Fig ) reveals little variation in T1/T2 values and the derived rotational correlation time ( tc ) of 18 . 2 ns is consistent with a ~ 24 kD CtDCEN homodimer . The homodimer interaction is defined by numerous NOEs ( S4C Fig ) and encompasses 470 Å2 of buried surface . The interface is largely hydrophobic with the majority of interactions resulting from packing of α6 of one monomer against α6 of the opposing monomer together with some contribution from hydrophobic side chains of residues on α5 ( Fig 4B ) . At the centre of the interface the side chains of I398 , L410 and M413 from one monomer pack against I398* , L410* and M413* of the opposing monomer and comprise a continuous apolar network . Disruption of this network by introduction of an L410E/M413E double mutation results in total loss of dimerisation as revealed by SV-AUC analysis ( Fig 4C ) . Notably , I398 , L410 and M413 are also involved in the NtDCEN-CtDCEN interface were they make apolar contacts with side chains of residues on α2 and α4 in NtDCEN ( Fig 1D ) . To probe the function of domain interface residues in a virological context , V375Q and L410E/M413E amino acid interface-disrupting mutations were introduced into PFV-Gag in a mammalian virus expression system . In addition , W371A or C368A alanine substitution mutations designed to disrupt hydrophobic packing of the Gag-NtDCEN domain were also made along with particles lacking reverse transcriptase ( iRT ) . The effects of these substitutions on virus Gag/Env/Pol processing , particle production , and infectivity were then assessed ( Fig 5 ) . In all instances , viral particles were produced and the composition and processing of Gag Pol and Env was comparable with wt PFV ( Fig 5A ) , although , overall particle production was reduced between 3–5 fold , in all of the mutants ( Fig 5B ) . In contrast to these small particle production defects , viral infectivity upon introduction of V375Q and L410E/M413E interface mutations was reduced by over 4 orders of magnitude ( Fig 5C ) comparable with 3–4 log reductions observed in W371A and C368A NtDCEN disruption mutants and 4 log reductions observed with a combined W371A/V375Q mutant or a Gag wt /Pol iRT virus . Given these large effects on viral infectivity , the morphology and integrity of particles was also assessed by cryo-electron microscopy ( cEM ) ( Fig 6 ) . Analysis of wt PFV ( Fig 6A and S2 Table ) reveals roughly spherical 1000 to 1300 Å diameter particles with external spikes of the Env protein and core structures as previously described [45 , 54] . We performed cryo-tomography to study virus particles in 3-dimensions . The majority of particles contain a dense core structure , 600 to 800 Å , in their interior . In some instances , two cores were present , often correlating with a larger virion size , as observed with other foamy virus [45] and orthoretroviral particles [55] . Inspection of the core morphology revealed that it comprised an 80–100 Å layer that is strongly faceted and contains vertices indicative of a polyhedral structure with underlying icosahedral order . By contrast , although of similar size and displaying Env spikes , no virus particles with V375Q and L410E/M413E interface mutations contained an internal dense core , indicating they have defects in core assembly ( Fig 6B and S2 Table ) . The particles appear either empty or in some cases contain a diffuse layer of density close to the inner side of viral envelope . Similarly , particles of NtDCEN disruption mutants C368A and W371A also have wt size distribution and external morphology but have no cores ( Fig 6B and S2 Table ) demonstrating that mutations affecting NtDCEN—CtDCEN interactions and those designed to interfere with Gag central domain folding are both deleterious to core assembly . The effects of the interface and NtDCEN disruption mutations on reverse-transcription of the viral genome were also examined by qPCR . These data ( Fig 7 ) revealed that all particles contained similar levels of PFV RNA suggesting that there was no requirement for an assembled viral core to recruit and/or package RNA genomes . However , quantitation of viral DNA revealed that in both the interface or Gag-NtDCEN disruption mutants that lack cores , there was a 100-fold reduction in the DNA genome content . The DNA genome content of the iRT mutant was reduced 1000-fold . Given that reverse transcriptase is recruited into particles in the mutants with a comparable efficiency to wt ( Fig 5A ) these data reveal a requirement for core formation in order for efficient reverse transcription to occur .
Gag is the major structural protein of both spuma and orthoretroviral subfamilies , required for viral assembly , genome packaging and budding from producer cells [56] . Nevertheless , despite the conservation of function , spuma and orthoretroviral Gag share little if any sequence identity [57] . Any relatedness in terms of structure therefore remains unclear . Previous studies have shown that an N-terminal domain from spumaretroviral Gag ( PFV-Gag-NtD ) , whilst possessing some of the functional properties of orthoretroviral Gag MA and CA maturation products , is entirely unrelated on a structural level [43] . We have now determined the solution structure of a central region of PFV-Gag ( NtDCEN-CtDCEN ) . By contrast with the N terminal region , this structure reveals that the central region of spumaretroviral Gag has unanticipated structural similarity to the NtDCA and CtDCA of orthoretroviruses . The NtDCEN and CtDCEN domains comprise 4 and 5 helical bundles , respectively , that in terms of topology align well with secondary structure elements of NtDCA and CtDCA domains . However , overall the alignment is relatively weak and although the core helical bundles are structurally very similar , the orthoretroviral NtDCA and CtDCA contain additional helices and loop insertions . We therefore applied an unbiased objective approach to assess the degree of similarity between PFV-NtDCEN and PFV-CtDCEN with NtDCA and CtDCA domains [52 , 53] . This analysis confirmed the relationship between the spuma- and orthoretroviral sequences and revealed that by far the preferred statistical alignment was also the most plausible on biological grounds , specifically a “forward pairing” where PFV-NtDCEN corresponds to NtDCA and PFV-CtDCEN relates to CtDCA . Based on these observations , it is reasonable to conclude that the related central regions of the Gag proteins of spuma- and orthoretroviruses , as well as having conserved functions have arisen as a result of genetic divergence from a common , double domain ancestor . The capacity to form an assembled lattice is a key feature of retroviral Gag proteins . These structures have been well characterised for mature orthoretroviruses [58] , though the versions present in immature viruses remain relatively poorly defined [59–61] . Nevertheless it is clear that the formation of CA hexamers is vital for the assembly process . By contrast , there is much less information available regarding spumavirus Gag mediated assembly . It has been demonstrated that PFV-Gag-NtD self-associates into dimers [43] . Our findings now identify PFV-Gag ( NtDCEN-CtDCEN ) that is structurally related to orthoretroviral CA , has the functional properties of a protein involved in capsid assembly and moreover , FV polyhedral core structure is dependant on PFV-Gag ( NtDCEN-CtDCEN ) structural integrity . A clue to how PFV Gag might assemble is revealed by the structure of PFV-CtDCEN ( Fig 4 ) . In isolation PFV-CtDCEN forms weak dimers , KD = 0 . 9 μM ( Fig 3 ) through homotypic interactions mediated by hydrophobic side chains located on helices α5 and α6 . This is in contrast , to the orthoretroviruses where the major CA-CtD interface is formed through homotypic interactions between residues on CA-CtD α9 that would align to α7 in PFV-CtDCEN and therefore appears unrelated . Nevertheless , in the context of intact PFV-Gag , formation of these CtDCEN-CtDCEN interactions would require conformational rearrangement to expose the α5-α6 interface that would consequently release the NtDCEN domains to make further homotypic interactions . However , given we have demonstrated the capacity for CtDCEN self-association it is a possibility that the CtDCEN-CtDCEN interface is utilised by FV-Gag in CA assembly . Moreover , since Gag conformational switching is a major driver in the maturation of orthoretroviruses [59–62] the notion of a conformational change in FV Gag is certainly plausible . In further support of this notion , notably the Major Homology Region ( MHR ) of orthoretroviral CA is a critical driver of maturation and assembly [63–65] . The MHR comprises a strand-turn-helix structure that makes intra-hexamer homotypic CA-CtD interactions in the immature CA lattice [60 , 61] and maps to α5 and α6 region of PFV-CtDCEN in our alignments . Therefore , although the α5 - α6 and MHR motifs are structurally unrelated their positioning suggests a conservation of assembly function in this region . Another prominent feature of PFV-Gag is the YxxLGL motif ( Fig 1A ) ( residues Y464-L469 ) that is conserved in all spumaretroviruses ( Fig 1B ) and is required for particle assembly [48] . In the PFV-Gag ( NtDCEN-CtDCEN ) structure this motif is found at the C-terminus of α9 in CtDCEN ( S5 Fig ) . The aromatic side chain of Y464 packs into a hydrophobic pocket and forms part of the core of the CtDCEN helical bundle . Notably , as only Y or F are observed at this position amongst FV Gags ( Fig 1B ) the conservation is likely a result of the structural requirement for a phenyl group at this position to be buried in the hydrophobic core . By contrast , the side chains in the LGL portion of the motif are exposed and abut residues from another highly conserved PGQA motif at the N-terminal of α8 in CtDCEN ( residues 431–434; Fig 1B ) to form a continuous surface hydrophobic patch located ~ 180° away from the α5 - α6 interface of CtDCEN ( S5 Fig ) . Given the requirement for capsid assembly , one notion is that α5 - α6 homotypic interactions and further self-association through YxxLGL/PGQA surface patch when combined with PFV-Gag-NtD dimerisation , might also give rise to hexameric assemblies analogous to those formed in orthoretroviruses . However , notably the helices containing the YxxLGL/PGQA patch actually align with α10 and α11 of orthoretroviral CA that are not major drivers of orthoretroviral CA assembly suggesting there might be an alternative packing arrangement of a spumaretroviral Gag assembly . Introduction of interface mutations V375Q and L410E/M413E or YxxLGL motif mutants [48] have little effect on virus assembly or RNA encapsidation . By contrast , dramatic effects are observed on the formation of morphologically intact cores , particle DNA content and infectivity . These seemingly incompatible data might be reconciled in the following way . It is known that initial FV capsid formation occurs within the cell cytoplasm and simultaneously viral RNA is recruited by Gag via the GR-regions [54] . Subsequently , FV Env leader peptide binds Gag to facilitate membrane targeting and particle release [45] . However , it has been demonstrated that cleavage of PFV p71-Gag to generate p68-Gag is required for the initiation of reverse transcription [66] . Furthermore , it has been shown that proteolytic processing of the Gag protein of S . cerevisiae Ty1 transposable elements that assemble in the cytoplasm is also required for reverse transcription and transposition activity [67 , 68] . Although we cannot rule out that in FVs Env binding to Gag might be a trigger to conformational rearrangement , we suggest that Gag cleavage to form p68 , initiates the rearrangement of Gag , resulting in the appearance of the discrete capsid layer observed by cEM . The absence of viral DNA genomes in released mutant virions ( Fig 7 ) implies that this Gag rearrangement and capsid shell formation is a requirement for one or more steps in reverse transcription and may be analogous to maturation in orthoretroviruses . Members of the Trim5α family of restriction factors block infection of cells by HIV-1 , as well as other lentiviruses , gammaretroviruses and the FVs [31 , 69] . Orthoretrovirus restriction requires interaction of Trim5α with the CA component of Gag in the context of an assembled capsid shell [32 , 70] consistent with the genetic mapping within CA of the amino-acid determinants for restriction specificity [71 , 72] . It appears that CA-hexamers , the basic building block for core assembly , represent the primary target for Trim5α restriction [73 , 74] and a similar picture is emerging for Fv1 [75 , 76] . However , given the apparent lack of sequence identity between orthoretroviral and FV Gag proteins , it has been unclear how such restriction factors might recognise and restrict FVs . Indeed , the molecular determinants for Trim5α restriction of FVs seem to map to the N-terminal region of FV Gag [43] . Our structural analysis of PFV Gag now reveals that FVs also contain a CA region comprising two domains with folds related to the NtDCA and CtDCA of orthoretroviral Gag . This might suggest a similar mechanism for FV recognition by restriction factors where self-association of the central region through Gag CtDCEN interactions in combination with dimerisation through the Gag N-terminal region [43] could also form hexameric arrays that are targeted by Trim5α . More detailed structural studies will be required to answer this question .
The DNA sequences coding for PFV-Gag residues 300–477 , 300–381 ( NtDCEN ) , and 381–477 ( CtDCEN ) were amplified by PCR from template plasmid pcziGag4 [77] containing the PFV Gag gene . PCR products were inserted into a pET22b expression vector ( Novagen ) using the NdeI and XhoI restriction sites in order to produce C-terminal His-tag fusions . The correct sequence of expression constructs was verified by automated DNA sequencing ( GATC Biotech ) . His-tagged PFV constructs were expressed in the E . coli strain Rosetta 2 ( DE3 ) and purified using Ni-NTA affinity ( Qiagen ) and size exclusion chromatography ( SEC ) on Superdex 75 ( GE healthcare ) . For NMR studies proteins were grown in minimal media supplemented with 15NH4Cl , 13C-Glucose and/or 2H2O and purified as described . All NMR experiments were carried out at 298 K on Bruker Avance 600- , 700- , 800- , and 950-MHz spectrometers . 1H/2H , 13C 15N-labeled PFV-Gag samples , PFV-Gag ( 300–477 ) ( residues 300–477 ) and PFV-Gag CtDCEN ( residues 381–477 ) were prepared in buffer containing 20mM Tris-HCl , 20 mM NaCl , 0 . 5 mM TCEP pH 7 . 0 . Protein concentrations for the NMR experiments were ~300 μM for PFV-Gag ( 300–477 ) and 1 . 6 – 2 mM for PFV-Gag CtDCEN . 1H , 13C and 15N resonance assignments for protein backbone were obtained from three-dimensional HNCA , HN ( CO ) CA , HNCACB , HN ( CO ) CACB , CBCA ( CO ) NH , HNCO , HN ( CA ) CO experiments . For side-chain chemical shift assignments 3D HBHA ( CO ) NH , CC ( CO ) NH , H ( CCO ) NH , ( H ) CCH-TOCSY , and CCHTOCSY spectra were also acquired . In addition , aromatic side-chain resonances were assigned from the analysis of the 1H-13C HSQC tuned to aromatic carbons , 2D ( HB ) CB ( CGCD ) HD , 2D ( HB ) CB ( CGCDCE ) HE as well as 3D 13C-edited NOESY-HSQC tuned to aromatic carbons . Inter-proton distance restraints for structural calculations were obtained from 3D 13C-edited NOESY-HSQC and 15N-edited NOESY-HSQC spectra recorded using a 100 ms mixing time . The dimer interface of PFV-Gag CtDCEN was identified by intermolecular distance restraints using 13C/15N-filtered 13C-edited NOESY spectra . The 3D-filtered spectra were obtained using an asymmetrically labelled dimer of PFV-Gag CtDCEN prepared by mixing equimolar unlabelled protein with uniformly 13C/15N-labeled protein ( 1 . 6 mM total protein concentration ) . For residual dipolar coupling ( RDC ) measurements , weakly aligned 15N-labelled samples of PFV-Gag ( 300–477 ) ( 200 μM ) and PFV-Gag CtDCEN ( 2 mM ) were prepared by the addition of 10mg/ mL filamentous phage Pf1 ( ASLA Biotech Ltd , Latvia ) . 1D NH RDCs were measured using the In-Phase and Anti-Phase method [78] . The RDC values were obtained by subtracting the reference value in isotropic solution . All spectral data were processed with NMRPipe [79] and analysed with CARA [80] . The solution structures for PFV-Gag ( 300–477 ) and the PFV-Gag CtDCEN dimer were calculated using the program ARIA ( Ambigious Restraints for Iterative Assignment v 2 . 3 ) [81] . Nine iterations of progressive assignment and structure calculation combined with NOE distance restraints , hydrogen bonds , dihedral angle restraints , predicted by the TALOS program [82] and RDC measurements were employed in a simulated annealing protocol . For the PFV-Gag CtDCEN homodimer the inter-proton NOE-derived distance restraints present in the filtered NOESY experiments were defined as intermolecular and the corresponding NOEs removed from the 3D 13C-NOESY-HSQC . Initial structures were used to determine the axial and rhombic components of the alignment tensors with the program MODULE [83] . Subsequently , the RDC restraints were added in the final refinement stage of structure calculations . Only data for residues located in rigid secondary structure elements ( 1H-15N NOE > ~0 . 75 ) were employed . A final ensemble of the 20 lowest energy structures derived from 100 calculated structures and refined in an explicit water box in the last iteration was selected . The superimposition of the 20 lowest-energy structures and the ribbon diagram of one representative PFV-Gag ( 300–477 ) and one PFV-Gag CtDCEN dimer structure are shown in S1A and S4A Figs . The quality of the calculated structure ensembles were assessed and validated with the Protein Structure Validation Suite-PSVS [84] and Procheck-NMR [85] . For the final 20 lowest-energy NMR structures , no distance or torsional angle restraint was violated by more than 0 . 5 Å or 5° , respectively . Structure determination details are summarised in Table 1 . The backbone 15N relaxation parameters of the spin-lattice relaxation time T1 , the spin-spin relaxation time T2 and the steady-state heteronuclear 1H-15N NOE relaxation were determined at 25°C on a 700 MHz spectrometer using a 15N-labeled NMR samples for PFV-Gag ( 300–477 ) . The time delays used for T1 experiments were 10 , 50 , 100 , 200 , 400 , 500 , 750 , 1000 , and 1400 ms , and those for T2 experiments were 8 , 16 , 32 , 48 , 64 , 80 , 96 , 112 , 128 and 160 ms . The T1 and T2 relaxation data were obtained by fitting the individual peak intensities using nonlinear spectral lineshape modelling and fitted to single exponential using routines within NMRPipe [79] . 1H-15N NOE values were calculated from peak intensity ratios obtained from spectra with and without 1H saturation prior to the 15N excitation pulse . The protein structure comparison service ( SSM ) at the European Bioinformatics Institute ( http://www . ebi . ac . uk/msd-srv/ssm/ ) was used to perform initial searches for structural homologues in the PDB . PFV-Gag NtDCEN and CtDCEN were superimposed upon orthoretroviral CA NtD and CtDs using SUPERPOSE [51] from the ccp4 program package . The fit qualities based on rmsd of Cα positions were ranked using the Q-score . Structural alignments were also produced using the SAP program [53] that uses a local structural environment based comparison that is less sensitive to local structural variation than the raw rmsd measure . The significance of the SAP comparisons were assessed using customized "decoy" models to provide a background of scores against which the comparison of the native domain structures could be evaluated [52] . A representative selection of five orthoretroviruses for which both NtDCA and CtDCA structures were available was used allowing a joint probability of their significance to be calculated for each domain pairing . Sedimentation velocity experiments were performed in a Beckman Optima Xl-I analytical ultracentrifuge using conventional aluminium double sector centrepieces and sapphire windows . Solvent density and the protein partial specific volumes were determined as described [86] . Prior to centrifugation , samples were prepared by exhaustive dialysis against the buffer blank solution , 20 mM Tris-HCl pH 8 , 150 mM NaCl and 0 . 5 mM TCEP ( Tris Buffer ) . Centrifugation was performed at 50 , 000 rpm and 293 K in an An50-Ti rotor . Interference data were acquired at time intervals of 180 s at varying sample concentration ( 0 . 5–2 . 0 mg/ml ) . Data recorded from moving boundaries was analysed in terms of the size distribution functions C ( S ) using the program SEDFIT [87–89] . Sedimentation equilibrium experiments were performed in a Beckman Optima XL-I analytical ultracentrifuge using aluminium double sector centrepieces in an An-50 Ti rotor . Prior to centrifugation , samples were dialyzed exhaustively against the buffer blank ( Tris Buffer ) . After centrifugation for 30 h , interference data was collected at 2 hourly intervals until no further change in the profiles was observed . The rotor speed was then increased and the procedure repeated . Data were collected on samples of different concentrations of PFV-Gag ( 300–477 ) and PFV-Gag CtDCEN at three speeds and the program SEDPHAT [90 , 91] was used to determine weight-averaged molecular masses by nonlinear fitting of individual multi-speed equilibrium profiles to a single-species ideal solution model . Inspection of these data revealed that the molecular mass of PFV-Gag ( 300–477 ) showed no significant concentration dependency and so global fitting incorporating the data from multiple speeds and multiple sample concentrations was applied to extract a final weight-averaged molecular mass . For PFV-Gag CtDCEN the molecular masses showed significant concentration dependency and so global fitting of a monomer-dimer equilibrium model incorporating the data from multiple speeds and multiple sample concentrations was applied to extract the dimerisation association constant ( KA ) . PFV Wild type and the Gag central domain mutants were examined by cryo-electron tomography . In summary , 2 μL stock virus solution was mixed with 10-nm gold particles ( British-Biocell ) diluted in buffer PBS and the total 2 . 5 μL solution was applied to amylamine glow-discharged 200 mesh copper Quantifoil ( R2/2 ) grids in the environment chamber ( 4°C , 100% RH ) of a Vitrobot Mark III ( FEI ) , blotted on both sides with a double layer of paper for 4 seconds before plunging into liquid ethane . The frozen grids were transferred to a Gatan 626 cryo tomography holder and inserted into the FEI Spirit TWIN microscope operated at 120keV with a tungsten filament source . Images were recorded unbinned at a nominal magnification of 30 , 000 ( 7Å/pixel ) on a 2Kx2K Eagle CCD camera at -2 . 5 μm defocus . Tilt series for tomography were recorded automatically using Serial EM from 0 to ±60° in 2° steps , typically with a total dose less than 70 e-/Å2 . Tomographic tilt series were aligned using IMOD software [92] . Alignment initially used cross-correlation and then used gold particles as fiducials . Reconstructed 3D volumes were generated by back-projection as well as SIRT method . For better visualization , individual virus particles were extracted from the whole tomograms and 50Å thick sections are shown in Fig 6 . The human embryonic kidney cell line 293T ( ATCC CRL-1573 ) [93] and the human fibrosarcoma cell line HT1080 ( ATCC CCL-121 ) [94] were cultivated in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% heat-inactivated fetal calf serum and antibiotics A four-component PFV vector system , consisting of the expression-optimized packaging constructs pcoPG4 ( PFV Gag ) , pcoPE ( PFV Env ) , pcoPP ( Pol ) , and the enhanced green fluorescent protein ( eGFP ) -expressing PFV transfer vector puc2MD9 , has been described previously [50 , 77 , 95] . In some experiments a previously described variant of the PFV Pol packaging construct with catalytically inactive reverse transcriptase ( pcoPP2 , Pol iRT , YVDD312–315GAAA mutation ) was used [50] . All PFV Gag packaging constructs used in this study are based on the parental pcoPG4 vector [95] . The PFV Gag packaging constructs encoding mutant Gag protein with alterations in central domains ( pcoPG4 C368A , pcoPG4 W371A , pcoPG4 V375Q , pcoPG4 W371A+V375Q , pcoPG4 L410E+M413E ) were generated by recombinant PCR techniques and verified by sequencing . Cell culture supernatants containing recombinant viral particles were generated by transfection of the corresponding plasmids into 293T cells using polyethyleneimine ( PEI ) as described previously [66 , 96] . For subsequent Western blot analysis the supernatant generated by transient transfection was harvested , passed through a 0 . 45-μm filter and centrifuged at 4°C and 25 , 000 rpm for 3 h in a SW32Ti rotor ( Beckman ) through a 20% sucrose cushion . The particulate material was resuspended in phosphate-buffered saline ( PBS ) . For cryo electron microscopy analysis viral particles were produced in serum-free medium and a further concentration step using Amicon Ultra 0 . 5 ml 100K Concentrators was included following the first concentration by ultracentrifugation through 20% sucrose similar as described recently [54] . Transduction efficiency of recombinant , eGFP-expressing PFV vector particles by fluorescence marker-gene transfer assay was analyzed 72 h post-transduction as described previously [54 , 95 , 97] . All transduction experiments were performed at least twice . In each independent experiment the values obtained with the wt construct pcoPG4 were arbitrarily set to 100% and values obtained with other constructs were normalized as a percentage of the wt values . Cells from a single transfected 100 mm cell culture dish were lysed in detergent-containing buffer and the lysates were subsequently centrifuged through a QIAshredder column ( QIAGEN ) . Protein samples from cellular lysates or purified particulate material were separated by SDS-PAGE on a 10% polyacrylamide gel and analyzed by immunoblotting as described previously [98] . Polyclonal rabbit antisera specific for PFV Gag [99] or residues1 to 86 of the PFV Env leader peptide ( LP ) , [98] as well as hybridoma supernatants specific for PFV PR-RT ( clone 15E10 ) or PFV integrase ( IN ) ( clone 3E11 ) [100] were employed . After incubation with species-matched horseradish peroxidase ( HRP ) -conjugated secondary antibody , the blots were developed with Immobilon Western HRP substrate . The chemiluminescence signal was digitally recorded using a LAS3000 ( Fujifilm ) imager and quantified using ImageGauge ( Fujifilm ) . Preparation of particle and cellular samples for qPCR analysis was performed as previously described [54 , 96] . Primers , Taqman probes and cycling conditions for specific quantification of PFV genome are summarized in ( S3 Table ) . All sample values obtained using a StepOne Plus ( Applied Biosystems ) qPCR machine were referred to a standard curve consisting of 10-fold serial dilutions of respective reference plasmid ( puc2MD9 ) containing the target sequences . All sample values included were in the linear range of the standard curves with a span from 10 to 109 copies . The values for the DNA or RNA content of viral particle samples obtained by the qPCR analysis were normalized for Gag content determined by quantitative WB as indicated above and are expressed as percentage of the wt ( generated by transfection of cells with pcoPG4 , pcoPP , pcoPE and puc2MD9 ) . | Foamyviruses ( FVs ) or Spuma-retroviruses derive their name from the cytopathic effects they cause in cell culture . However , infection in humans is benign and FVs have entered the human population through zoonosis from apes resulting in the emergence of Prototype FV ( PFV ) . Like all retroviruses , FVs contain gag , pol and env structural genes and replicate through reverse-transcription and host genome integration . Gag , the major structural protein , is required for genome packaging , virion assembly , trafficking and egress . However , although functionally equivalent , FV and orthoretroviral Gag share little sequence homology and it is unclear how they perform the same function . Therefore , to understand more about relationship between FV and orthoretroviral replication we have carried out structural studies of PFV-Gag . Here we present the structure of CA domains from a central region PFV-Gag and show that despite little sequence similarity they share the same fold as the CA domains of orthoretroviral Gag . These data provide the first information relating the Spuma and Orthoretrovirinae Gag proteins . We discuss our findings in terms of evolutionary divergence of spuma and orthoretroviral lineages . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"sequencing",
"techniques",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"viral",
"structure",
"retroviruses",
"viruses",
"immunodeficiency",
"viruses",
"rna",
"viruses",
"materials",
"science",
"mater... | 2016 | Structure of a Spumaretrovirus Gag Central Domain Reveals an Ancient Retroviral Capsid |
It is widely accepted that the growth and regeneration of tissues and organs is tightly controlled . Although experimental studies are beginning to reveal molecular mechanisms underlying such control , there is still very little known about the control strategies themselves . Here , we consider how secreted negative feedback factors ( “chalones” ) may be used to control the output of multistage cell lineages , as exemplified by the actions of GDF11 and activin in a self-renewing neural tissue , the mammalian olfactory epithelium ( OE ) . We begin by specifying performance objectives—what , precisely , is being controlled , and to what degree—and go on to calculate how well different types of feedback configurations , feedback sensitivities , and tissue architectures achieve control . Ultimately , we show that many features of the OE—the number of feedback loops , the cellular processes targeted by feedback , even the location of progenitor cells within the tissue—fit with expectations for the best possible control . In so doing , we also show that certain distinctions that are commonly drawn among cells and molecules—such as whether a cell is a stem cell or transit-amplifying cell , or whether a molecule is a growth inhibitor or stimulator—may be the consequences of control , and not a reflection of intrinsic differences in cellular or molecular character .
In biology , “control” is often used interchangeably with “regulation , ” but in engineering , control has a precise meaning: It refers to the strategies that enable a system to achieve desired ends , usually in a robust manner . To begin talking about the control needs of growing tissues and organs , we must first ask what are the “desired” ends , and to what kinds of uncertainties and perturbations must growth and differentiation be robust ? Perhaps the most obvious objective of a growth control system is to reach and maintain a specified size . Sizes of organs such as the brain , for example , are genetically specified within narrow tolerances ( e . g . , [16] ) . Moreover , self-renewing organs , such as the liver , seem to “remember” their appropriate sizes , as they accurately regenerate to their original sizes following even massive lesions [17] . The fact that many genetic alterations can affect final organ size ( e . g . , [18 , 19] ) suggests that there are diverse molecular pathways by which size may be regulated . A less obvious performance objective is control of growth rate . Consider , for example , a self-renewing tissue that maintains constant size by balancing continual cell death with cell production . Following an injury in which differentiated cells are destroyed , if there is no adjustment in cell production , those cells will be replaced only at the same ( often very slow ) rate at which they previously turned over . In regenerating tissues , however , it is common to observe a dramatic increase in proliferation following injuries , with rapid restoration of tissue morphology and size [17 , 20 , 21] . Even in tissues that do not regenerate , control of growth rate is likely to be important during development , so that the changing sizes of different organs are properly coordinated with each other . Other possible targets of control are the proportions of cell types in a tissue . For example , in a branched lineage ( one with more than one terminal-stage cell type ) a fixed ratio of end products may be important for tissue or organ function [22] . In lineages that operate continuously , it may also be desirable to ensure that stem and progenitor cells ( which do not usually contribute directly to tissue function ) are not too great a fraction of the tissue mass . How difficult should it be for tissues to achieve such objectives ? With control , the difficulty of the task depends upon the magnitude of the perturbations that are normally encountered ( e . g . , genetic and/or random effects on cell behavior , environmental fluctuations , injury , and disease ) ; the sensitivity of the system's behavior to those perturbations; and the level of imprecision in output that is acceptable . In recent years , increasing attention has been focused on the control challenges of biological networks , including those associated with metabolism , intracellular signaling , and gene regulation ( e . g . , [23–26] ) . Superficially , cell lineages look a great deal like these other kinds of pathways ( Figure 1 ) . Yet the components of lineages—cell stages—do not just transmit signals or material from one to another; they typically undergo autonomous , exponential expansion at the same time . This imparts a characteristic volatility to lineage dynamics that no doubt poses challenges for control . Given such challenges , it would not be surprising if the control of tissue and organ growth necessitates control strategies unlike those encountered elsewhere in biology . Here , we take steps toward identifying such strategies .
One way to identify the control needs of a system , and the strategies that may be used to address those needs , is to build models and explore their behavior . Figure 2A is a general representation of an unbranched cell lineage that begins with a pool of stem cells , ends with a postmitotic cell type , and possesses any number of transit-amplifying progenitor stages . If cells at each stage are numerous , and divisions asynchronous , then the behavior of such a system over time can be represented by a system of ordinary differential equations ( Figure 2B ) with two main classes of parameters . The v-parameters quantify how rapidly cells divide at each lineage stage ( in particular , v = ln2/λ , where λ = the duration of a cell cycle ) . The p-parameters quantify the fraction of the progeny of any lineage stage that remains at the same stage ( i . e . , 1-p is the fraction that differentiates into cells of the next stage ) . Thus p may be thought of as an amplification , or replication , probability . As each lineage stage has its own v and p , we use subscripts to distinguish them . Let us refer to the number of terminal-stage cells at any point in time as the output of a lineage system . From Figure 2B , we can see that a system is not stable—over time the output increases without bound—if pi > 0 . 5 for any i . In contrast , if pi < 0 . 5 for all i , stem and progenitor cells eventually run out , and the production of new terminal-stage cells stops . Provided terminal-stage cells do not die at an appreciable rate , such a system will reach a final state with a fixed number of terminal-stage cells . Finally , if p0 = 0 . 5 , and pi < 0 . 5 for i > 0 , then the system will eventually produce terminal-stage cells at a constant rate . If such cells die or are shed with a constant probability per unit time ( represented in Figure 2B by the rate constant d ) , then the output will approach a steady state , the solution for which is given in Figure 2C ( solutions for certain cases of final-state behavior are also given in Protocols S1–S3 , sections 5 and 6 ) . The result in Figure 2C describes a steady state that is quite sensitive to the system's parameters . For example , output is proportional to the number of stem cells ( χ0 , which remains constant at its initial value ) and the rate of stem cell division ( v0 ) , and inversely proportional to the rate of terminal-stage cell death ( d ) . Output varies even more sensitively with the pi . For example , increasing the value of a pi from 0 . 45 to 0 . 4725—a 5% change—necessarily produces a 74% increase in the output of terminally differentiated cells . In engineering , parameter sensitivity is usually quantified as the fold change in output for a given fold change in the parameter ( equivalent to the slope of a log-log plot of output vs . parameter ) . Thus , a linear relationship corresponds to a sensitivity of 1 ( directly proportional ) or −1 ( inversely proportional ) . From Figure 2C , we may calculate that the sensitivity of the output to any pi is pi/ ( 1 − 3pi + 2pi2 ) , which for pi < 0 . 5 is always greater than 1 , and grows without bound as pi approaches 0 . 5 . In well-regulated biological systems , parameter sensitivities ≥ 1 tend to be undesirable , since genetic or environmental variability can easily cause several-fold changes in the biological processes ( levels of proteins , cell growth rates , etc . ) that underlie parameters [27–29] . A system that cannot compensate for such variation is justifiably considered fragile ( the opposite of robust ) . Arguably , the most severe fragility of the system in Figure 2 is the constraint placed on the stem cell replication probability: p0 must be exactly 0 . 5 for a non-zero steady state to exist ( effectively , the system's sensitivity to p0 is infinite ) . This is simply another way of stating that , unless exactly half of all stem cell progeny are stem cells , lineages eventually either go extinct or explode . Meeting this constraint can be achieved by having every stem cell undergo perfect asymmetric divisions , but that does not seem to be what normally happens . Rather , individual stem cells behave stochastically , sometimes giving rise to two , one , or zero stem cells ( e . g . , [6 , 8 , 30] ) . For the exact condition p0 = 0 . 5 to arise as a population average , when such behavior is not a cell autonomous imperative , is an extraordinary—and yet poorly understood—feature of stem cell systems . The idea that negative feedback is used to regulate tissue size and enhance regeneration is an old one . Over 40 y ago , Bullough [31] introduced the term chalone to refer to secreted factors that inhibit growth of the tissues and organs that secrete them . When a tissue is injured or partially removed , reduction in chalone levels would thus result in an up-regulation of tissue production . The view that chalones are secreted factors was supported by in vitro experiments , and by experiments with parabiotically joined pairs of animals in which partial hepatectomy in one animal led to liver cell proliferation in the other [32] . Although many of the original , in vitro–defined chalones have yet to be fully characterized , genetic studies in the 1990s demonstrated that growth and differentiation factor 8 ( GDF8 ) /myostatin ( Mstn1 , MGI:95691 ) , a member of the transforming growth factor β ( TGFβ ) superfamily of secreted signaling molecules , is specifically expressed by striated muscle cells ( the terminal-stage cells of muscle lineages ) , inhibits the production of muscle , and when genetically eliminated from animals , results in the production of supernumerary muscle cells and an increase in muscle mass [33] . Subsequently , GDF11 ( MGI:1338027 ) —a close relative of GDF8—was shown to be produced specifically by cells of the neuronal lineage of the mouse OE , and to provide feedback to inhibit the production of neurons ( olfactory receptor neurons; ORNs ) in that system [34] . Animals deficient in GDF11 also develop supernumerary ORNs . In recent years , factors that exert negative feedback on growth have been described for many other tissues , including skin , liver , bone , brain , blood cells , retina , and hair ( Table S1 ) . Many of these factors turn out to be members of the TGFβ superfamily , especially the TGFβ/activin branch of that superfamily [35] . The OE of the mouse is a particularly useful system for studying lineage progression and feedback: It is continually self-renewing; its lineage stages are well defined; its cells can be studied in tissue culture; and it can be manipulated in vivo through genetic , chemical , or surgical means [36–38] . The OE neuronal lineage consists of a stem cell ( which expresses Sox2 [MGI: 98364] , a gene encoding an SRY-box transcription factor ) , that gives rise to cells that express the proneural gene Mash1 ( Ascl1 , MGI: 96919 ) , which in turn give rise to cells that express another proneural gene , Neurogenin1 ( Ngn1; Neurog1; MGI: 107754 ) , which in turn give rise to cells that exit the cell cycle and differentiate into ORNs . Recent data have raised the possibility that the Sox2+ and Mash1+ stages are not truly distinct , but rather are interchangeable states of the stem cell ( K . K . Gokoffski et al . , unpublished data ) . However , the Ngn1+ cell—which is usually referred to as the Immediate Neuronal Precursor , or INP—is clearly a distinct transit-amplifying cell stage ( Figure 3A; [34 , 39 , 40] ) . The INP appears to give rise solely to ORNs , i . e . , it does not represent a lineage branch point [39] . It is therefore interesting that the feedback actions of GDF11 seem to be directed specifically at INPs [34]: In vitro , GDF11 completely , but reversibly , arrests INP divisions , yet it has no effect on proliferation of Mash1+ or Sox2+ cells . In vivo , the increase in neuronal number observed in Gdf11−/− mice is accompanied by an increase in INPs , but not in Mash1+ or Sox2+ cells . These data imply that GDF11 regulates tissue size by inhibiting the proliferation of a committed transit-amplifying cell . Because GDF11 can slow and even arrest INP divisions , it is natural to model GDF11-mediated negative feedback as an increase in the cell-cycle length of the INP ( Figure 3B ) . Indeed , there is abundant literature showing that GDF11 , GDF8 , and other TGFβ superfamily members slow rates of progression through the cell cycle , at least in part by inducing cyclin-dependent kinase inhibitors [34 , 41–44] . Increasing the INP cell-cycle length is equivalent to decreasing its v-parameter , v1 ( Figure 3B ) . Unfortunately , the result in Figure 2C states that the steady state outputs of lineage systems are independent of all v except for that of the stem cell ( v0 ) . This makes intuitive sense: if one decreases the division rate of an intermediate-stage cell in a lineage , the unchanged influx of cells from the previous lineage stage will cause its numbers to rise proportionately . From the standpoint of the lineage output , the two effects will cancel . Apparently then , having GDF11 ( or any other factor ) feed back onto the INP cell division rate can be of no use in controlling the steady state level of ORNs . Could such feedback serve a function related to some other performance objective , such as rate control ? As mentioned earlier , without control , lineage systems would be expected to return to steady state after a perturbation ( i . e . , regenerate ) with a time scale similar to that over which terminal-stage cells normally turn over . In principle , feedback onto the cell division rate of a lineage intermediate could improve this . However , as explained below , the utility of this strategy turns out to be very limited: Figure 3C shows a simulated regeneration experiment in which output , via GDF11 , feeds back onto v1 . At the start of the experiment , all ORNs are synchronously destroyed , and the time course of the return to steady state is followed ( this type of perturbation can be produced experimentally by transecting the olfactory nerve or removing one or both olfactory bulbs of the brain [45] ) . For comparison , the figure also shows what the time course of the return to steady state would be in the absence of feedback ( dashed line ) . From Figure 3C , we can see that feedback enables the system to regenerate faster , but we also observe a very high proportion of INPs ( they are virtually as numerous , at steady state , as ORNs ) . It turns out that speeding up regeneration requires a large feedback gain ( the parameter h in Figure 3B ) , which in turn drives down steady state ORN numbers ( relative to other cells ) . If we define progenitor load as the percentage of the entire tissue that is composed of progenitors ( stem cells plus INPs ) , we find that requiring the steady state progenitor load to be less than 50% limits any improvement in regeneration speed to about 3 . 2-fold; restricting progenitor load to 10% drops this value to about 2 . 6-fold ( Figures S16 and S17 in Protocols S1–S3 ) . In fact , experimental data indicate that the progenitor load in the OE is below 10% [46–48] . There is another cost of achieving fast regeneration through feedback on v1: the lower the progenitor load , the more necessary it becomes to use values of p1 that are perilously close to 0 . 5 ( i . e . , nearly half the output of INPs needs to be more INPs; Figures S16 and S17 in Protocols S1–S3 ) . As discussed earlier , when p-parameters are close to 0 . 5 , system output becomes extremely sensitive to small variations in those parameters ( and thus very fragile ) . All told , feeding back onto the rate at which INPs divide does not seem to be a particularly good control strategy . We wondered whether GDF11 might do a better job if it fed back onto a different parameter of INP growth: p1 , the replication , or amplification , probability . Analysis of a model of this sort of feedback ( Figure 3D ) reveals several remarkable things: First , with feedback on p1 , the constraint p1 ≤ 0 . 5 goes away: Any INP replication probability allows for establishment of a steady state . Second , the fragility of the steady state output can be substantially reduced . In particular , sensitivity to the number of stem cells , the rate of stem cell division , and the death rate of terminally differentiated cells can be made arbitrarily small for appropriate parameter choices . Sensitivity to p1 can also be greatly reduced ( to values <1 ) , even if p1 is large ( Figures S1 and S2 in Protocols S1–S3 ) . Finally , such a system can mount explosive regeneration after a perturbation . In some cases , the return to steady state can be as much as 100 times faster than in the absence of feedback . Furthermore , this can be accomplished without the need for a high progenitor load . Figure 3E shows this behavior for a particularly effective set of parameters . Notice how , in response to an acute loss of terminal-stage cells ( ORNs ) , transit-amplifying cells ( INPs ) undergo a rapid , but transient , increase in number , following which , terminal-stage cells are restored rapidly to values close to steady state . This sort of behavior closely parallels what is seen in the OE following olfactory bulbectomy ( in which ORN degeneration is induced by olfactory bulb removal ) : a transient upsurge in progenitor cell numbers , followed by a wave of neuronal production [20 , 40 , 46 , 49–51] . The fact that feedback aimed at p1 can , in theory , produce more useful and realistic behaviors than feedback aimed at v1 , raised the possibility that the actual target of GDF11 might be p1 , and not v1 , as initially thought . To resolve this issue , we carried out tissue culture experiments in which mouse OE progenitor cells were pulse-labeled with 5-bromo-2-deoxyuridine ( BrdU; to label cells undergoing division ) , and evaluated at successive times thereafter to determine when the progeny of dividing cells acquire immunoreactivity for NCAM , a marker for terminally differentiated ORNs . As shown previously , most dividing cells in these cultures are INPs , and their cell cycle length is about 17 h [39] . If all INP divisions result in production of ORNs , the acquisition of NCAM immunoreactivity by all BrdU-labeled cells should occur after sufficient time to progress through the rest of S-phase , G2-phase , M-phase , and however long it takes for NCAM levels to rise above the threshold of detection . If some INPs replicate , however , then a fraction of labeled cells will not express NCAM until one cell cycle ( ∼17 h ) later ( if the replicating fraction is high enough , some progeny will go through several cell cycles before acquiring NCAM immunoreactivity; cf . [39] ) . Accordingly , delay in the onset of NCAM expression can be used as a measure of the INP replication probability . Figure 4 shows the effect of GDF11 ( added to the culture medium 12 h prior to BrdU labeling ) on acquisition of NCAM expression by BrdU pulse-labeled cells . In Figure 4J , data for two different “chase” periods are graphed . In the absence of GDF11 , about 60% of BrdU-labeled cells become NCAM-positive within 18 h . In the presence of low levels of GDF11 , this percentage rises as high as 75% , then falls again at high concentrations of GDF11 to less than 10% . The increase in neuronal differentiation in response to low levels of GDF11 documents that GDF11 indeed suppresses INP replication ( i . e . , it lowers p1 ) . The fact that this increase gives way to a large decrease in neuronal differentiation at high GDF11 levels is most likely due to the additional effect of GDF11 on the rate of cell cycle progression: As the INP cell cycle is progressively lengthened , one would expect that an 18-h chase period would cease being long enough to allow BrdU-labeled cells to go on to differentiate . This would lead to a sharp drop-off in the percentage of BrdU-labeled cells that acquire NCAM expression , but with longer chase times ( e . g . , 36 h ) , this effect would be overcome . That is indeed what is observed ( Figure 4J ) . A numerical simulation of the experiment , in which GDF11 negatively regulates both p1 and v1 , replicates both qualitative and quantitative features of the experimental data ( Figure 4K; Protocols S1–S3 , section 10 ) . Having the output of the OE lineage feed back onto p1 seems to be an effective strategy for meeting two control objectives: steady state robustness ( low sensitivity to stem cell number χ0 , cell division rates v0 , and v1 , and the death rate constant of the terminal-stage cell d ) and rapid regeneration . But the ability to meet each objective separately does not guarantee that both can be met together ( i . e . , for the same sets of parameters ) . As it turns out , the two strategies are largely incompatible . Numerical exploration of the parameter space shows a strong negative correlation between robustness and enhancement of regeneration ( Figure 5A ) . Cases for which the sensitivity to χ0 , v0 , or d is less than 0 . 4 ( i . e . , a 2-fold change in parameter will cause ≤32% change in output ) , generally do not exhibit acceleration in regeneration speed exceeding approximately 8-fold . In fact , this result is skewed by cases in which regeneration speed goes from extremely slow ( in the absence of feedback ) to merely very slow . If one restricts the analysis to cases in which regeneration from complete loss of terminal-stage cells is 80% complete in fewer than 29 transit-amplifying cell cycles ( ∼20 d for INPs ) , then to achieve parameter sensitivities less than 0 . 4 , the best possible improvement in regeneration speed is less than 2-fold ( Figure 5A and 5B ) . Upon closer inspection , other unfortunate tradeoffs can be seen: For the cases in Figure 5A , improvement in regeneration speed was calculated by simulating a complete loss of terminal-stage cells and then measuring the return to steady state . If we use a milder perturbation ( a 75% loss of terminal-stage cells ) , but otherwise the same parameters , the return to steady state is , unexpectedly , quite slow ( Figure 5C ) . The need to sustain injury that is massive before regeneration can be rapid hardly seems like a good strategy for an organism in the real world . To define the conditions under which this phenomenon occurs , we calculated , for all the cases in Figure 5A , the ratio of two regeneration times: the time for regeneration from a 100% perturbation , and the time for regeneration from a 75% perturbation . In Figure 5D , this value ( “speed ratio” ) is plotted against fold improvement in regeneration speed ( for the 100% perturbation , compared with no feedback ) . The data show that the speed of regeneration following massive injury cannot be improved by more than about 3-fold , without sacrificing the speed of regeneration following less-than-massive injury . Altogether , tradeoffs among regeneration speed , sensitivity to parameters , and sensitivity to initial conditions make the control strategy of having GDF11 feed back onto p1 less attractive than it originally seemed . Analysis of cases in which GDF11 inhibits both p1 and v1 ( which corresponds most closely to what GDF11 does in vitro; Figure 4J and 4K ) shows some improvement in the tradeoff between regeneration speed and parameter sensitivity , but the effect is not dramatic ( Figure S18 in Protocols S1–S3 ) . Accordingly , we wondered whether additional control elements might still be missing . As mentioned in Table S1 , many feedback inhibitors of tissue and organ growth belong to the TGFβ superfamily of growth factors , with those of the TGFβ/activin branch ( which signals through the intracellular proteins Smad2 and Smad3 ) being the most highly represented . Recently , we found that activinβB ( Inhbb; MGI: 96571; hereafter referred to simply as “activin” ) is highly expressed in the OE and , like GDF11 , has growth-inhibitory effects on the neuronal lineage . Unlike GDF11 , however , activin's effects are aimed specifically at the Sox2+ and Mash1+ populations , and not at INPs ( K . K . Gokoffski et al . , unpublished data ) . This implies that two feedback loops exist in the OE , one aimed at stem cells , and one aimed at transit-amplifying cells ( Figure 5E ) . Like GDF11 , activin could potentially feed back onto a v-parameter ( namely v0 , the rate of stem cell division ) or a p-parameter ( namely p0 , the stem cell replication probability ) , or both . For technical reasons , a pulse-chase experiment similar to that in Figure 4 cannot be performed to sort this out . However , we infer that feedback onto p0 must occur , because Sox2+ and Mash1+ populations are markedly expanded in the OE of ActβB−/− mice ( K . K . Gokoffski et al . , unpublished data ) . If activin only regulated v0 , loss of activin would result in stem cells that cycle faster , but it could not increase their numbers . Interestingly , when we add the feedback effects of both activin and GDF11 into the equations for the behavior of the ORN lineage , the expression for the steady state value of ORNs becomes very simple: ( 2p0 − 1 ) /j , where j is the feedback gain for activin ( Protocols S1–S3 , section 4 ) . This constitutes a dramatic improvement in robustness—the system will , at steady state , always produce the same number of terminal-stage cells regardless of how many stem cells it starts with , how fast stem cells divide , or how quickly terminal-stage cells are lost . Perhaps even more strikingly , the problematic constraint that the stem cell population must intrinsically “know” to replicate exactly half the time ( p0 = 0 . 5 ) vanishes . As long as p0 > 0 . 5 , feedback automatically ensures that the stem cell population behaves in the necessary way . All of these improvements in steady state control come solely from the single feedback loop of system output onto p0 . When such a loop is in place , however , feedback onto other p- and v-parameters can have additional useful effects: Consider , for example , the matter of regeneration speed , which we previously found could be increased through feedback onto p1 or v1 , but only by sacrificing robustness , low progenitor loads , or the ability to regenerate quickly from a variety of initial conditions ( Figures 3C and 5A–5D ) . When feedback is directed solely at stem cells , we also fail to achieve good performance: Feedback onto p0 hardly improves regeneration speed at all ( Figure S19 in Protocols S1–S3 ) , and although feedback onto p0 and v0 together can produce fast rates of regeneration ( Figure S21 in Protocols S1–S3 ) , those rates still show a very sensitive dependence on initial conditions ( Figure S22 in Protocols S1–S3 ) . In contrast , when feedback is directed at both stem and transit-amplifying cell stages—i . e . , the arrangement that actually occurs in the OE—it becomes possible to achieve very rapid regeneration , with low progenitor loads , from almost any starting conditions . This includes conditions in which variable numbers of stem , transit-amplifying , or terminal-stage cells are depleted . Figure 5F shows an example of such a case . Not only is such performance possible , it occurs over a substantial fraction of the parameter space ( that is , a substantial fraction of randomly chosen sets of parameters meet all of these performance objectives ) . Figure 6A shows graphically how , as feedback loops are added one at a time , good control ( robustness , stability , low progenitor load , and fast regeneration from a variety of conditions ) is found over an increasing fraction of the parameter space ( exploring wide ranges on all parameters ) . In evaluating the magnitude of this effect , it should be noted that fractions of parameter space in the range of 0 . 1%–1 . 5% are remarkably high , given the numbers of parameters in each model ( cf . [52] ) . For example , when there are eight independent parameters ( as there are when feedback is directed at p0 , v0 , p1 , and v1 ) , good performance over 0 . 1% of the parameter space means that the average parameter value “works” over 42% ( ∼0 . 0011/8 ) of its range . In Figure 6 , most parameters were explored over three orders of magnitude ( i . e . , they were randomly selected from a log-uniform distribution with a 1 , 000-fold range ) , so for such cases , 42% means that the average parameter can be varied over an 18-fold range ( 1 , 0000 . 42 ) without loss of good control . What is the significance of a control system that works over a large portion of its parameter space ? It means that the output of the system can be adjusted ( through changes to the parameters ) without the control strategy itself being jeopardized . From a biological perspective , this means that the system is evolvable , a feature we should expect to observe in most biological control systems [53] . So far , we have said much about the cell stages and processes that are targets for feedback in cell lineages , and little about the quantitative details of feedback signals . In Figures 3 and 5 , feedback was modeled using Hill functions; these are natural choices for the actions of secreted growth factors , since saturable binding of ligands to receptors is usually well described by them [54] . Hill functions typically employ a parameter n , the Hill coefficient , to fit dose-response relationships that are positively ( n > 1 ) or negatively ( n < 1 ) cooperative . In Figures 3 , 5 , and 6A , a Hill coefficient of 1 was used , but more detailed exploration of the two-loop feedback system ( with feedback on p0 , v0 , p1 , and v1 ) shows that system performance increases steadily as n goes from 0 . 5 to 2 ( Figure 6B and 6C ) . This makes intuitive sense if we consider that high values of n make Hill functions more switch-like . In the limit of a perfect switch ( infinite n ) , the drive for increased growth would be zero when output is at the desired value , yet maximal when output is even slightly below the desired value . Such a strategy clearly achieves the fastest possible regeneration following a perturbation . In biology , dose-response relationships that are fit by Hill coefficients other than 1 arise for a variety of reasons besides biochemical cooperativity; these include buffering , competition , feedback , and distributed multistep reactions [55–57] . Generally speaking , Hill coefficients quantify the sensitivity of output to input ( in the limit of high input , the Hill coefficient and the engineering definition of sensitivity are equivalent ) . Thus , in our models of feedback in the OE , Hill coefficients near 1 mean that the amount of activin and GDF11 signaling in stem cells and INPs ( respectively ) is roughly proportional ( over some range ) to the number of cells producing activin and GDF11 ( i . e . , the size of the tissue ) . It occurred to us that this situation—feedback proportional to tissue size—might not be so easy for tissues to achieve . As a tissue grows in size , one can certainly envision the total amount of material it produces increasing proportionally , but it is the concentrations—not the amounts—of factors like GDF11 and activin to which cells respond . How the concentrations of secreted ligands change as tissues grow turns out to depend both on issues of geometry ( tissue shape and boundary properties ) , and issues of cell biology ( rates of ligand capture and turnover ) . For example , consider a hypothetical tissue surrounded by a boundary across which macromolecules cannot diffuse . In this case , a secreted protein produced everywhere in the tissue should reach a steady state concentration determined by the balance between production and local degradation . If the tissue doubles in size , it will make twice as much of the protein , but distribute it over twice the volume . The result will be no change in concentration . In a truly “closed” tissue , secreted molecules cannot be used as part of a strategy for growth control . Fortunately , epithelia , such as the OE , are not closed systems . Although tight junctions between epithelial cells prevent escape of molecules from the apical surface , there appears to be little or no impediment to diffusion across a basal lamina into the underlying connective tissue stroma [58] . Within such a geometry , we may use approaches developed for the analysis of morphogen and signaling gradients [59–62] to calculate expected intraepithelial distributions of secreted molecules ( Protocols S1–S3 , section 11 ) . The results of these calculations ( Figure 7 ) show that when an epithelium is very thin , concentrations of secreted molecules in the intercellular space initially go up linearly with tissue size , but soon level off . Does the normal size range of the OE ( adult thickness ∼80 μm ) lie in the linear region , or on the plateau ? The answer depends on two factors: The first is the decay length of the molecule of interest . This is the average distance a molecule travels in tissue before being captured and degraded by cells , and is a function of its diffusion coefficient and rate of receptor-binding and degradation . The second factor is the ratio of decay length within the epithelium to decay length in the adjacent stroma ( which , in most cases , simply reflects how much faster or slower degradation proceeds in one location versus the other ) . If that ratio is low—i . e . , if molecules that diffuse into the stroma are not quickly degraded—then intraepithelial concentrations will be poorly sensitive to tissue size long before the epithelium reaches even a single decay length in thickness ( Figure 7A; Figure S27 in Protocols S1–S3 ) . In contrast , if the ratio of decay lengths between epithelium and stroma is high—i . e . , if the stroma acts as a sink , quickly eliminating molecules that enter it—then average intraepithelial concentrations will rise more gradually , and not plateau until the epithelium has reached a size of several decay lengths ( Figure 7B ) . This effect is more pronounced if the concentration that matters is the concentration close to the basal surface of the epithelium , and not the average concentration over the entire epithelial thickness . At this basal location , concentration varies linearly with tissue size for many decay lengths ( Figure 7B; Figure S28 in Protocols S1–S3 ) . Estimates of intraepithelial decay lengths of TGFβ superfamily polypeptides , obtained both from measurements of morphogen gradients and from first-principles calculations , tend to be in the range of tens of micrometers [59 , 63–65] , i . e . , on the order of , or less than , the normal thickness of the OE . This suggests that it would be difficult to use activin and GDF11 as “reporters” of OE size , if these molecules merely leaked into the stroma and were not rapidly degraded there ( as in Figure 7A ) : once the OE grew beyond 0 . 2 decay lengths in thickness , the poor sensitivity of activin and GDF11 concentrations to OE size would be functionally equivalent to feedback described by Hill coefficients less than 0 . 5 . As already demonstrated ( Figure 6B ) , such low Hill coefficients undermine good control . Accordingly , we infer that it would be strategically advantageous for the OE to possess a mechanism that rapidly removes activin and GDF11 in the underlying stroma , as well as a mechanism for restricting the location at which cells measure the level of activin and GDF11 , to the basal surface of the tissue . Remarkably , the OE seems to have both: First , the OE contains large amounts of the protein follistatin ( FST; MGI: 95586 ) in its basement membrane and stroma ( Figure 7C; [34 , 66] ) . FST not only binds and inhibits both activins and GDF11 , it does so irreversibly , effectively eliminating them [67–69] . That FST plays a central role in regulating GDF11 and activin function in the OE has recently been demonstrated genetically ( [34] and K . K . Gokoffski et al . , unpublished data ) ; what the analysis here provides is an explanation for why FST is used by the OE , and why it should be found primarily beneath the epithelium . Second , the progenitor cells of the OE that respond to activin and GDF11 become increasingly polarized , during early development , to the basal side of the epithelium; eventually they lie within a few cell diameters of the basement membrane . This is shown in Figure 7D and 7E , using in situ hybridization for Ngn1 to visualize INPs . Thus , the only concentrations of GDF11 and activin that progenitor cells sense are likely to be those near the basal surface of the epithelium . Interestingly , in many other types of epithelia , stem/progenitor cells also localize near the basement membrane , an observation that has long suggested the existence of a specialized microenvironment , or “niche , ” in this region [70] . The OE , a self-renewing tissue , maintains its size by continuous replacement of dying cells [51 , 71] . Some organs—such as the mammalian brain—achieve a final size during development and largely cease proliferating [72–74] . Such final-state ( as opposed to steady state ) systems also may be modeled using the equations in Figure 2 , by setting the terminal cell death rate constant , d , to zero , and allowing replication probabilities to be below 0 . 5 . Like steady state systems , they can be quite fragile . This point is well illustrated by the mouse brain , which is composed of approximately 108 cells of neural lineage ( neurons and glia; [16] ) . Although brain cell number varies from mouse to mouse , within a given strain , the coefficient of variation is small , about 5% [16] . If we hypothesize that the brain is “founded” by a pool of 105 progenitors ( probably an overestimate ) , and we make the simplifying assumption that no cells die during development , then a 1 , 000-fold expansion in cell numbers is needed ( Figure 8 ) . One way to accomplish this would be to have all progenitors replicate for a time equal to ten cell-cycle lengths ( 210 = 1 , 024 ) , and then stop . With this strategy , final cell number will be linearly sensitive ( i . e . , proportional ) to the initial size of the progenitor pool ( Figure 8A ) , and much more than linearly sensitive to the average length of the cell cycle , or the length of time allowed for proliferation ( a mere 5% change in either parameter would produce a 30% change in output ) . If the brain is founded by fewer progenitors , this fragility only becomes more severe . Now , let us consider a slightly more sophisticated strategy: a progenitor pool that undergoes a mixture of replicative and differentiative divisions , with a replication probability p set below 0 . 5 . Because proliferating cells replicate less than half the time , the progenitor pool runs out , and the tissue approaches a final state gradually , without need to count cell cycles or time . In this case , the final state is still linearly sensitive to the initial size of the progenitor pool , and although no longer sensitive to time or cell-cycle parameters , it is extremely sensitive to the value of p itself , which must be very close to 0 . 5 to produce a 1 , 000-fold expansion in cell numbers ( Protocols S1–S3 , section 5 ) . One way to circumvent this extreme fragility is to allow p to change over time , starting above 0 . 5 ( promoting progenitor expansion ) , then falling below 0 . 5 ( driving progenitor cell extinction ) . In fact , this very mechanism , illustrated in Figure 8B , was introduced by Nowakowski et al . [75] to explain the biphasic expansion and contraction of progenitor pools in the cerebral cortex , and it is supported by considerable experimental data ( e . g . , [76] ) . Mathematical analysis ( Protocols S1–S3 , section 5 ) shows that sensitivity to p is reduced by this strategy , but it still remains very high ( Figure S5 in Protocols S1–S3 ) . Moreover , the system now becomes quite sensitive to the rate at which p declines ( relative to the cell-cycle length; Figure S4 in Protocols S1–S3 ) . In addition , such a system is still linearly sensitive to the initial size of the progenitor pool ( Figure 8B ) . Given how difficult it seems to be to achieve even modestly robust final states , it is striking how much can be accomplished with the addition of just a single feedback loop . Figure 8C illustrates a case much like the one in Figure 8B , in which the p-value of a progenitor pool declines over time , but this time , the decline is caused by feedback from terminal-stage cells . Superficially ( that is , when not perturbed ) , it behaves just like the Nowakowski-Caviness model [75] , displaying expansion , contraction , and disappearance of a stem cell pool . Yet in this case , a 2-fold change in the initial number of stem cells produces only a minute ( 0 . 14% ) change in the final state ! Even sensitivity to the initial value of p can be much lower ( <5 ) than in the case without feedback ( Figures S6–S11 in Protocols S1–S3 ) . Just as with our analysis of steady state systems , this sort of behavior arises only when feedback regulates replication probabilities ( p-parameters ) , and not when it regulates cell cycle lengths ( v-parameters ) .
Using this approach , we showed that a feedback configuration that exists in the OE—with regulation at two sequential lineage stages—achieves a variety of important control objectives , including limited parameter constraints , decreased parameter sensitivities , improved regeneration speed , minimized influences of initial conditions , and evolvability . The core of this strategy is feedback inhibition of replication probabilities , referred to here as p-parameters . Such feedback is highly useful , not only to tissues that continuously turn over ( such as the OE ) , but also to tissues that are generated during a discrete period by a transient pool of progenitors ( such as the mammalian brain ) . In contrast , feedback on rates of cell division was found to be of only marginal value unless also combined with feedback on p . The data in Figure 4 provide experimental verification that GDF11 in fact acts by lowering the replication probability of neuronal transit-amplifying cells . Recent work suggests that GDF8/myostatin works similarly in muscle—lowering the probability that progenitors replicate and increasing the probability that they differentiate [78] . Thus , action on p may be a common feature of feedback inhibitors of tissue and organ growth . The molecular mechanisms by which such an action is achieved are currently unknown . Like many members of the TGFβ superfamily , GDF11 and GDF8 up-regulate the expression of cyclin-dependent kinase inhibitors ( e . g . , p21cip1/waf1 , p27kip1 ) , which are implicated in both inhibiting cell-cycle progression and promoting differentiation ( e . g . , [34 , 41 , 78–80] ) . Formally , it is possible that these two effects are linked , i . e . , the probability that a cell replicates or differentiates is determined by how long its cell cycle lasts . Indeed , in the developing mammalian brain , an observed progressive decline in p-values is matched by a progressive increase in cell cycle lengths [73 , 76] . However , we do not favor the interpretation that cell cycle length dictates replication probability , for two reasons . First , as implied by Figure 4F and 4G ( and unpublished data ) , the dose of GDF11 needed to maximally decrease p1 in the OE is considerably lower than that needed to prolong the cell cycle . Second , several growth factors are known to increase replication probabilities without altering cell cycle parameters . For example , the FGFs act in this way both on neural progenitors [39 , 81] ( including the INPs of the OE ) and on muscle progenitors [82] . The inhibitory effects of leukemia inhibitory factor on mouse embryonic stem cell differentiation also occur without changes to cell cycle parameters [83] . From this , we conclude that it is at least possible for p- and v-parameters to be regulated independently . In engineering , feedback control is often classified by the relationship between a measured “error”—usually the difference between actual and desired output values—and a control signal , i . e . , a quantity that is fed back . “Proportional control” means the control signal is proportional to the error . In “integral control , ” the signal is proportional to the integral , over time , of the error . “Derivative control” implies a control signal proportional to the derivative , with respect to time , of the error . Each strategy has strengths and weaknesses , and engineers often combine them . Proportional control , for example , can never fully compensate for a steady perturbation , because only when output is not at the desired level does a non-zero control signal exist . Proportional control can decrease a system's response time , but at the expense of gain ( the amount of amplification from input to output ) . In the lineage pathways described here , feedback onto v-parameters clearly exhibits the hallmarks of proportional control: Feedback onto v0 can reduce , but never eliminate , parameter sensitivities; and feedback onto v1 can speed regeneration , but only by decreasing the ratio of terminal-stage cells to progenitors . Integral control , in contrast , will fully compensate for a steady perturbation , producing a steady state that is completely independent of many external and internal influences; this phenomenon is sometimes referred to as “perfect adaptation” [84] . Integral feedback also tends to speed the rate of approach to steady state , but often at the risk of overshoots , undershoots , and oscillations . In the lineage pathways described here , feedback onto p0 exhibits the hallmarks of integral control: output that is independent of many parameters , very rapid regeneration , and a tendency toward oscillation ( the latter behavior is described in detail in [77] ) . To understand how feedback onto p0 implements integral control , it suffices to note that any steady deviation in the replication probability of stem cells above ( or below ) 0 . 5 leads to an ever-increasing ( or ever-decreasing ) effect on system output . In this way , output naturally follows the time integral of the difference between the effective value of p0 ( i . e . , p0 as modified by feedback ) and the value 0 . 5 . Feeding back output onto p0 thus represents true integral feedback control . Derivative control is often used by engineers to suppress instabilities associated with integral control , but it suffers from its own problems , such as a tendency to amplify noise . At this point , it is unclear whether derivative control is used in lineage pathways . Intriguingly , it has been noticed in the OE that the expression of GDF11 is stronger in immature than mature cells [34] , raising the possibility that GDF11 levels could track , at least to some degree , the rate of change ( i . e . , the time-derivative ) of system output , and not just the current output . In the biological literature , a sharp distinction between stem cells and transit-amplifying cells is classically drawn: the former are said to divide indefinitely and asymmetrically , regenerating themselves with each division , whereas the latter are said to have only limited capacity for self-replication [85 , 86] . The results of the present study lead us to question whether stem and transit-amplifying cells necessarily exist . By this we mean it is possible to have lineages in which all cells have the same intrinsic proliferative tendencies , yet typical stem and transit-amplifying behaviors are observed , solely as a consequence of feedback control . The only conditions required for this to happen are ( 1 ) cells should have an intrinsic tendency to self-replicate more than half the time ( p > 0 . 5 ) , and ( 2 ) the output of the lineage should negatively regulate replication probabilities ( feedback on p ) . For example , in a lineage with two sequential stages of dividing cells , if the output feeds back onto the p-parameters of both cell types , then either of two steady states is possible , depending on the relative strength of the two feedback loops ( see Protocols S1–S3 , section 4 ) . In one of these , the first cell stage exhibits classic stem cell behavior , i . e . , its population self-replicates exactly half the time , and the second cell exhibits classic transit-amplifying behavior , i . e . , its population appears to undergo limited divisions . In the other , the first cell stage is extinguished , and the second cell exhibits stem cell behavior ( see [77] and Protocols S1–S3 , section 4 , for further discussion; see also the related discussion in [87] ) . Which cell becomes the stem cell is thus determined by the feedback , and not anything intrinsic to that cell . It is easy to see how other typical behaviors of stem cell systems can also be the consequences of control . For example , with sufficiently large negative feedback onto v-parameters , progenitor cell populations will appear “slowly cycling” or even “resting , ” and would be observed to be “label-retaining” ( see Protocols S1–S3 , section 7 , especially Figure S13 ) . These arguments lend strong quantitative support to a view that has been gathering increasing support , namely that the definition of stem cell should be seen as one of context and condition , not of cell type [6 , 88 , 89] . The work presented here additionally suggests that much the same thing could be said about transit-amplifying cells . Interestingly , recent work on epidermis has shown that cells long thought to be classical transit-amplifying cells in fact do not display the seemingly essential property of limited self-replication [30 , 90]; instead they behave in a probabilistic manner that is fully consistent with the models presented here . In engineering , it is widely accepted that one cannot make a device robust in every possible way to every possible perturbation . Usually , strategies that eliminate one fragility come at the expense of creating new ones , a phenomenon underlying the characteristic “robust-yet-fragile” architecture of highly engineered systems [91 , 92] . Evidence for such tradeoffs can be seen in some of the data presented in this study ( for example , the fact that feedback onto p1 leads to rapid regeneration only at the expense of steady state robustness; Figure 5B ) . This suggests that even the two-loop OE feedback control system of Figure 5E must have an Achilles' heel somewhere , and indeed this is the case . For such a system to robustly control output , the feedback gain parameters ( the relationship between ORN number and the amount of feedback ) must be reliable . In essence , sensitivity to one set of parameters ( stem cell number , growth rates , death rates , etc . ) has been shifted onto another . Does this mean the control strategy is a failure ? Not at all . As engineers know , control is not about eliminating fragility , but managing it . One seeks to transfer fragility to parameters or inputs that are either intrinsically more reliable , or can themselves be controlled by other means , or to outputs in which error is more tolerable . The feedback mechanisms described in the present study end up transferring fragility from cell-intrinsic processes ( cell cycle length and death rate ) to cell-extrinsic quantities ( the level of GDF11 or activin in the extracellular space ) . This creates an opportunity for additional regulation , as well as opportunities to tie the behavior of cells in the OE neuronal lineage to each other , to other phenomena in the tissue , or even to the behaviors of cells in surrounding tissues . From a systems biology perspective , the present study has defined a control module , whose function can be appreciated in isolation , but whose real utility depends on how it integrates with other modules . The notion that elements like GDF11 , activin , FST , lineage stages , and epithelial architecture are components of an integrated system for controlling growth and regeneration emerges here mainly from the mathematical analysis and computational exploration of models . The models are firmly anchored in experimental data , but their primary use was not to generate experimental predictions ( although such things did occur , e . g . , Figure 4 ) . “Predictive” modeling can be valuable for testing mechanistic hypotheses , but it often requires a relatively complete picture of a system's components [64 , 93] . In tissue and organ growth control systems , it is indeed likely that components not considered here—such as Notch and Wnt signaling [94–98] , lineage branch points , and other feedback and feedforward factors—also play important roles . Rather , modeling was used here for its explanatory power , i . e . , as a way to achieve clarity in the face of complexity . Whether the precise control mechanisms suggested here are “right” or “wrong” is less important than the fact that they provide a more satisfying set of explanations than those yielded by traditional intuitive reasoning about the data . In the OE , for example , traditional pathway-centered reasoning—following from the analysis of phenotypes—would naturally emphasize the fact that GDF11 and activin are potentially redundant “antineurogenic” factors; that Fst is “proneurogenic , ” and that OE growth is somehow regulated by a balance among these factors . Although not inaccurate , this view draws attention away from what may be more fundamental relationships: that Fst extends the dynamic range over which tissue size can be sensed; that GDF11 and activin regulate a cell-fate decision ( to replicate or differentiate ) ; and that stem and transit-amplifying cell behaviors can be simple consequences of feedback . Such relationships fit better into the context of observations that Fst is highly expressed adjacent to several other epithelia that respond to Fst-sensitive ligands ( e . g . , in tongue , eye , and gut [66]; that GDF11 and GDF8 affect other kinds of cell fate decisions ( e . g . , in retina and muscle [99 , 100] ) ; and that stem and transit-amplifying cell behaviors are strongly context-dependent in many lineage systems [6 , 89 , 90 , 101] .
OE explants were prepared as previously described [39] and cultured with 10 ng/ml recombinant FGF2 and varying concentrations of GDF11 ( PeproTech ) . After 18 h , bromodeoxyuridine ( BrdU ) cell-labeling reagent was added at 1:10 , 000 ( #RPN201; Amersham ) . Two hours later , explants were washed with cold thymidine ( 10 μm; Sigma-Aldrich ) , growth factors replenished , and cultures grown for either 16 or 34 h longer ( total culture time was either 30 or 48 h ) . For 48-h cultures , FGF2 and GDF11 were refreshed after 40 h in vitro . Explants were fixed and stained with rat monoclonal anti-NCAM H28 and mouse monoclonal anti-BrdU antibody as described [39] . Immunoreactivity was visualized with Cy2-Donkey anti-rat IgG ( 1:50; Jackson Immunoresearch ) and Texas Red goat anti-mouse IgG1 ( 1:50; Jackson Immunoresearch ) . To compare the percentage of ORNs produced by INPs in each culture condition , total migratory BrdU+ cells were counted in at least 15 fields each of duplicate cultures per condition and scored for BrdU and NCAM immunofluorescence by an experimenter blind to the treatment condition , to ensure lack of bias . Embryos were dissected in room temperature phosphate-buffered saline ( PBS; pH 7 . 2 ) and heads fixed in 4% paraformaldehyde in PBS overnight at 4 °C , then cryoprotected , embedded , sectioned , and processed as described [34] . For Ngn1 in situ hybridization , tissue was processed using digoxigenin-labeled cRNA probes [34] . FST immunostaining was performed using R&D Systems goat anti-human FST antibody ( 10 μg/ml final concentration ) and visualized with biotinylated horse anti-goat IgG ( 1:250 ) in combination with Vector MOM Immunodetection Kit ( PK-2200; Vector Labs ) according to the manufacturer's instructions . Mathematical analysis and numerical simulation were carried out with the assistance of Mathematica ( Wolfram Research ) . Codes used for all cases shown are provided in Protocols S1–S3 . Gene accession numbers used in the manuscript refer to the Mouse Genome Informatics database , http://www . informatics . jax . org/ . | Many tissues and organs grow to precise sizes and , when injured , regenerate accurately and rapidly . Here , we ask whether the organization of cells into lineages , and the feedback interactions that occur within lineages , are necessary elements of control strategies that make such behavior possible . Drawing on mathematical modeling and the results of experimental manipulation of the mouse olfactory epithelium , we show that performance objectives , such as robust size specification , fast regeneration from a variety of initial conditions , and maintenance of high ratios of differentiated to undifferentiated cells , can be simultaneously achieved through a combination of lineage structures , signaling mechanisms , and spatial distributions of cell types that correspond well with what is observed in many growing and regenerating tissues . Key to successful control is an integral-feedback mechanism that is implemented when terminally differentiated cells secrete molecules that lower the probability that progenitor cells replicate versus differentiate . Interestingly , this mechanism also explains how the distinctive proliferative behaviors of stem cell and “transit-amplifying” cell populations can emerge as a consequence of feedback effects , rather than intrinsic programming of cell types . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"cell",
"biology",
"neuroscience"
] | 2009 | Cell Lineages and the Logic of Proliferative Control |
Latency reversal agents ( LRAs ) have proven to induce HIV-1 transcription in vivo but are ineffective at decreasing the size of the latent reservoir in antiretroviral treated patients . The capacity of the LRAs to perturb the viral reservoir present in distinct subpopulations of cells is currently unknown . Here , using a new RNA FISH/flow ex vivo viral reactivation assay , we performed a comprehensive assessment of the viral reactivation capacity of different families of LRAs , and their combinations , in different CD4+ T cell subsets . We observed that a median of 16 . 28% of the whole HIV-reservoir induced HIV-1 transcripts after viral reactivation , but only 10 . 10% of these HIV-1 RNA+ cells produced the viral protein p24 . Moreover , none of the LRAs were powerful enough to reactivate HIV-1 transcription in all CD4+ T cell subpopulations . For instance , the combination of Romidepsin and Ingenol was identified as the best combination of drugs at increasing the proportion of HIV-1 RNA+ cells , in most , but not all , CD4+ T cell subsets . Importantly , memory stem cells were identified as highly resistant to HIV-1 reactivation , and only the combination of Panobinostat and Bryostatin-1 significantly increased the number of cells transcribing HIV within this subset . Overall , our results validate the use of the RNA FISH/flow technique to assess the potency of LRAs among different CD4+ T cell subsets , manifest the intrinsic differences between cells that encompass the latent HIV reservoir , and highlight the difficulty to significantly impact the latent infection with the currently available drugs . Thus , our results have important implications for the rational design of therapies aimed at reversing HIV latency from diverse cellular reservoirs .
Current antiretroviral therapy ( ART ) is extremely effective at suppressing HIV viremia below the limit of detection of standard clinical assays and substantially reduces the morbidity and mortality associated with the HIV-1 infection . However , ART is unable to fully eliminate and eradicate HIV from the human body [1 , 2] . This is mainly due to the presence of latently HIV-infected cells generated in the early stages of the infection that are not susceptible to current antiretroviral drugs [3] . The development of new clinical strategies targeting the persistent virus may lead to a long-term drug-free remission of HIV infection , which currently represents a high priority for HIV-1 research [4 , 5] . Over the last years , the “kick and kill” therapeutic strategy has been pursued as an approach for eliminating HIV; latently HIV-infected cells are pharmacologically forced to induce HIV transcription with the hope that viral reactivated cells will be cleared by virus-induced cytopathic effects or by the immune system [6–8] . In this regard , drug discovery efforts have identified several latency reversal agents ( LRAs ) , compounds that can efficiently induce HIV expression . Vorinostat , Romidepsin and Panobinostat belong to the histone deacetylase inhibitor ( HDACi ) family . HDACi can suppress the histone deacetylases enzymes that enzymatically remove the acetyl group from histones , and as a consequence they induce gene expression . Importantly , HDACi successfully reactivated latent HIV in the first-in-human clinical trials [9–11] . Further , Disulfiram , a drug previously used to treat alcoholism , has been shown to increase HIV transcription in a subgroup of ART-suppressed patients after in vivo administration [12] . However , so far , none of the current LRAs tested in patients have proven to be effective at decreasing the size of the latent HIV reservoir . Other compounds , not yet tested in humans , have shown promising results ex vivo . In this sense , the PKC ( protein kinase C ) agonists Ingenol [13] and Bryostatin-1 [14] are involved in the PKC pathway , which plays an important role in cellular latency and reactivation of HIV via NF-κB ( nuclear factor kappa-light-chain-enhancer of activated B cells ) signaling and via P-TEFb ( positive transcription elongation factor b ) . The bromo and extra terminal ( BET ) bromodomain inhibitor JQ1 [15] also reactivates HIV by its effect through the P-TEFb . Lastly , a novel family of LRAs has been identified; the benzotriazoles successfully increase viral transcription dependent on STAT5 phosphorylation [16] . An important issue for current and future clinical trials aimed at curing HIV infection through the administration of LRAs is to determine how effective these compounds are in fully reactivating the virus from all latently-infected CD4+ T cell subpopulations . The CD4+ T cell pool encompasses a heterogeneous population of cells defined by the differential expression of cell surface receptors associated with different stages of cell maturation , activation and differentiation [17 , 18] . These CD4+ T cell subpopulations include naive cells ( TNA ) , stem cell memory ( TSCM ) , central memory ( TCM ) , transitional memory ( TTM ) , effector memory ( TEM ) and terminally differentiated cells ( TTD ) . As HIV transcription level and infection frequency differ by cell type [19–22] , the characterization of the responses of the different CD4+ T cell subpopulations to pharmacological HIV reactivation will guide us on the design of more effective therapies aimed at reducing HIV persistence . Currently , the most used assay for measuring the impact of LRAs on HIV reactivation is the quantification of intracellular HIV-1 RNA by conventional quantitative PCR assays [11 , 23–25] . Several other methodologies , as the quantitative viral outgrowth assay ( qVOA ) , Tat/rev induced limiting dilution assay ( TILDA ) or the quantification of viral DNA , have also been used to characterize the action of different LRAs in patient samples [26–28] . Recently , a new assay that detects HIV reactivation using a dual staining protocol of the viral protein p24 has been described [29] . However , due to the low number of cells responding to the LRAs in patients , current assays require the use of large quantities of cells to accurately measure viral transcription . Furthermore , the detailed characterization of the different cell subsets responding to LRAs has been scarce so far , since it requires the previous isolation of the specific cell subsets under evaluation . In order to overcome these limitations , we have recently reported a novel RNA FISH/flow method , which is based on the quantification of viral RNA by flow cytometry allowing the quantification and phenotyping of cells expressing HIV-1 RNA molecules at the single cell level [30 , 31] . Importantly , HIV-1 RNA expressed in different subpopulations of CD4+ T cells can be successfully determined by this novel system . Here , we have used and validated the RNA FISH/flow assay as a novel methodology suitable to evaluate compounds that can be pursued to reactivate the latent virus in patient-derived HIV infected cells . Using this methodology , we have characterized the specific responses of different CD4+ T cell subpopulations to the action of several LRAs families and their combinations . Overall , in CD4+ T cells we found that , on average , 16 . 28% of cells containing HIV-1 DNA were able to reactivate HIV with the most potent LRAs tested . From these cells , only a small fraction ( ~10% ) produced the viral protein p24 . Furthermore , we observed heterogeneous responses of specific cell differentiation phenotypes to these compounds , and we identified the combination of Romidepsin plus Ingenol as the most effective drug combination to efficiently increase HIV transcription and p24 production in most CD4+ T cell subsets . These findings highlight the difficulty to find LRAs able to reactivate HIV present in all cellular reservoirs; an essential requirement for the “kick and kill” therapeutic strategy to successfully impact persisting HIV in infected patients .
In order to evaluate the potency and timing of the different LRAs at reactivating latent HIV , we initially tested them in the latently infected cell line J-Lat ( clone 10 . 6 ) , which contains integrated but transcriptionally competent HIV proviruses that express the green fluorescence protein ( GFP ) after viral reactivation . We evaluated the following families of LRAs: HDACi ( Panobinostat and Romidepsin ) , PKC agonists ( Ingenol and Bryostatin-1 ) and a bromodomain inhibitor ( JQ1 ) . Drugs were used at concentrations previously shown to be effective at reversing HIV latency [23 , 32 , 33] . To best measure HIV reactivation and in order to avoid the loss of GFP signal due to the cell death of highly viral-reactivated cells , we treated cells with the pan-caspase inhibitor Q-VD-OPh before the addition of the compounds . Previous results showed that J-Lat cells and CD4+ T cells from patients stimulated with Panobinostat expressed higher levels of GFP and HIV-1 RNA , respectively , when cells were simultaneously treated with the pan-caspase inhibitor ( S1A and S1B Fig ) . In J-Lat cells , after incubation with single LRAs and a detailed monitoring of the viral dynamics , we observed that viral reactivation for most LRAs tested reached its maximum or a plateau after 20-24h of drug exposure , except for JQ1 that gradually increased the expression of GFP during the entire incubation period ( 50h ) . Romidepsin induced the highest reactivation level ( 24% of GFP+ cells ) , followed by Ingenol and Panobinostat ( 20 and 15% of GFP+ cells , respectively ) ( Fig 1 ) . In this model of latent infection , Bryostatin-1 did not induce a significant viral reactivation . In addition , we analyzed the effect that the combination of different families of LRAs had on HIV transcription . Romidepsin plus Ingenol was the most potent combination of LRAs , reaching values of 35% of GFP+ cells . Moreover , we also observed an additive effect at reactivating HIV when Ingenol was combined with Panobinostat and with JQ1 ( 27 and 28% of GFP+ cells , respectively ) . Importantly , in our experimental system , we observed that after 20-24h of drug treatment neither the single compounds nor their combinations induced more than 12% of cell apoptosis in J-Lat cells ( Fig 1 ) . Next , we tested the ability of the RNA FISH/flow assay to detect HIV-1 RNA and the viral protein p24 after the administration of LRAs . J-Lat cells were cultured with Romidepsin or with Romidepsin plus Ingenol for 22h ( S2A Fig ) . We observed that , as previously shown [30 , 34] , the RNA FISH/flow technology is able to distinguish two HIV-1 RNA positive populations: single HIV-1 RNA+ cells , and cells expressing both HIV-1 RNA and p24 . Importantly , the population of cells expressing HIV-1 RNA and p24 was highly abundant ( ~90% of cells ) when the culture was treated with the combination of the 2 LRAs , compared to single LRAs ( ~50% of cells ) ( S2A Fig ) . These values corresponded to percentages of GFP+ and HIV-1 RNA+ cells of 58 . 1% for Romidepsin alone and 90 . 2% for Romidepsin plus Ingenol ( S2B Fig ) . Differences in the proportion of positive cells between these results and those provided in Fig 1 are more likely due to the read out and the normalization method used to quantify viral reactivation with the different assays . Overall , we determined 22h as the more adequate timing to observe viral reactivation with all tested LRAs . First , we measured drug toxicity induced by the addition of LRAs to primary CD4+ T cells . Early apoptosis , late apoptosis and cell death were identified as shown in S3A Fig . Overall , LRAs induced a maximum median of 11 . 34% of cell death ( condition Romidepsin plus Ingenol ) when drugs were added for 22h to previously-isolated CD4+ T cells obtained from uninfected donors ( S3C Fig ) . However , under our experimental conditions , using the pan-caspase inhibitor Q-VD-OPh , no more than 3 . 04% of cell death was quantified ( S3B Fig ) . Moreover , in CD4+ T cell subsets we observed that TTD and TEM cells , the more differentiated cell subsets , were more susceptible to cell death , especially when treated with the combination of Romidepsin and Ingenol ( ~10% dead cells , S3D Fig ) . On the contrary , TCM and TNA cells showed the lowest percentages of cell death when treated with different LRAs ( maximum 2–3% of cell death ) ( S3D Fig ) . Drug toxicities in the absence of the caspase inhibitor are shown in S3E Fig . Of note , drug toxicity that remains despite Q-VD-OPh treatment might be caused by other cell death mechanisms , such as pyroptosis or cell necrosis [35] . Overall , Romidepsin plus Ingenol was the most toxic combination of drugs in all CD4+ T cell subpopulations . Nevertheless , even in this condition drug toxicity still remained relatively low after 22h of cell culture , and thus it is very unlikely that drug toxicity might preclude the interpretation of the viral reactivation assays . Then , we tested the potential of LRAs to reactivate latently HIV-infected cells in fresh samples from 9 ART-treated individuals . At least 6x106 isolated CD4+ T cells were cultured per condition during 22h , and a total of 13 conditions were set up for each patient . After viral reactivation , cells were subjected to the RNA FISH/flow assay in order to evaluate the frequency of cells that responded to the action of LRAs and were able to reactivate the latent provirus . The sensitivity of this assay at detecting HIV-1 RNA+ cells was previously established at 10 HIV-RNA+ cells per million cells [30] . The representative flow cytometry gating strategy used to identify HIV expression and production of the viral protein p24 in different subpopulations of CD4+ T cells is shown in S4A Fig . Overall , most tested LRAs and the combination of different families of LRAs significantly increased the frequency of cells expressing HIV-1 RNA in most patients compared to the non-reactivated control ( p<0 . 05 ) ( Fig 2A ) . For single LRAs , we observed that Ingenol , Romidepsin and Panobinostat increased the proportion of HIV-1 RNA expressing cells to higher levels ( median values of 67 for Ingenol , 66 for Romidepsin and 48 for Panobinostat , expressed as cells per million , p = 0 . 0039 , 0 . 0039 and 0 . 054 , respectively , compared to the media control ) than Bryostatin-1 or JQ1 ( median values of 19 for Bryostatin-1 and 33 for JQ1 , p = 0 . 015 and 0 . 039 , respectively ) . However , only Ingenol was effective at reactivating HIV in all tested patients , compared to Romidepsin ( 8 out of 9 patients ) and Panobinostat ( 7 out of 9 patients ) . For the combinations of LRAs , we observed that Romidepsin plus Ingenol promoted the highest induction of cells expressing HIV-1 RNA in all tested patients ( p = 0 . 0039 , fold change ( FC ) = 3 . 50 , compared to the negative control ) ( Fig 2A ) . Of note , in this specific condition the proportion of HIV-1 RNA+ cells was even higher than the resulting proportion of cells cultured with the positive control of PMA and Ionomycin ( median values of 180 for Romidepsin plus Ingenol and 64 for PMA and Ionomycin , p = 0 . 0078 ) . We also observed that the combination of JQ1 with Ingenol induced a high proportion of cells expressing HIV-1 RNA ( median value of 78 , p = 0 . 0039 ) in 8 out 9 patients ( Fig 2A ) . Furthermore , we calculated the synergistic , additive or antagonistic effects of the different combinations of LRAs using the Bliss independence Model [33 , 36] . We observed that Romidepsin and Ingenol presented the highest synergistic effect in 45% of the patients analyzed , while the other drug combinations promoted drug synergy in 22% of the patients at the most ( Fig 2B ) . Moreover , it should be noted that the combination of Panobinostat and Ingenol induced an important antagonistic effect in 89% of the patients tested ( Fig 2B ) . The synergistic effects between the different families of the LRAs in the individual patients are depicted in S4B Fig . Next , we normalized the values of RNA-expressing cells to the positive control , PMA and Ionomycin . Supporting the results showed in Fig 2B , we observed that Ingenol and Romidepsin were equally potent at reactivating cells expressing HIV than the positive control in 7 out of 9 and in 5 out of 9 patients , respectively . A more modest effect was observed for Panobinostat , Bryostatin-1 and JQ1 alone ( Fig 2C ) . The best drug combinations again were Romidepsin plus Ingenol and JQ1 plus Ingenol , which increased the proportion of HIV-1 RNA expressing cells at levels comparable to the positive control in 9 out of 9 , and 6 out of 9 patients , respectively . In addition , in patients #1 , 3 , 6 and 8 , Panobinostat only reactivated at most half of the values obtained with the positive control . In the same patients , Ingenol was equally potent than the positive control; however , the combination of both drugs decreased the proportion of cells expressing HIV-1 RNA compared with the single drug Ingenol , compatible with an antagonist effect when both compounds are combined ( Fig 2C ) . Besides , since we have recently described that CD4+ T cells expressing CD32dim are enriched in HIV transcripts in vivo , and that viral infection upregulates this marker [30 , 37] , we analyzed the expression of CD32dim in viral-reactivated cells using different LRAs . We observed that CD32dim was consistently upregulated after the pharmacological reactivation of HIV ( S4C Fig ) . First , we determined the sensitivity of the assay at detecting HIV-1 RNA+ and p24+ cells . Primary ex vivo infected cells were spiked into uninfected cells at different ratios and the mixture was then subjected to the RNA FISH/flow protocol . We observed that dual expression of HIV-1 RNA and p24 determined by the experimental curve showed consistency with the predicted curve at all of the dilutions tested , establishing a limit of detection of 10–20 positive events per million cells ( S2C Fig ) . Next , in order to determine whether LRAs and their different combinations were also capable of inducing the expression of the viral protein p24 , we performed the RNA FISH/flow protocol with the simultaneous staining of intracellular p24 . We analyzed the percentage of cells transcribing HIV-1 RNA that were also able to produce p24 . As shown in Fig 3A , cells expressing p24 significantly increased after the addition of all LRAs but two , Bryostatin-1 and JQ1 . Ingenol , Romidepsin and Panobinostat were the most potent LRAs at inducing the translation of viral transcripts ( median values of 5 . 62 , 4 . 17 and 3 . 64% , respectively ) . The combination of LRAs produced significant proportions of cells expressing p24 , except when JQ1 was combined with Bryostatin-1 ( median value of 0 . 99% ) . The positive control PMA and Ionomycin was the most potent drug ( median value of 8 . 33% ) , followed by the combination of Ingenol and Romidepsin ( median value of 7 . 14% , Fig 3A ) . However , in general , we did not detect more than 10% of HIV-1 RNA+ cells expressing p24 . Of note , the combination of JQ1 and Ingenol produced high levels of cells transcribing HIV-1 RNA , however this combination only induced p24 in a modest proportion of cells ( 3 . 23% ) . Moreover , no synergies were detected for the production of the viral protein p24 in any of the combinations tested and , in concordance with the transcription data , we determined an antagonistic effect in the majority of patients when the combination of Panobinostat and Ingenol was used for viral reactivation ( Fig 3B ) . Next , we normalized the proportion of p24+ cells to the maximum values obtained with the positive control ( Fig 3C ) . In agreement with the results described in Fig 2C , we observed a negative effect when we combined Panobinostat and Ingenol ( i . e patients #1 and 3 ) ( Fig 3C ) . Furthermore , we observed a statistical significant correlation between the proportion of HIV-1 RNA+ cells and the percentage of the cells that are able to produce p24 after different LRA treatments ( p<0 . 0001 ) ( S4D Fig ) . Taken together , individual LRAs have different capabilities of increasing the proportions of HIV-1 RNA and p24-expressing CD4+ T cells . In general , there was an agreement between the frequency of cells expressing HIV-1 RNA and p24 , but some disconnection was observed for some LRAs; Romidepsin and Ingenol was the combination inducing the larger proportion of RNA-expressing cells , however PMA and Ionomycin outperformed them in their ability to induce p24-expressing cells . This is in agreement with previous reported results that determined that the positive control ( anti-CD3/CD28 antibody-coated beads ) was able to induce higher levels of multiply spliced Tat-Rev HIV-1 transcripts compared to unspliced HIV-1 RNA . On the contrary , Romidepsin induced higher levels of unspliced HIV-RNA compared to Tat-Rev transcripts [38] . Moreover , we detected an antagonist effect , in both HIV-1 RNA+ and p24+ cells , when Panobinostat and Ingenol were combined . Next , we focused our investigations on the capabilities of LRAs at reactivating HIV in different CD4+ T cell subpopulations . To do so , we isolated fresh CD4+ T cells from ART-treated patients and after LRA addition , transcription of HIV was measured by the RNA FISH/flow assay . Firstly , we assessed the impact of the different LRAs on the phenotypic markers used to identify the different CD4+ T cell subpopulations . We observe that the proportions of the different subsets were , in general , well maintained after treatment with the different drugs . Only very small differences were detected in some conditions ( S5A Fig ) . We consider that these changes are negligible and it should not significantly impact the proportion of virally-reactivated cells . Moreover , we observed that Romidepsin increased the proportion of memory cells transcribing HIV-1 RNA compared to the control , including central memory ( TCM ) ( FC = 2 . 33 , p = 0 . 0039 ) , effector memory ( TEM ) ( FC = 4 . 24 , p = 0 . 007 ) and transitional memory ( TTM ) ( FC = 4 . 06 , p = 0 . 046 ) cells , and also naïve cells ( TNA ) ( FC = 1 . 72 , p = 0 . 0078 ) ( Fig 4A ) . However , Panobinostat was less potent at inducing HIV-1 RNA+ cells in TTM; indeed , we observed the highest effect in TCM ( FC = 2 . 11 , p = 0 . 0039 ) and a modest effect in TEM and TNA ( FC = 2 . 32 , p = 0 . 031 and FC = 1 . 27 , p = 0 . 0156 , respectively ) . Although significant , JQ1 induced a modest frequency of cells transcribing HIV-1 RNA in the majority of subsets analyzed , except for TSCM . Moreover , Ingenol preferentially reactivated TCM ( FC = 4 . 05 , p = 0 . 0039 ) and TTM ( FC = 5 . 27 , p = 0 . 0156 ) in most patients , which reached statistical significance . Although not significant , Ingenol also reactivated HIV in TEM cells in 4 out of 9 patients . Finally , Bryostatin-1 reactivated very modestly some subsets , including TNA , TTD and TCM ( FC = 1 . 49 , p = 0 . 0156; FC = 1 . 94 , p = 0 . 031; and FC = 1 . 22 , p = 0 . 0156 , respectively ) ( Fig 4A ) . A summary heatmap for the effect of single drugs is shown in S6A Fig . In addition , we analyzed the data stratified by CD4+ T cell subsets ( Fig 4B ) . In general , TCM and TNA subpopulations were successfully reactivated by almost all tested drugs . TCM and TTM were more efficiently reactivated by Ingenol , while TEM cells transcribed more HIV when cells were treated with Romidepsin . Moreover , HIV transcription was induced in TNA cells more robustly after the addition of Ingenol and Romidepsin , and in TSCM after Ingenol treatment . TTD cells showed a distinct pattern of reactivation , since Ingenol , Bryostatin-1 and JQ1 were the only single LRAs that increased the proportion of cells expressing HIV in this specific subset ( Fig 4B ) . Importantly , the combination of Romidepsin and Ingenol induced the largest proportion of cells transcribing HIV in most CD4+ T subsets , outperforming most LRAs and their combinations ( FC = 3 . 44 , p = 0 . 0039 for TCM; FC = 6 . 72 , p = 0 . 0078 for TTM; FC = 6 . 96 , p = 0 . 0156 for TEM; FC = 7 . 61 , p = 0 . 0078 for TTD; FC = 2 . 54 , p = 0 . 0039 for TNA; and FC = 2 . 61 , p = 0 . 062 for TSCM ) ( Fig 4B ) . However , within the TSCM subset only the combination of Panobinostat plus Bryostatin-1 was able to induce a significant increase of HIV-1 RNA+ cells ( Fig 4B ) . A summary heatmap on the effect of drug combinations in the different CD4+ T cell subsets is shown in S6B Fig . Finally , we calculated the interactions between each drug combination in the individual patients ( S6C Fig ) . In the majority of patients , the combination of Romidepsin and Ingenol was synergistic in memory cells ( TCM 45% , TTM 67% , TEM 44 . 5% and TTD 67% ) ( Figs 4C and S6C ) . However , the combination of drugs that induced significant synergy in TNA cells was JQ1 plus Ingenol ( 56% ) , and in TSCM Panobinostat plus Bryostatin-1 ( 44% ) . As we already observed in the total CD4+ T cell population , the combination of Panobinostat and Ingenol produced an antagonistic effect in memory cells ( TCM 89% , TEM 67% , TTM 56% , TSCM 67% and TTD 67% ) . However , for the TTM subset the combination of JQ1 and Ingenol was also antagonistic in most patients ( 78% ) ( Figs 4C and S6C ) . The reactivation induced by the positive control , PMA and Ionomycin , was not evaluated in CD4+T cells subsets due to the difficulty to gate accurately the different CD4+ T subpopulations . Next , we investigated not only the capabilities of LRAs to induce HIV transcription , but also to generate the viral protein p24 expression in the different CD4+ T cell subsets . We observed that the combination of Romidepsin and Ingenol was able to induce a substantial increase of cells producing p24 in TCM cells in most patients ( Fig 4D ) , although it did not induce a synergistic effect ( S6D Fig ) . Of note , for all remaining cell subsets , we were not able to detect significant number of cells expressing p24 . The low percentage of cells expressing p24 and the low absolute number of HIV-1 RNA+ cells detected in the remaining subsets may explain this observation . Overall , different CD4+ T cells subsets have different susceptibilities to LRAs and their combinations , but in general , we found that Romidepsin plus Ingenol was the most potent combination of LRAs , increasing significantly the proportion of HIV+ cells and producing a synergistic effect compared to the individual drugs . We also observed that TCM and TNA subpopulations presented broader susceptibility to the different families of LRAs , despite TTD and , specially , TSCM were more resistant to HIV reactivation . Furthermore , we determined a robust antagonistic effect when Panobinostat and Ingenol were used in combination in most subsets analyzed as we observed in the whole population of CD4+ T cells .
The elimination of the latently infected cell reservoir is believed to be the most important requirement to definitively eradicate HIV from the human body . Currently , therapeutic strategies named “kick and kill” are focused on the reactivation of these latent proviruses in ART-treated individuals using pharmacological compounds , the so-called LRAs . The ultimate goal of LRAs is to render infected cells susceptible to immune responses or to induce cell death by viral cytopathic effects . However , the impact of the drugs used to “kick” the virus on the subpopulations of cells that encompass the latent HIV reservoir is currently unknown . Importantly , clinical studies designed to perturb the latent HIV reservoir using LRAs have demonstrated an important increase in HIV transcription [10 , 11] . However , no effect was observed on the reduction of the size of the HIV reservoir following LRAs treatment . Since cell susceptibility to reactivating stimuli is the result of a complex interplay of individual viral and host factors , the inability of current drugs to reactivate latent HIV present in specific subsets of long-lived cells might help to explain the recent failures of clinical trials . In this study , we investigated for the first time the impact of several families of LRAs , such as the HDACi ( Romidepsin and Panobinostat ) , the PKC agonists ( Ingenol and Bryostatin-1 ) , and the bromodomain inhibitor JQ1 , on their ability to induce viral transcripts in freshly-isolated CD4+ T cells from aviremic ART-treated HIV-infected individuals . Using a novel flow cytometry technique , the RNA FISH/flow method , we studied the reactivation of HIV from cellular reservoirs . Although we do not detect all fully replication-competent viruses , positive cells detected using this method are able to produce elongated HIV-1 RNA upon LRA treatment , and in some cases , to produce the viral protein p24 . Importantly , the frequency of blood cells induced to transcribe HIV-1 RNA has been previously correlated with the frequency of cells induced to express infectious virus [39] . Therefore , detection of HIV-1 RNAs have biological importance . Here , we observed that the fraction of the total HIV-reservoir that can be reactivated by the tested LRAs varied between patients , ranging from 3 to 31% . These results concur with Banga et al . [25] , which indicated that only a fraction ( ≈2 . 6% ) of HIV-1 proviruses were reactivated to produce virions , supporting also results from Cillo et al . [24] . This low percentage of viral reactivation might be explained , in part , by the fact that most of HIV-1 DNA present in ART-treated patients contains fatal mutations; only 2–10% of proviruses are considered intact , and therefore , more likely to produce full length HIV-1 RNA transcripts [40 , 41] . Moreover , between 60–80% of the proviruses might present deletions in the gag-pol region [40] , precluding the detection of viral transcription by the RNA FISH/flow method . It should be note that we are using fresh CD4+ T cells from a single blood draw , and therefore it is uncertain if similar results will be obtained from blood collected at different time points . We speculate that the results using LRAs with cells collected at different time points will mainly depend on the size and stability of the transcriptionally-inducible HIV-1 reservoir . Of note , we have not measure the half-live of the transcriptionally-inducible HIV reservoir in ART-treated patients , but as demonstrated for other reservoir measurements , the stability will be more likely dependent on the individual patient; i . e . the half-live of the replication competent HIV reservoir is approximately 44 months ( if measured as IUPM or IDPA ) [40] , but other factors , such as clonal expansion , ongoing viral replication , and redistribution of infected cells from lymphoid tissue , will significantly affect its longitudinal stability [42] . Additionally , the intrinsic stability of the different cell subsets that contain the inducible HIV-1 , might also account for the variation in the level of viral reactivation . For all tested drugs we observed a significant increase in the proportion of HIV-1 RNA+ cells , but some LRAs were especially potent at increasing their frequency . As single drugs , and in concordance with our in vitro data generated in the J-Lat cell line , Romidepsin and Ingenol induced the highest frequency of cells expressing HIV-1 RNA in nearly all samples . These findings are in agreement with previous reports; Pandeló et al . showed that Ingenol disrupted HIV latency at higher levels compared to PMA or Vorinostat , both in latency cell models and in infected primary resting cells [13] , while Wei et al . demonstrated that Romidepsin induced an important increase in HIV-1 transcription compared to Vorinostat in both total memory and resting cells from HIV infected patients [43] . Moreover , we observed that JQ1 and Bryostatin-1 reactivated HIV-1 very poorly . In contrast to our results , it has been previously showed that in resting CD4+ T cells from HIV-patients only Bryostatin-1 induced an increment in the production of RNA compared to the HDAC inhibitors Romidepsin , Panobinostat and Vorinostat , and the bromodomain inhibitor JQ1 [23] . In the study , the authors used qPCR to measure viral reactivation , thus a potent induction of HIV in a limited number of cells might help to explain the discrepancy between both studies . Another study from Jiang et al . [44] , described a synergistic effect when JQ1 plus Ingenol were combined , but in our work we observed an antagonistic effect in the 56% of the patients . The fact that different Ingenol molecules can be used for viral reactivation studies , the different methods used to detect viral reactivation , and the discrepancies observed between cell lines and primary CD4+ T cells in viral reactivation protocols [32] , might explain this contradictory result . Furthermore , we characterized the responses of each CD4+ T subpopulation to different LRAs . These investigations have rarely been performed before , mainly due to the difficulty to obtain enough cells from each CD4+ T cell subset to comprehensively quantify viral reactivation . In order to overcome this limitation , we used the novel RNA FISH/flow methodology that allows the simultaneous detection of HIV-1 RNA transcripts and the viral protein p24 at the single cell level without the need to previously isolate the fraction of cells being evaluated [30] . In general , each LRA was impacting differently the CD4+ T cell subpopulations; even drugs belonging to the same family had a differential effect on the same cell subsets . For instance , Panobinostat successfully reactivated HIV in TCM cells , whereas Romidepsin was capable of impacting all memory cells ( TCM , TTM and TEM ) . Importantly , it has been shown that both drugs have different capacity to inhibit cell-associated HDAC activity [43] . Thus , it is tempting to speculate that differential expression of HDAC isoforms within different CD4+ T cell subsets could be associated to their intrinsic capability to reactivate latent HIV . In concordance with our results , in a recent study , cells treated with Panobinostatt that reactivated HIV appeared to be long-lived whereas Romidepsin appeared to reactivate HIV in shorter life span cells [45] . This study calculated the life span of cells that reactivated HIV in vivo using mathematical models . Consistently , Banga et al . showed that Panobinostat was slightly more robust than Romidepsin at reactivating HIV in isolated resting memory CD4+ T cells , a fraction enriched in long-lived central memory cells [25] . We also observed that TCM and TNA cells have the broadest susceptibility to the different families of LRAs , and Ingenol was extremely efficient at reactivating TNA , TSCM , TCM and TTM but did not show a significant effect on TEM cells . In this regard , a recent study determined that the majority of cells expressing HIV-1 RNA in the presence of Ingenol had a TCM/TTM and TEM phenotype [31] . Additionally , while the TNA subpopulation has not traditionally been considered as a cellular HIV reservoir , this subset has been recently described as a large inducible cell reservoir of both latent and replication competent virus at levels similar observed in TCM [46] , which is in concordance with our results . One of the main limitations of the present study is our inability to detect p24 in most of the cell subsets . In general , there was an agreement between the frequency of cells expressing HIV-1 RNA and cells producing p24 . However , we were only able to detect p24 in the whole CD4+ T cell population and in TCM cells . This is not the result of a poor sensitivity of the RNA FISH/flow method ( 10–20 positive cells per million ) , instead it might be explained by the low absolute number of HIV-RNA+ cells observed within subsets that were represented in small frequencies as i . e . TSCM , TTM , TEM and TTD ( all below 30% ) . We observed that long-lived cells such as TCM or TSCM , previously defined to be important in the long-term maintenance of HIV reservoirs in patients [19 , 20] , have different susceptibilities to the LRAs tested . For instance , TSCM were poorly reactivated with most drugs . Only the combination of Panobinostat and Bryostatin-1 , and to a lesser extent Ingenol , were able to significantly increase the proportion of HIV-1 RNA+ cells in this subset . This finding highlights the difficulty to identify LRAs with a mechanism of action broad enough to reactivate latent HIV present in all HIV-infected cells . Moreover , the lack of effect of the LRAs on TSCM cells is a concern , since viral recrudescence from these long-lived cells might significantly preclude the in vivo long-term efficacy of LRAs tested in clinical trials . Thus , our results have important implications for rational design of therapies aimed at reversing HIV latency; the knowledge of the individual mechanisms that lead to viral reactivation in the population of cells that encompass the latent HIV reservoir will help with the development of LRAs with which to impact HIV persistence . Importantly , we found that the combination of Romidepsin and Ingenol induced the highest frequency of HIV-1 RNA+ cells , even more than the positive control with PMA and Ionomycin , and this finding was consistent in all tested samples . To our knowledge , the combination of Romidepsin plus Ingenol has never been explored before in this setting . The independent mechanism of action of both drugs is most likely the responsible for the high number of HIV-1 RNA+ cells detected . This argument is supported by the observation that the combination of both drugs does not induce higher number of HIV-1 RNA molecules per cell ( mean fluorescence intensity ) ( S5B Fig ) , instead it induces a broader spectrum of cells that are able to express HIV-1 RNA upon viral reactivation . Moreover , the synergistic effect was particularly evident in the TCM and TTM memory subsets , indicating that the differentiation or maturation status of the cells may be a critical determinant for a successful viral reactivation with the different LRAs . In this sense , it has been recently reported that CD4+ T cell subsets have distinct transcriptional profiles that are related to the level of HIV-1 infection and might modulate the response to external stimulus [47] . We also determined a robust antagonistic effect ( 89% in whole CD4+ T cells ) when Panobinostat and Ingenol were combined . This is in agreement with the study presented by Larragoite et al . [48] , in which they showed that the co-treatment with both drugs inhibited the reactivation of HIV in an ex vivo model of resting CD4+ T cells isolated from aviremic patients , despite a synergistic relationship was demonstrated in an in vitro latency cell model ( J-Lat 10 . 6 ) . The authors speculate that the inhibition induced by Panobinostat of the chaperone heat shock protein 90 ( Hsp90 ) , which is directly involved in the reversion of HIV-1 latency by Ingenol [49] , might reduce the activation of the NF-kB pathway caused by the PKC agonist . This could explain the antagonistic effect observed when these two drugs are combined . In addition , it has also been observed that Panobinostat induces latency reversal by an Hsp90 independent way [48] . Further , these results manifest again the existing discrepancies between latently infected T cell lines and primary cell models of HIV-1 latency [32] . In conclusion , this study highlights the inability of current LRAs to fully reactivate HIV hidden in diverse cellular reservoirs . The identification of compounds with a broader reactivation capacity or the use of complementary drugs with different mechanisms of action will be needed to reactivate latent virus present in different cell types , where more likely diverse cellular pathways are implicated in silencing HIV .
PBMCs ( peripheral blood mononuclear cells ) from adults ( >18 years old ) HIV-1-infected patients were obtained from the HIV unit of the Hospital Universitari Vall d’Hebron in Barcelona , Spain . Written informed consent was provided by all patients who participated in this study , and the protocols used were approved by the Comitè d’Ètica d’Investigació Clínica ( Institutional Review Board numbers 39–2016 and 196–2015 ) of the Hospital Universitari Vall d’Hebron , Barcelona , Spain . All samples were obtained only from adults and were totally anonymous and untraceable . Samples from HIV-1-infected patients under ART with CD4+ T cell counts higher than 100 cells/mm3 and viral load <50 cop/ml for a mean ( min-max ) of 3 ( 1–6 . 5 ) years were recruited in the HIV unit of the Hospital Universitari Vall d’Hebron in Barcelona ( Spain ) and were included in this study . Information on plasma viral loads , CD4+ T cell counts , and time on ART for treated patients is summarized in S1 Table . Fresh PBMCs were obtained from a whole blood donation ( 400ml ) from HIV-infected patients by Ficoll-Paque density gradient centrifugation and cells were immediately used without previous cryopreservation . Isolated CD4+ T cells ( MagniSort Human CD4+ T Cell Enrichment; eBioscience ) were cultured in RPMI medium ( Gibco ) supplemented with 10% fetal bovine serum ( FBS; Gibco ) , 100 μg/ml streptomycin ( Capricorn Scientific ) and 100 U/ml penicillin ( Capricorn Scientific ) , ( R10 ) . The human latently infected cell line J-Lat ( clone 10 . 6 ) was obtained through the NIH AIDS Reagent Program from Eric Verdin [50]; cells were grown in R10 and maintained at 37°C in a 5% CO2 incubator . Isolated CD4+ T cells were stimulated during 22h with latency reversal agents ( LRAs ) at the following concentrations: 40 nM Romidepsin ( Selleckchem ) , 30 nM Panobinostat ( Selleckchem ) , 1 μM JQ1 ( Sigma-Aldrich ) , 100 nM Ingenol-3-angelate ( Sigma-Aldrich ) , 10 nM Bryostatin-1 ( Tocris Bioscience ) , the positive control ( PMA 81 nM plus Ionomycin 1 μM , both from Abcam ) , or the negative control ( media alone , R10 ) . Drugs were used at concentrations previously shown to be effective at reversing latency in studies performed in CD4+ T cells from HIV-infected individuals as well as studies performed in latency models in vitro [23 , 32 , 33] . All compounds were reconstituted in DMSO at the maximum concentration of 0 . 006% . Moreover , in order to prevent cell death induced by the reactivation of HIV and to evaluate the reactivation effect without confounding variables , cells were pre-treated with a pan-caspase inhibitor named Q-VD-OPh ( quinolyl-valyl-O-methylaspartyl-[-2 , 6-difluorophenoxy]-methyl ketone , Selleckchem ) [51 , 52] . Q-VD-OPh is a potent inhibitor for caspases 1 , 3 , 8 and 9 , which are involved in the intrinsic and extrinsic apoptotic pathways , inhibiting consequently the specific cell death induced by HIV [53–56] . In all experiments , cells were treated with 10 μM of Q-VD-OPh for at least 2h prior to the addition of the latency reversal agents . HIV reactivation and toxicity effects induced by the different LRAs were longitudinally and exhaustively determined in the latently infected cell line J-Lat 10 . 6 . Viral reactivation and cell death was monitored using the IncuCyte ZOOM live cell imaging system ( Essen Bioscience ) . The latently infected cell line J-Lat contains integrated but transcriptionally latent HIV proviruses , in which the reporter gene GFP replaces the nef coding sequence [50] . GFP was used to measure viral reactivation and the apoptotic marker Annexin V ( Essen Bioscience ) was used to determine cell death induced by the drugs or by cytopathic effect . Briefly , cells were pre-treated with the pan-caspase inhibitor Q-VD-OPh for at least 2h and then seeded at 25 . 000 cells per well in a 96 well plate . Single LRA or combination of different families of LRAs were added to the corresponding well and Annexin V reagent ( 1:200 ) was immediately added on cells , with a final well volume of 100μl . Images were captured every hour for 48h from 2 independent wells per condition . Green ( HIV expression ) and red ( Annexin V ) object counts per well ( 1/mm2 ) were quantified at each time point and values were normalized to the confluence of each well . Cell toxicity was assessed for single drugs and the combination of different LRA families in previously isolated CD4+ T cells from three independent uninfected donors . CD4+ T cells were pre-incubated with the pan-caspase inhibitor Q-VD-OPh for 2h . Afterwards , CD4+ T cells ( 200 , 000 cells per well ) were incubated for 22h with the compounds . Then , cells were stained with the apoptotic marker Annexin V ( PE , Biolegend ) and a viability dye ( LIVE/DEAD fixable Violet Dead Cell Stain kit , Invitrogen ) in order to identify the following stages of cell death: live cells ( Annexin V- Viability- ) , early apoptotic cells ( Annexin V+ Viability- ) , late apoptotic+necrotic cells ( Annexin V+ Viability+ ) and total cell death ( Annexin V- Viability+ ) . In addition , different surface markers , including CD3 ( Pe-Cy7 , BD Biosciences ) , CD4 ( AF700 , BD Biosciences ) , CD45RO ( BV605 , Biolegend ) and CD27 ( FITC , Biolegend ) , were used to identify drug toxicity induced in the different CD4+ T cell subpopulations . The CD4+ T cell subsets were identified as follows: Naïve ( TNA ) and Stem Cell Memory ( TSCM ) ( CD3+CD4+CD27+ CD45RO- ) , Central ( TCM ) and Transitional Memory ( TTM ) ( CD3+CD4+CD27+ CD45RO+ ) , Effector Memory ( TEM ) ( CD3+CD4+CD27- CD45RO+ ) and Terminally Differentiated cells ( TTD ) ( CD3+CD4+CD27- CD45RO- ) . PBMCs from nine ART-treated HIV-infected patients were obtained from a whole blood donation ( 400ml ) and CD4+ T cells were isolated by negative selection using magnetic beads ( MagniSort Human CD4+ T Cell Enrichment; eBioscience ) . A total of 13 conditions were assayed per patient and at least 6x106 of freshly-isolated CD4+ T cells were subjected to viral reactivation per condition , which included the individual LRAs , the combination of 2 different families , and the positive and negative controls . Prior to viral reactivation , cells were pre-incubated with the pan-caspase inhibitor Q-VD-OPh for 2h . In order to block new rounds of viral infection during viral reactivation , cells were treated with LRAs in the presence of Raltegravir ( 1uM ) during 22h . Afterwards , cells were subjected to the RNA FISH/flow protocol for the detection of HIV transcripts and the viral protein p24 following the manufacturer’s instructions ( Human PrimeFlow RNA Assay; eBioscience ) with some modifications , as previously described [30] . Briefly , PBMCs were stained with antibodies against cell surface markers and viability dye . Cells will be then fixed , permeabilized , and intracellularly stained for detection of the viral p24 protein . After an additional fixation step , cells will be ready for 3h of probes hybridization at 40±1°C with a high-sensitivity target-specific set of 50 probes spanning the whole Gag-Pol HIV mRNA sequence ( bases 1165 to 4402 of the HXB2 consensus genome ) . The cells will be then subjected to different amplification steps ( sequential 2h incubations at 40°C ) . Finally , multiple label probes will be hybridized with the specific amplifiers ( 1 h at 40°C ) and samples will be run on an LSR Fortessa four-laser flow cytometer ( Becton Dickinson ) . In these experiments , to identify the different CD4+ T cell subpopulations expressing HIV-1 RNA and the viral protein p24 , the following antibodies were used for cell surface staining: CD3 ( AF700 , Biolegend ) , CCR7 ( Pe-CF594 , BD Biosciences ) , CD27 ( FITC , BD Biosciences ) , CD45RO ( BV605 , Biolegend ) and CD95 ( Pe-Cy5 , BD Biosciences ) . The CD4+ T cell subset phenotypes were identified as follows: TNA ( CD3+ CCR7+ CD45RO- CD27+ CD95- ) ; TSCM ( CD3+ CCR7+ CD45RO- CD27+ CD95+ ) ; TCM ( CD3+ CCR7+ CD45RO+ ) ; TEM ( CD3+ CCR7- CD45RO+ CD27- ) ; TTM ( CD3+ CCR7- CD45RO+ CD27+ ) and TTD ( CD3+ CCR7- CD45RO- ) . The surface marker CD32 ( Pe-Cy7 , Biolegend ) was also included in the analysis . The expression of HIV-1 RNA transcripts was analyzed with target-specific AF647-labelled probes , and the expression of the Gag p24 viral protein was detected with a PE-anti-p24 antibody ( clone KC57 RD1; Beckman Coulter ) . Cell viability was determined using a violet viability dye for flow cytometry ( LIVE/DEAD fixable Violet Dead Cell Stain kit , Invitrogen ) . All values of HIV-1 RNA were normalized to the negative control ( R10 ) corresponding to the non-reactivated cells from each patient . To test the sensitivity of the assay at detecting cells expressing both HIV-1 RNA and p24 , primary infected CD4+ T cells from HIV-infected patients were expanded . We used the same protocol described for the qVOA assay [30] , and the positive wells were mixed up and diluted into uninfected cells at six different ratios . Samples were then subjected to the RNA FISH-flow assay . The predictive curve was determined by the basal expression of HIV-1 RNA and p24 and the subsequent theoretical values of the serial dilutions . The infection rate ( experimental curve of percent HIV RNA+p24+ cells ) was calculated by using the values obtained with the RNA FISH-flow assay . Linear regression was computed to determine the linearity of the relationship between the predicted and experimental values of the assay . CD4+ T cells were isolated by negative selection as mentioned above . For proviral quantification , 1 million CD4+ T cells were immediately lysed in a Proteinase K-containing lysis buffer ( at 55°C over-night and at 95°C for 5 minutes ) . Cell lysates were subjected to HIV-DNA quantification by qPCR using primers and probes specific for the 1-LTR HIV region ( LTR forward 5’-TTAAGCCTCAATAAAGCTTGCC-3’ , LTR reverse 5’-GTTCGGGCGCCACTGCTAG-3’ and probe 5' /56-FAM/CCAGAGTCA/ZEN/CACAACAGACGGGCA/31ABkFQ/ 3' ) , as previously described [57] . CCR5 gene was used for cell input normalization . Samples were analyzed in an Applied Biosystems 7000 Real-Time PCR System . Statistical analyses were performed using the Prism software ( GraphPad ) version 6 . 01 . Data are shown as the median and the min-max rank . Comparisons among the frequency of HIV-1 RNA expressing cells between unstimulated control ( R10 ) and viral-reactivated conditions were performed using the Wilcoxon signed rank test . For correlations , Spearman’s correlation coefficient was calculated . To test the linearity of the assay , a linear regression was performed . A Friedman ANOVA test was used to compare the frequency of HIV-1 RNA+ cells induced by Romidepsin plus Ingenol with the levels induced by the other drugs in the different CD4+ T cell subsets , with corrected p-values for multiple comparisons ( Dunn’s test ) . A p value of <0 . 05 was considered statistically significant . Synergies and antagonisms effects between drugs were calculated using the Bliss independence model ( values <-0 . 09 were considered as highly antagonistic , values > 0 . 09 were considered as highly synergistic . Intermediate values between 0 . 09 and -0 . 09 were considered to have an additive effect ) . Data are presented as the difference between the observed and the predicted responses of each combination ( ∆faxy = faxy , O—faxy , P ) , where faxy , O is the observed fraction affected and faxy , P is the predicted fraction affected . The faxy , P is calculated as faxy , P = fax+fay− ( fax·fay ) where fax is the fraction affected by drug X and fay is the fraction affected by drug Y [33] . | HIV infection is an incurable disease . Despite antiretroviral therapy , a pool of cells with HIV in a latent state persists and precludes fully eradication of the viral infection . The cells that contain this latent viral reservoir are very diverse , and therefore different therapeutic strategies would be necessary to target and eliminate all infected cells . Latency Reversal Agents ( LRAs ) are compounds able to awake the latent virus from its dormant state with the purpose of making infected cells visible to the immune system . But the ability of the LRAs to target different cell types containing HIV is currently unknown . Here , using a novel methodology that interrogates individual cells , we found that current LRAs do not impact equally all infected cells . In fact , certain types of memory lymphocytes , recognized to harbor latent HIV for decades , are not fully impacted by most of the LRAs tested . Our study highlights the difficulty to cure HIV with the currently available LRAs . Different therapeutic approaches aimed at reversing HIV latency from diverse cellular reservoirs are needed to reduce HIV persistence . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"blood",
"cells",
"cell",
"death",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"cell",
"processes",
"microbiology",
"toxicology",
"retroviruses",
"viruses",
"immunodeficiency",
"vir... | 2019 | Latency reversal agents affect differently the latent reservoir present in distinct CD4+ T subpopulations |
Maturation of vertebrate oocytes into haploid gametes relies on two consecutive meioses without intervening DNA replication . The temporal sequence of cellular transitions driving eggs from G2 arrest to meiosis I ( MI ) and then to meiosis II ( MII ) is controlled by the interplay between cyclin-dependent and mitogen-activated protein kinases . In this paper , we propose a dynamical model of the molecular network that orchestrates maturation of Xenopus laevis oocytes . Our model reproduces the core features of maturation progression , including the characteristic non-monotonous time course of cyclin-Cdks , and unveils the network design principles underlying a precise sequence of meiotic decisions , as captured by bifurcation and sensitivity analyses . Firstly , a coherent and sharp meiotic resumption is triggered by the concerted action of positive feedback loops post-translationally activating cyclin-Cdks . Secondly , meiotic transition is driven by the dynamic antagonism between positive and negative feedback loops controlling cyclin turnover . Our findings reveal a highly modular network in which the coordination of distinct regulatory schemes ensures both reliable and flexible cell-cycle decisions .
The mitotic division cycle is the sequence of events by which a growing cell replicates all its components , including DNA , and divides them , after mitosis , into two nearly identical daughter cells [1] . Meiosis is an alternative mode of cell division in which a diploid cell undergoes two successive divisions without intervening DNA synthesis , to create haploid cells called gametes or spores [2] . In vertebrate species , for instance , meiosis occurs during oocyte maturation , which is initiated in response to an hormonal signal with the specificity that oocytes are thereafter arrested , usually at the metaphase stage of MII , awaiting fertilization [3] . Meiotic maturation shares with mitosis many morphological events , such as metaphase and anaphase , as well as regulators such as the cyclin B-Cdk1 , known as the M-phase promoting factor ( MPF ) . However , it also involves a unique sequence of decision steps - meiotic resumption , transition and arrest - which clearly diverges from the mitotic one ( Fig . 1A ) . Investigating the regulation of meiotic maturation is therefore an opportune strategy to understand the remarkable plasticity of the cell cycle , which unfolds a diversity of decision patterns at different stages of multicellular development . The specific decision pattern of the oocyte meiotic maturation is intimately linked to the tightly controlled temporal dynamics of MPF ( Fig . 1B ) . The rise and the first peak of MPF activity triggers germinal vesicle break down ( GVBD ) and entry into MI . The transition from MI to MII is typified by an unusual partial decrease of MPF activity followed by an increase and stabilization at a plateau level associated with metaphase II arrest in Xenopus oocytes . The time course of MPF is shaped by a complex web of interaction with other cell-cycle regulators . At the first arrest of Xenopus oocyte in a G2-like state , MPF kinase is stored in an inactive state called pre-MPF in which , among the five isoforms of cyclin B described in this animal model , only cyclin B2 and B5 are found associated to Cdk1 [4] . As during mitosis , MPF activity is primarily regulated by its interaction with a dual protein-phosphatase ( Cdc25 ) , a cyclin-dependent kinase inhibitor ( Myt1 ) and the anaphase promoting complex ( APC ) . During meiotic maturation , this module is supplemented with a layer of control which involves the MAPK ( Mitogen Activated Protein Kinase ) /ERK ( Extracellular Regulated Kinase ) pathway , whose main upstream and output components in the context of meiotic maturation are proteins Mos and Rsk , respectively . These components of the MAPK pathway are involved not only in meiotic spindle morphogenesis during oocyte maturation [5] but also at several decision points of the oocyte maturation process including meiotic resumption ( G2/MI ) , meiotic transition ( MI/MII ) and maintenance of metaphase II arrest [6]–[8] . A key advance was to identify Rsk-mediated phosphorylation of APC inhibitor Emi2 as leading to MPF reaccumulation at the MI/MII transition [9]–[11] . In turn , MPF tightly controls phosphorylated levels of Mos [12] or Emi2 [13] . Two decades of experimental studies have thus documented manifold levels of interaction between the MPF and Mos/MAPK pathways , whose respective roles in various decision stages of maturation remain difficult to disentangle . In an attempt to clarify the interactions between both pathways , we use a modeling approach which has already been harnessed to gain insight into cell cycle control during animal development , as with the syncytial mitotic cycles in Drosophila embryos [14] , fertilization process in mammals [15] , the oocyte maturation initiation switch [16] but not yet for the whole oocyte meiotic maturation process . This approach has been remarkably successful , not only to check whether known molecular interactions can explain observed contextual and functional cell-cycle behaviors , but also to uncover the design principles of the molecular network in terms of feedback and feedforward topology [17]–[19] . Our modeling effort will thus be devoted to address two complementary issues: Are documented interactions between MPF and MAPK pathways necessary and sufficient to account for the observed properties of meiotic maturation ? What are the network design principles that robustly enforce the progression of cells through a specific sequence of meiotic decisions ? To answer these questions , we first build a computational model that incorporates the major signaling pathways involved in the meiotic maturation of Xenopus laevis oocytes . Appropriate parameterization of the model allows us to reproduce the temporal dynamics of MPF and of other key regulators when the oocyte progresses from meiotic entry to metaphase II arrest . The dynamical mechanisms underlying these transitions are further analyzed using bifurcation analysis , which unmasks the existence of two main positive-feedback systems in addition to the core negative-feedback loop along which MPF represses itself by upregulating its own inhibitor , the anaphase promoting complex ( APC ) . Remarkably , the architecture of these two subcircuits is unambiguously identified using a parameter sensitivity analysis , which reveals that they independently regulate meiotic resumption on the one hand , and meiotic transition on the other hand . The significance of the model is further assessed by simulating how alteration of the underlying molecular network using chemical manipulations or antisense strategies may induce maturation defects including initiation delays [8] or failures to transit from first to second meiosis [4] , [6] , [7] , [10] , [20] . Revisiting the relation between topology and dynamics in the maturation regulatory network leads us to identify and discuss the design principles that underlie the complex and reliable decision sequence studied here , and which could apply in various other cellular contexts .
The interaction graph shown in Fig . 1C incorporates all molecular actors and interactions known to be involved during the cell-cycle progression from prophase I to metaphase II arrest . Following the basic principles of biochemical kinetics , we translate this graph into a set of ordinary differential equations ( see Methods ) . The unknown rate constants of the model are estimated by fitting the qualitative model behavior to the available data , including the well-characterized temporal profile of MPF activity during the meiotic maturation ( Fig . 1B ) as well as the bistable behavior of the MAPK modules and the oscillatory dynamics of the MPF-APC module ( Methods ) . It was found that the behaviors observed depend little on the specific parameter set chosen under these constraints . This global maturation regulatory network of Fig . 1C connects together functional modules that so far have been studied only separately: ( i ) the MPF autoamplification loop , which triggers G2/M transition; ( ii ) the MAPK phosphorylation cascade , which is characterized by specific upstream and downstream components during meiotic maturation and is tightly bound to the MPF autoamplification loop; ( iii ) the underlying CPEB-dependent translational network , which controls temporal expression during the maturation process . We describe below how these subcircuits function and how they interact . A remarkable feature of oocyte meiotic maturation is that a basic hormonal signal ( a pulse-like or constant exposure of progesterone ) induces a complex non-monotonous MPF activity profile ( see Fig . 1B ) . The term non-monotonous refers to the fact that MPF activity does not continously increase or decrease during maturation but falls rapidly after rising to a first peak , before increasing again toward a plateau . In this section , we investigate the network dynamical properties underlying this sophisticated temporal profile of MPF activity , which drives the sequence of meiotic decisions from resumption to transition and arrest . Fig . 2A shows how the mathematical model responds to a constant exposure of progesterone . The numerical simulation reproduces the typical MPF temporal profile observed during meiotic maturation of Xenopus oocytes ( compare Fig . 2A and Fig . 1B ) . In contrast with the non-monotonous time course of MPF , components of the MAPK pathway ( Mos , MEK , ERK , Rsk ) or of the autoamplification loop ( Plx1 , Cdc25 , Myt1 ) exhibit a sharp activation ( or inactivation for Myt1 ) followed by a plateau . Besides CPEB1 , which is inactivated and degraded at the meiotic transition , APC is the only actor which exhibits a transient activation , following the MPF peak associated with anaphase events . Another key feature of the simulation is Emi2 activity rising only at the very end of meiosis I before reaching a plateau . As we shall see later , proper timing of Emi2 activation is crucial to allow MPF to reaccumulate after a full activation of APC . The activity profiles obtained in this simulation are fully consistent with experimental data collected for Mos , MAPK , Plx1 , APC/Cdc27 or Emi2 during oocyte maturation of Xenopus laevis [4] , [6] , [7] , [13] , [34] . The one-parameter bifurcation diagram in Fig . 2B shows how the steady state value of MPF activity varies as a function of progesterone level . At least two stable solution branches coexist for some range of progesterone concentration , including the case of no progesterone . The coexistence of two ( or more ) stable solutions for the same parameter value is a phenomenon known as bistability ( or multistability ) . Coexisting stable solution branches ( nodes ) are generally connected by an unstable branch solution ( saddle ) , which acts as a separatrix between them . Stable and unstable branches connect at saddle-node points , where they annihilate together ( the stable and unstable solution can be found on one side only of the saddle-node point ) . The lower branch ( low MPF activity ) corresponds to the prophase I-arrest state whereas the upper branch ( high MPF activity ) corresponds to the Metaphase II-arrest state . A sufficiently strong progesterone input , even transient , is therefore able to switch the cellular state from G2-arrest to metaphase-II arrest in an irreversible manner . An important feature of this bifurcation diagram is also the existence of four saddle-node bifurcation points ( I , II , III , IV ) instead of the two saddle-node points associated with the classic bistability scheme . The bifurcation point I controls the progesterone level required to destabilize the G2-arrested state and to trigger sharp MPF activation . The bifurcation point IV determines the stability of the metaphase II arrest characterized by high MPF activity . As long as this bifurcation point is associated with a negative value of progesterone signal , oocytes cannot leave the metaphase II state . If it is shifted to positive values of the progesterone signal by parameter changes , however , high MPF levels cannot be maintained , and the arrest state is unstable . The presence of the two additional saddle-node points II and III allows the slopes of the unstable solution branches originating from points IV and I to be largely independent , a feature which may persist even if the saddle-node points collide upon variation of another a parameter . This configuration effectively decouples the bifurcation points I and IV and allows them to be controlled separately , which will prove crucial in the following . The global structure of the bifurcation diagram of Figs . 2B , with its double bistability cycle , reflects in fact the coordinated actions of two bistable positive-feedback systems [18] , [35] , [36] , which are relatively independent although one tends to activate the other and which will be identified in the next section . The non-monotonous behavior of MPF activity during the transition from the low activity state to the high activity state is not directly related to the structure of the bifurcation diagram . The fact that MPF activity rises , then decreases before increasing again is due to a negative feedback control based on the interaction between MPF and APC . This feedback-based bifurcation structure underlying the maturation dynamics is expected to provide robustness against environmental or intrinsic noises that could bias the trajectory toward inappropriate cellular states ( e . g . G2-like , interphase-like , oscillations ) . Fig . 3 shows that such major disruption is very improbable . Indeed , the dynamical trajectory of MPF in state space starting from a G2-arrest state to a metaphase-II arrest state ( bottom panel of Fig . 3A ) is remarkably insensitive to changes in the progesterone input profile ( top panel of Fig . 3A ) , with fluctuations mostly affecting the timing of maturation ( middle panel of Fig . 3A ) . In particular , whether progesterone input is constant or transient has almost no effect on the dynamical trajectory of MPF , provided the input is sufficiently strong , which is consistent with experiments in which oocytes are either treated with transient or continuous exposure of progesterone [37]–[39] . It also confirms the result anticipated by the bifurcation diagram of Fig . 2B that oocyte meiotic maturation is indeed a bistable process in which the transition can be triggered by a transient perturbation . Similarly , the model also displays a robust behavior with respect to variability in kinetic rates since the qualitative structure of the trajectory remains unaffected when all kinetic parameter values are randomly changed with a coefficient of variation ( CV ) of ( Fig . 3B ) . For a CV of , only a few cases display abnormal MPF profiles . However , when the CV of parameter changes is increased beyond the large value of , maturation failures occur in more than half of the trials . The type of robustness oberved here emphasizes that achieving the appropriate sequence of decisions depends on the sequence of biochemical states traversed , not on the exact times at which they are reached . Thus , the state space trajectory is more relevant than time profiles . This robust dynamical behavior stems from the existence of an attracting slow manifold that canalizes the trajectory in state space . In the previous section , the bifurcation analysis revealed the existence of two positive-feedback control systems operating independently to sequentially drive the G2/M and meiotic transitions , in addition to the core MPF-APC negative feedback loop . To identify these two systems , numerical simulations and bifurcation analysis are here supplemented with a systematic parameter sensitivity analysis , which allows us to characterize the effect of each parameter on the maturation process . We first focus on quantitative indicators of the MPF activity profile , which are the time of occurence of the first MPF peak ( signaling the G2/MI transition ) , as well as MPF levels and associated with the trough and the plateau of the MPF time course ( signaling the MI/MII transition and the further metaphase II arrest ) . We measure their sensitivities to parameter variation as: ( 1 ) where is the perturbed parameter and the weigh the different indicators . As discussed above , G2 arrest and MII arrest are directly controlled by the saddle-node bifurcation points I and IV , respectively . Denoting by and the progesterone thresholds associated with bifurcation points I and IV , respectively , the sensitivity of and to parameter variation should also be a relevant parameter sensitivity measure of maturation dynamics . These sensitivities can be written as: ( 2 ) For both types of sensitivities , it is useful to define normalized sensitivities , defined by and with and . For example , a value of close to ( resp . , ) indicates that parameter affects the progesterone threshold ( resp . , ) much more than ( resp . , ) . These two complementary sensitivity measures are expected to indicate whether a given parameter tends to affect early or late stages of maturation as illustrated in the right panels of Figs . 4A and 4B . Fig . 4C provides a synthetic view of sensitivity values for all kinetic parameters that control interactions between two molecular actors and are therefore associated to a link in the network diagram of Fig . 1C . Interestingly , the values of sensitivities and ( and therefore and ) are seen to be highly correlated . The two sensitivities thus essentially provide the same information , confirming that dynamic response to progesterone signals is very much controlled by the bifurcation diagram . Noting that most values of the normalized sensitivities are either close to 0 or 1 , a natural partition of kinetic parameters into two classes emerges , according to whether they preferentially control transition G2/MI ( ) or the MI/MII transition ( ) . The classification so obtained allows us to disentangle the complex regulatory network shown in Fig . 1C by isolating two separate subnetwork module , such that all links in a module control the same transition . It is quite remarkable that most molecular actors appear in one module or the other but not in both , with the notable exception of Mos and MPF . Note that links corresponding to kinetic parameters with very small sensitivities have been neglected . Presumably , these molecular interactions have biological roles not directly related to maturation control or have a specific impact that could not be identified given the chosen model parameters . The first circuit drawn in Fig . 4D displays a coherent feedback organization where only positive feedback loops are present . The post-translational interactions between Plx1 , Cdc25 , MPF and Myt1 constitute the core set of positive-feedback loops that contributes to the MPF autoamplification loop . Additional feedforward loops mediated by the activation of the translation machinery ( i . e . , CPEB ) and positive-feedback loops mediated by the phosphorylation of Mos by MPF and of Myt1 by Mos are also involved in G2/MI transition . The architecture of the circuit controlling MI/MII transition ( Fig . 4E ) markedly differs from that of the meiotic resumption module . First , MPF is now regulated through CPEB-dependent synthesis and APC-dependent degradation , which control only its turnover . Second , this circuit relies on an antagonism between two negative feedback loops , where the direct interactions of MPF with APC and Emi2 promote its own inactivation through the degradation of its cyclin subunits , and a positive-feedback loop where MPF-dependent activations of MAPK and CPEB4 cooperate towards the accumulation and activation of Emi2 , which itself opposes the APC-dependent degradation of MPF . This feedback antagonism results in an incoherent feedforward loop , which is key to the precise temporal gap between the G2/MI and MI/MII transitions . Importantly , our model accounts not only for the main features of meiotic maturation in wild-type eggs , but also of phenotypes of eggs treated by antisense oligonucleotides-based strategies or by chemical inhibitors . Our simulations of these phenotypes are summarized in Fig . 5 and Table 1 . To identify the role of protein synthesis in the initiation of Xenopus oocytes maturation , experiments have been performed to inhibit cyclin B or/and Mos synthesis using antisense oligonucleotides [8] . They showed that ablation of either Mos or cyclin B alone does not prevent maturation initiation yet induces significant delays , whereas combined ablation impairs initiation . Our model is able to reproduce such delays in the absence of cyclin or Mos synthesis ( Fig . 5A and B ) , reflecting the existence of cooperative mechanisms between translational and post-translational controls during meiotic initiation ( e . g . , Mos synthesis and Mos-dependent inactivation of Myt1 ) . In addition , oocytes where cyclin B is disabled by antisense strategies fail to reaccumulate MPF at MI/MII transition [4] . This is also observed in simulations ( Fig . 5A ) where , after the post-translational activation of preMPF by the Plx1 pathway , depletion of preMPF and degradation of active MPF by APC are not counterbalanced by the synthesis of new cyclins , thereby precluding MPF reaccumulation . Meiotic transition also fails in Mos-ablated oocytes , due to the absence of MAPK activation [7] , with the possibility however to form a transitory interphase nucleus after completion of meiosis I and to reactivate MPF so as to mimic the mitotic cell cycle of early embryos [7] . In numerical simulations , oocytes lacking Mos are indeed unable to transit appropriately to MII . However , an oscillatory pattern of MPF activity may be also observed although it is highly sensitive to model parameters ( Fig . 5B ) . Besides the defects for maturation initiation associated with inhibition of protein synthesis , disruption of the progesterone-dependent Plx1 activation also significantly delays meiotic resumption in progesterone-treated oocyte [22] , [40] , which is reported as well in numerical simulations ( Fig . 5C ) . Note that any combination of the disruption of cyclin synthesis , Mos synthesis and Plx1 activation leads in model simulation to maturation initiation failures ( result not shown ) , emphasizing the synergistic role of multiple translational and post-translational mechanisms . Inhibition of MAPK activation in oocytes can also be achieved using MEK inhibitor U0126 [5] , [6] , [41] . In U0126-treated oocytes , MAPK inactivation prevented cyclin B reaccumulation after MI , by allowing APC-mediated degradation similarly as in the case of Emi2 ablation [10] . In simulations ( Fig . 5D ) , MPF concentration does not vanish as in the case of inhibition of cyclin synthesis but remains at an intermediate level as is observed in experiments [5] , [6] . Simulations do not reproduce the delay observed in these experiments , which can be due to our model not taking into account the regulation of cyclin B synthesis by MAPK as has been reported by Abrieu et al [42] . In addition , chemical inhibitor U0126 might target other translational regulators besides MEK1 , and such non-specific effects may account for discrepancies in the observations made when Mos is ablated . Experiments inducing deletion or overexpression of Emi2 demonstrate the crucial role of this protein in meiotic transition . Ectopic expression of Emi2 at physiological MII levels can arrest maturing oocytes at metaphase I [11] , which is easily explained by the fact that Emi2 counteracts APC activity and subsequently cyclin degradation , maintaining a sustained MPF activity ( Fig . 5E ) . Conversely , our simulations also reproduce the effect of inhibiting Emi2 synthesis ( Fig . 5F ) , which leads to complete and rapid degradation of cyclin B at MI exit , causing an inappropriate exit into interphase and a failure to reaccumulate cyclin B [10] , [11] , [13] . These experiments and simulations showing maturation failure for overexpression or deletion of Emi2 strongly support that a strict temporal control over Emi2 levels is critical for a reliable MI/MII transition .
The availability of a regulatory network model that qualitatively reproduces a broad spectrum of experimental data allows us to investigate the design principles that underlie a reliable maturation process . Bifurcation and sensitivity analyses of the model unveiled the existence of two independent subcircuits where feedback loops are subtly interlocked so as to achieve two coordinated but separable transitions ( see Fig . 6 ) . The first transition , meiotic resumption , relies on a circuit that involves several signaling pathways and positive-feedback loops . This module is organized around the core autoamplification loop which includes MPF , Myt1 , Cdc25 and Plx1 and drives the sharp post-translational activation of MPF associated with G2/MI transition ( Fig . 6A ) . This loop is supplemented with other positive-feedback loops and coherent feedforward loops featuring CPEB1 and Mos , which ensures a simultaneous activation of MPF , the Mos/MAPK pathway and the translational machinery ( Fig . 6B ) . The role of these combined positive-feedback motifs in the MPF and MAPK modules is not related to robustness against noise [47] , activation threshold tuning [48] or multistability [18] , [35] , but is rather aimed to induce and sustain high MAPK activity throughout maturation , independently of the MPF activity level which decreases due to the APC-dependent degradation of cyclin subunits at the end of the first meiosis ( Fig . 6C ) . The second transition from meiosis I to meiosis II indeed requires high MAPK levels to promote MPF stabilization . Late reactivation of MPF is driven by a delayed positive-feedback loop involving Emi2 that counteracts the negative feedback mediated by APC . Delayed activation of Emi2 is itself the result of the incoherent feedforward loop in which MPF both activates and inactivates Emi2 ( Fig . 6D ) . This sophisticated regulatory scheme provides an interesting example of how the combination between positive and negative feedback loops gives rise to complex dynamics such as non-monotonous bistable behaviors , besides those that have already been studied in the context of oscillatory , excitable and bistable dynamics [36] , [49] , [50] . Meiotic maturation poses a difficult challenge to oocyte cells . A single transient signal must be followed by a coordinated sequence of two crucial and distinct decisions , MI entry and MI/MII transition , which both require a sharp MPF activation . Our findings reveal the sophisticated molecular network mechanisms that provide an original solution to this problem . Firstly , like in other biological decision-making processes , the two main meiotic decisions rely on two distinct positive-feedback-based circuits , each of which combining multiple loops so as to create sharp and robust transitions . Secondly , interference and retroactivity between the two decision circuits are minimized by using separate and partly independent regulatory schemes based on post-translational modifications and protein turnover control , respectively . Lastly , the coordination of the decision systems is mediated by the existence of a negative feedback loop and an incoherent feedfoward loops , which are known to be efficient for scheduling temporal gaps between successive decisions [51] , [52] . Thanks to this specific regulatory and feedback architecture , a transient signal can trigger complex dynamical and phenotypical trajectories which are attracted by a one-dimensional slow manifold and follow it throughout maturation . This dynamical process is reminiscent of the phenomenon of canalization during multicellular development [53] . Overall , this encourages further efforts to decipher the dynamical behavior of molecular networks with complex feedback and feedforward topology , especially when they combine oscillatory and irreversible behaviors , as occuring during meiotic maturation .
The mathematical model for the maturation regulation network is based on the molecular interactions reported in Xenopus laevis between the 12 proteins CPEB1 ( abbreviation C1 ) , CPEB4 ( abbr . C4 ) , MPF , Cdc25 ( abbr . C25 ) , Myt1 ( abbr . Myt ) , APC , Mos , MEK , ERK , Rsk , Plx1 ( abbr . Plx ) and Emi2 ( abbr . Emi ) . We assume that the activity of each protein in the list above can be post-translationally regulated , typically through phosphorylation , such that they can be either in activated or inactivated forms . The 12 molecular actors can be distinguished according to whether their total concentration is also regulated ( class I: Mos , MPF , APC , Emi , C1 , C4 ) or can be considered as constant on the time scale of maturation ( class II: Plx , C25 , Myt , MEK , ERK , Rsk ) . For class-I proteins , the concentrations of the active and inactive proteins evolve in time according to a set of differential equations ( Table 2 ) . where and denote the concentration of the active or inactive forms of protein , whereas , , and denote , respectively , the synthesis , degradation , activation and inactivation rates of these proteins . We assume Michaelis-Menten kinetics for activating and inactivating reactions where and are the maximum rate of the reactions ( Table 2 ) . Only Emi2 is assumed to have more than two states: ( i ) partially activated when unphosphorylated with a dephosphorylation reaction rate ; ( ii ) inactivated through phosphorylation by Rsk with a reaction rate ; ( iii ) fully-activated through phosphorylation by Rsk of unphosphorylated or MPF-phosphorylated forms , with a reaction rate . The concentrations of these three forms are denoted by , and , respectively . The assumption that the total concentrations of class-II proteins remain relatively stable throughout the maturation process allows us to use the quasi-steady state approximation . Moreover , if phosphorylation and dephosphorylation reactions operate in the linear regime , the steady-state concentrations can be obtained as a function of the total concentration ( normalized here to 1 ) and of their maximum activating and inactivating reaction rates and . These expressions depend on whether activation is achieved through a one-step phosphorylation ( C25 , Myt , Plx ) or a two-step phosphorylation ( MEK , ERK , Rsk ) ( Table 2 ) . The kinetic rates , , and that appear in both differential equations and steady-state concentrations can be either considered as constant parameters on the time scale of maturation process or as time-dependent variable as they may depend on the concentration of other dynamic regulators of the maturation process . Such dependence is depicted as links in the network representation of Fig . 7 is given in Table 2 . The model contains a large number of kinetic parameters ( 73 ) , which , for the most , have not been estimated experimentally so far . A preliminary step toward the adjustment of parameters is to reduce their number . To account for the dynamical features of the maturation process , the mathematical model only needs to describe the evolution of protein concentrations relative to each other . We can therefore normalize protein concentrations . First , the total concentration of class-II proteins is normalized to 1 . Second , only the relative value between activation rates and inactivation rates are relevant for class-II proteins , such that we can introduce a free parameter which determines their absolute value . Third , the Michaelis constants for all activation and inactivation processes of class-I proteins are set to . Actual values of the concentrations can always be recovered by scaling the variables appropriately , keeping in mind that the present modeling study focuses on the temporal profile of protein activity rather than quantitative predictions . The normalization procedure can reduce the number of parameter to 54 . The other parameters used in this study ( Table 3 ) have been selected in a semi-arbitrary manner constrained by qualitative fitting of the time course of several components in various contexts . The kinetic parameters for the MAPK pathway have been adjusted to display the classic bistable behavior of this cascade ( Fig . 7A ) . The kinetic parameters for Cdc25 , Myt1 , MPF , APC and their respective interactions have been adjusted to produce an excitable or oscillatory behavior commonly associated with a specific underlying bifurcation structure of the dynamics: a saddle node bifurcation on an invariant circle ( Fig . 7B ) . Finally , the kinetic parameters coupling these two modules between themselves and to the input signal have been adjusted to match the temporal profile of MPF activity that is typically observed in various experimental prototocols ( Fig . 7C , see also Fig . 1B and 5 ) . | In the life cycle of sexual organisms , a specialized cell division -meiosis- reduces the number of chromosomes in gametes or spores while fertilization or mating restores the original number . The essential feature that distinguishes meiosis from mitosis ( the usual division ) is the succession of two rounds of division following a single DNA replication , as well as the arrest at the second division in the case of oocyte maturation . The fact that meiosis and mitosis are similar but different raises several interesting questions: What is the meiosis-specific dynamics of cell-cycle regulators ? Are there mechanisms which guarantee the occurence of two and only two rounds of division despite the presence of intrinsic and extrinsic noises ? The study of a model of the molecular network that underlies the meiotic maturation process in Xenopus oocytes provides unexpected answers to these questions . On the one hand , the modular organization of this network ensures separate controls of the first and second divisions . On the other hand , regulatory synergies ensure that these two stages are precisely and reliably sequenced during meiosis . We conclude that cells have evolved a sophisticated regulatory network to achieve a robust , albeit flexible , meiotic dynamics . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"biology"
] | 2012 | A Dynamical Model of Oocyte Maturation Unveils Precisely Orchestrated Meiotic Decisions |
The unique avian vocal organ , the syrinx , is located at the caudal end of the trachea . Although a larynx is also present at the opposite end , birds phonate only with the syrinx . Why only birds evolved a novel sound source at this location remains unknown , and hypotheses about its origin are largely untested . Here , we test the hypothesis that the syrinx constitutes a biomechanical advantage for sound production over the larynx with combined theoretical and experimental approaches . We investigated whether the position of a sound source within the respiratory tract affects acoustic features of the vocal output , including fundamental frequency and efficiency of conversion from aerodynamic energy to sound . Theoretical data and measurements in three bird species suggest that sound frequency is influenced by the interaction between sound source and vocal tract . A physical model and a computational simulation also indicate that a sound source in a syringeal position produces sound with greater efficiency . Interestingly , the interactions between sound source and vocal tract differed between species , suggesting that the syringeal sound source is optimized for its position in the respiratory tract . These results provide compelling evidence that strong selective pressures for high vocal efficiency may have been a major driving force in the evolution of the syrinx . The longer trachea of birds compared to other tetrapods made them likely predisposed for the evolution of a syrinx . A long vocal tract downstream from the sound source improves efficiency by facilitating the tuning between fundamental frequency and the first vocal tract resonance .
Direct visualization of the sound-producing larynx [23] and syrinx [12] and experiments with an excised larynx [24–27] and syrinx [11 , 13 , 15] confirm that both organs function as self-oscillating valves driven by airflow . The primary sound then travels along the respiratory tract above the source , which for laryngeal phonation consists of oral , nasal , and pharyngeal cavities [28–30] and in birds of the tracheal tube , larynx , oropharyngeal–esophageal cavity , and beak [31–34] . An obvious , major difference between the laryngeal and syringeal design is the relative length of the airway above and below the sound source . This difference has important consequences for how the vibrating tissue of the sound source and the air column in the vocal tract interact . The air columns above and below each sound source can affect the way energy is conveyed from the aerodynamic airflow to the vibrating tissue masses . Titze [35] used a surface-wave model to show how energy from the airstream becomes coupled to the vocal folds . The driving force for tissue vibrations in the syrinx and larynx is lung pressure . The lowest driving pressure required for triggering tissue vibration is referred to as phonation threshold pressure ( pth ) and provides an important estimate of the energy conversion . Phonation threshold pressure was derived as pth=kt ( ρ2 ) ( B2Llt ) 2 , ( 1 ) in which kt is a dimensionless pressure coefficient ( average of about 1 . 1 over a vibration cycle ) , ρ is the air density , B is vocal fold tissue damping , L is vocal fold length , and lt is the acoustic inertance of the downstream tube . Inertance is the sluggishness ( or inertia ) of the vocal tract air column . As the supraglottal column of air is driven forward and backward by airflow emerging from the glottis , the sluggishness of the air column creates an acoustic pressure that helps the vocal folds in their self-sustained oscillations [28 , Chapter 4] . The phonation threshold pressure thus varies inversely with the square of inertance . Greater inertance lowers the phonation threshold pressure , making it easier to produce vocal fold oscillations . For a tube that is acoustically short ( i . e . , much less than a quarter of a wavelength ) , the inertance can be expressed simply as lt=ρLtAt , ( 2 ) in which Lt is the length of the tube and At is the cross-sectional area . Eq 2 shows that a longer and narrower tube produces greater inertance . When the tube is lengthened much beyond a quarter wavelength , however , standing waves can be produced due to reflections from the distal end of the tube , and the inertance then becomes frequency dependent . In fact , for some frequencies , the vocal tract may become compliant rather than inertive , in which case supraglottal pressures hinder self-sustained oscillations of the vocal folds . To maximize energy transfer , it is then important that the bird develops an appropriate length–frequency combination that produces inertance at the input of the trachea , which passerine songbirds indeed do by matching fundamental frequency and first formant frequency [32] . Tissue oscillations are maintained much more easily , i . e . , with lower subglottal/subsyringeal pressure , at frequencies for which the air column is inertive rather than compliant , which generally occurs at frequencies below a resonance frequency of the tube . Tracheal lengths that are slightly below one-fourth , three-fourths , one and one-fourth , one and three-fourths , etc . wavelengths are theoretically ideal for a tube closed at one end and open on the other end . The exact boundary conditions may differ , however , with variable glottal impedance and radiation impedance . The effect of the vocal tract on self-sustained oscillation is reversed for a subglottal/subsyringeal airway system . Fletcher [36] expanded the theory of self-oscillating valves in a tube by including valves with both lateral and longitudinal degrees of freedom ( relative to the airflow and an upstream acoustic tube ) . For the important lateral degree of freedom in vocal fold vibration , inertance below the larynx was not favorable to vocal fold vibration , raising the threshold pressure rather than lowering it . Titze [37] showed that combining a compliant system below ( i . e . , upstream ) the sound source with an inertive system above ( i . e . , downstream ) the sound source provides the best assistance to self-sustained oscillation . A second benefit obtained from an inertive tube is the delay of the peak acoustic airflow through the glottis relative to the peak excursion of the lateral vocal fold movement , generating a “skewing of the airflow waveform” toward a sawtooth shape . In human subjects , these interactions between source and filter have different effects on the voice during spontaneous vocalization , including an increase in overall intensity , an increase of the higher harmonic energy , or an increase in the probability of nonlinear phenomena [38] . In summary , theoretical acoustic analysis predicts that ( 1 ) subglottal/subsyringeal inertance raises the phonation threshold pressure but increases glottal waveform skewing , the benefit and costs of which can offset each other , and ( 2 ) that supraglottal/suprasyringeal inertance lowers oscillation threshold pressure and increases glottal waveform skewing , an additive beneficial effect . Thus , a sound source deeper in the airway would appear to have an advantage , assuming that the extra energy losses in the longer transmission system do not negate the additional energy converted .
All procedures using birds were approved by the Institutional Animal Care and Use Committee of the University of Utah ( protocol number 16–03014 ) . The protocols are in compliance with the Animal Welfare Act regulations and Public Health Service Policy . The university maintains accreditation by the Association for the Assessment and Accreditation of Laboratory Animal Care International . In the first study , we used a model that consists of two vocal folds constructed from silicone [39] . The silicone model was a single-layer representation of the vocal folds or of labia and membranes in a syrinx [40 , 41] . The model cross section was uniform in the dorsoventral direction up to the point of intersection with the tube wall . Resonance properties of the model were determined by placing the model on a small shaker , which created small amplitude vibrations from 5 to 500 Hz . The beam of a laser Doppler vibrometer ( Polytec Scanning Vibrometer; Polytec , Inc . ) was positioned on one vocal fold in order to measure its frequency response . A Fourier analysis of the model vibration yielded a fundamental frequency of 78 Hz . A simple physical model was used that neither includes detailed features of syringeal or laryngeal morphology nor imitates the respective respiratory anatomies . By not including features such as air sacs and accessory elastic tissue components or musculature , we limit the number of variables to sound source position and trachea length . This provides a clear and simple test of whether one location of a sound source confers an acoustic advantage over another . Physical models such as that used here have been tested in previous studies investigating different questions related to human voice production and biomechanics [26 , 42–44] . The two vocal folds were mounted inside a 1-inch inner-diameter PVC ring that could be coupled to a 1-inch inner-diameter PVC tube . Blowing compressed air through a tube containing the vocal folds initiated vocal fold vibration and created sound . The sound source ( i . e . , the ring containing the vocal folds ) was placed either at the upper ( larynx ) or lower ( syrinx ) end of a PVC tube ( Fig 1 ) . The PVC tube length was varied from 0 to 248 cm in a stepwise fashion . For the syringeal position , the sound source was placed 2 cm above a y-shaped tube simulating the bronchial bifurcation . The laryngeal sound source was placed at the downstream end of the trachea and was equipped with a short 15-cm-long vocal tract above the source . Air was supplied from a tank with compressed air through a 5-m Silastic tube , which was connected to both bronchial tubes . Average airflow was measured 3 m upstream from the tracheal bifurcation in the Silastic tubing with a rotameter mounted in series ( KING instruments company , Garden Grove , CA; maximum flow rate: 4 SCFM ) and in one bronchial tube ( flow meter MC-5SLPM-D-15PSIA; Alicat Scientific ) . Pressure was measured below each sound source ( Fig 1 ) and , in the case of the laryngeal sound source , at the lower end of the trachea . The latter allowed us to monitor the pressure gradient along the tracheal tube , which never exceeded 20 Pa/m . Calibrated pressure measurements were made through small ( 1-mm inner diameter ) stainless steel tubes mounted into the wall of the PVC tubing and connected through Silastic tubing to a pressure transducer ( model FHM-02PGR-02; Fujikura , Tokyo , Japan ) . A calibrated microphone ( GRAS Sound and Vibration , Denmark; pressure microphone 40 AG , preamplifier 26 AK and 12 AD power module ) was placed perpendicular to the vocal tract opening at a distance of 10 cm . Sound , airflow , and pressure signals were recorded through a multichannel AD-acquisition board ( NI DAQ ) . Signals were digitized at 44 . 1 kHz sampling rate with Avisoft Recorder software ( Avisoft , Berlin , Germany ) . We measured driving pressure , tracheal airflow , sound pressure level , and fundamental frequency and estimated vocal efficiency as the ratio of radiated acoustic power ( Pr ) over aerodynamic power ( Pa ) : E=PrPa . ( 3 ) Radiated power is a measure of the amount of aerodynamic energy converted into acoustic energy and radiated into the air per second ( in Watts ) . Assuming spherical radiation , it can be calculated from the sound pressure level ( in dB ) at a radius R from the opening of the vocal tract ( lips or beak in animals; open tube end in our experimental setup ) : Pr=4πR2×10 ( SPL−120 ) /10 , ( 4 ) in which Pr is the radiated power , R is measured as distance between microphone and the opening of the vocal tract tube ( here , 10 cm ) , and SPL is the sound pressure level ( dB ) . Aerodynamic power is derived as the product of mean flow rate and subglottal/subsyringeal pressure: Pa=psV , ( 5 ) in which Pa is the aerodynamic power ( Watts , W ) , ps is the pressure below the sound source ( Pascal , Pa ) , and V is the mean flow rate ( cubic meters per second , m3/s ) . The vocal fold model , either in syringeal or laryngeal position , was coupled with 18 different tracheal lengths . The segment lengths were chosen so that the first tracheal resonance is either lower , higher , or equal to the eigenfrequency of the physical model of 78 Hz . The first and second resonances are provided in S1 Table . Studies linking fundamental frequency and body size [45] or body size and tracheal length [18] suggest that our vocal fold model would be that of a 2 to 30 kg bird with a tracheal length of approximately 40 cm . Therefore , the simulations with very short and very long tracheal lengths are representative for birds with extreme trachea morphologies [46] . It is the range around 40-cm trachea length that resembles most realistically a bird-like situation with average tracheal length . In a second study , we investigated the interdependency between sound source position , glottal efficiency , and vocal tract length by computational simulation . In agreement with the physical model construct , we did not make the self-oscillating sound source specific to any species or gender nor did we include any layered tissue morphology . Rather , we used a simple generic self-oscillating tissue surface model . The “vocal folds” were defined by five serially coupled sections of a soft-wall tube ( 1 . 6 mm each section in the caudal–cranial direction ) , giving the vibrating tissue an overall thickness of 8 . 0 mm . The sections had elliptical cross sections . The minor diameters ( also known as the prephonatory glottal widths ) were 1 . 0 , 0 . 8 , 0 . 6 , 0 . 6 , and 1 . 0 mm , caudal to cranial , whereas the major diameters ( also known as vocal fold length , ventral to dorsal ) were all 10 mm . The viscoelastic properties of the wall were patterned after the two-section model of [47] , with a Young’s modulus = 4 . 0 kPa , a shear modulus = 1 . 0 kPa , mass per unit area = 0 . 3 g/cm2 , and a damping ratio of 0 . 1 . This produced a natural tissue frequency of 130 Hz in each section . Fluid flow and acoustic wave propagation in all airway sections ( including the source sections ) were calculated on the basis of conservation of momentum and mass transfer using the Navier–Stokes and continuity equations for nonsteady , compressible airflow [48 , 49] . Fluid pressures on the surfaces of the five source sections provided the driving forces for self-sustained oscillation . The tracheal length was varied from 22 cm to 154 cm in steps of 22 cm . In case of the syrinx position , an additional 1 . 0-cm section length was added between the source and the bronchial termination , while for the larynx position , an additional 1 . 0-cm length was added between the source and the mouth radiation . Radiation from the mouth was computed with the piston-in-a-spherical-baffle model [49] . The cross-sectional area of the tube was length dependent , as defined below . The viscoelastic wall properties for all sections except the vocal fold sections were chosen according to Titze and colleagues [49]: Young’s modulus = 9 . 62 kPa , shear modulus = 1 . 67 kPa , mass per unit area = 1 . 5 g/cm2 , and damping ratio = 1 . 26 . The power calculations were as follows: P=1N∑n=1N ( pnUn ) totalpower , ( 6 ) PDC=pDCUDCsteadyflow ( DC ) power , ( 7 ) PAC=P−PDCacoustic ( AC ) power , ( 8 ) in which n is the time sample index , N is the number of samples simulated ( 22 , 050 in a 0 . 5-s window ) , pn is the instantaneous pressure in a given section , Un is the instantaneous flow rate , pDC is the steady ( DC ) pressure , and UDC is the steady ( DC ) flow rate , computed as time averages over the 0 . 5-s window . The instantaneous powers pnUn in Eq 6 varied dramatically in the vocal fold sections where self-oscillation took place . Therefore , to get a representation of the mean values of intraglottal pressure , flow , and power , the calculations in Eq 6 were averaged over the five adjacent vocal fold sections . The efficiency was computed as the acoustic power delivered to the mouth divided by the total input power . This was different from the efficiency calculation with Eq 3 for the physical model . Mouth power gave a more accurate difference calculation in Eq 8 . Radiated power was many orders of magnitude lower than the input power when the tube was more than 1 . 0 m long . Thus , the magnitudes of the efficiency calculations between the physical model and the computational model are not comparable , but the variations with length and source position are comparable . Unlike in the first experiment with the physical model , tracheal diameter was adjusted with changing tracheal length . This simulates conditions in avian archosaurs more realistically . As indicated in Eq 2 , not only the length but also the diameter of the downstream airway affects tissue vibration characteristics . The relationship between tracheal length ( TL ) and tracheal diameter ( TD ) was estimated with Eq 9 following published empirical data [18]: TD= ( 0 . 24*TL ) +0 . 1 . ( 9 ) We used tracheal diameter to estimate vocal fold length . We assume that vocal fold length is approximately equal to tracheal diameter [45] . According to Eq 9 , a 1-cm tracheal diameter would suggest a tracheal length of 41 cm in a hypothetical bird , i . e . , similar to the physical model . Tracheal lengths much shorter or longer could be representative of extreme tracheal morphologies . In a third study , we investigated the effect of tracheal length on sound production by a syrinx in situ . The approach of the in situ syrinx has been proven effective [11 , 50–53] . It is important to perform these experiments in situ because excised syringeal preparations may reveal unnatural vibratory behavior of the labia [54] . If the length of the airway above a sound source is a critical factor determining the acoustic output of a bird , we expect systematic changes associated with tracheal length changes . The syrinx can be phonated by blowing air into the posterior thoracic or abdominal air sac . The experiments were performed in freshly killed birds . Five male specimens from each of three species ( chicken , Gallus gallus; budgerigar , Melopsittacus undulatus; and zebra finch , Taeniopygia guttata ) were used . Birds were euthanized with an overdose of Ketamine/Xylazine . Compressed humidified and warm air was injected into the right posterior thoracic or caudal air sac through a Silastic tube . A microphone was placed 10 cm downstream from the cranial opening of the trachea . We measured fundamental frequency while phonating the syrinx . Subsyringeal air sac pressure , which is proxy for the driving pressure of the sound source , was measured in the right anterior thoracic air sac by inserting a flexible cannula through a small hole in the body wall ( Silastic tubing; 1 . 65 mm o . d . , 6 cm length ) . The free end of the tube was connected to a piezoresistive pressure transducer ( model FHM-02PGR-02; Fujikura , Tokyo , Japan ) . The numerical data used in all figures are included in S1 Data .
Phonation threshold pressure varied between 1 . 4 and 2 kPa for the syringeal and between 1 . 7 and 2 . 7 kPa for the laryngeal position of the physical model source . Phonation threshold pressure was lower for the syrinx for all tracheal lengths tested ( Fig 2A ) . Very high pressures were required to trigger phonation in the larynx for tracheal lengths between 36 and 86 cm ( Fig 2A ) when the vocal fold eigenfrequency was located to the left of the first tracheal resonance . For a very short trachea and for a tracheal length around 2 m , the laryngeal and syringeal sound sources generated comparable sound intensities , but in all other conditions , the syringeal source emitted louder sound ( Fig 2B ) . Tracheal resonances influenced the vibration rate of both sound sources ( Fig 2C ) . Increasing tracheal length was first accompanied by increased fundamental frequency , which peaked at approximately 60 cm tracheal tube length and then decreased with longer tracheae . Glottal efficiency of the syrinx was greater across almost all tracheal lengths ( Fig 2D ) . The difference was most dramatic in the range between 40 and 80 cm . Efficiency of the laryngeal sound source was comparatively flat across all tracheal lengths . In sum , the findings support the hypothesis that the position of the sound source within the respiratory tract critically affects vocal parameters . For a given body size ( approximated at 20 to 30 kg body mass associated with a tracheal length of 40 cm ) , the syringeal position is more efficient in energy conversion . Phonation threshold pressure varied between 0 . 4 kPa and 1 . 0 kPa for the syringeal position and between 0 . 5 and 1 . 5 kPa for the laryngeal position . This pressure was lower for the syrinx for all tracheal lengths tested ( Fig 3A ) . Fundamental frequency was categorically lower for the syringeal position . This is in agreement with analytical predictions that greater supraglottal acoustic inertance lowers F0 . Glottal efficiency was categorical greater for the syringeal position than for the laryngeal position . We also tested to what degree source position affects vocal parameters with the computational model . The critical length for the 130-Hz natural frequency of oscillation of the computational model was 67 . 5 cm , for which the tube resonated at a quarter wavelength at 130 Hz . For all lengths below this critical length , the vocal tract acoustic reactance is inertive , which means that there is a favorable source–vocal tract interaction [37] . This favorable interaction explains why in Fig 3 , in the region below 67 . 5 cm , the phonation threshold pressure is lower and fundamental frequency is slightly lower . Vocal tract inertance adds effective mass to the coupled oscillator system and therefore lowers fundamental frequency . Furthermore , acoustic power at the mouth is higher , and vocal efficiency is higher than in the region above the critical length . Phonation threshold pressure was on the order of 0 . 5 kPa in the inertive region and rose to 1 . 2 kPa in the noninertive region for both the syrinx and the larynx position . It reached its minimum at a tracheal length of about 40 cm ( Fig 3A ) . All variations with tube length and source position in Fig 3 are not as large as in the physical model . This is attributable to four important differences: ( 1 ) the wall properties of the tube , ( 2 ) a more realistic simulation of tracheal diameter , ( 3 ) the difference in vocal fold geometry and material properties , and ( 4 ) the difference in radiation from the tube . The computational model used soft walls throughout , while the physical model used a hard-wall PVC tube . The acoustic energy levels in hard-wall tubes are much greater than in soft-wall tubes , increasing the degree of interaction between the source and the vocal tract . The interaction in the computational model was also lessened by the fact that the cross-sectional area of the tube increased with length . It is well-known that vocal tract pressures are scaled by the characteristic tube impedance ρc/A , where ρ is the air density , c is the speed of sound , and A is the cross-sectional area . Greater cross-sectional area lowers all vocal tract pressures . Furthermore , the way vocal folds respond to airflow is governed by factors such as geometry , layer structure , and viscoelastic properties . While the physical and computational vocal fold models shared similar characteristics , the effects of geometric and material property differences are not fully understood . Finally , the radiation from a tube without a baffle , which was the case in the physical model experiments , may differ from radiation from a piston in a spherical baffle , which was assumed in the computation . A total of four chickens , five budgerigars , and five zebra finches were successfully phonated . The stepwise shortening of the trachea was accompanied by an increased fundamental frequency in chickens and budgerigars ( Fig 4A and 4B ) . In zebra finches , fundamental frequency remained relatively constant ( Fig 4C ) . The elongation of the trachea with tubes that fitted the respective trachea was associated with a decrease of fundamental frequency in chickens but not in budgerigars and zebra finches ( Fig 4A–4C ) . In male zebra finches , we observed nonlinear phenomena in the phonations more frequently if tracheal length resonance was near the fundamental frequency .
Our approach has addressed the origin of the syrinx as opposed to its diversification . It is therefore imperative to assume a simple sound source , rather than the diverse morphologies found in extant birds . Once the relocation of the sound source had occurred in an as of yet unknown ancestor of Aves , the further diversification may have explored many different avenues for further increasing vocal output , such as two sound sources , different interactions with the upper vocal tract , etc . The different mechanisms of interaction suggest a possible , albeit speculative , scenario for the origin of the syringeal sound source within Aves . If strong interaction leads to less control or uncertainty in fundamental frequency because of a sparsity of vibration modes , then perhaps the original syrinx represented a vocal organ with Level 2 interaction . Typically , Level 1 interaction arises in conjunction with histologically more complex vibratory tissue [37] . In extant birds , more complex vocal folds evolved , i . e . , in songbirds [20] , while other groups , i . e . , parrots or Galliformes , possess thin membranes but display variable degrees of muscular control of the syrinx [46] . The heliox data in intact and denervated budgerigars suggest that muscular control of the syrinx can modulate source–tract coupling . This initial investigation of possible selective advantages of a syringeal location of the sound source also highlights that the evolutionary origin of novelty can be addressed with specific tests of hypotheses about selective scenarios . Our data show that one likely selective advantage of the syringeal position is increased efficiency . The ability to generate loud sounds is important for long-range acoustic communication and in the context of courtship and territory defense [70 , 71] . Thus , both natural and sexual selective forces may have contributed to the evolution of the avian syrinx . To what degree an early syrinx may have coexisted with a laryngeal sound source remains to be determined . The modeling and the experiments conducted here deliberately constitute a test of a limited and small set of parameters rather than a physical replica of the avian vocal organ , with all its complexity . While this minimalist approach is likely to inform about a possible selective advantage for the switch in source location , it does not include a thorough test of other selective scenarios and does not explore other likely adaptations for increased efficiency in extant birds . Therefore , future work will have to test whether the dramatic efficiency advantage of a syringeal position is maintained for various syrinx designs or if other variables emerge as the main targets of selection . Syrinx morphology shows remarkable diversity , including features such as multiple sound sources [53] , multilayered vocal fold design [41] , or changes in vocal tract design and motility [32] . All of these features affect efficiency , and we do not know how they are influenced by trade-offs between vocal efficiency and those other acoustic features . Nevertheless , our approach presents a first test and sets the stage for testing additional hypotheses related to syrinx origin and syrinx diversification . The current study highlights vocal efficiency as an important selective force that may have played a role in the evolution of the syrinx as a vocal organ . The timing of the transition from larynx to syrinx in the theropod lineage leading to modern birds is unknown prior to 66–68 million years ago [7] . Whereas clarification awaits new fossil data , the results from this study allow some speculation about a possible scenario and therefore the timing of syrinx evolution . The response curves in both the physical model and computational simulation ( Fig 2 and Fig 3 ) demonstrate that the interactions between sound source and vocal tract are complex and nonlinear . There are regions that represent local optima and others that are unfavorable for sound production . For example , phonation threshold pressure is lowest in a region of tracheal length 75–150 cm ( Fig 2A ) and 40–70 cm ( Fig 3A ) . This demonstrates that for a given sound source , a certain tracheal length range is optimal , and both achieved maxima that are higher for the syringeal than the laryngeal position . There are also regions in which the rate of change reaches a maximum . For example , the region between 50 and 100 cm trachea length appears to be such an inflection point . Glottal efficiency begins to increase at about 100 cm and with smaller trachea lengths . Again , this is more dramatic for the syrinx than for the larynx ( Fig 2D and Fig 3F ) . Our data therefore highlight the possibility that the evolution of a simple syrinx may be tied to a specific constellation of body size and vocal fold morphology . The theropod lineage leading to birds underwent sustained miniaturization of body size and rapid diversification [72] , and in these processes , it is possible that combinations of body size–dependent vocal tract length and sound frequencies favored the evolution of a novel vocal organ . For the sound source used in our study , a tracheal length between 50 and 100 cm yields higher vocal efficiency for the syrinx than the larynx . An important question for the evolution of the syrinx is to what degree the advantageous interaction between sound source and vocal tract resonance , shown here for a specific size , can be generalized to other body sizes ? We postulate that this favorable interaction is not limited to a specific size; as long as the tracheal resonance remains above but close to the fundamental frequency , a favorable suprasyringeal inertive compliance will provide the best assistance to self-sustained oscillation [37] . While the one available study on avian tracheal length suggests that also in small birds , the trachea remains relatively longer than in similar sized other vertebrates [18] , a broader and more systematic sampling of avian tracheal anatomy seems warranted . However , even if the exceptional avian body size–trachea length relationship did not hold for smaller birds , many modern birds possess intrinsic syringeal musculature and are able to modulate vocal fold tension , i . e . , fundamental frequency can be actively adjusted to remain close and below the tracheal resonance . Furthermore , small birds have three potential mechanisms for dynamically adjusting their vocal tract resonances: ( 1 ) tracheal length changes [73] , ( 2 ) size changes of the laryngeal aperture [31] , and ( 3 ) size changes of the oropharyngeal–esophageal cavity [32] . The long tracheal vocal tract in addition to dynamic filter components might allow birds of all sizes to easily maintain tuning of the vocal tract resonance to a quarter wavelength of the fundamental frequency . If the syrinx is so much more efficient , why do other groups such as nonavian reptiles , frogs , and mammals continue to use the larynx as sound source ? The avian trachea is acoustically longer than those of mammals , nonavian reptiles , and frogs . “Acoustically long” means that tube length is near the quarter wavelength of the fundamental frequency of the sound source ( see Introduction , Eq 2 ) . The first ancestral bird with a syrinx most likely produced a low fundamental frequency and covered only a small frequency range . The ancestral syrinx did probably not possess any intrinsic muscles if we assume it resembled that of ostrich , emu , or cassowary [46] . Substantial frequency modulation probably only arose once tension of the vibratory tissue could be adjusted by muscular control [45] . Avian archeosaurs tend to have relatively long or very long necks . While almost no mammal has evolved more than seven cervical vertebrae , in birds , cervical vertebrae are more numerous and often elongated [74 , 75] . Long necks contain long tracheas . Consequently , tracheal length ( i . e . , suprasyringeal tracheal airspace ) of birds tends to be much closer to the quarter wavelength of their voice’s lowest fundamental frequency . Whereas tracheal length is greater in birds , tracheal diameter is not different between mammals and birds ( Fig 6 ) . Most importantly , tracheal length in birds is close to the quarter wavelength of the size-predicted lowest fundamental frequency , thus enabling a boost in vocal efficiency through the overlap of positive vocal tract reactance and fundamental frequency ( Fig 6C and 6D ) . For the first bird with a syrinx , the lowest fundamental frequency may have overlapped with the high positive reactance range just to the left of the first formant , which coincides with the most dramatic supportive interaction between source and filter . This can boost vocal efficiency . In contrast , the vocal tract of most mammals is acoustically short , i . e . , the first resonance is much higher than the lowest fundamental frequency . The wavelength of the fundamental frequency is much longer than the tracheal length ( Eq 2 , Fig 6D ) . Most living mammalian species and therapsid ancestors had short necks and neck length did not vary much [74] . Consequently , tracheal length is not sufficient for facilitating a similar boost in vocal efficiency , as was possible for long-necked birds . For example , the average adult female human trachea is about 12 cm long , and the upper vocal tract is about 14 cm long . Even if the vocal folds were located at the tracheobronchial junction ( 12 cm + 14 cm = 26 cm ) , the quarter wavelength of the fundamental frequency of a human female’s average speaking voice ( 210 Hz ) is much longer ( close to 42 cm ) . The discrepancy is even more pronounced for males . With the currently available data , we present the following testable model for the evolution of the avian syrinx . The unique avian respiratory system , with its unidirectional flow through the gas exchange tissue , made gas exchange very efficient . This freed the avian bauplan from an important constraint in the neck area by allowing for more dead space ( i . e . , a longer trachea without a simultaneous decrease in tracheal diameter ) . Consequently , respiratory needs were permissive of longer necks with longer tracheas . A longer trachea shifted the avian vocal system ( i . e . , sound source and vocal tract ) into a range for which an overlap of fundamental frequency and first tracheal resonance was possible . At this point , it became advantageous to move the sound source upstream near the tracheobronchial juncture . This model indicates that a multitude of different interacting systems must generate a permissive scenario in which novel structures for particular functions can emerge . Perhaps the evolution of a novel structure for an already existing function , such as the switch of the sound source from larynx to syrinx , particularly requires coinciding , permissive interactions [2] . | The larynx is an important valve in the respiratory system of all air-breathing vertebrates that is located at the upper end of the trachea . In some amphibians , in nonavian reptiles , and in mammals , it has also assumed the function of a vocal organ . In contrast , birds have evolved a new and unique vocal organ , the syrinx , which is located at the lower end of the trachea . The selective forces that underlie the evolution of the syrinx as a novel organ have remained unclear . Among all air-breathing vertebrates , birds have the longest necks , and long necks require a long trachea . With a vocal organ at the base of the trachea , this long tube can act as vocal tract resonator and , therefore , can improve the conversion of aerodynamic energy into acoustic energy if fundamental frequency and a resonance frequency are matched . Here , we conducted experiments with simplified physical models , with real birds and a computational simulation in order to investigate the effect of the two different positions of a sound source within the respiratory tract . We find that sound is produced with greater efficiency by a sound source in syrinx position and that favorable interactions between sound source and vocal tract occur with syringeal position . The data provide support for the hypothesis that a selective pressure for high vocal efficiency may have contributed to the evolution of the syrinx in its unique location within the air tract . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"acoustics",
"medicine",
"and",
"health",
"sciences",
"resonance",
"frequency",
"classical",
"mechanics",
"vibration",
"larynx",
"vertebrates",
"animals",
"mammals",
"throat",
"respiratory",
"system",
"sound",
"pressure",
"amniotes",
"trachea",
"birds",
"resonance",
"ph... | 2019 | The evolution of the syrinx: An acoustic theory |
Repeated exposure to a novel physical environment eventually leads to a mature adaptive response whereby feedforward changes in motor output mirror both the amplitude and temporal structure of the environmental perturbations . However , adaptive responses at the earliest stages of learning have been found to be not only smaller , but systematically less specific in their temporal structure compared to later stages of learning . This observation has spawned a lively debate as to whether the temporal structure of the initial adaptive response is , in fact , stereotyped and non-specific . To settle this debate , we directly measured the adaptive responses to velocity-dependent and position-dependent force-field perturbations ( vFFs and pFFs ) at the earliest possible stage of motor learning in humans–after just a single-movement exposure . In line with previous work , we found these earliest stage adaptive responses to be more similar than the perturbations that induced them . However , the single-trial adaptive responses for vFF and pFF perturbations were clearly distinct , and the disparity between them reflected the difference between the temporal structure of the perturbations that drove them . Critically , we observed these differences between single-trial adaptive responses when vFF and pFF perturbations were randomly intermingled from one trial to the next within the same block , indicating perturbation response specificity at the single trial level . These findings demonstrate that the initial adaptive responses to physical perturbations are not stereotyped . Instead , the neural plasticity in sensorimotor areas is sensitive to the temporal structure of a movement perturbation even at the earliest stage in learning . This insight has direct implications for the development of computational models of early-stage motor adaptation and the evolution of this adaptive response with continued training .
When voluntary movement encounters a physical perturbation , the motor system generates an adaptive response that counteracts the perturbation’s effects during subsequent movements . Several studies have suggested that this adaptive response is motion-state-dependent in the sense that it’s time course tends to be proportional to the time course of the position , velocity , and acceleration signals that characterize the motion . This is in line with the idea that motor adaptation acts to update an internal model of the physical environment which , based on Newtonian mechanics , should depend on motion state [1–9] . Several studies have shown that exposure to a perturbation can induce an adaptive response that is specifically tuned to the temporal structure of that perturbation [8 , 10–13] or a motion-state-dependent approximation of it [9] . This tuning can , however , be systematically biased . Sing et al . [8] found that that single trial exposure to pure position-dependent force-fields ( pFFs ) or pure velocity-dependent force-fields ( vFFs ) both induce adaptive responses with a partially velocity-dependent and partially position-dependent structure that gradually increase in specificity as adaptation proceeds . Due to this cross-adaptation , adaptive responses are not fully specific to the temporal structure of the perturbation . However , even at the earliest stage of learning , adaptive responses have been observed to be partially specific to the experienced perturbation , with the largest portion of the single-trial response being velocity-dependent for a vFF and position-dependent for a pFF [8 , 14] . Extended exposure to a particular FF environment further increases this specificity , and adaptive responses become highly specific after 60–100 exposures , even when washed out between exposures [14] . Two recent studies have reported , however , that single-trial adaptive responses display no specificity to the temporal structure of the perturbation . Fine and Thoroughman [15] examined brief force-impulse perturbations delivered at various points during movement , and Wei et al . [16] examined a variety of different perturbations including visuomotor rotation and linear and nonlinear position-dependent force-fields . Both reported identical single-trial adaptive responses to different perturbation types . This is grossly in line with the substantial cross-adaptation observed in Sing et al . [8] and Yousif and Diedrichsen [14] , but at odds with the partial specificity observed in these same studies . Interestingly , the different perturbation types were randomly interleaved in the studies which found no specificity but blocked in the ones that did , leading Wei et al . [16] to suggest that the specificity observed in blocked experiments was due to a meta-learning effect arising from the multiplicity of single-trial exposures to the same perturbation rather than from any perturbation specificity inherent in the initial adaptive response . As a counterpoint , both studies that reported non-specific responses largely based their findings on kinematic aftereffect data in which the temporal structure of adaptive response may have been obscured . This is the case because the effects of force adaptations would be filtered through the physical dynamics of the limb and combined with the effects of real-time feedback responses and limb impedance in producing aftereffect kinematics [17] . In contrast , Sing et al . [8] and Yousif and Diedrichsen [14] directly measured the temporal structure of the forces produced during the adaptive response using error clamp trials where no such filtering occurs and the effects of feedback control and limb impedance are minimized . Thus , it remains unclear whether the non-specific initial adaptation reported in the Fine and Thoroughman and Wei et al . studies resulted from better control of meta-learning-induced effects , or from lower fidelity kinematic measurements of the temporal structure of the adaptive response . Here , we address this question by directly measuring the forces produced during single-trial adaptive responses in an interleaved condition .
We found that the pFF and the vFF perturbed movements by similar amounts , with 3–4 cm maximum displacements in all cases ( Fig 3A ) . Note that 0 ms in Fig 3A–3D corresponds to the midpoint of the movement . The vFF produced more lateral displacement early in the movement but less displacement late than the pFF ( Fig 3A ) . This is consistent with the fact that the perturbing force in the vFF is larger in amplitude than in the pFF early in the movement but smaller late in the movement . Correspondingly , the peak displacement occurred significantly earlier for velocity-dependent FF perturbations ( 155 ± 5 ms ( mean ± SEM ) vs . 339 ± 10 ms for clockwise FFs and 140 ± 7 ms vs . 352 ± 12 ms for counter-clockwise FFs , p < 10−6 in both cases ) , in line with greater early-movement effects for vFF perturbations and greater late-movement effects for pFF perturbations . Fig 3B shows that the lateral displacements in EC trials were kept extremely small , generally less than 0 . 6 mm . We found longitudinal motion ( Fig 3C ) to be considerably less affected than lateral motion for both the pFF and the vFF perturbations , in line with the fact that both were curl FFs that produced forces in a direction orthogonal to motion ( see Methods ) . Correspondingly , the correlation coefficients between the pFF and vFF perturbation trial data were substantially higher for longitudinal position data ( 0 . 997 for CW fields , 0 . 985 for CCW fields , Fig 3C ) than for lateral position data ( 0 . 310 for CW fields , 0 . 321 for CCW fields , Fig 3A ) . For reference , we found correlations of 1 . 000 for both analogous comparisons for the longitudinal position data in the EC trials following each perturbation type ( Fig 3D ) . The single-trial adaptive responses displayed in Fig 4A show that pFFs and vFFs induce adaptive responses with different temporal structures . Note that 0 ms in Fig 4A corresponds to the midpoint of the movement . Although the longitudinal motion profiles associated with the pFF and the vFF data are essentially identical ( Fig 3D ) , and the peak force levels during the adaptive responses are quite similar ( 0 . 45–0 . 5N , see Fig 4A ) , the shapes of the force profiles that characterize these responses appear systematically different . The pFF response ( blue ) is smaller early in the movement and larger late in the movement than the vFF response ( red ) , as illustrated by the thick green trace representing the difference between the pFF and vFF responses in Fig 4A . Grossly , this response pattern echoes the temporal pattern of errors induced by the perturbation ( Fig 3A ) . The larger early-movement vFF response is consistent with the larger early-movement vFF perturbation effect shown in Fig 3A , and the larger late-movement pFF response is consistent with the larger late-movement pFF perturbation effect shown in Fig 3A . To quantify differences between the temporal structure of the single-trial adaptive response to pFFs and vFFs , we analyzed the force data from three distinct 100ms windows: centered at the mid-movement point ( mid ) , 150 ms before this point ( early ) , or 150 after it ( late ) . We operationally defined the mid-movement point as the time point where the longitudinal ( Y ) component of the position profile crossed the 5 cm point of the 10 cm-long movement . Analysis of the adaptive responses during these three windows reveals the pFF response to be 43 ± 12% smaller than the vFF response in the early-movement window , but 58 ± 13% larger than the vFF response in the late-movement window as illustrated in Fig 4B ( p < 0 . 008 and p < 0 . 002 , respectively , two-tailed paired t-tests ) . The adaptive responses are nearly equal during the mid-movement window , with a nominal difference of 12 ± 8% , p = 0 . 19 . These data indicate that the time course of the adaptive response is not stereotyped as previously suggested [15 , 16] . We previously found that adaptive responses to position-dependent and velocity-dependent force-fields were well-characterized by a linear combination of motion-dependent responses [8] . This is shown for the current data set in Fig 4A . Analyzing adaptive responses in the space of position and velocity gains ( the PV gain-space , see methods ) can provide a compact , low-dimensional ( two-parameter ) characterization of the shape of each adaptive response and allow a direct visualization of its specificity , as shown in Fig 5 . More specifically , this PV gain-space analysis directly relates the temporal structure of the adaptive responses to the temporal structures of the position-dependent and velocity-dependent perturbations that induced these responses . A vFF perturbation would induce a purely velocity-dependent response if perfect specificity were maintained , leading to an adaptation vector in PV gain-space with only a velocity-dependent component . This would be represented by an adaptation vector in Fig 5A perfectly aligned with the ( vertical ) velocity axis of the PV gain-space . Likewise , a perfectly specific response to a pFF perturbation would be represented by an adaptation vector perfectly aligned with the ( horizontal ) position axis of the PV gain-space . Consistent with the direct analysis of the adaptive response profiles shown in Fig 4 , the gain-space plots in Fig 5A show that the position- and velocity-dependent force-field perturbations induce adaptive responses with distinct shapes . We find that the vFF response vector is more strongly velocity-dependent ( i . e . closer to the y-axis ) than the pFF response which is more strongly-position dependent ( i . e . closer to the x-axis ) . Here the difference in the temporal structure of the response shape can be characterized , independent of response amplitude , by the angle in gain-space between the pFF and vFF responses . Note that gain-space angle ( θ ) for a particular response is a monotonic function of the velocity-position gain ratio: θ = arctan ( GV /GP ) where GP and GV are the position and velocity components of the gain-space response . Analysis of the gain-space angles for pFF and vFF responses reveals that the vFF responses are consistently greater in angle ( with respect to the x-axis ) than the pFF responses as shown in 5B ( Δθ = 18 . 3° ± 1 . 9° , p <10−5 ) . This indicates that although the adaptive responses to the pFF and vFF perturbations are far from perfectly specific , they are clearly distinct from one another . This finding corroborates the movement segment analysis illustrated in Fig 4 , which also finds differences in the pFF and vFF responses , and shows that the differences we observe in these adaptive responses directly correspond to response specificity: pFF perturbations elicit adaptive responses with a relatively greater position component leading to systematically smaller gain-space angles across participants , whereas vFF perturbations elicit adaptive responses with a relatively greater velocity component leading to systematically larger gain-space angles . Note that the gain space analysis presented in Fig 5 is intrinsically more powerful than the early and late movement segment comparisons presented in Fig 4 , because it considers the entire time course of the adaptive response . The randomly interleaved exposure to pFF and vFF perturbations in the current experimental paradigm ( Fig 2C ) was put in place so that meta-learning about the perturbation type would be minimized . Thus , the current results indicate an inherent temporal specificity to the adaptive response that cannot be due to meta-learning . However , it might be that repeated exposure to the same perturbation type might lead to meta-learning that further increases response specificity beyond that observed in our interleaved data . To examine this possibility , we compared the current results to a previous dataset [8] with a similar experimental design , but where only a single perturbation type was experienced by each participant . Like the current experimental design , single-trial adaptive responses were measured following occasional curl pFF and vFF perturbations with a probability of 0 . 2 and the direction of the perturbations was randomly interleaved; however , one group of participants experienced only vFF perturbations and a second group only pFF perturbations . We compared the gain-space representation of the single trial learning ( described in Fig 5 ) between the two experimental designs: single-field versus interleaved . We found that neither the angles nor amplitudes of the gain-space vectors were different between pFF single-field versus pFF interleaved data ( p > 0 . 2 in both cases ) or for the vFF single-field versus vFF interleaved data ( p > 0 . 3 in both cases ) . In particular , the adaptive response specificity as assessed by the difference in PV gain-space angles between vFF and pFF perturbations was similar for the single-field ( previous ) and interleaved ( current ) data ( 22 . 8° ± 4 . 7° versus 18 . 3° ± 1 . 9° , p > 0 . 3 ) . This data cannot rule out the possibility that meta-learning contributed to the adaptive response specificity observed in Sing et al . [8] , however it appears that meta-learning plays a substantially smaller roll , if any , than the inherent adaptive response specificity that we elucidate in the current study , suggesting that meta-learning effects are small for the isolated single-trial adaptation paradigm studied in Sing et al . [8] . Interestingly , we recently found large meta-learning effects for the amplitude of the adaptive response for training paradigms with high environmental consistency in highly-structured environments where multiple FF trials consecutively encountered [12] . It would be interesting to determine where such environment could also increase the temporal specificity of the adaptive response .
Although we have clearly demonstrated that adaptive responses to position-dependent and velocity-dependent force-field perturbations are distinct even after a single trial of adaptation , these single-trial responses are far from fully specific . We found that the difference in gain-space angles between the pFF and vFF responses to be 18 . 2 ± 1 . 9°– just over 20% of the 90° angle that would be expected for fully specific responses . Correspondingly , the correlation coefficient between the average pFF and vFF responses shown in Fig 4A is r = 0 . 84 , whereas the correlation coefficient between the shapes of the perturbations themselves was r = 0 . 02 . Correlated , but distinct adaptive responses for pFF and vFF perturbations , like those that we observe here , are consistent with motor primitives that display correlated tuning to the position and velocity of movement [8 , 9 , 13] . Correlated tuning to these motion states is observed throughout the sensorimotor system; motor spindle afferents [18 , 19] , neurons in premotor and primary motor cortex [20–22] , and the cerebellum [23] demonstrate codependent encoding of position and velocity during movement . There is increasing evidence that the motor system represents force perturbations to movement based on this tuning , and that this correlated representation of motion states influences the observed adaptive response . For example , in a previous study we showed that adaptive responses to single-trial force-impulse perturbations were highly motion dependent , although the dynamics of the perturbation was not [9] . This common tuning to position and velocity is also in line with the current results , insofar as we observe substantial cross-adaptation in response to position-dependent and velocity-dependent dynamics . We find the degree of cross-adaptation to be , however , incomplete , resulting in initial adaptive responses that are only partially stereotyped . And following extended exposure , adaptive responses gradually become more specific—revealing that the underlying neural networks are modified with training to better reflect the temporal structure of environmental dynamics [8 , 9] . Importantly , the specificity observed following single-trial exposure in the current study suggests that neural plasticity in sensorimotor areas reflect the temporal structure of the perturbation . In previous work [24] we showed that the pattern of generalization of the adaptive response in early learning matched that predicted by the actual rather than the planned motion ( motion-referenced learning ) during training . That is , when the movement goal was manipulated such that subsequent movements were aligned to the actual motion experienced during training ( the perturbed trajectory ) there was a significant improvement in the learning rate , and a significant reduction when there was a misalignment of the two , in line with the idea of motion-referenced learning . Further evidence for motion-dependent learning comes from a recent study in which adaptive responses to perturbations that were themselves not motion-dependent [9] . In this study , two different narrow force-impulse perturbations that could not be well-approximated by a motion-dependent representation comprised of a linear combination of position , velocity , and acceleration signals , induced adaptive responses that were consistently well-explained by such a motion-dependent representation . The study found this to be the case , both for single-trial adaptation and after extended exposure . As the duration of the training exposure increased , the internal composition of the motion-dependent representation evolved substantially but the overall degree of motion dependence remained . Together with the current results , these findings paint a picture of the initial adaptive response that is strongly motion-dependent and flexible enough within the space of motion-dependent representations to display a clear amount of inherent specificity to the temporal structure of the experienced perturbation . The current results for interleaved training are consistent with our previous findings for single-field training during which the FF type was held constant throughout the experiment [8] . In that study the direction of the perturbation was also changed randomly throughout the session with a short washout period between the error-clamp force-field measurement triplets . Before the current study , it could be argued that these prior results for single-field training resulted primarily from some form of meta-learning [16] , whereby the temporal structure of the adaptive response on any particular trial is fixed but can be gradually reshaped by repeated exposure to a particular perturbation . The idea was that meta-learning that involves gradual reshaping the temporal structure of the adaptive response over the course of training might be responsible for the adaptation specificity observed in non-interleaved training paradigms . However , in the current study we find that the same pattern of specificity is maintained when the perturbation types are interleaved so that the initial adaptive responses to different perturbations are measured in rapid succession . This indicates the adaptive responses are inherently specific at the earliest stage in the adaptive response . Moreover , when we examined whether meta-learning might further increase this inherent specificity , we found little effect: With our most powerful measure of response specificity ( the difference in PV gain-space angles between pFF and vFF responses ) , we found no significant increase for single-field learning where meta-learning might be able to contribute , and the nominal increase in this measure was only 1/5 as large as the inherent specificity we observed , suggesting that the inherent specificity of the initial adaptive response can dominate meta-learning driven effects . However , the current results appear grossly at odds with the findings from two previous studies that were unable to find evidence for temporal specificity in the initial adaptive response . In the first study by Fine and Thoroughman [15] , subjects occasionally experienced a single force pulse perturbation during point-to-point reaching movements . The force pulses were applied orthogonal to the movement direction , but the direction , magnitude and location within the movement the pulse was applied varied from trial to trial . The authors observed that the adaptive response , measured in the null aftereffect trials immediately after these force pulse movements , was sensitive to the direction , but insensitive to the amplitude and the time within the movement the force pulse was applied . However , the kinematic assessment of adaptation aftereffects with null trials is sensitive to the online feedback correction and the changes in limb impedance that dominate compensation late in the movement [3 , 7 , 17 , 25–30] . This obscures late-movement feedforward changes in adaptive control , which reduces the ability to accurately determine the temporal structure of the feedforward adaptive response . A key feature of the current study is the use of error-clamp trials to probe the dynamics of the feedforward adaptive changes in force output throughout the entire movement [8 , 31 , 32] . We show that the adaptive response to each force field continues throughout the course of the movement ( see Fig 4 ) , and that information about the entire time course–allowing the comparison of early and late forces during each movement–is required to demonstrate the temporal specificity we report . Compared to pFF perturbations , vFF perturbations result in an increased early-movement adaptive response alongside a decreased late-movement adaptive response . Indeed , it is the coexistence of these opposing early- and late-movement differences that indicates the specificity of the temporal structures of the adaptive responses to pFF and vFF perturbations . Consistent changes in the adaptive response early , mid , and late in movement could result from a gain change alone . However , opposing differences in the adaptive response at distinct time points such as early and late into the movement is strong evidence that the temporal structure of the adaptive response is specific to different perturbations . Recently , Wei et al . also looked at responses to various force perturbations during point-to-point reaching movements implemented as different functions of longitudinal position [16] . Although they characterized the set of perturbations they studied as “random” , four of the five different force perturbations they used closely resembled or exactly matched , linear functions of motion-state . In particular , their “ramp” perturbation was exactly linearly position-dependent , their “half-sine” and “triangle” perturbations were almost linearly velocity-dependent ( correlation coefficients > 0 . 92 if a bell-shaped minimum-jerk velocity profile is assumed in both cases ) , and their “sine” perturbation is almost linearly acceleration-dependent ( correlation coefficient > 0 . 85 ) . Thus , 3 of the 5 force perturbations employed in Wei et al . [16] were extremely similar to the position- and velocity-dependent dynamics examined in the current study , which should make much of the data highly comparable . In their first experiment , the authors looked at the motion aftereffects induced by these perturbations , which , as argued above are unlikely to give substantial insight into the time-course of the feedforward adaptive response . In their second experiment , Wei et al . [16] analyzed the adaptive responses observed on error-clamp trials following the application of these perturbations , although details of the experimental design and analysis make interpretation of the results , unfortunately , rather difficult . A key issue is that the experiments attempted to look at single-trial learning by examining aftereffects following perturbations that were densely spaced . Perturbations were levied on 50% of trials , some but not all of which were followed by aftereffect trials used to estimate the single-trial adaptive response . Critically , no attempt was made to wash out learning from one perturbation trial to the next , allowing the adaptive response to display a random-walk-like buildup across different perturbation types during the experiment . This buildup would preclude a clean interpretation of the aftereffect responses observed following each different perturbation type because the adaptive response following an individual perturbation trial would partly result from that perturbation itself and partly from perturbations that closely preceded the trial of interest . Due to the random 50% perturbation rate , one would , in fact , expect the trial immediately preceding a perturbation to itself include a perturbation in 50% of cases , and the two immediately preceding trials to both include perturbations in 25% of cases . Contamination from preceding trials with different perturbations might have been mitigated if the experiment had included probe trials both before and after each perturbation so that the learning-related change in the adaptive response could be estimated from the difference in the corresponding aftereffects to subtract out previous learning . However , such trials were not included , and no attempt to subtract out the buildup of adaptation from exposure to preceding perturbations was made . Both the current study and Fine and Thoroughman [15] included a number of washout trials between perturbations as well as probe trials before and after perturbations to mitigate the non-specific buildup mentioned above . Keeping non-specific effects at bay is critical when examining the specificity of the adaptive response . It is therefore unclear whether the Wei et al . findings were driven by the non-specific nature of the experimental design and analysis performed . In addition to temporal specificity , Fine and Thoroughman [15] also examined the amplitude specificity of the initial adaptive response by looking at single-trial adaptation following different perturbation amplitudes . They found that although brief force-pulse perturbations of 6N , 12N and 18N , induced motor errors in proportion to perturbation amplitude , the adaptive responses measured by the post-perturbation after-effect size were similar to one another . The authors interpreted this as evidence for a fixed-amplitude for the initial adaptive response . However , the data from the individual conditions presented in Fig 4 of the Fine and Thoroughman study reveal the 12N perturbation responses to be nominally larger that the 6N responses in 5 of 6 individual conditions examined , and the combined data presented in Fig 5 of that paper indicate a nominal increase in adaptive response strength of 30–50% in the 12N responses compared to the 6N responses . Both suggest that perturbation response amplitude may increase with perturbation amplitude . It should be noted , however , that 100% increases would be expected for a linear increase in perturbation response amplitude from 6N to 12N , and that no consistent increases in perturbation response strength were evident in going from 12N to 18N perturbations . Thus , the Fine and Thoroughman [15] data clearly demonstrate that the relationship between perturbation amplitude and adaptive response amplitude is sublinear . However , the claim of a fixed-amplitude single-trial adaptive response is unclear , as the data appear to suggest a relationship that increases for smaller perturbation amplitudes , in line with a saturating non-stereotyped adaptive response amplitude . Four more recent studies are also in line with a sublinear but non-stereotyped adaptive response amplitude for single-trial adaptation to both force [33 , 34] and visuomotor [35 , 36] perturbations . In these studies , initial adaptive responses systematically increased in amplitude with the amplitude of lateral perturbations , but the rate of the observed increased displayed a progressive reduction and then leveled off for large perturbation amplitudes . Taken together , these studies show that the amplitude of single-trial adaptive responses are not stereotyped , but systematically depend on perturbation amplitude . This indicates that adaptive responses are at least partially specific to the amplitude of environmental perturbations , consistent with the current findings that adaptive responses are specific to the temporal structure of environmental perturbations . In this study we have shown that single-trial learning of position- and velocity-dependent force-fields leads to distinct adaptive responses , contradicting the claim that initial adaptation to unexpected or random perturbations is stereotyped . This was true when the two perturbation types were interleaved throughout the training session—preventing effects due to meta-learning from constant exposure to a single perturbation . When we examined the effect of meta-learning from exposure to a single perturbation by comparing the adaptive responses from single-field vs interleaved conditions , there was little to no effect . There was no significant improvement in response specificity . Together these results suggest that even after a single perturbation exposure , the motor system is sensitive to the characteristics of the disturbance to movement and utilizes this information in the compensatory motor output .
Eleven healthy subjects without known neurological impairment were recruited from the Harvard University community to participate in the study . All participants were right handed and performed the task using their right hand . The study protocol was approved by the Harvard University Institutional Review Board and all participants gave informed consent . The experimental paradigm was based on the standard force-field ( FF ) adaptation paradigm [1] . Subjects were trained to move their hands to targets in the horizontal plane while grasping a robot manipulandum ( Fig 1A ) . The manipulandum measured hand position , velocity , and force , and its motors were used to apply forces to the hand , all at a sampling rate of 200 Hz . The position of the hand was shown as a small round cursor ( 3 mm ) on a vertically oriented computer monitor in front of the participant ( refresh rate of 75 Hz ) . Participants reached to circular targets 1 cm in diameter that were spaced 10 cm apart . We instructed participants to ‘‘make quick movements to the targets . ” In addition , subjects were told that the reaction time was not important—they could wait as long as they wished after target appearance before starting each movement—but when ready , they were to move in a rapid motion toward each target . The endpoint of each movement was used as the starting point for the subsequent movement , and movements were made in two target directions [37] . Three trial types were used in the experiment: null field trials , force field ( clockwise or counterclockwise ) trials , and error-clamp trials ( Fig 1B , 1C and 1D ) . Null field trials ( or simply , null trials ) were used for initial familiarization with the experiment and for washout between force field trial exposures in order to minimize the buildup of adaptive responses from one force field exposure to the next ( see below ) . During these trials the motors of the robot manipulandum were turned off . The second type of trial was a force field trial . We studied two types of force-field trials with different temporal structures so that the temporal specificity of the adaptive response could be assessed . During position-dependent force-field ( pFF ) trials ( Fig 1B ) the relationship between force ( f ) and position ( x ) vectors was determined by the 2x2 matrix: K = [0 δ;−δ 0] via the relationship f = Kx , with δ = ±45 N/m so that the forces imposed on the hand were proportional in magnitude and perpendicular in direction to hand displacement along the target axis . For velocity-dependent force-field ( vFF ) trials , the motors were used to produce forces on the hand that were proportional in magnitude and perpendicular in direction to the velocity of hand motion ( Fig 1C ) . In this case , the relationship between force ( f ) and velocity ( x˙ ) vectors was determined by the 2x2 matrix: B = [0 α;−α 0] via the relationship f=Bx˙ , with α = ±15 N/ ( m/s ) . The direction of force-field ( clockwise or counterclockwise ) changes when the sign of α ( for velocity force-fields ) or δ ( for position force-fields ) is changed . The third type of trial was an error-clamp ( EC ) trial . During EC trials , the robot motors were used to constrain movements in a straight line toward the target by counteracting any motion perpendicular to the target direction [7–9 , 11–14 , 24 , 31 , 32 , 34 , 37–41] in order to clamp lateral displacements and thus lateral errors to near-zero values . In these trials , perpendicular displacement from a straight line to the target was generally held to less than 0 . 6 mm and averaged about 0 . 2 mm in magnitude . This was achieved by applying a stiff one-dimensional spring ( 6 kN/m ) and damper ( 150 Ns/m ) in the axis perpendicular to the target direction . Subjects made repeated point-to-point movements between two target positions that were both in the participant’s midline . The movement thus alternated between outward movements ( that we refer to as 90° movements ) and backward movements ( that we refer to as 270° movements ) . The experiment began with a 270-trial familiarization period ( 135 trials in each movement direction ) that did not include FF trials and was divided into 3 blocks separated by short one-minute rest breaks . The main experiment period followed in which we studied the specificity of initial motor adaptation by examining adaptation after just a single exposure to either a pFF or vFF perturbation . To accomplish this , each FF trial was flanked by EC trials in the same movement direction ( thus each flanking EC trial was 2 trials away from the middle FF trial ) forming an EC-FF-EC measurement triplet . These EC trials provide the ability to accurately measure the temporal pattern of lateral forces associated with each movement [8] . Thus the measurement triplets provide the ability to accurately measure the temporal structure of the adaptive response to the laterally-directed pFF and vFF perturbations we studied by examining the difference in the lateral force profiles observed before vs after pFF and vFF perturbations . This post-FF minus pre-FF difference was the main experimental result that we analyzed ( see below ) . To prevent adaptive responses from building up from one triplet to the next , we followed each triplet with 3–5 null field trials in that triplet’s movement direction and randomly ordered the direction of the FF perturbations experienced in consecutive triplets ( clockwise vs counter-clockwise ) . FF exposures only occurred within measurement triplets and the 224 exposures in the experiment were balanced across movement direction ( 90° vs 270° ) , perturbation direction ( clockwise vs counter-clockwise ) , and perturbation type ( pFF vs vFF ) and presented in a randomized order . A critical feature of our experimental design was that pFF and vFF perturbations were randomly interleaved ( Fig 2C ) so that meta-learning about the perturbation type to be expected would be minimal . Furthermore any meta-learning that might occur could not systematically effect measurements of the adaptive response to pFF vs vFF perturbations ( because the type of perturbation in any given triplet would be independent of the perturbation type previously experienced ) . Based on the experimental design , the differences between the force profiles measured on the post-FF versus pre-FF trials from the measurement triplets for pFF and vFF perturbations was the main focus of the analysis . These difference are plotted for the pFF and vFF data in Fig 4A and analyzed in Figs 4 and 5 . We combined the data from CW and CCW perturbation directions , aligning the force profiles to the perturbation , and we additionally combined the 90° and 270° movement data . In general , lateral force could reflect an adaptive compensation of expected external force or an online feedback correction for errors detected during the course of movement . However , on the EC trials on which the lateral force were measured , lateral errors were small so that online feedback correction should have little effect . Thus the lateral forces measured on EC trial should primarily reflect feedforward motor adaptation . One of the eleven participants did not consistently remain on-task in terms of making the rapid movements instructed . We thus excluded his data . The participant’s movements were nearly 5 standard deviations slower than the mean of the other 10 participants . For the remaining data we excluded the small fraction of trials ( 2% ) that had very slow or very fast movement speeds ( peak speed < 0 . 22 m/s or > 0 . 50 m/s ) or very high peak force levels ( >15 N ) . Since the number of washout trials ( 3–5 ) between the triplets in the 90° and the 270° directions were randomized independently , 90° perturbations were presented in an order that was entirely independent of the 270° perturbations . Thus FF perturbations on 90° and 270° trials sometimes occurred several movements apart and sometimes occurred directly adjacent on another . The latter circumstance resulted in some “shared” triplets–triplets in which a perturbation trial in the 270° direction occurred adjacent a 90° perturbation trial and thus between the 90° pre-FF and post-FF EC trials , or vice versa . When we examined these shared triplets , we found that shared triplets in which perturbations were both in the same direction ( either both CW or both CCW ) appeared to show somewhat different pre-FF / post-FF changes than shared triplets in which perturbations were in opposite directions ( possibly providing interfering vs facilitating effects ) , even though the 90° and 270° movement directions were separated by 180°—a large difference in movement directions over which generalization in often minimal . To avoid the possibility that interactions between movement directions might play a role in the shaping of the single-trial adaptive responses we report , we included only the data from “solitary” triplets in our analysis of the force profile data illustrated in Fig 4A and the subsequent analyses based on these force profiles which follow . These solitary triplets comprise 58% of the measurement data , and the shared triplets comprise 42% . Including just the solitary triplet data versus the entire set of the triplet data turns out to have little systematic effect on the results as shown in S1 Fig plotted below , which reveals that systematic differences in the temporal structure of pFF vs vFF adaptive responses are maintained for both the all-triplet data and the solitary-triplet-only data . The most noticeable difference between these two is that the 42% smaller data solitary-triplet-only dataset appears noticeably noisier in shape than the entire data set , as would be expected given the smaller size . Regardless , we thought it might be best here to be methodologically cautious and include only the cleanly-designed solitary-triplet data . We took the approach of assessing the specificity of the temporal structure of the force profiles associated with single-trial adaptation by ( 1 ) analyzing whether pFF and vFF perturbations induced adaptive responses with distinct temporal structure and ( 2 ) examining whether the difference in the temporal structure of the adaptive response for pFF vs vFF perturbations reflected the differences in the perturbations themselves . We performed two different analyses , each of which could shed light on both of these issues . The first analysis , illustrated in Fig 4 , simply examined the time course of the adaptive responses to pFF vs vFF perturbations by comparing these adaptive responses at three time points: early , middle , and late in movement . Specifically , we computed the mean force during 100ms windows centered 150ms before the middle of the movement , at the middle , and 150 ms after the middle . Thus the early period ranged from -200 to -100 ms with respect to the mid-movement point , the mid period ranged from -50 to 50 ms , and the late period ranged from 100 to 200 ms . We operationally defined the mid-movement point as the time when the longitudinal position crossed 5 cm of the 10 cm-long movement . Distinct temporal structure would be indicated by differential findings at early vs mid vs late-movement time points such as significantly decreased lateral force at one time point alongside significantly increased force at another . More specifically , temporal specificity in the adaptive response would predict that pFF responses would be significantly decreased when compared to vFF responses early in the movement yet significantly increased when compared to vFF responses late in the movement because pFF perturbations peak later than vFF perturbations . The second analysis , illustrated in Fig 5 , directly assessed the position-dependence and velocity-dependence of the pFF and vFF adaptive responses , in line with the idea that temporal specificity in the adaptive response would predict that pFF responses should be more strongly position-dependent and vFF responses should be more strongly velocity-dependent when compared to one another . We performed this analysis by projecting the pFF and vFF adaptive responses into a position-velocity gain-space ( PV gain-space ) . This was accomplished by first regressing the experimentally measured force profiles onto a motion-dependent representation consisting of the position , velocity , and acceleration of movement as well as a constant offset , and then determining the gains ( GP and GV ) associated with the position-dependent and velocity-dependent regression coefficients ( CP and CV ) compared to the strengths of the pFF and vFF perturbations ( δ & α ) . Thus , GP = CP / δ and GV = CV / α . Note that this procedure is analogous to simultaneously computing adaptation coefficients [8 , 32 , 38 , 39] for both the pFF and VFF perturbations for each type of adaptive response . If the measured lateral force and the ideal force for one FF were to perfectly coincide ( i . e . , full FF compensation ) , the corresponding gain from the linear regression would be 1 , if the two profiles were directly opposed , the gain would be -1 , and if they were unrelated it would be zero . Note that we used a motion-dependent representation that contained acceleration because it has been shown to provide an efficient low-dimensional representation of the temporal structure of the adaptive response to a variety of different force perturbations–for multiple motion-dependent perturbations and force impulse perturbations [8 , 9] . In particular , this removes contamination of the PV gains by a significant acceleration-dependent response ( and note that pure PV projection also yields result of even greater amplitude and as statistically powerful as the more refined gain-space analysis we employ ) . The PV gain-space representations we computed in Fig 5 are summarized for statistical testing by the direction ( angle ) of the PV gain-space vector for each FF . | With repeated exposure to a perturbation , the sensorimotor system learns to develop an adaptive response that is highly specific to both the amplitude and temporal structure of that perturbation in order to effectively counteract it . It is widely known that the amplitude of the adaptive response starts small and gradually grows to the right size with repeated exposure . However , it is also the case that the temporal structure of the adaptive response starts somewhat generically and gradually grows into the right shape with repeated exposure . A key question is whether the adaptive response to a perturbation begins with a stereotyped temporal structure that only becomes specified with further practice , or if it begins with a degree of specificity for the experienced perturbation that need only to be refined by practice . Here , by precisely measuring the temporal pattern of motor output in the single-trial adaptive response to two different perturbations , we show that the initial adaptive response is indeed specific to the temporal characteristics of the perturbation , even when the disturbance randomly changed from one trial to the next . These results demonstrate that the sensorimotor system is sensitive to the temporal features of a disturbance , even when experienced just once . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"velocity",
"learning",
"medicine",
"and",
"health",
"sciences",
"classical",
"mechanics",
"education",
"adaptive",
"training",
"sociology",
"experimental",
"design",
"social",
"sciences",
"neuroscience",
"learning",
"and",
"memory",
"training",
"(education)",
"research",... | 2017 | Temporal specificity of the initial adaptive response in motor adaptation |
For persistent infections of the mammalian host , African trypanosomes limit their population size by quorum sensing of the parasite-excreted stumpy induction factor ( SIF ) , which induces development to the tsetse-infective stumpy stage . We found that besides this cell density-dependent mechanism , there exists a second path to the stumpy stage that is linked to antigenic variation , the main instrument of parasite virulence . The expression of a second variant surface glycoprotein ( VSG ) leads to transcriptional attenuation of the VSG expression site ( ES ) and immediate development to tsetse fly infective stumpy parasites . This path is independent of SIF and solely controlled by the transcriptional status of the ES . In pleomorphic trypanosomes varying degrees of ES-attenuation result in phenotypic plasticity . While full ES-attenuation causes irreversible stumpy development , milder attenuation may open a time window for rescuing an unsuccessful antigenic switch , a scenario that so far has not been considered as important for parasite survival .
Pathogenic bacteria and protozoan parasites often employ a coat of surface molecules to protect themselves from host immune attack . These surface coats are sometimes variable and hence , not only act as a physical shield but have evolved as an efficient camouflage strategy . The surface-exposed proteins are mostly members of large families and are subject to antigenic variation , i . e . they are sporadically exchanged . This allows the persistence of the pathogens in the host , as well as reinfection . The genetic mechanisms underlying antigenic variation differ greatly , ranging from transcriptional changes in Plasmodium to duplicative events for example in Borrelia or Neisseria [1] . An extensively studied model for antigenic variation is the protozoan parasite Trypanosoma brucei and the phenomenon was , in fact , first described in trypanosomes [2 , 3] . The surface coat of trypanosomes consists of millions of identical copies of a variant surface glycoprotein ( VSG ) [4 , 5] . The highly immunogenic VSGs cause a rapid host immune response , which is thought to lead to an almost complete elimination of the parasite population . Only parasites that have switched to the expression of an immunologically distinct VSG survive . Thus , at any given time just one VSG out of a repertoire of several hundreds of VSG genes is expressed and dominates the cell surface of the pathogen [6 , 7] . At all times the parasite has to maintain the shielding function of the coat and hence , the concentration of VSGs on the cell surface . This is not a straightforward task as the VSG coat is continuously endocytosed and recycled with unprecedented kinetics [8] . Consequently , VSGs are constantly produced in large quantities . Uniquely , this high level expression of VSG is driven by RNA-polymerase I [9] . T . brucei exploits both genetic and epigenetic mechanisms for antigenic variation [10 , 11] . Allelic exclusion , which may be achieved by epigenetic modifications [12 , 13] , ensures that only one VSG gene is expressed from one of 15 telomeric expression sites ( ES ) [14] . The open chromatin structure of the active ES is thought to facilitate its transcription by RNA polymerase I in a distinct extranucleolar compartment termed the expression site body ( ESB ) [15–17] . The large repertoire of silent VSG copies is subject to frequent rearrangements , resulting in the continuous production of new mosaic variants [7 , 18 , 6 , 19] . A VSG switch is recombinational when the actively transcribed VSG gene is replaced by another variant . Besides by gene conversion , antigenic variation can occur by telomere exchange , i . e . by recombinational cross-over of chromosome ends [20 , 21] . Alternatively , the expressed VSG can be exchanged by transcriptional silencing of the active ES and activation of another , previously non-transcribed ES [22] . This so-called ‘in situ switch’ does not involve genetic recombination but possibly epigenetic modifications [13] . Since VSG ESs are polycistronic transcription units , an in situ switch also silences the expression site associated genes ( ESAGs ) . The number and order of ESAG genes can vary between ESs and not all ESAGs have been functionally characterized [14] . Irrespective of the mode of VSG switching , the VSG mRNA levels must be kept rather constant , as down-regulation of VSG mRNA rapidly leads to cell cycle arrest followed by parasite death [23] . Therefore , recombinational switches have to be fast , and the activation of a new ES should precede silencing of the old one during a transcriptional switch . Antigenic variation is the trypanosome’s key-strategy for establishing a persistent infection in the mammalian host . For long-term survival , however , the trypanosomes must also limit the burden they impose on the host , as a constantly high parasitemia would be lethal [24] . Consequently , the parasites have evolved a way of limiting their population size: the proliferating slender forms differentiate to the cell cycle arrested and fly-infective stumpy stage . This developmental stage transition is triggered by a quorum sensing mechanism that involves secretion of the ‘stumpy induction factor’ ( SIF ) [25 , 26] . In a cell density-dependent manner SIF is thought to accumulate in the bloodstream , and once a threshold is reached , the irreversible transition from the slender to the stumpy bloodstream stage is initiated [27 , 28] . In this way , the trypanosomes not only regulate their population size , but also promote vector transmission , as only stumpy bloodstream form parasites are thought to establish an infection in the tsetse fly [29] . During stumpy development the protein expression pattern changes as a pre-adaptation for life in the insect [30] . The level of mitochondrial proteins is augmented and the ‘protein associated with differentiation’ ( PAD1 ) is exposed on the surface of stumpy parasites [31 , 32] . Microscopically , the parasites adopt the eponymous stout appearance , the free flagellum shortens and the mitochondrion elaborates [29 , 33] . In a previous study we discovered a connection between VSG switching and developmental competence [34] . We simulated the initiation of an in situ switch by inducible overexpression of an ectopic VSG . This caused attenuation of the complete VSG ES and growth retardation . The ES-attenuation was dependent on histone H3 methylation , because in the absence of histone methyltransferase DOT1B the phenotype was not detectable . As the growth retardation was accompanied by signs of developmental competence , we hypothesized that attenuation of the active ES might trigger stumpy development . Thus , in the present work we focused on the question whether , apart from SIF-mediated stumpy formation , there exists a second mechanism that induces stumpy differentiation . Here , we show that SIF is indeed not required for stumpy stage transition and that there is an alternative path , which is controlled by the VSG ES . We propose that the ES represents a switch that interfaces two aspects of parasite persistence: the survival in the host through antigenic variation and the vector transmissibility through stumpy stage development .
Previous data raised the question whether ectopic VSG overexpression-induced ES-attenuation could cause stumpy differentiation [34] . This is an important point as it would imply that besides the stumpy induction factor SIF , there exists a density-independent trigger for differentiation to the stumpy life cycle stage . This possibility , however , could not be adequately addressed with monomorphic culture forms of T . brucei , as they have lost the ability to differentiate from the proliferative long slender to the cell cycle arrested short stumpy stage . Only pleomorphic parasites possess full developmental competence and are suitable for analyses of trypanosome differentiation [35 , 36] . Therefore , we have now exclusively used the pleomorphic trypanosome strain EATRO 1125 ( serodeme AnTat1 . 1 ) to test whether ectopic VSG overexpression can induce stumpy formation . We initially established two reporter cell lines in parasites natively expressing the VSG AnTat1 . 1 ( A1 . 1 ) . The first cell line was generated to monitor the activity of the VSG ES . A GFP open reading frame was integrated just downstream of the ES-promotor , yielding cell line GFPESproA1 . 1ES ( Fig 1A , S1 Fig ) . The second trypanosome line was produced to observe a gain in developmental competence . The fluorescent stumpy stage reporter GFP:PAD1UTR was integrated into the tubulin locus ( courtesy of Mark Carrington; [34] ) . The construct consists of a GFP sequence with a nuclear localization signal , followed by the 3`UTR of the stumpy-specific ‘protein associated with differentiation 1’ ( PAD1 ) . A sequence motif in the 3`UTR mediates the early increase of PAD1 transcript abundance during stumpy development [37] . Therefore , the nuclear fluorescence of GFP:PAD1UTR reflects the expression of the cell surface protein PAD1 , and hence , is a direct indication for stumpy development ( Fig 1B ) . For ectopic overexpression of VSG 121 , a pLew82v4 construct , which inserts into the ribosomal spacer region , was used . The inducible ectopic VSG overexpression is driven by a T7-polymerase under the control of a tetracycline repressor . The construct for ectopic VSG overexpression was transfected into both reporter lines , generating the trypanosome lines GFPESproA1 . 1ES121tet ( Fig 1C ) and GFP:PAD1UTRA1 . 1ES121tet ( Fig 1D ) . The ectopic overexpression of VSG 121 yielded clones with different growth phenotypes . In a subset of clones , the parasites continued to grow with only slightly impaired population doubling times ( Fig 1C and 1D , proliferating ) . In other clones the parasites stopped growth after one cell cycle ( Fig 1C and 1D , arrested ) . Irrespective of the cell cycle response , all clones expressed the induced ectopic VSG 121 on the cell surface , as was revealed by immunofluorescence analyses . An example of a proliferating VSG overexpressor is shown in Fig 2 and flow cytometry analysis of the same clone in S3 Fig . The distinct responses of the parasite clones were not due to expression of a specific VSG , but were reproduced with another VSG . The ectopic overexpression of VSG 118 had either no effect on growth or initiated a rapid growth arrest . Irrespective of the growth response , the trypanosomes exchanged their cell surface coat , now presenting VSG 118 on their surface ( S4 Fig ) . Thus , ectopic VSG overexpression mimics an antigenic switch of VSG coats . Quantitative Northern blot analyses documented the very fast kinetics of ectopic VSG 121 mRNA expression and the virtually simultaneous loss of native VSG A1 . 1 mRNA . In both , proliferating and arrested cells , the induction of ectopic VSG 121 overexpression led to an increase in VSG 121 mRNA to wild type levels within 4 hours ( Fig 3A and 3B ) . In the same period , the transcripts of the endogenous VSG A1 . 1 dropped to below 50% . Likewise , within 8 hours of induction , the protein levels of VSG 121 increased to ES-levels in both proliferating and arrested populations ( Fig 3C and 3D ) . The amount of the endogenous VSG A1 . 1 protein decreased in both cases to about 25% within 24 hours . After 8 hours , the amount of VSG 121 transcripts started to decrease in growth arrested clones , whereas in proliferating parasites the levels of VSG 121 mRNA remained constant . The VSG 121 protein was highly expressed in all trypanosome lines . Thus , after 24 hours of induction , the ectopic overexpression of VSG 121 always resulted in an almost complete exchange of VSG coats . Consequently , the different growth phenotypes could not be explained by differences in VSG coat exchange . Therefore , we assessed the transcriptional status of the A1 . 1 ES to determine if the phenotypes were the consequence of differences in ES-activity . The promotor proximal GFP reporter mRNA was quantified using Northern blot analyses ( S5 Fig ) . In growth-arrested trypanosomes , the GFP mRNA decreased to less than 50% within 24 hours , when compared to non-induced cells , suggesting that the ES was less active ( S5A Fig ) . In the same period of time , GFP mRNA levels in proliferating VSG 121 overexpressors remained above 70% indicating that the ES was more active ( S5B Fig ) . These results were confirmed at the single cell level using quantitative in situ-hybridization ( Fig 4 ) . In growth arrested cells , the GFP mRNA signal dropped by 80% within the first 24 hours , and remained at this low level for two days ( Fig 4A ) . As a control , G1/0 arrested short stumpy trypanosomes ( st ) were used , because in this life cycle stage the ES is attenuated [38] . In the density-induced stumpy trypanosomes the GFP mRNA was down-regulated by 90% , when compared to the long slender stage ( 0 h ) of the same strain . In proliferating clones , the GFP mRNA levels remained unaffected for 24 hours , whereas after 48 hours of ectopic VSG 121 overexpression , the mRNA had decreased to 50% compared to slender cells ( Fig 4B ) . This emphasized that proliferating clones also had reduced the ES-activity , however , without consequences for cell growth . Transcript levels , monitored for another endogenous component of the active ES , supported these results . We quantified the transcripts of ESAG6 , encoding part of the essential trypanosome transferrin receptor , which is present in all ESs [14] . In a growth arrested clone , ectopic VSG overexpression lead to a decrease of ESAG6 mRNA levels to 75% within 24 hours when compared to the non-induced control ( Fig 4C ) ( unpaired t-test: p-value < 0 . 01 ) . After 48 hours , the ESAG6 mRNA had further decreased to 40% , which was comparable to the amount measured in the density-induced stumpy cells ( 30% ) . No significant changes in ESAG6 mRNA levels were detected in the proliferating population within 48 hours of ectopic VSG 121 overexpression ( Fig 4D ) . Thus , the very sensitive single-cell measurements supported the results obtained from Northern analyses . In summary , our results so far showed that in cells ectopically overexpressing VSG 121 , attenuation of the complete ES led to a growth arrest . In contrast , in proliferating clones the endogenous VSG gene was silenced , while other parts of the ES largely retained their transcriptional activity . Importantly , the induced VSG coat exchange was stable over prolonged periods in proliferating clones , as after one month the VSG 121 still dominated the VSG coat in the majority of the cells ( S6 Fig ) . Thus , all these experiments suggest that the VSG and the ES can be silenced independently and that this uncoupling can be stable for many parasite generations . In addition , it confirms that the VSG-coat forming mRNA neither has to be transcribed from a telomeric position nor from the active ES . Growth arrested ectopic VSG overexpressors had an attenuated ES . To test if the growth phenotype was linked to a specific cell cycle stage , we determined the kinetoplast/nucleus ( K/N ) configuration ( Fig 5A ) . An accumulation of non-dividing 1K1N cells ( G1-phase ) was detected in growth arrested clones . Already after 24 hours of ectopic VSG 121 overexpression 20% more 1K1N cells were present in the population compared to non-induced slender cells ( 0 h ) , and after 48 hours of induction , 86% of the parasites were in G1 . At the same time , the number of dividing cells ( 1Kd1N , 2K1N and 2K2N ) had decreased . Thus , the parasites were stalled in the G1/0-phase of the cell cycle , very much like the density-induced stumpy control ( st ) , which per definition is G1/0 arrested [39] . In addition , the ectopic VSG overexpressors changed their morphology within 48 hours of induction , now displaying the characteristic shortened flagellum and stout appearance of density-induced short stumpy parasites ( Fig 5B ) . Next , we tested for the presence of the green fluorescent GFP:PAD1UTR reporter , which is exclusively expressed in the stumpy stage [32] . After 24 hours of ectopic VSG overexpression , already 74±4% of all cells expressed the reporter . After 48 hours , 90±9% of the cells displayed a green fluorescent nucleus ( S7A Fig ) , which was comparable to the number of cells expressing the GFP:PAD1UTR reporter in density-induced stumpy parasites of the same cell line ( 97±3%; S7A Fig ) . We also analyzed the expression of a second protein that is strongly up-regulated during stumpy development , the mitochondrial lipoamide dehydrogenase ( LipDH ) [31] . Western blot analyses showed that after 48 hours of ectopic VSG overexpression , LipDH increased 10-fold in growth arrested clones , when compared to non-induced long slender cells ( 0 h ) ( Fig 5C ) . Thus , in ectopic VSG overexpressors , LipDH levels were similar to those of density-induced stumpy parasites . Next , the morphology of the mitochondrion , which is another hallmark for the discrimination of slender and stumpy parasites , was assessed . The organelle grows and branches during stumpy development as a metabolic pre-adaption to the loss of glucose homeostasis , which occurs upon uptake by the transmitting tsetse vector [29 , 40] . The morphology of the mitochondrion was visualized using Mitotracker in arrested ectopic VSG overexpressors ( 48 h ) and in non-induced slender parasites ( 0 h ) ( Fig 5D ) . As a control for mitochondrial expansion , density-induced short stumpy trypanosomes ( st ) of the same cell line were used ( S7B Fig ) . As expected , the mitochondria in the slender control trypanosomes had the characteristic slim and elongated shape . After 24 hours of VSG 121 overexpression , 70±9% of the parasites possessed a branched mitochondrion ( S7B Fig ) . After 48 hours of induction , 87±5% of the cells displayed a branched mitochondrion , which compares well to 90±4% in density-induced stumpy trypanosomes . Another , more subtle marker for stumpy differentiation is an increase in the expression of the glycosomal DxDxT class phosphatase PIP39 [41] . This protein is essential for stumpy to procyclic transition , as it is part of the citrate/cis-aconitate ( CCA ) signaling cascade , which promotes procyclic development upon entry of the stumpy parasites into the alimentary system of the tsetse fly [42] . Western blot analysis showed that PIP39 is upregulated in density-induced stumpy parasites , as well as in a growth arrested clone after 24 and 48 hours of ectopic VSG overexpression ( S7C Fig ) . Thus , VSG-induced ES-attenuation initiates growth arrest in G1/0 , expression of stumpy marker proteins , mitochondrial re-organization and changes to a stumpy cell morphology . We conclude that ectopic VSG 121 overexpressors with an attenuated ES are indistinguishable from density-induced short stumpy trypanosomes . Therefore , we introduce the term ‘ES-attenuation-induced stumpy trypanosomes’ for such cells . The stumpy development observed above was not the result of cell stress . As a control , we exposed slender parasites to mild acid conditions ( pH of 5 . 5 ) for 30 minutes and two hours , as reported by Rolin et al . [43] ( S8A Fig ) . Propidium iodide ( PI ) staining of the stressed cells showed that 30 minutes of mild acid treatment was sufficient to kill the majority of cells ( PI-positive ) . After 2 hours virtually no living cells ( PI-negative ) could be detected . Parasites treated for 30 minutes were washed and cultivated further to analyze if the surviving cells would differentiate to the stumpy stage . However , the parasites grew normally and did not arrest in the cell cycle ( S8B Fig ) , which means the surviving cells were slender stage trypanosomes . This was supported by monitoring the GFP:PAD1UTR stumpy reporter 24 and 48 hours after mild acid treatment ( S8C Fig ) . No increase in the number of fluorescent stumpy parasites was detected . Thus , mild acid treatment does not trigger stumpy development in slender parasites . Proliferating ectopic VSG 121 overexpressors that had exchanged the VSG surface coat , but maintained an ES-activity of above 50% , did not show any alterations in the cell cycle ( Fig 6A ) . Following induction of overexpression the parasites retained their slender morphology ( Fig 6B ) and did not express the GFP:PAD1UTR reporter ( Fig 6B and S9A Fig ) . LipDH expression remained at the same level as in non-induced slender cells ( Fig 6C ) and no mitochondrial restructuring could be observed ( S9B Fig ) . This all suggested that ES-attenuation is required to induce differentiation , whereas silencing of the ES-resident VSG alone does not lead to stumpy development . However , at this point we had not formally excluded that the proliferating clones simply were refractory to stumpy induction . To test this , non-induced parasites were grown to high densities to induce stumpy development . In this control we observed a G1/0 cell cycle arrest in more than 90% of the parasites ( st in Fig 6A ) , expression of the GFP:PAD1UTR reporter in a high proportion of the cells ( st in S9A Fig ) and an increase in the LipDH levels ( st in Fig 6C ) . In the next step , parasites were cultivated without dilution in the absence of tetracycline to directly compare the response to SIF of slender populations of proliferating and arrested clones . Population growth was recorded for 96 hours , and the number of GFP:PAD1UTR-positive cells indicated SIF-induced stumpy development . As all cells in this experiment were grown in the absence of tetracycline , i . e . without VSG overexpression , they were termed potentially proliferating ( 'proliferating' ) and potentially arrested ( 'arrested' ) . The ‘proliferating’ clone reached higher cell densities during SIF-induced stumpy development than the ‘arrested’ clone ( Fig 7A ) . Nevertheless , in both cases the parasite populations synchronously differentiated to the stumpy stage , with more than 90% of the cells expressing the stumpy reporter within 48 hours ( Fig 7B ) . Thus , non-induced slender cells of 'proliferating' as well as 'arrested' clones were fully competent for stumpy development . Thus , attenuation of the ES is required for the induction of stumpy stage transition in the ectopic VSG overexpressors . Silencing of the telomeric VSG alone , however , is not sufficient to induce developmental transition . Stumpy trypanosomes are thought to be the only bloodstream stage that can infect the tsetse fly [29] . The development of stumpy cells to the procyclic insect stage is accompanied by an early loss of cell surface VSG , which is replaced by an invariant EP-procyclin coat [44 , 45] . The stumpy-to-procyclic transition can be enforced in vitro by cold-shock and treatment with citrate and cis-aconitate ( CCA ) [35 , 46 , 47] . Therefore , we challenged growth arrested ectopic VSG 121 overexpressors that displayed stumpy morphology ( ES-attenuation-induced stumpy cells ) with CCA for 0 , 6 and 24 hours at 27°C , followed by immunodetection of EP1 ( Fig 8A and 8B ) . Non-induced long slender cells ( 0 h ) served as a negative control , and density-induced stumpy parasites ( st ) were used as a positive control . Flow cytometry showed that upon CCA-treatment , both ES-attenuation-induced and density-induced stumpy cells replaced the VSG coat with EP1 ( Fig 8A ) . Within 6 hours , 78% of the ES-attenuation-induced and 80% of the density-induced stumpy parasites were EP1 positive . In contrast , only 11% of the slender forms showed EP1 expression after 6 hours of CCA-treatment . The remarkably similar kinetics of EP1 expression suggested that ES-attenuation-induced and density-triggered stumpy trypanosomes possess the same developmental competence , at least in vitro . During procyclic development the parasites elongate at the posterior pole and the kinetoplast is repositioned towards the vicinity of the nucleus [48] . After 6 hours of CCA-treatment , no morphological changes were observed in ES-attenuation-induced stumpy cells . After 24 hours of CCA treatment , however , the distance between kinetoplast and nucleus had shortened from 4 . 81±0 . 6 μm to 2 . 77±0 . 9 μm . Within the same period , the distance between the posterior cell pole and the kinetoplast almost tripled from 1 . 45±0 . 42 μm to 4 . 13±1 . 39 μm . Thus , the parasites adopted the characteristic elongated shape of procyclic cells , and the kinetoplast was repositioned towards the nucleus ( Fig 8B ) . Importantly , 33% of the parasites were in the 2K1N or 2K2N cell cycle phase and thus , the trypanosomes had resumed growth as procyclic forms . Hence , the trypanosomes synchronously responded to CCA-treatment with EP1 expression on the surface , loss of the VSG surface coat and morphological alterations that are characteristic for development to the procyclic insect stage . In the next step , we tested if ES-attenuation-induced stumpy parasites would be able to initiate and complete the complex passage through the tsetse vector . For this , ES-attenuation-induced stumpy trypanosomes ( 2x 106 cells/ml ) were included in the blood meal of 50 tsetse flies . Control flies were fed with the same number of density-induced stumpy parasites of the parental GFP:PAD1UTR cell line . After >50 days of infection , the alimentary tract of the flies was dissected and examined for the presence of trypanosomes . Parasites were found in the salivary glands of 6 . 4% of flies infected with ES-attenuation-induced stumpy parasites ( n = 47 ) , whereas in the control experiment density-induced stumpy parasites completed the infection in 22 . 7% of flies ( n = 44 ) . Hence , independent of the differentiation trigger , the stumpy trypanosomes were able to passage through the insect . Fluorescence microscopy was used to probe for the characteristic trypanosome stages in the alimentary system of tsetse flies [49 , 50] . An antibody against the paraflagellar rod ( PFR ) visualized the length and location of the flagellum , and the DNA was stained with DAPI to analyze the configuration of kinetoplast and nucleus . All developmental stages of trypanosomes were present in the flies ( Fig 8C ) . Not only procyclic midgut parasites were found , but also mesocyclic , epimastigote and metacyclic stages . Thus , ES-attenuation-induced stumpy trypanosomes are not only able to establish an infection in tsetse flies but can also successfully complete the tsetse passage by developing the mammal-infective metacyclic stage in the salivary glands of the insect . Trypanosome development to the stumpy stage occurs in response to a cell density-dependent quorum sensing mechanism [25] . The parasites continuously secrete SIF , the as yet elusive stumpy induction factor . SIF is thought to accumulate in the host with rising parasitemia and to induce stumpy transition once a concentration threshold is reached [26] . In cell culture , this requires parasite densities of over 106 cells/ml [51] . We postulate that ectopic VSG overexpression-induced ES-attenuation leads to stumpy development in a non-density dependent and , hence , SIF-independent manner . As stumpy cells are irreversibly arrested in the cell cycle and have a lifespan of 2–3 days [52] , cell death should become apparent at day 4 post-induction of ectopic VSG overexpression . In fact , we did observe cell death , however , all populations resumed growth at later time points ( Fig 9A , S11 Fig ) . The timing of outgrowth varied and occurred between days 4 and 8 of induction . Several possible mechanisms would explain the outgrowth of the ES-attenuation-induced stumpy parasites: ( i ) a subpopulation of ES-attenuated parasites is refractory or less sensitive to stumpy differentiation; ( ii ) the complete A1 . 1 ES has been re-activated; ( iii ) a defect in the overexpression system has occurred , e . g . by mutation of the T7 polymerase or promoter; or ( iv ) a minority of parasites does not attenuate the ES completely and hence , escapes stumpy formation . To test the first possibility , a growing population of trypanosomes that appeared after 8 days of ES-attenuation-induced cell cycle arrest was treated with the stumpy induction factor ( SIF ) , or the downstream signal analogue pCPT-cAMP [26] . If the parasites were refractory to differentiation , they should not respond to these differentiation signals by expression of the GFP:PAD1UTR stumpy reporter . However , both SIF and pCPT-cAMP triggered synchronous differentiation to the stumpy stage with kinetics that were identical to those measured for non-induced parasites ( Fig 9B ) . Explanation ( ii ) was excluded by immunofluorescence analyses , showing that the outgrowing parasites , even after 28 days , still expressed the ectopic VSG 121 on their cell surface , and thus , had not re-activated the complete A1 . 1 ES ( S12 Fig ) . The same experiment also precluded ( iii ) as expression of the ectopic VSG 121 would not be inducible any more if a mutation in the T7 polymerase or promoter had occurred . This was further supported by the finding that the ectopic VSG overexpression system was re-inducible: tetracycline was removed after 48 hours of ectopic VSG 121 overexpression , and the parasites were cultivated for one week without tetracycline . At day 7 , the cells had resumed growth and expressed the endogenous VSG A1 . 1 coat ( Fig 9C , top ) . Then tetracycline was again added to the culture , and within 24 hours , the trypanosomes had once more exchanged their VSG coat , now again predominantly presenting the ectopic VSG 121 on the surface ( Fig 9C , bottom ) . Thus , the outgrowing cells were ( i ) neither refractory to SIF action , ( ii ) nor had they re-activated the endogenous VSG A1 . 1 ES . They were ( iii ) also not the product of a deficient gene expression system . Interestingly , no growth arrest could be observed when tetracycline was re-added in order to re-induce overexpression of ectopic VSG ( Fig 9D , S11B Fig ) . This means that the outgrowing population was based on parasites that had escaped ES-attenuation-induced stumpy development . The late onset of outgrowth shown in Fig 9A ( S11A Fig ) excluded that the starting population already contained cells that did not respond to ectopic VSG overexpression with ES-attenuation and subsequent growth retardation . As a fact , short stumpy parasites are cell cycle arrested and can only be rescued by developmental progression to the procyclic insect stage [39 , 44] . We postulate that this is also true for ES-attenuation-induced stumpy cells . Thus , the late appearing dividing trypanosomes must have escaped ES-attenuation-induced stumpy formation . To estimate the number of escapers , we used serial dilutions . ES-attenuation was induced by ectopic VSG overexpression and the trypanosomes were immediately diluted into 96-well plates , each well containing either 5 , 50 , 500 or 5 , 000 cells . As a control for the outgrowing cells , non-induced long slender parasites of the same cell line were used . When 5 cells were seeded per one well , growth resumed in 90% of the control wells , while no growth was observed with induced cells . However , when 50 or 500 induced parasites were seeded per well , cells grew in 1 and 4% of all wells , respectively . Correspondingly , growth was apparent in 40% of wells , which had been seeded with 5 , 000 induced cells . Assuming that the outgrowing population in one well can originate from just a single cell , at least 1 in 10 , 000 trypanosomes must have escaped ES-attenuation-induced stumpy formation . However , those cells also most likely had attenuated the ES prior to regaining proliferative capacity . We suggest that all cells in a clonal population respond to ectopic VSG overexpression with ES-attenuation , however , the ES-activity has to fall below a critical threshold in order to drive the cell cycle into the irreversible G1/0 state . In a few parasites , the ES does not reach this critical level . In these cells the native VSG A1 . 1 is completely silenced , while the A1 . 1 ES could still provide sufficient ESAG transcripts to support slowed growth . The ectopic VSG 121 continues to be expressed by T7 polymerase . The cells neither effectively proliferate nor do they differentiate into the cell cycle-arrested stumpy stage . They could rather linger in a prolonged G1-phase . This dormancy is an unstable state , which is either drifting towards ES shut-down and subsequent stumpy formation , or it is rescinded by re-activating the ES to permissive levels for re-entry into the cell cycle . In the latter case , the parasites would appear as normally growing long slender trypanosomes , ectopically expressing a VSG 121 surface coat . This was the case for the cells that grew out in the above experiment . We have shown that ES-attenuation can cause stumpy development and thus , represents a direct differentiation trigger . To further support this finding , we induced ectopic VSG 121 overexpression , and hence ES-attenuation , in a potentially arrested clone at cell densities of 2 . 5x 105 cells/ml ( high density , HD ) and 2 . 5x 104 cells/ml ( low density , LD ) ( Fig 10A and 10B; S13A and S13B Fig ) . Irrespective of the starting cell densities , the parasites only divided once after tetracycline addition . Thus , for the first four days , the cell numbers never exceeded 5x 105 cells/ml in HD cultures , and 5x 104 cells/ml in LD cultures . At these densities , SIF is initially not present in a sufficient amount for triggering density-induced stumpy development . This excludes that the immediate cell cycle arrest was SIF-driven . The possible action of SIF became evident only at later time points , and only when the HD parasites were incubated without an exchange of the culture medium , which allowed SIF to accumulate in the culture ( Fig 10A , no wash; S13A Fig ) . As the whole population is dying the accumulating SIF could have triggered stumpy formation also in those trypanosomes that did not attenuate the ES sufficiently , and which would have resumed growth in the absence of SIF ( ‘escapers’ , Fig 9A ) . Consequently , when the HD trypanosome population was provided fresh culture medium on either days 1 or 2 , the ES-attenuation escapers survived and grew out after 5 days ( Fig 10A , washed ) . This finding was supported by the control experiment using lower starting cell densities , i . e . a 10-fold slowed SIF accumulation ( Fig 10B; S13B Fig ) . In those cultures , SIF would never have accumulated in sufficient amounts to be able to drive the complete population into cell cycle arrest . An important conclusion that we can draw from the above experiment is that ES-attenuation leads to stumpy formation in less than one day ( S7 Fig ) . Hence , stumpy development in HD cultures in the first two days following induction was exclusively caused by ES-attenuation , and thus independent of SIF . With regard to signal penetration , our experiments suggested that SIF was the dominant differentiation trigger , as ES-attenuation escapers were still responsive to this . We hypothesize that ES-attenuation represents an ‘epigenetic’ signal , downstream of the chemical cue SIF . What , however , happens if the cells receive both triggers , ES-attenuation and SIF , at the same time ? To address this question , we exposed cells to both signals , assuming that a population that had been primed with ES-attenuation could react faster to the SIF signal than the non-induced control . We induced VSG overexpression-mediated ES attenuation in the PAD1 reporter cell line , and added SIF or its second messenger cAMP ( Fig 11 ) . The combination of ES-attenuation with either of these two chemical signals was by far more effective than each signal alone . Within 20 hours , ES-attenuation and 200 μM cAMP , produced 16% and 15% of GFP:PAD1-positive cells , respectively . When both triggers were combined , 70% of the parasites became stumpy . The combination of SIF and ES-attenuation was also more effective: while SIF alone produced just 10% stumpy parasites , the simultaneous induction of ES attenuation resulted in 50% of trypanosomes being PAD-positive ( Fig 11 ) . An extended experiment using different time frames of incubation and different concentrations of the differentiation triggers is shown in S14 Fig . We tentatively conclude that SIF and ES-attenuation are acting along the same signaling pathway , probably in a cooperative manner . All long slender trypanosomes respond to ectopic VSG overexpression with ES-attenuation . For stumpy development , the ES-activity has to fall below a critical threshold . In the presence of additional SIF this threshold is reached earlier and hence , more trypanosomes can differentiate within the same period . Thus , all our results are compatible with a mechanism , in which VSG-induced ES-attenuation triggers stumpy differentiation in a cell density-independent manner , downstream of the density-dependent quorum sensing factor SIF . Our experiments further underline the multiple roles of the VSG ES as a trypanosome virulence hub . The ES is not only essential for immune evasion and metabolism , but also controls parasite development .
Little is known about the control of in situ VSG switching . Basically , there are two possibilities: either the old ES is shut-down and then a new ES is activated , or a new ES is transcriptionally activated before the old one is switched off . Support for the first possibility comes from tagging two ESs with selectable markers [53 , 54] . In the presence of the drugs rapid switching between the tagged ESs occurred . This suggested that one silent ES lingers in a pre-active state and , thus , is immediately activated once the active ES is silenced . However , another study reported that the inducible block of ES transcription caused growth inhibition and subsequent probing of several silent ESs [13] . This suggested that the silencing of the active ES does not cause an immediate antigenic switch . In addition , depletion of VSG mRNA results in a rapid precytokinesis arrest , which suggests that an inactivation of the ES without the simultaneous activation of a new one would be lethal [23] . Therefore , in a previous study , we tested the possibility that a new VSG is activated , before the old one is silenced . This was achieved by inducible overexpression of a second VSG [34] . Surprisingly , the trypanosomes responded with attenuation of the active ES and growth retardation . It is important to note that these cells never stopped growth , i . e . they never arrested in the cell cycle , but rather lingered in a prolonged G1-phase . Interestingly , growth retardation was accompanied by signs of developmental competence . This raised the question whether ectopic VSG overexpression or ES-attenuation could lead to stumpy development . This possibility , however , remained unexplored , as the monomorphic trypanosome strains routinely used in the laboratory are developmentally deficient . Only more natural , pleomorphic parasites are suitable for analyses of trypanosome differentiation [35 , 36] , but large scale cultivation and genetic manipulation are very difficult . We established the tetracycline-inducible ectopic VSG overexpression system in the pleomorphic strain EATRO 1125 ( serodeme AnTat1 . 1 ) . The induction of ectopic VSG 121 overexpression produced an unexpected phenotypic variability . In a subset of recombinant clones , the ectopic VSG overexpression led to growth arrest . The clones rapidly stopped growing within the first cell division cycle , and did not linger in a prolonged G1-phase , as the monomorphic trypanosomes did . In another subset of clones , however , the pleomorphic trypanosomes did not halt the cell cycle at all , but rather proliferated normally , with only marginally prolonged population doubling times . Initially , we assumed that the latter parasites were simply refractory to VSG induction , but this was not the case . Irrespective of the growth response , all trypanosome clones exchanged the VSG surface coat with similar kinetics , i . e . the endogenous VSG A1 . 1 was replaced with the ectopic VSG 121 . This phenotypic variability was not VSG-dependent . When ectopic VSG 118 overexpression was induced , the VSG coat was exchanged , but only a subset of clones arrested in the cell cycle , while others grew normally . This confirmed that a VSG coat can be readily formed with protein transcribed from outside the active ES . In addition , it showed that the ES-resident VSG can be silenced without shutting off the other parts of the ES . In the proliferating ectopic VSG overexpressors , the VSG promoter-proximal GFP reporter transcripts decrease to about half of wild type levels within 48 hours , and the native VSG A1 . 1 was silenced over many parasite generations . We propose that such an expression of ESAGs and VSG from two different genomic locations might occur naturally , namely during an in situ switch . When the old ES is attenuated , the ESAGs of the new ES will have to functionally complement and , thus , can become limiting for growth . However , the ES-activity can be stalled at levels that still support growth . Consequently , these trypanosomes could potentially survive a switch to a defective or incompatible ES . It has long been known that T . brucei preferentially populates tissue spaces , enters the brain and , as only recently shown , thrives in fat tissue [55–58] . Assuming that not all ESAGs ( or other ES-derived elements ) are equally well suited for supporting growth in fat or other tissues , it would be an advantage to probe for the optimal ES and to select for the best adapted parasites as founders of a new population . Additionally , the blood feeding behavior of the tsetse fly is not very choosy and thus , the trypanosomes are confronted with a wide range of hosts . It has long been postulated that host serum compatibility is also a readout of ESAGs , especially ESAGs 6 and 7 , which encode the heterodimeric transferrin receptor [59–61] . Thus , any stochastic in situ switch could select for the most advantageous ES in a given host [7] . The ectopic overexpression of VSG yielded not only proliferative ectopic VSG overexpressors , but , with similar frequency , VSG overexpressors that rapidly stopped growth once they had attenuated the active ES . We have shown that these clonal populations halt the cell cycle in G1/0 . Furthermore , the cells are indistinguishable from the short stumpy life cycle stage in every possible aspect studied . They undergo the same morphological changes , express stumpy marker proteins , including a GFP-reporter for the ‘protein associated with differentiation 1’ ( PAD1 ) . They synchronously respond to the differentiation trigger cis-aconitate with development to the insect stage , and importantly , they infect the tsetse fly and successfully complete the weeks-long passage through the vector . Thus , the growth arrested VSG overexpressors with an attenuated ES are short stumpy trypanosomes . So far , it has been assumed that the stumpy stage transition is exclusively initiated through the quorum sensing factor SIF , which is continuously secreted by proliferating slender bloodstream trypanosomes [26] . In a paracrine manner SIF is sensed and limits the parasite population size by driving the trypanosomes into G1/0 cell cycle arrest . Thus , the SIF pathway is strictly cell density-triggered [25] . We show that ectopic VSG overexpression induced ES-attenuation yields stumpy stage parasites even at low cell densities and thus , in a SIF-independent way . This happens with very fast kinetics: while SIF-challenged ( already committed ) slender cells divide between 2 and 3 times before they exit the cell cycle [26 , 52] , ES-attenuation induced stumpy parasites divide just once before they arrest . At this time the ES is attenuated by about 80% . Thus , one or more products from the ES might signal the status of the ES , and initiate the stumpy induction pathway , bypassing the need for high cell density and , thus , SIF . This might be because SIF obviously acts upstream of ES-attenuation , as it is an extracellular cue . By combining SIF and ectopic VSG overexpression induced ES-attenuation we have shown that both triggers work cooperatively . We suggest that a reduction in the ES-activity can prime the parasite for stumpy development , which is triggered once the transcriptional activity drops below the critical threshold . Besides this , we also considered the question whether any cell stress or growth inhibition per se could enforce stumpy development . For several reasons the answer is no: first , in monomorphic VSG overexpressors , ES-attenuation precedes growth retardation [34] . Second , cell cycle arrest in pleomorphic parasites , for example due to VSG shortage , does not cause stumpy development [23] . Also , stressing trypanosomes by mild acid treatment does not trigger stumpy differentiation . Lastly , the possibility that stumpy development was caused by nutrient deprivation [62 , 63] , e . g . due to loss of ESAG function , is simply excluded by the fast kinetics of the event: ES-attenuation initiates the developmental progression within one cell cycle , which is clearly faster than expected from a metabolic penalty that depends on the decay of ESAG protein levels . So why should there be an alternative to cell density-triggered stumpy stage development ? In fact , when trypanosomes are injected into natural hosts the parasitemia is very low [56] . It is difficult to envisage how the secreted SIF should accumulate to concentrations that would induce stumpy development , at least outside of tissue spaces . Here , a second , synergistic differentiation trigger could be expedient . All trypanosomes undergoing a transiently or permanently unsuccessful in situ switch would contribute to the number of short stumpy cells . Since VSG switching occurs stochastically , ES attenuation-triggered stumpy formation should generate a constant background rate of stumpy differentiation . Such a density independent formation of stumpy parasites has in fact been suggested previously [64] . Based on mathematical simulations the authors state that a density-dependent model cannot explain the observed presence of stumpy cells before SIF reaches an effective concentration . They have termed this the background rate and we surmise that ES-attenuation accounts for this constantly occurring stage transition . All our data are compatible with a model that promotes the VSG ES as a master regulator of antigenic variation and development ( Fig 12 ) . When a new ES is activated , the new VSG replaces the old one immediately as the old VSG is transcriptionally silenced within a few hours [34 , 65] . If the new VSG protein is incompatible or defective , the trypanosome dies , as the parasite cannot form a proper surface coat . When a new VSG coat has been produced , the remainder of the old ES is attenuated , most likely by epigenetic mechanisms [13 , 34] . When the old ES is attenuated to a critical threshold the ES-transcripts become limiting . This shortage has to be compensated by the ESAGs of the newly activated ES . If the complementation is successful , the old ES is silenced , and the antigenic switch is completed . If , however , elements of the new ES are defective or incompatible , the trypanosomes can react in two ways . If the ES transcript levels remain above 50% , the cells stop further attenuation of the old ES , which allows the parasites to keep proliferating . If , however , the ES is silenced to levels below 50% , the irreversible transition to the stumpy life cycle stage is initiated and the trypanosomes arrest the cell cycle , thereby becoming fully competent for tsetse fly transmission . Thus , the parasites do not necessarily die when an ES is activated that does not provide a good complement of ESAGs ( or other essential ES transcripts , such as small RNAs ) . Instead , a ‘rescue program’ is launched ensuring the survival of the trypanosome population . Although it is not straightforward to experimentally test this hypothesis , our data make it even more difficult to exclude it .
All generated cell lines were based on the pleomorphic Trypanosoma brucei brucei strain EATRO 1125 ( AnTat1 . 1 13–90 ) expressing VSG AnTat1 . 1 , abbreviated as VSG A1 . 1 [35 , 66] . The bloodstream form parasites were cultured in HMI-9 medium , supplemented with 10% ( v/v ) fetal bovine serum and 1 . 1% ( w/v ) methylcellulose to increase viscosity ( Sigma-94378 ) , at 37°C and 5% CO2 [67 , 68] . To maintain expression of the T7-polymerase and the tetracycline repressor , cells were cultivated in the presence of 2 . 5 μg/ml hygromycin and 1 . 25 μg/ml G418 . Trypanosome cultures were strictly kept at densities below 5x 105 cells/ml in order to prevent developmental transition to the stumpy stage . To harvest the cells , methylcellulose had to be removed from the cultures . Therefore , we first diluted the cultures at least 1:4 with sterile trypanosome dilution buffer ( TDB; 5 mM KCl , 80 mM NaCl , 1 mM MgSO4 , 20 mM Na2HPO4 , 2 mM NaH2PO4 , 20 mM glucose , pH 7 . 6 ) . Next , the diluted culture was filtered ( MN 615 1/4 , Macherey-Nagel , Germany ) using sterile conditions and centrifuged ( 1 500 xg , 15 minutes , RT ) . The AMAXA Nucleofector II ( Lonza , Switzerland ) was used to transfect 3x 107 trypanosomes with 10 μg of linearized plasmid DNA . Transgenic clones were selected by serial dilution and the addition of the respective antibiotics . Monomorphic parasites of the Trypanosoma brucei brucei strain Lister 427 ( MITat1 . 6 wild type cells expressing VSG 121 or MITat1 . 2 wild type cells expressing VSG 221 ) were cultivated at 37°C and 5% CO2 in HMI-9 containing 10% ( v/v ) fetal bovine serum . For tagging of the active VSG expression site the open reading frame of an eGFP was inserted upstream of the puromycin resistance cassette into the pLF12 plasmid [12] . The resulting construct was targeted to the ES promotor region and consisted of the eGFP flanked by aldolase UTRs and the puromycin resistance cassette with actin 5´ and aldolase 3´UTRs . The plasmid was linearized with KpnI and SacI and transfected into the parental AnTat1 . 1 13–90 cell line ( A1 . 1ES ) . Selection with 1 μg/ml puromycin yielded the GFPESproA1 . 1ES cell line . To generate the GFP:PAD1UTR stumpy reporter cell line , the plasmid p4231 ( courtesy of M . Carrington; [34] ) was transfected into AnTat1 . 1 13–90 cells ( A1 . 1ES ) . This construct consists of a GFP sequence with a nuclear localization signal that is followed by the 3´UTR of PAD1 . As the stumpy stage specific transcript increase of PAD1 is controlled by parts of its 3´UTR [37] , early stumpy development can be monitored by the appearance of green fluorescent nuclei in the GFP:PAD1UTRA1 . 1ES cell line . For ectopic overexpression of VSG 121 , the reporter cell lines were transfected with the NotI-linearised pRS . 121 plasmid [34] , giving rise to the cell lines GFPESproA1 . 1ES121tet and GFP:PAD1UTRA1 . 1ES121tet . For ectopic overexpression of VSG 118 ( kindly provided by N . Jones ) , the VSG 118 open reading frame , flanked by its wild type 3´UTR and the EP 5´UTR , was inserted into the pLew82v4 vector ( 24 009; Addgene plasmid ) . The construct was linearized with NotI and transfected into the GFP:PAD1UTR cell line , giving rise to the GFP:PAD1UTRA1 . 1ES118tet cell line . Parasites were diluted to a concentration of 25 , 250 , 2 , 500 or 25 , 000 cells/ml and ectopic VSG overexpression was induced by the addition of tetracycline ( 1 μg/ml ) . Subsequently , the dilution was transferred to a 96-well microtiter plate , each well containing 200 μl . Thus , every well of the plate contained 5 , 50 , 500 or 5 , 000 cells . As a control , non-induced long slender cells were seeded at a concentration of 5 cells per well . As the medium color shifts from pink ( alkaline ) to orange ( acidic ) at high cell densities the outgrown wells were readily identified by changes of medium color after 18 days of incubation . To estimate the number of outgrowing cells we assumed that a single cell is able to establish an outgrowing population . Thus , the number of outgrowing cells was calculated by dividing the amount of seeded ectopic VSG overexpressors per plate by the number of outgrown wells . Slender parasites of the GFP:PAD1UTRA1 . 1ES cell line were harvested as described above and transferred to liquid HMI-9 ( pH 7 or pH 5 . 5 ) . The cells were incubated for 30 minutes or two hours in the medium at 37°C . Subsequently , 1 μl propidium iodide ( 1 mg/ml ) was added to 1 ml culture and analyzed with a BD Bioscience FACSCalibur Flow Cytometer . 20 , 000 cells were counted per sample and the data were analyzed with the BD CellQuest Pro Software ( BD Bioscience , USA ) . An aliquot of the pH treated cells was washed two times with TDB and further cultivated in HMI-9 , supplemented with methylcellulose . Population growth was recorded for 48 hours after treatment , and the expression of the GFP:PAD1UTR was monitored . For the generation of density-induced stumpy parasites , slender cells at a seeding density of 5x 105 cells/ml were cultivated without dilution for 48 hours . This allowed the accumulation of SIF and subsequent stumpy formation . To analyze the impact of SIF on growth arrested ectopic VSG overexpressors , the parasites were treated either with a SIF concentrate or the downstream analogue pCPT-cAMP ( Sigma-Aldrich , USA ) . To generate the SIF concentrate , monomorphic parasites at a density of 5x 104 cells/ml were grown for 50–52 hours to maximum cell density ( 0 . 7-1x 107 cells/ml ) . Subsequently , filtration was used to remove the cells and proteins were depleted from the supernatant via methanol precipitation . The protein free medium was lyophilized and resuspended to an x-fold concentration , whereby 1x corresponds to conditioned medium without further concentration steps . The SIF concentrate was diluted in pre-warmed TDB to a concentration of 1x or 1 . 5x and the SIF downstream analogue pCPT-cAMP to 400 or 800 μM . Parasites were diluted to a concentration of 1x 105 cells/ml , ectopic VSG overexpression and , thus , ES-attenuation was induced by the addition of tetracycline ( 1 μg/ml ) . Immediately , 1 . 5 ml of the recently induced trypanosomes were transferred to a 24-well plate . To each well 500 μl of the dissolved compounds or TDB alone was added ( control ) . Thus , SIF had a final concentration of 0 . 25x or 0 . 37x and pCPT-cAMP of 100 μM or 200 μM . In parallel , non-induced slender cells of the same strain were treated identically to the ectopic VSG overexpressors . Subsequently the parasites were incubated at 37°C and 5% CO2 . After 20 and 28 hours of incubation the amount of GFP:PAD1UTR-positive cells was microscopically analyzed . Trypanosomes ectopically overexpressing VSG 121 for 48 hours ( ES-attenuation-induced stumpy parasites ) , non-induced slender or density-induced stumpy cells were harvested from HMI-9 supplemented with 1 . 1% ( w/v ) methylcellulose . To trigger differentiation to the procyclic stage the parasites were resuspended in DTM culture medium [47] to a density of 2x 106 cells/ml . After the addition of 3 mM cis-aconitate and 3 mM citrate , the cultures were incubated at 27°C ( CCA treatment ) . Samples were collected after 0 , 6 or 24 hours of CCA treatment . The detection of EP1 was conducted as described by Batram et al . , 2014 using an Alexa647-conjugated anti-mouse antibody for FACS analyses and an Alexa594 conjugated anti-mouse antibody for the acquisition of microscopic images [34] . A BD Bioscience FACSCalibur Flow Cytometer was used for flow cytometry and 20 , 000 cells were counted for every sample . The data were analyzed with the BD CellQuest Pro Software ( BD Biosience , USA ) . Tsetse flies ( Glossina morsitans morsitans ) were kept at 27°C and a relative humidity of 70% . The insects were fed twice a week through a silicon membrane with defibrinated sterile sheep blood ( ACILA , Germany ) . After a maximum of 48 hours post-eclosion , the flies were infected with trypanosomes during their first blood meal . For this , the parasites were harvested and resuspended to a density of 2x 106 cells/ml in blood supplemented with 60 mM N-acetylglucosamine . 50 flies each were fed with density-induced stumpy trypanosomes of the parental GFP:PAD1UTRA1 . 1ES cell line or with parasites ectopically overexpressing VSG 121 for 56 hours ( ES-attenuation-induced stumpy parasites ) . After >50 days of infection the surviving flies were starved for at least 24 hours , before they were dissected as described by Rotureau et al . , 2011 [69] . First , the salivary glands were isolated immediately after dissection . Then , the complete tsetse alimentary tract was dissected in a drop of PBS and microscopically examined for the presence of trypanosomes ( density-induced: 44 flies; ES-attenuation-induced: 47 flies ) . Next , tsetse foregut and proventriculus were separated from the midgut in different drops of PBS and parasites were released from the tissues . Subsequently , immunostaining was performed as described below . Total RNA was extracted from 1x 108 trypanosomes using the RNeasy Mini Kit ( Qiagen , Netherlands ) . For fluorescent labeling , 3 μg of glyoxal-denaturated RNA was transferred to a nitrocellulose membrane using a Minifold Dotblotter ( Schleicher & Schuell , Germany ) . The blots were hybridized over night at 42°C with oligonucleotide probes coupled to IRDye 682 ( VSG 121: GCTGCGGTTACGTAGGTGTCGATGTCGAGATTAAG; VSG AnTat1 . 1: GTCTTTCTCTTCTTTCCCTTTGCACTTTTC ) or IRDye 782 ( tubulin: TCAAAGTACACATTGATGCGCTCCAGCTGCAGGTC ) . For radioactive quantifications the denatured RNA was separated on an agarose gel and transferred to a nylon membrane . GFP mRNA was detected with a 32P-labeled probe ( complete eGFP ORF , Thermo Scientific DecaLabel DNA Labeling Kit ) and quantified using a Phosphorimager . Protein samples were prepared by acetone precipitation and analyzed via protein dot-blotting ( 6x 105 cell equivalents ) as described by Batram et al . , 2014 [34] , or on Western blots ( 2x 106 cell equivalents ) . After blocking with 5% ( w/v ) milk powder in PBS , the primary antibodies were diluted in PBS containing 1% ( w/v ) milk powder and 0 . 1% ( v/v ) Tween 20: rat anti-VSG AnTat1 . 1 ( VSG A1 . 1 ) 1: 20 , 000 [35]; rabbit anti-VSG 121 1:2 , 000 ( courtesy of M . Carrington ) ; rabbit anti-LipDH 1: 10 , 000 [70]; rabbit anti-PIP39 1:750 ( courtesy of B . Szoor ) , rabbit anti-H3 1:10 , 000 [71]; guinea pig anti-H3 1:5 , 000 [72]; mouse anti-PFR ( L13D6 ) 1:200 [73] . Species-specific , IRDye coupled secondary antibodies ( LI-COR Biosciences , Netherlands ) were used for infrared detection of proteins ( 1:10 , 000 in PBS containing 1% ( w/v ) milk powder and 0 . 1% ( v/v ) Tween 20 ) . Analyses and quantification of fluorescently labeled protein and RNA was conducted using the Licor Odyssey Infrared Imaging System ( LI-COR Biosciences , Netherlands ) . To quantify mRNA levels via FISH the QuantiGene ViewRNA ISH Cell Assay kit ( Affymetrix , USA ) was used , essentially following the manufacturer’s instructions . At least 1x 107 trypanosomes were harvested , fixed with 4% ( w/v ) formaldehyde ( FA ) for 10 minutes at room temperature and , subsequently , washed two times with PBS . Cells were allowed to settle on poly-l-lysine-coated slides ( within hydrophobic circles ) for 30 minutes . For protease digestion the settled cells were incubated with the protease solution ( 1:1 600 in PBS ) for 15 minutes at 25°C . The following probes for mRNA detection were used in a 1:100 dilution of the original stock: eGFP ( full antisense ORF , red = type 1 ) and ESAG6 ( antisense to nucleotides 107–1206 Tb427 . BES40 . 3 , red = type 1 ) . Only samples from the same slide were compared for quantification of mRNA levels . Per slide , fixed cells ( non-induced slender , density-induced stumpy and ectopic VSG overexpression induced for 24 and 48 hours ) of a proliferating or growth arrested clone were incubated either with the ESAG6 or eGFP probe . The mitochondria of the parasites were visualized by incubation of trypanosome cultures with 50 nM MitoTracker Red CMXRos ( ThermoFisher Scientific , USA ) for 20 minutes at 37°C . The cells were then harvested as described above , washed with TDB and chemically fixed for 15 minutes at room temperature with 2% ( w/v ) formaldehyde ( FA ) and 0 . 05% ( v/v ) glutaraldehyde in PBS . For the detection of VSGs chemically fixed parasites ( 30 minutes , 2% ( w/v ) FA ) were allowed to settle on poly-l-lysine-coated slides . The cells were blocked for 30 minutes with 1% ( w/v ) BSA and incubated with a rat anti-VSG AnTat1 . 1 ( 1:4 , 000 , [35] ) and a rabbit anti-VSG 121 or anti-VSG 118 ( 1:500 ) antibody diluted in 0 . 1% ( w/v ) BSA in PBS . Alexa488- and Alexa594-conjugated anti-rabbit and anti-rat antibodies , were used at dilutions of 1:500 ( in PBS containing 0 . 1% ( w/v ) BSA; ThermoFisher Scientific , USA ) . For flow cytometric examination of ectopic VSG 121 expression , cells were blocked and stained with the rabbit anti-VSG 121 ( 1:500 ) in suspension . An Alexa647-conjugated anti-rabbit antibody was used ( 1:500 in PBS containing 0 . 1% ( w/v ) BSA; ThermoFisher Scientific , USA ) and 20 , 000 cells per sample were counted with a BD Bioscience FACSCalibur Flow Cytometer . The data were analysed with the BD CellQuest Pro Software ( BD Bioscience , USA ) . For the detection of PAD1 , fixed cells were permeabilized for 20 minutes with 0 . 05% ( v/v ) Triton X-100 and incubated with PBS containing 20% ( v/v ) FCS for 45 minutes . Next , a rabbit anti-PAD1 antibody ( 1:100 in PBS containing 20% ( v/v ) FCS , [32] ) was added , followed by incubation with an Alexa594-conjugated anti-rabbit antibody ( 1:500 in PBS containing 20% ( v/v ) FCS; ThermoFisher Scientific , USA ) . Parasites isolated from tsetse flies were spread on poly-l-lysine-coated slides , dried and fixed for 30 seconds in ice-cold methanol . After rehydration for 15 minutes in PBS a mouse monoclonal anti-PFR antibody ( L8C4 , 1:20 in PBS containing 0 . 1% ( w/v ) BSA , [73] ) was added to the cells and an Alexa594-conjugated anti-mouse antibody ( 1:500 in PBS containing 0 . 1% ( w/v ) BSA; ThermoFisher Scientific , USA ) was used as secondary antibody . Images were acquired using the iMIC wide field fluorescence microscope ( FEI—TILL Photonics , Germany ) , equipped with a CCD camera ( Sensicam qe , pixel size 6 . 45 μm , PCO , Germany ) . Z-stack images were recorded using 100x ( NA 1 . 4 ) or 60x ( NA 1 . 45 ) objectives ( Olympus , Germany ) and the filter cubes ET-mCherry-Texas-Red , ET-GFP and DAPI ( Chroma Technology CORP , USA ) . All equipment was controlled with the ‘Live acquisition’ software ( TILL Photonics , Germany ) . The 3D images consisting of 100 slices with a z-step size of 100 nm are displayed as maximum intensity projections ( ImageJ ) . For signal quantifications images were deconvolved using Huygens Essential software ( Scientific Volume Imaging B . V . , Netherlands ) and intensities were measured in Z-projections ( method sum slices ) . Alternatively , cells were recorded at 100x magnification using the DMI6000B wide field fluorescence microscope ( LEICA microsystems , Germany ) , equipped with a DFC365FX camera ( pixel size 6 . 45 μm , LEICA microsystems , Germany ) . DIC images are average projections of 10–20 slices with a z-step size of 67 nm and fluorescent images maximum intensity projection . Images are shown in false colors with green fluorescence in green , blue in grey and red in magenta to ensure the availability of color information for individuals with color vision deficiencies [74] . Pseudocoloring , intensity projections and intensity measurements were performed using ImageJ software ( National Institutes of Health ) . | African trypanosomes escape the mammalian host’s immune system by antigenic variation of their variant surface glycoprotein ( VSG ) coat . VSGs are expressed from a specialized region in the genome , the expression site ( ES ) , that contains essential expression site associated genes ( ESAGs ) . So far , it was assumed that only successful antigenic switches to an intact expression site are viable . Here we show that unsuccessful VSG switches are not a dead-end , but may rather contribute to the persistence of the trypanosomes at the population level . We have simulated an unsuccessful VSG switch in pleomorphic trypanosomes by expression of a second VSG from a locus without ESAGs . The parasites responded with surprising phenotypic plasticity . All parasites immediately exchanged the surface coat and reduced the abundance of ES-derived transcripts . However , depending on the degree of ES-attenuation , the transgenic trypanosomes either resumed growth , or stopped proliferation . We show that the growth-arrested populations synchronously differentiate to the stumpy life cycle stage and become infective for the tsetse fly . This occurs at low cell densities and in the absence of the quorum sensing factor SIF . Thus , unsuccessful VSG switches are not lethal and cell density-dependent quorum sensing is not the only path to the tsetse fly competence . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"antimicrobials",
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"nuclear",
"staining",
"parasitic",
"cell",
"cycles",
"cell",
"cycle",
"and",
"cell",
"division",
"drugs",
"cell",
"processes",
"microbiology",
"cloning",
"parasitic",
"diseases",
"animals",
"... | 2017 | A quorum sensing-independent path to stumpy development in Trypanosoma brucei |
During Coxsackievirus B3 ( CVB3 ) infection hepatitis is a potentially life threatening complication , particularly in newborns . Studies with type I interferon ( IFN-I ) receptor ( IFNAR ) -deficient mice revealed a key role of the IFN-I axis in the protection against CVB3 infection , whereas the source of IFN-I and cell types that have to be IFNAR triggered in order to promote survival are still unknown . We found that CVB3 infected IFN-β reporter mice showed effective reporter induction , especially in hepatocytes and only to a minor extent in liver-resident macrophages . Accordingly , upon in vitro CVB3 infection of primary hepatocytes from murine or human origin abundant IFN-β responses were induced . To identify sites of IFNAR-triggering we performed experiments with Mx reporter mice , which upon CVB3 infection showed massive luciferase induction in the liver . Immunohistological studies revealed that during CVB3 infection MX1 expression of hepatocytes was induced primarily by IFNAR- , and not by IFN-III receptor ( IFNLR ) -triggering . CVB3 infection studies with primary human hepatocytes , in which either the IFN-I or the IFN-III axis was inhibited , also indicated that primarily IFNAR- , and to a lesser extent IFNLR-triggering was needed for ISG induction . Interestingly , CVB3 infected mice with a hepatocyte-specific IFNAR ablation showed severe liver cell necrosis and ubiquitous viral dissemination that resulted in lethal disease , as similarly detected in classical IFNAR-/- mice . In conclusion , we found that during CVB3 infection hepatocytes are major IFN-I producers and that the liver is also the organ that shows strong IFNAR-triggering . Importantly , hepatocytes need to be IFNAR-triggered in order to prevent virus dissemination and to assure survival . These data are compatible with the hypothesis that during CVB3 infection hepatocytes serve as important IFN-I producers and sensors not only in the murine , but also in the human system .
Coxsackievirus B3 ( CVB3 ) is a single-stranded RNA virus that belongs to the genus of human Enterovirus [1] . CVB3 infections are very common , especially in children and neonates , and mostly cause only mild disease . However , occasionally also severe disease with fatal outcome , such as myocarditis , meningoencephalitis , or hepatitis , can occur . In adults , only few cases of CVB3-induced hepatic necrosis have been reported [2 , 3] , whereas in neonates CVB3-induced hepatitis is more frequent [4] . Especially in Taiwan several outbreaks of CVB3 infections with predominant hepatitis and occasionally lethal outcome of up to 30% have been reported [5–7] . Recently , the analysis of murine neonates revealed that increased susceptibility to CVB3 correlated with high expression of the Coxsackievirus-adenovirus receptor in the liver , which decreased with age [8] . Moreover , lower expression of IFN-α during the first year of human life might contribute to increased infection susceptibility in infants [9] . Type I IFN receptor ( IFNAR ) -deficient ( IFNAR-/- ) mice succumbed to CVB3 infection within days [10] . Such mice showed early elevated serum markers of fulminant liver damage and high virus titers in the liver [10] . Similarly , also in CVB3-infected IFN-β-deficient mice elevated virus titers were detected [11] , whereas in these mice liver injury was not reported and mortality was related to cardiomyocyte necrosis . However , so far it remains elusive , which cells critically depend on IFNAR triggering in order to control liver infection . Furthermore , it is not known , which cell types produce the protective IFN-I . Recently , Lind et al . demonstrated reduced CVB3 infection of primary human hepatocytes after treatment with IFN-III [12] . However , whether IFN-III plays an important role in the pathogenesis of CVB3 infection is still elusive . Here , we investigated spatio-temporal conditions of IFN-β induction and IFNAR signaling during CVB3 infection , and we addressed the role of IFN-III . We found that hepatocytes , and not myeloid cells , were the main IFN-β producers that also had to be IFNAR triggered in order to restrict viral dissemination and to promote survival . Furthermore , in the murine system type III IFN receptor ( IFNLR ) signaling of hepatocytes was dispensable for the control of fulminant hepatitis and to resolve CVB3 infection . In vitro CVB3 infection experiments with primary human hepatocytes revealed a dominant role of the IFN-I axis for ISG induction .
To investigate spatio-temporal conditions of IFN-β induction upon CVB3 infection , IFN-β reporter mice [13] were intraperitoneally ( i . p . ) injected with 2 × 104 PFU CVB3 and in vivo imaging was performed . Already 1 day post infection ( dpi ) moderate luciferase induction was detected in the abdominal region , which reached a maximum 2 dpi and then declined ( Fig 1A and 1B ) . Other significant bioluminescence imaging signals were detected in the cervical region with a maximum on 4 dpi ( Fig 1A and 1B ) . To more specifically identify tissues showing reporter expression , infected reporter mice were perfused with PBS , organs were dissected , and luciferase expression was analyzed ex vivo in organ homogenates . At 2 dpi , robust bioluminescent imaging signals were detected in liver and pancreas , whereas in secondary lymphatic organs , salivary gland , and heart less abundant signals were observed ( Fig 1C ) . To investigate the expression of IFN-I and IFN-III , organ homogenates were assessed by ELISA . Reminiscent to the in vivo imaging data , we found moderate amounts of IFN-I in spleen and maximal levels in cervical lymph nodes ( cLN ) 3 to 4 dpi ( S1 Fig ) . In contrast , lysates of secondary lymphoid organs contained only minimal IFN-III levels ( S1 Fig ) . In the liver , peak levels of IFN-I and IFN-III were detected 3 and 2 dpi , respectively ( Fig 1D–1F ) . In pancreas , maximal IFN-β induction was observed 1 dpi , whereas IFN-α and IFN-III peaked on 3 dpi ( Fig 1D–1F ) . Thus , upon CVB3 infection abundant amounts of IFN-I and IFN-III were produced primarily in liver and pancreas . To address whether in the liver primarily hematopoietic cells or non-hematopoietic cells were triggered to mount IFN-β responses upon CVB3 infection , lethally irradiated C57BL/6 recipient mice were reconstituted with bone marrow ( BM ) from IFN-βwt/Δβ-luc mice ( IFN-βwt/Δβ-luc>C57BL/6 ) , which resulted in BM chimeric mice that carried the IFN-β reporter only in hematopoietic cells . In vivo imaging of such mice after CVB3 infection revealed moderate bioluminescence imaging ( BLI ) signals in the abdomen 2 dpi and in the cervical region 4 dpi ( Fig 2A and 2B ) . To investigate the contribution of non-hematopoietic cells , C57BL/6>IFN-βwt/Δβ-luc BM chimeric mice were studied . In such mice abundant BLI signals were observed in the liver that were similar to the ones detected in IFN-βwt/Δβ-luc>IFN-βwt/Δβ-luc control mice ( Fig 2A and 2B ) . Thus , upon CVB3 infection IFN-β expression within the liver was contributed primarily by non-hematopoietic cells . To further elucidate the identity of IFN-β expressing cells in the liver , myeloid- and hepatocyte-specific IFN-β reporter mice were analyzed ( LysM-Cre+/-IFN-βwt/floxβ-luc and Alb-Cre+/-IFN-βwt/floxβ-luc mice , respectively ) . CVB3-infected LysM-Cre+/-IFN-βwt/floxβ-luc mice showed moderate reporter induction in the abdominal region , whereas the induction in the cervical region was comparable with that detected in ubiquitous IFN-βwt/floxβ-luc reporter mice ( Fig 2C and 2D ) . These data indicated that in the cervical region , but not in the liver , IFN-β induction was conferred by myeloid cells . Upon CVB3 infection of Alb-Cre+/-IFN-βwt/floxβ-luc mice similar BLI signals were detected in the abdominal region as in IFN-βwt/Δβ-luc mice ( Fig 2E and 2F ) . Thus , within the liver primarily hepatocytes conferred IFN-β responses upon CVB3 infection . To assess sites at which IFN responses exhibit biological function , Mx2Luc-reporter mice carrying a firefly luciferase gene under the control of the IFN-I and IFN-III-responsive Mx2 promoter [14] were evaluated upon CVB3 infection . In line with previous reports [14] , constitutive expression of the reporter was observed in non-infected mice in the liver ( Fig 3A ) . Upon CVB3 infection , significant reporter gene induction was detected in the upper abdomen ( Fig 3A ) . Notably , in CVB3 infected Mx2Luc mice the kinetics of the reporter induction in the upper abdominal region was similar to that detected in IFN-β reporter mice ( compare Fig 1A and Fig 3A ) . Ex vivo analysis revealed Mx2-dependent reporter induction in all organs analyzed with the highest values detected in the liver ( Fig 3B ) . To investigate Mx induction on the cellular level , Mx1 mice carrying a functional Mx1 gene were used [15] . In contrast to uninfected controls ( S2A Fig ) , CVB3-infected Mx1 mice showed MX1 protein induction in hepatocytes and bile duct cells 2 dpi ( Fig 3C ) . Interestingly , no MX1 expression was detected in hepatocytes of CVB3-infected Mx1-IFNAR-/- mice ( Fig 3C ) indicating that in vivo IFNLR triggering of hepatocytes did not play a major role . In contrast , bile duct epithelial cells showed MX1 expression in CVB3-infected Mx1-IFNAR-/- mice , which indicated triggering by locally produced IFN-III . MX1 expression in hepatocytes and bile duct epithelial cells in infected Mx1-IFNLR-/- mice was similar to that detected in infected Mx1 mice ( Fig 3C ) showing that both hepatocytes and bile duct epithelial cells responded to IFN-I . As expected , neither hepatocytes nor bile duct epithelial cells showed MX1 protein induction in CVB3-infected Mx1-IFNAR-/-IFNLR-/- mice ( Fig 3C ) . Pancreatic acinar cells showed MX1 expression in infected Mx1-IFNLR-/- , but not in Mx1-IFNAR-/- mice ( Fig 3C ) . Thus , acinar cells responded to IFN-I , but not to IFN-III . In contrast , pancreatic duct epithelial cells showed MX1 protein induction in both Mx1-IFNLR-/- and Mx1-IFNAR-/- mice indicating that these cells responded to IFN-I and IFN-III ( Fig 3C ) . Moreover , liver and pancreas of CVB3 infected as well as uninfected Mx1-WT , Mx1-IFNAR-/- , Mx1-IFNLR-/- and Mx1-IFNAR-/-IFNLR-/- mice were analyzed for ISG15 expression by immunohistology ( S2B and S2C Fig ) . These data revealed an overall very similar picture as the analysis of MX1 expression . Since Mx1-IFNLR-/- mice were as resistant to CVB3 infection as Mx1 controls , whereas Mx1-IFNAR-/- and Mx1-IFNAR-/-IFNLR-/- mice succumbed to CVB3 infection ( Fig 3D ) , IFN-III did not seem to play a major role in the pathogenesis of murine CVB3 infection . Furthermore , histological analysis of the liver of infected Mx1-IFNLR-/- mice did not reveal inflammation and necrosis ( S3A and S3B Fig ) . Taken together , CVB3 induces non-redundant IFNAR signaling in hepatocytes and acinar cells , whereas in bile duct epithelial cells and pancreatic duct epithelial cells concomitant IFNAR and IFNLR signaling was detected . As similarly published before [10] , IFNAR-/- mice succumbed to CVB3 infection within days , whereas C57BL/6 control mice did not show overt signs of disease ( Fig 4A ) . Nevertheless , several WT control mice had to be sacrificed due to body weight loss of more than 20% ( Fig 4A ) . To investigate the impact of IFNAR signaling of myeloid cells within the liver , we generated LysM-Cre+/-IFNARfl/fl mice with a selective IFNAR deletion in myeloid cells . Upon CVB3 infection , most of these mice appeared overall healthy during the observation period , whereas some mice had to be sacrificed due to body weight loss of more than 20% ( Fig 4A ) . Thus , IFNAR signaling in myeloid cells did not play a dominant role in the protection against CVB3 infection . Likewise , also Alb-Cre+/-IFNARfl/fl mice with a hepatocyte-specific IFNAR-deletion were CVB3-infected and interestingly these mice showed a similarly enhanced sensitivity to CVB3 infection as detected in IFNAR-/- mice ( Fig 4A ) . In infected Alb-Cre+/-IFNARfl/fl and IFNAR-/- mice dramatically enhanced ALT levels were found ( Fig 4B ) , whereas infected WT controls and LysM-Cre+/-IFNARfl/fl mice showed moderately increased ALT levels ( Fig 4B ) . Thus , CVB3-induced hepatitis correlated with impaired IFNAR signaling of hepatocytes . In contrast , all mice of the different genotypes analyzed showed a similar extent of pancreatitis , as indicated by enhanced lipase levels , although the kinetics of lipase level increase was delayed in LysM-Cre+/-IFNARfl/fl mice ( Fig 4B ) . In conclusion , in WT mice the development of pancreatitis and massive pancreatic viral replication ( Fig 4C ) were independent of functional IFNAR signaling . Next we assessed the impact of IFNAR signaling of hepatocytes and myeloid cells on viral dissemination . In the liver of infected WT mice viral titers increased by approximately 6 log until 3 dpi , whereas in IFNAR-/- mice they increased by approximately 10 log ( Fig 4C ) . Notably , also Alb-Cre+/-IFNARfl/fl mice showed massively increased viral titers in liver , spleen , heart , salivary gland , and brain ( Fig 4C ) . In contrast , viral titers in organs of infected LysM-Cre+/-IFNARfl/fl mice were similar as in organs of C57BL/6 mice ( Fig 4C ) . Thus , IFNAR signaling of hepatocytes played a key role in restricting viral dissemination . Next , we assessed the impact of myeloid cell or hepatic IFNAR ablation on liver pathology upon CVB3 infection . In histological analysis no signs of liver damage were detected in CVB3-infected C57BL/6 mice , indicating that the moderately elevated ALT levels did not translate into histopathological liver damage ( Fig 5A and 5B ) . Similarly , also liver sections from infected LysM-Cre+/-IFNARfl/fl mice did not show pathological changes ( Fig 5A and 5B ) . In contrast , IFNAR-/- mice developed focal liver cell necrosis 2 dpi ( Fig 5A , line 1 ) and widespread and severe coagulative hepatic necrosis at 3 dpi ( Fig 5A , line 2 ) . Notably , also Alb-Cre+/-IFNARfl/fl mice developed liver cell necrosis ( Fig 5A , line 1 ) . Interestingly , necrosis in CVB3-infected IFNAR-/- and Alb-Cre+/-IFNARfl/fl mice was not accompanied by infiltration of immune cells ( Fig 5A , line 1 and 2 ) . Immunohistochemical analysis of the liver from CVB3-infected IFNAR-/- and Alb-Cre+/-IFNARfl/fl mice revealed preferential infection of hepatocytes , whereas hepatocyte infection was absent in C57BL/6 and LysM-Cre+/-IFNARfl/fl mice ( Fig 5A ) . The area of CVB3-infected liver tissue correlated with the severity of liver injury and was similar in Alb-Cre+/-IFNARfl/fl and IFNAR-/- mice ( Fig 5B ) . Interestingly , bile duct epithelial cells did not show any abnormalities in light microscopy and were not infected , even in mouse strains in which this cell subset was IFNAR-deficient ( Fig 5A , inserts ) . In accordance with the elevated lipase levels , all CVB3-infected genotypes displayed infection of pancreatic acinar cells and developed a widespread necrosis of the exocrine pancreas , while pancreatic islet and duct epithelial cells were not affected ( S4 Fig ) . Taken together , IFNAR signaling of hepatocytes was necessary to prevent hepatocyte infection and necrosis , whereas necrosis of the exocrine pancreas was not affected by IFNAR signaling . To further address whether the IFN-β detected in liver homogenates originated from hepatocytes , as implied by the in vivo imaging results ( Fig 2 ) , primary murine hepatocytes were infected with CVB3 and analyzed for IFN-I and IFN–III mRNA and protein expression . Of note , the preparation protocol yielded hepatocytes with a purity of approximately 90% , as determined by FACS analysis ( S5A and S5B Fig ) . Upon CVB3 infection , primary murine hepatocytes indeed showed Ifn-β mRNA upregulation , whereas upregulation of Ifn-α or Ifn-λ2/3 mRNA was not detected ( Fig 6A ) . Moreover , also the expression of Isg15 was upregulated , indicating autocrine IFNAR triggering of the hepatocytes ( Fig 6A ) . In accordance with these results , IFN-β but not IFN-α or IFN-λ protein levels were detected in supernatants ( Fig 6B ) . To study conditions in the human system , primary human hepatocytes were CVB3 infected . Indeed , the virus replicated in these cells , as indicated by increased virus titers detected in the supernatant after 12 , 24 , and 48 hpi ( S5D Fig ) . Similar to primary murine hepatocytes , qPCR analysis of primary human hepatocytes 24 hr after CVB3 infection showed upregulation of Ifn-β and of Isg15 mRNA expression , but not of Ifn-α mRNA expression ( Fig 6C ) . In contrast to murine hepatocytes , human hepatocytes additionally showed upregulated Ifn-λ1 mRNA expression ( Fig 6C ) . This observation was in accordance with a previous study that showed IFN-λ production of CVB3 infected primary human hepatocytes that conferred ISG induction and mediated antiviral protection [12] . We detected only very low amounts of IFN-β protein in the supernatant of CVB3 infected primary human hepatocytes , while IFN-III levels were very abundant ( Fig 6D and S5F Fig ) . To further address the relative functional relevance of the IFN-I vs . the IFN-III axis during CVB3 infection of primary human hepatocytes , we inhibited the IFN-I and/or the IFN-III axis by treatment with either recombinant B18R , the soluble IFN-I receptor of vaccinia virus Ankara , and/or recombinant anti-interferon lambda receptor 1 antibody ( αIFNLR ) , respectively . In control experiments human hepatocytes were stimulated with either 1 ng/mL recombinant IFN-β or 100 U/mL IFN-α , or 10 ng/mL IFN-λ1 , 10 ng/mL IFN-λ2 , or 30 ng/mL IFN-λ3 and treated with recombinant B18R and/or recombinant anti-interferon lambda receptor 1 antibody ( αIFNLR ) . As expected , B18R and αIFNLR treatment efficiently inhibited IFN-I and IFN-III mediated Mx and ISG15 induction in hepatocytes , whereas B18R and/or αIFNLR treatment alone did not have any effect ( S5C Fig ) . Next we CVB3 infected human hepatocytes for 2 hr , washed with PBS , treated with B18R and/or αIFNLR and then analyzed the RNA induction by qPCR . These experiments revealed that infected human hepatocytes showed abundant Mx induction at 12 , 24 , and 48 hpi , whereas B18R treatment reduced this induction , while αIFNLR had only a minor effect ( Fig 6E and S5E Fig ) . To further study the relative effects of IFN-I and IFN-III on human hepatocytes upon CVB3 infection , RNAseq was performed on total RNA samples of two donors 24 hpi . The 5 most abundantly induced ISGs are shown ( Fig 6E ) . As observed before , B18R treatment resulted in the reduction of ISG induction , while αIFNLR treatment had only a modeate effect . Upon combined treatment with B18R and αIFNLR the ISG induction was almost comparable with uninfected controls ( Fig 6F ) . These data further underscored the strong antiviral effect of IFN-I on CVB3 infected primary hepatocytes , both in the murine as well as the human system .
Several studies demonstrated a predominant role of IFN-I and IFNAR signaling in the pathogenesis of CVB3-induced hepatitis [16] . However , the cellular subsets that contribute to these effects are not known . Therefore , in the current study we aimed at identifying cell subsets that mount protective IFN-I responses and show IFNAR triggering upon CVB3 infection . In CVB3-infected mice , we found abundant IFN-β , IFN-α , and IFN-λ responses in the liver , of which IFN-β was produced primarily by hepatocytes . Moreover , hepatocytes showed exclusively IFNAR- and not IFNLR-dependent ISG induction in mice . Interestingly , IFNAR triggering of hepatocytes prevented severe liver cell necrosis and dissemination of the virus to other organs . CVB3 infection of human hepatocytes induced IFN-β as well as IFN-λ responses , whereas ISG expression was mainly triggered by IFN-β . These results indicate that during CVB3 infection similar mechanisms apply in the murine and the human system . Consistent with previous reports that following CVB3 infection of mice Ifn-β mRNA expression was detected in the liver [17] , we found early and strong induction of IFN-β protein in liver lysates . Because within the liver hepatocytes as well as myeloid cells , such as Kupffer cells or infiltrating pro-inflammatory monocytes , have been reported to be able to mount IFN-β responses [18 , 19] , we addressed which of the two subsets accounted for the IFN-β responses upon CVB3 infection . Our studies with BM chimeric mice , in which either hematopoietic or non-hematopoietic cells reported on IFN-β expression , as well as experiments with hepatocyte- and myeloid cell-specific conditional IFN-β reporter mice , indicated that upon CVB3 infection IFN-β induction in the liver was contributed mainly by hepatocytes , and to a minor extent by myeloid cells . In line with that , ex vivo isolated primary murine hepatocytes mounted substantial IFN-β responses . Thus , our data indicate that in CVB3 infection hepatocytes are important IFN-β producers in the liver . In a recent study , CVB3-infected mice with a cardiomyocyte-specific IFNAR ablation showed enhanced virus replication in the heart [20] . However , in that study it was not addressed whether locally or extra-cardiacally produced IFN-I conferred protective effects in cardiomyocytes . Here , we detected only very minor IFN-β induction in the heart , whereas early and strong IFN-I responses were found in the liver , suggesting that hepatic IFN-I responses determined whether peripheral organs , such as the heart , were assaulted or not . In addition to IFN-β , we found early and significant IFN-α levels in liver lysates that were particularly abundant on 2 dpi , when IFN-α was also detected at low level in the serum ( S1D Fig ) . Since plasmacytoid dendritic cells ( pDC ) have been reported to be main IFN-α producers in the liver [21] , it is possible that hepatic IFN-α was derived from local pDC . However , upon in vitro incubation with CVB3 , human and murine pDC were unable to mount strong IFN-α responses in the absence of opsonizing CVB3 antibodies [22] . Since it is unlikely that CVB3-specific antibodies are present as early as 2 dpi , this raises the question whether pDC could be activated in vivo by other mechanisms . Indeed , for other viruses it was shown that direct virus infection of pDC was not required to trigger IFN-α responses [23] . IFN-α induction was even stronger when pDC were stimulated by infected cells [24] . Thus , CVB3-infected hepatocytes might be able to trigger IFN-α production by pDC , as previously shown for HCV-infected cells [25] . Finally , also extrahepatically produced IFN-α can reach the liver via the circulation . Livers of untreated mice showed constitutive Mx2 induction that was further enhanced after CVB3 infection . Mainly hepatocytes and bile duct epithelial cells , but not immune cells , showed MX1 and ISG15 protein induction . MX1 and ISG15 upregulation in hepatocytes was IFNAR- , and not IFNLR-dependent , whereas bile duct epithelial cells showed IFNAR- and IFNLR-dependent MX1 and ISG15 upregulation . The moderately enhanced ISG15 induction in CVB3 infected Mx1-IFNAR-/-IFNLR-/- mice can be explained by direct ISG15 induction through pattern recognition receptor engagement by virus associated molecular patterns , as similarly detected for other pathogens [26] . Our data highlighting the relevance of IFNAR signaling on hepatocytes during CVB3 infection are in accordance with a previous report in which upon infection with a hepatotropic influenza A virus strain MX1 induction in hepatocytes was also independent of IFNLR triggering [27] . Similar to previous studies , we also observed virus dissemination and severe liver pathology in CVB3-infected IFNAR-/- mice [10] . Liver pathology was characterized by severe necrosis and infection of hepatocytes . IFNAR-triggering of myeloid cells was shown to be crucial for the control of many different virus infections [28–31] . Nevertheless , mice with a selective IFNAR ablation on myeloid cells showed normal survival of CVB3 infection and only moderate or no hepatitis , as similarly detected in WT mice . In contrast , mice with a hepatocyte-specific IFNAR-deletion showed increased virus titers in the liver and the virus disseminated to other fully IFNAR competent peripheral organs , as similarly observed in IFNAR-/- mice that show an ubiquitous IFNAR deletion . Furthermore , it was shown that mice with a cardiomyocyte-specific IFNAR ablation had only increased viral titers in the heart , but not in other peripheral organs tested [20] . These data are compatible with the model that IFNAR signaling of hepatocytes is essential to control CVB3 infection and to prevent its dissemination . Therefore , it is possible that the responsiveness of hepatocytes to CVB3 infection as well as the IFN-I responsiveness of hepatocytes affect the individual risk to develop myocarditis . These data concur with the earlier observation that CVB3-infected IFN-β-/- mice showed higher virus titers in the liver than WT mice [11] . However , in CVB3-infected IFN-β-/- mice no hepatitis was detected , suggesting that under such conditions IFN-α responses modulated hepatitis and thus compensated the IFN-β deficiency . Interestingly , in hepatic necrosis of CVB3-infected IFNAR-/- and Alb-Cre+/-IFNARfl/fl mice we observed only very few infiltrating immune cells . This absence of infiltrating immune cells in hepatic necrosis was also detected in fatal cases of CVB3-infected neonates [3 , 4] . A similar phenotype was also described for CVB3-infected mice with an ablation either of the cytoplasmic dsRNA sensor melanoma differentiation-associated protein 5 ( MDA5 ) or its adaptor MDA-5-transducing mitochondrial antiviral signaling protein ( MAVS ) [16 , 17] . It was speculated that in MAVS-/- mice the induction of reduced IFN-I levels was responsible for the development of hepatocyte necrosis [16] , which suggested the need of MAVS-dependent signaling in hepatocytes for the induction of IFN-I responses . Interestingly , upon CVB3 infection WT , IFNAR-/- , Alb-CreIFNARfl/fl , and LysM-CreIFNARfl/fl mice showed high viral loads in the pancreas and all the different mouse strains tested displayed similar signs of pancreas injury ( elevated lipase levels ) at 2 dpi . Liu et al . [32] published earlier that the PKR-like endoplasmic reticulum kinase ( PERK ) induces degradation of the IFNAR1 chain in VSV and HCV infection , which leads to unresponsiveness to IFN-I upon virus infection . An important role of PERK action was observed in the pancreas . Mice treated with PERK inhibitor showed pancreas injury , which was due to a pathological IFNAR signaling [33] . In contrast to IFNAR-mediated protection against viral hepatitis , Bhattacharya et al . reported detrimental effect of IFNAR-signaling in LCMV-induced hepatitis [34] . Mice with a selective IFNAR ablation in hepatocytes were protected from LCMV-induced hepatitis , whereas in WT mice hepatitis was induced by oxidative damage . However , hepatitis in LCMV-infected WT mice was moderate and without associated hepatocyte necrosis [34] , whereas in our model CVB3 infection resulted in fulminant hepatitis with hepatocyte necrosis . In mice IFNAR , and not IFNLR , signaling mediated critical ISG induction in hepatocytes , whereas in humans the situation might be different . Recently , it was shown that primary human hepatocytes expressed IFNLR and responded in vitro to IFN-III treatment with ISG induction that inhibited CVB3 replication [12] . We showed that CVB3 infected human hepatocytes expressed IFN-β as well as IFN-λ , and showed enhanced ISG expression . Furthermore , we detected low protein levels of IFN-β and higher protein concentrations of IFN-III in the supernatant of CVB3 infected human hepatocytes . Despite the higher concentration of IFN-III , B18R treatment of CVB3 infected hepatocytes reduced the ISG induction in CVB3 infected human hepatocytes , whereas the inhibition of the IFN-III axis had only very minor effects . Bolen et al . [35] compared the reactivity of similar amounts of IFN-I and IFN-III proteins on primary human hepatocytes . They found that regarding ISG induction IFN-β had the highest potency , followed by IFN-α , IFN-λ3 , IFN-λ1 , and IFN-λ2 . This observation offers an explanation for the dominant effects of IFN-I in our experiments . Since we could only detect very minor luciferase induction in the heart of CVB3 infected IFN-βwt/Δβ-luc reporter mice , it remains elusive whether local IFN-I production would suffice to induce protective effects in cardiomyocytes [20] . Hepatocyte-derived IFN-β presumably exhibits biological effects also outside the liver , e . g . in the myocard . However , the relevance of IFN-I vs . IFN-III in the human liver during CVB3 infection needs to be better understood before IFN-I and/or IFN-III can be used for the therapy of CVB3-induced hepatitis or myocarditis . In conclusion , our data highlight hepatocytes as innate effector cells in CVB3 infection . Our observation that hepatic IFN-I is critical to control CVB3 infection encourages to search for individual variations in the IFN-I induction and IFNAR signaling pathways in fatal cases of neonatal hepatocyte necrosis . Furthermore , the impact of hepatic IFN-β in myocarditis and other CVB3-associated diseases has to be further analyzed .
IFNAR1 deficient mice [36] ( B6 . 129S2-Ifnar1tm1 ( Neo ) Agt referred to as IFNAR-/- ) were used that were more than 10 times backcrossed to the C57BL/6JOlaHsd background . IFN-βwt/Δβ-luc mice , in which the floxed IFN-β is deleted and thus the reporter is expressed ubiquitously ( B6 . Bruce4-Ifnbtm2 . 2 ( luc ) Lien [13] ) and Mx2Luc BAC transgenic reporter mice [14] used for in vivo imaging were backcrossed to the albino ( Tyrc2J ) C57BL/6 background [37] . Conditional IFN-βfloxβ-luc/floxβ-luc reporter mice ( B6 . Bruce4-Ifnbtm2 . 1 ( luc ) Lien [13] ) as well as IFNARfl/fl mice ( B6 . 129SV-Ifnartm ( flox ) kal [38] ) were intercrossed with LysM-Cre ( B6 . 129P2-Lyz2tm1 ( cre ) Ifo [39] ) and Alb-Cre mice ( B6 . Cg-Tg ( Alb-cre ) 21Mgn [40] ) to generate myeloid cell- and hepatocyte-specific IFN-β reporter or IFNAR-deficient mice , respectively . Mx1 mice ( B6 . A2G-Mx1 ) carried intact Mx1 alleles [41] , whereas Mx1-Ifnar1-/- mice additionally lacked the type I IFN receptor ( B6 . A2G-Mx1-Ifnar1-/- , referred to as Mx1-IFNAR-/- ) , B6 . A2G-Mx1-Il28rα-/- mice lacked the type III IFN receptor ( referred to as Mx1-IFNLR-/- ) and Mx1-Ifnar1-/-Il28rα-/- were double-deficient [15] ( referred to as Mx1-IFNLR-/-IFNAR1-/- ) . For experiments , 8–14 week-old mice were used . Mice were kept under specific pathogen-free conditions on regular diet in the central mouse facility of the Helmholtz Centre for Infection Research , Braunschweig , and at TWINCORE , Centre of Experimental and Clinical Infection Research , Hannover , Germany . C57BL/6 mice were purchased from Envigo . CVB3 ( strain Nancy ) was generated from infectious clone p53CB3/T7 [42] and passaged twice in Green monkey kidney cells ( Vero cells , ATCC CCL-81 ) . Virus titer was determined by plaque formation on Vero cells . Vero cells were grown to confluence in 6-well plates in MEM supplemented with 10% FCS and 1% GlutaMAX . Organs were homogenized using a FastPrep-24instrument ( MP Biomedicals , Eschwege , Germany ) and serial 10-fold dilutions were titrated on Vero cell monolayers . After 24 hr incubation at 37°C the cells were fixed with 5% ( w/v ) TCA for 2 hr at room temperature , and the monolayer was stained using 0 . 5% crystal violet dissolved in 5% formaldehyde , 50% ethanol , and 0 . 8% NaCl . For flow cytometry anti-CD45-Alexa Fluor 700 ( BD Biosciences , Franklin Lakes , USA ) was used . Data acquisition was performed using a LSR-II flow cytometer ( BD Biosciences , Franklin Lakes , USA ) . Data were analyzed with FlowJo software ( Tree Star , Ashland , USA ) . BM cells were isolated from femur and tibia . Red blood cells were removed by RBC lysis buffer ( Sigma-Aldrich , St . Louis , USA ) . For BM transplantation , mice were lethally irradiated with 9 Gy and received 1 x 107 BM cells via tail vein injection . Mice were used 8 weeks after transplantation . Organs from perfused mice were dissected and homogenized in 20 mM Tris-HCl ( pH7 . 3 ) containing 140 mM NaCl , 0 . 5% Triton X-100 , 2 mM Na3VO4 , and protease inhibitor ( complete ULTRA , Sigma Aldrich , St . Louis , USA ) . IFN-α , IFN-β , and IFN-λ3 levels were determined from murine homogenates , serum , and supernatants of murine hepatocytes by ELISA methods following the manufacturer’s instructions ( IFN-α ELISA and IFN-β ELISA: PBL Biomedical Laboratories , Piscataway , USA; IFN-λ3 ELISA: eBiosience , Thermo Scientific , Waltham , USA ) . IFN α , IFN-β , and IFN-λ1/2/3 levels were determined from supernatants of human hepatocytes by ELISA methods following the manufacturer’s instructions ( IFN-λ1/2/3 ELISA and High sensitivity IFN-β ELISA: PBL Biomedical Laboratories , Piscataway , USA; IFN-α ELISA: eBiosience , Thermo Scientific , Waltham , USA ) . Isoflurane anesthetized mice were intravenously injected with luciferin ( 100 μL of 30 mg/mL per 20 g mouse weight ) and imaged on an IVIS Spectrum CT ( PerkinElmer , Waltham , USA ) . Living Image 4 . 5 software was used for data analysis . For ex vivo detection of luciferase activity , the respective organs were homogenized in Glo Lysis Buffer ( Promega , Fitchburg , USA ) and bioluminescence activity was detected with a bright Glo Luciferase Assay System ( Promega , Fitchburg , USA ) . Mice were perfused with PBS , organs were removed , and fixed in 4% formalin and embedded in paraffin wax . Tissues were cut at 2–3 μm thickness and sections were placed on SuperFrost Plus slides ( Menzel GmbH , Thermo Scientific , Waltham , USA ) . For histology , a hematoxylin and eosin-staining was performed . Primary antibodies used for immunohistochemistry included rabbit polyclonal anti-mouse MX1 ( 1:8000 , [43] ) , rabbit polyclonal anti-ISG15 ( 1:750; PB9951 , Boster Biological Technology , Pleasanton , CA , USA ) , and monoclonal mouse anti-CVB3 mAB ( clone 31A2 , 1:2000 , Mediagnost , Tübingen , Germany ) . Of note , the mouse anti-CVB3 mAb has a lower sensitivity in detecting CVB3 infection than the plaque formation assay . For antigen retrieval , samples were incubated for 30 min in the microwave with citrate buffer . For visualization of the polyclonal and monoclonal antibodies , the ABC-method ( VECTASTAIN Elite ABC HRP Kit , Vector Laboratories Inc , Burlingame , USA ) and the EnVision+ System-HRP ( Dako/Agilent , Santa Clara , USA ) was used , respectively . For the MX1 and ISG15 immunohistochemistry , a secondary goat-anti-rabbit antibody [43] was applied . CVB3 immunopositive area was quantified with AnalySIS 3 . 2 software ( SOFT Imaging system , Münster , Germany ) on a digital photomicrograph . Artefacts were manually outlined and excluded . The positive area was calculated as percentage of the outlined immunopositive area . In addition , the intensity of ISG15 staining in liver and pancreas was quantified using a semiquantitative scoring system: 0 = no staining , 1 = mild staining , 2 = moderate staining , 3 = strong staining . RNA was extracted from primary human hepatocytes using NucleoSpin RNA Isolation kit ( Macherey-Nagel , Germany ) following the manufacturer’s instructions . RNA was reverse transcribed into cDNA using Prime-Script First Strand cDNA Synthesis Kit ( TaKaRa , Kyoto , Japan ) according to the manufacturer’s instructions . Primers and SYBR Green ( Bioline , London , UK ) were added to cDNA , and quantitative real-time PCR ( qPCR ) was carried out . All samples were measured as triplicates and PCR reactions were run in a LightCycler 480 ( Roche , Basel , Switzerland ) . Target genes were normalized to the housekeeping gene hypoxanthine phosphoribosyl transferase 1 ( HPRT1 ) . The following primers were used in this study for human hepatocytes: IFNB1 ( TGTGGCAATTGAATGGGAGGCTTGA; TCAATGCGGCGTCCTCCTTCTG ) , IFNA ( CGATGGCCTCGCCCTTTGCTTTA; GGGTCTCAGGGAGATCACAGCCC ) , IFNL1 ( AGCTTGGACCGTGGTGCTGGT; TCCAAGGCGTCCCTGGCCTTC ) , ISG15 ( TGTCGGTGTCAGAGCTGAAG; AGAGGTTCGTCGCATTTGTC ) Mx1 ( ACAGGACCATCGGAATCTTG; CCCTTCTTCAGGTGGAACAC ) , and HPRT1 ( GAACGTCTTGCTCGAGATGTG; CCAGCAGGTCAGCAAAGAATT ) and murine hepatocytes: IFNb ( CTGGCTTCCATCATGAACAA; CATTTCCGAATGTTCGTCCT ) , IFNa2 ( TACTCAGCAGACCTTGAACCT; CAGTCTTGGCAGCAAGTTGAC ) , IFNl2/3 ( AGCTGCAGGCCTTCAAAAAG; TGGGAGTGAATGTGGCTCAG ) , ISG15 ( GAGCTAGAGCCTGCAGCAAT; TTCTGGGCAATCTGCTTCTT ) , and GAPDH ( GTGGCAAAGTGGAGATTGTT; CTT GACTGTGCCGTTGAATT ) . For RNA sequencing ( RNAseq ) quality and integrity of total RNA was controlled on Agilent Technologies 2100 Bioanalyzer ( Agilent Technologies; Waldbronn , Germany ) . The RNAseq library was generated from 500 ng total RNA using Dynabeads mRNA DIRECT Micro Purification Kit ( ThermoFisher ) for mRNA purification followed by ScriptSeqv2 RNA-SeqLibrary Preparation Kit ( Epicentre ) according to manufacture´s protocols . The libraries were sequenced on Illumina HiSeq2500 using TruSeqSBS Kit v3-HS ( 50 cycles , single ended run ) with an average of 3 x107 reads per RNA sample . After thorough quality control , single reads were mapped to the human genome ( hg19 ) using CLC Genomics Workbench v11 . 0 . 1 ( Qiagen ) with standard parameters . Normalized transcript expression is given as reads per kilobase of transcript , per million mapped reads ( RPKM ) . A subset of 268 potential interferon regulated genes was generated by filtering transcript lists using the interferome database ( www . interferome . org ) search mask with filters on in vivo | in vitro ( in vivo ) , species ( homo sapiens ) , system ( gastrointestinal tract ) , organ ( liver ) , cell ( hepatocytes ) and a two-fold change up or down . For visualization of data GraphPad Prism version 7 . 04 for Windows ( GraphPad Software , La Jolla California USA , www . graphpad . com ) was employed . Murine hepatocytes were isolated from C57BL/6 mice using a 2-step liberase ( Sigma-Aldrich ) perfusion as described previously [44 , 45] and cultured in Primaria 6-well plates ( BD Biosciences , Franklin Lakes , USA ) at a density of 1 × 106 cells per well in 2 mL of HBM Basal Medium supplemented with HCM SingleQuots ( Lonza , Basel , Switzerland ) . Hepatocytes were infected with CVB3 at MOIs 0 . 01 , 0 . 1 , and 1 in dublicates or triplicates and incubated for 24 hr at 37°C . Dublicates and triplicates were averaged . Human primary hepatocytes were isolated from liver specimens obtained after partial hepatectomy and cultivated as described previously ( obtained from Dr . med . F . W . R . Vondran , Hannover Medical School ) [46] . Human hepatocyte cultures were infected with CVB3 at MOIs of 0 . 1 , as well as 0 . 3 and incubated for 24 hr at 37°C . 2 hr after infection or mock treatment hepatocytes were washed with PBS and overlayed with 500 μL fresh medium . As control uninfected cells were treated with 1 ng/mL αIFNLR ( PBL Biomedical Laboratories , Piscataway , USA ) or/and 100 ng/mL B18R ( eBiosience , Thermo Scientific , Waltham , USA ) and/or stimulated with 1 ng/mL recombinant IFN-β ( PeproTech , Hamburg , Germany ) , 100 U/mL IFN-α ( PeproTech EC , London , Great Britain ) , 10 ng/mL IFN-λ1 ( PeproTech , Hamburg , Germany ) , 10 ng/mL IFN-λ2 ( PeproTech , Hamburg , Germany ) , or 30 ng/mL IFN-λ3 ( kindly provided by Rune Hartmann ) . Infected hepatocytes were mock treated or incubated with 1 ng/mL αIFNLR or/and 100 ng/mL B18R . Upon 12 , 24 , and 48 hr incubation at 37°C the supernatants and cells were harvested . Dublicates were averaged . Serum alanine aminotransferase ( ALT ) and lipase levels were determined using commercially available kits from Fuji DRI-CHEM NX500 . Kaplan-Meier product-limit method was used to calculate survival rates . Differences between the groups were determined using log-rank statistics . Statistical analyses were performed by One-Way ANOVA or Mann-Whitney test . A P value < 0 . 05 was considered statistically significant . For statistical analysis , GraphPad Prism Version 6 . 0 ( Graph-Pad ) was used . | CVB3 belongs to human enteroviruses and is transmitted through the fecal-oral route . Infections with CVB3 are mostly unnoticed or cause flu-like symptoms , however , they can also cause severe disease , such as myocarditis , pancreatitis , and hepatitis . Although CVB3 does not efficiently trigger plasmacytoid dendritic cells , which are the main IFN-I producers in many other virus infections , IFNAR signaling plays a crucial role in CVB3 control . Therefore , we investigated which cells are stimulated to produce IFN-I following CVB3 infection and which cell types have to be IFNAR-triggered in order to confer anti-viral protection . We found that upon CVB3 infection IFN-β was mainly expressed within the liver , especially by hepatocytes and not by liver resident macrophages . This was corroborated by in vitro CVB3 infection experiments with primary murine and human hepatocytes . Interestingly , IFNAR signaling of hepatocytes was required to control the virus . Collectively , our data indicate that hepatocytes , and not immune cells , are the key innate effector cells that are relevant for the control of CVB3 infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"liver",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"in",
"vivo",
"imaging",
"epithelial",
"cells",
"bone",
"marrow",
"cells",
"signs",
"and",
"symptoms",
"immunologic",
"techniques",
"resear... | 2018 | Interferon-beta expression and type I interferon receptor signaling of hepatocytes prevent hepatic necrosis and virus dissemination in Coxsackievirus B3-infected mice |
Recent studies suggest that motor adaptation is the result of multiple , perhaps linear processes each with distinct time scales . While these models are consistent with some motor phenomena , they can neither explain the relatively fast re-adaptation after a long washout period , nor savings on a subsequent day . Here we examined if these effects can be explained if we assume that the CNS stores and retrieves movement parameters based on their possible relevance . We formalize this idea with a model that infers not only the sources of potential motor errors , but also their relevance to the current motor circumstances . In our model adaptation is the process of re-estimating parameters that represent the body and the world . The likelihood of a world parameter being relevant is then based on the mismatch between an observed movement and that predicted when not compensating for the estimated world disturbance . As such , adapting to large motor errors in a laboratory setting should alert subjects that disturbances are being imposed on them , even after motor performance has returned to baseline . Estimates of this external disturbance should be relevant both now and in future laboratory settings . Estimated properties of our bodies on the other hand should always be relevant . Our model demonstrates savings , interference , spontaneous rebound and differences between adaptation to sudden and gradual disturbances . We suggest that many issues concerning savings and interference can be understood when adaptation is conditioned on the relevance of parameters .
There is a large body of evidence to suggest that the nervous system maintains internal representations of variables that are relevant to the production of movement [1] , [2] , [3] . Internal models allow us to make repeatable and reliable movements despite a highly variable world and body , and our noisy perceptions of them . Ideally , these internal models ought to distinguish between the properties of the body and world , a crucial ability when generalizing movements [4] . Such a representation requires many parameters to represent how to control the body when interacting with external objects in the world . This in turn implies that many parameters of both the body and the world need to be estimated . When estimating changes in the many parameters necessary to describe the interaction of the body and the world , it seems sensible that some of these parameters will change rapidly , while others change more slowly . Consequently a number of recent studies have constructed linear time invariant models that model adaptation unfolding over multiple time scales [e . g . 5 , 6 , 7] . These models have explained a wide range of temporal adaptation and savings phenomena . While many linear models can explain motor phenomena associated with rapid re-adaptation , they are limited in their ability to explain phenomena of even short-term adaptation , as in savings after “washout” trials [e . g . 8] , let alone the long-term effects of adaptation . For instance , linear models predict that aftereffects should decay with the same rate behaviors are adapted to , in contrast with experimental evidence [9] , [10] . Linear models also predict that once a disturbance has been removed , its influence on movement is de-adapted and completely forgotten . This is clearly not the case , and subjects retain the ability to compensate for previously adapted behaviors over long periods of time [11] , [12] , [13] . In summary , while there are clearly multiple time scales at work , a linear time invariant process is not capable of explaining motor adaptation . Since neither washout nor intervening days delete motor adaptations , there must be some mechanism that guards newly adapted parameter values against de-adaptation when they are no longer relevant . Motor architectures that can guard entire forward and inverse models of limb dynamics by switching them on and off have been proposed [14] , [15] , [16] . However , it is unclear how these models can account for the patterns of apparently incomplete generalization observed experimentally [4] . What's more , these models do not make a distinction between the parameters of the body and the world , but rather estimate when an appropriate model of the coupled body and world dynamics is applicable . In contrast , we propose the nervous system should separately estimate the properties of the body and the world and when those individual parameters are relevant for control . For example , our estimate of a coffee cup's weight is only relevant while we are holding it and not after we have set it down on the table . Estimates of our arm's weight , on the other hand , are always relevant for limb movements . Conditioning on such obvious relevance the nervous system can know not to adapt estimates of the cup's weight unless we are holding it . Parameter relevance , however , is not always this obvious . If this relevance could be estimated , then the nervous system could guard newly adapted behaviors and later retrieve them when they are relevant again . To examine this idea , we designed an idealized model for computing the probability of relevance , and then using this estimated relevance to adapt . In a previous study we proposed a statistical inference model for motor adaptation that estimated a large number of parameters for the body and the world [4] . In a different study we proposed that the nervous system constantly estimates the relevance of errors for motor adaptation [17] . Here we combine these two approaches . We assume that parameters associated with the body are always relevant , whereas world parameters are only relevant under specific conditions . If the probability of a parameter's relevance is high , then it is subject to adaptation . If not , the motor errors may be due to sensorimotor noise or changes in body parameters . In contrast with the coffee cup example , the kind of experimental disturbances subjects are exposed to are not as evident . Therefore , we estimate relevance using a model that can predict the consequences of a class of world disturbances . As such , relevance defined here does not depend on a particular parameter value , but rather the particulars of that type of parameter's influence on motor behaviors . When movement patterns are consistent with a large world disturbance , regardless of the observed movement error ( see Fig 1 ) , then the likelihood of that parameter being relevant is high . For example , if the presence of a coffee cup in our hand , any coffee cup , can account for unexpected limb motions and forces on our hand , then parameters representing the cup's inertial properties should be subject to adaptation . If not , then those parameters should not be updated . In effect this allows for a rudimentary long-term memory , allowing for the retention and later retrieval of newly acquired world parameter values . We simulated a series of experiments to investigate how our model behaves when adapting to multiple motor behaviors in succession . The model was restricted to four free parameters , which were held constant for all simulations . The models' predictions are consistent with the findings of savings , interference , spontaneous rebound and the differences between adaptation to gradual and abrupt disturbances . Our model offers a formalization of how the nervous system may estimate and store motor parameters when adapting to disturbances .
The model used here is based on that used in a previous study [see 4 for details and code] . Briefly , the human upper limb is modeled as a nonlinear 2-link , 2 degree of freedom mechanism driven by feedforward torque components to compensate for estimated world and body dynamics , plus a feedback component to stabilize movements about a nominal , minimum jerk trajectory . For the results shown here only two parameters were inferred , a body-centric visuomotor rotation θb ( due to some possible combination of proprioceptive errors and relative head or torso rotations ) and a world-imposed visuomotor rotation , θw , the experimental disturbance of the cursor . The system observation , y ( t ) , is the visually observed ( displayed cursor ) position vector , x and velocity vector , dx/dt of the limb's endpoint ( or hand ) in a Cartesian reference frame , y = [x ( t ) , dx ( t ) /dt]T . We assume this observation is corrupted by measurement noise , n ( t ) , with zero mean and covariance R . We collate the parameters to be estimated in the vector , p = [θb , θw]T . To infer these parameters , we assume that they vary according a random walk , with a small forgetting factor , where wi is a zero mean random variable drawn from a normal distribution with variance , σi2 . These parameters influence the nonlinear dynamics of the limb , and the subsequent effects on movement are then observed in the output , y . However , we assume that influences of the world parameter , θw , are only observed when the limb is perturbed . To denote this state of being perturbed by a visuomotor rotation , we define the relevance variable , λrot . The variable can take on one of two values , one or zero . If a world rotation parameter is relevant then the relevance variable is one , if not , zero . The system's output then depends on the relevance in the following way , where y ( θ , t ) is shorthand for the observed output when visually rotated by θ . Though binary , we assume that the relevance parameter is also Markovian , and has a small non-zero probability of transitioning from one value to the other . We define a transition model ( or mixing matrix ) , M = [0 . 999 0 . 001; 0 . 001 0 . 999] , ensuring our prior probability of relevance never becomes fixed at 0 or 1 . In total the model has 4 free parameters , ab and aw , σb and σw . However , to assure that estimates of the world would be retained over long periods of time , we held aw fixed at 0 . 99999 . The model uses its observations of the limb's endpoint ( y ) to infer the probability that an external parameter is relevant and update its belief in the parameters in p . In this study we were focused on the interference and savings of visuomotor adaptations , therefore we limited our computations of relevance to the visuomotor variable , λrot . Key in this computation is how the likelihood of relevance is computed . Before examining this , we first briefly describe how the posterior probability of relevance is computed using a Bayesian update . For ease of notation , we shall refer to the visuomotor relevance variable , λrot , as λ for the remainder of this section . As defined above , λ = 1 if it's variable is relevant , and λ = 0 if not . The posterior probability that the world's rotation parameter is relevant , P ( λ ( t ) = 1|y ( t ) ) is found through Bayes' rule , where P ( y ( t ) |λ ( t ) = 1 ) is the likelihood ( P ( y|λ = 1 ) for brevity ) , and P ( λ ( t ) = 1 ) is the prior ( P ( λ = 1 ) for brevity ) . Note that the prior is the posterior found in the previous time step , modulo the transition model , m11 P ( λ = 1 ) + m12 P ( λ = 0 ) , since it summarizes the probability of being relevant based on all the observations made up to that time ( we assume λ is Markovian ) . Our definition of relevance is based on a type of parameter's ability to explain disturbances . To illustrate , consider reaches early during adaptation . The body and world parameter estimates of a visuomotor rotation are zero and there are large movement errors . These errors are consistent with a large world disturbance and the probability of the visuomotor parameter being relevant is computed as high ( Fig 1A ) . After adapting for some time , an updated estimate of the body parameter partially compensates for the disturbance . The newly estimated world disturbance further compensates for the disturbance . Any remaining errors are used to update these parameter values with a Bayesian update ( see below ) . However , even if the errors are driven to zero , there remains a large apparent error between the observed movement , and how much the body parameter can account for ( Fig 1B ) . If this mismatch can be explained as the result of a relatively large world rotation , then the likelihood of a world disturbance is high . Therefore the corresponding likelihood of relevance is based on the probability of observing the cursor ( y ) , given our current estimate of the body parameter , and a world rotation of any value perturbing our observations . To compute this we must integrate the probability of a perturbed observation over all possible world rotations , P ( y|θ , λ = 1 ) is the likelihood of observing the limb's endpoint with a given rotation , θ . Since body parameters are always relevant , this likelihood is a normal distribution centered on the internal model's prediction , N ( y ( θ+θb ) , R ) . Rather than integrate this distribution over the forward model's prediction over each movement and all possible world rotations we made the following simplifying assumption . Since the visuomotor disturbance influences movement observations in a relatively simple and unique manner ( a constant rotation ) , we redefined this likelihood using only visuomotor angles . We used the hand trajectory , to identify the unique rotation , θy , that minimized the root mean squared error between the observed limb path , y and the estimated path when only compensating with the , always relevant , body estimate . We then use a Normal distribution over θ centered on θy , with the variance associated with an observation of the rotated limb , σθ2 ( see below ) . Although the normal distribution is defined over all real numbers , the variance of this distribution is much smaller than our limits of integration , and can very accurately be described as restricted between − π to π . To define the prior over visuomotor angles , P ( θ/λ = 1 ) , we note the following considerations: relevance is based on the ability of any visuomotor disturbance to explain the data , and we want to avoid biasing the inference . The prior should be flat over all non-zero rotations , but avoid assigning high probabilities to the degenerate case of small ( relative to our observation noise ) or zero rotations . Based on these considerations we defined the prior as ( 1-exp ( −θ2/2σθ2 ) ) /Z where Z is an appropriate normalizing constant . Just as above , given that the variance for the Gaussian term is much smaller than the domain , 2π , Z is very accurately approximated as 2π . This form of a prior assigns high probability to all large valued rotations , and low probability to rotations that are near zero , or small relative to the size of the observation noise , σθ2 . After integrating the above equations we find an expression solely in terms of the rotation that corresponds to our observation , θy , To summarize , this likelihood assigns high probability when the observed rotation , θy is large relative to the observation noise . We also note that in this idealized model , θy is the angular displacement relative to a movement predicted using the current estimate of a body disturbance , θb . Thus estimated body disturbances influence the forward model's belief of where in space the limb is . Finally , we also need to compute the likelihood of the unperturbed condition , λ = 0 , We can define P ( y|θ , λ = 0 ) with a Normal distribution just as before . However , since this is for the case when the rotation is not relevant , this distribution should be centered on θ = 0 . The prior , however , will be different . The prior should only assign large probability to rotations that are small , or small relative to the size of the observation noise . Therefore we define the prior as a Normal distribution with zero mean and variance , σθ2 . Again , since the variance for these distributions is very small relative to the limits of integration , both the likelihood and prior can be accurately approximated as restricted between −π to π . After integrating , we arrive at With this final term found , we can express , P ( y ) = P ( y|λ = 1 ) P ( λ = 1 ) + P ( y|λ = 0 ) P ( λ = 0 ) , and compute both the posteriors , P ( λ = 1|y ) , and P ( λ = 0|y ) . The variance , σθ2 , was found by noting that the angle subtended by the arm's length , L , and one standard deviation of the observation noise in either direction , is approximately 2σ/L , where σ is 0 . 01 meters . Using either the upper or lower arm length for L , the angle is approximately 1 . 7° . Using the whole arm length for L , the angle is 3 . 4° . Therefore , we defined σθ2 = ( 2 . 5 degrees ) 2 . During the error clamp simulations the model's observation was artificially constrained to have zero error , regardless of the parameter estimates used to generate motor commands , or their relevance . The model's observations of movements that attempted to compensate for disturbances were no different from estimated movements without disturbances . To model this uncertainty , we held the likelihood fixed at 0 . 5 during these circumstances . We note that denying the model the evidence necessary to compute a likelihood ( as may occur in error clamps ) also has the same effect , as the transition matrix relaxes the probability of relevance to 0 . 5 as time passes . With the relevance probabilities in hand , we can then infer estimates of the parameters . The estimate of the world's rotation used by the model to make predictions and compute commands is conditioned on the prior probability of being relevant , since ( the rotation when not operating in a visuomotor rotation ) is assumed to be zero . This expected world estimate along with the body estimate is collated in the vector . If the probability of relevance is one , then the update for the rotations is the extended Kalman filter update , where A is a matrix with ab and aw on the diagonal . However , if the probability of relevance is zero , then the world rotation is guarded against adaptation , and the update is Therefore , we approximate the update with the maximum likelihood update , The parameters' covariance , P , was updated in a similar fashion . Defining Pn+1 = APnAT + Q , and the updated covariance , then the posterior covariance was approximated as We note that multiple approximations to the updates for the parameters and their covariance were attempted and the qualitative results did not change . Furthermore , the transitions from low to high relevance are relatively quick ( 2–3 trials ) . As such , the approximations for inference during the intermediate state of relevance/non-relevance ( 0<P ( λ = 1 ) <1 ) have only a limited influence on the estimated parameter values . The limb parameter values were based on [18] . For all simulated experiments , the targets and reaching distances were equivalent to that used in the studies . For all simulated movements we assumed the nominal limb trajectory was that of a minimum jerk profile specified by the target locations , via points ( 8 equally spaced locations ) and movement times reported . Parameter estimates were updated 6 times per movement , and movement targets were randomly selected . The probability of relevance was computed once per movement . The three free parameters , ab , σb and σw , were tuned by hand to create qualitative fits to the data from [19] . These values were then used for the remaining simulations . Our simulated visuomotor experiments display trial-by-trial adaptations , whereas experimental plots of the same data are of cycles ( data averaged over 8 consecutive trials ) . We have not made a distinction between trials and cycles because of the rescaling properties of the inference process . A single trial in our formulation need not represent a single trial or a cycle . The model is time invariant in this regard and we can scale all the parameters ( jointly ) to scale time by any specific value .
In our previous model [4] parameters were always relevant and subject to adaptation . For variables that describe the body this makes intuitive sense . Variables that describe the environment , however , may only be relevant in a particular circumstance [17] . We thus amended the source estimation model , partnering world parameters with relevance variables . The probability of being relevant is found by comparing the observed movement with the movement predicted if the estimated world disturbance were neglected . The estimate of a world parameter is then adapted using a Kalman update weighted by the probability of being relevant ( see Methods ) . This contextualization allows for the storage and later retrieval of newly acquired parameter values . In this study we focus on the paradigm of visuomotor adaptation , restricting the model to estimate two variables , a body-centric visual rotation ( e . g . a rotation of the head relative to the torso and/or arm ) and world-imposed rotation ( the experimental manipulation ) . As a result , the model can only entertain one visual disturbance due to the body and one due to the world . We restrict the model to four free parameters: two parameters to describe the magnitude of noise associated with them , and two decay rates or time scales . However , we further assume the decay rate for world parameters is essentially zero , allowing for the long-term retention of that estimate . The existence of a fast and slow time scale are consistent with previous findings [5] , and our previous work [4] which suggests the uncertainty associated with body parameters is large , and estimates should vary quickly . The resulting model offers predictions for how adaptation should proceed when it is statistically optimal . Though the relevance model we present here is nonlinear in both the limb dynamics and the adaptation scheme , the results we present share many similarities with those of previously published linear models of adaptation . Specifically , when adapting to a visuomotor rotation of the model's hand location the motor errors appear linear in the estimated disturbances . Furthermore , although these disturbances are not adapted with a fixed rate ( but instead estimated with an extended Kalman filter ) , trial-by-trial changes in the estimates are small and the resulting motor errors follow typical exponential trajectories . Due to these similarities the relevance model has the appearance of a linear estimation process with a nonlinearity that switches the estimated world disturbance in and out of the adaptation process . To examine short-term motor adaptation , many experiments expose subjects to a disturbance twice in quick succession , with either a counter disturbance or a washout period in between . Savings are observed on the second presentation of the disturbance in both cases . Linear models can explain savings after adaptation in the form of an increased learning rate when adapting to the counter disturbance paradigm [5] , [6] . However , linear ( time invariant ) models are not capable of explaining this same type of savings after a sustained washout period [8] . Once the perturbation has been removed , the model necessarily de-adapts its parameters . Therefore , a washout period lasting as long as the adaptation period would reverse any savings; a second exposure to the disturbance would proceed just as the initial one . Without a mechanism for guarding parameters against de-adaptation , linear models are incapable of displaying even this form of short-term motor adaptation . Consider how the model presented here adapts while making reaches with a visuomotor perturbation . Initially the model cannot predict the consequences of , nor compensate for , a visual disturbance , and there are large motor errors ( see Fig 1A , 2A ) . These errors drive adaptation of the estimated body rotation . At the same time , the model estimates that a large angular rotation of the hand's path is consistent with the observed reach ( Fig 1A ) . This large potential angular perturbation indicates that the probability of the world's visuomotor rotation relevance is high ( approximately 1 , Fig 2B ) . As a result the world's rotation estimate is adapted and rises to help compensate for the experimental perturbation ( Fig 2B ) . Although the motor errors progressively decrease , the model is still aware that a large visuomotor rotation is consistent with the ongoing observations; there remains a large discrepancy between the observed reaches and the model's estimate of an uncompensated reach . An estimate of the uncompensated reach is found by predicting a reach made without compensating for the estimated world rotation . The estimated body rotation however , is still used , and biases this estimate ( see Fig 1 ) . A large angular perturbation continues to be estimated and the probability of relevance remains high throughout the adaptation process . After an adequate number of trials , the contribution from the body and world rotations largely cancels the visual disturbance and the errors are small ( Fig 2A ) . The overall motor behavior is qualitatively consistent with adaptation to a novel visuomotor disturbance . Both linear models and our nonlinear model can correctly describe the resulting patterns of adaptation . Continuing with the short-term adaptation paradigm , when washout trials are subsequently presented , consistent with experimental findings , the relevance model produces large motor errors in the opposite direction ( Fig 2A ) . The model , now biased by its previously adapted body rotation , mistakenly estimates an angular perturbation now in the opposite direction . The probability that the world's visuomotor rotation estimate is relevant remains high and both the body and world estimates de-adapt ( this produces a short lasting overshoot in the error , Fig 2 green panel ) . As the body estimate quickly de-adapts the probability of relevance decreases back to zero . This change in the world's estimated relevance halts adaptation of the world rotation parameter . In contrast with similar linear multi-rate models , the motor errors are now only used to estimate the body's rotation parameter ( which is always relevant ) . The body's estimate continues to de-adapt and the motor errors vanish . This combination of the fast change in the world parameter's relevance , along with the fast adaptation rate for the body parameter , results in the relatively quick de-adaptation back to nominal reaches . When the disturbance is turned on again , large errors result . Just as before , the probability that the world's visuomotor rotation parameter is relevant increases . This quick change in the estimated relevance results in a relatively fast decrease in errors , as the world's rotation estimate begins to compensate for the rotated reaches ( Fig 2C ) . Thus the estimation of relevance allows the model to explain fast re-adaptation . The adaptation and short-term savings we have reviewed above have similar analogues over longer time frames . To examine savings over multiple days , subjects adapt to a disturbance , and then are presented with the same motor disturbance on a subsequent day . In another paradigm , subjects adapt to two disturbances in quick succession , the later often a counter disturbance , and then evidence for savings or interference is examined on a subsequent day [e . g . 13 , 19] . Both experimental paradigms demonstrate that many of the features of short-term motor adaptation also exist over longer time frames . Unfortunately linear models are not capable of describing some of these phenomena over these longer time frames . Using the relevance model to examine its predictions for saving over days , we simulated these experimental paradigms [e . g . 19] . No changes were made to the model for these long-term adaptation results . The first day's adaptation to a visuomotor disturbance proceeds just as described above ( Fig 3A ) . There is a subsequent washout period just as before , where the probability that a world rotation estimate is relevant quickly decreases towards zero . The world's parameter estimate , no longer relevant , is left for later use should it become relevant again . The body estimate then rapidly de-adapts ( Fig 3A ) . In this particular experiment , subjects do not undergo a period of washout with the robot , but instead leave the experimental setting . Therefore this washout is different from those of the previously described short-term experiments on two counts . First , the washout trials predicted by the model correspond to natural movements made by the subjects after the experiment has ended . Our model thus predicts that there should be aftereffects that persist after the subject has let go of the robot handle . This is consistent with recent evidence [e . g . 20] . Second , since this washout period does not take place while grasping the robot handle , the last interactions subjects have with the robot are associated with a disturbance; the robot is an unambiguous proxy for the relevance of a visuomotor rotation . Therefore we assume that the model's initial probability of the visuomotor parameter's relevance should be similar when the model next returns to the experiment . When adaptation on a subsequent day is simulated , the probability of the visuomotor parameter being relevant is initialized to a high value ( 0 . 75 ) , as discussed above , and the world's rotation estimate is believed to be relevant . The model is again presented with the same visuomotor rotation and the initial motor errors are lower than those of the previous day ( Fig 3B ) . At the end of this second day of adapting to the visuomotor disturbance the movement errors are lower than on the previous day . This is due to the relatively large contribution from the world estimate . The model's predictions for long-term savings are consistent with the observed experimental findings ( Fig 3C ) . We simulated the second experimental paradigm , now presenting two visuomotor rotations of opposite orientations in succession [19] . Adaptation to the first motor behavior proceeds just as above . When the model is presented with a second , oppositely directed rotation , the model again estimates a large angular discrepancy between the observed hand path and an estimated path that neglects the current world rotation estimate . However , this estimated rotation of the hand's path is now in the opposite direction . Regardless , the probability that the visuomotor rotation parameter is relevant remains high . Both the world and body parameters begin adapting to a rotation with the opposite sign ( Fig 3D ) . When the washout trials begin , the probability of relevance quickly decreases , the world's rotation estimate is no longer used , and adaptation is halted . However , by this point all adaptation to the first visuomotor rotation has largely been lost . Just as above , on a subsequent day the model begins with a belief in a visuomotor rotation's relevance , and uses its estimated world rotation . Yet , the small estimate has little influence on the movements . Consistent with experimental findings of interference , the model performs as if naïve on the second day's presentation of the disturbance ( Fig 3E , F ) . Our new model explains long-term savings in the form of retention of a previously adapted motor behavior and decreased initial errors . Further , the model demonstrates how adaptation to two similar disturbances can cancel each other's influences and result in interference . Both findings are widely observed in motor adaptation studies . Most studies examine adaptation after the sudden introduction of a perturbation . However , recent evidence has found marked differences when subjects adapt to a perturbation that is gradually introduced . These gradually introduced perturbations have been used to examine both interlimb generalization , and savings of motor behaviors across multiple days . In one study , subjects adapted to a force field that was either suddenly or gradually introduced [21] . After adapting , savings were examined when making test reaches with the non-dominant limb in the same force field ( at full strength ) . The test reaches made after adaptation to the gradually introduced perturbation exhibited relatively larger deviations from a straight path . The initial errors were roughly twice as large as those found after adapting to the suddenly introduced perturbation , suggesting generalization of the adapted force field to the other limb was relatively poor when the perturbation is gradually introduced . In another study examining the differences between gradually and abruptly introduced force fields , post-adaptation reaches made without grasping the robot handle were examined [22] . The aftereffects on these free reaches were larger when subjects adapted to a gradually introduced perturbation . This suggested adaptation to a gradually introduced force field , may have altered the way subjects controlled their limb . Another study examined savings across days with a visuomotor rotation that was either gradually or suddenly introduced [23] . After adapting on one day , subjects made reaches in the same visuomotor perturbation ( full strength ) on a subsequent day . Subjects that had adapted to the gradually introduced perturbation made slightly larger errors initially , even though they adapted over more trials than the other group . These three results , and other studies like them , with their distinctions in savings , may offer testable predictions for how the nervous system adapts . To examine our model's predictions we simulated the same gradual perturbation as the one used in [23] . During the early trials the motor errors are small and the body estimate quickly adapts to them . Because these errors are small the body estimate does an adequate job of compensating for the perturbation . The model does not detect a large angular perturbation and does therefore not believe the world's rotation estimate ( initially zero ) to be relevant . Only during later trials as the perturbation strength increases does the model believe the world's parameter is relevant . Thus , much of the adaptation is accounted for by the body estimate ( Fig 4A ) . After the simulated experiment has ended , the model has a world estimate that is little more than half as strong as would be otherwise ( compare with Fig 3 ) . Our model predicts three findings of interest . First , we can conclude that during a generalization trial with the other limb , the model's errors would be approximately twice as large as if the perturbation was suddenly introduced , consistent with experimental evidence [21] . Second , because the perturbation is largely attributed to the body , the model predicts relatively large aftereffects during reaches made without the force field , when the robot handle is not grasped and the probability of a disturbance parameter's relevance is zero [22] . Third , since the world estimate of a rotation is smaller than would be otherwise , movement errors on a subsequent day are larger initially , just as was found experimentally ( compare Fig 4A , B ) . Our model thus provides an interpretation of the effects that are associated with fast versus slow introductions of perturbations . One additional set of phenomena may be important to characterize the properties of motor adaptation . In several recent studies subject's motor behaviors are examined when they make reaches in an “error clamp” , or “force channel” , wherein force disturbances are removed and movements are constrained to be straight . This is done to examine how and if subjects alter their motor strategies in the absence of kinematic errors . In an early study , after subjects adapted to a velocity-dependent force field , an error clamp was unexpectedly turned on [24] . Even though there was no longer any need to compensate for the force field , subjects continued to produce considerable forces as if it were still present . These forces slowly decayed , over a longer period of time than the subjects required to adapt or de-adapt in the absence of an error clamp . This suggested that these erroneous forces and their slow decay were the result of some altogether different process . We can examine what the model would predict by simulating similar circumstances . The model is first presented with a visuomotor rotation , and then the reaches are “clamped” to constrain movement errors to be zero . Adaptation proceeds just as we have seen before ( Fig 5A ) . Under the simulated error clamp condition , regardless of what the model ( or subjects ) does to compensate for a perceived disturbance , they observe the same error-less outcome . The model cannot observe the consequences of using its estimated perturbations; this results in uncertainty in the relevance of the visuomotor parameter ( see Methods ) . As a result the model partially uses the world's estimate to compensate and both the body and world rotation estimates slowly decay towards zero . The results are qualitatively similar to experimental findings ( Fig 5B ) . In a somewhat different paradigm , after subjects adapt to one disturbance they are briefly presented with a counter disturbance and subsequently make reaches while errors were clamped [5] . Under these circumstances subjects temporarily make reaches as if they are compensating for the counter disturbance , even though it is not present . This phenomenon , termed spontaneous rebound , has been observed under a variety of conditions [25] , [26] , [27] . Ideally models of motor adaptation should be able to describe such a behavior . How would the source relevance model explain such findings of spontaneous rebound ? We can simulate the model's predictions to the same paradigm with a visuomotor rotation first , then a counter rotation , and then a “clamp” where we artificially constrain the movement errors to be zero . The model can predict spontaneous rebound through the interaction of two mechanisms . As with other linear multi-rate models there is the interaction of two or more processes with different adaptation rates [e . g . 5] . But more importantly for our model , under the simulated error clamp condition , the model ( and subjects ) observes an error-less outcome , regardless . This results in uncertainty in the relevance of the visuomotor parameter and the model partially uses the world estimate . The model appears to overcompensate for a nonexistent rotation and the results are similar to experimental observations ( Fig 5C , D ) . Though other multi-rate models can explain spontaneous rebound , our model offers a different explanation in terms subject's difficulty in gauging the circumstances under which they are adapting .
Here we have extended a body-world , multi-rate model to infer not only the parameter values but also their relevance to the current motor conditions . The discrepancy between observed movements and those predicted when neglecting world estimates is used for the computation of relevance . World parameters that are estimated as having little relevance are not used to generate motor commands and are not adapted . Body parameters , however , are assumed to always be relevant and subject to adaptation . In effect , this allows for a rudimentary long-term memory of world parameters , allowing for the retention and later retrieval of newly acquired parameter values . The entire process is dynamic and requires no intervention for describing behavior across short or long time frames . We have demonstrated that such a model can explain a wide range of findings on human motor control . Our results are consistent with the basic findings of savings and interference , error clamp results , and the differences between adapting to gradual and abruptly introduced disturbances . Though there are some clear similarities between the model we present here and other computational descriptions of motor control and adaptation , there are important distinctions . Our model makes a categorical distinction between parameters that represent the body and those that represent the world; thus it shares similarities with two-rate models [5] , [6] . Indeed , our model makes nearly identical predictions for short-term savings , interference and reduced learning rates with increased adaptation duration [28] . Since these models are linear , however , they cannot explain adaptation on longer time scales , as all their adapted parameters relax back to zero . Perhaps a more fundamental distinction , it is not clear what the “fast” and “slow” variables in multi-rate models represent computationally , although they may be related to distinct neural structures at the implementation level . The model we present offers explanations for a range of findings on both short and long-term motor adaptations as well as generalization [4] . Further , we model the estimation of body and world variables that can be tested through future experimentation . Since our model switches the world parameter values in and out based on their probability of relevance , it bears some resemblance to the other models that switch modules on and off , such as the mixture of experts and MOSAIC [14] , [15] , [16] . However , our representation of world and body parameters within a dynamical model is distinct from the MOSAIC controller's modules of paired forward and inverse models of whole body-world dynamics . The MOSAIC controller does not independently represent the body and the world ( which is a cornerstone of our model ) . In fact , even if the MOSAIC were altered to represent the body and the world in two different modules , they could not be “summed” to represent whole body-world dynamics , as these descriptions are coupled and highly nonlinear . Our proposed model represents distinct parameters within a model of the limb and body dynamics . Therefore it can uniquely adapt these parameters , and use them for generalization in a manner MOSAIC cannot . Furthermore , our use of a relevance parameter is distinct from the notion of context used in these switching controllers . In the MOSAIC model , modules are switched on and off based on the similarity between their predictions and the observed motor behavior . Each module's predictions are uniquely described by the current parameter values that make up that module ( e . g . its current estimate for a visuomotor rotation or force field ) . As a result , a module for a particular force field will not be switched on unless the limb makes reaches in a very similar force field . Our computation of relevance is based not on a parameter's value , but on the manner it influences motor behaviors . For example , the parameter for an inertial perturbation is likely whenever limb movements are consistent with an inertial perturbation of any sufficiently large value . In large part due to these differences in relevance and context , it is not clear if the MOSAIC model could also explain some of the findings we have presented here . For instance , consider adapting to a 30° visuomotor rotation . A module representing the perturbed limb dynamics would modify its parameters to compensate for the disturbance . When a −30° rotation is then presented , this module's prediction errors ( now ∼60° ) would in fact be larger than a baseline , null condition module ( only ∼30° ) . As a result the context variable for the module associated with the visuomotor rotation would be switched off , and this module would not continue adapting to the counter rotation; the model would not predict interference . Through a similar line of reasoning it is not clear how the MOSAIC model could explain the phenomena of spontaneous rebound . Other studies have used the idea of context in different ways . In one study context was defined as the implicit memory of the limb segments used during a motor behavior [29] . In a sense , this assigned relevance to different body effectors . In a more recent study context indicated visuomotor rotations of different magnitudes [6] . In those studies context was known unambiguously , not estimated based on errors or changes in the environment , as we have done here . Further , here we define relevance ( similar to context ) in terms of the existence of external disturbances , regardless of what limb segment is used or the strength of the particular magnitude of the disturbance . Our study can thus be seen as a generalization of these studies to unobserved contexts and changes in the environment , which makes new experimentally testable predictions about the role of relevance . In this work we have examined the effects of adapting to a visuomotor rotation , however , this model could be extended to adapt to other types of disturbances as well . In particular , several experimental studies have investigated how adapting to visuomotor rotations and altogether different motor disturbances in quick succession , effect interference and savings [19] , [30] , [31] . Interestingly , the results of these studies , having contrasting findings on savings , have motivated distinct interpretations concerning the nervous system's ability to represent kinematics and dynamics uniquely . Within that context , these results were argued to be incompatible . Our model makes no distinction between kinematics and dynamics but instead a distinction between parameters that represent how to control the body and how to interact with the world . Furthermore , our model predicts that if the effects of two different perturbations were similar ( in terms of their resulting motor errors and sensory consequences ) then their accompanying world estimates ( e . g . estimated world rotation , or estimate world force field ) would both be assumed relevant for adaptation . Therefore , adapting to a visuomotor rotation , and then a force field that perturbed the limb in a similar manner , might produce interference [19] , whereas the subsequent adaptation to a force field dissimilar to a visuomotor rotation wouldn't [30] . As such future work using this model may offer a unique perspective to examine the findings of these and similar experimental studies . For the sake of focus and instruction , we have modeled one estimate per disturbance , i . e . one estimated world rotation . This assumption played a crucial role in some of our findings on interference . If instead we had allowed for multiple estimates of a world rotation , it is not clear how the model would predict interference when modeling adaptation to counter disturbances across multiple days . Indeed , other studies have found that under appropriate conditions , a newly acquired motor behavior can be consolidated and resist retrograde interference [32] , [33] . Our model does not predict these findings but extensions that could also explain these effects would be interesting . Such extensions might be possible by introducing parameters to describe multiple visuomotor disturbances , each with their own uncertainty . Such a model could implement a form of supervised adaptation; after adapting to , or operating within , a specific visuomotor disturbance for a long time the model could grow certain of this parameter value . Then , adapting to a similar but oppositely directed disturbance would require adapting another , less certain , visuomotor parameter . Such a scheme might implement adaptation to multiple disturbances , consistent with the idea of consolidating a motor behavior and learning a second , distinct behavior without interference . We feel that much if not all of the model's value lays in the intuition it yields in trying to explain motor behavior phenomena . The studies and accompanying simulated results we present are those that we feel the model may help to explain . However , as with all models , this model is necessarily false [34] , and there are experimental findings the model either cannot explain or that are flatly at odds with its predictions . For example , though our model is consistent with the findings on adapting to gradually versus abruptly presented perturbations in the Klassen et al . study , a more recent examination found distinct results . In this new study rates of motor decay were probed during short-term adaptation to a force field , either abruptly or gradually introduced [35] . Though the aim and experimental protocol of this study was very different from the Klassen study , some apparent contradictions were found in that there were no effects on the re-adaptation to the force field between the abrupt and gradual groups . To be clear , this finding was made under conditions of short-term savings of a force field ( not long-term retention of a viruomotor disturbance ) , and obtained with the use of error clamps . However , despite their differences , it is not obvious to us how our model could account for these two distinct findings . In contrast with the gradual vs . abrupt findings presented above however , our model makes an interesting prediction that could readily be tested . According to our model the amount of adaptation for world parameters is due to both the size of disturbance and the amount of training; the larger the disturbance and the more training time , the more a world parameter is adapted . Similarly , the more world parameters are adapted , the more savings should be observed on a second day's presentation of the disturbance . Surprisingly though , our model predicts that even with a gradually introduced perturbation , and one that never reaches the strength of the abruptly presented one , more savings can be observed on a second day . If the model is presented with a visuomotor rotation that is ramped up slowly over many trials , the world estimate will have relatively more time to adapt , and the body estimate more time to de-adapt . As a result , even if adaptation ends before the visuomotor disturbance has reached , say 30° , the world estimate will surpass that seen in the abruptly presented paradigm . Thus more savings , not less , will be observed on the second day . The results of such an investigation would be very informative for the study of adaptation . Another study of force field adaptation offers both supporting and contradicting evidence for our model . In this study the rates at which subjects adapted ( as quantified through movement errors ) were compared when adapting either to the null field or a scaled down version of the force field [9] . Consistent with our model , de-adapting to the null field is much faster than adapting to the force field . It was also found that subjects adapted to the scaled down force field even faster than they did the null field . In contrast with this finding , our model would predict that both the body and the world would adapt to the scaled down force field , resulting in a relatively slow process . This is in sharp contrast with their findings and will provide an interesting target for future modeling efforts . In our model we have assumed that movement predictions always utilize body estimates . Since the body is always relevant , this seems sensible . One consequence of this is that the model is in effect “blind” to changes it has inferred are due to the body; the model cannot make predictions for movements that do not compensate for these adapted body estimates . Even if the inferred body estimates are due to an experimental perturbation , the model will have an altered prediction of where the limb will be in space . In effect , the act of adapting alters the model's perception of the limb . Interestingly , there is a growing body of experimental evidence for this same effect . In particular , the act of adapting to a visuomotor disturbance biases the perception of subjects' movement and hand position in a manner consistent with our model [36] , [37] . This perceptual bias was found to be nearly half of the adapted rotation , also consistent with our model . Importantly , this bias was found to be associated with the limb alone , and not the result of a global recalibration of visual space [36] . A similar finding demonstrates that adapting to a force field alters the perception of the limb in space as well [38] . On the whole , these results suggest a further link between our model's use of body and world parameters and how the nervous system adapts to new motor behaviors . Some of our results on interference rely on the relative duration of the counter disturbance , behavior B , in the A-B-A paradigm . Since both disturbances are presented for the same length of time , the counter disturbance almost completely degrades any estimate of the world parameter estimate . This results in motor patterns consistent with interference . If the counter disturbance was presented for approximately twice as long , our model predicts that the typical pattern of interference would not be observed . Rather than producing errors similar to naïve subjects , our model predicts subjects should produce larger errors , consistent with the expectation of the counter disturbance . As far as the authors are aware , this particular experimental result has not yet been performed and would be particularly informative . In this study we have implicitly assumed that savings is a form recall; previously adapted information is called upon resulting in reduced motor errors relative to naïve conditions . However , other researchers have asserted that savings could be a form of meta-adaptation instead , wherein adaptation rates are facilitated and motor errors decrease faster than during naïve conditions [e . g . 8 , 39] . By the same token , interference could either be a form of re-adaptation and hijacking of previously adapted behaviors ( as we have assumed ) , or an inability to recall previously adapted information . To the best of the authors' knowledge , both of these options for savings and interference are consistent with the known empirical evidence . However , our model does make some predictions that might speak to these possibilities . For example , our model predicts that on repeated days of training , the estimate of a world-imposed disturbance progressively increases . Assuming cues such as the experimental apparatus are salient for estimating relevance , each day's initial errors should be smaller than the previous . This implies that subjects should eventually display “one-shot” learning of a disturbance . This would be strong evidence that subjects were in fact recalling knowledge , rather than nearly instantaneously adapting . Future studies could examine a similar line of predictions to distinguish between savings as recall , and savings as meta-adaptation . Relevance as we have defined it here is a relatively simplistic indication of the motor system's current operating condition , or context . Clearly there is more to context than motor errors . For instance , whether or not one is holding the handle of a robot is a clear indicator of the kind of disturbances one might expect [20] . Similarly , while they may not be as salient , cues such as tones and colors may also serve for disambiguating context [40] . Finally , in this study we have completely neglected forces , both the contact forces between the limb and the robot handle , and the forces required to produce movements . This is clearly an oversimplification and it is known that these forces are relevant when adapting [e . g . 41 , 42 , 43] . Why some cues are easy to indicate context and others are difficult remains and open question . Which variables the nervous system uses to distinguish context are similarly unknown . We expect that future studies will shed more light on these issues . | Trying to explain how humans adapt to new motor behaviors and retain them over time is a central focus in motor control . Many aspects of adaptation , including savings and interference , have proven difficult to explain in a coherent manner . Linear dynamical models have been successful at describing the observed increase in performance while subjects familiarize themselves with an experimental perturbation . Many aspects of these experiments however , remain unexplained . In particular , while subjects display the ability to remember new motor behaviors for long periods of time , these linear models cannot . In this work we extend our previous body-world model of motor adaptation by estimating the relevance of inferred world disturbances . When these parameters are estimated to be relevant , they are used ( and motor behaviors are adapted ) , and when they are estimated to not be relevant they are stored ( and motor behaviors are remembered without being lost ) . Our model offers explanations for many observations on motor adaptation , savings and interference . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"motor",
"systems",
"computational",
"neuroscience",
"biology",
"computational",
"biology",
"neuroscience",
"learning",
"and",
"memory"
] | 2011 | Estimating the Relevance of World Disturbances to Explain Savings, Interference and Long-Term Motor Adaptation Effects |
Cellular fusion is required in the development of several tissues , including skeletal muscle . In vertebrates , this process is poorly understood and lacks an in vivo-validated cell surface heterophilic receptor pair that is necessary for fusion . Identification of essential cell surface interactions between fusing cells is an important step in elucidating the molecular mechanism of cellular fusion . We show here that the zebrafish orthologues of JAM-B and JAM-C receptors are essential for fusion of myocyte precursors to form syncytial muscle fibres . Both jamb and jamc are dynamically co-expressed in developing muscles and encode receptors that physically interact . Heritable mutations in either gene prevent myocyte fusion in vivo , resulting in an overabundance of mononuclear , but otherwise overtly normal , functional fast-twitch muscle fibres . Transplantation experiments show that the Jamb and Jamc receptors must interact between neighbouring cells ( in trans ) for fusion to occur . We also show that jamc is ectopically expressed in prdm1a mutant slow muscle precursors , which inappropriately fuse with other myocytes , suggesting that control of myocyte fusion through regulation of jamc expression has important implications for the growth and patterning of muscles . Our discovery of a receptor-ligand pair critical for fusion in vivo has important implications for understanding the molecular mechanisms responsible for myocyte fusion and its regulation in vertebrate myogenesis .
Cell–cell fusion is crucial for several biological processes , including placental development [1] , bone remodelling [2] , fertilisation [3] , and formation of skeletal muscle fibres [4] , but surprisingly remains poorly understood . Skeletal muscle forms the bulk of tissue in vertebrates and is composed of bundles of long syncytial fibres formed by the fusion of post-mitotic muscle precursor cells ( myocytes ) . It is a highly regenerative tissue , constantly undergoing repair and growth through the fusion of myocytes to form new fibres or supplement established ones . Impairment of the function of muscle , through age or genetic lesion , results in mild-to-severe pathologies that shorten lifespan , reduce quality of life , and demand a high burden of care . A more complete understanding of the molecular mechanisms of muscle development may lead to better treatment of muscle diseases and greater insights into regenerative medicine . Muscle development has been well characterised in the larval body wall musculature of Drosophila melanogaster , where fusion occurs between two sub-populations of myoblasts , referred to as the fusion-competent myoblasts ( FCMs ) and founder cells [5]–[7] . The process of fusion has been resolved into a series of intermediate steps through ultrastructural analysis [8]–[10] and identification of the molecular components through forward genetics screens [11] . A critical step in fusion is the initial recognition and adhesion between the two cell types . This is regulated by the mutually exclusive expression of the cell surface receptor proteins Kirre and Sns , which form a heterophilic receptor pair between neighbouring cells [12]–[17] . Mutations in genes encoding these cellular recognition receptors ( and their partially redundant paralogues Rst and Hbs ) result in a severe block in fusion between the muscle precursors . In vertebrates , a functionally equivalent heterophilic receptor pair that is essential for myocyte fusion in vivo has yet to be identified [18] . One approach to isolate the vertebrate receptors has focussed on using sequence orthology to the fruitfly proteins—a rationale which is validated by emerging evidence that the molecular pathways important for myocyte fusion are conserved across these species [19]–[23] . Cell culture experiments have also suggested the involvement of several other cell surface proteins in vertebrate myocyte fusion , for example BOC and CDO [24] . Loss-of-function studies of these candidates resulted in mild disruption of myocyte fusion in vivo , leading to the view that this process involves several partially redundant proteins in vertebrates [18] . Only one vertebrate receptor , Kirrel3l ( originally named Kirrel ) , has been identified as essential for myocyte fusion in vivo by using an antisense morpholino to knockdown the protein in zebrafish embryos [22] . There is no known Kirrel3l counter-receptor involved in the process of fusion , suggesting that other important vertebrate receptors remain to be discovered . In this study , we identify two vertebrate cell surface receptors that are crucial for myocyte fusion: Jamb and Jamc ( official nomenclature: Jam2a and Jam3b , respectively; Entrez gene: 100005261 , 569217 , respectively ) . The mammalian orthologues of both genes , commonly referred to as JAM-B and JAM-C ( after rationalisation of the gene family nomenclature within the field [25] ) , have well-characterized roles in leukocyte migration [26] , tight junction formation [27]–[29] , and spermatogenesis [30] . Mouse Jam-B ( Jam2 ) and Jam-C ( Jam3 ) are members of a small sub-group of immunoglobulin superfamily cell surface proteins that is restricted to the deuterostome lineage ( TreeFam [31] ) . They contain two extracellular immunoglobulin superfamily domains , a single transmembrane domain and a short cytoplasmic domain ending in a type II PDZ domain binding motif [32] . Heterophilic interactions between Jam-B and Jam-C are thought to be important for leukocyte transmigration across vascular endothelia [26] and the polarisation of spermatids necessary for complete differentiation into functional spermatozoa [30] , but to date , there is no reported function for Jam-B and Jam-C in muscle development . We have shown here that jamb and jamc are co-expressed in developing myoblasts and , by using mutant zebrafish , demonstrate that the physical interaction between them is essential for myocyte fusion in vivo . By analysing the mutant phenotypes and showing that jamc expression is misregulated in a muscle patterning mutant , we provide new insights into the regulatory mechanisms that govern vertebrate myogenesis .
To identify novel receptor pairs that might be involved in myocyte fusion , we queried our large database of extracellular protein interactions constructed by screening a library of 249 zebrafish receptor proteins using the AVEXIS assay and supported by embryonic expression patterns of the corresponding genes during zebrafish embryonic development [33]–[35] . One pair , Jamb and Jamc , was selected because both genes are expressed by dividing myoblasts during primary myogenesis , but in distinct patterns . jamb is expressed by all fast muscle myoblasts shortly after the formation of each somite ( Figure 1A ) . After approximately 10–13 somites have formed , jamc is initially highly expressed in a small , medial sub-population of fast muscle myoblasts along the dorsal-ventral axis ( Figure 1B , 10–13 somites ) . Over time , the expression domain of jamc expands to include all myoblasts in the hypaxial and epaxial regions of the myotome ( Figure 1B , 17–18 somites , 21 somites ) . jamb and jamc are co-expressed by all myoblasts in the anterior somites by the 17–18 somites stage ( Figure 1 , 17–18 somites ) and in posterior somites at later stages ( Figure 1 , 21 somites ) . Whilst highly expressed in developing muscle , jamc also appears to be expressed at a basal level throughout the embryo—an observation that is replicated using a second riboprobe specific to the 3′ UTR of jamc ( unpublished data ) . The expression of both genes in the myotome is attenuated in axial musculature by 24 h post-fertilisation ( h . p . f . ) , but subsequently upregulated in later-developing craniofacial , abdominal , and pectoral fin muscles ( Figure 1A , B ) . We conclude that both jamb and jamc are expressed in the somites of the embryo in a wave along the anterior-posterior axis . Within each somite , jamc expression begins medially and spreads laterally throughout the domain of jamb-expressing fast muscle myoblasts over time , so that both genes are co-expressed by myoblasts during the initial period of fusion between somitic precursors [20] , [36] . Dynamic co-expression of the jamb and jamc genes in the developing musculature and later forming muscles suggested a role for the interaction between these two cell surface receptor proteins in myogenesis . To establish whether jamb and jamc were important for myocyte fusion in vivo , mutant alleles of both genes were obtained from the Hubrecht Institute ( HU3319 ) and Sanger Institute Zebrafish Mutation Resource ( sa0037; Figure 2A ) . Mutations within selected exons of jamb and jamc were identified by amplifying and directly sequencing PCR products from libraries of chemically mutagenised zebrafish . The jambHU3319 allele is a nonsense mutation that results in a premature stop codon near the N-terminus of the protein . A truncating mutation was not recovered for the jamc gene , but one allele , jamcsa0037 , contained a missense point mutation in a cysteine residue ( C136 to Y ) that is predicted to form a structurally critical disulphide bond . Both jambHU3319 and jamcsa0037 homozygous mutant embryos exhibited the same striking phenotype: regimented lines of centrally positioned nuclei within each myotome ( Figure 2B ) . In wild-type embryos , somitic fast muscle myocytes fuse together to form multinucleate muscle fibres by approximately 24 h . p . f . [20] , [36] . In jambHU3319 and jamcsa0037 mutants , fast muscle myocytes did not fuse , but instead , fully elongated to form mononuclear fibres that spanned each somite by 48 h . p . f . ( Figure 2B ) and remained mononucleate until at least 5 days post-fertilisation ( Figure 2C ) . We quantified the lack of fusion in subsequent transplant experiments: 95% and 85% of fast fibres remained mononucleate in jambHU3319 donor into jambHU3319 host and jamcsa0037 donor into jamcsa0037 host transplants , respectively ( Table 1 ) . To provide independent evidence that the mutations in both jamb and jamc were responsible for the phenotype , we injected translation-blocking morpholino antisense oligonucleotides targeted to both jamb and jamc into wild-type embryos . Embryos injected with either morpholino phenocopied the mutants , demonstrating that the phenotype was not due to closely linked mutations in either the jambHU3319 or jamcsa0037 mutant lines ( Figure 2D ) . From these experiments we conclude that zebrafish jamb and jamc are essential for myocyte fusion . In teleost fish , two spatially segregated muscle populations form during primary myogenesis: superficially located slow-twitch muscle and medial fast-twitch muscle [37]–[39] . Fast muscle fibres are syncytial ( Figure 2B ) , but slow muscle fibres remain mononucleate during embryonic development [40] . To determine if the mononuclear muscle fibres in jambHU3319 and jamcsa0037 mutants do correctly differentiate as fast-twitch muscle , we used antibodies that are specific for the slow and fast isoforms of myosin heavy chain ( sMyHC and fMyHC ) . We observed that both mutants had the same number of normal , superficially positioned slow muscle fibres ( Figure 3A ) with no ectopic expression of sMyHC within the deeper fibres . The medially located , and more numerous , mononuclear fibres in both mutants expressed fMyHC ( Figure 3B ) , suggesting that specification of fast-twitch muscle was unaffected . Finally , we observed no difference in the ability and timing of spontaneous twitching and response to tactile stimuli in either mutant relative to wild-type , suggesting that the muscles were innervated and fully functional ( unpublished data ) . Together , these data suggest that both mutants are able to complete the myogenic programme , except for a specific defect in fusion . These findings also suggest that other aspects of terminal differentiation , such as elongation and sarcomerogenesis , do not depend upon myocyte fusion in vertebrates . We observed an overt overabundance of fast muscle fibres in both mutants relative to wild-type embryos ( Figure 2B ) . We quantified this increase by counting fibres outlined by a membrane-localised red fluorescent protein ( mRFP ) in optical cross-sections of wild-type and mutant embryos at 24 , 32 , and 48 h . p . f . ( Figure 4A ) , revealing a statistically significant increase ( p≤0 . 001 ) in fast fibre number in mutants by 1 . 6–1 . 8-fold , relative to wild-type ( Figure 4B; Table S1 ) . Interestingly , there was not as great an increase as might have been expected from the average number of nuclei in each wild-type fast muscle fibre ( approximately 2 . 7 and 3 . 2 at 32 and 48 h . p . f . , respectively; [20] ) . Staining mutant embryos with acridine orange did not reveal any increase in apoptosis relative to wild-type ( unpublished data ) . In addition , we did not observe any rounded , unelongated , unfused myoblasts expressing fMyHC in either mutant or wild-type embryos ( Figure 3B ) , suggesting that all somitic fast muscle myoblasts had undergone differentiation . Between 32 and 48 h . p . f . , myotome muscle fibre number increased by a similar proportion in both mutant and wild-type embryos ( Figure 4B ) . In contrast , the number of nuclei within each mutant myotome was decreased compared to wild-type embryos ( Figure 4C; Table S2 ) , suggesting that myoblast proliferation is limited in both mutants . In other words , growth of mutant , mononucleate fast muscle myotome requires less myocytes than the equivalent amount of growth of wild-type , syncytial fast muscle myotome . Taken together , these results reveal that the majority of fast muscle myoblasts could elongate and form functional mononuclear muscle fibres , resulting in an overabundance of fast muscle ( Figure S1 ) . This suggests that axial fast muscle precursors are not divided into distinct subpopulations . Both jamb and jamc are expressed in fast muscle myoblasts during primary myogenesis ( Figure 1 ) and loss-of-function of either gene results in a severe block in myocyte fusion ( Figure 2 ) , without overtly affecting any other aspect of axial muscle differentiation ( Figure 3 ) . Taken together , these results suggest that Jamb and Jamc are a receptor pair necessary for myocyte fusion . The mammalian orthologues of Jamb and Jamc are known to form both homophilic [41] , [42] and heterophilic [43] interaction pairs . Our large-scale systematic protein interaction screen identified a heterophilic interaction between zebrafish Jamb and Jamc , but no homophilic binding was observed [33] . Homophilic interactions are known to be the main class of false negatives in the AVEXIS assay used in these screens [33] , so to determine whether zebrafish Jamb and Jamc could interact homophilically and to quantify the relative biophysical binding parameters , we used soluble recombinant proteins and surface plasmon resonance . We found that both Jamb and Jamc were able to bind each other with an equilibrium binding constant typical of extracellular protein interactions between membrane-embedded cell surface receptors ( KD≈4 . 7±0 . 7 µM , Figure 5A; [44] ) . To compare between all three possible interactions of Jamb and Jamc , we used dissociation phase data of binding experiments to calculate dissociation rate constants ( Figure 5B ) . We took this approach because equilibrium measurements can be confounded by unreliable estimates of analyte activities , which are affected by homophilic interactions within the analyte . Dissociation rate constants are independent of analyte activity and can therefore be more appropriately compared . As expected from studies of the mammalian orthologues , both proteins could also self-associate , but with a much weaker interaction strength than that of the heterophilic interaction ( Figure 5B ) . All dissociation curves fitted a first-order decay equation well , suggesting a 1-to-1 binding mechanism . These experiments show that while zebrafish Jamb and Jamc are indeed able to form homodimers , the heterophilic interaction between them is significantly stronger . Having established that both proteins could physically interact ( Figure 5 ) and that jamb and jamc are co-expressed by myoblasts in wild-type ( Figure 1 ) and mutant embryos ( Figure S2 ) , we used cellular transplantation experiments to determine the mechanism of binding between Jamb and Jamc for myocyte fusion in vivo . Firstly , to demonstrate that jambHU3319 mutant myocytes are unable to fuse to each other , as observed in the mutant phenotype ( Figure 2 ) , we transplanted fluorescent dextran-labelled cells from jambHU3319 donors into jambHU3319 hosts and counted the number of labelled mononucleated or multinucleated fibres at 48 h . p . f . ( Table 1 ) . As expected , only 5% of myocytes derived from transplanted donor cells were able to fuse to mutant host myocytes , showing that expression of Jamc is unable to compensate for the loss of Jamb . To establish if myocytes lacking jamb are nevertheless competent for fusion , we transplanted cells from jambHU3319 donors into wild-type hosts , and vice versa ( Figure 6A and Table 1 ) . When transplanted into wild-type hosts , 93% of jambHU3319 mutant cells could form multinucleate fibres , suggesting they are able to fuse with wild-type myocytes . Similarly , 95% of wild-type myocytes were able to form multinucleate fibres when transplanted into jambHU3319 hosts ( Table 1 ) . These results demonstrate that Jamb acts non-cell-autonomously , and that Jamb and Jamc need to be expressed by neighbouring cells for fusion to occur . To determine if the Jamb and Jamc interaction between cells is necessary for fusion , we tested the prediction that transplanted jambHU3319 mutant cells ( that could nevertheless express wild-type Jamc ) would be able to fuse to jamcsa0037 hosts ( that could express wild-type Jamb ) . We observed that 96% of jambHU3319 mutant donor cells were able to fuse to jamcsa0037 mutant host cells ( Figure 6C , Table 1 ) . The cellular complementation between jamb and jamc mutant myocytes demonstrates that Jamb and Jamc must interact as a heterophilic pair on neighbouring cells and do not act as independent homophilic receptors . To show that the interaction between Jamb and Jamc proteins was necessary for fusion and did not require any additional factors , donor cells that were deficient in both Jamb and Jamc ( jambHU3319 embryos injected with a jamc-targeted morpholino ) were transplanted into wild-type hosts ( Figure 6E; Table 1 ) . Most doubly-deficient donor cells ( 88% ) could not fuse with wild-type host cells , demonstrating that expression of either Jamb or Jamc is essential for a myocyte to be competent for fusion . Doubly-deficient embryos are indistinguishable from jambHU3319 and jamcsa0037 mutant embryos , suggesting no further phenotypic enhancement from combined knockdown of both proteins ( Figure S3 ) . We repeated each of the transplant experiments described above , except we used jamcsa0037 mutants as donors to establish if myocytes from both mutants behaved similarly . As with jambHU3319 transplants , we observed that jamcsa0037 donor cells were unable to fuse to jamcsa0037 host cells ( Table 1 ) , showing that Jamb is unable to rescue the loss of Jamc; Jamb and Jamc are not redundant . Wild-type donor cells were able to fuse to jamcsa0037 host cells and vice versa ( Figure 6B , Table 1 ) , demonstrating that jamc mutant myocytes are competent for fusion and suggesting that Jamb and Jamc need to be expressed by neighbouring cells for fusion to occur between them . In addition , jamcsa0037 donor cells were able to complement jambHU3319 host cells and fuse with wild-type efficiency ( 95% , Figure 6D , Table 1 ) . This reinforces the conclusion that Jamb and Jamc must interact as a heterophilic pair between adjacent cells for fusion to occur . Finally , doubly deficient cells ( jamcsa0037 embryos injected with a jamb-targeted morpholino; Figure S3 ) were unable to fuse to wild-type host myocytes ( Figure 6F , Table 1 ) , further demonstrating that no other factor is interacting with either Jamb or Jamc . Interestingly , jamcsa0037 mutant cells transplanted into a wild-type host fused less efficiently than into a jambHU3319 host , suggesting that homophilic Jamb interactions between donor and host myocytes could inhibit fusion in the absence of Jamc on the donor cell ( compare jamcsa0037 donor , wild-type host and jamcsa0037 donor , jambHU3319 host; Table 1 , Figure 6B and D ) . Taken together , these data show that the physical interaction between Jamb-Jamc is required between neighbouring cells for myocyte fusion to occur in vivo . The overabundance of fast muscle fibres in the absence of myocyte fusion suggested that the regulation of jamb and jamc might play an important role in the control of muscle patterning and development . Slow-twitch muscle myocytes do not undergo fusion during primary myogenesis [40] . However , in zebrafish embryos mutant for the transcriptional repressor prdm1a , the premigratory progenitors of slow-twitch muscle [39] express fast muscle-specific genes [45] and inappropriately fuse with the neighbouring fast muscle myocytes , resulting in the absence of slow muscle fibres [40] . These observations suggest that prdm1a mutant adaxial cells must ectopically express critical cell surface proteins necessary for myocyte fusion . To test if either jamb , jamc , or kirrel3l [22] are ectopically expressed by prdm1a mutant adaxial cells , we performed wholemount in situ hybridization using riboprobes for each gene . We observed that jamc is misexpressed in the adaxial cells of prdm1atp39 embryos , but that jamb and kirrel3l are not ( Figure 7 ) . All three genes are expressed in fast muscle myoblasts of prdm1atp39 mutant embryos , as expected ( Figure 7 , Figure S4 ) . These results suggest that misregulation of jamc permits ectopic fusion of mutant slow muscle precursors with neighbouring jamb-expressing fast muscle myocytes; in wild-type embryos , prdm1a represses jamc in adaxial cells to prevent this occurring . This also implies that a heterophilic interaction of Jamb and Jamc between mutant slow muscle cells and fast muscle myocytes is necessary for ectopic fusion events to occur . Finally , these data also suggest that transcriptional regulation of jamc triggers fast muscle myocyte fusion events in vertebrate musculature . To determine if expression of jamc alone is sufficient to cause fusion events between slow muscle cells and fast muscle myocytes , we attempted to ectopically express jamc in wild-type slow muscle by microinjecting transgenic embryos containing a slow muscle marker , Tg ( smyhc1::egfp ) i104 [46] , with full-length , capped jamc mRNA or a plasmid containing full-length jamc . We did not observe any ectopic fusion events with slow muscle cells in injected embryos ( unpublished data ) , suggesting that other components , presumably also regulated by prdm1a , are necessary for fusion . Furthermore , we tested whether or not interaction between Jamb and Jamc is sufficient to drive fusion in heterologous cells , as described for the fusogen EFF-1 [47] , by mixing HEK293E cells transfected with plasmids containing either full-length jamb or jamc . No fusion events were observed in mixed cultures of jamb- and jamc-transfected cells , or control cultures containing only jamb- or jamc-transfected cells ( unpublished data ) . These results suggest that the context of Jamb and Jamc binding determines whether or not the interacting cells will fuse .
Using a combination of quantitative biochemistry , mutant zebrafish , and cell transplantation experiments , we have shown that we have identified a heterophilic interaction between a cell surface receptor pair that is essential in vivo for myocyte fusion in a vertebrate . This discovery has important implications for the molecular mechanism , regulation , and evolution of cellular fusion in the context of myogenesis . The identification of Kirrel3l as a functional orthologue of Kirre/Rst in zebrafish and of other key intracellular effectors [19]–[23] suggests conservation of the important signalling pathways for the process of myocyte fusion between vertebrates and invertebrates . Our discovery of Jamb and Jamc as a new deuterostome-restricted [31] receptor pair that is essential for fusion in the zebrafish axial musculature raises the possibility of vertebrate-specific adaptations of the components and the regulation of muscle development in vertebrates . For example , our results suggest that this interaction is independent of Kirrel3l , suggesting that multiple recognition steps between vertebrate myocytes are required for fusion . During differentiation , myocytes make a fundamental decision between founding a new muscle fibre or fusing to an existing one . In chicken and mouse embryos this decision seems to be temporally controlled: primary myocytes form an array of elongated mononucleate fibres , to which later differentiating myocytes fuse [48] , [49] . In the larval body wall of Drosophila , this decision is controlled by early specification of myoblasts into two distinct subtypes , ultimately defining the number of muscle fibres formed [7] , [12]–[16] , [50] . Our results show that in the absence of fusion in the zebrafish axial musculature , the number of fast-twitch muscle fibres almost doubles , suggesting that the fast muscle precursors are not divided into defined subpopulations , but that each myocyte is capable of founding a fibre if it does not fuse to an existing one . The co-expression of these essential receptors in all fast muscle myoblasts adds to the suggestion that the precursors are not restricted to specific fates . In addition , our transplantation experiments did not reveal any functional subdivision of myocytes; approximately 95% of jamb or jamc mutant donor myocytes were able to undergo fusion with jamc or jamb host myocytes , respectively . The dynamic nature of jamc expression in fast muscle myoblasts and repression in slow muscle precursors suggest that regulation of jamc plays a fundamental role in the patterning of muscles through the timing of fusion events , rather than specification . Furthermore , other elements of terminal differentiation , such as elongation and sarcomerogenesis , seem to be independent of the process of fusion . Our results suggest that the interaction between Jamb and Jamc expressed by neighbouring cells is essential for fusion . These cell surface receptors likely mediate an initial recognition and adhesion event similar to that of the cell surface receptors Kirre and Sns in Drosophila . It is unlikely that the interaction between Jamb and Jamc is sufficient for fusion because both proteins are known to be expressed and interact in other tissues that do not normally undergo fusion , such as the vascular endothelium [51] . Jamb and Jamc permit cellular recognition and adhesion , but do not cause fusion when expressed in heterologous cells such as CHO [52] , MDCK [51] , or HEK293E ( unpublished data ) , unlike EFF-1 , a known fusogen in C . elegans that causes spontaneous fusion between Sf9 insect cells transfected with membrane-bound splice variants [47] . In addition , ectopically expressing either Jamb or Jamc in zebrafish slow muscle cells did not result in inappropriate fusion with fast muscle precursors ( unpublished data ) . We hypothesise that the biological context of Jamb and Jamc binding determines the productive output of that interaction; for example , cellular fusion or tight junction formation . Interaction between Kirre and Sns is thought to be the initiation event for the formation of a crucial adhesion and signalling complex between a founder cell and a fusion-competent myoblast , termed FuRMAS [9] . Both Kirre and Sns are thought to be involved in localising important signalling components to this complex , such as Rols7 and Mbc , in order to build and maintain a complex branched F-actin structure necessary for fusion [11] . Similarly , Jam-B and Jam-C are known to be involved in forming tight junctions between cells and localising other proteins such as ZO-1 to those sites [27]–[29] . A specialised fusion structure has not been reported or characterised in vertebrates to date , but Jamb and Jamc may form part of a similar complex that defines the site of fusion between myocytes . A conserved role for JAM-B and JAM-C in myocyte fusion in other vertebrate organisms is an important focus for our future research . In support of this hypothesis , both genes have been shown to be expressed in developing skeletal muscle of mouse [53] , [54] and human embryos [55] . Knockout mice models have been generated for both genes and studied in the context of fertility [30] , immunity [56] , [57] , cardiac development [58] , neurobiology [59] , and stem cell biology [60] . Two independent Jam-C−/− models have been reported with high perinatal mortality [30] , [57]; approximately two-thirds of mutant pups die within 48 h after birth and are described as cyanotic and gasping [57] , [58] . The formation , structure , and integrity of the diaphragm has not been studied in these mice . Surviving Jam-C mutant mice also exhibit significant growth retardation starting from the second week of perinatal development [56] , [58] , megaeosophagus [56]–[58] , weaker forepaw grip strength [59] , and “jitteriness” [58] . These characteristics could conceivably be a result of underlying muscle defects . Mice deficient in Jam-B display no overt phenotype [53] , [60] although skeletal muscle development and growth have not been specifically examined in detail . We believe that identification of the critical function of jamb and jamc in zebrafish myocyte fusion presents us with an opportunity to better understand myogenesis in higher vertebrates and cellular fusion in other biological contexts . A molecular explanation of the intercellular recognition processes that are necessary for fusion in , for example , placenta formation and sperm-egg interactions remains incomplete . The identification of Jamb and Jamc as an in vivo validated receptor-ligand pair required for cellular fusion in vertebrates may now provide impetus to shed more light on these biological processes .
Zebrafish mutants carrying alleles jambHU3319 and jamcsa0037 were obtained from the Hubrecht laboratory and Wellcome Trust Sanger Institute Zebrafish Mutant Resource and maintained according to standard fish husbandry conditions and UK Home Office and Institute regulations and guidelines . Both jamb and jamc mutant lines were homozygous viable and fertile in our aquarium , but did not thrive . Embryos were fixed in either 4% paraformaldehyde or , for EB165 immunohistochemistry , in methanol . We refer to the zebrafish homologues of JAM-B and JAM-C as jamb and jamc , respectively , for the sake of clarity and consistency with other recent literature concerning the JAM family [26] . The official symbols and accession/reference numbers are as follows: jamb ( official symbol jam2a ) - Entrez gene: 100005261; jamc ( official symbol jam3b ) - Entrez Gene: 569217 . The extracellular domain of Jamb or Jamc were expressed as a soluble fusion protein with rat Cd4 domains 3 and 4 and either a 6-histidine ( Cd4d3+4-6H ) or an enzymatically biotinylatable peptide ( Cd4d3+4-bio ) C-terminal tag . These were purified and used in surface plasmon resonance experiments , essentially as previously described [33] . The activity of the Jamc analyte used in binding experiments cannot be accurately determined , as Jamc is capable of homophilic association . Dissociation rate constants ( kd ) , which are not confounded by analyte activities ( and can therefore be directly compared ) , were calculated by averaging the dissociation phase of three different concentrations of purified Jamc-Cd4d3+4-6H or Jamb-Cd4d3+4-6H protein and fitting a simple first-order decay curve . Fits to the data were good , suggesting a 1∶1 stoichiometry of binding . Half lives ( t½ ) were calculated by t½ = ln 2/kd . Wholemount in situ hybridisations using digoxygenin-labelled riboprobes were performed using standard protocols [61] . Riboprobe templates were generated from plasmids containing the extracellular domain of jamb , jamc , or kirrel3l . Wholemount immunohistochemistry was performed according to standard methods , using mouse monoclonal antibodies F59 , EB165 ( 1∶200; Developmental Studies Hybridoma Bank ) and anti-mouse IgG , Alexa-488- or Alexa-568-conjugated secondary antibodies ( 1∶5 , 000; Molecular Probes ) . Embryos were mounted in Slowfade Gold with DAPI ( Molecular Probes ) and/or treated with Alexa-488-conjugated phalloidin ( 1∶40; Cambrex Biosciences ) . Capped membrane-targeted red fluorescent protein mRNA was transcribed from a linearised plasmid [62] using the mMessage mMachine kit ( Ambion ) and SP6 polymerase . 1–2 cell stage embryos were microinjected with approximately 4 nl of mRNA ( ∼25 ng/µl ) diluted in sterile water , 0 . 1% phenol red ( Sigma-Aldrich ) , fixed with 4% paraformaldehyde , and observed by confocal microscopy . Optical cross-sections of fixed , 48 h . p . f . mRFP-labelled embryos were computed from z-stacks collected from myotomes 10–15 in each embryo , using Leica Application Suite Advanced Fluorescence software ( LAS AF; Leica Microsystems ) . Fibres were manually counted in each cross-section; superficial slow muscle fibres were excluded from analysis . Estimation of nuclei was determined by mfhnh+ ( 1−m ) fh , where m is the fraction of multinucleated fibres ( quantified in same donor into same host genotype transplant controls; Table S1 ) , fh is the number of fibres , nh is the average number of nuclei per fibre reported [20] , and h is the developmental stage in h . p . f . 1- and 2-cell stage embryos were injected with approximately 4 nl of translation blocking morpholinos ( ∼200 µM , 5–7 . 5 ng per embryo ) diluted in sterile water with 0 . 1% phenol red . Translation blocking morpholino sequences were as follows: jamb: GCA CAC CAG CAT TTT CTC CAC AGT G; jamc: TTA ACG CCA TCT TGG AGT CGG TGA A . Transplants were performed essentially as described [63] . Briefly , 1–2-cell stage donor embryos were injected with lysine-fixable fluorescein or rhodamine labelled dextran ( 10 , 000 kDa , 1% in sterile water; Molecular Probes ) . Fluorescently labelled donor cells were transplanted into the marginal cells of unlabelled host embryos between high/sphere to ∼30% epiboly stages . Transplanted embryos were maintained in embryo media supplemented with penicillin ( 50 U/ml ) and streptomycin ( 50 µg/ml ) , fixed in 4% paraformaldehyde at 48 h . p . f . , and analysed by confocal microscopy . Confocal microscopy images were collected using a Leica SP5/DM6000 confocal microscope and LAS AF software . Wholemount in situ hybridisation images were obtained using a Zeiss Imager M1 microscope , Zeiss AxioCam Hrc camera , and Zeiss AxioVision software . Entire images were adjusted for contrast , brightness , dynamic range , and resampled to a standardised resolution ( 300 d . p . i . ) using Adobe Photoshop CS2 . Statistical significance between wild-type and mutant fibre counts and nuclei estimates were determined by one-tailed Student's t test , modified to take unequal sample size and variance into account . The number of embryos is presented in Table S1 . | The fusion of precursor cells is a crucial step in many biological processes , one of which is the development of skeletal muscle . The molecular and cell biology of fusion of muscle precursors has been well described in Drosophila melanogaster larvae , leading to insights into the process in vertebrates . However , the identity and mechanism of action of essential cell surface proteins for fusion between vertebrate muscle precursors has previously been lacking . Here , we describe a vertebrate-specific cell surface receptor pair that is essential for fusion in zebrafish: Jamb and Jamc . Loss of function of either receptor causes a near-complete block in fusion , resulting in an overabundance of mononucleate muscle fibres that are otherwise overtly normal . We demonstrate that Jamb and Jamc physically interact and are co-expressed by muscle precursors . Moreover , we show that the interaction between them is essential for fusion between neighbouring precursors in an embryo . We hypothesise that binding of Jamb to Jamc is a necessary recognition and adhesion step permissive for , but not sufficient to cause , myocyte fusion . Knowledge of these molecular components in vertebrates will lead to better understanding of how fusion is controlled to pattern skeletal muscle tissue . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biomacromolecule-ligand",
"interactions",
"protein",
"interactions",
"animal",
"models",
"model",
"organisms",
"membrane",
"receptor",
"signaling",
"proteins",
"muscle",
"fibers",
"biology",
"molecular",
"biology",
"biochemistry",
"zebrafish",
"signal",
"transduction",
"tr... | 2011 | Jamb and Jamc Are Essential for Vertebrate Myocyte Fusion |
Haplotype maps ( HapMaps ) reveal underlying sequence variation and facilitate the study of recombination and genetic diversity . In general , HapMaps are produced by analysis of Single-Nucleotide Polymorphism ( SNP ) segregation in large numbers of meiotic progeny . Candida albicans , the most common human fungal pathogen , is an obligate diploid that does not appear to undergo meiosis . Thus , standard methods for haplotype mapping cannot be used . We exploited naturally occurring aneuploid strains to determine the haplotypes of the eight chromosome pairs in the C . albicans laboratory strain SC5314 and in a clinical isolate . Comparison of the maps revealed that the clinical strain had undergone a significant amount of genome rearrangement , consisting primarily of crossover or gene conversion recombination events . SNP map haplotyping revealed that insertion and activation of the UAU1 cassette in essential and non-essential genes can result in whole chromosome aneuploidy . UAU1 is often used to construct homozygous deletions of targeted genes in C . albicans; the exact mechanism ( trisomy followed by chromosome loss versus gene conversion ) has not been determined . UAU1 insertion into the essential ORC1 gene resulted in a large proportion of trisomic strains , while gene conversion events predominated when UAU1 was inserted into the non-essential LRO1 gene . Therefore , induced aneuploidies can be used to generate HapMaps , which are essential for analyzing genome alterations and mitotic recombination events in this clonal organism .
Researchers are using Haplotype Maps ( HapMaps ) with increasing frequency , due to their utility in revealing underlying sequence variation [1] . HapMaps facilitate the study of factors influencing recombination and genetic diversity , as the three primary activities that alter haplotype structure are mutation , recombination , and selection [2 , 3] . The haplotype is the map of the location of specific gene alleles on each specific chromosome homolog . If an organism is haploid , containing only one copy ( homolog ) of each chromosome , the haplotype is the genetic sequence of that chromosome . In diploid organisms , alleles of two different genes that reside on the same chromosome can be located on the same homolog ( in cis ) or on different homologs ( in trans ) . In most organisms , HapMaps are produced by analysis of Single-Nucleotide Polymorphism ( SNP ) segregation in large numbers of meiotic progeny . Candida albicans , the most common human fungal pathogen , is an obligate diploid organism that is not currently known to undergo meiosis and , unlike its close relative Saccharomyces cerevisiae , no haploid or homozygous form of C . albicans is available . As a result , the haplotypes of each chromosome have yet to be determined; researchers usually do not know if two heterozygous mutations , in genes that are found on the same chromosome , occur on the same homolog or on different homologs . As a consequence , linkage mapping or recombinational analysis is not possible in C . albicans using standard methods . However , investigation of recombination is vitally important , as a large proportion of events leading to antifungal drug resistance in clinical settings may involve recombinational mechanisms [4] . Sequencing of the C . albicans genome revealed a high level of natural heterozygosity ( 55 , 655 SNPs for the entire 32Mb diploid genome ) [5] . Additional studies showed that differences in SNPs are associated with different phenotypes [6] . Two newly-developed microarray technologies – whole genome single-nucleotide polymorphism ( SNP ) analysis , and comparative genome hybridization ( CGH ) analysis , permit the analysis of specific genotypes and of the number of copies of each allele , respectively . SNP microarrays are composed of DNA sequences known to be polymorphic in the genome; both alleles are present on the array , and hybridization of DNA from the strain of interest to one or both of the allele sequences indicates the homozygosity or heterozygosity at that position in that strain [7] . SNP arrays can detect loss of heterozygosity ( LOH ) but do not distinguish between alleles that are present in cis or in trans and thus cannot detect reciprocal cross-over events . In C . albicans , cross-overs between highly repetitive Major Repeat Sequence ( MRS ) regions , which are found on essentially all chromosomes , are thought to have resulted in multiple translocation events found in divergent clinical strains [8] . CGH microarrays are composed of an array of DNA sequences from every ORF in the genome , and hybridization of DNA from the strain of interest determines the copy number of each ORF relative to the copy number of the corresponding ORF in the diploid reference strain [9] . When the CGH intensity data is plotted as a function of position on the genetic map , chromosomes or chromosomal segments that exhibit a deviation in copy number ( aneuploidy ) are readily identified . The combined use of both SNP and CGH microarrays can identify heterozygous DNA sequence polymorphisms ( which will be present in a 1:1 ratio in a diploid cell ) and demonstrate the copy number of the chromosomes bearing the SNPs . C . albicans tolerates aneuploidy of all of its chromosomes [8–10] . For example , growth on sorbose induces chromosome 5 ( Ch5 ) monosomy , and duplication of the remaining Ch5 homolog occurs when the monosomic strain is grown on rich media [11] . Similarly , chromosome loss followed by duplication of the remaining homolog is one mechanism for spontaneous MTL homozygosis [12] . Natural whole chromosome trisomies have been reported in C . albicans lab strains: Ch1 trisomy and Ch2 trisomy occur in commonly used strains [8 , 9] and Ch3 trisomy was reported in strains selected for fluconazole resistance [13] . Furthermore , CGH analysis demonstrated that all C . albicans chromosomes can be aneuploid and that approximately 50% of FluR laboratory and clinical strains contained at least one aneuploidy [4] . It has been hypothesized that changes in chromosome copy number provide a means for genetic variation in C . albicans [14] . We realized that strains that are aneuploid can be exploited to determine the cis/trans linkage of the SNPs in a non-meiotic heterozygous organism like C . albicans . Strains in which a chromosome is homozygous ( either due to monosomy or chromosome duplication followed by loss of the heterozygous chromosome ) will give SNP ratios of 1:0 or 0:1 . Addition of a third copy of the chromosome will change the ratio of the SNPs on that chromosome from 1:1 to 1:2 or 2:1 , depending on which allele is present on the extra chromosome , allowing assignment of a particular SNP allele to a specific chromosome . In C . albicans , the UAU1 deletion cassette [15] was developed to generate homozygous disruption or deletion alleles using only a single transformation step . The cassette includes 3 segments: a nonfunctional 3′ deletion copy of URA3 , a functional ARG4 and a nonfunctional 5′ deletion copy of URA3 that shares 530bp of sequence identity with the 3′ deletion segment . In the UAU1 state , the cassette expresses ARG4 but not URA3 . Recombination between the 530bp of homologous sequence of the two ura3 segments produces an intact URA3 gene and excision of ARG4 , thus expressing URA3 but not ARG4 . Transformation of the UAU1 cassette into a locus , followed by screening for segregants that express both ARG4 and URA3 , results in the isolation of homozygous mutations , although the mechanism by which homozygosis occurs ( gene conversion or chromosome duplication followed by chromosome loss ) has not been determined . When an essential gene is targeted , the target locus becomes trisomic , carrying copies of URA3 , ARG4 and a wild-type copy of the targeted gene . However , the presence and extent of the trisomy ( whole-chromosome versus segmental ) has not been investigated . We have developed a novel approach to determine the haplotype of C . albicans strains using SNP and CGH array analysis of strains carrying either naturally-occurring or UAU1-induced aneuploidies . If a strain is monosomic ( or if it is a disomic homozygote ) the ratio of the two SNP alleles on that chromosome will be 1:0 or 0:1 . If a strain is carrying an extra copy of a chromosome ( trisomy ) , the ratio of the SNPs on that chromosome will be 1:2 or 2:1 , depending on which allele is present on the extra chromosome . These altered ratios enable the assignment of all SNP alleles to a specific chromosomal homolog . We exploited this feature of aneuploid strains to generate SNP haplotype maps of laboratory and clinical C . albicans strains , finding that a significant amount of recombination has occurred in the clinical isolate relative to the laboratory strain . We also investigated the genome changes resulting from the activation of the UAU1 cassette in essential and non-essential genes located on Ch1 , and found that trisomy of the full-length Chromosome 1 carrying the target gene results when an essential gene is targeted , while homozygous mutants are obtained primarily by gene conversion when a nonessential gene is targeted . Our results show that the SNP haplotype map can be used to investigate recombination events across the entire genome .
C . albicans was routinely cultured in YEPD ( 1% Yeast Extract , 2% Peptone , 2% Dextrose , 2% Agar ) supplemented with 20mg/L uridine at 30 °C . Selection was done on minimal medium ( 6 . 7% yeast nitrogen base plus ammonium sulfate , without amino-acids , 2% dextrose , 2% agar ) supplemented with the appropriate amino-acid mix . The strains used to determine SC5314 haplotype maps are derivatives of SC5314 that were shown to be homozygous or trisomic for a specific chromosome when analyzed by CGH and SNP arrays ( Table 1 ) . The strains used to determine T118 haplotype maps are drug-resistant derivatives from fluconazole-sensitive clinical isolate T118 [16 , 17] that were found to be homozygous or trisomic for a specific chromosome when analyzed by CGH and SNP arrays ( Table 2 ) . For the UAU1 analysis , chromosome trisomy was artificially induced in SN76 [18] , which is derived from SC5314 . The strains constructed for the UAU1 analysis are presented in Table 3 . Integration of the UAU1 cassette into the C . albicans genome was done by homologous recombination using a PCR-based cassette method [19] . Oligonucleotides used in this work are listed in Table 4 . The orc1::UAU1 and lro1::UAU1 cassettes were obtained by amplifying the UAU1 cassette from pBME101 [15] with oligonucleotides CaORC1-pBME101-F/R and CaLRO1-pBME101-F/R , respectively . PCR products were concentrated by ethanol precipitation and then transformed into SN76 , using a modification of the standard Lithium/Acetate transformation protocol . Overnight cultures were inoculated into 50ml YEPD+uri at an OD600 of ∼0 . 05 and incubated with shaking at 30°c until the OD600 reached ∼0 . 5 . Cells were pelleted , washed with 5ml sterile water and resuspended in 500ul of TE/LiOAc ( 10mM Tris HCl [pH 7 . 5] , 1mM EDTA [pH 8] , 0 . 1M Lithium Acetate ) . Cells were transferred to a 1 . 5ml eppendorf tube , pelleted and resuspended in 300 ul of TE/LiOAc . One hundred microliters of this cell suspension was mixed with 5ul of Salmon Sperm Single Stranded DNA ( 10 mg/ml ) and the transforming DNA , and then incubated at room temperature for 30 minutes . 700ul of PLATE mix ( 10mM Tris HCl [pH 7 . 5] , 1mM EDTA [pH 8] , 0 . 1M Lithium Acetate , 50% polyethylene glycol 3350 ) was added and mixed by pipetting slowly . The mixture was incubated at room temperature overnight and then incubated at 42 °C for one hour . Cells were pelleted , resuspended in 200 ul of sterile water and plated on appropriate media at 30 °C for 3 days . Transformed cells were plated on Min+His+Uri plates to select for Arg+ transformants . The ORC1-UAU1 and LRO1-UAU1 transformants were screened by colony PCR with oligonucleotides CaARG4detect-F/CaORC1+374-R and Ca1322/2294Chr1-SNP-F/CaURA3+289-F , respectively . The genomic DNA of positive transformants was extracted from a single colony and the genotype of the ORC1/orc1::UAU1 heterozygotes was confirmed by PCR with oligonucleotides CaORC1+2707-F/CaURA3+289-F and CaARG4detect-F/CaORC1+374-R . Genotypes of the LRO1/lro1::UAU1 heterozygotes were confirmed by PCR with oligonucleotides Ca1322/2294Chr1-SNP-F/CaURA3+289-F and CaARG4detect-F/CaLRO1+2416-R . The presence of the wild type gene in the heterozygotes was confirmed by PCR with oligonucleotides CaORC1+2707-F/CaORC1+2072-R for ORC1 and Ca1322/2294Chr1-SNP-F/CaLRO1+1659-R for LRO1 . The SNP present within the ORC1 open reading frame was amplified by PCR with oligonucleotides CaARG4detect-F/CaORC1+374-R and sent for sequencing to determine which Ch1 homolog was targeted . DKCa157 is an ORC1/orc1::UAU1 heterozygote in which the UAU1 cassette integrated into one of the Ch1 homologs , while DKCa158 is a second heterozygote in which the UAU1 cassette integrated into the other Ch1 homolog . The SNP located within the LRO1 open reading frame was amplified by PCR with oligonucleotides Ca1322/2294Chr1-SNP-F/R . The PCR product was then incubated overnight at 37 °C with restriction enzyme BccI . We selected two LRO1/lro1::UAU1 strains such that a different homolog was targeted in each strain ( DKCa616 and DKCa623 ) . DKCa157 , DKCa158 , DKCa616 and DKCa623 cells were patched on YEPD+uri , grown at 30°C for 48h and replica-plated on Min+His to select for Arg+ Ura+ derivatives . Genomic DNA was extracted from single Arg+ Ura+ colonies , as described previously . The presence/absence of the wild type ORC1 and LRO1 genes was assessed by PCR with oligonucleotides CaORC1+2707-F/CaORC1+2072-R and Ca1322/2294Chr1-SNP-F/CaLRO1+1659-R , respectively . As potential Ch1 trisomy candidates , we selected Arg+ Ura+ derivatives in which a wild-type copy of the target gene ORC1 or LRO1 is still present ( DKCa169/DKCa634 for ORC1 and DKCa632/DKCa681 for LRO1 ) . We also selected 10 Arg+ Ura+ derivatives from DKCa616 and from DKCa623 that lacked the wild type LRO1 gene , to investigate the mechanism by which homozygosis occurs for non-essential genes disrupted by the UAU1 cassette . To confirm trisomy of the target locus , Southern blot analysis was performed as described previously [9] . Genomic DNAs from orc1::UAU1 and lro1::UAU1 Arg+ Ura+ derivatives were digested with MfeI and XbaI + BbsI respectively . DNA probes were generated by PCR using the DIG DNA Labeling Kit ( Roche ) according to the manufacturer's instructions . The ORC1 probe was generated with the oligonucleotides CaORC1+3155-F/CaORC1+2676-R and the LRO1 probe was generated with the oligonucleotides CaLRO1+2332-F/CaLRO1+2683-R . The size of each fragment was determined based on the genome sequence . CGH and SNP arrays were performed as described previously [7 , 9] and were plotted without data smoothing [4] . Out of 150 defined SNPs [20] , 122 SNPs were used in this study . Most of those omitted were homozygous in SC5314 or were part of one large SNP marker - only one SNP for each of this type of locus is presented on the map . The order of SNP alleles presented along each chromosome is based on the physical map and the published sequence of the genome [20] , as it cannot be determined directly from SNP haplotype mapping .
Single Nucleotide Polymorphism ( SNP ) microarrays determine if specific regions of a diploid genome are heterozygous or homozygous [7] . However , SNP arrays alone cannot determine the cis or trans arrangement of linked heterozygous alleles . To generate a haplotype map of C . albicans , we determined the cis/trans linkage of SNPs in strains carrying aneuploidies . The altered chromosomal copy number was first detected by Comparative Genome Hybridization ( CGH ) microarrays [9] . Theoretically , the addition of a third copy of a chromosome will change the ratio of the SNPs on that chromosome from 1:1 to 1:2 or 2:1 , while loss of one of the two homologs will result in a 1:0 allele ratio for one of the alleles for each SNP . We exploited these alterations in allelic ratio to assign a particular SNP allele to a specific chromosome ( Figure 1A ) . To test the feasibility of this SNP haplotype mapping approach , we analyzed three C . albicans yeast strains derived from laboratory strain SC5314 for correlations between changes in chromosomal copy number and SNP ratios . The allelic fractions of the chromosome R ( ChR ) SNPs in these strains were compared to the allelic fractions in the diploid strain SC5314 ( Figure 1B and 1C ) . As expected , SC5314 alleles were heterozygous for ChR ( an allelic fraction of ∼0 . 5 , which is equivalent to a SNP ratio of 1:1 ) whereas alleles in strains YJB10698 and YJB10699 were generally homozygous ( allelic fraction ∼0 or ∼1 ( 0:1 or 1:0 ) respectively ) . Importantly , all SNP alleles on ChR that were 0:1 in strain YJB10698 were 1:0 in YJB10699 , and vice versa . We used these results to define the set of ChR SNP alleles in YJB10698 as allele “a” and the set of SNP alleles in YJB10699 as allele “b” . Interestingly , in the third strain , YJB10700 , most SNPs had an allelic fraction of ∼0 . 66 ( 2:1 ) for ChR ( Figure 1B and 1C ) , suggesting that the strain is trisomic for ChR . A CGH microarray confirmed the YJB10700 ChR trisomy ( Figure 1D ) . This result , combined with the SNP data , indicates that YJB10700 has two copies of SNP allele set “a” ( the ChRa homolog ) and only one copy of SNP allele set “b” ( the ChRb homolog ) . Thus , allelic fractions from strains that are aneuploid for a specific chromosome can be used to generate SNP haplotype maps of that chromosome . We next used aneuploidies to determine the SNP haplotype for all of the chromosomes in SC5314 , the most common C . albicans laboratory strain ( Tables S1 and S2 ) . SC5314 is the wild-type strain that was sequenced; it is the progenitor of CAI4 and BWP17 , the major strains used in laboratory studies . Previous analysis of a large set of strains identified twenty SC5314 derivatives in which at least one of the eight chromosomes was either homozygous or trisomic ( Table 1 ) . For example , we included MTLa/a and MTLα/α derivatives from the MTLa/α SC5314 that had been selected previously for their ability to grow on sorbose [7] . Because these two isolates are homozygous for Chr5 , SNP array analysis defined haplotype maps for each Chr5 homolog . The data from multiple aneuploid strains were combined to construct a complete SNP haplotype map of strain SC5314 ( Figure 2; Table S1 ) . The current C . albicans SNP map contains 150 SNP markers comprising 561 SNPs and 9 insertions-deletions . On average the map has 1 SNP marker every 111kb across the 16Mb genome [20] . The SNP haplotype map determined for SC5314 in this paper will serve as a reference for subsequent C . albicans haplotype analysis . As was done with the primary DNA sequence [5] , we have set the SNP haplotype map of SC5314 as the reference for comparison with other strains . Presentation of the SNP map of SC5314 as un-rearranged is not meant to imply any evolutionary or phylogenetic relationships . One striking feature of the C . albicans genome is its plasticity . Although the genome of laboratory strains grown under standard growth conditions is relatively stable , half of clinical isolates have variant karyotypes [21] . In addition , between 3% and 7% of clinical isolates are naturally homozygous at the MTL locus [22 , 23] . One mechanism leading to loss of heterozygosity ( LOH ) at MTL is homozygosis of the entire chromosome due to a chromosome loss event followed by duplication of the remaining chromosome [12] . Another alternative mechanism for homozygosis requires chromosome duplication followed by loss of the single heterozygous chromosome and retention of the two remaining homozygous chromosomes . In addition , Soll and coworkers have reported conflicting data on the significance of gene conversion at the mating type locus [12 , 24] . Recent whole-genome CGH array analysis of fluconazole-resistant clinical isolates revealed that approximately 50% of FluR clinical strains exhibited chromosomal aneuploidies , with up to 20% of the isolates carrying an isochromosomal derivative of Ch5 , in which the left arm of the chromosome had been duplicated [4] . In order to gain a better understanding of the genome rearrangements occurring in clinical isolates , we compared the SNP haplotype of SC5314 with the haplotype of a clinically-derived isolate . T118 is a diploid FluS strain obtained from an HIV-infected patient [16] . Preliminary SNP analysis of T118 detected SNP heterozygosity on Ch2 and Ch4 , while at least seven regions ( on ChR , Ch1 , Ch3 , Ch5 , Ch6 and Ch7 ) exhibited LOH in T118 . To construct a complete SNP haplotype map , we used isolates previously derived from T118 by serial passaging in the presence of fluconazole [17] . We identified seven individual T118-derived strains that were trisomic or homozygous for one or more chromosomes ( Table 2 ) , and used these strains for SNP haplotype analysis ( Table S3 ) . Comparison of the T118 haplotype map ( Figure 3 ) with the SC5314 map showed that the T118 clinical isolate has a number of chromosomal alterations involving every chromosome relative to the SNP map for SC5314 . However , these alterations are only those that are detectable by the current SNP array , and so represent only the minimal amount of variation between the two strains . Also , as discussed above , presentation of the T118 data as having undergone recombination or alteration does not imply that SC5314 was the un-recombined precursor to T118; interpretations are presented in this manner to facilitate discussion of the relative differences in the two strains . Finally , note that chromosome homolog assignments ( i . e . Ch1a or Ch1b ) are given based on the allele identity of the SNP closest to the centromere of each chromosome , and that the exact location of each reciprocal exchange event is unknown , although the crossover sites are shown as occurring half-way between the flanking SNPs in Figure 3 . Chromosomes 2 , 4 and 7 exhibited very simple alterations in T118 . Ch2 had five crossovers relative to SC5314 , while Ch4 had six . Ch7 exhibited a single gene conversion ( GC ) event , at SNP 141 . Chromosomes R , 1 and 3 had slightly more complex alterations , consisting of both crossovers and GCs . On ChR , there were two crossovers , between SNPs 45 and 150 and between 19 and 18 , with a region of homozygosity between these flanking crossovers . Ch1 had two simple crossover events on the left arm of the chromosome . On the right arm , a third crossover between SNPs 32 and 33 was flanked by a GC event at SNP 34; this alteration could be an independent GC or it could have occurred at the same time as the adjacent crossover . Ch3 had two GC events , one near the left telomere ( SNP 73 ) and the other to the left of the centromere ( SNP 81 ) . A crossover occurred between the two GC events . Because it is the most telomere-proximal SNP marker assayed , the homozygosity observed at SNP 73 could be the result of a standard gene conversion event , a reciprocal crossover followed by chromosome mis-segregation , or break-induced replication ( BIR ) . Chromosomes 5 and 6 exhibited a complex pattern of rearrangements . In strain T118 , Ch5 was completely homozygous for homolog 5a except for two regions of sequence deviation; SNPs 117 to 120 on the right arm of Ch5 and SNPs 106 to 110 on the left arm of the chromosome ( Figure 3 ) . SNPs 117 and 120 are homozygous for the Ch5b allele , rather than the Ch5a alleles present elsewhere . This change must have occurred prior to the events that led to the homozygosis of Ch5 , and resulted either from multiple crossover events centromere proximal to both 117 and 120 and on either side of SNP 118 , or from linked crossover and GC events . Subsequent formation of the tract of internal heterozygosity at SNPs 106 to 110 was more complicated . Either initial crossovers occurred between SNPs 110 and 111 and between SNPs 106 and 109 , or the entire region was involved in a gene conversion event . This would have been followed directly by a chromosome mis-segregation event in which the two Ch5a sister chromatids segregated together , resulting in homozygosis of the majority of Ch5 sequences and leaving only a small heterozygous tract on the left arm of the chromosome . Ch6 was completely homozygous in T118 except for SNP 131 . This homozygosis likely resulted from a failure to segregate the Ch6b sister chromatids followed by loss of the heterozygous Ch6a homolog; chromosome loss followed by duplication of the remaining homolog would not have accounted for the heterozygosity at SNP 131 . Comparison of Ch6 SNP maps in T118 and SC5314 indicates that the chromosome mis-segregation event leading to Ch6 homozygosis must have been preceded by a crossover event between SNPs 128 and 129 and a gene conversion event at SNP 131 . While some of the chromosomal recombination events occurred in or adjacent to the Major Repeat Sequence ( MRS ) tracts ( on Ch3 , Ch4 and Ch6 ) , the majority of the changes ( 27/30 events ) were not associated with an MRS . Thus , the MRS appears to be a site of recombination , albeit not one that is highly predominant . An expansion of this type of haplotype analysis using further clinical isolates will allow us to identify recombination events that may be linked to the acquisition of antifungal drug resistance in Candida albicans . The SNP haplotype mapping method described above requires aneuploid strains . To make this approach more widely applicable , a method for inducing trisomy of any desired chromosome is needed . One potential method is the use of the UAU1 deletion cassette [15] , which was designed to construct homozygous C . albicans deletion strains . Previous studies have shown that when the UAU1 cassette is inserted into an essential gene and Ura+ Arg+ selection is applied , strains that arise have three copies of the locus of interest . However , the organization of the extra copy has not been explored . We used the UAU1 cassette to ask if triplication arises by whole chromosome or by segmental trisomy . We used the UAU1 cassette to target an essential gene as well as a non-essential gene . Both genes were on Ch1 and both contained SNPs that allowed us to follow the fates of the individual homologs . We selected orf19 . 3000 , CaORC1 , a homolog of the essential S . cerevisiae ORC1 gene , which encodes the largest subunit of the origin recognition complex . Because of its central role in DNA replication initiation , we assumed that the C . albicans Orc1p also would be essential for cell viability . A SNP present in the ORC1 open reading frame allowed us to distinguish between the alleles on each Ch1 homolog . We deleted one copy of CaORC1 by integrating the UAU1 cassette into the ORC1 locus in parental strain SN76 [18] , an SC5314 derivative . Colony PCR identified two correct transformants out of 24 transformants screened . Sequencing of the SNP in the two heterozygotes indicated that the UAU1 cassette integrated into one ORC1 allele in the first transformant ( DKCa157 ) and into the other ORC1 allele in the second transformant ( DKCa158 ) . Single colonies from both heterozygotes were grown under selection for Arg+ Ura+ derivatives , which were analyzed by PCR to detect the presence of the UAU1 cassette ( Arg+ ) , the URA3 recombinant cassette ( Ura+ ) and the wild-type ORC1 allele ( see Materials and Methods ) . One hundred percent of the Arg+ Ura+ derivatives maintained a wild type ORC1 allele , providing strong support for the idea that CaOrc1p is essential for cell viability in C . albicans . To assess the extent of the trisomy in these strains , we performed SNP microarray analysis on seven Arg+ Ura+ derivatives ( three from DKCa157 and four from DKCa158 ) that by PCR and Southern analysis were trisomic at the ORC1 locus . Trisomy of the full-length Ch1 was observed in three of the seven strains ( 43% ) ( DKCa157-derived DKCa169 and DKCa633 and DKCa158-derived DKCa634 ) ( Figure 4 ) , which allowed us to determine the SNP haplotypes of Ch1 in those isolates . In the four remaining Arg+ Ura+ derivatives ( 57% ) , we observed a 1:1 SNP ratio , although we did detect unselected aneuploidies including trisomy of other chromosomes ( e . g . , Ch2 and Ch7 in DKCa170 ) . Further analysis of these strains indicated that they possessed four copies of the ORC1 locus – two wild-type , one bearing the UAU1 cassette and the other bearing the recombined UAU1 Ura+ derivative . These data explain the 1:1 Ch1 SNP ratio , and may indicate that ORC1 exhibits a mild haplo-insufficiency in Candida albicans , resulting in selective pressure to increase the copy number of the wild-type ORC1 gene when one copy is deleted . In all strains , including the parental strain , we observed a partial loss of heterozygosity on Ch2 , which is also observed in RM1000 , an ancestor of SN76 . This change occurred in the process of constructing RM1000 and therefore is expected to be present in all descendants . We also observed trisomy of Ch6 in parental strain SN76 as well as in all of its derivatives . This change is specific to the SN76 isolate used in this study , since CGH analysis of an independent SN76 isolate did not detect Ch6 trisomy ( data not shown ) . We next asked about the fate of non-essential genes disrupted with the UAU1 cassette . Previous reports have documented the presence of three copies of genes ( allelic triplications ) as a result of UAU1 activation in a non-essential gene [15] . We disrupted LRO1 ( orf19 . 6018 ) , which is also on Ch1 and includes a SNP ( 1322/2294 ) . The homolog in S . cerevisiae , Lro1p , is an acyltransferase whose function is not essential for viability in S . cerevisiae . We integrated the UAU1 cassette into the LRO1 locus and screened 24 transformants by colony PCR , obtaining 10 transformants that had correctly integrated into the LRO1 locus . Two heterozygotes ( DKCa616 and DKCa623 ) , representing an insertion in each of the two LRO1 alleles , were selected after SNP typing . Singles colonies from both heterozygotes were grown on SDC-arg-ura to select for Arg+ Ura+ derivatives in which the UAU1 cassette has been activated . The majority of the Arg+ Ura+ derivatives were homozygous diploid deletion strains in which the wild-type LRO1 sequence was no longer present , consistent with the idea that LRO1 is non-essential in C . albicans . The frequency of Arg+ Ura+ cells that maintained the wild-type LRO1 gene was low , as determined by colony PCR ( 3% for DKCa616 and 25% for DKCa623 ) . Two of these Arg+ Ura+ derivatives , one from each heterozygote , were analyzed by Southern-blot and more detailed PCR analysis . The results confirmed that they each contained three different LRO1 loci ( LRO1 , lro1::UAU1 and lro1::URA3 ) ( data not shown ) . CGH microarray analysis detected trisomy of all of Ch1 for one of the two isolates ( DKCa632 ) ( Figure 4 ) . The other isolate ( DKCa681 ) , appeared to have approximately 2 . 5 copies of Ch1 , a result that is typical of an intermediate ploidy state between disomy and trisomy ( Figure 4 ) , suggesting that a copy of the chromosome is being lost ( or gained ) . Thus , activation of the UAU1 cassette within both essential and non-essential genes can result in whole chromosome trisomies . A major difference between the essential and non-essential UAU1 insertion results was the frequency ( 100% for an essential gene , of which 42% exhibited whole-chromosome trisomy; 3%–25% for a non-essential gene , all of which exhibited whole-chromosome trisomy ) at which trisomic strains are observed among the Arg+ Ura+ derivatives . When essential genes are disrupted , selection for the essential gene and for the ARG4 and URA3 genes maintains all three copies; for the non-essential gene there is less selective pressure to maintain the copy that carries the wild-type gene . To date , no study has addressed the precise mechanism by which homozygous insertion mutations are generated following transformation of the UAU1 cassette . The construction of homozygous mutants with the UAU1 cassette could be the result of a gene conversion or a chromosome duplication followed by chromosome loss . A consequence of the latter would be LOH of Ch1 . We isolated ten Arg+ Ura+ derivatives from DKCa616 and DKCa623 for which PCR showed that the wild-type LRO1 sequence was absent , indicating that these isolates are likely to be disomic for Ch1 . We analyzed SNPs flanking the LRO1 gene ( 1799/2450 , centromere-distal to the LRO1 locus and located within an AluI restriction site , and SNP 1449/2362 , centromere-proximal to LRO1 and located within a FokI restriction site ) . Both SNPs are located approximately 128kb away from the LRO1 gene . Most ( 18/20 ) isolates remained heterozygous for both of these SNPs . One isolate remained heterozygous for SNP 1449/2362 but became homozygous for SNP 1799/2450 , while the last isolate became homozygous for both flanking SNPs . Thus , when the Ch1 gene that is targeted is not essential , the activation of the UAU1 cassette primarily occurs by gene conversion , in which a relatively short region flanking the target sequence becomes homozygous . This is in contrast to the insertion of the UAU1 cassette within an essential gene on Ch1; in this case , Arg+ Ura+ derivatives can arise through whole chromosome duplication , leading to trisomy . In conclusion , we used SNP and CGH microarray analysis of strains bearing naturally-occurring or induced aneuploidies to elucidate the SNP haplotype map for all eight chromosomes of the Candida albicans reference strain SC5314 . Similar analysis of the genome of a clinical isolate , T118 , revealed multiple regions of LOH and rearrangements on all eight chromosomes . This implies that crossover and gene conversion recombination events led to the different haplotypes in these two strains . Further , we used SNP haplotype analysis to determine the molecular mechanisms that generate homozygous gene deletions in strains bearing the UAU1 deletion cassette on Ch1 of Candida albicans . In theory , any organism should be amenable to this type of analysis , if trisomies can be induced or identified in the population . This approach to SNP haplotype mapping promises to greatly increase our understanding of the genome rearrangement and recombination events that occur in C . albicans , especially during the development of antifungal drug resistance . | Candida albicans , a heterozygous diploid yeast , is the most prevalent fungal pathogen . It often acquires resistance to anti-fungal drugs via genome-altering recombination events . In many organisms , recombination events are analyzed using Haplotype Maps ( HapMaps ) , which show the location of different alleles on each chromosomal homolog . Conventional HapMaps are constructed by following allelic markers as they segregate in meiotic progeny . Because C . albicans has not been shown to undergo meiosis , construction of a Candida HapMap has not been possible . We exploited the presence of whole chromosome aneuploidies in mitotic progeny of C . albicans to detect skewed ratios of different alleles , thereby determining the relationships between these alleles on each chromosomal homolog . This facilitated the construction of a HapMap for the most commonly used C . albicans laboratory strain . We then used this HapMap to identify all of the recombination events in a clinical isolate relative to the laboratory reference strain . Finally , we used this mapping approach to investigate the molecular mechanisms that affect the C . albicans genome when it is subjected to a common gene disruption technique . Our rapid HapMap construction method is generally applicable to any organism for which whole-chromosome aneuploidy events can be identified . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] | [
"yeast",
"and",
"fungi",
"infectious",
"diseases",
"genetics",
"and",
"genomics",
"eukaryotes"
] | 2008 | Haplotype Mapping of a Diploid Non-Meiotic Organism Using Existing and Induced Aneuploidies |
The structure of BG505 gp140 SOSIP , a soluble mimic of the native HIV-1 envelope glycoprotein ( Env ) , marks the beginning of new era in Env structure-based immunogen design . Displaying a well-ordered quaternary structure , these subtype A-derived trimers display an excellent antigenic profile , discriminating recognition by broadly neutralizing antibodies ( bNAbs ) from non-broadly neutralizing antibodies ( non-bNAbs ) , and provide a solid Env-based immunogenic platform starting point . Even with this important advance , obtaining homogeneous well-ordered soluble SOSIP trimers derived from other subtypes remains challenging . Here , we report the “rescue” of homogeneous well-ordered subtype B and C SOSIP trimers from a heterogeneous Env mixture using CD4 binding site-directed ( CD4bs ) non-bNAbs in a negative-selection purification process . These non-bNAbs recognize the primary receptor CD4bs only on disordered trimers but not on the native Env spike or well-ordered soluble trimers due to steric hindrance . Following negative selection to remove disordered oligomers , we demonstrated recovery of well-ordered , homogeneous trimers by electron microscopy ( EM ) . We obtained 3D EM reconstructions of unliganded trimers , as well as in complex with sCD4 , a panel of CD4bs-directed bNAbs , and the cleavage-dependent , trimer-specific bNAb , PGT151 . Using bio-layer light interferometry ( BLI ) we demonstrated that the well-ordered trimers were efficiently recognized by bNAbs and poorly recognized by non-bNAbs , representing soluble mimics of the native viral spike . Biophysical characterization was consistent with the thermostability of a homogeneous species that could be further stabilized by specific bNAbs . This study revealed that Env trimers generate different frequencies of well-ordered versus disordered aberrant trimers even when they are genetically identical . By negatively selecting the native-like well-ordered trimers , we establish a new means to obtain soluble Env mimetics derived from subtypes B and C for expanded use as candidate vaccine immunogens .
The HIV-1 envelope glycoprotein ( Env ) is a trimer of heterodimers composed of two non-covalently associated subunits: the receptor-binding gp120 and the fusion machinery-containing gp41 . Each subunit is derived from a gp160 precursor glycoprotein following cleavage by cellular furins [1] . HIV-1 gp120 binds the CD4 molecule on the surface of human target T cells to initiate the viral entry process , and following co-receptor engagement , fusion is mediated by gp41 [2]–[4] . The surface-exposed HIV-1 Env trimer is the sole target for antibodies capable of neutralizing the virus [5] . Recently , a myriad of Env-directed broadly neutralizing antibodies ( bNAbs ) were isolated from numerous HIV-1-infected individuals , demonstrating that the human B cell response can effectively inhibit this variable pathogen [6]–[11] . Infection of macaques by a chimeric model virus , SHIV , can be prevented by prior passive immunization of all bNAbs so far tested , confirming the capacity of neutralizing antibodies to prevent HIV infection [12]–[15] . Along with virus-specific T cells , an efficacious HIV-1 vaccine therefore would likely need to generate bNAbs targeting Env . Although the premise is simple , in actuality , it is a tremendous challenge without precedent in the history of vaccinology . The difficulty to vaccinate against HIV arises from the extensive variability of Env present on the large number of HIV-1 isolates simultaneously circulating in the human population as well as other mechanisms of immune evasion selected for by strong pressure from the human immune system . Generally , vaccine-generated antibodies using either or both gp120 or gp41 sequences do not recognize native Env on the surface of cells or virus , do not neutralize primary isolates in vitro , and do not prevent infection in laboratory animals [16]–[18] . Non-neutralizing antibodies directed to the major variable region two ( V2 ) of gp120 are associated with modest efficacy in a single human clinical trial [19] , [20] , while , in general , Env-elicited antibodies fail to demonstrate protection in previous human clinical trials [21]–[23] . Many Env-based trimeric candidate immunogens are engineered to eliminate cleavage between gp120 and gp41 ( so called uncleaved gp140 trimers ) , usually generating imperfect mimetics of the functional spike based on antigenic profiling or EM analysis [18] , [24] . As a group , the defined , or presumed to be , disordered trimers ( in adjuvant ) generate high self-binding antibody titers . However , these vaccine-elicited antibodies do not efficiently neutralize most HIV-1 primary isolates , that is , strains representative of those circulating in the human population [17] , [25]–[27] . Antibodies elicited by these immunogens target epitopes exposed only on the free gp120 and trimeric post-fusion forms of gp41 or disordered gp140s and thus are ineffective at accessing their epitopes buried within the ordered , quaternary structure achieved in the native Env spike . We recently described the limitations of two CD4 binding site ( CD4bs ) -directed non-bNAbs , ( GE148 and GE136 ) generated following immunization of uncleaved gp140 trimers ( YU2 gp140-foldon ) in non-human primates ( NHP ) . Non-bNAbs , represented by GE136 and GE148 , can only neutralize the sensitive so-called “tier 1 viruses” that are not representative of the more neutralization resistant tier 2 primary isolates circulating in the human population . Using crystallography , EM reconstructions , paratope scanning and molecular modeling we determined that these vaccine-elicited antibodies fail to reach the CD4bs due to steric barriers imposed by quaternary packing of the native Env on neutralization resistant primary isolates , a property that we use to our advantage in the negative-selection strategy presented here [18] . The cumulative historical data have led to the hypothesis that a more faithful mimic of the HIV-1 spike that better recapitulates the native , pre-fusion form of Env , selectively displaying neutralizing determinants while occluding non-neutralizing determinants , may better elicit antibodies capable of accessing the native spike . A soluble Env mimetic , containing a disulfide linkage between gp120 and gp41 ( SOS ) , first described in the 2000 s , and further developed over the next decade , displays many of these properties , culminating in the determination of the high resolution structures of the well-ordered BG505 SOSIP trimers by crystallography and EM [28]–[31] . A sub-nanometer EM reconstruction of KNH1144 SOSIP is also available but does not provide atomic level details [32] . The BG505 SOSIP and KNH1144 SOSIP trimers are derived from the Env sequences of the subtype A BG505 and KNH1144 strains . These soluble trimers possess an engineered disulfide linkage between the gp120 and gp41 ( at residues 501C and 605C , respectively ) and an additional mutation in the heptad repeat 1 ( HR1 ) of gp41 ( I559P ) that facilitates trimerization [33] , [34] . A truncation of the membrane proximal external region ( MPER ) at residue 664 that enhances expression while decreasing aggregation is incorporated into the so-called BG505 SOSIP . 664 trimers [30] , [35] . Although SOSIP molecules based on other HIV-1 primary strains were attempted over the past decade , the BG505- and KNH1144-derived SOSIP trimers are the two limited examples of SOSIPs that yield homogeneous trimers suitable for high resolution biophysical and structural analysis . The structural explanation for the difficulty to readily transfer the SOSIP design to other HIV-1 strain-derived sequences is not yet fully understood and would be valuable information to broaden the trimer design horizon . Here , we describe two SOSIP trimer molecules derived from the B subtype strain , JRFL , and the subtype C strain , 16055 . We selected these two Envs for the initial results reported in this study as follows . The JRFL SOSIP trimer , truncated at residue 663 ( JRFL SOSIP ) derives from the JRFL HIV-1 strain isolated from the frontal lobe ( FL ) of an HIV-1-infected individual . This Env is often used because it displays the unusual property that its gp160 Env precursor is efficiently cleaved into the gp120 and gp41 subunits when expressed on the cell surface of 293F HEK cells [36] . The 16055 SOSIP trimer , also truncated at residue 663 , derives from a HIV-1 Indian strain and displays the unusual property that its monomeric gp120 is weakly recognized by the quaternary epitope-preferring bNAbs , PG9 and PG16 , which is relatively infrequent amongst most HIV-1 Env sequences [37] , and is also observed for BG505 gp120 [38] , [39] . In the current study , we demonstrate that the JRFL and 16055 SOSIP trimers were purified to homogeneity by a novel means of isolation that utilizes non-bNAbs targeting the CD4bs in a negative-selection process that effectively separates well-ordered trimers from a mixture also containing disordered aberrant trimers and other oligomeric states of Env . By bio-layer light interferometry ( BLI ) binding analysis , we demonstrated that the purified JRFL and 16055 SOSIP trimers were efficiently recognized by bNAbs but were poorly recognized by the non-bNAbs . By negative stain EM , we confirmed that negative selection results in homogeneous , well-ordered JRFL and 16055 SOSIP trimers displaying a 3-lobed architecture resembling the native HIV spike and the previously described subtype A SOSIP trimers . We obtained 3D EM reconstructions of the unliganded and liganded JRFL and 16055 SOSIP trimers and demonstrated that the negatively selected trimers adopt conformational changes upon sCD4 engagement that emulate those of the native HIV spike [40] . Differential scanning calorimetry ( DSC ) and differential scanning fluorimetry ( DSF ) revealed that the negatively selected JRFL and 16055 SOSIP trimers were stable at relatively elevated temperatures , exhibiting melting temperatures ( Tm ) exceeding 58 and 63°C , respectively . We conclude that the negative-selection process resulted in highly homogenous well-ordered JRFL and 16055 trimers , expanding the SOSIP family of Env mimetics to HIV-1 subtypes B and C . This advance provides opportunities for HIV Env structural comparisons at high resolution as well as a wider array of ordered trimers for sequential or simultaneous inoculation regimens to evaluate enhanced immunogenicity toward more broadly effective antibody responses .
As an overall approach , the JRFL SOSIP and 16055 SOSIP trimer glycoproteins , designed on the established SOSIP template as described in Methods and S1A Fig . , were purified in three steps consisting of lectin-affinity chromatography , followed by size exclusion chromatography ( SEC ) , followed by a final negative-selection procedure ( Schematic , Fig . 1A ) . Blue native polyacrylamide gel electrophoresis ( BN-PAGE ) analysis of the lectin-purified proteins revealed bands corresponding to the expected size of SOSIP trimers , along with undesired monomers , dimers and higher-order oligomeric forms . The distribution of oligomers was slightly different for JRFL SOSIP than for 16055 SOSIP . The JRFL SOSIP glycoproteins presented as a predominant band corresponding to the trimeric species with lower intensity bands corresponding to dimeric and momeric forms . In contrast , 16055 SOSIP displayed bands of similar magnitude for all oligomeric forms detected ( Fig . 1B ) . The lectin-purified glycoproteins of both SOSIP types were subjected to SEC and the corresponding chromatograms corroborated the distribution of oligomeric forms observed by BN-PAGE . Specifically , the JRFL SOSIP protein peak corresponding to the trimeric form of SOSIP eluted at approximately 11 ml , with a shoulder at 12 ml , corresponding to dimers , with a smaller peak at 13 ml corresponding to monomers ( Fig . 1C ) . The 16055 SOSIP SEC profile showed three overlapping peaks of similar magnitude , suggesting a less efficient tendency of this Env to form SOSIP trimers compared to JRFL ( Fig . 1C ) . Elution fractions containing the expected trimers ( elution volume 10–12 ml ) were collected and contained primarily trimers along with associated dimers and monomers that could not be resolved by SEC . With the goal of resolving the mixture of SOSIP oligomeric states , we reasoned that the CD4bs-directed non-bNAbs GE136 or GE148 might be able to absorb out disordered trimers , dimers and monomers . We also included the similar , but infection-elicited CD4bs-directed non-bNAb , F105 , in our analysis . Each of these non-bNAbs inefficiently target the CD4bs on the trimeric HIV spike with a vertical angle of approach that clashes with the variable region cap on the well-ordered native spikes [18] and therefore do not neutralize either JRFL or 16055 virus . We sought to use these CD4bs-directed non-bNAbs to remove the disordered , and presumably more conformationally open , trimers , dimers and monomers from the oligomeric mixture in a negative-selection purification step . To begin , we first assessed which non-bNAb most efficiently immuno-precipitated the unresolved Env forms from the mixture , selecting F105 for JRFL and GE136 for 16055 , which displayed favorable binding kinetic parameters for the respective gp120s ( S1B Fig . ) . F105 did not efficiently immuno-precipitate 16055 Env forms and also displayed a faster dissociation rate for 16055 gp120 ( S1B Fig . ) . Subsequently , lectin- and SEC-purified SOSIP samples were flowed over protein A affinity columns with F105 or GE136 previously immobilized on this matrix . Analyzing the flow-through from the affinity-column , we observed that the SOSIP trimers migrated as a highly homogenous single peak by SEC , suggesting that the negative-selection approach removed aggregates , dimers , and monomers ( Fig . 1C ) . Negative selection retained disordered trimers , dimers and monomers on the solid phase , presumably by allowing the non-bNAbs F105 or GE136 access to the CD4bs on these aberrant forms of Env . This retention is readily apparent for 16055 , where the disappearance of the dimer and monomer bands on the BN-PAGE gel can be observed following negative selection compared to before ( Fig . 1B ) . BN-PAGE analysis of the lectin- and SEC-purified , negatively selected JRFL and 16055 SOSIP trimers revealed a single band corresponding to the expected size of a trimeric SOSIP protein ( Fig . 1B ) . To confirm the effectiveness of the separation process , we examined the SOSIP samples before and after the negative-selection affinity chromatography process by negative stain EM . Visual inspection of the EM micrographs showed a pronounced reduction of aberrant or disordered SOSIP oligomers following negative selection ( Fig . 2A ) . Negative selection yielded highly homogeneous well-ordered native-like trimers in the respective eluates based on EM 2D classification ( Fig . 2B ) . Yields after negative selection were typically 1 . 5 mg per liter for JRFL SOSIP and 0 . 5 mg per liter for 16055 SOSIP . Negative selection removed approximately 50-60% of disordered forms of Env from the SEC trimer-isolated fraction . As expected , reducing SDS-PAGE revealed that both negatively selected JRFL and 16055 SOSIP trimers appeared predominantly as two bands on the gel , corresponding to Env glycoprotein subunits gp120 and gp41 , indicating effective furin cleavage of the SOSIP trimers ( S1C Fig . ) . We next investigated the effect of negative selection on trimer antigenicity using a set of CD4bs-directed bNAbs and non-bNAbs . We employed BLI ( Octet ) to assess mAb binding to JRFL and 16055 SOSIP trimers in solution , before and after negative selection . To begin the analysis , we plotted the BLI maximal response values from binding curves as a bar graph , permitting a semi-quantitative relative assessment of binding for each antibody . We observed that negative selection eliminated nearly all recognition of JRFL and 16055 SOSIP trimers by the CD4bs-directed non-bNAbs ( “F105-like” ) , compared to before negative selection ( Fig . 3A , S2A Fig . and S3A Fig . ) . Assessing trimer recognition by quaternary epitope preferring bNAbs such as PG16 , PGT151 , PGT145 , VRC06 and VRC03 was especially useful for this antigenic analysis . Their trimer recognition , or lack thereof , provided a means to discern between a native-like , well-ordered trimer and an “open” disordered conformation of the trimer . The glycan-specific bNAbs PG16 , PGT145 and PGT151 are known for their quaternary epitope specificity and , as such , they do not efficiently recognize monomeric gp120 ( Fig . 3B , S2A Fig . and S3A Fig . ) [8] , [41]–[43] . The CD4bs-directed VRC03 and VRC06 , unlike VRC01 , possess a framework region insertion that extends their paratope beyond the CD4bs into the V3 loop of the adjacent protomer ( within a trimer ) conferring upon them a trimer-preferring character [28] , [44] . VRC06 and VRC03 did not recognize gp120 while the non-trimer preferring control mAbs , VRC01 and CD4-IgG , did recognize the soluble monomer ( Fig . 3B , S2A Fig . and S3A Fig . ) . In fact , all of these trimer-preferring antibodies showed increased recognition of SOSIP trimers , as compared with their recognition of monomeric gp120 ( Fig . 3B , S2A Fig . and S3A Fig . ) . The preferential recognition by bNAbs following negative selection suggested that this process efficiently eliminated disordered oligomers , consistent with the EM analysis ( Fig . 4A ) . We also assessed binding of bNAbs 2G12 , PGT121 , and PGT135 that target an array of glycans clustered around the N332 glycan . While the subtype B JRFL isolate is naturally glycosylated at the N332 site , the subtype C 16055 Env lacks this N-linked glycan . JRFL SOSIP was robustly recognized by all glycan-dependent bNAbs tested , whereas 16055 SOSIP trimers was poorly recognized by the mAb PGT135 ( Fig . 4B , S2A Fig . and S3A Fig . ) . 2G12 and PGT121 binding remained relatively strong , despite faster off-rates compared to JRFL SOSIP , suggesting that the latter two antibodies may use other glycans that compensate for the missing 16055 332 N-linked glycan , as recently suggested for PGT121 ( Fig . 4B ) [45] . PGT121 is the only antibody that we tested targeting this N332 glycan “site of vulnerability” that neutralizes the HIV-1 subtype C strain , 16055 ( S1 Table ) . Additionally , the CD4bs-specific bNAb PGV04 , recently used to determine the high-resolution cryo-EM structure of the BG505 SOSIP trimer [28] , and b12 , both efficiently recognized the JRFL SOSIP trimer , but not the 16055 SOSIP trimer ( S2 Fig . and S3 Fig . ) . Consistent with this differential binding , PGV04 and b12 neutralize the parental JRFL HIV strain while they do not neutralize the 16055 clade C strain in a TZM-bl assay . ( S1 Table ) . Utilizing Fabs derived from a selected panel of antibodies , we determined kinetic constants for their interaction with the JRFL SOSIP trimer His-captured on the biosensor . All neutralizing antibodies tested in this minipanel had affinities ranging from 5 nM to 20 nM , while the non-neutralizing mAb F105 inefficiently recognized the negatively selected trimers with micromolar affinity ( Fig . 5A ) . With a few exceptions , binding of the SOSIP trimer was associated with a neutralizing phenotype of the antibody for the parental HIV strain . Non-neutralizing mAbs targeting other Env sites , such as 17b , C11 , 7b2 , did not recognize the negatively selected JRFL and 16055 SOSIP trimers . The V3-directed non-bNAb 19b did recognize the SOSIP trimers even after F105 or GE136 negative selection ( S2 and S3 Fig . ) . By EM , the 19b Fab infrequently bound the negatively selected JRFL or 16055 SOSIP , demonstrating binding by only one Fab to ∼20–25% of trimers , respectively ( S4A Fig . and S1 Table ) . This occupancy was comparable to that displayed by the BG505 SOSIP . 664 where 30% of the trimers bound one V3-directed Fab [30] . Recognition by the V3 antibodies as assessed by BLI with the multivalent SOSIP trimers as an analyte , may increase the sensitivity of detection due to avidity effects that are eliminated with the Fab in the EM context . We obtained 3D reconstructions of the SOSIP trimers in the unliganded state by EM negative stain ( Fig . 5B ) . The overall morphology of the unliganded JRFL and 16055 SOSIP trimers at 21 Å and 18 Å resolution , respectively , is similar to that previously described for BG505 SOSIP . 664 . All trimers display three-lobed structures and an overall density that is wider at the top and narrower at the bottom corresponding with the association of three gp120 units at the top of the spike and three gp41 units near the bottom ( Fig . 5B ) [30] . We fitted the cryo-EM derived model of BG505 SOSIP . 664 ( PDB 3J5M ) within the JRFL and 16055 SOSIP EM reconstructions to demonstrate that no gross differences were observed . A superimposition of the unliganded JRFL and 16055 SOSIP densities revealed small differences when comparing their surface contours ( Fig . 5B , right panel ) . These differences may be in part due to the low resolution of the reconstructions . We do however note that there are differences in the glycosylation patterns of the two trimers , as the 16055 HIV-1 Env possesses 28 glycosylation sites while JRFL Env has 25 glycosylation sites , and that difference may partially account for the observed surface contour variation . We compared the unliganded state to complexes with a soluble version of the HIV-1 primary receptor , soluble four-domain CD4 ( sCD4 ) ( Fig . 5B , lower panels ) . The 3D reconstructions of the JRFL and 16055 SOSIP trimers liganded with sCD4 at 21 Å and 23 Å resolution , respectively , show conformational changes in agreement with cryo-EM images of the sCD4-liganded native BaL Env and the previously published KNH1144 SOSIP:CD4 complexes ( S4B Fig . ) [40] , [46] . During natural infection , such conformational changes presumably follow the engagement of the cellular receptor , CD4 , to form or expose the co-receptor binding site . By EM analysis , CD4-induced conformational changes result in the lateral movement of the gp120 subunits and the appearance of a protrusion attributed to the displacement of the V1V2 loops ( Fig . 5B and S4B Fig . ) [40] , [46] . Also , in the current analysis of the JRFL and 16055 soluble spike mimetics , the putative gp41 unit density , located at the bottom of the trimer , opened and flattened when in complex with sCD4 ( Fig . 5B ) . As expected from previous results , CD4 engagement did not abrogate trimerization of the SOSIP trimers despite the large conformational changes observed and despite the truncation of the MPER in these constructs . sCD4 displayed the same angle of approach to the CD4bs in both CD4-liganded JRFL and 16055 SOSIP trimers , consistent with the previous KNH1144 SOSIP-sCD4 and BaL-Env EM analysis ( Fig . 5B and S4B Fig . ) [40] , [46] . We investigated if the bNAb VRC01 , which also targets the CD4bs , resulted in similar quaternary conformational changes in the trimer architecture as those induced by sCD4 . Accordingly , we obtained EM 3D reconstructions of VRC01-liganded JRFL and 16055 SOSIP at 20 Å and 22 Å resolution , respectively ( Fig . 6A ) . VRC01 did not induce any apparent conformational changes in the overall architecture of the JRFL SOSIP trimer at the resolution obtained in this study . However , we did detect conformational changes induced by VRC01 interaction with the 16055 SOSIP trimers . These conformational changes were not as pronounced as those induced following engagement with sCD4 , however , substantial differences between JRFL and 16055 were observed in the superimposition of the two complexes ( Fig . 6A ) . Specifically , VRC01 adopts an apparent more horizontal angle of approach when bound to 16055 SOSIP trimers as compared to its interaction with the JRFL SOSIP trimers . This angle difference is likely due to a more “open” state of the 16055 SOSIP trimer induced by VRC01 rather than a difference in the interaction of the antibody with its epitope on the corresponding gp120 subunits . The VRC01-bound 16055 SOSIP reconstruction displayed differences in the gp41 subunit that resembled those induced by sCD4 ( Fig . 6A and Fig . 5A ) . To investigate further differential conformational changes induced by VRC01 Fab on the JRFL versus 16055 SOSIP trimers , we performed isothermal titration calorimetry ( ITC ) . We detected much larger favorable enthalpy and unfavorable entropy changes induced by VRC01 Fab in complex with the 16055 SOSIP trimers relative to the JRFL SOSIP trimers , consistent with the EM analysis ( Fig . 6B and S5 Fig . ) . In contrast , ITC parameters assessed with sCD4 were similar for both trimers ( Fig . 6B and S5 Fig . ) . Next , we investigated if the conformational flexibility exhibited by the 16055 SOSIP trimer was a result of the interaction with VRC01 or an intrinsic property encoded by the 16055 primary sequence and subsequent quaternary assembly . We used VRC03 , a VRC01-related bNAb also targeting the CD4bs that , unlike VRC01 , showed preferential binding to the trimer relative to the monomer by BLI ( Fig . 3B , S2A Fig . and S3A Fig . ) . We obtained 3D reconstructions of JRFL and 16055 SOSIP trimers in complex with VRC03 at 20 Å and 19 Å resolution , respectively , revealing that VRC03 binding did not result in any apparent conformational changes in either of the SOSIP trimers ( Fig . 6A ) . Unlike the VRC01 densities , the superimposition of the two VRC03-bound trimer densities was highly concordant and the angle of approach of VRC03 was the same for both JRLF and 16055 SOSIP trimers . Taken together , these data suggest that the conformational changes observed in 16055 SOSIP upon VRC01 binding may be induced by the interaction of the mAb with these soluble trimers and not due to increased flexibility of the 16055 SOSIP trimers themselves , consistent with the ITC data ( Fig . 6 ) . To solidify that these two SOSIP trimers faithfully mimic the virion native spike conformation , we made complexes with the recently described , trimer-preferring and cleavage-specific bNAb PGT151 . This bNAb binds specific N-linked glycans at the interface of four Env subunits , two gp120 and two gp41 protomers [41] , [42] . The EM 2D class averages of PGT151 in complex with JRFL and 16055 SOSIP trimers revealed mostly two or one Fabs per trimer ( Fig . 7A ) . Computed stoichiometries based on EM micrographs revealed that ∼36% of the JRFL SOSIP trimers possessed two Fabs , ∼28% with one bound Fab and ∼11% with 3 bound Fabs ( S1 Table ) . PGT151 displayed similar stoichiometry in its interaction with the native JRFL envelope extracted from the cell membrane , as recently described [41] . The subtype C 16055 SOSIP 2D class averages displayed mostly one PGT151 Fab bound per trimer , although two Fabs were occasionally detected ( Fig . 7A ) and the computed stoichiometry was slightly different than for JRFL ( ∼34% with one bound Fab , ∼20% with two Fabs and 0% with three Fabs ) ( S1 Table ) . The lower stoichiometry for 16055 is consistent with the observation that in ∼60% of cases , only one PGT151 Fab bound the subtype C C22 Env following extraction from the cell membrane [41] . Based on the more favorable stoichiometry with PGT151 and on the availability of the native JRFL Env-PGT151 complex EM density [41] , we obtained a 3D EM model of the JRLF SOSIP bound to PGT151 at 24 Å resolution ( Fig . 7B ) . This 3D model displays two Fabs bound per trimer and its superimposition with that of the JRFL cleaved full-length Env purified in complex with PGT151 showed a high degree of correspondence , with the expected exceptions of the MPER and TM gp41 regions lacking in the SOSIP trimers ( Fig . 7B ) . We also superimposed the JRFL SOSIP-PGT151 3D reconstruction with that of the published BG505 SOSIP-PGT151 density and , as expected , they were highly concordant ( S6A Fig . ) . We assessed the stability of the negatively selected SOSIP trimers by two biophysical methods , differential scanning calorimetry ( DSC ) and differential scanning fluorimetry ( DSF ) . By DSC , the JRFL SOSIP trimer displayed a melting temperature ( Tm ) of 58 . 5°C , about 1°C higher than the JRFL gp120 monomer Tm of 57 . 1°C . In contrast , the 16055 SOSIP trimer melted at 63 . 7°C , approximately 6 degrees higher than the 16055 gp120 monomer ( 57 . 6°C ) ( Fig . 8A ) . Just as some mAbs can induce conformational changes on the trimer , we reasoned that some might also stabilize the trimeric ground-state . Accordingly , we used DSF to measure melting temperatures in the context of liganded-SOSIP trimers , a comparable method to DSC that requires less protein and is more amenable to higher throughput analysis . DSF employs a real-time PCR instrument to detect fluorescence emission of a dye with specificity for hydrophobic residues . The exposure of hydrophobic residues as the protein unfolds with increasing temperature/energy results in a sigmoidal curve that allows the determination of the protein melting temperature . By DSF , the melting temperatures of the JRFL and 16055 SOSIP trimers alone were 55 . 1°C and 62 . 8°C , respectively , comparable to those determined by DSC ( Fig . 8B ) . We selected the CD4bs-directed bNAbs ( VRC01 , PGV04 and VRC06 ) and the trimer-preferring mAb ( PGT151 ) to investigate their stabilizing or destabilizing effect on the SOSIP trimers . While VRC01 and VRC06 had no significant effect in trimer stability , the antibodies PGT151 and PGV04 increased the Tm of the JRFL SOSIP-mAb complex by 2 . 1 and 3 . 6°C , respectively ( Fig . 8B ) . The melting temperatures of the individual Fabs were plotted separately for clarity ( S6C Fig . ) . These data may be of value for subsequent immunogenicity assessments using immune complexes to enhance SOSIP stability in vivo . In contrast , 16055 SOSIP trimers in complex with either VRC01 or PGT151 mAbs did not increase the Tm beyond that of the trimer alone ( Fig . 8B ) . In addition , to further assess trimer stability by EM , we compared the 3D models of JRFL SOSIP bound to the PGV04 Fabs at days 0 and 7 following “trimer alone” incubation at 4°C . We observed that the trimeric complexes appeared similar at both time points , indicating no deterioration in quaternary structure over this time interval ( S7 Fig . ) .
In this study , we selected two HIV-1 Env sequences from subtypes B and C to produce soluble SOSIP trimers to complement the already available subtype A BG505 SOSIP trimer . Obtaining soluble mimetics of the native HIV-1 spike from subtypes representing the majority of global infections is of high interest for additional structural analysis as well as preclinical immunogenicity studies and candidate vaccine trials . The JRFL and 16055 SOSIPs did form well-ordered trimers , but not as readily as the subtype A SOSIP trimers derived from the BG505 Env sequences [24] , [30] , indicating molecular heterogeneity . Since , following SEC , we detected well-ordered SOSIP trimers within a mixture of Env forms , we used negative selection to purify the JRFL and 16055 SOSIP trimers to a high level of homogeneity . We then were able to obtain 3D EM reconstructions of these trimers in both the unliganded state and in complex with sCD4 and selected bNAbs . By EM , we demonstrated that sCD4 induced conformational changes in these SOSIP trimers that parallel those observed for the native BaL-Env spike . In addition , the cleavage- and trimer-specific bNAb PGT151 recognized the JRFL SOSIP trimers in a manner similar to its recognition of the native JRFL Env spike . We demonstrated that the antigenic profile of the negatively selected trimers was consistent with a well-ordered state , mimicking the viral spike and that the trimers exhibited degrees of thermostability consistent with a homogenous species by calorimetry and fluorimetry . To obtain the well-ordered trimers , we used CD4bs-directed non-bNAbs to selectively adsorb the disordered oligomers to the solid phase . F105 readily removed the disordered oligomers from the JRFL SOSIP mixture but was insufficient for 16055 , likely due to its faster off-rate for the 16055 gp120 monomer . GE136 was a better negative selecting agent for the 16055 mixture showing a considerably slower dissociation rate for monomeric 16055 gp120 ( S1B Fig . and S3 Fig . ) . A slower dissociation rate of a given mAb for the gp120 monomer is likely advantageous to efficiently capture the undesirable disordered oligomers within the SOSIP mixture and may be a key factor for successful negative selection . Following negative selection , we isolated highly homogeneous well-ordered JRFL and 16055 SOSIP trimers , which share conformational and antigenic similarities with the BG505 SOSIP . 664 trimers . BG505 SOSIP . 664 do not require this negative-selection purification step since , following initial 2G12 positive selection , they form well-ordered trimers that can be isolated by SEC alone [30] . Why most HIV-1 Env sequences do not form SOSIP trimers with this degree of homogeneity is not yet clear but seems to be a relatively infrequent feature associated with the BG505 Env , perhaps by specific structural interaction focused around the I559P change that alters gp41 conformational flexibility to inhibit efficient six-helix bundle formation , the post-fusiogenic form of gp41 . The negatively selected JRFL and 16055 SOSIP trimers displayed efficient recognition by bNAbs , including those recognizing quaternary epitopes , and low or undetectable reactivity to CD4bs-targeting non-bNAbs and other non-neutralizing antibodies targeting other sites than the CD4bs , such as 17b and C11 . These data suggest that the CD4bs-directed negative-selection process eliminated generally disordered trimers and was not specifically restricted to the CD4bs . Generally , most bNAbs that efficiently recognized the negatively selected trimers , also neutralized the parental sequence viral strain , suggesting that these spike soluble mimetics faithfully recapitulate the quaternary packing of the native Env spike . This interpretation is consistent with the reported correlation between bNAb HIV-1 neutralizing activity and binding to the ordered BG505 SOSIP . 664 trimers [30] . Similar to that study , and as reported here , the exception was the V3-targeting antibody 19b that does not neutralize the parental JRFL or 16055 viruses , but did recognize the soluble SOSIP trimers . The binding of 19b was slightly reduced after negative selection , but not fully abrogated , suggesting that the V3 might be in a more exposed or “triggered” conformation at least in one of the protomers within some of the population of trimers . Consistent with this , EM negative stain using 19b Fab with negatively selected JRFL and 16055 SOSIP detected a low percentage of Fab binding and exclusively to one protomer . A similar level of V3 reactivity was also observed previously for BG505 SOSIP . 664 . This undesirable reactivity might be overcome with additional design modifications [30] . This V3 exposure that might be due to “breathing” of this region in the relatively well-ordered trimeric context , may reflect the metastable condition of the Env spike itself since activation of the HIV-1 spike occurs by protein:protein interaction . It may be that a low energy barrier is required to trigger HIV-1 Env by this means , making conformational breathing more likely . HIV-1 Env is not triggered by pH as is , for example , influenza HA or other endosomally triggered viral fusion units , which may allow a wider degree of trimer stability in the native Env state . In one other exception , the N332-lacking 16055 SOSIP trimers were recognized by the N332-targeting bNAbs 2G12 and PGT121 . 2G12 does not neutralize the parental virus while PGT121 neutralizes the parental virus strain , which is likely related to , and consistent with , the promiscuity of “nearby” glycan usage displayed by this bNAb that does not absolutely require the presence of the 332 N-glycan [45] . Both JRFL and 16055 SOSIP trimers are relatively stable , displaying melting temperatures of 58 . 5°C and 63 . 7°C , respectively . These values are substantially in excess of perhaps more relevant temperatures such as room or physiological body temperature . Since the published melting temperature of BG505 SOSIP . 664 trimer is higher ( 68°C ) [30] , one could postulate that the Tm of the SOSIP trimer will correlate with a higher degree of well-ordered trimer formation . However , the melting temperature of 16055 SOSIP was almost 6°C higher than that of JRFL SOSIP and 16055 had a lower percentage of “spontaneous” trimer formation as revealed by the SEC profiling and related analysis . Other factors such as the level of glycosylation or the propensity of the V1/V2/V3 to adopt a near native arrangement even within a protomer may contribute to the higher thermostability of the 16055 or BG505 SOSIP trimers . BG505 and 16055 gp120s , but not JRFL gp120 , are weakly recognized by the V2-directed , trimer-preferring antibodies PG9 and PG16 . BG505 SOSIP and 16055 SOSIP display higher Tms than does JRFL SOSIP , suggesting that the propensity of the 16055 and BG505 monomers to adopt a native , trimer-like conformation may contribute to the thermostability of the SOSIP oligomers . Antibodies PGV04 and PGT151 increased the Tm of the JRFL SOSIP trimer . This could be a result of specific inter-protomer contacts established by the paratope of the antibody , simultaneously bridging two protomers and conferring structural rigidity to the architecture of the trimer . In contrast , VRC01 shows a lower level of this putative inter-protomer bridging as suggested indirectly in the recent publication of the EM structure of the BG505 SOSIP trimer bound to PGV04 [28] . VRC01 did not show a significant stabilization effect on the SOSIP trimers as measured by DSF , and even , destabilized the 16055 SOSIP trimer as evidenced in the EM 3D reconstruction of the complex . Obtaining soluble mimics of the HIV-1 spike representative of different subtypes/strains of HIV will be of benefit toward potential advancement of a global HIV-1 vaccine . The recently published structural characterization of the subtype A BG505 SOSIP . 664 [28] , [29] , a soluble mimetic of the HIV native spike , provides fundamental insights regarding the organization of the gp120 and gp41 within the trimer . Extending this structural information to other subtype strains of HIV is of high interest in the field . For now , SOSIP represents the best and only well-ordered soluble trimer mimetic . Frustratingly , many HIV sequences do not readily form ordered homogeneous SOSIP trimers to the extent that BG505 Env does , generating different frequencies of well-ordered versus disordered trimers even when they are genetically designed in an identical manner . This trend toward trimer micro-heterogeneity likely explains why , in part , previous attempts to obtain high-resolution crystal structures of the JRFL and KNH1144 SOSIP trimers were not fruitful until BG505 was identified . Our trimer isolation strategy leads to a high-degree of conformational homogeneity that may allow the determination of higher resolution structures of SOSIP trimers derived from other HIV subtypes . In summary , in this study , we offer a new means to obtain homogenous well-ordered SOSIP trimers of subtypes B and C that potentially can be extended to more HIV-1 Env strains by the use of the non-bNAb negative-selection strategy used to rescue well-ordered trimer sub-fractions of JRFL and 16055 SOSIP oligomers .
To generate the HIV-1 subtypes B JRFL SOSIP and C 16055 SOSIP expression plasmids we generally followed established SOSIP design parameters [30] . In brief , the JRFL and 16055 SOSIP trimers were engineered with a disulfide linkage between gp120 and gp41 ( residue 501 in gp120 to residue 605 in gp41 ) that covalently links the two subunits of the heterodimer ( SOS ) [33] . As previously described , we included the I559P mutation in the heptad repeat region 1 ( HR1 ) of gp41 that promotes trimerization of the heterodimer , and a deletion of part of the hydrophobic membrane proximal external region ( MPER ) , in this case residues 664–681 of the Env ectodomain [31] , [34] , [35] . The furin cleavage site between gp120 and gp41 ( 508REKR511 ) was altered ( 506RRRKKR511 ) to enhance cleavage [47] . The JRFL SOSIP trimer includes an additional mutation ( E168K ) that is associated with PG9/PG16 neutralization sensitivity in the pseudovirus neutralization assay that is naturally present in the 16055 Env [48] , [49] . The CD5 leader sequence was positioned at the 5′ end of the SOSIP encoding DNA to enhance secretion and expression . JRFL SOSIP and 16055 SOSIP expression constructs were transfected into 293F cells along with a plasmid encoding the cellular protease furin to ensure efficient cleavage of the Env precursor gp160 at a 2∶1 Env:furin ratio [50] . The transfected 293F cells were cultured in a CO2 humidified shaking incubator at 37°C for 5–6 days to transiently express the soluble SOSIP trimers . Culture supernatants were collected and cells were removed by centrifugation at 3800 x g for 20 min , filtered twice , first with a 0 . 45 µm pore size filter device ( Nalgene ) and subsequently with a 0 . 2 µm pore size filter . SOSIP proteins were purified by flowing the supernatant over a lectin ( Galanthus nivalis ) affinity chromatography column overnight at 4°C . Proteins were eluted from the lectin column with 3 column volumes of 0 . 5 M methyl-α-D-mannopyranoside and 0 . 5 M NaCl . The eluate was concentrated with a Millipore concentrator ( MWCO 100 kDa ) to 500 µL and loaded onto a Superdex 200 10/300 GL column to separate the trimer-size oligomers from aggregates and gp140 monomers . Fractions corresponding to the trimer ( approximately eluate volumes 10–12 mL ) were pooled and loaded into an agarose protein A column previously loaded with a 1 mg of mAb ( F105 or GE136 ) per ml of column material . This quantity can be customized in relation to the amount of SOSIP protein loaded . Generally , we used 2-fold weight excess of mAb with respect to SOSIP protein amount obtained after SEC . Specifically , we used F105 for JRFL SOSIP and GE136 for 16055 SOSIP trimer purification . The column was rocked at 4°C for 45 min , the solid phase was allowed to settle for 5 min and the flow-through collected by flowing one column volume of PBS through the column . The flow-through , containing the well-ordered trimers , was concentrated using a 100 kDa molecular weight cut off filter device from Millipore to approximately 1 . 5 mg/mL for analysis or cold storage . Final yields of JRFL SOSIP well-ordered trimers following negative selection were approximately 1 . 5 mg/L and 0 . 5 mg/L for the well-ordered 16055 SOSIP trimers . To perform immunoprecipitation experiments on supernatants containing the SOSIP trimers , 20 µL of protein A agarose beads were added to a 1 . 5 ml Eppendorf tube , washed twice with PBS , resuspended in 500 µL of PBS and 5 µg of antibody were added . The protein A agarose-mAb mixture was rocked for 30 min at 4°C and then washed twice with PBS 500 mM NaCl . One mL of filtered supernatant was added to the microtube and rocked for 30 min at 4°C . The microtube was then centrifuged at 1000×g for 5 min and the supernatant discarded . The protein A-agarose pellets containing the bound antibody-Env protein were washed twice with 1 mL of PBS before resuspending them in 20 µL of SDS-PAGE loading buffer to resolve over SDS PAGE minigels for 50 min at 200 V . For the binding experiments shown in Fig . 3 , Fig . 4 , S2 Fig . and S3 Fig . we used an Octet Red instrument immobilizing IgGs on hydrated ( PBS pH 7 . 4 ) anti-human IgG Fc sensors ( AHC: ForteBio ) . The SOSIP trimers and gp120 monomers were analyzed as analytes in solution ( PBS pH 7 . 4 ) . Briefly , the bio-sensors were immersed in PBS pH 7 . 4 containing IgGs at a concentration of 10 ug/mL for 2 min and with vibration at 1000 rpm prior to encounter with the analyte ( SOSIP trimers or gp120 monomer , 200 nM and 600 nM respectively ) . The IgG-immobilized sensor was immersed in the analyte in solution for 3 min at 1000 rpm and then removed from the analyte solution and placed into PBS , pH 7 . 4 , for an additional 3 min . The 3 min binding intervals generated the association and dissociation binding curves reported in this study . For the data reported in Fig . 5A , Fabs and monovalent 2G12 IgG were used as analytes in solution ( 400 nM–25 nM ) and the JRFL SOSIP trimer were immobilized on an anti-His biosensors ( HIS2; ForteBio ) at a concentration of 10 ug/mL . Negatively selected JRFL and 16055 SOSIP trimeric proteins were incubated with a ten molar excess of selected Fabs at RT for 1 hour . The following complexes were analyzed 1 . JRFL-SOSIP with four-domain sCD4 , 2 . JRFL-SOSIP with VRC01 , 3 . JRFL-SOSIP with VRC03 , 4 . JRFL-SOSIP with PGT151 , 5 . JRFL-SOSIP with PGV04 , 6 . 16055 SOSIP with four-domain sCD4 , 7 . 16055 SOSIP with VRC01 , 8 . 16055 SOSIP with VRC03 , and 9 . 16055 SOSIP with PGT151 . A 3 µL aliquot containing ∼0 . 05 mg/ml of the Fab+JRFL-SOSIP complex or the Fab+16055 complex was applied for 15 s onto a carbon coated 400 Cu mesh grid that had been glow discharged at 20 mA for 30 s , followed by negative staining with 2% uranyl formate for 30 s . Data were collected using a FEI Tecnai Spirit electron microscope operating at 120 keV , with an electron dose of ∼36 e−/Å2 and a magnification of 52 , 000x that resulted in a pixel size of 2 . 05 Å at the specimen plane . Images were acquired with a Tietz 4 k×4 k TemCam-F416 CMOS camera using a nominal defocus of 1000 nm and the Leginon package at 10° tilt increments , up to 50° . The tilts provided additional particle orientations to improve the image reconstructions . Particles were picked automatically using DoG Picker and put into a particle stack using the Appion software package [51] , [52] . Initial , reference-free , two-dimensional ( 2D ) class averages were calculated using particles binned by two via Xmipp Clustering 2D Alignment [53] and sorted into classes . Particles corresponding to complexes were selected into a substack and binned by two before another round of reference-free alignment was carried out using the Xmipp Clustering and 2D alignment and IMAGIC programs [54] . Fabs and sCD4 were clearly visualized in the 2D class averages if they are bound to the trimer , allowing the percentage of bound trimers relative to unbound trimers to be tabulated ( S1 Table ) [28] . ab initio common lines models were calculated from reference-free 2D class averages in EMAN2 [55] without imposing symmetry . All ab initio common lines models were the same . One of those models was then refined against raw particles for an additional 89 cycles . EMAN [56] was used for all 3D reconstructions . The resolutions of the final models were determined using a Fourier Shell Correlation ( FSC ) cut-off of 0 . 5 ( S6B Fig . and S8 Fig . ) . The number of particles used for the 3D reconstructions are shown in S1 Table . The cryo-EM structure of PGV04-liganded BG505 SOSIP . 664 ( 3J5M ) , and gp120 with sCD4 ( 1RZK ) were manually fitted into the EM densities and refined by using the UCSF Chimera [57] ‘Fit in map’ function . Thermal denaturation was analyzed with a N-DSC II differential scanning calorimeter from Calorimety Sciences Corp . ( Prov , UT ) , at a scanning rate of 1 K/min under 3 . 0 atmospheres of pressure . Samples were dialyzed in PBS pH 7 . 4 and protein concentration was adjusted to 0 . 5 mg/mL prior to measurement . Data collected were analyzed after buffer correction , normalization and baseline subtraction . Fluorescence measurements were performed using a CFX96 RT-PCR detection system ( BIO-RAD , Hercules , CA ) . SYPRO Orange dye was diluted 1∶5000 in PBS pH 7 . 4 and added to 30 µg of protein in clear PCR tubes to a final volume of 25 µL . For samples containing trimer and Fab complexes , 30 ug of trimer protein were mixed with 10 ug of Fab and incubated at 4°C for 1 hr prior to adding the dye . The fluorescence emission was collected using a fluorescence resonance energy transfer filter ( 560–580 nm ) and an excitation wavelength of 450–490 nm . During the DSF experiment , the temperature was increased from 20 to 95°C at an increment of 0 . 5°C with an equilibration time of 5 s at each temperature prior to measurement . The data were exported into CFX Manager version 1 . 6 for analysis . The melting temperature ( Tm ) is defined as the temperature corresponding to the minimum value of the negative first derivative of the first fluorescence transition . We note that the high initial fluorescence is likely due to exposure of hydrophobic pockets on the surface of the trimer . | The HIV envelope glycoprotein ( Env ) is the sole virally encoded gene product on the surface of the virus and , as such , is the only target of neutralizing antibodies . A broadly efficacious HIV vaccine will likely need to generate a robust neutralizing antibody response directed at conserved elements of the variable Env . For a successful antibody-based vaccine , a soluble mimic of the HIV spike will likely be required to generate high-titer anti-Env antibodies capable of neutralizing a wide array of HIV isolates . Due to the global sequence diversity of Env , generating a diverse array of these soluble spikes will benefit immunization strategies designed to cope with such viral diversity . Here , we report a novel purification strategy followed by a comprehensive characterization of two soluble HIV spikes from infection-prominent subtypes , B and C . We demonstrate that these homogeneous soluble trimers are faithful mimics of the HIV spike by neutralizing antibody binding , electron microscopy and other biophysical assessments . Possessing soluble and stable mimics of the HIV spike derived from diverse strains improves both our knowledge of HIV spike architecture as shown here and extends subtype coverage of potential vaccine candidates . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"viral",
"envelope",
"virology",
"biology",
"and",
"life",
"sciences",
"microbiology",
"viral",
"structure"
] | 2015 | Well-Ordered Trimeric HIV-1 Subtype B and C Soluble Spike Mimetics Generated by Negative Selection Display Native-like Properties |
Evidence for minimally symptomatic Ebola virus ( EBOV ) infection is limited . During the 2013–16 outbreak in West Africa , it was not considered epidemiologically relevant to published models or projections of intervention effects . In order to improve our understanding of the transmission dynamics of EBOV in humans , we investigated the occurrence of minimally symptomatic EBOV infection in quarantined contacts of reported Ebola virus disease cases in a recognized ‘hotspot . ’ We conducted a cross-sectional serosurvey in Sukudu , Kono District , Sierra Leone , from October 2015 to January 2016 . A blood sample was collected from 187 study participants , 132 negative controls ( individuals with a low likelihood of previous exposure to Ebola virus ) , and 30 positive controls ( Ebola virus disease survivors ) . IgG responses to Ebola glycoprotein and nucleoprotein were measured using Alpha Diagnostic International ELISA kits with plasma diluted at 1:200 . Optical density was read at 450 nm ( subtracting OD at 630nm to normalize well background ) on a ChroMate 4300 microplate reader . A cutoff of 4 . 7 U/mL for the anti-GP ELISA yielded 96 . 7% sensitivity and 97 . 7% specificity in distinguishing positive and negative controls . We identified 14 seropositive individuals not known to have had Ebola virus disease . Two of the 14 seropositive individuals reported only fever during quarantine while the remaining 12 denied any signs or symptoms during quarantine . By using ELISA to measure Zaire Ebola virus antibody concentrations , we identified a significant number of individuals with previously undetected EBOV infection in a ‘hotspot’ village in Sierra Leone , approximately one year after the village outbreak . The findings provide further evidence that Ebola , like many other viral infections , presents with a spectrum of clinical manifestations , including minimally symptomatic infection . These data also suggest that a significant portion of Ebola transmission events may have gone undetected during the outbreak . Further studies are needed to understand the potential risk of transmission and clinical sequelae in individuals with previously undetected EBOV infection .
Despite over 28 , 000 reported cases of Ebola virus disease ( EVD ) in the 2013–16 pandemic as of March 27 , 2016 [1] , we are only beginning to trace the complex biosocial processes that have promoted spread of the virus [2 , 3] . Important questions remain , including how to best use tools such as new vaccines [4] and rapid diagnostic tests [5] to contain future outbreaks , the extent to which symptomatic individuals do not present for care , how to identify and manage clinical sequelae of EVD [6] , and the incidence and transmission dynamics of minimally symptomatic Ebola virus ( EBOV ) infection . Evidence for minimally symptomatic EBOV infection is limited . During the 2013–16 outbreak in West Africa , it was not considered epidemiologically relevant to published models or projections of intervention effects [7–10] . Moreover , it is not known if clinical sequelae seen in survivors of EVD ( e . g . , uveitis ) exist in individuals who had minimally symptomatic EBOV infection . In order to improve our understanding of the transmission dynamics of EBOV in humans , we investigated the occurrence of minimally symptomatic EBOV infection in a recognized Ebola ‘hotspot’ [11] , which we defined as an area with a reverse transcription polymerase chain reaction ( RT-PCR ) -confirmed EVD attack rate above 2% in a two-month period .
The study protocol was approved by the Sierra Leone Ethics and Scientific Review Committee and the Stanford University Institutional Review Board ( Protocol ID: 33882 ) . We held meetings with Paramount , Sectional , and Town Chiefs to discuss the proposal and supply them with written information . We then held town meetings to describe the project and answer community questions . Individuals provided written informed consent or placed a thumbprint after hearing a consent script read in the Krio or Kono languages ( both ethics committees approved the use of an oral script and thumbprint for those participants who could not read or write ) ; parents signed for children under the age of 18 , and children and adolescents provided verbal assent . Subjects received 50 , 000 Leones ( ~$10 US ) for their participation in the study . Kono is a diamond-rich district in eastern Sierra Leone which reported 301 cases of EVD ( that is , RT-PCR confirmed individuals who presented for isolation or were identified by surveillance teams ) between August 2014 and February 2015 ( Kono District Ebola Response Centre ( DERC ) , personal communication ) . Several of the authors worked closely with the Kono DERC , British military , and International Federation of the Red Cross on containment , isolation , and surveillance during the time of active transmission in the district . Parts I and II of this study were conducted from October 2015 to January 2016 by a team consisting of two physicians , a physician-anthropologist , a laboratory technician , and two community health workers .
One year after the peak of the Ebola epidemic in Sierra Leone , we identified positive IgG responses to Zaire-EBOV in 14 individuals not known to have had EBOV infection from a village classified as a hotspot for Ebola transmission . Of these , the majority reported having had no symptoms consistent with EVD during the epidemic while two reported only fever . Thus , we provide further evidence that Ebola , like many other viral infections , presents with a spectrum of clinical manifestations , including minimally symptomatic infection . In addition , our data suggest that a significant portion of Ebola transmission events may have gone undetected during the epidemic . Although it was difficult to verify symptoms through our retrospective interviews ( given the considerable denial of EVD during the outbreak due to stigma and the fear of being admitted to an ETU where those admitted were seldom discharged ) , our data indicate that 25% of EBOV infections may have been minimally symptomatic , which is similar to the data from empiric studies and to recent modeled estimates [14] . The phenomenon of previously undetected , minimally symptomatic EBOV infection was evident around the discovery of the virus in 1976 . Using an immunofluorescence assay , the World Health Organization/International Study Team found that 19% of contacts of EVD cases—very few of whom gave any history of illness—had antibodies to the virus [15] . In 2000 , Leroy and colleagues published a study ( based on ELISA/Western blot ) and found that of 24 asymptomatic close contacts of Gabonese patients with EVD , 11 developed both IgM and IgG responses to Ebola Zaire antigens , indicating viral infection [16] . Other investigators have found evidence of seropositive individuals in areas without large outbreaks using ELISA and postulate that there may have been active circulation of filovirus without apparent clinical manifestations [17 , 18] . Heffernan and colleagues also used ELISA in Gabon and found that 1% of individuals in an epidemic zone had IgG antibodies to Ebola Zaire virus , yet no history of exposure [19] . In another study in Gabon , Becquart and colleagues found a 15 . 3% Ebola Zaire IgG seroprevalence in 220 randomly selected villages and concluded that most of the seropositive persons identified “probably had mild or asymptomatic infection” [20]; however , they used uninfected individuals in France as negative controls . We found that unexposed expatriates ( not included as negative controls ) had a significantly lower mean log anti-GP ( M = 5 . 25 U/mL , SD = 0 . 54 , N = 12 ) than unexposed Sierra Leoneans ( M = 6 . 40 U/mL , SD = 1 . 06 , N = 132 ) using the two-sample t-test for unequal variances , t ( 20 . 05 ) = 6 . 40 , P< = 0 . 001 ( see Fig 3 ) , potentially due to cross-reactivity of our assay with closely related pathogens circulating in the region . Our study has several possible limitations . First , if asymptomatic EBOV infection is a common occurrence , it is possible that some of the “true negatives” used for the validation of our ELISA assays could have been infected with EBOV . We did not use a microneutralization assay for comparison purposes , nor did our pool of EVD survivors represent all of the possible antibody titers in the regional population . Although the serologic assay conferred sensitivity and specificity greater than 95% , the assay protocol produced antibody titers that were considered qualitative in nature . Second , we relied on study participants and their household contacts’ memories of events that had taken place up to a year prior when we classified them as symptomatic or asymptomatic ( potential recall bias ) ; however , during the outbreak , most quarantined households were monitored daily by surveillance teams that conducted symptom screens and measured temperatures , and individuals were brought to treatment facilities for EBOV testing if they screened positive . Third , our IgG assays indicate previous infection but provide no information on when that infection took place . The 3 negative controls with positive tests either represent false positives due to cross-reactive antibodies or previously infected individuals . ( We did not perform IgM ELISAs , as other investigators have demonstrated that Ebola IgM titers largely diminish within 60 to 90 days of symptom onset [21 , 22] . ) Fourth , we did not ascertain whether there were non-quarantined individuals who had minimally symptomatic infection . Our study focused on the quarantined population of one village . Extrapolation of our findings to other villages and generalizability to the epidemic should be approached with caution . Although many of the study participants were followed by surveillance teams during the time of active EBOV transmission in their village , the timing of the possible infection cannot be known from IgG data , and it is improbable that surveillance teams followed each individual throughout the duration of the outbreak in Sierra Leone . Thus , the concern that IgG positive persons indeed had but denied symptoms cannot be excluded . Despite these limitations , our serosurvey provides a deeper perspective on EBOV transmission , and more village-level serosurveys could enhance our understanding of undetected EBOV transmission at the epidemic level . Furthermore , our findings suggest there would be value in exploring the interaction of seropositive persons and EVD cases to improve our understanding of exposure risk . As a result , we may learn more about how efforts at containment can be improved . The data also have important implications for future vaccine studies that rely on detecting antibody to EBOV . Lastly , the findings support the World Health Organization’s interim guidance on clinical care for survivors of EVD , which defines a survivor as a person: There is ongoing discussion in West Africa over the definition of survivorship , usually specified by having a positive EBOV RT-PCR result and discharge from an ETU . Plans are underway to develop national registries and provide ID cards to such survivors , so as to delineate individuals eligible for free social and medical support services . Should the notion of survivorship be extended to all those who are IgG positive , including those who had minimally symptomatic infection or who were sick but were never tested at the time of illness ? How we define a community of suffering is always problematic and should be revisited given that this definition has implications for identity , stigma , and access to social and medical services . In conclusion , by using ELISA to measure Zaire-EBOV antibody concentrations , we identified a significant number of individuals with previously undetected minimally symptomatic EBOV infection in a ‘hotspot’ village in Sierra Leone , approximately one year after the village outbreak . Further studies are needed to understand the potential risk of transmission and clinical sequelae in individuals with minimally symptomatic EBOV infection . | With over 28 , 000 reported cases , the 2013–16 West African Ebola virus disease epidemic is the largest and longest on record . This study provides further evidence that Ebola , like other viruses , causes a spectrum of clinical manifestations that may include minimally symptomatic infection . The findings also suggest that many episodes of human-to-human transmission of Ebola virus in West Africa may have gone undetected in the recent outbreak . This has implications for the definition of Ebola virus disease survivorship , delineation of transmission chains , and future vaccine studies . | [
"Abstract",
"Introduction",
"Methods",
"and",
"Results",
"Discussion"
] | [
"reverse",
"transcriptase-polymerase",
"chain",
"reaction",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"immune",
"physiology",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"immunology",
"tropical",
"diseases",
"microbiology",
"ge... | 2016 | Minimally Symptomatic Infection in an Ebola ‘Hotspot’: A Cross-Sectional Serosurvey |
FGF signaling is a potent inducer of lacrimal gland development in the eye , capable of transforming the corneal epithelium into glandular tissues . Here , we show that genetic ablation of the Pea3 family of transcription factors not only disrupted the ductal elongation and branching of the lacrimal gland , but also biased the lacrimal gland epithelium toward an epidermal cell fate . Analysis of high-throughput gene expression and chromatin immunoprecipitation data revealed that the Pea3 genes directly control both the positive and negative feedback loops of FGF signaling . Importantly , Pea3 genes are also required to suppress aberrant Notch signaling which , if gone unchecked , can compromise lacrimal gland development by preventing the expression of both Sox and Six family genes . These results demonstrate that Pea3 genes are key FGF early response transcriptional factors , programing the genetic landscape for cell fate determination .
During development of a complex multicellular organism , organ identity is determined by the combination of lineage-specific and signal-induced transcription factors . In mammalian lacrimal gland development , the extracellular signals include Fibroblast Growth Factor ( FGF ) , Bone Morphogenetic Protein ( BMP ) , Notch and Wnt that either cooperate or antagonize each other during budding , elongation and branching morphogenesis [1] . In particular , genetic evidence has revealed that FGF signaling initiated by the binding of FGF10/Fgf10 , sent from the periocular mesenchyme , to FGFR2B/Fgfr2b on the conjunctival epithelium is indispensable for lacrimal gland development in both human and mouse [2–5] . Demonstrating the striking potency of FGF signaling in driving the lacrimal gland fate , ectopic expression of either rat Fgf10 or human FGF7 in the lens led to the formation of lacrimal gland-like cells in an area that under normal physiological conditions develops into the planar corneal epithelium [6 , 7] . This is at least partly mediated by both the FGF-induced Sox9 expression required for lacrimal gland induction and the Sox10 expression for acini formation [8] . However , unlike BMP , Notch and Wnt which have well established downstream transcription effectors Smad , NICD and β-catenin , respectively , how FGF signaling triggers its transcriptional responses in lacrimal gland cell fate determination is not known . The Pea3 family of transcription factors , composed of Pea3 ( Etv4 ) , Erm ( Etv5 ) and Er81 ( Etv1 ) , are E26 transformation-specific ( ETS ) -domain proteins that can be phosphorylated by Mitogen-Activated Protein Kinase ( MAPK ) to control their subcellular localization , DNA binding and transactivation [9] . They have been shown to act as oncogenes in melanoma , breast , lung and prostate cancer , mimicking the aberrant activation of RAS-MAPK pathways commonly present in a multitude of malignancies [10] . During embryonic development , expression of the Pea3 genes closely correlates with the activities of FGFs , making these genes suitable candidates for being the downstream effectors of FGF-Ras-MAPK signaling [11 , 12] . Indeed , conditional inactivation of Pea3/Erm in the lung epithelium disrupted the Fgf10-Shh feedback loop , resulting in smaller lung sizes , but mice were grossly healthy and exhibited normal life-span [13 , 14] . In the limb buds , Pea3 and Erm mediate FGF signaling in the proximal-distal ( P-D ) and anterior-posterior ( A-P ) patterning , which was evident by the growth retardation and mild polydactyly in the Pea3/Erm mutants [15 , 16] . Nevertheless , these Pea3/Erm mutant phenotypes were relatively modest compared to the FGF signaling mutants in the same tissues . In this study , we show that the MAPK-regulated Pea3 family of transcription factors are critical for lacrimal gland duct elongation and branching . Deletion of all three Pea3 genes from the lacrimal gland epithelium resulted in ectopic expression of epidermal markers , shifting the lacrimal gland progenitor cells toward a cutaneous cell fate . In addition to previously reported FGF signaling response genes , we also identify Six1 and Six2 as being novel targets of the FGF-Pea3 axis , showing that these two genes cooperate in regulating lacrimal gland branching . Loss of Pea3 results in aberrant upregulation of Notch signaling in the lacrimal gland primordia driven by Jag1-mediated lateral activation and concurrent downregulation of the Notch modulator , lunatic fringe . Aberrant Notch signaling sustains this auto-stimulatory loop by upregulating Jag1 expression , leading to the downregulation of FGF signaling effector genes and failure of lacrimal gland induction . The shift of cellular identity and discordance of FGF-Notch crosstalk in the absence of Pea3 transcription factors establishes Pea3 genes as cell fate determinants in lacrimal gland development .
Mouse lacrimal gland development commences at E13 . 5 with the thickening of the conjunctival epithelium , which subsequently forms a bud , entering the surrounding periocular mesenchyme by E14 . 5 . This process is triggered by the mesenchymal release of Fgf10 which activates FGF signaling in the epithelium . This signaling leads to the activation of the Pea3 family of ETS transcription factors , Pea3/Etv4 , Erm/Etv5 , Er81/Etv1 ( Fig 1A–1C , dotted lines ) [5 , 17 , 18] . We conditionally deleted Mek and Erk using an Le-Cre transgenic mouse line , in which Cre-recombinase linked to an IRES-GFP reporter was expressed in the conjunctival epithelium and the lacrimal gland [19] . In both Le-cre; Mek1fl/fl; Mek2-/- ( Mek KO ) and Le-cre; Erk1-/-; Erk2fl/fl ( Erk KO ) lacrimal gland epithelia , expressions of Pea3 transcription factors were abolished ( Fig 1D–1I , dotted lines ) . To study the function of these transcription factors , we conditionally deleted Erm and Er81 , two members of the Pea3 family of transcription factors , in a Pea3-null background using Le-Cre . Indicated by the lacrimal gland progenitor cell marker Pax6 , the lacrimal gland primordia in E15 . 5 Le-cre; Pea3-/-; Ermfl/fl; Er81fl/fl ( hereafter referred to as Pea3 TKO ) embryos were noticeably smaller in size compared to the control ( Fig 1J and 1M , dotted lines ) . As reflected by TUNEL staining , this was consistent with an increase in apoptosis seen in the lacrimal gland primordia ( Fig 1K and 1N , arrows ) . Analysis of the malformed gland marked by GFP expression additionally showed that both duct elongation and branching were severely compromised at the post-natal P1 stage ( Fig 1L and 1O , arrows ) . In contrast , the lacrimal gland phenotype was considerably less severe in mice carrying at least one normal copy of Pea3 ( Fig 1P ) , indicating the importance of Pea3 gene . Of note , unlike Mek and Erk KO that displayed complete lacrimal gland aplasia , Pea3 TKO still presented with residual lacrimal glands . These results suggest that Pea3 transcription factors mediate some but not all of MAPK-dependent processes in lacrimal gland development . In order to decipher the gene regulatory network of Pea3 transcription factors , E14 . 5 lacrimal gland epithelial tissue from control ( Le-Cre ) and mutant ( Pea3 TKO ) mouse embryos were micro-dissected using laser capture microscopy and subjected to RNA-seq ( Fig 2A , n = 3 per condition ) . Unsupervised clustering analysis of the normalized data revealed that control and mutant samples were separated into two distinctive groups and that data from individual samples within each group were highly correlated ( Fig 2B , r = 0 . 8 ) , indicating the robustness of the obtained results . Gene ontology analysis showed that biological processes such as protein degradation , ECM interaction , glycosaminoglycan biosynthesis and cell adhesions are significantly downregulated in Pea3 TKO mutants ( Fig 2C ) , which is in line with the previous findings that proteoglycans and ECM proteins play important roles in lacrimal gland development [5 , 8 , 17 , 18] . In addition , PI3K and Ras pathways were also impaired in Pea3 TKO mutants , suggesting that downstream effectors of FGF signaling may also be compromised . To validate this idea , we compared our dataset with the previously published result from the Fgfr2 conditional knockout [8] . Gene set enrichment analysis ( GSEA ) revealed that there was indeed a significant overlap in downregulated genes between Pea3 TKO and Fgfr2 mutants ( NES = -6 . 8 , p = 0 . 01 ) ( Fig 2D ) [20] . Taken together , these results are consistent with the notion that the Pea3 family of genes act downstream of the FGF signaling cascade . Further analysis revealed that Pea3 transcription factors were uniquely positioned to fine tune the FGF signaling outcome . First , Pea3 transcription factors promoted their own expressions in the lacrimal gland bud , as Pea3 , Erm and Er81 transcripts were reduced in Pea3 TKO RNA-seq dataset ( Fig 3A ) . Second , expression of heparan sulphate biosynthetic enzymes Ext1 , Hs3st and Hs6st was also down regulated ( Fig 3A ) . Since heparan sulphate proteoglycans are known to act as co-receptors for Fgf10 , this was expected to dampen the positive feedback mechanism of FGF signaling . Third , Pea3 transcription factors were required for the expression of Dusp6 and Spry4 ( Fig 3A ) , which are both inhibitors of Ras-MAPK signaling . Reevaluating the available ChIP-seq data from the human LoVo and GIST48 cancer cell lines [21 , 22] , we found that all of the above negative and positive feedback genes could be bound by either PEA3 , ERM or ER81 in their promoter ( within 5000 bp of the transcriptional start site ) and/or enhancer regions ( beyond 5000 bp upstream or downstream to the promoter site ) ( Fig 3A and 3B ) . Many of the ChIP-seq peaks for PEA3 proteins overlapped with H3K4Me1 , H3K4Me3 and DNAse I sensitivity sites , signifying an open chromatin conformation in these regions ( Fig 3B ) . Since Erm was the most highly expressed Pea3 transcription factor during lacrimal gland development ( Fig 1B ) , we searched for the putative Erm binding sites in the corresponding mouse genomic regions using TRANSFAC database ( S1 Fig ) . By chromatin immunoprecipitation , we confirmed that Erm protein indeed bound to the Ext1 , Dusp6 , Col2a1 and Mmp2 loci in P4 lacrimal gland cells ( S1 Fig ) . In addition , RNA in situ hybridization experiments confirmed that Pea3 , Erm , Er81 and Dusp6 genes were down regulated specifically in the Pea3 TKO lacrimal gland primordia ( Fig 3C ) . These data show that Pea3 transcription factors play a central role in modulating the levels of FGF signaling by regulating the positive and negative feedback loops involved in the fine tuning of this pathway . The Sox family of transcription factors Sox9 and Sox10 have been previously identified as downstream targets of FGF signaling important for lacrimal gland development [8] . The expression levels of Sox10 were severely diminished in Pea3 TKO mutants , whereas the reduction in Sox9 expression was less dramatic ( Fig 3A and 3C ) . Interestingly , the ChIP-seq analysis suggested that the promoter of SOX9 but not that of SOX10 harbored direct binding sites for PEA3 factors ( Fig 3A ) . Sox9 was previously shown to regulate the expression of extracellular matrix related genes Col2a1 , Col9a1 , Mia1 and MMP2 , which is consistent with the dynamic remodeling of the extracellular matrix during lacrimal gland development [8] . Interestingly , these genes were also occupied by PEA3 transcription factors in their promoter/enhancer regions in GIST48 and LoVo cells , with their expressions being down regulated in Pea3 TKO mutants ( Fig 3A–3C ) . Therefore , by controlling both Sox9 and its downstream targets , Pea3 transcription factors activate a feedforward mechanism in regulating lacrimal gland development . Strikingly , the transcriptome analysis additionally revealed that many keratin genes were upregulated in Pea3 TKO mutants ( Fig 4A ) . This result was especially unexpected because the keratins that were ectopically expressed are typically found in the cutaneous epithelium during embryonic development , rather than in the lacrimal gland . This led us to hypothesize that there was a shift in cell identity from the lacrimal gland fate to the epidermal-like fate in the absence of Pea3 genes . To test this idea , we performed GSEA of differentially upregulated genes in Pea3 TKO mutants compared to the published gene expression datasets of E14 . 5 mouse embryonic skin [23] . This analysis showed that the transcriptome of the Pea3 TKO lacrimal gland primordia was significantly enriched in genes prevalent in the epidermis ( Fig 4B , NES = 11 . 99 , p<0 . 001 ) and hair follicle placode ( NES = 9 . 0 , p<0 . 01 ) . In contrast , no significant similarities were observed when compared with the dermal condensates , skin fibroblast , melanocyte or Schwann cells . We next examined a set of genes that displayed nested expressions from the epidermis , to the conjunctiva to the lacrimal gland . At E14 . 5 , Krt14 was mostly restricted to the epidermis , and Krt5 and Sfn were only present in the skin epidermis and the conjunctival epithelium , whereas Krt7 expression was expanded into the stalk region of the lacrimal gland but excluded from the bud ( Fig 4C ) . In the Pea3 TKO mutant , all these genes were expressed in the lacrimal gland primordia . These data indicated that Pea3 proteins prevented the lacrimal gland progenitors from adopting the epidermal fate . To further understand the molecular mechanism of Pea3 mutant defects , we sought to determine the most differentially regulated genes in our dataset . For this analysis , the gene expression changes depicted by Log2 ( fold change ) were plotted on the x-axis against the corresponding statistical significance depicted by -Log10 ( p-value ) on the y-axis ( Fig 5A ) . Apart from the aforementioned FGF-responsive genes Spry4 , Dusp6 , Col2a1 , Col9a1 , Sox9 and Sox10 , transcription factors Six1 and Six2 also emerged as significantly downregulated genes in Pea3 TKO mutants . Importantly , both SIX1 and SIX2 loci in GIST48 and LoVo cells displayed significant ChIP-seq peaks for PEA3 , Erm and ER81 in open chromatin conformations marked by histone H3K4Me1 and H3K4Me3 methylations and DNase I sensitivity , suggesting they could be direct targets of PEA3 transcription factors ( Fig 5B ) . Indeed , in situ hybridization for Six1/ Six2 revealed that their expressions were significantly reduced in E14 . 5 Pea3 TKO lacrimal glands ( Fig 5C , dotted lines ) and abolished in Le-Cre; Fgfr2fl/fl mutants ( Fig 5C , arrows ) . Therefore , Six1 and Six2 are transcriptional targets of Pea3 and FGF signaling in the lacrimal gland epithelium . While a Six1 deletion has been shown to affect lacrimal gland duct elongation and branching [24] , a Six2 mutant phenotype hasn’t previously been reported . We examined lacrimal gland development in Six2 knockout embryos at E15 . 5 , but did not observe any gross abnormalities ( S2 Fig ) . This could be due to compensation by Six1 during lacrimal gland development . To test this idea , we used siRNAs against Six1 and Six2 genes , which resulted in significant down regulation of their expressions in a cell based assay ( Fig 5D ) . In the ex-vivo lacrimal gland culture , exogenous Fgf10 induced significant growth of the E17 . 5 lacrimal gland primordia , which was dampened by siSix1 but not by scrambled siRNA ( Fig 5E and 5F ) . Although siSix2 did not display any effect , combined application of siSix1 and siSix2 led to significant reduction in the size of lacrimal gland buds induced by Fgf10 . These results show that Six1 and Six2 act synergistically to regulate lacrimal gland development . Although Pea3 proteins generally function as transcriptional activators , they can also act as repressors in certain contexts [25] , thus we examined genes upregulated in the Pea3 TKO mutants . Notably , pathway analysis revealed activation of the Notch signaling pathway reflected by an increase in expression of the ligand Jag1 , receptors Notch1 , Notch 2 and Notch3 , downstream target Hes1 and a reduced expression of Lunatic fringe ( Lfng ) ( Fig 6A ) . This was further confirmed by GSEA of Notch signaling genes in the Pea3 TKO transcriptome ( Fig 6B ) . Indeed , RNA in situ hybridization showed that Jag1 mRNA was normally restricted to the surface ectoderm and conjunctiva at E14 . 5 , but in the Pea3 TKO mutants , Jag1 transcripts were ectopically expressed in the lacrimal gland primordia , with its translated protein form being prominently induced in the same area ( Fig 6C ) . This was in sharp contrast to Lfng , a gene that was readily detectable in the control lacrimal gland with its expression being significantly reduced in the Pea3 TKO mutants ( Fig 6C ) . In line with these findings , Pea3 TKO mutant lacrimal gland primordia displayed readily detectable staining patterns of Notch1 intracellular domain ( Notch1-ICD ) , demonstrating that Notch signaling was aberrantly activated . After establishing the misplaced activation of Notch signaling in the lacrimal gland , we subsequently investigated the functional significance of its activation in this developing tissue . Lfng is a glycosyl transferase that prevents Jag1-mediated Notch signaling in a context dependent manner [26–28] . Consistent with its negative role in Notch signaling , genetic ablation of Lfng resulted in a moderate increase in Notch1-ICD staining in the tip of the E14 . 5 lacrimal gland ( S3A–S3D Fig ) . Interestingly , lacrimal gland size was reduced in P10 Lfng knockout pups , suggesting that the loss of Lfng expression likely contributed to the Pea3 TKO lacrimal gland phenotype ( S3E and S3F Fig ) . Nevertheless , the Lfng knockout did not fully activate Notch signaling to the extent seen in the Pea3 TKO mutants . This prompted us to directly express the Notch1 intracellular domain in the developing lacrimal gland using the Cre-inducible R26-N1CD allele . In E14 . 5 Le-Cre; R26-N1CD embryos , expressions of Six1 and Six2 were lost , but the lacrimal gland progenitor cell markers Pax6 and E-cadherin were retained ( Fig 7A and 7B , dotted lines ) . The downstream targets of FGF signaling such as Sox10 , Pea3 , Erm and Dusp6 were also downregulated ( Fig 7A ) . Interestingly , Jag1 was upregulated in the fornix of the conjunctiva where the lacrimal gland progenitors resided , suggesting that Notch signaling acted in an auto-stimulatory loop to increase Jag1 expression ( Fig 7B , dotted lines ) . At P1 , no lacrimal gland was found in Le-Cre; R26-N1CD embryos ( Fig 7B , n = 10 ) , demonstrating that aberrant activation of Notch was deleterious to lacrimal gland development .
FGF signaling plays an instructive role in lacrimal gland development , controlling its fate determination and morphogenesis . Mediated by the canonical Ras-MAPK pathway , FGF signaling induces expression of Pea3 transcription factors during the formation of both the epithelial and mesenchymal compartments of the lacrimal gland [18 , 29] . In this study , we showed that Pea3 transcription factors were necessary to establish the identity of the lacrimal gland epithelium , turning it away from epidermal and conjunctival cell fates ( Fig 7C ) . This was further attributed to the loss of Six1 and Six2 during lacrimal gland development , leading to both the disruption of duct elongation and branching morphogenesis . In addition , we found that Pea3 transcription factors inhibited the Notch signaling pathway which , when activated , prevents the expression of Six and Sox genes and causes the abortion of lacrimal gland induction . Collectively , our data demonstrate that Pea3 transcription factors control the expression profiles of key genes involved in the promotion of lacrimal gland identity and morphogenesis . The regulatory mechanisms controlling the expression levels of Six1 and Six2 are not well understood . Six1 deficiencies cause defects in organs that include the inner ear and kidney , both of which also develop through an epithelial-mesenchymal interaction like that which occurs in the lacrimal gland . However , contrary to what we observed in the lacrimal gland , analyses of inner ear development showed that Pea3 negatively regulated the pre-placodal genes Six1 and Eya2 , and Six1 acted upstream of Jag1 in the Notch signaling pathway [30 , 31] . In kidney development , both Six1 and Six2 are expressed in the cap mesenchymal area where they are required for the ureteric budding and branching process [32–34] . Although Pea and Erm transcription factors are present in both the ureteric bud and the mesonephric mesenchyme , they are only required in the epithelial compartment to mediate Ret signaling [35] . SIX1/Six1 have been previously implicated in lacrimal gland development in humans and in mice . A heterozygous missense mutation in the SIX1 gene causes autosomal dominant lacrimal gland stenosis whereas Six1 knockout mouse embryos displayed small lacrimal glands with duct elongation and branching defects [24] . Our RNA-seq analysis showed that Six2 was expressed at 5 . 6 folds higher than Six1 in the developing lacrimal gland epithelium , but surprisingly , Six2 null mutant embryos did not exhibit any lacrimal gland phenotype . This was likely due to compensation by Six1 , as our explant culture experiments showed that knockdown of both Six1 and Six2 synergistically disrupted branching morphogenesis of the lacrimal gland . We further showed that Six1 and Six2 were controlled by Pea3 transcription factors downstream of FGF signaling in the lacrimal gland epithelium . Thus , Six1 and Six2 genes are novel targets of Pea3 transcription factors in regulating lacrimal gland morphogenesis . Although Notch activity is important for maintaining the postnatal homeostasis of the lacrimal gland [36] , its temporal requirement during development has yet to be established . We have shown that Pea3 transcription factors prevent ectopic activation of Notch signaling during lacrimal gland induction . Analysis of our RNA-seq data for the modulators of Notch signaling showed that Lfng was the only Fringe family gene expressed abundantly in the lacrimal gland and its expression was significantly downregulated in the Pea3 TKO mutants . Lfng is a glycosyltransferase that adds O-linked fucose residues to the extracellular domain of the Notch receptor in order to modulate its ligand binding [37] . Lfng has been shown to potentiate the Dll-mediated but inhibit the Jag1-mediated Notch1 signaling pathways [26–28] . During sensory hair cell development in the inner ear , Lfng co-expresses with Jag1 and , when mutated in mice , partially rescues the Jag2 knockout phenotype [38 , 39] . During lacrimal gland development , Pea3 transcription factors may turn on the expression of Lfng to down regulate Jag1-mediated Notch signaling . This model was supported by the down regulation of Lfng levels in the absence of Pea3 transcription factors , and increased Notch1-ICD staining with reduced size of the lacrimal gland in the Lfng knockout . Lfng was not the only component of Notch signaling targeted by the Pea3 transcription factors in the lacrimal gland . In fact , the expression levels of Jag1 , Notch and Hes1 were all elevated in the Pea3 TKO mutants , resulting in a much higher level of Notch1-ICD staining compared to that seen in the Lfng mutants . To replicate such strong activation of Notch signaling , we directly expressed Notch1-ICD in the ocular surface , resulting in loss of both the Six and Sox genes and abrogation of lacrimal gland induction . These results highlight the importance of inhibiting aberrant premature Notch signaling during lacrimal gland development . Our study revealed a previously underappreciated FGF signaling network systematized by the Pea3 transcription factors targeting Sox , Six and Notch signaling pathways during development of the lacrimal gland . We also showed that Pea3 transcription factors not only directly promote the expression of heparan sulfates involved in potentiating FGF signaling but also activate expression of the inhibitory factors Sprouty4 and Dusp6 . We would like to suggest that by inducing both positive and negative feedback loops the Pea3 family of proteins may amplify the transcription response to low levels of FGF signaling but dampen the response to strong FGF signals . This non-linear transcriptional response mechanism can stabilize the FGF signaling network output given a wide range of FGF signal input , buffering the developmental system in face of environmental perturbations .
The animal experiments were approved by Columbia University Institutional Animal Care and Use Committee ( Protocol number: AAAR0429 ) . Mice carrying Erk1-/- , Erk2flox , Lfng-/- , Mek1flox and Mek2-/- alleles were bred and genotyped as described [28 , 40 , 41] . We obtained Er81flox mice from Dr . Silvia Arber ( University of Basel , Basel , Switzerland ) , Pea3-/- and Ermflox mice from Dr . Xin Sun ( University of California at San Diego , San Diego , CA ) , Fgfr2flox from Dr . David Ornitz ( Washington University Medical School , St Louis , MO ) and Le-Cre mice from Dr . Ruth Ashery-Padan ( Tel Aviv University , Tel Aviv , Israel ) . [15 , 19 , 42 , 43] . Rosa-N1-ICDflox/+ mice were obtained from Jackson lab ( Stock # 008159 ) . Animals were maintained in a mixed genetic background . Lacrimal gland growth and morphology were identical in Le-Cre and Le-Cre;Pea3+/-;Ermflox/+;Er81flox/+ mice , which were used as controls throughout the conducted experiments . Mice were housed in specific pathogen free ( SPF ) facility that employed a 12-hour light-dark cycle and were given standard mouse feed . RNA in situ hybridization was performed as previously described [44] . Briefly , the mouse embryos were harvested , fixed overnight in 4% PFA , equilibrated in 30% sucrose and cryo-frozen in OCT . On the day of the experiment , OCT blocks were sectioned at 10 μm , hybridized with the diluted probe at 68°C overnight in a wet chamber and moistened with solution containing 50% Formamide and 1X Salt ( 0 . 2M NaCl , 10mM Tris , 5mM NaH2PO4 , 5mM Na2HPO4 , 5mM EDTA ) . The probe was diluted at 1:200–500 in a pre-warmed hybridization buffer and incubated at 70°C for at least 10 minutes . On the next day , slides were washed 3X in wash buffer ( 1X SSC ( 150mM NaCl , 15mM Sodium citrate , pH 7 ) , 50% Formamide ) at 68°C . After cooling , slides were washed 2X with MABT ( 100mM maleic acid , 150mM NaCl , pH 7 . 5 , 0 . 1% Tween 20 ) and incubated at room temperature for 30 min . Slides were then blocked with 20% Sheep serum in MABT for 1 hour , followed by an overnight incubation with anti-DIG antibody ( 1:1500 ) at 4°C . On the next day , slides were washed 4-5X with MABT and 2X with alkaline phosphatase buffer . For color development , slides were covered with BM purple substrate and incubated at room temperature for 4–24 hrs . The following probes were used: Pea3 , Erm5 ( from Dr . Bridget Hogan , Duke University Medical Center , Durham , NC , USA ) , Er81 ( from Dr . Gord Fishell , New York University Medical Center , New York , NY , USA ) , Jag1 ( from Dr . Doris Wu , National Institute on Deafness and Other Communication Disorders , National Institutes of Health , Bethesda , MD ) , Lfng ( from Dr . Andy Groves , Baylor College of Medicine , Houston , TX ) , Six1 ( from Dr . Bernice Morrow , Albert Einstein College of Medicine , New York , NY , USA ) , Six2 ( from Dr . Thomas Caroll , UT Southwestern Medical center , Dallas , TX , USA ) , Sox10 ( from Dr . Anthony Firulli , Indiana University School of Medicine , Indianapolis , IN , USA ) , Dusp6 ( full length cDNA IMAGE clone: 3491528 , Open Biosystems , Huntsville , AL , USA ) , Sfn and Krt7 ( full length cDNA IMAGE clone: 184592 and 40614 , the DNA Resource Core , Harvard Medical School , Boston , MA , USA ) . For immunohistochemistry of paraffin samples , sections were deparaffinized and rehydrated by serial treatment with histosol followed by decreasing percentages of ethanol solutions [44 , 45] . For cryosections , sections were briefly washed with PBS to remove OCT . Antigen retrieval was performed with microwave boiling in citrate buffer ( 10 mM sodium citrate , pH 6 . 0 ) for 1–2 minutes followed by heating for 10 minutes at a low power setting . Sections were then washed with PBS and blocked with 5% NGS/0 . 1% Triton in PBS . Primary antibody incubation was performed overnight at 4°C in a humid chamber followed by incubation with fluorescent-conjugated secondary antibodies for 1 hour at room temperature in the dark . For signal amplification , HRP-conjugated secondary antibodies were used , followed by washing and equilibration with TNT buffer . The slides were then incubated with Tyramide reagent for 10 minutes , washed with TNT buffer , stained with DAPI and mounted with anti-fade reagent , 0 . 2% NPG , 90% glycerol in 1X PBS . The following primary antibodies were used: Pax6 ( PRB-278P ) and Krt14 ( PRB-155P ) ( both from Covance , Berkeley , CA , USA ) , Ecad ( U3254 , Sigma , St Louis , Missouri , USA ) , Jag1 ( sc-8303 , H-114 , Santa Cruz Biotechnology , Santa Cruz , CA , USA ) , Krt 5 ( 905901 , Biolegend , San Diego , CA , USA ) , N1-ICD ( #4147 , Cell signaling Technology , Boston , MA , USA ) 3T3/HeLa cells were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin ( Invitrogen ) at 37°C . For the Six1 knockdown , transient transfection of Six1 siRNA ( s73792 , Ambion , Carlsbad , CA ) was performed in 3T3 cells . Total RNA from 3T3 cells was extracted using the MiniRNA Plus kit ( Qiagen , Hilden , Germany ) and converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) . Quantitative-PCR was performed using the PCR SYBR green 2X master mix ( Invitrogen , Carlsbad , CA ) in the StepOne plus Real time PCR instrument [46] . For the Six2 knockdown , transient transfection of Six2 cDNA ( clone TCM1304 , Transomic , Huntsville , AL ) was performed with Lipofectamine 3000 ( cat#L3000015 , Invitrogen , Carlsbad , CA ) according to the manufacturer's instruction . After 18 hours , cells were transfected with Six2 siRNA ( Silencer Select , s73794 , Ambion , Carlsbad , CA ) or scrambled siRNA with a final concentration of 20 nM using RNAi Max ( cat#13778150 , Invitrogen , Carlsbad , CA ) according to the manufacturer's instruction . siRNA silencing was conducted a second time after 8 hours . Cells were collected for Quantitative-PCR analysis following the Six2 cDNA overexpression for 48 hours and the Six2 siRNA knockdown for 24 hours . The primer sequences used were: Six1: 5’- ATGCTGCCGTCGTTTGGTT -3’ , 5’-CCTTGAGCACGCTCTCGTT -3’ , Six2: 5’- CACCTCCACAAGAATGAAAGCG-3’ , 5’-CTCCGCCTCGATGTAGTGC -3’ , Gapdh: 5’-AGGTCGGTGTGAACGGATTTG-3’ , 5’-TGTAGACCATGTAGTTGAGGTCA-3’ . P4 old lacrimal glands collected from 40 mouse pups were incubated with 1ml trypsin for 5 minutes and pipetted a few times to dissociate into single cells . 4ml DMEM+10% FBS was added to neutralize the trypsin before addition of 270μl formaldehyde ( 37% ) in 10ml of DMEM containing 10% FBS to fix the cells with shaking for 10 minutes . The cross linking was stopped by addition of DMEM with 10% FBS and 0 . 125M glycine for 5 minutes . After washed with cold 1xPBS twice ( 5 minutes each ) and centrifuged in 3000 rpm for 3 minutes , the cells were collected and re-suspended in 1ml of ChIP lysis buffer ( 10mM Tris-Cl , pH8 , 85mM KCl , 0 . 5% NP-40 , 5nM EDTA , 0 . 25% Triton; RIPA- 1% Triton , 150mM NaCl , 0 . 1% SDS , 0 . 1% Na-Deoxycholate , 10mM Tris-Cl , pH8 , 5mM EDTA ) with 1X protease inhibitor and kept in rocker at 4°C for 10 minutes . The cells were spun at 3K rpm , re-suspended with 1ml of RIPA buffer with 1X protease inhibitor before being sonicated with the power 1 second “on” , 2 second “off” for 8 minutes and spun in 15000 rpm for 10 minutes at 4°C . Pre-cleared by incubating with 45 μl agarose beads for 2 hrs at 4°C , the supernatant was spun at 3K rpm and incubated overnight with 1μg of antibody for 1mg of protein at 4°C , followed by 20 μl protein G bead for 2 hrs . The beads were washed with RIPA , Wash buffer A ( 50mM HEPES , pH7 . 9 , 500mM NaCl , 1mM EDTA , 1% Triton , 0 . 1% Na-deoxycholate , 0 . 1% SDS ) , wash buffer B ( 20mM Tris-Cl , pH8 , 1mM EDTA , 250 mM LiCl , 0 . 5% NP-40 , 0 . 5% Na-deoxycholate ) and TE buffer twice respectively ( 5 min each wash ) . After they were spun at 2000 rpm , the collected beads were incubated with 480μl elution buffer ( 1% SDS , 30mM Tris-Cl ( pH8 ) , 15mM EDTA , 200mM NaCl ) at 50°C overnight before adding the same volume of phenol:chloroform and centrifuging at 15000 rpm for 5 minutes . The supernatant was mixed with 2X ethanol ( 100% ) at -80°C for 30 minutes , recovered to RT and centrifuged at 15000 rpm at 4°C for 15 minutes . The pellet was washed with 70% ethanol and the air dried DNA was dissolved in 15μl of distilled water for PCR reaction . The mouse monoclonal antibody against Erm was from Proteintech ( Catalog number: 66657-1-Ig ) . The primers used are: CAGCGACTGGAATGAGAACA and GCTGGAACAGGTTGTGTTGA for Dusp6 , ACTTGGGACTGCCACACTG and AACAACCCCCTCCCTTCTAA for Col2a1 , TACGATGATGACCGGAAGTG and AGGTTGTTCCAGGTCAGGTG for Mmp2 , AGTCCCGCTTGATACCTTGA and GTGGCTTTCTCGCTGTCTTT for Ext1 . The lacrimal glands from E16 . 5–17 . 5 embryos were harvested and gently transferred onto filter paper ( 0 . 45 um ) in 35 mm low bottom dishes ( ibidi , Martinsried , Germany ) in medium ( DMEM , 5%FBS , 400ng/ml Fgf10 , 250ng/ml Heparin , 1X ITS , P/S ) containing either scrambled , Cy3-labeled negative control ( AM4621 , Invitrogen , Carlsbad , CA ) , Six1 ( s73792 ) or Six2 ( s73794 ) siRNA . Lipoefectamine-siRNA complexes were prepared in Optimem medium as per the manufacturer’s instructions . To test the genetic redundancy , 45nM of scrambled siRNA , 15nM Six1 + 30nM scrambled siRNA , 30nM Six2 +15nM scrambled siRNA and 15nM Six1 + 30nM Six2 siRNA were used . 10μl of matrigel and medium in a 1:1 ratio was added on top of each gland . The glands were cultured for 24–48 hrs at 37°C . Laser capture microdissection and RNA sequencing were performed as previously described [29] . The RNAseq data is available at the GEO repository under accession number GSE114509 . Unsupervised clustering analysis was performed in MATLAB using the Clustergram function . We determined interquartile ranges of the gene expression levels in all samples and the top 200 genes were plotted . GSEA was performed using MATLAB implementation of the same method as described [20] . KEGG pathway enrichment analysis and functional annotation was performed in DAVID . For the functional annotation of downregulated genes , a list of 476 genes was used for the analysis based on cutoff points for the normal expression levels ( > 50 units ) , Log2 ( fold change ) ( <-1 ) and p-values ( <0 . 05 ) . Volcano plots representing Log2 ( p-value ) vs Log2 ( fold change ) were plotted in MATLAB . -Log2 ( p-value ) > 50 were set to 50 in order to avoid the scaling issues in the plot . ChIP-seq analysis was performed using MACS [47] . SRA files of ETV1 ( ER81 ) , ETV4 ( PEA3 ) and ETV5 ( ERM ) ChIP-seq data were retrieved from the GEO database [21 , 22] . SRA files were converted to a Fastq format using sratoolkit , followed by mapping of the sequence reads on the genome ( hg18 ) to generate a SAM file . Peak calling was done using MACS ( using default parameters ) . The mapped ChIP-seq file was visualized on the human reference genome assembly ( hg18 ) using the UCSC genome browser . The Erm binding sites in the mouse genome were scanned using MATCH algorithm based on TRANSFAC database . | FGF signaling regulates cell fate decision by inducing genome-wide changes in gene expression . We identified Pea3 family transcription factors as the key effectors of FGF signaling in reprograming the epithelia transcriptome . Pea3 factors control both the feedback and feedforward circuities of FGF signaling in lacrimal gland development . They also activate specific expression of Six and Sox family genes and suppress aberrant activation of Notch signaling . In the absence of Pea3 genes , the lacrimal gland progenitors become epidermal-like in their gene expression patterns . The study of Pea3 function resolves the long standing conundrum of how FGF induces the lacrimal gland fate , providing direction for regenerating the lacrimal gland to treat dry eye diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"gene",
"regulation",
"regulatory",
"proteins",
"dna-binding",
"proteins",
"notch",
"signaling",
"dna",
"transcription",
"endocrine",
"physiology",
"developmental",
"biology",
"organism",
"development",
"gland",
"development",
"tra... | 2018 | FGF-induced Pea3 transcription factors program the genetic landscape for cell fate determination |
CHTF18 ( chromosome transmission fidelity factor 18 ) is an evolutionarily conserved subunit of the Replication Factor C-like complex , CTF18-RLC . CHTF18 is necessary for the faithful passage of chromosomes from one daughter cell to the next during mitosis in yeast , and it is crucial for germline development in the fruitfly . Previously , we showed that mouse Chtf18 is expressed throughout the germline , suggesting a role for CHTF18 in mammalian gametogenesis . To determine the role of CHTF18 in mammalian germ cell development , we derived mice carrying null and conditional mutations in the Chtf18 gene . Chtf18-null males exhibit 5-fold decreased sperm concentrations compared to wild-type controls , resulting in subfertility . Loss of Chtf18 results in impaired spermatogenesis; spermatogenic cells display abnormal morphology , and the stereotypical arrangement of cells within seminiferous tubules is perturbed . Meiotic recombination is defective and homologous chromosomes separate prematurely during prophase I . Repair of DNA double-strand breaks is delayed and incomplete; both RAD51 and γH2AX persist in prophase I . In addition , MLH1 foci are decreased in pachynema . These findings demonstrate essential roles for CHTF18 in mammalian spermatogenesis and meiosis , and suggest that CHTF18 may function during the double-strand break repair pathway to promote the formation of crossovers .
Precise chromosome segregation is crucial to ensure that germ cell development proceeds normally during meiosis , and that genetic information is accurately transmitted to the gametes . For chromosome segregation to proceed flawlessly during meiosis , homologous chromosomes must undergo several processes that allow them to pair and remain physically joined until anaphase I . The physical connections between homologous chromosomes are established by at least three different mechanisms . Sister chromatids are connected between arms and at centromeres by cohesion , a process mediated by cohesins , i . e . multiprotein complexes that are established during S-phase [1]–[6] . Different cohesin complexes exist depending on the cell type and stage , and each consists of at least four subunits , some of which are specific to meiosis [7] , [8] . The physical connection between homologues also occurs by synapsis during meiotic prophase I , when homologous chromosomes pair through formation of a tripartite protein structure: the synaptonemal complex ( for a review see [9] ) . Meiosis-specific cohesin complexes are believed to form a scaffold to which components of the synaptonemal complex can attach [10] . During synapsis , additional physical contacts occur at points of DNA crossover ( chiasmata ) through reciprocal recombination between nonsister chromatids ( reviewed in [11] , [12] ) . At the end of prophase I , the synaptonemal complex disassembles , but homologous chromosomes remain joined across sister chromatid arms and at centromeres . At anaphase I , dissolution of cohesion between sister chromatid arms and resolution of chiasmata allow homologous chromosomes to migrate away from the metaphase plate [13] . Although cohesion between sister chromatid arms is dissolved , cohesion at centromeres is preserved to keep sister chromatids connected until they segregate in anaphase II following attachment to the spindle [14] . Thus , cohesion and chiasmata between sister chromatid arms prevent homologues from separating prematurely [10] , [11] . Maintenance of genome integrity is mediated in part by Replication Factor C- like complexes ( RLCs ) which function in DNA replication , chromosome cohesion , and the DNA damage checkpoint [15] . CTF18 , a component of RLC-CTF18 , was initially discovered in Saccharomyces cerevisiae [16] . In yeast , RLC-CTF18 is essential for establishment of sister chromatid cohesion and genome stability [15] , [17] , [18] . CTF18 is also crucial for germline development in the fruitfly . In CTF18 mutant flies , termed Cutlet , a loss-of-function mutation causes failure of germline stem cells to proliferate normally , resulting in sterility [19] . Recently , studies of the human RLC-CTF18 ( termed RLC-CHTF18 ) complex in vitro and in immortalized cell lines have demonstrated a role for CHTF18 in mammalian DNA replication [20]–[22] . Previously , we cloned and characterized Chtf18 , the murine orthologue of CTF18 . We showed that CHTF18 is expressed throughout the male and female germline of the mouse , suggesting a role for it in gametogenesis [23] . However , the role of CHTF18 in mammals has not been fully elucidated . Here we report the crucial roles CHTF18 plays during male meiosis in vivo . Our data demonstrate that CHTF18 functions in mammalian spermatogenesis to ensure fertility in males . Our results also suggest a role for CHTF18 in male meiotic recombination , and that it may function in maintaining the linkage of homologous chromosomes during meiotic prophase I .
CHTF18 is expressed throughout the male germline of the mouse , suggesting a role for it in spermatogenesis [23] . In order to study CHTF18 function in vivo , we employed gene targeting to derive Chtf18 mutant mice . We constructed Chtf18-null and conditional alleles by homologous recombination in embryonic stem ( ES ) cells ( Figure 1A ) . The mouse Chtf18 gene consists of 22 exons spanning 8 kb of genomic DNA [23] . We chose to target exons 7–10 , because these exons encode sequence motifs with high sequence similarity to Replication Factor C ( RFC ) ( Figure 1A ) . These sequence motifs are called RFC boxes and are required for the function of RFC in yeast and in human cells [24]–[26] . Following electroporation and screening of 300 ES cell clones , five correctly targeted clones containing the Chtf18loxP-flanked ( Chtf18flox ) allele were identified ( three are shown in Figure 1B ) . Cells from each of three clones were injected into mouse blastocysts and yielded 19 highly chimeric ( >90% ) male mice , which resulted in germline transmission of the targeted allele . Mice carrying this allele were then mated with transgenic Cre mice under the control of the E2A promoter [27] . The resulting heterozygotes were bred to homozygosity to generate Chtf18−/− mice . Absence of CHTF18 protein in Chtf18−/− testes was confirmed by Western blot analysis , indicating that this is a null allele ( Figure 1C ) . To derive Chtf18flox/− TNAP-Cre mice , mice homozygous for the Chtf18flox allele were bred with transgenic Cre mice under the control of the germ cell-specific promoter of the tissue non-specific alkaline phosphatase ( TNAP ) gene [28] , following FLP-mediated excision of the neomycin resistance cassette in vivo ( Figure 1 and Figure S1 ) . Although Chtf18−/− mice were viable with no overt defects , they were smaller in body weight ( Chtf18−/− adult mean weight about 15% less than adult mean weight of wild-type ) , and were born at submendelian ratios ( mean ratio of expected/observed embryos , Chtf18−/− 0 . 6 , Chtf18+/+ 1 . 2 , Chtf18+/− 1 . 1 , p<0 . 007 using ANOVA , Figure 2C ) . Data collected from Chtf18+/− intercross matings revealed approximately 50% of the expected number of Chtf18−/− offspring , compared to Chtf18+/+ and Chtf18+/− offspring ( Figure 2A ) . Data collected postnatally and from embryos at 14 . 5–18 . 5 dpc ( Figure 2B and 2C ) revealed virtually the same ratios of observed/expected for each genotype , and confirmed that these numbers were due to higher rates of death among Chtf18−/− mice during embryonic development . Although Chtf18−/− mice appeared grossly normal but smaller in body weight , testes of Chtf18−/− males were significantly smaller ( testis weight per body weight for Chtf18+/+ and Chtf18−/− mice mg/g , means ± SEM , Chtf18+/+ 6 . 98±0 . 30 , n = 9 males; Chtf18−/− 3 . 03±0 . 32 , n = 8 males , P<0 . 0001 using the Student's t-test , Figure 3A and 3B ) and morphologically abnormal compared to those of control littermates ( Figure 4 ) . Although seminiferous tubules of Chtf18−/− and wild-type testes contained the complete spectrum of spermatogenic cells , including spermatogonia , spermatocytes , spermatids , and spermatozoa , indicating that there is not a block in spermatogenesis at any specific stage , the cells demonstrated a range of abnormalities . Chtf18−/− seminiferous tubules contained large multinucleated and aberrant-appearing spermatogenic cells , while others were almost devoid of spermatogenic cells ( Figure 4B ) , although a few tubules showed almost normal morphology ( Figure 4D ) . In addition , the stereotypical arrangement of spermatogenic cells demonstrating the orderly progression of spermatogenesis within seminiferous tubules appeared absent in most Chtf18−/− tubules , suggesting that spermatogenesis is impeded or disrupted ( Figure 4E and 4F ) . To determine the physiologic consequences of the disruption seen in seminiferous tubules of Chtf18−/− mice , we quantified the number of sperm recovered from the caudal region of the epididymis . We found that the sperm counts of Chtf18−/− mice were reduced more than 5-fold compared to those of wild-type males ( Figure 5A ) . To evaluate the impact of this severe oligospermia on fertility of Chtf18−/− males , we mated Chtf18−/− or wild-type males with pairs of wild-type females over a period of five months . As expected from the low sperm counts , we found that Chtf18−/− males are subfertile compared to wild-type controls ( Figure 5B ) . Loss of Chtf18 leads to oligospermia and not meiotic arrest , since some mature spermatids are indeed produced . Thus , the phenotype results in subfertility and not sterility in males . To assess whether apoptosis was an underlying cause of the paucity of spermatogenic cells in Chtf18−/− seminiferous tubules , we performed TUNEL assays . We found an increased number of apoptotic cells in Chtf18−/− tubules ( mean number of apoptotic cells per seminiferous tubule , 7 . 66 and 3 . 08 for three Chtf18−/− and three wild-type adult males , respectively , p<0 . 0001 , Figure 5C and 5D ) . Because CHTF18 is expressed in both the somatic and germ cell lineages in the testes , we wanted to determine whether Chtf18 is required specifically in germ cells . To this end , we derived Chtf18flox/− TNAP Cre mice ( cKO ) , in which Chtf18 is deleted only in germ cells ( Figure S1 ) . Several studies have demonstrated the use of the TNAP Cre transgenic mouse to effect highly specific and efficient germ cell-specific deletion [28]–[32] . As shown in Figure 5E and 5F , the morphological phenotype of affected cKO spermatogenic cells is indistinguishable from those seen in Chtf18−/− testes , suggesting that Chtf18 is required cell-autonomously in germ cells . While pre-meiotic effects cannot be ruled out , somatic effects of the testes can be excluded since TNAP is not expressed in these cells . CTF18 mutant flies ( called Cutlet ) exhibit cessation of germline stem cell proliferation in mitotic stages of amplification [19] . Therefore , we speculated that a defect in establishment or maintenance of the early spermatogonial pool ( prospermatogonia ) might contribute to the paucity of germ cells seen in Chtf18−/− tubules . To evaluate this population of cells we stained postnatal day 3 ( P3 ) testis sections from Chtf18−/− compared to wild-type tubules with an antibody to mouse vasa homolog ( MVH ) , a germ cell-specific marker . Although we did not see a progressive loss of spermatogonial cells in Chtf18−/− tubules , we found that the number of prospermatogonia was significantly decreased in seminiferous tubules of Chtf18−/− compared to wild-type mice ( mean number of germ cells per seminiferous tubule , 2 . 399 and 1 . 750 for four males each , respectively , p<0 . 0001 , Student's t-test , Figure 6A–6C ) . In addition , the number of tubules completely lacking prospermatgonia was significantly greater in Chtf18−/− than wild-type testes ( 61 , N = 280 tubules and 30 , N = 278 tubules for four males each , respectively , p = 0 . 0005 , Fisher's exact test , Figure 6D ) . These data suggest that that there is a defect in the early germ cell lineage of Chtf18−/− males . To begin to identify the molecular basis underlying the observed defects in Chtf18−/− mice , we examined meiosis . Surface spread analysis of Chtf18−/− spermatocytes , immunostained with anti-SYCP1 and anti-SYCP2 antibodies which label central and axial/lateral elements of the synaptonemal complex , were used to evaluate the progression of meiotic prophase I . We found that the leptotene and zygotene stages of prophase I progressed normally in Chtf18−/− spermatocytes as demonstrated by accumulation of SYCP1 and SYCP2 on homologues ( Figure 7A and 7B ) . Synapsis of homologous chromosomes during the pachytene stage was also normal as demonstrated by the complete co-localization of SYCP1 and SYCP2 on autosomes of 105 Chtf18−/− pachytene spermatocytes compared to 100 pachytene wild-type cells ( Figure 7C ) . However , examination of the diplotene stage of prophase I revealed the presence of separated homologues , consistent with univalent chromosomes ( Figure 7D ) . To confirm the presence of univalent chromosomes we used CREST autoimmune serum , which stains centromeres , and anti-SYCP3 , which stains the axial/lateral elements of the synaptonemal complex ( Figure 7E and 7F ) . We quantified the number of CREST foci on homologues in Chtf18-null compared to wild-type spermatocytes . Univalent chromosomes were counted in diplotene spermatocytes containing greater than 21 CREST foci on separated homologues . We found that 42% of Chtf18−/− diplotene spermatocytes ( 66 cells counted in four males ) contained univalent chromosomes ( Figure 7F ) , while no univalent chromosomes were seen in wild-type diplotene spermatocytes ( 70 cells counted in three males , Figure 7E ) . In affected Chtf18−/− diplotene spermatocytes , we found two or more univalent chromosomes . These data are consistent with premature separation of homologous chromosomes during prophase I and not asynapsis because pairing of homologues and chromosomal synapsis through the pachytene stage in Chtf18−/− mice were normal . Next we performed metaphase I spread analysis of spermatocytes to determine whether univalent chromosomes persist after synaptonemal complex dissolution at the end of prophase I ( Figure 7G and 7H ) . Univalent chromosomes ( as many as six ) were present in 20% ( 35 cells counted ) of Chtf18−/− metaphase I spermatocytes ( Figure 7H ) , a number that is highly significant in light of the fact that such chromosomes were not observed in spermatocytes of wild-type males ( 50 cells counted ) . These findings reveal that homologues in Chtf18−/− spermatocytes separate prematurely during meiotic prophase I and that the defect persists through metaphase I , resulting in formation of univalent chromosomes . In order to evaluate progression of meiotic recombination and DNA double-strand break ( DSB ) repair , we performed immunostaining with antibodies to γH2AX and RAD51 , markers of DSB repair . Following formation of DSBs during leptonema , γH2AX is found along chromatin of both autosomes and sex chromosomes during normal meiotic progression [33] . γH2AX staining decreases during prophase I as DSBs are repaired until it is confined to the sex body , a region containing both the X and Y chromosomes , during pachynema . RAD51 foci appear as early meiotic recombination nodules , and they are abundant throughout prophase I . The number of RAD51 foci peaks in leptotema and early zygonema , and decreases in late pachynema as DSBs are repaired [34]–[36] . In both wild-type and Chtf18-null spermatocytes meiotic recombination initiated normally as demonstrated by the appearance of γH2AX during leptonema ( Figure 8A and 8E ) . Although γH2AX staining decreased similarly in both wild-type and Chtf18-null spermatocytes during zygonema ( Figure 8B and 8F ) and became restricted to the sex body in wild type spermatocytes in pachynema and diplonema ( Figure 8C and 8D ) , it persisted on the autosomes of Chtf18−/− spermatocytes into pachynema and diplonema ( Figure 8G and 8H ) . This suggests that DSBs are formed but not repaired efficiently in the absence of CHTF18 . Immunostaining with anti-RAD51 showed normal deposition of RAD51 on wild-type homologues in zygonema and pachynema ( Figure 8I and 8L ) , but revealed the persistence of meiotic recombination nodules in Chtf18-null spermatocytes into zygonema and pachynema ( Figure 8J and 8M ) . While the number of RAD51 foci decreased in wild-type spermatocytes by pachynema , Chtf18-null spermatocytes maintained a significantly greater number ( mean number of RAD51 foci per nucleus , 10 . 27 and 19 . 95 , in four control and four mutant males , respectively , p<0 . 0001 , Figure 8N and 8O ) . DSB repair also appeared to be delayed as suggested by a significantly greater number of RAD51 foci detected on Chtf18-null compared to wild-type homologues during zygonema ( mean number of RAD51 foci per nucleus , 195 . 2 and 166 . 2 for four Chtf18−/− and four wild-type males , respectively , p<0 . 0001 , Figure 8K and 8O ) , indicating that the early stages of meiotic recombination are affected by loss of Chtf18 . Next we used an antibody to the mismatch repair protein , MLH1 , to evaluate DSB repair and formation of meiotic crossovers in Chtf18−/− spermatocytes . MLH1 localizes to meiotic nodules that are believed to be the sites where chiasmata form [37] , [38] , and resolution of chiasmata is necessary for homologue disjunction . While each homologue should have at least one crossover ( MLH focus ) , we found a slight but statistically significant decrease in the number of MLH1 foci in Chtf18−/− compared to wild-type spermatocytes ( 21 . 87 and 23 . 77 for four Chtf18−/− and four wild-type males , respectively , p<0 . 0001 , Figure 8Q and 8R ) . In addition , 16 . 9% of Chtf18−/− spermatocytes contained at least one autosome that completely lacked a MLH1 focus ( N = 83 late pachytene cells ) compared to 3 . 4% of wild-type spermatocytes ( N = 87 late pachytene cells ) . Analysis excluding these cells revealed that the average number of MLH1 foci was still significantly decreased in Chtf18−/− spermatocytes ( that did not lack foci ) compared to wild-type spermatocytes ( 22 . 17 and 23 . 82 for four Chtf18−/− and four wild-type males respectively , p<0 . 0001 , Figure S2 ) . Since each homologue normally forms at least one crossover ( the obligate CO ) , these data are consistent with fewer Chtf18−/− spermatocytes ( i . e . those not lacking MLH1 foci ) containing autosomes with two or more MLH1 foci ( instead of one ) compared to wild-type spermatocytes . These findings indicate that the meiotic recombination defects seen in Chtf18−/− spermatocytes persist well into the late stages of meiotic recombination , and suggest a role for Chtf18 in facilitating normal rates of crossover during prophase I . In addition , the presence of univalent chromosomes in Chtf18-null diplotene and metaphase I spermatocytes ( Figure 7 ) is due , at least in part to defective crossover formation at the pachytene stage .
We derived mice lacking CHTF18 , an evolutionarily conserved protein that is crucial for fertility in the fruitfly , and essential for accurate chromosome segregation in yeast . We demonstrated that spermatogenesis is severely disrupted and fertility is significantly impaired in Chtf18-null males . Chtf18 is the murine orthologue of CTF18 , a subunit of the replication factor C-like complex ( RLC ) , RLC-CTF18 , which consists of seven subunits ( CTF18-CTF8-DCC1-RFC2-RFC3-RFC4-RFC5 ) , and was initially discovered in Saccharomyces cerevisiae [16] . During DNA replication CHTF18 protein forms a complex with other RFC components to load PCNA ( proliferating cell nuclear antigen; a replication fork protein essential for DNA replication ) onto DNA . Studies in budding yeast have shown that CTF18 also functions in homologous recombination and DSB repair [39] . Loss of CTF18 in yeast results in improper establishment of sister chromatid cohesion , genetic instability , and aneuploidy [15] , [17] , [18] . RLC-CTF18 seems to couple DNA replication with sister chromatid cohesion because it is recruited to the replication fork in response to replication arrest [40] , but the way in which this occurs is unknown . Consistently , RLC-CTF18 is implicated in the replication checkpoint and functions as an efficient unloader of PCNA in S . cerevisiae [41] , [42] . Moreover , CTF18 stabilizes replication forks to facilitate sister chromatid cohesion in Schizosaccharomyces pombe [43] . As mentioned above in the fruit fly , CTF18 ( called Cutlet ) is necessary for fertility [19] . In humans , formation of the RLC-CHTF18 complex in vitro and in cell lines suggests a role for CHTF18 in mammalian DNA replication; human RLC-CHTF18 interacts with proliferating cell nuclear antigen and bound chromatin preferentially during S phase [20]–[22] . Recently , RLC-CHTF18 was shown to be necessary for the speed of DNA replication fork progression and efficient acetylation of cohesin in human epithelial cells , important processes that are necessary for continued advancement of DNA synthesis [44] . Thus , the functions of RLC-CTF18 appear to be conserved among eukaryotes . Our data reveal that homologous chromosomes separate prematurely during meiosis I in Chtf18−/− spermatocytes . While homologous chromosomal pairing and synapsis are complete during pachynema , as revealed by deposition and co-localization of SYCP1 and SYCP2 ( Figure 7C ) , univalent chromosomes are detected in diplonema of prophase I and in metaphase I in mutant spermatocytes ( Figure 7F and 7H ) . Our findings suggest that the pachytene checkpoint is not activated in Chtf18−/− spermatocytes , since spermatocytes progress beyond pachynema . Meiotic recombination is initiated and the early steps appear to proceed normally in Chtf18-null homologues , but DNA double-strand break repair ( DSB ) repair and crossover formation are defective . This is indicated by persistence of both γH2AX and RAD51 , and decreased MLH1 foci number during prophase I . In addition , DSB repair appears to be delayed as suggested by a significantly greater number of RAD51 foci detected on Chtf18-null homologous chromosomes during zygonema ( Figure 8J , 8K , and 8O ) . These data suggest that CHTF18 plays crucial roles in mammalian meiosis and that CHTF18 may function in preventing early homologue disjunction . These observations are consistent with a role for CHTF18 in maintaining homologue linkage and possibly in DSB repair . CHTF18 may prevent homologue disjunction through a mechanism that affects DSB repair and/or crossover formation . Homologue disjunction during anaphase I normally occurs with resolution of chiasmata , which necessitates removal of cohesin from chromosome arms distal to chiasmata but not at centromeres [45]–[47] . Persistence of DSBs seen in pachynema and diplonema may arise due to DSBs occurring before meiosis and not as a result of SPO-11 action during early prophase I . However , it is not likely a major contributing defect since synapsis of homologues occurs normally in Chtf18-null spermatocytes . The possibility of pre-meiotic defects during the spermatogonial stages of spermatogenesis cannot be excluded . CHTF18 protein is expressed in all stages of developing germ cells in adult males , and in fetal male germ cells from 13 . 5 through 15 . 5 dpc [23] , and the number of prospermatogonia is significantly decreased in Chtf18-null compared to wild-type neonatal seminiferous tubules . Although the decreased number of prospermatogonia must originate during establishment or proliferation of the primordial germ cell population , there does not appear to be a defect in spermatogonial stem cell renewal in adult mutant mice . Adult Chtf18-null seminiferous tubules do not progressively lose spermatogonia as seen in the Plzf mutant mouse [48] . The defect in establishment or proliferation of prospermatgonia in Chtf18-null males is not as severe as that observed in the germline of Drosophilia CTF18 mutants ( termed Cutlet ) . In Cutlet mutant ovaries there is cessation of germline stem cell proliferation , and few if any egg chambers are formed , leading to sterility [20] . Cutlet mutant flies also exhibit eye and wing defects , but these defects are relatively mild and the adult organs appear to function normally . Cutlet mutations result in both decreased cellular proliferation and increased apoptosis in affected tissues , and studies implicate Cutlet as an accessory factor for DNA replication [19] . Similarly , Chtf18−/− mice are healthy but smaller in size than wild-type controls , suggesting a role for CHTF18 in cellular proliferation and DNA replication of somatic cells in mammals . The milder phenotype of early germ cell defects observed in Chtf18 mutant mice compared to Cutlet mutant flies suggests a non-essential but more specialized role for CHTF18 in mammalian gametogenesis . Premature homologue disjunction and a defect in DSB repair in Chtf18−/− spermatocytes are consistent with cohesion-dependent and cohesion-independent mechanisms . Cohesion is mediated by cohesin complexes , which form a ring-like structure and embrace chromatin fibers of sister chromatids from DNA replication until their separation during anaphase [5] . Segregation of homologues during meiosis I is elicited by loss of cohesin complexes along chromosome arms distal to chiasmata [49] . During meiosis , cohesin complexes are necessary for establishing and maintaining cohesion between sister chromatids , and for synapsis and recombination between homologous chromosomes [50] . DSB repair during meiosis also requires cohesion between sister chromatid arms to be maintained [45] . Although the exact role cohesion plays in homologous recombination and DSB repair during meiosis is not known , a recent study in Caenorhabditis elegans reveals that meiotic cohesin promotes DSB processing and recruitment of DNA damage checkpoint proteins early in the DNA damage response . Absence of cohesin from meiotic chromosomes causes loss of chiasmata and the persistence of DSBs with accumulation of recombination intermediates [51] . Therefore , it is possible that CHTF18 is involved in both cohesion action and its effects on DSB repair during meiosis . However , a role for CHTF18 in maintenance of homologous chromosome linkage in mammals has not been shown previously . In both budding yeast and vertebrates efficient repair of DSBs by homologous recombination relies on the ability of cohesin complexes to mediate sister chromatid cohesion , and cohesin complexes accumulate on chromatin at DSBs [52]–[56] . While it has been shown that cohesin accumulates at sites of DSBs in mitotically dividing mammalian cells [57] , it is not known whether cohesin complexes can be loaded onto chromosomes of meiotic cells after S phase [8] in mammals . CHTF18 has been shown to interact with the cohesin complexes in human immortalized cells [58] , [59] . Thus , CHTF18 may preserve homologue linkage by maintaining cohesion between sister chromatid arms that was established prior to S phase; this could occur by interaction of CHTF18 with cohesin complexes loaded at sites of DSBs during meiosis , by a crossover mechanism , or by a combination of both these mechanisms . Studies have provided evidence that cohesion is directly coupled to DNA replication by physical interaction between cohesin proteins and proteins involved in DNA replication at the replication fork [60] . Possible mechanisms of replication-dependent sister chromatid cohesion have been provided by studies in yeast and human cell lines [20]–[22] , [40]–[43] . Recent studies in yeast have led to a model in which RLC-CTF18 associates with chromosomes to regulate PCNA and establish sister chromatid cohesion at the replication forks [40]–[43] . Thus , CHTF18 may interact with cohesin complexes at the replication fork . While prior studies have demonstrated a clear role for CHTF18 in DNA replication and establishment of sister chromatid arm cohesion , our data suggest a role for CHTF18 in promoting crossover formation through a possible interaction with cohesin complexes . Our data also suggest a role for CHTF18 in DSB repair and ensuring a wild-type number of crossovers in spermatocytes . Interestingly , approximately 17% of Chtf18-null spermatocytes in late pachynema contain at least one autosome that completely lacks a MLH1 focus ( i . e . the majority of Chtf18-null spermatocytes have autosomes that each contain at least one MLH1 focus ) , yet homologues separate prematurely in 42% of Chtf18-null spermatocytes during diplonema . A possible explanation is that one crossover per homologue in the presence of impaired sister chromatid cohesion does not provide enough stability to prevent premature separation of homologues in Chtf18-null spermatocytes . This would suggest that in addition to its canonical function during DNA replication , CHTF18 acts as a cohesion factor to facilitate crossover formation during meiotic recombination . It is unclear exactly how CHTF18 might interact with cohesion factors . It is possible that CHTF18 affects this process during pre-S phase and/or during the DSB repair pathway in meiosis . Both chiasmata and cohesion between sister chromatid arms distal to chiasmata prevent homologues from separating prematurely [10] , [11] and Chtf18-null male mice exhibit both premature homologue disjunction and decreased DNA crossovers . Thus , involvement of CHTF18 in chromosome cohesion during meiosis is biologically plausible . Loss-of-function mutations in cohesin genes have been described in mice . The phenotype of Chtf18-null males is not as severe as those for the SMC1β or REC8 cohesin mouse mutants . Both SMC1β mutant males and females are sterile; while SMC1β-deficient spermatocytes exhibit pachytene arrest , oocytes from SMC1β-deficient females show loss of sister chromatid cohesion in metaphase II [61] . REC8 mutant male and female mice are also sterile and show severe defects in synapsis , sister chromatid cohesion , and meiotic recombination [50] , [62] . Although CHTF18 is not absolutely required for meiotic recombination , it may serve as functional link between DSB repair and crossover formation in mammals . We propose a model whereby CHTF18 associates or interacts with cohesin proteins to facilitate and maintain linkage of homologues during meiotic prophase I . Our data support a function for CHTF18 downstream of SPO-11 mediated DSB formation , during early stages of RAD51-mediated DSB repair and upstream of MLH1- mediated DSB repair and crossover formation . In summary , we derived Chtf18-null mice and demonstrated that the gene is essential for male meiosis . Our work reveals important new functions of CHTF18 in mammals , and suggests compelling roles for CHTF18 in male fertility and meiosis . Deletion of Chtf18 leads to a phenotype in which there is significant impairment of spermatogenesis , meiotic defects , and subfertility . These findings closely resemble those found in humans , where the majority of infertile men present with defects more subtle than complete spermatogenic failure . The requirements for Chtf18 in mammalian spermatogenesis demonstrated above suggest that malfunctioning of CHTF18 may be a cause of oligospermia and infertility in men . Hence , this work provides an important framework for future studies , which may elucidate the functions of CHTF18 in mammalian meiosis and fertility , and may ultimately shed more light on the processes of DSB repair and chromosome cohesion .
A 129SV mouse BAC library was screened by PCR and colony hybridization to obtain the Chtf18 genomic clone . Fragments of genomic DNA were then amplified by PCR from the Chtf18 clone and subcloned into the PND1 plasmid ( given by G . Radice , Jefferson Medical College ) to construct the PND1-Chtf18 targeting vector . Following electroporation and selection of cells , targeted clones were enriched by culture in G418 . Approximately 300 surviving colonies were isolated , expanded , and screened for homologous recombinants by Southern blot analysis and PCR . Cells from three correctly targeted clones were expanded further , analyzed for a normal karyotype , and injected into C57BL/6 blastocysts , yielding 19 highly chimeric ( ≥90% ) male mice . The male chimeric mice were mated to C57BL/6 female mice , resulting in successful germline transmission of the Chtf18flox allele . Mice carrying this allele were then mated with transgenic Cre mice under the control of the E2A promoter [27] . The resulting heterozygotes were bred to homozygosity to generate Chtf18-null mice . To derive Chtf18flox/−; TNAP Cre mice , mice heterozygous for the Chtf18flox allele were bred with transgenic Cre mice under the control of the germ-cell specific promoter tissue non-specific alkaline phosphatase ( TNAP ) [28] following FLP-mediated excision of the neomycin resistance cassette in vivo . For protein analyses of mouse testes , 50 mg of total protein were electrophoretically separated by 4–12% SDS-PAGE , and transferred to polyvinylidene difluoride membranes ( Millipore Co . , Bedford , MA ) . Membranes were blocked ( Tris-buffered saline solution containing 5% nonfat dry milk and 0 . 1% Tween 20 [TBST] ) , and then incubated with IgG-purified mouse CHTF18 antibody ( 0 . 31 mg/ml ) [23] at 4°C overnight . The blots were washed in TBST and incubated with a goat anti-rabbit immunoglobulin conjugated to horseradish peroxidase ( 0 . 2 mg/ml , Jackson ImmunoResearch Laboratories , Inc . , West Grove , PA ) for 1 h at room temperature . After washing , the CHTF18 protein was detected with Super Signal chemiluminescent substrate ( Pierce , Rockford , IL ) . The caudal epididymides of wild-type and Chtf18−/− adult mice were dissected , and their sperm content was released into PBS . Sperm number and concentration were determined using a hemocytometer . Statistical analysis was performed using the Student's t-test . The number of offspring from wild-type females bred with 3 Chtf18−/− and 3 wild-type male mice over a five month period was documented . Each male was paired with two wild-type females , and the total number of pups from 12 pairs of wild-type females was recorded . Statistical analysis was performed using the Student's t-test . Testes from wild-type and Chtf18−/− mice were fixed in 4% paraformaldehyde and embedded in paraffin . TUNEL assays were performed with the In Situ Cell Death Detection Kit , Fluorescein ( Roche Applied Science , Indianapolis , IL ) according to the manufacturer's instructions . Two hundred seminiferous tubules from 3 mice of each genotype were counted . Only cross sections and tubules containing at least one apoptotic cell were counted . For histology , testes from adult male mice were fixed in Bouin's solution , embedded in paraffin , sectioned , and stained with hematoxylin and eosin . For germ cell counts , testes from postnatal day 3 mice were fixed in 2% paraformaldehyde , embedded in paraffin , sectioned , and stained with anti-DDX4/MVH antibody ( rabbit , Abcam , 1∶250 ) and DAPI . MVH-positive cells were counted in at least 250 tubules from four different mice per genotype . Surface spreads of spermatocyte nuclei were prepared as previously described [63] , [64] . Briefly , mouse testes were removed , seminiferous tubules gently minced with tweezers in DMEM , and cells mechanically separated . The cellular suspension was then spun to pellet cellular debris , and the nuclear suspension was pipetted onto slides . Slides were then fixed for 3 minutes each in freshly prepared 2% paraformaldehyde in PBS containing 0 . 03% SDS , and in 2% paraformaldehyde alone . Slides were rinsed three times for 1 minute each in 0 . 4% PHOTO-FLO 200 solution ( Eastman Kodak Company , Rochester , NY ) , dried , then blocked in TBST containing 10% goat serum . Slides were then incubated with primary antibodies for 1 hour at 37°C or overnight at 4°C . Primary antibodies used for immunofluorescence were as follows: anti-SYCP2 ( guinea pig , 1∶100 ) , anti-SYCP1 serum 458 ( rabbit , 1∶500 ) , anti-SYCP3 ( rabbit , Abcam , 1∶200 ) or anti-SYCP3 ( mouse , 1∶1 provided by R . Jessberger , Dresden University of Technology , Dresden , Germany ) , anti-γH2AX ( rabbit , Millipore , 1∶500 ) , anti-RAD51 ( rabbit , Calbiochem , 1∶400 ) , anti-CREST ( human Immunovision , 1∶100 ) , anti-MLH1 ( mouse , BD Pharmingen , 1∶50 ) . Metaphase spreads were stained with Giemsa . All experiments involving mice were approved by the Institutional Animal Care and Use Committees at the University of Pennsylvania and Drexel University College of Medicine . The data comparing testis size , caudal epididymal sperm concentration , and number of offspring for Chtf18−/− mice and Chtf18+/+ controls were subjected to the Student's t-test . Results from expected/observed ratios of Chtf18+/+ , Chtf18+/− , and Chtf18−/− embryos were analyzed by analysis of variance ( ANOVA ) . Germ cell and immunofluorescence focus counts were analyzed using the Chi square test , Fisher's exact test or Student's t-test . All data were expressed as mean ± standard error of the mean ( SEM ) , and p values<0 . 05 were considered statistically significant . Values were calculated using Prism 4 . 0 for Macintosh ( GraphPad Software , Inc . , La Jolla , CA ) . | Meiosis is the specialized process of cell division during germ cell development that results in formation of eggs and sperm . Genetic exchange between maternal and paternal chromosomes occurs during meiosis in a process called homologous recombination , in which DNA double- strand breaks are made and then repaired to allow DNA crossovers to form . These are essential processes that keep homologous chromosomes joined until anaphase I and ensure proper chromosome segregation . Errors in meiotic recombination lead to chromosome mis-segregation and ultimately aneuploidy , an abnormal chromosome number . Although it is well known that defects in these processes contribute greatly to infertility , birth defects , and pregnancy loss in humans , their molecular basis is not well understood . We demonstrate here a Chtf18 mutant mouse that exhibits subfertility and defects in meiotic recombination . Specifically , DNA double-strand breaks are incompletely repaired , DNA crossovers are significantly decreased , and homologous chromosomes separate during prophase I in Chtf18-null males . Our findings suggest roles for CHTF18 in DNA double-strand break repair and crossover formation , functions in mammals not previously known . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"model",
"organisms",
"genetics",
"biology",
"genetics",
"and",
"genomics"
] | 2012 | Disruption of Chtf18 Causes Defective Meiotic Recombination in Male Mice |
Leprosy is a chronic dermato-neurological disease caused by Mycobacterium leprae infection . In 2016 , more than 200 , 000 new cases of leprosy were detected around the world , representing the most frequent cause of infectious irreversible deformities and disabilities . In the present work , we demonstrate a consistent procoagulant profile on 40 reactional and non-reactional multibacillary leprosy patients . A retrospective analysis in search of signs of coagulation abnormalities among 638 leprosy patients identified 35 leprosy patients ( 5 . 48% ) which displayed a characteristic lipid-like clot formed between blood clot and serum during serum harvesting , herein named ‘leprosum clot’ . Most of these patients ( n = 16 , 45 . 7% ) belonged to the lepromatous leprosy pole of the disease . In addition , formation of the leprosum clot was directly correlated with increased plasma levels of soluble tissue factor and von Willebrand factor . High performance thin layer chromatography demonstrated a high content of neutral lipids in the leprosum clot , and proteomic analysis demonstrated that the leprosum clot presented in these patients is highly enriched in fibrin . Remarkably , differential 2D-proteomics analysis between leprosum clots and control clots identified two proteins present only in leprosy patients clots: complement component 3 and 4 and inter-alpha-trypsin inhibitor family heavy chain-related protein ( IHRP ) . In agreement with those observations we demonstrated that M . leprae induces hepatocytes release of IHRP in vitro . We demonstrated that leprosy MB patients develop a procoagulant status due to high levels of plasmatic fibrinogen , anti-cardiolipin antibodies , von Willebrand factor and soluble tissue factor . We propose that some of these components , fibrinogen for example , presents potential as predictive biomarkers of leprosy reactions , generating tools for earlier diagnosis and treatment of these events .
Leprosy remains an important public health problem worldwide . Damage to peripheral nerves and bone absorption [1] results in the deformities and disability that are hallmarks of the disease . There are two separate aspects to the disease . The first aspect involves direct infection of Schwann cells [2] and macrophages [3] by M . leprae and systemic immune response against the infection [4] . The second aspect includes reactional episodes that may affect nearly 50% of patients [5] . In contrast with mild and slow direct effects due to the presence of bacilli in the cells , reactional episodes are acute and highly deleterious to tissues and the peripheral nervous system , frequently causing medical emergencies in this chronic disease . Clinically , type 2 reactional episodes are considered a necrotizing panvasculitis , presenting different ranges of endothelium inflammation , followed by cyanosis with necrotic-hemorrhagic lesions on the extremities and trunk in more severe cases , such as Lucio’s phenomenon [6 , 7] . Hemostatic disorders are frequently associated with acute and chronic infections due to the fact that platelet functions , blood coagulation and fibrinolysis are intimately correlated with the immune system [8–10] . Platelet disorders have previously been described in lepromatous and tuberculoid leprosy patients as impairments in adhesiveness and aggregation to collagen [11] . It was already reported that patients developing erythema nodosum leprosum ( ENL ) present prolonged activated partial thromboplastin time ( aPTT ) with high fibrinogen and platelet titers [7] , together with platelet activation [12] . Deep thrombophlebitis followed by multiple pulmonary embolism was already described in leprosy patients suffering ENL [13] , as well as edema , a frequent complication of leprosy reactional episodes [14] . Decades before the development of an efficient treatment for leprosy , Rogers and colleagues observed that from a total of 101 leprosy patients , 9 of them died from coronary thrombosis , and 5 of them from cerebral vascular accident , indicating that advanced non-treated leprosy could be related to higher incidence of stroke [15] . With the success of WHO multidrug therapy implementation around the world in the ‘80s , deep vein coagulation started to be reported in leprosy patients treated with rifampin , the main mycobactericidal drug of the multidrug therapy , and thalidomide , the anti-inflammatory drug of choice to treat ENL [16 , 17] . For that reason ENL patient’s are the most susceptible ones to develop coagulation disorders due to the ENL pathophysiology per se , as well as the concomitant use of rifampin and thalidomide . In a retrospective study of a cohort of 638 leprosy patients , we identified 35 patients who presented an atypical clot formation during sera harvesting . Until now , there have been no efforts to describe the composition or physiological explanation of this material , which has been identified by the medical routine as a lipid precipitation at the top of the blood clot that invades the serum fraction , here named as leprosum clot . This study aimed to describe , for the first time , the occurrence of coagulation cascade exacerbation in some of these patients , as well as the protein profile of the leprosum clot , an abnormal lipid enriched clot which correlates with serum prothrombotic markers .
The present work was developed through the analysis of two groups of patients: a prospective group , which plasma samples were collected before multidrug therapy against leprosy , composed of 11 non-reactional ( multibacillary leprosy; MB-NR ) , being 10lepromatous leprosy ( LL ) and 1 borderline lepromatous ( BL ) . The erythema nodosum leprosum patients group ( MB-ENL ) included 13 LL and 1 BL individuals . These two groups include 6 female , 19 male with median age of 45 . 2 years , ranging from 23 to 80 ( S1 Table ) . All samples were harvested before starting treatment , adopting the following criteria for exclusion: pregnancy , recent vaccination , presence of co-infections , autoimmune , and/or allergic diseases . A retrospective cohort were composed of 638 leprosy outpatients at the Souza Araújo Outpatient Unit of Oswaldo Cruz Institute , Fiocruz , from 2012 to 2014 , where 35 patients presented the leprosum clot during serum harvesting . Samples were collected along multidrug therapy against leprosy , separated in: before and during the leprosum clot occurrence . Among these leprosum clot positive patients 48% were diagnosed with multibacillary leprosy ( 13 female , 22 male; medium age , 42 . 3 years; range 10–76 ) , and 45 , 7% developing some reactional episode ( 4 patients suffering type 1 and 12 patients developing type 2 ) ( S2 Table ) . Blood serum and plasma samples were harvested using BD Holder ( adaptor ) and sterile one-use needles . For serum samples , ten milliliters of venous blood was collected in sterile BD Vacutainer SSTII Advanced tubes from all studied individuals , without additive or clot activator . The tube was centrifuged for 15 minutes at 800 x g in room temperature without brake . After this step , the supernatant fluid ( serum; ±4 mL in normal samples , ±1 mL in samples where leprosum clot occurred ) was collected with sterile serological pipete , aliquoted into cryovials ( 500μL/vial ) and stored at -20°C until use . Plasma was also harvested from venous blood , collected from all volunteers with 5 mL vacutainer tubes , containing ~1 . 8 mg K2EDTA/mL blood . The tube was gently inverted for 10 times to mix blood and anticoagulant and the sample was centrifuged immediately for 10 minutes at 600 x g at room temperature . The supernatant ( plasma ) was carefully aspirated with sterile serological pipete , aliquoted into cryovials and store at -20°C until use . The leprosy patients followed routine examinations and were classified according to the Ridley and Jopling criteria [18] . The multibacillary/pauciballary proportion and occurrence of reactional episodes were described . In addition , fibrinolysis/coagulation parameters were determined in 50 non-leprosy patients ( 23 male , 27 females; median age , 48 years; range: 28–66 ) . To establish the proteomic profile of the control and leprosum clot , leprosum clots were collected and immediately frozen at -70°C when occurred during patients serum harvesting . For comparison purposes , we generate control clots as followed: plasma samples from 6 healthy donors ( 3 male , 3 females; median age , 40 years; range: 26–60 ) were collected as described before . Immediately after , 5ml of each plasma sample was clotted by the addition of calcium chloride ( 0 . 5M ) . Control and leprosum clots were transferred using sterilized tweezers to glass tubes where proteins were extracted by maceration in extraction solution ( 7 M urea , 2 M thiourea , 4% CHAPS , 40 mM Tris and 60 mM DTT ) followed by 5 freeze-thaw cycles . Protein content was measured with the commercially available 2D Quant-Kit ( GE Healthcare , Ohio , USA ) according to the manufacturer’s instructions . For SDS-PAGE analysis , 30 μg of leprosum clot and control clot protein extracts were solubilized in sample buffer ( 1 . 25 ml of pH 6 . 8 Tris to 0 . 5 M , 4 ml of glycerol , 0 . 2 g of SDS , 0 . 5 ml of β-mercaptoethanol , 0 . 25 ml of bromophenol blue and 0 . 05% deionized water ) and applied in a 10% polyacrylamide gel with a 4% stacking gel . The electrical conditions employed were 30 min at 10 mA/gel and 20 mA/gel . For 2D electrophoresis , 500 μg of protein extract was subjected to isoelectric focusing using 24-cm IPG strips with a linear pH range between 4 and 7 [19] . The samples were solubilized in rehydration solution ( 7 M urea , 2 M thiourea , 4% w/v CHAPS , 0 . 002% w/v bromophenol blue , 60 mM DTT and 1% v/v IPG buffer of pH 3–10 or 4–7 ) and applied to the IPG strips . The electrical conditions used in the first dimension in the Ettan IPGphor system ( GE Healthcare , Ohio , USA ) were 30 volts for 12 h at 20°C for strip rehydration , followed by 200 volts for 1 h , 500 volts for 1 h , 1000 volts for 1 h , 1000–3500 volts for 0 . 5 h and 3500 volts for 4 h . After isoelectric focusing , the strips were incubated for 15 min with agitation in 10 ml of equilibrium solution ( 1 . 5 M of pH 8 . 8 Tris-HCl , 6 M urea , 30% v/v glycerol , 2% w/v SDS w/v , 0 . 002% w/v bromophenol blue ) containing 100 mg of DTT , which was then replaced by another equilibrium solution containing 400 mg of iodoacetamide . Then , the strips were placed on a polyacrylamide gel ( 12% ) with SDS [20] , and the system was sealed with 0 . 5% w/v agarose at 80°C in Tris-glycine electrode buffer . The electrical conditions were 5 mA/gel for 30 min , followed by a constant 10 mA/gel until the end of the run . The gels were stained with ammoniacal silver for visual detection or colloidal Coomassie for identification by mass spectrometry . The spots were taken from the gel , digested with trypsin and analyzed by spectrometry MALDI-TOF/TOF 5800 ( AB SCIEX , Clotachusetts , USA ) . The mass spectrometry protein identifications were obtained with a 5800 Proteomics Analyzer ( Applied Biosystems , Foster City , CA ) . Both MS and MS/MS data were acquired in positive and reflectron mode using a neodymium-doped yttrium aluminum garnet ( Nd:YAG ) laser with a 200-Hz repetition rate . Typically , 1 , 600 shots were accumulated for spectra in the MS mode , whereas 3 , 000 shots were accumulated for spectra in the MS/MS mode . Up to ten of the most intense ion signals with a signal-to-noise ratio greater than 20 were selected as the precursors for MS/MS . External calibration in MS mode was performed using a mixture of four peptides: des-Arg1-Bradykinin ( m/z = 904 . 47 ) , angiotensin I ( m/z = 1 , 296 . 69 ) , Glu1-fibrinopeptide B ( m/z = 1 , 570 . 68 ) and ACTH ( 18–39 ) ( m/z = 2 , 465 . 20 ) . MS/MS spectra were externally calibrated using known fragment ion masses observed in the MS/MS spectrum of Glu1-fibrinopeptide B . MS/MS database searching was performed against the NCBInr databases using the Mascot software ( www . matrixscience . com ) . The search parameters included two missed tryptic cleavages allowed and non-fixed modifications of methionine ( oxidation ) and fixed cysteine ( carbamidomethylation ) . The peptide per sample plate ( pps ) and peptide per well ( ppw ) files were generated from the raw ( or native ) MS data according to the following parameters using the Data Explorer Software ( Applied Biosystems ) . The parameters for MS1 were as follows: mass range , 900–3 , 500 Da; peak density , 200 peaks per 200 Da; signal-to-noise ratio 30; minimum area 1000 μm2; and maximum peaks per spot 60 . The parameters for MS2 were as follows: mass range 60 Da until the mass of the precursor; peak density 55; 200 peaks per 200 Da; signal-to-noise ratio 2; minimum area 10 μm2; maximum peaks per precursor 60 . Protein identifications based MS/MS peptide were validated in Scaffold 2 software ( Proteome Software Inc . , Portland , OR ) , and the identifications were accepted if they could be established at greater than 95% probability , as specified by the Peptide Prophet algorithm [21] , and contained at least 2 identified peptides . Protein probabilities were assigned using the Protein Prophet algorithm [22] . Gels were stained with colloidal Coomassie Brilliant Blue G-250 and documented using a GS-800 auto-calibrating imaging densitometer ( Bio-Rad ) . Image analysis was performed using PDQuest software , version 8 . 0 . 1 ( Bio-Rad ) . Comparative 2D data were derived from three biological replicates from each clot type ( control clot and leprosy clot ) . The spots were quantified based on their relative ‘volume’: the amount of a protein spot was expressed as the sum of the intensities of all pixels composing that spot . To compensate for subtle differences in sample loading , gel staining and de-staining , the volume of each spot was normalized relative to the total density of valid spots present in the gel image . After automated detection and matching , manual editing was conducted . Following MS acquisition , each spectrum was submitted to a peptide mass fingerprinting search for MS/MS spectra using Mascot version 2 . 5 ( Matrix Science: http://www . matrixscience . com/ ) . For protein identification , the search was performed against the NCBI-nr nonredundant database ( NCBI-nr201512 , National Center for Biotechnology Information , http://www . ncbi . nlm . nih . gov/ ) taxonomy restricted to Homo sapiens . For Mascot searches , the parameters used were trypsin as the enzyme of choice and two missed cleavage , ±50 ppm peptide tolerance , and ±0 . 6 Da for the fragment ion mass ( MS/MS tolerance ) . Oxidation of methionines was allowed as variable modification , whereas alkylation of cysteines ( carbamidomethyl cysteines ) was set as constant modification . Identification was considered valid for Mascot protein scores greater than 50 and a significance threshold of p<0 . 05 . If a protein ‘hit’ was identified by only one peptide , the MS/MS data were required to exhibit a clear spectrum with sequence tags that matched at least three consecutive y or b fragment ion series . Lastly , a good correlation between the experimental and theoretical molecular mass and pI was also considered for positive identifications . A functional protein association network , STRING ( http://www . string-db . org ) , was used for interaction networks . The search was based in the UniProt name , and Homo sapiens was the selected organism . The leprosum and control clots were macerated , and lipids were extracted with chloroform , methanol , and water ( 1:2:0 . 8 , v/v/v ) as described previously [23] . Neutral lipids and phospholipids were analyzed through one-dimensional HPTLC on silica gel 60 plates ( Merck , Darmstadt , Germany ) . For neutral lipids analysis , the plates were first developed on hexane-ethyl ether-acetic acid ( 60:40:1 , v/v/v ) until the solvent border reached the middle of the plate and then on hexane-chloroform-acetic acid ( 80:20:1 , v/v/v ) . For phospholipids analysis , the plates were developed on chloroform-methanol-acetone-acetic acid-water ( 40:13:15:12:8 ) . HPTLC plates were stained by spraying with a charring solution consisting of 10% CuSO4 and 8% H3PO4 and then heating to 180 °C for 5–10 min as described previously [24] . The charred TLC plates were then subjected to densitometric analysis using ImageJ software . The percentage of each lipid was calculated from the total amount of lipid ( set as 100% ) isolated in each clot . Live Mycobacterium lepraeThai-53 strain was prepared from athymic nu/nu mouse footpads immediately before use and was provided by Dr . Patricia Sammarco Rosa ( Lauro de Souza Lima Institute , Department of Biology , Bauru-SP , Brazil ) . M . leprae preparation , viability determination and purity were performed as described elsewhere [25] . HEPG-2 human hepatocyte cell lineages were obtained from American Type Culture Collection ( ATCC ) and maintained in high-glucose D-MEM ( LCG Bioscience , São Paulo , Brazil ) supplemented with 10% fetal bovine serum ( CULTILAB , Campinas , Brazil ) without antibiotics . Cultures were kept at 37°C in a humidified 5% CO2 atmosphere . Infection was performed over 48 h , with a multiplicity of infection of 50 M . leprae per cel at 33°C in a humidified 5% CO2 atmosphere . Complement component 4 ( C4 ) and inter-alpha-trypsin inhibitor protein ( IHRP ) release were measured in the supernatant as described below . In order to avoid rifampicin and thalidomide interference of in our data , aPPT , PT , d-dimer and fibrinogen parameters were determined in all patients before treatment . In the present study we applied Stago’s STA-R Evolution instrument ( Stago , Asnièressur Seine , France ) to determine partial thromboplastin time ( aPPT ) and prothrombin time ( PT ) in all plasma samples , according the manufacturer instructions . The levels of von Willebrand and soluble tissue factor , C4 complement , and anti-cardiolipin IgM antibody in the serum of leprosy patients were determined using the following commercial kits: Human von Willebrand Factor ELISA kit and Human Tissue Factor ELISA kit ( Abcam , Clotachusetts , US ) and C4 turbiquest ( Labtest , Minas Gerais , Brazil ) , respectively . HDL-cholesterol , total cholesterol and triglycerides were determined through high-throughput enzymatic colorimetric test Cobas 8000 module 702 ( Roche , Pleasanton , USA ) . When triglyceride levels were less than 400 mg/dL , cholesterol was determined using the Friedwald formula . Fibrinogen levels were determined using automatic Clauss methodology with a BCS high-throughput reader ( Siemens , Berlin , Germany ) . D-dimers were determined using imunoturbidimetric methodology with a BCS reader ( Siemens , Berlin , Germany ) . All tests were performed according to the manufacturer’s instructions . For quantification , the absorbances of samples were compared with the standards using a mathematical correlation by linear regression of the standard curve data . All numerical data were analyzed using non parametric tests , Kruskall-Wallis with post-test to compare relevant groups , or Mann Whitney test to compare continuous variables . Fisher Test was used to compare categorical data , with GraphPad Prism software . This project was approved by the Oswaldo Cruz Foundation Research Ethics Committee ( protocol number 275 . 648 ) and all methods were performed in accordance with the Brazilian Guidelines and Norms for Research Involving Human Beings ( CNS 466/2012 ) , and to the principles expressed in the Declaration of Helsinki . Informed written consent was obtained from all individuals prior to specimen collection . Parents and guardians provided consent on behalf of all patients below 18 years .
To evaluate the hemostatic parameters among leprosy patients , we prospectively analyzed plasma samples from non-leprosy patients and multibacillary ( MB ) patients developing or not a type 2 reaction ( ENL ) ( S1 Table ) . During this prospective phase of the study , in a serum harvesting performed in parallel , none of the patients developed the leprosum clot . We first investigated the contact activation and tissue factor coagulation pathway efficiency by measuring the activated partial thromboplastin ( aPTT ) and prothrombin time ( PT ) , respectively ( Fig 1A and 1B ) . Both MB patient groups exhibited significantly prolonged aPTT and PT times , indicating dysfunction or intravascular consumption of one or more factors involved in both coagulation pathways . Next , we determined the d-dimer levels in leprosy MB patients , demonstrating that it is significantly increased in both MB patient groups . Since high d-dimer levels indicate that both thrombin and plasmin generation have occurred , this data suggests increase of intravascular coagulation and fibrinolysis in MB patients ( Fig 2A ) . In contrast , fibrinogen , an acute phase reactant , is increased in ENL patients , as expected ( Fig 2B ) . Interestingly , the prothrombotic state of leprosy patients had a minor impact on platelet counts ( Fig 2C ) , thus ruling out the occurrence of disseminated intravascular coagulation in these patients . In a second approach , we retrospectively analyzed the clinical history of 638 leprosy patients treated at the Souza Araujo Outpatient Unit . Interestingly , during blood harvesting , 35 patients presented a milky white mass that had formed on the top of the blood clot during serum harvesting . Herein , this material is referred to as “leprosum clot” ( Fig 3 ) . Among these patients , 48% were diagnosed with multibacillary leprosy and 45 , 7% developing some reactional episode ( S2 Table ) . Therefore , our data indicated that formation of the leprosum clot is a rare event , commonly associated with the MB pole of the disease ( p<0 . 0001 ) ( Fig 3E ) . We also observed the occurrence of vascular abnormalities , such as upper and lower limbs cyanosis , edema and ulcerations , more frequently observed in the cohort of patients who formed the leprosum clot during serum harvesting ( Fig 3G and 3H , S1 and S2 Tables ) . Following WHO multidrug therapy , we collected serum samples of all patients at three times: before treatment , during treatment when some intercurrence such as ENL occurs , and after treatment . Among these 35 patients listed in S2 Table , leprosum clot occurred randomly , before , during or after treatment , only once per patient . We were able to assess frozen serum samples collected from patients which were negative for leprosum clot formation before treatment and during leprosy treatment developed the leprosum clot , observed in a subsequent blood harvesting . In some cases the leprosum clot was observed before treatment , and none of the individuals presented the leprosum clot after 12 doses of multidrug therapy . Unfortunately , functional analysis such as aPPT , PT and D-dimers , are not possible to be performed in such sample . On the other hand , we successfully observed that levels of anti-cardiolipin IgM , von Willebrand factor ( vWF ) and soluble tissue factor ( TF ) in the leprosum clot positive samples were moderately elevated compared with control sera ( Fig 4 ) . In contrast , these factors were dramatically elevated in the samples which formed the leprosum clot during harvesting . Therefore , leprosum clot formation appears to be directly correlated with increased levels of factors associated with prothrombotic states . Through the complementation of calcium in plasma samples from healthy donors , we generated control clots , determining the lipid profile of both clots by high-performance thin layer chromatography ( HPTLC ) analysis . Control and leprosum clots exhibited a distinct macroscopic patterns; the leprosum clot was white in color and had a solid appearance , whereas a control clot had characteristic transparent , gelatinous and delicate appearance . By comparing neutral lipids amounts from leprosum and control clots , we demonstrated that leprosum clots present approximately ten-fold more cholesteryl esters and triglycerides than control clots ( Fig 5A and 5B ) , explaining the visual differences between the clots . These data corroborate the intuitive conclusion of physicians and nurses , which erroneously recognized the leprosum clot as a lipid mass . Interestingly , multibacilary leprosy patients present low levels of total and HDL-related cholesterol in their sera , while other related sera parameters such as triglycerides , LDL and VLDL-related cholesterol were normal ( S2 Fig ) . To understand the differences between leprosum and control clot proteomic profiles , we performed a comparative 2D gel electrophoresis proteomic analysis of three biological replicates from each clot type ( S1 Fig ) . We extracted approximately 137 spots from each gel and identified these spots using MALDI-TOF analysis . Most spots were identified as fibrin ( alpha , beta and gamma chains ) . We also identified apolipropotein A1 , a hallmark of the high-density lipoprotein ( HDL ) fraction , among other serum proteins . Table 1 listed the 15 most abundant proteins found in the leprosum clot . After analysis using PDQuest software , protein spots exclusively presented in one type of clot were identified ( Fig 6A ) . Using the STRING interaction network analysis , we constructed an interactome of these proteins based on different types of evidence ( Fig 6B ) . From this set of proteins , three were only identified in the control clot: tropomyosin alpha-4 chain isoform Tpm4 ( TPM4 ) , tyrosine 3-monooxygenase/tryptophan 5-monooxygenase ( TMO ) and kininogen 1 ( KNG1 ) . The proteins inter-alpha-trypsin inhibitor family heavy chain-related protein ( IHRP ) and complement component 4A ( C4A ) were only identified in leprosum clot ( Fig 6B ) . A graph theoretic network analysis revealed that six of these 15 proteins are involved in important biological processes , such as blood coagulation and inflammatory response ( Fig 6C and 6D ) . To validate these identifications , we performed an ELISA in order to quantify C4 and IHRP proteins in leprosy patients’ sera samples prior to and during leprosy clot occurrence ( Fig 7A and 7B ) . We observed that both proteins are increased during and even before leprosum clot formation . In order to mimics this phenomenon in vitro , we exposed HEPG2 , a cell line derived from hepatocytes , the maintainer of these proteins blood levels [26 , 27] , to live M . leprae . As results we observed that M . leprae where able to induce IHRP synthesis and exportation to the medium after 48 h of infection . The same was not observed to C4 component , which probably is not directly related to M . leprae infection , but immune stimulation of hepatocytes ( Fig 7C and 7D ) , or macrophages .
Leprosy persists as a significant public health problem worldwide . Despite the drastic reduction in the number of cases in recent years , new cases continue to be detected [28] . Procoagulant disorders in untreated leprosy patients were described along decades associated [6 , 7] or not [11 , 12 , 13] to reactional episodes . The immune response to infections by pathogens , such as bacteria and fungi , can be accompanied by changes in key metabolic pathways , which may include coagulation and lipid metabolism [29 , 30] . In addition , expression of the clotting initiator protein , tissue factor , by blood immune cells plays a central role in the immunothrombosis , promoting microvessel thrombosis to capture microbes , thus limiting pathogen dissemination [31] . Hepatic infection by M . leprae was extensively reported in the past [32 , 33] . Based on these data , we believe that the presence of high loads of M . leprae antigens in the blood stream of MB patients , and consequently in their liver , as well as the hepatic modulation by immune system in response to these antigens , could be responsible to induces acute phase protein synthesis with a high thrombogenic effect . For this reason , the exacerbation of fibrin clot generation observed in our patients could be attributed to the hepatic increment in expression of fibrinogen , von Willebrand factor and tissue factor , that are likely associated with pro-inflammatory cytokines previously described in ENL [34 , 35] . The literature described that leprosy patients developing erythema nodosum leprosum ( ENL ) present prolonged activated partial thromboplastin time ( aPTT ) , normal prothrombin time ( PT ) and high fibrinogen and platelet titers [7] . Our data , in contrast , showed both coagulation time parameters prolonged in leprosy patients’ plasma , which indicates a disturbance not only in the tissue factor pathway , but also in the contact activation pathway . We suggested that these data discrepancy could be attributed to differences between methods , populations and patient cohorts . Fibrinogen , abundance and cleavage , were also increased in these patients , as demonstrated by the high levels of d-dimers . Because the association of fibrin-von Willebrand [36] and von Willebrand-apolipoprotein A1 [37] which has been previously described , we first hypothesized that the high levels of von Willebrand factor , followed by low levels of HDL in leprosy patients sera , could be explained by cross-linking of HDL to the fibrin , increasing its amount of neutral lipids , resulting in the leprosum clot . Unfortunately , we were not able to observe differences in apolipoprotein A1 abundance between control and leprosy clots in 2D gels , and because of this , the source of the neutral lipids contained in the leprosum clot remains a subject of investigation . During sepsis treatment , exacerbation of soluble tissue factor serum levels and the resultant coagulation activation is a major cause for infection-associated mortality and inflammation[34] . It has been already described that those patients with antiphospholipid syndrome associated to high levels of blood tissue factor presents higher risk to develop thromboembolic complications[35] . Here , a correlation between high levels of soluble tissue factor , anti-cardiolipin IgM , increased von Willebrand factor , and leprosum clot occurrence was observed ( Fig 4 ) . We attribute this to the fact that mycobacterial cell wall components are able to induce a series of response in a large number of cells , as example , tissue factor expression in macrophages and endothelial cells [38] . M . leprae infection has been shown to induce antiphospholipid antibodies and complement factors , such as the membrane attack complex or MAC , that contribute to the peripheral nervous damage observed in leprosy [12 , 39] . The complement system plays a major role in immune and inflammatory responses , comprising more than 30 proteins associated with cell membranes or found in plasma . The pathway activation results in the formation of anaphylatoxins ( C3a , C4a , C5a ) and MAC [40] . The differential proteomics analysis of the leprosum clot demonstrated the presence of anaphylatoxins ( C4 ) and IHRP protein . These proteins are related but not directly involved in the leprosum clot formation , because high levels of both proteins were also observed in leprosy patients sera weeks prior to leprosum clot occurrence . It was already demonstrated that complement activation products are highly abundant in leprosy reactional episodes , and their presence in the leprosum clot is probably circumstantial [41] . IHRP , which is also increased in patient’s sera and blood clot , belongs to the inter-alpha-trypsin inhibitor family of acute phase proteins , comprising the common light chain bikunin . Its protease inhibitory activity is involved in suppression of TNF–α signaling in macrophages [42] , as well as binding to polimorphonuclear cell surface actin , inhibiting their phagocytic activity [43] , a process which could be involved in the well-known immune responsiveness against M . leprae during infection [44] . Bikunin high levels is also related to stabilization of fibroblasts extracellular matrix [45] , a processes related to irreversible nerve damage in leprosy [46] . The consequences of IHRP increase in leprosy patient’s sera , as well as its induction in hepatocytes infected by M . leprae in vitro represent an interesting line of investigation . More important , the absence of kininogen 1 in the leprosum clot , which deficiency was already associated with prolonged aPPT [47] , could be a reasonable explanation for the observation of prolonged aPPT in leprosy patients . There are clinical symptoms , frequently observed in leprosy patients , which could be related to intravascular coagulation , such as limbs cyanosis and edema , as well as non-diabetic foot ulceration , related to the occlusion of superficial vessels . One possibility that cannot be ruled out is that this leprosy multibacilary patients procoagulant status could contribute to the disturbance in venous return dynamics observed in the ENL , responsible for the classic skin lesions and plaques shared by ENL and lipodermatosclerosis [48] . Taken together , our data strongly suggest that although the occurrence of leprosum clot represents a rare phenomenon associated with procoagulant exacerbation and vascular abnormalities , all leprosy MB patients enrolled in the present work develop a procoagulant status , presenting exacerbation of in vitro clot formation in some acute cases due to high levels of fibrinogen , anti-cardiolipin antibodies , von Willebrand factor and soluble tissue factor . We characterize the nature of leprosum clot , describing a panel of serum proteins which are up-regulated in multibacilary leprosy patients , such as D-dimers , anti-cardiolipin , von Willebrand factor , soluble tissue factor , C4 and IHRP . We also observed plasma fibrinogen level increased in patients developing leprosy reactional episodes . Monitor multibacilary patients fibrinogen levels in order to predict reactional episodes [35] could represent an advance in the treatment , avoiding ENL complications such as nerve and tissue damage . We propose that multibacilary patients with high levels of fibrinogen could be beneficiated from a prophylactic use of xanthine derivatives such as pentoxifylline , in order to prevent some of the acute clinical symptoms observed during severe cases of leprosy reactional episodes , such as cyanosis and tissue necrosis , probably related with superficial vein thrombosis [49 , 50] . | Hemostatic illnesses are frequently associated with acute and chronic infections . In the present work we demonstrated that leprosy patients developed hemostatic abnormalities , like the formation of an atypical lipid clot mass during sera harvesting , a phenomenon previously observed and never unraveled . We characterize the nature of the “leprosum clot” , formed during a protrombotic state developed by some patients . During the proteomic analysis of the leprosum clot we discovered a set of potential serum biomarkers to leprosy reactional episodes diagnosis , which at this moment is based only in clinical features . Taking together , our data suggest that leprosy patients are suffering from a procoagulant status , being beneficiated by the introduction of routine coagulation tests during their treatment , which will aloud physicians to prevent some of the acute clinical symptoms related with superficial vein thrombosis such as cyanosis and tissue necrosis observed during severe cases of leprosy reactional episodes . | [
"Abstract",
"Introduction",
"Materials,",
"subjects",
"and",
"methods",
"Results",
"Discussion"
] | [
"mycobacterium",
"leprae",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"fibrinogen",
"tropical",
"diseases",
"neutral",
"lipids",
"fibrin",
"bacterial",
"diseases",
"neglected",
"tropical",
"diseases",
"glycoproteins",
"bacteria",
"infectious",
"diseases",
... | 2018 | Blood coagulation abnormalities in multibacillary leprosy patients |
Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics . We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data . The procedure , called Genome Order Optimization by Genetic Algorithm ( GOOGA ) , couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations . We demonstrate the method using both simulated data ( calibrated from experiments on Drosophila melanogaster ) and real data from five distinct crosses within the flowering plant genus Mimulus . Application of GOOGA to the Mimulus data corrects numerous errors ( misplaced sequences ) in the M . guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex . Finally , we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus ( QTL ) mapping .
Over the last decade , genetic research has been revolutionized by the availability of whole genome sequences for many of the world’s medically , ecologically , and agriculturally important species . It has also become clear that a single reference genome sequence is insufficient for many species . For example , a comparison of two maize accessions found that over 2 , 500 genes were present in only one of the two genomes [1] . Even in humans , which have much less genetic diversity than maize , structural and gene content polymorphisms are abundant [2] . Differences in gene copy number [3–6] , variation in gene order [1 , 7 , 8] and chromosomal inversions [9–13] are not captured by a single reference genome , nor can they be annotated succinctly in relation to a single reference as is possible for Single Nucleotide Polymorphisms ( SNPs ) . These structural and gene content variants have important phenotypic consequences in many species , highlighting the need for intensive study [14–18] . Recognizing structural variation is important for many of the experimental applications of genomic science . For example , trait-mapping approaches , such as bulked segregant analysis , rely on accumulating signals from adjacent genomic regions ( windows ) to establish significance . If gene order in the population under study differs from the reference genome , the proximity of polymorphisms will be incorrectly inferred , which in turn , undermines inference of both the location and significance of QTLs [19] . Similar issues can arise in population genomic analyses , such as scans for selection or introgression [20 , 21] or of response in “Evolve and Resequence” experiments [22–26] . One solution is to make reference genomes for every divergent accession under study [4] . An alternative approach is to construct ‘pseudo-chromosome’ assemblies to better match structural variation in the focal accessions . Regardless , accounting for structural variation is an important challenge for the continued development of evolutionary genomics . In this paper , we develop an approach to pseudo-chromosome construction based on comparative genetic mapping . In species with repeat rich genomes , whole genome shotgun sequencing and assembly typically yields many thousands of scaffolds . These scaffolds can be stitched together to form pseudo-chromosomes . There are various techniques for making pseudo-chromosomes , such as following a BAC-tiling path [27] , optical mapping using nanochannel arrays [28] , or by localizing the scaffolds to markers on a genetic map [29 , 30] . Genetic mapping has proved to be effective for initial genome construction and pseudo-chromosome assembly , especially for large genomes [31–33] . We extend this approach using comparative genetic maps from five distinct crosses , allowing us to simultaneously improve the pseudo-chromosome representation of the reference genome and also identify large-scale variation in gene order , including chromosomal inversions and translocations . Our approach utilizes data from low-coverage sequencing . Restriction site associated DNA-sequencing ( RAD-seq ) [34 , 35] and related reduced-representation methods [36–38] allow cost-effective genotyping of hundreds of recombinant individuals in species with limited genetic markers . While RAD-seq data is often used to create de novo markers [39 , 40] , RAD-seq reads can also be directly mapped to genomic scaffolds . Recombinant genotypes located to genomic scaffolds can then be used to assemble pseudo-chromosomes . Unfortunately , there are substantial challenges in constructing genetic maps from low-coverage sequencing data and in inferring map differences ( i . e . , the evidence for structural variation ) . New approaches are needed to address the following methodological questions: What is the optimal means to convert sequence data into markers ? How should we accommodate genotyping error in these markers given that the error rate is often high and variable among samples ? After locating markers to genomic scaffolds , how do we obtain the optimal order and orientation of scaffolds into pseudo-chromosomes ? Finally , how do we determine if putative differences between maps are real ? We develop a statistical procedure called Genome Order Optimization by Genetic Algorithm ( GOOGA ) that detects large structural mutations , such as inversions or translocations , using marker data from multiple genetic mapping populations . Importantly for error-prone low-coverage genotyping , GOOGA propagates genotype uncertainty throughout the model , thus accommodating this source of uncertainty directly into the inference of structural variation . GOOGA couples a Hidden Markov Model ( HMM ) with a Genetic Algorithm ( GA ) . The HMM yields the likelihood of a given ‘map’ ( hereafter used to denote the ordering and orientation of scaffolds along a chromosome ) conditional on the genotype data . The GA searches map space by creating new candidate orders which are recurrently fed to the HMM to diagnose their likelihood . Inference of recombination rate parameters and/or tests for differences in gene order are enabled by the fact that all calculations are conducted in the currency of likelihood . As proof of concept , we first apply GOOGA to mapping data simulated from a known genome sequence . After demonstrating its effectiveness in this context , the pipeline is applied to real RAD-seq data ( multiplexed shotgun genotyping ( MSG ) type [36 , 41] ) from five different mapping populations , each synthesized from a cross between lines within the M . guttatus species complex . The complex is a highly diverse clade of inter-fertile North American wildflowers [42–45] . The five mapping populations include both intra- and inter-specific crosses as well as multiple cross types ( F2s , F3s , and recombinant inbred lines ( RILs ) ) . Recombinant individuals from each mapping population were scored genome-wide for SNPs and then input to GOOGA . Starting from a rough-draft scaffold order [46] , GOOGA produces an optimized order and orientation of genomic scaffolds for each population . The M . guttatus reference genome derives from an inbred line used as a parents in two of our five crosses , which allows us to correct many errors ( misplaced scaffolds ) in the reference genome . Improved estimates for recombination rates indicate the effects of gene density and transposable elements on chromosomal variation in recombination . Comparisons among maps identify eight distinct structural polymorphisms , five of which were suggested by previous mapping studies [12 , 13 , 47–50] . Finally , we demonstrate GOOGA clarifies the results of a QTL mapping study by correcting errors in the reference genome .
We used the "A4" genome build [51] of D . melanogaster to serve as the reference in the simulation study ( downloaded from https://www . ncbi . nlm . nih . gov/assembly/GCA_002300595 . 1/ ) . This is a high quality build; likely close to the true genome sequence of the line . To estimate multiplexed shotgun genotyping ( MSG ) [36 , 41] of a mapping population using this genome , we extracted MSG reads from a QTL mapping study in D . melanogaster [52] . We mapped reads , downloaded from https://datadryad . org/resource/doi:10 . 5061/dryad . gc182 , to the A4 sequence using the methods described below for Mimulus MSG reads . The mapping locations of restriction site associated DNA-tags ( RAD-tags ) were thinned to at most one tag per kb , and then used for subsequent simulation of MSG data in a population of F2 individuals . To create the mapping population , we consider a cross between isogenic lines that differ at one SNP per RAD-tag . For simplicity , we focused on chromosome 2 with F1 flies heterozygous at each RAD-tag along this chromosome . We synthesize each F2 by crossing a male that produces gametes without recombination , to a female that produces gametes based on the recombination frequency map of D . melanogaster [53] . Given the locations and true genotypes at each RAD-tag , we simulate genomic data by sampling a specified number of reads per individual per tag . After forming the entire F2 population , we fractured the reference genome into 31 scaffolds by randomly choosing 30 break-locations across the chromosome . The genomic data were then input to the GOOGA pipeline . The genomic coordinates of RAD-tags , the recombination map , and the code to simulate F2 genotype and break the reference genome into scaffolds are provided in S1 Data . Fig 1 is an overview of the data analysis pipeline . The first programs take short-read data from recombinant individuals within the mapping population and make “putative” genotype calls . From the parental genomes and offspring of each mapping population we identified SNPs with GATK [54] . We retained only diagnostic SNPs , where bases could be unambiguously assigned to one of the parents ( A or B ) . Window based calls are based on the aggregate of sequence data from all diagnostic SNP sites located between a lower and upper coordinate ( all reads that map between these coordinates ) . The size of windows is a user defined variable chosen based on the density of SNPs and the average number of reads per SNP per individual . The count of reads matching each parent ( A or B ) is the basis for making putative calls although thresholds for these decisions are also user defined . In our application to both simulated Drosophila data and real Mimulus data , we delineated markers as windows 100 kb in length within each scaffold . The last marker on each scaffold included all remaining sequence beyond the last complete 100 kb segment . Given the number of reads scored as A and B ( across SNPs ) within a window , the individual was called AA if the fraction of reads matching parent A exceeded 95% . They were called BB if the fraction was less than 5% , and AB if the fraction was between 25% and 75% . We default to ignorance ( NN = No Call ) if none of these conditions are met or if the number of reads within the window for that individual is less than a minimum depth threshold ( set to 6 for our applications ) . Ambiguity can result either from mis-mapping of reads ( in which case the read counts are misleading ) or if recombination occurs within the marker ( in which case the true genotype is a combination of two different genotypes , e . g . AA-AB ) . Scoring either scenario as NN is suitable for downstream analysis by the HMM–data from neighboring markers will strongly inform inference of the underlying genotype at the NN marker . The detailed Methods for making putative genotype calls in for the Mimulus data is reported in S1 Appendix . We use a window-based approach instead of SNP-specific genotyping because the latter can be highly error prone , especially with low coverage sequencing data and mapping to preliminary ( error filled ) genome builds . Even after numerous filtering steps ( see S1 Appendix ) , SNP-level calls remain problematic ( see S1 Fig for an illustration with data from three Mimulus recombinants ) . Under-calling of heterozygotes is often problematic with MSG data; both alleles appear less frequently than the binomial distribution predicts [55 , 56] . Aggregating signal from closely linked SNPs addresses both low coverage and allele dropout in heterozygotes . However , SNP level genotype calling may be preferable if both the reference scaffolds and recombinant sequencing are sufficiently high in quality . The downstream components of the GOOGA pipeline are fully compatible with SNP-level calls in this circumstance . The putative calls are the observed or “emitted” states for the HMM and the hidden states are the true underlying genotypes ( AA , AB , or BB ) [36 , 57 , 58] . The structure of the model and transition probabilities depends on the breeding design ( e . g . F2 or F3 or RILs ) and on recombination rates . Fig 2 illustrates the HMM for an F2 population . The HMM is non-homogeneous [59] and we report the transitions for F2 , F3 , and RIL designs in S2 Appendix . The emission probabilities are determined by individual-specific genotyping error rates ( Fig 2 ) . Three distinct error rates are estimated for each individual ( i ) : the probability that a true homozygote yields a putative call to heterozygote ( e0i ) or to the opposite homozygote ( e1i ) , and the probability that a heterozygote yields a call to one of the two homozygotes ( e2i ) . Regarding the last rate , we assume that errors to either of the alternative homozygotes are equally likely . The HMM is applied in different ways at four stages of the pipeline ( Fig 1 ) . The first objective is to estimate individual-specific genotyping error rates from transitions between genotypes within scaffolds . The transition probabilities depend on the true recombination rate per base pair , which is unknown and can vary across the genome . However , to estimate e0i , e1i , and e2i , we fit a simple ‘homogeneous’ model assuming that recombination rate is proportional to physical distance between markers within scaffolds . Based on data from prior Mimulus studies [47 , 50] , we set rates to the genomic average of 5 . 0 cM/Mb for F2 populations and 10 . 0 cM/Mb for F3 and RIL populations . For the simulation study , we use the specified ( known ) recombination rates . The likelihood of data from each scaffold of an individual plant is then a function of e0i , e1i , and e2i , and the likelihood for the entire plant is a product across scaffolds . For each plant we obtain the maximum likelihood estimation ( MLE ) of e0i , e1i , and e2i via application of the forward-backward algorithm [60] coupled with the bfgs bounded optimization routine [61] of scipy . optimize ( https://docs . scipy . org/doc/scipy/reference/optimize . html ) . We also used the bfgs optimizer to obtain MLE for recombination rates as described below . The individual-specific error rates ( reported as S1 Table ) can be used to cull highly error prone individuals from mapping populations ( S2 Appendix ) . The next step is to obtain preliminary recombination rate estimates between markers within scaffolds ( Fig 1 ) . While error rates were estimated by taking a likelihood across all scaffolds within a single individual , we here maximize the likelihood within each individual scaffold ( with respect to r values between all adjacent markers ) across all individuals within a mapping population . These intra-scaffold rate estimates are used in the GA search ( described below ) , but not in the final maps . In our application to the five Mimulus datasets , the intra-scaffold r estimates were nearly always small , consistent with close linkage . However , we noticed one very high rate between two adjacent markers on scaffold 13 of the DUNTIL cross . The same interval exhibits normal ( r < 0 . 01 ) recombination rates in other crosses . Given evidence for an inversion breakpoint ( further evidence below ) , we split scaffold 13 into 13a and 13b . The third application of the HMM is within the GA as it searches for the optimal map ( ordering and orientation of scaffolds within a chromosome ) . Here , the log-likelihood is for the entire chromosome of an individual , summed across all individuals in the mapping population . This requires an assignment of scaffolds to linkage groups . In the absence of other information , this can be obtained by applying a standard map making program such as R/QTL [57] to the putative genotype calls . We wrote a simple end matching program ( make . ends . meet . py ) to accomplish this task for the simulation study . This program , and all others used in the pipeline , are available at ( https://github . com/flag0010/GOOGA ) . For a chromosome , there are L– 1 + K recombination rates to be estimated , where L is the number of scaffolds on the chromosome and K is the number of intra-scaffold rates within these scaffolds . The likelihood of a particular map is determined mainly by the data at the “joins” ( where two scaffolds meet ) . The genetic algorithm searches map space with intra-scaffold rates held at their estimates from the second application of the HMM ( Fig 1 ) . Evaluation is based on the likelihood of the map after optimizing the inter-scaffold rates . However , we apply the full model , with all intra- and inter-scaffold recombination rates re-estimated , to our final map for each chromosome of each mapping population . In both cases , we obtain the maximum likelihood via application of the forward-backward algorithm . GOOGA maximizes the likelihood of the HMM using a genetic algorithm ( GA ) applied to each chromosome of each mapping population . A GA is an algorithmic optimization scheme inspired by sexual reproduction and natural selection [62] . Below we use terms such as “individual” , “mutation” , “recombination” , and “fitness” as they are frequently used in the GA literature; not to the biological processes . To build the GA , we first coded unique scaffold orders , including scaffold orientations ( i . e . forward strand vs . reverse complement ) , to make an "individual . " Each individual represents a candidate solution among a population of size N competing in a given generation . The likelihood of the map provides the fitness of individuals . To preserve the best scaffold orders , we used a strategy called elitism ( E ) , which allows a predetermined number ( E = 4 in our case ) of the best individuals ( i . e . , highest likelihood scaffold orders ) to go on to the next generation unchanged . To fill the remaining N-E spots in the next generation , we applied rank-based selection to select pairs of individuals to “mutate” and “recombine” into new individuals for the next generation . The details of the mating and mutation scheme , and well as the strategy for optimizing computation , are reported in S3 Appendix . After extracting the optimal map from the GA , we run the HMM with both inter- and intra-scaffold rates as free parameters . This yields the final MLE for recombination rates across each chromosomes and also the posterior probabilities of genotypes for each individual in the mapping population . We extracted MLE recombination rates from each Mimulus mapping population to compare to DNA level features such as amount of coding DNA , number of transposable elements , and the presence of centromeric DNA . For these analyses , we defined a 200 kb interval around each 100 kb-long marker , starting at the midpoint of the preceding marker and ending at the mid-point of the next marker . The analysis is defined at this scale to absorb recombination events that occur mid-marker . Consider the first three markers on a scaffold defined on the position ranges 0–100 kb , 100 kb-200 kb , and 200 kb-300 kb , respectively . We related the sequence interval from 50 kb-250 kb to the sum of the two rates ( r1 , 2 + r2 , 3 of Fig 2 ) . This analysis neglected very small scaffolds , the sequence at the ends of longer scaffolds , and the estimated rates between scaffolds ( the amount and the features of the interceding DNA are unknown ) . In this analysis , we also excluded regions where recombination is suppressed due to inversions ( S2 Table ) . We obtained a single rate for each interval by first standardizing map specific rates by the total length of each map and then calculated a weighted mean across populations . The weight given to estimates from each cross is proportional to the reciprocal of the genome-wide recombination rate variance: IMPR = 1 . 0 , IMSWC = 0 . 899 , IMNAS = 0 . 790 , and DUNTIL = 0 . 677 , and IMF3 = 0 . 380 ( see Table 1 for mapping population abbreviations ) . We compared quantitative trait locus ( QTL ) mapping results for the IMSWC cross using either the optimized scaffold order generated by our pipeline or the scaffold order of the M . guttatus V2 reference genome . For each of the 873 F2s used for genetic map construction , genotype posterior probabilities were emitted for each of the 111 markers defined for chromosome 11 . This chromosome harbors a major QTL that contributes to variation in the ability to flower under 13/11 hours of light/dark ( Kooyers et al . 2018 , in prep . ) . Genotypes were then assigned at each marker for each individual based on the genotype with a posterior probability > 0 . 95; otherwise , the genotype was called as missing . For each marker order , we calculated recombination frequencies ( Haldane map function ) , imputed genotype probabilities at 1 cM steps ( error probability = 0 . 001 ) , and performed interval mapping using the binary model in R/QTL [57] .
We simulated data with Drosophila chromosome 2 , fragmented in different ways into 31 scaffolds , which were then input to the GOOGA pipeline . Fig 3 is a typical result . The initial map , constructed from putative genotype calls at scaffolds ends , is misassembled in several places , mainly where recombination rates are low . GOOGA converged in 26 steps for this simulation replicate , increasing the log-likelihood ( lnLk ) by over 500 and reducing the map length by ca . 40% . Genetic map shrinkage occurs because maximum likelihood adjustments of the rate parameters will compensate for bad joins by increasing recombination fraction ( r ) values . Over the course of a run , bad joins are corrected . Importantly , the final GOOGA order is not completely correct–two small scaffolds near the centromere are inverted . The likelihood of the data under the GOOGA final map and the correct map are exactly equivalent . There are simply no recombinant individuals in the mapping population to resolve the orientation of these scaffolds because it is a low recombination region . In all of simulation replicates , GOOGA converged on the correct map or on a map with an equivalent likelihood to the correct map . We used GOOGA to optimize chromosome-scale genetic maps for all 14 chromosomes among five Mimulus mapping populations ( Table 1 , S2–S15 Figs , and S3 Table ) . In each case the current M . guttatus genome assembly was used as the starting point with 100 kb windows as genetic markers . For each chromosome , GOOGA produced an MLE pseudo-chromosome construction . The overall map lengths for the F2 crosses are 1278 cM for the DUNTIL cross , 1523 cM for the IMNAS cross , and 1258 cM for the IMSWC cross . The average map length , 1353 cM , is shorter than previous F2 maps generated in Mimulus through PCR-based genotyping methods [12 , 47 , 50] . The map from the IMF3 cross is ≈50% longer than the F2 average ( 2043 cM ) , as expected given the extra generation of recombination between F2 and F3 generations . Finally the map from the IMPR cross ( 1489 cM ) is only slightly longer than the F2 average . There is additional recombination in the formation of these RILs , but the recombination parameter is specified differently ( as related to crossover events ) in the RIL HMM ( see S2 Appendix ) . Comparison of the five GOOGA optimized maps to the M . guttatus V2 reference genome order ( hereafter V2 map; [46] ) indicates that a large number of updates to the reference genome are necessary . Although the five mapping populations differ importantly from each other , changes in scaffold order and orientation from the V2 map are nearly always shared–the five estimated maps differ from the reference in the same way . These regions likely reflect errors in genome assembly given that the reference genome was sequenced from an inbred line ( IM62 ) that is used in two of the crosses that we analyze here ( IMF3 and IMSWC ) . Fig 4 illustrates this point , comparing the maximum likelihood map of the IMF3 data from GOOGA to the V2 map for three chromosomes . This intra-population cross is likely to be most congruent with the true order of the IM62 reference genome . The difference in log-likelihood ( ΔlnLk ) provides a measure of improvement in fit of the GOOGA relative to V2 . ΔlnLk is computed by fitting the genotype data to both the GOOGA optimized and V2 maps , and then subtracting the former from the latter . In each case , recombination rates are estimated independently , and so ΔlnLk is determined entirely by differences in scaffold order and orientation . The maps for chromosome 14 ( Fig 4A ) are largely similar . However , differences such as the changes in location and orientation of scaffolds 127 , 211 , 178 , and 140b ( inconsistency near the center of Fig 4A , S3 Table ) , are sufficient for a large improvement in likelihood . The ΔlnLk of 77 . 1 ( S4 Table ) corresponds to a likelihood improvement of 1033 , suggesting this new order fits the segregation data in the IMF3 population far better than the V2 map . Importantly , the updated ordering of scaffolds 127 , 211 , 178 , and 140b is shared by the IMSWC , DUNTIL , and IMPR maps ( S14 Fig and S3 Table ) . The IMNAS map retains the [211 , 127] ordering of the V2 map , albeit with a flip of 127 . However , this inconsistency is not compelling because genome assembly by genetic mapping will fail when there is no recombination to provide signal , and there is no evidence of recombination in this region among the 91 F2s genotyped in the IMNAS population . Chromosome 2 ( Fig 4B ) is typical of most IMF3 contrasts . Numerous scaffolds are rearranged ( e . g . the IMF3 sequence [44a , 212 , 249] is inverted in the V2 map ) and several are flipped in place ( including scaffold 81 that flanks [44a , 212 , 249] ) . There is an increase in likelihood from the V2 map to the GOOGA optimized order ( ΔlnLk = 255 . 8 ) , and the genetic length of chromosome 2 shrinks by ≈15% from V2 to GOOGA . This effect is even more pronounced for chromosome 10 ( Fig 4C ) , where there is a large increase in likelihood ( ΔlnLk = 710 . 1 ) . Among 70 chromosomes ( 5 crosses x 14 chromosomes ) , 23 ( 33% ) chromosomes had ΔlnLK improvements greater than 500 , while only 5 ( 7 . 1% ) improved less than 100 ( S4 Table ) . The most significant alterations to the V2 map are in genomic regions harboring inversions , particularly on chromosomes 5 , 8 , and 10 ( Fig 4C , S6 Fig , S9 Fig , and S11 Fig ) . The V2 map is based partly on genetic mapping data from approximately 70 recombinant inbred lines from a cross between IM62 and DUN10 ( J . Willis pers . comm . ) . DUN10 is a parent in our DUNTIL cross [35] and the aggregate of evidence ( see below ) suggests that DUN10 has chromosomal inversions ( relative to IM62 ) on each of these chromosomes . Our five mapping populations ( Table 1 ) contain one intra-population cross ( IMF3 ) , two inter-population crosses ( IMPR , IMSWC ) , a close interspecific-cross ( IMNAS ) , and a more distant interspecific cross ( DUNTIL ) . We observe structural polymorphisms by aligning the five maps to each other by chromosome . Fig 5 compares the maps for chromosome 10 which had previously been shown to harbor an inversion in IMPR [48] . As described in METHODS , we broke scaffold 13 into 13a and 13b based on a preliminary analysis of the DUNTIL data . GOOGA reassembled 13a and 13b into a continuous sequence for the IMF3 cross , but not the other crosses ( Fig 5 ) . There is minimal recombination between 13a and 13b in IMPR , IMNAS , and IMSWC because 13b is flanked by a large block of markers with nearly complete recombination suppression . This suppressed region , which represents at least 4 . 5 Mb of DNA , is freely recombining in IMF3 and DUNTIL but with a perfect reversal of marker order/orientation between those two crosses . From this , we infer that the IMF3 parents ( IM62 and IM767; Table 1 ) each have inversion karyotype “A” , the DUNTIL parents ( LVR and DUN ) each have karyotype “B” , and the other three crosses are heterokaryotypic ( one parent A and one B ) for this inversion ( S3 Fig ) . Noting that IM767 is orientation A , recombination suppression in the IMPR suggests the other parent in this cross ( Point Reyes ) has orientation B . By similar reasoning , we can conclude that SF5 and SWC also have orientation B ( S3 Fig ) . The right half of chromosome 10 is largely collinear among all five crosses , indicating the inversion is the primary influence on chromosome-wide likelihood . The GOOGA lnLk of the IMF3 data is -3784 . If the IMF3 data is forced into the optimized DUNTIL order , the lnLk drops to -4074 ( ΔlnLk = 290; S5 Table ) . This gives strong statistical support of the inversion between the A and B homokarytypic crosses . The effect is less pronounced in the heterokaryotypic crosses . For example , the ΔlnLks of the IMNAS data when forced into the DUNTIL and IMF3 maps are 59 and 124 , respectively ( S5 Table ) . Thus , as expected , the recombination suppression in this heterokaryotypic cross results in relatively weak support for either a pure A or B inversion orientation . The inversion on chromosome 10 is the only case among these crosses where we see free recombination in both homokaryotypes and suppression in the heterokaryotypes . In all other cases , one or more crosses reveal recombination suppression , with at least one homokaryotypic cross also present among our five populations ( S11 Fig and S3 Table ) . Lowry and Willis [13] showed a reversal of marker order ( as in Fig 5 ) for the inversion on chromosome 8 . This feature is associated with annual versus perennial life-history within M . guttatus . Here , we see free recombination over the inverted region on chromosome 8 in IMF3 , IMSWC , and IMNAS ( annual x annual crosses ) , and suppression in IMPR and DUNTIL ( annual x perennial M . guttatus and perennial M . guttatus x perennial M . tilingii , respectively ) . A similar pattern is noted for previously hypothesized inversions on chromosomes 5 ( suppression in DUNTIL and IMPR ) and 13 ( suppression in DUNTIL ) [49] and for the meiotic drive locus on chromosome 11 ( suppression in IMF3 ) [63] . Given comparable evidence , we also identify three novel putative inversions ( S2 Table ) . A region of at least 1 . 2 Mb spanning scaffolds 19 , 73 and 65 on chromosome 2 is completely suppressed in the IMPR ( S3 Fig ) . There is substantial recombination across this region in other crosses: 8 cM in DUNTIL , 10 cM in IMF3 , 7 cM in IMNAS , and 2 cM in IMSWC . A larger physical region ( ≈5 Mb on chromosome 7 ) is fully suppressed in IMPR but not the other crosses ( 20–45 cM; S8 Fig ) . Finally , a stretch of ≈4 Mb on chromosome 14 is suppressed in the IMSWC cross but not in other crosses ( about 30 cM , S15 Fig ) . To provide a more general comparison of the extent of gene order differences among the crosses , we imposed the optimal map in every cross onto the genotypic data from every other cross and computed the ΔlnLk . Then we summed these for each chromosome . The larger this sum , the greater degree of structural discrepancy between maps for each cross ( Table 2 ) . As quantified by this metric , chromosome 10 has the greatest degree of structural discrepancy , an unsurprising result given the large polymorphic inversion ( Fig 5 ) . Chromosomes 5 and 11 are next , both of which show large tracts of reordered scaffolds and shared regions of recombination suppression among several crosses ( Table 2 ) . Surprisingly , the lowest value is for chromosome 8 , which has a pairwise sum of ΔlnLk of only 75 . 4 . The large inversion on chromosome 8 suppresses recombination in annual x perennial mapping populations , and as a consequence , the ordering of scaffolds within the inverted region is fairly arbitrary in those heterotypic crosses . There are few map changes for chromosome 8 . This result arises because the strongly supported , co-linear maps from the homokaryotypic crosses ( IMF3 , IMSWC , and IMNAS ) are also largely reiterated in the suppressed crosses ( DUNTIL and IMPR ) . However , all GOOGA maps of chromosome 8 represent a vast improvement over the V2 map ( mean ΔlnLk vs V2 = 985 , S4 Table ) , suggesting the shared order that emerges from the inversion region is a large improvement over the reference genome . We tested whether recombination rate is associated with the proportions of DNA annotated as coding sequence , transposable elements ( TEs ) , and putative M . guttatus cent728 centromeric repeats [55] within a genomic window ( Fig 6 ) . Recombination rate is positively correlated with coding sequence density ( Pearson’s r = 0 . 218 ) and is negatively correlated with TE density ( Pearson’s r = -0 . 478 ) . To test the impact of centromeric repeats [63] on recombination rate , we binned our 200 kb windows into those with < 5% centromeric repeat sequence vs . those with > 5% . Centromeres are expected to suppress recombination , and consistent with this prediction , we see a significant drop in recombination in windows with > 5% centromeric repeats ( t-test p-value = 0 . 0003; Fig 6C ) . These results indicate that the recombination rates estimated by GOOGA fit well with biological expectations .
A tangible product of the application of GOOGA to Mimulus guttatus is that we substantially revise the reference genome of this species . The reference line ( IM62 ) is used in two of our crosses , including in a cross to another line from the same population ( IM767 ) . Excepting the meiotic drive locus on chromosome 11 [63] , IM767 appears to be largely collinear with IM62 ( the two lines have the same orientation at other putative inversions ) . Despite this , the GOOGA realignment of scaffolds yields a dramatic increase in IMF3 likelihood over the V2 assembly: ΔlnLk is 5464 when summed over all chromosomes ( S4 Table ) . Map revisions are found on each chromosome , and supported not only by ΔlnLk within IMF3 , but also the maps from the other crosses . Incomplete assembly is a common and important problem in genomics , particularly in species with complex patterns of repeats . The promising implication of Fig 4 is that the rough genome assembly of many species can be dramatically improved with a low-coverage sequencing of a mapping population . Moreover , GOOGA quantifies the magnitude of improvements in terms of increase in likelihood . The alignment of maps for Mimulus chromosome 10 illustrates the effect of an inversion . A 5 Mb region ( left portion of Fig 5 ) was previously identified as a putative inversion from recombination suppression in the IMPR [48] . This is clearly confirmed here by the inclusion of both homokaryotypic crosses ( AxA and BxB ) and the ‘map flip’ ( top two panels in Fig 5 ) effectively ascertains the scaffolds within the inversion and their ordering . This example also highlights the importance of marker construction . While it is possible to construct markers de novo with RAD-seq data [40] , here we delineate markers on a previously assembled set of reference DNA sequences ( the genomic scaffolds from the M . guttatus reference genome ) obtained from sequencing of the IM62 inbred line . There are clear advantages to defining markers in this fashion , but care must be taken with this approach , especially in distantly related populations . For example , we initially assumed that the IM62 reference genome scaffolds correctly reflect the gene order for the other mapping populations . However , in our analysis of chromosome 10 , we found it necessary to break scaffold 13 into two parts ( 13a and 13b ) , though it is continuous in IM62 . GOOGA reannealed 13a and 13b in the IMF3 cross but inserted other scaffolds between them in other crosses due to a segregating inversion with a breakpoint contained within scaffold 13 ( particularly DUNTIL; Fig 5 ) . This implies that scaffold 13 was correctly assembled for IM62 , but not for DUNTIL . In the five Mimulus crosses , chromosome 10 is the only of the eight putative inversions where both homokaryotypic crosses are included . Reversal of marker ordering between homokaryotypic crosses was previously demonstrated for chromosome 8 [13] , and could be demonstrated for others with appropriate selection of parental lines . However , the sort of reversal of genetic maps evident in Fig 5 requires polymorphism within both karyotypes . The derived karyotype is essentially homogeneous ( few or no SNPs ) for several structural polymorphisms in M . guttatus [12 , 73] . Polymorphism should be evident within both karyotypes for inversions that have had time to accumulate variants through mutation or gene flux [74 , 75] , but perhaps not for very young inversions . The inclusion of phenotype data with the IMSWC cross illustrates the importance of scaffold ordering for downstream genetic analyses ( Fig 7 ) . Here , each F2 plant was scored for progression to flowering , a dichotomous trait in the experimental photoperiod , which was restrictive to floral induction for one parent . Application of the same QTL mapping procedure to the data produces radically different outcomes if markers are placed according to the current M . guttatus reference genome ( V2 map is the top panel of Fig 7 ) or by the GOOGA optimized map ( bottom panel of Fig 7 ) . Using the latter , which has a ΔlnLk improvement of 1089 over the V2 map ( S4 Table ) , the data suggest a single large-effect QTL localized to a map position 44–45 cM into chromosome 11 . QTL mapping to the V2 orientation yields three distinct peaks , each with a high LOD score . The specific markers near the QTL peak in the GOOGA map are jumbled in the V2 map which splits the genotype-phenotype association into three distinct parts . The V2 map is also “stretched”–expanded in recombination length by over 20 cM–likely to compensate for bad scaffold joins . Both of these effects impede QTL inference in places where the reference genome is misassembled . The GOOGA pipeline is a set of modules ( Fig 1 ) : ( A ) procedures to make genetic markers from low-coverage sequencing data in conjunction with a collection of genomic scaffolds , ( B ) a method to estimate genotyping error rates specific to each individual , ( C ) an HMM to estimate recombination rates and obtain a likelihood for a specific ordering and orientation of genomic scaffolds , and ( D ) a genetic algorithm ( GA ) to search map space to obtain pseudo-chromosomes that maximize the likelihood of the data . While GOOGA was developed as an integrated series of steps , one or more of the components might be used apart from the rest . Below , we outline a few options that could be appropriate for different species or scenarios . Most basically , one might use ( A ) to create markers and then apply standard map making software [76 , 77] to the resulting genotype matrix . If this is done with the Mimulus mapping populations ( or comparable datasets ) , the resulting matrices contain a great excess of missing data . As a consequence , extensive culling ( both of individuals scored for too few markers and markers scored for too few individuals ) is then required to apply standard map making programs . An alternative is to apply ( A ) - ( B ) - ( C ) to generate genetic markers . Assuming that the genomic scaffolds are generally reliable , the HMM will leverage data from neighboring windows to inform genotype calls . After obtaining the MLE on rates , one can extract posterior probabilities on genotypes and then impose ‘hard calls’ , e . g . [41] , to create a genotype matrix . We found this approach effective with as few as 10 informative reads per windows ( see METHODS ) . Another possibility is to replace the front end of the pipeline . If one has high certainty in the validity of individual SNPs ( their location and scoring ) , it is natural to replace windows ( A ) with individual SNPs as the observed states of the HMM ( [36] , Fig 2 ) . Finally , while we found the GA effective for searching map space ( D ) , other map optimization methods may prove useful in searching order/orientation space given that the HMM provides a likelihood for each ( e . g . , [78 , 79] ) . Like Mimulus , many species and species complexes harbor significant segregating inversions and other gene order polymorphisms including Drosophila , Zea , Anopheles and Helianthus [80–83] . The simulation study suggests a way that GOOGA could test whether sequences in a novel population under study are collinear with the reference genome . One could start by breaking the reference genome into ‘pseudo-scaffolds’ to be reassembled using data from the novel population . This is essentially what we did in the simulation experiment on D . melanogaster chromosome 2 ( Fig 3 ) . Sequences from recombinant individuals of the novel population would be mapped to the pseudo-scaffolds and the data input to GOOGA . Metrics like ΔlnLk can evaluate the reference genome map and identify necessary changes ( as we have done in IMSWC in Fig 7 ) . In this way , a necessary solution for species with incomplete genome assemblies ( e . g . Mimulus ) could be used in species with high-quality reference assemblies ( e . g . D . melanogaster ) but in populations that are structurally diverged from the reference genome . This application would be particularly convenient in cases where trait mapping is being performed via RAD-Seq genotyping , as no additional data would need to be generated . | Genome sequences are an essential resource for genetic research in many species . However , most species exhibit considerable variation in genomic organization , making a single reference sequence inadequate . This variation complicates quantitative trait mapping and population genomics . We introduce a new statistical method and computational tools that use linkage information to improve genome assembly and identify structural differences between individuals or populations . We first use the method to correct many assembly errors in the reference genome of Mimulus guttatus . Analyzing five crosses from the M . guttatus species complex , we detect eight large chromosomal inversions and improve the resolution of a trait mapping study . This work illustrates how genetic mapping can be applied to a greater diversity of species to address genetic and evolutionary questions . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"markov",
"models",
"chromosomal",
"inversions",
"invertebrate",
"genomics",
"chromosome",
"mapping",
"mathematics",
"dna",
"recombination",
"molecular",
"biology",
"techniques",
"dna",
"genotyping",
"research",
"and",
"analysis",
"methods",
"gene",
"mapping",
"chromosome... | 2019 | GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity |
Hepatitis C virus ( HCV ) nonstructural protein 2 ( NS2 ) is a hydrophobic , transmembrane protein that is required not only for NS2-NS3 cleavage , but also for infectious virus production . To identify cellular factors that interact with NS2 and are important for HCV propagation , we screened a human liver cDNA library by split-ubiquitin membrane yeast two-hybrid assay using full-length NS2 as a bait , and identified signal peptidase complex subunit 1 ( SPCS1 ) , which is a component of the microsomal signal peptidase complex . Silencing of endogenous SPCS1 resulted in markedly reduced production of infectious HCV , whereas neither processing of structural proteins , cell entry , RNA replication , nor release of virus from the cells was impaired . Propagation of Japanese encephalitis virus was not affected by knockdown of SPCS1 , suggesting that SPCS1 does not widely modulate the viral lifecycles of the Flaviviridae family . SPCS1 was found to interact with both NS2 and E2 . A complex of NS2 , E2 , and SPCS1 was formed in cells as demonstrated by co-immunoprecipitation assays . Knockdown of SPCS1 impaired interaction of NS2 with E2 . Our findings suggest that SPCS1 plays a key role in the formation of the membrane-associated NS2-E2 complex via its interaction with NS2 and E2 , which leads to a coordinating interaction between the structural and non-structural proteins and facilitates the early step of assembly of infectious particles .
Over 170 million people worldwide are chronically-infected with hepatitis C virus ( HCV ) , and are at risk of developing chronic hepatitis , cirrhosis , and hepatocellular carcinoma [1] . HCV is an enveloped virus of the family Flaviviridae , and its genome is an uncapped 9 . 6-kb positive-strand RNA consisting of the 5′ untranslated region ( UTR ) , an open reading frame encoding viral proteins , and the 3′ UTR [2] . A precursor polyprotein is further processed into structural proteins ( Core , E1 , and E2 ) , followed by p7 and nonstructural ( NS ) proteins ( NS2 , NS3 , NS4A , NS4B , NS5A , and NS5B ) , by cellular and viral proteases . The structural proteins ( Core to E2 ) and p7 reside in the N-terminal region , and are processed by signal peptidase from the polyprotein . NS2 , NS3 , and NS4A are prerequisites for proteolytic processing of the NS proteins . NS3 to NS5B are considered to assemble into a membrane-associated HCV RNA replicase complex . NS3 also possesses activities of helicase and nucleotide triphosphatase . NS4 is a cofactor that activates the NS3 protease . NS4B induces vesicular membrane alteration . NS5A is considered to play an important but undefined role in viral RNA replication . NS5B is the RNA-dependent RNA polymerase . It is now accepted that NS proteins , such as NS2 , NS3 , and NS5A , contribute to the assembly or release of infectious HCV [3]–[9] . NS2 protein is a transmembrane protein of 21–23 kDa , with highly hydrophobic N-terminal residues forming transmembrane helices that insert into the endoplasmic reticulum ( ER ) membrane [5] , [10] . The C-terminal part of NS2 resides in the cytoplasm , enabling zinc-stimulated NS2/3 autoprotease activity together with the N-terminal domain of NS3 . The crystal structure of the C-terminal region of NS2 reveals a dimeric cysteine protease containing two composite active sites [11] . Prior work showed that NS2 is not essential for RNA replication of subgenomic replicons [12]; however , the protein is required for virus assembly independently of protease activity [5] , [6] . Several adaptive mutations in NS2 that increase virus production have been reported [13]–[17] . In addition , there is increasing evidence for genetic and biochemical interaction of NS2 with other HCV proteins , including E1 , E2 , p7 , NS3-4A , and NS5A [10] , [18]–[25] . Thus , NS2 is now suggested to act as a scaffold to coordinate interactions between the structural and NS proteins for viral assembly . However , the molecular mechanism by which NS2 is involved in virus assembly remains unclear . In this study , we identified signal peptidase complex subunit 1 ( SPCS1 ) as a host factor that interacts with NS2 by yeast two-hybrid screening with a split-ubiquitin system . SPCS1 is a component of the microsomal signal peptidase complex which is responsible for the cleavage of signal peptides of many secreted or membrane-associated proteins . We show that SPCS1 is a novel host factor that participates in the assembly process of HCV through an interaction with NS2 and E2 .
To gain a better understanding of the functional role of NS2 in the HCV lifecycle , we screened a human liver cDNA library by employing a split-ubiquitin membrane yeast two-hybrid system with the use of NS2 as a bait . It is known that the split ubiquitin-based two-hybrid system makes it possible to study protein-protein interactions between integral membrane proteins at the natural sites of interactions in cells [26] . From the screening , several positive clones were identified from the 13 million transformants , and the nucleotide sequences of the clones were determined . A BLAST search revealed that one of the positive clones encodes a full-length coding region of signal peptidase complex subunit 1 ( SPCS1 ) . SPCS1 is a component of the microsomal signal peptidase complex which consists of five different subunit proteins in mammalian cells [27] . Although catalytic activity for SPCS1 has not been indicated to date , a yeast homolog of this subunit is involved in efficient membrane protein processing as a component of the signal peptidase complex [28] . To determine the specific interaction of NS2 with SPCS1 in mammalian cells , FLAG-tagged NS2 ( FLAG-NS2; Fig . 1A ) was co-expressed in 293T cells with myc-tagged SPCS1 ( SPCS1-myc; Fig . 1A ) , followed by co-immunoprecipitation and immunoblotting . SPCS1 was shown to be co-immunoprecipitated with NS2 ( Fig . 1B ) . Co-immunoprecipitation of SPCS1-myc with NS2 was also observed in the lysate of Huh-7 cells infected with cell culture-produced HCV ( HCVcc ) derived from JFH-1 isolate [29] ( Fig . 1C ) . To determine the region of SPCS1 responsible for the interaction with NS2 , deletion mutants of myc-tagged SPCS1 were constructed ( Fig . 1A ) and co-expressed with FLAG-tagged NS2 . Since the expression of C-terminal deletion mutants , d3 and d4 , was difficult to detect ( Fig . 1D ) , N-terminal deletions ( d1 and d2 ) as well as wild-type SPCS1 were subjected to immunoprecipitation analysis . SPCS1-myc , -d1 , and -d2 were co-immunoprecipitated with NS2 ( Fig . 1E ) , suggesting that the SPCS1 region spanning amino acids ( aa ) 43 to 102 is involved in its interaction with NS2 . Next , to identify the NS2 region responsible for its interaction with SPCS1 , deletion mutants for FLAG-NS2 ( Fig . 1A ) were co-expressed with SPCS1-myc-d2 in cells , followed by being immunoprecipitated with anti-myc antibody . SPCS1 was co-immunoprecipitated with the NS2 deletions , except for a mutant lacking transmembrane ( TM ) 2 and TM3 ( dTM23 ) domains ( Fig . 1F ) . These finding suggests that the TM3 region of NS2 is involved in the interaction with SPCS1 . To investigate SPCS1-NS2 interaction in situ , the proximity ligation assay ( PLA ) [30] , which is based on antibodies tagged with circular DNA probes , was used . Only when the antibodies are in close proximity , the probes can be ligated together and subsequently be amplified with a polymerase . We were able to detect PLA signal predominantly in the cytoplasm of the cells expressing FLAG-NS2 and SPCS1-myc-d2 tagged with V5 at N-terminus ( Fig . 1G ) . By contrast , the PLA signal was not observed in the context of NS2-Core co-expression . We further analyzed the SPCS1-NS2 interaction by the monomeric Kusabira-Green ( mKG ) system [31] , which is based on fusion proteins with complementary fragments ( mKG-N and mKG-C ) of the monomeric coral fluorescent reporter protein . When the mKG fragments are in close proximity due to the protein-protein interaction , the mKG fragments form a beta-barrel structure and emit green fluorescence . Co-expression of SPCS1-mKG-N and NS2-mKG-C fusion proteins in cells reconstituted green cellular fluorescence as shown in Fig . 1H . Thus , these results represented structures with SPCS1 and NS2 in close proximity , and strongly suggest their physical interaction in cells . To investigate the role ( s ) of endogenous SPCS1 in the propagation of HCV , four small interfering RNAs ( siRNAs ) for SPCS1 with different target sequences or scrambled control siRNA were transfected into Huh7 . 5 . 1 cells , followed by infection with HCVcc . Among the four SPCS1-siRNAs , the highest knockdown level was observed by siRNA #2 . siRNAs #3 and #4 showed moderate reductions of SPCS1 expression , and only a marginal effect was obtained from siRNA #1 ( Fig . 2A ) . As indicated in Fig . 2B , the infectious viral titer in the culture supernatant was significantly reduced by the knockdown of SPCS1 . It should be noted that the infectious titers correlated well with the expression levels of endogenous SPCS1 . siRNA #2 reduced the HCV titer to ∼5% of the control level in Huh7 . 5 . 1 cells . To rule out the possibility of off-target effect of SPCS1-siRNA on HCV propagation , we also used “C911” mismatch control siRNAs in which bases 9 through 11 of siRNAs are replaced with their complements but other parts of antisense- and sense-strand sequences are kept intact . These mismatch designed-control siRNAs have been shown to reduce the down-regulation of the targeted mRNA , but maintains the off-target effects of the original siRNA [32] . The C911 controls against SPCS1-siRNA #2 , #3 , and #4 ( C911-#2 , -#3 , and -#4 ) showed little effect on knockdown of SPCS1 as well as propagation of HCV ( Fig . S1A and B ) . We further determined the loss- and gain-of-function of SPCS1 on HCV propagation in an SPCS1-knockdown cell line . To this end , Huh-7 cells were transfected with a plasmid encoding a short hairpin RNA ( shRNA ) targeted to SPCS1 and were selected with hygromycin B , resulting in clone KD#31 where little or no expression of SPCS1 was detectable ( Fig . 2C ) . KD#31 cells and parental Huh-7 cells were transfected with an RNA polymerase I ( pol ) -driven full-genome HCV plasmid [33] in the presence or absence of an expression plasmid for shRNA-resistant SPCS1 ( SPCS1- shr ) . Western blotting confirmed the expression levels of SPCS1 in cells ( Fig . 2D ) . As expected , viral production in the culture supernatants of the transfected cells was significantly impaired in SPCS1-knockdown cells compared with parental Huh-7 cells ( Fig . 2E white bars ) . Expression of SPCS1- shr in KD#31 cells recovered virus production in the supernatant to a level similar to that in the parental cells . Expression of SPCS1- shr in parental Huh-7 cells did not significantly enhance virus production . Taken together , these results demonstrate that SPCS1 has an important role in HCV propagation , and that the endogenous expression level of SPCS1 is sufficient for the efficient propagation of HCV . A typical feature of the Flaviviridae family is that their precursor polyprotein is processed into individual mature proteins mediated by host ER-resident peptidase ( s ) and viral-encoded protease ( s ) . We therefore next examined the role of SPCS1 in the propagation of Japanese encephalitis virus ( JEV ) , another member of the Flaviviridae family . SPCS1 siRNAs or control siRNA were transfected into Huh7 . 5 . 1 cells followed by infection with JEV or HCVcc . Although knockdown of SPCS1 severely impaired HCV production ( Fig . 3A ) , the propagation of JEV was not affected under the SPCS1-knockdown condition ( Fig . 3B ) . Expression of the viral proteins as well as knockdown of SPCS1 were confirmed ( Fig . 3C ) . This suggests that SPCS1 is not a broadly active modulator of the flavivirus lifecycle , but rather is involved specifically in the production of certain virus ( es ) such as HCV . Since SPCS1 is a component of the signal peptidase complex , which plays a role in proteolytic processing of membrane proteins at the ER , it may be that SPCS1 is involved in processing HCV proteins via interacting with ER membranes . To address this , the effect of SPCS1 knockdown on the processing of HCV precursor polyproteins in cells transiently expressing the viral Core-NS2 region was analyzed . Western blotting indicated that properly processed core and NS2 were observed in KD#31 cells as well as Huh-7 cells ( Fig . 4A ) . No band corresponding to the unprocessed precursor polyprotein was detected in either cell line ( data not shown ) . We also examined the effect of SPCS1 knockdown on the cleavage of the NS2/3 junction mediated by NS2/3 protease . Processed NS2 was detected in both cell lines with and without SPCS1 knockdown , which were transfected with wild-type or protease-deficient NS2-3 expression plasmids ( Fig . 4B & C ) . Signal peptidase plays a key role in the initial step of the protein secretion pathway by removing the signal peptide and releasing the substrate protein from the ER membrane . It is now accepted that the secretion pathways of very-low density lipoprotein or apolipoprotein E ( apoE ) are involved in the formation of infectious HCV particles and their release from cells [34] , [35] . ApoE is synthesized as a pre-apoE . After cleavage of its signal peptide in the ER , the protein is trafficked to the Golgi and trans-Golgi network before being transported to the plasma membrane and secreted . As shown in Fig . 4D , the secreted levels of apoE from Huh-7 cells with knocked-down of SPCS1 were comparable to those from control cells . In addition , the level of albumin , an abundant secreted protein from hepatocytes , in the culture supernatants of the cells was not influenced by SPCS1 knockdown ( Fig . 4E ) . These data suggest that the knockdown of SPCS1 has no influence on the processing of viral and host secretory proteins by signal peptidase and HCV NS2/3 protease . To further address the molecular mechanism ( s ) of the HCV lifecycle mediated by SPCS1 , we examined the effect of SPCS1 knockdown on viral entry and genome replication using single-round infectious trans-complemented HCV particles ( HCVtcp ) [33] , of which the packaged genome is a subgenomic replicon containing a luciferase reporter gene . This assay system allows us to evaluate viral entry and replication without the influence of reinfection . Despite efficient knockdown of SPCS1 ( Fig . 5A ) , luciferase activity expressed from HCVtcp in SPCS1-knockdown cells was comparable to that in control or non-siRNA-transfected cells ( Fig . 5B ) , suggesting that SPCS1 is not involved in viral entry into cells and subgenomic RNA replication . As a positive control , knockdown of claudin-1 , a cell surface protein required for HCV entry , reduced the luciferase activity . We also examined the effect of SPCS1 knockdown on full-genome replication using HCVcc-infected cells . Despite efficient knockdown of SPCS1 , expression of HCV proteins was comparable to that in control cells ( Fig . 5C ) . By contrast , knockdown of PI4 Kinase ( PI4K ) , which is required for replication of HCV genome , led to decrease in expression of HCV proteins . As cells that had already been infected with HCV were used , knockdown of claudin-1 had no effect on HCV protein levels . These data suggest that SPCS1 is not involved in viral entry into cells and the viral genome replication . We also observed properly processed Core , E2 , NS2 and NS5B in SPCS1-knockdown cells in consistent with the result as shown in Fig . 4A , indicating no effect of SPCS1 on HCV polyprotein processing . Next , to investigate whether SPCS1 is involved in the assembly or release of infectious particles , SPCS1-shRNA plasmid along with a pol I-driven full-genome HCV plasmid [33] were transfected into CD81-negative Huh7-25 cells , which can produce infectious HCV upon introduction of the viral genome , but are not permissive to HCV infection [36] . It is therefore possible to examine viral assembly and the release process without viral reinfection . The infectivity within the transfected cells as well as supernatants was determined 5 days post-transfection . Interestingly , both intra- and extracellular viral titers were markedly reduced by SPCS1 knockdown ( Fig . 5C ) . Taken together , in the HCV lifecycle , SPCS1 is most likely involved in the assembly of infectious particles rather than cell entry , RNA replication , or release from cells . It has been shown that HCV NS2 interacts with the viral structural and NS proteins in virus-producing cells [18]–[21] , and that some of the interactions , especially the NS2-E2 interaction , are important for the assembly of infectious HCV particles . However , the functional role of NS2 in the HCV assembly process has not been fully elucidated . To test whether SPCS1 is involved in the interaction between NS2 and E2 , cells were co-transfected with expression plasmids for E2 , FLAG-NS2 , and SPCS1-myc . E2 and NS2 were co-immunoprecipitated with SPCS1-myc , and E2 and SPCS1-myc were co-immunoprecipitated with FLAG-NS2 ( Fig . 6A ) , suggesting the formation of an E2-NS2-SPCS1 complex in cells . To investigate the interaction of SPCS1 with E2 in the absence of NS2 , HCV Core-p7 polyprotein or E2 protein were co-expressed with SPCS1-myc in cells , followed by immunoprecipitation with anti-myc antibody . As shown in Fig . 6B and Fig . S2 , E2 was co-immunoprecipitated with SPCS1-myc . The interaction between SPCS1 and E2 was further analyzed in situ by PLA and mKG system . Specific signals indicating formation of the SPCS1-E2 complex were detected in both assays ( Fig . S3 ) , suggesting physical interaction between SPCS1 and E2 in cells . We further determined the region of SPCS1 responsible for the interaction with E2 by co-immunoprecipitation assays . Full-length and deletion mutant d2 of SPCS1 ( Fig . 1A ) were similarly co-immunoprecipitated with E2 , while only a limited amount of d1 mutant SPCS1 ( Fig . 1A ) was co-precipitated ( Fig . 6C ) . It may be that the aa 43–102 region of SPCS1 , which was identified as the region involved in the NS2 interaction ( Fig . 1D ) , is important for its interaction with E2 , and that deletion of the N-terminal cytoplasmic region leads to misfolding of the protein and subsequent inaccessibility to E2 . Finally , to understand the significance of SPCS1 in the NS2-E2 interaction , Huh7 . 5 . 1 cells with or without SPCS1 knockdown by siRNA were transfected with expression plasmids for Core-p7 and FLAG-NS2 , followed by co-immunoprecipitation with anti-FLAG antibody . As shown in Fig . 6D , the NS2-E2 interaction was considerably impaired in the SPCS1-knockdown cells as compared to that in the control cells . A similar result was obtained in the stable SPCS1-knockdown cell line ( Fig . 6E ) . In contrast , in that cell line , the interaction of NS2 with NS3 was not impaired by SPCS1 knockdown ( Fig . 6E ) . These results , together with the above findings , suggest that SPCS1 is required for or facilitates the formation of the membrane-associated NS2-E2 complex , which participates in the proper assembly of infectious particles .
In this study , we identified SPCS1 as a novel host factor that interacts with HCV NS2 , and showed that SPCS1 participates in HCV assembly through complex formation with NS2 and E2 . In general , viruses require host cell-derived factors for proceeding and regulating each step in their lifecycle . Although a number of host factors involved in genome replication and cell entry of HCV have been reported , only a few for viral assembly have been identified to date . To our knowledge , this is the first study to identify an NS2-interacting host protein that plays a role in the production of infectious HCV particles . NS2 is a hydrophobic protein containing TM segments in the N-terminal region . The C-terminal half of NS2 and the N-terminal third of NS3 form the protease , which is a prerequisite for NS2-NS3 cleavage . In addition , it is now accepted that this protein is essential for particle production [4]–[6] , [12] . However , the mechanism of how NS2 is involved in the assembly process of HCV has been unclear . So far , two studies have screened for HCV NS2 binding proteins by yeast two-hybrid analysis [37] , [38] . Erdtmann et al . reported that no specific interaction was detected by a conventional yeast hybrid screening system using full-length NS2 as a bait , probably due to hampered translocation of the bait to the nucleus [37] . They further screened a human liver cDNA library using NS2 with deletion of the N-terminal TM domain , and CIDE-B protein , a member of the CIDE family of apoptosis-inducing factors , was identified . However , whether CIDE-B is involved in the HCV lifecycle and/or viral pathogenesis is unclear . de Chassey et al . reported several cellular proteins as potential NS2 binding proteins using NS2 with N-terminal deletion as a bait [38] . Involvement of these proteins in the HCV lifecycle is also unclear . In our study , to screen for NS2-binding partners using full-length NS2 as a bait , we utilized a split-ubiquitin yeast two-hybrid system that allows for the identification of interactions between full-length integral membrane proteins or between a full-length membrane-associated protein and a soluble protein [39] . SPCS1 was identified as a positive clone of an NS2-binding protein , but proteins that have been reported to interact with NS2 were not selected from our screening . SPCS1 is a component of the signal peptidase complex that processes membrane-associated and secreted proteins in cells . The mammalian signal peptidase complex consists of five subunits , SPCS1 , SPCS2 , SPCS3 , SEC11A , and SEC11C [27] . Among them , the functional role of SPCS1 is still unclear , and SPCS1 is considered unlikely to function as a catalytic subunit according to membrane topology [40] . The yeast homolog of SPCS1 , Spc1p , is also known to be nonessential for cell growth and enzyme activity [28] , [41] . Interestingly , these findings are consistent with the results obtained in this study . Knockdown of SPCS1 did not impair processing of HCV structural proteins ( Fig . 4A ) or secretion of apoE and albumin ( Fig . 4B and C ) , which are regulated by ER membrane-associated signal peptidase activity . The propagation of JEV , whose structural protein regions are cleaved by signal peptidase , was also not affected by the knockdown of SPCS1 ( Fig . 3B ) . SPCS1 , SPCS2 , and SPCS3 are among the host factors that function in HCV production identified from genome-wide siRNA screening [42] . It seemed that knockdown of SPCS1 had a higher impact on the later stage of viral infection compared to either SPCS2 or SPCS3 , which are possibly involved in the catalytic activity of the signal peptidase . Further analyses to address the mechanistic implication of SPCS1 on the HCV lifecycle revealed that SPCS1 knockdown impaired the assembly of infectious viruses in the cells , but not cell entry , RNA replication , or release from the cells ( Fig . 5 ) . We thus considered the possibility that the SPCS1-NS2 interaction is important for the role of NS2 in viral assembly . Several studies have reported that HCV NS2 is associated biochemically or genetically with viral structural proteins as well as NS proteins [10] , [18]–[25] . As an intriguing model , it has been proposed that NS2 functions as a key organizer of HCV assembly and plays a key role in recruiting viral envelope proteins and NS protein ( s ) such as NS3 to the assembly sites in close proximity to lipid droplets [21] . The interaction of NS2 with E2 has been shown by use of an HCV genome encoding tagged-NS2 protein in virion-producing cells . Furthermore , the selection of an assembly-deficient NS2 mutation located within its TM3 for pseudoreversion leads to a rescue mutation in the TM domain of E2 , suggesting an in-membrane interaction between NS2 and E2 [21] . Another study identified two classes of NS2 mutations with defects in virus assembly; one class leads to reduced interaction with NS3 , and the other , located in the TM3 domain , maintains its interaction with NS3 but shows impaired interaction between NS2 and E1-E2 [20] . However , the precise details of the NS2-E2 interaction , such as direct protein-protein binding or participating host factors , are unknown . Our results provide evidence that SPCS1 has an important role in the formation of the NS2-E2 complex by its interaction with both NS2 and E2 , most likely via their transmembrane domains , including TM3 of NS2 . As knockdown of SPCS1 reduced the interaction of NS2 and E2 as shown in Fig . 6D and E , it may be that SPCS1 contributes to NS2-E2 complex formation or to stabilizing the complex . Based on data obtained in this study , we propose a model of the formation of an E2-SPCS1-NS2 complex at the ER membrane ( Fig . 7 ) . In summary , we identified SPCS1 as a novel NS2-binding host factor required for HCV assembly by split-ubiquitin membrane yeast two-hybrid screening . Our data demonstrate that SPCS1 plays a key role in the E2-NS2 interaction via formation of an E2-SPCS1-NS2 complex . These findings provide clues for understanding the molecular mechanism of assembly and formation of infectious HCV particles .
A split-ubiquitin membrane yeast two-hybrid screen was performed to identify possible NS2 binding partners . This screening system ( DUALmembrane system; Dualsystems Biotech , Schlieren , Switzerland ) is based on an adaptation of the ubiquitin-based split protein sensor [26] . The full-length HCV NS2 gene derived from the JFH-1 strain [29] was cloned into pBT3-SUC bait vector to obtain bait protein fused to the C-terminal half of ubiquitin ( NS2-Cub ) along with a transcription factor . Prey proteins generated from a human liver cDNA library ( Dualsystems Biotech ) were expressed as a fusion to the N-terminal half of ubiquitin ( NubG ) . Complex formation between NS2-Cub and NubG-protein from the library leads to cleavage at the C-terminus of reconstituted ubiquitin by ubiquitin-specific protease ( s ) with consequent translocation of the transcription factor into the nucleus . Library plasmids were recovered from positive transformants , followed by determining the nucleotide sequences of inserted cDNAs , which were identified using the BLAST algorithm with the GenBank database . Human embryonic kidney 293T cells , and human hepatoma Huh-7 cells and its derivative cell lines Huh7 . 5 . 1 [43] and Huh7-25 [36] , were maintained in Dulbecco's modified Eagle medium supplemented with nonessential amino acids , 100 U of penicillin/ml , 100 µg of streptomycin/ml , and 10% fetal bovine serum ( FBS ) at 37°C in a 5% CO2 incubator . Plasmids pCAGC-NS2/JFH1am and pHHJFH1am were previously described [33] . The plasmid pCAGC-p7/JFHam , having adaptive mutations in E2 ( N417S ) and p7 ( N765D ) in pCAG/C-p7 [44] , was constructed by oligonucleotide-directed mutagenesis . To generate the NS2 expression plasmid pCAG F-NS2 and the NS2-deletion mutants , cDNAs encoding the full-length or parts of NS2 possessing the FLAG-tag and spacer sequences ( MDYKDDDDKGGGGS ) were amplified from pCAGC-NS2/JFH1am by PCR . The resultant fragments were cloned into pCAGGS . For the NS2-NS3 expression plasmid pEF F-NS2-3 , a cDNA encoding the entire NS2 and the N-terminal 226 amino acids of NS3 with the N-terminal FLAG-tag sequence as above was amplified by PCR and was inserted into pEF1/myc-His ( Invitrogen , Carlsbad , CA ) . The plasmid pEF F-NS2-3 H956A , having a defective mutation in the protease active site within NS2 , was constructed by oligonucleotide-directed mutagenesis . To generate the NS3 expression plasmid pCAGN-HANS3JFH1 , a cDNA encoding NS3 with an HA tag at the N terminus , which was amplified by PCR with pHHJFHam as a template , was inserted downstream of the CAG promoter of pCAGGS . To generate the SPCS1-expressing plasmid pCAG-SPCS1-myc and its deletion mutants , cDNAs encoding all of or parts of SPCS1 with the Myc tag sequence ( EQKLISEEDL ) at the C-terminus , which was amplified by PCR , was inserted into pCAGGS . pSilencer-shSPCS1 carrying a shRNA targeted to SPCS1 under the control of the U6 promoter was constructed by cloning the oligonucleotide pair 5′- GATCCGCAATAGTTGGATTTATCTTTCAAGAGAAGATAAATCCAACTATTGCTTTTTTGGAAA-3′ and 5′- AGCTTTTCCAAAAAAGCAATAGTTGGATTTATCTTCTCTTGAAAGATAAATCCAACTATTGCG-3′ between the BamHI and HindIII sites of pSilencer 2 . 1-U6 hygro ( Ambion , Austin , TX ) . To generate a construct expressing shRNA-resistant SPCS1 pSPCS1-shr , a cDNA fragment coding for SPCS1 , in which the 6 bp within the shRNA targeting region ( 5′-GCAATAGTTGGATTTATCT-3′ ) was replaced with GCTATTGTCGGCTTCATAT that causes no aa change , was amplified by PCR . The resulting fragment was confirmed by sequencing and then cloned into pCAGGS . Full-length SPCS1 and N-terminal region of NS2 ( aa 1–94 ) were amplified by PCR and cloned onto EcoRI and HindIII sites of phmKGN-MN and phmKGC-MN , which encode the mKG fragments ( CoralHue Fluo-chase Kit; MBL , Nagoya , Japan ) , designated as pSPCS1-mKG ( N ) and pNS2-mKG ( C ) , respectively . Transmembrane domain of the E1 to E2 was also amplified by PCR and cloned onto EcoRI and HindIII sites of phmKGC-MN . To avoid the cleavage of E2-mKG ( C ) fusion protein in the cells , last alanine of the E2 protein was deleted . Positive control plasmids for mKG system , pCONT-1 and pCONT-2 , which encode p65 partial domain from NF-κB complex fused to mKG ( N ) and p50 partial domain from NF-κB complex fused to mKG ( C ) respectively , were supplied from MBL . For PLA experiments , cDNA for SPCS1 d2-myc with the V5 tag at the N-terminus was amplified by PCR , and inserted into pCAGGS . For expression of HCV E2 , cDNA from E1 signal to the last codon of the transmembrane domain of the E2 , in which part of the hypervariable region-1 ( aa 394–400 ) were replaced with FLAG-tag and spacer sequences ( DYKDDDDKGGG ) , was amplified by PCR , and inserted into pCAGGS . For expression of FLAG-core , cDNAs encoding Core ( aa 1–152 ) possessing the FLAG-tag and spacer sequences ( MDYKDDDDKGGGGS ) were amplified from pCAGC191 [45] by PCR . The resultant fragments were cloned into pCAGGS . Monolayers of 293T cells were transfected with plasmid DNA using FuGENE 6 transfection reagent ( Roche , Basel , Switzerland ) in accordance with the manufacturer's instructions . Huh-7 , Huh7 . 5 . 1 , and Huh7-25 cells were transfected with plasmid DNA using TransIT LT1 transfection reagent ( Mirus , Madison , WI ) . The assay was performed in a humid chamber at 37°C according to the manufacturer's instructions ( Olink Bioscience , Uppsala , Sweden ) . Transfected 293T cells were grown on glass coverslips . Two days after transfection , cells were fixed with 4% paraformaldehyde in phosphate-buffered saline ( PBS ) for 20 min , then blocked and permeabilized with 0 . 3% Triton X-100 in a nonfat milk solution ( Block Ace; Snow Brand Milk Products Co . , Sapporo , Japan ) for 60 min at room temperature . Then the samples were incubated with a mixture of mouse anti-FLAG monoclonal antibody M2 and rabbit anti-V5 polyclonal antibody for 60 min , washed three times , and incubated with plus and minus PLA probes . After washing , the ligation mixture containing connector oligonucleotide was added for 30 min . The washing step was repeated , and amplification mixture containing fluorescently labeled DNA probe was added for 100 min . Finally , the samples were washed and mounted with DAPI mounting medium . The signal representing interaction was analyzed by Leica TCS SPE confocal microscope . The assay was performed according to the manufacturer's instructions ( CoralHue Fluo-chase Kit; MBL ) . 293T cells were transfected by a pair of mKG fusion constructs . Twenty-four hours after transfection , cell were fixed and stained with DAPI . The signal representing interaction was analyzed by Leica TCS SPE confocal microscope . The siRNAs were purchased from Sigma-Aldrich ( St . Louis , MO ) and were introduced into the cells at a final concentration of 10 to 30 nM using Lipofectamine RNAiMAX ( Invitrogen ) . Target sequences of the siRNAs were as follows: SPCS1 #1 ( 5′-CAGUUCGGGUGGACUGUCU-3′ ) , SPCS1 #2 ( 5′-GCAAUAGUUGGAUUUAUCU-3′ ) , SPCS1 #3 ( 5′-GAUGUUUCAGGGAAUUAUU-3′ ) , SPCS1 #4 ( 5′-GUUAUGGCCGGAUUUGCUU-3′ ) , claudin-1 ( 5′-CAGUCAAUGCCAGGUACGA-3′ ) , PI4K ( 5′-GCAAUGUGCUUCGCGAGAA-3′ ) and scrambled negative control ( 5′-GCAAGGGAAACCGUGUAAU-3′ ) . Additional control siRNAs for SPCS1 were as follows: C911-#2 ( 5′-GCAAUAGUaccAUUUAUCU-3′ ) , C911-#3 ( 5′-GAUGUUUCuccGAAUUAUU-3′ ) and C911-#4 ( 5′-GUUAUGGCgccAUUUGCUU-3′ ) . Bases 9 through 11 of the siRNAs replaced with their complements were shown in lower cases . Huh-7 cells were transfected with pSilencer-SPCS1 , and drug-resistant clones were selected by treatment with hygromycin B ( Wako , Tokyo , Japan ) at a final concentration of 500 µg/ml for 4 weeks . HCVtcp and HCVcc derived from JFH-1 having adaptive mutations in E2 ( N417S ) , p7 ( N765D ) , and NS2 ( Q1012R ) were generated as described previously [33] . The rAT strain of JEV [46] was used to generate virus stock . Mouse monoclonal antibodies against actin ( AC-15 ) and FLAG ( M2 ) were obtained from Sigma-Aldrich ( St . Louis , MO ) . Mouse monoclonal antibodies against flavivirus group antigen ( D1-4G2 ) were obtained from Millipore ( Billerica , MA ) . Rabbit polyclonal antibodies against FLAG and V5 were obtained from Sigma-Aldrich . Rabbit polyclonal antibodies against SPCS1 , claudin-1 , PI4K and myc were obtained from Proteintech ( Chicago , IL ) , Life Technologies ( Carlsbad , CA ) , Cell Signaling ( Danvers , MA ) and Santa Cruz Biotechnology ( Santa Cruz , CA ) , respectively . An anti-apoE goat polyclonal antibody was obtained from Millipore . Rabbit polyclonal antibodies against NS2 and NS3 were generated with synthetic peptides as antigens . Mouse monoclonal antibodies against HCV Core ( 2H9 ) and E2 ( 8D10-3 ) and rabbit polyclonal antibodies against NS5A and JEV are described elsewhere [47] . To determine the titers of HCVcc , Huh7 . 5 . 1 cells in 96-well plates were incubated with serially-diluted virus samples and then replaced with media containing 10% FBS and 0 . 8% carboxymethyl cellulose . Following incubation for 72 h , the monolayers were fixed and immunostained with the anti-NS5A antibody , followed by an Alexa Fluor 488-conjugated anti-rabbit secondary antibody ( Invitrogen ) . Stained foci were counted and used to calculate the titers of focus-forming units ( FFU ) /ml . For intracellular infectivity of HCVcc , the pellets of infected cells were resuspended in culture medium and were lysed by four freeze-thaw cycles . After centrifugation for 5 min at 4 , 000 rpm , supernatants were collected and used for virus titration as above . For titration of JEV , Huh7 . 5 . 1 cells were incubated with serially-diluted virus samples and then replaced with media containing 10% FBS and 0 . 8% carboxymethyl cellulose . After a 24 h incubation , the monolayers were fixed and immunostained with a mouse monoclonal anti-flavivirus group antibody ( D1-4G2 ) , followed by an Alexa Fluor 488-conjugated anti-mouse secondary antibody ( Invitrogen ) . Transfected cells were washed with ice-cold PBS , and suspended in lysis buffer ( 20 mM Tris-HCl [pH 7 . 4] containing 135 mM NaCl , 1% TritonX-100 , and 10% glycerol ) supplemented with 50 mM NaF , 5 mM Na3VO4 , and complete protease inhibitor cocktail , EDTA free ( Roche ) . Cell lysates were sonicated for 10 min and then incubated for 30 min at 4°C , followed by centrifugation at 14 , 000× g for 10 min . The supernatants were immunoprecipitated with anti-Myc-agarose beads ( sc-40 , Santa Cruz Biotechnology ) or anti-FLAG antibody in the presence of Dynabeads Protein G ( Invitrogen ) . The immunocomplexes were precipitated with the beads by centrifugation at 800× g for 30 s , or by applying a magnetic field , and then were washed four times with the lysis buffer . The proteins binding to the beads were boiled with SDS sample buffer and then subjected to SDS–polyacrylamide gel electrophoresis ( PAGE ) . Transfected cells were washed with PBS and lysed with 50 mM Tris-HCl , pH 7 . 4 , 300 mM NaCl , 1% Triton X-100 . Lysates were then sonicated for 10 min and added to the same volume of SDS sample buffer . The protein samples were boiled for 10 min , separated by SDS-PAGE , and transferred to polyvinylidene difluoride membranes ( Millipore ) . After blocking , the membranes were probed with the primary antibodies , followed by incubation with peroxidase-conjugated secondary antibody . Antigen-antibody complexes were visualized by an enhanced chemiluminescence detection system ( Super Signal West Pico Chemiluminescent Substrate; PIERCE , Rockford , IL ) according to the manufacturer's protocol and were detected by an LAS-3000 image analyzer system ( Fujifilm , Tokyo , Japan ) . To determine the human albumin level secreted from cells , culture supernatants were collected and passed through a 0 . 45-µm pore filter to remove cellular debris . The amounts of human albumin were quantified using a human albumin ELISA kit ( Bethyl Laboratories , Montgomery , TX ) according to the manufacturer's protocol . | Viruses hijack host cells and utilize host-derived proteins for viral propagation . In the case of hepatitis C virus ( HCV ) , many host factors have been identified that are required for genome replication; however , only a little is known about cellular proteins that interact with HCV proteins and are important for the viral assembly process . The C-terminal half of nonstructural protein 2 ( NS2 ) , and the N-terminal third of NS3 , form the NS2-3 protease that cleaves the NS2/3 junction . NS2 also plays a key role in the viral assembly process independently of the protease activity . We performed split-ubiquitin yeast two-hybrid screening and identified signal peptidase complex subunit 1 ( SPCS1 ) , which is a subunit of the microsomal signal peptidase complex . In this study , we provide evidence that SPCS1 interacts with both NS2 and E2 , resulting in E2-SPCS1-NS2 complex formation , and has a critical role in the assembly of infectious HCV particles . To our knowledge , SPCS1 is the first NS2-interacting cellular factor that is involved in regulation of the HCV lifecycle . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"biology"
] | 2013 | Signal Peptidase Complex Subunit 1 Participates in the Assembly of Hepatitis C Virus through an Interaction with E2 and NS2 |
In the visual system , large ensembles of neurons collectively sample visual space with receptive fields ( RFs ) . A puzzling problem is how neural ensembles provide a uniform , high-resolution visual representation in spite of irregularities in the RFs of individual cells . This problem was approached by simultaneously mapping the RFs of hundreds of primate retinal ganglion cells . As observed in previous studies , RFs exhibited irregular shapes that deviated from standard Gaussian models . Surprisingly , these irregularities were coordinated at a fine spatial scale: RFs interlocked with their neighbors , filling in gaps and avoiding large variations in overlap . RF shapes were coordinated with high spatial precision: the observed uniformity was degraded by angular perturbations as small as 15° , and the observed populations sampled visual space with more than 50% of the theoretical ideal uniformity . These results show that the primate retina encodes light with an exquisitely coordinated array of RF shapes , illustrating a higher degree of functional precision in the neural circuitry than previously appreciated .
In primates , high-resolution visual information is encoded by the magnocellular and parvocellular pathways , which respectively originate in the retina as populations of parasol and midget retinal ganglion cells ( RGCs ) [1] . These populations are expected to represent the visual scene efficiently and completely . Contrary to this expectation , indirect evidence suggests that the receptive fields ( RFs ) of individual parasol and midget cells have irregular and inconsistent shapes [2–5] , and thus that the visual representation may be patchy , with inhomogeneous gaps and overlap [3] . The problem of uniformly sampling visual space has an intriguing conceptual correlate , and potential solution , in the anatomical literature: in certain ganglion cell types such as primate midget cells , dendritic fields ( DFs ) are coordinated to uniformly cover the physical surface of the retina [6–8] . However , despite a rough alignment of RF and DF shapes [4 , 9 , 10] , it is not clear whether these shapes match at a fine spatial scale [3] . Thus the coordination of DFs may or may not produce coordination of RFs . Moreover , the DFs of primate parasol cells overlap substantially , with no obvious signs of coordination of their spatial extent [11] . We set out to test whether and how RGCs in the high-resolution visual pathways of primates are coordinated to transmit a uniformly sampled image to the brain .
To measure directly how ganglion cell populations sample visual space , large-scale simultaneous recordings were obtained from hundreds of identified neurons in patches of peripheral primate retina [12 , 13] . Stable recordings over several hours allowed RFs to be mapped at a fine spatial scale . Because hundreds of cells were recorded simultaneously , they could be grouped into clear functional classes defined by physiological properties such as latency , light response polarity , and spike train autocorrelation ( see Materials and Methods ) [13–15] . These properties , combined with the density of each functional class , were used to identify the distinct classes as on and off parasol and on and off midget cells , morphologically distinct cell types with distinct projection patterns in the brain . Frequently , every cell of a type was recorded in a local region [15] , presenting a unique opportunity for the study of collective encoding . Parasol and midget cell RF shapes strongly deviated from the theoretical ideal of a smooth surface defined by a difference of Gaussians [16] . In particular , RF shapes exhibited fine structure and irregular outlines , with shapes and sizes varying significantly from cell to cell ( Figure 1A–1E ) , consistent with previous studies [2–5] . The observed irregularity of individual RFs suggested that the collective visual coverage by each cell type might be uneven and irregular , potentially posing a problem for high-resolution vision . Examination of the entire population , however , revealed an elegant resolution to the problem of irregular RF shapes: RFs were coordinated , interlocking to sample visual space more uniformly ( Figures 1F and 2 ) . To visualize this coordination , each RF was summarized by interpolating a contour line at a single sensitivity level , similar to an iso-elevation line on a topographical map . For each cell type , a single contour level was selected that , on average , assigned each spatial location to a single cell . The contour lines for all RFs of a single type were then plotted together to illustrate the structure of the collective visual sampling . As expected from previous anatomical and physiological studies , the locations of the RFs of each cell type formed an approximately regularly spaced lattice ( Figures 1F and 2 ) [7 , 11 , 13 , 14] . Surprisingly , however , RFs showed a striking tendency toward coordinated structure: irregular outlines of neighboring cells complemented each other , interlocking like jigsaw puzzle pieces . RFs were precisely coordinated in all four major cell types ( Figure 2 ) . There appeared to be no coordination between cells of different types , emphasizing the importance of clearly distinguishing one cell type from another when studying sensory encoding by a neural population . The observed coordination of RFs produced more uniform visual sampling than expected by chance , as demonstrated using a geometric test . The null hypothesis was that visual sampling is no more uniform than expected from random interaction between irregular RF shapes , where “irregular” is defined as deviation from circular symmetry . Under this null hypothesis , mirroring each RF around its center point should not affect sampling uniformity [6] . To test this hypothesis , the arrangement of simultaneously recorded RFs of a single type ( Figure 3A , first column ) was compared to the arrangement obtained after each RF was artificially mirrored ( Figure 3A , second column ) . Visual inspection showed that mirroring severely disrupted visual coverage: the area covered by exactly one RF contour ( gray ) was significantly reduced , and there were many more gaps ( black ) and overlaps ( white ) . Thus RF shapes were not arranged randomly , but rather were coordinated in a way that provides more uniform coverage of visual space . The spatial features of RFs that are important for uniform visual coverage were not captured by the most common and accurate idealized RF model , an elliptical difference of Gaussians [5 , 14 , 17] . This was demonstrated using a second test in which the null hypothesis is that deviations from elliptical ( rather than circular ) symmetry do not produce more uniform coverage . The hypothesis was tested by rotating each RF by 180° around its center point , a perturbation that leaves elliptical shapes intact but disrupts any coordination between the nonelliptical structure of adjacent cells . Rotating each RF substantially disrupted coverage ( Figure 3A , first and third columns ) , rejecting the null hypothesis . Thus , although RF shapes exhibited apparently “noisy” deviations from smooth elliptical models , these deviations were coordinated to produce a more uniform sampling of visual space ( see Figure 4 , and below ) . The above observations were confirmed quantitatively using a numerical measure of the regularity of visual coverage: the uniformity index ( UI ) ( Figure 3B ) . For a collection of RFs represented by a single contour level , the UI is the proportion of visual space covered by exactly one contour , computed only in regions where all cells of a type were apparently recorded ( see Materials and Methods ) . Graphically , the UI represents the fraction of space in Figure 3A that is colored gray . Higher UI values indicate more uniform coverage; if RF shapes interlocked perfectly , the UI would equal 1 . Scatter plots in Figure 3B show that the UI was always reduced when RFs were mirrored or rotated by 180° , confirming the visual inspection of perturbed RFs . This finding was not affected by setting the threshold level defining RF contours substantially above or below its optimal value ( see Materials and Methods ) . The striking coordination of RF structure suggested that retinal circuits may sample the visual scene with high precision , perhaps in a manner that approaches the optimum for high-resolution vision . To measure the precision of interlocking , RFs were artificially perturbed , and the minimal perturbation that significantly disrupted visual coverage was identified ( Figure 4A–4D ) . Rotating RF shapes around their respective center points by as little as 15° led to a significant reduction in the UI . The minimal angle was similar for both parasol and midget cells , showing that in both populations , the arrangement of RF shapes is exquisitely tuned to sample visual space more uniformly . Cell to cell variability contributed importantly to uniform coverage . When each RF in the observed population was replaced with the average RF , the UI was substantially reduced ( Figure 4E , “observed” vs . “average RF” ) . This observation suggests that uniform sampling is more important for visual encoding than homogeneous RF shapes . The observed RFs approached an optimal arrangement . This was demonstrated by comparing the UIs of various simulated populations . RFs are commonly modeled as a hexagonal lattice of identical circular difference of Gaussians functions . Because of regular spacing and regular shapes , this idealization produces a very uniform sampling ( Figure 4E , “Gaussians on hexagonal lattice” ) . When the smooth ideal RFs were replaced with the observed irregular and variable RFs , uniformity dropped substantially ( Figure 4E , “RFs on hexagonal lattice” ) . When these RFs were placed on the observed quasiregular lattice , uniformity fell further ( Figure 4E , “scrambled RFs” ) . Thus , as expected , uncoordinated irregular RF shapes can degrade the uniformity of visual sampling [3] . With coordination , however , uniformity increased substantially . Compared to the baseline of uncoordinated RFs ( Figure 4E , “scrambled RFs” ) , the observed RFs exhibited uniformity 53% of the optimum given by perfectly interlocking shapes ( Figure 4E , “interlocking polygons” ) . Thus , the coordination of retinal RFs produces a substantially more uniform visual representation than would occur if RFs were independently formed .
The present results demonstrate that the visual representation in the primate retina is finely coordinated to achieve a homogeneous sampling of visual space . This finding has several important implications for retinal circuitry , retinal development , and the precision of neural population codes . The discovery of RF coordination is distinct from previous studies of RF overlap . Those studies focused on the average degree of overlap between neighboring RFs within a population [15 , 18–21] . Complementing the empirical measurements , several studies have suggested that the observed average overlap may be nearly optimal ( e . g . , [18 , 21–23] ) . For example , it has been suggested that the observed spacing of RGC RFs can produce a relatively uniform sensory surface [18] , without excessive spatial pooling [21] , and may maximize the information transmitted from the retina to the brain about natural scenes [21] . It should be noted that the present recordings exhibited RF overlap consistent with previous studies ( e . g . , [15] ) . However , the overlap was not apparent in the figures because the contour visualization focused on RF shape alone . Overlapping RFs may be important for representing fine spatial detail [21] , but RF overlap was not the primary focus of the present study . Instead , the present study focused on the coordinated fine structure of individual RFs relative to their neighbors , a property that is independent of the average overlap . The coordination of RF shapes produces more consistent sampling of visual space , contributing to the uniformity of the visual representation irrespective of the average overlap . Although there is undoubtedly a link between this uniformity and the neural representation of visual space , new theoretical frameworks will need to be developed to assess exactly how RF coordination improves visual encoding . What retinal mechanisms precisely coordinate RF shapes ? One study comparing the RF and DF shapes of individual rabbit ganglion cells suggested that DFs do not determine the fine structure of RFs [3] . However , it is possible that primate parasol and midget cells exhibit a different relationship between RF and DF . In fact , the interlocking midget cell RFs observed here exhibited deformations similar in size and shape to the interlocking midget cell DFs observed previously [7] . This suggests that , at least among midget cells , RF shapes might match DF shapes at a fine scale . Although parasol cell DFs have too much overlap to interlock in this fashion [11] , interactions among dendrites of neighboring parasol cells could contribute to complementary RF shapes . Thus it will be interesting for future studies to determine how the relationship between RF and DF varies among different cell types , particularly those in which RFs interlock with neighbors . Alternatively , the precise coordination of RF shapes could rely primarily on the layers of circuitry that connect photoreceptors to ganglion cells [24] . For example , the regular lattice of bipolar cells [25–27] might contact ganglion cells in a partly exclusive fashion , so that two neighboring ganglion cells would not both receive strong input from the same bipolar cell [27 , 29] . This hypothesis is supported by the finding that the spatial arrangement of bipolar cell synapses onto ganglion cells is highly variable [30 , 31] , consistent with each ganglion cell requiring a unique pattern of bipolar cell inputs to achieve coordination with its neighbors . Another possibility is that inhibitory amacrine cell inputs reduce the sensitivity of RF lobes that would otherwise jut too far into a neighboring RF [32] . In any case , the discovery of RF coordination provides a guiding framework for understanding the retinal circuit elements that determine the fine features of RF shape . How might RF coordination arise during development ? Given the diversity of circuit elements that must be arranged to precisely align interlocking RF shapes , one possibility is that RF shapes arise from plasticity driven by visual input [33 , 34] . Under this hypothesis , the mechanisms that modify retinal circuitry would be sensitive to the coordination of visual signals in neighboring RFs , as distinct from anatomical growth cues or patterns of spontaneous activity [35] . It may be interesting to test this hypothesis by investigating how early in development RF shapes appear to be coordinated and how the coordination is affected by rearing in light- or form-deprived visual environments [36] . The present results have surprising implications for how populations of neurons produce an efficient and complete representation . Recorded in isolation , single neurons frequently exhibit irregular response properties , suggesting that large populations must rely on averaging or interpolation to produce accurate sensory performance or behavior ( e . g . , see [37–39] ) . The present results , however , show that in a complete population , irregular features can be integral to a finely coordinated population code . This suggests that the nervous system operates with a higher degree of precision than previously thought , and that irregularities in individual cells may actually reflect an unappreciated aspect of neural population codes ( e . g . , [40] ) .
In accordance with institutional guidelines , retinas were obtained from deeply anesthetized monkeys ( Macaca mulatta , Macaca fascicularis ) being euthanized for other experimental procedures , as described previously [15] . Eyes were enucleated and hemisected , and the vitreous was removed in room light . Retinas remained attached to the pigment epithelium and were incubated in the dark for at least 30 min prior to recording . For recording , patches of peripheral retina 3–5 mm in diameter and 6–12 mm from the fovea were isolated from the pigment epithelium and held flat against a planar array of 512 extracellular recording electrodes . The preparation was perfused with oxygenated and bicarbonate-buffered AMES medium ( Sigma; [pH 7 . 4] 32–35 °C ) . The visual stimulus was produced using the optically reduced image of a computer display focused on the photoreceptors . Voltage data for each electrode were digitized at 20 kHz . Offline , spike waveforms were sorted into clusters in a multistep procedure [15] , and clusters with a minimum refractory period between spikes were identified as single neurons . All analyses were performed using custom software . RFs were mapped by computing the spike-triggered average ( STA ) stimulus obtained in the presence of a white noise stimulus . Features of the STA were parameterized using a separable model consisting of a two-dimensional difference of Gaussians spatial profile , a biphasic time course , and a spectral profile . For analysis of RF shape , the spatial component of the STA was extracted using singular value decomposition ( SVD ) across time ( see below ) . Light sensitivity was normalized across neurons by regressing the RF against an elliptical single Gaussian fit with a peak of 1 . RFs were low-pass filtered by convolving with a two-dimensional Gaussian function . The standard deviation ( SD ) of the Gaussian was typically 0 . 3 to 0 . 9 pixels . After spatial smoothing , contour lines were linearly interpolated in each RF . For each cell type examined , a common contour level was applied to all cells and adjusted to maximize the UI . The UI was equal to the proportion of space covered by exactly one contour ( excluding both gaps and overlaps ) . It was computed only within the area in which all cells appeared to have been recorded , as defined by an automated procedure ( see below ) . The contour level that maximized the UI tended to produce RF contours that just touched their neighbors , providing the greatest amount of information about whether RF shapes were complementary . If a contour level significantly higher or lower had been used , the area covered by exactly one cell would have been very small , yielding little or no information about the coordination of RF shapes . For analysis of mirrored or rotated RFs , the contour level used for the UI calculation was reoptimized after transforming the RFs . Simulated RFs ( Figure 4E ) were defined in spatial bins the same size as the pixels of the measured RFs , and independent Gaussian noise was added to each bin to match the noise in measured RFs . For reshuffling of measured RFs , each RF was translated to its new location without additional noise . The UI of simulated RFs was computed using the same procedure as for the measured RFs . In particular , the contour level used for the UI calculation was reoptimized after RFs were altered . Within each pixel of the flickering checkerboard stimulus , the red , green , and blue monitor primaries were modulated based on random draws from a binary distribution , chosen independently in space and time . Pixel sidelengths ranged from 18 to 60 μm ( on the retina ) and the color changed every 8 . 33 to 50 ms . For each neuron , the spatiotemporal RF was estimated by computing the STA stimulus over the 250 ms preceding a spike . This RF included the contribution of both center and surround . Although the surround time course was delayed compared to the center time course , the stimulus update temporal period was sufficiently long that this delay typically did not significantly influence the spatial RF estimate . Some RFs analyzed here were originally recorded for use in other studies , but could also be used to study RF shape coordination . A recording was used only if it satisfied two criteria: ( 1 ) the pixels were small enough to resolve the fine shape of each RF ( generally at least four to five pixels per RF diameter ) , and ( 2 ) the measurements of RFs exhibited low noise . These criteria were met by stimuli of various spatial and temporal scales , producing a large range of stimulus parameters in the dataset . The color value of each pixel was chosen from a binary distribution rather than a Gaussian distribution to more rapidly characterize RF shape . The small pixel sizes used produced a relatively low effective contrast , and thus responses were approximately linear . In the linear regime , a binary distribution produces an unbiased estimate of RF shape [41] . Each space–time STA described both the RF shape and kinetics . To extract only the spatial component , the STA was approximated by a space–time separable function . First , the STA was put into a matrix M in which each row was the time course of a single pixel . The singular value decomposition ( SVD ) was performed , yielding a standard decomposition M = UDVt , where U and V are orthogonal matrices and D is diagonal . The first column of U contained the primary spatial component of the STA . Visual inspection showed that this spatial component resembled individual frames of the STA movie in which the spatial structure of the RF was most clear . The UI was computed for the mosaic formed by each cell type in two steps . The first step identified regions in which all cells appeared to have been recorded . The second step revealed the contour level that maximized the area covered by exactly one cell . The details of these steps and justifications are described below . In some preparations , a complete lattice of RFs was recorded ( e . g . , Figure 2A ) , whereas in other preparations , many RFs appeared to be missing ( e . g . , Figure 2D ) . This variability was likely due to mechanical factors , such as contact with the electrode array . A lattice of RFs was only included in the analysis if sufficiently many RFs appeared to have been recorded , usually about 50%; and in each preparation , only regions with contiguous RFs were used for quantitative analysis . If RFs were precisely uniformly spaced , contiguous regions would be easy to identify . However , RFs exhibited somewhat variable spacing , and thus a multi-step algorithm was required to exclude regions of space not covered by recorded cells . First , the entire recorded region was subdivided into triangular areas using the Delaunay triangulation [15 , 42] of the collection of Gaussian fit center points . The area within a triangle was considered to be covered by contiguous RFs only if the cells at its vertices were sufficiently close together , with the cutoff distance equal to 1 . 9 times the median nearest neighbor spacing . The parameters of this algorithm were chosen based on the statistics of RF mosaics , as described below . The median was used to estimate the typical nearest-neighbor spacing because it is relatively robust to outlying points . In a complete mosaic , nearest-neighbor distances follow an approximately Gaussian distribution . In an incomplete mosaic , however , nearest-neighbor distances will form a distribution composed of a Gaussian plus a long tail that corresponds to cells whose nearest neighbors were not recorded . Thus the median was the most robust estimate of nearest-neighbor spacing . The scale factor , 1 . 9 , was chosen empirically to match the observed variability of cell spacing . Figure 5 demonstrates the procedure that was used to arrive at this number . To simulate an observed mosaic with missing cells , a complete mosaic ( Figure 5A ) was randomly subsampled . For each subsampled mosaic , a range of scale factors was used in the algorithm to identify regions of contiguous cells . Figure 5D–5F shows an 85% subsampled mosaic in which scale factors of 1 . 5 , 1 . 9 , and 2 . 3 were used , respectively . The areas identified as containing contiguous cells are colored in blue . For a low scale factor ( Figure 5D ) , the colored region is clearly too small; several groups of contiguous cells were missed . For a high scale factor ( Figure 5F ) , the colored region is too large , and includes several gaps where cells were not recorded . An intermediate scale factor of 1 . 9 appears to describe the region of contiguous cells most accurately ( Figure 5E ) . Quantifying this trend revealed that a scale factor of 1 . 9 was optimal . For each subsampled mosaic , the region of truly contiguous cells was identified by testing which cells were missing from the original , complete mosaic . As an example , Figure 5G shows the region of truly contiguous cells for the mosaic shown in Figure 5A . For the subsampled mosaic shown in Figure 5D–5F , the region of truly contiguous cells is shown in Figure 5H in red and purple . For each scale factor , the estimated region of contiguous cells was compared to the actual region , thus creating three areas: the area correctly identified as containing contiguous cells , the area of false negatives , and the area of false positives . Figure 5H shows these three areas for the scale factor value 1 . 9 . The relative sizes of the three areas were summarized by taking the correct area minus the sum of the error areas . Figure 5I shows this summary value for several scale factors , ranging from 1 . 5 to 2 . 3 . Data were pooled from mosaics subsampled at 100% , 85% , 70% , and 55% . Higher numbers represent more accurate identification of contiguous regions , and the curve shows that scale factors of 1 . 8 , 1 . 9 , and 2 . 0 are approximately equally effective . Thus a value of 1 . 9 was chosen to robustly identify regions of contiguous cells . Only regions of contiguous cells were used for subsequent analysis . The degree of RF interlocking , and thus the uniformity of RF coverage , was measured by considering how precisely RF shapes fit together . To efficiently describe the RF shapes , each RF was represented by a single contour level at which neighboring cells just touched . To avoid bias , this was done automatically by finding a single contour level for all cells that maximized the total area covered by exactly one cell . To visualize why this was the most informative contour level , Figure 6 shows a collection of on parasol RFs at a variety of contour levels . For low contours ( left column , upper rows ) , each RF contour was large , and the contours overlapped so much that RF shape interactions were difficult to distinguish . For high contours ( left column , lower rows ) , each RF was too small for neighbor relationships to be revealed . The contour level that provided the most information about RF shape interlocking was the level at which RFs on average just touched their neighbors ( 0 . 36 , left column , center row ) , and equivalently , the level that maximized the area covered by exactly one cell ( scatter plot ) . The optimal contour level was different for each preparation and varied from 0 . 16 to 0 . 40 ( median 0 . 24 ) . Note that the absolute contour value reflects several factors , including the amount of noise in the measurement ( and thus the duration of the recording ) , the degree of blurring that was applied , and the degree of RF overlap . The observation of RF coordination was not sensitive to the particular contour level chosen , as illustrated in Figure 6 . Over a broad range of contour levels , the area covered by one cell always declined when RFs were mirrored or rotated ( bottom plot ) . At extreme contour levels , however , there was no effect of mirroring or rotation , because very high or low contours do not reveal the interlocking of RF shapes ( upper and lower rows ) . Error bars for the measured UI were produced as follows . Within each Delaunay triangle , a local UI was computed as the area covered by exactly one cell within the triangle . The overall reported UI was computed across the area occupied by all triangles . Error bars were equal to the standard error of the mean ( SEM ) of a subset of the local UIs . The subset was chosen so that no two triangles shared an edge , ensuring that local correlations in the UI did not artificially reduce the SEM . The SEM was validated using a bootstrap simulation . The simulation concluded that the SEM was a conservative estimate , typically overestimating the standard deviation of the UI by 30% to 60% . Each RF was rotated about its Gaussian fit center point by the same angle; and for each such rotation , the contour level was chosen to maximize the UI . After generating rotated contours , the following procedure was applied to each cell type . First , data were pooled from all preparations in which sufficiently many cells of that type were recorded . A subset of the Delaunay triangles was chosen in each preparation as described above , and the local UI values of these triangles were averaged from all datasets to determine the mean UI at each rotation angle ( including 0° ) , with error bars equal to the SEM . For each nonzero rotation angle , a one-tailed two-sample t-test was used to determine whether the UI was significantly lower ( p < 0 . 01 ) than the UI of the unrotated RFs . Mirror test . Each RF was mirrored about an axis passing through the Gaussian fit center point . The angle of the mirror axis was chosen using a procedure that ensured it was both arbitrary and unique . For visualization ( Figure 3A ) , the axis was parallel to the short edge of the boundary of the region shown . For quantitative analysis ( Figure 3B ) , the axis was parallel to the short edge of the recording array . Interlocking polygons . Voronoi domains were computed based on the center points of measured RFs . Each RF was shaped like the Voronoi domain with amplitude following a Gaussian taper matched to the taper of the observed RFs . Gaussians on a hexagonal lattice . Lattice spacing was equal to the median nearest-neighbor spacing of the measured RFs . Each RF was a circular difference of Gaussians function with center radius equal to 0 . 5 times the lattice spacing , surround radius equal to twice the center radius , and surround amplitude equal to 0 . 2 times the center amplitude . RFs on a hexagonal lattice . Cell centers were located on a regular hexagonal lattice , and the cell at each location was a randomly chosen RF from the original population . Average RF . Each RF was replaced by the average RF with noise added to match the noise in observed RFs . Scrambled RFs . Each RF was replaced by a randomly chosen RF from the same preparation . The lattice of RF center locations was held constant . | All visual information reaching the brain is transmitted by retinal ganglion cells , each of which is sensitive to a small region of space known as its receptive field . Each of the 20 or so distinct ganglion cell types is thought to transmit a complete visual image to the brain , because the receptive fields of each type form a regular lattice covering visual space . However , within each regular lattice , individual receptive fields have jagged , asymmetric shapes , which could produce “blind spots” and excessive overlap , degrading the visual image . To understand how the visual system overcomes this problem , we used a multielectrode array to record from hundreds of ganglion cells in isolated patches of peripheral primate retina . Surprisingly , we found that irregularly shaped receptive fields fit together like puzzle pieces , with high spatial precision , producing a more homogeneous coverage of visual space than would be possible otherwise . This finding reveals that the representation of visual space by neural ensembles in the retina is functionally coordinated and tuned , presumably by developmental interactions or ongoing visual activity , producing a more precise sensory signal . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience"
] | 2009 | Receptive Fields in Primate Retina Are Coordinated to Sample Visual Space More Uniformly |
Malaria , caused by Plasmodium parasite infection , continues to be one of the leading causes of worldwide morbidity and mortality . Development of an effective vaccine has been encumbered by the complex life cycle of the parasite that has distinct pre-erythrocytic and erythrocytic stages of infection in the mammalian host . Historically , malaria vaccine development efforts have targeted each stage in isolation . An ideal vaccine , however , would target multiple life cycle stages with multiple arms of the immune system and be capable of eliminating initial infection in the liver , the subsequent blood stage infection , and would prevent further parasite transmission . We have previously shown that immunization of mice with Plasmodium yoelii genetically attenuated parasites ( GAP ) that arrest late in liver stage development elicits stage-transcending protection against both a sporozoite challenge and a direct blood stage challenge . Here , we show that this immunization strategy engenders both T- and B-cell responses that are essential for stage-transcending protection , but the relative importance of each is determined by the host genetic background . Furthermore , potent anti-blood stage antibodies elicited after GAP immunization rely heavily on FC-mediated functions including complement fixation and FC receptor binding . These protective antibodies recognize the merozoite surface but do not appear to recognize the immunodominant merozoite surface protein-1 . The antigen ( s ) targeted by stage-transcending immunity are present in both the late liver stages and blood stage parasites . The data clearly show that GAP-engendered protective immune responses can target shared antigens of pre-erythrocytic and erythrocytic parasite life cycle stages . As such , this model constitutes a powerful tool to identify novel , protective and stage-transcending T and B cell targets for incorporation into a multi-stage subunit vaccine .
Unlike other infectious diseases , malaria parasites continue to defy the development of a protective vaccine . One main difference between pathogens currently amenable to vaccination and malaria parasites is the degree of complexity of the parasites causing malaria , Plasmodium spp . These eukaryotic parasites have complex genomes that control elaborate life cycles . They progress through multiple , antigenically distinct stages of replication and infection within mammalian hosts and mosquito vectors—making it difficult to target with traditional vaccination methods[1] . Infection is initiated when a parasitized Anopheles mosquito injects tens to hundreds of sporozoites into the dermis of the host . Sporozoites traverse through multiple host cell types in the dermis for minutes to hours until they traverse the vascular endothelium and into the circulation . The sporozoites are then carried into the sinusoids of the liver where they again traverse multiple cell types to reach and infect hepatocytes . This begins the clinically silent liver stage development of infection , during which each parasite undergoes many rounds of replication in a single hepatocyte and eventually forms tens of thousands of red blood cell-infectious exoerythrocytic merozoites . They are released in to the circulation and begin the asexual blood stage ( BS ) cycle whereby cyclic infection , replication within and lytic release from red blood cells ( RBCs ) occurs . This rapidly propagates the parasite and causes all malaria-associated morbidity and mortality as parasite numbers expand into the billions . A fraction of parasites terminally develop into gametocytes , which can be transmitted back to a mosquito during blood meal acquisition . To date , malaria vaccination strategies have largely focused on either the sporozoite and liver stages ( “pre-erythrocytic” , PE ) or BS of infection by targeting parasite antigens specific to each stage[2] . However , success has been limited with these stage-specific approaches , raising the question as to whether there should be a greater emphasis on multi-stage vaccination approaches . PE vaccines have the advantage of targeting a bottleneck in the parasite population with only tens to a few hundred sporozoites injected in the skin and even fewer successfully infecting the liver . In addition , PE infection is clinically silent and completely eliminating PE parasites ( termed “sterile protection” ) would prevent BS infection and thus both disease and transmission . Both humoral and cellular immune defenses can contribute to PE immunity . Antibodies against sporozoites can act in the skin to immobilize the parasite and can bind to sporozoites in circulation to prevent hepatocyte infection[3–7] . Once parasites are within hepatocytes , CD8 T cells can target the infected hepatocyte and kill it[8] . However , successful infection of the liver by even a single parasite can lead to fulminant BS infection . Indeed , the stringent requirement for both antibodies and T cells to eliminate 100% of PE parasites has contributed to the limited success of PE subunit vaccine candidates in clinical trials . The first malaria vaccine candidate to reach phase III clinical trials is RTS , S , which targets only the circumsporozoite protein ( CSP ) . RTS , s is capable of significantly reducing the cases of severe disease , but the long-term efficacy of RTS , S in eliciting protection is limited[9 , 10] . An alternative strategy for PE vaccination is immunization with live-attenuated sporozoites . Radiation-attenuated sporozoites ( RAS ) and sporozoites administered under chloroquine cover ( known as “infection treatment immunization” , ITI ) can confer 100% sterilizing PE protection in humans[11–13] Similarly , immunization with genetically attenuated parasites ( GAP ) has been shown to elicit complete sterile protection against PE infection in mice[14–16] . CD8 T cells are required for live-attenuated sporozoite protection in mice and also correlate with protection in non-human primates[17–22] . However , immunization with RAS requires the development of very large numbers of sporozoite-specific CD8 T cells for complete protection which in animal models needs to account for ~1% of the total CD8 repertoire[23] . While antibodies elicited by attenuated whole parasites are also able to strongly reduce liver infection , they have not been shown to be essential for protection[4 , 5 , 17 , 19 , 21 , 24 , 25] . The parasite BS have been the other major focus of malaria vaccine development efforts . However , attempts to create BS subunit vaccines have been stymied by suboptimal clinical performance perhaps due to the large degree of antigenic variation and polymorphisms within BS proteins and the high parasite burden as compared to the PE stages[26] , [27] . In contrast to the stage-specific approaches to malaria vaccine development , targeting PE and BS life cycle stages simultaneously may be more fruitful as PE immunity can reduce the number of developing liver stages , which in turn reduces the number of merozoites released from the liver—thus potentially making BS infection easier to control and eliminate by an immune response . However , there is scant evidence that such stage-transcending protection ( STP ) is possible and the antigens and immunological mechanisms potentially capable of mediating STP remain undefined . Thus , establishing an STP model and understanding the mechanisms required for potent immunity against multiple parasite stages could be critical in developing fully protective , multi-stage subunit malaria vaccines . Our previous work has indicated that late liver stage-arresting GAP confer STP[28] . Herein , we build upon this evidence to show that STP can be mediated by both T cells and by antibodies . Furthermore , protective antibodies predominantly rely on FC-mediated effector mechanisms and recognize potentially novel protective antigens shared between the late liver stages and BS parasites . Not only do our findings provide a rationale for the development of a late liver stage-arresting GAP as a vaccine candidate , but they also offer a platform to identify novel antigens and investigate the immune mechanisms mediating robust STP against PE stages and BS of Plasmodium .
P . yoelii ( Py ) fabb/f—parasites that are deficient in endogenous fatty acid biosynthesis undergo substantial liver stage growth , develop into late stage exoerythrocytic schizonts but fail to complete differentiation into exoerythrocytic merozoites[29] As a consequence , mice immunized with Pyfabb/f—parasites only experience PE infection and are not exposed to BS parasites . As such , they constitute late liver stage-arresting GAP ( LAGAP ) . Mice immunized with LAGAP are not only protected against sporozoite challenge but are also protected against direct intravenous challenge with Py-infected RBCs ( iRBCs ) [28] . However , the immune mechanisms elicited by PE LAGAP-immunization that control and eliminate BS infection remain to be elucidated . To assess the relative importance of antibodies and T cells in protection , BALB/cJ and C57BL/6 mice were immunized with LAGAP sporozoites , isolated from mosquito salivary glands , and given an intravenous ( iv ) challenge of 10 , 000 Py lethal strain iRBCs 25 days after the final immunization . While mock-immunized mice ( mice injected with uninfected mosquito salivary gland debris ) succumbed to hyperparasitemia within a week after challenge , LAGAP-immunized mice of both strains controlled parasitemia and cleared infection ( Fig 1A ) . Interestingly , C57BL/6 immunized mice controlled the BS infection more robustly than BALB/cJ mice , displaying a lower peak parasitemia ( ∼2% vs . ∼13% at day 8 after challenge Fig 1A ) . To examine the respective roles of antibodies ( Ab ) and T cells in protection , we depleted T cells using monoclonal Ab ( mAb ) specific for CD4 and CD8 24 hours prior to lethal iRBC challenge ( S1A and S1B Fig ) . Strikingly , only LAGAP-immunized C57BL/6 mice were able to control BS infection in the absence of T cells whereas BALB/cJ mice succumbed to hyperparasitemia similar to mock-immunized control mice ( Fig 1C ) . Thus , the immune mechanisms of STP depend on the mouse genetic background . Whereas humoral immunity is sufficient to protect C57BL/6 mice from a lethal BS challenge , T cells are required to protect LAGAP-immunized BALB/cJ mice . Given the stage-transcending immunity observed , we wanted to ensure that there was no self-limiting blood stage infection caused by breakthrough during LAGAP PE stage immunization , which could be inducing blood stage immunity . To do this , 250μL of pooled blood from C57BL/6 mice immunized with LAGAP sporozoites three days prior was injected into naïve C57BL/6 mice . None of the 5 recipient mice became blood stage patent . This contrasts with transfer of just 2 LAGAP blood stage parasites , which can result in patency of 100% of mice by day 7[29] . Thus , LAGAP PE immunization did not induce a submicroscopic blood stage infection that could be causing the observed blood stage immunity . To further examine if antibodies elicited by LAGAP immunization are required for protection against BS infection , we immunized C57BL/6 AID-/- mice , which possess B cells that are incapable of producing class-switched antibodies [30 , 31] . These mice developed a robust CD4+ and CD8+ T cell response to immunization as measured by markers of antigen-experienced cells in the peripheral blood[32] ( S2A and S2B Fig ) but failed to control a lethal BS challenge ( Fig 1C ) . Furthermore , passive transfer of wildtype C57BL/6 immune sera to BALB/cJ mice conferred protection against a lethal BS challenge in all mice ( Fig 1D ) . This confirms the ability of antibodies raised in C57BL/6 LAGAP-immunized mice to control BS infection and demonstrates that the difference in protection between the two strains is not explained by a higher susceptibility to infection in BALB/cJ mice . Thus , these data indicate that the antibodies elicited by LAGAP immunization of C57BL/6 mice are potent and essential for STP against BS infection . To further investigate the host strain-specific differences in STP , we characterized the titers of IgG antibodies against sporozoites and BSs in sera obtained from LAGAP-immunized mice of both strains . Both C57BL/6 and BALB/cJ had similar IgG responses to CSP after immunization ( Fig 2A ) , indicating that humoral immune responses to the major sporozoite surface protein are similar in both strains . Although both strains showed an increase in total IgG against BS parasites following LAGAP immunization , C57BL/6 mice had significantly higher titers than BALB/cJ mice ( Fig 2B ) . Taken together , these data show that both cell-mediated and humoral immunity protects against BS infection following LAGAP immunization . Furthermore , antibodies from LAGAP immunized C57BL/6 but not BALB/cJ mice are both sufficient and essential for STP . The observation that immunization of C57BL/6 mice produces higher antibody titers against BS proteins when compared to immunization of BALB/cJ mice might contribute to the superior protection against a lethal BS challenge observed in the former . Antibodies can function independent or dependent of the FC portion of antibodies . FC-independent mechanisms include interference with pathogen activities by steric hindrance or blocking of target proteins ( e . g . pathogen ligands for host cell infection ) . The FC-dependent mechanisms include complement-mediated lysis of the target pathogen and opsonization of the pathogen or pathogen-infected cell , flagging it for phagocytosis or destruction by FC-receptor ( FCR ) -bearing cells . To determine which mechanisms were playing a role in the antibody-mediated STP observed , we immunized C57BL/6 mice with LAGAP as before , depleted them of T cells and then additionally depleted complement via injection of cobra venom factor ( CVF ) . CVF is a C3 convertase , which rapidly and efficiently depletes complement within hours of administration for 3–5 days[33] ( S3A Fig ) . We injected 30 μg of CVF 6 hours prior to challenge and 4 days after challenge to ensure complement depletion throughout BS challenge . When LAGAP-immunized C57BL/6 mice were depleted of complement and T cells , only 40% survived a lethal blood stage challenge ( Fig 3A ) . In contrast , 100% of immunized C57BL/6 mice lacking T cells but not depleted of complement survived the same challenge ( Fig 1B ) . This indicates a strong role for complement-mediated destruction of opsonized parasites and/or iRBCs in the elimination of a BS infection in immunized mice . We also performed similar immunizations with C57BL/6 FCRγ-/- mice—which lack the γ-chain subunit of the FcγRI , FcγRIII and FcεRI receptors—to determine the role of FC-receptor binding in protection . These mice developed antibody titers against sporozoites and BS parasites that were comparable to wild type C57BL/6 mice ( S3B and S3C Fig ) . Yet , lack of FCR functions also resulted in a reduction of mouse survival after lethal BS challenge from 100% ( Fig 1B ) to 40% ( Fig 3B ) , implicating this effector pathway in LAGAP-elicited antibody-mediated protection . Elimination of all FC-dependent effector functions by CVF administration in immunized FCRγ-/- mice further reduced survival to 20% ( Fig 3C and 3D ) . This again indicates a strong role for FC-dependent antibody effector mechanisms in LAGAP-immunized mice . The survival of a small proportion of mice suggests that FC-independent basic neutralization of parasites by LAGAP-elicited antibodies is also contributing to protection , although this was minor when compared to FC-dependent protection . LAGAP elicit STP and antibodies play a pronounced role in this protective immunity . It has been shown previously that RAS and early liver stage-arresting GAP ( EAGAP ) do not elicit STP[14 , 34] . Thus , we predicted that the antibodies mediating STP are elicited by antigens expressed in late liver stage parasites and that these antigens are shared with BS parasites . To analyze the targets of STP , we investigated the stages of the parasite that are recognized by LAGAP-elicited antibodies using immunofluorescence assay ( IFA ) . As a control , we used serum collected from C57BL/6 mice immunized with the EAGAP , Pysap1- , which efficiently invades hepatocytes but is completely attenuated by 6h post infection[35] . Antibodies from both EAGAP and LAGAP-immunized mice recognized sporozoites with a circumferential surface-staining pattern , likely indicative of CSP recognition ( Fig 4A ) . Staining of liver stage parasites 24h post-infection with the same immune sera also showed a circumferential pattern for both sera ( Fig 4B ) . Interestingly , both immune sera showed little/no reactivity against 33h-old liver stage parasites ( Fig 4B ) . However , we observed pronounced differences in reactivity against 48h late liver stage parasites , a time when exoerythrocytic merozoites begin to differentiate . While there was little/no detectable reactivity with EAGAP immune serum ( Fig 4B ) , LAGAP immune serum showed robust reactivity that localized to the exoerythrocytic merozoite surface and to the parasitophorous vacuole membrane ( Fig 4B ) . We next performed IFA with immune sera on BS parasites to determine if the antibodies cross-reacted with these stages . While EAGAP immune serum had no detectable reactivity , LAGAP immune serum displayed an intense circumferential staining on merozoites that co-localized with MSP1 ( Fig 4B ) . Interestingly however , we did not detect antibodies against either the 19 or 42kD fragment of merozoite surface protein 1 ( MSP1 ) in LAGAP-immunized mice ( S4 Fig ) . This provides an unprecedented demonstration that LAGAP immunization elicits antibodies against the late liver stages and BSs , which are mostly reactive with merozoite surface determinants . This could constitute one major mechanism by which STP is achieved . Both LAGAP immunized BALB/cJ and C57BL/6 immunized mice produce antibodies that can recognize BS proteins by ELISA with C57BL/6 producing slightly higher titers ( Fig 2B ) . This quantitative difference , however , cannot explain the inferior protection afforded by antibodies in BALB/cJ as passive transfer of C57BL/6 immune serum to naive BALB/cJ mice results in antibody titers as low as actively immunized BALB/cJ mice ( S5 Fig ) , yet these passively immunized BALB/cJ mice are still protected against a BS challenge ( Fig 1D ) . Thus , the differential protection could be due to different BS antigens being recognized by antibodies from the two strains or , given the demonstrated role of FC-mediated functions , by differences in the isotype distribution of the antibodies . To determine if the antibodies produced by the two strains of mice differ qualitatively by either specificity or isotype , we performed IFAs on iRBCs using serum from both LAGAP-immunized BALB/c and C57BL/6 immunized mice and secondary antibodies representing different IgG isotypes . Immunized C57BL/6 mice produced IgG of both IgG1 and 2b isotypes which co-localized with MSP1 at the surface of exoerythrocytic merozoites ( Fig 5 ) . In contrast , IFAs using serum from LAGAP-immunized BALB/cJ mice showed antibodies that are primarily of the IgG2b isotype and recognized the parasite interior ( Fig 5 ) . Quantification of immune serum staining patterns in 65 iRBCs confirmed the dichotomy of C57BL/6 serum recognizing the periphery of schizonts , whereas BALB/c immune serum recognized the parasite interior ( Table 1 ) . Western blots probing BS lysates with immune sera also demonstrated a distinct set of proteins recognized by sera from C57BL/6 immunized mice that were not apparent in serum from BALB/cJ immunized mice ( S6 Fig ) . Thus , although immunized BALB/cJ mice produce anti-BS antibodies that were detectable by ELISA , IFA and Western blot , these antibodies offered no protection against a lethal BS challenge ( Fig 1C ) . Therefore , whereas LAGAP-immunized BALB/cJ mice produce non-protective anti-BS antibodies , LAGAP-immunized C57BL/6 mice produce antibodies that recognize a unique set of BS antigens capable of potent STP .
LAGAP are unique amongst all current malaria immunization strategies in that they are designed to arrest the immunizing parasites late in liver stage development , cause no exposure to BS parasites and yet protect against PE parasite- and BS parasite challenge [28] . Here , we show that STP is mediated by T cells and antibodies , with that the latter recognizing antigens shared between the late liver stage and BS parasites . Immunization strategies focusing on single stages of infection must either be 100% effective in preventing PE infection of the liver or they must overcome the significant antigenic diversity , immune evasion mechanisms and high parasite burden present during BS infection in order to prevent disease and transmission . Other whole sporozoite vaccination types such as RAS or EAGAP provide potent , antigenically diverse PE immunity but they require complete prevention of development of even a single liver stage parasite . Otherwise they would fail to confer protection . In contrast , LAGAPs that elicit STP can maintain efficacy in the face of potentially leaky PE protection and breakthrough BS infection if one or a few parasites escapes PE immunity . Here , we demonstrate for the first time that in addition to PE immunity , immunization with LAGAP invokes both protective cellular and humoral BS immune responses . This not only provides a platform for investigation of novel cross-protective antigens and immune mechanisms , but together with the robust PE immunity observed after LAGAP immunization[24] provides further rationale for development of LAGAP for potential use in human immunization . It has been previously shown that immunization with LAGAP elicits both robust cellular and humoral PE immunity[24 , 28] . Thus , it was reasonable to hypothesize that the observed STP against a BS challenge in LAGAP-immunized mice could be mediated by antibodies , CD4+ or CD8+ cells , as all have also been implicated in controlling BS infection[36–41] . Our data demonstrate that immunization with LAGAP elicits functional T cell responses to BS parasites that are essential for protection in BALB/cJ mice . Further studies using antibody-deficient mice on the BALB/cJ background would be required to determine which cell types are involved and if these cells are sufficient for protection in the absence of antibodies . Conversely , immunized AID-/- mice on the C57BL/6 background were unable to control a lethal BS challenge , pointing to antibodies as critical for protection . However , their parasitemia was curtailed and their time to death longer than WT controls , indicating a role for effector T cell immunity in this strain as well . CD8 T cells are widely recognized as essential effectors in eliminating liver stage parasites[8] , and their role in BS protection is becoming more evident[38 , 39 , 41] . Although data demonstrating a clear role for CD8 T cells in mediating blood stage immunity in humans is lacking , identification of the antigens recognized by CD8 T cells in LAGAP-immunized BALB/cJ mice might be useful as these antigens are potentially present in multiple stages , are protective targets and thus could be prime candidates for a cross-stage protective T cell subunit vaccine . In contrast to the increasingly appreciated role of T cells in BS immunity , antibodies have long been considered the main mechanism of protection against BS parasitemia and disease . This is based on early studies showing that passive transfer of convalescent serum from malaria-experienced individuals to unprotected individuals resulted in protection against BS disease [42 , 43] and high antibody titers against BS antigens correlate with reduction of morbidity and mortality in endemic areas [43 , 44] . However , whether or not this is mediated simply by antibody binding and impairment of merozoite activities , such as invasion , or mediated also by FC-dependent effector mechanisms still remains unclear . One study in mice using passive transfer of Py hyperimmune sera or an anti-MSP1 mAb to wildtype and FCRγ-/- mice suggested that FC-mediated mechanisms are dispensable[45] . However , additional studies in animal models and naturally immune individuals highlighted the importance of “cytophilic” antibodies ( IgG1 and IgG3 in humans , IgG2a/b in mice ) acting through FC-dependent functions for control of BS parasitemia[46–54] . In our current study , the antibodies engendered by LAGAP immunization are strongly dependent on FCR binding as the majority of immunized FCRγ-/- mice lost protection despite high levels of antibodies . FC-mediated complement fixation and destruction of antibody-bound iRBCs , merozoites or parasite proteins in immune complexes ( the “classical complement pathway” ) has been poorly defined . Only a few in vitro studies[55–57] have implicated complement fixation in the destruction of parasites while the one in vivo study conducted in non-human primates concluded that complement depletion via CVF had no impact on natural control of parasitemia[58 , 59] . In contrast , opsonized-iRBC phagocytosis by macrophages has been well documented and has been correlated with protection in naturally immune individuals[52 , 60] . Our data suggest a strong role for the classical complement pathway as immunized mice lacking complement showed a severe defect in controlling BS infection in the presence of LAGAP-induced antibodies . Importantly , localization data indicate that the antibodies do not preferentially bind the surface of iRBC but strongly react with the merozoite surface , suggesting that these antibodies bind and fix complement directly on the merozoite . This is in line with a previous study demonstrating that antibodies recognizing the merozoite protein SERA have enhanced inhibitory capacity in the presence of complement in vitro[57] . Complement fixation by opsonized merozoites could enhance parasite clearance by a number of mechanisms including direct killing via the membrane attack complex , recruitment of leukocytes via generation of anaphylatoxins ( C3a and C5a ) or by opsonization and subsequent phagocytosis of complement-bound parasites . Regardless of the mechanism , our data provide the first in vivo evidence of the functional importance of the classical complement pathway playing a major role in the control of blood stage parasitemia . We have previously speculated that the STP resulting from LAGAP immunization is targeting protective antigens that are shared between the late liver stages and BS parasites[28] . This arises from the observation that parasites arresting development early in the liver , such as RAS or EAGAP , do not afford STP[15 , 34] . Here , we provide direct evidence that it is indeed antigens shared between the late liver stages and BSs that are the targets of protection . IFAs using serum from mice immunized with an EAGAP ( Pysap1- , ) and the LAGAP show that immunization with both parasites elicits antibodies against sporozoites and liver stages up to 24 h post infection . This is consistent with the presence of CSP on the sporozoite- and liver stage surface and the abundant anti-CSP antibody titers in the immune sera . In contrast , only LAGAP-immune serum recognized late liver stages , exoerythrocytic merozoites and BS merozoites . Combined with our data showing that LAGAP-induced antibodies alone provide protection from iRBC challenge , this confirms that there are indeed yet to be identified antigens in late liver stage parasites and BS parasites capable of eliciting STP . Targeting these antigens by both T cells and antibodies ( i . e . with viral vectors ) could allow for multiple opportunities to eliminate the parasite in both the liver and blood if it is indeed the same antigens providing both PE and blood stage protection . Importantly , this immunity does not appear to target MSP119 or MSP142 , which although capable of conferring protection in mice , has failed to protect in clinical studies[26 , 27] . How exactly LAGAP antigens are acquired and presented by antigen presenting cells ( APC ) remains to be elucidated . Numerous types of cells in the liver are capable of antigen uptake and presentation including Kupffer cells ( KC , liver-resident macrophages ) , multiple types of dendritic cells ( DC ) , liver sinusoidal endothelial cells ( LSEC ) and even hepatocytes[61 , 62] . Hepatocytes only possess MHCI and can present parasite antigens[63] but lack MHCII and thus this cannot explain the antibody responses to late liver stages we observed . Even though LSEC are very efficient at presentation of exogenous antigen[62] , their ability to generate the type of mature , class-switched IgG response seen in LAGAP immunization has not been established[61] . A likely scenario is that as the LAGAP parasite dies late in liver stage development , the hepatocyte undergoes apoptosis[64 , 65] and releases parasite antigens to liver-resident DC or KC[66] , which prime responses to late liver stage/blood stage antigens . Priming against early PE antigens likely occurs against extracellular sporozoites and liver stage parasites that die early as a part of normal parasite infection[29 , 64 , 65] . This speculation is bolstered by the fact that we see serum reactivity to early ( 12 and 24h ) and late ( 48h ) liver stages but not to 33h as fewer parasites are dying at this mid liver stage . These liver-resident APCs can then migrate to draining lymph nodes where they prime productive adaptive immune responses . In support of this , a recent report by Lau et al . demonstrates that following immunization with RAS by iv injection , substantial parasite-specific T cell activation occurs in the liver-draining lymph nodes at a rate that is surpassed only by the spleen[67] . Another study showed accumulation of CD8α+ DCs in the liver of RAS-immunized mice and that these DCs were capable of directly activating T cells in vitro[68] . Several reports identify peripheral lymphoid organs such as the spleen as instrumental in immune priming following sporozoite immunization[67 , 69] , but direct evidence of peripherally primed T cells being the mediators of protection after whole sporozoite immunization by iv injection is lacking . As the antigens mediating STP in our model are present in late liver stages , it is more likely that liver-resident APCs are responsible for priming the immune response to late liver antigens in the liver-draining lymph nodes . Antibodies in LAGAP immune sera overwhelmingly recognize the surface of the developing and mature merozoite and co-localize with MSP1 in late liver stage and BS parasites . MSP1 is the most abundant protein on the merozoite surface and antibodies against this protein have been shown to be protective against BS infection in mice [70–73] . However , we were unable to detect antibodies against either protective MSP1 regions in serum from LAGAP-immunized mice[74 , 75] . Thus , the protective antigen ( s ) are as yet unidentified merozoite surface protein ( s ) . Our data from IFAs and Western blot indicate that the protective antibodies in LAGAP-immunized C57BL/6 mice recognize different antigens than non-protective antibodies from BALB/c immunized mice . Accordingly , by identifying the antigens uniquely recognized by C57BL/6 immune serum , it is conceivable that a subset of these antigens could be incorporated into a multi subunit vaccine that could induce STP . The potent STP observed in our studies also lends support to using LAGAP as a whole parasite vaccination strategy . The only other example of true STP with live parasites has been observed in mice immunized with iRBC under chloroquine cover[76] . This immunization strategy in mice controlled liver infection via CD4+ and CD8+ T cells , conferred partial BS immunity and has been demonstrated as protective against sporozoite challenge in humans[77] . Multi-stage protection has also been demonstrated in animal models of ITI ( infection with wild type sporozoites under drug cover ) where there is both antibody-dependent and independent protection against both a sporozoite and BS infection[25 , 76 , 78] . However , this is not truly stage transcending protection as the development of BS immunity requires exposure to low levels of BS parasitemia during immunization[25 , 78] . Trials using cryopreserved RAS in humans have demonstrated that administration of live , attenuated sporozoites is effective , safe and well-tolerated[79] . Significant hurdles remain in manufacturing and delivering a live , attenuated sporozoite vaccine , but the success of RAS in humans has provided the impetus for creative solutions to these barriers . Yet , no gene knockout in the human-infective P . falciparum species has been created that is phenotypically similar to the LAGAP described here and knockout of the orthologous P . falciparum gene results in a parasite incapable of forming sporozoites[80] . Given the promise of superior and stage-transcending immunity , development of a late liver-arresting P . falciparum GAP that is free from breakthrough during immunization is of high priority and should be under intense investigation . In summary , we have shown that immunization with LAGAP can elicit both T cell and antibody-mediated immunity to BS parasites via recognition of antigens shared between the late liver stage- and BS parasites . Furthermore , antibodies act through complement and FCR binding to control and eliminate BS parasitemia . Since these T cells and antibodies are both highly efficacious and directed against potentially novel antigens , mechanistic studies using this model can critically inform the development of the next generation of subunit vaccines . These should be designed to elicit T cells as well as antibodies of the correct isotype , each directed against critical antigens and effective in eliminating liver stages and blood stage parasites .
6–8 week old female BALB/cJ and C57BL/6 mice were purchased from the Jackson Laboratory . Age-matched female FCRγ-/- mice on the C57BL/6 ( B6 . 129P2-Fcer1gtm1Rav N12 , model 583 ) background were purchased from Taconic Biosciences , Inc . All mice were maintained in a pathogen-free facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care at the Seattle Biomedical Research Institute . All experiments were conducted in accordance with animal protocols approved by the Institutional Animal Care and Use Committee . Six-to-eight week old female SW mice were injected with blood from Py knockout ( fabb/f- or sap1- ) -infected mice to begin the growth cycle . The infected mice were used to feed female Anopheles stephensi mosquitoes after gametocyte exflagellation was observed . On days 14–17 post infectious blood meal , salivary gland sporozoites were isolated from the mosquitoes for experimentation . Mice were immunized by injecting 50 , 000 sporozoites intravenously via the tail vein two weeks apart . As a control , equivalent amounts of salivary gland debris from uninfected mosquitos were used . Frozen blood stocks of PyXL or PyYM-infected blood containing 1% iRBCs was ip-injected into BALB/c or C57BL/6 mice and allowed to develop for 2–4 days until parasitemia reached a maximum of 1% as determined by Giemsa-stained thin smear . These mice were terminally bled via cardiac puncture and the blood diluted in PBS to contain 10 , 000 iRBCs/200μL . iRBCs were then iv-injected at a volume of 200μL/mouse into congenic recipient mice . Parasitemia was monitored by Giemsa-stained thin smears beginning on day 3 post-infection . Mice were euthanized when parasitemia reached 60% or became moribund . Serum from mice immunized with three doses of Pyfabb/f- sporozoites or uninfected salivary gland debris ( mock ) was collected on day 7 and day 14 after the final immunization and pooled . Naïve mice were intravenously injected with 300 μl of pooled serum on day 0 , 3 and 5 after a challenge with 10 , 000 lethal PyXL or PyYM iRBCs injected intravenously . CD8+ and CD4+ T cells were depleted in mice as previously described [22] . Briefly , 0 . 5 mg of anti-CD8 mAb 2 . 43 ( BioXCell ) and 0 . 35 mg of anti-CD4 mAb 1 . 5 ( BioXCell ) , or 0 . 85 mg of isotype control rat IgG2b ( BioXCell ) was iv-injected into mice 24 hours prior to parasite challenge . T cell depletion was confirmed before each challenge by collecting 50–100 μl of peripheral blood via the retro-orbital plexus from each mouse and assaying peripheral blood lymphocytes by flow cytometry staining for CD19 , CD3 , CD4 and CD8 . Cobra venom factor ( CVF ) is a complement activating C3b analog that when administered rapidly depletes complement ( Vandenberg 1991 ) . For complement depletion , mice were administered 30 μg of CVF intraperitoneally 6h prior and 4 days after iRBC challenge . Depletion of complement was confirmed prior to challenge by C3 sandwich ELISA ( Genway Biotech ) . Serum was isolated from peripheral blood at day 25 post-immunization , immediately prior to challenge as previously described[24] . ELISA plates ( Corning , Inc . ) were coated with full length PyCSP protein at a concentration of 0 . 1 μg/mL or with 2 μg/mL of either Py sporozoite or BS lysate in calcium bicarbonate/sodium carbonate coating buffer overnight at 4°C . For MSP1 ELISAs , plates were coated at 0 . 1μg/mL of either the 19 or 42kD fragment ( generously provided by Dr . James Burns ) as above . Plates were washed prior to addition of a 1:800 ( for PyCSP ) , 1:20 ( for sporozoite and BS lysate ) or 1:2000 ( for MSP1 19 and 42kD ) dilution of serum in duplicate followed by incubation at 37°C for two hours . After washing , anti-mouse IgG conjugated to HRP ( SouthernBiotech ) was added at a 1:2000 dilution for an additional 2h at 37°C . Plates were again washed and 100 μL of SigmaFast OPD ( Sigma-Aldrich ) substrate was added for 2–10 minutes prior to colorimetric detection of antibodies by measuring absorbance at 450 nm . Sporozoites , infected hepatocytes and infected red blood cells were harvested , fixed and stained as previously described [81 , 82] . Briefly , fixed cells were stained with a 1:200 dilution of serum collected from Pysap1- and Pyfabb/f- immunized C57BL/6 mice . Rabbit antibodies against BiP and MSP-1 were used as control antibodies for early liver stages and late liver stages/BSs , respectively . Fluorescently labeled secondary antibodies ( Alexa Fluor 488 or Alexa Fluor 594 ) from Life Technologies were used to detect mouse IgG ( catalog # A-11059 ) , IgG1 ( catalog # A-21125 ) and IgG2b ( catalog #A-21145 ) . Images were acquired using Olympus 1 x 70 Delta Vision deconvolution microscopy . For quantification of staining pattern , slides were prepared as above and blinded to the microscopist . Infected red blood cells containing schizonts were identified by MSP1 staining and anti-mouse IgG staining was classified as “interior” , “exterior” or “both” based on the MSP1 border . Calculations and statistical tests indicated in the figure legends were performed using GraphPad Prism . A p value < 0 . 05 was considered significant . All animal procedures were conducted in accordance with and approved by the Seattle BioMed Institutional Animal Care and Use Committee ( IACUC ) under protocol SK-09 . The Seattle Biomed IACUC adheres to the NIH Office of Laboratory Animal Welfare standards ( OLAW welfare assurance # A3640-01 ) . | Malaria is arguably one of the deadliest infectious diseases in human history . Today , it infects nearly 300 million people each year and kills up to 1 million of those—mostly women and children under the age of 5—and no effective malaria vaccine has been developed . Traditional subunit vaccines for pathogens work by training the immune system to recognize a single pathogen target . Attempts at developing a subunit malaria vaccine have , however , been stymied by the complexity of the parasite genome which encodes a complex life cycle with specific stages in the mosquito , as well as in the liver and blood of the mammalian host . Only the blood stage parasites cause malaria symptoms and mortality . Previously , it was assumed that immunity to malaria is stage-specific , either targeting parasites in the liver or in blood , but not both . The herein described vaccination approach uses genetically engineered , attenuated rodent malaria parasites that are able to infect the mouse liver and replicate , but die shortly before red blood-infectious parasite stages are formed and released . Immunization with these attenuated parasites induces the immune system to build defenses against both parasite stages in the liver and blood . Protection is mediated by multiple arms of the immune system . The antibody arm recognizes parasite targets shared between liver stages and blood stages . This not only demonstrates the optimal potency of this live-attenuated vaccination strategy , but also provides a potential source of new malaria subunit vaccine targets . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Mechanisms of Stage-Transcending Protection Following Immunization of Mice with Late Liver Stage-Arresting Genetically Attenuated Malaria Parasites |
Entamoeba histolytica is a protist parasite that is the causative agent of amoebiasis , and is a highly motile organism . The motility is essential for its survival and pathogenesis , and a dynamic actin cytoskeleton is required for this process . EhCoactosin , an actin-binding protein of the ADF/cofilin family , participates in actin dynamics , and here we report our studies of this protein using both structural and functional approaches . The X-ray crystal structure of EhCoactosin resembles that of human coactosin-like protein , with major differences in the distribution of surface charges and the orientation of terminal regions . According to in vitro binding assays , full-length EhCoactosin binds both F- and G-actin . Instead of acting to depolymerize or severe F-actin , EhCoactosin directly stabilizes the polymer . When EhCoactosin was visualized in E . histolytica cells using either confocal imaging or total internal reflectance microscopy , it was found to colocalize with F-actin at phagocytic cups . Over-expression of this protein stabilized F-actin and inhibited the phagocytic process . EhCoactosin appears to be an unusual type of coactosin involved in E . histolytica actin dynamics .
Human amoebiasis is caused by the protist parasite E . histolytica . The parasite is highly motile and displays high level of phagocytic activity in the trophozoite stage . Motility and phagocytosis are essential processes for the survival and invasion of host tissues by the parasite , and largely depends on a highly dynamic actin cytoskeleton . Moreover , there are other processes , such as phagocytosis that also require dynamic actin filament reorganization . Molecular mechanisms that regulate actin dynamics in E . histolytica have not been studied in detail . Preliminary investigations suggest an overall similarity with those described in other eukaryotic cells , but with crucial differences . For example , a number of calcium-sensing calcium-binding proteins appear to directly regulate actin recruitment and dynamics [1] , [2] , [3] . Several actin-binding proteins are encoded by the E . histolytica genome and many of these proteins are homologs of those that have been studied in other systems . Not many of these amebic actin-binding proteins have been characterized . Understanding structural-functional relationship of these proteins would help to decipher mechanisms of actin dynamics in E . histolytica . In E . histolytica as well as many other cells , actin dynamics involves both assembly and disassembly of filaments regulated by several actin-binding proteins . The actin-binding protein coactosin was first identified in Dictyostelium discoidedeum and has been classified as a member of actin depolymerising factor ( ADF ) /cofilin family [4] . The ADF/cofilin family members are expressed in all eukaryotes studied to date . The human coactosin-like protein ( HCLP ) binds F-actin and interferes with capping of filaments . However it does not affect actin polymerisation [5] . HCLP is also known to bind 5-lipooxygenase [6] . The binding of members of the ADF/cofilin family to the F-actin results in severing and depolymerisation of F-actin [7] . However the precise function of this family may vary from actin nucleation to severing depending on the cellular concentration gradient of cofilin [7] . The E . histolytica genome contains only one copy of the coactosin gene , whose product we refer to as EhCoactosin . Since the role of EhCoactosin in the actin dynamics of E . histolytica has not been previously investigated , we have carried out structural and functional analyses of this protein and present the results here . They show that a single conserved ADF homology domain of EhCoactosin is involved in binding F-actin , and that F-actin is stabilized when EhCoactosin is bound . Moreover , mutation of conserved lysine 75 to alanine does not result in loss of F-actin binding , in contrast to that observed in the case of HCLP , and the binding of this mutant EhCoactosin yields a similar level of F-actin stabilization as does the binding of native EhCoactosin . But deletion of complete F-loop completely abolishes G-actin binding with loss of F-actin stabilization activity , albeit still binds to F-actin . We also propose a mechanism for the binding of EhCoactosin to actin based on a structural model obtained by X-ray crystallography . Overall our results suggest that EhCoactosin displays some features not seen in coactosin from other organisms .
A multiple sequence alignment of EhCoactosin [Acc No XP_650926 . 1 from the NCBI database] with homologous proteins from different organisms allowed us to identify numerous residues that are conserved in this family of proteins , as well as those unique to EhCoactosin ( Figure 1 ) . The amebic Coactosin sequence displays 40% similarity with both human and D . discoidedeum CLPs . Among the conserved residues is a critical lysine at position 75 , known to be involved in F-actin binding [8] . The binding of EhCoactosin to F-actin was assessed by a sedimentation assay as described previously ( 1 ) . The full-length wild-type ( WT ) protein binds F-actin , as it was found in the pellet fraction after ultracentrifugation ( Figure 2A ) . A similar level of F-actin binding was also observed for truncated versions of EhCoactosin where either the N-terminal seven amino acid residues ( EhCoΔN , Figure 2B ) or C-terminal 14 residues ( EhCoΔC , Figure . 2C ) were deleted . In an attempt to narrow down the specific region involved in actin binding , we deleted the F-loop of EhCoactosin ( from 71–76 amino acids ) and also mutated the critical Lys75 residue . The F-loop deleted version of EhCoactosin ( ΔF ) retained actin binding property ( Figure 2D ) . The K75A mutant of EhCoactosin was able to bind F-actin , ( Figure 2E ) , which is in contrast to the complete loss of F-actin binding caused by the same mutation in HCLP [8] . These observations suggest that F-actin binding by EhCoactosin does not solely depend on F-loop and Lys75 . G-actin binding was determined by a G-actin sequestering and solid phase assay as described previously [1] . G-actin sequestering assay uses fluorescently labelled G-actin and when a protein binds the labelled actin its florescence decreases mostly in dose dependent manner . The WT EhCoactosin shows G-actin sequestering in dose dependent manner ( Figure 2F ) while the EhCoΔF has no G-actin binding activity as seen in Figure 2G . We also confirmed G-actin binding for other truncated versions of proteins by this assay . EhCoΔC and EhCoΔN showed dose-dependent G-actin binding but affinity of EhCoΔN was more than EhCoΔC as 10 µM of EhCoΔN was able to sequester same amount of G-actin as 25 µM of EhCoΔC ( Figure S1A and B ) . EhCoactosin displays specific G-actin binding was also confirmed by binding to a plate coated with G-actin . The level of binding was 2-fold higher than that of the known G-actin-binding protein EhCaBP1 [1] ( Figure S1 ( C ) ) . While EhCoΔC showed a 33% decrease in binding when compared to the WT protein , EhCoΔN exhibited a 2-fold increase in G-actin binding in comparison to WT . The homolog pfADF1 , which binds G-actin strongly [9] is positively charged at the N-terminal region compared to EhCoactosin . The deletion of N-terminal residues in EhCoactosin exposes more positive charges in this region ( Figure S2 ) which , by analogy with pfADF1 , may explain the increased affinity of this mutant for G-actin . The F-loop deleted ( EhCoΔF ) version exhibited complete loss of G-actin binding which was also observed with G-actin sequestering assay . The role of EhCoactosin in F-actin stabilization was determined by a pyrene-actin assay where fluorescence of pyrene-labelled F-actin decreases upon depolymerisation . The assay showed relative stabilization of F-actin by EhCoactosin compared to that by Xenopus cofilin1 ( Xac1 ) ( Figure 3A ) , and the stabilization effect was confirmed by the ability of EhCoactosin to antagonize the F-actin severing activity of Xac1 ( Figure 3B ) [10] . That is , while addition of Xac1 led to a sharp decrease in fluorescence , indicating its severing effect on F-actin , in the presence of EhCoactosin no decrease in fluorescence was observed and values were similar to that seen with only actin . The results suggest that EhCoactosin may be protecting F-actin from severing ( Figure 3B ) . We also checked possibility of interaction between Xac1 and EhCoactosin by pull down assay which may lead to similar results . We found that Xac1 and EhCoactosin and its mutants do not interact directly with each other ( Figure S3 ) . EhCoΔC and EhCoΔN showed actin stabilization similar to that of the wild-type protein ( Figure 3C and 3E ) , and a similar stabilization effect was also observed in the case of the K75A mutant ( Figure 3G ) . Moreover , both truncated versions and K75A mutant of EhCoactosin antagonised Xac1-dependent F-actin severing ( Figure 3D , 3F and 3H ) . However , EhCoΔN and Xac1 at 2∶1 ratio did show mild F-actin severing ( Figure 3D ) ; the apparent weaker protection conferred by this mutant may be result of its high affinity for G-actin ( Figure S1 ) . However , the EhCoΔF had lesser F-actin stabilizing property than WT protein ( Figure 3I ) as in presence of the protein F-actin depolymerised to an extent . Also EhCoΔF was not able to protect F-actin from Xac1 activity ( Figure 3J ) . These results indicate that F-loop is very essential for stable F and G-actin binding . The deletion of F-loop results in lower affinity towards F-actin making it accessible for Xac1 activity . Hence the whole F-loop plays an essential role in stable binding rather than conserved lysine residue at 75th position . EhCoactosin consists of a central core of β-sheets surrounded by α-helices . The central core is made up of five strands: β1 ( 26–32 ) , β2 ( 37–44 ) , β3 ( 60–69 ) , and β4 ( 76–85 ) forming antiparallel strands while β5-strand ( 113–117 ) forms parallel strand with β3 and β4 . The central β-sheets are flanked on both sides by a total of five helices; α1 ( 9–17 ) and α3 ( 92–107 ) are located on the N-terminal side , and α2 ( 48–54 ) , α4 ( 120–122 ) and α5 ( 125–137 ) are located on the C-terminal side ( Figure 7A and B ) . This arrangement of secondary structural elements is a common structural feature of proteins belonging to the ADF/cofilin family . EhCoactosin has a long N-terminal end protruding outside with Ser repeats and this signature Ser repeats is expected to bind G-actin as seen in PfADF1 [9] , however wild type EhCoactosin binds to F-actin and EhCoΔN shows higher affinity for G-actin , indicating the “Ser” repeats on the N-terminal are not involved in G-actin binding . The loop connecting strands β3 and β4 , which has a conserved lysine at position 75 , is called the “F-loop” and it is expected to participate in stabilizing and binding to F-actin [11] . As described in more detail below , the surface of EhCoactosin is highly negatively charged , and this F-loop is part of the negatively charged surface . The N-terminal end and the F-loop are at two opposite sides of the globular structure ( Figure 7 ) suggesting that EhCoactosin binds G-actin and F-actin in very different ways . Although there is a general similarity of the overall conformation of EhCoactosin with that of related proteins in other organisms , the surface charge distributions of EhCoactosin is markedly distinctive . The surfaces of both sides of EhCoactosin are quite negatively charged , although one surface has overall higher level of negative charge as compared to the other surface . Just a small positively charged surface is found in the α3 and α4 region , as well as is between the β4 and α3 regions , and a hydrophobic pocket is formed between β3 and α5 ( Figure 8A and 8A′ ) . In contrast , human coactosin-like protein ( HCLP ) is positively charged on one side , while negatively charged on the other , which is a characteristic feature of the ADF/cofilin family . The F-loop surface , which is negatively charged on both sides in EhCoactosin , is positively charged on one side and hydrophobic on the other in HCLP ( Figure 8B and 8B′ ) . The surface charge distributions of pfADF1 and pfADF2 also differ from that of EhCoactosin . For pfADF1 , one side is highly positively charged and the other has a relatively hydrophobic surface . The N-terminal region of pfADF1 is positively charged relative to that of EhCoactosin [11] . Also , α1 of pfADF1 has three positively charged residues and is relatively long whereas in EhCoactosin it is relatively small and negatively charged [Figure 8C and 8C′] . The surface of pfADF2 , while more negatively charged than that of pfADF1 , is less negatively charged than that of EhCoactosin ( Figure 8D and 8D′ ) . Note that the N-terminal regions of EhCoactosin and PfADF2 were also found to be different; while , as indicated above , the former has Ser repeats , the latter does not [12] , [13] . The overall structure of EhCoactosin is quite similar to that of human coactosin-like protein ( HCLP ) , with an RMSD of 1 . 56 Å and few major differences . The N-terminal regions of the two proteins do deviate by up to 14 . 7 Å , with that of HCLP bent towards the inside of the structure while in EhCoactosin this N-terminal region is extended . Also , α1 of HCLP is longer by 3 residues compared to that of EhCoactosin ( Figure 9A ) . The overall structure of EhCoactosin is also fairly similar to the structures of the two types of ADF proteins of Plasmodium falciparum , pfADF1 and pfADF2 . Although pfADF1 is functionally different than other ADF/Cofilin proteins , since it binds G-actin [12] and only transiently interacts with F-Actin [12] , its overall structure differs from that of EhCoactosin by an RMSD of just 2 . 0 Å . Certain structural differences are quite notable: The F-loop is absent in pfADF1; β3 and β4 of EhCoactosin , which are extended towards its F-loop , are shorter in pfADF1; and a long C-terminal α-helix present in EhCoactosin is absent in pfADF1 . All these observations suggest that the F-loop , β3 , β4 and the C-terminal helix of EhCoactosin could be involved in binding to F-actin ( Figure 9B ) . Note also that in pfADF1 , the N-terminal end is relatively short , and connected to a short β-sheet , which is a characteristic feature of ADF/cofilin , while in EhCoactosin the N-terminal region is long with characteristic serine repeats , which is thought to participate in G-actin binding . However , both these proteins bind G-actin and it is difficult to suggest a possible mechanism with this data . The RMSD between pfADF2 and EhCoactosin is 2 . 13 Å . pfADF2 binds F-actin as well as G-actin [12] , and in pfADF2 , the F-loop , β3 , β4 , β5 and β6 are similar to those in EhCoactosin . Moreover , the C-terminal helix , which is missing in pfADF1 , is present in pfADF2 . This helix is nevertheless longer in EhCoactosin . These regions are likely to be involved in F-actin binding ( Figure 9C ) . EhCoactosin directly binds F-actin but the mechanism of preventing depolymerisation is not understood . The structural differences of EhCoactosin with Coactosins from other organisms may be responsible for the distinct functional properties . Properties of mutants helped us to model F-actin binding . Here we have sought to analyse the nature of interactions between actin and EhCoactosin by computational modelling . We propose different mode of binding of EhCoactosin to G-actin and F-actin to explain the actin binding properties . Based on the crystal structure of the mouse twinfilin C-terminal ADF homology domain in complex with actin [14] and the recent 9 Å EM model of human Cofilin-2 in complex with actin filaments [15] ( Figure S6A and S6B ) , we built two different models , one for G-actin binding and one for F-actin binding to explain and understand actin binding mechanism of EhCoactosin . EhCoactosin superimposes well with the cofilin of the cofilin-actin complex filaments [15] . In the energy-minimized model , EhCoactosin fits well between the subdomain 1 of the actin monomer and the subdomain 2 of the next actin monomer ( Figure 10A ) . As seen in the model , the N-terminal region of EhCoactosin interacts with subdomain-1 of the actin monomer1 and the C-terminal region of EhCoactosin is placed at the binding interface between two actin molecules ( Figure 10B ) . The α-3 helix forms extensive contacts with subdomain-1 of the actin monomer-1 whereas the F-loop ( S69-K75 ) interacts with the subdomain-2 of the adjacent actin monomer ( Figure 10C and D ) . The C-terminal α-5 helix is docked inside the cavity formed by the two actin molecules . The N-terminal sequence and F-loop region behave like clamps anchoring well within the F-actin structure along the length of the filaments , hence resulting in its stabilization . This explains the effect of EhCoΔF as the mutation of the F-loop results in loss of F-actin stabilization suggesting F-loop is one side of the clamp interacting with F-actin . Thus EhCoΔF can bind F-actin but can't stabilize it . The homology model of the EhCoactosin-F-Actin complex suggests that various regions of the protein , such as the N-terminal sequence , helices α-3 and α-5 and the F-loop play important roles in binding F-actin – and also suggests , in agreement with our mutational studies described above , that no single region or feature of EhCoactosin is indispensible for binding F-actin . Such is the case for EhCoactosin Lys75 , for example , despite it being conserved and completely responsible for F-actin binding in other systems; EhCoactosin is unique in this regard . EhCoactosin deletion mutants EhCoΔC as well as EhCoΔN also displayed F-actin binding and stabilization abilities similar to that of the wild type protein . The model for globular monomeric actin ( G-actin ) binding to EhCoactosin was obtained using the mouse twinflin ADF homology domain in complex with actin ( Figure S4B ) . Based on the energy minimized model , α3 of EhCoactosin binds the cleft between subdomain 1 & 3 of actin as shown in Figure 11 . The modelling data suggest that deletion of the N-terminal region and development of positive charge may loosen interaction with a hydrophobic patch on domain 1 of actin ( Figure S2 ) . Due to this , α3 can enter in the groove between domain 1 and 3 of G-actin ( see G-actin binding model , Figure 11 ) , helping to explain our result described above that EhCoΔN binds G-actin more strongly than does wild-type EhCoactosin . Interestingly the EhCoΔF abolishes G-actin binding suggesting F-loop deletion might have altered the orientation of α3 and thus loss in G-actin binding .
The protist parasite E . histolytica undergoes extensive pseudopod extension , and displays high level of motility , phagocytosis and macro-pinocytic activities . These processes are crucial for amebic biology as these are associated with food intake and pathogenesis . Since actin dynamics drives all of these processes , we have been investigating many molecules that are known to participate in actin dynamics . Actin-binding proteins , such as those of the ADF/cofilin family , play a major role in actin dynamics . In the current study , we have investigated structural and functional features of the ADF/cofilin protein EhCoactosin . Our results indicate EhCoactosin to be both a G- and F-actin-binding protein , and that it stabilizes F-actin by direct binding . This set of unusual functional feature is due to presence of unique structural motifs not observed in other coactosins or other homologs . EhCoactosin displays an overall conformational similarity with other ADF/cofilin family members such as HCLP , pfADF1 and pfADF2 , yet also displays distinct differences ( Figure S7A ) . Some of the features , such as presence of helices α1 and α3 at the N-terminal region as well as the F-loop , which contains conserved Lys75 , are also present in coactosins from other organisms including D . discoideum which are structurally conserved in this family ( Figure S7B ) . Distinctive features of EhCoactosin include a longer N-terminal sequence and a more negatively charged surface . As a result of the latter feature , both sides of the F-loop in EhCoactosin is negatively charged while , for example , one side of the F-loop of HCLP is positively charged while the other side is hydrophobic . The observation that certain features found in EhCoactosin are absent in other coactosins suggests that this molecule in E . histolytica may impart novel functional properties . Our data clearly show that EhCoactosin is both an F- and G-actin-binding protein in vitro . It is associated with the actin cortex and co-localises with F-actin during pseudopod formation and erythrophagocytosis . The presence of EhCoactosin in phagocytic cups is parallel to the F-actin during the phagocytic cup formation . In vitro functional assays suggest that EhCoactosin is a F-actin stabilizing protein which implies its role in maintaining integrity at the leading edge . Nearly all coactosins studied previously , including human CLP , have not shown a direct effect on actin polymerisation or depolymerisation , although they can interfere with capping of filaments . Chick coactosin is an exception which has been shown to be involved in actin polymerisation downstream of Rac signalling and to promote polymerisation [16] . EhCoactosin is a novel member of the coactosin family with direct effect on F-actin stabilization with F-loop playing important role in binding . The functional difference between EhCoactosin and other coactosins can be attributed mainly to increased length of the N-terminal part and altered charge distribution . These distinct properties of EhCoactosin are likely to contribute to its binding of G-actin and stabilization of F-actin . Deletion of the N-terminal part EhCoactosin , for example , increases the binding affinity for G-actin on the solid phase . The C-terminal part may also have a role in regulating G-actin binding . When it is deleted affinity for G-actin decreases but not drastically and this is similar to HCLP where C-terminal does not play significant role in F-actin binding [17] . Our in silico analysis suggests that one molecule of EhCoactosin binds to two adjacent actin molecules in the filament . The binding model also suggests that interactions between EhCoactosin and F-actin involve several regions rather than just the F-loop as in other systems . The N-terminal and F-loop of EhCoactosin function as clamps in F-actin binding and decorate the filament along its length . The long serine rich N-terminal region plays a role in F-actin binding whereas deletion of which results in F-actin severing activity . The model and solid phase data suggest that this may be due to high affinity for G-actin displayed by the mutant as a result of uninhibited binding of α3 between subdomain 1 and 3 . Although the Lys75 residue is needed by HCLP for binding F-actin , it is not required in case of EhCoactosin since the mutant K75A protein has similar experimentally determined F-actin-binding and other properties as does the wild-type protein . Computational modelling also supports these results as K75A mutant does not show any significant change in binding of EhCoactosin to actin , as K75 is not directly interacting with F-actin . This implies that binding of EhCoactosin and actin involves interactions other than F-loop and Lys75 residue unlike other homologs . But complete deletion of F-loop results in loss of F-actin stabilization suggesting F-loop is one side of the clamp interacting with F-actin . Thus EhCoΔF can bind F-actin but can't stabilize it . Our experiments have shown that EhCoactosin stabilises F-actin , but we also need to understand the underlying contributions to actin dynamics in E . histolytica since both depolymerization as well as stability of F-actin are required for critical cellular processes . Many drugs that stabilize F-actin have deleterious effect on processes that require actin dynamics [18] , [19] , and over-expression of EhCoactosin in E . histolytica yields cells that display impaired growth and phagocytosis , presumably due to the protein's stabilization of F-actin . This consequence of overexpression is not seen with other coactosins and appears to be a unique property of the E . histolytica protein . E . histolytica is an early branching eukaryote displaying unique biology , and although it shares many of the participants of the cytoskeleton remodelling machinery with metazoan organisms , it also uses a few novel proteins in regulating the actin cytoskeleton [1] , [2] , [3] . The calcium-binding proteins EhCaBP1 and EhCaBP3 are such examples , and they have been shown to be involved in actin dynamics and phagocytic cup formation [2] , [3] , [20] . All these studies including present study show that E . histolytica proteins can also undergo functional diversification in order to fulfil its needs , high rate of actin dynamics . The detailed study of this binding protein will lead to better understanding of the cytoskeletal remodelling in this parasite and also as well evolution of this process in other eukaryotes . The erythrophagocytosis results indicate in vitro concentration of EhCoactosin above critical level may affect actin remodelling . Phagocytosis involves both actin polymerisation and depolymerisation which is mediated by several actin-binding proteins . The high levels of EhCoactosin in cell may promote excess stability of F-actin in vitro by preventing access of actin remodelling protein to F-actin required during the phagocytosis . Taken together this leads to increased rigidity in actin cytoskeleton which impairs its dynamic remodelling required for processes like motility and phagocytosis . In conclusion , EhCoactosin is directly involved in F-actin stabilization , which has not been reported earlier . In vivo EhCoactosin may actively contribute to the maintenance of F-actin during erythrophagocytosis and pseudopod formation . The interactions between EhCoactosin and F-actin depend on several regions in the protein rather than specific residues such as Lys75 . The evolutionary basis of development of specific interaction in higher organisms can be understood by studying primitive eukaryotes like E . histolytica . This study will also lead to better understanding of actin dynamics in this organism and as well as evolution of actin dynamics as a process in organisms .
The coding sequence of coactosin gene ( GenBank accession no . XP_650926 ) was amplified by PCR from genomic DNA of Entamoeba histolytica strain HM1:IMSS using the forward primer 5′-CCGCCATGGCAATGTCTGGATTTGATCTTAG-3′ and the reverse primer 5′-CCGCTCGAGCTTAATTTTAGCAGCGATTTC-3′ . The EhCoactosin gene was cloned in pET28b ( Novagen ) between Nco1 and Xho1 sites with a C-terminal 6× His tag . Four constructs were prepared for biochemical experiments: wild-type EhCoactosin ( EhCoWT ) ; an EhCoactosin in which 14 amino acid residues were deleted from the C-terminus because it was predicted to form a loop ( EhCoΔC ) ; another for which 7 residues were deleted from the N terminus ( EhCoΔN ) , F-loop spanning from 71–76 amino acid was also deleted ( EhCoΔF ) and a single site substitution mutant ( K75A ) . The cloning was confirmed by restriction digestion by Nco1 and Xho1 followed by DNA sequencing . The CAT gene of the shuttle vector pEhHYG-tetR-O-CAT ( TOC ) was excised using KpnI and BamHI and the EhCoactosin gene was inserted in its place in either the sense or the antisense orientation . The expression in this vector was tetracycline inducible and expressed sense ( S ) and antisense ( AS ) RNA of the gene in E . histolytica trophozoites . For the study of co-localization in E . histolytica cells we carried out HA tagging at the N-terminus of EhCoactosin . The Forward Primer 5′-CGGGGTACCATGTATCC ATATGATGTTC CAGATTATGCTATGTCTGGATTTG-3′ and the reverse primer 5′- GCGGGATCCTTAAGCATAATCTGGAACATCATATGGATAATT TGAGGTGG-3′ were used for HA tagging . The recombinant plasmid containing the EhCoactosin gene was transformed into E . coli BL21 ( DE3 ) cells ( Novagen ) . Primary culture was grown overnight in 50 ml LB media from the single colony of transformed BL21 cells supplemented with 50 µg/ml Kanamycin at 37°C . Secondary culture was grown by inoculating 1% of primary culture in the same media at 37°C until the OD600 reached 1 . 0 . The culture was induced with 1 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) ( Sigma ) and allowed to grow for another 4 hrs at the same temperature . Cells were harvested by centrifugation at 6000 rpm for 10 minutes at 4°C . These cells were stored at −80°C until further processing . The harvested cells were resuspended and homogenized in resuspension buffer containing 50 mM Tris HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA and 0 . 1 mM DTT . Resuspended cells were lysed with 3 cycles of flash-freezing in liquid nitrogen and subsequent thawing in water-bath at 37°C . The lysate was subjected to 5–6 cycles of sonication on ice at 25% amplitude with each pulse of 30 sec and 1 min interval . The sonicated cell lysate was centrifuged at 13 , 000 rpm for 30 minutes at 4°C . Supernatant was filtered with Whatman filter paper no . 1 and clear lysate was passed through a Nickel-NTA column ( GE healthcare ) pre-equilibrated with resuspension buffer . Thereafter , the column was washed with 2 bed volumes of buffer containing 50 mM Tris HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA , 0 . 1 mM DTT and 10 mM imidazole . The bound protein was eluted with buffer comprising 50 mM Tris-HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA , 0 . 1 mM DTT and 100 mM imidazole . The purified fractions of protein were concentrated using Centricon filters ( Millipore ) and subjected to gel filtration chromatography on HiLoad Superdex 75G 16/60 column ( GE Healthcare ) pre-equilibrated with buffer containing 50 mM Tris-HCl ( pH 8 . 0 ) , 0 . 5 mM EDTA , 0 . 5 mM DTT and 1 mM sodium azide . Homogeneity of protein was assessed on 12% SDS-PAGE ( Figure S8 ) . Peak fractions were concentrated using Centricon filters ( Millipore ) and concentration was estimated with A280 . Selenomethionine-labelled EhCoactosin was purified under reducing conditions using specific media ( by Molecular Dimensions , United Kingdom ) . The concentration of selenomethionine was maintained at about 25 mg/litre . Initially , the primary culture was grown in LB medium overnight . Cells were then harvested by centrifuging at 4000 rpm for 6 min . Harvested cells were resuspended in the complete selenomethionine media , and washed once with same media to completely remove any leftover LB medium . Secondary culture was grown by inoculating 1% of primary culture in the same media at 37°C until the OD600 reached 1 . 0 . Culture was allowed to grow at 37°C for about 4 hrs after inoculation until OD600 reached 1 . 0 . Cells were induced with 1 mM IPTG and allowed to grow for another 4 hrs at same temperature . Cells were harvested at 6 , 500 rpm for 6 min and stored at −80°C for further processing . Subsequent processing and purification were done by the same method used for native EhCoactosin . G-actin was purified from rabbit skeletal muscle acetone powder [21] . Further Actin was labelled with N- ( 1-pyrene ) iodoacetamide ( P-29 , Molecular Probes ) by the protocol described previously [22] for performing the pyrene-actin assay . Native EhCoactosin protein was crystallized using the hanging drop vapor diffusion method in 24-well Linbro plates against a reservoir solution containing 25–35% PEG 1500 , 100 mM sodium acetate , 0 . 2 mM CaCl2 , 10 mM MgCl2 and 100 mM HEPES , pH 7 . 3–7 . 7 . Two µl of [∼75 mg/ml] protein and 2 µl of reservoir solution were mixed and allowed to equilibrate at 16°C . The crystals that formed in these drops were flash frozen in a cryoprotectant solution containing additional 5% PEG 400 mixed with mother liquor . Selenomethionine-labelled protein was prepared and crystallized using similar conditions . The crystal appeared in condition containing 28–33% PEG 3350 , 100 mM sodium acetate , 0 . 2 mM CaCl2 , 10 mM MgCl2 , 5% isopropanol and 100 mM HEPES pH 7 . 4–7 . 7 ( Figure S9 ) . The crystals were flash frozen in the same cryo-protectant . The X-ray data for selenomethionine-substituted crystals were collected at the BM14 synchrotron beamline , ESRF , Grenoble , France at a selenium peak wavelength of 0 . 97860 Å . Data sets were indexed and scaled using HKL2000 [23] . Anomalous data collected for Se-Met labelled EhCoactosin crystals were used to calculate FA values using the program SHELXC [24] . Each of the two heavy atoms expected were found using the program SHELXD [24] . Initial phases were calculated after density modification using SHELXE [25] . The reflection file was further used in the Autobuild program [25] of the Phenix suite [26] for automated model building . Then missing residues were traced into the electron density and refined by iterative model building using the COOT graphics package combined with REFMAC5 [27] . HEPES , Na , and water molecules were added by COOT guided by Fo-Fc electron density >3σ . The final model was validated by the Procheck [28] program of the CCP4 suite . Structure factors and co-ordinates have been validated and deposited in the Protein Data Bank with accession id 4LIZ . Data statistics are listed in Table 1 . A model of the F-actin–EhCoactosin complex was built using the 9 Å electron microscopy derived model of F-actin ADF/cofilin protein complex [16] . The crystal structure of EhCoactosin was then superimposed onto the human ADF/cofilin molecule from the EM model using the RAPIDO server [29] . The ADF/cofilin molecule used showed an extended N-terminal region which did not superimpose well . The overall structure ( 119 atoms ) , however , did superimpose well with an RMSD of 1 . 15 Å ( Figure S4A ) . The final model of the complex with five actin and six coactosin molecules was then subjected to energy minimization with 2500 cycles of steepest descent and followed by 2500 cycles of steepest descent algorithm using AMBER molecular dynamics package [30] . Similarly the model of the G-actin-EhCoactosin complex was obtained using the crystal structure of mouse twinfilin C-terminal ADF homology domain in complex with actin [14] . The root mean square deviation obtained was 2 . 17 Å ( Figure S4B ) . The electrostatic surface charge distribution was calculated using the ABPS plugin in PyMOL . The negative electrostatic surface is shown in red , and the positive surface in shown in blue; all surfaces are drawn at 3 e/kBT . The images were prepared using Pymol software [31] . E . histolytica strain HM-1: IMSS and all transformed parasites were maintained and grown in TYI-S-33 medium [1] containing 125 ml of 250 U ml−1 benzyl penicillin and 0 . 25 mg ml−1 streptomycin per 100 ml of medium . The transformants containing tetracycline inducible system were grown in presence of 10 µg ml−1 of Hygromycin B . The cells were first grown for 48 h ( 60–70% confluent ) and then 20 µg ml−1 tetracycline was added to the medium for 36 h for induction . Cells carrying constructs with constitutive expression system ( such as GFP ) were maintained at 10 µg ml−1 of G418 . But the experiments were carried out in presence of 30 µg ml−1 of G418 . Transfection was performed by electroporation . Briefly , trophozoites in log phase were harvested and washed with phosphate buffer saline ( PBS ) , followed by incomplete cytomix buffer ( 10 mM K2HPO4/KH2PO4 ( pH 7 . 6 ) , 120 mM KCl , 0 . 15 mM CaCl2 , 25 mM HEPES ( pH 7 . 4 ) , 2 mM EGTA , 5 mM MgCl2] . The washed cells were then re-suspended in 0 . 8 ml of complete cytomix buffer ( incomplete cytomix containing 4 mM adenosine triphosphate , 10 mM glutathione ) containing 200 mg of plasmid DNA and subjected to two consecutive pulses of 3000 V/cm ( 1 . 2 kV ) at 25 mF ( Bio-Rad , electroporator ) . The transfectants were initially allowed to grow without any selection . Drug selection was initiated after 2 days of transfection in the presence of 10 µg ml−1 G-418 for constructs with GFP or 10 µg ml−1 of hygromycin B was used for tetracycline inducible constructs . Immunofluorescence staining was carried out as described previously [1] . Briefly E . histolytica cells resuspended in TYI-33 medium were transferred onto acetone-cleaned coverslips placed in a petri dish and allowed to adhere for 10 min at 35 . 5°C . The culture medium was removed and cells were fixed with 3 . 7% pre-warmed paraformaldehyde ( PFA ) for 30 min . After fixation , the cells were permeabilized with 0 . 1% Triton X-100/PBS for 1 min . This step was omitted for non-permeabilized cells . The fixed cells were then washed with PBS and quenched for 30 min in PBS containing 50 mM NH4Cl . The coverslips were blocked with 1% BSA/PBS for 30 min , followed by incubation with primary antibody at 37°C for 1 h . The cover slips were washed with PBS followed by 1% BSA/PBS before incubation with secondary antibody of 30 min at 37°C . Antibody dilutions used were: Anti- EhCoactosin at 1∶200 , anti-HA at 1∶50 , TRITC-Phalloidin at 1∶250 and anti-rabbit Alexa 488 ( Molecular Probes ) at 1∶300 . The preparations were further washed with PBS and mounted on a glass slide using DABCO ( 1 , 4-diazbicyclo [2 , 2 , 2] octane ( Sigma ) 10 mg/ml in 80% glycerol ) . The edges of the coverslip were sealed with nail-paint to avoid drying . Confocal images were visualized using an Olympus FluoView FV1000 laser scanning microscope . 17 . 5 µM G-actin with 10% pyrene labeled was polymerized for one and half hour at 25°C in F-buffer ( 10 mM Tris-Cl pH8 . 0 , 0 . 2 mM DTT , 0 . 7 mM ATP , 50 mM KCl , 2 mM MgCl2 ) . Depolymerization kinetics was started with the addition of 2 µL of preassembled actin with 58 µL of F-buffer , and the volume was made up to 70 µl with HEKG5 or protein solution . N-pyrene fluorescence was monitored with excitation at 365 nm and emission was measured at 407 nM for 600 seconds ( QM 40 PTI NJ ) . The de-polymerizing protein Xenopus cofilin1 ( Xac1 ) was used as a positive control [10] . The solid-phase assay experiments were performed to monitor the binding of wt- and mutant EhCoactosin proteins to G-actin . The wells of the ELISA plate were coated with 5 µM G-actin in PBS buffer and incubated for 12 h at 4°C . The wells were washed with PBS-T buffer . 5 µM protein was added to the wells in duplicates . Bound protein was detected with anti-EhCoactosin antibody followed by HRPO-lined anti-rabbit IgG using the colorimetric substrate TMB ( Sigma ) . The reaction was stopped with 2N H2SO4 and absorbance was monitored at 405 nm with ELISA plate reader ( Bio-Rad , USA ) . 5 µM of rabbit muscle actin was polymerized for one and half hour at 25°C in F-buffer ( 10 mM Tris-Cl pH8 . 0 , 0 . 2 mM DTT , 0 . 7 mM ATP , 50 mM KCl , 2 mM MgCl2 ) . After polymerization , actin was mixed with appropriate target protein ( 5 µM ) in a total volume of 150 µl and incubated for 30 min at RT . The samples were centrifuged at 100 , 000 g for 45 min at 4°C . The supernatant and pellet fractions ( total ) were analyzed by 12% SDS-PAGE followed by Coomassie blue staining . All target proteins were ultracentrifuged at 1 , 00 , 000× g for 1 h and the supernatant was used for the assays in order to avoid aggregates . The human erythrocytes used in the experiments were collected from Somlata . The blood was taken by piercing the ring finger by sterile needle and transferred into a sterile tube containing PBS . 107 red blood cells ( RBC ) were washed with PBS and incomplete TYI-33 and were incubated with 105 amoeba for varying time periods at 37°C in 0 . 5 ml culture medium . The amoebae and erythrocytes were pelleted down , non-engulfed RBCs were bursted with cold distilled water and recentrifuged at 1000 g for 2 min . This step was repeated twice , followed by resuspension in 1 ml formic acid to burst amoebae containing engulfed RBCs . The absorbance was measured at 400 nm . The human erythrocytes used in the experiments were collected from Somlata . The blood was taken by piercing the ring finger by sterile needle and transferred into a sterile tube containing PBS . The consent letter was obtained from the individual for taking blood sample before carrying out the experimental studies . | E . histolytica is an important pathogen and a major cause of morbidity and mortality in developing nations . High level of motility and phagocytosis is responsible for the parasite invading different tissues of the host . Phagocytosis and motility depend on highly dynamic actin cytoskeleton of this organism . The mechanisms of actin dynamics is not well understood in E . histolytica . Here we report that coactosin like molecule from E . histolytica , EhCoactosin is involved in F-actin stabilization . The crystal structure obtained for the protein provides explanation for some functional differences observed with respect to the human homologue , such as ability to bind G-actin . Moreover , computational modelling along with crystal structure helps to explain the F-actin binding and stabilization by wild type protein . The mutational analysis further suggests that F-actin binding property does not depend on conserved Lys75 residue as observed in Human coactosin like protein ( HCLP ) but other regions present in protein are involved in binding . Overexpression of this protein in trophozoites leads to stabilization of actin filaments which are not accessible to actin remodelling machinery thereby reducing the growth of parasite due to decreased rate of actin dependent endocytosis . Overall , EhCoactosin behaves as F-actin stabilizing protein in vitro and it also participates in processes like phagocytosis and pseudopod formation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"biochemistry",
"cytoskeletal",
"proteins",
"cell",
"motility",
"actin",
"filaments",
"cell",
"biology",
"proteins",
"protein",
"structure",
"structural",
"proteins",
"biology",
"and",
"life",
"sciences",
"dynamic",
"actin",
"filaments"
] | 2014 | EhCoactosin Stabilizes Actin Filaments in the Protist Parasite Entamoeba histolytica |
This study aimed to evaluate the risk factors associated with developing leprosy among the contacts of newly-diagnosed leprosy patients . A total of 6 , 158 contacts and 1 , 201 leprosy patients of the cohort who were diagnosed and treated at the Leprosy Laboratory of Fiocruz from 1987 to 2007 were included . The contact variables analyzed were sex; age; educational and income levels; blood relationship , if any , to the index case; household or non-household relationship; length of time of close association with the index case; receipt of bacillus Calmette-Guérin ( BGG ) vaccine and presence of BCG scar . Index cases variables included sex , age , educational level , family size , bacillary load , and disability grade . Multilevel logistic regression with random intercept was applied . Among the co-prevalent cases , the leprosy-related variables that remained associated with leprosy included type of household contact , [odds ratio ( OR ) = 1 . 33 , 95% confidence interval ( CI ) : 1 . 02 , 1 . 73] and consanguinity with the index case , ( OR = 1 . 89 , 95% CI: 1 . 42–2 . 51 ) . With respect to the index case variables , the factors associated with leprosy among contacts included up to 4 years of schooling and 4 to 10 years of schooling ( OR = 2 . 72 , 95% CI: 1 . 54–4 . 79 and 2 . 40 , 95% CI: 1 . 30–4 . 42 , respectively ) and bacillary load , which increased the chance of leprosy among multibacillary contacts for those with a bacillary index of one to three and greater than three ( OR = 1 . 79 , 95% CI: 1 . 19–2 . 17 and OR: 4 . 07–95% CI: 2 . 73 , 6 . 09 ) , respectively . Among incident cases , household exposure was associated with leprosy ( OR = 1 . 96 , 95% CI: 1 . 29–2 . 98 ) , compared with non-household exposure . Among the index case risk factors , an elevated bacillary load was the only variable associated with leprosy in the contacts . Biological and social factors appear to be associated with leprosy among co-prevalent cases , whereas the factors related to the infectious load and proximity with the index case were associated with leprosy that appeared in the incident cases during follow-up .
The primary aim of all disease control measures is to reduce the incidence , prevalence , morbidity and/or mortality rates to the lowest level possible in a given population . However , once control program objectives have been met , continuous interventions are necessary to maintain these minimal rates [1] . In 2007 , the Brazilian Ministry of Health adopted new case detection rates for all ages and for children under 15 years of age as indicators of the effectiveness of leprosy control measures in the country . Because detection of leprosy in those under 15 years of age is considered indicative of recent Mycobacterium leprae ( ML ) transmission , evaluating these cases for epidemiologic markers was especially important [2] . In early 2009 , the global prevalence of leprosy was approximately 213 , 000 cases; however , the annual detection rate of leprosy worldwide has declined . In 2002 , more than 620 , 000 cases were detected; whereas , in 2008 , there were approximately 249 , 000 cases . In Brazil , in 2008 , there were 38 , 914 new leprosy cases detected . Nevertheless , there seems to be a tendency for the detection rates to stabilize in Brazil at somewhat higher levels in the North , Midwest and Northeast regions of the country . In the state of Rio de Janeiro , there is a clear decreasing trend from 1990 to 2008 . For instance , detection rates ranged from 27 . 30 cases per 100 , 000 population in 1997 to 11 . 84 cases per 100 , 000 population in 2008 . The detection rates in Rio de Janeiro for children less than 15 years old in the period 2001–2008 had very high ratings ( 6 . 00/100 , 000 population to 2 . 69/100 , 000 population ) . In addition to the administration of multidrug therapy ( MDT ) to patients diagnosed with leprosy , disease control strategies in Brazil include early new case detection , routine clinical examinations , and Bacillus Calmette-Guérin ( BGG ) vaccination of the patient's contacts , which is a group considered to be at high risk to develop the disease [3] . One activity of early detection of leprosy is contact surveillance , which aims to interrupt disease transmission and prevent the development of disabilities [4] . The notion that group-level factors are important in understanding the risk of disease has long been present in infectious disease epidemiology , because the risk of an individual contracting an infectious disease depends not only on his or her own risk behavior and biological and socio-economic factors , but also on his or her population group . With regard to scientific validity and the practical implications for disease prevention , the growing consensus is that investigations into the causes of disease must include factors defined on multiple levels , such as the individual and communities . In infectious disease epidemiology , multilevel analysis can be used to examine how both group- and individual-level factors are related to individual-level infectious disease outcomes and how factors on both levels affect group differences in the risk of disease . The application of multilevel analysis has only recently begun to emerge in the infectious disease literature [5] , [6] . Several potential risk factors associated with individual features of leprosy patients and their contacts have been suggested but , to date , these factors' effects have yet to be evaluated . In-depth investigation of these factors may allow for the simultaneous examination of group-level and individual-level factors , assessment of the demonstrable interaction between contacts- and index case-level constructs , and exploration of how factors at multiple levels contribute to differences in disease risk . The aim of the present study was to identify potential risk factors of the index cases and their contacts on development of leprosy among contacts .
Since 1987 , the Leprosy Outpatient Clinic , a National Reference Center at the Oswaldo Cruz Foundation in Rio de Janeiro , RJ , Brazil , has conducted routine clinical examinations of the contacts of leprosy patients diagnosed at the Clinic . The Clinic provides health care recommendations to leprosy patients and their families at diagnosis and during treatment . The study population consisted of 6 , 158 contacts of 1 , 201 newly-diagnosed leprosy patients of the cohort treated at the Leprosy Outpatient Clinic from 1987 to 2007 . The average duration of follow-up of contacts was 16 . 9 years . Among the patients , 454 had paucibacillary leprosy , and 747 had multibacillary leprosy . After confirmation of the leprosy diagnosis , patients were given educational information about the disease , and medical visits were scheduled for their close contacts ( within and outside of the household ) . During the initial visits , contacts answered a questionnaire regarding socio-economic status ( income and education level ) and type of contact with the index case . The contacts were examined by specialized dermatologists and neurologists to confirm a leprosy diagnosis and the existence of a BCG scar . The Brazilian Ministry of Health recommends that all leprosy contacts receive the BCG vaccine [3] . Between 1987 and 1991 , all contacts were instructed to attend the Clinic at least once a year . From January 1992 throughout December 2007 , they were also requested to return to the Clinic if and when symptoms and/or skin lesions appeared . Follow-up visits included medical consultations with specialized dermatologists and neurologists . Those presenting signs or symptoms that were suggestive of leprosy were assessed through bacteriological , histopathological , and immunological examinations . In September 2009 , the Brazilian Disease Notification System ( SINAN ) , covering December 1987 to September 2009 , was searched to locate the healthy contacts to ascertain whether any leprosy cases had been missed in contact follow-up procedures . SINAN records were matched to the database of the study group with respect to the variables present in both: name of contact , date of birth and mother's full name . Contacts that had not been identified as leprosy patients in SINAN by September 2009 were considered healthy . Co-prevalent cases were the contacts diagnosed with leprosy at the first examination after the index case was diagnosed . Incident cases were apparently leprosy-free contacts at the time of index case diagnosis but developed the disease at some point during follow-up . Household contacts were defined as individuals who had lived in the same dwelling during the five-year period prior to the index case diagnosis . Non-household contacts were defined as those indicated by the index case as having had other types of contact , such as next-door neighbors , blood relatives , friends and/or co-workers , etc . , during the five-year period prior to the index case diagnosis . Variables that described the contact included sex; age; educational and income levels; blood relationship , if any , and type ( household and non-household ) and length of time of close association with the index case . With regard to BCG vaccination , contacts were examined to verify the presence or absence of a BCG scar , which was considered the first dose . Once a leprosy diagnosis is excluded , the BCG vaccine is administered to a healthy contact , and this vaccination corresponded to the second dose . For the index cases , the variables included sex , age , educational level , family size , bacillary index ( BI ) from the slit skin smear test at the beginning of treatment and disability grade . The patients were classified as paucibacillary , based on a zero BI , or multibacillary , based on an above-zero BI . We classified the initial disability/impairment grade according to the present World Health Organization classification system [7] , which consisted of three grades ( 0 , 1 and 2 ) . Grade 0 indicates no loss of sensation or visible deformity , grade 1 is defined by the loss of sensation without visible deformity , and grade 2 indicates the presence of a visible deformity . All disability grade evaluations were conducted by specialized professionals . A two-level logistic model with a random intercept was used , and the contacts were considered first-level units and grouped with their respective index cases , who were considered second-level units . For the empty models , the Variance Partition Coefficient ( VPC ) was calculated according to the simulation method proposed by Goldstein et al [8] . The total number of simulations was 5 , 000 . Initially , a bivariate analysis was conducted separately for the co-prevalent and incident cases . The association between the occurrence of leprosy disease and a set of independent variables was assessed using the crude odds ratio ( OR ) and the associated 95% confidence interval ( CI ) . The second step of the analysis involved adjusting the multilevel logistic regression model for all the contact and index case variables ( full model – data not shown . ) The final model consisted of all the variables that were statistically significant after adjustment for all other factors related to the contacts and their respective index cases . Additional variables in the final included those recognized for epidemiological relevance or were frequently regarded as confounding variables , such as age of contact , sex , and contact and index case educational levels . The estimated measure of association was the OR . The OR associated with incident-case risk factors may be interpreted as a relative risk ( RR ) when the disease frequency is low , as in the present study . The OR of prevalence cases also estimates the RR if the disease duration among the exposed and unexposed is the same [9] . The software MlWin 2 . 10 was used to perform the multilevel statistical analysis . The estimation method of Penalized Quasi-Likelihood , second order , was adopted throughout the analysis . All contacts who returned to the clinic for examination were eligible for the study . All adult participants and the guardians or parents of the children that were included in the study provided written consent . This study was approved by the Ethics Research of the National School of Public Health .
This study included 6 , 158 contacts of 1 , 201 leprosy patients , with an average 5 . 12 contacts per patient . Of the contacts studied , 57 . 6% ( 3546/6158 ) were female . The mean age was 25 . 6 ( ±17 , 8 ) years . Of the index cases , 63 . 9% ( 767/1201 ) were male , and the mean age was 38 . 2 ( ±16 , 9 ) years . Among the contacts , 452 ( 7 . 3% ) new cases of leprosy were diagnosed . The first contact examination found 319 ( 5 . 2% ) co-prevalent cases , and during the follow-up , 133 ( 2 . 3% ) incident cases were diagnosed . Among the incident cases , this study found an incidence rate of 3 . 32 cases per person-year . The average period for the incident cases of leprosy diagnosis was 4 . 1 years after the index case diagnosis . Among the contacts diagnosed with leprosy , 89 . 4% ( 404/452 ) had multibacillary leprosy , 74 . 5% ( 337/452 ) had paucibacillary leprosy , and 65 . 8% of them ( 222/337 ) had borderline-tuberculoid leprosy . Table 1 shows the numbers and proportion of contacts with leprosy according to the clinical classification of index cases . The VPCs were approximately 18% and 13% for the co-prevalent and incident cases , respectively , i . e . , the proportion of the outcome variability due to the determinants on the first level was somewhat greater in incident patients than in co-prevalent patients . The frequencies and the bivariate analyses for the contacts and index cases , for the co-prevalent and incident cases are shown in Table S1 . A significant association was observed between the contacts diagnosed with leprosy at the initial examination ( co-prevalent cases ) and several of the variables under study; these included few years of schooling ( OR = 1 . 50 , 95% CI: 1 . 03–2 . 19 ) , a monthly family income under three minimum wages ( OR = 1 . 85 , 95% CI: 1 . 35–2 . 54 and OR = 2 . 18 95% CI: 1 . 50–3 . 17 ) , consanguineous relationship with ( OR = 1 . 50 , 95% CI: 1 . 15–1 . 96 ) and close proximity to the index case for a minimum five-year period ( OR = 2 . 64 , 95% CI: 1 . 75–3 . 98 ) . Household contacts were more likely than non-household contacts to present with leprosy , for both co-prevalent cases ( OR = 1 . 44 , 95% CI: 1 . 11–1 . 86 ) and incident cases ( OR = 2 . 05 , 95% CI: 1 . 35–3 . 11 ) . Having received a neonatal BCG vaccine was a protective factor in both co-prevalent and incident cases . In addition , the application of the BCG vaccine , as recommended by the Ministry of Health , was also a protective factor in the follow-up . Among the index case variables , some were associated with a leprosy diagnosis in co-prevalent cases; these included up to 4 years of schooling ( OR = 3 . 31 , 95% CI: 1 . 87–5 . 58 ) , between 4 to 10 years of schooling ( OR = 2 . 53 , 95% CI: 1 . 37–4 . 64 ) , monthly family income up to two minimum wages ( OR = 2 . 17 , 95% CI: 1 . 34–3 . 52 ) , having an income between two and three minimum wages ( OR = 2 . 31 , 95% CI: 1 . 44–3 . 70 ) , and a disability grade = 2 ( OR = 1 . 50 , 95% CI: 1 . 04–2 . 16 ) . The contacts who were 15 years and older had an increased odds ratio ( OR = 8 . 37 , 95% CI: 1 . 12–62 . 4 ) of contracting leprosy , compared with those who were under 15 , only among incident cases . Contacts of male index cases were more likely to have leprosy than contacts of female index cases . This was true for both prevalent and incident leprosy cases among contacts . BIs of index cases over three was significantly associated with the diagnosis of co-prevalent leprosy cases ( OR = 4 . 37 , 95% CI: 2 . 95–6 . 46 ) . BIs of one to three ( OR = 4 . 30 , 95% CI: 2 . 12–8 . 71 ) and more than three ( OR = 7 . 31 , 95% CI: 3 . 63–14 . 75 ) were associated with incident leprosy cases , considering as reference a negative BI . Table 2 summarizes the results of the multivariate analysis . In the final model for co-prevalent cases , the variables that remained associated with leprosy between contacts were household contact ( OR = 1 . 33: 95% CI: 1 . 02–1 . 73 ) and consanguinity with the index case ( OR = 1 . 89 , 95% CI: 1 . 42–2 . 51 ) . With respect to the index case model , the variables associated with leprosy included up to 4 years of schooling and 4 to 10 years of schooling ( OR = 2 . 72 , 95% CI: 1 . 54–4 . 79 and 2 . 40 , 95% CI: 1 . 30–4 . 42 , respectively ) , and bacillary index , which increased the risk of leprosy among contacts for those with index cases with BI of one to three and greater than three ( OR = 1 . 79 , 95% CI: 1 . 19–2 . 70 and OR: 4 . 07 , 95% CI: 2 . 73–6 . 09 , respectively ) . In the multilevel model for incident cases , household exposure was associated with leprosy in the incident case contacts , with OR = 1 . 96 ( 95% CI: 1 . 29–2 . 98 ) . The consanguinous relationship of contacts with their index case was also a significant risk factor for contracting leprosy ( OR = l . 54 , 95% CI: 1 . 00–2 . 37 ) . In connection with index case variables , an elevated bacillary load was the only variable whose association was maintained after adjusting for the other variables under consideration . The presence of a BCG scar showed a highly statistically significant protective effect in both models for co-prevalent and incident cases , with OR = 0 . 28 ( 95% CI: 0 . 21–0 . 37 ) and 0 . 45 ( 95% CI: 0 . 30–0 . 68 ) , respectively . The contacts who received the BCG vaccine also demonstrated significant protection against the disease: OR = 0 . 44 ( 95% CI: 0 . 29–0 . 64 ) . There were no statistically significant differences in the odds between male and female contacts in either incident or co-prevalent cases . Finally , the presence of overdispersion in the final models was not detected . The overdispersion parameter in the model for co-prevalent cases was 0 . 89 and that for incident cases was 0 . 94 .
In this study , we found that the major risk factor among contact incident cases was proximity to the index case . Among the characteristics of the index cases , bacillary load was the only risk factor associated with developing leprosy . A BCG scar and the application of the vaccine after index case diagnosis independently contributed as protective factors . However , among co-prevalent cases , the variables most strongly associated were a consanguinous and household relationship with the index case . Furthermore , a BCG scar contributed independently as a protective factor . Factors related to the index cases included up to 4 years and between 4 to 10 years of schooling and bacillary load , both associated with leprosy among their contacts at the first examination . Although men make up most of the leprosy cases in Brazil , our study did not find any gender differences in the risk of contracting the disease among contacts , suggesting that the gender differences in the detection rates for the general population may be due to differences in their exposure . These findings are in agreement with those of other studies that likewise did not observe any gender differences in the likelihood of acquiring leprosy [10]–[12] Nevertheless , Ali et al . [13] , in a prospective contact study and two other retrospective studies , found that the attack rate was , in fact , lower among women [14] , [15] . Conversely , Fine et al . [16] reported a significantly higher attack rate among men . In the present study , contact age was not associated with leprosy among either co-prevalent or incident cases . Our decision to categorize the age of minors and those over 15 years to conform to the indicator adopted by the Brazilian Leprosy Control Program may be an explanation for this lack of association . Other studies have shown that among contacts the risk of leprosy is significantly higher for those younger than 14 , particularly for contacts of multibacillary index cases [11] , [13] , [17] . Likewise , Moet et al . [12] reported a bimodal distribution according to age: the risk increased for those between 5 to 15 years of age , reached a peak for those aged 15 to 20 , decreased for those aged 20 to 29 , and gradually increased after a 30-year lag . Leprosy has traditionally been associated with lower socio-economic status . An ecological study recently conducted in Brazil by Kerr et al . [17] showed an association between social inequality , population growth and a high prevalence of leprosy . Population-based studies have also described an increased risk of leprosy associated with fewer years in school , poor housing and low income [18] . Our findings suggested an association between level of education and leprosy . However , in our study , poor schooling was associated with disease duration in index case patients and with a higher prevalence of leprosy among their close contacts ( co-prevalence ) . Poor schooling among index case patients is likely to be a proxy for lower socio-economic status and could be associated with late diagnosis of leprosy , allowing for longer periods of exposure among their contacts . This finding is most certainly related to both the unavailability and inaccessibility of health care facilities , making it more difficult for individuals to maintain good health and prevent disease . In the present study , the lack of association between socio-economic markers and the risk of disease could be understood in light of the homogenous distribution of these markers along the study sample; everyone involved in this study was from the same socio-economic strata . From the moment of the index case diagnosis , the consanguinous relatives had a higher risk of developing leprosy ( OR = 1 . 89 , 95% CI: 1 . 42–2 . 51 ) . Most likely due to their increased vulnerability , genetic susceptibility , and type of immune response , these contacts were more likely to become ill . In turn , the confidence interval of the probability of association with incidence cases was just above the cut-off probability of 0 . 05 . A cross-sectional study on determinants of the transmission of leprosy showed that consanguinous relatives had a 2 . 8 higher risk than non-consanguineous contacts [19] . Similarly , Moet et al . [12] , in the initial evaluation of a contact cohort , calculated that consanguinous contacts had an increased odds ( OR = 1 . 65 , 95% CI: 1 . 05 , 2 . 57 ) , regardless of physical distance from their index case . As expected , contact/index case co-habitation was shown to be a key risk factor in developing leprosy . However , the strength of this association was different for both co-prevalent and incident cases . Household contacts had a higher risk for leprosy in the follow-up . Among incident cases , the risk of household contacts developing the disease was twice that among non-household contacts , which also corroborated findings of aforementioned studies . To reiterate , a number of reports have indicated that household contacts are at the highest risk , compared with the general population [11] , [14] , [20] and non-household contacts [16] , [21] . As in other , similar studies , the most important association determining leprosy disease among contacts was the bacillary load of the index case . These findings were in agreement with the literature that demonstrates that multibacillary patients are primarily responsible for ML transmission in endemic areas [10]–[13] , [15] , [20] , [22] . In the follow-up , index cases with BIs over three were eight times more likely to transmit leprosy to their contacts ( incident cases ) than were paucibacillary patients . The contacts of multibacillary index cases also had a four-fold higher chance of being diagnosed with leprosy ( co-prevalent cases ) than did the contacts of index cases with a negative BI . A previous study conducted in Brazil demonstrated that a high familial bacillary index and the presence of more than one source of contamination in the family at the time of first examination of contacts were associated with greater risk of developing leprosy , especially among those younger than 15 years [23] . Again , in the present study , the BCG vaccine administered in infancy was shown to effectively protect against leprosy in 72% [ ( 1-OR ) ×100] of all co-prevalent cases and 55% of incident cases . During the follow-up , the protective rate conferred by the BCG vaccine applied after index case diagnosis was 56% [ ( 1-OR ) ×100 ) ] . Other Brazilian studies have confirmed the significant impact of neonatal BCG on the incidence and transmission of leprosy [24] , [25] . In our study , of the contacts vaccinated who developed leprosy in the follow-up period , 89% have presented with the paucibacillary form of the disease , indicating the protective effect of BCG vaccine against the development of multibacillary forms , consistent with other studies that point to the role of vaccine in the interruption of leprosy transmission [26] , [27] . In summary , socio-economic factors appear to be more strongly associated with leprosy among the contacts found to be ill at the first examination ( co-prevalent cases ) , compared with the association among incident cases . This finding among co-prevalent cases may be secondary to the difficulties that patients with lower educational level have in finding adequate health care facilities and information . With regard to the incident cases , bacillary load factors , i . e . , intensity of transmission , increased the likelihood of contracting leprosy , in comparison with other social and biological factors . Moreover , incident cases developed the disease even when the associated co-prevalent and index cases were undergoing treatment , had neurological and skin examinations , and received the BCG vaccination . A major strength of this study was the multilevel approach in analyzing the data , which allowed for the simultaneous observation of the effects of the predictor variables on both the group ( index case ) and individual levels ( contacts ) . Importantly , inter-group-dependent observations were taken into account , which highlighted and did not disregard the dependency of leprosy as an infectious disease . According to the evaluation of VPC in Goldstein [8] , with regard to empty models , the leprosy variance among contacts that can be attributed to the differences among index cases was 18% of the co-prevalent and 13% of the incident cases . Moreover , we observed that outcome variability at the superior hierarchical level was sufficient to justify the use of this model . We also found that the VPC evaluation of the final models indicated that only 2 . 4% and 4 . 0% of the explained variables continued to be attributable to the index cases , whereas the model appears to be well fitted for both the co-prevalent and incident cases . The ability to accurately identify contacts of leprosy patients who are at high risk of disease is of utmost importance for leprosy control . Surveillance and appropriate health education of household contacts should be strongly reinforced and extended to all close contacts of index case-patients , including their consanguineous relatives . In our study , however , we have identified a group of contacts who , despite all appropriate intervention measures , acquired leprosy . Therefore , household contacts of MB index case-patients , especially those with high bacillary load at diagnosis , should be considered for chemoprophylaxis in addition to immunoprophylaxis with BCG vaccination , once the efficacy of chemoprophylaxis is proven . | Leprosy is an infectious disease that can lead to physical disabilities , social stigma , and great hardship . Transmitted from person to person , it is still endemic in developing countries , like Brazil and India . Effective treatment has been available since 1960 , but early diagnosis of the disease remains the most effective way to stop the transmission chain and avoid late diagnoses and subsequent disabilities . Knowledge of the risk factors for leprosy can facilitate early detection; therefore , our study aimed to investigate the factors presented by leprosy patients and their contacts , who are considered at highest risk of contracting the disease . We studied 6 , 158 contacts of 1 , 201 patients under surveillance from 1987 to 2007 in a Public Health Care Center in the City of Rio de Janeiro , Brazil . We evaluated the ways patient and contact demographics and epidemiological characteristics were associated with the detection of leprosy . Statistical analyses took into account both individual and group characteristics and their interrelationships . The main characteristics facilitating the contraction of leprosy among contacts were shown to be consanguinity and household association . Conversely , the bacillary load index of leprosy patients was the principle factor leading to disease among their contacts . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"epidemiology",
"neglected",
"tropical",
"diseases",
"leprosy"
] | 2011 | Leprosy among Patient Contacts: A Multilevel Study of Risk Factors |
Identifying etiologies of acute febrile illnesses ( AFI ) is challenging due to non-specific presentation and limited availability of diagnostics . Prospective AFI studies provide a methodology to describe the syndrome by age and etiology , findings that can be used to develop case definitions and multiplexed diagnostics to optimize management . We conducted a 3-year prospective AFI study in Puerto Rico . Patients with fever ≤7 days were offered enrollment , and clinical data and specimens were collected at enrollment and upon discharge or follow-up . Blood and oro-nasopharyngeal specimens were tested by RT-PCR and immunodiagnostic methods for infection with dengue viruses ( DENV ) 1–4 , chikungunya virus ( CHIKV ) , influenza A and B viruses ( FLU A/B ) , 12 other respiratory viruses ( ORV ) , enterovirus , Leptospira spp . , and Burkholderia pseudomallei . Clinical presentation and laboratory findings of participants infected with DENV were compared to those infected with CHIKV , FLU A/B , and ORV . Clinical predictors of laboratory-positive dengue compared to all other AFI etiologies were determined by age and day post-illness onset ( DPO ) at presentation . Of 8 , 996 participants enrolled from May 7 , 2012 through May 6 , 2015 , more than half ( 54 . 8% , 4 , 930 ) had a pathogen detected . Pathogens most frequently detected were CHIKV ( 1 , 635 , 18 . 2% ) , FLU A/B ( 1 , 074 , 11 . 9% ) , DENV 1–4 ( 970 , 10 . 8% ) , and ORV ( 904 , 10 . 3% ) . Participants with DENV infection presented later and a higher proportion were hospitalized than those with other diagnoses ( 46 . 7% versus 27 . 3% with ORV , 18 . 8% with FLU A/B , and 11 . 2% with CHIKV ) . Predictors of dengue in participants presenting <3 DPO included leukopenia , thrombocytopenia , headache , eye pain , nausea , and dizziness , while negative predictors were irritability and rhinorrhea . Predictors of dengue in participants presenting 3–5 DPO were leukopenia , thrombocytopenia , facial/neck erythema , nausea , eye pain , signs of poor circulation , and diarrhea; presence of rhinorrhea , cough , and red conjunctiva predicted non-dengue AFI . By enrolling febrile patients at clinical presentation , we identified unbiased predictors of laboratory-positive dengue as compared to other common causes of AFI . These findings can be used to assist in early identification of dengue patients , as well as direct anticipatory guidance and timely initiation of correct clinical management .
As malaria incidence continues to decrease throughout the tropics , a new area of research has focused on identifying etiologies of non-malaria , acute febrile illness ( AFI ) [1 , 2] . Knowledge is limited in this area , in large part because AFIs often have similar non-specific clinical presentations early in the clinical course when most patients are likely to present for care . In addition , rapid point-of-care diagnostics are often not readily available . Surveillance for AFIs , if done , is largely passive and relies on clinical identification of cases and voluntary case reporting . Therefore , burden of disease for the etiologic agents of AFI are likely underestimated . An improved understanding of the major causes of AFI is important to guide clinical management , develop diagnostics , inform public health policy , and direct prevention efforts [3] . In Mexico , South and Central America , and the Caribbean , AFI are common among patients of all age groups . In the last four decades , dengue , a mosquito-borne AFI caused by four genetically distinct dengue viruses ( DENV 1–4 ) , has become an increasingly common cause of AFI [4 , 5] . The burden of dengue is thought to be less in Latin America than in Southeast Asia [6]; however , several studies have found that the incidence of dengue is likely underestimated in Latin America due to reliance on passive case surveillance [6–8] . Understanding region-specific etiologies of AFI and estimating the true incidence of dengue is necessary to plan large scale interventional trials for assessing the impact of mosquito control measures and vaccines . In addition , collecting clinical signs and symptoms from AFI patients of all ages with identified etiologic agents has utility in developing unbiased case definitions and identifying early clinical predictors to guide clinical management . Prospective studies enrolling patients with AFI provide a methodology to systematically identify causes of AFI in a population and describe variation in the clinical course by patient age and etiologic agents . Since 2000 , nine such studies have evaluated AFIs including dengue among both pediatric and adult patients [9–18] . While these studies are comparable in many ways , the studies differ in that several excluded either infants and young children [9–13 , 16] , older adults [12 , 13] , severe or hospitalized cases [13 , 16] , or cases with a known source of fever [9–12 , 14] . In addition , most studies were conducted in low resource settings in Southeast Asia where malaria is still endemic [9 , 10 , 12–18] . In fact , two studies enrolled based on a potential participant meeting national eligibility criteria for malaria testing [12 , 13] , and two other studies excluded cases based on malaria blood smear positivity [14 , 15] . In this manuscript , we describe a 3-year prospective study of AFI among all age groups that used a pre-defined diagnostic testing algorithm for DENV 1–4 and 21 other pathogens . We conducted this study in Puerto Rico , where malaria was eradicated in 1962 [19] and dengue has been endemic since the late 1960s [20] . We describe the frequency of dengue and other AFIs , and the distribution of these diseases in terms of person , place and seasonality . Last , we describe clinical predictors of dengue by timing of presentation compared to other AFIs .
The study was conducted in southern Puerto Rico at Saint Luke’s Episcopal Hospital ( SLEH ) –Ponce , a 425-inpatient bed , tertiary care teaching hospital during May 7 , 2012–May 6 , 2015; and SLEH—Guayama , a 161-inpatient bed hospital during February 1 , 2013–May 6 , 2015 . SLEH-Ponce is one of four hospitals serving 481 , 708 residents of Ponce and 11 neighboring municipalities [21] . SLEH-Ponce has about 50 , 000 emergency department ( ED ) visits and 11 , 000 inpatient admissions annually . SLEH-Guayama is one of two hospitals that serve 96 , 439 residents of Guayama and three adjacent municipalities . SLEH-Guayama has 35 , 000 ED visits and 6 , 000 inpatient admissions annually . Patients presenting to the ED or as a direct hospital admission were eligible for enrollment if fever was present ( defined by a body temperature of ≥38 . 0°C [oral] or ≥38 . 5°C [axillary] ) or they reported a history of fever of ≤7-day duration . After informed consent was administered , vital signs , signs and symptoms of current AFI , history of exposures and chronic disease , and clinical laboratory results were recorded on an enrollment case report form ( CRF ) . A physician examined the participants and recorded the clinical diagnosis on the CRF . Participants discharged to home after enrollment were asked to return 7–10 days post-illness onset ( DPO ) . At the follow-up visit , a second completed CRF included a report of any healthcare services received and signs and symptoms experienced since enrollment . Participants admitted to the hospital upon enrollment had their hospital course summarized on a standardized data collection form that included treatment received , results of clinical laboratory and radiologic investigations , and disease manifestations . Prior to enrollment , informed consent was administered in accordance with Puerto Rico law ( Article 13 , Section 13 , Regulation 7617 of the Office of Patient Ombudsman , Act #194 ) . Specifically , written informed consent was obtained from eligible adults >20 years old and emancipated minors 14–20 years old . Written informed assent was obtained from non-emancipated minors 14–20 years old and written informed consent was obtained from the parents or guardians . Verbal informed assent was obtained from children 7–13 years old and written informed consent was obtained from the parents or guardian , and the parents or guardian of children <7 years old . The Institutional Review Boards at the Centers for Disease Control and Prevention ( CDC ) and Ponce Health Sciences University approved the study protocol . All study participants had blood ( 5 mL in EDTA , 7 mL whole blood ) , urine ( 15 mL ) , nasopharyngeal ( NP ) , and oropharyngeal ( OP ) specimens collected at enrollment . Convalescent blood ( 5 mL in EDTA , 5 mL whole blood ) and urine ( 10 mL ) were collected at the follow-up visit or hospital discharge . NP and OP specimens were placed in a vial containing viral transport medium . Serum , blood , and urine specimens and inoculated vials were kept at 4°C until transported to CDC Dengue Branch ( CDC-DB ) in San Juan , Puerto Rico . Molecular diagnostic testing for DENV 1–4 , influenza A and B viruses ( FLU A/B ) , and 12 other respiratory viruses ( ORV ) including adenovirus ( AdV ) , human respiratory syncytial virus ( HRSV ) , human metapneumovirus ( HMPV ) , parainfluenza virus 1–4 ( PIV-1–4 ) , human rhinovirus ( HRV ) , and four human coronaviruses ( HCoV ) ( 229E , OC43 , NL63 and HKU1 ) , was performed at CDC-DB . However , testing for HRV , PIV-2 , PIV-4 , and the four HCoV was discontinued after the first year because of low yield ( i . e . , only 1 PIV-2 , 37 HCoV and 4 HCoV co-infections identified ) . In brief , RNA was extracted from NP and OP specimens and tested for ORV and FLU A/B viral genome by real time , reverse transcriptase-polymerase chain reaction assay ( rRT-PCR ) [22] . Serum specimens collected ≤6 DPO were tested by DENV-serotype specific rRT-PCR [23 , 24] , and those collected ≥4 DPO were tested by an antibody-capture enzyme-linked immunosorbent assay ( MAC-ELISA ) ( InBios International , Inc . , Seattle , WA ) [25–27] . Beginning in May 2014 , specimens collected ≤6 DPO were tested by CHIKV-specific real-time RT-PCR [28] , and those collected ≥6 DPO were tested by anti-CHIKV MAC-ELISA [25] . Remaining serum , whole blood , and urine were stored at -70°C until shipped to CDC in Atlanta , Georgia . At CDC , serum specimens collected ≤3 DPO were tested in the Picornavirus Laboratory by a pan-enterovirus real-time RT-PCR assay that targets the VP1 region [29]; positive specimens were sequenced . Paired serum specimens from enrollment and the follow-up visit or hospital discharge were tested for Leptospira spp . , and Burkholderia pseudomallei at the Bacterial Special Pathogens Branch Laboratory . Specimens were tested by microscopic agglutination test ( MAT ) for 20 Leptospira reference antigens representing 17 serogroups [30] . All convalescent serum specimens were tested for presence of Burkholderia pseudomallei and Leptospira antibodies by an indirect hemagglutination assay ( IHA ) [31] and MAT respectively , and acute specimens were tested in cases for which the corresponding convalescent specimen was positive . The first 250 patients with Leptospira spp . and Burkholderia pseudomallei negative specimens and for which paired specimens were available were tested by IFA for Rickettsia spp . , Ehrlichia spp . , and Coxiella spp . at the Rickettsial Zoonoses Branch Laboratory . Whole blood and/or acute serum from cases with a reactive IFA were assessed for C . burnetii , R . rickettsii , R . typhi , and/or E . chaffeensis DNA by PCR . A laboratory-positive dengue case had DENV nucleic acid or anti-DENV IgM detected in a single specimen . A laboratory-negative dengue case had no anti-DENV IgM detected in serum collected ≥6 DPO . A laboratory-positive influenza case was defined by presence of FLU A/B nucleic acid in a NP or OP specimen . Laboratory-positive HMPV , HRSV , ADENO , PIV-1 , PIV-2 , PIV-3 , PIV-4 , HRV , and HCoV cases had the respective viral nucleic acid present in a NP or OP specimen . A laboratory-positive leptospirosis case was defined by ≥4-fold increase in MAT titers in paired specimens , or MAT titer ≥800 in a single specimen . A laboratory-positive melioidosis case was defined by presence of Burkholderia pseudomallei nucleic acid in a clinical specimen and/or a ≥4-fold rise in IHA titer in paired specimens . A laboratory-positive enteroviral case was defined by presence of enterovirus nucleic acid in serum collected ≤3 DPO . A laboratory-positive ehrlichiosis case was defined by presence of Ehrlichia chaffeensis IgG reciprocal titer >1:128 by IFA , a ≥4-fold rise in IgG titer in paired serum specimens , or a positive PCR on an acute whole blood or serum specimen . A laboratory-positive Rickettsia case was defined by presence of R . rickettsii or R . typhi IgG titer >1:128 by IFA , a ≥4-fold rise in IgG titer in paired serum specimens , or a positive PCR in a whole blood or serum specimen . A laboratory-positive Coxiella case was defined by presence of C . burnetii IgG titer >1:128 by IFA , a ≥4-fold rise in IgG titer in paired serum specimens , or positive PCR on an acute whole blood or serum specimen . Leukopenia was defined as a white blood cell count ≤5 , 000 cells/μL . Thrombocytopenia was defined as a platelet count ≤100 , 000/μL . Severe hemoconcentration was defined by a hematocrit ≥20% above the U . S . population mean hematocrit for age and sex , and moderate hemoconcentration was defined by a hematocrit >97 . 5th percentile for age and sex to less than the cut-off for severe hemoconcentration [32] . A skin bleed was defined by presence of skin bruising and/or petechiae . Mucosal bleeds included epistaxis , gingival bleed , hematemesis , melena , hematochezia , menorrhagia , or hematuria ( >5 red blood cells per high powered field ) in a male or non-menstruating female . Frequencies were calculated for demographic characteristics and medical history by study year . Clinical and laboratory features were compared by sex , age group , and laboratory diagnostic groups including infection with DENV , FLU A/B , ORV , and CHIKV . Differences in proportions were tested by applying the chi-square test , and medians were compared using the Mann-Whitney-Wilcox test . Bonferroni correction was used to account for simultaneous multiple comparisons . The Woolf test was performed to evaluate the homogeneity of odds ratio across DPO group for death among adult participants by sex , and the Mantel-Haenzel test was used to determine significance . Multiple imputation was used to predict an independent plausible value for missing values using generalized linear regression on non-missing variables to create 40 imputed complete data sets [33] . To identify predictors of laboratory-positive dengue as compared to all other AFI cases , stepwise Akaike Information Criterion ( AIC ) variable selection was used for each imputed data set . Variables retained at least once in the 40 models were included in a pooled logistic regression model [34] . Odds ratios ( OR ) and 95% confidence intervals ( CI ) were calculated for significant early ( <3 DPO ) and late ( 3–5 DPO ) predictors . Data were analyzed using the “mi” and “MASS” packages from R software ( V3 . 3 . 0 , R Foundation for Statistical Computing , Vienna , Austria ) .
Most ( 71 . 8% ) participants were enrolled <3 DPO ( median DPO at enrollment = 1 , range: 0–8 days ) ( Table 2 ) . The timing of presentation did not differ by sex but did differ by age , with a higher proportion of child participants ( i . e . , <20 years old ) presenting <3 DPO than adult participants ( 74 . 9% child vs . 68 . 2% adult females , p <0 . 001; and 73 . 4% child vs . 67 . 4% adult males , p <0 . 001 ) . One quarter ( 24 . 9% ) of participants were admitted to the hospital at enrollment . Adult participants were less likely to be admitted than child participants; a higher proportion of female adult participants than male adult participants were sent home after enrollment ( 78 . 3% vs . 74 . 6% respectively , p <0 . 05 ) . However , a higher proportion of male versus female adult participants died after enrollment ( 0 . 8% vs . 0 . 2% respectively ) , in fact , adult males were five times more likely to die than adult females when adjusting by DPO ( OR = 5 . 4 , CI: 1 . 5–19 . 0 ) . There were no statistical significant differences between female and male participants <20 years old in terms of the timing of presentation and disposition . The most common signs and symptoms ( aside from fever ) at enrollment were tiredness/lethargy ( 73 . 5% ) , anorexia ( 65 . 0% ) , chills ( 64 . 5% ) , headache ( 64 . 3% ) , muscle , bone or back pain ( 60 . 0% ) , cough ( 53 . 4% ) , red conjunctiva ( 49 . 2% ) , rhinorrhea ( 49 . 1% ) , nausea ( 48 . 9% ) , and joint pain ( 48 . 9% ) . Slightly more than half ( 54 . 8% , 4 , 930 ) of the 8 , 996 participants had a pathogen detected ( Fig 2 ) . CHIKV was detected in 1 , 635 ( 18 . 2% ) participants and was the most common pathogen detected , followed by FLU A/B ( 1 , 074 , 11 . 9% ) , DENV 1–4 ( 970 , 10 . 8% ) , and ORV ( 904 , 10 . 3% ) . Most chikungunya ( 1 , 499 , 91 . 7% ) and dengue ( 685 , 70 . 6% ) cases were confirmed by RT-PCR . Among PCR-positive cases , DENV-1 was detected most frequently ( 645 , 94 . 2% ) , followed by DENV-4 ( 38 , 5 . 5% ) , and DENV-2 ( 2 , 0 . 3% ) ; no DENV-3 infections were identified . The majority ( 736 , 68 . 5% ) of influenza cases had FLU A virus detected . Among the ORV cases , adenovirus was detected most frequently ( 284 , 31 . 4% ) , followed by RSV ( 175 , 19 . 4% ) , HMPV ( 168 , 18 . 6% ) , PIV-3 ( 138 , 15 . 3% ) , PIV-1 ( 101 , 11 . 2% ) , HCoV ( 37 , 4 . 1% ) , and PIV-2 ( 1 , 0 . 1% ) . Overall , enterovirus ( 80 , 0 . 9% ) , leptospirosis ( 11 , 0 . 1% ) , and melioidosis ( 2 , 0 . 02% ) cases were infrequently identified . Positive blood , urine or other culture , taken at the discretion of the site physician , were available for 145 ( 1 . 6% ) participants . Co-infection was identified by molecular detection of two pathogens in 109 participants ( Table 3 ) . Co-infections most commonly occurred among participants infected with enterovirus ( 13/80 , 16 . 3% of all enterovirus cases ) , followed by ORV ( 67/904 , 7 . 4% ) , FLU A/B ( 46/1074 , 4 . 3% ) , DENV ( 34/970 , 3 . 5% ) , and CHIKV ( 27/1635 , 1 . 7% ) . The distribution of pathogens causing AFI varied by age ( Fig 2 ) . The proportion of chikungunya cases increased with age , accounting for 9 . 3% of all AFI cases in participants <5 years old versus 33 . 4% in participants ≥50 years old . In contrast , the contribution of ORV to AFI cases decreased with age , making up 21 . 6% of AFI cases in participants <5 years old , 6 . 4% in participants 5–19 years old , 3 . 7% in participants 20–49 years old , and 4 . 1% in participants ≥50 years old . Dengue was the most common cause of AFI in participants 5–19 years old , accounting for 20 . 3% of all cases; 2 . 8% of AFI cases in participants <5 years old were dengue , 9 . 8% in participants 20–49 years old , and 7 . 4% in participants ≥50 years old . The contribution of influenza was similar among age groups making up 8 . 4% of AFI cases in participants <5 years old , 13 . 9% in 5–19 years old , 15 . 4% in 20–49 years old , and 9 . 7% in ≥50 year-old participants . Analysis of the temporal disease trends demonstrated that a dengue epidemic occurred in 2012 and continued through 2013 , during which a total of 921 dengue cases were detected ( Fig 3 ) . In comparison , few ( n = 49 ) dengue cases were detected in 2014 to the end of the study period in 2015 . The first chikungunya case was detected in May of 2014 , and was followed by a six-month outbreak during which 1 , 558 cases were detected . Few ( n = 61 ) chikungunya cases were detected in 2015 . A large bimodal influenza epidemic took place in 2013 with increased case detection in the dry months of January–April ( n = 225 ) , and during the rainy season , July–October ( n = 302 ) . Fewer influenza cases ( n = 356 ) were detected in 2014 and 2015 , and those detected occurred primarily in dry months with no obvious bimodal distribution . An increase in AFI cases due to ORV was detected at the same time influenza cases were detected , with the exception of 2013 when the peak time of ORV case detection appeared to follow that of influenza . Subject demographics at enrollment differed by subsequent laboratory diagnosis ( Table 4 ) . A lower proportion of participants with dengue and ORV illness were females when compared with participants with chikungunya . Participants with ORV illness were significantly younger ( median age = 3 . 2 years , p <0 . 001 ) than participants with dengue ( 15 . 4 years ) , chikungunya ( 24 . 3 years ) , or influenza ( 14 . 1 years ) . In contrast , the median age of participants with chikungunya was significantly greater than participants in all other diagnostic groups , and they were more likely to report having a chronic medical condition . A higher proportion of participants with dengue reported having a household member with dengue at enrollment than participants with other diagnoses ( 11 . 8% of dengue cases versus ≤5% in other diagnostic groups , p <0 . 001 ) . Over half of all participants reported having mosquito bites in the 30 days before enrollment; however , a higher proportion of participants with chikungunya reported mosquito bites than participants with other laboratory diagnoses ( 73 . 9% versus <55% in other diagnostic groups , p<0 . 001 ) . Clinical presentation and disposition varied by laboratory diagnostic group ( Table 4 ) . Participants with laboratory-positive dengue presented later ( median = 3 days ) , and a higher proportion were admitted at enrollment than participants with other laboratory diagnoses; nearly half ( 46 . 6% ) of dengue cases were admitted compared with 27 . 3% of participants with ORV illness , 18 . 8% with influenza , and 11 . 2% with chikungunya . A significantly higher proportion of participants with dengue had chills , signs of poor circulation , eye pain , headache , dizziness , anorexia , nausea , abdominal pain , and diarrhea at enrollment than participants with influenza , ORV illness or chikungunya . A higher proportion of participants with dengue versus these other diagnoses had thrombocytopenia and leukopenia . Compared to influenza and ORV illness cases , a significantly higher proportion of dengue cases had a skin rash , pruritic skin , any bleeding , a skin bleed , and muscle , bone , back , and joint pain , whereas a higher proportion of chikungunya versus dengue cases had these findings . A higher proportion of dengue versus influenza and ORV illness cases had facial and/or neck erythema and mucosal bleeding . In contrast , a significantly higher proportion of participants with influenza and ORV illness than dengue had cough , rhinorrhea , and sore throat . Among 6 , 349 participants who presented early ( <3 DPO ) in the clinical course , leukopenia , thrombocytopenia , headache , eye pain , nausea , and dizziness were significant positive predictors of laboratory-positive dengue as compared to all other AFI cases across all age groups ( Table 5 ) . Presence of rhinorrhea and irritability predicted non-dengue AFI . Age group had a statistically significant effect on multiple predictors ( Table 6 ) . Rash was a positive early predictor of dengue among participants <5 years old , and being male was a positive predictor among adults 20–49 years old . Chills and cough were positive predictors for those >50 years old while cough was a negative predictor among those <20 years old . Muscle , bone or back pain was a negative predictor in those >50 years old . Pruritic skin as a predictor varied by age group , but most significantly between the <5 and 50+ year-old groups . Among the 2 , 146 participants who presented 3–5 DPO , thrombocytopenia , leukopenia , facial and/or neck erythema , nausea , eye pain , signs of poor circulation , and diarrhea were significant positive predictors of dengue across all age groups ( Table 7 ) . Presence of rhinorrhea , red conjunctiva and cough predicted non-dengue AFI . Again , age group significantly affected multiple predictors ( Table 8 ) . Abdominal pain was a positive predictor for participants 20–49 years old . Red and/or swollen joints was a positive predictor among participants <5 years old but a predictor of non-dengue AFI among participants ≥50 years old . Leukopenia was a significant positive predictor across all age groups , but to varying degrees . Chills; muscle , bone , back and joint pain; and any bleeding as predictors varied depending on the age group .
As a clinical syndrome , AFIs are a diagnostic challenge for clinicians especially early in the clinical course when anticipatory guidance and supportive care may pre-empt medical complications . Our study identified the AFI etiology in over half ( 55% ) of participants and most were infected with one of nine viral pathogens . This detection frequency was higher than that of other recent prospective AFI studies that tested for multiple pathogens ( 55% versus 36–41% ) [9–12 , 15] . This difference may in part be explained by the greater contribution of chikungunya in our study than in the other studies that tested for this pathogen [9 , 10 , 17] . However , unlike other studies , we were unable to detect any evidence of disease caused by Rickettsia or Coxiella spp . , which made up 4–13% of all AFIs in other studies [10–13]; and unlike other areas , malaria [9 , 11–13 , 18] and typhoid fever [9 , 10 , 12 , 13 , 15] were not part of our diagnostic algorithm as they are only occasionally detected among travelers returning to Puerto Rico . While DENV was not as commonly identified as CHIKV or FLU A/B , participants with dengue were more likely to be admitted to the hospital at enrollment . The proportion of AFI cases with dengue in our study was comparable to other recent studies which found 4–9% of AFI cases had dengue [9–13 , 15–18] . One exception to this was a study that found 34% of AFI patients had dengue; however , the study’s eligibility criteria likely enhanced enrollment of dengue cases [14] . In our study , dengue incidence varied by age group with a nearly a 10-fold difference between participants <5 years old and 10–19 years old ( 3% versus 27% ) , which may be due to differences in likelihood of seeking medical care in primary versus secondary DENV infections [35] . Of note , 6% of participants ≥65 years old had dengue as a cause of AFI , a finding comparable to a Puerto Rico study in which 5% of 17 , 666 laboratory-positive dengue cases detected by surveillance were ≥65 years old [36] . In contrast , other recent prospective studies [37 , 38] and a cross-sectional serosurvey [39] conducted in other dengue endemic countries found few , if any , symptomatic dengue cases among older participants . Whether this is due to a lower force of infection in Puerto Rico , immunosenescence , evolution of genotypes/strains of DENV , differences in prevalence of underlying chronic disease or health care seeking behavior in Puerto Rico , or lack of life-long homotypic immunity is not known [40–42] . However , DENV-1 has been in circulation in Puerto Rico since the 1970s and involved in every major outbreak since [35 , 43] . Chikungunya , the most commonly identified AFI overall , was least likely to result in hospital admission , although two male participants with CHIKV infection died . These cases were older individuals ( >75 years old ) who had underlying co-morbidities which may have complicated their clinical course . Nonetheless , since autopsy was not performed for either case , ascertaining whether CHIKV infection played a role in either fatality is difficult . However , in our study chikungunya was disproportionally identified among older participants , with positivity increasing from <10% of pre-school aged children to about one-third of participants ≥50 years old . This pattern of disease has been seen in other areas with recent CHIKV emergence [18] , and may be due to older individuals having an increased likelihood of complications due to preexisting co-morbidities [44 , 45] . Co-infections confirmed by molecular assays were detected among 1% of our participants , most commonly involving enteroviral or ORV infections; less than one-third of all co-infections included a DENV or CHIKV infection . Another recent prospective study found that 1% of AFI participants had co-infections involving molecularly diagnosed dengue or influenza , malaria , and positive blood culture [9] . Interestingly , we did not detect any co-infections involving CHIKV and DENV . An analysis of island-wide surveillance data from Puerto Rico during the same time period found only one CHIKV/DENV co-infection among approximately 1 , 000 specimens tested by RT-PCR for both DENV and CHIKV [46] . These findings are consistent with another prospective AFI study conducted in Sri Lanka [17] . Although a recent study has shown that Aedes aegypti can be infected with as many as three arboviruses simultaneously and can likely transmit these viruses to humans [47] , the frequency of co- or tri-infection of mosquitoes in the wild depends upon the geographic spread and degree of circulation of each virus . During our study , DENV transmission decreased significantly before CHIKV transmission peaked , making co-infections less likely . In addition , in Puerto Rico , where Aedes aegypti is the sole vector for CHIKV and DENV , viral interaction and viral interference within the mosquito may reduce the likelihood of co-infection [48–50] . However , RT-PCR positive DENV/CHIKV co-infections have been documented at higher rates in five countries [51] . We identified differences in clinical predictors of laboratory-positive dengue by timing of presentation and age group highlighting the importance of considering these factors when developing prediction algorithms for clinical management [52–60] . We found , as have others [61] , that even early ( <3 DPO ) in the clinical course leukopenia and thrombocytopenia are predictive of dengue across all age groups , and thrombocytopenia strengthened as a predictor over time . In our study , headache and eye pain were the only “aches and pains” that were predictive of dengue for all age groups [62] . Eye pain was a predictor early and later in the clinical course , a finding consistent with pediatric [63] and adult [56] prospective cohort studies , as well as a surveillance study conducted in Puerto Rico [52] . We also found that rash among children <5 years old presenting early and erythema on the face and/or neck in all age groups presenting 3–5 DPO , were positive predictors of dengue . While the presence of skin rash has been found to be a predictor of dengue in several prospective studies [61] , few studies have evaluated erythema as a predictor [18 , 64 , 65] . Last , like other prospective studies [56 , 66] , we found that nausea is an early predictor for dengue . We were also able to show that adults aged 20–49 years presenting 3–5 DPO were more likely to have abdominal pain than those with other AFIs , and dengue cases of all ages presenting 3–5 DPO were also more likely to have diarrhea and poor circulation in addition to nausea , findings that lend support to the idea that warning signs for severe dengue develop after the early phase of the illness . Our study , which enrolled all patients presenting with fever regardless of age , sex , or clinical characteristics , may be limited in generalizability . The study was conducted in southern Puerto Rico which may differ from neighboring islands and other parts of the island with regard to population demographics , preexisting immunity to DENV and other flaviviruses , and exposure to infections . Second , while we enrolled nearly 600 older adults ( ≥65 years old ) , we were unable to adequately evaluate predictors of dengue among this population because we had only 36 dengue cases and most presented early in the clinical course . Last , we did not systematically collect stool and test for potential enteric pathogens , and bacterial infections were likely under recognized because blood cultures were only done on patients in whom sepsis was suspected . While our study identified an etiology in over half of all AFI cases , the etiology of 45% of AFI remained unknown even after extensive testing and the majority of diagnosed cases were caused by one of nine viral pathogens that typically do not require empiric therapy . In fact , we were unable to find any cases of Rickettsia spp . , Ehrlichia spp . , and Coxiella spp . , and only sporadic cases of melioidosis and leptospirosis were identified . Our findings demonstrate that dengue is not only one of the leading causes of AFI in Puerto Rico , but results in greater morbidity than other AFIs as measured by need for hospitalization . Moreover , dengue affects people of all ages including older adults who may be at higher risk of developing medical complications . Clinicians should include dengue on the differential diagnosis of AFI among older adults so that timely anticipatory guidance can be offered . We found that the presence of leukopenia and thrombocytopenia were the best predictors of dengue in both time periods overall and for all age groups . Our findings suggest that eye pain should be reevaluated as a predictor of dengue . Future studies should focus on improving clinical diagnosis of AFI including dengue by timing of presentation and age of the patient . | We conducted a prospective study of acute febrile illness ( AFI ) in Puerto Rico to better understand the etiology of AFI among all age groups in the tropics . Such findings could assist clinicians to identify disease-specific characteristics , which can then be used to initiate proper patient management . We enrolled 8 , 996 AFI patients and tested them for dengue viruses 1–4 ( DENV 1–4 ) and 21 other pathogens . A pathogen was detected in 55% of patients , most frequently chikungunya virus ( CHIKV , 18% ) , influenza A or B virus ( FLU A/B , 12% ) , DENV 1–4 ( 11% ) , or another respiratory virus ( ORV , 10% ) . Participants with dengue presented later after symptom onset and were hospitalized more often ( 47% ) than patients with another etiology of AFI ( 27% with ORV , 19% with FLU A/B , and 11% with CHIKV ) . Predictors of patients with dengue differed by timing of presentation but included eye pain , nausea , and low white blood cell or platelet counts; negative predictors included symptoms of respiratory illness . By enrolling febrile patients at clinical presentation , we identified unbiased predictors of patients with dengue as compared to other common AFI . Findings can be used to diagnose dengue patients to provide early and appropriate clinical management . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"dengue",
"virus",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"togaviruses",
"chikungunya",
"infection",
"influenza",
"pathogens",
"tropical",
"diseases",
"microbiology",
"geographical",
"locations",
"orthomyxoviruses",
"alphaviru... | 2017 | Clinical and epidemiologic characteristics of dengue and other etiologic agents among patients with acute febrile illness, Puerto Rico, 2012–2015 |
It has been demonstrated that Terminal Flowering 1 ( TFL1 ) in Arabidopsis and its functional orthologs in other plants specify indeterminate stem growth through their specific expression that represses floral identity genes in shoot apical meristems ( SAMs ) , and that the loss-of-function mutations at these functional counterparts result in the transition of SAMs from the vegetative to reproductive state that is essential for initiation of terminal flowering and thus formation of determinate stems . However , little is known regarding how semi-determinate stems , which produce terminal racemes similar to those observed in determinate plants , are specified in any flowering plants . Here we show that semi-determinacy in soybean is modulated by transcriptional repression of Dt1 , the functional ortholog of TFL1 , in SAMs . Such repression is fulfilled by recently enabled spatiotemporal expression of Dt2 , an ancestral form of the APETALA1/FRUITFULL orthologs , which encodes a MADS-box factor directly binding to the regulatory sequence of Dt1 . In addition , Dt2 triggers co-expression of the putative SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( GmSOC1 ) in SAMs , where GmSOC1 interacts with Dt2 , and also directly binds to the Dt1 regulatory sequence . Heterologous expression of Dt2 and Dt1 in determinate ( tfl1 ) Arabidopsis mutants enables creation of semi-determinacy , but the same forms of the two genes in the tfl1 and soc1 background produce indeterminate stems , suggesting that Dt2 and SOC1 both are essential for transcriptional repression of Dt1 . Nevertheless , the expression of Dt2 is unable to repress TFL1 in Arabidopsis , further demonstrating the evolutionary novelty of the regulatory mechanism underlying stem growth in soybean .
Stem growth habit is an important morphological and adaptation trait in flowering plants , which is primarily shaped by regulatory processes converting the vegetative shoot apical meristems ( SAMs ) that form leaves to the inflorescence meristems ( IMs ) and then floral meristems ( FMs ) that form flowers [1–3] . Such processes have been best studied in Arabidopsis [4 , 5] . Upon floral induction by both environmental signals ( e . g . , day length , light quality , and temperature ) and endogenous cues ( e . g . , age and hormone ) , the primary shoot meristems of Arabidopsis begin to produce the determinate IMs on its flanks , where the floral identity genes such as LEAFY ( LFY ) and APETALA1 ( AP1 ) are expressed to develop flowers [6–8] . However , the SAMs in the center of the stem tips sustain the indeterminate growth due to the spatial expression of a floral repressor Terminal Flower1 ( TFL1 ) [1 , 9 , 10] , which represses the expression of LFY and AP1 and thus prevents the formation of FMs [11–12] . As a result , the wild-type ( TFL1 ) Arabidopsis produces the indeterminate apical stems that grow indefinitely . By contrast , LFY and AP1 are expressed in the center of stem tips of the loss-of-function tfl1 mutants to give rise to determinate growth , with terminal flowers on the stem tips [1 , 8 , 11 , 13 , 14] . In addition , both LFY and AP1 are able to bind to the TFL1 locus to suppress its expression in floral meristems [15 , 16] . Although the full functions of TFL1 in Arabidopsis remain to be elucidated , it is documented that the putative orthologs of TFL1 are widely conserved among diverse plant species including many leguminous and solanaceous species , and in particular , their roles as floral repressors , such as Dt1 in soybean ( Glycine max ) [17 , 18] , PvTFL1y in common bean ( Phaseolus vulgaris ) [19] , Det in pea ( Pisum sativum ) [20] , Sp in tomato ( Solanum lycopersicum ) [21] , and CaSP in peppers ( Capsicum annuum ) [22] , in producing indeterminate stems are conserved . In general , the wild progenitor species of these individual crops and the majority of the cultivated varieties from these species exhibit indeterminate stem growth . Nevertheless , determinate growth habit in these crops was also selected through domestication or modern breeding , and adapted to specific eco-regions for agricultural production [23–25] . The determinate soybean varieties rose originally from independent human selections of four distinct single-nucleotide substitutions in the Dt1 gene during soybean domestication from its wild progenitor Glycine soja , each of which led to a single amino acid change that resulted in a recessive dt1 allele specifying determinate stem growth [17] . In general , determinate soybean cultivars have distinctly separate vegetative and reproductive stages and are relatively late maturing and grown in the southern eco-regions of both the United States and China . By contrast , the indeterminate cultivars have more overlap of vegetative growth with reproductive development , providing better adaptation to shorter growing season in the north [26] . In addition to these two major types of stem growth habit , semi-determinate cultivars , which produce stems with terminal racemes similar to those observed in determinate cultivars but show an intermediate phenotype have been developed through breeding in the past few decades and deployed for production in the north . While semi-determinate cultivars usually produce slightly fewer stem nodes than indeterminate cultivars , the former are somewhat shorter than the latter and thus provide some degree of lodging resistance that is desirable for production in the high fertility and irrigated environments [27] , representing an alternative for enhancement of soybean yield potential , similar to that achieved by the “green revolution” gene in cereals . Classic genetic analysis demonstrated that semi-determinacy in soybean is specified by a dominant allele , designated Dt2 , in the Dt1 genetic background [23] . As shown in S1 Table , the dt2dt2;Dt1Dt1 and Dt2Dt2;Dt1Dt1 genotypes produce indeterminate and semi-determinate plants respectively , whereas both the dt2dt2;dt1dt1 and Dt2Dt2;dt1dt1 genotypes produce determinate plants , indicating a recessive epistatic effect of dt1 on the Dt2/dt2 locus . Semi-determinate stem growth habit was also observed and genetically investigated in tomato [21 , 28–30] and two other leguminous crops , pigeon pea ( Cajanus cajan ) [24] and chickpea ( Cicer arietinum ) [25] . However , unlike observed in soybean , semi-determinacy in tomato is specified by a recessive allele sdt in the recessive sp genetic background , and the dominant allele Sp , the functional equivalent of TFL1/Dt1 , exhibits dominant epistatic effect on the Sdt/sdt locus [21 , 28–30] ( S1 Table ) . More intriguingly , the legume crops pigeon pea and chickpea , two close relatives of soybean , both show an inheritance pattern of stem growth habit and a digenic epistasis distinct from observed in soybean but similar to observed in tomato [24 , 25] . A more recent study demonstrated that the genetic control of stem growth habit in pea is also distinct from observed in soybean [31] ( S1 Table ) . These observations reflect the uniqueness and evolutionary novelty of genetic control of stem growth habit in soybean . Recently , Dt2 has been isolated by a map-based cloning approach using a segregating population derived from a high-yielding semi-determinate elite soybean cultivar NE3001 and a high-yielding indeterminate elite cultivar IA3023 [32] ( S1 Fig ) . Dt2 was demonstrated to be a dominant gain-of-function MADS-domain factor gene belonging to an AP1/SQUAMOSA subfamily that includes floral identity genes AP1 , CAULIFLOWER ( CAL ) , FRUITFUL ( FUL ) in Arabidopsis [33–35] . It was also found that the causative mutation that converting dt2 into Dt2 is located in the non-coding regulatory region of the gene . Quantitative real time-polymerase chain reaction ( qRT-PCR ) analysis revealed that Dt2 is primarily expressed in the stem tips at vegetative 2 ( V2 ) stage , when the first trifoliate leaflets at node 2 are fully expanded but the second trifoliate leaflets at node 3 are not yet unfolded . It was proposed that , at this stage , floral induction occurs in all meristems ( apical and lateral ) , abruptly in the case of the determinants , less abruptly in the case of semi-determinants , but not in the terminal apical meristems in indeterminants , suggesting the essential role of Dt2 , as a floral activator , in promoting terminal flowering with the presence of Dt1 . However , except of the observed phenotypic epistasis , it is not yet known how this recently selected dominant gain-of-function Dt2 allele interacts with Dt1 and other genes to modulate the semi-determinate growth habit . Here , we report molecular dissection of the Dt2-mediated molecular mechanism regulating stem growth habit in soybean , with an emphasis on the evolutionary novelty of the regulatory pathways reshaped by artificial selection .
The expression patterns of the Dt1 and Dt2 loci in the main stem tips of NE3001 ( Dt2Dt2;Dt1Dt1 ) and IA3023 ( dt2dt2;Dt1Dt1 ) have been previously examined by qRT-PCR [32] ( Fig 1A ) . The expression level of either the Dt2 or dt2 allele was increased from the V0 ( when the cotyledons at node 0 are fully extended but the unifoliate leaflets at node 1 are not yet unrolled ) to V2 stages and then decreased at the V3 stage ( when the second trifoliate leaflets are fully expanded but before the third trifoliate leaflets are still unrolled ) . By contrast , the expression level of Dt1 in either the Dt2 or dt2 backgrounds was consistently reduced over these developmental stages . In addition , the expression level of Dt1 in the Dt2 background was lower than detected in the dt2 background . Such an expression pattern , particularly at the V2 stage , together with the epistatic interaction between the two genes as deduced from the phenotypes [23] , suggest that Dt2 may be a transcriptional repressor of Dt1 . However , because the apical meristems only made up a small portion of the main stem tips , the relative abundance of the transcripts from the two genes in apical meristems could not be precisely reflected by qRT-PCR analysis . Therefore , how the difference in levels of Dt2 and dt2 expression determines indeterminate or semi-determinate stems was not understood . To further elucidate the effects of the Dt2 expression on the transcription of Dt1 , we analyzed the expressional changes of Dt1 under ectopic expression of Dt2 driven by the Cauliflower Mosaic Virus ( CaMV ) 35S promoter . As shown in Fig 1B , the expression level of Dt1 in stem tips at the V2 stage was significantly reduced upon the ectopic expression of the transgene Dt2 in the Throne ( dt2dt2;Dt1/Dt1 ) genetic background , which resulted in an conversion from indeterminate stems to semi-determinate stems [32] . Because the expression level of Dt1 is extremely low relative to that of Dt2 or dt2 in the stem tips [32] , it is quite difficult to accurately determine the extent of repression of the Dt1 expression . Previous work has demonstrated that Dt1 is expressed at the highest level in soybean roots [17] , where both the Dt2 and dt2 alleles are expressed at extremely low levels [32] . Thus , in the soybean root system , there appear to be little or no effects of the native Dt2/dt2 locus on the expression of Dt1 , making the system ideal for investigation of the effect of Dt2 on the expression of Dt1 . Using an indeterminate soybean cultivar Kefeng 1 ( dt2/dt2;Dt1/Dt1 ) , we generated the Dt2 over-expression transgenic hairy roots . As shown in Fig 1C , the Dt2 transgene driven by the 35S promoter in the transgenic hairy roots was expressed at a level ~30 times higher than the native dt2 . By contrast , the expression level of the native Dt1 was reduced ~10 times in the Dt2 transgenic roots , indicating that the level of the Dt2 expression is a key factor controlling Dt1 expression . Since Dt2 is a MADS-box domain transcription factor ( TF ) localized in nucleus [32] , we wondered whether Dt2 could directly interact with the Dt1 promoter to inhibit the transcription of Dt1 . To this end , we created a fusion of the Dt2 protein to the hormone-binding domain of the rat glucocorticoid receptor ( GR ) , under the control of the constructive 35S promoter ( Pro35S:Dt2-GR ) , and the construct was transformed into the hairy roots of the soybean cultivar Kefeng 1 . To determine the effect of Dt2 activation on the expression of Dt1 in the Pro35S:Dt2-GR transgenic roots , we treated the roots with the steroid hormone dexamethasone ( DEX ) , the protein synthesis inhibitor cycloheximide ( CHX ) or both and then measured the changes of Dt1 expression by qRT-PCR , using non-transgenic hairy roots as a control . As shown in Fig 2A , the level of the Dt1 mRNA was reduced significantly after the treatment with DEX , indicating that DEX activation of the Dt2-GR fusion protein by nuclear translocation [13 , 36] resulted in repression of Dt1 . The level of Dt1 mRNA was also reduced significantly in the presence of both DEX and CHX , but not reduced in the presence of CHX , which has been proven to be able to terminate de novo protein synthesis [13 , 36] , suggesting that Dt2 can transcriptionally repress Dt1 directly without the requirement for protein synthesis . As MADS box domains are generally able to bind to DNA sequences of high similarity to the motif CC[A/T]6GG termed the CArG-box [37] , we first examined the Dt1 sequence and its flanking sequences from NE3001 and identified five putative CArG-boxes within ~2kb upstream of the Dt1 coding sequence ( CDS ) and one putative CArG-box at ~1 . 5kb position downstream of the CDS ( Fig 2D ) . To determine whether Dt2 directly binds to the CArG-box sequences in the regulatory region of Dt1 , we performed chromatin immunoprecipitation ( ChIP ) . We first raised a Dt2-specific antibody , anti-Dt2 ( Fig 2B ) based on a highly unique peptide composed of 19 amino acids from Dt2 ( S2 Fig ) , according to the soybean reference genome sequence [38] , and its specificity was further indicated by a substantially higher level of the Dt2 protein detected in the stem tips of NE3001 than that of dt2 detected in IA3023 at the V2 stage ( Fig 2C ) . We then used the anti-Dt2 antibody to enrich DNA fragments bound by Dt2 in NE3001 and then measured the relative enrichment by quantitative PCR ( qPCR ) . As shown in Fig 2D , fragments containing the 1st , 2nd , and 5th putative CArG-boxes , respectively , were enriched by >5–9 fold compared with the control DNA fragment amplified from the soybean ATP binding cassette transporter gene Cons4 [39] in the same genome . By contrast , the 3rd and 4th putative CArG-boxes , and the 7th one downstream of the Dt1 gene were not enriched compared with the control , indicating that these three of the six CArG-boxes are recognized and bound by Dt2 . These results were consistent with the observations from the electrophoretic mobility-shift assay ( EMSA ) analysis ( Fig 2E ) , which reveals that only 1st , 2nd , and 5th CArG-boxes can be bound by a Dt2-6×His fusion protein isolated from an Escherichia coli strain BL21 ( Fig 2B ) . The essentiality of the three CArG boxes for the repression of Dt1 transcription by Dt2 was further demonstrated by the observed ineffectiveness of the repression activity upon the truncation of the Dt1 promoter region involving these CArG-boxes or point mutations within each of the three CArG boxes using a luciferase ( LUC ) as a reporter ( Fig 2F and 2G ) . Together , these observations suggest that Dt2 functions as a repressor of Dt1 expression by binding directly to the three CArG boxes in the Dt1 promoter region through its MADS-box domain , and that all these three CArG boxes , bound by Dt2 , are essential for repression of the activity of the Dt1 promoter . In addition to the MADS-box domain that binds to the three CArG-boxes in the Dt1 promoter region , a Keratin ( K ) -box domain , I domain , and a C-terminal or C domain were predicted in the Dt2 protein based on the homolog searches against the conserved domain database ( Fig 3A ) . Compared with MADS-box domains , K-domains are generally less conserved and often involved in protein-protein interactions to form heterodimers for performing their functions [40–43] . It was reported that the C-domains of some MADS-box factors are also important for translational regulation [13] . To test if the K-domain and C-domian in Dt2 are required for fulfillment of the Dt2 function of repressing Dt1 expression , we investigated the effects of constitutive expression of the intact Dt2 protein , an incomplete Dt2 protein without the K-domain ( Dt2ΔK ) , and an incomplete Dt2 protein without a C terminal domain ( Dt2ΔC ) , driven by the 35S promoter , respectively ( Fig 3A ) , on the activity of the Dt1 promoter in Arabidopsis using GUS as a reporter . As shown in Fig 3B and 3C , the activity of the Dt1 promoter was inhibited under the constitutive expression of Dt2 , was partially inhibited under the constitutive expression of Dt2ΔC , and was not inhibited under the constitutive expression of Dt2ΔK . By contrast , the expression levels of Dt2 , Dt2ΔC , Dt2ΔK did not show obvious difference in levels of expression under the control of the 35S promoter ( Fig 3D ) . These observations suggest that the K-domain is essential , and perhaps , so are its interacting proteins , for the Dt2 function of repressing Dt1 expression . We then carried out Yeast Two Hybrid ( Y2H ) -screening of a cDNA library constructed with V2-stage stem tips of soybean using Dt2 as the bait , and identified eight unique cDNA clones each with an insert from a soybean gene ( S2 Table ) , including a putative orthologs of the Arabidopsis SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 ( SOC1 ) , dubbed GmSOC1 . SOC1 in Arabidopsis encodes a MADS-domain factor protein , which integrates multiple flowering signals derived from photoperiod , temperature , hormone , and age-related signals [44 , 45] . However , similar to AP1 , SOC1 is not expressed in the main shoot of Arabidopsis to maintain the stem’s indeterminate growth [2] . The interaction between Dt2 and GmSOC1 was further validated by Y2H ( Fig 4A ) and bimolecular florescence complementation ( BiFC ) using leaf cells of Nicotiana benthammiana ( Fig 4B ) . The interaction signals between Dt2ΔC and GmSOC1 were detected by BiFC , whereas no interaction signals between Dt2ΔK and GmSOC1 were detected . Because both the Dt2ΔK and GmSOC1 proteins were expressed at substantially high levels ( Fig 4C ) , the lack of interaction signals between Dt2ΔK and GmSOC1 would be indicative of the lack of interaction between the two proteins . These results are consistent with the ineffectiveness of Dt2ΔK expression on suppression of Dt1 expression , as illustrated in Fig 3 , suggesting that Dt2 interacts with GmSOC1 via its K-domain , and that GmSOC1 is important for fulfillment of the Dt2 function . As a MADS-domain factor , GmSOC1 , as expected , was localized to the nucleus ( Fig 4D ) . To test whether GmSOC1 interacts with Dt1 , a GmSOC1-GR fusion protein was created and expressed in the hairy roots of Kefeng 1 directed by the 35S promoter to evaluate the effect of GmSOC1 activation in the GmSOC1-GR fusion protein on the expression of Dt1 . The levels of Dt1 mRNA was reduced significantly in the presence DEX , and in the presence of DEX and CHX , but was not changed significantly in the presence of CHX , compared with the transgenic roots without any treatment ( Fig 5A ) , suggesting that GmSOC1 was involved in repression of Dt1 transcription by direct binding to the promoter region of Dt1 . Nevertheless , the effect of constructive expression of GmSOC1 on repression of the Dt1 promoter activity is not as strong as the effect of constructive expression of Dt2 on repression of the Dt1 promoter activity ( Fig 5C and 5D ) . This is also consistent with the observed effects of GR-fusion proteins on Dt1 expression ( Figs 2A and 5A ) . A GmSOC1-6×His fusion protein was isolated from the Escherichia coli strain BL21 and used to examine whether the MADS-domain of GmSOC1 can bind to any of the CArG-boxes in the promoter region of Dt1 by EMSA . As shown in Fig 5B , only binding of GmSOC1 with the 5th CArG box in the Dt1 promoter region was detected . Expression analysis by qRT-PCR using the stem tips from NE3001 and IA3023 revealed consistent expression patterns between GmSOC1 and Dt2 in NE3001 from the V0 stage through the V5 stage ( when the fourth trifoliate leaflets are fully expanded but before the fifth trifoliate leaflets are still unrolled ) ( Fig 6A ) . It is particularly noticeable that both GmSOC1 and Dt2 were expressed at the highest levels in NE3001 at the V2 stage . By contrast , GmSOC1 in IA3023 showed an expression pattern distinct from dt2 . In particular , the expression levels of GmSOC1 in IA3023 continued to be elevated after the V2 stage through the V5 stage , suggesting that GmSOC1 may have different regulatory roles between the Dt2 and dt2 backgrounds . Given that Dt2 is primarily expressed in the stem tips , we thus performed in situ hybridization to localize the transcripts of Dt2/dt2 , GmSOC1 , and Dt1 alleles in specific sections within the V2-stage stem tips in semi-determinate NE3001 and indeterminate IA3023 ( Fig 6B ) . It was found that the Dt2 transcripts were concentrated in the central zone of the SAMs at the V2 stage in NE3001 , where the Dt1 expression was not detected . By contrast , the dt2 transcripts were not detected in the central zone of SAMs in IA3023 , where abundant Dt1 transcripts were observed . In the Dt2 ( i . e . , NE3001 ) background , the GmSOC1 transcripts were detected in SAMs , whereas no expression of GmSOC1 was detected in SAMs in the dt2 ( i . e . , IA3023 ) background . The spatiotemporally specific co-expression of Dt2 and GmSOC1 in NE3001 and absence of dt2 and GmSOC1 transcripts in SAMs of IA3023 suggest that the observed expression of GmSOC1 in SAMs and thus its novel function are Dt2-depedent , and that both Dt2 and GmSOC1 are involved in transcriptional repression of Dt1 , responsible for the formation of semi-determinacy . It is also noticeable that dt2 was expressed in lateral meristems in IA3023 , where the transcripts of Dt1 was not detected , and that GmSOC1 was detected in lateral meristems in both NE3001 and IA3023 , where no or minimal expression of Dt1 was observed ( Fig 6B ) , suggesting that the lateral meristems at the stem tips of the V2 stage may be in the state of transition from IMs to FMs in both NE3001 and IA3023 , and that both dt2 and GmSOC1 may be involved in floral induction in the lateral meristems of both NE3001 and IA3023 , most likely , by suppressing Dt1 transcription , as Dt2 and GmSOC1 do in the SAMs of NE3001 ( Fig 6C ) . Our previous study demonstrated that the TFL1/tfl1 promoter was able to drive the expression of Dt1 in the Arabidopsis tfl1 mutant to convert the mutant phenotypes ( determinate stem and early flowering ) to the wild-type phenotypes ( indeterminate stem and late flowering ) [17] . Also , since the activity of the Dt1 promoter could be detected by the GUS gene in Arabidopsis ( Fig 3B ) , we were thus curious about whether the expression of Dt1 driven by its own promoter in Arabidopsis tfl1 mutants could recover the wild-type phenotype , and if so , whether ectopic expression of Dt2 alone or in combination with Dt1 in the tfl1 mutant could produce semi-determinate stem growth habit that has not been observed in Arabidopsis . To address these questions , we created a ProDt1:Dt1 construct comprised of the promoter of Dt1 and its rest genomic sequence , and introduced it to an Arabidopsis tfl1 mutant line to produce ProDt1:Dt1 transgenic lines . We also developed Pro35S:Dt2 transgenic lines with the wild-type ( TFL1 ) Arabidopsis . These two transgenic lines were crossed to generate progeny lines containing both ProDt1:Dt1 and Pro35S:Dt2 in the tfl1 background . We found that the ProDt1:Dt1 transgenic line with the tfl1 background recovered the wild-phenotypes that are typically shown by the wild-type Arabidopsis ( Fig 7D and 7J ) , suggesting that the Dt1 promoter functions as the TFL1/tfl1 promoter in driving Dt1 expression to fulfill the TFL1 function . The ectopic expression of Dt2 in the wild-type genetic background did not affect the indeterminate stem growth determined by TFL1 . By contrast , the ectopic expression of Dt2 expression in the ProDt1:Dt1 transgenic line with the tfl1 background exhibited semi-determinate stem growth habit similar to shown by semi-determinate soybean ( Fig 7E and 7K ) , indicating that the TFL1 promoter activity was not repressed by Dt2 . The spatiotemporal co-expression of Dt2 and GmSOC1 and their co-binding to the Dt1 promoter suggests the essential role of GmSOC1 in the formation of semi-determinacy . To further test this hypothesis , we crossed the semi-determinate Arabidopsis transgenic line ( Fig 7E and 7K ) with the Arabidopsis soc1 mutant [44 , 45] , and obtained F2 plants containing both ProDt1:Dt1 and Pro35S:Dt2 in the tfl1 and soc1 background . As exemplified in Fig 7F and 7L , the plants with such a combination of genes showed indeterminate stem growth , indicating that SOC1 is indeed essential to repress Dt1 transcription in the Dt2 background in SAMs .
As reflected by the timing and spatial patterns of it’s expression , Dt1 in indeterminate soybean appears to function in a way similar to what TFL1 does in the wild type Arabidopsis to repress stem terminal flowering . In Arabidopsis , TFL1 starts to be expressed weakly in the center of the SAMs during the vegetative phase and its expression level is up-regulated at the stage when the SAMs make cauline leaves that bear shoot meristems in their axils [47 , 48] . The expression of TFL1 remains in SAMs of the main shoot afterwards to repress the expression of the floral identity genes such as AP1 , LEAFY and thus sustain the indeterminate growth until the plant cease to grow [35] . In emerging floral meristems on the flanks , AP1 and LFY were found to repress TFL1 by direct binding to its 3’ regulatory region [15 , 16] . A more recent study demonstrated that AP1 recruits SEP4 , SOC1 , AGL24 , and SVP to form a regulatory complex that represses the expression of TFL1 to initiate lateral flowering [49] . In soybean , the highest level of Dt1 in main stem tips was detected at the V0 stage , which seems to be equivalent to the Arabidopsis stage when TFL1 is up-regulated . Because the functional equivalents of the Arabidopsis floral identity genes such as AP1 and LEAFY in soybean have not been identified , it remains unclear when the floral meristems at the flanks are exactly initiated in soybean . Nevertheless , Dt1 expression was detected in the center zone of SAMs of IA3023 , but not detected in the lateral meristems in either NE3001 or IA3023 , suggesting that floral induction may have occurred in the lateral meristems in both varieties . Several lines of evidence indicate that Dt2 is not the functional equivalent of the Arabidopsis of AP1 , although the functional orthologs Dt1 and TFL1 are their respective direct targets . Firstly , Dt2 is a rare gain-of-function allele that is present only in semi-determinate soybean , while AP1 is a floral identity gene in the wild-type Arabidopsis; Secondly , Dt2 is not one of the four soybean duplicates orthologous to the Arabidopsis AP1 [32] . Instead , Dt2 appears to be an ancestral copy of MADS-box factor gene proceeding the divergence of AP1 from FUL that had occurred before the split of Arabidopsis from soybean; Thirdly , Dt2 is expressed in the central zone of SAMs to repress the expression of Dt1 ( Fig 6B ) , whereas AP1 is not expressed in SAMs of the main shoots of Arabidopsis [35] . Fourthly , Dt2 binds to the promoter region of Dt1 ( Fig 2E , 2F and 2G ) , whereas AP1 binds to the 3’ regulatory region of TFL1 to achieve suppression of the transcription of the two target genes [13] . Nevertheless , Dt2 appears to be responsible for initiation of floral meristems in SAMs similar to that was achieved by AP1 in the lateral meristems in Arabidopsis . The transcripts of GmSOC1 were detected not only in the SAMs of NE3001 , but also the lateral meristems of both NE3001 and IA3023 by in situ hybridization ( Fig 6B ) . Further , the expression level of GmSOC1 in the main stems with both apical and lateral meristems continues to increase in IA3023 but that starts to decrease in NE3001 after the V2 stage ( Fig 6A ) . Because the main stems of IA3023 continue vegetative growth at their apical meristems and floral induction at their flanks until all meristems are consumed and the plants get matured , whereas the main stems of NE3001 appear to have undergone the transition from IMs to FMs at the V2 stage ( Fig 6B ) , the elevated expression levels of GmSOC1 in IA3023 after the V2 stage would be considered as additional evidence in support of the role of GmSOC1 as a floral identity gene in soybean . In Arabidopsis , TFL1 expression is repressed by SOC1 in an AP1-dependent manner [49] . Contrastingly , the repression of Dt1 by GmSOC1 appears to be Dt2-dependent . Such a similarity reflects not only the functional conservation between GmSOC1 and SOC1 as repressors of Dt1/TFL1 , but also the way in which they function . Although the dt2 transcripts were also detected in the lateral meristems of IA3023 ( Fig 6B ) , the expression level of dt2 was declined after the V2 stage in IA3023 , suggesting that , unlike GmSOC1 , dt2 may not be essential for floral induction in the lateral meristems . If GmSOC1 , indeed , is the functional equivalent of the Arabidopsis SOC1 , as directly indicated by the recovered indeterminacy by Dt1 , with the constitutive expression of Dt2 , in the tfl1 and soc1 double mutants of Arabidopsis ( Fig 7F and 7L ) , the expression of GmSOC1 would be essential for initiating terminal flowering through suppressing Dt1 expression and perhaps through activating the expression of other flowering identity genes in soybean such as functional equivalents of the Arabidopsis LEAFY and FUL in SAMs . Several observations obtained in this study , such as the spatially specific and co-expression pattern of GmSOC1 and Dt2 ( Fig 6 ) , their direct interaction ( Fig 4A and 4B ) , the lack of Dt2Δk for interacting with GmSOC1 and repressing Dt1 expression ( Fig 3B and 3C ) , and the repressive effect of GmSOC1 on Dt1 expression ( Fig 5C and 5D ) , suggest that GmSOC1 plays an essential role in forming the semi-determinate stem growth habit , and this role was likely fulfilled by its dimerization with Dt2 . As the Arabidopsis SOC1 is not expressed in the shoot meristems , such a pattern of GmSOC1 expression would indicate its novel function , which was specifically triggered by Dt2 . As similarly observed in Arabidopsis , rice SOC1 , AGL24 , SVP , and SEP4 orthologs regulate panicle branching through suppressing the TFL1 orthologs in rice , indicating the genetic pathways underlying inflorescence architecture are highly conserved between monocot and dicot species [49] . Intriguingly enough , such an inter-specifically conserved pathway is not conserved between the apical and lateral meristems in initiating flowering in semi-determinate soybean due to the spatiotemporal expression of Dt2 ( Fig 6 ) . It is possible that the activation of GmSOC1 was initiated through suppression of Dt1 by Dt2 . Alternatively , GmSOC1 could be directly activated by Dt2 . It would be interesting to further investigate how such specific expression of Dt2 was achieved simply through the gain-of-function mutation ( s ) that occurred outside of its CDS [32] , and how the spatiotemporal expression of GmSOC1 was triggered by the Dt2 mutation , and to what extent the regulatory networks underlying soybean stem growth habit was reshaped by the Dt2 mutation . The formation of the semi-determinate Arabidopsis by heterologous expression of Dt2 and Dt1 in the tfl1 mutants is an applausive observation ( Fig 7 ) , which suggests that all the functions of the soybean genes involved in the regulatory complex suppressing Dt1 expression can be fully provided by the Arabidopsis genes . By contrast , the overexpression of Dt2 in wild Arabidopsis did not result in any stem architectural changes , suggesting that Dt2 did not interact with TFL1 . This may be explained by the absence of any CArG-boxes in the promoter region of TFL1 as potential target sites of Dt2 . Therefore , the heterologous expression experiment demonstrated both conservation and divergence of regulatory sequences between TFL1 and Dt1 for precise control of their switch-on and switch-off . It would be important to further identify floral identity genes in FMs developed from the apical IMs and secondary IMs , respectively , towards a more in-depth understanding of the spatiotemporal specificity and commonality of floral regulation that determine that plant’s inflorescence architecture . Given such a long period of divergence of soybean and Arabidopsis from a common ancestor , the formation of semi-determinacy by Dt2 and Dt1 in Arabidopsis would suggest a feasibility and potential application of this novel regulatory mechanism for modification of stem growth habit in many other plants , particularly , the legume crops , towards optimizing plant architecture for enhanced yield potential and adaptability .
Semi-determinate elite soybean line NE3001 ( Dt2Dt2;Dt1Dt1 ) , indeterminate soybean elite lines IA3023 and Thorne ( dt2dt2;Dt1Dt1 ) , and a Dt2 over-expression transgenic line ( #2 ) in the Thorne genetic background were previously described [32] . An indeterminate elite line Kefeng 1 ( dt2dt2;Dt1Dt1 ) , used for hairy root transformation , was obtained from the USDA Soybean Germplasm Collection . The tfl1 mutant ( tfl1-1 ) was obtained from The Arabidopsis Information Resource ( TAIR ) Arabidopsis Stock Centers . The Arabidopsis seeds were surface-sterilized with 10% bleach plus 0 . 01% Triton X-100 for 12 min , followed by washing five times with sterile water . The sterilized seeds were stratified at 4°C for 2 days and transferred to culture media or soil for further growth at 22°C under the condition of 16 h of 120 μE·m−2·s−1 light and 8 h of dark . Genomic DNA isolation , PCR primer design , PCR amplification with genomic DNA , PCR product purification , RNA isolation , cDNA synthesis by reverse transcription-PCR ( RT-PCR ) , quantitative real-time-PCR ( qRT-PCR ) , and sequencing of DNA and cDNA fragments were performed using protocols previously described [17 , 32] . In the qRT-PCR experiments , three biological replicates were analyzed to quantify the levels of gene expression in NE3001 , IA3023 , Kefeng 1 and three technical replicates were performed to measure the levels of gene expression in Thorne and the Thorne transgenic line , and the soybean ATP binding cassette transporter gene Cons4 [39] was used as the internal control , and we normalized the relative expression levels of the genes/alleles Dt2 , dt2 , Dt1 , GmSOC1 in each experiment by setting the lowest expression level as 1 . 0 . Primers used for PCR , RT-PCR , qRT-PCR and sequencing are listed in S3 Table . The full-length or portions of the CDSs of Dt2 , and GmSOC1 were amplified by RT-PCR using KOD hot start DNA polymerase ( Novagen catalog no . 71087 ) , and the Dt2ΔK fragments were obtained by overlapping PCR with two overlapped CDS fragments as templates in a same reaction . The CDS and fused CDS fragment were then inserted to pCR8/GW/TOPO vector ( Invitrogen , catalog no . K2500-20 ) and verified by sequencing . Subsequently , the verified inserts were cloned into the binary vector pGWB17 [50] to obtain the three constructs Pro35S:Dt2 , Pro35S:Dt2ΔC , Pro35S:Dt2ΔK , and Pro35S:GmSOC1 , and the verified inserts were cloned into pBI-ΔGR-GW [51] to generate the Pro35S:Dt2-GR and Pro35S:GmSOC1-GR constructs . The ~2 . 4-kb upstream sequence from the start codon ( dabbed the promoter region or pProDt1 ) , the truncated promoter without the cluster of the five putative CArG-boxes ( dabbled ProDt1Δ ) , the CDS , and the ~1 . 1kb downstream sequence from the stop codon ( dabbed terminator region ) of Dt1 were amplified from the indeterminate soybean cultivar Williams 82 . The obtained PCR fragments were cloned into the pGEM-T Easy Vector ( Promaga , catalog no . A1360 ) and then sequenced . The verified clones with the promoter region , the CDS , and the terminator region , were digested by PstI and SalI , SalI and XbaI , and XbaI and BamHI , respectively , and then integrated into pPZP212 [32] . The construct of ProDt1Δ:LUC was made by integrating pProDt1 into pGWB435 [50] . The Dt1 promoter sequences with point mutations within each of the three CArG boxes were created by using QuikChange II Site-Directed Mutagenesis Kit ( Agilent Technologies , Catalog #200523 ) with specifically designed primers ( S3 Table ) . These constructs were introduced into Agrobacterium tumefaciens strain GV3101 or Agrobaterium rhizogenes strain K599 . The Arabidopsis transgenic lines each from a single construct were obtained by A . tumefaciens-mediated transformation , and the transgenic lines with genes from two distinct constructs were generated by crossing two transgenic lines with respective transgenes and subsequent screening of the progeny lines . The soybean transgenic hairy roots were produced by A . rhizogenes-mediated transformation following a protocol previously described by Kereszt et al . [40] . The seedlings with transgenic roots were sprayed a solution of 0 . 03mM DEX with 0 . 005% Silwet L-77 , a solution of 1 . 8mM CHX with 0 . 005% Silwet L-77 or a solution of 0 . 03mM DEX and 1 . 8mM CHX with 0 . 005% Silwet L , and levels of gene expression was measured four hours after each treatment . Thermo Scientific Antigen Profiler , a bioinformatics protein sequence analysis tool and custom peptide design algorithm , was provided by Pierce Biotechnology , Inc . and employed to design a unique 19 amino-acid peptide from Dt2 , which was then used to raise a Dt2-specific antibody from a rabbit ( dabbed anti-Dt2 , Pierce Biotechnology , Inc . ) . The specificity of the anti-Dt2 was tested by Western blot using total proteins isolated from the stem tips of NE3001 at the V2 stage . ChIP assays with anti-Dt2 were performed following the protocols described previously [52 , 53] , with minor modification . Stem tips of NE3001 collected at the V2 stage were immersed in 1×Phosphate Buffered Saline ( PBS ) buffer containing 1% formaldehyde ( Macron , catalog no . K15754 ) for cross-linking . Enrichment of the precipitated DNA by anti-Dt2 relative to DNA recovered from the control treatment without anti-Dt2 was measured by qRT-PCR with three biological replicates . The primers used in ChIP-PCR are listed in S3 Table . The CDSs of Dt2 and GmSOC1 were cloned into the expression vector pET-DEST42 containing a 6×His tag ( ThermoFisher Scientific , catalog no . 12276–010 ) , separately , to generate the Dt2-6×His and GmSOC1-6×His constructs . The two constructs were transformed into the Escherichia coli strain BL21 and Rosetta ( DE3 ) , respectively , The Dt2-6×His and GmSOC1-6×His fusion proteins were then induced in the transformed cells by growing at 37°C for 5h in the 2×YT medium with 1 mM isopropyl β-D-1- thiogalactopyranoside and then extracted and purified with Ni-NTA Agarose ( Qiagen , catalog 30210 ) . EMSAs were performed using digoxigenin-labeled probes and the DIG Gel Shift Kit ( Roche , 3353591910 ) following the manufacturer’s instructions . Ten transgenic plants for each construct were mixed for protein extraction and histochemical staining . The Gus activities were measured in a method described earlier [54] . The stem tips from Williams 82 at the V2 stage were used to isolate total RNA , which was used to synthesize cDNA by reverse transcription . The pool of cDNA fragments were cloned into pGADT7 ( Clontech , catalog no . 630442 ) and then transformed into the yeast strain Y187 ( Clontech , catalog no . 630457 ) , following the manufacturer's instructions The CDS of Dt2 was inserted into vector pGBKT7 as a bait and introduced into the yeast strain Y2H Gold ( Clontech , catalog no . 630498 ) . Mating between the Dt2 strain and the cDNA library were screened on the quadruple dropout medium ( QDO ) with SD/–Ade/–His/–Leu/–Trp ( Clontech , catalog no . 630322 ) . The positive cDNA clones were sequenced and the interaction between Dt2 and one of the positive clones , which contains a fragment from the CDS of GmSOC1 , was further validated by co-transformation of pGBKT7 with the Dt2 CDS and pGADT7 ( Clontech , catalog no . 630442 ) with the GmSOC CDS into Y2HGold and grown on the selection medium QDO supplemented with X-a-Gal ( Clontech , catalog no . 630462 ) and Aureobasidin A ( Clontech catalog no . 630466 ) following the manufacturer's instructions . For BiFC assays , the Dt2 , Dt2ΔC , and Dt2ΔK were cloned into the pEarleyGate201-YN vector [55] and the GmSOC1 was cloned into the pEarleyGate202-YC vector [55] . These constructs were introduced into the A . tumefaciens strain GV3101 , together with the p19 strain , and the strains carrying GmSOC1 and Dt2 , Dt2ΔC , or Dt2ΔK were co-infiltrated into leaf epidermal cells of 3- to 4-week old tobacco ( Nicotiana benthamiana ) , following a protocol described previously [56] . The transformed cells were observed and photographed using a confocal scanning microscope ( Nikon 90i ) 24 h after infiltration . For subcellular localization , the CDS of GmSOC1 was cloned into the plasmid pGWB405 to form a fusion protein of GmSOC1 and a green fluorescent protein ( GFP ) under the control of 35S promoter , which are provided by the plasmid . The construct was introduced into leaf epidermal cells of 3- to 4-week old tobacco by A . tumefaciens infiltration . The transformed cells were observed and photographed using a confocal scanning microscope ( Nikon 90i ) 24 h after infiltration . RNA in situ hybridization was performed according to a previously described protocol [57] . A 120-bp fragment specific to the Dt2/dt2 cDNA , a 123-bp fragment specific to the Dt1 cDNA , and a 188-bp fragment specific to the GmSOC1 cDNA were amplified with respective primer sets ( S3 Table ) , and then integrated into the pGEM-T Easy vector , respectively . Digoxigenin-labeled sense and anti-sense probes were obtained from EcoR-digested linear pGEM-T Easy Vectors with 120-bp , 123-bp , or 188-bp inserts by in vitro transcription with SP6 or T7 RNA polymerase ( Roche , catalog no . 11175025910 ) according to the manufacturer’s protocol . Hybridization signals were detected and photographed using a confocal scanning microscope ( Nikon A1R ) . | Similar to the “green revolution” semi-dwarf cereals , semi-determinate soybean varieties are lodging-resistant and particularly suitable for planting in high fertility and irrigated environments . Nevertheless , molecular mechanisms underlying semi-determinate stem growth have not been deciphered in any flowering plants . We demonstrate that semi-determinacy is originated from an innovation of spatiotemporal expression of an ancient MADS-box gene and consequent changes of spatiotemporal expression of its interacting genes in soybean , which occurred post-domestication of soybean and selected by breeding . The findings from this study not only provides new insights into the evolutionary novelty of molecular mechanisms regulating stem growth habit reshaped by artificial selection , but also exhibited potential application of such an innovative mechanism for molecular design of stem architecture in other crops towards enhanced adaptability and yield potential . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"gene",
"regulation",
"dna-binding",
"proteins",
"cloning",
"plant",
"physiology",
"habits",
"plant",
"science",
"crops",
"molecular",
"biology",
"techniques",
"dna",
"plants",
"flowering",
"plants",
"promoter",
"regions",
"research",
"and",
"analysis",
"methods",
"cr... | 2016 | Innovation of a Regulatory Mechanism Modulating Semi-determinate Stem Growth through Artificial Selection in Soybean |
Innate immunity is the first line of defense against invading microorganisms . Trypanosome Lytic Factor ( TLF ) is a minor sub-fraction of human high-density lipoprotein that provides innate immunity by completely protecting humans from infection by most species of African trypanosomes , which belong to the Kinetoplastida order . Herein , we demonstrate the broader protective effects of human TLF , which inhibits intracellular infection by Leishmania , a kinetoplastid that replicates in phagolysosomes of macrophages . We show that TLF accumulates within the parasitophorous vacuole of macrophages in vitro and reduces the number of Leishmania metacyclic promastigotes , but not amastigotes . We do not detect any activation of the macrophages by TLF in the presence or absence of Leishmania , and therefore propose that TLF directly damages the parasite in the acidic parasitophorous vacuole . To investigate the physiological relevance of this observation , we have reconstituted lytic activity in vivo by generating mice that express the two main protein components of TLFs: human apolipoprotein L-I and haptoglobin-related protein . Both proteins are expressed in mice at levels equivalent to those found in humans and circulate within high-density lipoproteins . We find that TLF mice can ameliorate an infection with Leishmania by significantly reducing the pathogen burden . In contrast , TLF mice were not protected against infection by the kinetoplastid Trypanosoma cruzi , which infects many cell types and transiently passes through a phagolysosome . We conclude that TLF not only determines species specificity for African trypanosomes , but can also ameliorate an infection with Leishmania , while having no effect on T . cruzi . We propose that TLFs are a component of the innate immune system that can limit infections by their ability to selectively damage pathogens in phagolysosomes within the reticuloendothelial system .
Human blood is a potentially hostile environment to colonizing pathogens due in part to effectors of innate immunity . Trypanosome Lytic Factors ( TLFs ) are a subset of high-density lipoproteins ( HDLs ) that protect against infection by many but not all species of the African trypanosome . Two TLFs have been characterized in human blood: TLF1 and TLF2 . TLF1 is a large ( 500 kDa ) lipid rich HDL composed predominantly of apolipoprotein A-I ( apoA-I ) , haptoglobin-related protein ( Hpr ) , and apolipoprotein L-I ( apoL-I ) [1] , [2] . TLF2 is a 1000 kDa lipid-poor HDL , which is an immunocomplex composed of apoA-I , Hpr , apoL-I , and IgM [1] , [3] . Hpr and apoL-I are the two unique protein components of TLFs that are required to give optimal trypanolytic activity . African trypanosomes are single cell eukaryotes ( from the order Kinetoplastida ) that live extracellularly in the bloodstream and tissue spaces of their host , from which they endocytose transferrin and lipoproteins for growth . TLF , a lipoprotein , is endocytosed by trypanosomes and trafficked to the lysosome , wherein the acidic pH activates TLF [4]–[7] . TLF forms ion selective pores in trypanosome membranes , which leads to the loss of osmoregulation allowing water influx , swelling and lysis of the trypanosomes [8] , [9] . The pore forming activity has been assigned to apoL-I because a purified recombinant preparation can kill trypanosomes [9] , [10] . However , in vitro experiments show that the association of Hpr and apoL-I in the same HDL particle is necessary to achieve optimal TLF activity , because reconstitution of individual components reveal that the combination of Hpr and apoL-I are ten-fold more lytic than either component alone and native HDLs with either Hpr or apoL-I alone have levels of activity several hundred fold lower than HDL with both Hpr and apoL-I [11] . Hpr promotes the efficient uptake of TLFs via a putative trypanosome receptor [12] , [13] . The presence of an Hp ( Hpr ) receptor was initially reported by Drain et al . [13] . Recent data indicates that the trypanosome receptor ligand is in fact the complex of Hpr bound to hemoglobin ( Hpr-Hb ) [13] , [14] and/or haptoglobin bound to hemoglobin ( Hp-Hb ) [15] . There are two other parasites from the order Kinetoplastida , Leishmania sp . and Trypanosoma cruzi , which represent important human pathogens . These parasites , which are primarily intracellular , do not have an ortholog of the Hpr-Hb receptor identified in African trypanosomes [15] . However , they do reside in an acidic parisitophorous vacuole ( PV ) ( permanently or transiently ) , where TLF could be delivered , activated and act against them . We hypothesize that TLF may function more broadly as a reservoir of antimicrobial proteins such as apoL-I and Hpr-Hb that could be released from the carrier HDL and activated , in the case of Leishmania within the intracellular acidic PV of macrophages . Leishmania is the causative agent of leishmaniasis , a disease whose manifestations in humans range from mild cutaneous and mucocutaneous lesions to fatal visceral infections . Leishmania undergoes a complex life cycle; human infection initiates with the deposition of non-dividing metacyclic promastigotes by sand flies biting the host skin . The parasites are then taken up by professional phagocytes [16] , [17] . The major host cell is the macrophage in which the parasite resides within the PV , a phagosome that ultimately fuses with endosomes and lysosomes , forming an organelle with an acidic pH . Inside the PV , the parasites differentiate to amastigotes , multiply , and eventually rupture the cell and spread to uninfected cells [18] . T . cruzi is the causative agent of Chagas' disease in humans . An infected triatomine insect vector feeds on blood and deposits metacyclic trypomastigote forms of the parasite in its feces , which can enter the host through breaches in the skin or through intact mucosal membranes . T . cruzi trypomastigotes circulate and are disseminated to the heart and other organs through the blood , where they could encounter innate effectors . The parasite is capable of invading and replicating in a wide variety of nucleated cells in the vertebrate host . The trypomastigote form of the parasite enters a host cell and is enclosed within a membrane-delimited vacuole that rapidly fuses with lysosomes [19] , [20] providing the acidic environment that is essential for vacuole disruption and parasite replication in the cytosol [21] , a critical step in the T . cruzi life cycle . In the present study we evaluated the effect of TLF on the intracellular growth of Leishmania sp . and T . cruzi , because they are parasites that traffic to acidified PVs to which TLF may be delivered and activated . We find that in axenic acidic conditions TLF damages metacyclic promastigotes externally and reduces their infectivity . Furthermore , TLF ameliorates infection by Leishmania by accumulating within the PVs of macrophages , thereby reducing the pathogen number . In vivo , TLF reduces the pathogen burden of Leishmania in mice , whereas TLF has no measurable effect on T . cruzi infection .
The infective form of Leishmania , metacyclic promastigotes do not divide . They are covered in a dense glycocalyx of lipophosphoglycan , which contributes to their resistance to complement killing . After deposition in the skin by the bite of a sandfly , metacyclic promastigotes are rapidly opsonized and phagocytosed by macrophages and gradually fuse their PV with endosomes and lysosomes . The vacuoles containing Leishmania are acidified and eventually reach pH 5 [22] , [23] . We tested the effect of lytic HDL on L . major and L . amazonensis purified metacyclic promastigotes under neutral conditions ( such as those encountered in the tissues spaces and blood ) and acidic conditions ( such as those ultimately encountered in the PV ) . After 24 hours of co-incubation with a physiological concentration of lytic HDL ( 1 . 5 mg/ml ) , which contains TLF at physiological concentrations ( ∼10–15 µg/ml ) at 27°C in acidic media ( pH 5 . 2 ) the L . major metacyclic promastigotes became swollen but remained motile ( Figure 1A ) , we could not detect any uptake of propidium iodide indicating that the parasites are still viable ( data not shown ) . In contrast there was no visible effect of lytic HDL in neutral pH media ( Figure 1B ) . TLF binds to the parasites independently of the pH . Incubation with Alexa Fluor-488 labeled human TLF ( 10 µg/ml ) ( pH 5 . 2 , Figure 1I , and pH 7 . 5 , Figure 1J ) reveals a net shift in fluorescence of the whole population of parasites . Bovine HDL , which does not kill trypanosomes and does not contain TLF , was used as a non-lytic HDL control at an equivalent concentration . The parasites remained motile and elongated in acidic or neutral media in the presence of bovine HDL ( data not shown ) . The pretreatment of L . major metacyclic promastigotes with lytic HDL in acidic media substantially reduced their infectivity ( p<0 . 01 compared to bovine HDL , Figure 1C ) , measured by their ability to infect BALB/c bone-marrow derived macrophages . In contrast , there was no change in infectivity after pretreatment of metacyclic promastigotes with lytic or non-lytic HDL in neutral media ( Figure 1D ) . We observed the same outcome after pretreatment with lytic HDL of L . amazonensis parasites before infection of BALB/c bone-marrow derived macrophages ( Figure 1E and 1F ) . Pretreatment of promastigotes with lytic HDL in acidic media significantly reduced their infectivity ( p<0 . 01 compared to bovine HDL , Figure 1E ) . There was no change in infectivity after pretreatment in neutral media ( Figure 1F ) . In contrast pretreatment of amastigote-like forms ( day 13 of axenic transformation ) with lytic HDL in acidic or neutral media did not reduce their infectivity for macrophages ( Figure 1G and 1H ) . We conclude that lytic HDL ( which contains TLF ) can damage L . major and L . amazonensis promastigotes under acidic conditions thereby affecting their shape and infectivity . In contrast , amastigote like forms are apparently resistant to lytic HDL . Inside the macrophages , the Leishmania parasite resides in an acidic vesicular compartment , the PV , which has phago-endosomal/lysosomal properties . The fusion properties of the PV are dependant upon the life cycle stage used for infection in vitro i . e . the use of purified metacyclic promastigotes versus a heterogeneous promastigote population and the source and activation status of the host cells [23]–[28] . TLFs are a subset of HDLs and macrophages have receptors for binding and endocytosing HDLs [29]–[31] and haptoglobin [32] , [33] , 1% of which circulates bound to HDLs [34] . We therefore reasoned that TLF might bind to one or all of these macrophage receptors , be endocytosed , traffic to PVs and exert lytic activity against Leishmania at acidic pH . We used confocal fluorescent microscopy to visualize the potential uptake and colocalization of TLF with L . major within macrophages . BALB/c bone-marrow derived macrophages were infected with L . major parasites for 2 hours and physiological concentrations of lytic human TLF ( 10 µg/ml ) labeled with Alexa Fluor-594 ( Figure 2 ) . After 2 hours incubation TLF ( red image ) and parasites ( small blue dots ) were found within the phagolysosome delineated by Lamp-1 antibodies , which label all lysosomal compartments within the macrophages ( green image ) . When all three images were merged , we observed that the parasites and TLF are found within the PV of the macrophage ( Figure 2 , merged panel , solid arrows ) . To determine whether the parasites endocytosed the TLF or were coated by the TLF within the parasitophorous vacuole we used GFP-L . major , which express GFP in the entire cytoplasm of the parasite . BALB/c bone-marrow derived macrophages were infected with GFP-L . major parasites for 2 hours . After 2 and 24 hours incubation with Alexa Fluor 594 labeled TLF there was no detectible colocalization of the two dyes , as revealed by the 2D cytofluorograms ( Figure 3B , 2 h; and 3D , 24 h ) , which represent the data collected from 25 individual z-stacks of the two maximum projection images ( Figure 3A , 2 hours; and 3C , 24 hours ) . Therefore , we conclude that TLF is taken up by the macrophages and surrounds the parasites within the PV but may not be endocytosed by the parasite . The fusion properties of the PV vary with the infecting species of Leishmania . Initially the PVs fuse with the late endosomes/lysosomes of the macrophage and eventually become fully acidified [22]–[28] , [35] . Within 24 hours , Leishmania differentiation into amastigotes begins . L . major ( organism of the Old World ) and L . amazonensis/L . mexicana ( of the New World ) cause cutaneous leishmaniasis but diverged from each other 40–80 million years ago . Consequently , significant differences in host-parasite interactions have evolved , including differences in the PV . For example , the PVs that harbor L . amazonensis or L . mexicana ( large communal PVs ) versus those that harbor L . major or L . donovani ( small individual PVs ) indicate that the fusion/fission processes occurring at the level of these organelles differ mechanistically or kinetically in macrophages infected with these different species [35] . In order to assess the effect of lytic HDL on intracellular Old World L . major parasites within macrophages we added different concentrations of human lytic HDL two hours post-infection of peritoneal macrophages from Swiss-Webster mice with purified metacyclic promastigotes . Bovine HDL , which does not kill trypanosomes and does not contain TLF , was used as a non-lytic HDL control . Two hours post-infection we observe an equivalent infection rate of all macrophages ( Figure 4A ) . However , in the presence of lytic HDL the initial parasite burden of ∼11 parasites/100 macrophages was reduced to ∼5 parasites/100 macrophages ( Figure 4A ) after 24 hours . To evaluate the lytic capacity of HDL in large communal PVs generated by New World Leishmania , we repeated the 2 and 24 hours incubation with lytic HDL using BALB/c mice bone-marrow derived macrophages infected with L . amazonensis . Two hours post-infection we observe an equivalent infection rate of all macrophages ( Figure 4B ) . At 24 hours L . amazonensis was also killed intracellularly by lytic HDL , reducing the parasite burden by ∼65% ( p<0 . 05 compared to bovine HDL , Figure 4B ) . At 72 hours post-infection the parasites begin to divide within the macrophages . Of note the Leishmania with macrophages incubated with lytic HDL are growing at 72 hours , which suggests that the parasites have escaped the effect of lytic HDL ( TLF ) , either by transforming or remodeling their PV or both . Once inside macrophages metacyclics differentiate into amastigotes and begin to divide , this takes 1–3 days . We tested the susceptibility of axenically cultivated amastigote-like forms within macrophages to lytic HDL . BALB/c bone-marrow derived macrophages were infected with promastigotes ( Figure 5A ) or amastigote-like forms ( Figure 5B ) of L . amazonensis before treating with lytic HDL for 24 hours . There was no reduction in amastigote numbers within macrophages ( Figure 5B ) . We conclude that amastigote-like forms of L . amazonensis are resistant to lytic HDL ( TLF ) in macrophages . Binding and endocytosis of lytic human HDL ( TLF ) does not activate BALB/c mice bone-marrow derived macrophages infected with metacyclic promastigotes of L . major ( Figure 6A ) . There was no measurable increase in nitrite oxide ( NO ) production unless the macrophages were treated with IFNγ and LPS . Furthermore , lytic HDL effectively reduced the parasite number in murine bone-marrow macrophages harvested from inducible NO synthase knock-out mice ( iNOS−/− ) , which are unable to make NO ( p<0 . 01 , Figure 6B ) . Taken together the data indicate that lytic HDL does not require nor generate NO to exert anti-leishmanial activity within infected macrophages . In addition , lytic HDL effectively reduced the parasite number in murine bone-marrow macrophages harvested from NAD ( P ) H oxidase knock-out mice ( gp91phox−/− ) ( p<0 . 001 , Figure 6C ) , indicating that reactive oxygen species are not required for the anti-leishmanial activity of lytic HDL within macrophages . Lytic HDL effectively reduced the parasite number in bone-marrow macrophages harvested from the parental wild-type mice ( C57BL/6 mice , p<0 . 01 , Figure 6D ) . The magnitude of parasite killing in the presence of human HDL inside macrophages harvested from all three murine strains was the same ( ∼50% ) . Overall the data show that macrophages are not activated by lytic HDL and do not require activation for lytic HDL to reduce the parasite burden . We next examined whether TLF can ameliorate an infection with intracellular L . major in vivo . Previously in our laboratory human TLF was reconstituted in transgenic mice , by generating human HDL particles that contain both apoL-I and Hpr , in vivo [36] . This was achieved using hydrodynamics-based gene delivery ( HDG ) , by which single or multiple transgenes can attain a significantly high level of expression within days of DNA injection [37] . The main organs that are transfected by this technique are the liver and lungs [38] . As the liver is the main tissue that expresses the genes that encode Hpr and apoL-I ( and lung ) , we found sufficient production and correct processing of Hpr and apoL-I occurs by this in vivo transfection technique [36] . To test the effect of reconstituted lytic HDL ( TLF ) on L . major in vivo , we transfected mice with a single plasmid that contains either apoL-I or Hpr . We also transfected mice with a single plasmid which contains both apoL-I and Hpr ( apoL-I∶Hpr ) under the control of individual promoters , which results in HDL particles that contain both apoL-I and Hpr [36] . We used C57BL/6 mice , which have the capacity to resolve a leishmanial footpad infection within 8–12 weeks and best “mimic” a human course of infection . ApoL-I , Hpr and apoL-I∶Hpr plasmids were injected 3 days before an L . major footpad infection and protein levels in the plasma and footpad size were monitored . Serial dilution of transgenic-murine plasma revealed that the level of apoL-I was approximately equivalent to that found in human plasma ( Figure 7A ) , while Hpr was expressed at a lower level in the dual plasmid ( apoL-I∶Hpr ) than in the single Hpr plasmid ( Figure 7B ) . Within 2–3 weeks post infection we observed a 50% reduction in the size of the lesion in mice expressing TLF ( apoL-I∶Hpr ) ( p<0 . 05; Figure 8A ) , which translates into a significant three-fold reduction in parasite burden 3 weeks post-infection ( p<0 . 05; Figure 8B ) . Although HDL particles that contain both apoL-I and Hpr are more robust and have greater lytic capacity than apoL-I alone [11] , [12] , we have found that apoL-I is necessary and sufficient to control a trypanosome infection in vivo [36] . Therefore , we next investigated the individual contributions of apoL-I and Hpr toward controlling the L . major infection in vivo . In order to decrease the burden of disease and maximize the effectiveness of human TLF , the mice expressing different human TLF genes were infected with 50% fewer parasites , and the L . major isolate was slightly decreased in virulence by passaging one additional time in vitro . We found that human apoL-I ( closed squares ) exerted an anti-leishmanial effect that was measurable by a reduction in the footpad lesion size ( p = 0 . 004 ) , while the effect of Hpr ( open triangle ) was not significant . When Hpr and apoL-I were both expressed ( closed inverted triangles ) the anti-leishmanial effect appeared to be co-operative ( p<0 . 001; Figure 8C ) . These results suggest that both apoL-I and Hpr are required to attain the optimal effect against L . major infection . Whether mice , were transfected with a single plasmid that expresses both apoL-I and Hpr ( apoL-I∶Hpr ) , which allows for synthesis in the same transfected cell or transfected with two individual plasmids encoding apoL-I and Hpr ( apoL-I+Hpr ) , we found that both methods of gene delivery and protein expression afford protection compared to control ( Figure 8D; apoL-I∶Hpr , p = 0 . 045; apoL-I+Hpr , p = 0 . 006 ) . Notably , complete resolution of the lesion follows a similar time course irrespective of the innate immune modulator ( apoL-I alone or apoL-I and Hpr ) , indicating that adaptive immunity plays a key role in the resolution of the disease . In order to assess the contribution of Hpr to lytic HDL ( TLF ) activity on intracellular L . major parasites in vivo , we evaluated the role of Hpr as a potential ligand that facilitates the uptake and thus activity of lytic HDL in macrophages . Hp is an abundant serum protein , which when complexed with heamoglobin ( Hp-Hb ) is an effective inhibitor of lytic HDL ( TLF ) uptake into African trypanosomes [14] , [15] . Therefore , we incubated BALB/c bone-marrow derived macrophages with human lytic HDL ( 1 . 5 mg/ml ) for 24 hours with or without the addition of Hp ( 1 mg/ml ) two hours post-infection with purified metacyclic promastigotes . Hp prevented lytic HDL from killing the intracellular parasites ( Figure 9 ) . In order to determine if TLF would have an effect on a pathogen that transiently localizes within a phagolysosomal vacuole we compared the kinetics of infection with T . cruzi in wild-type mice to our TLF expressing mice . T . cruzi is another member of the Kinetoplastida that invades cells ( including macrophages , smooth and striated muscle cells , and fibroblasts ) passing transiently through lysosomes before escaping to the cytosol to replicate . The acute phase of infection is characterized by high blood parasitemia and tissue parasitism . Mice injected with either apoL-I or Hpr plasmid alone or both were infected three days later with T . cruzi trypomastigotes intraperitoneally . Expression of the apoL-I and Hpr proteins were confirmed by western blot ( data not shown ) . The acute phase of the infection was followed by monitoring blood parasitemia daily ( Figure 10 ) . No difference in parasitemia was observed between the control mice and mice expressing apoL-I or Hpr , alone or in combination . This suggests that TLF does not have an effect on the acute stage of T . cruzi infection .
Our data shows that TLF has broad anti-microbial properties , with the ability to kill other organisms beyond trypanosomes . Because TLF requires an obligate acidic environment to become activated for pore-forming activity , we have focused on microbes that reside in an acidic environment . Leishmania metacyclic promastigotes are phagocytosed by macrophages wherein they transform into amastigotes within membrane-bound organelles of the endocytic pathway , progressively acquiring late endosomal/lysosomal characteristics . The phagosome acidification and fusion with the late endosomes/lysosomes is variable [22]–[28] . The differentiation to amastigotes starts in the hours following phagocytosis and takes 1 to 3 days to complete [39] . During this differentiation the parasite may be vulnerable to attack because HDL and TLF can be endocytosed and delivered to acidic endo/lysosomes in cells that have an appropriate lipoprotein scavenger receptor , such as SRB-I [29] , [31] or SR-BII [30] , or Hp receptors that are expressed on macrophages [32] , [33] . Our results show that L . major parasites pretreated with lytic HDL in acidic media have a drastic change in morphology whereas in neutral media they maintained normal morphology ( Figure 1A and 1B ) . We observed that TLF bound equally well to the parasites irrespective of the pH ( Figure 1I and 1J ) and that propidium iodide was excluded from the treated parasites , which suggests that the parasites remain “viable” ( data not shown ) . However , the pretreatment of L . major or L . amazonensis promastigotes with lytic HDL in acidic media substantially reduced their infectivity; whereas , there was no change in infectivity after pretreatment in neutral media ( Figure 1C–1F ) . We interpret this data as follows; TLF increases susceptibility to host macrophage microbicidal processes by damaging the parasites . African trypanosomes , are killed in neutral media , because lytic HDL ( TLF ) is endocytosed by the parasites via a Hp-Hb receptor and activated within the acidified lysosome of the parasite , wherein it forms pores [7]–[12] , [14] , [15] . Leishmania do not have a homologue of the trypanosome Hp-Hb receptor [15] and may not be able to accumulate sufficient lytic HDL ( TLF ) within 24 hours . Given that the binding of TLF is equivalent in neutral or acidic media , the data suggest either ( 1 ) lytic HDL ( TLF ) may interact with the surface of Leishmania promastigotes and damage the plasma membrane when activated under acidic conditions , possibly by forming pores; or ( 2 ) TLF was endocytosed by Leishmania parasites and the promastigote lysosome is weakly acidified in neutral media , as it stains poorly with the pH sensitive probe , lysotracker [40] , but in acidic media the parasite lysosome will be fully acidified , allowing the activation of the TLF . TLF accumulates within the PV of macrophages ( Figure 2 ) . The observation that all PVs contain TLF , which surrounds the parasites but does not appear to be endocytosed by the parasites ( Figure 3 ) , concurs with the axenic data; TLF may act directly at the parasite plasma membrane within the PV , though we cannot rule out that some TLF may be endocytosed by the parasite . We find that the number of parasites within macrophages decreased by 24 hours post-addition of lytic HDL in a dose-dependent manner ( Figure 4 ) . However , the clearance of the parasites was not complete , which may be due to individual differences each in PVs acidification process . Overall these data indicate that addition of lytic HDL ( TLF ) decreases the number of metacyclic promastigotes in vitro in macrophages . In contrast , we find that amastigotes are resistant to lytic HDL ( TLF ) axenically ( Figure 1G and 1H ) and within macrophages ( Figure 5 ) . Therefore , we conclude that the window of Leishmania susceptibility to lytic HDL is after phagocytosis of the metacyclic promastigotes during acidification of the PV and before transformation into amastigotes ( Figure 11 ) . Our data also show that the effect of lytic HDL on Leishmania is independent of macrophage activation ( Figure 6 ) . In vivo infections with L . major lead to the development of cutaneous lesions , which are considered to arise from growth within tissue macrophages . In our in vivo system , apoL-I and Hpr were both required to maximally reduce the Leishmania lesion ( Figure 8 ) . The reduction of the Leishmania lesion by apoL-I was statistically significant ( p = 0 . 004; Figure 8C ) . However , the dual expression of Hpr and apoL-I reduced lesion size significantly compared to apoL-I alone ( p = 0 . 001 ) . The reduction in lesion size was effective whether the genes were expressed from individual plasmids such that expression is from different transfected cells ( p = 0 . 006 ) or the same plasmid , which allows for expression from the same transfected cell ( p = 0 . 045; Figure 8D ) . Therefore , the two proteins appear to be acting co-operatively . ApoL-I likely forms a pore in the membrane of the Leishmania parasite directly at the plasma membrane and/or at the lysosomal membrane ( Figure 1 ) . Hpr appears to be a ligand , which binds to a putative receptor on macrophages and enhances the uptake of TLF into macrophages . We draw this conclusion from the in vitro competition data in Figure 9 , which showed that Hp prevented the lytic HDL from killing the intracellular parasites . Recent studies demonstrate that neutrophils are the initial host cell that phagocytose a substantial fraction of L . major parasites after sandfly transmission [17] . Neutrophils can bind and endocytose HDLs particles [41] and Hp [42] 1% of which circulates bound to HDLs [34] . It is plausible that in vivo , in addition to macrophages , TLF might be endocytosed and traffic to PVs within neutrophils and exert lytic activity against Leishmania at acidic pH . The co-operative effect of Hpr and apoL-I may also require Hb as proposed for African trypanosomes [14] , [15] . Hb ( from the FBS in culture media and in murine blood ) can be bound to TLF via Hpr , and thereby be taken up by infected macrophages . The Hpr-Hb complex may be the ligand that facilitates uptake of apoL-I ( in TLF complexes ) into macrophages . It has been proposed that Hpr-Hb complexes may generate free radicals by reacting with hydrogen peroxide within the acidified lysosomes [14] . Although free radicals could contribute to the damage of Leishmania parasite membranes , we find that macrophages devoid of any NAD ( P ) H oxidase , which generates superoxide that can dismutate to hydrogen peroxide , are able to kill Leishmania ( Figure 6C ) as effectively as wild-type macrophages . Furthermore , Hpr-mice did not change the lesion size significantly in vivo ( Figure 8C ) . While the transgenic-TLF mice do not completely clear the Leishmania infection , they substantially reduce the parasitemia . The fact that Leishmania infection is not eliminated in the presence of TLF is not unexpected since humans have TLF but remain susceptible to Leishmania infection . Thus , TLF may serve to reduce the initial pathogen numbers and limit dissemination of the parasite until adaptive immunity takes effect . Other possible explanations for partial parasite clearance may be that TLF is less abundant in tissue spaces ( ∼25% ) than in blood , and therefore TLF levels may not be optimal at the footpad lesion derma in order to act against the parasite . In addition , the hydrodynamic gene delivery system allows maximal expression of the proteins for ∼10 days . Indeed , ∼2 weeks post-injection of the plasmid , the protein expression in plasma drops below the limit of detection . Nevertheless , some low level of protein is maintained for months since mice can be infected with T . brucei several months post-injection of apoL-I plasmid , and resist the infection ( data not shown ) . In contrast to L . major , we could not detect any effect of transgenic-TLF against T . cruzi parasites ( Figure 10 ) . This finding suggests that bloodstream trypomastigotes , which accumulate to high numbers in the circulation during the acute stage of infection and invade both phagocytic and non-phagocytic cell types , are refractory to TLF . This may reflect the ability of the parasite to infect non-phagocytic cells that may not take up HDL efficiently . Additionally , it is possible that because T . cruzi resides transiently ( 8–16 hours ) within acidified vacuoles , the parasites are not exposed to active TLF for a sufficient period of time . These new findings support the hypothesis that TLF not only kills African trypanosomes , but also contributes to the innate immunity against other pathogens , such as Leishmania . The efficiency of killing other pathogens by TLF may depend on both a physical interaction as well as an extended period of contact between the susceptible pathogen and TLF . African trypanosomes grow in the blood and tissues spaces of the human host and constantly endocytose TLF , whereas Leishmania parasites grow within phagocytic cells in fully acidified PVs to which TLF may be delivered , but then transform to evade TLF action . In contrast , T . cruzi parasites infect non-phagocytic cells as well as professional phagocytes , and are only transiently localized within acidified vacuoles , such that constant exposure to active TLF is unlikely . We conclude that TLFs are a component of the innate immune system , which can limit infections by their ability to selectively damage pathogens such as Leishmania , that reside within the reticuloendothelial system .
HDL was purified from normal human serum by adjusting to a density of 1 . 25 g/ml with potassium bromide ( KBr ) and ultracentrifuged at 49 , 000 rpm ( NVTi 65; Beckman ) for 16 hours at 10°C . The lipoprotein fraction was collected and the density of this fraction was adjusted to 1 . 3 g/ml with KBr and 4 ml aliquots were layered under 8 ml of 0 . 9% NaCl . The lipoproteins were then centrifuged at 49 , 000 rpm for 3 hours at 10°C ( NVTi 65 rotor; Beckman ) . HDL was harvested and dialyzed against Tris-buffered saline ( TBS; 50 mM Tris-HCl , 150 mM NaCl ( pH 7 . 5 ) at 4°C and then concentrated by ultrafiltration ( XM300 filter membrane; Amicon ) . HDL was concentrated to about 50 mg of protein/ml . TLF was obtained by affinity purification of human HDL using a mouse anti-human Hp monoclonal ( H6395 , Sigma ) coupled to a HiTrap column ( Amersham Biosciences ) . The fractions containing Hpr ( TLF ) were pooled and concentrated . HDL and LDL from bovine serum cannot be efficiently separated by density , unlike human HDL and LDL . Therefore when bovine HDL was used as a control the lipoproteins were purified by adjusting their density to 1 . 25 g/ml with KBr and ultracentrifuged for 16 hours at 49 , 000 rpm , 10°C . The lipoprotein fraction ( density 1–1 . 25 g/ml ) was then collected , and size-fractionated on a Superdex 200 HR 10/30 column ( Amersham ) equilibrated with TBS [1] . Fractions containing apoA-I , the canonical HDL apolipoprotein , were pooled and concentrated . L . major strain Friedlin V1 ( MHOM/JL/80/Friedlin ) and L . major FV1 SSU: GFP+ ( b ) -SAT promastigotes were grown as previously described in medium M199 [43] ( neutral medium 1 ) , and infective-stage metacyclic promastigotes were isolated from stationary cultures ( 5-days old ) by density centrifugation on a Ficoll gradient [44] . L . amazonensis IFLA/BR/67/PH8 strain promastigotes were maintained in vitro as previously described [45] ( neutral medium 2 ) . L . amazonensis axenic amastigote-like forms were cultured at 32°C in the same medium supplemented with 0 . 25% glucose , 0 . 5% trypticase , and 40 mM Na succinate ( acidic medium ) [45] . L . major and L . amazonensis metacyclics and amastigote-like forms were incubated for 24 hours at 27°C and 32°C respectively in corresponding neutral medium or in amastigote acidic medium in the presence or the absence of HDL . They were washed and checked for integrity under the microscope . Thereafter they were allowed to invade macrophages in DMEM containing 10% heat-inactivated FBS , 5% penicillin-streptomycin , 5 mM L-glutamine ( DMEM culture medium ) , at a multiplicity of infection of 3 to 6 parasites per macrophage for 24 hours at 33°C ( 5% CO2 , 95% air humidity ) . Intracellular parasites were assessed after staining with DAPI ( 3 µmol/L ) by fluorescence microscopy . Bone marrow-derived macrophages were prepared as described previously [46] . Cells were prepared from femurs of BALB/c mice ( Taconic ) , B6;129P2-Nos2tm1Lau/J or B6 . 129S6-Cybbtm1Din/J , C57BL/6/J and after 3 days in culture , non-adherent progenitor cells were taken and cultured for an additional 7 days in culture medium supplemented with 30% ( v/v ) L cell-conditioned medium as a source of CSF-1 . Adherent cells were harvested with cold DMEM+0 . 5 mM EDTA and seeded into an 8-well Lab-Tek II ( Nalge Nunc International , Naperville , IL ) chambered coverglass at a concentration of 50 , 000 cells/chamber and allowed to adhere for 24 hours ( 37°C , 5% CO2 , 95% air humidity ) before being used for infections . Unactivated intraperitoneal macrophages were isolated by lavage of the intraperitoneal cavity of Swiss-Webster Mice ( Taconic ) . The cells were resuspended in DMEM culture medium , seeded into an 8-well Lab-Tek II ( Nalge Nunc International , Naperville , IL ) chambered coverglass ( 50 , 000 cells/chamber ) , and allowed to adhere for 24 hours ( 37°C , 5% CO2 , 95% air humidity ) . Thereafter , non-adherent cells were removed by three extensive washings with culture medium before being used for infections . L . major metacyclics and L . amazonensis promastigotes or amastigotes were opsonized by 30 min incubation in DMEM medium containing 4% BALB/c or Swiss-Webster mouse serum and allowed to invade strain matched macrophages in DMEM culture medium , at a multiplicity of infection of 3 parasites per macrophage for 2 hours at 33°C ( 5% CO2 , 95% air humidity ) . Thereafter , non-phagocytosed parasites were washed off , and the cultures were further incubated in the presence or the absence of HDL with or without Hp ( H3536 , Sigma ) for indicated times . Intracellular parasites were assessed after staining with DAPI ( 3 µmol/L ) by fluorescence microscopy . Bone marrow-derived macrophages BALB/c mice were seeded into an 8-well Lab-Tek II chambered coverglass at a concentration of 150 , 000 cells/chamber before being used for infections with L . major at a multiplicity of infection of 3 parasites per macrophage for 2 hours at 33°C ( 5% CO2 , 95% air humidity ) . Thereafter , non-phagocytosed parasites were washed off , and the cultures were further incubated in the presence or the absence of HDL ( 1 . 5 mg/ml ) with or without murine IFNγ ( 5 µg/µl , 315-05 , Preprotech and LPS ( 100 µg/µl , L6511 , Sigma ) for 24 hours . Nitrite quantification was measured by the Griess reaction according to the manufacturer ( G7921 , Molecular Probes ) . Metacyclic promastigotes ( 1×106 ) were inoculated intradermally into the right hind footpad of C57BL/6 mice ( Taconic ) in a volume of 50 µl using a 28 . 5-gauge needle ( 5 mice per group ) . The evolution of the lesion was monitored by measuring the lesion thickness with a direct-reading Vernier caliper . A non-parametric approach for several independent groups , Kuskal Wallis test , was used to analyze the data . For post-hoc comparisons , Mann Whitney tests were used with a Bonferroni correction . Parasite titrations were performed with footpad tissue homogenates obtained from individual mice and serially diluted . Each dilution was dispensed into 36 wells to give sufficient data for Poisson distribution . After 10 days , the growth of parasites was determined microscopically . The number of viable parasites in each sample was determined from the highest dilution at which promastigotes could be grown out after 7 days of incubation at 27°C . For treatment comparisons Mann Whitney tests were used . TLFs were labeled with Alexa Fluor-594 or Alexa Fluor-488 protein labeling kit ( Molecular Probes ) according to the manufacturer's instructions . L . major metacyclics FV1 or FV1 SUU: GFP+ ( b ) -SAT purified metacyclics were opsonized by 30 min incubation in DMEM medium containing 4% serum from BALB/c mice and allowed to invade BALB/c bone-marrow derived macrophages for 2 hours at 33°C ( 5% CO2 , 95% air humidity ) . Thereafter , non-phagocytosed parasites were washed off , and the cultures were further incubated in the presence of Alexa labeled TLF for 2 or 24 hours . Live parasites within macrophages were fixed with 2% paraformaldehyde . Cells were permeabilized with 0 . 05% saponin . Lamp-1 staining was performed using a rat monoclonal antibody to mouse Lamp-1 ( 1∶100 , 1D4B; Developmental Studies Hybridoma Bank , Iowa City , IA ) , followed by goat anti-rat IgG conjugated to FITC antibodies ( 1∶200 , Sigma ) . Intracellular parasites were observed by staining with DAPI ( 3 µmol/L ) or direct GFP fluorescence of parasites . The samples were visualized and analyzed with a Leica TCS SP2 AOBS confocal laser scanning microscope . For flow cytometry on live Leishmania , purified L . major metacyclics were washed twice in PBS and incubated ( 2×107/ml ) with 10 µg/ml Alexa Fluor-488 labeled TLF in bicine-buffered saline with glucose ( pH 5 or 7 . 5 ) for 30 min . Cells were washed twice in FACS buffer ( PBS , 5% FBS , and 0 . 1% sodium azide ) before being analyzed . Flow cytometry was performed with a Becton Dickinson FACSCalibur system . Expression of human Hpr and apoL-I in plasma of mice was achieved using hydrodynamics-based gene delivery [38] . Briefly , 20 g male C57BL/6 mice ( for L . major experiments ) , and Swiss-Webster ( for T . cruzi ) were injected IV , in less than 10 seconds with 2 ml of sterile 0 . 9% NaCl solution containing 50–100 µg of plasmids [36] . Three days after injections and every other day thereafter , blood samples ( 20 µl ) were taken from the animals via tail bleeds and expression of the human proteins was evaluated by western blotting . Plasma samples were separated on 7 . 5% Tris-glycine PAGER Gold precast Gels ( Cambrex Bio Science Rockland , Inc . ME ) . Gels were transferred onto PDVF membranes ( GE Healthcare Bio-Sciences , Uppsala , Sweden ) . For western blot analysis membranes were blocked with 5% skimmed milk and 0 . 1% Tween-20 in TBS and probed for 1 hour with the following antibodies: mouse monoclonal anti-Hpr ( 1∶5000 ) ; mouse monoclonal anti-apoL-I ( 1∶10 , 000; kindly provided by Dr . Stephen Hajduk ) . The secondary antibodies were conjugated to horseradish peroxidase , and used at the following dilutions: anti mouse IgG ( 1∶50 , 000; Promega , Madison , WI ) . Primary and secondary antibodies were diluted into 2 . 5% skimmed milk and 0 . 1% Tween–20 in TBS . Bound antibodies were detected by chemiluminescence using ECL ( GE Healthcare Bio-Sciences , Uppsala , Sweden ) . Tissue culture-derived T . cruzi trypomastigotes ( Y strain ) were generated by weekly passage in confluent monolayers of LLcMK2 cells in DMEM containing 2% FBS as described previously [47] . Trypomastigotes harvested from culture supernatants were washed three times in serum free DMEM prior to use . T . cruzi trypomastigotes ( 106 ) were injected intraperitoneally into Swiss-Webster mice ( Taconic ) three days after transfection ( 3 mice per group ) . Parasitemia was monitored in peripheral blood of infected mice by microscopic examination of non-fixed blood . Apolipoprotein L-I , NM_003661; Haptoglobin-related protein , NM_020995 . | Innate immunity ( present from birth ) is the first line of defense against microorganisms and provides an initial barrier against disease . Here we show that a minor sub-fraction of human high-density lipoprotein ( the good cholesterol ) , known as Trypanosome Lytic Factor ( TLF ) , not only kills the parasite Trypanosoma brucei , but is also a more broadly acting antimicrobial component of the innate immune system in humans . As TLF is activated under acidic conditions , we evaluated the activity of TLF against the intracellular parasite Leishmania , which infects and grows within acidic compartments of macrophages , cells in our blood that normally destroy invading microorganisms . Here we show that TLF acts directly on Leishmania parasites , causing them to swell , thereby decreasing their infectivity . Furthermore , microscopy of macrophages infected with Leishmania reveal that TLF is taken up and delivered to the same compartment as Leishmania , concomitant with a reduction in the intracellular parasite number . Finally , we made mice that expressed the genes for human TLF; these mice reduced the pathogen burden and thereby controlled the Leishmania infection better than unmodified mice . In contrast , TLF mice were not protected from infection by Trypanosoma cruzi , a related parasite , which transiently passes through acidic compartments within cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"immunology/innate",
"immunity",
"cell",
"biology/microbial",
"growth",
"and",
"development",
"biochemistry/biomacromolecule-ligand",
"interactions",
"microbiology/parasitology",
"infectious",
"diseases/protozoal",
"infectio... | 2009 | Trypanosome Lytic Factor, an Antimicrobial High-Density Lipoprotein, Ameliorates Leishmania Infection |
Uncontrolled trachoma is a leading cause of blindness . Current global trachoma burden summary measures are presented as disability adjusted life years but have limitations due to inconsistent methods and inadequate population-based data on trachomatous low vision and blindness . We aimed to describe more completely the burden of blinding trachoma in Southern Sudan using health expectancies . Age and gender specific trachomatous trichiasis ( TT ) prevalence was estimated from 11 districts in Southern Sudan . The distribution of visual acuity ( VA ) in persons with TT was recorded in one district . Sudan life tables , TT prevalence , and VA were used to calculate Trichiasis Free Life Expectancy ( TTFLE ) and Trichiasis Life Expectancy ( TTLE ) using the Sullivan method . TTLE was broken down by VA to derive TTLE with normal vision , TTLE with low vision , and TTLE with blindness . Total life expectancy at birth in 2001 was 54 . 2 years for males and 58 . 1 for females . From our Sullivan models , trichiasis life expectancy at the age of 5 years was estimated to be 7 . 0 ( 95% confidence interval [CI] = 6 . 2–7 . 8 ) years ( 12% [95% CI = 11–14] of remaining life ) for males and 10 . 9 ( 95% CI = 9 . 9–11 . 9 ) years ( 18% [95% CI = 16–20] of remaining life ) for females . Trichiasis life expectancy with low vision or blindness was 5 . 1 ( 95% CI = 3 . 9–6 . 4 ) years ( 9% [95% CI = 7–11] of remaining life ) and 7 . 6 ( 95% CI = 6 . 0–9 . 1 ) years ( 12% [95% CI = 10–15] of remaining life ) for males and females , respectively . Women were predicted to live longer and spend a greater proportion of their lives with disabling trichiasis , low vision , and blindness compared to men . The study shows the future burden associated with doing nothing to control trachoma in Southern Sudan , that is , a substantial proportion of remaining life expectancy spent with trichiasis and low vision or blindness for both men and women , with a disproportionate burden falling on women .
Trachoma is one of the oldest infectious diseases known to mankind and is the leading infectious cause of blindness , estimated to be responsible for 2 . 9% of blindness worldwide [1] . Recurrent infection with ocular Chlamydia trachomatis results in chronic inflammation , scarring , trichiasis , corneal opacification , and blindness [2]–[4] . Blindness due to trachoma is preventable through the World Health Organization ( WHO ) SAFE strategy which comprises: Surgery , eyelid surgery to correct in-turned eyelashes which stops pain and minimizes risk of corneal damage; Antibiotic treatment for active trachoma using single-dose oral azithromycin or topical tetracycline; Facial cleanliness , promotion of clean faces especially in children through sustained behaviour change; and Environmental improvements to increase access to water and sanitation [5] . Summary measures of population health , including disability adjusted life years ( DALYs ) [6] and handicap adjusted life years ( HALYs ) [7] , have been used to estimate the global burden attributable to trachoma . DALYs and HALYs are population health measures permitting morbidity and mortality to be simultaneously described within a single number and estimate the gap between a population's health and some defined goal . The methodology and data sources describing trachoma DALYs and HALYs have differed such that direct comparisons are not possible . For instance , Evans and Ranson estimated global burden of trachoma for the year 1990 to be 80 . 0 million HALYs [7]; while for the same years the Global Burden of Disease ( GBD 1990 ) project reported trachoma burden to be 1 . 0 million DALYs [8] . Additionally , studies describing the global burden of trachoma for the year 2000 yielded different estimates of 2 . 2 million DALYs [6] and 3 . 6 million DALYs [9] . These previous estimates also have limitations arising from paucity of population-based data on trachomatous low vision and blindness [7] , [9] , [10] . We aimed to demonstrate the application of the health expectancies approach for trachomatous trichiasis health states ( any trichiasis , trichiasis with normal vision , trichiasis with low vision , and trichiasis with blindness ) as a summary measure of trachoma burden using population-based survey data from Southern Sudan . Health expectancy is a measure that combines information on both mortality and morbidity to derive lengths of time spent in different states of health . The methods presented can be applied to other trachoma endemic areas and presents estimates of the potential burden of blinding trachoma if control measures are not implemented .
The Institutional Review Board of Emory University approved the survey protocols and clearance to conduct surveys was obtained from the Sudan Peoples Liberation Movement Secretariat of Health ( SPLM/Health ) . Verbal informed consent to participate was sought from the heads of the household , from each individual and the parents of children aged less than 10 years in accordance with the declaration of Helsinki . Consent for household interviews and eye examination was documented by interviewers and examiners on the data collection forms . Personal identifiers were removed from the data set before analyses were undertaken . Surveys for trachoma were conducted in eleven districts in Southern Sudan between 2001 and 2006 [11]–[13] . For each district , the sample size was calculated to allow for estimation of at least 50% prevalence of active trachoma signs in children aged 1–9 years within a precision of 10% given a 95% confidence limit and a design effect of 5 . We also aimed to estimate at least 2 . 5% prevalence of trachoma trichiasis ( TT ) in persons aged 15 years and above within a precision of 1 . 5% at 95% confidence limit and a design effect of 2 . The districts were selected on the basis of pragmatic program implementation criteria of: 1 ) anecdotal reports of blinding trachoma; 2 ) security and accessibility; and 3 ) feasibility of initiating trachoma control interventions after the survey . A two-stage cluster random sampling with probability proportional to size was used to select the sample population in each district . A cluster was defined as the population within a single village . Using a line listing of all the villages in each survey district , villages were grouped into sub-districts . Villages that were inaccessible and/or insecure were excluded from the sampling frame . In the first stage , villages were randomly selected with probability proportional to the estimated population of the sub-district . In the second stage , households were selected from the villages selected in the previous stage using the random-walk method [14] , except in Ayod district where the compact segment method [15] was used for sampling households . All residents of selected households were enumerated and those present were eligible for eye examination . It was not possible to return later to the households to pick up any absentees and households where residents were not available were skipped . Trainee examiners comprising of auxiliary nurses and community health workers were trained using the WHO simplified grading system [16] by a senior examiner experienced in trachoma grading ( ophthalmologist or ophthalmic nurse ) . The minimum accepted inter-observer agreement was set at 80% and reliability assessed in two stages . In the first stage , trainee examiners identified trachoma grades using the WHO sets of trachoma slides [17] , [18] . Those examiners who achieved at least 80% agreement then proceeded to the second stage of field evaluation . During field evaluation a reliability study comprising 50 persons of varying age and gender were selected by the ophthalmic nurse to represent all trachoma grades . Each trainee examiner evaluated all 50 subjects independently and recorded their findings on a pre-printed form . Inter-observer agreement was then calculated for each trainee using the senior examiners' observation as the ‘gold standard’ . Only trainees achieving at least 80% inter-observer agreement after the field evaluation were included as trachoma graders . All persons living within each selected household who gave verbal consent were examined using a torch and a ×2 . 5 magnifying binocular loupe in accordance to the simplified grading system . Alcohol-soaked cotton-swabs were used to clean the examiner's fingers between examinations . All examined participants were assigned a dichotomous outcome for each trachoma sign based on the worst affected eye . TT was defined by the presence of at least one eye lash touching the eyeball or evidence of epilation of the eyelashes . Individuals with signs of active trachoma were offered treatment with 1% tetracycline eye ointment . Patients TT were referred to the health centre where free eyelid surgery was available . In one district ( Mankien ) , visual acuity ( VA ) testing was conducted in all eligible participants [19] . Experienced Integrated eye care workers ( IECW ) were re-trained in VA testing , basic eye examination and trachoma grading and their reliability assessed . Only trainees achieving an inter-observer agreement of 80% and above were eligible to participate as examiners . Prior to the survey , the minimum age for visual acuity ( VA ) testing was predetermined to be 5 years . VA testing was conducted outdoors in adequate sunlight using the Snellen E chart at 6 meters . In persons with VA<6/60 , VA was evaluated with the Snellen chart at 3 meters . Further VA assessment was done in persons with VA<3/60 by counting fingers , hand movement and light perception as appropriate . All participants then underwent basic eye examination . Using a torch and a ×2 . 5 magnifying binocular loupe , each eye was examined first for in-turned lashes ( TT ) , and the cornea was then inspected for corneal opacities ( CO ) , and the lens examined for cataract . Persons with visual impairment were referred to attend an eye surgery-camp conducted after the survey . Data were recorded on a customized form and the cause of visual impairment determined for all subjects with a presenting VA of <6/18 for each eye separately . The principal disorder responsible for low vision or blindness was determined for the participant by taking into account the main cause for each individual eye . Vision loss was attributed to trachoma in persons presenting with trichiasis and corneal opacity . In the instance where different causes of vision loss had been identified for each eye separately in a given individual , the principal disorder was chosen to be the one that was most readily curable or , if not curable , most easily preventable ( i . e . cataract , trachoma , non-trachomatous CO , and other causes in that order ) . To define the vision status we adopted the WHO categories of visual impairment based on presenting visual acuity ( Box 1 ) . Our model of the distribution of vision status has been described previously [19] . In brief; using VA data for persons presenting with TT from Mankien survey , age specific distributions of vision status were calculated for 5-year age intervals . We then fitted an ordinal logistic regression model to the observed data to explore the age and gender distribution of the three categories of vision status: normal vision; low vision; and blindness . Persons with visual impairment not directly attributable to trichiasis were excluded from the final model . Children aged 0–4 years were assumed to have normal vision . Predicted probabilities were derived to smooth age-specific curves for the three categories of vision status . Life tables are frequently used in demography , actuarial science and health services . They trace the life expectancy in pre-determined intervals for a hypothetical population size ( frequently 100 , 000 births ) based on parameters usually derived from vital registration data . Abridged life tables for Sudan for the year 2001 were obtained from the World Health Organisation Statistical Information System ( WHOSIS ) for males and females separately [20] . Demographic estimates for Sudan are based on model life tables because vital registration data are poor or not available . The life tables were derived using the Modified Logit model life table system which is extensively used for countries with poor vital registration . The Modified Logit system has been modelled using data from other populations judged to be similar and is indexed on the number of survivors at age five years and the number of survivors at age 60 years [21] . The health states used to describe the burden due to trachoma were defined as follows: Total Life Expectancy , the total lifespan at birth ( years ) ; Trichiasis Free Life Expectancy ( TTFLE ) , the expectation of life without any trichiasis; and Trichiasis Life Expectancy ( TTLE ) , the expectation of life with any trichiasis . TTLE was then broken down into three health states: 1 ) TTLE with normal vision , the expectation of life with any trichiasis and normal vision ( presenting VA≥6/18 in better eye ) ; 2 ) TTLE with low vision , the expectation of life with any trichiasis and low vision ( presenting VA<6/18 but ≥3/60 in better eye ) ; and 3 ) TTLE with blindness , the expectation of life with any trichiasis and blindness ( presenting VA<3/60 in the better eye ) . The data analysis framework is summarised in Figure 1 . Microsoft Excel spreadsheets developed by the European Health Expectancy Monitoring Unit were adapted for the calculation of health expectancies [22] . Age and gender specific prevalence of trichiasis was estimated from cross-sectional surveys and modelled using logistic regression to smooth the prevalence estimates . The distribution of vision status was derived from Mankien survey whereby visual acuity was categorized into normal vision , low vision and blindness; and modelled by ordinal logistic regression to provide proportions for each category of VA by age and gender [19] . The prevalence of vision status in the sample population was then calculated by multiplying the age and gender specific proportions of vision status with the smoothed prevalence of trichiasis . Life tables were collapsed to represent 5-year age-groups from age zero ( 0 ) to 75 years and above . Trichiasis Free Life Expectancy ( TTFLE ) and Trichiasis Life Expectancy ( TTLE ) were then calculated using the Sullivan method [23] . The Sullivan method combines use of life tables and age specific prevalence of morbidity to partition life expectancy into years with and without morbidity . The Sullivan health expectancy reflects the current health of the population adjusted for mortality levels and independent of age structure . Health expectancy calculated by the Sullivan method is the number of remaining years , at a particular age , that an individual can expect to live in a specified health state . Trichiasis Life Expectancy was further broken down by vision status to derive TTLE with normal vision , TTLE with low vision and TTLE with blindness .
Table 1 summarises the study population . A total of 23 , 139 ( 87 . 2% of those enumerated ) people , in 11 districts , were examined for trachoma of whom males comprised 43% . The overall prevalence of trachomatous trichiasis ( all ages ) was 6 . 0% ( 95% confidence interval [CI] = 5 . 2–7 . 0 ) and varied by district ranging from 0 . 7% in Katigiri to 10 . 0% in Kimotong . Of 341 people with TT in Mankien district , 319 were included in modelling of the distribution of vision status ( i . e . TT with normal vision , TT with low vision and TT with blindness ) . The distribution of proportions of vision status by age and gender in persons with trichiasis is shown in Table 2 [19] . Table 3 summarises the age and gender specific prevalence of trichiasis and breakdown of prevalence of trichiasis vision status . The prevalence of TT increased with age and females were more likely to have TT compared to males , age adjusted Odds Ratio ( OR ) = 1 . 5 ( 95% CI = 1 . 3–1 . 7 ) . Consistent with prevalence of trichiasis , prevalence of visual impairment ( low vision and blindness ) increased with age ( Table 3 ) . In the 2001 Sudan life table , women had a higher life expectancy at birth than men . The life expectancy at birth for Sudan was 58 . 1 years for females and 54 . 1 years for males . Life expectancy increased in age 5–9 years compared to life expectancy at birth ( age 0–4 years ) to 56 . 8 years in males and 60 . 7 for females; indicating the high under-five mortality rate . Table 4 , Figure 2 and Figure 3 show the life expectancy ( LE ) and proportions of total life expectancy , trichiasis free life expectancy ( TTFLE ) , trichiasis life expectancy ( TTLE ) , and TTLE with normal vision , TTLE with low vision , and TTLE with blindness . Females had a greater life expectancy at all ages ( Figure 2 ) than males and a larger proportion of remaining life spent with trichiasis low vision or blindness ( Figure 3 ) . At age five , TTFLE was 49 . 8 years ( 88% of remaining life ) and 49 . 8 years ( 82% of remaining life ) in males and females , respectively . Males expected to live 7 . 0 ( 95%CI = 6 . 2–7 . 8 ) years ( 12% [95% CI = 11–14] of remaining life ) with trichiasis at age five; of which 1 . 9 years ( 3% of remaining life ) , 3 . 5 years ( 6% of remaining life ) and 1 . 6 years ( 3% of remaining life ) would be lived with normal vision , low vision and blindness , respectively . At age five , females TTLE was 10 . 9 ( 95%CI = 9 . 9–11 . 9 ) years ( 18% [95% CI = 16–20] of remaining life ) of which trichiasis with normal vision , low vision and blindness comprised 3 . 3 years ( 6% ) , 4 . 9 years ( 8% ) and 2 . 7 years ( 4% ) , respectively . For both genders , the proportion of life spent with trichiasis increased with age ( Figure 3 ) , by age 50 years , TTLE was 30% ( 6 . 4 years ) for males and 40% ( 9 . 5 years ) for females . The proportion of remaining life with trachoma visual impairment ( low vision or blindness ) at age 50 was 26% ( 5 . 5 years ) for males and 33% ( 7 . 7 years ) for females .
We have presented the burden of trachomatous vision loss by age and gender using health expectancies . These data are of value in advocacy for trachoma control in engagement with politicians and donors . Unless action is taken by further delivery of trachoma control interventions , then populations in Southern Sudan can expect to spend a substantial proportion of their life with low vision or blindness due to trachoma . | Summary measures of population health attempt to express disease burden in terms of a common “currency” and are useful in establishing public health priorities . Disability adjusted life years ( DALYs ) , a health gap measure , have previously been used to estimate burden due to trachoma; however , their methods and results have limitations . This study demonstrates the application of the health expectancies to estimate burden due to trachoma . The study illustrates the future burden associated with doing nothing to control trachoma in Southern Sudan: a substantial proportion of remaining life expectancy spent with trichiasis and low vision or blindness for both men and women , with a disproportionate burden falling on women . The results presented are intuitively meaningful for policy makers and a non-technical audience and compare favourably with other indicators such as mortality and incidence rates or DALYs , which are not generally easily understood . Unless action is taken by further delivery of trachoma control interventions , then populations in Southern Sudan can expect to spend a substantial proportion of their life with low vision or blindness due to trachoma . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"public",
"health",
"and",
"epidemiology/epidemiology",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"ophthalmology/eye",
"infections"
] | 2009 | What Will Happen If We Do Nothing To Control Trachoma: Health Expectancies for Blinding Trachoma in Southern Sudan |
Neurons process and convey information by transforming barrages of synaptic inputs into spiking activity . Synaptic inhibition typically suppresses the output firing activity of a neuron , and is commonly classified as having a subtractive or divisive effect on a neuron’s output firing activity . Subtractive inhibition can narrow the range of inputs that evoke spiking activity by eliminating responses to non-preferred inputs . Divisive inhibition is a form of gain control: it modifies firing rates while preserving the range of inputs that evoke firing activity . Since these two “modes” of inhibition have distinct impacts on neural coding , it is important to understand the biophysical mechanisms that distinguish these response profiles . In this study , we use simulations and mathematical analysis of a neuron model to find the specific conditions ( parameter sets ) for which inhibitory inputs have subtractive or divisive effects . Significantly , we identify a novel role for the A-type Potassium current ( IA ) . In our model , this fast-activating , slowly-inactivating outward current acts as a switch between subtractive and divisive inhibition . In particular , if IA is strong ( large maximal conductance ) and fast ( activates on a time-scale similar to spike initiation ) , then inhibition has a subtractive effect on neural firing . In contrast , if IA is weak or insufficiently fast-activating , then inhibition has a divisive effect on neural firing . We explain these findings using dynamical systems methods ( plane analysis and fast-slow dissection ) to define how a spike threshold condition depends on synaptic inputs and IA . Our findings suggest that neurons can “self-regulate” the gain control effects of inhibition via combinations of synaptic plasticity and/or modulation of the conductance and kinetics of A-type Potassium channels . This novel role for IA would add flexibility to neurons and networks , and may relate to recent observations of divisive inhibitory effects on neurons in the nucleus of the solitary tract .
The activity of a neuron is driven by barrages of synaptic inputs . Synaptic inputs are classified as either excitatory ( those that promote spike generation ) and inhibitory ( those that impede spike generation ) . The interplay between these two “opposing” inputs influences how neurons process and transmit information in the brain . To characterize the nature of inhibition , researchers often distinguish between inhibition that has a subtractive effect on neural firing , versus inhibition that has a divisive effect [1] . Inhibition is said to be subtractive if it reduces the firing activity of a neuron by ( roughly ) a constant amount , regardless of the strength or amount of synaptic excitation . Inhibition is said to be divisive if it reduces the firing activity of a neuron by an amount that is ( roughly ) proportional to the neuron’s firing rate . We illustrate this distinction in Fig 1 , by showing output firing rate of a neuron as a function of the rate of its excitatory inputs ( not actual data ) . The differences between these modes of inhibition has important consequences for neural coding . Subtractive inhibition suppresses responses to “non-preferred” stimuli that evoke infrequent responses in the absence of inhibition . This can be useful for promoting the representation of “preferred” inputs . In contrast , divisive inhibition is a mechanism for neural gain control: it reduces the firing rate of a neuron while preserving the overall range of inputs to which the neuron is responsive [2] . Understanding the physiological mechanisms that determine how and why inhibition acts in these two modes is key for understanding how neurons and networks function . Past studies have identified numerous possibilities for mechanisms underlying these two modes of inhibition , including the stochastic ( noisy ) nature of synaptic inputs [3] , the balance between excitatory and inhibitory inputs [4] , shunting inhibition [5 , 6] , synaptic depression [7] , and circuit structure [2 , 8] , and see [1] for additional review . In this study , we identify a novel role for A-type voltage-gated potassium current in determining whether inhibition acts in a subtractive or divisive manner . Voltage-gated K+ channels play an important role in regulating neuronal excitability [9 , 10] . Here , we focus on the class of K+ channels that produce an A-type current [11] . These outward currents are mediated by a variety of membrane-bound channels [12–14] , found primarily on dendrites [15] but with a somatic location in some cells [16 , e . g . ] . A-type currents vary greatly in their voltage dependence and kinetics . Although a limited number of channels are typically open ( active ) at the resting membrane potential , producing a “window current” [17 , 18] , additional hyperpolarization further “primes” [19] or de-inactivates the membrane [20] , making more channels available for activation by a depolarizing stimulus . Thus , the magnitude of A-type currents are particularly sensitive to inhibitory inputs . Inactivation kinetics vary greatly , ranging from less than 20 ms to as much as 600 ms , even within populations of neurons sharing a single potassium channel subfamily [14 , e . g . ] . Through mathematical analysis and simulations , we explore the combined effects of synaptic inputs and voltage-gated ion currents on spiking dynamics of a neuron model . We find that if the A-type current is sufficiently large and activates rapidly , then it combines with inhibitory inputs to suppress firing activity in a subtractive manner . If , instead , the A-current is sufficiently weak or activates slowly ( relative to spike initiation dynamics ) , then inhibition has a divisive effect on firing rates . Our work identifies a route through which adaptive or dynamic changes to the intrinsic dynamics of neurons ( for example , through modification of ion currents [21] ) can modulate the effects of inhibition . This capability for individual neurons to switch between different inhibition “regimes” could provide useful flexibility to neural systems .
The dynamics of membrane potential , V , in the one-compartment neuron model are C V ′ = - I L - I K - I A - I N a - I S y n , E - I S y n , I ( 1 ) where the membrane capacitance is C = 1 μF/cm2 . Here , and throughout , we use V′ to indicate the time-derivative of V , i . e . dV/dt . The ionic currents ( leak , potassium , A-type potassium , and sodium ) are given by the equations IL=gL ( V−VL ) , IK=gKn4 ( V−VK ) , IA=gAa3b ( V−VK ) , INa=gNam3h ( V−VNa ) . ( 2 ) We use the following fixed parameter values for maximal conductances: gL = 1 mS/cm2 , gK = 45 mS/cm2 , and gNa = 37 mS/cm2 . We use a range of values for the maximal conductance of the A-current ( gA ) to observe transitions between subtractive and divisive effects of inhibition . The reversal potentials are VL = −70 mV , VK = −80 mV , and VNa = 55 mV . We make several simplifications , similar to those first suggested in [22] , to the gating variables in the model . We identify sodium activation as a fast process and assume it evolves instantaneously to its voltage-dependent steady-state value . That is , we let m = m∞ ( V ) = 1/ ( 1 + e− ( V+30 ) /15 ) . In addition , we observe an approximately linear relationship between sodium inactivation and potassium inactivation and thus set h = 1 − n . The remaining gating variables are n , a , and b . Their dynamics are described by equations of the form X ′ = ϕ X X ∞ ( V ) - X τ X ( V ) , X = n , a , b . ( 3 ) The voltage-dependent steady-state functions are of the form X ∞ ( V ) = 1 / ( 1 + e ( X - θ X ) / σ X ) . For the potassium activation variable , n , we assume that ϕn = 0 . 75 , θn = −32 and σn = −8 . The time-scale for the n variable is voltage-dependent: τn ( V ) = 1 + 100/ ( 1 + e ( V+80 ) /26 ) . Similar to the model presented in [23] , we assume that ϕa = 1 , θa = −50 and σa = 20 for A-type potassium activation , and ϕb = 1 , θb = −70 and σb = −6 for A-type potassium inactivation . The time-scales for the A-type current are constants: τa = 2 ms and τb = 150 ms . Inputs to the model include synaptic excitation ( ISyn , E ) and inhibition ( ISyn , I ) . Excitatory current is ISyn , E = gSyn , E sE ( V − VE ) and inhibitory current is given by an analogous equation . The maximal excitatory and inhibitory conductances ( gSyn , E and gSyn , I ) are parameters that we vary in simulations . The reversal potentials are VE = 0 mV for excitation and VI = −85 mV for inhibition . The gating variables , sE and sI , reset to one at the time of a synaptic event and decay with an exponential time-course . That is , the excitatory gating variable is defined as s E ( t ) = { 1 if t = t E e - β E ( t - t E ) if t > t E ( 4 ) where tE is the time of the most recent excitatory event and the decay time constant is βE = 0 . 2 ms−1 . A similar equation holds for the inhibitory gating variable sI , but with a time constant βI = 0 . 18 ms−1 . Excitatory event times are randomly distributed according to a homogeneous Poisson process with rate rE . Inhibitory event times are periodic with rate rI . We vary the values of the rate parameters ( rE , rI ) in our investigations . Our choice of these input patterns simplifies some of our mathematical analysis . In addition , our choice of periodic inhibitory events was motivated by the design of in vitro experiments , presented in [24] , in which inhibitory interneurons were activated periodically using optogenetic techniques . In additional simulations included as S1 and S2 Figs we allowed the timing of inhibitory inputs to be random with event times drawn from a homogeneous Poisson process . We did not observe substantially different results in simulations that used these non-periodic inhibitory inputs . In some simulations we augment the one-compartment ( point neuron ) model by attaching additional compartments that represent a dendritic process . We assume that the dendrite consists of nine equally-sized compartments . Moreover , the neuron receives inhibitory input at its soma ( the first compartment ) and excitatory input at a dendritic compartment . Voltage in the first compartment ( soma ) is denoted V1 and is given by Eq 1 with synaptic excitation removed and with a new term representing the flow of current between compartments ( axial current ) : C V 1 ′ = - I L , 1 - I K - I A - I N a - I A x , 1 - I S y n , I . ( 5 ) The remaining dendritic compartments do not include potassium , A-type potassium , or sodium currents , and thus Vj for 2 ≤ j ≤ 10 follows the linear dynamics of a passive cable: C V j ′ = { - I L , j - I A x , j - I S y n , E at location of excitatory inputs - I L , j - I A x , j at other locations . ( 6 ) Leak conductance in the dendrite compartments is gL = 0 . 1 mS/cm2 ( one-tenth the value in the first compartment ) . Axial current is I A x , j = { g A x ( V 1 - V 2 ) for j = 1 g A x ( - V j - 1 + 2 V j - V j + 1 ) for 2 ≤ j ≤ 9 g A x ( V 10 - V 9 ) for j = 10 ( 7 ) where gAx = 10 mS/cm2 . Input currents are defined in a manner identical to inputs in the one-compartment model . Excitatory and inhibitory gating variables follow Eq 4 . Excitatory synaptic event times are drawn from a homogeneous Poisson process with rate rE and inhibitory synaptic event times are periodic with rate rI . These constants , as well as synaptic input strengths ( gSyn , E , gSyn , I ) and the compartment targeted by the excitatory inputs , are parameters we vary in our investigations . We simulated the point-neuron and multi-compartment neuron models using software written in the C computer programming language . We integrated differential equations using the fourth order implicit Runge-Kutta method available in the GNU scientific library . We also simulated the one-compartment model and a reduced model version of the one-compartment using XPPAUT , and performed bifurcation analysis of these models using the AUTO feature of XPPAUT [25] . Simulation code is available for download at https://github . com/jhgoldwyn/Gain-Control-With-IA .
We first study the relationship between excitatory input rate ( rE ) and firing output rate ( rout ) of the one-compartment model . In Fig 2A , we plot examples of this input/output relationship for simulations without inhibition ( empty circles , gSyn , I = 0 ) and with inhibition ( filled circles , gSyn , I = 1 ) . The A-channel conductance in these simulations is gA = 20 mS/cm2 . For these parameters , we observe that inhibition reduces the model neuron’s output firing rate , but the neuron continues to fire in response to arbitrarily low input rates . An additional way to view the effect of inhibition is to plot output firing rates in the presence of inhibition as a function of output firing rates in the absence of inhibition , as we have done in Fig 2C . There is a roughly linear relationship between these output firing rates , which we describe by fitting these data with a threshold-linear function of the form y = [ m ( x - x 0 ) ] + ( 8 ) where the symbol [⋅]+ indicates we set y = 0 if the argument m ( x − x0 ) is negative . We obtain the slope parameter m and the x-intercept parameter x0 by applying a curve-fitting procedure ( using the fminsearch command in MATLAB ) to the portion of data for which the output firing rate in the presence of inhibition is less than five spikes per second . In this example , inhibition affects the value of the slope parameter m , but the value of x0 is nearly zero . We identify responses with these characteristics as cases in which the effect of inhibition is divisive . In Fig 2B , we increase the A-channel conductance to gA = 40 mS/cm2 . We observe that inhibition has a different effect on the input/output curve in these simulations . In the presence of inhibition ( filled circles ) , there is now a non-zero value of the input rate below which the neuron model does not spike ( rout = 0 for rE ⪅ 30 ) . Moreover , when we view the relationship between output firing rates with and without inhibition in Fig 2C , we observe a rightward shift of the threshold-linear function fit to these data ( positive-valued x-intercept ) . We identify responses with these characteristics as cases in which the effect of inhibition is subtractive . Although we refer throughout to the effect of inhibition on responses as being either divisive or susbtractive , this is a simplification of a more complicated and subtle reality . In fact , responses can show characteristics of both divisive and subtractive inhibition . In particular , the input/output curves can be right-shifted ( evidence of subtractive inhibition ) and have slopes that are decreased relative to slopes for gA = 0 ( evidence of divisive inhibition ) . We provide evidence of such “mixed” responses in S3 Fig . To be clear: we refer to scenarios in which the input/output curves have only a change of slope as divisive , and scenarios in which the input/output curve is right-shifted as subtractive . In other words , the subtractive case will also include “mixed” responses . We identify two parameters in the one-compartment model that are key factors in determining whether inhibition has a divisive or subtractive effect on firing rate responses: the A-channel conductance ( gA ) and the excitatory synaptic conductance ( gSyn , E ) . In Fig 3A we show a set of threshold-linear functions computed using gA = 20 , 30 and 40 , and synaptic excitation strength fixed at gSyn , E = 0 . 5 . The transition from divisive to subtractive inhibition is evident in the rightward shift of these threshold-linear functions with increasing values of gA . This transition occurs , for this parameter set , for gA ≈ 33 , a point we investigate in more detail below , with simulations and phase plane analysis . In Fig 3B , we show a set of threshold-linear functions with gA = 30 fixed , but now varying the value of gSyn , E from 0 . 4 to 0 . 7 . The stronger excitatory inputs ( gSyn , E = 0 . 5 , 0 . 7 ) cause inhibition to have a divisive effect , while the weaker excitatory input ( gSyn , E = 0 . 4 ) causes inhibition to have a subtractive effect . In these simulations , we do not vary the parameters associated with inhibition . They are gSyn , I = 1 and rI = 50 Hz . From these simulations , we conclude that the effect of inhibition on firing rates in the one-compartment model can switch from divisive to subtractive for sufficiently strong A-current conductance or sufficiently weak excitatory synaptic conductance . In the parameter plane of gA and gSyn , E , then , there is a boundary that separates parameter sets that produce divisive inhibition from parameter sets that produce subtractive inhibition . We map this boundary by performing simulations throughout the ( gA , gSyn , E ) parameter space . For each simulation , we fit threshold-linear functions to characterize the relationship between output firing rates in the presence and absence of inhibition . We then find the smallest value of gA for which the x-intercept of the threshold-linear function is right-shifted by more than two spikes per second and label this as boundary between subtractive and divisive inhibition . In Fig 4 , we show the results of this parameter exploration . We performed these simulations and classification procedure for several values of inhibition conductance strength ( varying values of gSyn , I , in Fig 4A ) , and for several values of inhibition rate ( varying values of rI , in Fig 4B ) . The lines in each panel separate parameter regions for which inhibition is divisive ( lower right corners in each panel ) from parameter regions in which inhibition is subtractive . This confirms our earlier observation that the effect of inhibition is subtractive if A-channel conductance is sufficiently strong or excitatory inputs are sufficiently weak . These simulations also demonstrate that inhibition parameters modify ( weakly ) the location of the boundary between divisive and subtractive inhibition in the ( gA , gSyn , E ) parameter plane . Stronger inhibition ( either through larger gSyn , I or larger rI values ) decreases the portion of the ( gA , gSyn , E ) parameter plane in which inhibition has a divisive effect on firing rate responses . We use mathematical analysis to derive the parameter regions in which the model exhibits either a divisive or subtractive response to inhibition . We begin by considering a reduced model in which activation of the A-current is instantaneous; that is , a = a∞ ( V ) . Later , we discuss how the model’s response to inhibition may change if this assumption does not hold . A key assumption in the formulation and analysis of the reduced model is that the A-current activates sufficiently fast so that the dynamical variable a can be set to its voltage-dependent steady state value; that is , we set a = a∞ ( V ) . One effect of this change from a evolving dynamically with τa = 2 ms ( the full model ) , to a evolving instantaneously as a∞ ( V ) ( the reduced model ) , is that excitation must be much stronger in the reduced model to observe subtractive inhibition . Typical values of gSyn , E in the full model are around 0 . 5 ( see Fig 4 ) , and typical values of gSyn , E in the reduced model are around 3 . This suggests that the speed of A-current activation ( not just the strength of the A-current ) plays a role in switching the effect of inhibition from divisive to subtractive . For inhibition to have a subtractive effect , responses to infrequent excitatory inputs must be suppressed . In the reduced model , this occurs when gA is sufficiently strong because the A-type channel activates “instantaneously” and can prevent spike initiation . In the one-compartment model with “non-instantaneous” a variable , large gA could switch the effect of inhibition to subtractive , but only if excitatory input strength was also sufficiently small ( recall Fig 4 ) . The importance of small gSyn , E is demonstrated in Fig 11 . We show time-courses of voltage in the one-compartment model for gSyn , E = 0 . 2 , 0 . 5 , and 1 , and with gA = 0 and gI = 0 . In all cases , the input evokes an output spike . Notice , however , that as input strength weakens , there is a marked delay in the time before spike initiation . For the weakest input used ( gSyn , E = 0 . 2 ) , there is a delay of roughly 2 ms before the rapid upstroke of V at the onset of the action potential . During this “pause” , voltage is slowly ramping up and , simultaneously , recruiting additional A-current as the a variable activates . The amount of IA available to suppress spike initiation depends , therefore , on A-channel maximal conductance ( gA ) and also the time-constant of IA activation ( τA ) . From this observation , we draw the following conclusion: “non-instantaneous” IA can act to switch the effect of inhibition from divisive to subtractive , but only if it activates rapidly enough relative to the dynamics of spike initiation . To illustrate our point , we simulated the model with three values of A-channel activation time constant ( τA = 0 . 5 , 1 , 2 ) , using gSyn , E = 0 . 5 , gSyn , I = 1 , and rI = 50 Hz . As shown in Fig 11B , inhibition is divisive for slower activation kinetics ( τA = 1 , 2 ) and subtractive for faster activation kinetics ( τA = 0 . 5 ) . In Fig 11C , we map the boundary between divisive and subtractive inhibition in the ( gSyn , E , gA ) parameter plane . There is a strong effect of τA . Faster activation kinetics ( smaller τA values ) shift the critical point at which inhibition switches from divisive to subtractive to lower values values of gA . Our prior observation , that delaying spike initiation allows inhibition to have a subtractive effect for “non-instantaneous” A-channel activation , led us to investigate other cellular mechanisms that could have a similar effect . To this end , we considered a multi-compartment neuron model that describes a soma and passive dendrite . Inhibition and voltage-gated currents are restricted to the soma compartment , and excitation targets a location somewhere on the dendrite . Passive cable theory tells us that the amplitudes of excitatory post-synaptic potentials attenuate and their rising slopes become less steep as signals spread along the cable [28] . By varying the location of excitatory synaptic inputs to the dendrite in the multi-compartment model , we can , therefore , adjust the shape of excitatory post-synaptic potentials as they arrive in the soma . Examples of action potentials , recorded in the soma compartment , in response to inputs at different locations along the dendrite are shown in Fig 12A . Synaptic conductance strength is constant ( gSyn , E = 2 in these simulations ) . Inputs that arrive proximal to the soma are large and fast-rising relative to responses to more distal inputs , and thus evoke action potentials with shorter latencies . The parameter cptin identifies the compartment that receives synaptic excitation . It takes values from 1 ( proximal ) to 9 ( distal ) . We included synaptic inhibition in the model targeting the soma , and used simulations to characterize the effect of inhibition as either subtractive or divisive . In Fig 12B we observe a transition from divisive to subtractive inhibition as we move the location on the dendrite at which synaptic excitation targets the cell . This matches our expectation: synaptic excitation placed at more distant locations will generate weaker and slower rising inputs in the soma . This will , in turn , lead to spikes that initiate slowly and that give time for A-channel conductance to activate and prevent spike generation . In additional simulations included as S4 Fig we targeted inhibitory inputs to the dendrite and did not observe substantially different results . We explored the ( gA , gSyn , E ) parameter plane and identified the boundary separating regions in which inhibition has a divisive effect on firing rates and regions in which inhibition has a subtractive effect ( following the procedure used previously for Fig 4 ) . We find that there is a dramatic effect of input location , as shown in Fig 12C . The region of the ( gA , gSyn , E ) parameter plane over which inhibition has a divisive effect is smaller when inputs are more distant from the soma .
Using simulations and phase plane analysis , we systematically investigated conditions under which inhibition acts on firing rate outputs in a divisive or subtractive manner . We first identified critical values of IA conductance ( gA ) at which the effect of inhibition switched from divisive ( for lower gA values ) to subtractive ( for higher gA values ) ( Fig 4 ) . In the reduced model , we approximated this critical value of gA using bifurcation analysis . By tracking the left-knee of the V-nullcline ( Fig 5 ) , we identified this critical value of gA as a bifurcation point at which the neuron model ceased to be excitable in response to synaptic inputs ( Fig 8 ) . Key in this analysis was the separation of time scales between fast variables ( V ) and slow variables ( n , b ) . In fact , the inactivation variable b was sufficiently slow that it could be treated as a constant with a value that depended on the input rate ( Fig 7 ) . This simplification enabled further analysis . By viewing the spiking output of the model as a Poisson process modified by a refractory period and inhibition-dependent firing threshold , we approximated firing rates at the point of spiking onset ( Fig 9 ) as well as for arbitrary input rates ( Fig 10 ) . A-type potassium current is a source of dynamic , voltage-gated negative feedback that is fast activating . We leveraged this property to obtain analytical results by assuming that the gating variable for IA activation , a , evolved instantaneously to its voltage-dependent equilibrium value ( see also [23] ) . We also performed simulations without this assumption and discovered a delicate interaction between the speeds of IA activation and spike initiation . In particular , subtractive inhibition required that IA is sufficiently strong and that it activates sufficiently rapidly to prevent spike initiation ( Fig 11 ) . For our standard value of IA activation ( τA = 2 ms ) , we found that , in conditions of slow spike initiation , IA could “ramp up” during slowly-developing spikes and suppress spike initiation . Weak excitatory inputs , or excitatory inputs targeting more distal regions in a model that included a spatially-extended dendritic process ( Fig 12 ) produced spikes that were slow to initiate , and were therefore scenarios in which inhibition was subtractive for low to modest levels of gA . Divisive inhibition is a mechanism of neural gain control and has been the subject of numerous studies; see [1] for review . We found that , at lower levels of gA , inhibition can have a divisive effect on the input/ouput properties of a spiking neuron responding to a mixture of random excitatory inputs and periodic inhibitory inputs . The amount of gA altered the slope ( gain ) of the output firing rate , and thereby tunes the gain control in this system . This result is consistent with the results of a recent in vitro study of neurons in the rostral nucleus of the solitary tract [24] . In that experiment , Chen and colleagues controlled inhibition using optogenetic techniques and constructed threshold linear functions to express the relation between firing responses with and without inhibition ( analogous to our Fig 2C , and similar figures ) . They observed that slopes of threshold-linear function were more shallow for neurons with IA , as compared to neurons in the same nucleus that did not have IA . Thus , the presence of IA enhanced the divisive effect of inhibition . Previous modeling work has identified similar gain control effects by IA [29] . We observed , additionally , that at higher levels of gA , the A-type current can switch the effect of inhibition from divisive to subtractive . This demonstrates a novel example of how the internal dynamics of a neuron interact with synaptic inhibition to change the neuron’s computational properties ( input/output relation ) . Previous studies have explored the multi-faceted ways in which IA current can alter neural dynamics . Connor and Stevens established IA current as a mechanism to prolong interspike intervals of repetitively-firing neurons to arbitrary lengths ( “type I” firing dynamics ) [11] . Other identified functions of IA include prolonging first spike latency [30] , producing burst firing patterns and preventing anodal break ( rebound ) firing [23] , filtering synaptic inputs in favor of slow time-scale NMDA receptor-mediated inputs [31] , and affecting the correlation in spiking among neurons responding to common inputs [32] . Our contribution adds to the rich repertoire of IA function . We have identified routes to subtractive inhibition that depends only on mechanisms that could be readily adjusted by processes of plasticity and neuromodulation . In particular , we have shown that strong and fast IA can lead to subtractive inhibition . The strength and kinetics of the A-type Potassium channels can be modulated in a variety of ways [33–36] . For example , in neurons involved in gastro-intestinal function , A-type potassium channels were modified both by diet [37–39] and gastric disorders [40] . We also found that weak excitatory inputs or more distally-located excitatory inputs led to subtractive inhibition by slowing the onset of action potentials . Synaptic plasticity and modulation of the electrical properties of dendrites can adjust the strength and propagation of excitatory inputs [41] , and plasticity of spike initiation zones could change the dynamics of spike initiation [42 , 43] . These changes happen at the level of the output neuron . They do not require “global” modulatory effects to change background network activity , circuit structure , or the balance of excitation and inhibition . We conclude , then , that IA can add flexibility to neural systems by allowing neurons to “self-regulate” whether inhibition acts in a subtractive or divisive manner . | Neurons process information by generating spikes in response to two types of synaptic inputs . Excitatory inputs increase spike rates and inhibitory inputs decrease spike rates ( typically ) . The interaction between these two input types and the transformation of these inputs into spike outputs is not , however , a simple matter of addition and subtraction . Inhibitory inputs can suppress outputs in a variety of ways . For instance , in some cases , inhibition adjusts the rate of spiking activity while preserving the range of inputs that evoke spiking activity; an important computational principle known as gain control . We use simulations and mathematical analysis of a neuron model to identify properties of a neuron that determine how inhibitory inputs affect spiking activity . Specifically , we demonstrate how the gain control effects of inhibition depend on the A-type Potassium current . This novel role for the A-type Potassium current provides a way for neurons to flexibly regulate how they process synaptic inputs and transmit signals to other areas of the brain . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"pharmacologic",
"analysis",
"knees",
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"legs",
"membrane",
"potential",
"limbs",
"(anatomy)",
"electrophysiology",
"neuroscience",
"simulation",
"and",
"modeling",
"compartment",
"models",
"one-compartment",
... | 2018 | Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition |
The mechanism of rapid energy supply to the brain , especially to accommodate the heightened metabolic activity of excited states , is not well-understood . We explored the role of glycogen as a fuel source for neuromodulation using the noradrenergic stimulation of glia in a computational model of the neural-glial-vasculature ensemble ( NGV ) . The detection of norepinephrine ( NE ) by the astrocyte and the coupled cAMP signal are rapid and largely insensitive to the distance of the locus coeruleus projection release sites from the glia , implying a diminished impact for volume transmission in high affinity receptor transduction systems . Glucosyl-conjugated units liberated from glial glycogen by NE-elicited cAMP second messenger transduction winds sequentially through the glycolytic cascade , generating robust increases in NADH and ATP before pyruvate is finally transformed into lactate . This astrocytic lactate is rapidly exported by monocarboxylate transporters to the associated neuron , demonstrating that the astrocyte-to-neuron lactate shuttle activated by glycogenolysis is a likely fuel source for neuromodulation and enhanced neural activity . Altogether , the energy supply for both astrocytes and neurons can be supplied rapidly by glycogenolysis upon neuromodulatory stimulus .
The management of energy in the brain is organized by an oligocellular cooperative called the neural-glial-vasculature ensemble ( NGV ) . Each component is assigned distinct tasks during the chain of events that extract reducing equivalents from glucose to support every brain function . While the continuous supply of energy to the brain is critical for basal functions , rapid boosts in energy demand during higher states of alertness , often in response to neuromodulatory signals , must also be met . There is much controversy about how this kind of brain activity is supported energetically . What is agreed upon is that glucose , glycogen and lactate are the lead actors , with a cadre of support from intermediate metabolites [1–10] . The plot is complicated by dynamic changes in the relative contributions and timing of their roles; sorting all this out requires the insights provided by computational models . The relationship among the NGV components is still being revealed with increasing interest in the role of glycogen—a form of polymerized glucose that constrains the energy storage capacity in the brain [2 , 9–15] . It has long been observed that brain glycogen resides almost exclusively in astrocytes [16–18] , although its conservative presence in neurons has been noted and associated with hypoxia resistance [19] . Recent studies have more precisely located glycogen granules to the astrocytic lamelliform processes that ensheath synapses[20–23] . In fact , among the first indications of the complexity of coupling between neurons and astrocytes were the observations that synaptic and neuromodulatory activity promote glycogen hydrolysis in the mouse cerebral cortex [5 , 24] . Brain glycogen is the largest repository of energy in the brain , retaining more glucose equivalents than the amount dissolved in the cytosol , and can supplement the brain for more than an hour under conditions of hypoglycaemia [10] . The concept of the role of glycogen has evolved from a mere glucose storage depot for crisis management [25] to being part and parcel of the dynamic energy milieu [15 , 26–29] . The on-going turnover of glycogen involves the so-called glycogen shunt in which some of the blood-borne glucose imported into the astrocyte is stored as glycogen before becoming available for glycolysis via glycogenolysis [9 , 15 , 30 , 31] . Glycogenolysis not only contributes to commonplace energy supply [2 , 5 , 6 , 8 , 15 , 32–40] , but also to handling special requests including stability maintenance during hypoglycemia [41] , responding to rapid and high-demand needs signaled by neuromodulatory factors such as norepinephrine ( NE ) [4] , higher local energy demand due to regional stimulation [42–45] , memory formation and consolidation [35 , 46–51] drug addiction [52] , as well as sleep and development [29 , 53 , 54] . The locus coeruleus ( LC ) in the brainstem sends far-reaching projections throughout numerous brain regions . In the cortex , these inputs effect neuromodulatory control of arousal , attention and memory via the LC-norepinephrine ( LC-NE ) arousal circuit [55–57] . The NE is released from axonal varicosities from which it diffuses to find adrenergic receptors on neurons [58] and astrocytes [59] . The activation of β2-adrenergic receptors ( β2R ) on astrocytes by the volume transmitted NE [60] is thought to mediate the neuromodulatory stimulus-demanding energy supply and consumption in the NGV , with glycogen implicated as a key supplier of lactate [61–71] . Turnover of glycogen in astrocytes is triggered by NE from LC inputs and involves signal transduction mediated by adenyl cyclase and the second messenger cAMP [68 , 72 , 73] . Glycogen and β-adrenergic dysregulation are associated with neurodegeneration [46 , 74] and astrocytic β2 receptors mediate hippocampal long-term memory consolidation and stress response management through training-dependent lactate production [47] . Neuromodulatory stimuli can mobilize more than half of stored glycogen; such glucose dumping could provide rapid and large energy injections into the NGV system [75] . In the cortex , NE containing varicosities are found near glia throughout development and adulthood concomitant with the expression of glycogen , suggesting a persistent role for this pathway , [6 , 48 , 66 , 76–79] , and NE release from the LC modulates glycogenolysis and memory consolidation via β2-adrenergic receptors [77 , 80] . The consumption of glycogen upon circuit activity in cortex [81 , 82] and its activation and mobilization appear to be rapid [35] . Of particular importance to brain energy supply is the lactate derived from glycolysis in the astrocyte and which is required to support higher metabolic brain activities , including during intense exercise [83] , in response to neuromodulation [61 , 68 , 71 , 84] and in support of memory formation [47 , 50 , 85] . The production of lactate by whatever means is followed by its export to neighboring neurons through monocarboxylate transporters ( MCTs ) in a process called the astrocyte-to-neuron lactate shuttle ( ANLS ) [7 , 86–89] . This computational model tests the feasibility that glycogenolysis within the NGV ensemble can respond rapidly and sufficiently to provide energy for both astroctyes and neurons in response to neuromodulatory signals [90] . We built on our previous computational model of ANLS to explore the dynamics of glycogen mobilization by NE release from LC terminals and test whether existing knowledge of the enzymatic cascades supports the role of glycogen as a source of energy both to astrocytes and neurons . We observed a rapid degradation of glycogen , expected enzymatic cascades , the production of NADH and ATP and lactate for the neuron via ANLS [7 , 8 , 87] . In addition , volume transmission resulting from differences in release distances between the LC terminals and the astrocyte is unlikely to influence outcome , at least in a high ligand affinity second messenger transduction pathway . These results support the idea that glycogenolytic energy supports the enhanced metabolic demand of neuromodulation .
After using 3D electron microscopy ( EM ) to determine the locations of glycogen granules in the somatosensory cortex , we employed a computational approach to elucidate the role of glycogen in supporting neuromodulation by building upon our previous NGV model [87] . New model features include a complex , multi-step glycogenolysis pathway , neuromodulation via the LC-NE system in the cortex , and second messenger transduction ( cAMP ) [91] . We simulated astrocytic stimulation by LC noradrenergic inputs with a focus on the contribution of glycogenolysis to the local and exported energy supplies including the role of lactate shuttling from the astrocyte to the neighboring neuron ( ANLS ) [7 , 89] . While it has been established that glycogen is located in astrocytes , we further explored the subcellular distribution of glycogen granules within six astrocytic processes from layer I mice somatosensory cortex[92 , 93] ( Fig 1A ) . We measured the number of granules apparent over a period of 4 ( n = 3 ) and 24 ( n = 3 ) months in 3D reconstruction from EM stacks of 125 cubic micrometers volumes of neuropil . In order to obtain the density of glycogen granules , we divided the total number of granules per each of the reconstructed volumes ( 3038 , 3738 and 11809 in 4 months old , and 6588 , 7758 and 4287 in 24 months old ) per the volume of the reconstructed astrocyte ( 10 . 7 μm3 , 10 . 8 μm3 , 17 . 9 μm3 and 10 . 6 μm3 , 12 . 3 μm3 , 6 . 5 μm3 for 4 and 24 months old , respectively ) and found a stable distribution between the two populations ( Fig 1B ) .
The value of the role of glycogen in balancing the energy budget of the brain should not be discounted given its abundance in astrocytes in the vicinity of synapses [21 , 23] and experimental evidence for its involvement in supporting brain activity [15 , 35 , 40 , 48 , 49 , 65 , 68 , 82 , 94] . What is not clear is the feasibility of glycogen being able to respond rapidly and sufficiently enough to neuromodulators that regulate neuronal circuit activity and to what degree the ANLS is involved [47 , 51 , 83 , 85 , 89 , 95–97] . Since glycogenolysis has been suggested to provide energy to both neurons and astrocytes during learning , the involvement of lactate would be a likely candidate in this mechanism [49] . Accordingly , we have investigated the role of astrocytic glycogen in fueling and mediating neuromodulation in a computational model of glycogenolytic and noradrenergic transduction pathways along with elements of our previous NGV model [87] . The results of our 3D electron microscopy and computational modeling study supports the plausibility that glycogenolysis plays a major mechanistic role in fueling and transducing the neuromodulatory signals mediated by cAMP . Significantly , we conclude that 1 ) glycogen granule density in layer 1 of somatosensory cortex is stable between 4–24 months , the type of reliable expression that would be consistent with expectations for a fuel source responsible for support of on-demand activity; 2 ) the distance of NE release from the astrocyte is not critically important , implying that volume transmission effects can be mitigated by high-affinity receptor or rapid transduction systems; 3 ) glycogenolysis evoked by cAMP elevations generate energy in the form of ATP , NADH and lactate production , thus supplying energy to both the astrocyte and the neuron; and 4 ) astrocytic lactate derived from glycogen is shuttled rapidly and preferentially to the neuron ( ANLS ) . 5 ) Altogether , our model supports observations of the involvement of glycogen and lactate in supplying energy to both astrocytes and neurons during learning events related to neuromodulatory inputs , as well as their involvement in related disease states [35 , 45 , 47–49 , 51 , 52 , 97 , 108 , 109 , 139] . 6 ) The success of the model validates our bottom-up modeling approach as a tool to complement and guide basic and disease-related experimental studies .
We reconstructed astrocytic processes and the glycogen granules within the astrocytic profiles of six volumes of 5x5x5 μm3 from FIBSEM image stacks ( courtesy of Graham Knott , BioEM , EPFL , Switzerland ) . Original samples were acquired from layer I somatosensory cortex of wild type mice aged 4 and 24 months ( N = 3 per sample ) . Astrocytes were reconstructed using the carving , semi-automated algorithm [140] of the ilastik 1 . 2 software ( www . ilastik . org ) . Glycogen granules were reconstructed using the trakEM2 software , by placing a sphere in the center of each granule and adjusting its diameter to the size of the granule ( Fig 1 ) Our modeling approach was to adapt our previous NGV model [87] by adding new modules without changing the previous equations or parameters except where required for integration of the new modules into the original model . We provide all the equations in this manuscript for ease of reference . | Although efficient compared to computers , the human brain utilizes energy at 10-fold the rate of other organs by mass . How the brain is supplied with sufficient on-demand energy to support its activity in the absence of neuronal storage capacity remains unknown . Neurons are not capable of meeting their own energy requirements , instead energy supply in the brain is managed by an oligocellular cartel composed of neurons , glia and the local vasculature ( NGV ) , wherein glia can provide the ergogenic metabolite lactate to the neuron in a process called the astrocyte-to-neuron shuttle ( ANLS ) . The only means of energy storage in the brain is glycogen , a polymerized form of glucose that is localized largely to astrocytes , but its exact role and conditions of use are not clear . In this computational model we show that neuromodulatory stimulation by norepinephrine induces astrocytes to recover glucosyl subunits from glycogen for use in a glycolytic process that favors the production of lactate . The ATP and NADH produced support metabolism in the astrocyte while the lactate is exported to feed the neuron . Thus , rapid energy demands by both neurons and glia in a stimulated brain can be met by glycogen mobilization . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"carbohydrate",
"metabolism",
"neurochemistry",
"chemical",
"compounds",
"astrocytes",
"carbohydrates",
"neuroscience",
"organic",
"compounds",
"macroglial",
"cells",
"glucose",
"glucose",
"metabolism",
"camp",
"signaling",
"cascade",
"cell",
"signaling",
"protein",
"kinase... | 2018 | Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate |
Lens induction is a classical developmental model allowing investigation of cell specification , spatiotemporal control of gene expression , as well as how transcription factors are integrated into highly complex gene regulatory networks ( GRNs ) . Pax6 represents a key node in the gene regulatory network governing mammalian lens induction . Meis1 and Meis2 homeoproteins are considered as essential upstream regulators of Pax6 during lens morphogenesis based on their interaction with the ectoderm enhancer ( EE ) located upstream of Pax6 transcription start site . Despite this generally accepted regulatory pathway , Meis1- , Meis2- and EE-deficient mice have surprisingly mild eye phenotypes at placodal stage of lens development . Here , we show that simultaneous deletion of Meis1 and Meis2 in presumptive lens ectoderm results in arrested lens development in the pre-placodal stage , and neither lens placode nor lens is formed . We found that in the presumptive lens ectoderm of Meis1/Meis2 deficient embryos Pax6 expression is absent . We demonstrate using chromatin immunoprecipitation ( ChIP ) that in addition to EE , Meis homeoproteins bind to a remote , ultraconserved SIMO enhancer of Pax6 . We further show , using in vivo gene reporter analyses , that the lens-specific activity of SIMO enhancer is dependent on the presence of three Meis binding sites , phylogenetically conserved from man to zebrafish . Genetic ablation of EE and SIMO enhancers demostrates their requirement for lens induction and uncovers an apparent redundancy at early stages of lens development . These findings identify a genetic requirement for Meis1 and Meis2 during the early steps of mammalian eye development . Moreover , they reveal an apparent robustness in the gene regulatory mechanism whereby two independent "shadow enhancers" maintain critical levels of a dosage-sensitive gene , Pax6 , during lens induction .
Cellular and molecular mechanisms of vertebrate lens development are objects of intense studies for many decades , reviewed in [1] . In particular , lens induction represents a classical developmental model allowing investigation of cell specification , spatiotemporal control of gene expression , as well as the integration of signaling pathways and transcription factors into highly complex gene regulatory network ( GRN ) . At the end of neural plate formation , the vertebrate lens originates from the multipotent pre-placodal ectoderm [2 , 3] through a series of cell-type specifications , governed by DNA-binding transcription factors Pax6 , Six3 and Sox2 , and including another transitional population of cells , the presumptive lens ectoderm ( PLE ) . The PLE gives rise to the lens placode , readily observed as a thickening of the head surface ectoderm ( SE ) that is in close contact with the underlying optic vesicle , an evaginating part of the future diencephalon . Genetic dissection of lens induction has mainly focused on the function of Pax6 , Six3 and Sox2 , coupled with studies of BMP , retinoic acid and Wnt signaling in the surface ectoderm , neuroectoderm , and surrounding periocular mesenchyme , reviewed in [1] . Pax6-deficient ( Pax6 Sey/Sey ) mice are anophthalmic with eye development arrested at the optic vesicle stage [4–6] . Numerous studies have shown that Pax6 is essential for lens formation through its expression in the SE and PLE , and in the subsequent stages of lens placode formation [7–9] . In contrast , the role of Six3 and Sox2 are less clear , although it is known these factors play major roles in anterior forebrain development and optic cup formation [10–12] , further enforcing Pax6 as an ideal node to decipher genetic wiring of lens induction . Despite a well-established genetic role , much less is known about the factors operating upstream of Pax6 and their interaction with cis-regulatory elements that direct Pax6 expression to the lens ectoderm . Since lens development is sensitive to Pax6 dosage [4] accurate regulation of Pax6 expression level during lens development is therefore of great importance . Transcriptional control of Pax6 gene expression is very complex and different cells and tissues choose specific promoters and distal regulatory regions from an archipelago of enhancers scattered within the large Pax6 genomic region [13 , 14] . The expression of Pax6 in lens ectoderm was initially shown to be driven by an ectoderm enhancer ( EE ) located approximately 4kb upstream of the Pax6 P0 promotor [15 , 16] . However , genetic studies in which EE was inactivated provided strong evidence that EE is not the only regulatory element responsible for Pax6 expression in the lens placode [17] . Surprisingly , detectable expression of Pax6 in lens placode of EE mutants remains . In fact , the relatively small reduction of Pax6 levels in EE mutants leads to only mild lens defects ( such as a lens placode of reduced thickness and a small lens pit/vesicle ) that do not phenocopy Pax6 deficiency in the PLE [7 , 17] raising the possibility that additional regions compensate for the loss of EE . Genetic analysis of human aniridia patients has identified a highly conserved long-range cis-regulatory element called SIMO , located 150 kb downstream of Pax6 [18] that can also direct transgene expression to the developing lens [19 , 20] suggesting a role as a tissue-specific enhancer . Mouse-human sequence conservation around the SIMO breakpoint revealed 85% nucleotide identity over a 1400 bp fragment with 500 bp core region showing 96% identity [20] . Recently , de novo point mutation within the SIMO region has been identified in patient suffering aniridia . This mutation disrupts an autoregulatory PAX6 binding site in SIMO , causing defective maintenance of PAX6 expression [19] . Remarkably , a Pax6 autoregulatory loop has also been described in the case of the EE [21] . While autoregulation of Pax6 is critical for lens cell-type identity , and represents a key mechanistic property of both Pax6 lens enhancers , such a mechanism does not address the critical issue , namely the identification of upstream regulators of Pax6 . To date , functional interactions of Meis1/2 , Prep1 , Six3 , Sox2 and Oct1 have only been demonstrated at the EE [22–25] . Three amino acid loop extension ( TALE ) homeobox genes are evolutionarily highly conserved developmental regulators present in both vertebrate and invertebrate genomes . In vertebrates , TALE homeoproteins are represented by the Pbx and Meis/Prep subfamilies . Pbx proteins interact with Prep and Meis through a conserved amino-terminal domain while an independent protein surfaces allow Pbx to form trimeric complexes with Prep or Meis and Hox , reviewed in [26] . Prep and Meis alone preferentially bind DNA motifs with the sequence TGACAG/A , whereas Prep-Pbx and Meis-Pbx dimers bind the sequence TGATTGACAG . In mouse and human , three Meis homologs ( Meis1 , Meis2 and Meis3 ) and two homologues of Prep ( Prep1 and Prep2 ) have been identified . Genome-wide analysis of Meis and Prep binding sites using a ChIP-seq approach have revealed their substantial specialization as well as significant regulatory coordination between these factors [27] . Biochemical and transgenic reporter studies have implicated Meis1 and Meis2 in the regulation of the EE of Pax6 [22] . In addition , binding of Prep1 to the EE has been shown to control Pax6 levels and the timing of Pax6 activation in the developing lens [25] . However , Meis1 knockout mice exhibit only a mild lens phenotype at later developmental stages [28] . As Meis1 and Meis2 exhibit similar expression patterns during the early stages of lens development ( detailed in this study ) we hypothesized that they are genetically redundant . To test this hypothesis , we have generated a Meis2 floxed allele and subsequently investigated the effect of Meis2 and Meis1/Meis2 defficiency on lens development using a lens-specific deleter Le-Cre recombinase [7] . We provide genetic evidence that Meis2 alone is not essential for lens development , however combined depletion of both Meis1 and Meis2 proteins at the early stages of lens development demonstrate that Meis1/2 are redundantly required for lens placode formation . Chromatin immunoprecipitation and transgenic reporter studies further dissect the molecular mechanism of Meis-dependent regulation of Pax6 gene expression . Deletion of SIMO region by genomic engineering in vivo suggests its redundancy with EE and uncovers SIMO function in lens development . Moreover , simultaneous deletion of EE and SIMO in vivo resulting in loss of lens formation confirms the essential role of the two Pax6 enhancers for lens induction . Remarkably , our data demonstrate the existence of two independent and partially redundant Meis-dependent enhancers , with similar molecular architecture , involved in the regulation of Pax6 expression during lens placode formation , thereby providing an unexpected level of robustness to the system .
In this study , we sought to determine the genetic hierarchy during early lens development by investigating the role of Meis1 and Meis2 homeoproteins using knockout mice . In addition , we wanted to examine the extent of Meis-mediated regulation of the critical eye specification gene Pax6 during lens induction . It was previously shown that specific deletion of Pax6 in the PLE resulted in a failure of lens development from the lens placode stage onward [7] . The main prerequisite for transcriptional regulation of placodal Pax6 expression by Meis proteins is their co-expression in the same tissue . Immunoflourescence using specific antibodies against Meis1 and Meis2 [22 , 29 , 30] revealed that both proteins were expressed in developing lens: in the PLE , lens placode and later in the lens pit ( S1A–S1F Fig ) . Moreover the expression pattern of both Meis1 and Meis2 were overlapping with Pax6 expression in the PLE [31] . Meis1 mutants ( Meis1-/- ) do not present with arrested lens development [28] . We therefore questioned whether deletion of Meis2 may affect lens development . Accordingly , mice containing a Meis2 floxed allele ( Meis2f/f ) were generated ( S1G Fig ) and [32] , and subsequently zygotic Hprt1-Cre mice were employed to create whole-body knockout of Meis2 ( Meis2-/- ) . Meis2-/- embryos displayed strong hemorrhage and other developmental defects and died by E14 . 5 [32] . However , lens development was not affected in these mutants ( S2 Fig ) . To overcome the embryonic lethality of Meis2 whole-body knockout and to conditionally inactivate Meis2 specifically in PLE from E9 . 0 , Le-Cre mice [7] , ( S1H and S1I Fig ) were crossed with Meis2f/f mice . In Le-Cre;Meis2f/f embryos Meis2 protein was efficiently deleted in the lens placode and surface ectoderm at E9 . 5 ( S1J Fig ) . We accordingly analyzed lens development in the absence of Meis1 , Meis2 or both factors . The morphology of lens development was examined at stages E10 . 0 and E12 . 5 on tissue sections stained with hematoxylin-eosin . As shown in Fig 1 , both Meis1 and Meis2 deficient embryos developed beyond the lens placode stage and subsequently and invariantly formed a lens . Therefore , we decided to generate embryos simultaneously deficient for both Meis1 and Meis2 in PLE; Le-Cre;Meis1-/-;Meis2f/f ( referred thereafter as Meis1/Meis2 double mutant ) . Deletion of Meis1 and Meis2 in the PLE of Le-Cre;Meis1-/-;Meis2f/f embryos resulted in arrested lens development , characterized by a failure of the PLE to thicken and form the lens placode ( Fig 1 ) . Histological analysis at E12 . 5 confirmed an absence of lens tissue on a morphological level in all analyzed Meis1/Meis2 double mutants , where only folded retina was present ( Fig 1O ) . Interestingly , one functional allele of Meis1 in Le-Cre;Meis1+/-;Meis2f/f embryos was sufficient to ensure lens placode and later lens formation , although the lenses were typically smaller ( Fig 1N ) . These results demonstrate a requirement for Meis proteins during lens placode and subsequent lens formation . To determine , whether the morphological arrest of lens development was accompanied by a loss of Pax6 expression and other lens placode markers , we performed immunofluorescent marker analyses at E10 . 0 . Strikingly , we discovered a dramatic decrease in Pax6 expression in the PLE of Meis1/Meis2 double mutants ( Fig 2A–2B’ ) . In addition , the expression of the lens differentiating gene Foxe3 , which is known to be highly Pax6-sensitive [33] , was also not initiated ( Fig 2C–2D’ ) . Conversely , Sox2 expression persisted in the PLE of E10 . 0 Meis1/Meis2 double mutants ( Fig 2E–2F’ ) , which is consistent with Pax6-independent regulation of Sox2 at the lens placode stage [34] . Finally , Six3 expression that is mutually dependent on Pax6 expression in the PLE [23 , 35] , was also decreased in Meis1/Meis2 double mutants ( Fig 2G–2H’ ) . Immunofluorescent analysis of E12 . 5 Meis1/Meis2 double mutant embryos also confirmed the loss of α-crystallin-positive lens tissue , Prox1-positive differentiating lens fiber cells , Foxe3-positive lens epithelial cells and γ-crystallin-positive lens fiber cells ( S3 Fig ) . Nevertheless , the presence of Pax6 and Sox2 proteins in the neural retina , and Otx2 in the retinal pigmented epithelium suggested that the specification of these tissues was not affected by the arrest of lens development ( S3 Fig ) . Taken together , these results demonstrate that simultaneous inactivation of Meis1 and Meis2 results in early arrest of lens development and phenocopies Pax6 deficiency in the PLE [7] . A previous study has shown that Meis1 and Meis2 directly bind to the Pax6 ectoderm enhancer ( EE ) and thus control Pax6 expression during early vertebrate lens induction [22] . Here we show that the simultaneous inactivation of Meis1 and Meis2 leads to the dramatic downregulation of Pax6 in PLE and arrested lens development , in a manner reminiscent of that observed in Pax6 mutants [7] . However , as deletion of the EE does not phenocopy Pax6 loss [17] , we hypothesized that Meis proteins might , in addition to the EE , interact with another enhancer such as the SIMO to drive appropriate levels of Pax6 expression in the developing lens . Thus , we focused on a 1400 bp evolutionarily conserved fragment of SIMO and used chromatin immunoprecipitation ( ChIP ) to analyse whether Meis proteins bound the SIMO element in vivo ( Fig 3 ) . We initially screened the 1400 bp fragment for the presence of Meis consensus binding site sequence motif , 5’ TGACAG/A 3’ [36] , ( Fig 3B ) . In the most conserved core region of the SIMO , we identified five Meis binding sites named SIMO_A , SIMO_B , SIMO_C , SIMO_D , SIMO_E with SIMO_B/C/D clustered in DNA region of 77 bp ( Fig 3A ) . As a positive control for Meis binding ChIP analyses , we used the EE as it has been previously described to be bound by Meis [22] and as negative controls , the Axin2 promoter and Neurod1 coding sequences were used . Chromatin immunoprecipitation was performed on wild-type E10 . 5 embryos and the αTN-4 cell line [37] representing a model of mouse lens epithelial cells . qRT-PCR analysis of DNA fragments immunoprecipitated with mixture of Meis1+Meis2 specific antibodies from in E10 . 5 embryos showed significant enrichment at the EE as well as at the SIMO_B/C/D putative Meis-interacting sites ( Fig 3C ) . No enrichment was observed at the negative controls regions or at the predicted Meis binding site SIMO_A . Similar results were also obtained when αTN-4 cells were used for immunoprecipitation ( Fig 3D ) . Taken together these data show that Meis proteins bind the SIMO element in vivo and suggest that simultaneous binding of both the EE and SIMO may be required for appropriate Pax6 expression in the early lens . To test the functional significance of identified Meis interactions with the SIMO enhancer we prepared reporter gene constructs expressing lacZ gene under the control of a minimal hsp68 promoter fused to the mouse SIMO enhancer ( Fig 4A and 4B ) . To determine the specificity of any interactions , a single point mutation was introduced into Meis binding site that changed the recognition sequence from TGACAG/A into TcACAG/A . The same G to C mutation has previously been shown to abbrogate Meis binding and has been used in functional characterization of the EE and pancreatic enhancer in transgenic mouse models [22 , 38] . In accordance with previous studies , FLAG-tagged Meis2 was able to specifically bind double-stranded oligonucleotides ancompassing wild-type Meis binding site but not its mutated version ( S4 Fig ) . DNA constructs containing either the wild-type SIMO enhancer ( SIMO WT ) or the enhancer simultaneously mutated in conserved Meis binding sites SIMO_B , SIMO_C and SIMO_D ( SIMO MUT ) , respectively , were introduced into the chick eye forming region by in ovo electroporation at embryonic stage HH10-11 . The electroporated embryos were collected at stage HH20-21 and tested for β-galactosidase activity . As shown in Fig 4C and 4E and S5 Fig , wild-type SIMO enhancer mediated efficient expression of the lacZ reporter gene in the developing chick lens . In contrast , when all three Meis binding sites were mutated in SIMO , the lens-specific activity of the resulting reporter gene construct was abbrogated ( Fig 4D and 4F and S5 Fig ) . Next , we wanted to determine a possible contribution of individual Meis binding sites to SIMO enhancer activity . Mutation of SIMO_B Meis binding site alone resulted in decreased expression of reporter gene in lens as compared to wild-type SIMO , whereas simultaneous mutation of both SIMO_B and SIMO_C binding sites led to a complete loss of lens-specific expression of reporter gene ( S6A Fig ) . These data suggest additive effect of three Meis binding sites on SIMO enhancer activity . We noticed that Meis binding sites ( sequence TGACAA in SIMO_B , SIMO_C and SIMO_D ) in wild-type SIMO enhancer do not constitute the perfect match to the optimal Meis DNA-binding site motif TGACAG ( http://jaspar . genereg . net/ ) indicating that they might represent a medium affinity sites . In order to evaluate the functional significance of these non-optimal Meis binding sites for expression in lens we prepared reporter gene constructs expressing lacZ gene under the control of a minimal hsp68 promoter fused to the most conserved region of mouse SIMO enhancer ( hereinafter referred to as minSIMO ) containing either wild-type or optimized Meis binding sites . As shown in S6B Fig , substitution of wild-type Meis binding sequence in SIMO_B , SIMO_C and SIMO_D for optimal Meis binding sequence motif resulted in higher level of reporter activity in the developing lens . These data are in accord with the key functional role of Meis proteins in SIMO regulation and indicate that strong but restricted SIMO enhancer activity relies on a cluster of three medium affinity non-optimal Meis binding sites . Notably , recent systematic study of a model enhancer shows that enhancer specificity depends on a combination of suboptimal recognition motifs having reduced binding affinities . Conversion of suboptimal binding sites to perfect matches to consensus mediates robust but ectopic patterns of gene expression [39] . Finally , in order to gain further insight into enhancer architecture we used JASPAR database ( http://jaspar . genereg . net/ ) to screen throughout the most evolutionarily conserved core region of SIMO ( minSIMO region ) for consensus binding sites of additional transcription factors . We identified potential binding sites for Six3 , Ets/Tead , Maf and homeodomain-containing transcription factors ( S6C Fig ) . We performed site-directed mutagenesis of SIMO introducing dinucleotide changes in the conserved residues of the consensus binding sites ( LOGOs in JASPAR database ) . In addition , we mutagenized an evolutionarily conserved GCTC box present in SIMO of all species analyzed in Fig 3A . Reporter gene constructs expressing lacZ gene under the control of minimal hsp68 promoter fused either to the wild-type SIMO enhancer , or to the enhancer mutated in binding site for each particular transcription factor , were introduced into the chick eye forming region by in ovo electroporation at embryonic stage HH10-11 . As shown in S6C Fig , none of the mutations resulted in a complete abbrogation of lens-specific reporter gene activity as did mutations in Meis binding sites SIMO_B and SIMO_C ( S6A Fig ) . Notably , mutation of Six3 binding site resulted in decreased expression of reporter gene ( S6C Fig ) , suggesting the requirement of similar Six3 input in SIMO enhancer as in EE [23] . Mutations in homeodomain binding sites HD1 and HD2 but not in HD3 lead to a subtle decrease of reporter activity ( S6C Fig ) . Taken together , reporter gene assays in chick demonstrated an essential role of Meis transcription factors for SIMO enhancer activity . Intrigued by the fact that Meis binding sites SIMO_B , SIMO_C and SIMO_D were phylogenetically conserved between mouse and zebrafish we next examined the functional significance of these sites in the context of zebrafish SIMO element . It was previously shown that the region encompassing zebrafish SIMO was able to drive expression to the lens of 48 hpf zebrafish [19] . We made a zebrafish EGFP reporter gene transgenic using wild-type and Meis-mutated versions of zebrafish SIMO element fused to minimal gata2a promoter ( Fig 4G and 4H ) . In order to control for successful transgenesis and to quantitate results between the two constructs , ZED vector containing surrogate muscle-specific DsRed marker gene separated from EGFP reporter gene by an insulator was used [40] . In accordance with a previous study [19] , transgenic fish carrying wild-type SIMO enhancer exhibited high level of EGFP in the lens at 48hpf ( Fig 4I and 4J ) . In contrast , mutation of the phylogenetically conserved Meis binding sites resulted in the loss of EGFP due to the loss of lens-specific enhancer activity of SIMO while the muscle-specific surrogate reporter gene was still active ( Fig 4K and 4L ) . These results suggest an evolutionarily conserved role of Meis proteins in the regulation of the Pax6 SIMO enhancer . Combined , our data establish that the SIMO enhancer is a natural target of Meis1 and Meis2 and that this physical interaction conveys expression of Pax6 in developing vertebrate lens . In order to get an insight into SIMO function in vivo we generated mice carrying deletion of its evolutionarily conserved central core . Targeted engineering of genomic DNA in Pax6 locus was achieved using a pair of transcription activator-like effector nucleases ( TALENs ) designed to delete approximately 200 bp of the most evolutionarily conserved core region of SIMO ( S7A Fig ) . Several lines of mice were established ( S7B Fig ) from which the line #710 designated Pax6SIMOdel710/+ was used for most of further studies . Enhancer region deleted in line #710 encompass Pax6 autoregulatory element and Meis1/2 binding sites SIMO_B , SIMO_C and SIMO_D , respectively , and is absolutely required for lens-specific activity based on transgenic reporter assay in chick ( S7C Fig ) . To our surprize , mice carrying a homozygous deletion of SIMO ( Pax6SIMOdel710/ SIMOdel710 ) did not manifest a major lens developmental phenotype ( S7D Fig ) . To test whether lowering the dose of Pax6 may phenotypically uncover SIMO function during early lens development , we combined Pax6SIMOdel710/+ allele with Sey allele ( Pax6 loss-of-function ) , ( Fig 5 ) . Under these conditions , only one allele of Pax6 carries SIMO enhancer deletion , while the second is genetically inactive in Sey . Although there are several lens phenotypes associated with the complete inactivation of one Pax6 allele in Sey mice , lens is always formed [5 , 6] , ( Fig 5B ) . Remarkably , when the function of the second allele of Pax6 in Sey mice is compromised by SIMO deletion , lens development is arrested prior to lens pit stage ( Fig 5B , the bottom panel ) and no lens is detected in compound Pax6 heterozygote embryos at E13 . 5 ( Fig 5B , the middle panel ) . Finally , to demonstrate redundant role of Pax6 enhancers EE and SIMO for lens induction , we generated mice carrying deletion of both enhancers SIMO and EE simultaneously . For that purpose , we used CRISPR/Cas9 system to delete approximately 500 bp long critical region of EE [15 , 16] on the Pax6SIMOdel710/SIMOdel710 genetic background . Several transgenic lines of Pax6ΔEE;ΔSIMO/ ΔEE;ΔSIMO mice were estabilished ( hereinafter referred to as Pax6 EE/SIMO double mutant ) , from which line containing 477bp deletion of EE simultaneously with SIMO deletion was used for further analysis ( Fig 6A ) . Histological analysis of mice lacking all four copies of lens enhancers at E11 . 0 revealed arrest of lens development prior to lens pit formation ( Fig 6B ) . Immunofluorescent staining for lens marker Prox1 at E12 . 5 confirmed the absence of lens tissue in Pax6 EE/SIMO double mutant embryos ( Fig 6B , the bottom panel ) . Remarkably , a single copy of a functional enhancer in Pax6ΔEE;ΔSIMO/ EE+;ΔSIMO embryo was sufficient for lens induction albeit the resulting lens was much smaller at E11 . 0 as compared to control and lens stalk was apparent in Pax6ΔEE;ΔSIMO/ EE+;ΔSIMO mice at E12 . 5 indicating delayed development ( Fig 6B ) . Genetic data indicated redundancy as well as potential additive activity of EE and SIMO . To provide further evidence that both EE and SIMO might be additively required for high level of Pax6 expression during lens induction we tested synergistic role of SIMO and EE on strength and specificity of expression of reporter genes in the developing chick lens . For that purpose we used reporter gene constructs expressing lacZ gene under the control of a minimal hsp68 promoter fused to either SIMO alone , EE alone , or combination of both enhancers ( S8 Fig ) . As expected , combination of full-length EE [16] with SIMO elicited stronger expression of lacZ reporter gene than did SIMO alone ( S8B Fig ) . Similarly , combination of minimal functional EE [15] with the most conserved region of SIMO ( minSIMO ) ensured stronger expression than did either of the minimal enhancers alone ( S8C Fig ) . Strong and specific reporter gene activity may also be achieved by duplication of the same type of enhancer ( S8C Fig ) . Reporter gene assays suggest that simultaneous use of both EE and SIMO enhancers may be beneficial for achieving high-level tissue-specific Pax6 gene expression during lens induction . Combined , our data demonstrate simultaneous requirement of EE and SIMO Pax6 enhancers for normal lens development and provide evidence of their apparent redundancy and synergistic activity at early stages of lens induction .
GRNs provide a system level explanation of development in terms of the genomic regulatory code [41 , 42] . While significant insights into the functional role of many transcription factors during the lens placode formation have been realised , much less is known about the upstream regulation of these critical factors and the intricate wiring of the GRN that controls the earliest stages of lens development . Previous studies have shown that the GRN of mammalian lens induction is governed by a multitude of mutual cross-regulations , including the transcription factors Pax6 , Six3 and Sox2 ( summarized in the BioTapestry visualization Fig 7 ) . Six3 appears to regulate the onset of Pax6 expression in the PLE while Pax6 subsequently maintains Six3 levels [23 , 35 , 43] . Only a small fraction of Six3 f/del;Le-Cre embryos , type III in [23] , exhibit a complete arrest of lens development prior to the lens pit stage , a phenotype comparable to Pax6 knockout phenotype , although this might be due to the inefficient deletion of Six3 . Consequently , the level of Six3 ablation in lens-derived tissue correlates well with the grade of phenotype and Pax6 and Sox2 downregulation [23] . Epistasis of Pax6 and Sox2 is stage-dependent . In pre-placodal ectoderm , Pax6 and Sox2 are regulated independently . By contrast , after the lens placode has formed , Sox2 expression is dependent on Pax6 [34] . Genetic data presented here reveal a fundamental and redundant role of Meis1 and Meis2 homeoproteins in the regulation of lens induction . Meis1 and Meis2 transcription factors have previously been identified as upstream regulators of the Pax6 EE [22] . However , Meis1- and EE-deficient mice surprisingly do not display eye phenotypes at placodal stage of lens development [17 , 28] and therefore are not comparable to that of the lens-specific ablation of Pax6 [7] . This indicates that ( i ) Meis2 may compensate for the loss of Meis1 , and that ( ii ) another Pax6 enhancer driving expression to lens may substitute for missing EE [17 , 44] . Until recently , interrogation of the combined role of Meis1/2 proteins on lens induction and Pax6 expression in vivo has been hampered by the lack of suitable Meis2 knockout allele . Herein , we have conducted a comprehensive genetic analysis of Meis1 and Meis2 function in mouse to show that simultaneous depletion of Meis1 and Meis2 in the presumptive lens ectoderm results in the failure of lens placode formation and a marked reduction of Pax6 and Six3 expression in the presumptive lens areas . In contrast , expression of Sox2 is maintained in the Meis1/Meis2 mutated ectoderm . The Meis-related TALE homeodomain protein Prep1 ( also known as Pknox1 ) apears to control the timing of Pax6 activation and its expression level in the developing lens via direct binding to the EE [25] . The available data regarding the genetic requirement for Prep1 suggest it has a cell-nonautonomous function in lens induction . Prep1 trans-heterozygotes composed of a germline knockout and retroviral insertion allele ( a hypomorph ) , respectively , demonstrate defects at the lens induction step [25] . In contrast , conditional gene targeting of Prep1 at pre-placodal and placodal phases of lens induction using Ap2alpha-Cre and Le-Cre did not reveal any developmental phenotype [45] . We were unable to detect any changes in Prep expression using imunohistochemistry ( S9 Fig ) , making it unlikely that the observed phenotype in Meis1/2 double knockout mice is due to Prep1 deficiency . Our data are consistent with the scenario in which Meis1/2 function as regulators of lens placode development primarily via activation of Pax6 enhancers . However , it is likely that Meis1 and Meis2 regulate other factors contributing to early lens development such as the ones identified for Meis1 [46] . It was recently shown that Meis1 regulates either directly or indirectly the expression of genes involved in patterning , proliferation and differentiation of the neural retina , and that haploinsufficiency of Meis1 causes micropthalmic traits and visual impairment in adult mice [46] . Based on the fact that Marcos et al . could not detect Meis2 expression at early stages of eye development , authors considered only Meis1 function to be critical for early mouse eye development [46] . In contrast , in this study we detected Meis2 expression in early stages of lens development ( S1 Fig ) . Furthermore , Meis2 expression is lost upon genetic ablation of Meis2 gene ( S1J Fig ) . This data together with the fact that only simultaneous deletion of Meis1 and Meis2 in PLE leads to an arrest of lens development in pre-placodal stage strongly suggests that both Meis1 and Meis2 are expressed and essential for early eye development . Nevertheless , it is very likely that Meis1 and Meis2 fulfill the redundant function only in specific developmental stages and processes ( our data and [46] ) , while having many discrete functions in the embryo even within the eye development . Mammalian eye development is highly sensitive to the levels of Pax6 as haploinsufficiency causes aniridia in humans and multiple ocular defects in mice [4 , 47–50] . In contrast , increased levels of Pax6 result in various ocular abnormalities [51] . In the mammalian lens , Pax6 controls all known steps of tissue morphogenesis [7 , 34 , 52] but its dosage appears to be especially critical during the earliest developmental stages . The data presented here show that the molecular mechanisms of Meis1/2 regulation of Pax6 are mediated by at least two "shadow enhancers" ( Fig 7 ) : a 3‘-located ultraconserved SIMO identified as a Meis target here , and a 5‘-located ectoderm enhancer ( EE ) , identified as a target of TALE proteins earlier [22 , 25] . The concept of the seemingly redundant "shadow enhancers" driving expression of a given gene to overlapping or identical patterns has been pioneered in Drosophila as a potential source of evolutionary novelty [53] . It was hypothesized that "shadow enhancers" may evolve novel binding sites and achieve new regulatory activities without disrupting the core patterning function of a developmental control gene . As cis-regulatory mutations are the main driving force of animal evolution [54 , 55] buffering loss-of-function situations during enhancer evolution may be critical . "Shadow enhancers" analyzed in detail in Drosophila to date provide robustness and precision to the system [56–58] . A remote "shadow enhancer" identified in the human ATOH7 gene , by virtue of its deletion in patients suffering with nonsyndromic congenital retinal nonattachment , displays identical spatiotemporal activity to the primary enhancer when tested by transgenesis [59] . Although the function of the primary and "shadow enhancer" are not firmly established , dual enhancers may reinforce Atoh7 expression during early critical stages of eye development when retinal neurogenesis is initiated . It is tempting to speculate that the two apparently redundant distal "shadow enhancers" ( EE , SIMO ) ensure robust and tight regulation of Pax6 gene expression during mammalian lens induction . In our view robustness of Pax6 "shadow enhancer" system provides stable high level of Pax6 gene expression and confers compensation for deleterious effects and protection to expression level fluctuations due to environmental influences . Recent systematic analysis of "shadow enhancers" during Drosophila mesoderm development revealed that their spatio-temporal redundancy is often partial in nature , while the non-overlapping function may explain why these enhancers are maintained within a population [60] . Reporter gene assays and genetic ablation experiments shown here provide evidence for redundant ( "shadow" ) enhancer function of SIMO and EE selectively during early stages of lens induction . Later on the two enhancers may indeed act more independently with some overlap of transcription factor use while their distinctness is likely elicited by different sets of transcription factors co-expressed and co-bound at different times and in different combinations and stoichiometry . It is nevertheless intriguing that the two enhancers responsible for lens placode expression of Pax6 utilize similar molecular logic , namely Meis1/2-dependency ( [22] and this study ) , Six3 regulatory input ( [23] and this study ) and autoregulatory function [19 , 21] . Furthermore , two Meis/Prep binding sites , L1 and L2 , were identified in the EE [22 , 25] while at least three evolutionarily conserved Meis binding sites are present in SIMO ( this study ) . In theory , the accumulation of homotypic binding sites may aid the enhancer robustness and may protect the enhancer from vulnerable mutations leading to the loss of responsivness to a particular transcriptional regulator . Phylogenetic footprinting and reporter gene transgenics indicate that SIMO enhancer activity in zebrafish not only depends upon Pax6 autoregulation [19] but also on functional Meis binding sites ( this study ) . Given the profound difference in the early stages of lens development in mice ( lens formed by invagination ) and fish ( lens arises by delamination ) it is remarkable that the SIMO enhancer maintains its Meis-dependent regulation albeit not for the comparable developmental stage . In fact , SIMO enhancer becomes active in zebrafish only at 48 hours post fertilization when the lens is already formed [19] . This illustrates that species-specific adaptation of enhancer function is combined with a developmental change . It will be interesting to see if other features of SIMO regulation , such as Six3 interaction , are maintained in zebrafish . No functional data exist for the zebrafish EE , although at the sequence level this regulatory element is evolutionarily conserved from human to fish [13 , 15 , 25] . It remains to be seen if the evolutionary strategy of maintaining lens "shadow enhancers" in the Pax6 locus is utilized in zebrafish , or the developmental robustness is achieved via Pax6 gene duplication giving rise to Pax6 . 1a and Pax6 . 1b paralogues [61] . Pax6 is considered as an extreme case of an evolutionarily conserved developmental regulator promoting eye formation in vertebrates and Drosophila [62] . Meis genes belong to the TALE homeobox family found in genomes across all Metazoa [63] . In contrast to Pax6 , Homothorax , a Drosophila orthologue of vertebrate Meis/Prep genes , suppresses eye development rather than promoting it [64] . Homothorax together with the Cut homeoprotein supresses expression of Pax6 orthologue Eyeless in the antenna disc [65] . Conversely , Sine oculis , a downstream target of Eyeless , supresses Homothorax and Cut in the eye disc thus allowing eye development to proceed [65] . The different genetic wiring of Pax6/Eyless and Meis/Homothorax in vertebrate and Drosophila eye developmental programs may merely reflect the vast evolutionary distance between the respective species , morphological differences in the eye types being built and a general strategy of re-purposing individual components from the common genetic toolkit during the course of evolution . In conclusion , this study identifies a genetic requirement for Meis1 and Meis2 for early steps of mammalian eye development and reveals an apparent robustness of the gene regulatory mechanism whereby two independent "shadow enhancers" of similar molecular architecture maintain critical levels of a dosage-sensitive gene , Pax6 , during lens induction . These results allow us to establish a genetic hierarchy during early vertebrate eye development and provide novel mechanistic insights into the regulatory logic of this process .
Housing of mice and in vivo experiments were performed in compliance with the European Communities Council Directive of 24 November 1986 ( 86/609/EEC ) and national and institutional guidelines . Animal care and experimental procedures were approved by the Animal Care Committee of the Institute of Molecular Genetics ( study #174/2010 ) . Mice were sacrificed by cervical dislocation . To inactivate Meis1 , Meis1+/- [28] mice were used . A conditional mutant allele of the Meis2 gene ( Meis2f/f ) was generated by inserting loxP sites in the introns 2 and 6 , flanking exons 3 and 6 in the Meis2 gene ( S1G Fig ) at the Gene Targeting & Transgenic Facility , University of Connecticut , USA [32] . To generate whole-body knockout of Meis2 , Meis2f/f mice were crossed with Hprt-Cre mice ( strain 129S1/Sv-Hprttm1 ( cre ) Mnn /J , stock 004302 , The Jackson Laboratory ) that display the zygotic Cre recombinase activity . For specific deletion of Meis2 in presumptive lens ectoderm , Le-Cre [7] mice were used . ROSA26R [66] and Pax6Sey-1Neu[4] mice ( herein designated as Pax6Sey/+ ) have been described previously . SIMO enhancer was deleted using a pair of TALENs targeting sequences TCAGCCCCCACCCATACTCtcaaaaggaatgtcgTCGAGCGTCAGTGCCTGAA and TGCACTTGTCACTCAGCATTAtccatcctcattaaTGACAATGGGAAAGTTTA ( recognition sequence shown in capital letters ) . TALENs were designed using TAL Effector Nucleotide Targeter 2 . 0 ( https://tale-nt . cac . cornell . edu/ ) , assembled using the Golden Gate Cloning system [67] , and cloned into the ELD-KKR backbone plasmid [68] . Polyadenylated TALEN mRNAs were prepared using mMESSAGE mMACHINE T7 ULTRA Kit ( Ambion ) and were injected into the cytoplasm of fertilized mouse oocytes . EE [16] was deleted using CRISPR/Cas9 system . A sequence containing EE region was submitted to CRISPR Design Tool ( http://crispr . mit . edu/ ) to select for a set of sgRNAs‘ . Oligonucleotides used to make sgRNA constructs are listed in S1 Table and were cloned into pT7-gRNA ( pT7-gRNA was a gift from Wenbiao Chen , Addgene plasmid # 46759 ) . Cas9 mRNA was prepared using mMESSAGE mMACHINE T7 ULTRA Kit ( Ambion ) using plasmid pCS2-nCas9n ( pCS2-nCas9n was a gift from Wenbiao Chen , Addgene plasmid # 47929 ) . The sgRNAs were transcribed using MEGAshortscript kit ( Ambion ) . A mixture of Cas9 mRNA ( 100ng/μl ) and specific sgRNAs ( 25ng/μl each ) was injected into the cytoplasm of fertilized mouse oocytes with homozygous or heterozygous deletion of SIMO enhancer ( genetic background Pax6SIMOdel710/SIMOdel710 or Pax6SIMOdel710/+ ) . Multiple independent lines were estabilished and the extent of EE deletion was analysed in F1 animals by DNA sequencing . Mouse embryos were staged by designation the noon of the day when the vaginal plug was observed as embryonic day 0 . 5 ( E0 . 5 ) . Embryos of desired age were disected , fixed in 4% paraformaldehyde ( PFA ) from 45 minutes up to 4 hours at 4°C , washed with PBS , cryopreserved in 30% sucrose and frozen in OCT ( Sakura ) . The cryosections ( 10–12 μm ) were permeabilized with PBT ( PBS with 0 . 1% Tween ) , blocked with 10% BSA in PBT and incubated with primary antibody ( 1% BSA in PBT ) overnight at 4°C . Sections were washed with PBS , incubated with fluorescent secondary antibody ( Life Technologies , 1:500 ) for one hour at room temperature , washed with PBS , counterstained with DAPI and mounted in Mowiol . The images were taken on Leica SP5 confocal microscope and were processed ( contrast and brightness ) with Adobe Photoshop . For hematoxylin-eosin staining , embryos were fixed in 8% PFA overnight , processed , embedded in paraffin , sectioned ( 8 μm ) , deparaffinized and stained . For β-galactosidase staining , embryos were fixed in 2% PFA , washed with rinse buffer ( 0 . 1 M phosphate buffer pH 7 . 3 , 2 mM MgCl2 , 20 mM Tris pH 7 . 3 , 0 . 01% sodium deoxycholate , and 0 . 02% Nonidet P-40 ) and incubated in X-Gal staining solution ( rinse buffer supplemented with 5 mM potasium ferricyanide , 5 mM potassium ferrocyanide , 20 mM Tris pH 7 . 3 , and 1 mg/ml X-gal ) at 37°C for 2 hours and at room temperature overnight shaking . For chromatin immunoprecipitation whole E10 . 5 embryos or murine lens epithelial cells αTN4 [37] were used . A chromatin immunoprecipitation assay was performed according to manufacturer’s protocol ( Upstate Biotech ) with slight modifications as previously described [69] . The assay was repeated twice for both embryonic and tissue culture samples . The immunoprecipitated DNA was analyzed by qRT-PCR . In silico analysis to identify putative Meis binding sites in SIMO was performed using high-quality transcription factor binding profile database JASPAR [70] . Electrophoretic mobility shift assays ( EMSAs ) was performed using double-stranded oligonucleotides comprising binding sites SIMO_B . A single point mutation was introduced into binding site changing Meis recognition sequence TGACAG/A into TcACAG/A . 32P-labeled oligonucleotides were incubated with in vitro-synthesized FLAG-Meis2 ( TNT Quick , Promega ) in binding buffer ( 10 mM HEPES pH 7 . 9 , 100 mM KCl , 1mM EDTA , 4% Ficoll , 0 . 05mg/mL poly-dIdC ) at room temperature for 15 minutes . For supershift experiment , anti-FLAG M2 antibody was included in the binding reaction . Samples were analysed by 6% polyacrylamide gel electrophoresis and autoradiography . The wild-type mouse SIMO enhancer was amplified from genomic DNA using primers shown in S1 Table and introduced into the electroporation vector containing hsp68-lacZ reporter cassette [20] . Transcription factor binding sites within SIMO were mutagenized using QuickChange mutagenesis kit ( Stratagene ) . Constructs carrying minimal EE and minimal SIMO enhancers were generated using synthetic double stranded oligonucleotides shown in S1 Table . All reporter gene constructs were verified by DNA sequencing . Brown Leghorn eggs were incubated until reaching HH10–11 stages and electroporation was performed as described [71] . The DNA mixture was injected outside of the right developing optic cup and electroporated using voltage of 12 V , length of pulse 20 ms , interval length 100 ms . The embryos were collected in stage HH20-HH21 , fixed for 15 minutes in 2% formaldehyde and proceeded to X-gal staining . The wild-type zebrafish SIMO enhancer was introduced into ZED vector upstream of minimal gata2a promoter [40] . Meis binding sites within SIMO were mutagenized using QuickChange mutagenesis kit ( Stratagene ) . For transgenesis , the Tol2 transposon/transposase method [72] was used with minor modifications . A mixture containing 30 ng/μl of transposase mRNA , 30 ng/μl of Qiagen column purified DNA , and 0 . 05% phenol red was injected in the cell of one-cell stage embryos . Embryos were raised at 28 . 5 oC and staged by hours post fertilization ( hpf ) . Embryos selected for imaging were anaesthetised with tricaine and mounted in low-melting agarose . Images were taken on Leica SP5 confocal microscope . All used oligonucleotides are listed in S1 Table . All used primary antibodies are listed in S2 Table . | While significant insights into the functional role of some transcription factors during lens formation have been accomplished , much less is known about the intricate wiring of the gene regulatory network ( GRN ) that controls the earliest stages of lens development . Our genetic experiments presented here demonstrate a fundamental and redundant role of Meis1 and Meis2 genes in the process of lens induction . Furthermore , we present evidence that the robustness and dose-dependent regulation of Pax6 , a key node of lens GRN , occurs via employment of "shadow enhancers" powered by Meis transcription factors . Combined , this study significantly extends knowledge about the genetic wiring of the earliest stages of eye development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"gene",
"regulation",
"regulatory",
"proteins",
"ocular",
"anatomy",
"dna-binding",
"proteins",
"vertebrates",
"reporter",
"genes",
"animals",
"animal",
"models",
"osteichthyes",
"developmental",
"biology",
"model",
"organisms",
... | 2016 | The Gene Regulatory Network of Lens Induction Is Wired through Meis-Dependent Shadow Enhancers of Pax6 |
The transmission of mosquito-borne diseases is strongly linked to the abundance of the host vector . Identifying the environmental and biological precursors which herald the onset of peaks in mosquito abundance would give health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control . We analysed a 15-year time series of monthly abundance of Aedes vigilax , a tropical mosquito species from northern Australia , to determine periodicity and drivers of population peaks ( high-density outbreaks ) . Two sets of density-dependent models were used to examine the correlation between mosquito abundance peaks and the environmental drivers of peaks or troughs ( low-density periods ) . The seasonal peaks of reproduction ( r ) and abundance ( ) occur at the beginning of September and early November , respectively . The combination of low mosquito abundance and a low frequency of a high tide exceeding 7 m in the previous low-abundance ( trough ) period were the most parsimonious predictors of a peak's magnitude , with this model explaining over 50% of the deviance in . Model weights , estimated using AICc , were also relatively high for those including monthly maximum tide height , monthly accumulated tide height or total rainfall per month in the trough , with high values in the trough correlating negatively with the onset of a high-abundance peak . These findings illustrate that basic environmental monitoring data can be coupled with relatively simple density feedback models to predict the timing and magnitude of mosquito abundance peaks . Decision-makers can use these methods to determine optimal levels of control ( i . e . , least-cost measures yielding the largest decline in mosquito abundance ) and so reduce the risk of disease outbreaks in human populations .
Only a few of the approximately 3000 mosquito species known worldwide feed on human blood [1] . Unfortunately , these human pest species are responsible for the deaths of millions of people each year by transmitting some of the deadliest-known diseases , such as malaria , yellow fever , dengue , and Rift Valley fever [2] . Different mosquito species transmit different diseases; for example , Aedes aegypti is the primary vector of the arboviruses dengue and yellow fevers [3] , [4]; several Anopheles species carry different forms of the malaria protozoan parasite [5] , and Aedes vigilax transmits Ross River and Barmah Forest virus [6] , [7] . The transmission of mosquito-borne diseases is strongly linked to the abundance of the host vector [6] , [8] , making rigorous surveillance and control programs an essential component of disease suppression . To develop effective control measures , most studies have focused on describing and quantifying habitat associations and the particular spatio-temporal distributions of vector species . The fluctuations in mosquito abundance over time are driven both by endogenous ( negative density feedback ) and exogenous ( stochastic environmental variation ) components [9] , [10] . Indeed , a recent analysis of Ae . vigilax population dynamics in northern Australia demonstrated that negative density feedback alone accounts for over 31% of the deviance in population growth rate , with another 40% of the deviance explained by the addition of high tide frequency , rainfall and relative humidity [10] . This combination of negative density feedback and environmental influences contribute to the characteristic oscillatory pattern of peaks and troughs in mosquito abundance over the course of a single year . These characteristic fluctuations in abundance produce large amplitude changes in the peak timing of biting adult mosquitoes , with some extreme events occurring at wave lengths of several years . High-magnitude peaks in abundance are often correlated with the outbreak of diseases [11]–[13] . As such , the ability to predict the timing and magnitude of population peaks is an essential precursor to effective control and risk management ( e . g . , public warnings ) . A previous analysis of density fluctuations in mosquitoes in northern Australia focused on the interplay between density feedback and environmental stochasticity [9] , [10] . However , there is now a need to determine what conditions lead specifically to a higher probability of high-abundance peaks that precipitate disease outbreaks . Identifying the timing and intensity of future abundance peaks is also important for maximising control efficiency because the sub-optimal and liberal application of chemical and biological pesticides has generated several ancillary problems , including excessive financial costs , the evolution of insecticide resistance via repeated exposure , safety risks for humans and domestic animals , and environmental contamination [14]–[16] . The ability to define the most effective timing and spatial configuration for applying control measures , using the smallest quantity of insecticide , will not only reduce the frequency and severity of disease outbreaks , it will alleviate many of the environmental and logistical problems associated with control and reduce costs . There are a number of approaches developed to explain the basic causes underlying fluctuations in natural and controlled populations [17]–[20] . Various approaches have stressed the importance of weather variability , the dynamics of natural predators , prey or competitors , overcrowding , and so on [21] , [22] . Yet few have been constructed to forecast simultaneously the magnitude and timing of peak population sizes in species experiencing regular density irruptions . One notable exception is the use of marginal logistic regression models to predict the timing of southern pine beetle ( Dendroctonus frontalis ) outbreaks [17] . This approach examined the influence of explanatory variables on host availability , physiography , climate , extreme weather events , and management protocols after accounting for spatial and temporal autocorrelation [17] . Modelling chaotic behaviour in population dynamics provides another approach to identify optimal intervention times . Hilker and Westerhoff [18] presented a simple method to guide management efforts in preventing crashes , peaks , or any other undesirable state in chaotic population dynamics . The method is illustrated by two examples based on captive populations of the flour beetle ( Tribolium sp . ) : ( 1 ) alleviation of extinction risk in the Ricker model: ( where = relative abundance at time t , r [population rate of change] = loge ( / ) and K = carrying capacity ) , and ( 2 ) control of outbreaks in a stage-structured demographic model . First , an abundance time series of the population is analysed to identify paths that lead to crashes or outbreaks . The second step is to manipulate population abundance to force it out of the danger zone preceding either a crash ( prevention of extinction ) or a peak ( eliminating outbreaks ) . Ecological data are normally the emergent or phenomenological expression of a set of complex processes that are difficult to model mechanistically , so they cannot normally be well-represented by any one model . Model selection may help to identify which simplification of reality provides the most parsimonious explanation of the phenomenon , and multi-model inference ( MMI ) will draw conclusions from a weighted average over predictions made by the suite of candidate models considered [23] , [24] . Previously , we used MMI for a series of phenomenological models to examine the principal drivers of population change in two well-monitored mosquito species from northern Australia [9] , [10] . Using weekly relative abundance data ( from CO2 traps ) , collected over 15 years in Darwin , northern Australia , we previously determined the subtle and complex interactions between density and environmental conditions [9] , [10] . But this study did not examine the conditions leading to peaks in abundance over time . Here we extend our earlier approach to investigate explicitly whether the timing and magnitude of abundance peaks in Ae . vigilax can be determined ( and so predicted ) from a suite of intrinsic and measureable extrinsic components . High tide frequency and low rainfall lead to higher population growth rates in this species [10] , with low rainfall in the late dry season and early wet season , in particular , facilitating mosquito breeding and subsequent abundance peaks , especially if it occurs during favourable tides . Ae . vigilax breeds primarily in saline to brackish wetlands along the coast , where females lay their eggs on moist mud and at the base of plants in high marshlands dominated by brackish water reeds ( Schoenoplectus spp . ) or mangroves ( Avicennia spp . or Bruguiera spp . ) [10] . With the highest tides able to flood the salt marshes , Ae . vigilax eggs are hatched immediately; however , as the tide retreats , more eggs are laid on moist substrata where they mature quickly and become drought-resistant and persist until the onset of the next high tide cycle or rain event [10] . The interval between hatchings may take weeks to months depending on the tidal patterns and rainfall , but low rainfall following the first high tide is likely to maintain sufficient soil moisture and salinity to support continued hatching [10] , thus maintaining the surge of recruitment into the adult population . For the Darwin region , the first high tide>7 . 4 m in the late dry season ( September ) , followed by light rains , will normally precipitate a rapid rise in the mosquito's rate population of change ( r ) and a subsequent peak in abundance in November [10] , [25] . Using weekly mosquito-capture data collected over 15 years in the greater Darwin area , tropical north Australia , our specific objectives were to ( i ) evaluate the oscillation pattern of both the rate of population increase ( r ) and abundance over the 15 years , and ( ii ) estimate the relationship between peak occurrence and magnitude and changes to previously identified environmental correlates of abundance ( i . e . , tidal factors , rainfall and relatively humidity ) . Rather than simply repeat our previous work examining the processes influencing fortnightly relative abundance [9] , [10] , our approach was to simplify the time series and focus only on periods of highest relative abundance , indicative of peaks ( and by proxy , potential disease outbreaks ) . Our overarching aim is to provide decision makers charged with suppressing mosquito abundance with practical advice on the timing for optimal control strategies in this and other species of mosquitoes implicated in the spread and maintenance of human infectious diseases .
Eleven monitoring locations were selected in the geographical region between 12° 22–25′S latitude and 130° 51–56′E longitude , in the swampy regions surrounding Darwin , Northern Territory , Australia [9] , [10] , [26] . CO2-baited mosquito traps [27] were checked weekly at each location by the Medical Entomology Branch of the Northern Territory Department of Health and Community Services . We assume the light intensity of the traps was the same throughout the duration of the observations . Therefore , the variation in population density is mainly a reflection of total population abundance rather than an artefact of sampling effort ( see [9] for a more detailed justification ) . Previously [9] , [10] , we argued that the comparison of monthly summary data avoids the potential confounding effects of age structure and overlapping generations , as well as providing a reasonable representation of fluctuations in population density over time . Ae . vigilax also exhibits only weak spatial heterogeneity within the study region , allowing us to pool population density data among the 11 trap locations as an arithmetic mean [9] . The final dataset covered 180 months from January 1991 to December 2005 ( 15 years ) . A visual examination of the oscillations of mosquito abundance time series clearly shows the semi-regular occurrence of high-abundance ‘peaks’ interspersed with low-abundance ‘troughs’ ( Fig . 1A ) . Our a priori hypothesis is that environmental variation can be used to predict the frequency and magnitude of abundance peaks after taking density feedback into account . To test this hypothesis , we split the relative abundance time series for Ae . vigilax from 1991 to 2005 into periods representing seasonal periodicity: relatively high ( September–February ) , and low ( March–August ) abundance periods ( 28 seasonal intervals over 15 years ) ( Fig . 1B ) . For each six-month period , the numbers of mosquitoes were summed in subsequent analyses . Monthly population growth rates ( r ) were taken as rt = loge ( / ) , where is the measure of relative abundance at time point t . Monthly environmental data covering the same interval as the mosquito abundance dataset were provided by the Australian Government Bureau of Meteorology ( www . bom . gov . au ) . Frequency of high tide ( Fig . 2A ) , rainfall ( Fig . 2B ) and relative humidity have all been previously identified as correlated with the rate of population change for this species , with the strongest explanatory variable being high tide frequency [10] . For the question at hand , we also considered two additional descriptors of tidal patterns that measure the magnitude of tidal influence in these ephemeral saltwater habitats: ( 1 ) accumulated tide height per month , and ( 2 ) maximum tide height per month ( Fig . 2C ) . Our previous work [10] determined that the ecosystem's carrying capacity is relatively invariant compared to Ae . vigilax's response to fluctuating extrinsic conditions . Therefore , periodicity in relative abundance ( ) represents mainly the population's response to environmental conditions promoting egg hatching , larval development and adult dispersal . Periodicity in r , on the other hand , indicates variation in the population's relative distance from carrying capacity , and so is more indicative of internal feedback mechanisms . We therefore examined periodicity in both properties to determine the oscillatory dynamics of Ae . vigilax . Let and be the monthly mosquito abundance and population growth rate , respectively . We fitted a seasonality model through the harmonic curves of and of the following form: ( 1 ) ( 2 ) which can be simplified to: ( 3 ) ( 4 ) Here , amplitude ( a and b are the estimated coefficients for the sine and cosine terms in Equations 1 and 2 ) ; ( 1 month/12 months ) ; is the phase , and defines any linear temporal trends in the response . The month where the seasonal peak occurs is then: ( 5 ) where n = is the number of the cycle . The fundamental time-series modelling tool for spectral analysis is the periodogram , which is based on the squared correlation between the time series and sine-cosine waves of frequency . Periodograms were analyzed using the spec . pgram function in the R Package v2 . 4 . 0 [28] . Two sets of models were used to examine the correlation between mosquito abundance peaks ( Fig . 1B ) and the environmental drivers considered: ( 6 ) ( 7 ) where and denote total mosquito population size during the identified peak and trough intervals , respectively , is the vector of coefficients for the n environmental drivers considered , and and represent the vector of environmental drivers ( , ) during the peak and trough intervals , respectively . Our previous identification of a strong density feedback component in the oscillation dynamics of this mosquito species [9] argued for the inclusion of the previous value as an explanatory covariate ( Equations 6 and 7 ) . The mid-point of the trough interval ( 6-month duration ) occurs exactly 6 months prior to the identified peak date given the definition of the ‘trough’ and ‘peak’ periods and the identification of dominant 12- and 6-month periodicities ( see Results ) . We contrasted a total of 44 models comprising various combinations of the terms of interest , fitted using maximum-likelihood estimation . All analyses were done using the R Package [28] . Model comparisons were based on multi-model inference ( MMI ) using Akaike's Information Criterion corrected for small sample size bias ( AICc ) as an estimate of Kullback-Leibler ( K-L ) information loss [24] , [29] . The difference between the model's criterion and the top-ranked model ( ΔAICc ) and the relative model weights ( wAICc ) were calculated . Thus , the strength of evidence ( wAICc ) for any particular model varies from 0 ( no support ) to 1 ( complete support ) relative to the entire model set . For each model we also calculated the % deviance explained ( %DE ) as a measure of the model's goodness-of-fit , the predicted R2 as a measure of the % variance explained , and the leave-one-out cross-validation prediction error ( C-Vε ) based on the cv-glm command in the R Package [28] to validate the robustness of the predictions .
Figure 1A shows the time series of monthly mosquito abundance ( ) and population rate of change ( r ) between 1991–2005 , clearly depicting the strong seasonality in the temporal pattern of both and r . In Figure 1B , the reduced peak-trough dataset is presented . For the time series , amplitude ( A ) of the time-series curve was 62 . 98 trap-caught individuals: fitted mosquito abundance fluctuated between 0 and 125 . 96 per trap ( 2A ) . The seasonal peak of the mosquito population occurs in early November ( month 11 . 3 ) ; the September–February period is considered the high-abundance season , and March–August the low-abundance season ( Fig . 1B and 3 ) . The coefficients for the sine and cosine elements in the periodicity model were −15 . 95 and 60 . 93 , respectively . We found weak evidence for a slight uptrend in ( coefficient = 0 . 06242 [SE = 0 . 116] ) over time between 1991–2005 . For r , the amplitude ( A ) of the time series = 0 . 66 , with the seasonal peak occurring at the beginning of September ( 9 . 0 ) , which is over two months earlier than the expected mosquito abundance peak . The periodograms of and r ( Fig . 4 ) confirm the seasonality with strongest periodicities of 12 months ( largest peak with frequency 1/12 = 0 . 08 ) , followed by a weaker periodicity of 6 months ( second highest peak with frequency 1/6 = 0 . 17 ) . The coefficients for the sine and cosine elements in the r periodicity model were −0 . 58 and −0 . 32 , respectively . Among the two sets of models , the frequency of high tides above 7 m per month in the previous trough interval had the strongest wAICc support for explaining variance in during the high-abundance period ( ) after accounting for the previous trough's abundance ( Table 1 ) . In the top-ranked model , the first ( trough ( t−1 ) ) and second ( tide>7 m frequency ) term's coefficient estimates were −0 . 98 and −39 . 07 , respectively . This demonstrates that the combination of low mosquito abundance and a low frequency of high tide exceeding 7 m in the previous low-abundance ( trough ) period were the most parsimonious predictors of a peak's magnitude ( Fig . 5A ) , with the model explaining over 50% of the deviance in . This was also confirmed by the leave-one-out cross-validation . The top wAICc-ranked model also had the lowest prediction error ( Table 1 ) . Model weights were also relatively high for those including maximum tide height , accumulated tide height or total rainfall in the trough period ( Table 1 , Fig . 5B–D ) , with high values in the trough correlating negatively with the onset of a high-abundance peak . For the second highest-ranked model including trough ( t−1 ) , tide frequency and maximum tide height ( Table 1 ) , the coefficients were −0 . 69 , −76 . 92 and 4281 , respectively . For the third highest-ranked model including trough ( t−1 ) , tide frequency and accumulated tide height ( Table 1 ) , the coefficients were −1 . 41 , −68 . 75 and 20 . 38 , respectively . We used the three most highly ranked models ( Table 1 ) , accounting for 72% of the model weights ( the fourth-ranked model was the intercept-only model ( no predictors ) , so this was excluded along with the remaining models that accounted for ∼18% of the remaining weights ) , to calculate a model-averaged prediction ( Fig . 6 ) . Based on these results , it can be seen that predictions mimic observed values rather well . With the primary applied aim of predicting the highest-abundance peaks , our method thus provides an adequate tool for managers to prepare and mitigate mosquito outbreaks .
Ae . vigilax is a particularly notorious and heavily controlled mosquito species in Australia because of its role in the spread and maintenance of several serious infectious human diseases such as Ross River and Barmah Forest fevers [30] , [31] . Indeed , these two diseases were the most common mosquito-borne diseases reported in Australia in 2005–2006 [32] , with over 5000 cases of Ross River fever reported annually . Previous studies suggested that human infection rates are related to the appearance of large numbers of adult Ae . vigilax [8] , with the recommendation that targeted control of adults in disease-prone areas could reduce the number of cases substantially . As such , identifying the environmental and biological precursors that herald the onset of peaks in mosquito abundance provide health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control . Our results presented here on the amplitude and timing of outbreaks , when combined with the more general previous work on intrinsic and environmental determinants of mosquito population dynamics [9] , [10] , clearly demonstrate that a relatively simple set of conditions – low abundance of adult mosquitoes in the trough season , coupled with a low frequency of high tide and low rainfall – can predict peaks in mosquito abundance and potentially outbreaks of human disease with sufficient reliability to be a useful decision-making tool for managers . The rate of population change ( r ) has been widely used to model the way in which animal or plant populations change with time [33] , [34] . We found that the low relative abundance during the dry season months were generally indicative of impending population peaks . Hence , the development of ideal environmental conditions ( see below ) , coupled with rapidly rising abundances , indicate the optimal times to apply control in swamps surrounding human settlements to minimize the magnitude of subsequent peaks . Further , we found that maximum r generally occurred around two months prior to the appearance of abundance peaks , so that regular monitoring should provide managers with ample preparatory time in which to organise and implement widespread control ( e . g . , in September for Ae . vigilax in Darwin ) . Our results suggest that control operations for Ae . vigilax in northern Australia should target the period approximately two months leading up to an eventual peak , when ‘trough’ abundances are relatively low and few recent high tide events have occurred . Thus , following low tide events in the dry season , targeted control such as spraying of mosquito breeding swamps in early September will allow for more effective control close to human settlements . Another element to optimise reduction efficiency is the spatial configuration of control measures . In Darwin , the bacterial larvicide , Bacillus thuringiensis var . israelensis ( B . t . i ) , and temephos ( an organophosphorus insecticide ) , are widely used for larval control and are broadcast via ground and aerial ( helicopter ) operations [35] . Adulticides are generally less effective due to the ability of adult mosquitoes to disperse over wide areas , including human settlements ( e . g . , [36] ) . Control is often done at a local , administrative scale , with the choice of spatial configuration depending traditionally on accumulated trial-and-error knowledge rather than any systematic analysis of spatial data . Many other mosquito control studies have placed emphasis on determining the optimal spatio-temporal distribution of adult mosquitoes [8] , [37] , [38] . We suggest that such approaches should also be applied to Ae . vigilax larvae to improve control efficiency further . Perhaps counter-intuitively , we found that the magnitude of mosquito peaks was negatively associated with the frequency of high tide above 7 m and maximum high tide over the previous season of low abundance . This may be explained by considering the chaotic population dynamics typical of oscillating populations . Both aquatic ( e . g . , algal blooms ) [39] and terrestrial ( e . g . , insect outbreaks ) [40] studies have found that population crashes are often preceded by an immediate peak in population size , and vice versa . For instance , in a series of microcosm experiments , Hilker and Westerhoff [18] showed that the addition of adult flour beetles ( Tribolium castaneum ) immediately prior to the occurrence of an anticipated population peak reduced the probability that one would occur . This is generally attributed to the strong negative feedback on the survival and perhaps fertility of individuals invoked by intra-specific density-related competition and predation [9] , [18] . In the case of Ae . vigilax , the lack of high tides during the low-abundance phase necessarily impedes the hatching of eggs , leading to low initial population densities as the season progresses . The low densities and accumulation of eggs therefore provide the opportunity for en masse hatching and high post-hatching survival once conditions become favourable , leading inevitably to high-magnitude peak in adult abundance . In conclusion , we have shown that basic environmental monitoring data can be coupled to relatively simple density-feedback models to assist in predicting the timing and magnitude of mosquito peaks which lead to disease outbreaks in human populations . Our results demonstrate this capacity for the control of a mosquito species in northern Australia which is responsible for many cases of infectious viral diseases . We propose that our model can be applied to any other mosquito populations where the appropriate monitoring and environmental data are available , so that optimal levels of control ( i . e . , least-cost measures bringing the largest decline in mosquito abundance ) can be implemented to alleviate suffering and save lives and money in tropical regions worldwide . | Mosquitoes carry several diseases that are potentially fatal to people . The risk of disease transmission is high when mosquitoes are abundant in an area , and it is therefore the job of health professionals to control or prevent mosquito outbreaks in certain areas , especially those close to human habitation . Biologists that study mosquito populations have the ability to predict peaks in mosquito population abundance by relating measures of these with environmental variables , such as tidal events and rainfall . Here we analysed data of mosquito ( Aedes vigilax ) populations from northern Australia over 15 years . We compared the highs and lows in mosquito numbers to possible drivers of these , such as tides . We found that low tide events prior to the mosquito peaks were followed by a boom in mosquito numbers . We also found the highest population growth rate is in September , which is two months earlier than the peak of mosquito abundance . Thus , following low tide events in the dry season , targeted control ( such as spraying in earlier September ) of mosquito breeding areas may allow for more effective control of mosquitoes close to human settlement , and therefore reduce the likelihood of disease outbreaks . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/viral",
"infections",
"ecology/population",
"ecology",
"public",
"health",
"and",
"epidemiology"
] | 2009 | Predicting the Timing and Magnitude of Tropical Mosquito Population Peaks for Maximizing Control Efficiency |
The clinical use of the anthracycline doxorubicin is limited by its cardiotoxicity which is associated with mitochondrial dysfunction . Redox cycling , mitochondrial DNA damage and electron transport chain inhibition have been identified as potential mechanisms of toxicity . However , the relative roles of each of these proposed mechanisms are still not fully understood . The purpose of this study is to identify which of these pathways independently or in combination are responsible for doxorubicin toxicity . A state of the art mathematical model of the mitochondria including the citric acid cycle , electron transport chain and ROS production and scavenging systems was extended by incorporating a novel representation for mitochondrial DNA damage and repair . In silico experiments were performed to quantify the contributions of each of the toxicity mechanisms to mitochondrial dysfunction during the acute and chronic stages of toxicity . Simulations predict that redox cycling has a minor role in doxorubicin cardiotoxicity . Electron transport chain inhibition is the main pathway for acute toxicity for supratherapeutic doses , being lethal at mitochondrial concentrations higher than 200μM . Direct mitochondrial DNA damage is the principal pathway of chronic cardiotoxicity for therapeutic doses , leading to a progressive and irreversible long term mitochondrial dysfunction .
Doxorubicin ( DOX ) is an anthracycline antibiotic with potent antineoplastic properties [1] . It has a broad-spectrum and is widely prescribed in the treatment of many types of cancers , including solid tumors and leukemias [2] . Yet , the clinical use of this drug is restricted by its severe side effects . DOX presents dose dependent , cumulative and irreversible cardiotoxicity that can lead to cardiomyopathy and ultimately congestive heart failure [3] . However , the underlying biochemical mechanisms of its toxicity are still not fully elucidated . Different processes are involved in DOX cardiotoxicity , including apoptosis , intracellular calcium dysregulation and myofibrillar detereoration , among others [4] . DOX cardiotoxicity is also strongly associated to mitochondrial dysfunction that leads to increased reactive oxygen species ( ROS ) production and cardiac oxidative stress [5] . DOX can inhibit the electron transport chain ( ETC ) by binding to cardiolipin which is present in the inner mitochondrial membrane [6] . As cardiolipin is required for normal ETC activity , this interaction leads to ETC inhibition [7] . During acute exposure , DOX also increases ROS production by undergoing redox cycling . The drug is capable of oxidising Complex I of the ETC , stealing electrons and transfering them directly to oxygen , producing ROS [8] . Furthermore , DOX acts as a topoisomerase II poison [9] and can form DNA adducts [10] , which can damage the DNA and inhibit gene transcription and DNA replication [11–14] . It has been suggested that these interactions can form vicious cycles that could continue operating even after the termination of the treatment , accumulating over time , and ultimately leading to bioenergetic failure [15] . DOX can potentially influence all elements of these cycles as depicted in Fig 1 where two possible vicious cycles can be observed . Vicious cycle one involves increased ROS levels which can inactivate the ETC and cause a further increase in ROS production . In vicious cycle two , increased ROS levels cause mtDNA damage which can lead to a downregulation of the ETC proteins that are mtDNA encoded , exacerbating mitochondrial dysfunction and ROS production . The objective of this study is to test the hypothesis that such vicious cycles could be formed by developing a computational model that quantitatively links alterations in ETC activity , ROS production and mtDNA damage with mitochondrial dysfunction . The goal is to quantify the contributions of each of these different toxicity pathways and possible vicious cycles to mitochondrial dysfunction associated with acute and chronic DOX cardiotoxicity .
To quantify the relevance of vicious cycle one depicted in Fig 1 , the acute effects of DOX were initially studied taking only ETC inhibition and redox cycling into account . This allowed us to investigate these two mechanisms in isolation and combined , prior to accounting for mtDNA damage . The predicted acute effects of redox cycling and ETC inhibiton at different concentrations of DOX can be observed in Fig 2 . This experiment consisted of introducing a constant concentration of the drug and performing a simulation until the model reached a steady state . This allowed us to quantify how mitochondrial function varies in the presence of different concentrations of the drug . Across all simulations we can observe similar features . For low drug concentrations , the ATP concentration and the membrane potential were only barely reduced , with an associated increase in the O2 consumption and concentrations of [•OH] , [ O 2 . - ] and [H2O2] . We can also observe that , for concentrations of up to 160μM , redox cycling is the main contributor for the increase in ROS , while for concentrations higher than that , the effect of ETC inhibition becomes dominant . For high drug concentrations , mitochondrial function gradually deteriorate until a threshold is reached and the mitochondria completely collapse . This causes a complete loss of membrane potential and ATP concentration , a sharp increase in the ROS concentrations and a reduction in the O2 consumption to residual levels , indicating that the dose may be lethal . This threshold happened at 480μM in the simulations including redox cycling only , 270 μM taking only ETC inhibition into account and 210 μM taking both into account . A series of dynamic simulations were performed to investigate if ETC inhibition and redox cycling could lead to any permanent alteration in mitochondrial function by forcing the mitochondria into a new steady state . In these simulations , time varying concentrations of DOX were used as an input to the model , respecting the drug pharmacokinectics . A fast absorption of the drug was assumed with half-time of 5 minutes while the elimination of the drug was considered to be slower with a half-time of 24 hours [16] as depicted in Fig 3 . Four supratherapeuthic doses were tested spanning a range of concentrations lower than the lethal dose of 210 μM , that was predicted from the simulations presented in Fig 2 . For low doses , mitochondrial function is only marginally affected , but at high doses , some significant variations can be observed . Mitochondrial function deteriorates as the dose increases , with a decrease in ATP concentration , membrane potential and NADH levels and an increase in ROS concentrations and O2 consumption . Additional simulations revealed that decreases in both the membrane potential and matrix pH equally contribute to an alteration in Complex IV activity which results in the increased O2 consumption observed . In all the dynamic simualtions , these effects are always temporary and all quantities return to their baseline values after the drug is fully eliminated from the system . This indicates that ETC inhibition and redox cycling , and thus the possible vicious cycle one in Fig 1 , are not sufficient to explain long term mitochondrial dysfunction associated with DOX . As our model including redox cycling and ETC inhibition was not capable of reproducing any permanent and long term alteration in mitochondrial function , the mtDNA model represented in Eq 1 was introduced to investigate if mtDNA damage , and vicious cycle two in Fig 1 , could explain the chronic toxicity of DOX . Prior to investigating the damaging effects of DOX to the mtDNA , we performed simulations to evaluate how mitochondrial function is affected by variations in the mtDNA content without including any DOX effect . In our model , as the mtDNA content is altered , the expression of all proteins and enzymes encoded by it are scaled , namely , Complexes I , III , IV and ATP synthase . In order to generate a phase plot and to predict the dependency of mitochondrial function on the mtDNA content , multiple experiments were performed by holding the mtDNA content constant at different values and simulating until a new steady state was achieved . The results of these simualtions can be seen in Fig 4 . We can observe that at baseline conditions with a mtDNA content of 0 . 75 , its time derivative , dmtDNA/dt , is equal to zero , in a stable condition with all the indices of mitochondrial function also at baseline . For mtDNA contents higher than baseline , no significant improvement in mitochondrial function is observed and dmtDNA/dt is negative . This is caused by an attenuation of the mtDNA repair activity such that intrinsic ROS production causes enough damage to mtDNA to reduce its content to baseline conditions over time . For mtDNA contents lower than baseline , dmtDNA/dt has a biphasic behaviour with a positive and a negative region . A stable region is observed for mtDNA contents between 0 . 75 and 0 . 73 . In this region , dmtDNA/dt is positive , as the mtDNA repair activity is greater than the mtDNA damage caused by ROS , causing the mtDNA content to recover back to baseline . An unstable region is observed for mtDNA contents lower that 0 . 73 . In this region , dmtDNA/dt is negative , as the increased ROS concentrations generate more mtDNA damage than the mtDNA repair system can handle . This indicates that 0 . 73 is a bifurcation point that is a threshold of how much mtDNA damage a mitochondrion can recover from . Any reduction in mtDNA content below 0 . 73 leads to a perpetuating and progressive decrease in mitochondrial function . It is also possible to observe that reductions in the mtDNA content also lead to an increase in the O 2 . - concentration . As the mtDNA content is reduced , O 2 . - production by Complex I is reduced , but a concomitant increase in O 2 . - production by Complex III is observed and the combined O 2 . - production monotonically increases . The superoxide production by Complex III is increased as a reduction in the density of this complex leads to a reduction in the rate of the reactions involved in the Q-cycle . This causes changes in the concentrations of the substrates involved in these reactions , including an increase in the semiquinone radical ion concentration . This increase in the semiquinone radical ion concentration consequently leads to an increase in the rate that this radical is oxidized by O2 , which is the source of O 2 . - production by Complex III . Fig 5 shows the model predictions for how mitochondria function is affected over time by different numbers of weekly doses of 1mg/kg of DOX , which are equivalent to doses of 30μM in our model . The red errorbars in the first panel are the experimental data points used to fit the model parameters [15] , and are related to in vivo measurements of mtDNA content after seven weekly doses of DOX in rats . With only one dose , we can already observe long term alterations in mitochondrial function , however , the mtDNA content is only slightly reduced and the mitochondria manage to recover through mtDNA repair . With four doses the damage is already large enough to trigger a vicious cycle , but the progression of the mitochondrial dysfunction is slow as the damage is relatively small and may not lead to observable symptoms . With seven doses and more , the damage is significant , triggering a fast degradation of mitochondrial function . We can observe a progressive reduction in the mtDNA content and ATP concentrations and an increase in ROS levels . The spikes observed in the curves are related to the DOX doses that have a peak while the drug is still in the system , but keeps a cumulative dysfunction even after the drug is eliminated due to the mtDNA damage . Our model also predicts that direct mtDNA damage by DOX is the main pathway that triggers this vicious cycle , being responsible for over 75% of the mtDNA content reduction during the acute stages of DOX intoxication . Free iron plays an important role in modulating DOX cardiotoxicity by serving as a catalyst to the formation of hydroxyl radicals through the Haber-Weiss reaction [17] . Iron chelators have demonstrated cardioprotection properties when co-administred with DOX as they bind to iron and eliminate this heavy metal from the body [18 , 19] . More specifically , co-administration of Dexrazoxane with DOX has been shown to prevent a rise in free iron levels observed when administring DOX in isolation , reverting this cardiotoxic effect and keeping the the free iron levels at baseline [20] . Co-administration of Dexrazoxane has also been shown to mitigate mtDNA damage and the loss of mtDNA content associated with DOX [21] . To test if our model is capable of capturing this protective property , we used a simplified model of chelating therapy by assuming that the free iron levels are kept constant at baseline during chelating treatment [20] . A detailed description can be found in the supplemental material . Our simulations reproduced the setup of this in vivo experiment where seven weekly doses of 0 . 8 mg/kg of DOX , which are equivalent to doses of 24μM in our model , were administred in rats , with and without the co-administration of iron chelators , and the mtDNA content was measured 37 weeks after the termination of the treatment [21] . Fig 6 shows the variation in the mtDNA content observed in these simulations , while the errorbars are the mtDNA content measured in the in vivo experiment . We can see that our model was able to capture the protective properties of the chelating therapy , although to a lesser extent than the ones observed in vivo . Our model also predicts that extending the chelating treatment to two of three times the duration of the DOX treatment might considerably increase the cardioprotection offered by further decreasing the loss of mtDNA content .
In this work , a biophysical model of the mitochondria was adapted to represent the cardiotoxic effects of DOX . Computational models have already been used to study mitochondrial dysfunction [22] , ROS generation [23 , 24] and ROS scavenging [25 , 26] . However , this is the first time , that this kind of models has been used to study drug cardiotoxicity . Three pathways of DOX cardiotoxicity were modeled , with all parameters constrained using experimental data , and their contributions to mitochondrial dysfunction were quantified . Our model predicts that although redox cycling is the main contributor to acute increases in ROS concentrations at clinically relevant concentrations , of approximatelly 30μM [16] , it has a minor role in DOX cardiotoxicity as any considerable loss of mitochondrial function can only be obeserved at much higher concentrations , as observed in Fig 2 . ETC inhibition also showed negligible effects at clinically relevant concentrations , however , when a critical mitochondrial concentration of 210 μM is reached , it is the principal mechanism for a sharp and rapid collapse in mitochondrial function . For doses higher than this critial concentration , the mitochondria are not able to sustain the membrane potential , which causes a collapse in mitochonrial function and depletion of ATP . These results are in agreement with experiments where mice treated with a single 15mg/kg dose of DOX , which correspond to a dose of 450μM in our model , were used to test the hypothesis of redox cycling mediated cardiotoxicity [14] . This experiment showed a reduction in ETC activity and a rapid depletion of ATP , followed by a decrease in the expression of myocardial ETC genes . Our results also showed that redox cycling and ETC inhibition alone are not capable of generating any long term alterations in mitochondria function , as depicted in Fig 3 . The chronic cardiotoxicity of DOX was only reproducible when taking mtDNA damage into account , which was necessary and sufficient to trigger a vicious cycle that leads to a progressive loss of mitochondrial function . These findings highlight the importance of dosing for in vivo and in vitro experiments when investigating DOX cardiotoxicity as the dominant toxicity pathways of acute therapeuthic dosing , acute supratherapeuthic dosing and chronic therapeuthic dosing could be different . To study chronic DOX toxicity , a novel mtDNA damage and repair model was proposed , including the subsequent alterations in the expression of mtDNA encoded proteins that was fit to experimental data [15] . This model was capable of reproducing the cumulative and progressive long-term effects of DOX toxicity in the time course of weeks and even years . We observed that the effect of a single clinical dose is not sufficient to lead to progressive mitochondrial dysfunction as the mitochondria manage to recover . However , mtDNA damage accumulates after sucessive doses and vicious cycle 2 depicted in Fig 1 is triggered . With mtDNA damage , the expression of mtDNA encoded proteins is reduced , leading to progressive mitochondria dysfunction until bioenergetic failure . As observed in Fig 4 , a mtDNA content reduction of approximately 5% with respect to baseline is enough to trigger a vicious cycle by moving mitochondria function from a stable to an unstable state . The assumptions made in the model , related to •OH production , potentially overestimate oxidative mtDNA damage ( see section S3 of the supplemental material for details ) , however , this is unlikely to alter the study conclusions as we identified direct damage to mtDNA by DOX as the main pathway to trigger the vicious cycle responsible for DOX chronic cardiotoxicity . It was quantified that direct mtDNA damage by DOX is responsible for over 75% of the mtDNA content reduction during the acute stages of intoxication . Although oxidative mtDNA damage by ROS has a secondary role during the acute stages , it allows this vicious cycle to be sustained after the chemotherapy treatment is completed and the drug has been eliminated . These results are in agreement with experiments that showed that cardiomyocite specific deletion of the gene encoding topoisomerase-IIβ , involved in mtDNA damage by DOX , protects cardiomyocytes from doxorubicin induced defective mitochondria and ROS formation [9] , while co-administration of ROS scavengers and antioxidants failed to prevent cardiac toxicity both experimentally [27] and clinically [28] . The only approved cardioprotective agent that has shown efficacy when co-administred with DOX in clinical settings is the iron chelator Dexrazoxane [28 , 29] . Our model was capable of capturing this protective property of iron chelator co-administration , which reduces the initial insult to mtDNA , as shown in Fig 6 . However , this protection is partial , not only because mtDNA damage by ROS has a secondary role during the acute stages , but also because , even when the iron levels are kept at baseline , an increase in the hydroxyl concentration and mtDNA damage by ROS is still observed as a consequence of increased peroxide concentrations . The model predicts that extending the iron chelating therapy to time periods longer than the DOX treatment can enhance this protective property . This generates a longer time period with reduced oxidative damage to mtDNA by ROS , allowing the mitochondria to repair more of the initial damage , potentially reverting the vicious cycle or at least slowing down the progression of dysfunction . All models are inherently simplifications and aim to represent the salient features of the underlying system . Here we discuss the limiations and assumptions of the models and the potential impact on the study conclusions . The repair systems of mtDNA are complex and still poorly understood , with mulitple mechanisms reported in the literature [30] . Due to the sparsity of experimental data available , a simplified model was adopted , with all mtDNA repair activity lumped into a single enzymatic term . Also , due to the limited data to constrain the model’s parameters , an additional 15 , 000 simulations were performed , exhaustively exploring the space of potential parameter combinations , to test if the study results were dependent on the specific parameter set evaluated . All of the evaluated parameter combinations , that generated results within the errorbars of the experimental data , support the conclusion that direct damage to mtDNA by DOX is the main toxicity pathway responsible for triggering the vicious cycle that leads to mitochondrial dysfunction . More details can be found in section S3 of the supplemental material . In the model , the expression of mtDNA encoded proteins was considered to be proportional to the mtDNA content . Although these quantities are correlated , there could be delays between the mtDNA damage and the reduction in the density of mtDNA encoded proteins , and this could play a role especially during the initial stages of the cardiotoxicity . Also , although redox cycling and oxidative damage to mtDNA are represented in our model , we do not take into account oxidative damage to any other structures or proteins . It is possible that the damage caused by the elevated ROS levels to other structures contributes to DOX cardiotoxicity . This may effect proteins , lipids and other pathways not represented in this model , including calcium dysregulation [31] and mitochondrial permeability transition [32] . It has also been proposed that DOX removes proginator cells that may contribute to a heart failure phenotype [33] . These may be contributing factors , however , the observed increase in ROS production and decrease in mtDNA content are consistent with the mitochondria playing a prominent role in DOX cardiotoxicity . Despite these limitations , this work presents a computational model for DOX mitochondrial cardiotoxicity that gives new insights into the drug’s toxicity mechanisms and cardioprotection alternatives and allows us to combine and evaluate multiple hypothesis concurrently within a common framework . The models developed here can be further used to test different DOX treatment protocols , cardioprotection strategies or to study the cardiotoxicity of other drugs . The framework of this study and the novel mtDNA damage and repair model developed here have applications even beyond drug cardiotoxicity , as mitochondrial dysfunction and mtDNA damage are associated with multiple other pathologies and applications such as heart failure [34] , cardiac and cerebral ischemia reperfusion injury [35 , 36] and aging [37] .
When present in the mitochondria , DOX binds onto cardiolipin in the mitochondrial membrane which in turn inhibits the complexes of the ETC . The activity of each of the four ETC complexes has been recorded in isolation at multiple concentrations of DOX , and the IC50 values have been reported in the literature [7] . In our model , this data along with corresponding fitted Hill coefficients , were used to construct dose dependent functions to scale the activity of each of the ETC complexes . More details can be found in section S1 of the supplemental material . Increased ROS production by redox cycling was represented by augmenting superoxide production by Complex I , which has been identified as the redox cycling site for DOX [8] . This increase in superoxide production was considered to be proportional to the concentration of the drug and fitted to experimental data [15] . In this experiment , a 7% increase in the superoxide concentration was measured two hours after the administration of 1mg/kg of DOX in rats . In humans , this dose is equivalent to a clinically relevant concentration of 37mg/m2 [40] which generate mitochondrial concentrations in the range of 5 to 30μM [16] . In this study , the redox cycling parameters were manually adjusted to generate a similar 7% increase in the superoxide concentration for a DOX dose of 10μM . More details can be found in section S2 of the supplemental material . To take into account the damaging effects of DOX in the mtDNA , we propose a new mass action model for mtDNA damage and repair . This model includes a variable for the mtDNA content , which was considered unitless and normalized . At baseline conditions the damaging term is equal to the repair term , keeping the mtDNA content constant at 0 . 75 [41 , 42] . The expression of all proteins and enzymes encoded in mtDNA was considered to be scaled by the mtDNA concentration . More specifically , Complexes I , III , IV and ATP synthase have their expression and protein densities scaled by the mtDNA content . We consider that when mtDNA is damaged , its content is reduced and if mtDNA is repaired , its content is increased as represented in Eq 1: d ( m t D N A ) d t = α · ( 1 - m t D N A ) ( 1 - m t D N A ) + κ - β · [ • O H ] n · m t D N A - γ [ D O X ] · m t D N A . ( 1 ) Where α is the mtDNA repair maximum rate , κ is the mtDNA repair half-saturation coefficient , β is the coefficient for mtDNA damage by ROS , [•OH]n is the normalized hydroxil radical concentration and γ is the coefficient for mtDNA damage by DOX . The first term of the equation represents the mtDNA repair system . As the mtDNA repair activity is conducted by enzymes , this term was considered to have an assymptotic behaviour [30 , 43] . If the mtDNA is damaged and its content is decreased , the repair activity increases until a saturation is achieved where the system is working at full power . The second term represents mtDNA damage by [•OH] which is a ROS capable of damaging DNA [44] . This highly reactive oxidant has been shown to be produced in biological systems through iron-catalyzed Haber-Weiss reaction , which make use of Fenton chemistry [45] . If [•OH] levels rise above baseline conditions , the mtDNA damaged rate is increased and its content reduced . The third and last term represents direct damage to the mtDNA by DOX and was considered to be proportional to the mtDNA content and the drug concentration . The model parameters were fitted using data from in vivo experiments [15 , 21] where mtDNA content reductions were measured after treating rats with seven weekly doses of DOX . An extended description of the model’s assumptions , parameters fitting and sensitivities can be found in section S3 of the supplemental material . | Doxorubicin is a potent anticancer drug , but its efficacy is limited by a cumulative dose-dependent cardiotoxicity . Multiple pathways are involved in the drug cardiotoxicity , however , the underlying mechanisms are still not fully elucidated . Here we developed a computational model to study doxorubicin mitochondrial cardiotoxicity , which allowed for the first time , a systematic test of different hypothesis in a unified framework . By quantitatively comparing the effect of multiple toxicity mechanisms , we could identify that electron transport chain is the main cause of acute toxicity , while direct damage to the mitochondrial DNA is the principal pathway of chronic cardiotoxicity . This is a crucial step in developing new antitumor therapies , toxicity screens and developing treatments to mitigate doxorubicin cardiotoxicity . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"mitochondrial",
"dna",
"dose",
"prediction",
"methods",
"toxicology",
"dna",
"damage",
"toxicity",
"pharmaceutics",
"forms",
"of",
"dna",
"mitochondria",
"dna",
"bioenergetics",
"p... | 2016 | A Biophysical Systems Approach to Identifying the Pathways of Acute and Chronic Doxorubicin Mitochondrial Cardiotoxicity |
Messenger RNA splicing is an essential and complex process for the removal of intron sequences . Whereas the composition of the splicing machinery is mostly known , the kinetics of splicing , the catalytic activity of splicing factors and the interdependency of transcription , splicing and mRNA 3′ end formation are less well understood . We propose a stochastic model of splicing kinetics that explains data obtained from high-resolution kinetic analyses of transcription , splicing and 3′ end formation during induction of an intron-containing reporter gene in budding yeast . Modelling reveals co-transcriptional splicing to be the most probable and most efficient splicing pathway for the reporter transcripts , due in part to a positive feedback mechanism for co-transcriptional second step splicing . Model comparison is used to assess the alternative representations of reactions . Modelling also indicates the functional coupling of transcription and splicing , because both the rate of initiation of transcription and the probability that step one of splicing occurs co-transcriptionally are reduced , when the second step of splicing is abolished in a mutant reporter .
The splicing of precursor messenger RNA ( pre-mRNA ) is an essential process in the expression of most eukaryotic genes . The five small nuclear ribonucleoproteins ( snRNPs ) and the many non-snRNP-associated proteins that constitute the splicing machinery , assemble anew on each precursor RNA to form the spliceosome complex that catalyses the two chemical reactions of splicing [1] . Both the spliceosome components and the spliceosome assembly process are largely conserved between human and yeast . The complexity of the spliceosome is indicated by the 170 proteins that are associated with it [1] . Adding to the complexity , splicing may occur partly , or entirely , concurrently with transcription . In eukaryotes , the interaction of the spliceosome with the precursor RNA can be considered to be an allosteric cascade in which early recognition steps induce conformational changes required for subsequent steps and for catalytic activation ( reviewed by [2] ) . However , the wealth of knowledge of molecular interactions , obtained mainly through extensive biochemical and genetic analyses , has yet to be formalised as a systems model of transcription and splicing . Spliceosome assembly is thought to occur via a series of events with many points of regulation [3] . In the first step , U1 snRNP binds to the 5′ splice site ( 5′SS ) , followed by the U2 snRNP at the branchsite . The U4 , U5 and U6 snRNPs join as a tri-snRNP complex and , after the association of other , non-snRNP proteins , the spliceosome complex is activated for the first chemical step of splicing . The 5′ splice site is cleaved and , simultaneously , the 5′ end of the intron becomes covalently attached to the branchsite to form a branched , lariat structure . In the second step , the 3′ splice site ( 3′SS ) is cleaved , which excises the intron , and the exons are joined to produce the mature mRNA . Between the two steps of splicing , a conformational change is required in the catalytic centre of the spliceosome [4] , and at several stages during the cycle of spliceosome assembly , splicing and spliceosome dissociation , proofreading mechanisms are thought to operate [5] . Nascent transcripts also have to be matured at their 3′ end , by cleavage and polyadenylation . Figure 1 A illustrates spliceosome assembly and the two steps of splicing for a pre-mRNA with one intron that has already been polyadenylated and released from the DNA template . Splicing can also occur co-transcriptionally , prior to 3′ end maturation ( Figure 1 B ) , and there is considerable experimental evidence for functional coupling of transcription , splicing and 3′ end maturation in vivo [6]–[12] . However , little is known about the impact of coupling on kinetic rates . Splicing has been modelled , but not to the same level of detail as transcription , and models of transcription have yet to fully incorporate the splicing reaction . Quantifying the dynamics of these processes remains a challenge [13] , and modelling may have an important role to play in distinguishing functional dependencies from coincidental and contemporaneous effects , and in identifying and characterising the interactions that effect coupling . Existing models of splicing have allowed splicing efficiency to be defined [14] , and have shown that transcription by RNA polymerase II ( Pol II ) greatly increases splicing efficiency in comparison with transcription by T7 polymerase [15] . A correlation between splicing efficiency and the pausing of Pol II on short terminal exons has been reported [11] . Splicing has been represented as a single irreversible reaction that creates the product mRNA from pre-mRNA [11] , [14] , and as a single irreversible reaction that creates mRNA from the pre-mRNA+spliceosome complex [15] . To-date , steps one and two of splicing have not been modelled as separate reactions , nor have the co- and post-transcriptional splicing pathways been distinguished . Further insights into splicing can be expected by more detailed modelling and analysis . As noted above , splicing can occur during messenger RNA transcription . Transcription begins with the assembly of the pre-initiation complex at the promoter . This complex includes Pol II , which , after initiation , begins the transcript elongation process that transcribes DNA into RNA . Early in elongation , the pre-mRNA is capped at its 5′ end by the capping enzymes . Elongation involves a sequence of many hundreds of individual polymerisation reactions , and hence the time required to complete the elongation of a transcript is predicted to have less variability than a single-step process with an equivalent rate [16] , [17] . The mature 3′ end of the RNA is formed by an endonucleolytic cleavage at the so-called poly A site and the newly formed 3′ end is extended by polyadenylation ( reviewed by [18] ) . The elongation process and the 3′ end formation steps can also be accounted for when modelling transcription [16] . The recruitment of Pol II enzymes and spliceosomal proteins are important steps in transcription and splicing , but are not believed to be rate limiting under normal conditions . Kinetic studies of Pol II complexes indicate that a minority of them are actively involved in transcription at any given time . The remainder move by diffusion through the nucleus [19] , as do the product mRNA molecules [20] . Three kinetically distinct populations of Pol II have been identified at the site of transcription; those bound to the promoter , those initiating transcription , and those engaged in elongation [21] . The movement of the spliceosomal proteins that catalyse the splicing reactions can be modelled as Brownian diffusion [22]: these RNPs move continuously throughout the nucleus independently of transcription and splicing . We have developed a stochastic model that represents splicing in the context of transcript elongation and RNA 3′ end maturation , as shown diagrammatically in Figure 1 C . ( All pathway models are provided as files in Dataset S1 . ) A stochastic formulation allows the effects of small numbers of molecules to be explored , and simulations of the model can be averaged in order to obtain the population mean over time . Experimental values for the model species ( population averages in copies/cell ) , including fully-spliced mRNA ( see Materials and Methods ) and two precursor species in both 3′ uncleaved and cleaved/polyadenylated forms , have been obtained by a rapid sampling protocol that is capable of capturing transient species [23] . We first describe the structure of the pathway , then present the data , and subsequently discuss alternative representations of the steps in the RNA pathway in the light of the data . The simplest description that might be adopted for the elongation , 3′ end formation and splicing steps is a single irreversible reaction . However , we find this provides a poor fit to the available data , and consequently a number of alternative representations for these reactions are considered . The extent to which the alternative pathways fit the data is assessed by the Akaike information criterion ( AIC ) for optimal parameter choices .
We propose a multistep model for transcription by dividing the gene into sections to be transcribed . Each section ( ) of the reporter DNA represents approximately 30 nucleotides , corresponding to the footprint of Pol II on the DNA [24] . As the length of the Ribo1 reporter ( described below ) used in the experimental studies is 1240 bases , we define 40 sections of DNA: . Each section of DNA can be occupied by at most one Pol II , and the progression of Pol II from the 5′ to the 3′ end of the gene is equated with successful extension of the transcript . The number of sections of DNA defines an upper limit on Pol II occupancy , and can limit the effective rate at which a Pol II can complete elongation . Beginning with the initiation of transcription , the reaction ( see Figure 1 C ) places a Pol II enzyme in the active promoter complex ( APC ) when the gene is active . Thereafter , this Pol II can progress along the gene at elongation rate ( the number of sections of DNA transcribed per unit of time ) . Letting the rate of polymerisation of nucleotides be ( the number of nucleotides incorporated per unit of time ) : . ( Equivalently , the mean time for n polymerisation events: , equals the mean time for one elongation event: ) . This multistep model of elongation is comparable with the kinetic model of Pol I elongation proposed in [25] . The pathway proposed here does not include a transition between active and inactive states of the promoter , as the rapid rate of mRNA production does not indicate that the promoter switches off during the period immediately after induction . However , such a transition is needed to explain the mRNA distribution in steady state [26] and can easily be included in this model . Kinetic competition between splicing and elongation has been discussed extensively [8] , [27] , [28] , and is modelled here as taking place at the sections of DNA after the branchsite . In these sections , the occurrence of the first step of splicing of an RNA is represented in the model by a change of state of the associated Pol II , which can make a transition to the co-transcriptional splicing path . Sections and represent the same n nucleotides of the DNA and so at most one of these sections can be occupied ( by at most one Pol II ) . The rate for the transition between splicing pathways is 0 prior to the completion of the splicing activation process . The splicing activation process is triggered at rate when the gene switches on . When splicing is active , the transition rate is , where is a constant that determines the ratio of the competing reactions ( elongation and splicing ) and thereby the probability of co-transcriptional splicing . Activation of co-transcriptional splicing involves co-transcriptional spliceosome assembly as well as the first step splicing reaction ( i . e . co-transcriptional spliceosome assembly alone is not sufficient ) . Each Pol II completes elongation either at , having completed the first step of splicing , or at having failed to do so . Subsequently , on the post-transcriptional path , 3′ end maturation ( ) produces polyadenylated pre-mRNA , step one of splicing ( ) produces polyadenylated lariat-exon2 , and step two of splicing ( ) produces mature mRNA and lariat , as indicated in Figure 1 C . On the co-transcriptional pathway , the second step of splicing ( ) produces uncleaved mRNA and lariat , and 3′ end maturation ( 3′ cleavage , polyadenylation and release; ) , produces mature mRNA . It is important to note that the species measured experimentally are pre-mRNA , lariat-exon2 ( the branched lariat structure ) and mRNA , and that the uncleaved and polyadenylated forms can also be distinguished ( as illustrated in in Figure 1 C ) . The assays for these species are described below . Initial estimates for some parameters can be obtained from the literature: the rate of initiation of transcription in yeast has been estimated as [29] , [30] . Polymerisation rates in the mammalian nucleus of up to 72 nucleotides/s have been reported for polymerases that do not pause . This is a significant increase on earlier estimates of 18–42 nucleotides/s [13] that may reflect an average or effective rate . A Pol I elongation rate of 90 nucleotides/s has been reported [25] . The time for pre-mRNA 3′ cleavage in HIV-1 has been reported to be 55 s , with release taking 9 s [16] . The probability of co-transcriptional splicing is not known , and this , along with precise values for all other parameters , will be inferred from fitting the pathway to the data . The pathway was developed to explain data from the Ribo1 reporter [23] . Ribo1 is a chimeric yeast gene that contains the single intron from the ACT1 gene and the 3′ end processing signal from PGK1 , as shown in Figure 2 A ( modified from [31] ) . The reporter gene is integrated in the genome , transcribed under the control of a doxycycline-responsive promoter in a doxycycline-inducible strain of Saccharomyces cerevisiae . By modelling splicing in this reporter , we aim to define the splicing pathways and to quantify reaction rates . The impact of splice site mutations on the coupling between splicing and transcription can also be explored . Three replicate experiments were performed in which doxycycline was added to a culture to induce reporter gene expression , and transcript levels were measured by reverse transcription and real-time quantitative PCR ( RT-qPCR; see Materials and Methods ) . A time series of values was obtained for accumulation of pre-mRNA , lariat-exon2 , and mRNA . The RT-qPCR data were converted to copies per cell ( see Materials and Methods; [23] ) , which allows a quantitative comparison of data obtained for the different RNA species and from different cultures . The merged time series derived from three biological replicates for Ribo1 is shown in Figure 2 B ( referred to as Expt 1 ) . In the 120 s interval 420 s–540 s after the addition of doxycycline to the cell culture , the level of Ribo1 mRNA increases from 11 to 45 copies/cell ( Figure 2 B ) . Messenger RNA then reaches 60 copies/cell , on average , 180 s later . The high level of mRNA is notable , as is the rapid rate of transcript synthesis . The delay between the rise in pre-mRNA and the rise in mRNA may indicate a slow , or delayed , splicing reaction . In the substantive phase of transcriptional activity ( after 420 s in Figure 2 B ) , the levels of pre-mRNA and lariat intermediate are only a fraction of the mature mRNA species which shows that the first and second steps of splicing must be rapid . To investigate the effects of blocking the first or second step of splicing , two modified Ribo1 reporters were created with point mutations at the 5′ splice site ( 5′SSRibo1 ) or 3′ splice site ( 3′SSRibo1 ) respectively [23] . The mutant reporters were induced with doxycycline and the splicing intermediates detected using the primers shown in Figure 2 A . The merged time series for 3′SSRibo1 and 5′SSRibo1 are shown in Figure 2 C and 2 D respectively . As indicated by the error bars in Figure 2 C , the synthesis of lariat-exon2 in the mutant reporter varied between biological replicates , but technical error within each replicate remained at typical levels . The level of pre-mRNA measured in the modified reporters is greater than was observed for Ribo1 . This may be attributed to changes in the rates of transcript synthesis , splicing step one or degradation , and modelling can help to resolve this question . For practical reasons , co-transcriptional splicing is defined here as splicing that is completed before the transcript has been released from the transcription complex by 3′ end cleavage . The data shown in Figure 2 were produced using a cDNA primer that hybridises to exon2 ( at position C1 in Figure 2 A ) , which does not distinguish between transcripts that are cleaved and polyadenylated or uncleaved at the 3′ end . Therefore , in order to differentiate between these species and to estimate the rates for 3′ end formation , a modified 3′ cleavage assay used two alternative primers for cDNA synthesis from 3′ end sequences of Ribo1; oligo ( dT ) , anneals to cleaved and polyadenylated transcripts , whereas primer C2 is complementary to a sequence downstream of the mapped 3′ end cleavage sites ( Figure 3 A; [23] ) . By amplifying these cDNAs with specific primers for detection of pre-mRNA , lariat-exon2 and mRNA ( Figure 3 A ) , uncleaved and cleaved/polyadenylated pre-mRNA , lariat-exon2 and mRNA were successfully distinguished in Expt 2 ( Figure 3 B and 3 C ) . The 3′ cleavage assay detected a sharp , transitory peak in uncleaved pre-mRNA at 540 s , followed by a similar peak in polyadenylated pre-mRNA 30 s later ( Figure 3 B ) . This indicates pre-mRNA that is not spliced prior to 3′ end cleavage , i . e . is not spliced co-transcriptionally . However , the rapid accumulation of uncleaved mRNA between 540 and 600 s prior to detection of polyadenylated spliced mRNA at 600 s , clearly shows that co-transcriptional splicing occurs before post-transcriptional splicing . By formally modelling the splicing pathway , we aim to quantify the extent to which mature mRNA is derived from co-transcriptional splicing , and from post-transcriptional splicing respectively . The reactions in the model must be enabled ( switched on ) progressively in order to explain the data . Following the induction of transcription by doxycycline , a burst of pre-mRNA is postulated to occur . At this time , splicing is not active , and additional transcripts are not initiated . These initial pre-mRNAs are cleaved and polyadenylated , and may then splice or degrade . This process explains the accumulation of pre-mRNA in Figure 2 B , and the peak in uncleaved pre-mRNA in the 3′ cleavage data in Figure 3 B . After a delay ( defined by the rate ) , splicing steps 1 and 2 and the initiation of new transcripts start . This explains the drop in pre-mRNA in Figure 2 B , and the peak in polyadenylated pre-mRNA ( Figure 3 B ) as the activation of splicing removes these species also . Figure 1 in Text S1 illustrates the sequence of events . The proposition that there are advantages to modelling elongation in detail can be tested . Pathways that include 40 steps of elongation are compared with simpler pathway models where competition between elongation and splicing step one is represented by two reactions and that have APC as the substrate and and as the respective products . The proportion of co-transcriptional splicing can be calculated from these reaction rates and this proportion can be compared with that predicted for the 40 step model ( as defined by equation 1 in Materials and Methods ) . The total time allocated to elongation can also be compared in the alternative models . On completion of elongation , the RNA transcripts undergo 3′ end formation . This involves cleavage , polyadenylation and transcript release , and requires three multi-subunit factors [32] . Polyadenylation adds up to approximately 250 nucleotides to the end of the transcript . Hence , it is uncontroversial to model 3′ end formation as a multi-step process as many steps of maturation are clearly required . When fitting the splicing pathway to the Ribo1 data , a much better qualitative and quantitative fit is obtained when 3′ end maturation is modelled as a five-step process ( each of the five steps has rate ) in comparison with a single step model . The characteristics of 3′ uncleaved spliced RNA also fit the data better by modelling 3′ end formation in this way . As shown in Figure 3 C , uncleaved mRNA quickly peaks towards its steady state of 10 copies/cell rather than making a slow progression to this level . Replacing the single step model with the 5 step model of 3′ end maturation ( reactions and ) significantly improves the fit to the data . It is easily shown that a process of five steps , each at the same rate , has a kinetic response that differs significantly from that of a single step . ( The distribution of waiting times follows a gamma distribution rather than an exponential distribution . ) We do not aim to determine the exact number of steps , rather we aim to test whether a process of multiple steps of maturation or senescence provides a better quantitative and qualitative explanation than a single reaction . Henceforth , we assume that 5 steps constitute an adequate model of a multi-step process for the purpose of testing this hypothesis . Text S2 presents an analysis of Ribo1 degradation kinetics that further illustrates the approach . Genetic studies have identified many splicing factors , but their impact on splicing kinetics in-vivo is difficult to quantify . These factors , and the five snRNPs , are not believed to be rate-limiting and have not been included in the model: We initially consider the kinetics of the splicing intermediates alone . However , we find once more that simple unimolecular models for steps one and two of splicing do not fit the data well . Consequently , we propose two alternative characterisations of the splicing reactions prior to steady state , and quantify the extent to which these models improve the fit of the pathway to the data . The first alternative model of splicing we propose represents these processes as a sequence of several reactions that reflect the many known steps of spliceosome assembly . The precursors and products of multi-step processes show sharp transitions in their kinetics , as observed for pre-mRNA and lariat-exon2 in the experimental data . A multi-step model of this kind has been shown to explain fluorescence recovery after photobleaching ( FRAP ) data obtained from a splicing reporter in mammalian cells [33] . The second alternative explanation of the rapid processing of pre-mRNA and lariat-exon2 that we propose is based on the proposition that the splicing reactions are catalysed in a manner such that the propensity of the reaction increases on successive splicing events . It is necessary for the initial propensity to be low as otherwise no accumulation of splicing precursor or lariat intermediate would be observed , and for the propensity to increase to remove the accumulation rapidly . The reduction of uncleaved lariat-exon2 over the period of time when mRNA increases rapidly ( 600 s-700 s in Figure 3 C ) may indicate such an increase in reaction propensity: the substrate decreases while the rate of increase of product remains constant . It therefore appears that step two of splicing may not be governed by first-order kinetics when it is co-transcriptional . The observations can be modelled by positive feedback in the splicing reaction . This requires the involvement of additional molecular species in the splicing reaction - the enzyme Y - a role that can be played by factors required for step two of splicing . The following positive feedback mechanism has the property of increasing reaction propensity: Let the enzyme Y have an initial copy number of 1 , and increment the copy number on each splicing event to effectively increase the propensity . The enzyme contributes to the reaction propensity according to the formula for bimolecular reactions ( ) . The positive feedback model is proposed for the kinetics of high rates of induction prior to steady state . Due to the uncertainties in pathway structure , and parameter identifiability and estimation , it is of considerable value to explore multiple models of a biological system [34] . The goodness of fit to the experimental data of eight versions of the RNA processing pathway is compared in Table 1 . The alternative pathways are distinguished by their representation of elongation , of co-transcriptional splicing step two , and of post-transcriptional splicing steps one and two . Elongation is modelled either as a single step or as 40 steps , as described above . The alternative models considered for the splicing reactions are: a single step , multiple steps ( each at the same rate ) or positive feedback . It is important to consider each pathway as a whole as the goodness of fit for each observed species is dependent on the reactions that act directly on the observed species , and those that act on its precursors and thereby shape the kinetics of the precursors . Table 1 defines each pathway and lists the AIC scores obtained using the optimal parameters ( see Table 1 in Text S1 for the parameter values ) . Note that pathway slowromancap VIII makes the simplest assumptions about elongation and splicing steps , namely that they are single-step unimolecular reactions , and that the poor fit of this pathway to the data motivates the search for alternative models . Pathway parameters were optimised by a simulated annealing algorithm ( see Materials and Methods; [35] ) that identified the best fit between each pathway and the nine data series obtained for Ribo1 ( those plotted in Figure 2 B , 3 B and 3 C ) . The total AIC ( defined in Materials and Methods ) is calculated from the combined residuals from all species/experiments . All data and pathway models are provided as files in Dataset S1 . An executable version of the Dizzy simulator [36] is also provided to allow the models to be executed . The AIC scores for pre-mRNA , lariat-exon2 and mRNA are represented separately in the columns of the heat map in Figure 4 . It is apparent from the A-pre-mRNA column that all pathways fit well to the pre-mRNA data in Expt 1 , and fit to a comparable extent . The majority of pathways also fit the mature mRNA data well ( A-mRNA and P-mRNA columns ) . Pathways I-IV can be optimised to the lariat-exon2 data simultaneously . In contrast , pathways V-VIII have a poor fit to one or more of the lariat-exon2 species . Pathway I has the best overall AIC as a result of fitting the nine data series most consistently . Pathways I-IV incorporate the feedback mechanism for co-transcriptional splicing step two and this feature correlates with a good fit ( low AIC ) for all lariat-exon2 species . Within these pathways , a multi-step representation of post-transcriptional splicing , combined with a multi-step representation of elongation has the best overall score ( pathway I ) . Feedback in post-transcriptional splicing , combined with a multi-step representation of elongation also explains the data well ( III ) . Pathway VII is ranked in third place , failing to explain the polyadenylated lariat-exon2 data in Expt 2 ( as indicated by the white cell in row VII , column P-lariat-exon2 in Figure 4 ) . The predictions of the pathways for each of the nine species measured in Expt 1 and 2 are plotted in Figure 2 in Text S1 . Important qualitative differences between the pathways can be seen in these graphs . Pathways with a single elongation step require an initiation rate of 0 . 4 , and a rate for elongation of 0 . 4–0 . 54 , giving an implausible time of 2–3 s for the elongation of a 1240 nucleotide gene . As a consequence of defining a more realistic elongation time , pathways with a multi-step representation of elongation typically fit the data better , see Table 1 . In pathway I , pre-mRNA 3′ end maturation takes 35 s and uncleaved 3′ end mRNA maturation takes 49 s using the measure of the time taken for the sum of intermediate species undergoing the five steps of 3′ end processing to reduce by half ( an equivalent to the half life of a single step reaction ) . The completion of splicing co-transcriptionally in yeast has been a topic of debate . Genome-wide ChIP studies indicated that co-transcriptional spliceosome assembly may not have time to occur if the 3′ exon is short [28] . More recent studies provide evidence for polymerase pausing 3′ of introns , suggesting a mechanism to slow transcription , allowing more time for splicing [10] , [11] . With Ribo1 we observe that the initial burst of 3′ uncleaved pre-mRNA is not spliced before it is 3′ end cleaved , as shown by the successive blue and purple peaks in Figure 3 B , and it may undergo post-transcriptional splicing . After this initial burst , the majority of transcripts splice co-transcriptionally , as seen by the accumulation of uncleaved lariat-exon2 and uncleaved mRNA prior to cleaved/polyadenylated mRNA ( red , green and black , respectively in Figure 3 C ) . Optimisation of pathway I computes to be 11 . 39 , and by substituting this value into equation 1 ( see Materials and Methods ) it follows that 12% of Ribo1 RNA transcripts splice post-transcriptionally , and 88% of transcripts splice co-transcriptionally . Values for in pathways III , V and VII imply the same proportion of co-transcriptional splicing , as do the values of and in the four remaining pathways where the proportion of co-transcriptional splicing is approximately 85% . Pathways I-IV show a good qualitative fit to the uncleaved lariat-exon2 data ( see Figure 2E in Text S1 ) . All four pathways specify a positive feedback mechanism for with estimated rate constants in the range 0 . 0061–0 . 0068 ( see Table 1 in Text S1 ) . In pathway I , the half life of this reaction is 110 s for the first transcript to splice , and , with feedback , the half life reduces to 5 . 5 s at 670 s after induction . As , initially , the half life is much greater than the time to transcribe exon 2 ( approximately 11 s ) , the decision to model the second step of splicing as a process that occurs after elongation is justified . The feedback mechanism may be a result of the disassembly and recycling of the snRNPs of the spliceosome for subsequent rounds of splicing [37] . It has been proposed that the branchpoint binding protein ( BBP ) and Mud2 are recycled between two steps in pre-spliceosome assembly: BBP is released during or after the second step and efficiently recycled to promote the first [38] . The finding that snRNPs do not assemble on a nascent transcript in response to a signal , but move randomly [22] , does not preclude them impacting on splicing kinetics in a transcription-dependent manner through an influence on rates of spliceosome assembly , disassembly or recycling . Maintenance of the transient Cajal body ( responsible for the maturation of snRNPs ) requires continuous recycling of pre-existing snRNPs after each round of spliceosome assembly [22] , and may therefore be indirectly dependent on transcription when splicing is co-transcriptional . If recycling mechanisms existed for second step factors , increasing the effective second step reaction rate , this could explain the peak and dip in uncleaved lariat-exon2 in Expt 2 . The allosteric effects of second step splicing factors would provide an alternative explanation . Pathways I and III specify a multi-step representation of elongation and feedback in co-transcriptional splicing . They account for 99% of the probability mass available in the Akaike weight analysis ( see Table 1 ) . These two pathways differ on the post-transcriptional splicing mechanism: a multi-step representation is more probable ( P = 0 . 845 ) but a feedback mechanism cannot be ruled out ( P = 0 . 152 ) . As pathway I has a better fit to the polyadenylated pre-mRNA and polyadenylated lariat-exon2 data ( the precursor and products of post-transcriptional splicing ) , we tentatively conclude that post-transcriptional splicing has multi-step characteristics . The difficulty in modelling the post-transcriptional splicing process lies in its transient activation . The characteristic features of the feedback mechanism are not clearly revealed . For a multi-step model , the times for the sum of intermediate species undergoing splicing to reduce by half are 34 s for step one and 36 s for step two of splicing . The 3′SSRibo1 data are explained by a variation of the transcription and splicing pathway where step one of splicing can be co-transcriptional or post-transcriptional ( as in the full pathway ) , but where the lariat-exon2 species goes through the five-step cleavage process ( at rate ) instead of step two of splicing ( = 0 and = 0 ) . The pathway used to explain the 5′SSRibo1 data has no co-transcriptional splicing path ( cannot be reached ) , and no post-transcriptional splicing can occur ( ) . The induction of the 3′SSRibo1 reporter ( Figure 2 C ) shows a greater accumulation of pre-mRNA than observed for Ribo1 . The lariat-exon2 product is not spliced , but accumulates and is subject to degradation . The data can be explained by pathway I using the rate inferred for Expt 1 and 2 . However , to predict the pre-mRNA response is increased to 30 , is reduced to 0 . 015 , and is reduced to 0 . 175 . The probability of step 1 occurring co-transcriptionally is therefore reduced to 56% compared with 88% in Ribo1 , the time taken for splicing to become active increases two fold , and the rate for the initiation of transcription reduces to 70% of the rate in Ribo1 . The prediction for lariat-exon2 is greater than observed , and this may indicate that 3′ end maturation and/or lariat-exon2 degradation pathways differ in the mutant reporter . The induction of 5′SSRibo1 ( Figure 2 D ) shows that pre-mRNA accumulates and does not splice . The response can be explained by further reduction in to 0 . 1 , that is , 40% of the rate in Ribo1 . The induction of 3′SSRibo1 was repeated using the primers of Expt 2 in order to validate the finding that the probability of co-transcriptional splicing is reduced . The new data are shown in Figure 3A in Text S1 . The pathway model predicts only a slow removal of the accumulating uncleaved pre-mRNA ( and consequently of polyadenylated pre-mRNA ) that is consistent with the new data . In contrast , the large reduction in pre-mRNA that is predicted when the rate for is 11 . 39 ( as inferred for Ribo1 ) does not fit the new data , see Figure 3B in Text S1 . The overestimation of lariat-exon2 by the model ( Figure 2 C ) might be explained by a significant underestimation of the degradation rate for this species . This rate has been determined in the 3′SSRibo1 ‘OFF’ strain where transcription is halted by doxycycline , see Text S2 . ( A second experiment using alternative primers confirms this result [23] . ) Alternatively , the assumption made when modelling 3SSRibo1 that uncleaved lariat-exon2 would be able to complete 3′ end maturation and contribute to the total population of polyadenylated lariat-exon2 may be incorrect . Modelling shows that polyadenylated lariat-exon2 may be the product of polyadenylated pre-mRNA alone , with no contribution from the co-transcriptional pathway .
Despite the biochemical and genetic evidence for multiple steps in the cycle of splicing events , previous in vivo studies of mRNA splicing kinetics have revealed simple first-order monomolecular reactions that exclude the action of a catalyst . The allosteric cascade is yet to be revealed at the systems level , either in terms of the existence of multiple steps , or the impact of enzyme kinetics , and we argue that this is due to the course-graining phenomena associated with stochastic processes [39] and to the lack of experimental quantification of mRNA and its precursors . Using rapid sampling of cultures , combined with RT-qPCR assays that detect the intermediates and products of the splicing reaction in a way that permits quantitative comparisons between different RNA species and between different cultures , we are able to present kinetic data with an unprecedented level of resolution , monitoring pre-mRNA production , the two steps of splicing and 3′ end processing of a reporter transcript in yeast . Our data cannot be explained satisfactorily by single-step unimolecular splicing reactions . We conclude that a systems model of transcription and splicing must distinguish the two steps of splicing , account for their occurrence co- and post-transcriptionally , represent spliceosome assembly , and include the action of an additional partner in the splicing reactions , as we find evidence in the data for each of these processes . While developing the model , we considered including a transition from uncleaved lariat-exon2 to polyadenylated lariat-exon2 , which would permit pre-mRNAs that have already undergone the first step of splicing co-transcriptionally to undergo 3′ end maturation . However , when this transition was added to model I , the AIC was found to increase by 1 . 4 ( after optimisation ) , meaning model I fits the data better without the additional transition . The proposed transition occurs very slowly , and consequently rarely , does not assist modelling the data , and , therefore , was excluded from the models we analysed further . The model proposed here specifies that pre-mRNAs that have already undergone the first step of splicing co-transcriptionally will be fully spliced co-transcriptionally prior to 3′ end cleavage . This is in contrast with the mammalian model proposed in [40] where splicing is completed after 3′ cleavage ( in HeLa nuclear extracts ) . Both models stipulate that partially-spliced transcripts are not released from the DNA , and both allow for a post-transcriptional splicing pathway . Our model is consistent with the recycling of splicing factors [3] . Recycling of BBP and Mud2 has been proposed for pre-spliceosome formation [38] , and similar mechanisms may exist for subsequent spliceosome assembly steps . Alternatively , it has been proposed that an increase in the local concentration of splicing factors is linked to transcription via the C-terminal domain of Pol II [15] . Nuclear speckles may also have a role in keeping spliceosomal components concentrated near nascent transcripts [37] . Cooperativity in the interaction of splicing factors with the spliceosome or with the nascent pre-mRNA may also contribute to the kinetics of co-transcriptional splicing . Addressing the interdependency between RNA processing steps , modelling indicates that mutations at the 3′ and 5′ splice sites reduce the rate of initiation of transcription , and , in the 3′SS mutant , reduce the probability of step one of splicing occurring co-transcriptionally . Quantitative analysis of the mutant data requires establishing a parameterised model for the ‘wild type’ in order to define and test the alternative explanations of the differences observed . A half life for splicing in HeLa cell nuclear extracts of 23 min ( splicing rate of 0 . 03/min ) has been reported [15] . In vivo half-lives of 6–12 min have been reported in mammalian cells [41] , as have estimates of 5–10 min for the completion of splicing after intron synthesis [42] . Half lives for splicing in the range 0 . 4–7 . 5 min have also been reported for the splicing of introns in mammalian cells [43] . The inferred rates for post-transcriptional splicing in Ribo1 equate to half lives of 0 . 6 min for each of steps one and two , and are at the faster end of the spectrum reported in [43] . On the co-transcriptional pathway , splicing step one is concurrent with the transcription of the 800 bases from the branchsite until the polyA site ( taking approximately 11 s ) . Co-transcriptional step two occurs with a half life of 110 s for the first transcript , and , with feedback , the half life reduces to 5 . 5 s at 670 s after induction . Therefore co-transcriptional splicing is the more efficient pathway under the high induction conditions studied here . This study proposes a mechanistic kinetic model that represents some of the complexity and flexibility of the splicing pathway that is known from biochemical and genetic studies [3] . Co-transcriptional splicing is evident in the data , and modelling shows that this pathway may be activated after a delay . Furthermore , the second step of splicing benefits from positive feedback when co-transcriptional . These could be explained by the coordination of splicing factor recruitment/recycling with transcription , possibly facilitated by polymerase pausing [10] , [11] and/or dynamic chromatin modification [9] , [44] , [45] .
To analyse the transcription , splicing , degradation and 3′ end formation of yeast pre-mRNA , the Ribo1 reporter was integrated into the yeast genome at the his3 locus . The reporter is based on a hybrid ACT1/PGK1 gene [31] , modified as described in [23] by inserting two copies of the boxB sequence ( 57 bp each ) in the ACT1 intron , enabling it to be readily distinguished by RT-qPCR from the endogenous ACT1 intron without affecting splicing . Primer pairs were created to measure the unspliced pre-mRNA ( 5′ primer upstream of ATG , 3′ primer over the exon 1 - intron junction ) , the lariat-exon2 intermediate ( 5′ primer upstream of 3′end of intron , 3′ primer over exon 2; the pre-mRNA level was subtracted from this measurement ) and the spliced mRNA ( 5′ primer upstream of ATG , 3′ primer over exon 2 ) . Measurements of mRNA in copies per cell , averaged over a population , were obtained by carefully quantifying the efficiency of cell lysis , recovery of RNA , reverse transcription and qPCR . For full details see [23] . The first step of post-transcriptional splicing , and all transitions to the path , decrease pre-mRNA and increase lariat-exon2 . The second step of splicing decreases lariat-exon2 and increases spliced mRNA , according to the pathway . All species in the pathway , with the exception of the excised intron product of step two , are measurable by RT-qPCR , provided that they extend beyond the position of the cDNA primer . Splicing events on transcripts that have not been elongated to the cDNA point are not detected until this sequence is transcribed , and the calculation of RT-qPCR signal intensity from the species in the pathway reflects this . For example , the ( simulated ) pre-mRNA signal is not incremented until the species is incremented , despite the PCR primers for pre-mRNA being located several hundred bases upstream . Considering a single Pol II complex ( and ignoring the effect of other Pol IIs on its movement ) , the probability of transitioning from states to the co-transcriptional path is simply calculated from the elongation rate and the transition rate . This choice can be made 25 times , allowing the probability of the Pol II exiting on the post-transcriptional pathway to be estimated independently of by: ( 1 ) Unless otherwise stated , reaction rates are expressed as the probability density per unit time , per distinct combination of reactant molecules . Where there is a single reactant species , the number of distinct combinations is just the population of reactants . The half life is the time a molecular species takes to reduce by half , and is computed for unimolecular reactions by in units of seconds . Pathway models were optimised by the simulated annealing algorithm specified in [35] ( see Figure 1 ) . Following [35] , the error E is defined by equation 2 where S is a time series simulated from a pathway model , D is the observed data , n is the number of time points and d the number of dependent variables ( the dimension of and is d ) . ( 2 ) On each iteration of the algorithm , each parameter is assigned a new value ( ) and the error for the new set of parameters ( ) is calculated from a simulation of the model using the updated parameter set . The new parameter value is always accepted if , otherwise it is accepted with probability , where T is the current temperature and E is the error of the current parameter set . The new parameter value is generated from the current value by adding a normally-distributed random value . We define the scale constant k in equation 3 using the error of a set of parameter values that are given as input at the start of optimisation ( these must provide an approximate fit to the data ) , and then update each parameter value according to equation 4 , where N ( 0 , 1 ) is a normally-distributed random value ( mean 0 , standard deviation 1 ) and and are the maximum and minimum values respectively that is allowed to take . See [35] for further details . ( 3 ) ( 4 ) The Akaike information criterion ( AIC; eqn . 5 ) was used to assess the fit between a time series S simulated from a pathway model of k optimised parameters , a data set D of n values [46] . Assuming normally distributed errors , AIC can be computed from the model residuals ( eqn . 6 ) [47] . The values for total AIC incorporate the 2 k penalty for the number of parameters optimised . ( 5 ) ( 6 ) When comparing m pathway models , the Akaike weight w of model i can be defined in terms of the relative likelihood , where is the difference between the AIC for model i and the AIC of the best model [47] . Akaike weights computed by equations 7 and 8 are listed in Table 1 . ( 7 ) ( 8 ) | The coding information for the synthesis of proteins in mammalian cells is first transcribed from DNA to messenger RNA ( mRNA ) , before being translated from mRNA to protein . Each step is complex , and subject to regulation . Certain sequences of DNA must be skipped in order to generate a functional protein , and these sequences , known as introns , are removed from the mRNA by the process of splicing . Splicing is well understood in terms of the proteins and complexes that are involved , but the rates of reactions , and models for the splicing pathways , have not yet been established . We present a model of splicing in yeast that accounts for the possibilities that splicing may take place while the mRNA is in the process of being created , as well as the possibility that splicing takes place once mRNA transcription is complete . We assign rates to the reactions in the pathway , and show that co-transcriptional splicing is the preferred pathway . In order to reach these conclusions , we compare a number of alternative models by a quantitative computational method . Our analysis relies on the quantitative measurement of messenger RNA in live cells - this is a major challenge in itself that has only recently been addressed . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"systems",
"biology",
"rna",
"processing",
"biophysic",
"al",
"simulations",
"gene",
"expression",
"genetics",
"molecular",
"genetics",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics"
] | 2011 | Modelling Reveals Kinetic Advantages of Co-Transcriptional Splicing |
TDP-43 proteinopathies have been observed in a wide range of neurodegenerative diseases . Mutations in the gene encoding TDP-43 ( i . e . , TDP ) have been identified in amyotrophic lateral sclerosis ( ALS ) and in frontotemporal lobe degeneration associated with motor neuron disease . To study the consequences of TDP mutation in an intact system , we created transgenic rats expressing normal human TDP or a mutant form of human TDP with a M337V substitution . Overexpression of mutant , but not normal , TDP caused widespread neurodegeneration that predominantly affected the motor system . TDP mutation reproduced ALS phenotypes in transgenic rats , as seen by progressive degeneration of motor neurons and denervation atrophy of skeletal muscles . This robust rat model also recapitulated features of TDP-43 proteinopathies including the formation of TDP-43 inclusions , cytoplasmic localization of phosphorylated TDP-43 , and fragmentation of TDP-43 protein . TDP transgenic rats will be useful for deciphering the mechanisms underlying TDP-43–related neurodegenerative diseases .
TAR DNA-binding protein ( TDP-43 ) is a highly conserved ribonucleoprotein that is encoded by the TDP gene and can bind to RNA , DNA , and proteins [1]-[3] . In mammals , the primary transcript of the TDP gene can be alternatively spliced to generate 11 mRNA molecules . The major splice variant is full-length and encodes TDP-43 [4] . While the functions of this complex molecule remain largely unknown , ubiquitinated and phosphorylated TDP-43 accumulates in the nucleus and cytoplasm of affected cells in sporadic amyotrophic lateral sclerosis ( ALS ) and frontotemporal lobe degeneration ( FTLD ) [5] , [6] . TDP-43 resides predominately in the nucleus and its translocation to the cytoplasm appears to be an early event in the pathological process underlying sporadic ALS [7] . At the end-stages of sporadic ALS and FTLD , C-terminal fragments of TDP-43 are remarkably increased in the brain [5] , [6] , but the full-length protein remains the major species in spinal cord [8] , suggesting that regional differences exist in the metabolism and pathological mechanisms of TDP-43 . Although TDP-43 proteinopathies have been identified in a wide range of neurodegenerative diseases including sporadic ALS , FTLD , Alzheimer's disease , and dementia with Lewy bodies [5]–[9] , TDP-43 inclusions have not been detected in familial ALS caused by mutation of the SOD1 and FUS genes [10]–[13] . These findings imply that TDP-43 proteinopathy is common to neurodegenerative diseases and that divergent pathological processes may underlie sporadic and familial cases of ALS . Mutations in the TDP gene segregate with ALS and FTLD associated with motor neuron disease ( FTLD-MND ) in geographically unrelated families [14]–[18] , suggesting that TDP mutation is pathogenic in a subset of neurodegenerative diseases . Transient expression of the mutant , but not the normal , human TDP gene leads to apoptotic death of spinal motor neurons in chicken embryos [15] . In Drosophila melanogaster , depletion of the TDP homolog results in deficient locomotor activity and defects at neuromuscular junctions ( NMJs ) [19] . Suppression of TDP gene expression induces cell death in cultured neuroblastoma cells [20] . Previous studies indicate that mutation of the TDP gene is neurotoxic and that normal TDP-43 is important to cellular function; however , how mutations in the TDP gene cause neurodegeneration remains unknown . To study the consequences of TDP mutation in an intact system , we expressed a mutant form of the human TDP gene in rats , which were chosen over mice because they are the preferred animals for pharmacological studies . Overexpression of a mutant , but not the normal , human TDP gene caused widespread neurodegeneration , which predominantly affected the motor system . Transgenic rats that constitutively or conditionally expressed a mutant form of human TDP with a valine-to-methionine substitution at position 337 ( M337V ) developed similar phenotypes at early ages , the phenotypes that were characterized by motor neuron degeneration accompanied by astrocyte and microglial activation in the spinal cord .
TDP-43 is widely expressed in mammalian tissues [21] . To mimic the expression profile of the endogenous TDP gene , we extracted the minimal human TDP gene ( mini TDP gene ) from a BAC clone and discarded the excess flanking sequences . The mini human TDP transgene contains essential elements for regulating transgene expression but does not carry unwanted genes into transgenic rats ( Figure 1A ) . Among all known mutations in the TDP gene , the M337V substitution is found in geographically unrelated families and thus is an excellent representative of TDP gene mutations [15] , [17] . We introduced the M337V mutation into the mini TDP transgene using a recombineering technique [22] . Using pronuclear injection , we generated three transgenic founders ( two males: founders 1 and 2; one female: founder 3 ) that robustly expressed the miniTDP43M337V transgene ( Figure 1A–1C and 1F ) . The mutant TDP transgenic founders were indistinguishable from their nontransgenic littermates at birth; however , they soon lost mobility and died at postnatal ages . Founder 3 died at the age of 10 days . Founder 2 showed weakness in the limbs at the age of 13 days and became paralyzed by the age of 18 days . Founder 1 showed weakness in a forelimb at the age of 21 days and became paralyzed by the age of 29 days . We examined founder 1 using immunohistochemistry and observed a reduction in motor neurons in the ventral horn of the lumbar spinal cord ( Figure 1F ) . Since none of the mutant TDP ( miniTDP43M337V ) transgenic rats survived to sexual maturity , mutant TDP transgenic lines could not be established . In parallel , we generated two transgenic founder rats that carried the normal human TDP transgene ( miniTDP43wt ) , which had an identical DNA composition as miniTDP43M337V except that it lacked the M337V mutation ( Figure 1A–1E ) . The miniTDP43wt transgenic rats expressed human TDP-43 protein at levels comparable to those detected in the miniTDP43M337V transgenic founder rats but did not develop paralysis by the age of 200 days . These findings suggest that the disease phenotypes observed in the miniTDP43M337V transgenic founder rats result from toxicity of the TDP gene mutation . Since constitutive expression of a mutant human TDP gene caused a severe phenotype in transgenic founders , we used a tetracycline ( Tet ) regulatory system to express the mutant TDP transgene in a controlled manner . In this way , we could establish transgenic rat lines expressing the human TDP transgene with a pathogenic mutation . The Tet-off system is commonly used in transgenic studies and is comprised of only two elements— a Tet-controlled transactivator ( tTA ) and a tTA-activated promoter ( TRE ) [23] . Using pronuclear injection , we established two transgenic lines ( line number corresponds to transgene copy ) that carry 7 or 16 copies of the TRE-TDP-43M337V transgene under the control of the TRE promoter ( Figure 2A ) . The transcriptional activator , tTA , is inactive in the presence of the Tet derivative , Doxycycline ( Dox ) , allowing for inactivation of a TRE promoter-controlled gene through Dox administration in the bigenic rats that carry the TRE-TDP-43M337V and the tTA transgenes ( Figure 2A ) . In the absence of Dox , tTA constantly activates the TRE-TDP-43M337V transgene , producing an expression pattern that is indistinguishable from constitutive transgene expression [24] . Constitutive expression of the miniTDP-43M337V transgene caused postnatal death in the transgenic founder rats ( Figure 1 ) , suggesting that the mutant TDP gene is highly toxic . To test whether the severe phenotype observed in the constitutive transgenic rats could be reproduced in conditional transgenic rats , we produced the TRE-TDP-43M337V and tTA double transgenic rats by crossing the TRE-TDP-43M337V transgenic lines with a tTA transgenic line that expresses the tTA transgene at levels sufficient to vigorously activate tTA reporter genes [24] . To obtain a constitutive pattern of transgene expression , we allowed the TRE-TDP-43M337V transgene to be expressed from early embryogenesis by withholding Dox treatment . Consistent with findings in constitutive transgenic rats ( Figure 1 ) , expression of the TRE-TDP-43M337V transgene from early embryonic stages caused severe phenotypes in the conditional transgenic rats of line 16 ( Figure 2B ) . Transgenic rats of line 16 became paralyzed and died by postnatal day 20 ( P20 ) . The similarity in phenotypes between the constitutive and conditional transgenic rats indicates that the observed defects did not result from an insertional mutation . Expression of the TDP-43M337V transgene from early embryogenesis caused early death in transgenic rats , making functional analysis of this model a challenge . To facilitate analysis of motor function , we added Dox to the drinking water ( 50 µg/ml ) of breeding rats to suppress transgene expression during embryonic development . We then withdrew Dox at 4 days before delivery to allow for recovery of transgene expression in postnatal rats . As a result , the transgene was not expressed in newborn pups but was fully expressed in postnatal rats by P10 ( Figure S1 ) . The TRE-TDP-43M337V transgenic rats of line 16 showed a rapid progression of disease phenotypes , exhibiting limb weakness by P20 and paralysis before P35 ( Figure 2E ) . In contrast , the TRE-TDP-43M337V transgenic rats of line 7 showed a later onset and a slower progression of similar phenotypes ( Figure 2C–2E ) . Disease progression in line 7 could be divided into four distinct stages [25]: the nonsymptomatic stage , disease onset , the paralysis stage , and the disease end stage . Disease onset was defined as an unrecoverable reduction in running time on a rotating Rotarod . The paralysis stage was defined as visible dragging of a limb . The disease end stage was defined as paralysis in two or more limbs . Postnatal rats aged 21 days were subjected to a Rotarod test to determine disease onset ( Figure 2D ) . Since transgenic rats of line 16 developed early paralysis and had a rapid disease progression , determining the time of disease onset for this high-copy line was technically difficult . Transgenic rats of line 16 showed limb weakness by an age of 20 days and became paralyzed in the legs by an age of 35 days , with no sexual dimorphism existing in the rate of disease progression ( Figure 2E ) . In contrast , transgenic rats of line 7 displayed sexual dimorphism in the time of disease onset and in the rate of disease progression ( Figure 2D and 2E ) . Sexual dimorphism in phenotypic onset has also been observed in an ALS animal model expressing mutant human SOD1 genes [26]–[29] . The disease phenotypes observed in the mutant TDP ( TRE-TDP-43M337V ) transgenic rats were not observed in normal TDP transgenic rats ( miniTDP-43WT ) by an age of 200 days , though these rats expressed the human TDP transgene at comparable levels as TRE-TDP-43M337V rats ( Figure 2B–2E ) . An examination of TRE-TDP-43M337V transgenic offspring revealed that , consistent with findings in miniTDP-43M337V transgenic founders ( Figure 1 ) , the disease phenotypes in these animals were related to mutation of the TDP gene . Anatomical analysis revealed that motor neurons in the spinal cord robustly expressed the human TDP transgene ( Figure 3A–3C ) . The number of spinal motor neurons was significantly reduced in mutant TDP transgenic rats but not in normal TDP transgenic rats ( Figure 3D–3F and 3I ) , although the mutant and normal TDP transgenic rats expressed human TDP-43 at comparable levels ( Figure 2B ) . Large-caliber neurons were preferentially affected in mutant rats at the end stages of disease ( Figure 3D–3F and 3L ) . During the paralysis stage , degenerating axons were clearly visible in the ventral ( Figure 3G–3I and 3M ) and dorsal roots ( Figure S2 ) , with motor axons of the corticospinal track also being affected ( Figure S3 ) . Confocal microscopy revealed that denervation of synaptic endplates in skeletal muscle occurred at disease onset ( Figure 4B and 4D ) and worsened at the end stage of disease ( Figure 4C and 4D ) . Electron microscopy confirmed that , in the mutant transgenic rats , degeneration of motor neuron axons occurred at disease onset ( Figure 4F and 4G ) ; however , no loss of motor neurons was detected in the mutant TDP transgenic rats at this time . These findings suggest that axon terminals are the primary targets of degeneration associated with pathogenic mutation of TDP . In the mutant TDP transgenic rats , denervation of skeletal muscle fibers was confirmed by electromyography , which detected frequent fibrillation potentials—a characteristic of muscle denervation and regeneration ( Figure 4E ) . As results of denervation , groups of skeletal muscles were atrophied ( Figure 4H and 4I ) . These pathological changes were correlated with progressive paralysis in the mutant transgenic rats ( Figure 2C–2E ) . Silver staining revealed that , in mutant TDP transgenic rats , spinal motor neurons degenerated during end-stage disease ( Figure 5A and 5B ) . A previous study showed that transient expression of the mutant , but not the normal human TDP gene , causes apoptotic death in the spinal cord of chicken embryos [15] . Consistent with the finding from this transient transfection study [15] , motor neurons in the spinal cord underwent apoptosis in paralyzed transgenic rats ( Figure 5C and 5D ) . Studies of mutant SOD1 mice suggest that glial cells play an important role in ALS pathogenesis [28] , [30]–[33] . Therefore , we examined glial reactions in our paralyzed rats . We found that astrocytes and microglia were increased around the motor neurons in the spinal cord ( Figure 6A–6D ) . The finding suggests that a glial reaction occurs in response to motor neuron degeneration . TDP-43 inclusions are found in the brain and spinal cord of patients with sporadic ALS , FTLD , Alzheimer's disease , or dementia with Lewy bodies [5]–[9] , suggesting that TDP-43 proteinopathies are common to neurodegenerative diseases . Pathogenic mutations in the TDP gene have been identified not only in ALS , but also in FTLD-MND [14]–[18] . Degeneration associated with mutations in the TDP gene may not be restricted to motor neurons . Indeed , silver staining revealed that neurodegeneration occurred in the cortex , hippocampus , and cerebellum of mutant transgenic rats with end stages of disease ( Figure 7A–7F ) but not in those with earlier stages of disease ( Figure 2D and data not shown ) . Nevertheless , degenerating neurons were not detected in the substantia nigra of paralyzed rats ( data not shown ) , despite the fact that transient overexpression of the normal human TDP gene in rats has been shown to induce a loss of dopaminergic neurons in this brain region [34] . Neuropathological findings were correlated with phenotypic expression in mutant TDP transgenic rats ( Figure 2 , Figure 3 , Figure 4 ) . Toxicity of the pathogenic TDP gene mutation was not restricted to motor neurons , though these neurons were affected by the mutation to a greater degree than all the other neuron types examined . Phosphorylated TDP-43 inclusions are a signature pathological feature of sporadic ALS and FTLD [5] , [6] , [35]–[37] . To detect phosphorylated TDP-43 inclusions in our transgenic rats , we tested a polyclonal antibody specific to phosphorylated TDP-43 on brain sections of FTLD patients and TDP transgenic rats . This phospho-TDP-43 antibody detected cytoplasmic accumulation of phosphorylated TDP-43 in FTLD patients , but not in control subjects ( Figure 8A ) . Similarly , phosphorylated TDP-43 was diffusely distributed in affected neurons in transgenic rats expressing the mutant or normal human TDP transgene ( Figure 8D ) . We generated a polyclonal antibody recognizing both phosphorylated and non-phosphorylated human TDP-43 ( Figure 1C–1F ) and detected a robust expression of the human TDP transgene in transgenic rats ( Figure 8B and 8C ) . TDP-43 was diffusely distributed in the nucleus and cytoplasm of cells within transgenic rats ( Figure 8 ) . However , TDP-43 inclusions were detected rarely , being present only in the cortex ( Figure 8B ) and not in the spinal cord ( Figure 8C ) of transgenic animals . Immunohistochemistry revealed that typical ubiquitin-positive inclusions were not present in the spinal cords of normal or mutant TDP transgenic rats , though the intensity of ubiquitin immunostaining was greater in these animals than in nontransgenic rats ( Figure S4 ) . Since TDP-43 inclusions were rare in transgenic rats , even at end-stage disease , we further examined TDP-43 ubiquitination using immunoprecipitation combined with immunoblotting analysis . Ubiquitinated TDP-43 was detected in the mutant TDP transgenic rats ( Figure S4 ) . Immunoblotting revealed that a small amount of TDP-43 fragments ( less than 43 kDa ) was present in TDP transgenic rats ( Figure 2B and Figure S5 ) . TDP-43 fragments were detected in urea tissue extracts from rats at the paralysis stage , but not in extracts from those at disease onset ( Figure S5 ) . The finding suggests that the solubility of the small TDP-43 fragment is reduced as the disease progresses .
Expression of the human TDP gene containing a M337V substitution reproduced the phenotypes of ALS in rats . That is , these animals exhibited progressive degeneration of motor neurons and denervation atrophy of skeletal muscles . In this transgenic rat model , neurodegeneration was not restricted to motor neurons and could be seen in other types of neurons including cortical neurons , hippocampal neurons , and cerebellar neurons . However , TDP mutation affected motor neurons earlier and more severely than other neurons in the central nervous system ( CNS ) . This robust rat model also recapitulated features of TDP-43 proteinopathies , including the formation of TDP-43 inclusions , cytoplasmic localization of phosphorylated TDP-43 , and fragmentation of TDP-43 . While our transgenic rat model developed the phenotypes of ALS , it displayed degeneration of CNS neurons other than motor neurons at the end stages of the disease . Our findings in mutant TDP transgenic rats do not necessarily contradict observations in ALS patients . ALS is traditionally thought to affect only motor neurons , but recent studies showed that neurons other than motor neurons also degenerate in ALS [38] . This point is strikingly illustrated by the observation in some ALS patients who live with the disease much longer than the average disease duration [38]–[40] . Moreover , some ALS and FTLD cases share symptoms and pathological characteristics [41] . Although mutations of the TDP gene are primarily associated with ALS [14]–[17] , a recent study found that a novel mutation in the TDP gene is associated with FTLD-MND [18] , suggesting that the toxicity ( if any ) of the TDP gene mutation is not restricted to motor neurons [18] . Further studies are warranted to ascertain whether a correlation exists between the pathological changes induced by TDP mutation and TDP-43 proteinopathies observed in sporadic ALS and FTLD . The fact that TDP-43 proteinopathy is observed in a wide range of neurodegenerative diseases suggests that mutations in the TDP gene are generally neurotoxic [5] , [6] , [9] , [42]–[45] . Neurodegenerative diseases may share common pathological mechanisms , with a certain subgroup of neurons being predominantly affected under each disease condition . Our mutant TDP transgenic rat is a robust model of neurodegeneration caused by mutation of the TDP gene . Many features of TDP-43 proteinopathies were reproduced in our TDP transgenic rats . Redistribution , phosphorylation , and aggregation of TDP-43 are all hallmarks of sporadic FTLD and ALS [5] , [44] , [45] . A recent clinical study showed that TDP-43 redistribution appears to be an early event in TDP-43 proteinopathy [7] , suggesting that TDP-43 redistribution underlies the pathogenesis of neurodegeneration . Our results showed that phosphorylated TDP-43 was diffusely distributed in the cytoplasm and nucleus of affected cells in paralyzed mutant TDP transgenic rats as well as in non-paralyzed , normal TDP transgenic rats . The presence of phosphorylated TDP-43 in normal TDP transgenic rats does not exclude the possibility that TDP-43 phosphorylation contributes to pathogenesis induced by TDP mutation . Specifically , TDP mutation may impart toxicity by enhancing the normal functions of the gene . For example , mutation of the LRRK2 gene causes Parkinson's disease by enhancing ( at least partially ) the kinase activity of LRRK2 [46] , [47] . Gene mutations can be classified into three types based on their effect on protein function: gain of function , loss of function , and dominant negative effect . Pathogenic mutation of the TDP gene may cause disease through any one of these three effects on protein function . Resolving the nature of the TDP gene mutation will require a more sophisticated model such as a knockin mouse . TDP-43 inclusions and fragmentation were rarely observed and were present only at end-stage disease , suggesting that these pathologies may be consequence of , rather than a cause of , neurodegeneration in TDP transgenic rats . C-terminal truncated products of TDP-43 are thought to result from caspase cleavage of full-length TDP-43 [48] . Accordingly , C-terminal fragmentation of TDP-43 is likely a consequence , instead of a cause , of neurodegeneration because caspase activation is a terminal feature of cell death . In addition , we cannot exclude the possibility that overexpression of the TDP transgene interferes with rat development , since the mutant TDP transgenic rats died at postnatal ages . Typical ALS has a late onset and rapidly progresses [12] , [13] , [17] , [29] , [49]–[51] . In contrast , mutant TDP transgenic rats developed paralysis at early ages , with the paralysis being similar to that seen in ALS . Early onset of disease phenotypes in our rat model likely results from toxicity of the TDP gene mutation , as evidenced by the following three findings . First , paralysis and lethality were observed in the mutant miniTDP43M337V transgenic founders , but not in the normal miniTDP43WT transgenic founders . Second , paralysis and neurodegeneration were observed in the inducible mutant TDP transgenic rats , but not in offspring of the constitutive normal TDP transgenic rats , despite the fact that both lines exhibited comparable expression of the human TDP transgenes . Third , similar phenotypes were observed in the constitutive mutant TDP transgenic founders and in the inducible mutant TDP transgenic offspring . One transgenic founder rat carried only six copies of the mini mutant TDP transgene and developed paralysis in postnatal age . The copy number of the mutant TDP transgene that is required for phenotypic expression in transgenic rats is much lower than the copy threshold of mutant SOD1 transgenes [52] , [53] . To activate the inducible mutant TDP transgene , we used a low-copy tTA transgenic line that carries only two copies of the tTA transgene [24] . Therefore , expression levels of the TDP transgene in the inducible transgenic rats were comparable to those in the constitutive normal TDP transgenic rats . Transgenic rats expressing the mutant TDP gene displayed a wider range of neurodegeneration than transgenic rodents expressing mutant SOD1 genes [29] , [52]–[54] , with neurodegeneration predominantly affecting the motor system . Such unrestricted toxicity of the TDP gene mutation may lead to an early onset of the disease . In some aspects , phenotypes observed in our transgenic rats are similar to those detected in transgenic mice that express the human TDP gene with a A315T substitution [55] . In these rodent models , both upper and lower motor neurons are affected and TDP-43 inclusions are rare . However , our rat model developed paralysis at postnatal ages and experienced a rapid disease progression , while the mutant TDP transgenic mice develop disease phenotypes during middle age and have varying disease durations [55] . Different mutations in the TDP gene and different animal species may contribute to phenotypic variation between the rat and mouse models . Our findings in TDP transgenic rats indicate that mutation of the TDP gene is highly toxic in rodents , though the nature of the pathogenic mutation in the TDP gene remains to be resolved . Since deletion of the TDP gene in Drosophila causes defects at NMJs [19] , the possibility that the TDP gene mutation produces a dominant-negative effect cannot be excluded . Although the nature of TDP gene mutation will need to be determined using a more sophisticated model , our TDP transgenic rats will be useful for mechanistic study of TDP-43-related neurodegenerative diseases .
Animal use followed NIH guidelines . The animal use protocol was approved by the Institutional Animal Care and Use Committees ( IACUC ) at Thomas Jefferson University . The Committee for Oversight of Research Involving the Dead at the University of Pittsburgh School of Medicine approved the use of human tissue from the University of Pittsburgh ALS Tissue Bank . Age-matched tissue sections from two FTLD and two non-neurological disease controls were used for the study . The 22-kb mini human TDP gene was extracted from a BAC clone ( RP11-829B14 ) , and a M337V substitution was introduced into the mini TDP gene by homologous recombination in Escherichia coli [22] . The normal and mutant mini TDP transgenes were linearized by restriction digestion , purified from agarose gels , and then used to produce transgenic rats through microinjection . To generate Tet-regulatable TDP transgenic rats , we PCR-amplified the human TDP-43 ORF from a human brain cDNA pool ( Invitrogen ) and generated a mutant carrying the M337V substitution using site-directed mutagenesis ( Stratagene ) . The mutated human TDP-43 cDNA gene was inserted downstream of a tTA-dependent promoter that was constructed by fusing seven tetracycline-responsive elements ( TRE ) with a minimal cytomegalovirus promoter ( TRE-miniCMV ) . To enhance gene splicing and expression , we inserted the first intron of the human ubiquitin C gene between the TRE-miniCMV promoter and the TDP-43 ORF [24] . Linearized miniTDP43 and TRE-TDP43 transgenes were injected into the pronuclei of fertilized eggs of Sprague-Dawley rats . The injected eggs were then transplanted into pseudopregnant females for embryonic development [56] . Transgenic founders carrying miniTDP-43 transgenes were analyzed for disease phenotypes . Transgenic founders carrying TRE-TDP-43 transgenes were crossed with CAG-tTA transgenic rats to produce double transgenic offspring , which were analyzed for transgene expression and disease phenotypes . The TDP transgenic rats were identified by PCR amplification of the human TDP gene using the following primer pair: 5′-TGCGGGAGTTCTTCTCTCAG ( forward ) and 5′-AGCCACCTGGATTACCACCA ( reverse ) . The copy number of the transgene was determined by quantitative PCR using two primer pairs . The first primer pair was designed to amplify a DNA fragment of the same composition from both the human and the rat TDP gene: 5′-TGAGCCCATTGAAATACCATC-3′ and 5′-TACACTGAGACACTGGATTC . The second primer pair was designed to amplify the rat prolactin gene as an internal control: 5′-CCTCTATGAACGAAACCCAC-3′ and 5′-CTTCCGGCTAATCCA CAATG-3′ . A rabbit polyclonal antibody was produced by Genemed Company . Rabbits were immunized with the synthetic peptide , ( N-terminal ) -EDELREFFSQYGDVM . Antiserum was then affinity-purified using a peptide-conjugated column ( Pierce ) . Under deep anesthesia , animals were transcardially perfused with 1X PBS ( pH 7 . 4 ) and then with 4% paraformaldehyde ( PFA ) dissolved in 1X PBS buffer . The brain , spinal cord , and gastrocnemius muscle of perfused animals were collected and further fixed in the same fixative overnight . Some tissue blocks were embedded in paraffin and sectioned into 10 µm-thick slices . Paraffin-embedded sections were treated with 10 mM sodium citrate buffer ( pH 6 . 0 ) to retrieve antigens for immunostaining . Paraffin-embedded coronal sections of the brain and transverse sections of the spinal cord were deparaffinized and immunostained with human TDP-43-specific antibody ( 1∶1 , 000; made in house ) or a phospho-TDP-43-specific antibody ( 1∶1 , 000; COSMO Bio Co . , TIP-PTD-P02 ) . Immunostaining was visualized using an ABC kit in combination with diaminobenzidine ( Vector ) . The immunostained sections were lightly counterstained with hematoxylin to display nuclei . After antigen retrieval , paraffin-embedded sections of the lumbar spinal cord were immunostained for human TDP-43 ( 1∶300 ) and ChAT ( goat antiserum; Millipore ) . Immunofluorescence staining for human TDP-43 ( red ) and ChAT ( green ) was visualized using a Nikon fluorescence microscope , and images were acquired using a Nikon digital camera . Paraffin-embedded sections of the gastrocnemius muscle were stained with hematoxylin and eosin ( H&E ) to visualize tissue structures . For NMJ detection , gastrocnemius muscles were fixed in 4% PFA for 2 h and sectioned on a cryostat into 100 µm-thick sections . Serial sections of the muscles were incubated with α-bungarotoxin ( Invitrogen ) for 30 min , washed in PBS three times , incubated overnight with mouse monoclonal antibodies to neurofilament ( Sigma ) and synaptophysin ( Millipore ) , and then incubated for 1 h with a secondary antibody ( FITC goat anti-mouse IgG1; Jackson Immunology ) . For detection of apoptotic cells and glial cells , 4% PFA-fixed lumbar spinal cords were cut into three sets of 10 µm-thick serial sections on a cryostat . Every first section was incubated with TUNEL staining reagent ( Millipore ) and goat anti-ChAT antibody . Every second section was incubated with the ChAT antibody and mouse anti-GFAP . Every third section was incubated with the ChAT antibody and mouse anti-CD68 antibody . Sections were then incubated with appropriately labeled secondary antibodies . The antibodies were purchased from Millipore . Images were captured using a Zeiss LSM510 META confocal system . The NMJ was reconstructed using z-stack projections produced from serial scanning every 1 µm . Fresh gastrocnemius muscle was snap-frozen in liquid nitrogen and cut into 12 µm-thick sections on a cryostat . Nonspecific esterase activity was detected using the α-napthyl acetate protocol . Denervated muscle fibers were stained a red-brown color , with normal fibers displaying a yellow-to-brown color . Degenerating neurons were visualized using the Bielschowski silver-staining method as well as the FD NeuroSilver kit ( FD Neurotechnologies , Baltimore , MD ) . For the Bielschowski silver method , paraffin-embedded spinal cord was transversely cut into 10 µm-thick sections . For staining using the FD NeuroSilver kit , 40 µm-thick coronal sections were obtained by slicing through the forebrain and cerebellum using a cryostat and then stained per the manufacturer's instructions . Rats were anesthetized and perfused with a mixture of 4% PFA and 2% glutaraldehyde in 0 . 1 M phosphate buffer ( pH 7 . 4 ) . The L3 and L4 ventral and dorsal roots were removed and post-fixed in the same fixative at 4°C overnight . The roots were then further fixed in 1% osmium tetroxide in 0 . 1 M phosphate buffer ( pH 7 . 4 ) for 1 h . The well-fixed tissues were dehydrated in graded ethanol and embedded in Epon 812 ( Electron Microscopic Sciences , Fort Washington , PA ) . Thin sections ( 80 nm ) were then stained with uranyl acetate and lead citrate for observation under a transmission electron microscope ( Hitachi H7500-I ) . For toluidine staining , roots were transversely cut into 1 µm-thick sections . Axons in the nerve roots were examined in the semi-thin sections under a light microscope ( Olympus AX70 ) . A 1-mm central segment of the L3 spinal cord was cut into 30-µm thick sections using a cryostat . Every third section was stained with cresyl violet and mounted in sequential order ( rostral-caudal ) . Neurons with a diameter larger than 25 µm were counted in the ventral horns on both sides . The number of targeted neurons was estimated using a fractionator-based unbiased stereology software program ( Stereologer ) , which was run on a PC computer that was attached to a Nikon 80i microscope with a motorized XYZ stage ( Prior ) . At low magnification ( 40x ) , the targeting area was outlined , and a random sampling grid was created . At 1000× magnification , an optical dissector probe was randomly generated by the program in the designated area . The presence of clearly definable neurons was noted according to defined inclusion and exclusion limits of the dissector . This process was repeated on all selected sections . The total number of defined neurons was calculated by the software based on values obtained from random counts . Animals were anesthetized during electromyography ( EMG ) examination . The fibrillation potential of the gastrocnemius muscle was recorded with an EMG instrument ( CMS6600; COTEC Inc . ) using a 27-gauge monopolar needle electrode and a 29-gauge reference needle electrode . During recording , a sub-dermal ground electrode was placed in the forelimb . Spontaneous electrical activity of selected skeletal muscle was recorded for 2 min . The number of defined neurons in the ventral horn was compared between groups of transgenic rats . The difference in the number of neurons was analyzed using an unpaired t test . The null hypothesis was rejected at the 0 . 05 level . | Amyotrophic lateral sclerosis , a condition also known as Lou Gehrig's disease , is characterized by progressive degeneration of motor neurons , denervation atrophy of skeletal muscles , and eventual paralysis of affected limbs . The signature pathology of Lou Gehrig's disease is the formation of intracellular inclusions containing phosphorylated TDP-43 protein . Most cases of Lou Gehrig's disease do not have a clear cause , while only about 10% of the cases are caused by mutation of individual genes . Here , we describe a novel rat model that expresses a mutated form of the human gene encoding TDP-43 and manifests the phenotypes and pathological features observed in patients with Lou Gehrig's disease . Laboratory rats are the preferred animals for pharmacological studies . Therefore , this new rat model will be useful not only for mechanistic study of Lou Gehrig's disease , but also for the development of therapies for this devastating disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"genetics",
"and",
"genomics/disease",
"models",
"neurological",
"disorders/movement",
"disorders"
] | 2010 | Transgenic Rat Model of Neurodegeneration Caused by Mutation in the TDP Gene |
One of the hallmarks of neocortical circuits is the predominance of recurrent excitation between pyramidal neurons , which is balanced by recurrent inhibition from smooth GABAergic neurons . It has been previously described that in layer 2/3 of primary visual cortex ( V1 ) of cat and monkey , pyramidal cells filled with horseradish peroxidase connect approximately in proportion to the spiny ( excitatory , 95% and 81% , respectively ) and smooth ( GABAergic , 5% and 19% , respectively ) dendrites found in the neuropil . By contrast , a recent ultrastructural study of V1 in a single mouse found that smooth neurons formed 51% of the targets of the superficial layer pyramidal cells . This suggests that either the neuropil of this particular mouse V1 had a dramatically different composition to that of V1 in cat and monkey , or that smooth neurons were specifically targeted by the pyramidal cells in that mouse . We tested these hypotheses by examining similar cells filled with biocytin in a sample of five mice . We found that the average composition of the neuropil in V1 of these mice was similar to that described for cat and monkey V1 , but that the superficial layer pyramidal cells do form proportionately more synapses with smooth dendrites than the equivalent neurons in cat or monkey . These distributions may underlie the distinct differences in functional architecture of V1 between rodent and higher mammals .
The concept of the cortical “column” is one of the few organising principles for cortical circuits that we have , yet the characteristic orientation columns in the primary visual cortex ( V1 ) of the cat and monkey appear to be completely absent in rodent V1 . In place of the ordered maps of orientation seen in cat and monkey , the distribution of orientation preferences in rodent V1 appears to be essentially random [1]–[3] . This “salt-and-pepper” arrangement in the rodent must reflect differences in the wiring of superficial layer neurons in rodents compared to cat and monkey . Another striking difference between V1 of mouse and those of cat and monkey is the tuning properties of inhibitory neurons . While in cat and monkey the receptive fields of smooth ( putative GABAergic inhibitory ) neurons are typically orientation selective [4]–[10] , with only occasional exceptions [11] , in the mouse they are essentially weakly tuned [12] , [13] ( but see Runyan and colleagues [14] ) . A third striking difference is that neurons in mouse V1 receive many more synapses on average [15] , [16] than a neuron in primary visual cortex of cat [17] , [18] or monkey [15] , [19]–[21] . In rodent barrel cortex a significant proportion of these synapses are probably contributed by neighbouring pyramidal cells , which form their synapses on the basal dendrites [22] , [23] . In cat , superficial layer pyramidal neurons are estimated to receive more than 60% of the excitatory synapses from their neighbouring pyramidal neurons [24] . This suggests that positive feedback loops are more likely between superficial layer pyramidal cells than between pyramidal cells in other layers , whose principal projections tend to project out of their home layers ( see Douglas and Martin [25] ) . By implication , the large number of excitatory synapses per neuron in the mouse may require a stronger component of recurrent inhibition . Clear evidence for an enhanced inhibitory component in the recurrent circuit came from a recent ultrastructural study by Bock et al . [26] designed to investigate whether the broadly tuned receptive fields of GABAergic inhibitory neurons could be explained by the convergence of input from excitatory neurons with different orientation preferences . This work involved partial reconstruction of 13 pyramidal cells and one smooth ( putative GABAergic inhibitory ) neuron in a single 50 µm thick section of V1 from a mouse that had undergone calcium imaging in vivo [26] . Their main conclusion was that pyramidal cells of different orientation preferences converged on individual smooth neurons . No synapses were formed between any of the 13 pyramidal cells . A remarkable statistic from Bock et al . was that 51% of the synapses formed by the pyramidal axons were targeting smooth neurons . This is a staggeringly high proportion , and it implies a very different wiring strategy from the cat or monkey V1 , where the proportion of excitatory synapses formed by layer 2/3 pyramidal neurons with smooth neurons is 5% [27] and 19% [28] , respectively . The results of Bock et al . thus raise the question of whether this arises because there are proportionately more smooth neuron targets in the mouse , or whether pyramidal cells select smooth neurons as their targets in a way they do not in the cat or monkey . To answer these questions we made detailed analyses not just of the synaptic targets of superficial layer pyramidal cells , but also of the content of the neuropil in mouse V1 in our material , and made the same analyses of the neuropil in the material of Bock et al . [26] . Our analyses indicate that the superficial pyramidal cells do not connect randomly to dendrites in the neuropil , as Braitenberg and Schüz [29] have proposed ( and named “Peters' rule” ) , but instead form a far higher proportion of their synapses with neighbouring smooth neurons than would be expected by chance .
Our goal was to determine the proportion of pyramidal cell synapses with smooth and spiny neurons and to determine whether the composition of the neuropil reflected the proportion of pyramidal axon targets found experimentally . To replicate the results of Bock et al . [26] , we used the same mouse strain and , as they did , used in vivo calcium imaging with two-photon microscopy ( 2PM ) to record the responses of V1 neurons to visual stimulation . In contrast to the Bock study , however , we did not reconstruct the unlabelled axons by serial section electron microscopy ( EM ) . Instead , we reduced the load of EM reconstruction considerably by filling individual imaged neurons with biocytin by electroporation after their functional characterization using 2PM , and making a correlated light/electron microscopic examination of their axons ( Figure 1 ) . We successfully characterized the morphology and synaptic ultrastructure of six neurons in layer 2/3 of five mice . The visual tuning properties were obtained for five neurons in five mice . In one mouse , a second serendipitously filled neuron was also reconstructed . Figure 1 shows the steps from imaging to electroporation to recovering the functionally characterised neuron . After 2PM calcium imaging of the neuronal population and functional characterization using drifting gratings , a reliably responsive and selective neuron was selected for electroporation . The white arrow in Figure 1A shows the neuron selected , which was tuned for vertically oriented drifting gratings ( Circular Variance Index [CVI] = 0 . 62; Direction Selectivity Index [DSI] = 0 . 17 ) . We then used targeted electroporation to label this neuron ( Figure 1B ) . We observed that the electroporated neuron maintained its selectivity and responsiveness ( Figure 1C , Figure S1 ) . By aligning a stack of 2PM images with the serial 80 µm thick sections imaged with bright field light microscopy ( LM ) , the recorded neuron was identified and recovered for morphological examination ( Figure 1D , E ) . Figure 2 shows the full extent of the axon contained within the single 80 µm thick section containing the cell body . The position of the soma and the distribution of the axons were very similar to those reconstructed by Bock et al . [26] . The position of the cell body relative to the laminae is indicated by a triangle and the laminar borders are indicated with dashed and dotted lines . The circles on the black axons show the positions of the boutons that formed synapses . Filled circles indicate synapses formed with spines and open circles indicate synapses formed with dendritic shafts . Arrowheads indicate boutons where the composition of the surrounding neuropil was analysed . The traces below each reconstruction are averages from calcium imaging and show that all five neurons were orientation tuned and/or directionally biased . Serial ultrathin sections were taken through the axon to examine 21–31 boutons per neuron . These segments of the axon were correlated with the light microscopy ( LM ) reconstructions to define the precise position of the synapses and their targets along the axon . A total of 163 boutons were investigated in the EM . They formed a total of 170 synapses ( 148 boutons formed one synapse , 11 boutons formed two synapses , and four boutons formed no synapses ) . The different targets of the pyramidal axons were classified by standard criteria [27] . Figure 3 shows two examples of spines ( Figure 3A , B ) forming synapses with the biocytin-labelled boutons , which are electron-dense and filled with vesicles . The large postsynaptic density ( arrow head ) indicates a typical asymmetric synapse formed by pyramidal neurons . Two unlabelled vesicle-filled boutons forming asymmetric synapses are also indicated in Figure 3A ( arrowheads ) for comparison . In Figure 3C the bouton formed an asymmetric synapse with the dendritic shaft of a smooth neuron . Unlike the dendrites of spiny neurons , where most asymmetric synapses are formed with spines , the asymmetric synapses formed with dendrites of smooth ( i . e . spine-free ) neurons are naturally found on the shafts ( arrow heads in Figure 3C ) . In all cases the classification of the targets was made on the basis of serial section analyses of the postsynaptic dendrite . By reconstructing the axons at the LM level , we were able to identify the particular branch segments that contained the synapses examined with subsequent EM . Figure 4 shows a summary dendrogram that reveals the branch ordering of the axons and the relative location of the 170 synapses examined on the axons . As in Figure 2 , the target type is indicated by closed circles for spines and open circles for dendritic shafts . The axon leaving the soma descends vertically before branching and forming collaterals with boutons in layers 2 and 3 . The dendrogram shows that the synapses we sampled were found on all orders of the branches , even on the main descending axon . A total of 126 synapses were formed with spiny neurons ( 120 formed with dendritic spines and 6 with dendritic shafts ) and 44 with dendritic shafts of smooth neurons . The data for each neuron in terms of target type are plotted in the histograms of Figure 5A . These histograms show that although spines formed the majority of targets , the variance between individual neurons was surprisingly high . If Peters' rule [29] applied , we would expect the proportion of different targets to reflect the local average proportions of smooth and spiny neurons in layer 2/3 . The question was whether this high variance reflected some local heterogeneities in distribution of targets in the neuropil , or whether it was due to specific targeting of smooth neurons by some pyramidal cells . We tested this using the unbiased disector counting technique ( see Materials and Methods [30] , [31] ) to determine the distribution of asymmetric synapses formed with spiny or smooth neurons in the neuropil at the vicinity of each of the reconstructed neurons . These results show that in the neuropil many more synapses were formed with spines than were found for the labelled axons ( Figure 5C ) . Next , we explored the possibility that the observed specificity was due to the fact that labelled boutons formed synapses in regions of the neuropil where there were more dendritic shaft targets . Again using the physical disector method we placed a 5 µm×5 µm sampling square centred on labelled boutons ( randomly selected boutons indicated by arrowheads in Figure 2 ) . The results plotted in Figure 5B show that in the region around any labelled bouton , virtually all the synapses were formed with spines . This was again different from the distribution of targets of the labelled boutons , which formed significantly more synapses with smooth neurons than Peters' rule would predict . Finally , to test whether the difference in targeting between labelled axons and the unlabelled neuropil could be due to a random process , we ran a simulation of an axon growing through a virtual neuropil and connecting to its targets by chance . The location of targets in the neuropil was uniformly distributed , as found in the large disectors ( 30 µm×30 µm ) , and each simulation was performed 10 , 000 times . When the simulations used the percentage of smooth dendritic targets collected from locations surrounding labelled boutons ( Figure 5B ) , the Monte Carlo analysis ( Figure S2 ) revealed that , with the exception of neuron M20 ( p = 0 . 077 ) the other neurons showed a strong statistical difference ( p = 0 ) between the number of inhibitory targets observed experimentally and that predicted from a random process . When the simulations used the percentage of smooth dendritic targets collected from random locations in the neuropil ( Figure 5C ) , the Monte Carlo analysis ( Figure S3 ) revealed that , with the exception of neuron M31 ( p = 0 . 48 ) , the other neurons showed strong a statistical difference ( p = 0 ) between the number of inhibitory targets observed experimentally and that predicted from a random process . We also tested whether the biases observed by Bock et al . [26] followed the same trend as our data . We applied the unbiased disector method on their web-based data to estimate the proportion of synapses formed with spiny and smooth neurons in the neuropil of the superficial cortical layers of their mouse ( pie charts in Figure 6 , right column ) . We found that 80% of the targets were on spiny dendrites ( 35 synapses on spines and one on a spiny shaft ) and 20% on smooth dendrites ( nine synapses ) . Our analyses of their data indicate that the axons contained in their reconstructed volume targeted far more smooth neurons than would be expected from the composition of the neuropil through which they passed ( compare Figure 6 , data from imaged neurons in the lower pie chart with disector counts in the upper pie chart ) . Thus , although on average our labelled neurons formed proportionately fewer synapses with smooth neurons than did those of Bock et al . [26] , in both studies the proportion of targeted smooth neurons was far higher than would be expected on the basis of random connectivity . Thus the data from both studies indicate that these superficial layer pyramidal cells in mouse V1 appear to select smooth neurons as their targets .
Our goal was to establish whether the salt-and-pepper representation of orientation in rodent V1 is reflected in the synaptic connections formed by the superficial layer pyramidal cells . After 2PM calcium imaging , individual pyramidal neurons were labelled with biocytin , sectioned , and reconstructed with LM . The synaptic targets of their axons as well the synaptic complement of the surrounding neuropil were quantified using EM . Previous physiological studies suggested that pyramidal cells connect specifically to one another [32]–[36] and to GABAergic neurons [37] . Moreover , in mouse V1 , the probability of pyramidal neurons connecting to neighbouring fast-spiking interneurons is much higher than the probability of pyramidal neurons connecting to each other [38] . Our data further indicate that pyramidal cells make specific connections with smooth , putative GABAergic neurons . A previous combined 2PM calcium imaging and electron microscopy study by Bock et al . [26] of 13 pyramidal cells in one mouse indicated that the pyramidal cells formed a consistently high proportion ( 50% ) of their synapses with smooth , putative GABAergic neurons . This is an astonishingly high fraction , since more extensive analyses of superficial layer pyramidal cells in V1 of other species indicate that typically 20% or fewer of the synapses are formed on smooth neurons . One explanation for the data from Bock et al . might be that the neuropil of mouse V1 contains a higher proportion of smooth neurons than other species . This seems not to be the explanation since no major differences have been noted in the proportion of pyramidal cells and smooth neurons in the superficial layers of rodent , cat , or monkey V1 [20] , [39] , [40] . The critical question is then whether the result obtained from the single section in one mouse by Bock et al [26] is an outlier , or whether it really reflects a wiring strategy to increase the local component of recurrent inhibition in V1 . The composition of the neuropil based on our own samples and those of Bock et al . [26] indicates that the superficial layer pyramidal cells in mouse V1 form a significantly higher proportion of their synapses with smooth , putative inhibitory neurons than would be predicted by Peters' rule [29] , which assumes that axons and dendrites connect in the proportions in which they are found in the neuropil . Our disector counts indicated that virtually all the unlabelled synapses in the neuropil within a 5 µm radius of any of the labelled pyramidal cell boutons were formed with spines , not smooth dendrites . Bock et al . [26] concluded that geometry dominates over function , since the proximity of two pyramidal cells , not their receptive field similarity , was the strongest indicator that their axons would converge onto a smooth neuron . However , our own results , and our new analyses of the cortical tissue from Bock et al . , indicates that the pyramidal cell connections to smooth neurons are far from being determined purely by geometry , for if geometry were the sole determinant , the pyramidal neurons should connect to smooth neurons in proportion to their occurrence in the neuropil . Instead , some pyramidal cells preferentially formed a subset of their synapses with smooth neurons . What is unexplained , however , is why the variance across the pyramidal cells is so high . In this context it is noteworthy that all the pyramidal cells in the mouse of Bock et al . had consistently high proportions of smooth targets , as did the mouse in which we examined two pyramidal cells . This suggests that the source of variance might not be within the individual , but between individuals of the same strain . These interesting observations across the two studies raise both a warning and an interesting challenge as to how we might discover the principles by which mouse brain wires itself if such high variance does exist between individuals . Our results and those of Bock et al . have implications for the functional architecture of mouse visual cortex ( Figure 7 ) and its operation . If layer 2/3 smooth neurons receive more than their fair share of synapses from local pyramidal cells than would be expected from Peters' rule , this implies that they receive proportionally fewer synapses from other excitatory projections into layers 2 and 3 . These other excitatory inputs arise from spiny neurons in layer 4 and 5 of V1 as well as other cortical areas and subcortical nuclei , like the thalamus . By this argument , pyramidal cells then have proportionately fewer synapses to devote to connections to other pyramidal neurons in the same layer . If this is the case , then in layer 2/3 of mouse visual cortex one might expect proportionately less recurrent excitation from within these layers than is present in the cat . Smooth neurons , like basket cells and chandelier cells , form their axonal arbours largely within the same layer as the cell body . Therefore , the smooth neurons targeted by our labelled pyramidal cell axons most likely are recurrently inhibiting the pyramidal cells that excite them . The fact that in mouse the smooth neurons have more convergent input from neurons with a variety of orientation tuning produces a circuit configuration that is very reminiscent of a winner-take-all ( WTA ) circuit . In this circuit , excitatory neurons have a map of some parameter ( e . g . orientation [35] , [36] ) , and the inhibitory neurons receive input from all excitatory neurons in the map and provide inhibition proportional to the overall excitation in the circuit [41] . While this study focuses on mouse V1 , previous work on superficial layer pyramidal cells in V1 of cat and monkey gave dramatically different results to those presented here . In monkey V1 , McGuire et al . [28] have shown that the axons of intracellularly filled layer 2/3 pyramidal neurons formed 19% of their targets with smooth dendritic shafts ( 28% if one considers spiny and dendritic shafts ) . As we did , McGuire et al . [28] analysed the neuropil surrounding one of their neurons , but unlike us found no evidence for preferential targeting of smooth GABAergic neurons by the superficial layer pyramidal neurons ( see also Beaulieu and colleagues for other counts of targets in monkey V1 neuropil [20] ) . In cat V1 , Kisvarday et al . [27] found that the axons of intracellularly filled layer 3 pyramidal neurons formed only 5% of their synapses with GABAergic neurons . They did not analyse the neuropil surrounding the labelled neurons , but in a different study Beaulieu and Colonnier analysed the neuropil of cat layer 2/3 of and found that 18% ( mean of layers 2 , 3A , and 3B ) of the asymmetric synapses are formed with dendritic shafts , some of which may be of spiny neurons [18] . These data strongly suggested that in cat there is no preferential targeting of inhibition by layer 2/3 pyramidal neurons , unlike what we , and Bock et al . [26] , now find for mouse V1 . This was also the conclusion of a theoretical study by Stepanyants and colleagues [42] , who found that the results of Kisvarday et al . [27] were consistent with Peters' rule . The conclusion of Stepanyants et al . makes it very clear that in the cat the proportion of GABAergic smooth neurons that are targets of superficial layer pyramidal axons is well below that of the mouse V1 . One idea for the generation of orientation “columns” in cat is that the orientation selectivity of neurons is created in layer 4 and then simply fed-forward to neurons in the superficial and deep layers [43] . In the macaque monkey , the situation is somewhat different , because most layer 4C neurons have non-oriented receptive fields , whereas neurons in the superficial and deep layers are orientation selective and form an orderly map of orientation , as in the cat . Development of the acolumnar salt-and-pepper arrangement of rodent V1 demands a high degree of specificity if it were to be achieved by feed-forward connections alone . Here , the stronger bias in the connections to smooth cells in the mouse may reflect increased demands on the inhibitory circuitry to shape the receptive field mediated by the superficial layer pyramidal cells .
All animal procedures were carried out according to the guidelines of the University of Zurich , and were approved by the Cantonal Veterinary Office . C57BL/6 mice ( 2–4 months old , of either sex ) were either first sedated with chlorprothixene ( Sigma; 0 . 2 mg/mouse ) and anaesthetized with urethane ( 0 . 5–1 . 0 g/kg ) or anaesthetized by 2 . 7 ml/kg of a solution containing one part fentanyl citrate and fluanisone ( Hypnorm; Janssen-Cilag , UK ) and one part midazolam ( Hypnovel; Roche , Switzerland ) in two parts of water , both delivered by intraperitoneal injections . Atropine ( 0 . 3 mg/kg ) and dexamethasone ( 2 mg/kg ) were administered subcutaneously to reduce secretions and oedema . Lactate-Ringer solution was regularly injected subcutaneously to prevent dehydration . Pinch reflexes were used to assess the depth of anaesthesia . The location of the primary visual area , V1 , was determined by stereotaxic coordinates ( V1 monocular segment – 1 . 0 mm anterior to lambda and 2 . 5 mm lateral from the midline [44] ) and confirmed by subsequent intrinsic imaging . Briefly , the skull above the estimated visual cortex was carefully thinned until a noticeable transparency of the bone was achieved . We then illuminated the cortical surface with 630-nm LED light , presented drifting gratings for 5 s , and collected reflectance images through a 4× objective with a CCD camera ( Toshiba TELI CS3960DCL ) . Intrinsic signal changes were analysed as fractional reflectance changes relative to the pre-stimulus average . V1 was the largest area active during visual stimulation at a location in accordance with stereotaxic coordinates . After identification with intrinsic imaging , a small craniotomy ( from 500 µm×500 µm to 1 mm×1 mm ) was opened above V1 , the dura removed and the exposed cortex superfused with artificial cerebrospinal fluid ( ACSF ) ( 135 mM NaCl , 5 . 4 mM KCl , 5 mM Hepes , 1 . 8 mM CaCl2 , 1 mM MgCl2 , pH 7 . 2 , with NaOH ) . Calcium indicator loading was performed using the “multi cell bolus loading” technique [45] . Briefly , 50 µg of the acetoxymethyl ( AM ) ester form of the calcium-sensitive fluorescent dye Oregon Green BAPTA-1 ( OGB-1; Invitrogen , Basel , Switzerland ) were dissolved in 2 µl of DMSO plus 20% Pluronic F-127 ( BASF , Germany ) and diluted with 37 µl standard pipette solution ( 150 mM NaCl , 2 . 5 mM KCl , 10 mM Hepes , pH 7 . 2 ) yielding a final OGB-1 concentration of about 1 mM . 1 µl of Alexa Fluor 594 ( Invitrogen; 2 mM stock solution in distilled water ) was added for visualization of the pipette during 2PM guided staining . The dye was pressure ejected under visual control through a glass pipette ( 4–5 MΩ ) at a depth between 150–300 µm to stain layer 2/3 neurons . Brief application of sulforhodamine 101 ( SR101; Invitrogen ) to the exposed neocortical surface resulted in co-labelling of the astrocytic network [46] . Following dye injection the craniotomy was filled with agarose ( type III-A , Sigma; 1% in ACSF ) and covered with an immobilized glass cover slip . Visual stimuli were presented on a 7-inch TFT monitor ( 75 Hz refresh rate ) 7 cm in front of the right eye roughly at 60° along the body axis of the anesthetized mouse . For the majority of the study , the visual stimuli were full contrast square wave gratings generated by the VisionEgg software [47] moving for 3 s in eight different directions spaced by 5 s blank ( grey screen presentation ) . The temporal frequency ( TF ) was 0 . 5 to 1 Hertz ( Hz ) and spatial frequency ( SF ) was 0 . 02 to 0 . 05 cycles per degree ( cyc/° ) , which have been shown to activate most neurons . For one animal , the stimulation used was full contrast square wave gratings moving back and forth during 4 s for each of four orientations . Calcium transients were acquired using a custom-built two-photon microscope equipped with a 40× water immersion objective ( LUMPlanFl/IR; 0 . 8 NA; Olympus 2 ) . 128×128 pixel frames or 256×256 pixel frames were acquired at rates from 2 to 4 Hz using custom written software ( LabView; National Instruments , USA ) . Data were analysed with ImageJ ( National Institute of Mental Health , NIH ) and MATLAB ( Mathworks ) . Cells were detected manually by drawing a region of interest around cell bodies . Relative percentage changes in fluorescence ( ΔF/F ) were calculated using as baseline the blank just before each stimulation . Traces were filtered using a Savitzky-Golay filtering approach . Responses were calculated by averaging 3–6 points around the peak fluorescence change ( time window of 1 . 5 s around the peak ) for each stimulation epoch . We defined a selectivity criterion using circular variance over gratings responses . Circular variance is defined as , where θ is the average drift direction of the grating: This measure of circular variance combines aspects of amplitude modulation and tuning width and takes into account all the responses to each direction of drift [48] . To use it as an index comparable to orientation selectivity indexes , the values given in this manuscript are 1 – circular variance ( see Niell and Stryker [49] ) referred to in the text as CVI , for Circular Variance Index ) . Consequently , a perfectly tuned neuron would have a CVI value close to 1 , and a perfectly untuned neuron close to 0 . For further analysis of selectivity we used the Direction Selectivity Index ( DSI; see below ) . We determined the direction selectivity as previously described [48] , [49] . It is defined as:Where Rpref is the response at the preferred angle θpref and Ropposite is the responses at the opposite direction θpref+π . If DSI >0 . 5 , the neuron is considered direction selective . Glass pipettes of resistance from 4 to 6 MΩ were filled with a standard pipette solution containing 2%–5% biocytin . These concentrations of biocytin were reached by mixing 4% biocytin ( ε-Biotinoyl-L-Lysine; Invitrogen ) diluted in some cases with the red dye Alexa 594 ( 20 µM; Invitrogen ) with a solution of 0 . 8 to 1 . 5% 5- ( and-6 ) -Tetramethylrhodamine biocytin ( Biocytin TMR; Invitrogen ) . The tip of the pipette was placed near the selected neuron for electroporation and a loose seal was formed to record extracellular spikes . Spikes were recorded at 5 kHz using a patch-clamp amplifier ( npi , Reutlingen , Germany ) and Spike2 software ( CED , Cambridge , UK ) . Once a stable configuration was reached , pulses from 300 to 400 mV of 10 ms duration were applied until successful electroporation was verified visually by uptake of the red indicator dye . In addition , we verified in some experiments the viability of the neuron by retesting the responses to visual stimulation after a recovery period of 10–20 min . This recovery period allows sufficient time for the pores formed during the electroporation to reseal , which usually occurs within 1 min [50] . At the end of the experiment the mouse was given an extra dose of anaesthesia and perfused transcardially with normal 0 . 9% NaCl solution , followed by a warm solution of 4% paraformaldehyde ( w/v ) , 0 . 5% glutaraldehyde ( v/v ) and 15% saturated solution of picric acid ( v/v ) in 0 . 1 M PB pH 7 . 4 . After fixation the mouse was perfused with solutions of 10% , 20% , and 30% sucrose in 0 . 1 M phosphate buffer ( PB ) . Once the brain was removed it was allowed to sink in a 30% sucrose solution in 0 . 1 M PB to provide cryoprotection and then freeze-thawed in liquid nitrogen . The brains were then washed in 0 . 1 M PB for at least 2 h to allow them to recover from the shrinkage provoked by the incubation in sucrose solution . Sections containing V1 were cut at 80 µm in the coronal plane and collected in 0 . 1 M PB . After cutting , the sections were washed several times in buffer in order to remove any remaining fixative . To reveal biocytin the sections were washed in TBS and then incubated overnight ( 5°C ) with an avidin-biotin complex ( Vector ABC kit – Elite ) . The peroxidase activity was identified using 3-diaminobenzidine tetrahydrochloride ( DAB ) with nickel intensification . After assessment by LM , regions of tissue containing the imaged area were treated with 1% osmium tetroxide in 0 . 1 M PB , dehydrated through alcohols ( 1% uranyl acetate in the 70% alcohol ) and propylene oxide , and flat mounted in Durcupan ( Fluka ) on glass slides . Serial light micrographs were taken from the osmicated sections at different magnifications , and the blood vessel pattern surrounding labelled neurons was reconstructed using TrakEM2 [51] . A similar blood vessel reconstruction was done on the 2PM stacks acquired in vivo . These reconstructions were used to find the recorded neurons in the osmicated histological sections . Finally the micrographs taken from histological sections were superimposed on the 2PM images to confirm the correspondence of the recorded neurons . The dendritic arbour and the proximal axon the neurons of interest were then reconstructed first in 2D using a drawing tube attached to a light microscope , and then in 3D from serial light micrographs using TrakEM2 [51] . Afterward , the tissue was serially resectioned at 50 nm thickness and collected on Pioloform-coated single slot copper grids . The axons of labelled neurons were then found in the ultrathin sections , and synapse connectivity between labelled axons and neuropil targets investigated with transmission electron microscopy ( TEM ) . Synapses and associated structures were classified using conventional criteria [52] , [53] . Estimations of the percentage of dendritic targets ( spines or shafts ) were performed at the EM level using the physical disector method [30] . The disector was composed of two serial sections of known thickness ( 50 nm ) separated by one intervening section . Synapses that disappeared from reference to lookup section were counted and the target was classified as dendritic spine or shaft as in [54] . Both sections were used as reference and lookup doubling the number of disectors per site . Electron micrographs were collected at a magnification of ( 13 , 500× , pixel size 2 . 5 nm ) with a digital camera ( 11 megapixels , Morada , Soft Imaging Systems ) . Four sets of counts were performed . The first set was done on randomly selected location in the neuropil surroundings the labelled neurons . The disectors had a size of 5 µm×5 µm and were sampled from the first intact section of every fourth grid ( each grid contained eight sections on average ) . The sampling sites ( five sites per animal ) and grids were selected according to a systematic random sampling scheme [31] , [55] . The second set was done on the neuropil surrounding the labelled boutons of recorded neurons . The counts were done in six randomly selected boutons per neuron and the disectors had a size of 5 µm×5 µm . The third set was from a single animal and the exact 2D location of the synapses was also collected for use in the Monte Carlo simulations described below . Three randomly located large disectors ( size 30 µm×30 µm ) were collected . The fourth set was collected from the dataset of Bock et al . [26] which was made available through CATMAID [56] . The disectors had a size of 12 . 7 µm×6 . 8 µm and the sampling sites ( eight sampling sites ) and sections were chosen according to a systematic random sampling scheme . A Monte Carlo analysis was performed to test whether the observed statistics of synaptic targets by labelled axons could be due to a random process . We ran a simulation in MATLAB ( Mathworks ) of an axon growing through a virtual neuropil of size 200 µm×200 µm×200 µm . Each simulation was run 10 , 000 times with the parameters from each labelled neuron/neuropil and was terminated when the virtual axon reached the number of synapses reconstructed for each labelled neuron . The result of each simulation was the proportion of smooth dendritic targets on the virtual axon . The location of targets in the virtual neuropil followed a uniform distribution as found in the biological data obtained from three large disectors ( 30 µm×30 µm ) . The proportion of spiny and smooth dendritic targets in the virtual neuropil were taken from the counts shown in Figure 5 and the density of synapses used was 109 synapses/mm3 following the work of Schüz and Palm [16] . The p-value estimate was given by the proportion of simulations that showed results larger than or equal to the measurements made in the real neurons . | The mammalian visual cortex , which is part of the cerebral cortex , contains 50 to 100 thousands of neurons per cubic millimetre , most of which are excitatory ( 85% ) and the minority , inhibitory ( 15% ) . Unlike neurons in the retina , neurons in the visual cortex are preferentially activated by lines or edges of a particular orientation . This is termed a neuron's “orientation preference . ” In the visual cortex of higher mammals like cats and monkeys , neurons that share an orientation preference are clustered in functional columns . However , in rodents like mice , orientation preferences are randomly distributed . In this study , we investigate whether the differences between columnar and non-columnar cortex is correlated with differences in the connectivity patterns between excitatory and inhibitory neurons . Using light and electron microscopy , we mapped the connectivity of pyramidal neurons—the primary excitatory neurons—in the superficial layers of the primary visual cortex ( V1 ) of mice . Our results show that the ratio of excitatory-inhibitory neurons in mouse V1 is similar to that of cat or monkey V1 , but in mouse V1 local pyramidal neurons target proportionately many more inhibitory neurons compared to what other studies found in cat or monkey . This difference may indicate the significance of inhibition in maintaining orientation selectivity in the non-columnar visual cortex of rodents like mice and is a distinct difference in the architecture of V1 between mice and higher mammals . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"connectomics",
"calcium",
"imaging",
"visual",
"system",
"cellular",
"neuroscience",
"neuroanatomy",
"anatomy",
"neuronal",
"morphology",
"neural",
"networks",
"nervous",
"system",
"biology",
"and",
"life",
"sciences",
"sensory",
"systems",
"neuroscience",
"neuroimaging"... | 2014 | Pyramidal Cells Make Specific Connections onto Smooth (GABAergic) Neurons in Mouse Visual Cortex |
Encoding properties of sensory neurons are commonly modeled using linear finite impulse response ( FIR ) filters . For the auditory system , the FIR filter is instantiated in the spectro-temporal receptive field ( STRF ) , often in the framework of the generalized linear model . Despite widespread use of the FIR STRF , numerous formulations for linear filters are possible that require many fewer parameters , potentially permitting more efficient and accurate model estimates . To explore these alternative STRF architectures , we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli . We compared performance of > 1000 linear STRF architectures , evaluating their ability to predict neural responses to a novel natural stimulus . Many were able to outperform the FIR filter . Two basic constraints on the architecture lead to the improved performance: ( 1 ) factorization of the STRF matrix into a small number of spectral and temporal filters and ( 2 ) low-dimensional parameterization of the factorized filters . The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex , despite requiring fewer than 30 parameters , about 10% of the number required by the FIR filter . After accounting for noise from finite data sampling , these STRFs were able to explain an average of 40% of A1 response variance . The simpler models permitted more straightforward interpretation of sensory tuning properties . They also showed greater benefit from incorporating nonlinear terms , such as short term plasticity , that provide theoretical advances over the linear model . Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function . They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models .
Encoding models provide a powerful , objective means to evaluate our understanding of how sensory neural systems represent complex natural stimuli [1 , 2] . An encoding model describes any time-varying neural signal ( single- or multiunit activity [3 , 4] , local field potential [5] , hemodynamic activity [6] , or behavior [7] ) as a function of the input stimulus , and it can predict the neural response to an arbitrary novel stimulus , including complex natural sounds . Prediction accuracy provides a quantitative measure of how well a model describes sensory-evoked activity; a completely accurate model should predict neural responses to any stimulus without error . More accurate models of sensory neural activity provide insight into algorithms that can be integrated into automated systems , such as speech recognizers and image classifiers . In the auditory system , the linear spectro-temporal receptive field ( STRF ) , implemented as a finite impulse response ( FIR ) filter , is the established “standard model” for neural representation [2 , 4 , 8–13] . This filter forms the core of generalized linear models ( GLMs ) applied to the auditory system [14 , 15] , and models sharing the same analytical form as the FIR STRF have been developed for studying visual [16–18] , somatosensory [19 , 20] , and olfactory systems [21] . Despite its widespread use , careful assessments of how well the linear STRF actually describes auditory neural activity are limited [22] . A few studies have shown that the linear STRF can explain only a limited portion of sound-evoked activity in cortex , especially for complex natural stimuli [9 , 23] . Others have argued that nonlinear variants of the classical linear STRF can improve predictive power [3 , 24–33] . These nonlinear variants of the STRF show improved predictive power under specific experimental conditions . However , the more complex models are difficult to estimate reliably when experimental data are limited [1 , 18 , 22] , especially for natural stimuli [12 , 23 , 34] . Difficulties associated with fitting and testing have prevented any single alternative from replacing the linear STRF as a new standard . The challenges encountered when evaluating alternatives to the FIR STRF highlight the trade-off between model performance , how accurately it predicts neural activity , and complexity , the degrees of freedom governing the stimulus-response relationship [35 , 36] . In order to completely describe a system’s function , an encoding model must account for all the degrees of freedom of the actual system . If the system is not well understood , some degrees of freedom in a model are likely to be mismatched to the system’s function . Any mismatched complexity does not provide additional explanatory power , but it does introduce noise into model parameter estimates . Because this complexity does not improve performance , there should exist a model with fewer degrees of freedom that can perform as well as the more complex model . In this study we focus on the problem of complexity . Rather than simply seeking the model that performs best , we identify the simplest possible model that attains a minimum level of performance . Specifically , we ask , can we produce a low-dimensional approximation of the linear STRF that performs as well as the full FIR STRF ? The idea of improving STRF performance by dimensionality reduction has been proposed previously . Isolated studies have shown benefits of low-rank approximations of the STRF [28 , 31 , 37 , 38] . In the visual system , several studies have also proposed low-dimensional , system-specific parameterizations [18 , 29 , 39–43] . Despite the many parameterizations that have been proposed , however , direct comparisons between them have been limited , especially for natural stimuli . Thus it remains difficult to identify the important features of these different models . We approached the complexity problem directly by systematic comparison of a large set of alternative parameterizations . We generated a collection of models that instantiate a variety of low-dimensional approximations to the FIR STRF . We then compared their performance on single-unit data collected from primary auditory cortex during presentation of natural vocalizations . By exploring the performance of this family of models , we were able to identify the minimal essential components required by linear STRFs that best described the data and to study the relationship between the amount of data available and optimal model complexity . We found that the standard FIR STRF is suboptimal according to the complexity criterion . Instead , a much simpler model , which defines the STRF as a product of three Gaussian-tuned spectral filters and biphasic temporal filters , outperformed the FIR STRF , while requiring only about 10% of the parameters ( 29 vs . 276 free parameters ) . These results indicate that , for the average A1 neuron , a model with about 30 free parameters is able to capture its linear filter properties . The total degrees of freedom of a comprehensive nonlinear model is likely to be higher , but our minimally complex linear STRF provides a starting point for developing better-performing nonlinear models .
We recorded single-unit neural activity from the auditory cortex ( A1 ) of awake , passively listening ferrets during presentation of natural ferret vocalizations . The same set of 42 3-second vocalizations was presented during recordings from all neurons ( N = 176 ) . We then fit a large number of encoding models with different architectures to data from each neuron and compared their performance . Data for each neuron were grouped into an estimation data set ( 40 vocalizations ) , which was used for fitting , and a validation data set ( 2 vocalizations ) , which was used only to test how well each fit predicted responses to a novel stimulus ( Fig 1A ) . Our primary performance metric was prediction correlation , i . e . , the correlation coefficient ( Pearson’s R ) between the actual peri-stimulus time histogram ( PSTH ) , r ( t ) , and the PSTH predicted by the model , p ( t ) ( Fig 1C ) . Other commonly used performance metrics showed the same pattern of results ( e . g . , log-likelihood and mutual information , see below ) . Models were structured as a sequence of signal transformations , or functional modules , corresponding to the block diagram in Fig 1B , x 0 ( t ) → f 1 ( · ) x 1 ( t ) → f 2 ( · ) ⋯ → f n ( · ) y ( t ) ( 1 ) where the output , xi ( t ) , of each module , fi ( ⋅ ) , provides the input into the subsequent module . The final module produced the predicted time-varying spike rate , y ( t ) . In most models tested , this sequence consisted of three modules , a cochlear filterbank [26 , 44] , followed by a linear spectro-temporal filter [8 , 9 , 11 , 12] , and finally an output nonlinearity to account for spike generation thresholds [13 , 17] . Alternative model architectures were compared by replacing one or more modules in Eq 1 , while keeping the others the same . Thus the impact of the choice for each module on model performance could be tested individually ( see Fig 2C ) . Using this empirical approach , we selected optimal modules for the cochlear filterbank ( Eqs 11–13 ) and output nonlinearity ( Eq 14 ) for the same linear filter module ( FIR filter , see below , Eq 3 ) . These modules were then held constant while we compared performance for the different formulations of the linear filter module that follow . Models were fit using an iterated coordinate descent ( a . k . a . boosting ) algorithm [34] . On each iteration , the algorithm cycled through each module sequentially and performed a few steps of coordinate descent within that module before moving on to the next one ( see Methods ) . We have previously demonstrated that this coordinate descent algorithm is able to accurately recover linear STRFs in simulation [30 , 34] . Because datasets are finite , the performance of any model will be limited by sampling noise . This noise impacts the analysis at two stages: producing error in the estimation of model parameters and in validation of prediction accuracy [18 , 22 , 45] . Accounting for the first problem is a nuanced issue: more complex models that require a large number of parameters are more susceptible to noise than simpler models . We address the issue of finite estimation data in a later section ( see Parameterized models perform similarly to FIR models in the limit of infinite data , below ) . To account for the latter problem , measures of prediction correlation were normalized by a factor reflecting response reliability in the validation stimulus ( Eq 23 , [45] ) . This factor was fixed for an individual neuron’s validation data . Thus it does not affect the performance of one model relative to another . Numerically , this correction increased prediction correlations in A1 by a mean of 20% ( ranging from 3% to 39% for individual neurons ) . Model complexity is often factored into cost functions for model fitting , in order to positively weigh simpler models [35 , 46] . Our goal was to study in depth the relationship between model complexity and performance . Thus , rather than combining them into a single cost function , we studied the trade-off between these criteria in detail , exploring the family of solutions that are optimal with respect to both . This optimal set of solutions is known as the Pareto front [36 , 47] . Formally , all items belonging to this front are non-dominated in the Pareto sense [47] which means that for all pairs of models on the front , one is less complex while the other fits more closely to the data . All models below the Pareto front are non-optimal: there is at least one model on the front that is both less complex and more accurate . We generated Pareto plots for the 1061 different linear STRF architectures tested , comparing model parameter count against average prediction correlation for estimation data ( Fig 2A ) and validation data ( Fig 2B ) . Most models lie under the Pareto front ( red line ) and are suboptimal relative to models that are less complex , better performing , or both . More complex models tend to perform better for estimation data , but they do not necessarily predict novel validation data more accurately . The differences between estimation and validation plots illustrate the problem of overfitting when available estimation data are finite . Among the more complex models , the FIR STRF falls below the Pareto front for the validation data ( black point , Fig 2B ) . Instead , best performance in the current dataset is achieved by a model requiring just 29 parameters ( orange point ) . In the following sections , we discuss in detail the subset of 260 architectures in which only the linear filtering module was varied , while all other modules ( cochlear filterbank , input nonlinearity , output nonlinearity ) and the fitting algorithm were held constant ( dark gray points , Fig 2A and 2B ) . Our focus is on identifying model architectures that fall on or near the Pareto front , making them optimal for a given level of complexity . The remaining models were generated by manipulating one or more modules other than the linear filter ( Fig 2C ) . Varying the other modules had less dramatic effects on model complexity and performance , but they provide a dense sampling of the complexity-performance space . A complete list of architectures evaluated is included in the supplementary materials ( S1 Table ) . Parameterized STRFs are approximations of the FIR STRF . Thus , in theory , the FIR STRF should perform as well as or better than any parameterized STRF . In practice , however , data available for estimation are finite , and simpler models can be estimated more accurately than the full FIR STRF . Thus simpler models are able to perform better than the FIR STRF in our analysis ( Fig 6 ) . The results so far demonstrate a clear practical advantage of the factorized and parameterized models , but they do not answer the question of whether any simpler model fully accounts for the linear STRF . Such a question can only be answered by comparing the relative performance of these models in the limit of infinite estimation data [18 , 22] . Extrapolating performance to infinite estimation data is challenging because there is no widely agreed upon model of variability in sensory-evoked neural activity . We made a simplifying assumption that prediction error from estimation noise is additive and inversely proportional to the square root of the number of samples used to estimate the STRF , T ( see Methods , Eq 31 , [18 , 45] ) . When these assumptions hold , then the effect of noise on model variance explained ( square of prediction correlation , R2 ) also decreases proportionally to T . We varied T by subsampling the available estimation data ( 10%–75% ) and measured the average RT across neurons for models fit with the different data subsets . We then fit the free parameters in Eq 31 to determine the theoretical limit on performance for each model , Rinf . We measured the asymptotic performance limit of four model architectures , ranging from high to low complexity: the full FIR model ( FIR , 276 parameters ) , D = 3 factorized model ( Factorized x3 , 109 parameters ) , D = 3 Gaussian spectral/P3Z1 temporal parameterization ( P3Z1x3 , 29 parameters ) , and D = 1 Gaussian spectral/P3Z1 temporal parameterization ( P3Z1x1 , 13 parameters ) . We removed very noisy data and focused on the subset of 124 neurons that produced reliable auditory-evoked responses ( SNR > 0 . 005 , see Methods , Eq 21 ) . For all models , performance improved as more estimation data became available ( Fig 8A ) . As expected , the lower-dimensional models performed better for small data sets and neared asymptotic performance sooner than higher-dimensional models . Consistent with this observation , performance of the FIR STRF showed the greatest improvement in the asymptote ( Rinf = 0 . 63 , Fig 8B ) . However , performance of the Factorized x3 ( Rinf = 0 . 63 ) and P3Z1x3 models ( Rinf = 0 . 62 ) was not significantly different from the FIR STRF ( jackknifed t-test ) . Thus within the precision we could achieve with this analysis , both models captured the essential features of the FIR STRF . Error bars on asymptotic performance are relatively large , especially for the FIR STRF , so a strong conclusion about relative performance of these models is difficult . However , asymptotic performance of the P3Z1x1 model was significantly worse than the other models ( Rinf = 0 . 56 , p < 0 . 001 ) , indicating a failure of this very simple model to capture the full linear model . For comparison with a previous analysis [22] , we also measured asymptotic performance for the FIR STRF with no output nonlinearity . This model performed better than the standard FIR STRF for smaller estimation sets , presumably due to its reduced complexity , but its advantage diminished for larger datasets . Asymptotic performance was slightly lower than the standard FIR STRF that included an output nonlinearity ( Rinf = 0 . 61 , p < 0 . 05 , Fig 8B ) . In addition to outperforming the FIR model in finite data conditions , reduced-dimensionality factorized and parameterized STRFs demonstrated several other benefits over the FIR STRF , which we detail below . For brevity in this section , factorized model refers specifically to the D = 2 factorized model , and parameterized model refers to the D = 3 Gaussian spectral parameterization with P3Z1 temporal parameterization . These models were chosen because they represent the best-performing models , respectively , among the factorized and parameterized models tested ( Fig 6C ) .
The finite impulse response ( FIR ) STRF represents the current standard model for stimulus-response filtering in the auditory system [2 , 4 , 8–13] . Our results agree with previous findings that , as a general architecture , the linear STRF accounts only partially for the neural response to natural sounds in A1 [9 , 23] . However , we find that the same level of performance can be achieved by much simpler models . A model requiring fewer than 30 parameters not only matches performance of the FIR STRF ( > 250 parameters ) but actually outperforms it for large but finite datasets . The simplest parameterization that works optimally for a neural population provides insight into the neural circuitry underlying system function [36] . According to this logic , the average linear STRF of an A1 neuron can be captured by the sum of three channels with Gaussian spectral tuning and an IIR temporal filter . When data are finite , a critical issue is that a simpler model with fewer free parameters will be less susceptible to estimation noise than a more complex model . Thus the simpler model may perform better , even if it fails to account for important degrees of freedom in the more complex one . Accounting for the impact of estimation noise on model performance is difficult , as it requires extrapolation to the condition where data are infinite [18 , 22] . By assuming that estimation noise is additive , we found that a simple inverse relationship between estimation set size and prediction error accurately described performance for several different architectures ( Fig 8A ) . In the limit of infinite data and under these assumptions , the FIR STRF did not perform significantly better than the simple parameterized model . These results should be confirmed with a larger dataset , but the current analysis suggests that the essential degrees of freedom for the linear STRF are much closer to 29 than to the 276 specified by the FIR STRF . The average linear STRF in A1 may be described by about 30 parameters , but STRFs for individual neurons do vary substantially in their complexity . Some neurons require only one spectral channel for optimal performance while others require four or more channels ( Fig 12B ) . The fact that only a minority of neurons were best described by a single dimension argues that most linear STRFs are not frequency-time separable [37 , 51] . At the other extreme , even STRFs with four or five spectral channels required substantially fewer parameters than the standard FIR STRF . This low dimensionality generalizes across other natural and synthentic stimuli in A1 , but our analysis of data from the belt area dPEG indicates that more complex models are required for non-primary cortex ( Fig 11 ) . Moreover , even in A1 , the full dimensionality of encoding models is likely to be greater than what is required to specify the linear STRF . As demonstrated by the enhanced performance of the nonlinear STP STRFs ( Fig 11 ) , introducing additional dimensionality that extends outside of the linear STRF architecture can improve model performance . How well can the linear STRF actually describe sensory responses in A1 ? Issues surrounding finite sampling of experimental data again make it difficult to answer this question definitively [18 , 45] . After implementing our estimation noise model , we found that the FIR STRF is able to account for 40% of A1 response variance on average ( i . e . , variance explained is 100R2 for R = 0 . 63 , Fig 8 ) . Factorized and parameterized STRFs very nearly matched performance of the FIR model ( 39% of response variance ) , indicating that these approximations capture the essential features of the more complex model , despite requiring only about 50% and 10% of the parameters , respectively . These measurements establish baseline performance by the linear STRF that must be surpassed by any more accurate model . At the Pareto frontier , a better model must either produce more accurate predictions or require fewer parameters and perform as well . Only one previous study has attempted to answer this question rigorously , using activity driven by random chord stimuli in anesthetized mice [22] . Although we focused primarily on models that included an output nonlinearity [13 , 14] , we also computed asymptotic performance of STRFs without this nonlinear term in order to make a more direct comparison to the previous analysis of asymptotic performance . Without a spiking nonlinearity , the average FIR STRF was able to account for about 37% of response variance . This result falls in the range of 18–40% reported previously [22] , although several factors make a direct comparison difficult . In the current study , recordings were performed in awake ferrets and used natural vocalizations rather than anesthetized mice and noise stimuli . Anesthesia can impact auditory neural activity [58 , 59] , and natural sounds evoke nonlinear response properties in a different functional domain than noise stimuli [9 , 60] . The number of models tested here was relatively large , but they are still likely to be suboptimal compared to as-yet-untested parameterizations . The current study explored only two spectral parameterizations ( Gaussian and Morlet functions ) and the pole-zero family of IIR temporal filters . Numerous other basis functions could be considered , including Gabor wavelets [42 , 61] or empirically-derived basis functions [29 , 31 , 33] . There is a clear trade-off between basis function complexity and the number of spectral dimensions needed . Better-performing temporal kernels like the P3Z1 filter reach their peak performance when D = 3 , while simpler kernels like P1Z0 need D ≥ 4 to reach the same performance . Thus the interaction between channel count and basis function complexity will be relevant to identifying optimal parameterizations . The efficiency of estimating parameterized STRFs allows the introduction of new , nonlinear terms that can account for encoding properties that are not captured by the linear model [30 , 31] . When nonlinear short-term plasticity was introduced to the FIR STRF , it did not change model performance , but when it was introduced to the parameterized model , it improved predictive power . Thus the benefits of nonlinear terms may only become apparent when sufficient statistical power is available in the current dataset . The family of models used in this study incorporate static nonlinearities that are commonly part of STRFs . This include log-compression of the input spectrogram to account for basilar membrane mechanics [25 , 26] and an output nonlinearity to account for spike threshold and saturation [13 , 14] . Other studies have incorporated nonlinear terms into the core computation of the filter . Some use general Volterra series expansions to account for second- and higher-order nonlinearities [3 , 27 , 57 , 62] . Others incorporate more specific terms aimed at capturing contextual influences [28 , 29] or mimicking biological circuit elements [26 , 31] . These additional nonlinear terms can be incorporated into the parameterized framework , potentially providing substantial improvements in predictive power . Neurons also undergo plasticity at multiple timescales due to stimulus context [12 , 30 , 63 , 64] , changes in behavioral state [50 , 65 , 66] , and learning [49 , 67] . In many experimental settings , the quantity of data available in a single behavioral state may provide a critical limitation on statistical power . Low-dimensional parameterized models may be particularly beneficial for exploring changes in spectro-temporal response properties in these experimental settings . From a general analytical perspective , parameterization is similar to regularization during model estimation [1 , 12 , 46 , 68] . In both cases , pre-existing knowledge or a hypothesis about the system’s function is used to constrain model fits . The idea that sensory receptive fields should vary smoothly in space and time has motivated the use of priors for smoothly varying STRFs [46 , 68] . Similarly , the idea that receptive fields should have a relatively small number of non-zero parameters has motivated a sparse prior on model fits [14 , 46] . Constraining the STRF to have analytical form of the factorized or parameterized models serves the same purpose of imposing a prior on the fit [38] . In the current study , the spectral and temporal parameterizations constrain both sparseness ( limiting the model’s degrees of freedom ) and smoothness ( Gaussian spectral tuning and exponential temporal tuning ) . To simplify model comparisons in this study , we used a single fit algorithm across all models . Thus it was not optimized specifically for the FIR STRF . Incorporating stricter stop criteria and sparseness constraints improve FIR STRF performance , but even after tuning the cost function , it did not match the performance of the parameterized model . The factorized and parameterized models were less sensitive to details of the fit algorithm such as the stop criterion , emphasizing the benefits of regularization effectively built into parameterization . Most real world optimization problems involve the simultaneous minimization of several objectives [69] . Thus when comparing different model architectures , it may be helpful to consider trade-offs separately along different dimensions [36 , 70 , 71] . The current study focused in particular on the trade-off between model prediction accuracy and parameter count . In general , however , such an approach can be used to define an N-dimensional Pareto front containing the best models according to numerous other measures , including alternative performance metrics ( Fig 13 , see also [72] ) , alternative model complexity metrics [73 , 74] , data required to fit ( Fig 8 ) , computational cost [75] , or model plausibility [76] . Pareto fronts are extensively used in the context of multiobjective optimization for the formulation of heuristics [69] . Given the complexity of performing a search on the space of model architectures , we relied here on inspection of the Pareto front to guide model design . While developing new analytic models to test , we found it most helpful to generate new models by adding to or discarding from a model on the current Pareto front . Variants of non-Pareto-optimal models rarely improved performance or provided insight into the relevancy of new parameters . Of particular note , the FIR implementation falls far from the Pareto front ( Fig 2B ) , making it difficult to test variants based on the FIR STRF .
Single-unit neural activity was recorded from five awake , passively listening ferrets . For the main analysis of responses to vocalizations , a total of 176 single units were recorded in primary auditory cortex ( A1 ) and 130 units in belt auditory cortex ( dPEG ) . For one analysis ( Fig 11C and 11D ) , responses were analyzed for 808 A1 units recorded during the presentation of continuous speech ( reanalyzed from a previous publication [9] ) and for 139 A1 units recorded during the presentation of 1/f noise [56] . Data used in this study will be made publicly available online via the Neural Prediction Challenge ( http://neuralprediction . berkeley . edu/ ) . The relationship between the time-varying input auditory stimulus , x ( t ) , and simultaneously recorded single-unit firing rate response , y ( t ) , is described by the spectro-temporal receptive field ( STRF [8 , 9 , 11 , 12] ) or , more generally , any function that maps x to y . In the current study , this mapping was cast as a sequence of functional modules , in which each function was applied to the output of the previous one ( Eq 1 , Fig 1C ) . The series of functions maps roughly to the physical elements that transmit auditory information to cortex . A detailed list of all models tested in this framework is included in S1 Table . For most models , stimulus and response data were binned at 10 ms ( 100 Hz ) and averaged across repetitions . Stimulus binning was applied after transformation to the spectrogram . Data recorded from each neuron were divided into two subsets , one used only for model estimation ( 4–6 repetitions of 40 3-sec vocalization sequences ) and the other for validation ( 20 repetitions of 2 3-sec sequences ) . Model parameters were fit using an iterated , greedy version of boosting that minimized mean-squared error prediction of the neural PSTH in the estimation dataset ( details below ) . Each model was then evaluated based on its ability to predict the time-varying PSTH response in the reserved validation data set . Prediction accuracy was measured as the correlation coefficient ( Pearson’s R ) between the predicted and observed PSTH [12 , 34] . The correlation coefficient provides a useful metric because it scales performance between 0 ( completely random ) and 1 ( perfect correlation ) . Model performance can be variable across single neurons . Thus to compare models we focused on average performance across the entire set of neurons studied , using the nonparametric Wilcoxon signed rank test ( sign test ) to assess significant differences in performance . Error bars for average prediction correlation plots were computed on the difference between prediction correlation for each model and the FIR STRF fit to the same neuron . Computing error bars based on the difference per neuron removed variability in overall neural response SNR ( e . g . , Figs 4A and 10B ) and revealed model differences commensurate with the sign test . Our goal was to compare the ability of different analytical model structures to describe the neural data . Ideally , the details of the fitting algorithm used to fit the different models should not be relevant to this comparison , but in practice , there is no single algorithm that can be applied to different models without some bias [1] . Thus , the best fitting algorithm and model analytical structures are not separable in practice . We tested a variety of fit algorithms ( Fig 2 , S1 Table ) , but we focused on a single algorithm that performed best , on average , across all the models tested . The fit algorithm consisted of nested iterations through each STRF module , initially optimizing each module with a conservative stop criterion . Once all modules had converged for the current stop criterion , its value was reduced and procedure was repeated for the smaller criterion . When fitting each module , two different coordinate descent algorithms were used . For non-parameterized modules ( FIR filter , factorized spectral filter , and factorized temporal filter ) , a standard coordinate descent algorithm was used . For the remaining , parameterized modules ( including the input filterbank and spike nonlinearities ) , greedy coordinate descent was used . The details of the fit algorithm are as follows: In general , we found that fitting parameters separately within modules and iterating through modules with progressively smaller stop criteria helped avoid local minima . Fitting first without the spike nonlinearity also helped avoid local minima . The greedy algorithm increased the risk of overfitting complex models , but on average greatly improved predictions for models with nonlinear and parameterized modules . The non-greedy algorithm worked best for non-parameterized modules where all parameters are of similar scale . Experimental procedures were approved by the Oregon Health and Science University Institutional Animal Care and Use Committee and conformed to standards of the National Institutes of Health . | Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers . Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems . However , the complexity of sensory neural systems , combined with practical limitations on experimental data , make it difficult to apply arbitrarily complex analyses to neural data . In this study we pushed analysis in the opposite direction , toward simpler models . We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex . We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models . These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | The Essential Complexity of Auditory Receptive Fields |
Synonymous mutations do not alter the specified amino acid but may alter the structure or function of an mRNA in ways that impact fitness . There are few examples in the literature , however , in which the effects of synonymous mutations on microbial growth rates have been measured , and even fewer for which the underlying mechanism is understood . We evolved four populations of a strain of Salmonella enterica in which a promiscuous enzyme has been recruited to replace an essential enzyme . A previously identified point mutation increases the enzyme’s ability to catalyze the newly needed reaction ( required for arginine biosynthesis ) but decreases its ability to catalyze its native reaction ( required for proline biosynthesis ) . The poor performance of this enzyme limits growth rate on glucose . After 260 generations , we identified two synonymous mutations in the first six codons of the gene encoding the weak-link enzyme that increase growth rate by 41 and 67% . We introduced all possible synonymous mutations into the first six codons and found substantial effects on growth rate; one doubles growth rate , and another completely abolishes growth . Computational analyses suggest that these mutations affect either the stability of a stem-loop structure that sequesters the start codon or the accessibility of the region between the Shine-Dalgarno sequence and the start codon . Thus , these mutations would be predicted to affect translational efficiency and thereby indirectly affect mRNA stability because translating ribosomes protect mRNA from degradation . Experimental data support these hypotheses . We conclude that the effects of the synonymous mutations are due to a combination of effects on mRNA stability and translation efficiency that alter levels of the weak-link enzyme . These findings suggest that synonymous mutations can have profound effects on fitness under strong selection and that their importance in evolution may be under-appreciated .
Synonymous mutations have traditionally been considered to be silent with respect to fitness because they do not change the encoded amino acid . However , synonymous mutations can alter mRNA structures in ways that alter translation initiation , mRNA stability , or even protein folding due to changes in the tempo of translation . Codon choice is not necessarily optimized in every gene , as natural selection operates to favor only mutations that impact survival , growth and/or reproduction , and even detrimental mutations can be fixed when population sizes are small or population bottlenecks occur . Nevertheless , codon choice appears to be reasonably close to optimal under the normal conditions in which organisms grow and reproduce , and synonymous mutations under those circumstances are often detrimental . Indeed , synonymous mutations have been implicated in over 50 human diseases [1] , including cystic fibrosis , breast cancer [2] , melanoma [3] and Crohn’s disease [4] . The same holds true for microbes; most of the 38 synonymous mutations introduced into genes encoding the E . coli ribosomal proteins S20 and L1 were slightly deleterious , with an average selection coefficient of -0 . 0096 [5] . While most synonymous mutations are neutral or slightly detrimental under normal conditions [6] , their effects may be magnified under strong selection . Synonymous mutations that generate a new promoter for a critical enzyme encoded by a downstream gene have been reported . In E . coli , for example , a synonymous mutation in the gene upstream of inhA , which encodes the target of isoniazid ( used to treat tuberculosis ) , generates a new promoter and increases inhA expression by 3-4-fold [7] . A similar case is our discovery of a synonymous mutation in the gene upstream of a gene encoding an inefficient bifunctional enzyme; this mutation also generates a new promoter for the downstream gene [8 , 9] . The effects of synonymous mutations can also play out at the mRNA level . Synonymous mutations can influence the stability of helices and stem-loops in ways that affect translation efficiency , mRNA stability or both . Synonymous mutations can also affect recognition of mRNAs by small regulatory RNAs and even the tempo of translation by the ribosome [10] . An infrequently used codon recognized by a rare tRNA can cause a pause in translation and allow the polypeptide to fold in the absence of interference from downstream sequences [11] . Thus , synonymous mutations can lead to different folding pathways and consequent effects on protein function [12–15] . Here we report strikingly large effects–both beneficial and detrimental–of synonymous mutations in a gene encoding a “weak-link” enzyme that catalyzes two reactions essential for growth of a mutant strain of Salmonella enterica subsp . enterica serovar Typhimurium str . SL1344 in M9/glucose . ProA ( L-γ-glutamyl phosphate reductase ) has a promiscuous ability to handle the substrate for ArgC ( N-acetyl-L-glutamyl phosphate reductase ) ( Fig 1 ) . This activity is not sufficient to sustain growth when argC is deleted from Salmonella enterica . However , a point mutation that changes Glu382 to Ala allows ProA to catalyze both reactions . ( This mutation is analogous to the mutation that enables survival of ΔargC E . coli on glucose that we previously described [8] . ) The inefficiency of E382A ProA ( referred to as ProA* hereafter ) severely limits growth of ΔargC proA* S . enterica on M9/glucose ( Fig 2 ) , imposing strong selective pressure for emergence of mutants that have an improved ability to synthesize proline and arginine . We evolved four populations founded by the ΔargC proA* strain under conditions selecting for faster growth for 260 generations . A mutation in the -10 region of the promoter of the proBA* operon that increases growth rate by 8-fold appeared first in every lineage . Subsequently , we detected an intergenic mutation just upstream of proA* , two synonymous mutations within the first six codons of proA* and one non-synonymous mutation in proA* . When re-introduced into the ΔargC proA* strain carrying the promoter mutation , these mutations increased growth rate by 30–83% . The striking effects of the two synonymous mutations inspired us to introduce all possible synonymous mutations in the first six codons of the proA* mRNA . Among these mutations , one doubled growth rate , while another completely abolished growth on M9/glucose . These fitness effects exceed previously reported effects for synonymous mutations [6 , 16 , 17] . Growth rate in strains containing the promoter mutation and mutations near the head of proA* ( the second gene in the proBA* operon ) correlates with changes in the level of proA* mRNA , suggesting that the mutations impact mRNA stability . Computational analyses of the structures of this region of wild-type and mutant proA mRNAs suggest that the mutations alter the efficiency of translation initiation , a conclusion supported by the observation that the mutations cause larger fold-changes in ProA* levels than in proA* mRNA levels . Thus , we attribute the striking fitness effects of synonymous mutations in the head region of the proA* mRNA to increases in production of ProA* due to both an increase in the level of proA* mRNA and an increase in the amount of protein produced from each mRNA molecule .
The genesis of this project was an interest in how recruitment of a promiscuous enzyme to serve a new function and the subsequent process of gene duplication and divergence to alleles encoding two specialist enzymes might differ in different organisms . We previously found that a point mutation in proA allows E383A ProA to substitute for ArgC in E . coli . In this work , we deleted argC from Salmonella enterica subsp . enterica serovar Typhimurium str . SL1344 and introduced a point mutation changing Glu382 ( which is homologous to Glu383 in E . coli ) to Ala . The inefficiency of E382A ProA ( ProA* ) limits growth rate to only 4% that of wild-type cells ( Fig 2 ) . Full restoration of growth requires addition of both proline and arginine , indicating that ProA* is the weak-link enzyme limiting growth rate of the ΔargC proA* strain , and that both L-γ-glutamylphosphate reductase and N-acetyl-L-γ-glutamylphosphate reductase activities are insufficient for optimal growth in this strain . When the ΔargC proA* strain was spread onto plates containing M9/glucose , we observed a few large colonies among a background of small colonies ( S1 Fig ) , suggesting that cells in some colonies had acquired a mutation that increased growth rate . In previous experiments with the comparable system in E . coli , 9% of the large colonies contained amplifications of a region surrounding the proBA* operon [9] . We assessed the copy number of the proBA* operon in 100 large colonies of the ΔargC proA* S . enterica strain but detected no amplifications . We sequenced the proBA* operon in 50 of these colonies and detected a mutation in the -10 region of the promoter in each colony . ( proB encodes γ-glutamyl kinase , which synthesizes the substrate for ProA . ) This mutation ( M1 ) , which changes the -10 region of the promoter from TAAAAC to TAAAAT , increased the levels of both proB and proA* mRNA by about 11-fold ( Fig 3A ) . We evolved four populations of the ΔargC proA* S . enterica strain in M9/glucose for approximately 260 generations ( Fig 4 ) . Growth rate , as approximated by generations per day , increased 8-10-fold over this period . We measured the copy number of proA* by qPCR every 60 generations , but no amplification was detected . We sequenced the proBA* operon by Sanger sequencing ( which will only detect the most abundant clones ) in genomic DNA isolated from each population at 3–4 points during the evolution experiment . In addition to the promoter mutation M1 previously identified in large colonies on plates , we identified an intergenic mutation at -3 relative to the proA* start codon , synonymous mutations in codons 2 and 6 of proA* , and a non-synonymous mutation in codon 34 of proA* ( Fig 5A ) . Fig 5B shows the mutations detected in the proBA* operon in each population at various times during adaptation . M1 occurred first in each lineage . Subsequently , 1–3 additional mutations were found in each lineage . Sequencing of the proBA* operon in individual colonies revealed that M1 occurred with each of the other mutations . Colonies with more than two mutations had either M1 , M2 and M5 , or M1 , M3 and M4 . We re-sequenced the genomes of the parental strain and of 7 colonies isolated from the final populations to determine whether the fitness increase during adaptation resulted from mutations in the proBA* operon or elsewhere in the genome ( Table 1 ) . All colonies , as well as the parental strain , contained identical mutations in menC , manX , ybdN , and SL1344_2940 compared to the reference genome . menC encodes an enzyme involved in menaquinone biosynthesis , which is primarily required for anaerobic growth [18] . manX encodes a mannose-specific phosphotransferase IIAB component . Since all of our experiments were carried out under aerobic conditions and with glucose as a sole carbon source , these mutations likely had little effect on growth rate . The other two genes in which mutations were found are genes of unknown function . The genome sequences of two colonies from population 2 were identical , as were those of two colonies from population 3 . Deletions in cmk were found in one colony from population 1 , both colonies from population 3 , and the single colony from population 4 . A point mutation in speF was found in population 3 and 4 . The parallelism in mutation targets among the small number of lineages strongly suggests that these mutations contributed to fitness during the adaptation . speF encodes ornithine decarboxylase . Ornithine is an intermediate in arginine biosynthesis downstream of the reaction catalyzed by the weak-link ProA* . Decreasing ornithine decarboxylase activity , which diverts ornithine toward putrescine synthesis , might prevent diversion of material from the compromised arginine synthesis pathway . cmk encodes cytidylate kinase , which is involved in pyrimidine salvage . Two of the three observed mutations introduced stop codons and likely caused loss of function . It is not obvious why loss of function of cytidylate kinase would be useful under these selection conditions . We introduced each of the point mutations identified in the proBA* operon , as well as combinations of mutations observed after the conclusion of the adaptation , back into the ΔargC proA* strain and re-sequenced the genomes of the constructed strains to identify any mutations acquired during genome editing ( Table 2 ) . The strain in which M1 had been introduced ( JC559 ) had an additional mutation near the 3’-end of glnD . GlnD encodes a uridyl transferase/uridyl removing enzyme that transfers a uridyl group to PII under nitrogen-limiting conditions , leading to activation of glutamine synthetase . Since each of the reconstructed strains contains this mutation , the effects of the additional introduced mutations can be evaluated in a common genetic background . Two other reconstructed strains had acquired additional mutations . The strain containing M1 and M5 ( JC622 ) had a deletion in bssS , a gene involved in biofilm formation . BssS was not among the 1607 proteins detected by proteomic analyses of samples grown under planktonic conditions; thus , a deletion in bssS should not affect fitness . The strain containing M1 , M3 and M4 ( JC663 ) had a point mutation in napH . NapH is one component of the NapGH quinol dehydrogenase that is involved in nitrate reduction under anaerobic conditions . Because there is no nitrate in M9/glucose , this mutation should not affect planktonic growth in M9/glucose under aerobic conditions . Fig 6 shows the growth rates of the reconstructed strains . The promoter mutation M1 increases growth rate by 9 . 2-fold over that of the parental ΔargC proA* strain . In the background of M1 , each of the additional point mutations further increases growth rate by factors ranging from 1 . 3- to 1 . 8-fold . The combinations of M2 and M5 and of M3 and M4 observed at the end of the evolution experiment provided further increases in growth rate , resulting in strains with a 17-18-fold improvement over the growth rate of the parental strain as a result of only three mutations . We also introduced the synonymous mutations M3 and M4 into the parental strain in the absence of the promoter mutation M1 . Both mutations increased growth rate less in the presence of M1 than in the parental background ( S2 Fig ) . In the parental background , M3 and M4 conferred a 2 . 1- and 4 . 0-fold increase in growth rate , respectively , while they conferred only a 1 . 4- and 1 . 7-fold increase in growth rate in the M1 background . These negative epistatic effects indicate that the increase in ProA* due to the synonymous mutations is less impactful after the growth limitation has been partially solved by the promoter mutation M1 . The occurrence of synonymous mutations in codons 2 and 6 of proA* that increased growth rate by 1 . 4- and 1 . 7-fold , respectively , in the background of the promoter mutation M1 prompted us to ask whether other synonymous mutations in this region would affect fitness . We introduced all possible synonymous mutations into codons 2 , 3 , 4 and 6 . ( Codons 1 and 5 specify Met , and there is only one codon choice for this amino acid . ) The whole genome of each constructed strain was sequenced to identify any adventitious mutations introduced during genome editing ( Table 2 ) . Fig 3B shows the effects of these mutations on the levels of proB and proA* mRNAs in the ΔargC proA* strain containing the promoter mutation M1 . Because mRNA levels are often not well-correlated with protein levels , we measured the abundances of ProA* and ProB in strains containing promoter mutation M1 and synonymous mutations relative to those in the strain containing just M1 ( S1 Table ) using label-free high resolution Orbitrap mass spectrometry [19] . Fig 7 shows that ProA* levels varied over a 5 . 4-fold range , whereas ProB levels varied by only 20% . Growth rate increases as a function of the level of ProA* , leveling off at 2-fold higher at the highest levels of the protein . In contrast , there is a surprising anti-correlation between growth rate and the level of ProB . Whether a decrease in ProB level contributes to fitness independently of an increase in ProA* level is uncertain . We utilized the RNAstructure package ( version 5 . 8 . 1 ) ( https://rna . urmc . rochester . edu/RNAstructureWeb/ ) [20] with the default parameters to predict the secondary structures of the head region of the proA* mRNA . The choice of the fragment length for the modeling was considered carefully . mRNA will be exposed behind a translating ribosome and will begin to fold as soon as base pair interactions are possible . Because the ribosome occludes about 11 codons of mRNA [21] , a new initiation cycle cannot be started until the preceding ribosome moves at least 11 codons away from the start codon . Thus , we chose to model the structures of 53-nucleotide mRNA fragments beginning 4 nucleotides upstream of the Shine-Dalgarno sequence and continuing 34 nucleotides past the AUG start codon . The lowest-energy structure for the 53-nucleotide region of the proA* mRNA generated by the Fold algorithm is shown in Fig 8 . The color used for each nucleotide conveys the probability that the nucleotide is found in the depicted state . The Shine-Dalgarno sequence [22] is predicted to be single-stranded and therefore accessible for binding to the 30S subunit of the ribosome in this structure , as well as all of the mutant structures to be discussed below . Notably , the AUG start codon is sequestered in a 5-bp stem that includes parts of codons 2 and 7 and all of codon 6 . The mutations discussed below do not alter the structure of the second stem-loop at the lower right of Fig 8A , so this part of the structure will not be shown in the following figures . Minimal free energies for each calculated structure are shown in Table 3 . Fig 9 shows the effect of mutations M3 , M4 and two of the synonymous mutations in codons 2 and 6 that increase growth rate on the lowest energy structure predicted by the Fold algorithm . M3 and M4 destabilize the structure by 0 . 5 and 2 . 0 kcal/mol , respectively , and decrease the probability that the nucleotides in the stem will be found in the depicted base-paired stem-loop structure . The effect of combining M3 and M4 is additive , suggesting that the effects of the two mutations are independent . A synonymous mutation in codon 2 that changes CUG to UUG weakens the stem by changing a GC base pair into a GU base pair . The most dramatic effects were seen for mutations in codon 6 that disrupt the middle base pair in the 5-base-pair stem . Changing GGC to GGG ( Fig 9 and S3 Fig ) or GGA ( S3 Fig ) is predicted to disrupt the stem-loop entirely , leading to a structure that contains a different stem-loop with only three base pairs . Equally dramatic effects are caused by synonymous mutations that increase the stability of the stem structure ( Fig 10 ) . A synonymous mutation that adds a GU base pair increases stability by 0 . 7 kcal/mol , and a mutation that adds a GC base pair increases stability by 2 . 9 kcal/mol . Notably , a strain carrying the latter mutation does not grow at all in M9/glucose . Fig 11 shows that growth rate is linearly related to the stability of the minimal free energy structure for this set of synonymous mutations in codons 2 and 6 . In contrast to synonymous mutations in codons 2 and 6 , the intergenic mutation M2 and synonymous mutations in codons 3 and 4 do not change the minimal folding energy ( Fig 12 ) . However , they have a striking effect on the probabilities that nucleotides preceding the start codon are single-stranded . The mutation at -3 ( M2 ) increases the probability that the region preceding the start codon will be single-stranded and increases growth rate by 84% . In contrast , synonymous mutations in codons 3 and 4 decrease the probability that the region preceding the start codon will be single-stranded and decrease growth rate by 56 and 16% , respectively .
Deletion of argC requires S . enterica to recruit a promiscuous enzyme within its proteome to serve an essential function during growth on glucose . This system provides a good model for many situations in which a new enzyme is needed , such as the presence of a toxin or the availability of a new source of carbon , nitrogen or phosphorus . Recruitment of a mutant version of ProA solves the immediate problem of providing an enzyme that is newly required for growth . ( Wild-type ProA cannot substitute for the missing ArgC . ) This situation is particularly interesting because both the new and the original functions of the enzyme are required to support growth . When growth of microbes is limited by the inefficiency of a weak-link bifunctional enzyme , selection favors emergence of mutants that have managed to increase the level of one or both growth-restricting activities . This is often accomplished , at least initially , by promoter mutations or gene amplification . Both mechanisms substantially improve growth rate in an ΔargC proA* strain of E . coli [9] . In the Salmonella ΔargC proA* strain , a point mutation in the -10 region of the promoter ( TAAAAC to TAAAAT ) increases its similarity to the sigma 70 consensus sequence ( TATAAT ) [23] , increases expression of the proBA* operon by 11-fold , and increases growth rate by 9-fold . However , we did not observe gene amplification in either large colonies on plates or in four lineages that were adapted in glucose medium for 260 generations , a time by which massive amplification of the region surrounding the proBA* operon had occurred in E . coli [9] . The lack of amplification is surprising , as previous studies have shown that duplications ( the precursors of amplifications ) occur readily in many regions of the S . enterica genome [24] . Since gene duplication is the prerequisite for divergence of gene copies toward alleles encoding two specialist enzymes , the lack of duplication constrains the prospects for evolution of a new enzyme with specialized ArgC activity , as well as the potential for reversion of the proA* allele to restore the previous level of ProA activity . These results suggest that the abilities of even relatively closely related microbes to evolve a new enzyme in the face of an environmental challenge may be dramatically different . The growth rate of the Salmonella ΔargC proA* strain is substantially lower than that of wild-type cells even after acquisition of a promoter mutation , so there is still selective pressure for mutations that improve the ability to make proline and arginine . We discovered only one beneficial non-synonymous mutation in proA* in the four adapted lineages . This mutation changes Glu34 to Gly . The corresponding residue in the crystal structure of Thermatoga maritima ProA ( PDB 1O20 ) is over 30 Å away from the catalytic Cys residue ( S4 Fig ) . Efforts to determine the effect of this mutation are in progress . Striking improvements in fitness of the ΔargC proA* strain were also attained due to an intergenic mutation just upstream of the start codon ( M2 ) and two synonymous mutations in the first six codons ( M3 and M4 ) . To explore the role of codon choice in this region of the proA* mRNA , we introduced every possible synonymous mutation into codons 2 , 3 , 4 and 6 in a background containing the M1 promoter mutation . These mutations had surprisingly large effects on fitness , ranging from a doubling of growth rate to complete inhibition of growth ( Fig 7 and Table 3 ) . Although six mutations in this region increased growth rate , we found only two in the adapted lineages . This discrepancy is likely due to the limited number of clones ( seven derived from only four evolved lineages ) whose genomes were sequenced . Further , three of the four mutations that were not found are transversions , which are 2-7-fold less common than transitions such as M3 and M4 [25] , and the other is unlikely to occur over a short period of selection because it requires two point mutations . Synonymous mutations might affect fitness via any of several mechanisms discussed in the introduction , including generation of a new promoter , alteration of mRNA stability and/or translation efficiency , binding of a small non-coding RNA to a mRNA , and alteration of protein folding due to changes in the tempo of translation . Since the synonymous mutations we found are within the body of the gene encoding the weak-link enzyme , they cannot have generated a new promoter for proA* . We can also dismiss effects on protein folding . The use of rare codons at specific points in a mRNA has been suggested to allow folding of translated polypeptide sequences in the absence of downstream sequences that might interfere with proper folding . We identified high-impact synonymous mutations within the first six codons . Any effects of translation rate on protein folding would be irrelevant in the initial six amino acids . There is insufficient space for folding in the ribosome exit tunnel until the chain has reached the last 20 Å of the exit tunnel , which requires translation of at least 20 amino acids [26 , 27] . Several of the synonymous mutations and the mutation at -3 relative to the start codon ( M2 ) had substantial effects on the level of proA* mRNA , and , not surprisingly , growth rate increased as a function of proA* mRNA level . The effects of these mutations on the levels of proA* mRNA might be due to effects on transcription , degradation , or both . The former is unlikely; mutations around the head of the proA* mRNA should not affect the rate at which the entire proBA* operon is transcribed because proA* is the second gene in the operon . Further , these mutations are too close to the start codon for proA* to generate a new promoter . Thus , the mutations most likely affect the rate of degradation of the proA* mRNA . These effects might be due to alterations in binding to a small regulatory RNA . However , of the 140 recognized small regulatory RNAs in S . enterica serovar Typhimurium , none is known to bind in the proBA operon [23] . A final possibility is that differences in degradation are due to the well-established link between mRNA degradation and translation efficiency . Ribosome occupancy protects mRNA from degradation by physically shielding the mRNA from endonucleases [28] . Ribosome binding also prevents premature transcription termination , which occurs when translation is not initiated promptly after the initial part of the transcript is produced by RNA polymerase [29] . Translation efficiency is determined by the efficiency of initiation [30] , which depends on a host of factors , including the strength of the Shine-Dalgarno sequence , the strength of the start codon , the spacing between these elements , the nature of the codons in the head of the mRNA , and the accessibility of the region between the Shine-Dalgarno sequence and start codon . Biases in codon usage in the heads of mRNAs have been recognized since the 1980s [31 , 32] and have been ascribed to either the importance of minimizing secondary structure in the region that must bind to the 30S ribosomal subunit to initiate ribosome assembly [31–33] , or to the benefits of a slow ramp in translation speed due to rare codons at the beginning of transcripts ( 30–50 codons ) that prevents ribosome traffic jams [34] . Both factors may be important , and cannot always be deconvoluted . In our case , every synonymous mutation except one–both beneficial and detrimental–changed a common codon into a rare codon ( S4 Table ) , suggesting that the beneficial effects cannot be attributed to the latter mechanism . Our modeling results suggest that the intergenic mutation at -3 and synonymous mutations in the first six codons alter the propensity for secondary structure around the Shine-Dalgarno sequence and start codon . This region must be single-stranded to bind to the 30S subunit of the ribosome prior to assembly of the full ribosome [35] , as the 30S initiation complex does not contain a competent GTPase that can use energy to unwind secondary structures . ( IF2 , which is present in the initiation complex , is not activated to hydrolyze GTP until after the 50S subunit binds [36 , 37] . ) Mutations that decrease the stability of the stem-loop that sequesters the start codon increase the level of proA* mRNA and growth rate by up to 2-fold . Other beneficial mutations do not affect the stability of the stem-loop structure , but increase the probability that the 10 nucleotides preceding the start codon will be single-stranded . In contrast , detrimental mutations either decrease the probability that the 10 nucleotides preceding the start codon will be single-stranded or increase the stability of the stem-loop structure that sequesters the start codon . Notably , a mutation that adds an extra GC base pair to the stem-loop structure is sufficient to prevent growth entirely . Our computational results support the hypothesis that synonymous mutations in the head region of the proA* mRNA affect translation efficiency . Experimental support for this notion is provided by the observation that the slope of a plot of fold-change in ProA* vs fold-change in proA* mRNA is 1 . 7 ( Fig 13 ) ; if translation efficiency were unaffected by the mutations , the slope would be 1 . 0 . This result indicates that changes in ProA* levels are due not only to changes in levels of proA* mRNA , but also to changes in the efficiency of translation . Our results are consistent with recent computational studies showing that folding energies in 39-nucleotide windows in every mRNA encoded by 414 bacterial genomes are less negative around the start codon than in the rest of the transcripts; this decrease in stability is associated with the use of rare codons that tend to be AU-rich [38] . The importance of low secondary structure around the ribosome binding site was reinforced by a study of a library of recoded GFP variants in which synonymous mutations were introduced randomly throughout the gene ( with an average of 114 differences between pairs of sequences ) . Expression was highest for sequences for which secondary structure was minimal around the ribosome binding site ( 30 nucleotides centered around the start codon ) [39] . Finally , an analysis of >14 , 000 synthetic reporters in E . coli demonstrated that increased secondary structure in the first 120 nucleotides of a mRNA decreases translation efficiency [40] . Substantial increases in growth rate due to synonymous mutations in a weak-link enzyme have been reported in two other cases . Agashe et al . recoded the gene encoding formaldehyde activating enzyme ( FAE ) , which is required for growth of Methylobacterium extorquens AM1 on methanol and methylamine , with 46–150 synonymous mutations . Every re-coded version caused a significant decrease in growth rate on methanol and in the level of FAE [41] . In a subsequent adaptive evolution experiment , growth of three strains carrying recoded versions of fae was substantially improved by synonymous mutations in codons 4 , 9 and 13 [42] . Further analysis of a set of 37 mutants showed no correlation between growth rate and either the computed folding energies of a 100-nucleotide fragment surrounding the start codon or the affinity between the Shine-Dalgarno sequence and the anti-Shine-Dalgarno sequence on the 30S ribosomal subunit , suggesting that epistatic interactions within the widely different fae alleles influenced the effects of single synonymous mutations on fitness . In another interesting case , Bailey et al . discovered two synonymous mutations in codons 15 and 38 of a glucose permease in a Pseudomonas fluorescens population evolved in the presence of glucose for 1000 generations . The mechanistic basis for the beneficial effects of these synonymous mutations , which increased expression of the glucose permease and increased fitness by 7–9% , was not clear [16] . The results reported here indicate that synonymous mutations can have unexpectedly large effects on fitness when growth rate is limited by a weak-link enzyme . While it has been recognized for decades that codon choice can have substantial effects on the levels of the expressed proteins , most previous work has been directed at either understanding the mechanism of translation or at optimizing protein expression for biotechnological purposes . The magnitude of the fitness effects of synonymous mutations under strong selective pressures we have reported here suggests that synonymous mutations may have played a previously unappreciated role in adaptation of microbes to novel stresses , particularly when gene duplication/amplification is not advantageous . However , the mechanisms responsible for increasing fitness are clearly not the same in this study and those of Agashe et al . [42] and Bailey et al [16] , suggesting that there is more to learn about the interplay between synonymous mutations and adaptive evolution , particularly under strong selection .
Salmonella enterica subsp . enterica serovar Typhimurium str . SL1344 ( hereafter S . enterica ) was obtained from the Detweiler lab at the University of Colorado Boulder . Strains we derived by genome editing of this ancestral strain are listed in Table 2 . S . enterica strains were grown in LB or M9 medium [43] containing 0 . 2% glucose supplemented with 100 μg/mL ampicillin ( amp ) , 35 μg/mL chloramphenicol ( chl ) , or 100 ng/mL anhydrotetracycline ( atc ) as required for selection of strains during genome editing . S . enterica is naturally resistant to streptomycin ( strep ) , so streptomycin was added to media at a concentration of 50 μg/mL to minimize contamination with other bacteria . When proline and/or arginine were added to the medium , the final concentrations ( 0 . 4 mM proline and 5 . 4 mM arginine ) correspond to those found in EZ-rich medium [44] . Primers ( S2 Table ) were ordered from IDT with standard desalting . A nonsense mutation in hisG in the ancestral strain of S . enterica was reversed using a genome editing method developed in our laboratory [45] . The same method was used to delete argC and change the GAA codon specifying Glu382 to GCA specifying Ala to generate the parental strain for this work . We performed these editing steps in two biological replicates . Whole genome sequencing indicated that the replicated were identical , and therefore both are designated JK328 . Fifty-μL aliquots of freezer stocks of JK328 were streaked onto 15 plates containing LB/strep and the plates were incubated at 37°C overnight . A single colony from each plate was washed twice with 100 μL sterile PBS , suspended in 100 μL sterile PBS , and diluted 1:10 , 000 . Fifty-μL aliquots of each suspension were spread onto 15 plates containing M9/glucose/strep and the plates were incubated at 37°C for 5–6 days . One hundred colonies that were larger than average were suspended in 20 μL sterile H2O . Each suspension was streaked onto plates containing M9/glucose/strep and the plates were incubated for two days at 37°C . Genomic DNA was prepared from a single colony from each of the 100 colonies that had been streaked onto the isolation plates . The copy number of the proBA* operon was determined by qPCR for all 100 colonies as described below; the entire proBA* operon was sequenced in 50 colonies using Sanger sequencing ( Macrogen ) . Four starter cultures of 2 mL LB/strep were inoculated with individual colonies of the parental strain JK328 and the cultures were incubated at 37°C with shaking . Cultures were harvested at mid-log phase by centrifugation at 10 , 000 x g for 1 . 5 min at room temperature . The pellets were washed twice with 1 mL ice-cold sterile PBS and then suspended in 1 mL ice-cold sterile PBS . Aliquots of each suspension were inoculated into 5 mL M9/glucose/strep to give an initial OD600 of 0 . 001 and the four parallel cultures were incubated at 37°C with shaking . The change in OD600 by the next day was used to calculate the number of generations that had occurred during growth of the culture . At approximately mid-log phase , aliquots of each culture were transferred to fresh medium to give an initial OD600 = 0 . 001 . ( On two occasions after growth rate improved , the initial OD was adjusted to 0 . 0001 or 0 . 00001 to ensure that the cultures would not reach stationary phase before the next day . After that point , dilutions were carried out to an initial OD600 of 0 . 001 twice a day . ) At each transfer , one mL of each culture was used to make a freezer stock by adding DMSO to 10% ( v/v ) ) and one mL was harvested via centrifugation at 10 , 000 x g for 1 . 5 min at room temperature . The cell pellets were stored at -20°C for later preparation of genomic DNA for Sanger sequencing and/or qPCR . The evolution experiment was carried out for one month ( approximately 260 generations ) . Construction of S . enterica strains containing mutations in the proAB* operon using the methods described by Kim et al . [45] is described in the Supporting Material . Aliquots of freezer stocks were streaked onto plates containing LB/strep and grown overnight at 37°C . Single colonies were used to inoculate 2 mL LB and grown to early log-phase at 37°C . One mL aliquots were harvested via centrifugation at 4 , 500 x g for 8 min at room temperature . Pellets were washed four times with 1 . 0 mL sterile PBS and re-suspended in 500 μL M9/glucose . The cells were diluted to an OD600 of 0 . 01 in M9/glucose and 10 μl aliquots were used to inoculate wells in a 96-well plate containing 90 μL M9/glucose to give an initial OD600 of 0 . 001 . The plates were incubated in a Varioskan ( Thermo ) plate reader at 37°C with shaking every 5 minutes . The absorbance at 600 nm was measured every 20 minutes for up to 500 hours . The baseline absorbance for each well ( the average over several smoothed data points before growth ) was subtracted from each point of the smoothed growth curve . Growth parameters ( maximum specific growth , μmax; lag time , λ; maximum growth , Amax ) were estimated by non-linear regression using the modified Gompertz equation [46] . Non-linear least-squares regression was performed in Excel using the Solver feature . We sequenced the whole genomes of adapted clones and reconstructed strains to identify mutations in the proBA* operon as well as elsewhere in the genome that might contribute to fitness . Genomic DNA was extracted from overnight cultures using the Invitrogen PureLink Genomic DNA mini kit . Libraries were prepared using a modified Illumina Nextera protocol and multiplexed onto a single run on an Illumina NextSeq500 to produce 151-bp paired-end reads [47] . These resulted in coverage of the ancestral S . enterica str . SL1344 genome ranging from 50 to 200 . Reads were trimmed using BBtools v35 . 82 ( DOE Joint Genome Institute ) and mapped using breseq v0 . 28 . 1 [48] . Procedures for determination of proA* copy number by qPCR are described in the Supporting Material . Procedures for analysis of transcript levels by RT-qPCR are described in the Supporting Material . Procedures for analysis of ProA* and ProB levels by label-free proteomics are described in the Supporting Material . | When a new enzyme is needed , microbes often recruit a pre-existing enzyme with a promiscuous activity corresponding to the newly needed activity . Such enzymes are often the “weak-link” in metabolism because they have not evolved to efficiently catalyze the new reaction . Under these circumstances , increasing the level of the weak-link enzyme can improve fitness . We evolved a strain of S . enterica in which a weak-link enzyme–E383A ProA–serves essential functions in synthesis of proline and arginine for 260 generations and then sequenced the genomes of several evolved strains . A mutation in the promoter of the operon encoding E383A ProA increased growth rate 9-fold . More surprisingly , a mutation upstream of the start codon and two synonymous mutations within the first six codons also increased growth rate by up to 68% . Introduction of all possible synonymous mutations in the first six codons showed that some doubled growth rate , while others slowed or even prevented growth . Computational and experimental data suggest that these effects were due to enhanced translational efficiency of the weak-link enzyme . These results show that synonymous mutations , once assumed to be selectively neutral , can have strong impacts on fitness when growth rate is limited by a weak-link enzyme . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"messenger",
"rna",
"microbiology",
"operons",
"bacterial",
"diseases",
"mutation",
"enterobacteriaceae",
"dna",
"cellular",
"structures",
"and",
"organelles",
"bacteria",
... | 2018 | Synonymous mutations make dramatic contributions to fitness when growth is limited by a weak-link enzyme |
Leishmaniasis is increasingly reported among travellers . Leishmania species vary in sensitivity to available therapies . Fast and reliable molecular techniques have made species-directed treatment feasible . Many treatment trials have been designed poorly , thus developing evidence-based guidelines for species-directed treatment is difficult . Published guidelines on leishmaniasis in travellers do not aim to be comprehensive or do not quantify overall treatment success for available therapies . We aimed at providing comprehensive species-directed treatment guidelines . English literature was searched using PubMed . Trials and observational studies were included if all cases were parasitologically confirmed , the Leishmania species was known , clear clinical end-points and time points for evaluation of treatment success were defined , duration of follow-up was adequate and loss to follow-up was acceptable . The proportion of successful treatment responses was pooled using mixed effects methods to estimate the efficacy of specific therapies . Final ranking of treatment options was done by an expert panel based on pooled efficacy estimates and practical considerations . 168 studies were included , with 287 treatment arms . Based on Leishmania species , symptoms and geography , 25 clinical categories were defined and therapy options ranked . In 12/25 categories , proposed treatment agreed with highest efficacy data from literature . For 5/25 categories no literature was found , and in 8/25 categories treatment advise differed from literature evidence . For uncomplicated cutaneous leishmaniasis , combination of intralesional antimony with cryotherapy is advised , except for L . guyanensis and L . braziliensis infections , for which systemic treatment is preferred . Treatment of complicated ( muco ) cutaneous leishmaniasis differs per species . For visceral leishmaniasis , liposomal amphotericin B is treatment of choice . Our study highlights current knowledge about species-directed therapy of leishmaniasis in returning travellers and also demonstrates lack of evidence for treatment of several clinical categories . New data can easily be incorporated in the presented overview . Updates will be of use for clinical decision making and for defining further research .
Leishmaniasis , infection by Leishmania parasites , is increasingly reported among travellers , especially in adventurous and eco-tourists [1]–[6] and military personnel [7]–[11] . Three syndromes are distinguished: visceral ( VL ) , cutaneous ( CL ) and mucocutaneous leishmaniasis ( MCL ) . VL is caused by L . donovani and L . infantum , rarely by other species . If left untreated , it will generally be fatal . CL is caused by L . major and L . tropica in the Old World ( OW: Europe , Africa , Asia ) and by parasites of the L . mexicana and L . braziliensis complexes in Central and South America ( NW , the New World ) . L . infantum and L . donovani can also cause CL . In Ethiopia and Kenya , L . aethiopica causes CL and diffuse cutaneous leishmaniasis , a difficult-to-treat condition . Most L . major and L . mexicana lesions heal spontaneously within 3 to 6 months [12]–[14] , L . tropica infections within one to two years [14] but L . braziliensis lesions may take much longer to heal [15] . MCL , involving nose , palate and often also pharynx and larynx , is usually caused by L . braziliensis . L . guyanensis , L . panamensis and L . amazonensis rarely cause MCL . Mucosal leishmaniasis of the OW is due to extension of CL to mucosa of mouth or nose , or to local primary infection by the sand-fly; the pathophysiology is different from that of MCL . It is estimated that 500 , 000 new cases of VL and 1 . 5 million new cases of CL occur per year , resulting in loss of 2 , 357 , 000 disability-adjusted life years [16] . In non-endemic regions , experience with diagnosis and management of leishmaniasis is limited . This may lead to delay in diagnosis and an unfavourable outcome of treatment [17] . In addition to the traditional diagnostic methods of microscopy , culture and serology , molecular techniques are increasingly used . Molecular techniques allow for fast and reliable identification of the aforementioned clinically relevant Leishmania species [6] , [18] , [19] . Leishmania species vary in sensitivity to available drugs [20] . Current choice of treatment is mainly based on the region where the infection was acquired and on the local experience with treatment . Molecular species identification makes species-directed treatment possible [17] . Development of guidelines for the treatment of leishmaniasis remains difficult . Cochrane Reviews of the treatment of CL and MCL highlight the poverty of current information and emphasize the need for high-standard trials [21] , [22] . Absence of parasitological confirmation and species characterization , lack of clearly defined treatment end-points , limited or no follow-up and small sample sizes are amongst the problems encountered , as described in the report of the expert committee of WHO [16] , a report wherein the clinical responsibility of the attending health care worker is acknowledged . CL is a self-healing disease which poses particular problems for the evaluation of therapies . These problems are not addressed in many reports . Published guidelines for the treatment of leishmaniasis in travellers do not aim to be comprehensive or do not provide an easy-to-use tool that quantifies overall success of available treatments [17] , [23]–[27] . Confronted with increasing numbers of patients with leishmaniasis , we aimed at providing comprehensive , yet easily digestible treatment guidelines based on symptoms , knowledge of the Leishmania species involved and the region where leishmaniasis was contracted , whilst taking data quality into account .
We searched PubMed with keywords “Leishmania AND therapy” , “leishmaniasis AND therapy” , “Leishmania AND treatment” and “leishmaniasis AND treatment” ( limited to humans and published in English ) , from January 1979 to December 2010 . Additional searches were performed on August 24th 2012 and December 19th 2012 . All treatment studies included in Cochrane reviews on therapy for CL and MCL [21] , [22] , all references included in the Deutsche Gesellschaft für Tropenmedizin guidelines on therapy for VL and CL/MCL [24] and all references from reviews on treatment of leishmaniasis in travellers [2] , [17] , [23] , [25]–[28] were considered for inclusion . Only original papers were considered . Randomized controlled trials ( RCTs ) and observational studies were included according to the inclusion criteria , summarized in Table 1 . If studies comprised several treatment options , separate analyses were performed for each treatment option with a minimum of 5 patients . To guide ranking of treatment options , results from studies were pooled to estimate the efficacy of specific therapies . For each treatment , the number of patients with a successful outcome and the total number treated with that treatment were extracted , irrespective of the dosing regimen , as only few publications report the actual dose per kg body weight [30] . If clinical end-points were related to the number of treated lesions , these were used to calculate the proportion with successful treatment success as separate analyses . Patients lost to follow-up before completing therapy were excluded from analysis . Patients who stopped therapy prematurely because of adverse events or treatment failure were regarded as treatment failures . Patients showing apparent cure that were subsequently lost to follow-up were grouped with patients classified as definite cure ( best case scenario ) . If absolute numbers of treatment successes and failures were not reported , but the proportion of successful treatment responses and the total number of patients per study arm were known , the latter data were used to calculate the absolute numbers . For each combination of diagnosis , species , location and specific treatment , a pooled efficacy and 95% confidence interval was calculated by pooling raw proportions of successfully treated patients from individual studies using the DerSimonian-Laird random effects method after Freeman-Tukey double arcsine transformation of raw proportions using the Meta package ( http://CRAN . R-project . org/package=meta ) . Because substantial heterogeneity between studies was expected , in particular due to differences in patient populations and study procedures , the choice was made to pool estimates using random effects , rather than fixed effects methods . Pooling was done separately for RCTs and for observational studies . Efficacy data from RCTs and observational studies were combined to arrive at the pooled efficacy of all included studies of a particular treatment . Treatments were summarized in relation to Leishmania species , clinical diagnosis and geography and ranked according to efficacy data from literature with type and number of studies , number of patients included and data of pooled efficacy ( tables 2 and 3 ) . Final ranking , with therapy of choice ( TC ) and up to 3 alternative therapies ( AT 1–3 ) , was done by the expert panel using the criteria mentioned above . Dosages for treatment options mentioned in tables 2 and 3 are provided in table 4 . Because of toxicity of amphotericin B , liposomal vehicles have been developed , notably liposomal amphotericin B ( Ambisome ) , amphotericin B-lipid complex ( Abelcet ) and amphotericin B colloidal dispersion ( Amphocil ) . All formulations are effective antileishmanial drugs , provided an adequate total dose of amphotericin B is given . Therefore , we pooled the treatment results of all amphotericin B formulations , including non-liposomal amphotericin B ( deoxycholate ) . Liposomal formulations are recommended because higher doses can be given in short courses with limited toxicity . Ambisome is preferred as it is the most widely used and evaluated of the available formulations , it is registered in many countries and is available at reduced price in endemic countries [16] and for free for selected poor countries [16] . Moreover , costs are carried by insurance companies in several industrialized countries . Azole drugs ( ketoconazole , fluconazole and itraconazole ) have been applied in the treatment of cutaneous and mucocutaneous leishmaniasis [31]–[39] although their efficacy is still debated [36] . Recent papers suggest that doses might not have been adequate [40] , [41] . More effective , better evaluated alternatives that require shorter treatment duration are available . We consider the azole drugs ‘reserve drugs’; they were not included in the analysis . Studies on cryotherapy [42]–[46] and intralesional antimony treatment ( ilSbv ) [42] , [45]–[47] as monotherapy have shown that these are effective treatments of L . tropica and L . major infections . Recently , ilSbv has been applied in CL from the New World too [48] . Literature on cryotherapy and ilSbv as monotherapy is provided , but these studies have not been included because several trials have shown superior effect of combination therapy [45] , [46] , [49] , which we therefore prefer . Aminosidine ( paromomycin ) ointment ( 15% aminosidine with 12% methylbenzethonium ) proved effective for treatment of L . major/tropica and L . mexicana infections [50] . A new formulation of aminosidine with gentamicin , WR 279 , 396 , shows very promising results [51] , [52] . Moreover , parenteral aminosidine for VL has recently become available for endemic countries [53] . If proven effective , topical aminosidine would be a welcome treatment especially for children for whom new treatments are eagerly awaited [54] . Registration and availability of these topical formulations are limited; they are not discussed but referred to and mentioned in Table 4 . Combination therapy is advocated for endemic areas in particular , in order to prevent development of resistance . Available data are limited [53] , [55] . The contribution of travellers to development of resistance is negligible and combination treatment is not considered here . Until the introduction and wide application of HAART , HIV-Leishmania co-infection was a frequent problem in southern European countries [56] . More recently , this has become a major problem in eastern Africa [57] . Treatment has been diverse and no universal guideline was developed . Guidelines for secondary prophylaxis ( prevention of relapses after apparent cure ) were never thoroughly evaluated . Treatment reports focusing exclusively on HIV-Leishmania co-infection and secondary prophylaxis before HAART are not included .
To optimize efficacy of treatment of leishmaniasis , the clinical diagnosis of the patient , the Leishmania species involved , and geography of infection should be considered . With these aspects as major determinants and with results of literature studies , we created a comprehensive guideline for species-directed treatment of leishmaniasis in the returning traveller . All English literature about treatment of leishmaniasis with a minimum of five persons per treatment was analysed . Efficacy data were pooled , resulting in an overall efficacy figure . Data are presented in easy-to-read overviews ( tables 2 and 3 ) . To our knowledge , this form of analysis and presentation has not been used before in overviews for treatment of leishmaniasis . It provides fast insight in current knowledge of treatment choices in clinical practice but also highlights the missing data needed to optimize treatment of leishmaniasis . It is difficult to define and rank evidence based treatment recommendations for leishmaniasis . Insufficiencies of reported studies include lack of parasitological proof , of species identification , clear definitions and end-points and insufficient follow-up or considerable loss to follow-up . Only few well-designed placebo-controlled trials have been performed and not all specific treatments were compared side-by-side , precluding a comprehensive meta-analysis of RCTs . Cochrane reviews revealed these limitations and called for initiatives to improve studies [21] , [22] , a plea supported by WHO [16] . In this study , a significant proportion of the included analyses were from observational studies ( 106/287 = 37% ) , including case-series of a single treatment regimen . In order to present a comprehensive overview of all available treatment options , we included both randomized controlled trials and observational studies . To allow estimation of pooled efficacy for all treatment modalities and across study designs , we chose a pragmatic approach of calculating the treatment success per treatment arm . As no comparisons between treated and untreated control groups were made , the external validity of the presented ( pooled ) efficacy estimates depends on the assumption that self-cure was negligible within the study period . However , because of similar timing of outcome assessment across studies , one may assume that when therapies for a specific species and clinical modality are compared , the degree of over-estimation of the treatment effect will have been comparable across studies , thus preserving the relative ranking of treatments which form the basis for our recommendations . A potential risk of not randomising treatment , as in observational studies , is that patients receiving a new treatment may have better or worse prognosis than the average patient . Nevertheless , pooled results from observational studies were comparable with those from RCTs ( tables 2 and 3 ) . Diverging results should be viewed with caution . For several clinical categories as defined in this study , limited literature was available , thus capitalizing on an expert panel was important . Efficacy figures of the literature study were combined with aspects of toxicity , convenience , and possibility of out-patient treatment to develop the guideline ( tables 2 and 3 ) . Combination of intralesional antimony with cryotherapy is advised for all cases of CL with less than 5 lesions . Exceptions are infections with L . guyanensis ( pentamidine ) and L . braziliensis from Bolivia , Peru and Ecuador ( systemic treatment ) . Treatment of complicated CL and MCL differs per species . For VL in immunocompetent travellers , treatment of choice is liposomal amphotericin B , total dose 20 mg/kg in 2–7 days , preferably 10 mg/kg o . d . on 2 consecutive days . Western European travellers mostly acquire VL in the Mediterranean region , occasionally in Latin America and rarely in the Indian continent or East Africa . For immunodeficient patients a total dose of 40 mg/kg is advised , administered over 4 to 8 days . In India , cost of treatment is a major consideration and slightly lower cure rates with lower dosages and retreatment of relapsed cases are accepted , if cost-effective . For travellers , highest cure rates and convenience are priorities and cost of drugs is less important; a reason to propose use of higher dosages of liposomal amphotericin B than are currently used in India . Leishmania species differ in in vitro or in vivo sensitivity to available drugs , in risk of development of complicated disease and in time required for spontaneous healing , thus knowledge of the Leishmania species and/or strain involved is important . Nowadays , PCR is an established diagnostic method , and species identification by molecular methods , e . g . sequence analysis , is available at reasonable cost . With the advent of these techniques , fast , precise and relatively cheap species differentiation has come within reach of many laboratories [6] , [18] , [19] . There are several limitations to our study . Firstly , we restricted our literature search in PubMed to the English language and may have missed relevant studies , since there is a fairly large body of literature published in other languages , especially in Latin America . Secondly , using the “best case scenario” for evaluation of treatment success for CL studies with considerable loss to follow-up may have led to overestimation of treatment results . However , this choice seems not unreasonable in view of the spontaneous healing tendency of at least 50% and the assumption that cured patients are less inclined to return for evaluation . Moreover , very few patients with initial cure ultimately fail treatment . Thirdly , azole drugs and aminosidine ointments are not fully discussed for the aforementioned reasons . New developments regarding aminosidine ointment WR 279 , 396 are promising , in particular for treatment of children [60] . Fourthly , methods of species identification have not been standardized and the exact status of several species is debated [83] . E . g . it has been argued that L . panamensis is a geographically confined subcluster or subspecies of L . guyanensis rather than a distinct species [84] , [85] . L . guyanensis may consist of several ( sub ) species or of different strains with different behaviour and different sensitivity to drugs [72] . Moreover , in many studies , species identification was not universally performed but based on prior surveys and studies , with inherent uncertainties of older , different ways of typing and possibilities of shifts of endemicity . As molecular techniques are becoming more widely available , we will likely get more and better information in the near future . Fifthly , due to the relative scarcity of reports of non-L . braziliensis MCL , it is difficult to evaluate the risk of developing MCL due to L . panamensis or L . amazonensis . The decision on local or systemic treatment for CL due to these species requires an individual risk benefit analysis . Finally , for several drugs a standard dosing schedule was reported in the included studies , but the exact doses in mg/kg for the individual patients ( or a mean or median with ranges for the studied population ) were hardly ever reported . Since the publication of Herwaldt and Berman [86] , antimony treatment of VL is with 20 mg Sbv/kg o . d . during 28–30 days , and this is how antimony treatment of VL has been reported in the literature since then . Dose and duration of antimony have varied in several studies of CL of the New World but WHO [16] advises 20 mg/kg o . d . , for 20 days . However , it is impossible to know if in the real world patients actually received 20 mg/kg . Are they actually weighed , and if so , by a calibrated scale ? Three antimony preparations are available: meglumine antimoniate ( Glucantime ) containing 81 mg Sbv per ml according to the WHO [16] , although others [30] mention 85 mg/ml , sodium stibogluconate ( Pentostam ) and generic sodium stibogluconate ( SSG , produced in India ) both containing 100 mg Sbv per ml . Glucantime is used in Latin America and French speaking countries and comes in ampoules of 5 ml [16] . There will be a tendency to use full ampoules . Pentostam is used in English speaking countries while SSG is mostly used by MSF . The latter two come in bottles of 100 ml; in general full millilitres will be used . Doses will be rounded off , upwards and downwards , regularly leading to under- or overdosing [30] . Actual total doses given per patient are rarely recalculated and reported . This is relevant for other drugs as well , e . g . for miltefosine that comes in capsules of 50 and 100 mg . An adult with bodyweight ≥50 kg receives 3×50 mg/d . In one study on L . major infections this led to doses of 1 . 3 to 2 . 1 mg/kg/d [67] . The difference in individual doses is important for several drugs; too much may lead to toxicity and adverse events , too low doses to treatment failure and development of resistance . We chose a practical approach of extracting all successfully treated patients irrespective of the dosing regimen . Our study highlights current knowledge of species-directed therapy of leishmaniasis in returning travellers . It also clearly demonstrates the lack of evidence in the literature for treatment of several clinical categories with different Leishmania species . More , well-designed and properly executed trials are needed to optimize advice on treatment [87] . This paucity of knowledge [21] , [22] has been recognized and prompted new initiatives to improve study design , diagnosis and evaluation of studies of the leishmaniases [16] , [87] . Moreover , in Europe , a study group has been established to integrate research on optimal treatment of VL , CL and MCL among travellers [88] . Updated versions of our overview will be of use both for clinical decision making and for defining further research . | Human leishmaniasis is caused by unicellular parasites that are injected into the skin by sand-flies , small , flying insects . Many different Leishmania species cause various manifestations of disease , both of the skin and internal organs . Leishmaniasis is a curable disease but clear guidelines on the best available treatment are lacking . Leishmania species differ in sensitivity to available drugs . Until recently , identification of the infecting Leishmania parasite was laborious , thus therapy could not precisely be targeted to the infecting species , in contrast to many other infectious diseases . Nowadays , Leishmania parasites can be identified relatively easily with new DNA techniques . We studied efficacy of therapies for diseases due to different Leishmania species , limited to the English literature . Efficacy was summarized and presented in an easy to read format . Because of difficulties with identification of parasite species in earlier studies , quality of evidence was often limited . Our findings are a major help for clinicians to easily find optimal treatment for specific patients . Moreover , our results demonstrate where additional research is needed to further improve treatment of leishmaniasis . | [
"Abstract",
"Introduction",
"Methods",
"Discussion"
] | [
"dermatology",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"medicine",
"and",
"health",
"sciences",
"skin",
"infections",
"travel-associated",
"diseases",
"leishmaniasis",
"neglected",
"tropical",
"diseases",
"biology",
"and",
"life",
"sciences",
"trop... | 2014 | Species-Directed Therapy for Leishmaniasis in Returning Travellers: A Comprehensive Guide |
Bone is a common site for cancer metastasis . To create space for their growth , cancer cells stimulate bone resorbing osteoclasts . Cytokine RANKL is a key osteoclast activator , while osteoprotegerin ( OPG ) is a RANKL decoy receptor and an inhibitor of osteoclastogenesis . Consistently , systemic application of OPG decreases metastatic tumor burden in bone . However , OPG produced locally by cancer cells was shown to enhance osteolysis and tumor growth . We propose that OPG produced by cancer cells causes a local reduction in RANKL levels , inducing a steeper RANKL gradient away from the tumor and towards the bone tissue , resulting in faster resorption and tumor expansion . We tested this hypothesis using a mathematical model of nonlinear partial differential equations describing the spatial dynamics of OPG , RANKL , PTHrP , osteoclasts , tumor and bone mass . We demonstrate that at lower expression rates , tumor-derived OPG enhances the chemotactic RANKL gradient and osteolysis , whereas at higher expression rates OPG broadly inhibits RANKL and decreases osteolysis and tumor burden . Moreover , tumor expression of a soluble mediator inducing RANKL in the host tissue , such as PTHrP , is important for correct orientation of the RANKL gradient . A meta-analysis of OPG , RANKL and PTHrP expression in normal prostate , carcinoma and metastatic tissues demonstrated an increase in expression of OPG , but not RANKL , in metastatic prostate cancer , and positive correlation between OPG and PTHrP in metastatic prostate cancer . The proposed mechanism highlights the importance of the spatial distribution of receptors , decoys and ligands , and can be applied to other systems involving regulation of spatially anisotropic processes .
Based on the model described above , the presence of osteoprotegerin in bone metastases should lead to reduced bone destruction and tumor growth . In agreement with this prediction , the systemic application of OPG leads to a decrease in tumor burden [8] , and Corey and colleagues [9] demonstrated that OPG produced locally by cancer cells has a similar anti-metastatic effect . However , several lines of experimental evidence contradict the present model . First , it was repeatedly demonstrated that high circulating levels of osteoprotegerin in prostate cancer patients predict more bone metastases and more osteolysis [10] , [11] . Even more interestingly , Fisher and colleagues [12] reported that local overexpression of osteoprotegerin by MCF-7 breast carcinoma cells co-expressing parathyroid hormone-related protein leads to increased osteolytic bone destruction and tumor growth in vivo - a result that appears to be in direct contradiction with the study of Corey and colleagues [9] . It has been suggested that the tumor-inducing effect of OPG is due to its inhibition of another ligand , TNF-related apoptosis-inducing ligand ( TRAIL ) [12] . TRAIL also acts as a modulator of osteoclast apoptosis [13] and differentiation [14] . However , it was shown that TRAIL cannot interfere with the anti-osteoclastogenic properties of osteoprotegerin [15] , therefore OPG-TRAIL interactions cannot fully explain the enhanced osteolysis induced by OPG-overexpressing MCF-7 cells [12] . Altogether , these results indicate that osteoprotegerin plays a controversial role in bone metastases: while a large set of experimental data supports its osteoclast- and hence tumor-inhibiting role , in certain situations osteoprotegerin is documented to stimulate osteolysis and tumor growth . We have recently demonstrated a potential role of OPG in enhancing RANKL gradients [6] , which in turn are responsible for chemotactic movement of osteoclasts [16] . Based on these observations , we propose the following hypothesis regarding the action of osteoprotegerin in bone metastases: 1 ) When OPG is applied globally ( i . e . systemically ) , it uniformly reduces RANKL levels , thus acting as an inhibitor of osteoclast formation and tumor growth . 2 ) When OPG is produced locally by cancer cells , the outcome is determined by the rate of OPG expression . At low expression rates , OPG enhances the chemotactic RANKL gradient responsible for osteoclast movement , thus stimulating osteolysis and tumor growth . At high expression rates , the RANKL-inhibiting effect of OPG becomes predominant and results in an overall decrease in osteolysis and tumor burden . The distinction of the two regimes for tumor-derived osteoprotegerin provides a potential explanation of the differential experimental outcomes in [9] and [12] . To test this hypothesis we developed a mathematical model of tumor-osteoclast interactions , including the cytokine fields of RANKL , OPG and PTHrP , and examined the model predictions by means of appropriate in silico experiments focusing on the following main questions: 1 ) How does the impact of systemic OPG compare to the impact of cancer–cell derived OPG production ? 2 ) How is indirect stimulation of RANKL production via PTHrP different from direct production of RANKL by tumor cells ?
The mathematical model consists of 6 state variables: osteoclast population density ( ) , RANKL concentration ( ) , OPG concentration ( ) , PTHrP concentration ( ) , bone density ( ) and tumor density ( ) . We introduce the model in several steps , and start with the osteoclast population density ( ) . The dynamics of osteoclast formation and death are modeled as ( 1 ) where and are formation and apotheosis rates , respectively , and the exponent represents autocrine interactions among osteoclasts . We refer to [33] , [34] for a complete discussion of such power-law models in the context of bone remodeling . Physiological parameters are such that equation ( 1 ) admits a stable fixed point , and we split the total osteoclast population into and a residual , where . Note that for all , provided that ( see [17] for details ) . We regard cells below as inactive precursors , and consider an increase of above as differentiation of precursors into active , resorbing osteoclasts . After adding the stimulation of osteoclast formation and chemotaxis by RANKL , we obtain the following evolution equation for the osteoclast population density: ( 2 ) where represents the chemotactic sensitivity of active osteoclasts to the RANKL gradient , is the rate of osteoclast stimulation by RANKL , and the sigmoid function in the last term describes the half-saturation of the binding of RANKL to RANK receptors on osteoclasts . In general , depends on the local bone density , as live osteoclasts have to attach to the bone surface [35] . We can usually relax this dependence due to the fact that the remodeling front is moving away from resorbed areas , and hence no active osteoclasts are present in areas that do not contain any bone tissue . Therefore , unless stated otherwise , . The bone density , initially constant at 1 , is degraded by resorbing osteoclasts ( rate ) as . As explained in Model Assumptions , the tumor density is described as . The dynamics of the RANKL field are governed by production by cancer cells ( rate ) , diffusion ( rate ) , degradation ( rate ) and binding to RANK receptors on active osteoclasts ( rate , half-saturation ) , ( 3 ) We assume that the concentration of membrane-bound RANKL is kept constant on expressing cells , and hence we neglect its decay rate , i . e . we set . To reconcile the known osteolysis-inhibiting effects of systemically administered osteoprotegerin [8] , and osteolysis-inducing effects of OPG locally produced by metastasizing cancer cells [12] , we extend the model to account for OPG produced locally by cancer cells . The evolution equation for the OPG concentration includes expression by cancer cells ( rate ) and systemic sources ( rate ) , diffusion ( rate ) , degradation ( rate ) as well as binding to RANKL ( rate ) , ( 4 ) The last term on the right-hand side of ( 4 ) is also added to the RANKL equation ( 3 ) . It is well-established that cancer cells metastasizing to bone commonly produce a mediator , such as parathyroid hormone-related protein , which in turn promotes RANKL production by osteoblastic and stromal cells [1] , [36] , [37] . To model this scenario , we introduce the PTHrP concentration as a new state-variable: once produced by cancer cells ( rate ) , PTHrP diffuses across the tissue ( rate ) and is degraded by proteases ( rate ) , ( 5 ) While diffusing across the tissue , PTHrP induces the expression of RANKL by osteoblastic cells in the bone tissue , and we describe this by adding a source term ( ) to the RANKL equation ( 3 ) . Finally , combining equations ( 2 ) , ( 3 ) , ( 4 ) and ( 5 ) with the modifications and extensions described in the text , the complete system of equations reads ( 6 ) The variables and model parameters are summarized in Table 1 , and a visualization of the spatial distribution of the fields is found in Figure 2 . The initial RANKL field consists of host-tissue RANKL only , and is of constant concentration . The initial profile of active osteoclasts is placed in the middle of the domain , and there is no tumor present ( see Figure 3 ) . The initial bone tissue is intact , , and the OPG and PTHrP concentrations are uniformly zero . The results presented in Figures 4–8 are based on numerical solutions of different versions of system ( 6 ) , together with periodic boundary conditions and initial conditions as specified above . The parameter values are matched to in vivo observations where available , and a tuning method is applied to the set of unmatched parameters as explained in Text S1 . The time stepping is performed with a fractional step method as described in [38] . Thereby , adaptive Runge-Kutta solvers are used for the advection and reaction parts , and a TR-BDF2 solver for the diffusion parts . Spatial discretisations are performed by means of finite differences ( chemotactic term ) and spectral collocation ( diffusion terms ) . See Text S1 for details .
We first assess how different levels of RANKL in the host tissue influence tumor growth . We solve equation ( 6 ) in absence of the OPG ( ) and PTHrP ( ) fields , and we set in the -equation . The host-tissue level of RANKL , , is modeled in the initial RANKL field , i . e . we set , see Figure 3 . It is important to note that the parameter denotes the concentration of active RANKL , i . e . the total concentration of tissue-derived RANKL minus the concentration of RANKL which is bound to OPG . In agreement with the known action of RANKL as a potent stimulator of osteoclast differentiation [28] , we observe a positive correlation between RANKL levels and tumor growth ( Figure 4-A ) . At first , the initial osteoclast profile splits up symmetrically into two individual resorption fronts ( note that since the field is symmetric , we only depict the right half of the modeling domain ) . The resorption front propagates in a wave-like manner in the case of sufficiently high RANKL levels ( ) , or dies out in the case of insufficient stimulation by RANKL ( ) . This suggests the existence of a threshold concentration of RANKL necessary for a sustainable resorption event . Next , we investigate the impact of systemically administered OPG by introducing equation ( 4 ) with and a spatially uniform systemic source . We assume that the OPG administration only starts after 20 days of tumor growth , and that the source is then continuously applied until the end of the simulation . The resulting evolution of the fields is depicted in Figure 4-B . As expected , systemic application of OPG considerably decreases the tumor burden after 90 days . The simulations in Figure 4 are relevant for two aspects of osteolytic bone metastases . 1 ) The tissue RANKL level is known to positively correlate with bone metastases [39] , and tumors preferentially metastasize to actively remodeled skeletal sites , likely containing higher RANKL levels [40] , [41] . 2 ) Systemic application of osteoprotegerin , which binds to RANKL in the bone tissue , lowering its levels , is known to inhibit osteolysis associated with cancer metastases to bone [8] . Next , we assess how the local production of OPG by cancer cells affects the progression of bone metastases . We consider system ( 6 ) in absence of the PTHrP equation , set , and repeat the same scenario for varying levels of osteoprotegerin production , see Figure 5-A ( a dynamic representation of these simulations is found in Video S1 ) . In comparison to the control case with no OPG expression ( ) , higher levels of OPG production by cancer cells ( and ) lead to an increase in osteoclast advance ( see OC after 90 days ) , and hence a bigger resorption area . A closer look at the RANKL field after 90 days in Figure 5-B reveals that tumor-produced OPG removes residual RANKL left behind the remodeling front , resulting in the formation of steeper RANKL gradients , and hence increased speed of osteoclast migration . Note that the RANKL gradients of in our simulations are consistent with the gradients of which were shown to induce osteoclast chemotaxis in experimental studies [16] . In Figure 5-C , we present a systematic study of the effect of OPG production by cancer cells on osteoclast migration , the number of active osteoclasts and tumor mass . These results demonstrate that the interplay of two main factors is important in determining the overall outcome of OPG action . First , the OPG-induced increase in RANKL gradient and osteoclast speed ( evident by the distance traveled in 90 days ) is accompanied by a decrease in the number of active osteoclasts . This results in a non-trivial dependence of the tumor mass on the rate of OPG production by cancer cells . While low and intermediate expression of osteoprotegerin by cancer cells correlates with an increase in osteolysis and hence tumor burden , at high OPG expression , the remodeling front is too small to completely resorb all bone tissue , leading to an overall decrease in tumor mass . Second , the effect of tumor-produced OPG strongly depends on the levels of RANKL in the bone tissue: at low RANKL levels , OPG is predominantly inhibitory , while at high RANKL levels , tumor-produced OPG becomes more effective in inducing osteolysis ( Figure 5-C , compare and ) . Thus , the model predicts the existence of two different regimes for the impact of tumor-produced OPG , which correspond well to experimental findings of inhibition of osteolysis by cancer cell–produced OPG [9] , and stimulation of osteolysis by cancer cell–produced OPG [12] . Since high levels of RANKL in the tissue are important for the osteolysis-enhancing effects of OPG , we assess now if cancer cells could promote osteolysis by directly producing RANKL . We model this situation by adding a tumor-derived RANKL source to the -equation , i . e . we solve system ( 6 ) in absence of the OPG and PTHrP fields , set , and repeat the same scenario for varying values of . Note in particular that for this scenario it is necessary to model the osteoclast-stimulation rate to be dependent on the bone density ( see Text S1 for details ) , i . e . we replace the reaction term in the osteoclast equation of ( 6 ) byAs shown in Figure 6 , the tumor-derived production of RANKL leads to a reversal of the RANKL gradient . Rather than moving away from the tumor and resorbing more bone to provide new space for proliferating cancer cells , osteoclasts move towards the tumor . Consequently , no traveling remodeling front is formed , osteolysis is disrupted , and tumor growth decreases with increase in RANKL production rate . Although the RANK-RANKL dynamics are known to play an important role in bone metastases [16] , [39] , there is uncertainty regarding the actual source of RANKL . While some studies report direct expression of RANKL by metastasizing squamuous cell carcinoma and prostate cancer cells [42] , [43] , others suggest that there is no direct production of RANKL by cancer cells [44] , [45] . In addition , it has been shown that breast cancer cells cease to express RANKL upon embedding into the bone environment [46] . Our simulations suggest that expression of RANKL does not provide cancer cells with an advantage in the bone microenvironment . We consider now the case where cancer cells produce PTHrP ( but no OPG ) , which in turn promotes RANKL production by osteoblastic and stromal cells . More precisely , we consider system ( 6 ) in absence of the -equation , set , and repeat the same scenario for varying values of PTHrP production . If the initial tissue level of RANKL is sufficient for the formation of a traveling wave-like front of osteoclasts in the absence of PTHrP production ( Figure 7-A , ) , switching on the PTHrP production leads to faster and bigger resorption fronts , and hence a further increase in tumor mass after 90 days ( Figure 7-A , ) . Moreover , if the initial RANKL level is insufficient to sustain a traveling resorption front ( Figure 7-B , ) , the expression of PTHrP by cancer cells induces a traveling wave of active osteoclasts , and hence an increase in tumor mass ( Figure 7-B , ) . Thus , in good agreement with experimental data [1] , [12] , the tumor is able to efficiently promote its own growth by producing PTHrP . We assess now the impact of simultaneous production of OPG and PTHrP by cancer cells . This leads to the most comprehensive scenario considered , and is captured by the complete system ( 6 ) with . First , we study osteolysis and tumor growth for varying tumor-derived osteoprotegerin production rates ( ) at a fixed level of PTHrP production ( ) , see Figure 8–A . An increase in osteoprotegerin production leads to an enhanced RANKL gradient , and the resulting increase in the speed of the remodeling front is accompanied by an increase of the resorbed area and a decrease in the number of active osteoclasts . A systematic study of the impact of varying OPG and PTHrP production rates on the tumor mass , see Figure 8-B , reveals that at low to intermediate OPG expression rates by cancer cells ( ) , there is an increase in overall tumor burden after 90 days , for all levels of PTHrP production ( ) . On the other hand , high levels of OPG expression lead to a decrease in tumor burden , which eventually drops below the value for . We examined the expression of OPG , RANKL , and PTHrP in patient samples from normal prostate tissue , prostate carcinoma , and metastatic prostate carcinoma tissues , as reported in the studies [47]–[55] . We used the publicly available gene expression data analysis engine Oncomine Research Edition ( www . oncomine . org ) , and processed data as described in Text S2 for details . We found that expression of osteoprotegerin was significantly increased in samples from metastatic prostate cancer compared to normal prostate ( ) , as well as prostate carcinoma ( ) ( Figure 9-A ) . In contrast , expression of RANKL and PTHrP did not exhibit significant changes ( Figures 9-B and 9-C ) . Since our simulations suggest that most effective in promoting bone metastases is the combination of OPG and PTHrP , we further assessed the correlation between the expression of OPG , PTHrP and RANKL in samples from metastatic prostate carcinoma only . We found that expression of osteoprotegerin by metastatic prostate cancer cells exhibited significant positive correlation with PTHrP , ( Figure 9-D ) , while no correlation was found between OPG and RANKL , ( Figure 9-E ) or PTHrP and RANKL , ( Figure 9-F ) . Thus , consistent with our modeling findings , gene expression data demonstrate an increase in OPG , rather than RANKL , in metastatic prostate cancer , as well as a positive correlation between the expression of OPG and PTHrP .
The goal of this study was to propose and test a novel hypothesis explaining the differential and seemingly contradictory experimental results regarding the role of osteoprotegerin in bone metastases . Whereas systemic application of osteoprotegerin is known to decrease osteolysis and tumor growth [8] , two similar experiments have shown that osteoprotegerin produced locally by metastatic cancer cells in the bone environment can lead to a decrease [9] or an increase [12] in osteolysis and tumor growth . Given the well-established role of osteoprotegerin as an osteoclast inhibitor [56] , the outcome of systemic osteoprotegerin application does not bear any surprises , but the osteolysis promoting effects in [12] , as well as the increased osteolysis in metastatic cancer patients with high levels of circulating osteoprotegerin [10] , [11] , appear to be contradictory . To resolve this apparent contradiction , we proposed that the spatial configuration of the tumor-bone interface in conjunction with the magnitude of tumor-derived osteoprotegerin expression determines the resulting effect of OPG . We hypothesized the existence of two distinct dynamical regimes for locally produced osteoprotegerin: ( 1 ) at low expression rates , tumor-produced OPG primarily enhances the chemotactic RANKL gradient oriented towards the unresorbed bone tissue , thus stimulating osteoclast movement , and resulting in an increase in osteolysis and hence tumor mass . ( 2 ) at high expression rates of tumor-derived OPG , the RANKL-inhibiting effect of OPG becomes predominant and results in an overall decrease in tumor burden . Based on a previously presented mathematical model of bone remodeling [6] , [17] , we developed a nonlinear partial differential equations model describing the interactions between metastatic cancer cells and the bone environment . In good agreement with our hypothesis , the model suggests the existence of two distinct dynamic regimes where tumor growth is either accelerated or slowed down by cancer-derived osteoprotegerin . These observations are further substantiated by a meta-analysis of gene expression , which shows that osteoprotegerin expression in metastatic prostate tissue is increased compared to normal prostate and prostate carcinoma samples . The model simulations point out another interesting aspect related to the spatial configuration of the tumor-bone interface . Our model predicts that the direct expression of osteoclastogenic cytokine RANKL by cancer cells may result in a reversal of the chemotactic gradient , thus slowing down osteolysis and tumor growth . The model suggests that it is crucial for cancer cells to express a mediator ( such as parathyroid hormone-related protein ) that diffuses across the tissue before triggering the expression of RANKL on osteoblastic cells . The involvement of such a mediator assures that the RANKL gradient is correctly oriented to induce osteoclast movement away from the tumor into unresorbed bone tissue . In accordance , the meta-analysis of gene expression reveals that osteoprotegerin expression in metastatic prostate tissue is positively correlated with the expression of PTHrP , but not RANKL . Due to the large number of a priori unknown parameters in the model , our results are predominantly of qualitative nature . While the emergence of two distinct regimes of OPG action is observed across a large span of parameter values , and is in particular independent of the production rate of parathyroid hormone-related protein PTHrP by cancer cells , further experimental investigation will be indispensable for a full validation of our hypothesis . Thereby , an experimental assessment of the diffusion rates of different molecules in tumor and bone tissues would be useful . In addition , the potential role of OPG in stimulating osteoclast movement could be studied in vitro , and the metastatic capacity of prostate cancer cells expressing different levels of OPG could be assessed in vivo . In the meantime , our qualitative predictions are valuable in suggesting a new conceptual model which is consistent with all the experimental data available to date . In particular , our model suggests that future experimental designs should take into account the directional movement of the constituent cells , as well as the geometry of the tumor-bone interface . The proposed mechanism emphasizes the importance of the spatial configuration of molecular densities , and thus may be relevant to other systems where distinct spatial patterns are imperative . An interesting example is the regulation of immune cell migration by chemokines . It has recently been shown that in addition to signaling receptors , there exist several decoy receptors that bind to chemokines , but do not induce any cellular changes [57] . Our hypothesis suggests that a potential role for these decoy receptors is the creation and enhancement of chemokine gradients . Another example is the difference in action of tumor-produced and host tissue-produced angiogenic factors , such as nitric oxide [58] , which is in agreement with the importance of spatial coordination of tumor vascularization for tumor growth at the metastatic site . In summary , our study demonstrates that taking into account the spatial distribution of regulators , receptors and decoy receptors can reveal novel mechanisms inaccessible to conventional models based on global regulator-receptor ratios . | Breast and prostate cancers commonly metastasize to bone . To create more space for their expansion , metastatic tumors activate osteoclasts , the only cells capable of bone destruction . The main osteoclast stimulator is the cytokine RANKL , while osteoprotegerin ( OPG ) acts as a RANKL inhibitor . Systemic application of OPG leads to a decrease in tumor-associated bone destruction , but surprisingly , OPG produced locally by metastasizing cancer cells can enhance bone destruction and tumor growth . Here , we provide a novel explanation for these apparently contradictory experimental results: the osteolysis-promoting effect of OPG is due to a local reduction in RANKL levels , resulting in a spatial RANKL gradient oriented from tumor towards bone tissue . At low rates of OPG expression by cancer cells , such gradients result in the correct orientation of osteoclast movement and intensified bone resorption . We positively test our hypothesis by means of a partial differential equations model , and further substantiate our results with a meta-analysis of gene expression . Even though developed for the specific problem of bone metastases , our model naturally applies to other systems operating within a geometrically anisotropic environment . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] | [
"theoretical",
"biology",
"physiology",
"biology",
"anatomy",
"and",
"physiology",
"molecular",
"cell",
"biology"
] | 2012 | Osteoprotegerin in Bone Metastases: Mathematical Solution to the Puzzle |
Leishmania donovani is known to induce myelopoiesis and to dramatically increase extramedullary myelopoiesis . This results in splenomegaly , which is then accompanied by disruption of the splenic microarchitecture , a chronic inflammatory environment , and immunosuppression . Chronically inflamed tissues are typically hypoxic . The role of hypoxia on myeloid cell functions during visceral leishmaniasis has not yet been studied . Here we show that L . donovani promotes the output from the bone marrow of monocytes with a regulatory phenotype that function as safe targets for the parasite . We also demonstrate that splenic myeloid cells acquire MDSC-like function in a HIF-1α-dependent manner . HIF-1α is also involved in driving the polarization towards M2-like macrophages and rendering intermediate stage monocytes more susceptible to L . donovani infection . Our results suggest that HIF-1α is a major player in the establishment of chronic Leishmania infection and is crucial for enhancing immunosuppressive functions and lowering leishmanicidal capacity of myeloid cells .
Elimination of intracellular pathogens requires the induction of pro-inflammatory cytokines and cytotoxic molecules secretion . Unfortunately , this process also leads to local tissue disruption and inflammation . Inflamed tissues represent a challenging microenvironment , characterized by hypoxia , acidosis and hypoglycemia . This microenvironment typically causes the stabilization of the transcription factor HIF-1α , the master regulator of the response to hypoxia [1 , 2] . HIF-1α has pleiotropic functions aimed at protecting tissues from injury and helping cells to adapt to a difficult microenvironment . However , stabilization of HIF-1α in some cells of the immune system , such as myeloid cells , may also have unwanted consequences . For instance , HIF-1α is responsible for the polarization towards the M2-like phenotype of tumor-associated macrophages ( TAM ) [3] , promoting therefore tumor growth . HIF-1α was also shown to enhance function and differentiation of myeloid derived suppressor cells ( MDSC ) in the tumor microenvironment [4] . Moreover , we have reported that HIF-1α stabilization in dendritic cells inhibited their function and consequently limited the expansion of protective CD8 T cell responses during experimental visceral leishmaniasis ( VL ) [5] . The HIF-pathway is also exploited by some pathogens for their replication and/or survival inside the host’s cell [6–9] . One example of such a pathogen is Leishmania . The protozoan parasite Leishmania is the causative agent of leishmaniasis , a disease with multiple clinical manifestations ranging from self-healing cutaneous and mucocutaneous lesions to potentially lethal visceral infections . The promastigote form of the parasite is transmitted to the host by a sandfly vector . Once inside the host , promastigotes transform into amastigotes . Macrophages are the main target cells of the parasite . However , to survive inside macrophages , Leishmania needs to attenuate their microbicidal potential [10] . One of the many strategies is the stabilization of HIF-1α [11] , which appears to be essential for the survival of the promastigote form inside the cell [6 , 11] . HIF-1α stabilization can occur following massive infiltration by pro-inflammatory cells in the tissue and/or as a consequence of pathogen invasion . These two phenomena are associated with increased oxygen consumption , which causes a local hypoxic environment [12] . During visceral leishmaniasis , HIF-1α stabilization is also induced in uninfected cells by the inflammatory environment and appears to hamper DC functions [5] . To date , the role of HIF-1α in other myeloid cells during in vivo Leishmania infections has not yet been explored . Dendritic cells and neutrophils have been extensively studied in various models of leishmaniasis; however , the contribution of monocytes to susceptibility and/or resistance to infection is still unclear . The early literature proposes a possible role of “undifferentiated macrophage-granulocytes” as safe targets for Leishmania , contributing therefore to disease susceptibility [13] . Passos et al . [14] demonstrate that intermediate monocytes are involved in mediating immunopathology in patients infected with L . braziliensis . Another study reports the upregulation of A2B adenosine receptors on human monocytes and the association of this upregulation with pathogenicity in patients exposed to L . donovani [15] . In contrast , monocyte-derived DC appear to be essential for priming protective Th1 responses in L . major infected mice [16] and classical monocytes are thought to be able to kill L . major [17] and L . braziliensis via reactive oxygen species [18] . In this study , we wanted to investigate the role of HIF-1α stabilization in myeloid cells , particularly monocytes , during experimental chronic VL . We found that myeloid cells are increasingly recruited to the spleen during chronic infection . Splenic myeloid cells upregulate HIF-1α and display HIF-1α-dependent inhibitory function on protective Th1 responses . Moreover , HIF-1α limits their leishmanicidal functions and regulates the differentiation and output of inflammatory monocytes from the bone marrow .
The literature about the role of monocytes during experimental visceral leishmaniasis is scarce . Hence , we wanted to have a full picture of the monocytes and neutrophils recruitment kinetics to the spleen over the course of experimental L . donovani infection , before assessing the role of HIF-1α in splenic myeloid cells . We first monitored the frequency of CD11bhi Ly6Ghi neutrophils . As shown in Fig 1A , the percentage of neutrophils present in the spleen gradually increased during the first 4 weeks of infection . Similarly to neutrophils , Ly6Chi monocytes were increasingly recruited to the spleen over the course of infection ( Fig 1B ) . In contrast , the frequency of Ly6Clo/int monocytes did not vary substantially as disease progressed ( Fig 1B ) . Interestingly , the two monocyte populations were less easily distinguishable during the chronic phase of infection . We next examined whether splenic myeloid cells expressed CD11c at various time points of infection . At d14 p . i . about 55% of all CD11b+ cells in the spleen expressed CD11c; the percentage of CD11c+ cells increased over the course of infection and at d35p . i . 80% of the splenic CD11b+ cells were also CD11c+ ( Fig 1C ) . As expected , all Ly6Chi monocytes were CD11c+ and about 85% of the Ly6Clo/int monocytes expressed CD11c ( Fig 1D ) . Because LysM-specific HIF-1α-deficient mice are not a good model to study the role of HIF-1α in monocytes/macrophages in the spleen [19] and the vast majority of splenic CD11b+ cells during VL were CD11c+ , we decided to use CD11c-specific HIF-1α deficient mice [5] to investigate the role of HIF-1α in myeloid cells , particularly monocytes , during chronic VL . To note , neutrophils did not express CD11c , hence they are HIF-sufficient in both groups of mice . We have previously reported that Hifflox/flox–Cd11c-Cre+ mice are highly resistant to L . donovani infection ( [5] and S1A Fig ) . During the acute phase of infection , HIF-1α impairs dendritic cell functions and limits CD8 T cell expansion [5] . At this stage of disease , parasite clearance in these mice is mainly CD8 T cell-dependent [5]; however , it is still unclear how these mice control L . donovani growth during chronic VL , when CD8 T cells are exhausted [20] . CD8+ dendritic cells are thought to be responsible for CD8 T cell cross-priming [21] . These DC subpopulation , unlike CD4+ DCs , mainly expresses DNGR1 ( S1B Fig ) and thus directly descends from DC precursors rather than being monocyte-derived [22] . Hence , we decided to extend our investigation on the role of HIF-1α to other myeloid cells , particularly monocytes and monocytes-derived cells . Because monocytes contribute to parasite clearance in other models of leishmaniasis [16–18] , we first compared the recruitment of monocytes to the spleen in Hifflox/flox–Cd11c-Cre+ mice ( HIF-1α-deficient ) and their Cre- littermates ( HIF-1α-sufficient ) at various time points of infection . Before , though , we monitored HIF-1α expression in purified CD11b+ cells from both mouse groups to confirm that HIF-1α was indeed deleted in Hifflox/flox–Cd11c-Cre+ myeloid cells ( S2A and S2B Fig ) . As observed in C57BL/6 mice , the frequency and the number of Ly6Chi monocytes increased over the course of infection in the Cre-and Cre+ group ( Fig 2A and 2B ) . However , a significantly higher number of inflammatory monocytes was present in the spleen of Cre+ mice . Non-classical Ly6Clo/int monocytes ( Fig 2A and 2C ) and neutrophils ( Fig 2D and S3A Fig ) displayed similar frequencies in both mouse groups , but cell numbers were higher in Cre+ mice , reflecting a slightly more pronounced splenomegaly in HIF-1α conditional knockouts . Similar results were obtained when we examined F4/80 expression in myeloid cells ( Fig 2E and S3B Fig ) . Next , we further characterized splenic monocytes by monitoring the expression of CCR2 and F4/80 , and MHCII on Ly6Clo/int and Ly6Chi cells . 85% of Ly6Chi monocytes co-expressed CCR2 and F4/80 at d14 and 21p . i . ; the frequency then decreased to 50% at later time points of infection ( Fig 2F and S4A Fig ) . No differences were observed between HIF-1α-sufficient and deficient monocytes . The frequency of CCR2+F4/80+ Ly6Clo/int monocytes steadily increased over the course of infection to reach a plateau of about 70% at d21p . i . ( Fig 2F and S4B Fig ) in both groups of mice . These monocytes possibly represent an intermediate stage in the differentiation process towards macrophages . Surprisingly , 50% of Cre- Ly6Chi monocytes were positive for MHCII; by d21p . i . , the frequency of MHCII+ inflammatory monocytes increased to 80–90% and was maintained at this level during chronic infection ( Fig 2G and S4C Fig ) . The percentage of MHCII+ Ly6Chi monocytes was slightly higher in HIF-1α-deficient mice at d14 , d28 , and d35p . i . Recently , Ly6Chi monocytes with a regulatory phenotype have been described [23] . These monocytes are induced by IFNγ in the bone marrow and express MHCII and Sca-1 . Hence , we assessed Sca-1 expression on monocytes . From d21 p . i . on , the majority of the Ly6Chi monocytes expressed Sca-1 , suggesting that inflammatory monocytes may also display a regulatory phenotype during chronic VL ( Fig 2H ) . Based on our surface marker analysis , splenic monocytes resembled monocytic myeloid-derived suppressor cells ( M-MDSC ) [24] and/or monocyte with a regulatory phenotype [23] . The other known subset of MDSC originates from polymorphonucleated cells ( PMN-MDSC ) and is characterized by the co-expression of Ly6G and Ly6C ( Ly6G+Ly6Clo ) [24] . To determine whether PMN-MDSC were also present in the spleen of L . donovani infected mice , we monitored the surface expression of Ly6C on CD11bhiLy6Ghi neutrophils . 100% of the neutrophils were Ly6C+ already at d14p . i . ( Fig 2I and S4D Fig ) ; Ly6C expression was maintained during the chronic phase . This suggests that neutrophils express similar markers to PMN-MDSC and could potentially exhibit immune suppressive properties . In the following , we sought to characterize myeloid cell function . To this end , CD11b+ cells were purified from the spleen of infected Cre- and Cre+ mice at various time points of infection; the expression of several genes was assessed by qPCR . Interestingly , CD11b+ cells from Hifflox/flox–Cd11c-Cre+ mice showed a lower expression of TNF ( Fig 3A ) , arginase ( Fig 3B ) , Fizz1 ( Fig 3C ) , Mgl1 , and Mgl2 ( Fig 3D and 3E ) ; in contrast , they expressed higher iNOS mRNA levels ( Fig 3F ) . Hence , HIF-1α seems to sustain the differentiation towards the M2-like macrophage subtype . Between d14 and 21 p . i . , splenic stromal cells are killed by excessive TNF production [25]; consequently , the splenic microarchitecture is altered [26] . Disruption of the microarchitecture is typically accompanied by the progressive loss of B cell Germinal Centers [27] . Interestingly , the splenic microarchitecture in infected Cre+ mice appeared to be more intact than in the Cre- controls at d28 p . i . ( Fig 3G ) . This may be a consequence of the lower TNF production by myeloid cells ( Fig 3A ) . Notably , myeloid cells ( Fig 3G , blue ) were increasingly present in the splenic red pulp of infected mice after d14 p . i . HIF-1α has been reported to promote iNOS expression [28–30] . Hence , we were surprised to observe an increase in iNOS mRNA levels in myeloid cells from infected Cre+ mice ( Fig 3F ) . To verify our in vivo observation , we infected HIF-1α-sufficient and deficient bone marrow-derived macrophages ( BMM ) with L . donovani amastigotes and analyzed iNOS production by flow cytometry . CD38 was used as an M1 marker . As expected , stimulation of BMM with IFNγ increased the percentage of CD38+ cells ( Fig 4A and 4B ) and the production of iNOS ( Fig 4A and 4C ) , which was slightly higher in HIF-1α-deficient cells . In contrast , treatment with IL-4 failed to promote iNOS ( Fig 4C ) and reduced the frequency of CD38+ cells ( Fig 4B ) , independently from the presence or absence of HIF-1α . However , when we infected BMM with L . donovani amastigotes , a dramatic increase in iNOS production was observed in HIF-1α deficient BMM but not in HIF-1α sufficient cells ( Fig 4A and 4C ) , confirming our in vivo observation ( Fig 3F ) . We also analyzed the expression of M2 markers Arg-1 ( Fig 4D ) , Fizz-1 ( Fig 4E ) , and IL-10 ( Fig 4F ) . A slight decrease in the levels of Arg-1 and Fizz-1 mRNA was detected in Cre+ compared to Cre- cells; moreover , IL-10 mRNA was not upregulated in HIF-1α deficient BMM following infection with L . donovani . To be sure that HIF-1α was indeed deleted in BMM from conditional knockouts , we assessed the expression of HIF-1α and two HIF-1α downstream targets , Pgk-1 and Glut-1 in cytokine-treated and infected BMM . HIF-1α ( Fig 4G ) , Pgk-1 ( Fig 4H ) and Glut-1 ( Fig 4I ) were not induced in HIF-1α deficient BMM following L . donovani infection or cytokine treatment , suggesting that recombination occurred in BMM from Cre+ mice . Because HIF-1α is known to regulate cell metabolism , we next measured intracellular lactate ( Fig 5A ) and glucose levels ( Fig 5B ) . Interestingly , HIF-1α-sufficient myeloid cells had a higher intracellular lactate concentration compared to HIF-1α-deficient cells ( Fig 5A ) , reflecting the metabolic switch towards anaerobic glycolysis [31 , 32] . Cre- cells also displayed a slightly higher intracellular glucose concentration ( Fig 5B ) . We also assessed the production of reactive oxygen species ( ROS ) , which are typically not generated by M2 macrophages [32] . HIF-1α–deficient splenocytes expressed higher levels of ROS ( Fig 5C and S5A Fig ) . Neutrophils ( Fig 5D and S5B Fig ) and inflammatory monocytes ( Fig 5E and S5C Fig ) lacking HIF-1α contributed to this difference . To rule out the possibility that myeloid cells acquired an M2-like phenotype because of higher levels of IFNγ present in the environment , we assessed the expression of the INFγ receptor by FACS . As shown in Fig 5F , the frequency of CD11bhiLy6C+ cells expressing IFNγR was similar in both groups of mice , with exception of d21 p . i . , when the expression was lower in Cre+ mice . Taken together , these results suggest that HIF-1α may be involved in the differentiation towards macrophages with an M2-like phenotype , which is unable to kill Leishmania [31 , 33] . We next investigated the inhibitory potential of splenic myeloid cells . CD11b+ cells were purified from the spleen of L . donovani infected mice at d14 and 28 p . i . and co-cultured at a 1:1 ratio with naïve CD4 T cells stimulated with plate-bound anti-CD3 and with anti-CD28 and rIL-12 . Myeloid cells purified from infected Cre- mice at d14 p . i . only slightly inhibited the differentiation towards IFNγ-producing CD4 T cells ( Fig 6A and 6B ) ; a similar result was obtained with HIF-1α-deficient myeloid cells purified at the same time . Remarkably , d28 p . i . CD11b+ cells from infected HIF-1α sufficient mice strongly inhibited Th1 differentiation ( Fig 6A and 6B ) ; a significantly lower degree of inhibition was observed in samples containing d28 p . i . HIF-1α-deficient myeloid cells . Taken together , our results suggest that myeloid cells purified during chronic infection inhibit T cell responses , implying that these cells are phenotypically and functionally similar to MDSC . This inhibitory function requires HIF-1α . Thus , this transcription factor is not only involved in attenuating the leishmanicidal capacity of myeloid cells , but also in enhancing their inhibitory function . To determine whether HIF-1α-deficient monocytes were more resistant to infection by L . donovani , we infected bone marrow-derived monocytes in vitro with fluorescently labelled amastigotes and monitored the infection for 24h by FACS and Image Stream . Monocytes were either activated or not with IFNγ 2h prior to infection; cells were kept under hypoxic conditions at all time to mimic the bone marrow [34] and the splenic environment ( S6A Fig ) . We first confirmed that Cre+ cells had a reduced HIF-1α expression ( S6B Fig ) . About 25–30% of HIF-1α–sufficient Ly6Chi/int monocytes contained parasites after 12h of infection; at 24h , 40–45% of the cells harbored parasites ( Fig 7A ) . Interestingly , when monocytes were exposed to IFNγ prior to infection , the percentage of parasitized cells dramatically increased to 60% at 12h and 80% at 24h of infection ( Fig 7A and S6C Fig ) . This is probably due to the fact that IFNγ induces regulatory Ly6Chi/int monocytes [23] and that these may be more permissive for L . donovani amastigotes . In contrast , HIF-1α-deficient inflammatory monocytes were significantly less parasitized at 12 and 24h in the absence of IFNγ ( Fig 7A and S6C Fig ) ; as for their HIF-1α-sufficient counterparts , the addition of IFNγ dramatically increased the rate of infection ( Fig 6A ) , suggesting that HIF-1α is not involved in inducing regulatory monocytes . Nevertheless , IFNγ-pulsed HIF-1α-deficient Ly6Chi/int monocytes were slightly more resistant to infection than wild type inflammatory monocytes . Similar results were obtained when we analyzed the degree of infection of Ly6Clo monocytes ( Fig 7B and S6D Fig ) . We next determined whether the number of parasites per cell was equal in both groups of mice using the ImageStream technology ( examples of analysis are depicted on Fig 7C ) . As shown in Fig 7D and 7E , no major differences were observed in the percentage of cells harboring various numbers of parasites between HIF-1α-deficient and HIF-1α-sufficient monocytes . Because HIF-1α is upregulated during the differentiation of monocytes to macrophages [35] , we were intrigued to know whether HIF-1α would play a role in resistance/susceptibility to infection if we kept M-CSF in the medium during infection with L . donovani to allow differentiation into macrophages . Surprisingly , we observed a highly significant reduction in the percentage of Ly6Chi cells harboring parasites in HIF-1α-deficient monocytes ( Fig 7F and S7A Fig ) , independently whether they were pulsed or not with IFNγ . Similar results were obtained when we analyzed the rate of infection in Ly6Clo/int monocytes ( Fig 7G and S7B Fig ) . Because Leishmania amastigotes survival in macrophages is not impaired in the absence of HIF-1α [5 , 36] , this suggests that HIF-1α mainly increases the susceptibility to infection of transitional forms of monocytes/macrophages . We previously showed that L . donovani induces the proliferation of myeloid-biased hematopoietic stem progenitor cell ( HSPCs ) in the bone marrow , and that this infection-induced change in hematopoiesis could promote parasite expansion [37] . Furthermore , our myeloid cells resembled MDSC and MDSC are derived from the myeloid lineage and are a result of altered hematopoiesis during cancer or chronic infections . We therefore investigated the state of myeloid progenitors in the bone marrow of L . donovani Cre+ and Cre- mice . Steady state bone marrow myeloid progenitors were gated as negative for all lineage markers , positive for c-kit and negative for Sca-1; they were then subdivided according to the expression of CD41 , CD150 , CD16/32 to define granulocyte-monocyte progenitors ( GMP ) as CD16/32hi CD150- cells ( Fig 8A , left panels ) . Interestingly , along with the increase in HSPCs expansion , we detected a shift in Sca-1 expression on HSPCs ( Fig 8A , right panels ) . In order to include emergency GMPs , known as Sca-1+ GMPs [38] , GMPs were identified as Sca-1+/-Lin-c-Kit+CD16/32hiCD150- CD41- ( Fig 8A , right panels ) . In agreement with our observations in the spleen , HIF-1α conditional knockout mice had a higher frequency and numbers of GMPs ( Fig 8B ) at day 28 and 35 p . i . This was paralleled by a significantly enhanced output of granulocytes at d35 p . i . ( Fig 8C ) and Ly6Chi monocytes ( Fig 8D ) at day 28 and 35 p . i . No differences were observed for the Ly6Clo/- monocytes ( Fig 8E ) . It is important to note that the granulocyte and monocyte output does not increase during the first 3 weeks of L . donovani infection , suggesting that the bone marrow , like the spleen , does not majorly react to the infection during the acute phase . Furthermore , the absence of difference in the bone marrow was expected as the progenitor cells were HIF-1α sufficient ( no CD11c expression ) . To assess possible differences in the capacity to exit the bone marrow between monocytes from Hif flox/flox-Cd11c-Cre+ and Cre- mice , we monitored CCR2 and CXCR4 expression . No differences were found between both groups of mice ( Fig 8F and 8G ) . When we monitored the surface expression of Sca-1 and MHCII , we could find a significant difference in terms of percentage of Sca-1 expression at d28 p . i . in infected Cre+ compared to Cre- mice ( Fig 8H ) . A similar result was obtained when we assessed MHCII expression ( Fig 8I ) , suggesting that the inflammatory stimuli received by Ly6Chi monocytes were similar in both groups of mice . To note , Sca-1 expression increased at d21 p . i . to peak at d28p . i . and gradually decreased thereafter ( Fig 8H ) , showing a different expression kinetics than on splenic Ly6Chi monocytes ( Fig 2H ) . HIF-1α CD11c-conditional knockouts are highly resistant to L . donovani infection in the spleen ( [5] and S1 Fig ) , but the hepatic parasites number does not significantly differ from that of the control mice [5] . Hence , we were curious to assess the parasite burden in the bone marrow of our HIF-1α conditional knockouts . In agreement with the literature [39] , we observed a dramatic increase in the number of parasite after 3 weeks of infection in the cre- control group ( Fig 9 ) . The parasite burden peaked at d28 p . i . in Cre+ mice as well , but to a lesser extent . Indeed , we observed more than 50% reduction in conditional knockout mice , suggesting that HIF-1α expression in CD11c+ cells is detrimental to the outcome of L . donovani infection in the bone marrow as well .
The hematopoietic system rapidly increases myeloid cell output to fight pathogens . Emergency myelopoiesis also occurs during visceral leishmaniasis [37] . Our study shows that emergency myelopiesis in the context of VL results in the generation of regulatory monocytes that are more permissive to Leishmania parasites . Moreover , myeloid cells acquire an MDSC-like phenotype in the spleen and display HIF-1α-dependent T cell inhibitory functions . HIF-1α also drove the polarization towards M2-like macrophages and rendered intermediate stage monocytes more susceptible to L . donovani infection . Our results suggest that HIF-1α largely contributes to the establishment of chronic Leishmania infection by enhancing immunosuppressive functions and lowering leishmanicidal capacity of myeloid cells . During experimental VL , myeloid cell generation is enhanced in the bone marrow by a parasite- induced boost in GM-CSF production [40]; extramedullary myelopoiesis in the spleen is also dramatically increased [39] . We observed a significant increase in GMPs in the bone marrow , which mainly resulted in a selective enhanced output of Ly6Chi monocytes ( see also [37] ) . Interestingly , inflammatory monocytes started upregulating Sca-1 and MHCII , two markers associated with regulatory monocytes [23] , at day 21 after infection . In L . donovani infected mice , CD4+ Th1 responses typically peak between day 21 and 28 of infection . Hence , it is possible that Th1 cells prime Ly6Chi monocytes for regulatory functions in the bone marrow . It is not surprising , thus , that IFNγ-primed monocytes are more permissive to in vitro L . donovani infection . Acquisition of regulatory functions was not dependent on HIF-1α , since Hifflox/flox–CD11c-Cre- monocytes showed a similar infection rate to Cre+ cells . HIF-dependent inhibitory functions were first acquired during differentiation into macrophages . Adaptation to hypoxia in human monocytes was shown to be governed by NFƙB1 and not HIF-1α [41] . However , during the differentiation towards macrophages , HIF-1α translocates from the cytosol to the nucleus , changing therefore the adaptation mechanism to hypoxic conditions [41] . This may also apply to IFNγ-primed mouse monocytes . Hence , Leishmania induces an increased output of inflammatory monocytes in the bone marrow that acquire HIF-1α-independent regulatory functions prior to egress and represent therefore “safe targets” for the parasite , as postulated by an early study by Mirkovich et al . [13] . Increased rate of medullary or extramedullary myelopoiesis often leads to the induction of MDSC . MDSC are a heterogeneous population of various intermediate stages of myeloid cell differentiation that are best defined by their characteristic T cell-inhibitory activity . Based on the current classification [24] , the majority of our splenic CD11b+ cells were phenotypically similar to Mo-MDSC and PMN-MDSC for the Ly6GhiLy6C+ neutrophils . Mo-MDSC-like markers were expressed by most of the Ly6Chi and Ly6Clo/int monocytes , suggesting that these two splenic populations represent a mixture of various intermediate differentiation stages . MDSC have been best studied in the context of cancer , where they are known to inhibit T cell proliferation and/or function [24 , 42 , 43] . Recent literature , however , highlights their role in parasitic diseases as well [44] . Heligmosomoides polygyrus , for instance , induces MDSC capable of suppressing Th2 cell proliferation and IL-4 secretion , promoting therefore chronic infection [45] . In contrast , during experimental L . major infection , MDSC appear to be required for protective immunity even if they inhibit Th1 cell proliferation [46]; surprisingly , in this model , MDSC effector functions seem to be mouse-strain specific [47] . Our results indicate that myeloid cells purified from L . donovani infected mice at day 28 post infection are rather inhibitory . Indeed , they are able to significantly reduce IFNγ production of anti CD3/CD28-stimulated CD4 T cells . Interestingly , cells purified at day 14 post infection , before they acquire a MDSC-like phenotype , didn’t display any suppressive effect . At this time point of infection , mice have not yet developed severe splenomegaly , the splenic architecture is still intact [48] , and HIF-1α expression in myeloid cell is very low ( S2A and S2B Fig ) . This suggests that the splenic environment further shapes myeloid cell’s function to acquire inhibitory competence during the chronic phase of infection . Suppression of Th1 cells was dependent upon HIF-1α expression . In fact , HIF-1α-deficient MDSC only displayed minor inhibitory functions even when purified from chronically infected mice . HIF-1α is known to enhance MDSC differentiation and effector functions in tumor immunology [4 , 49] . MDSC mediate suppression through various mechanisms , such as upregulation of PD-L1 , induction of IL-10 , secretion of NO or ROS , or increased arginase activity [24] . In our model , we do not know what is responsible for suppressing Th1 responses . NO has been reported to inhibit T cell proliferation during L . major and H . polygyrus infections . In our case , HIF-1α-deficient MDSC-like cells expressed higher Inos mRNA levels than HIF-1α-sufficient , yet their inhibitory functions are significantly attenuated . Moreover , we didn’t see a substantial difference in IL-10 expression between Cre+ and Cre- cells , suggesting that IL-10 may not play a major role . However , arginase expression was downregulated in HIF-1α-deficient myeloid cells compared to their HIF-1α-sufficient counterpart . In human cutaneous leishmaniasis , increased arginase activity has been associated to chronic infections [50 , 51]; in these studies , neutrophils were the enzyme’s main source . Moreover , parasite-derived arginase was reported to contribute to the regulation of CD4 T cell exhaustion during experimental L . major infection [52] . It is thus possible that arginase may play a role in our model as well . Further investigations are needed to characterize the mechanism of suppression of MDSC-like myeloid cells in experimental VL . Myeloid cells during L . donovani infection not only possess inhibitory capacities , but have also a propensity to be more permissive to L . donovani infection . Indeed , we found elevated mRNA levels for markers typically associated with the M2 macrophage phenotype , which is unable to kill the parasite [33] . Interestingly , HIF-1α conditional knockouts expressed significantly lower mRNA for M2-like markers during chronic experimental VL , suggesting that HIF-1α plays a major role in the induction of a M2-like phenotype . In a Lewis lung carcinoma model , polarization of tumor associated macrophages towards an M2-like phenotype was dependent on HIF-1α induced by tumor-derived lactic acid , a by-product of glycolysis [3] . In our model , splenocytes from infected Hif flox/flox-Cd11c-Cre- mice showed significantly higher intracellular lactate concentrations compared to Cre+ mice . It is thus possible that lactate contributes to HIF-1α stabilization in our model as well . We also noticed a lower glucose concentration in HIF-1α-deficient splenocytes . This may reflect a higher metabolic activity or a decreased capacity to import glucose into the cell , since HIF-1α induces the glucose transporter Glut-1expression [53] . Although our results are in agreement with Colegio et al [3] , other groups have shown that HIF-1α promotes NOS2 in myeloid cells [28–30 , 36] and is associated with M1-like macrophages . This discrepancy may be a reflection of the environment and the stimuli responsible for the stabilization of HIF-1α within the cells . In some infection models or in vitro experiments , HIF-1α appears to be mainly expressed by M1-like macrophages and to promote pathogen clearance [28 , 30 , 36 , 54]; while in models of chronic inflammation , HIF-1α has a more immunosuppressive role [3 , 49] . Under normoxic conditions , this transcription factor can be induced by inflammatory cytokines , TLR agonists , or directly by pathogens [55 , 56] [8 , 29 , 57 , 58] . In contrast , chronically inflamed tissues are generally hypoxic , acidic , hypoglycemic , and full of free oxygen radicals [59 , 60] . Thus , depending on the model , HIF-1α stabilization occurs through very different pathways and this could lead to different outcomes . In our model , ex-vivo purified myeloid cells expressed higher iNOS mRNA levels in the absence of HIF-1α . This was not due to a compensatory upregulation of HIF-2α , which was only transiently higher at d14 p . i . in HIF-1α-deficient myeloid cells ( S8 Fig ) . These results were confirmed using in vitro infection of BMM . Indeed , L . donovani strongly induced iNOS in HIF-1α deficient BMM , suggesting that this enzyme’s regulation in BMM may be manipulated by the parasite and that HIF-1α is somehow involved . However , the interpretation of these results could be tainted by the fact that some cells may escape recombination and that the interaction of HIF-1α + and HIF-1α- cells may play a role in the total iNOS induction . Further investigations are definitely warranted to better understand iNOS regulation in L . donovani infected monocytes/macrophages and the role HIF-1α may have in this process . Despite the fact that HIF-1α is involved in promoting endothelial cell proliferation [61] and Ly6Chi monocytes contribute to red pulp vasculature remodelling [62] , this transcription factor doesn’t seem to be the major player in tissue neovascularization and splenomegaly in experimental VL . Indeed , CD11c-HIF-1α conditional knockout and HIF-sufficient animals developed similar levels of splenomegaly and myeloid cells from both group of mice expressed similar levels of vascular endothelial growth factor ( VGEF ) mRNA . To conclude , our results demonstrate that emergency myelopoiesis following L . donovani infection results in the output of monocytes primed in the bone marrow to acquire regulatory functions in a HIF-1α-independent manner . Once monocytes reach the spleen and start differentiating into macrophages or dendritic cells , the chronically inflamed splenic environment induces the stabilization of HIF-1α , which is then taking control over their functions . HIF-1α is responsible for the acquisition of MDSC-like functions by myeloid cells , and for lowering their leishmanicidal capacity . Because myeloid cell can also be produced locally in the spleen , it would be interesting to compare the function of bone marrow and splenic-derived monocytes . Finally , our study demonstrates how L . donovani exploits a physiological response to hypoxia to establish persistent infection .
C57BL/6-Tg ( OT-I ) -RAG1tm1Mom mice were purchased from The Jackson Laboratory . Conditional Hif-1α knock-out in CD11c+ cells were generated as previously described [5] . All mice were housed at the INRS animal facility under specific pathogen-free conditions and used at 6–10 weeks of age . Leishmania donovani ( strain LV9 ) was maintained by serial passage in B6 . 129S7-Rag1tm1Mom mice , and amastigotes were isolated from the spleens of infected animals . Hif-1α Cd11c-Cre+ mice and their littermates Hif-1aflox/flox-Cd11c-Cre- were infected by injecting 2×107 amastigotes intravenously via the lateral tail vein . Splenic parasite burdens were determined by examining methanol-fixed , Giemsa stained tissue impression smears [5] . Bone marrow parasite burden were calculated by limiting dilutions [5] . Data are presented as number of parasites present in the bone marrow of one femur and one tibia or as Leishman Donovan Units ( LDU ) . Experiments were carried out under protocols approved by the Comité Institutionel de Protection des Animaux of the INRS-Institut Armand-Frappier ( 1602–02 , 1510–02 ) . These protocols respect procedures on good animal practice provided by the Canadian Council on animal care . Myeloid cell responses in infected mice were analyzed by flow cytometry . Fc receptors were blocked by adding supernatant of 2 . 4G2–producing hybridomas for 5 min at 4°C to block to the homogenized splenocytes . Cells were then washed with FACS buffer and stained with the following antibodies: anti-MHCII FITC conjugated , anti-CD11c-APC ( BD Biosciences ) , anti-CD11b Pacific Blue ( PB ) , anti-Ly6C-Percp , anti-Ly6G-PE ( Biolegend ) , anti-F4/80-PE-Cy7 ( eBioscience ) , anti-IFNγR ( eBioscience ) , and anti-CCR2-Alexa Fluor 700 ( R&D Systems ) . The bone marrow ( BM ) was harvested by flushing tibias and femurs from the hind limbs in phosphate-buffered saline ( PBS ) . Cells were passed through 25-gauge needles to obtain single cell suspensions . Single cell suspensions were prepared in PBS containing 0 . 1% bovine serum albumin ( BSA ) and 0 . 5mM ethylene-diamine-tetra-acetic acid ( EDTA ) . To analyze adult BM progenitor cell populations , biotin-conjugated anti-lineage mAbs anti-CD3e ( 145-2C11 ) , anti-CD11b ( M1/70 ) , anti-CD45/B220 ( RA3-6B2 ) , anti-Gr1 ( RB6-8C5 ) , and anti-Ter119 were used as the lineage mix . For secondary detection streptavidin conjugated to Brilliant Violet-500 was used . The hematopoietic stem progenitor cell ( HSPC ) population was analyzed by staining with PE anti-CD117 ( c-Kit , 2B8; BD-Biosciences ) and PE-Cy7 anti-Sca-1 ( Ly6A/E , D7; BD-Biosciences ) in addition to the lineage mix . Granulocyte-monocyte progenitor GMPs , were determined by staining with PE anti-CD41 ( eBioscience ) , PerCP-Cy5 . 5 anti-CD16-32 ( Biolegend ) and Alexa-Fluor 647 anti-CD150 ( TC15 , BD Biosciences . 300 , 000 events were acquired on a BD LSRFortessaTM cell analyzer ( Becton Dickinson ) ; analysis was performed using the FlowJo and/or FACSDiva software . The expression of Total Reactive Oxygen Species ( ROS ) from infected mice was assessed using the ( ROS ) Assay Kit 520 nm kit ( eBiosciences ) following manufacturer’s instructions ( catalogue number , 88-5930-74 ) . In order to characterize ROS production in myeloid cell subpopulations , the antibodies previously described were added after fixation with 2% paraformaldehyde ( PFA ) . Cells were acquired with a BD LSRFortessaTM cell analyzer ( Becton Dickinson ) . Detection of hypoxia in the spleen of infected and naïve mice was performed using the Hypoxyprobe-RedAPC kit ( Hypoxyprobe Inc . , Burlington , MA ) following manufacturer’s instructions . Cells were acquired with a BD LSRFortessaTM cell analyzer ( Becton Dickinson ) . Real-time PCR ( Stratagene mx3005p Real time PCR System ) was used to analyze transcripts levels of HIF-1α , HPRT [5] , TNF [63] , Arg1 , Fizz1 , Mgl1 , Mgl2 [3] , and iNOS [64] . Total RNA was isolated using RNeasy ( Qiagen ) to perform real-time RT-PCR . cDNA was prepared using 500 ng of total RNA using High capacity cDNA Reverse Transcription kit ( Bio Rad ) . Real time PCR was performed using standard cycle of amplification . Primers used to determine the relative gene fold expression by quantitative PCR ( qPCR ) are shown in Table 1 . Macrophages were derived from the bone marrow of naïve mice in IMEM medium ( Life Technologies ) supplemented with 10% FBS , pen/strept , L-glutamine , and 15% L929 cell-conditioned medium as a source of colony-stimulating factor-1 ( CSF-1 ) . Cells were then left for 6 days at 37°C in a 5% CO2 incubator . For one set of experiments , BMM were washed and resuspended in supplemented DMEM . Cells were classically activated ( M1 ) with 20 ng/ml of murine IFNγ ( Peprotec ) , alternatively activated ( M2 ) with 20 ng/ml of murine IL-4 ( Peprotec ) , or infected with L . donovani amastigotes at a MOI of 1:10 . BMM were then incubated for 24h and stained with anti-CD11b-BV421 , iNOS-APC ( Biolegend ) , and CD38-BV711 ( eBioscience ) or processed for qPCR analysis . Samples were acquired on a BD LSRFortessa cell analyzer ( Becton Dickinson ) . A million splenocytes from infected mice were washed with PBS and lysed with RIPA buffer ( Sigma , catalogue number R0278-50 ) . Intracellular lactate and glucose concentrations were measured using respectively a Lactate Assay Kit and Glucose Assay Kit ( BioVision ) as per manufacturer’s instructions . Samples were prepared as triplicates for the colorimetric lactate assay . The absorbance was measured at 570 nm using an xMarkTM microplate absorbance spectrophotometer ( BioRad ) immediately after preparation . Splenic CD4+ T cells were enriched from naïve mice using magnetic cell sorting ( MACS ) following manufacturer’s instructions ( Miltenyi Biotec ) . The purity comprised between 90–95% . CD11b+ cells were purified using MACS from spleens of infected and naïve mice previously digested with collagenase D; the purity of the samples was 80–90% . Microtest 96 well plates ( Sarstedt ) were coated with/without 200μl of PBS containing 1 μg/ml anti-mouse CD3 ( eBiosiences ) and incubated for 90 min at 37°C . Plates were then washed twice with PBS and equilibrated for 15 min at 37°C with 100μl per well of RPMI-1640 medium ( Life technologies ) , supplemented with 10% fetal bovine serum ( FBS ) , pen/strep and L-glutamine . Th1 polarization was induced as follows: CD4+ T cells were seeded at 2 × 105/well in anti-CD3-coated 96-well plates with anti-CD28 ( 2 μg/ml ) , rIL-12 ( 30 ng/ml ) , and rhIL2 ( 0 . 5 μg/ml ) ( eBiosciences ) . CD11b+ cells enriched as described above were added or not to the culture at a 1:1 ratio . Cells were then incubated at 37°C in a 5% CO2 incubator and 5 days later stimulated with phorbol 12-myristate 13-acetate ( PMA ) /ionomycin in the presence of Brefeldin A ( BD Biosciences ) . Production of IFNγ was analyzed by FACS using anti-CD4-FITC , anti-CD3-PB , and anti-IFNϒ-APC ( BD-Bioscience ) . 50 , 000 events were acquired on a BD LSRFortessa cell analyzer and analyzed using the FlowJo software . Monocytes were derived from the bone marrow of naïve mice under hypoxia ( 2% ) in IMEM medium ( Life Technologies ) supplemented with 10% FBS , pen/strept , L-glutamine , and 15% L929 cell-conditioned medium as a source of colony-stimulating factor-1 ( CSF-1 ) . Cells were then left for 3 days at 37°C in a hypoxia chamber . For one set of experiments , differentiated monocytes were washed and resuspended in supplemented DMEM without CSF-1 prior to a 2h-activation with 100 U/ml murine IFNγ ( Peprotec ) ; for another set of experiments , CSF-1 was left in the culture for the entire duration of the test . L . donovani amastigotes were stained with PKH67 ( Sigma ) following manufacturer’s instructions and added at a MOI of 1:10 for 1-24h under hypoxic condition . Cells were then stained with anti-CD11b-PB , Ly6C-PerCp , and CD11c-APC acquired on a BD LSRFortessa cell analyzer ( Becton Dickinson ) and Image stream ( Amnis ) . Total cell protein extracts of CD11b+ cells purified by MACS from infected and naive mice were pooled and lysed in RIPA buffer ( sigma Aldrich , Germany ) . Cre+ and Cre- bone marrow-derived monocytes infected with L . donovani amastigotes were lysed as described above . Equal amounts of protein ( 15 μg ) were fractionated by 10% SDS-PAGE . Monoclonal anti-HIF-1α antibody Hif-1α67 ( Novus Biologicals , Littleton , CO , USA ) was used for immunoblot assays . Blots were stripped and reprobed with a polyclonal antibody against β-actin to confirm equal protein loading [65] . Densitometric analysis was performed by spot densitometry using AlphaImager 3400 imaging software ( Alpha Innotech Corporation ) and normalized to ß-actin control . Values are presented as fold induction compared to the level in naive mice . Monocytes were differentiated , treated , and stained as described above . After fixation with 2% PFA nucleus were stained with 4' , 6-diamidino-2-phénylindole ( DAPI ) and washed with PBS . Samples were then acquired on the ImageStreamX MarkII imaging cytometer ( Amnis ) . The analysis was performed using the IDEAS software ( Amnis ) . Freshly harvested spleens were snap frozen in OCT ( Electron Microsopy Sciences , Hartfield , PA , USA ) and stored at −80°C . Immunohistochemistry was performed on 8-μm frozen sections . Tissue sections were fixed in 75% acetone and 25% ethanol ( v/v ) for 10 min at -20°C , rehydrated in PBS for 10 min at room temperature , and incubated for 1 hour with 5%-BSA in PBS supplemented with 2 . 4G2 supernatant ( 1:100 ) . Slides were then incubated over night at 4°C with anti-CD11b-BV421 ( BD Bioscience , 1:300 ) , anti-B220-FITC ( BioLegend , 1:500 ) , and anti-CD169-A594 ( BioLegend , 1:500 ) . Tissue sections were then washed in PBS , mounted with Fluoromount-G ( Electron Microscopy Sciences , Hatfield , PA , USA ) , and analyzed using a LSM780 confocal micoscope ( Carl Zeiss , Oberkochen , Germany ) . Statistical analysis was performed using a multi-way ANOVA or Student’s t-test ( only Figs 5 and 6 ) , with p<0 . 05 considered significant . All experiments were conducted independently at least three times . | The protozoan parasite Leishmania donovani causes chronic infection in the spleen , which is accompanied by a chronic inflammatory environment , an enlargement of the organ , and immunosuppression . The environment of chronically inflamed tissues is characterized by low oxygen levels and tissue disruption , which induce the expression of the transcription factor HIF-1α in all cells . The kinetics of monocyte production and differentiation in the bone marrow and the spleen , and the role of hypoxia in myeloid cell functions during visceral leishmaniasis have not yet been studied . Here we show that L . donovani promotes the output from the bone marrow of monocytes with a regulatory phenotype that function as safe targets for the parasite . We also demonstrate that HIF-1α potentiates inhibitory functions of myeloid cells and is involved in driving the polarization towards M2-like macrophages and rendering them more susceptible to L . donovani infection . Our results suggest that HIF-1α is a major player in the establishment of chronic Leishmania infection and is crucial for enhancing immunosuppressive functions and lowering leishmanicidal capacity of myeloid cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"methods"
] | [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"immune",
"physiology",
"spleen",
"immunology",
"microbiology",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"protozoan",
"life",
"cycles",
"bone",
"marrow",
"cells",
"developmental",
... | 2017 | HIF-1α is a key regulator in potentiating suppressor activity and limiting the microbicidal capacity of MDSC-like cells during visceral leishmaniasis |
The activation of phase-specific cyclin-dependent kinases ( Cdks ) is associated with ordered cell cycle transitions . Among the mammalian Cdks , only Cdk1 is essential for somatic cell proliferation . Cdk1 can apparently substitute for Cdk2 , Cdk4 , and Cdk6 , which are individually dispensable in mice . It is unclear if all functions of non-essential Cdks are fully redundant with Cdk1 . Using a genetic approach , we show that Cdk2 , the S-phase Cdk , uniquely controls the G2/M checkpoint that prevents cells with damaged DNA from initiating mitosis . CDK2-nullizygous human cells exposed to ionizing radiation failed to exclude Cdk1 from the nucleus and exhibited a marked defect in G2/M arrest that was unmasked by the disruption of P53 . The DNA replication licensing protein Cdc6 , which is normally stabilized by Cdk2 , was physically associated with the checkpoint regulator ATR and was required for efficient ATR-Chk1-Cdc25A signaling . These findings demonstrate that Cdk2 maintains a balance of S-phase regulatory proteins and thereby coordinates subsequent p53-independent G2/M checkpoint activation .
Cdks associate with cyclins to form heterodimers that are sequentially activated during the cell cycle . Metazoan cells have multiple Cdks and cyclins that are temporally regulated [1] , [2] . In normal cell cycles , Cdk4 and Cdk6 pair with D-type cyclins during G1 , Cdk2 pairs with E- and A-type cyclins during S and G2 , and Cdk1 pairs with A- and B-type cyclins during G2 and M . The importance of Cdks in cell cycle transitions was suggested by studies in which expression of dominant negative mutants or introduction of inhibitory antibodies or small molecule inhibitors caused phase-specific cell cycle arrest [3] . However recent genetic studies have called into question the requirement for multiple Cdks [3]–[5] . RNAi-mediated depletion of Cdks in human cells [6] and gene knockouts in mice [7]–[10] showed that Cdk2 , Cdk4 and Cdk6 are dispensable for cell cycle progression . Cdk1 can bind D- , E- , and A-type cyclins and functionally substitute for the non-essential Cdks [11] . While it is clear that Cdk1 alone can drive unperturbed cell cycle progression , it remains unclear whether the non-essential Cdks have non-redundant functions in cell cycle responses to stress . Cdks are targeted by checkpoints that halt the cell cycle in response to DNA damage . Cdk2 is primarily considered a downstream target of the S-phase checkpoint [12] , [13] . However Cdk2 can also signal upstream via the phosphorylation of ATRIP , a binding partner of the ATR kinase [14] . Despite this data suggesting a role for Cdk2 in the regulation of checkpoint signaling , conflicting genetic evidence challenges the functional requirement for Cdk2 in DNA damage responses . Both the G1/S and G2/M checkpoints appear to remain fully functional in CDK2−/− mouse embryonic fibroblasts ( MEFs ) [15] , [16] . As Cdk2 and Cdk1 are functionally redundant in supporting DNA replication , it would seem plausible that Cdk1 could similarly substitute for Cdk2 in checkpoint pathways . Such redundancy would account for the lack of apparent checkpoint defects in the mouse CDK2-knockout . Here we show that Cdk2 uniquely activates the G2/M checkpoint and that this function is masked by the presence of p53 , which functions independently to arrest cells in G2 after DNA damage . Unlike the functions of Cdk2 during unperturbed S-phase , the role of Cdk2 in the G2/M checkpoint is non-redundant and cannot be performed by Cdk1 .
Does Cdk2 contribute to human checkpoints ? We first tested whether Cdk2 is required for the regulation of Cdc25A , a common target of checkpoint kinases and a critical mediator of cell cycle transitions . Depletion of Cdk2 with siRNA resulted in increased Cdc25A protein levels in human colorectal cancer cells ( Figure 1A ) . To unambiguously evaluate the role of Cdk2 in checkpoint responses , we disrupted both CDK2 alleles in the human colorectal cancer cell line HCT116 ( Figure 1B ) . HCT116 cells have intact DNA damage-responsive checkpoints [17]–[19] and detailed analysis of these cells has revealed that p53 is required for maintaining stable arrest at G1/S and at G2/M after ionizing radiation ( IR ) [17] . To compare the contributions of p53 and Cdk2 , we disrupted P53 and CDK2 individually to generate P53−/− and CDK2−/− cells , respectively , and together to generate double knockout cells ( CDK2−/− P53−/− ) . Two double knockout clones were obtained in independent experiments . As expected , homozygous disruption of P53 and CDK2 led to loss of protein expression in a genotype-specific manner ( Figure 1C ) . Consistent with the established role of Cdk2 in promoting the G1-S transition , asynchronous CDK2−/− cells exhibited an elevated G1 fraction with fewer cells in S-phase ( Figure 1E ) . Following IR treatment , 60% of CDK2−/− cells arrested at G1/S ( Figure 1E ) , consistent with previous observations of an intact G1/S checkpoint in CDK2−/− MEFs [15] , [16] . P53 disruption caused a characteristic loss of the G1/S checkpoint , irrespective of CDK2 genotype ( Figure 1E ) . Stabilization of p53 and the induction of its downstream target p21 after IR were not affected by CDK2 disruption ( Figure S1C ) . In P53+/+ and P53−/− backgrounds , Cdk2 deficiency resulted in increased Cdc25A ( Figure 1C ) . Cdc25A protein levels are known to be tightly controlled by phosphorylation , in both stressed and unstressed cells [20] , [21] . To determine if increased Cdc25A protein following loss of Cdk2 was due to changes in stability , we assessed Cdc25A turnover by treating HCT116 and CDK2−/− cells with the protein synthesis inhibitor cycloheximide . While Cdc25A was degraded by 90 min in CDK2−/− cells , the rate of degradation was decreased ( Figure 1D ) indicating that Cdk2 contributes to normal Cdc25A protein turnover . To assess the integrity of the G2/M checkpoint response to DNA double strand breaks , we treated isogenic cultures with IR and trapped the cells that subsequently entered mitosis with the microtubule-destabilizing drug nocodazole . Cells of all genotypes arrested normally in mitosis when treated with nocodazole alone ( Figure 2A ) . p53-deficient cells do not stably arrest at G2/M following IR [17] , and therefore exhibited a modest increase in mitotic entry after 48–60 h , compared with wild type cells in which the mitotic index remained below 4% ( Figure 2A ) . The extent of mitotic entry was greatly elevated in double knockout cells ( CDK2−/− P53−/−; Figure 2A ) . Accordingly , the mitotic marker phospho-histone H3 S10 ( H3S10-P ) was strongly expressed in CDK2−/− P53−/− cells 48 h following IR/nocodazole treatment ( Figure 2B ) . Unirradiated cells entered mitosis within 24 h of the addition of nocodazole ( Figure 2A ) . The temporal delay in the mitotic entry of irradiated double knockout cells compared with unirradiated controls suggests that checkpoint pathways were activated in the absence of Cdk2 and p53 , but were apparently insufficient to facilitate stable arrest . This G2/M checkpoint defect was apparent over a range of IR doses ( Figure S1A ) and could be detected as early as 24 h after IR/nocodazole treatment ( Figure 2 and Figure S1A ) . In contrast , the majority of CDK2 knockout-P53 wild type cells ( CDK2−/− ) arrested at G1/S after IR treatment , and the remaining subpopulation ( about 40% ) of these cells arrested at G2/M with 4N DNA content ( Figure 1E ) . A very small number of these 4N cells entered mitosis over the course of the experiment ( Figure 2 and Figure S1A ) . We conclude that Cdk2 plays an important role in G2/M arrest after DNA damage and that the requirement for Cdk2 was masked by the function of p53 at both the G1/S and G2/M checkpoints . The G2-M transition is controlled in part by Cdk1 localization . In unperturbed cells , Cdk1 is cytoplasmic during interphase and enters the nucleus in prophase to trigger mitosis . After DNA damage , Cdk1 is excluded from the nucleus , thus contributing to arrest in G2 [22] , [23] . CDK2−/− MEFs have been reported to exhibit altered Cdk1 localization [15] . In these cells , deregulated Cdk1 localization has been attributed to the redistribution of cyclin E , normally associated with Cdk2 , to Cdk1 [15] . Several studies demonstrate a similar redistribution of the nuclear protein cyclin A to Cdk1 in the absence of Cdk2 in both mouse and human cells [11] , [15] , [24] , [25] , and such complexes have been shown to promote mitotic entry [26]–[30] . Consistent with these previous studies , the amount of cyclin E and cyclin A associated with Cdk1 was increased in CDK2-knockout human cells ( Figure 3A ) . As Cdk localization is dependent on its partner cyclin [31] , [32] , we asked whether the changes in Cdk1-cyclin complexes observed in CDK2−/− cells might affect Cdk1 localization . Total Cdk1 protein levels were unaffected by CDK2 genotype or IR ( Figure 3A and Figure S1C ) . After IR treatment , the amount of Cdk1 in the nucleus was increased in CDK2−/− cells compared to wild type cells ( Figure 3B ) . The increase in nuclear Cdk1 was independent of P53 genotype , and temporally preceded entry of double knockout cells into mitosis ( Figure 3B ) . Together , these data suggest that aberrant nuclear Cdk1 was a cause rather than a consequence of defective G2/M checkpoint function in CDK2−/− P53−/− cells . The failure of CDK2-knockout cells to exclude Cdk1 from the nucleus in response to IR was confirmed by immunofluorescence . The localization of Cdk1 in untreated cells was similar in HCT116 and all isogenic derivatives ( data not shown ) . By 24 h after IR , virtually all cells with wild type CDK2 had sequestered Cdk1 in the cytoplasm , while CDK2−/− cells exhibited Cdk1 staining in both the nuclear and cytoplasmic compartments ( Figure 3C ) . To determine which cyclin partners might contribute to the altered Cdk1 localization in CDK2−/− cells , we examined the localization of cyclin B1 , cyclin A and cyclin E after IR . Cyclin B1 was cytoplasmic in all cell lines ( Figure 3D ) , suggesting that aberrant nuclear localization of Cdk1 was not caused by deregulated cyclin B1 localization . In contrast , cyclins E and A were nuclear in checkpoint-proficient P53-wild type cells ( Figure 3E and 3F ) . Cyclin E was barely detectable in P53−/− cells after IR ( Figure 3E ) , presumably because these cells bypass the G1/S checkpoint and progress to G2/M wherein cyclin E is not normally expressed; cyclin A , which is normally expressed from interphase until prometaphase , was located in the nucleus in these cells ( Figure 3F ) . In agreement with studies of CDK2−/− MEFs [15] , these findings suggest that the redistribution of cyclin E and cyclin A to Cdk1 results in its aberrant localization to the nucleus after DNA damage . In addition to localization , Cdk1 is also controlled by inhibitory phosphorylation . The Cdc25A phosphatase promotes mitotic entry by removing the inhibitory Y15 phosphate moiety on Cdk1 ( Cdk1Y15-P ) [33] . This mode of activation is turned off following IR , when Cdc25A is degraded in a Chk1-dependent manner [20] . Cdk1 Y15 phosphorylation is a p53-independent checkpoint mechanism [33] . The increased Cdc25A protein levels in untreated , Cdk2-deficient cells ( Figure 1C and 1D ) prompted us to examine whether Cdc25A was also aberrantly regulated in response to IR . While Cdc25A was rapidly degraded after IR in wild type cells , Cdc25A levels remained high in cells lacking Cdk2 ( Figure 4A ) . Next , we asked whether the failure to degrade Cdc25A after IR affected the ability of CDK2-knockout cells to induce Cdk1Y15-P and arrest at G2/M . As expected , p53-deficient cells normally induced Cdk1Y15-P after IR ( Figure 4B ) ; in contrast , the levels of Cdk1Y15-P did not increase after IR in cells that were also Cdk2-deficient . Interestingly , the phosphorylation of Cdk1 Y15 was increased in untreated Cdk2-deficient cells compared to untreated cells with wild type Cdk2 ( Figure 4B ) . This increase in basal Cdk1Y15-P could be a consequence of the redistribution of Cdk1 to alternative Cdk1-cyclin heterodimers and the consequent expansion of the role of Cdk1 to multiple phases of the cell cycle , in Cdk2-deficient cells ( Figure 3A ) [11] , [15] , [24] , [25] . It is unknown if non-canonical Cdk1-cyclin heterodimers are efficient substrates for activating phosphatases and inhibitory kinases . To assess the relevance of increased Cdc25A protein levels to the observed checkpoint defect , we tested whether stable depletion of Cdc25A could restore checkpoint function . Depletion of Cdc25A in CDK2−/− P53−/− cells using short hairpin RNAs ( Figure 4C ) suppressed histone H3S10-P induction ( Figure 4D ) and mitotic chromosome condensation ( Figure 4E ) after IR/nocodazole treatment . Mitotic entry of unirradiated cells was not affected by Cdc25A knockdown ( Figure S1B ) . These results show that elevated Cdc25A contributes significantly to the checkpoint defect . Cdc25A protein stability is regulated by two pathways in response to IR . ATM phosphorylates the checkpoint kinase Chk2 [34] , which then triggers Cdc25A degradation [12] . Cdc25A is also targeted for IR-dependent degradation by Chk1 [21] , which is activated after phosphorylation by ATR on residues S317 and S345 [35] , [36] . The ATR-Chk1 signaling pathway is active at a reduced physiological level during unperturbed cell growth , and regulates basal Cdc25A protein turnover during S-phase [21] . IR-induced phosphorylation of Chk1 S317 and S345 was reduced in CDK2-knockout cells , irrespective of P53 genotype ( Figure 5 ) . Levels of IR-induced Chk1 S345 and S317 phosphorylation ( Chk1S345-P and Chk1S317-P ) were stably high in wild type cells but increased over time in p53-deficient cells . This effect is likely due to the entry of G1/S checkpoint-defective P53−/− cells into S-phase ( Figure 1C ) , when ATR activity is known to increase [37] . RNAi-mediated knockdown of Cdk2 in a diverse panel of human cell lines consistently reduced IR-dependent Chk1 phosphorylation ( Figure S2A ) . In contrast , Chk2 phosphorylation by ATM and the formation of DNA damage foci containing phosphorylated ATM were unaffected by CDK2 genotype ( Figure S1C and S1D ) . These observations suggest that Cdk2 is required for efficient ATR- but not ATM-mediated checkpoint signaling . To determine how Cdk2 might control ATR signaling , we first examined the status of ATR localization and known interacting proteins . The ability of ATR to localize to DNA damage foci was unaffected by disruption of CDK2 ( Figure S2C ) , as was the interaction between ATR and ATRIP , the requisite ATR binding partner [38] ( Figure S2D ) . ATRIP is phosphorylated on residue S224 by Cdk2 [14] and this phosphorylation event has been shown to contribute to G2/M checkpoint arrest . We observed that cells lacking Cdk2 exhibited somewhat reduced ATRIP S224 phosphorylation ( Figure S2B ) . Given the modest deficiency in ATRIP phosphorylation , we investigated additional mediators that might also contribute to checkpoint signaling by Cdk2 . One compelling candidate is the Cdk2 substrate Cdc6 . Cdc6 is a loading factor for the DNA replicative helicase complex required for replication origin licensing [39] . In addition to its role in DNA replication , Cdc6 has also been implicated as a regulator of checkpoint function and mitotic entry [39]–[44] . In human cells , depletion of Cdc6 causes cells with actively replicating DNA to aberrantly enter mitosis [42] , while overexpression of Cdc6 causes Chk1 phosphorylation and G2/M arrest [40] . Cdc6 has also been implicated in ATR-Chk1 signaling in fission yeast [41]and Xenopus [44] . Inherently unstable , Cdc6 can be stabilized as a direct result of phosphorylation by Cdk2 [45] . Although Cdc6 is an essential DNA replication protein , cells lacking functional Cdk2 are able to progress through S-phase despite significantly reduced Cdc6 levels [6] , [45] . Therefore the relatively small amount of Cdc6 remaining in Cdk2-deficient cells is clearly sufficient to support DNA replication and cell cycle progression . In concordance with previous studies , Cdc6 protein levels were decreased ( Figure 5 and Figure S3A ) , and turnover was increased ( Figure 6A ) , in Cdk2-deficient cells . In checkpoint-deficient P53−/− cells , Cdc6 levels increased after IR in tandem with Chk1 phosphoprotein ( Figure 5 ) . These results are consistent with a potential role for Cdc6 in the regulation of the upstream kinase of Chk1 , ATR . In fission yeast , a direct interaction between the Cdc6 homologue Cdc18 and the ATR homologue Rad3 is induced in response to replication stress; this complex then activates checkpoint signaling [41] . ATR and Cdc6 also interact following replication stress in human cells [43] . We asked whether Cdc6 and ATR might similarly interact after IR . Complexes of endogenous ATR and Cdc6 were detected after IR in HCT116 cells ( Figure 6B ) . To confirm this interaction we exogenously expressed HA-tagged Cdc6 ( HA-Cdc6; [45] ) in HCT116 and CDK2-knockout cells and probed for an interaction with ATR ( Figure 6C ) . Exogenous expression of HA-Cdc6 was higher in wild type cells ( Figure 6C ) , which mirrored the relative abundance of endogenous Cdc6 and further illustrated the stabilizing effect of Cdk2 ( Figure 6B and Figure S3A ) . ATR co-precipitated with HA-Cdc6 , and the amount of ATR bound increased after IR treatment ( Figure 6C , Figure S3C and S3D ) . In the reciprocal experiment , ATR was able to pull down increased HA-Cdc6 after IR treatment in both HCT116 and U2OS cell lines ( Figure 6C and Figure S3B ) . The coimmunoprecipitation of Cdc6 and ATR was not disrupted by 50 µg/ml ethidium bromide , suggesting that this interaction is specific and not simply mediated by DNA ( data not shown ) . We could not reliably detect ATR-Cdc6 complexes in CDK2−/− cells , before or after IR ( Figure 6B and 6C ) . As Cdc6 stability and protein levels were markedly decreased in CDK2-knockout cells ( Figure 6A–6C and Figure S3A ) , we asked whether the lack of detectable interaction was simply due to reduced Cdc6 protein levels or , alternatively , if loss of Cdk2-dependent phosphorylation on Cdc6 directly disrupted its interaction with ATR . Mutant Cdc6 proteins , wherein the Cdk2-phosphorylated serine residues ( S54 , S74 , S106 ) were replaced either with non-phosphorylatable alanine residues ( HA-Cdc6AAA ) or with phosphomimetic aspartic acid residues ( HA-Cdc6DDD; [45] ) , were expressed and pulled down . ATR co-precipitated with HA-Cdc6AAA in wild type HCT116 cells and with HA-Cdc6DDD in CDK2−/− cells ( Figure S3C and S3D ) . These results indicate that the ATR-Cdc6 interaction is independent of Cdc6 phosphorylation by Cdk2 per se , and that the differences in complex formation observed were most likely the result of decreased Cdc6 levels caused by Cdk2 deficiency . To examine whether Cdk2-mediated stabilization of Cdc6 could functionally contribute to ATR-Chk1 signaling , we experimentally manipulated Cdc6 levels . First , we knocked down Cdc6 by siRNA . Cdc6 protein levels could be transiently lowered in U2OS cells , which express wild type p53 , by siRNA transfection ( Figure 6D ) . The effects of Cdc6 levels on cell growth have been intensively studied . Depending on the extent and timing of Cdc6 depletion and the type of target cell , Cdc6 knockdown has been shown to result in variable changes to cell cycle distribution as well as cell death [42] , [46]–[51] . In many cases , transient depletion of Cdc6 in various cell types , including HCT116 [46] , [48] , [51] , has been reported to have minimal effects . Normal cells and cancer cells have been observed to respond differently to Cdc6 knockdown [49] , [52] , but the genetic alterations in cancer cells that might underlie such differences have not been conclusively identified . We observed that changes to the cell cycle distribution 48 h after partial Cdc6 knockdown were minimal , with a similar proportion of cells in S-phase and a small decrease of cells in G1 ( Figure 6E ) . Knockdown of Cdc6 caused a reduction in IR-induced Chk1 phosphorylation ( Figure 6D ) that was reminiscent of the observed changes in checkpoint signaling after CDK2 knockdown ( Figure S2A ) or knockout ( Figure 5 ) . While knockdown of Cdc6 was less efficient in HCT116 cells , decreased Cdc6 also led to reduced IR-induced Chk1 phosphorylation , irrespective of P53 genotype ( Figure S3E ) . Knockdown of Cdc6 also led to increased levels of the Chk1 target Cdc25A , before and after IR treatment ( Figure S3F and S3G ) . To determine if Cdc6 protein levels could functionally impact G2/M checkpoint arrest , we assessed mitotic index after CDC6 knockdown followed by sequential treatment with IR and nocodazole . Twenty-four hours after IR/nocodazole treatment , cells pretreated with CDC6 siRNA entered mitosis in higher numbers as compared with control siRNA ( Figure 6F ) . We next increased Cdc6 levels by transient transfection of an untagged Cdc6 expression construct . While partial restoration of Cdc6 expression in CDK2−/− P53−/− knockout cells did not appreciably alter the cell cycle profile ( Figure 6H ) , it did result in increased IR-induced Chk1 phosphorylation ( Figure 6G ) and reduced mitotic entry after IR/nocodazole treatment ( Figure 6I ) . Together , the results of these overexpression and knockdown experiments suggest that stabilization of Cdc6 by Cdk2 contributes to efficient IR-induced Chk1 phosphorylation by ATR and p53-independent G2/M checkpoint function . The Cdk2-Cdc6 pathway appears to have a direct affect on ATR-Chk1 signaling , as cell cycle profiles were only minimally changed by Cdc6 manipulation under these conditions ( Figure 6E and 6H ) .
Studies of knockout mice have now unequivocally shown that the essential S-phase functions previously attributed to Cdk2 can also be conducted by Cdk1 in somatic cells [3]–[5] . These seminal observations raised the question of why mammalian cells express multiple Cdks that appear to be non-essential . In this report , we demonstrate that loss of Cdk2 alters the regulation of several proteins that are known to regulate S-phase progression , but also control mitotic entry , including Cdc25A , Chk1 , Cdc6 and ATRIP [33] , [39] , [53] . The altered balance of these bifunctional proteins did not affect the transition of CDK2−/− cells through the phases of the unperturbed cell cycle , but did compromise their ability to mount effective checkpoint signaling through the ATR-Chk1 pathway . Cdk2 and Cdk1 are therefore redundant with respect to essential cell cycle functions , but have distinct , non-redundant roles in a key DNA damage response . IR-induced ATR activation is restricted to the S- and G2-phases [37] , when Cdk2 is normally active . We show that Cdk2 plays a unique role in facilitating robust DNA damage checkpoint control by the ATR-Chk1-Cdc25A pathway . Cdk2 appears to promote the formation of active ATR complexes in at least two ways: via the phosphorylation of ATRIP and by the stabilization of Cdc6 ( Figure 7 ) . It is possible that Cdk2 also controls checkpoint signaling through additional mechanisms such as the recently described Cdk2 interacting protein ( CINP ) which facilitates robust ATR signaling [54] . At present the precise mechanism by which Cdc6 affects ATR signaling remains unclear . Our data suggest that Cdk2-mediated phosphorylation of Cdc6 merely regulates Cdc6 levels but is not otherwise required for ATR-Cdc6 complex formation ( Figure S3C ) . In agreement with our results , a paper published during the preparation of this manuscript has reported an interaction between ATR and Cdc6 in human and Xenopus cells [51] and showed that the non-phosphorylatable Cdc6-AAA mutant could interact with ATR , albeit with somewhat lower efficiency . The fission yeast Cdc6 homologue Cdc18 is required to anchor the ATR homologue Rad3 to chromatin [41] , but our results suggest that Cdc6 may not perform an analogous function in human cells ( Figure S2B ) . Further study is required to determine if Cdc6 might directly affect ATR catalytic activity , or if Cdc6 might promote the assembly of higher order complexes required for full ATR activation . In conjunction with checkpoint pathways that target Cdk1 via Cdc25 phosphatases , the exclusion of Cdk1 from the nucleus is an important G2/M checkpoint mechanism [23] . In mouse [15] and human ( Figure 3B and 3C ) cells lacking Cdk2 , Cdk1 becomes aberrantly localized to the nucleus . It would therefore appear that the formation of non-canonical Cdk1-cyclin heterodimers that allow Cdk1 to compensate for Cdk2 in the completion of S phase in unperturbed cells [11] , [24] , [25] also impairs the ability of damaged cells to arrest at G2/M . We propose that the temporal division of respective S-phase and G2-phase functions between Cdk2 and Cdk1 is a critical feature of the metazoan cell cycle that allows its progress to be efficiently halted after DNA damage . Defective checkpoints are a feature of the majority of human cancers [55]–[57] . In many cancers , checkpoint deficiencies are caused by loss-of-function mutations in P53 . The genetic interaction between P53 and CDK2 described here demonstrates a novel , non-redundant requirement for Cdk2 in the p53-independent G2/M checkpoint pathways that remain intact in cancer cells .
Endogenous CDK2 and P53 loci were disrupted in HCT116 cells using recombinant adeno-associated virus ( rAAV ) -based gene targeting methods [58] , [59] . Briefly , the targeting constructs pAAV-CDK2 and pSEPT-p53 [58] were packaged into infectious rAAV subsequently used to generate transgenic clones . Identification and expansion of homologous recombinant cell lines was performed as described [59] . At least two independent clones were isolated and analyzed for each cell line . HCT116 , SW480 and derivatives were cultured in McCoys 5A supplemented with 6% FCS . U2OS cells were cultured in DMEM supplemented with 10% FCS . CDK2 and CDC6 siRNA pools and non-targeting control pools were purchased from Dharmacon . Transfections were performed with 100 nM siRNA and Lipofectamine 2000 ( Invitrogen ) . In HCT116 , optimal Cdk2 knockdown was achieved by two transfections 48 h apart and cells were analyzed 96 h after initial transfection . In SW480 , optimal Cdk2 knockdown was achieved by a single transfection and cells were analyzed after 72 h . Cdc6 knockdown was achieved by a single transfection and cells were analyzed after 48 h . IR and nocodazole treatment were performed as described [17] . For cell cycle analysis cells were fixed , stained with Hoechst 33258 and analyzed by flow cytometry or microscopy for mitotic chromosome condensation as described [17] . Mitotic index for U2OS cells were determined by immunofluorescence for histone H3S10-P staining . Total cell lysates were prepared using NuPAGE sample buffer ( Invitrogen ) . Non-denatured cell lysates for immunoprecipitation were collected in Cell Lysis Buffer ( Cell Signaling ) . Immunoprecipitations were performed by incubation of lysates with antibody and Protein A/G PLUS-Agarose beads ( Santa Cruz ) overnight at 4°C . Beads were washed , resuspended and boiled in NuPAGE lysis buffer . Proteins were separated on NuPAGE gels ( Invitrogen ) , transferred to PVDF membranes , probed with antibodies and developed using Enhanced Chemiluminescence ( Amersham ) . Primary antibodies were directed against α-tubulin , ATR , Cdc6 , Cdk1 , Cdk2 , Chk1 , cyclin A , cyclin E , cyclin B1 , HA , p53 ( Santa Cruz ) , Cdc25A ( Neomarkers ) , ATMS1981-P , Cdk1Y15-P , Chk1S317-P , Chk1S345-P , Chk2T68-P ( Cell Signaling ) , ATRIP , histone H3S10-P ( Millipore ) , Orc2 ( BD Biosciences ) , and ATRIPS224-P ( a gift from D Cortez ) , as indicated . The Quantity One 4 . 6 . 1 software package ( Bio-Rad ) was used for quantitation . For analysis of protein stability , cells were incubated in 100 µg/ml cycloheximide prior to lysis; band intensities were measured and normalized to α-tubulin abundance . Protein levels were expressed as a percentage of untreated control cells . Subcellular fractionation was performed as described [60] . For immunofluorescence , cells were grown on chamber slides and fixed with 3 . 75% paraformaldehyde/2% sucrose . Fixed cells were permeabilized in 0 . 2–0 . 5% Triton X-100 and blocked in BSA . Immunofluorescence staining was performed using Cdk1 , cyclin A , cyclin B1 , cyclin E or histone H3S10-P antibodies followed by biotin-conjugated secondary antibody ( Santa Cruz ) and Alexa-488 conjugated avidin ( Molecular Probes ) . Cells were counterstained with 4′-6′-diamidino-2-phenylindole ( DAPI ) and mounted with Fluoromount-G ( Southern Biotech ) . Images were captured at room temperature using an AxioImager Z1 microscope equipped with an AxioCam HRm camera , Axiovision 4 . 6 . 3 software , and a Plan Neofluar 20x/0 . 25NA , 40x/1 . 3NA or 63x/1 . 25NA lens ( Zeiss ) , as indicated . Images were processed for brightness and contrast using Adobe Photoshop . CDC25A shRNA was cloned from pSUPER-Cdc25A [61] into the retroviral plasmid pBabe to generate pBabe-shCDC25A . Retroviral production using Amphopack293 cells ( Clontech ) and subsequent gene transfer was performed according to protocols supplied by the manufacturer . Plasmids encoding HA-Cdc6 , HA-Cdc6AAA and HA-Cdc6DDD were previously described [45] . To generate the untagged full length Cdc6 construct , human Cdc6 cDNA was cloned into pCDNA3 . 1/Hygro ( Invitrogen ) . Cells were transfected using Lipofectamine 2000 ( Invitrogen ) and analyzed after 24–48 h . | Metazoan cells contain multiple Cdks that regulate cell cycle progression . Recent studies have shown that mouse cells can grow normally with just Cdk1 . The roles of the non-essential Cdks remain a fundamental question . In this study , we describe the generation and detailed characterization of CDK2-knockout human somatic cells . Our study demonstrates that Cdk2 is required for robust DNA damage checkpoint signaling . Loss of Cdk2 caused a marked deficiency in the G2/M arrest—a basic response to DNA damage—in cells that were also nullizygous for P53 . We propose that the multiple Cdks present in metazoan cells provide a mechanism by which the cell cycle can be efficiently halted after DNA damage . Significantly , this study reveals a heretofore unrecognized dependence for Cdk2 in p53-deficient cancer cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses"
] | 2010 | Cdk2 Is Required for p53-Independent G2/M Checkpoint Control |
One of the major breakthroughs in oncogenesis research in recent years is the discovery that , in most patients , oncogenic mutations are concentrated in a few core biological functional pathways . This discovery indicates that oncogenic mechanisms are highly related to the dynamics of biologic regulatory networks , which govern the behaviour of functional pathways . Here , we propose that oncogenic mutations found in different biological functional pathways are closely related to parameter sensitivity of the corresponding networks . To test this hypothesis , we focus on the DNA damage-induced apoptotic pathway—the most important safeguard against oncogenesis . We first built the regulatory network that governs the apoptosis pathway , and then translated the network into dynamics equations . Using sensitivity analysis of the network parameters and comparing the results with cancer gene mutation spectra , we found that parameters that significantly affect the bifurcation point correspond to high-frequency oncogenic mutations . This result shows that the position of the bifurcation point is a better measure of the functionality of a biological network than gene expression levels of certain key proteins . It further demonstrates the suitability of applying systems-level analysis to biological networks as opposed to studying genes or proteins in isolation .
Cancer is one of the most important diseases affecting human health today [1] . Although cancer is considered a genetic disease [2] , with a variety of oncogenes and tumour suppressor genes identified , the specific genomic alterations vary wildly between and within cancer types . In 2008 , three high-throughput cancer genomic studies reported that cancer gene mutations are concentrated in a limited number of core cellular pathways and regulatory processes [3]–[5] . This discovery suggests that oncogenesis is highly related to the dynamics of biologic regulatory networks , which govern the behaviour of functional pathways . Clearly , to understand the mechanisms underlying oncogenesis , we need to take a systems and dynamics approach . A number of studies have proposed a network-based approach to investigate oncogenesis . For example , Torkamani and Schork identified functionally related gene modules targeted by somatic mutation in cancer [6]; Cerami et al . proposed an automated network analysis approach to identify candidate oncogenic processes [7] . A more recent approach by Stites et al . sought to explain mutations in Ras pathway , which are commonly found in cancer , by investigating the steady state concentrations of cellular proteins in parameters changes [8] . In this paper , we propose a new way to identify high-frequency gene mutations in cancer cells . We reason that because gene mutations may affect the activities of their corresponding proteins in a biological regulatory network , they can be considered as perturbations of the system's dynamics . Therefore , those mutations that qualitatively affect biological network function should correspond to mutation hot spots in cancer . From a dynamics point of view , a qualitative change in a system relates to bifurcations—oncogenic mutations should therefore significantly affect certain bifurcation points . One of the hallmarks of cancer is evasion of apoptosis; in fact p53 mutations are found in most human cancers [9] . We therefore chose the DNA damage-induced p53-centered apoptosis pathway , as an example , to evaluate our hypothesis . We evaluated the sensitivity of bifurcation points to different network parameters , and compared the results with the cancer gene mutation spectrum . We found that parameters that significantly affect the bifurcation points corresponded to high-frequency oncogenic mutations . This study investigates the mutation spectrum found in cancer cells and provides a useful tool for predicting oncogenic mutations .
We focused on the apoptotic pathway that responds to sustained DNA damage , induced by the chemotherapeutic compound , etoposide [10] , [11] . A recent study showed that while low-dose etoposide induces oscillations in p53 levels , caspase3 levels remain low , and most cells survive; in contrast , high-dose etoposide induces a monotonic increase in p53 concentration , followed by a rapid increase in caspase3 with most cells undergoing apoptosis [11] . This experiment further justifies the use of p53 in our model . A schematic of the corresponding regulatory network , which is a modification of the p53 DNA damage response network established and analysed by Li et al . [12] , is shown in Figure 1 . Nuclear p53 induces mdm2 transcription , while MDM2 antagonizes p53 by promoting multistep ubiquitination and proteasome-dependent degradation of p53 [13] , [14] . In unstressed cells , p53 is kept at a low concentration by its negative regulator MDM2 . DNA damage reduces the binding affinity between p53 and MDM2 by inducing phosphorylation of p53 and MDM2 [15] — phosphorylated MDM2 undergoes rapid degradation [16] and p53 is subsequently activated by phosphorylation to a “response state” , triggering downstream events , such as apoptosis and cell-cycle arrest [17] . As shown in Figure 1 , mono-ubiquitinated p53 is exported to the cytoplasm , while poly-ubiquitinated p53 undergoes degradation [18] . At elevated p53 levels , apoptosis can be initiated by both nuclear and cytoplasmic ( or mitochondrial ) p53 [19] . While nuclear p53 regulates the transcription of pro-apoptotic proteins such as Puma , Noxa , Bax and Bak [20] , mitochondrial p53 exerts a direct pro-apoptotic effect by interacting with Bax and Bak to form a positive feedback loop that activates caspase3 [20]–[24] . In the experiments conducted by Chipuk et al . [21] and Chen et al . [11] , p53 appears to regulate apoptosis through its cytoplasmic pro-death activity and not its nuclear activity . Many previous studies of p53 dynamics have focused on the response to transient DNA damage induced by ionising radiation or UV; following cell-cycle arrest , cell proliferation resumes once the DNA damage is repaired [25] , [26] . In our model , we consider sustained DNA damage , which is maintained at a constant damage level until the cell initiates apoptosis [12] . In this way , we can ignore the cell-cycle arrest pathway , and instead concentrate on the apoptosis pathway . We first present an overview of network dynamics in response to DNA damage at two typical doses of etoposide . As shown in Figure 2 , at 1 µM etoposide ( low DNA damage ) , the concentration of nuclear p53 ( including inactivated- and activated-p53 ) oscillates around basal levels ( blue line ) , while the concentration of caspase3 remains low ( green line ) , indicating that apoptosis has not been trigged . At 100 µM etoposide ( high DNA damage ) , nuclear p53 increases monotonically ( black line ) , which is followed by a rapid increase in caspase3 ( red line ) , which in turn triggers downstream apoptotic processes . These results are consistent with the experimental observations of Chen et al . [11] , indicating that our model qualitatively reflects the real system . Having created a suitable model , we next conducted bifurcation analysis to identify potential qualitative changes in the system . Using the level of DNA damage as the control parameter , two types of bifurcations were found in this analysis: two Hopf bifurcations and one saddle-node bifurcation . The transition diagrams of these bifurcations are presented in Figure 3 . In Figure 3A , as the level of DNA damage increases , nuclear p53 undergoes two Hopf bifurcations . In the first bifurcation , with increasing DNA damage , the system changes from a low steady state to an oscillatory state; in the second bifurcation , with further DNA damage , the oscillatory state changes to a high steady state . This result is consistent with previously published observations [10] , [12] , [27]–[29] . In the case of caspase3 , there exists a saddle-node bifurcation as a function of the level of DNA damage , where a stable node collides with an unstable saddle at the bifurcation point , as shown in Figure 3B . This bifurcation separates the system into two dynamic regimes: a mono-stable steady state regime and a bistable regime . In the bistable regime , one steady state corresponds to a low caspase3 concentration and the other to a high caspase3 concentration . In a wild-type cell , the caspase3 concentration remains low . Once the DNA damage level increases beyond the bifurcation point ( about 26 µM etoposide in Figure 3B ) , the system will switch to a high caspase3 concentration that turns on the apoptosis pathway . Notice that this switch is irreversible—once the apoptosis pathway is turned on , caspase3 can maintain the high level state even when the level of DNA damage falls below the initial threshold . Our model is based on biological facts , together with certain assumptions and simplifications . The details of our model are presented in the Supporting Information ( Text S1 ) . Notice that in our model , ac-p53 refers to phosphorylated p53 . In biological systems , many biological functions are controlled through dynamic bifurcations . A good example is the saddle-node bifurcation in G1/S transition of the cell cycle [30]–[33] , where the qualitative behaviour of the system is significantly affected by changes in control parameters , which may dramatically affect the location of the critical point . Figure 3C represents such an example in the apoptosis pathway , where increasing the control parameter kf5 ( which corresponds to the rate of association of mono-ub-p53 and MDM2 in the regulatory network of Figure 1 ) 1 . 9-fold , shifts the critical point to the right . If kf5 is increased 4 . 2-fold , the high caspase3 state can never be reached under medium or high drug dose . This indicates that if the parameter kf5 is increased due to certain mutations , apoptosis will not be initiated properly , or will not initiate at all , even when the DNA is seriously damaged . The damaged cells therefore have a chance to bypass apoptosis , which may facilitate oncogenesis . The effect of parameter changes on the location of the bifurcation point is not evenly distributed: some parameters significantly impact the bifurcation points , while others do not . We believe that genes that fall in the former category play important roles in oncogenesis . To identify which parameters have a major impact on the location of the bifurcation point , we conducted parameter sensitivity analysis by increasing and decreasing each of the 54 parameters in our model 1 . 2-fold and recording the percentage change of the bifurcation points . In this way , we established a spectrum of parameter sensitivity , which allowed us to compare the result with the oncogenic mutation spectrum . High-throughput cancer genomic projects , such as the Cancer Genome Atlas ( TCGA ) and the Catalogue of Somatic Mutations in Cancers ( COSMIC ) [34] , are major resources to obtain the spectrum of genetic variants in different cancer types [35] . To investigate the relationship between parameter changes and the spectrum of cancer gene mutations , we chose skin cancer mutated genes from the Catalogue of Somatic Mutations in Cancers ( COSMIC ) and glioblastoma multiforme mutated genes from the “CAN-genes” by Parsons et al . [4] , and TCGA [5] . Based on the knowledge of biochemical reactions and gene expression [3]–[5] , we concentrated on three types of gene mutations: somatic mutations , amplifications , and deletions . Each mutation corresponds to specific parameters in ordinary differential equations ( ODEs ) in our model , the basis and details of which are given in the Supporting Information ( Text S1 ) . The main result of our calculation is summarized in Figure 4 . Parameter sensitivities of the saddle-node bifurcation point of each parameter of the apoptosis pathway are shown in Figure 4A ( see also Supporting Information Table S2 ) ; parameters that cause large or small changes in bifurcation points are marked in yellow , and green , respectively . For the apoptosis pathway about 70% of the parameters are non-influential: the bifurcation point varies very little when changing those parameters . This suggests that the apoptosis pathway is robust , a hallmark of biological networks . However , about 26% of parameters have significant effects on the critical bifurcation points , such as gc_Bax ( the basal generation rate of Bax ) and kex ( nuclear-export rate of mono-ubiquitinated p53 ) , see Figure 4A . A small change in these parameters will induce large changes in the bifurcation points , as shown in Figure 3B . Increasing gc_Bax causes the critical bifurcation point to shift to the left . Therefore , in a biological experiment , to achieve a given rate of apoptosis , increasing the basal generation rate of Bax will require a lower dose of the DNA damaging drug compared with the unaltered basal generation rate of Bax . Similarly , increasing kex will cause the critical bifurcation point to shift to the left , so that for a given rate of apoptosis , increasing the nuclear-export rate of mono-ubiquitinated p53 will require a lower dose of the DNA damaging drug compared with the unaltered nuclear-export rate . Our parameter sensitivity analysis of critical bifurcation points is in agreement with the literature . For example , overexpression of Bax significantly increases the rate of radiation-induced apoptosis in human breast cancer cells [36] , indicating that a perturbation of the basal generation rate of Bax ( in our model the corresponding parameter is gc_Bax ) would significantly affect the rate of apoptosis—as is indeed predicted by our model . Furthermore , mitochondrial p53-translocation and -accumulation may be induced by a variety of apoptotic stimuli , [37] , [38] . In fact Marchenko et al . found that the rate of apoptosis is significantly increased after redirecting p53 from the nucleus to the mitochondria by using mitochondrial import leader peptides [37] . This means that a perturbation in the nuclear-export rate of p53 ( in our model the corresponding parameter is kex ) could greatly alter the rate of apoptosis . Again , we confirmed this effect in our model . Moreover , Dewson et al . recently reported that following apoptotic signalling in cells and mitochondrial fractions , Bax homodimerises via a BH3:groove interface interaction [39] , a necessary step in the apoptotic pathway . The key interaction domains that affect apoptotic function are located in the α2–α5 regions ( 54–126A ) of Bax; mutations in one of these key residues disrupt apoptotic function , thereby reducing the rate of cell death following treatment with etoposide [39] . According to the COSMIC database , cancer mutation hot spots do exist in the α2–α5 helices of Bax . These loss-of-function mutations in the Bax BH3 domain decrease the dimerization rate of activated Bax ( in our model the corresponding parameter is kf10 ) . Our model showed that kf10 is indeed a sensitive parameter and a slight decrease in kf10 shifts the critical bifurcation point to the right . We next sought to compare our results to those of Stites et al . who investigated mutations in Ras pathway by measuring the steady state concentrations of cellular proteins in parameter changes [8] . In addition to parameter sensitivity analysis of the bifurcation points , we therefore used the steady-state concentration of caspase3 as a measure of oncogenesis . To achieve this , we increased and decreased each of the 54 parameters 1 . 2-fold and recorded the percentage change in the steady-state concentration of caspase3 . The results are presented in Figure 4B ( See also Supporting Information Table S3 ) . The parameters that cause a large or small percentage change in the steady-state concentration of caspase3 are marked in magenta and blue , respectively . Overall , we found that the bifurcation point and the steady-state concentration of caspase3 are sensitive to mutually exclusive sets of parameters . As shown in Figure 4A , the critical points of bifurcation are largely affected by 15 parameters ( yellow in Figure 4A ) , which we then selected to compare with the skin cancer and glioblastoma multiforme gene mutation spectrum , as shown in Figure 5A . Changes in the bifurcation point ( yellow bar ) and in the steady-state concentration of caspase3 ( blue bar ) are displayed alongside mutation frequencies in skin cancer and glioblastoma multiforme . Almost all influential parameters correspond to mutation hot spots in skin cancer and glioblastoma multiforme . This result supports our hypothesis that bifurcation points are sensitive to parameters corresponding to mutations that are most likely oncogenic . Here , we note that the definition of “mutation hot spot” is not quantitative; we will discuss this issue in the last section of the paper . For the sake of comparison , we also selected the parameters to which the bifurcation point was insensitive; the result is presented in Figures 5B and 5C . We found that a part of the insensitive parameters correspond to very small number of mutation hot spots ( Figure 5B ) , but the other part correspond to a large number of mutation hot spots ( Figure 5C ) . Several factors may contribute to the inconsistency . It is well established that alteration of a single gene may not be oncogenic in itself [40]—in most cases , multiple hits are necessary [41] . We therefore suggest a synergistic effect for these parameters ( Figure 5C ) , where two or more parameter changes , which are non-influential in isolation , may induce sensitivity in the bifurcation point when they co-occur . Indeed , we found that by decreasing kf3 ( association rate of p53 and MDM2 ) and increasing kr3 ( dissociation rate of p53/MDM2 complex ) at the same time by 1 . 2-fold , the bifurcation point changes by about 22% . However , when changing these two parameters in isolation , the bifurcation point only changes by 5% and 2% . This may partially explain the observed inconsistency . Furthermore , one gene mutation may affect several parameters in our model , and one model parameter may involve several genes . In our analysis , the one-to-one mapping between the model parameters and the genes involved in the network is certainly an oversimplification . Fully understanding this behaviour requires detailed knowledge of the effect of mutations on the parameters , which is not available except in a few special cases . Similarly , we investigated the effect of different parameters on the steady state concentration of caspase3 as a measure of oncogenesis . We identified two parameters ( magenta in Figure 4B ) , which led to the largest changes in the steady-state concentration of caspase3 . We also calculated changes in the critical point when changing the parameters ( Figure 6 ) . According to the results in Figures 5A and 6 , and compared with the results of bifurcation points , the relationship between the protein steady-state concentration and the cancer mutation spectrum is very weak .
Our work is based on the hypothesis that key regulators in physiological networks and oncogenesis are closely correlated and that the critical point of bifurcation is a good measure of network functionality . Therefore , mutations that cause variations in parameters that affect the bifurcation point are more likely to be oncogenic . In our apoptosis model , the location of the saddle-node bifurcation point reflects the DNA damage threshold where apoptosis is activated; when this threshold is exceeded , the system will switch from the low to the high state , which is accompanied by a rapid increase in caspase3 levels . A mutation may increase the apoptotic threshold , thereby allowing cells to evade apoptosis even at high levels of DNA damage , which may facilitate oncogenesis—a hypothesis that was confirmed by our analysis . Distinguishing driver- from passenger-mutations is a central challenge in cancer research [42]–[44] , and recently a network-based approach to identify cancer driver mutations has been proposed [6] , [7] . Similarly , our strategy may be applied to identify driver mutations by identifying parameters with the greatest impact on the bifurcation point . Several issues need to be addressed in this analysis: First , what is the impact of the Hopf bifurcation of nuclear p53 on oncogenesis ? Although a number of studies have investigated the oscillatory behaviour of p53 in response to stress [25] , [45]–[47] , the functional role of these oscillations in DNA damage response remains unclear . We also conducted parameter sensitivity analysis of the Hopf bifurcation of nuclear p53 as a function of the level of DNA damage [12] , and found a strong correlation between the spectrum of parameter sensitivities and the oncogenic mutation spectrum ( see Figure S1 ) . This may indicate that nuclear p53 oscillation plays a crucial role in protecting the cell against malignant transformation [11] . Second , as previously stated , our model parameters do not have one-to-one correspondence with gene mutations: changing one parameter may correspond to mutations in different genes or different types of mutations in the same gene . For example , the association and dissociation constants of two proteins may relate to mutations in either of the associated genes; an increase in a given protein may be caused by an increase in gene copy number , or by an increase in the catalytic efficiency of the relevant transcription factor ( corresponding to the mutation ) . Moreover , different mutations in a single gene may correspond to different parameters: a gene mutation may change the functionality of the protein , reduce the binding capacity of the protein with another protein , or alter its phosphorylation efficiency . As molecular biology advances , information regarding the function of different mutations in regulatory networks will become more quantitative , which will allow for more precise analysis using our model . The third concern relates to the definition of an oncogenic mutation hot spot . Different genes may be involved in different regulatory pathways , and have very large differences in mutation frequency . For example , p53 is involved in several regulatory pathways and has hundreds of known mutations , while Puma ( BBC3 ) is involved in the apoptosis pathway , with only four or five known mutations . A method for normalising mutation frequency is necessary to allow quantitative analysis using our model . However , due to the lack of detailed information on the impact of each mutation on different regulatory pathways and on the model parameters , our analysis can only be qualitative . In this work , we used arbitrary thresholds to define a “mutation hot spot” , that were determined by our knowledge of the specific gene . As such , for genes involved in several regulatory pathways and with a high mutation frequency ( like p53 ) , we set a high threshold value ( >10 ) for mutation hot spots . For genes involved in only one or two pathways , and which have very low mutation frequencies ( like Puma ) , we believe that in spite of the low mutation frequency , they are still mutation hot spots . Because of the lack of detailed information on the impact of each mutation on the model parameters , our analysis can only achieve qualitative conclusions . The fourth issue relates to the simplicity of our model network: although the apoptosis pathway involves both the extrinsic and intrinsic pathways [48] , we used a simplified , qualitative network model to conduct our research . To prove the validity of our model , we extended the current apoptosis pathway and repeated our analysis . Compared with the original model , the extended model consisted of 10 additional nodes including Noxa , Mcl-1 , Bcl-xL and the complexes that they formed . Puma , Noxa , Bcl-2 , Mcl-1 and Bcl-xL are all proteins of the Bcl-2 family . Like Puma , Noxa is a pro-apoptotic protein , which is regulated by nuclear p53 at the transcriptional level . Bcl-2 , Mcl-1 and Bcl-xL are pro-survival proteins that inhibit cell apoptosis [48] . Puma binds Bcl-2 , Bcl-xL and Mcl-1 , whereas Noxa binds only Mcl-1 [49] . The corresponding extended regulatory network is shown in Figure 7 . The details of the extended model are presented in the Supporting Information ( Text S1 , Figure S2and Figure S3 ) . The results of the parameter sensitivity analysis and the correspondence between parameter sensitivity and mutations are shown in Figure 8 . Notably , sensitive parameters in the extended pathway are very similar to those of the simplified pathway , with the 15 most influential parameters shared between the simplified ( Figure 4A ) and the extended pathways ( Figure 8A ) . However , in the extended pathway , we found three additional sensitive parameters . Similar to the results of the simplified model ( Figure 5A ) , we found that all sensitive parameters in the extended pathway correspond to mutation hot spots found in skin cancer and glioblastoma multiforme ( Figure 8C ) . The analysis of the extended version of the network produces almost the same results as the simplified network , which supports the applicability and validity of our method . Finally , in our analysis , the change of each mutated property ( parameter in ODE ) was counted only once within our model , despite the general consensus that more than one mutation is needed for oncogenesis to occur [50] . Our primary goal is to study the role of mutated genes in cancer-related biological functions . In future , we will analyse the role of multiple mutations on network functionality .
We compiled a set of ordinary differential equations ( ODEs ) ( Text S1 . ) to model the apoptotic pathway in response to DNA damage . Model parameters were chosen based on the literature and biochemical constraints [51] . The skin cancer mutated genes database was obtained from COSMIC ( http://www . sanger . ac . uk/genetics/CGP/cosmic ) , glioblastoma multiforme mutated genes from the “CAN-genes” by Parsons et al . [4] , and TCGA [5] . The “CAN-genes” by Parsons et al . [4] included genes frequently mutated in 22 glioblastoma multiforme samples . The TCGA project has catalogued somatic mutations and recurrent copy number alterations in 91 glioblastoma multiforme cases [5] . The basis and details of the correspondence of three forms of gene mutations and specific parameters in ordinary differential equations ( ODEs ) in our model are given in Text S1 . | Among complex genetic diseases affecting humans , cancer is a major cause of death . In 2008 , a genome-wide analysis of hundreds of tumour samples showed that oncogenic mutations are concentrated in a few core functional pathways , revealing a new conceptual framework for cancer biology research , where the role of oncogenic mutations and oncogenic mechanisms are addressed from a network perspective . We therefore propose a new way of identifying high-frequency gene mutations in cancer: gene mutations may affect their corresponding proteins' activity in the biological regulatory network and can be considered as perturbations of the dynamical system . Therefore , mutations that induce qualitative changes in biological networks should correspond to high-frequency mutations in cancer . This concept can help us identify and understand the function of genes that play an important role in oncogenesis , thereby allowing targeted and effective design of gene-based therapy in cancer . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"systems",
"biology",
"biology"
] | 2014 | Correlation between Oncogenic Mutations and Parameter Sensitivity of the Apoptosis Pathway Model |
The basic metabolic cytochrome P450 ( CYP ) system is essential for biotransformation of sterols and xenobiotics including drugs , for synthesis and degradation of signaling molecules in all living organisms . Most eukaryotes including free-living flatworms have numerous paralogues of the CYP gene encoding heme monooxygenases with specific substrate range . Notably , by contrast , the parasitic flatworms have only one CYP gene . The role of this enzyme in the physiology and biochemistry of helminths is not known . The flukes and tapeworms are the etiologic agents of major neglected tropical diseases of humanity . Three helminth infections ( Opisthorchis viverrini , Clonorchis sinensis and Schistosoma haematobium ) are considered by the International Agency for Research on Cancer ( IARC ) as definite causes of cancer . We focused our research on the human liver fluke Opisthorchis felineus , an emerging source of biliary tract disease including bile duct cancer in Russia and central Europe . The aims of this study were ( i ) to determine the significance of the CYP activity for the morphology and survival of the liver fluke , ( ii ) to assess CYP ability to metabolize xenobiotics , and ( iii ) to localize the CYP activity in O . felineus tissues . We observed high constitutive expression of CYP mRNA ( Real-time PCR ) in O . felineus . This enzyme metabolized xenobiotics selective for mammalian CYP2E1 , CYP2B , CYP3A , but not CYP1A , as determined by liquid chromatography and imaging analyses . Tissue localization studies revealed the CYP activity in excretory channels , while suppression of CYP mRNA by RNA interference was accompanied by morphological changes of the excretory system and increased mortality rates of the worms . These results suggest that the CYP function is linked to worm metabolism and detoxification . The findings also suggest that the CYP enzyme is involved in vitally important processes in the organism of parasites and is a potential drug target .
The heme-containing enzymes cytochromes P450 ( CYPs ) are widely distributed in living organisms from bacteria to mammals [reviewed in Refs . 1 , 2–3] . CYP enzymes act as components of a monooxygenase system; they display biological functions ranging from detoxification of environmental pollutants to synthesis and degradation of endogenous signaling molecules [1] . More than 80% of existing prescription drugs undergo the unavoidable step of biotransformation mediated by cytochromes Р450 . This step typically limits the speed of the biotransformation process and of excretion of the drugs [1] . Given these roles , CYP are functionally important for survival of invading pathogens . For example , CYP in Leishmania donovani is essential for cell growth , infection , ergosterol biosynthesis , and other processes . Parasites with one allele of CYP show impaired growth , lower membrane potential , reduced virulence , and higher sensitivity to drugs [2] . Inactivation of both copies of a CYP gene is lethal for this microbe [2] . In the free-living nematode Caenorhabditis elegans , one of the CYP enzymes participates in the synthesis of a steroid hormone necessary for worm development [3] , whereas the other CYP enzyme is involved in fatty acids homeostasis [4] . It is known that in fungi , the CYP system is an important pharmacological target because it participates in the synthesis of the cell wall of spores , in the metabolism of membrane sterols , and in production of metabolites with antibacterial properties [1] . It is noteworthy that CYP enzymes may be linked to the synthesis of unique sterol-like metabolites , oxysterols , and catechol-estrogens of specific structure known from trematodes , specifically Opisthorchis viverrini and Schistosoma haematobium [5 , 6] . Oxysterols , which are oxidation products of cholesterol and are generated by enzymatic ( cytochromes P450 ) or non-enzymatic processes [7] , can be mutagenic and genotoxic and may possess pro-oxidative and proinflammatory properties that promote carcinogenesis [8–9] . Accordingly , analysis and characterization of the function of CYP of parasites can be anticipated to lead to a deeper understanding of diverse aspects of parasite physiology that contribute to cell survival , drug resistance , and maintenance and evolution of the host-parasite relationship . Despite importance of the CYP system for the physiology of parasites and other pathogens and for drug detoxification , CYP enzymes of eukaryotic parasites remain poorly understood . It had long been thought that parasitic worms lost CYPs [10] . Recently , monooxygenase transformation of certain drugs into inactive metabolites was demonstrated in flukes [11–12] . The role of this activity in the drug resistance of parasites was also described [11–13] . For example , increased formation of oxidized inactive metabolites of albendazole and triclabendazole in resistant isolates of parasitic flatworms has been reported; furthermore , inhibition of the oxidative metabolism increases sensitivity of the worms to anthelminthic drugs [13] . In addition , a monooxygenase activity was discovered in S . mansoni and S . haematobium [14] . These findings indicated the existence of a CYP system in parasitic clades of the phylum Platyhelminthes; however , data are not yet available on either the composition of the CYP system or the function of these enzymes in flatworms . The liver flukes O . felineus , O . viverrini , and Clonorchis sinensis of the family Opisthorchiidae ( Class Trematoda ) cause serious human diseases affecting bile ducts and the gall bladder . The International Agency for Research on Cancer ( IARC ) recognizes infection with two of these three helminths as a definitive cause of cholangiocarcinoma: the liver flukes O . viverrini and C . sinensis [15] . Carcinogenic effects of an infection are also possible for O . felineus , given the similar signs of infection development and disease course [16] . O . felineus ( Rivolta , 1884 ) occurred primarily on the territory of the former USSR , but increasingly it is being found in other regions of Europe [see Refs . 17–18] . It is estimated that worldwide , there are 1 . 6 million cases of opisthorchiasis resulting from infection with O . felineus . Despite its public health significance , this widespread Eurasian species is one of the most poorly studied human liver flukes . The ‘CYPome’ of parasitic flatworms , including liver flukes ( Opisthorchiidae , Fasciolidae ) , blood flukes ( Schistosomatidae ) , and cestodes ( Taeniidae ) contains only one CYP gene [19] . Additionally , flavin monooxygenase genes appeared to be absent . Apparently , in these parasites , the main enzyme that has a monooxygenase activity toward xenobiotics is CYP . We cloned and sequenced CYP cDNA of O . felineus ( GenBank ID: JF920147 ) , and predicted the structure of the protein it encodes . The predicted protein has conserved structure , contains functional domains ( characteristic of mammalian microsomal CYP enzymes involved in biotransformation of xenobiotics ) and turned out to be most closely related in its structure to the mammalian CYP2 subfamily . Furthermore , this CYP gene exhibits strong expression of mRNA . Nevertheless , little or nothing is known about the functions of the protein . The aim of the present study was to analyze the functional organization of the metabolic system of cytochromes P450 in O . felineus , the liver fluke of humans and other fish-eating mammals . In particular , we set out to determine the functional significance of the monooxygenase of O . felineus , to assess its ability to metabolize xenobiotics , to identify the possible spectrum of substrate specificity of this CYP , evaluate the inducibility of the CYP gene , and to determine the necessity of expression of this gene on the phenotype of the liver fluke .
All of the procedures were in compliance with The Code of Ethics of the World Medical Association ( Declaration of Helsinki ) for animal experiments http://ec . europa . eu/environment/chemicals/lab_animals/legislation_en . htm . The hamsters were kept and treated according to protocols approved by the Committee on the Ethics of Animal Experiments of the Institute of Cytology and Genetics ( Permit Number: 25 of 12 . 12 . 2014 ) . Golden Syrian hamsters ( Mesocricetus auratus ) were purchased from the Puschino Animal Facility ( Russia ) and subsequently bred at the Animal Facility of the Institute of Cytology and Genetics , SB RAS ( RFMEFI61914X0005 ) , ( Russia ) . Euthanasia was performed by decapitation , and all efforts were made to minimize suffering . Metacercariae of O . felineus were collected from naturally infected fish ( Leuciscus idus ) from the Ob River near the city of Novosibirsk , Western Siberia . Territories where sample collection ( fishing ) took place were neither conservation areas nor private , nor otherwise protected; hence , no fishing permits were required . The fish species collected are not considered endangered or rare , and the fishing methods complied with the Federal Law N166-F3 of 20 . 12 . 2004 ( ed . 18 . 07 . 2011 ) , "Fishing and conservation of water bio-resources” . Adult liver flukes were recovered from the hepatobiliary tract of hamsters infected three months earlier with 100 metacercariae [20 , 21] . Newly excysted metacercariae ( NEM ) were hatched from metacercariae using incubation with 0 . 1% trypsin in sterile saline solution at 37°C for 15 min [21] . The parasites were incubated in the RPMI medium supplemented with 1× antibiotic/antimycotic ( Sigma–Aldrich , USA ) and 1% glucose at 37°C for either 4 or 24 h with one of the xenobiotics . Xenobiotics that were dissolved in dimethyl sulfoxide ( DMSO , Sigma–Aldrich ) were added in the form of 100× stock solutions to final concentrations of 10 nM 2 , 3 , 7 , 8-tetrachlorodibenzodioxin ( TCDD ) , 50 μM phenobarbital ( PB; Fluka , Switzerland ) , 10 μM dexamethazone ( DEX; Sigma–Aldrich ) , 50 mM ethanol , 0 . 1 μg/mL praziquantel ( PZQ; Bayer , Germany ) , or 40 μM ketoconazole ( Zdorovie , Russia ) . Cholesterol ( Sigma–Aldrich ) was dissolved in 96% ethanol at the concentration of 10 mg/mL to prepare a 2000× stock solution . Bile acids ( Sigma–Aldrich ) were dissolved in water to prepare a stock solution 400 mg/mL and were added to the medium as a 100× solution . Hemoglobin ( Sigma–Aldrich ) was dissolved in 10 mM Tris-HCl ( pH 8 ) to prepare a 2% stock solution and was added to the incubation media as a 20× solution . Forty micromolar resorufin , pentoxyresorufin ( PR ) , and methoxyresorufin ( MR ) ( S1 Fig ) ( AnaSpec , USA ) were dissolved in DMSO to prepare 100× solutions and were added to the final concentrations 0 . 1 , 0 . 5 , and 5 μM , respectively . The controls received an equivalent amount of the vehicle ( DMSO ) . The incubation assays for each xenobiotic concentration and each developmental stage of the parasite were conducted in triplicate . Ethylenediaminetetraacetate ( EDTA ) ( anticoagulant ) -treated blood and bile were isolated from a hamster and were diluted 4-fold for the experiments . Primers for real-time PCR were designed using the transcriptome of maritae of O . felineus from Solexa . The primers and probes were as follows: the Ub ( ubiquitin ) gene ( GenBank ID: JK649790 ) ( UB_F: 5’-TCCGCCACTCCGTCTTACGC , UB_R: 5’-ACTAGCCGATGACATGCGGTGGA ) and MrpL16 ( mitochondrial ribosomal protein L16 ) gene ( GenBank ID: JK649791 ) ( MrpL16_F: 5’-TCCCTTCCCGGCTCGTTTCGT , MrpL16_R: 5’-AGTGCTTGGCGAGCATCAGCA , MrpL16 Probe: 5’-R6G-ACAAGAGTTGCTGGACTGCGAGAA-BHQ2 ( Synthol , Russia ) ; paramyosin gene ( GenBank ID:AF311774 . 1 ) ( Paramyosin_F: 5’-AGAACGTCGCCTGCGCGAGG; Paramyosin_R: 5’-GGGCCCGATCGGCGGCTT ) ; alfa-tubulin gene ( GenBank ID: JK624299 ) ( TUA_F: 5’-CGCGTCCGATGGTGTACCGTCC; TUA_R: 5’-GGTGCGAACCGGCACTTACCGT ) ; CYP gene ( GenBank ID: JF920147 ) ( CYP_F2: 5’-ACTGGAGAATAGCAACCAAACGCCA; CYP_R1: 5’-CCCGTTCTCCATCTCGCACATCG ) . To measure the level of CYP gene expression in experiments with dsRNA , the CYP primers were as follows: CYP_F3: 5’-GCCCTTCGGCTTACCCCACA; CYP_R2: 5’-CCGCTGGACCTCTTGTAAGCCCA ( the full ORF spanning positions 1053–1354 ) . For droplet digital PCR primers and probes were as follows: MrpL16 ( mitochondrial ribosomal protein L16 ) gene ( MrpL16_F: 5’-TCCCTTCCCGGCTCGTTTCGT , MrpL16_R: 5’-AGTGCTTGGCGAGCATCAGCA , MrpL16 Probe: 5’-R6G-ACAAGAGTTGCTGGACTGCGAGAA-BHQ2 ( Synthol , Russia ) and CYP gene ( CYP_F2: 5’-ACTGGAGAATAGCAACCAAACGCCA; CYP_R1: 5’-CCCGTTCTCCATCTCGCACATCG; CYP Probe: 5’-FAM-TGCCG-ATTAT-TCGCC-GAACT-ATCTG-G–RTQ1 ( Synthol , Russia ) ) . In the real-time PCR assay , we used adult worms and newly excysted metacercariae ( NEM ) of O . felineus . For each data point , we used five adult worms extracted from the same hamster and 400 to 500 NEM for RNA isolation . For the real-time PCR assays with dsRNA , total RNA was isolated from individual adult worms . Total RNA for real-time PCR was isolated from flukes using the TRI reagent ( Ambion , USA ) . Concentrations of RNA were determined using a NanoDrop spectrophotometer ( ND1000 , NanoDrop Technologies , USA ) . One microgram of total RNA was used for the synthesis of single-stranded cDNA . First-strand cDNA synthesis was performed using the RevertAid Kit ( Fermentas , EU ) . Expression levels of the genes were measured by means of real-time PCR using the EVA Green Reagent Mix ( Synthol , Russia ) on a CFX96 real-time PCR system ( Bio-Rad , USA ) . As endogenous internal controls for normalization , we chose genes Ub and MrpL16 because these genes had the lowest M-value ( Bio-Rad ) [19 , 20] . Triplicate real-time PCR reactions were conducted for each sample . After the PCRs , a dissociation curve was constructed using the melting curve program of the thermal cycler to confirm the presence of a single PCR product , which was further confirmed using gel electrophoresis . Tenfold serial dilution of standard cDNA samples was used for PCR efficiency calculations . The fold change in the target gene expression ( that was normalized to the control ) relative to the control was calculated from the threshold cycle values ( Ct ) . Data analysis was performed using the CFX96 software . To identify satisfactory RT-PCR reference genes , we evaluated expression stability across developmental stages and among seven treatment regimens with xenobiotics . Expression stability for a particular gene is reflected by the M-value calculated as a mean standard deviation of the log-transformed expression ratio across samples for the particular gene relative to other reference genes remaining in the gene panel . The calculation was performed by stepwise exclusion of individual genes with the highest M-value from the other genes until reaching the last two genes with the smallest M value . Various investigators defined M < 1 . 5 as an acceptable criterion for selection of RT-PCR reference genes [22] . Mann–Whitney U test was used to determine whether the differences existed between experimental mean values . P-values ≤ 0 . 05 were considered significant ( Statistica 6 . 0 ) . The data are shown as mean ± SEM . Results of three independent experiments are presented . Duplex ddPCR reaction mixes were prepared as follows . 10 μL of 2× ddPCR Master Mix ( Bio-Rad , USA ) and CYP and MRPL16 primers ( final concentration of 300 nM ) and CYP and MRPL16 probes ( final concentration of 180 nM ) were mixed , and cDNA template added . ddPCR workflow and data analysis were performed according to the manufacturer’s instructions . Briefly , droplets were generated in 8-well cartridges , using the QX100 droplet generator ( Bio-Rad , USA ) as described . Water-in-oil emulsions were transferred to a 96-well plate and amplified . Thermal cycling conditions were: 10 min denaturation at 95°C , followed by 40 cycles of a two-step thermal profile comprising 15 s denaturation at 95°C , and 60 s annealing/extension at 100% ramp rate at 60°C . After amplification , products were denatured at 98°C for 10 minutes , then cooled to 12°C . Plates were then transferred to the QX100 droplet reader ( Bio-Rad , USA ) . Data acquisition and analysis was performed using QuantaSoft ( Bio-Rad , USA ) . CYP gene expression levels were quantified and values were simultaneously normalized to MrpL16 reference gene expression . The data are shown as the normalized ratio of CYP to MrpL16 ± SD . Each run was made in duplex . Results of three independent experiments are presented . To measure 6-OH-CLZ hydroxylation activity , we used 10 adult flukes per sample . The parasites were incubated in the RPMI medium ( supplemented with a 1× antibiotic ( Sigma–Aldrich ) and 1% glucose ) at 37°C for 24 h with 170 μg/mL CLZ in an atmosphere of 5% CO2 . 40 μM ketoconazole was added to the medium followed by incubation for 2 h before addition of CLZ . Benzoxazole ( BZ ) served as an internal control and was added into samples after treatment for 24 h with CLZ . The incubation medium was centrifuged to remove eggs as described above and was mixed with one volume of ACN . A 400-μL aliquot of a sample was mixed with 0 . 6 mL of a solution containing four units of beta-glucuronidase of Helix pomatia ( Sigma–Aldrich ) and 0 . 1 M acetic-acid buffer ( pH 4 . 5 ) . The samples were incubated 1 hour at 37°C and were extracted twice with 2 mL of ethyl acetate and then dried at room temperature . The samples were dissolved in 26% acetonitrile and were analyzed using high performance liquid chromatography ( HPLC ) on a C18 reverse-phase column ( 250 × 2 mm , 3 μm ) at the flow rate of 0 . 1 mL/min . CLZ , 6-OH-CLZ , and BZ were monitored by means of absorbance at 287 nm; the retention times for 6-OH-CLZ and CLZ were 6 . 4 and 9 . 45 min , respectively . For each product , retention time of the substance and of chromatographic standards was established . The worms were placed in Petri dishes and washed five times with sterile saline . The medium was changed to the RPMI medium containing a 1× antibiotic ( Sigma–Aldrich ) , 1% glucose , and 5 μM PR , methoxyresorufin ( MR ) , or benzoxyresorufin ( BR ) . The worms were incubated at 37°C in an atmosphere of 5% CO2 for 18 h . Ketoconazole was added to the final concentration 40 μM to the incubation medium 2 h before addition of PR . After incubation for 18 h , the worms were washed gently three times with saline ( 0 . 9% NaCl ) , were fixed with 4% formaldehyde ( Sigma–Aldrich ) in saline for 15–20 min , and were mounted on a microscope slide with the Prolong Gold Antifade Reagent ( Invitrogen , USA ) . The slides were examined under a microscope with DAPI , rhodamine , and fluorescein filters under an AxioImager fluorescent microscope ( Zeiss ) [21] . As a positive control for in situ visualization , we used other worms after treatment for 30 min with 0 . 01 μg/mL resorufin ( S2 Fig ) . The images were processed in the AxioVision software ( Zeiss ) . The dsRNA was designed to span 646 bp of the CYP gene ( full ORF spanning at positions 331–976 ) . The target sequence was amplified from the plasmid using primers flanked with a T7 RNA polymerase promoter sequence , indicated in underlined italic bold faced , at the 5’end . The CYP fragment was generated using primers T7CYPF: taatacgactcactatagggCTGGCTCAAGGTCATGGAAT and T7CYPF: taatacgactcactatagggCCAGGTAAGTAAACGCCCAA . An irrelevant negative control , luciferase ( LUC ) dsRNA , was constructed from the plasmid pGL3 ( Promega ) because this sequence does not match any targets in the O . felineus genome . The plasmid pGL3 ( Promega ) was kindly provided by Maerova A . L . , Institute of Molecular and Cellular Biology SB RAS , Novosibirsk , Russia . The fragment was amplified using primers T7LUCF: taatacgactcactatagggTGCGCCCGCGAACGACATTTA and T7LUCR: taatacgactcactatagggGCAACCGCTTCCCCGACTTCCTTA [23] . The size of the amplicons was determined using electrophoresis , and these DNA fragments were purified using standard phenol–chloroform extraction . The dsRNA was synthesized using the MEGAscript RNAi Kit ( Ambion , USA ) . The dsRNA was precipitated with one volume of 5 M ammonium acetate and 2 . 5 volumes of 95% ethanol , after which , the RNA was dissolved in water . Concentrations of dsRNA were determined by spectrophotometry ( ND1000 , NanoDrop Technologies , USA ) . After recovery from hamsters at euthanasia , the worms were rinsed in sterile 0 . 9% NaCl to remove residual host cell debris . The flukes were maintained in the RPMI medium containing 1× antibiotic/antimycotic ( Sigma–Aldrich ) and 1% glucose at 37°C , in an atmosphere of 5% CO2 for 18–24 h . To deliver CYP dsRNA , adult worms ( 18–40 per treatment group ) were transferred to a cuvette ( 4-mm gap; Bio-Rad ) in 0 . 1 mL electroporation buffer ( RPMI-1640 , 1× antibiotic/antimycotic , and 1% glucose ) containing 50 μg of CYP dsRNA or LUC dsRNA . Each group of worms was subjected to square wave electroporation with a single 20-ms pulse at 125 V ( Gene PulserXcell , Bio-Rad ) . Subsequently , the worms were maintained in the RPMI culture medium 9 ( three mL of the culture medium in each well of a 12-well plate ) containing 1× antibiotic/antimycotic and 1% glucose at 37°C in the atmosphere of 5% CO2 in the air . Similarly treated worms in electroporation buffer without the dsRNA served as controls . The worms were soaked for eight days with daily refreshment of the medium . The worms were collected for analysis on days 1 , 3 , 5 , 6 , and 8 after electroporation . Images were acquired using the inverted microscope Axiovert 40CFL equipped with Axiocam ICC3 ( Zeiss ) . This experiment was repeated three times . To assess the capacity of worms after the RNA interference to metabolize PR , 3 randomly-selected parasites from each treatment group five days after the knockdown were treated for 20 h with pentoxyresorufin , and images were acquired using multiple fluorescence filters . The total size of the resorufin particles in each worm was measured . The images were processed in the AxioVision software ( Zeiss ) . The data were further analyzed in Microsoft Excel spreadsheets . To estimate the mortality rates , the Kaplan-Meier survival curves were built using the 'survival‘ ( v . 2 . 38 ) R package . A 95% confidence interval was calculated ( out of three independent experiments ) by the log-rank test using the 'survival‘ ( v . 2 . 38 ) R package . Statistical difference in survival log-rank ( Mantel-Haenszel ) test between each pair of samples was calculated . Ub ( ubiquitin ) gene—GenBank ID: JK649790 MrpL16 ( mitochondrial ribosomal protein L16 ) gene— ( GenBank ID: JK649791 ) Prm ( paramyosin ) gene—GenBank ID: AF311774 . 1 TUA ( alfa-tubulin ) gene—GenBank ID: JK624299 CYP ( cytochrome P450 ) gene— ( GenBank ID: JF920147 )
The level of CYP mRNA expression was assessed using real-time PCR . As endogenous internal controls for normalization , four candidate genes were chosen: paramyosin , α-tubulin ( TUA ) , mitochondrial ribosomal protein L16 ( MrpL16 ) , and ubiquitin-related protein ( Ub ) . Based on the outcome of preliminary experiments ( see Materials and Methods section ) , for assessment of the CYP mRNA level , we selected two genes: Ub and MrpL16 . The CYP mRNA level was measured using simultaneous normalization to these two genes . The level of CYP mRNA was 10-fold lower at the NEM stage than in adult worms . We tested the main types of inducers of CYP on adult worms and on NEM: DMSO ( dimethyl sulfoxide ) and TCDD ( typical aryl-hydrocarbon receptor ligands ) , phenobarbital ( constitutive androstane receptor activator ) , dexamethasone ( DEX , a pregnane X receptor ligand ) , ethanol . In addition we used potential endogenous inducers: blood plasma [EDTA-treated blood] and bile from Mesocricetus auratus and hemoglobin ( Sigma ) . Results of three independent experiments are presented ( Fig 1 ) . Treatment of adult worms and NEM for 4 h ( S3 Fig ) and for 20 h ( Fig 1A and 1B ) with any of the inducers did not change the CYP mRNA level . Additionally , we decided to use a quantitative droplet digital PCR assay to confirm the data that hemoglobin does not induce CYP mRNA expression . There was no difference between the CYP and MrpL16 duplex and CYP and MrpL16 singleplex assays . ddPCR was set as a duplex assay . CYP gene expression levels were quantified and values were simultaneously normalized to MrpL16 reference gene expression using QuantaLife ( Bio-Rad , USA ) . The data are shown as the normalized ratio of CYP to MrpL16 ± SD . Each run was carried out in duplex . Results of three independent experiments are presented . No difference in CYP gene expression after hemoglobin treatment of adult worms for 20 hours was evident . So , none of the compounds tested , including hemoglobin , had any effect on the expression of the P450 mRNA ( Fig 1A , 1B and 1C ) During isolation of the microsomal fraction of O . felineus proteins , CYP is isolated in the inactive state P420 [19] . Therefore , it was not possible to determine the monooxygenase activity using the method that is widely used for studies of the activity of microsomal enzymes of mammals . Previously , genes encoding other types of monooxygenases have not been observed in datasets of nucleotide sequences of O . felineus , S . mansoni , C . sinensis , O . viverrini and other parasitic species . Thus , the main enzyme that is responsible for the monooxygenase activity in O . felineus seems to be the cytochrome Р450 . We hypothesized that the ability of the parasite to metabolize certain substrates of mammalian cytochromes P450 would mean functional activity of the cytochrome P450 of the parasite . It is believed that passive xenobiotic transfer through the external helminth surface is the predominant entry mechanism for most chemicals [24] . Furthermore , by adding into the incubation medium various substrates for CYP and by quantifying them , we could determine the possible spectrum of substrate specificity of the monooxygenase . Chlorzoxazone ( CLZ ) , a commonly used systemic myorelaxant acting on the central nervous system [25] , proved to be a highly specific substrate of CYP2E1 . CLZ is readily hydroxylated to 6-OH-chlorzoxazone ( 6-OH-CLZ ) by mammalian CYP2E1 [25] . Fig 2 shows a typical chromatogram of separation of the products of CLZ metabolism . In a sample of the incubation medium containing CLZ , we detected a peak corresponding to 6-OH-CLZ ( Fig 2B ) . To confirm whether the metabolite formed can be produced by active CYP450 , during the cultivation , we used treatment with ketoconazole . This compound is a widely known inhibitor of microsomal cytochromes . It can directly interact with different CYPs at a concentration 40nM [26 , 27] and inhibit enzymatic activity of CYPs at concentration 3–40 μM [28 , 29] . After simultaneous addition of ketoconazole and CLZ , the concentration of 6OH-CLZ significantly decreased and was only 10% of the 6OH-CLZ level under normal culture conditions ( Fig 2B , 2C and 2G ) . To the sample of the medium during treatment with ketoconazole , we also added a standard of 6OH-CLZ: there was an increase of precisely the peak that corresponds to the retention time of 6OH-CLZ ( Fig 2D ) . Alkoxyresorufins PR , MR , and benzoxyresorufin ( BR ) are fluorogenic substrates of cytochrome P450 that yield a fluorescent product ( resorufin ) after enzymatic cleavage of the alkyl group ( a monooxygenase dealkylation reaction ) [30] . We hypothesized that an active monooxygenase in O . felineus tissues would produce a fluorescent product , which would be visible under a rhodamine filter . After 20 h of incubation with PR , large aggregates of fluorescent particles were visible ( size ~5 μm ) in the region of excretory channels and excretory bladder of the fluke ( Fig 3D , 3E and 3F ) . It is noteworthy that fluorescence was not evident in the caeca and the surrounding tissues . After incubation with BR , we also observed formation of resorufin particles but in smaller amounts ( Fig 3C ) , whereas after MR treatment , no particles were formed ( Fig 3B ) . After joint treatment with PR and with ketoconazole , and inhibitor of microsomal CYP , the amount of aggregates of the fluorescent particles decreases substantially ( Fig 3G ) . This provided additional evidence that the fluorescent substance that formed in the fluke tissues was a product of catalysis by CYP . Therefore , it is apparent that O . felineus can metabolize substrates specific to monooxygenases of the mammalian CYP2B and CYP3A families ( PR and BR , respectively ) [28] but does not metabolize the CYP1 substrate . CYP gene expression was suppressed in O . felineus in vitro by introducing a gene-specific dsRNA using electroporation ( Fig 4B ) . Fig 4A shows data on the relative level of CYP gene expression 1–8 days after the electroporation . The level of expression decreased by 61% , 58% , 64% , 80% , and 70% , on days 1 , 3 , 5 , 6 , and 8 , respectively . The level of expression is presented in percentages of the CYP expression in control worms , which were also subjected to electroporation ( with vehicle ) and were kept for eight days under the same conditions ( mock control ) . According to Fig 4A , treatment of the worms with the nonspecific probe LUC did not change expression of the target gene . Worms with the suppressed expression of CYP exhibited alterations of the phenotype ( Fig 4D , S1 Dataset ) . Primarily , there were changes in the shape and size of the excretory system of the worm . In particular , the size of the excretory channels and excretory bladder increased ( S1 Dataset ) . These alterations started on days 3 after the gene knockdown and were maintained for eight days . Similar phenotype we also observed in other groups of worms , but the number of worms with phenotypic changes was less than number of similar worms in the CYP group ( S1 Dataset , S1 Table; Fig 4E ) . Percentage of worms with changed phenotypes in each group is shown ( Fig 4E ) . As an additional control , to reduce the CYP protein activity , we added ketoconazole ( a CYP inhibitor ) to the medium with control worms . We cultured the worms for eight days under similar conditions , and the ketoconazole-containing medium was refreshed every day ( Fig 4H and 4E; S1 Dataset , S1 Table ) . Changes in the phenotype of the worms were evident; the size of their excretory channels and excretory bladder was also increased ( Fig 4H ) . Thus , the worms exhibited similar changes in phenotype after suppression of CYP expression and after treatment with the CYP inhibitor . Furthermore , knockdown of the CYP gene lead to some mortality among the flukes . The worms are considered dead when all evidence of motility , including gut peristalsis , had ceased and worms had a dark colour . Eight days after the gene knockdown , viability of worms in the CYP knockdown group was 30% versus 62–79% in the control groups ( Fig 5A , S1 Table , S2 Dataset ) . A 95% confidence interval was calculated ( out of three independent experiments ) by the log-rank test using the 'survival‘ ( v . 2 . 38 ) R package ( S2 Dataset ) . There was no significant difference between the survival curves of worms unexposed to dsRNA ( mock control ) and worms exposed to LUC dsRNA . Each of the survival curves of the three control groups was significantly different to survival data obtained from worms exposed to CYP dsRNA ( Fig 5A , S2 Dataset ) . To test the hypothesis that CYP suppression reduced survival in a medium similar to the situation with the biliary tree in vivo , we conducted the following experiment . Eight days after CYP dsRNA treatment , the worms were kept for one day in RPMI medium containing hemoglobin , bile acids , and cholesterol . It turned out that the changes in the medium composition did not cause alterations of the phenotype , motility , or viability of the worms . To assess functional activity of the CYP enzyme five days after the knockdown of CYP mRNA , we tested the worms in situ for the ability to metabolize pentoxyresorufin ( PR ) in the tissues of the worms . The ability to metabolize PR into resorufin was decreased but remained . Fig 4F and 4G show granules of resorufin in tissues of worms . To assess the capacity of worms to metabolize PR , 3 randomly-selected parasites from each treatment group were treated for 20 h with pentoxyresorufin , and images were acquired using multiple fluorescence filters . The total size of the resorufin particles in each worm was measured . After being exported , the data were further analyzed in Microsoft Excel . The data are presented as means ± SD . *** p<v0 . 005 , ** p<v0 . 01 ( F-test ) , Fig 5B . Thus , the suppression of CYP mRNA in adult O . felineus by RNA interference led to reduction of CYP activity . The activity was significantly decreased and was only 18% of the PR metabolizing activity under control culture conditions ( Fig 5B ) .
This is apparently the first study of CYP enzymes in parasitic flatworms . CYP enzymes participate in metabolism of xenobiotics and , in addition , can metabolize selective substrates for mammalian CYP2E1 , CYP2B , CYP3A , but not CYP1A . It appears that the function of this CYP is linked to the excretory system of the fluke and possibly to metabolism and detoxification . Given the high level of the CYP gene expression , the search for the key endogenous substrate of CYP of parasitic flatworms is warranted , and can be anticipated to facilitate elucidation of the biochemical processes of this carcinogenic liver fluke and indeed flatworm parasites at large , and for understanding of the development of mechanisms of resistance to antiparasitic drugs . Finally , the enzyme represents a promising drug target . Cytochromes P450 are a group of proteins involved in the synthesis of physiologically active compounds , in drug metabolism , and in biotransformation of xenobiotics . We identified only one CYP450 enzyme in O . felineus . Both from the point of view of identifying drug targets and understanding the physiology of the Opisthorchis parasite , CYP450 needs to be investigated . | The basic metabolic system CYP ( cytochrome P450 ) is essential for biotransformation of sterols and xenobiotics , for synthesis and degradation of signaling molecules in all living organisms . Most eukaryotes including free-living flatworms evolved numerous paralogues of the CYP gene . Notably , by contrast , flukes and tapeworms–the etiologic agents of major neglected tropical diseases of humanity , have only one gene . However , the role of P450 in the physiology and biochemistry of helminths is not known . This report presents the first functional study of the CYP enzyme of any of the parasitic flatworms . We focused our research on the food-borne human liver fluke , Opisthorchis felineus , an emerging source of biliary tract diseases in Russia , Kazakhstan and central Europe . Here we report that this liver fluke has evolved a highly expressed functional monooxygenase system with broad substrate specificity . Tissue localization studies and suppression of CYP mRNA by RNA interference revealed the CYP function is linked to the excretory system and possibly to metabolism and detoxification . The fluke’s monooxygenase likely is a model for orthologues of the singular CYP of parasitic flatworms at large , where it plays a critical role in the pathogen’s metabolism that contributes to worm survival and drug resistance . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Functional Analysis of the Unique Cytochrome P450 of the Liver Fluke Opisthorchis felineus |
In every motor task , our brain must handle external forces acting on the body . For example , riding a bike on cobblestones or skating on irregular surface requires us to appropriately respond to external perturbations . In these situations , motor predictions cannot help anticipate the motion of the body induced by external factors , and direct use of delayed sensory feedback will tend to generate instability . Here , we show that to solve this problem the motor system uses a rapid sensory prediction to correct the estimated state of the limb . We used a postural task with mechanical perturbations to address whether sensory predictions were engaged in upper-limb corrective movements . Subjects altered their initial motor response in ∼60 ms , depending on the expected perturbation profile , suggesting the use of an internal model , or prior , in this corrective process . Further , we found trial-to-trial changes in corrective responses indicating a rapid update of these perturbation priors . We used a computational model based on Kalman filtering to show that the response modulation was compatible with a rapid correction of the estimated state engaged in the feedback response . Such a process may allow us to handle external disturbances encountered in virtually every physical activity , which is likely an important feature of skilled motor behaviour .
Neural transmission delays present a major challenge because the brain cannot directly use sensory feedback to guide motor actions . In order to compensate for feedback delays , the brain must build internal models of the dynamical interaction between the body and the environment , including sensory and motor prediction mechanisms . On the one hand , motor predictions use forward models to convert motor commands into estimates of the state of the body [1] . On the other hand , sensory prediction uses current sensory data to anticipate future events in various contexts . For instance , with enough sensory information , humans can easily anticipate the re-appearance of a visual target that is briefly occluded [2] , [3] . Another example is the anticipatory scaling of grip-force with expected load constraints estimated from fingertip sensory encoding prior to the object lift [4] . An important question is whether the motor system uses similar processes to guide feedback responses to mechanical perturbations . Indeed , perturbation loads applied on the upper limb evoke very quick , task-related responses ( long-latency , ∼50 ms ) [5] . Because delays as short as tens of milliseconds can destabilize motor corrections , we hypothesize that a rapid sensory prediction is performed to update the estimated state of the limb . This problem has received little attention because previous modeling studies have often assumed that delays are equivalent to instantaneous but noisier signals [6]–[10] . This approach is partially justified by the fact that increasing the feedback delay or the feedback noise similarly increases the variability of unperturbed behaviour [11] , but it is inadequate when abrupt perturbations induce large amounts of joint displacement . Also , previous work suggesting the presence of such a sensory-based prediction did not test directly whether such a process was engaged on a time scale corresponding to long-latency delays [12]–[14] . Thus , it remains unknown how quickly the internal estimation is corrected and used in the motor response . The words ‘sensory prediction’ and ‘motor prediction’ have been often used in the literature to designate the same process , which is the prediction of the consequences of motor commands based on efference copy and internal forward models [15] . In the present paper , we make a distinction between the prediction based on forward models , which we referred to as ‘motor prediction’ , and the process under investigation , which converts delayed sensory data into estimates of the actual state . We refer to this process as ‘sensory prediction’ , in the sense that it does not rely on efference copy of the motor command . In theory , sensory prediction is expected if optimal state estimation is performed while taking feedback delays into account ( Kalman filter ) . In this framework , the present state of the limb ( Figure 1 A , Δθ ( t ) ) is corrected based on the delayed sensory signal available at time t ( Δθ ( t−δt ) ) combined with an internal model of how the perturbation affects limb motion ( Figure 1 A–B ) . This model makes two important predictions: ( i ) perturbations of varying amplitude should be easily handled as long as their profile corresponds to the participants' internal model; ( ii ) corrective responses for unexpected time-varying perturbations would be initially biased towards responses for the expected ones . We tested these predictions by manipulating the probabilities of different perturbations applied on the upper limb . The odd perturbations shared similar initial force profiles but changed rapidly ( Figure 1 C ) , causing unexpected variations in the joint motion that should impact the motor response . In agreement with the model , we show that responses to step perturbations scaled with the step magnitude , regardless of whether changes in magnitude were expected or not . In contrast , initial responses to other unexpected perturbation profiles matched the response for the expected perturbation profile , suggesting that internal models are engaged in these rapid corrective responses . These priors started to influence the motor response within the long-latency time window ( ∼50–100 ms ) . Changes in long-latency responses correlated with the expected relationship between the initial joint displacement and the true state of the limb at the onset of the motor response as predicted by simulations using optimal state estimation . Altogether , our results suggest that state estimation guides long-latency motor responses to mechanical perturbations .
The effect of feedback delays on motor performances have been studied in the context of voluntary movement control , with feedback delays typically greater than those characterizing rapid motor responses to perturbations ( for instance , visuomotor delays are >100 ms ) [14] , [16] , [17] . Although responses to mechanical perturbations can be quicker , delays of the order of tens of milliseconds can also destabilize feedback responses . The effect of feedback delays is illustrated in Figure 2 with simulations from a feedback controller that must keep a joint at a prescribed angle with two distinct state estimators ( see Methods ) . In the first case ( Figure 2 A ) , the state estimator directly weighted the current feedback signal with the internal prior , taking only the variances of each signal into account and ignoring the feedback delay ( δt = 60 ms , see Methods ) . This control mechanism could generate stable reaching movements of varying amplitude , but it was prone to instability in the presence of external perturbations ( Figure 2 B ) . We observed numerically that decreasing the weight of the feedback signal by increasing sensory noise could stabilize the process because the controller relies less heavily on sensory feedback . However , we could reject this possibility because the resulting feedback corrections were too slow and incompatible with human motor behaviour . Note that the simulations presented in Figure 2 B were obtained after decreasing the weight of sensory feedback by a factor of 20 relative to the parameters used otherwise . Stability issues can also be encountered with control processes based a Smith predictor [16] , because these controllers are extremely sensitive to mismatch between the internal model and the actual plant [18] . However , prediction errors are ubiquitous in biological motor control because of the multiple sources of neural noise [19] , and the presence of external disturbances . In order to produce stable and accurate feedback responses , we suggest that motor systems rely on optimal state estimation while taking feedback delays into account ( Figure 2 C , Methods ) . The resulting controller generated stable reaching movements as well as feedback responses to the same perturbation loads ( Figure 2 D ) . Such a rapid correction involves a prediction based on the actual sensory data combined with an internal model ( or prior ) about the effect of the perturbation on the limb ( Figure 2 C and D ) . Observe that this mechanism is distinct from the usual motor prediction because there is no causal relationship between the motor command and the motion of the body . If participants rely on a similar mechanism , the theory predicts that internal models of the perturbation profiles must be engaged at the onset of the motor response . This prediction was confirmed by the experiments presented below . We first emphasize that internal priors modulate long-latency responses to perturbations ( Experiment 1 ) . The second experiment shows a trial-by-trial adaptation of these priors to changes in perturbation profiles . Finally , we present two control experiments confirming that these priors do not depend on the muscle pre-activation ( Experiment 3 ) , and are specific to the shape , and not the amplitude , of the perturbation loads ( Experiment 4 ) . We tested the hypothesis that the brain uses sensory prediction to drive the motor response by exposing participants to a large number of step torque perturbations ( 1 Nm , 2 Nm and 3 Nm , see Methods ) , of which typical evoked motion is depicted in Figure 3 A . Different directions and magnitudes were used to ensure that participants were expecting a step profile , regardless of the step amplitude . The effect of unexpected amplitude changes is thoroughly addressed below ( see Experiment 4 ) . We used ramp-up and ramp-down perturbations as catch trials in order to induce unexpected variations in the joint displacement ( Figure 3 B ) . We reasoned that if the perturbation has a ramp-down or ramp-up profile while a step torque is expected , the prediction based on sensory feedback would lead to over- or under-estimation respectively , which should be expressed in the motor response ( as illustrated in Figure 3 C for ramp-down profiles ) . Elbow displacements are illustrated in Figure 4 A: notable is the variation in the time of the peak elbow displacement ( dashed vertical lines ) following ramp profiles ( ramp-up , red; ramp-down , blue ) relative to those of step torque perturbations ( black traces ) . The inset in Figure 4 A shows for all subjects that the initial joint displacement for the first ∼10 ms following ramp-down or ramp-up profiles corresponds to the 3 Nm and 1 Nm step perturbations , respectively ( gray rectangle , inset ) . Therefore , readouts of the initial limb motion do not permit to determine whether the underlying torque is a ramp-up ( ramp-down ) or a step perturbation ( 3 Nm or 1 Nm ) , inducing errors in state estimation at the onset of the motor response . We found that the reversal time was a sensitive parameter that captured the effect of the profile on the kinematics of the corrective movements as well as the modulation of the feedback responses across contexts ( catch or blocked , see Methods ) . We measured this parameter in joint coordinates as the time of maximum elbow displacement , or in Cartesian coordinates as the time of the first hand-speed minimum . Overall , we found that there was no main effect of the step magnitude on the reversal times and hand speed minimum ( one-way ANOVA across step magnitudes , F<0 . 65 , P>0 . 1 ) . A closer look revealed a significant difference between reversal times following 1 Nm and 3 Nm perturbations ( paired t-test , t ( 12 ) = 3 . 6 , P<0 . 01 ) . This trend was not observed for the timing of hand speed minimum . The effect of the profile on the reversal time was robust and independent of the coordinate system: both elbow reversal time and hand speed minimum following ramp-down occurred significantly earlier than those of step torque responses regardless of the amplitude of the step ( Figure 4 C , t ( 11 ) >6 . 5 , P<0 . 001 ) . The opposite effect was observed following ramp-up profiles , with a significant increase for the time of hand-speed minimum relative to those of step-torque responses ( t ( 11 ) >2 . 1 , P<0 . 05 ) , and a significant increase in elbow reversal time relative to 3 Nm step torque responses ( t ( 11 ) = 3 . 03 , P<0 . 01 ) . Importantly , the changes in reversal times observed in Figure 4 C are not a simple consequence of physics and of the time-varying ramp profiles . Instead , these changes reflect that participants relied on a feedback control strategy that depended on the context . When participants had to counter the same ramp-up or ramp-down torques presented in a blocked manner , they altered their feedback responses and the timing of corrective movements shifted towards the values previously measured for step torque profiles ( Figure 4 A , bottom and 4 D ) . Following a ramp-down perturbation , both elbow reversal times and times of hand-speed minimum significantly increased towards values corresponding to step torques ( t ( 11 ) >1 . 88 , P<0 . 05 ) . For the ramp-up torques , the time of hand speed minimum decreased significantly ( t ( 11 ) = 2 . 95 , P<0 . 01 ) . Elbow reversal times followed the same trend ( t ( 11 ) = 1 . 54 , P = 0 . 075 ) . The model based on Kalman filtering explains the effect of the perturbation profiles on the kinematics of the corrective movements ( Figure 4 B ) . Prior expectations in the model were determined by the dynamics of the external torque ( Methods , Eqn . 3 ) . The time course of the actual and estimated state variables is shown in Figure 5 . Under the hypothesis that the external torque is constant , the estimates of this variable can be seen as a delayed and filtered version of the actual perturbation ( Figure 5 A , top ) . This produces an over- ( under- ) estimation following the ramp-down ( up ) perturbation as illustrated by the estimation error ( Figure 5 B ) . These estimation errors are propagated to the other state variables , leading to an over- ( under- ) estimation of the actual joint velocity and displacement . These estimation errors result from the fact that the Kalman filter simultaneously corrects the present and past states under the assumption that the external torque was constant throughout the feedback delay period ( δt = 60 ms ) . Our simulations capture three critical aspects of the data . First , the model predicts an invariant reversal time across the different values of step magnitude ( Figure 4 B , C ) . This property is a consequence of the superposition principle of linear systems , whereby scaled amounts of perturbation-related motion result in scaling of the motor response . Our data was compatible with this prediction , except for the difference observed between reversal times following 1 Nm and 3 Nm step perturbation . This difference may reflect the limitations of the linear approximation . Second , the model also reproduces the changes in the reversal times following ramp perturbations in a way that is compatible with our experimental results ( Figure 4 B , C ) . Third , our hypothesis of a rapid update of the state estimate accounts for the observed changes in reversal times depending on whether ramp perturbations were expected or not ( catch or blocked designs , Figure 4 D ) : simulations were obtained by feeding the controller with exact state information after artificially delaying the response , so that reversal times following step responses were exactly matched ( see Methods ) . The difference between reversal times of step or ramp profiles is markedly reduced when the controller can rely on perfect state estimation ( Figure 4 B , bottom ) , and the shifts in reversal times were clearly compatible with participants' behaviour ( Figure 4 D ) . This result is an important prediction of the model: indeed the effect of the profile on the corrective movement does not solely result from physics . Instead , they reflect the model's beliefs about the external torque and their effect on the corrective response . It is important to realize that estimation and control processes are independent in our model . Therefore , as the control policy was always the same across all simulations , we can ascribe the changes in feedback responses to the estimation algorithm . We collected the activity of elbow flexors and extensor muscles in order to determine the time when prior-related components of the response influenced the feedback correction . When participants expected a ramp-down perturbation ( blocked condition ) , the evoked response diverged from the response evoked by 3 Nm step perturbations after 44 ms for Brachioradialis ( Figure 6 A , ROC Analysis ) and 40 ms for Triceps Lateralis . In contrast , the same analysis revealed that in the catch condition , responses followed those evoked by 3 Nm step torques until 60 ms after perturbation onset ( 76 ms for Triceps Lateralis ) , whereas the elbow displacement was equal across catch and block conditions until >100 ms ( Figure 6 A ) . Observe also that the shoulder did not move until >100 ms as a result of the multi-joint torque , which validates the single joint model to address the problem of state estimation following the perturbation . The onset of divergence between ramp-down responses from the 3 Nm step torque across catch and block conditions must be compared with the onset of divergence measured across the step perturbations when participants relied on adequate priors . In this case , responses diverged in less than 35 ms for Brachioradialis and Triceps Lateralis in all pair wise comparisons ( Figure 6 B ) . Changes in activity resulting from mistakenly tracking the corresponding step function were significant in the long-latency time window . Following ramp-down profiles , the pre-perturbation activity ( −50–0 ms , see Methods ) , and the short latency response ( R1 , 20–45 ms ) were not significantly different across catch and block conditions ( Figure 6 C , one-tail paired t-test , t ( 11 ) <1 . 31 , P>0 . 1 ) , whereas significant context-related modulation was found in the long-latency and early voluntary epochs of time ( R2 , 45–75 ms: t ( 11 ) = 2 . 04 , P = 0 . 03; R3 , 75–105 ms: t ( 11 ) = 5 . 84 , P<0 . 001; Vol . , 120–180 ms: t ( 11 ) = 2 . 98 , P = 0 . 006 ) . This effect means that , for similar baseline and short-latency muscle activity , the long-latency response was significantly reduced when participants were expecting a ramp-down profile . The down-regulation of the response started in the R2 time window and likely resulted from internal processing of sensory data given that the joint displacement was identical across conditions . The opposite tendency was observed following ramp-up perturbations: responses in the blocked condition displayed significant modulation in R2 ( t ( 11 ) = 2 . 34 , P = 0 . 019 ) , whereas the other epochs displayed statistically similar activity ( t ( 11 ) <1 . 64 , P>0 . 05 ) . We performed an additional control experiment to address why the response modulation was smaller following the ramp-up perturbations and found that it was likely due to the relatively high perturbation magnitudes ( 3 Nm ) , generating very high response rate . We observed a stronger response modulation after reducing the perturbation loads ( see Methods ) . In all , the prior-related component influences the muscle response within about 60 ms of perturbation onset , in a way that correlated with changes in the expected relationship between the initial joint displacement and the state of the limb at the onset of the motor response . A surprising result from Experiment 1 is that , on average , the difference between ramp-down responses across conditions persisted for a prolonged period of time ( Figure 6 A ) . This suggests that the internal priors are quite strong , and that the sensory data does not fully overwrite it even after the time varying portion of the ramp-down perturbation . Given the strength of these priors in the corrective response , an important question is how rapidly they can be updated should a distinct perturbation profile be experienced . We designed the second experiment to test this prediction . We used a random adaptation paradigm and tested the influence of changes in perturbation profiles on the response to the next trial [20] , [21] . This paradigm presents the advantage to test the effect of a change in the perturbation profile on a large number of trials , which is typically required for the analysis of EMG data . The 2 Nm step and ramp-down were chosen based on the results of the first experiment . After the habituation blocks ( see Methods ) , the two perturbations profiles were randomly interleaved and equally likely . We sorted responses to each torque profile ( step or ramp ) by the preceding trial and found that the responses following a step perturbation displayed more vigorous corrections for either perturbation type ( quicker reversal times and smaller total displacement ) than those following a ramp-down perturbation ( Figure 7 A and B ) . EMG responses sorted by the same criterion correlated with the trial-by-trial changes in the behavior: up- or down-regulation was observed depending on whether the preceding trial was a step or a ramp-down perturbation , respectively ( Figure 7 C and D ) . Importantly , significant changes in muscle responses from all muscle samples pooled together were found from the onset of the R2 time window ( 45–75 ms , Figure 7 D ) , which confirms the results of Experiment 1 . The difference between perturbation responses to the same profile , ( step or ramp-down ) depending on the previous trial was found at 66 ms , within the long-latency time window ( ROC on the differential signal relative to the pre-perturbation variability ) . Observe that this divergence onset is found later than those measured in the first experiment because , in this case , the divergence were measured relative to the 3 Nm step responses rather than across conditions . These results emphasize that internal models of the perturbation profiles can be adjusted following the occurrence of a single unexpected perturbation profile . We first addressed whether inverting the internal prior affected the response to the previously expected step perturbation profiles . As predicted , reversal times following step perturbations tended to be delayed when participants were expecting a ramp-down profile , although this trend was only close to significant ( Figure 8 A , t ( 7 ) = 1 . 87 , P = 0 . 051 ) . Importantly , the long-latency and early voluntary epochs displayed significant modulation across catch and blocked conditions ( Figure 8 C , t ( 7 ) >1 . 9 , P<0 . 05 ) , showing that the priors used in Experiment 1 can be reversed and modulate the response to the step perturbations . Second , this experiment was designed to investigate whether the response modulation persisted when the muscles were pre-activated . This experiment was motivated by the response differentiation found at ∼44 ms in the first experiment , which , in theory , indicates that the short-latency pathway may have contributed to the response modulation . We applied a background load on the elbow joint ( −1 Nm ) to evoked the same baseline activity across the two series of blocks in which ramp-down trials were presented as catch trials or in blocked fashion ( Pre . across conditions , t ( 7 ) = 0 . 4 , P = 0 . 65 ) . A short-latency response was clearly evoked by each perturbation profile ( R1 versus Pre . , t ( 7 ) >2 . 7 , P<0 . 05 ) , but these R1 responses were statistically similar across catch and block conditions ( t ( 7 ) <0 . 4 , P>0 . 25 ) . In contrast , long-latency ( 45 ms–105 ms ) and early voluntary responses ( 120 ms–180 ms ) exhibited significant modulation across conditions ( Figure 8 B and C , t ( 7 ) >1 . 9 , P<0 . 05 ) . The onset of divergence across conditions was found at 55 ms ( ROC on the differential signal relative to the pre-perturbation activity ) . As in Experiment 1 , the modulation of the muscle response correlated with the change in reversal time ( Figure 8 A ) . Therefore , the modulation of long-latency responses could be reproduced with similar gains in the short-latency stretch response . In this experiment , we verified that the effect reported above was specifically related to the perturbation profiles independent of their magnitude . In theory , the controller only needs to know the perturbation profile to correct the state estimate , independently from the perturbation magnitude . A direct prediction of the model is that participants expecting a step torque should be able to respond to any perturbation magnitude provided that it follows a step function . Alternatively , if changes in control gains are involved , we expect to see a delayed corrective movement following the unexpected 3 Nm step torques since subjects were expecting a smaller perturbation ( 2 Nm ) . Feedback responses should also overcompensate for an unexpected 1 Nm perturbation . We tested these predictions by exposing participants to a large number of step torques of 2 Nm and presented step perturbations of 1 Nm or 3 Nm as catch trials following the same distribution as in the first experiment ( see Methods ) . We found that reversal times were essentially invariant across all step magnitudes even when the large ( 3 Nm ) and small ( 1 Nm ) perturbations were unexpected . Figure 9 shows the reversal times and the time of hand speed minimum . As observed in Experiment 1 , the reversal times displayed little variation across the different values of the step perturbation magnitude . We used the same axis as in Figure 4 C to emphasize that unexpected changes in step magnitude cannot account for the effect of unexpected ramp-profiles on the reversal times . Indeed , the variation in reversal times evoked by ramp-down torques are of the order of −40 ms on average ( Figure 4 C ) , which is clearly outside of the range of values reported in Figure 9 A . While the effect of ramp-up torques was overall smaller , the shift in reversal time of ∼10 ms on average ( Figure 4 C ) is also outside of the range reported in this experiment . These results suggest that the variation in the kinematics of corrective movements emphasized above is specific to the shape of the perturbation . Muscle responses of an elbow flexor are shown in Figure 9 B: the scaling of the response with the magnitude of the step can be observed very early . The measured onset of divergence across all paired comparisons of response populations was found in the short-latency time window ( ROC , 35 ms vertical arrow ) . This result shows that although changes in magnitude were unexpected , participants did not track any inadequate response strategy as observed following ramp perturbations .
This study shows that internal models of the perturbation loads influence long-latency responses to mechanical perturbations . Simulations based on optimal feedback control suggest that these priors reflect a rapid correction of the estimated state of the limb based on sensory prediction . In general , internal priors strongly influence decisional processes [22] , [23] , multi-sensory integration [24]–[27] and forward predictions [12] , [16] , [17] , [28] . This study shows that internal priors also influence the feedback control strategies following mechanical perturbations . Although previous studies have suggested that the brain uses sensory prediction following a perturbation [13] , [14] , [29] , direct evidence was missing because the latter studies addressed changes in feedback responses over longer time windows ( >100 ms ) , during which the usual forward dynamic model is engaged ( Figure 2 , Motor Prediction ) . Also , these studies did not investigate how quickly the prediction performed on sensory signals was used to guide motor responses . In order to disambiguate sensory from motor prediction mechanisms , it was necessary to manipulate the perturbation over a time window during which the motor command does not influence the motion of the limb . We addressed this concern by varying the load profiles over a time window corresponding to the shortest sensorimotor delays , as we suspected that the sophistication of long-latency responses is at least partially due to a rapid update in state estimation [30] . Our approach focuses on the rather simple case of a constant external torque , which is easy to model in the framework of linear systems . However , the limitations of linear systems are only theoretical and our data suggest that participants were able to learn more complex priors corresponding to non-linear ramp-up or ramp-down perturbations . Whether we are able to learn any perturbation profile , or equivalently any mapping between the sensed initial motion and the actual state of the limb , is an open question . Another important question is how multiple priors can be acquired . Our daily lives suggest that we can acquire motor skills in distinct tasks ( such as biking and skating ) without re-learning every time that we switch between tasks . A recent study in the context of force field learning has emphasized that multiple internal models can be acquired provided that the internal representation of the movements are distinct [31] . If a similar mechanism underlies internal models for sensory predictions , we expect that contextual factors play a key role for the acquisition of multiple priors associated with distinct motor tasks . Overall , the effects of prior expectations on the muscle response as well as on the kinematics of the corrective movements were quite small . This is not surprising as perturbations were manipulated over a very short time interval ( ∼50 ms ) , and the resulting unexpected change in limb motion can only be small . A clear difficulty is that it is not possible to investigate the case where no estimation at all is engaged in the response . Instead , we had to manipulate participants' expectations to extract the evidence for a sensory predictor . Although our approach evoked small effects in terms of magnitude , the results were consistently reproduced across experiments . Importantly , we also showed with simulations that ignoring the use of sensory predictions could lead to instability that should clearly be avoided at all cost . We also demonstrate two key properties of the sensory predictor . First , we show that the influence of a prior during mechanical perturbations occurs from ∼45 ms to ∼60 ms , at which time the motor response started to diverge towards the appropriate profile . Assuming a contribution of the transcortical feedback with sensory and motor delays of about 30 ms [32] , [33] , it is possible that the internal prior uses at most 15 ms of sensory information . Accumulating sensory evidence overrides this prior with a further 15 ms of information . However , we found that the responses remained biased by the expected profile well beyond this early time period , which may reflect the continued influence of the prior . A second key property of sensory predictors is that it is modified on a trial-by-trial basis , which parallels the properties of the voluntary motor system observed in force-field learning studies [20] , [34] . We randomly interleaved two response profiles and found that perturbation responses were also modified by the perturbation applied on the previous trial . This result emphasizes that similar mechanisms underlie voluntary control and rapid feedback responses to perturbations [30] . In principle , it is also possible that feedback gains were changed independently from any update in state estimation . Such changes in feedback gains may originate from internal set of the control strategy , or from changes in the peripheral motor apparatus through co-contraction and stiffness modulation [35]–[37] . While it is difficult to completely rule out such alternative interpretation , we believe that , in the present case , several features of our data argue against non-specific changes in control gain . First , we showed that applying control gains to delayed sensory feedback was likely to generate unstable oscillations . Although the control performances in such cases should be thoroughly investigated , our simulations suggest that delays on the order of tens of milliseconds cannot be ignored to produce fast and accurate feedback responses ( see Figure 2 ) . Second , we found that the modulation of long-latency responses according to prior expectation was present even after controlling for the pre-perturbation activity and short-latency reflex ( Experiment 3 ) . This experiment was partially motivated by the divergence onset between the expected ramp-down from the 3 Nm perturbations that we found at the end of the short-latency time window ( Experiment 1 ) . However , even with similar R1 responses , it is possible that rapid sensory predictions occurred at the periphery [38] , and that the sensitivity of the spindles to changes is muscle velocity and acceleration was adjusted according to participants' expectations [39]–[41] . Besides possible adjustments of the peripheral apparatus , our suggestion is that a similar sensory input is mapped into a distinct motor output as a result of a learned relationship between the initial joint displacement and the state of the limb . An important question is to determine under which circumstances motor systems rely on non-specific modulation of the short latency pathway as opposed to a novel sensorimotor mapping . Finally , unexpected changes in the step magnitude did not generate any over nor under compensation . Responses to 1 Nm and 3 Nm step perturbations were clearly similar regardless of whether changes in perturbation magnitudes were expected or not . Therefore , changes in reversal times evoked by ramp perturbations could not be explained by a possible modulation of control gains involved in response to unexpected changes in perturbation magnitude . These results were predicted by the model: the Kalman filter can correct the present estimate of the state of the limb by combining the sensed step magnitude of each individual trial with prior assumptions about the perturbation profile . As a consequence , time-varying feedback responses result from a constant feedback control policy applied to time varying estimates of the state of the limb , which does not require any prior knowledge about the perturbation magnitude . The controller only needs to know the perturbation profile . Future studies should investigate the underlying neural pathway . The latency of the prior-related component already sets physiological constraints on the possible candidates . The cerebellum is clearly a candidate region given its known implication in prediction processes associated with descending commands [16] , [42]–[44] . Our sensory-based prediction is similar in many respects; the main differences are that sensory information is used as input rather than the motor command , and the time interval over which the prediction is computed is distinct . Otherwise , these two prediction processes need the same internal model of limb dynamics . The cerebellum also responds to mechanical perturbations in the required time window [45] , [46] and projects to the primary motor cortex that is known to contribute to long-latency activity [47]–[49] . In addition , cerebellar dysfunction induces oscillatory feedback responses to perturbations [50]–[52] , which recalls the stability issue encountered when feedback delays were ignored ( Figure 3 ) . From this perspective , cerebellar modulation of reflex gains could be a stabilizing mechanism that anticipates what the motor system should do in the present time . A sensory prediction is critical when abrupt perturbations induce large displacement as in the present study . However , disturbances can also be encountered at smaller scales including noise in neural circuits , and feedback responses are likely engaged at the level of small deviations corresponding to natural variability [53] . Even small deviations in the limb motion must be processed to accurately adjust the ongoing motor command . In this respect , the sensory predictor must be engaged during voluntary movements as well as following external perturbations . Motor learning and development of motor skills is also clearly contingent upon the acquisition of both sensory and motor predictive models since feed-forward and feedback processes must incorporate knowledge of the dynamical interaction with the environment [14] . Biking on a bumpy road , skating or countering wind gusts pushing one's sail are examples of tasks that we could hardly learn to stabilize without adaptive sensory prediction of the state of the body .
The Queen's University Research Ethics Board approved the experimental protocol and participants gave written informed consent following standard procedures . Subjects interacted with a virtual reality display showing visual targets and a right-hand aligned cursor in the horizontal plane . Participants' right arm was placed on an exoskeleton that can selectively apply torques at the shoulder and/or elbow joints ( KINARM , BKIN Technologies , Kingston , ON [54] , [55] ) . Arm motion was constrained to the horizontal plane . The target ( radius 1 . 2 cm ) was located at 45 and 90 degrees of shoulder and elbow angles for each subject ( Figure 3 A ) . Perturbations were applied after a random delay ( between 1 s and 2 s ) following stabilization at the start target . In all cases , perturbations were built up in 5 ms and equal amounts of torque were applied at the shoulder and elbow joints . This procedure allows compensating for interaction torques at the elbow joint , which cancels the initial shoulder acceleration and produces pure elbow motion for ∼150 ms [56] . The hand-aligned cursor was extinguished at perturbation onset . Participants were instructed to return to the target within 800 ms of perturbation onset and stabilize for 2 s . We used different time varying perturbation profiles to produce an ambiguous relationship between the present state of the limb and the initial joint displacement sensed after the feedback time delay . The different perturbation profiles are illustrated in Figure 3 B . The step perturbations of different magnitudes followed a linear buildup of 5 ms . The ramp-down perturbation followed a linear ramp from 0 Nm to 3 Nm in 5 ms , and then from 3 Nm to 1 Nm in 50 ms . The ramp-up perturbation followed a linear build up from 0 to 1 Nm in 5 ms , followed by a second linear build up from 1 Nm to 3 Nm in 50 ms ( Figure 3 B ) . Shoulder and elbow motion were collected at 1 kHz and digitally filtered at 50 Hz ( 4th order dual-pass Butterworth filter ) . We considered both the kinematics of elbow motion as well as hand paths in Cartesian coordinates to validate the use of the single joint model presented below . Muscle activity was collected by means of surface electrodes attached on the muscle belly after light abrasion of the skin with alcohol ( DE-2 . 1 , Delsys , Boston , MA ) . We concentrated on the mono-articular elbow muscles for Experiment 1 , 3 and 4 ( Brachioradialis , Br . ; Triceps Lateralis , Tl . ) , and on the mono- and bi-articular elbow muscles for Experiment 2 ( Biceps , Bc; and Triceps Long , Tg . , in addition to Br . and Tl . ) . The raw EMG signal was amplified ( gain = 104 ) , digitally band-pass filtered ( 10–400 Hz ) , rectified , and averaged across trials . EMG signals were normalized to the average activity measured against a 2 Nm background load for all muscle samples ( except in Experiment 3 where we used the activity evoked by the 1 Nm background load ) , while participants maintained postural control in the initial joint configuration ( elbow = 90 deg and shoulder = 45 deg ) . The binned analysis of muscles activity was based on average EMG across the different epochs following classical definitions ( Pre . , −50 to 0 ms , R1 , 20 to 45 ms; R2 , 45 to 75 ms; R3 , 75 to 105 ms and early voluntary from 120 to 180 ms [57] ) . Statistical comparisons of kinematics or integrated EMG were based on one-tailed paired t-tests across the different conditions . We used Receiver Operating Characteristics ( ROC ) to determine the onset of divergence between time series of EMG signals [58] . The importance of the model is to provide a rationale for the experimental design as well as predictions about the effect of the perturbation profile on the kinematics of the corrective movement . The hypothesis that the brain uses a process similar to a Kalman filter was found to be a very powerful approach to characterize the online combination of internal priors with multisensory information [25] , [26] , [59] . We used this model in the context of optimal control to emphasize the consequences of feedback delays within a framework that is compatible with current approaches in sensorimotor control . We considered the angular motion of a rigid body as a model of the elbow joint . The choice of a single joint model was compatible with the perturbation-related motion immediately after the perturbation onset . Indeed , because we applied similar amounts of torque at the shoulder and elbow , the initial shoulder acceleration is zero as a result of the initial joint configuration and dynamics . Our data confirmed this property as the shoulder did not move until >100 ms following the perturbation . Therefore , the problem of state estimation following the perturbation reduces to the estimation of the elbow joint displacement in agreement with the single joint model . In addition , more complex models ( e . g . nonlinear models including inter-segmental dynamics ) are not necessary because the single-joint model captures the problem caused by feedback delays . Thus , we kept the model as simple as possible . The differential equation of the joint motion was coupled with a first order , low-pass model of muscle dynamics linking the control variable to the muscular torque . The net torque was the sum of a viscous torque proportional to the angular velocity , a controlled torque ( TC ) and an external torque ( TE ) . The different parameters ( inertia , viscosity and time constants ) were estimated from physiological models [60] , [61] . The controlled torque was a first order , low-pass response to the control variable ( u ) with time constant τ = 60 ms . The inertia ( I = 0 . 065 Kg m2 ) was estimated from the robot structure and average anthropometric data . The viscous constant was set to G = 0 . 05 N/s . The angular motion of the joint is described by the following system of differential equations ( θ is the joint angle and the dot represents time derivative ) : ( 1 ) ( 2 ) ( 3 ) This system was transformed into a discrete time control system by using classical Euler integration with 10 ms time step in order to take noise disturbances into account . Feedback delays were set to 60 ms . This value of feedback delay is compatible with the long-latency transmission delays , and also takes into account the fact that the controller , unlike EMG , can change the control value instantaneously . We therefore added on time step to the usual ∼50 ms considered for long latency delays in order to generate more realistic simulations . The state vector is composed of the joint angle , the joint velocity , the torques and the target location ( noted θ* ) at each time step: ( 4 ) The dependency of the state variables on time was omitted for clarity . In order to take feedback delays into account , the state vector must be augmented to include the previous time steps until the first time step observable by the controller . We define the augmented state as follows: ( 5 ) where h = 6 represents the feedback delay expressed in number of sample times ( 60 ms ) . After reduction to the non-delayed case by system augmentation ( Equation 5 ) , the discrete dynamics and feedback can be written as: ( 6 ) ( 7 ) The matrices A and B are determined by the system dynamics and augmentation ( Eqns . 1–3 ) , and H expresses that only the most delayed time-step of the augmented state vector is observable by the controller ( On and In are zeros and identity matrices of appropriate dimension ) : ( 8 ) We considered additive Gaussian noise ( ξt and ωt ) affecting the control and feedback signals to ensure that the state estimation was independent from the control mechanism [62] . However , all simulated results were similar in the presence of signal-dependent noise . The motor noise ( ξt ) only affected the control signal ( Equation 2 ) while the feedback noise ( ωt ) affected all entries of the observed state vector ( Equation 7 ) . For this class of system , the Kalman filter gives an unbiased estimate of the state vector ( Equation 5 ) that minimizes the estimation variance [63] . The state estimation is performed in two steps . We used to designate the estimated state at time step t following standard notations . First , a prior estimate is computed based on the motor commands and internal models of the systems dynamics ( ) . This prior estimate was also corrupted by additive Gaussian noise ( ζt ) : ( 9 ) Then , the prior estimate is corrected by the difference between expected and actual sensory feedback , weighted by the Kalman gain: ( 10 ) The rapid update of state estimation results from the definition of the augmented state . Indeed , the second term in Equation 10 corrects the prior estimate ( Equation 9 ) , which itself contains the past state vectors ( Equation 5 ) . Hence , the Kalman filter simultaneously corrects the sequence of joint angle , joint velocities and torques over the time interval corresponding to the feedback delay . Because a constant external torque is assumed ( Equation 3 ) , the controller treated changes in the external torque as step function . Hence , the sensory prediction results from the estimation of the augmented state vector ( Equation 5 ) , under the hypothesis that the external torque was constant . The consequences of assuming an external torque on the state estimation following the perturbation is illustrated in Figure 5 . The task of the controller was to stabilize the joint at a given angle against the external torque and noise disturbances . The cost-function that penalized deviation from the prescribed joint angle was: ( 11 ) N is the time horizon expressed in number of time steps; w and Rt , t<N , are constant scaling parameters and RN = 0 . This cost-function simply penalizes deviation from θ* at minimum motor cost . For this class of control problems , the optimal control sequence is a linear function of the state estimate that can be written as follows: ( 12 ) All noise parameters were Gaussian with zero mean and variance equal to 10−6 . This small value of noise variance is due to the fact that random disturbances are generated at each time step , and the variance should therefore scale according to the magnitude of the time step . When the process is simulated , we obtained a standard deviation of the joint angle of ∼0 . 1 deg over a 100 ms time window , which is compatible with the natural variability of unperturbed postural control [64] . The cost parameters were adjusted to match the perturbation related motion across simulations and data . Changing these parameters , as well as the delay in the feedback loop , had qualitatively no impact on the simulation results . The full control algorithm consisted in applying optimal feedback gains to estimates of the system state obtained from adaptive Kalman filter ( Eqn . 12 ) . The derivation of optimal feedback gains and Kalman gains followed procedures fully described earlier [62] , [64] , [65] . The simulations of reaching movements presented in Figure 2 were obtained by letting the system free to move for 600 ms ( w = 0 in Eqn . 11 ) , and then penalizing deviations from the prescribed joint angle ( 10 , 15 or 20 deg ) for 400 ms . Regarding simulations of postural control with perturbations , we used a time horizon that was sufficiently large so that the feedback gains ( Lt in Eqn . 12 ) were constant , approximating a steady-state postural control task . The different perturbation profiles were reproduced by changing the value of the external torque numerically ( TE ) . We tested whether the forward update in state estimation could be ignored by implementing a Kalman filter with the following feedback signal instead of Equation 7: ( 13 ) where xt was defined in Equation 4 . In fact , ignoring the system augmentation violates the assumption that the Kalman filter uses the conditional distribution of the feedback signal given the present state [63] , and the control design is therefore prone to instability as a consequence of time delays in the feedback loop . Finally , the blocked condition for the ramp-up/down perturbation profiles was simulated based on the assumption that the ideal control performance would be achieved if the controller could rely on perfect state information . To approximate this , we artificially set the control signal to 0 for a time interval corresponding to the feedback delay following the perturbation , and then applied the feedback gains to the true state of the system . In this case , the perfect state information corresponds to an estimation error that is zero , and the performance of the resulting control process corresponds to the best-case scenario . The artificial delaying of the response was used to generate a realistic displacement of the joint following the perturbation . We verified that the reversal times following step perturbations were identical with artificially delaying of the response , allowing us to compare changes in reversal times following ramp-perturbations . We should emphasize that the simulations based on perfect state information indicate what the system should do in the ideal case , without dealing explicitly with more complex priors . A theoretical limitation is that such complex profiles are difficult to reproduce within the framework of linear systems without additional dimensions and parameters . We performed additional simulations in which the external torque follows linear profiles ( by setting the derivative of TE to a non-zero value ) , and found the same results as with perfect state information . We decided to concentrate on the simulations with veridical state information because it provided the same prediction with fewer assumptions . In general , the variability in the reversal times from the simulations was lower than variability observed experimentally . The confidence interval was further reduced by considering the average reversal times across 50 simulation runs . In order to emphasize that effect of the estimation algorithm on corrective movements , we did not attempt to reproduce the experimental variability and chose to concentrate on the average reversal times across simulations ( Figure 4 ) . A shortcoming of our approach is that we change the value of the external torque ( TE ) during the simulations , while the feedback gains and Kalman gains depend on the initial condition ( and uncentered covariance matrices ) for which TE was set to 0 . However , this procedure has no impact on the simulation results because we only used additive noise , making the process variability independent from the values of the state variables . In the presence of signal dependent noise , small changes in control gains and Kalman gains were observed following changes in the external torque value because higher motor commands induced more variable control signals . However , this small reduction in gains did not impact the simulation results presented above . | It is commonly assumed that the brain uses internal estimates of the state of the body to adjust motor commands and perform successful movements . A problem arises when external disturbances deviate the limb from the ongoing task . In such cases , the estimated state of the body must be corrected based on sensory feedback . Because neural transmission delays can destabilize feedback control , an important challenge for motor systems is to correct the estimated state as quickly as possible . In this paper , we tested whether such a rapid correction is performed following mechanical loads applied to the upper limb . Our results indicate that long latency responses ( ∼50–100 ms ) exhibit knowledge of the relationship between the delayed sensed joint displacement and the current state of the limb at the onset of the motor response . Importantly , this knowledge can be adjusted from one perturbation response to the next , should a distinct perturbation profile be experienced . These results suggest that a correction of state estimation is performed within the limb rapid-feedback pathways , allowing fast and stable feedback control . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"motor",
"systems",
"computational",
"neuroscience",
"biology",
"neuroscience",
"neurophysiology"
] | 2013 | Priors Engaged in Long-Latency Responses to Mechanical Perturbations Suggest a Rapid Update in State Estimation |
NSMCE2 is an E3 SUMO ligase and a subunit of the SMC5/6 complex that associates with the replication fork and protects against genomic instability . Here , we study the fate of collapsed replication forks generated by prolonged hydroxyurea treatment in human NSMCE2-deficient cells . Double strand breaks accumulate during rescue by converging forks in normal cells but not in NSMCE2-deficient cells . Un-rescued forks persist into mitosis , leading to increased mitotic DNA damage . Excess RAD51 accumulates and persists at collapsed forks in NSMCE2-deficient cells , possibly due to lack of BLM recruitment to stalled forks . Despite failure of BLM to accumulate at stalled forks , NSMCE2-deficient cells exhibit lower levels of hydroxyurea-induced sister chromatid exchange . In cells deficient in both NSMCE2 and BLM , hydroxyurea-induced double strand breaks and sister chromatid exchange resembled levels found in NSCME2-deficient cells . We conclude that the rescue of collapsed forks by converging forks is dependent on NSMCE2 .
Replication-associated DNA damage is common in human cells and can lead to the development of somatic mutations . DNA damage during replication can be induced by DNA lesion-producing chemicals , proteins bound to the DNA , DNA polymerase inhibitors , or nucleotide limitation [1] . Hydroxyurea ( HU ) triggers fork stalling due to nucleotide limitation through inhibition of ribonucleotide reductase [2] . Human cells exposed to HU for up to six hours are capable of restarting 80% of their replication forks [3–6] . However , forks that are stalled for 16 to 24 h are unable to restart [7] , indicating they have collapsed . Collapsed replication forks must be rescued by active forks initiated at dormant origins to complete genome duplication . In both human cells and yeasts , the induction of a double strand break ( DSB ) is associated with repair of collapsed forks [8 , 9] . Although factors essential for the formation of DSBs during fork collapse have been identified [10] , the mechanism generating DSBs when the new replication forks converge with the collapsed forks is unknown . The DNA helicase mutated in Bloom’s syndrome BLM possesses multiple functions in DNA replication fork stabilization and homologous recombination ( HR ) , which is a mechanism that operates in the repair of replication-associated DSBs [11] . Recruitment of BLM to replication forks is part of the mechanism that stabilizes forks in both unperturbed and replication-stressed cells [12 , 13] . Excessive DSBs accumulate in BLM-deficient cells released from replication blockade after prolonged fork stalling [14] , suggesting that BLM plays a role in collapsed-fork rescue . In the absence of BLM , after fork collapse , under-replicated DNA and unresolved HR intermediates persist into mitosis where they cause DNA damage [15 , 16] . The E3 SUMO ligase NSMCE2 is a component of the SMC5/6 complex , which is present at stalled replication forks and a key component of the stalled fork proteome [17] . In budding yeast , deletion of the NSMCE2 homolog MMS21 is lethal; however , sumoylation-deficient hypomorphs are viable and have defects in replication-specific DNA repair [18] . These cells also accumulate excess RAD51-dependent recombination intermediates during replication stress and are deficient in HR [18 , 19] . During fork stalling , MMS21 undergoes auto-sumoylation during replication stress , and it then recruits the BLM homolog Sgs1 via SUMO binding sites on Sgs1 [20 , 21] . Once recruited , Sgs1 resolves HR intermediates generated during repair of damaged replication forks . In human cells , forks adopt a RAD51-dependent structure during stalling , which resembles a Holliday junction [22] . RAD51 is required to prevent replication-induced DSBs , and RAD51 levels increase at stalled forks as they transition from a restart-competent state to a collapsed state [7] . BLM regulates the exchange of RAD51 recombinase for RPA [23 , 24] , and in previous work we showed that sumoylation of BLM regulates a switch between BLM’s pro- and anti-recombinogenic functions [14] . If negative regulators of RAD51 such as BLM and the recently described RADX are ablated , excess RAD51 is loaded at stalled forks and excess DSBs accumulate [25] . In other situations , however , induction of excessive RAD51 can instead trigger inhibition of HR repair [26] . Because NSMCE2 regulates BLM recruitment and RAD51-dependent HR intermediates accumulate in yeast mms21 mutant cells , we hypothesized that NSMCE2 may be a critical regulator of RAD51 function at collapsed replication forks . Here we studied the role of NSMCE2 in repair and rescue of collapsed replication forks . We found that NSMCE2 is essential for formation of DSBs during collapsed-fork rescue . Interestingly , lack of DSBs during collapsed-fork rescue is associated with hyper-accumulation of RAD51 and impaired sister chromatid recombination . Defects in the rescue of collapsed replication forks in NSMCE2-deficient cells lead to DNA damage in mitosis .
Experiments in yeast suggested that NSMCE2 is required for efficient sumoylation of BLM [20] . To measure SUMO-BLM levels , we used human U2OS cells that express a His-tagged SUMO2 to carry out pull down assays . Analysis of SUMO-conjugates revealed that sumoylated BLM levels increased approximately eight fold after prolonged fork stalling by treatment with 2 mM HU for 16 hours ( Fig 1A and 1B ) . We then tested if sumoylation of BLM is dependent on NSMCE2 by knockdown of endogenous NSMCE2 . Depletion of NSMCE2 using two different siRNAs resulted in a 60% decrease in sumoylated BLM in HU-treated cells compared to HU-treated control cells ( Fig 1A and 1B ) . These data indicate that BLM sumoylation is dependent on NSMCE2 . Residual SUMO-BLM could result from the incomplete depletion of NSMCE2 or from residual sumoylation catalyzed by other E3 SUMO ligases . We previously reported that a BLM protein mutated at its preferred sumoylation sites K317R and K331R is recruited normally to stalled replication forks [14]; consequently , we hypothesized that BLM localization at stalled forks might be normal in NSMCE2-deficient cells . On the contrary , siRNA-mediated depletion in HeLa cells led to three-fold reduction in BLM foci in cells treated with HU for 24 h compared to HU-treated control cells ( Fig 1C ) . NSMCE2 depletion was not associated with a change in the levels of BLM protein in NSMCE2-deficient HeLa cells ( S2A Fig ) , and overexpression of siRNA-resistant NSMCE2 in depleted HeLa cells substantially rescued the defect in localization of BLM at stalled forks ( S3A Fig; representative low power images of BLM localization are shown in S4A Fig ) . These data show that efficient recruitment to or retention of BLM at collapsed replication forks is dependent on NSMCE2 . BLM can promote dissociation of RAD51 recombinase from ssDNA [23] . Because BLM’s role in dissociation of RAD51 at stalled forks could be defective in NSMCE2-deficient cells , we tested whether NSMCE2 plays a role in regulation of RAD51 accumulation . NSMCE2-deficient cells , both untreated cells and cells treated with HU for 24 hours , exhibited increases in the number , intensity , and size of RAD51 foci compared to controls ( Fig 1D and 1E; S3B Fig; representative low power images of RAD51 localization are shown in S4C Fig ) . Western blot analysis showed that total cellular RAD51 protein levels were similar in NSMCE2-deficient and control cells ( S2A Fig ) . Because over 90% of stalled replication forks are unable to restart after 24 hours of HU treatment [7 , 27] , we tested whether the excess RAD51 was localized to collapsed replication forks . To test this , we labeled nascent DNA synthesis with 10 μM EdU for 12 min prior to HU treatment , treated cells with HU for 24 hours , and then analyzed RAD51 foci . As expected , RAD51 co-localized with EdU foci in HU-treated control and NSMCE2-deficient cells ( S3C Fig ) . These data show that NSMCE2 is required to prevent over-accumulation of RAD51 at forks under conditions that lead to their collapse . RAD51 is normally loaded onto ssDNA by exchange with ssDNA binding protein RPA [28] . We therefore tested whether the excess RAD51 accumulation in NSMCE2-deficient cells might correlate with a diminished accumulation of RPA at stalled forks . For this experiment , we measured the accumulation of chromatin-bound RPA after nucleoplasmic extraction of cells treated with HU for 24 hours . After siRNA-mediated depletion of NSMCE2 and HU treatment , cells displayed 40% fewer RPA foci in both HeLa ( Fig 1F and 1G; representative low power images of RPA localization are shown in S4B Fig ) and U2OS cells ( S3D Fig ) compared to control cells . Overexpression of siRNA-resistant NSMCE2 in depleted HeLa cells rescued the defect in RPA foci accumulation ( S3E Fig ) . In addition , HU-treated HEK293T NSMCE2 null cells displayed reduced levels of chromatin-bound RPA compared to control normal cells ( S3F and S3G Fig ) . We conclude that RAD51 accumulates in excess over RPA in NSMCE2-deficient and NSMCE2 null cells , perhaps due to a failure to recruit BLM to stalled forks . The lower levels of RPA foci suggest that there are lower levels of ssDNA . To test this possibility , we measured the levels of ssDNA by incorporation of BrdU for two cell divisions prior to HU treatment , followed by immunodetection with anti-BrdU antibodies in non-denaturing conditions . Unlike control cells , which displayed at least a two-fold increase in BrdU foci after treatment with 2 mM HU for 24 hours , NSMCE2-deficient cells displayed no induction of BrdU foci after HU treatment ( S3H Fig; representative low power images of BrdU localization are shown in S4D Fig ) . Thus , the lower levels of focal RPA in HU-treated NSMCE2-deficient cells are evidence of lower levels of ssDNA detectable by anti-BrdU antibodies at collapsed replication forks . Because anti-BrdU antibodies cannot detect BrdU in the ssDNA-RAD51 nucleoprotein filament [29] , these results do not rule out the possibility that the excess RAD51 is bound to ssDNA . To test whether the RAD51-bound chromatin in NSMCE2-deficient cells is competent for HR , we measured the frequency of sister chromatid exchanges ( SCEs ) after prolonged fork stalling by 24-hour treatment with HU . The SCE assay measures crossovers generated after resumption of DNA synthesis that can be detected in the subsequent mitosis . NSMCE2-deficient cells had a 45% reduction in the number of HU-induced SCEs/metaphase compared to control cells ( Fig 1H ) . Thus , the excess RAD51 observed at stalled forks is not associated with increased sister chromatid recombination . Contrary to previous reports using murine cells [30] , we found that basal levels of SCEs in HEK293T NSMCE2 null cells were similar to normal HEK293T cells ( S3I Fig ) . Because HU-induced SCEs were suppressed in NSMCE2-deficient cells , we hypothesized that the excess RAD51 leads to a defect in DSB formation during rescue of collapsed forks . To test this possibility , we measured DSB accumulation in control and NSMCE2-deficient cells after prolonged exposure to HU . In HeLa cells exposed to control siRNA , a 16-hour treatment with HU did not induce DSBs; however , a 48-hour treatment with HU led to an accumulation of DSBs detectable by pulsed-field gel electrophoresis ( PFGE ) ( Fig 2A ) . Interestingly , we found that NSMCE2-deficient cells failed to produce a detectable increase in DSBs after a 48-h exposure . Ionizing radiation with 4 Gy followed by a 30-min repair period results in equal levels of DSBs in both control and NSMCE2-deficient cells , indicating that NSMCE2-deficient cells are not defective in DSB formation per se but in replication stress-induced DSBs . Because NSMCE2-deficient cells are defective in DSB formation at stalled forks after prolonged HU treatment , we tested whether the DNA damage response was also reduced . We measured γ-H2AX levels by analysis of DNA damage foci and flow cytometry . We found by both measures that NSMCE2-deficient cells accumulate two- to three-fold less γ-H2AX after prolonged HU treatment ( S3J and S3K Fig ) despite normal levels of phosphorylation of CHK1 and of RPA32 ( S2A Fig ) . Substantial rescue of the levels of γ-H2AX foci was observed by overexpression of siRNA-resistant NSMCE2 ( S3K Fig ) . To investigate the ability of cells to generate DSBs during collapsed-fork rescue , we measured the kinetics of accumulation of DSBs over time after release from HU . After release from the HU block , normal cells linearly accumulated DSBs , whereas NSMCE2-deficient cells failed to accumulate DSBs four and eight hours after release ( Fig 2A ) . The accumulation appears to be replication-dependent , because normal cells released into 10 μM aphidicolin after HU arrest did not accumulate similar levels of DSBs ( S5A Fig ) . Flow cytometry confirmed that control and NSMCE2-deficient cells show similar cell-cycle distributions 6 and 12 hours after release from HU , suggesting that differences in cell-cycle progression after release from HU do not explain these results ( S5B Fig ) . Moreover , no significant differences in the levels of apoptosis were observed in control and NSMCE2-deficient cells after release from HU , ruling out apoptosis as a confounder of differences in DSBs ( S5D Fig ) . Levels of DSBs in untreated HEK293T NSMCE2 null cells were higher than in untreated normal HEK293T cells; however , similar to the results obtained with NSMCE2 depletion with siRNAs , after treatment with HU and during release into normal medium we observed a defect in accumulation of DSBs in NSMCE2 null cells ( S5C Fig ) . Similar to NSMCE2-deficient HeLa cells , NSMCE2 null cells were also defective in their γ-H2AX response after HU treatment ( S5E Fig ) . Collectively , the results suggest that in the absence of NSMCE2 the levels of DSBs that are normally generated during collapsed-fork rescue are reduced . We next tested whether NSMCE2-deficient cells have a defect in the dynamics of RAD51 localization during collapsed-fork rescue . Because RAD51 protein accumulates during HU treatment , we hypothesized that converging forks displace the RAD51 over time . We therefore released cells from prolonged fork stalling and measured levels of the RAD51 foci at collapsed replication forks in a time course . Two , four , and eight hours after release from HU , control HeLa cells exhibited a steady decrease in RAD51 foci whereas NSMCE2-deficient cells retained them ( Fig 2B ) . RAD51 foci increased in normal cells between 8 and 12 h after release from HU , possibly due to RAD51-dependent DNA repair in late S or G2 phase . In addition , we also observed a persistence of RAD51 foci at stalled forks in NSMCE2 null cells compared to normal cells after release from HU treatment ( S6A Fig ) . In both normal and NSMCE2 null HEK293T cells , RAD51 foci co-localized with RPA and γ-H2AX foci ( S6B Fig ) . We considered the possibility that persistence of excessive RAD51 at collapsed replication forks might disturb replication dynamics in S phase after release from HU . To measure replication fork dynamics , we performed microfluidics-assisted replication track analysis ( maRTA ) [31] . We found that replication fork speed , fork restart , and dormant origin firing were similar in NSMCE2-deficient cells compared to control cells , after either 5 or 16 hours of HU treatment ( Fig 2C ) . These data indicate that both the replication dynamics of unperturbed forks and of dormant origin activation in replication-stressed cells are not adversely affected by NSMCE2 deficiency . NSMCE2-deficient cells maintained normal cell-cycle progression in the absence of HU treatment ( Fig 2D ) ; however , after release from prolonged HU treatment , NSMCE2-deficient cells displayed an arrest in the next G1 phase ( S7A Fig ) . Defects in collapsed-fork rescue could lead to under-replicated DNA in S phase and DNA damage in mitosis . Similar to previously reported results [30 , 32] , we found increases in the frequencies of abnormal anaphases , micronuclei , and G1 53BP1 nuclear bodies after release from HU block in NSMCE2-deficient cells compared to controls indicating that excess mitotic DNA damage is induced in NSMCE2-deficient cells ( S7B–S7F Fig; representative low power images of 53BP1 localization are shown in S4E Fig ) . To investigate further the nature of the mitotic damage invoked in HU-treated , NSMCE2-deficient cells , we measured the frequency of ultra-fine bridge ( UFB ) formation in cells undergoing mitosis . In order to obtain a sufficient number of cells in anaphase , cells were pretreated or not with HU for 24 hours; they were then blocked in G2 with the CDK1 inhibitor RO-3306 at 7 . 5 μM for 15 hours and then finally released into metaphase for 1 hour prior to fixation ( Fig 3A ) . Flow cytometry confirmed effective G2 arrest by RO-3306 treatment ( S7G Fig ) . We visualized UFBs using the PICH repair helicase , which localizes to UFBs and DNA under tension [3] . The number of UFBs measured by PICH staining was increased after HU treatment in NSMCE2-deficient cells but not control cells ( Fig 3B–3D and 3F ) . PICH-positive UFBs were also positive for BLM ( Fig 3C ) , indicating that localization of BLM to these structures is not dependent on NSMCE2 . The crosslink repair protein FANCD2 is sometimes associated with the ends of UFBs , and has been used as a marker for under-replicated DNA persisting into mitosis [3 , 16] . FANCD2-flanked , PICH-positive UFBs ( Fig 3D ) were infrequently observed in both NSMCE2-deficient and control cells . Thus , the excess UFBs produced in NSMCE2-deficient cells are not equivalent to the UFBs produced in cells treated with low-dose aphidicolin [15 , 16] . We then tested whether mitotic DNA damage originated from collapsed forks in the previous S phase . As a marker for damaged DNA and repair in anaphase cells , we counted the number of FANCD2 foci and found a 1 . 8 fold increase in NSMCE2-deficient cells treated with HU compared to control cells ( Fig 3E and 3G ) . In order to monitor the location of collapsed forks generated by prolonged treatment with HU , we labeled cells with EdU for 20 min before treatment with HU . FANCD2 foci co-localized with the EdU label at a greater frequency in HU-treated , NSMCE2-deficient cells compared to control cells ( Fig 3H ) , indicating that the recruitment of FANCD2 observed in mitosis had occurred in regions of chromatin where replication forks had previously stalled and collapsed . These data suggest that defective collapsed-fork rescue in NSMCE2-deficient cells leads to increased under-replicated DNA persisting into mitosis , which results in mitotic DNA damage . In order to measure DNA damage in metaphase cells , we measured the levels of γ-H2AX associated with metaphase chromosomes and the levels of chromosome aberrations detectable at metaphase . For the analysis of γ-H2AX levels , normal HEK293T and NSMCE2 null cells were treated with 2 mM HU for 24 hours , released into medium with RO-3306 to block them in G2 , then released into normal medium and prepared for analysis of γ-H2AX levels by immunofluorescence ( S8A and S8B Fig ) . HU-treated NSMCE2 null cells exhibited a nearly 50% increase in median fluorescence intensity of chromosome-associated γ-H2AX in phospho-H3-positive cells compared to HU-treated HEK293T normal cells . For the analysis of chromosome aberrations , cells were treated or not with 2 mM HU for 24 hours and then metaphase chromosomes were prepared and analyzed by fluorescence microscopy ( S9A Fig ) . We identified increased frequencies of chromatid arm breaks , telomere fusions , and secondary constrictions in NSMCE2 null cells compared to control cells . Chromatid arm breaks and secondary constrictions were induced by HU treatment ( S9B Fig ) . The increase in secondary constrictions in HU-treated NSMCE2 nulls cells is consistent with increased under-replicated DNA and the increase in chromatid breaks could arise from chromosome breakage in mitosis or under-replication as seen at common fragile sites . Because NSMCE2 is essential for proper localization of BLM to stalled replication forks but displays phenotypes distinct from BLM-deficient cells , we asked whether NSMCE2 is epistatic to BLM during rescue of collapsed forks . In these experiments , we used siRNAs to deplete BLM in NSMCE2 null and control cells ( Fig 4A ) . In HU-treated normal HEK293T cells depleted for BLM , levels of phosphorylated RPA and γ-H2AX were similar to levels in HU-treated control cells as evidence by Western blot analysis . In contrast , in HU-treated NSMCE2 nulls cells depleted for BLM , the levels of phosphorylated RPA and γ-H2AX were reduced in comparison to HU-treated control cells , but they were similar to levels in HU-treated NSCME2 null cells . The data for phosphorylated H2AX were confirmed by analysis of focal γ-H2AX and flow cytometry with γ-H2AX antibodies ( Fig 4B , 4D and 4E ) . Prolonged HU treatment of BLM-deficient normal HEK293T cells resulted in a 2 . 6 fold increase in the levels of SCEs from 8 . 2 to 21 . 1 SCEs/metaphase , whereas prolonged HU treatment of BLM-deficient NSMCE2 null cells resulted in an only small increase in SCEs from 4 . 6 to 6 . 3 SCEs/metaphase ( Fig 4C ) . Consistent with the suppression of HU-induced SCEs , the levels of HU-induced DSBs were also suppressed in BLM-depleted NSMCE2 null cells relative to BLM-deficient normal cells . These data indicate that NSMCE2 is epistatic to BLM with respect to HU-induced phenotypes . Because DSB accumulation is suppressed in NSMC2-deficient cells , we tested whether depletion of RAD51 would restore HU-induced DSB levels to normal in NSMCE2 null cells . After transfection of control and RAD51 siRNAs , we treated cells with 2 mM HU for 24 hours , then released into normal medium for 6 hours and quantitated the DSB marker γ-H2AX by flow cytometry and measured DSBs by PFGE . In HU-treated , control-depleted normal HEK293T cells , γ-H2AX levels increased approximately 10 fold compared to baseline after release into normal medium for 6 hours ( S10A and S10C Fig ) . Contrary to expectation , RAD51 depletion in normal cells resulted in much smaller induction of γ-H2AX , similar to the levels observed in HU-treated control-depleted NSMCE2 null and HU-treated , RAD51-depleted NSMCE2 null cells . Consistent with the γ-H2AX results , in HU-treated , control-depleted normal HEK293T cells , DSB levels increased approximately three fold compared to baseline after release into normal medium for 6 hours ( S10B Fig ) . In contrast , RAD51 depletion in normal cells resulted in almost no induction of DSBs compared to baseline , which again was similar to the levels of DSBs in HU-treated control-depleted NSMCE2 null and HU-treated , RAD51-depleted NSMCE2 null cells . These data suggest that , despite the fact that RAD51 has hyper-accumulated at collapsed replication forks , in the absence of NSMCE2 , the RAD51 at collapsed forks is nonfunctional .
DNA combing experiments in many mammalian cell lines have shown that , after prolonged fork stalling due to HU exposure , DNA synthesis does not normally resume at the site where the fork stalled ( see reference 2 and Fig 2 ) . These data rule out rescue mechanisms , such as HR-mediated restart or break-induced replication , in which replication is re-established at the site of fork stalling . The majority of collapsed forks must therefore be rescued by converging forks initiated at dormant origins after release from prolonged replication arrest . DSBs have been previously associated with resumption of DNA synthesis after release from prolonged HU block [7 , 14] , but the mechanism by which these breaks are generated is not well understood . Here we show that normal rescue of collapsed replication forks is dependent on NSMCE2 . After resumption of DNA synthesis , NSMCE2-deficient cells do not accumulate normal numbers of DSBs . The large increase in UFBs in mitosis indicates that many forks fail to complete DNA replication . NSMCE2 deficiency is associated with higher levels of RAD51 foci at collapsed forks and persistence of foci after release from replication arrest . These results are consistent with results obtained in yeast mutants of the SMC5/6 complex and of MMS21 , in which excess RAD51-dependent recombination intermediates accumulate at stalled forks [19 , 33 , 34] . Pathological accumulations of RAD51 have been associated with DNA repair defects [26 , 35] . We do not know the structure of the RAD51-bound DNA in NSMCE2-deficient cells , and the excess RAD51 could be at the fork itself , associated with ssDNA gaps behind the fork , or associated with other abnormal structures . There are established roles for RAD51 at stalled replication forks that do not involve DSBs . For instance , RAD51’s role in reversal of the replication fork is epistatic to its BRCA2-mediated fork protection function [36 , 37] . Our experiments with RAD51 depletion in normal cells suggest that reversed fork structures , catalyzed by RAD51 , are required for the formation of DSBs during fork rescue , very likely as substrates for nucleases such as MUS81 . Biochemical studies suggest that BLM performs a quality-control function on stressed replication forks by dissociating nonfunctional , ADP-bound RAD51 from the nucleoprotein filament [23] . Because BLM does not accumulate normally at stalled forks in the absence of NSMCE2 , it is possible that the RAD51 that accumulates excessively in NSMCE2-defcient cells is in the nonfunctional ADP-bound state . That said , the epistasis experiments indicate that NSMCE2 controls other factors besides BLM that contribute to the function of RAD51 at collapsed forks . We propose a model in which the excess nonfunctional RAD51 prevents DSB formation during the convergence of active replication forks with collapsed forks , leading to excess under-replicated DNA that is detectable at anaphase ( Fig 5 ) . However , more definitive tests of this proposition are required to rule out other possible models . For example , it could be informative to use molecular combing to monitor the replication dynamics of forks from newly fired origins as they converge upon collapsed replication forks . We found that BLM sumoylation is dependent on the presence of NSMCE2 . We and others [32] have shown that BLM does not accumulate normally at stalled forks in NSMCE2-deficient cells . BLM has multiple functions in the resolution of recombination intermediates during replication stress , normally ensuring that recombination intermediates are resolved without exchange . Yet the levels of SCEs are low in the absence of NSMCE2 , suggesting additional roles of NSMCE2 in promotion of crossover events when BLM is absent . Our evidence suggests that RAD51-depenedent intermediates in NSMCE2-deficient cells are not resolved until M phase , whereas RAD51 foci are normally resolved during S phase . Because BLM localization to UFBs is not dependent on NSCME2 , BLM could have a role in resolving RAD51-depenedent intermediates in mitosis . Our findings that excess γ-H2AX accumulated on metaphase chromosomes but not during S phase suggest that under-replicated DNA is not resolved until G2/M phase . For example , prometaphase DNA repair [3] or mitotic resolvases [38] could decatenate under-replicated DNA to permit disjunction of inter-linked sister chromatids . We cannot rule out the possibility that the loss of NSMCE2 in cells affects the function of the SMC5/6 complex . However , in agreement with previous results in human U2OS and DT40 cells [39 , 40] , we found SMC5 levels were normal in NSMCE2-deficient HeLa cells ( S2B Fig ) . These data suggest that , unlike in S . cerevisiae [41] , SMC5 levels are not dependent on the presence of NSMCE2 in human cells . Whether human NSMCE2 plays a structural role in collapsed-fork rescue and other repair processes remains to be determined . Our results here agree with previous results showing lower levels of SCEs in NSMCE2-deficient cells [42] . Hypomorphic NSMCE2 mutation in humans is associated with a syndrome characterized by short stature and acanthosis nigricans [32] . Cells derived from these patients display increased micronuclei , nuceloplasmic bridges at cytokinesis , and binucleated cells . Despite a defect in BLM localization at replication forks in patient cells , untreated cells have normal SCE levels , and UV treatment induces only a small increase in SCEs [32] . We found that cells deficient for both NSMCE2 and BLM exhibit reduced levels of HU-induced DSBs and SCEs , indicating that NSMCE2 is epistatic to BLM during collapsed-fork rescue . In contrast to results with human cells , murine cells that are null for Nsmce2 exhibit increased SCEs , and Blm knockdowns in the murine Nsmce2 null cells display an additive increase in SCE levels [30] . The explanation for these different outcomes of NSMCE2 deficiency between humans and mice is unknown . We used HU to generate and study collapsed forks and to block repair-coupled DNA synthesis ( e . g . , break-induced replication , gap filling , lesion bypass , etc . ) . We therefore cannot rule out the possibility that NSMCE2 plays other roles during the unperturbed cell cycle or in situations where template switching can bypass DNA damage during replication , such as in cells treated with methyl methanesulfonate or UV irradiation . We observed higher levels of DSBs in untreated NSMCE2 null cells ( S4 Fig ) , but observed no increase in basal SCE levels ( Fig 4C ) . Our analysis using maRTA indicated that deficiency of NSMCE2 did not alter replication dynamics in untreated cells , which is consistent with results reported in yeast [43] . We suggest that the increased DSBs in untreated NSMCE2-deficient cells may originate from incomplete replication at common fragile sites , leading to DNA breakage in mitosis and the formation of micronuclei in the next cell cycle . Micronuclei are known to be prone to replication-associated DNA breakage [44] . HR-directed DSB repair is dependent on NSMCE2 [45 , 46]; consequently , these breaks would most likely be repaired by non-homologous end joining . Studies in both mammalian cells [30 , 47] and yeasts [48] indicate that HR can be increased in NSMCE2-deficient cells under some conditions , emphasizing the general complexity of NSCME2’s roles in maintenance of genomic integrity . The importance of mechanisms that regulate RAD51 protein levels is underscored by studies that have identified increased RAD51 protein levels as a negative predictor of patient outcome in several cancer types [49] . The present work has uncovered a connection between NSMCE2 and the formation of DSBs at collapsed replication forks during rescue . The identification of NSMCE2 as a potential controller of HR-mediated fork rescue highlights NSMCE2’s potential as a new therapeutic target for combinatorial therapy of HR-dependent cancers .
Antibodies for immunofluorescence and western blots were obtained as follows: anti-RPA2 ( Abcam; mouse monoclonal ab2175 ) , anti-RAD51 ( Abcam; rabbit monoclonal ab133534 ) , anti-NSMCE2 ( OriGene; mouse monoclonal TA501632 ) , anti-BLM [50] , anti-RANGAP ( Thermo Fisher Scientific; rabbit polyclonal PA1-5866 ) , anti-histone H3 ( Cell Signaling Technology; rabbit polyclonal 9715 ) , anti-PCNA ( OriGene; mouse monoclonal TA800875 ) , anti-SMC5 ( Bethyl; rabbit polyclonal A300-236A ) , anti-HSP90 ( OriGene; mouse monoclonal TA500494 ) , γ-H2AX ( BioLegend; 613406; or Upstate; mouse monoclonal 05–636 ) , anti-bromodeoxyuridine ( BrdU ) ( Bio-Rad; mouse monoclonal OBT0030 ) , anti-RPA p70 ( Santa Cruz; mouse monoclonal SC-53497 ) , anti-PML ( Santa Cruz; mouse monoclonal SC-966 ) , Phalloidin-546 ( Thermo Fisher Scientific; Alexa Fluor A22283 ) , and anti-phospho-histone H3 ( serine 10; Cell Signaling; mouse monoclonal 9706 ) . siRNAs for NSMCE2 , BLM , and RAD51 were as follows: siNSMCE2-2: 5'-rUrUrArCrArUrArArUrGrGrUrUrUrArGrUrUrGrCrCrGrArUrCrCrA-3' 5'-rGrArUrCrGrGrCrArArCrUrArArArCrCrArUrUrArUrGrUdAdA-3' siNSMCE2-6: 5'-rUrArUrArUrUrCrArCrUrArCrUrCrArCrUrUrCrArGrUrCrUrGrArC-3' 5'-rCrArGrArCrUrGrArArGrUrGrArGrUrArGrUrGrArArUrAdTdA-3' siBLM: 5’-rArUrUrCrUrUrGrArGrArGrCrArGrUrArUrCrCrCrGrGrGrArUrU-3’ 5’-rUrCrCrCrGrGrGrArUrArCrUrGrCrUrCrUrCrArArGrAdAdAdT3’ siRAD51: 5’-rGrArGrCrUrUrGrArCrArArArCrUrArCrUrU-3’ 5’-rCrArCrCrUrUrGrArArGrUrArGrUrUrUrGrU-3’ Genome editing was carried out with Integrated DNA Technologies ( IDT ) ALT-R CRISPR-Cas9 system with crRNA guide ( GTCCATACCAGAGTTGATAC ) targeting the first coding exon . Ribonucleoprotein particles were introduced into HEK293T cells using FuGENE HD ( Promega ) . After 48 hours , single cells were deposited in a 96-well plate by flow cytometry . After four to six weeks , clones were analyzed by the T7 endonuclease assay ( New England Biolabs ) , and clones that scored positively were PCR sequenced . >50 clones were analyzed in the first screen and a single heterozygous NSMCE2 null mutant was obtained . This clone was genome edited again to obtain two NSMCE2 null mutants . Cells were lysed in RIPA buffer ( 150 mM NaCl , 1% Triton X-100 , 0 . 25% sodium deoxycholate , and 50 mM Tris-HCl , pH 8 . 0 ) supplemented with 5 mM EDTA , 1 mM EGTA , 25 mM sodium fluoride , 1 mM sodium orthovanadate , 1 mM phenylmethane sulfonyl fluoride ( PMSF ) in 1x EDTA-free Halt protease inhibitor ( Thermo Scientific ) . Protein concentration was measured using Pierce BCA Protein Assay . 30–50 μg of total protein from cell lysates was separated by electrophoresis through 4–20% gradient polyacrylamide gels and transferred onto Hybond nitrocellulose membranes by semi-dry transfer . Before addition of primary antibodies , membranes were probed with Ponceau S ( Sigma ) for 7 min , imaged , and washed with 1% glacial acetic acid in water . Membranes were blocked for 1 hour in Tris-buffered saline with 0 . 1% polysorbate 20 ( TBST ) containing 5% Bio-Rad Blotting-Grade Blocker , then incubated with primary antibody in 3% BSA in TBST overnight at 4°C . U2OS cells that were stably transfected with a His-tagged SUMO2 were kindly provided by Dr . Michael Matunis at Johns Hopkins University , who obtained them from Dr . Mary Dasso’s lab at NIH . The cells were reverse-transfected with siRNAs using LifeTechnologies’ Lipofectamine RNAiMAX . Cells were treated with 2 mM HU for 16 hours . His-SUMO2 conjugates were purified as described by Tatham et al . [51] . Briefly , cells were washed with ice-cold phosphate-buffered saline ( PBS ) and directly lysed in 4 ml lysis buffer ( 6 M guanidine-HCl , 100 mM NaCl , 10 mM Tris-HCl pH 7 . 4 , 3 mM imidazole and 2 mM β-mercaptoethanol ) and sonicated to reduce viscosity . Lysates were incubated with 50 ml Talon Metal Affinity Resin ( Clontech Laboratories , Inc . ) overnight at 4°C with gentle mixing , and then washed 2 times with 4 ml guanidine wash buffer ( 6 M guanidine-HCl , 100 mM NaCl , 10 mM Tris-HCl pH 7 . 4 , 0 . 1% Triton X-100 , 2 mM β-mercaptoethanol ) followed by 3 washes in 4 ml urea wash buffer ( 8 M urea , 100 mM NaCl , 10 mM Tris-HCl pH 7 . 4 , 0 . 1% Triton X-100 , 2 mM β-mercaptoethanol ) . The beads were transferred to 1 . 5 ml microfuge tubes for one more wash with urea wash buffer and proteins were eluted for 1 hour at room temperature with elution buffer ( 63 mM Tris-HCl pH 6 . 8 , 2% SDS , 200 mM imidazole , 1 . 5% β-mercaptoethanol , 10% glycerol , with bromophenol blue ) . The eluates were boiled for 5 min and cleared by centrifugation prior to loading on a 10% polyacrylamide gel . The whole cell lysates were lysed in RIPA buffer . Laemmli buffer was added to 1X in aliquots representing 10% of the eluate , then boiled for 5 min prior to gel loading . The protocol from Mendez and Stillman for chromatin isolation by small-scale fractionation was followed [52] . HEK293T normal and NSMCE2 null cells treated or not with 2 mM HU for 16 hours were harvested by scraping , centrifuging , and washing twice with PBS . The cells were resuspended such that there were 1 x 107 cells per 200 μl of Buffer A ( 10 mM HEPES pH 7 . 9 , 10 mM KCl , 1 . 5 mM MgCl2 , 0 . 34 M sucrose , 10% glycerol , 1 mM DTT , 0 . 1 mM PMSF , and 1x Protease Inhibitor Cocktail ) . Triton X-100 was added to a concentration of 0 . 05% , and the cells were incubated for 5 min on ice . Nuclei were collected by low-speed centrifugation ( 4 min , 1300 x g at 4°C ) and the supernatant was reserved as the cytoplasmic fraction . The nuclei were washed once in Buffer A , and then lysed in 100 μl Buffer B ( 3 mM EDTA , 0 . 2 mM EGTA , 1 mM DTT , 1x Protease Inhibitor Cocktail ) . Nucleoplasmic proteins were separated from chromatin-bound proteins by centrifugation ( 5 min , 1700 x g at 4 oC ) . Nucleoplasmic fractions were collected in the supernatant . The chromatin pellet was resuspended in 250 μl Laemmli buffer and the material was sonicated . The cytoplasmic and nucleoplasmic fractions were clarified by high-speed centrifugation ( 5 min , 20 , 000 x g at 4°C ) . The proteins in the fractions were analyzed by Western blot . Initially , cytoplasmic and nucleoplasmic fractions were analyzed separately; however , because the cytoplasmic fraction contained varying amounts of different RPA components , for comparisons of the amounts of RPA70 we combined equal parts of the cytoplasmic and nucleoplasmic fractions . The protocol was adapted from Dimitrova and Gilbert [53] . Cells were grown on coverslips overnight and washed with cold CSK buffer ( 10 mM HEPES pH 7 . 4 , 300 mM sucrose , 100 mM NaCl , 3 mM MgCl2 ) and nucleoplasm was extracted for 90 seconds with cold extraction buffer ( 0 . 5% Triton X-100 in CSK buffer with 1 mM PMSF , 50 mM sodium fluoride , 0 . 1 mM sodium orthovanadate and 1x EDTA free Halt protease inhibitor ) prior to 30-min fixation in 4% formaldehyde at room temperature . Cells were washed twice with cold PBS then treated with 0 . 5% Triton X-100 at room temperature before staining . Cells were blocked at room temperature for 1 hour using sterile filtered 3% BSA in PBS , then probed using primary antibodies for 1 hour at room temperature . Secondary antibodies ( Alexa Fluor 488 and 546 ) were used at 1:1000 for 45 min and nuclei were stained using Molecular Probes NucBlue reagent ( R37606 ) . For 5-ethynyl-2´-deoxyuridine ( EdU ) labeling , cells were incubated in 10 μM EdU for 20 min . EdU labeling and detection was performed using Life technologies Click-iT EdU Alexa Fluor 647 imaging kit according to manufacturer’s instructions . Cells were mounted in Molecular Probes ProLong Gold Antifade Reagent . Fixed and stained cells were imaged using the Leica SP5-II spectral confocal microscope using the 63x/1 . 4 NA PL Apo objective . Using the nuclear signal to mask the region of interest enabled accurate measurement of the number and intensity of nuclear foci and the percent of nucleus occupied by signal for each antibody target . No less than 10 , 000 foci were analyzed per experimental group . Box and whisker plots were used to visualize the distribution of foci . For analysis of EdU labeled forks and ultra-fine bridges ( UFBs ) , z-stacks were created using 100x objective and deconvolved using the GE DeltaVision Elite High Resolution Microscope . Images were analyzed and 3D representations were created using the NIS Elements software . Cells were harvested with trypsin/EDTA , resuspended in ice cold PBS , and fixed and stained using BioLegend True-Nuclear Transcription Factor Buffer . Cells were then stained for γ-H2AX using directly conjugated antibody and counterstained using 7AAD to monitor cell DNA content . For cell-cycle assays , analysis was performed using the BD Pharmingen FITC BrdU Flow Kit . A minimum of 30 , 000 events was recorded for each group using the BD FACSCanto II or BD LSR II flow cytometer . Apoptosis analysis was carried out using the BioLegend FITC Annexin V Apoptosis Detection Kit with propidium iodide ( PI ) . For SCE analyses , cells were cultured with 10 μM BrdU ( Sigma-Aldrich ) . After 60 hours , the cells were incubated with 0 . 02 μg per ml colcemid ( Invitrogen ) for up to 2 hours , harvested and processed as described earlier [14] . For the epistasis experiments using HEK293T cells , the cells were incubated in 0 . 6 μg per ml colcemid for 16 hours prior to harvest . The slides were examined under the microscope at 100× , and SCEs were counted from metaphases with an acceptable quality of sister-chromatid discrimination . For measurements of HU-induced SCEs , cells were cultured in 10 μM BrdU for 30 hours , washed one time with 1× PBS , and treated with 2 mM HU for 24 hours . Cells were then released into medium containing 10 μM BrdU for an additional 20 hours . Metaphases were collected in colcemid and processed as described above . For analysis of micronuclei , normal or NSCME2-depleted HeLa cells were seeded onto coverslips , treated or not with 2 mM HU for 24 hours , and fixed with 4% formaldehyde . The cells were stained with Nuc-Blue ( Thermo Fisher ) at 2 drops per ml in PBS for 30 min , washed briefly , mounted , and imaged using the GE DeltaVision Elite High Resolution Micro . For analysis of chromosomes at metaphase , normal and NSMCE2 null HEK293T cells were seeded into 60 mm dishes and incubated overnight . For analysis of γ-H2AX labeled chromosomes , cells were treated with 2 mM HU for 24 hours , then washed and incubated in medium with 7 . 5 μM RO-3306 for 10 hours for HEK293T control or 20 hours for NSMCE2 null cells . Cells were then released into normal medium and harvested at the indicated time points . Cells were fixed with 4% formaldehyde and stained with anti-phospho-histone H3 ( serine 10 ) . For analysis of chromosome spreads , cells were treated with 0 . 02 μg per ml colcemid for 45 min or with 0 . 6 μg per ml colcemid for 16 hours prior to harvest . Cells from both experiments were harvested and metaphases prepared as described [14] . Metaphase chromosomes were stained with Nuc-Blue . Cells or chromosomes were imaged using the GE DeltaVision Elite High-Resolution Microscope . The procedure was performed as previously described [8 , 14] . Sub-confluent cultures of HeLa cells or HEK293T cells untreated , treated with 2 mM HU for 24 hours , or treated with HU for 24 hours and released into normal medium for different times were harvested by trypsinization . Agarose plugs of 2 . 5 x 105 cells were prepared in disposable plug molds ( Bio-Rad Laboratories ) . In some experiments , cells were released into medium containing 10 μM aphidicolin for 24 hours . Plugs were then incubated in lysis buffer ( 100 mM EDTA , 1% wt/vol sodium lauroyl sarcosinate , 0 . 2% wt/vol sodium deoxycholate , and 1 mg/ml proteinase K ) at 37°C for 16 hours . Plugs were then washed four times in 20 mM Tris-HCl , pH 8 . 0 , and 50 mM EDTA before loading onto an agarose gel . Electrophoresis was performed for 21 hours at 14°C in 0 . 9% ( wt/vol ) agarose containing Tris-borate/EDTA buffer in a PFGE apparatus ( CHEF DR III; Bio-Rad Laboratories ) , according to the following protocol: block I: 9 hours , 120° included angle , 5 . 5 V/cm , 30 to 18-s switch; block II: 6 hours , 117° included angle , 4 . 5 V/cm , 18 to 9-s switch; block III: 6 hours , 112° included angle , 4 . 0 V/cm , 9 to 5-s switch . The gel was then stained with SYBR Gold ( 1 part in 10 , 000 in water; Invitrogen ) and analyzed by the AlphaImager system ( ProteinSimple ) . Relative DSB levels were assessed by comparing DSB signals for each treatment to the background levels observed in untreated conditions using Image J . Data were analyzed with GraphPad Prism software . maRTA was performed as previously described with some modifications [31] . Briefly , 36 hours after siRNA transfection , HeLa cells were pulse-labeled with 50 μM iododeoxyuridine ( IdU ) for 40 min . Cells were then treated or not with 2 mM HU for 5 hours or 16 hours . The cells were released in fresh medium containing 50 μM of chlorodeoxyuridine ( CldU ) for 40 min . Cells were then harvested and embedded into agarose plugs containing 20 , 000 cells/plug . After proteinase K digestion and agarose digestion by beta-agarase , DNA fibers were stretched on 3-aminopropyltriethoxysilane coated slides ( LabScientific ) using polydimethylsiloxane molds fashioned with micro-capillary channels prepared as described [31] . DNA fibers were then denatured in 2 . 5 M HCl , and probed with the following antibodies: mouse IgG1 anti-BrdU/IdU ( clone BD44 , Becton Dickinson ) , rat anti-BrdU/CldU ( clone B1/75 , Bio-Rad OBT0030 ) , and mouse IgG2a anti-ssDNA ( clone 16–19 , Millipore ) . Secondary antibodies included Alexa Fluor 488 anti-mouse IgG1 , Alexa Fluor 594 anti-rat , and Alexa Fluor 647 anti-mouse IgG2a , respectively ( Life Technologies ) . Images were acquired on Leica DMI6000 epifluorescence microscope using Leica LAS-AF software . Signals were measured using NIH ImageJ software with custom-made modifications and the data analyzed with GraphPad Prism software . Statistical evaluations of experiments with continuous variables ( e . g . , quantitation of SUMO-BLM and DSBs ) or discrete variables in which the data was normal were carried out by paired Students t-test . Evaluations of experiments with non-normal discrete variables ( e . g . , focal counts ) were analyzed by Mann-Whitney test . All p-values were two-sided . | DNA damage encountered by the replication fork causes fork stalling and is a major source of mutations when not adequately repaired . Fork stalling can lead to fork collapse , that is , a state of the fork in which normal DNA synthesis cannot be resumed at the site of stalling . Collapsed forks must be rescued by replication forks initiated nearby , but little is known about the rescue mechanism by which an active fork merges with a collapsed fork . We used an inhibitor of DNA replication to generate collapsed replication forks and then studied genetic control of collapsed-fork rescue . We found that NSMCE2 , which is a gene product that is known to regulate repair responses to replication stress , is required for cells to effectively rescue collapsed replication forks in order to complete DNA synthesis . DNA double strand breaks that are associated with normal collapsed-fork rescue do not accumulate in cells that are deficient for NSMCE2 , suggesting that DNA breakage is part of the rescue and repair mechanism . Failure to rescue collapsed forks leads to DNA damage in mitosis and DNA damage in the following cell cycle . Our work highlights a unique role for NSMCE2 in rescue of collapsed replication forks . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"blood",
"cells",
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"hela",
"cells",
"gene",
"regulation",
"cell",
"cycle",
"and",
"cell",
"division",
"biological",
"cultures",
"immunology",
"cell",
"processes",
"dna",
"damage",
"mitosis",
"dna",
"replic... | 2019 | Rescue of collapsed replication forks is dependent on NSMCE2 to prevent mitotic DNA damage |
Molecular evolution is an established technique for inferring gene homology but regulatory DNA turns over so rapidly that inference of ancestral networks is often impossible . In silico evolution is used to compute the most parsimonious path in regulatory space for anterior-posterior patterning linking two Dipterian species . The expression pattern of gap genes has evolved between Drosophila ( fly ) and Anopheles ( mosquito ) , yet one of their targets , eve , has remained invariant . Our model predicts that stripe 5 in fly disappears and a new posterior stripe is created in mosquito , thus eve stripe modules 3+7 and 4+6 in fly are homologous to 3+6 and 4+5 in mosquito . We can place Clogmia on this evolutionary pathway and it shares the mosquito homologies . To account for the evolution of the other pair-rule genes in the posterior we have to assume that the ancestral Dipterian utilized a dynamic method to phase those genes in relation to eve .
Molecular phylogenies based on protein coding genes have greatly enhanced evolutionary theory , and in favorable cases even allow a reconstruction of the last common ancestral gene or even full evolutionary pathways [1] . However regulatory sequence evolves more rapidly than coding sequence and the functional binding sites can move around without impacting the function of a ∼1kb functional regulatory module [2 , 3] . Thus one is often in the situation where gene homologies are obvious , yet there is no visible sequence homology in the regulatory regions . At the phenotypic level , gene expression domains can be easily mapped by in-situ hybridization yet a molecular understanding is limited outside of model organisms . There is considerable need for a computational tool that can take sparse phenotypic information , e . g . , broadly defined space-time gene expression , and construct the simplest phylogenetic relationships consistent with data , thereby highlighting interesting events for molecular follow up . Drosophila segmentation is a paradigmatic example of dynamic developmental network . Positional information propagates from maternal gradients such as bicoid ( bcd ) and caudal ( cad ) to gap genes such as hunchback , giant , knirps and Kruppel ( respectively hb , gt , kni , Kr ) , and then to the striped expression of primary pair-rule genes such as even-skipped ( eve ) , hairy ( h ) , runt ( run ) , and partially fushi-tarazu ( ftz ) [4 , 5] . The pair-rule genes in turn control the segment polarity genes that are broadly conserved across the arthropods [6] . Mutagenesis and bioinformatics studies have revealed the main DNA motifs controlling the expression of gap and pair-rule genes [7] while systematic quantitative imaging has led to phenomenological models for segmentation dynamics [8 , 9] . Recent evo-devo studies have started to map the segmentation hierarchy in other dipterans ( Anopheles [10] , Clogmia [11] , Megaselia [12] ) . Almost all information comes from localizing the relevant mRNA by in-situ hybridization , and knocking down ( KD ) various transcripts with RNA interference . Information in each of these three species is still very sparse: while we know the position of the gap genes and the single pair-rule gene eve , there is only few information on the phasing of the other pair-rule genes relative to eve . Whether they are positioned by the gap genes or other so-called primary pair-rule genes is not known in those Dipterans . There are no defined gene regulatory modules in these species , so all information about gap gene regulation is inferred from their position and shifts in putative targets under KD . In spite of this sparse information , some interesting questions can be posed . The anterior gap gene pattern appears invariant in all species as do the eve stripes , though there are only six in Clogmia before gastrulation vs 7 in Drosophila and up to 8 in Anopheles . There is more variability in the posterior . The relative positions of the posterior domains of hb and gt are inverted in Anopheles with respect to Drosophila , while in Clogmia , neither of these gap genes are expressed posteriorly before gastrulation . It is reasonable to assume that the primary pair-rule stripes are positioned by gap gene repression , so the evolutionary interchange of the posterior hb and giant domains poses problems for individual eve stripe regulatory modules . For instance , eve 5 in Drosophila is repressed posteriorly by gt so if the posterior gt domain is removed , eve 5 extends broadly posteriorly in Drosophila [13] . So how can gt domain be much more posterior in Anopheles , and virtually nonexistent in Clogmia ? Similarly the two nested modules eve3+7 and 4+6 are both defined by kni repression from the interior and hb repression from the exterior [14] , which seems less plausible in Anopheles based on the relative positions of the eve stripes and gap genes . How is computational modeling best harnessed to the task of inferring the evolutionary path between fly and mosquito with such sparse information about one endpoint and intermediates ? One very general lesson from the machine learning field is to avoid overfitting [15] [16] . More parameters make less predictive , “hairball” models [17] that can always be complexified rather than falsified . The temptation in the present instance is to import into the evolutionary simulation all the molecular details we have accumulated about Drosophila . A realistic model for the AP patterning in Drosophila with multiple factors , short range repression and cooperativity , was formulated in [18] , and applied to the evolution of new enhancers in [3] . When guided by strong selection for the correct domain of expression [3] , new modules can evolve on the time scale of 107 years [19] . The key point made in these and related papers is that de novo evolution of enhancers is fast because their genotype to phenotype map can be optimized by point mutations and hill climbing . These papers also observe that under the quick and sloppy logic of evolution , the excess of binding sites or the prevalence of generic activators and position specific inhibitors can all be understood as the most quickly realized solutions to the fitness optimization problem . We do not see the creation of new modules in response to strong selection as necessary for the transition from fly and mosquito back to their last common ancestor ( LCA ) . Rather via the logic of evolutionary bricolage [20] , organic evolution and thus computation , should seek the most quickly evolved repurposing of existing components that connects the two defined endpoints subject to the constraint of viability for all intermediates . We will show that gap and pair-rule regulation in fly can be continuously adjusted to accommodate the observed changes in the posterior gap gene expression patterns . Given the range of times we have to cover , the high rate of churn in regulatory sequence among the Drosophlids [19] ( with little effect on phenotype ) , and the changes in regulatory factors such as the absence of bicoid in Anopheles , it is thus most practical and informative to simulate the phenotype and ignore the molecular level . Phenotypic models have been informative in other areas [21 , 22] and in the present context fit quantitative genetic data as to how expression domains shift when upstream factors are altered . Similar approaches are found in [23] , and [8] . We then use an evolutionary computation that initializes the network model with Drosophila parameters , and mutates and selects with a ‘fitness’ that directs the model towards Anopheles . Putting aside the specific molecular information we have for Drosophila makes our approach applicable to a wider range of problems . Invariably we find , eve stripe 5 disappears and either ( or both ) the eve 4+6 or 3+7 modules add a third posterior stripe to compensate . Thus the posterior eve stripes are not homologous in Drosophila and Anopheles . When we consider regulation of the other primary pair rule genes in fly , we conclude that the most plausible common long-germ ancestor of fly and mosquito employed a dynamic patterning system based on a forward shift in the eve pattern as observed in Clogmia and Drosophila [24] to impose phase relationships on the remaining pair rule genes . Thus there should be no homology in the posterior gap gene regulation of run , h , or ftz between fly and mosquito . We emphasize that no computation , no matter how complex , will ever prove one evolutionary scenario over another . Computation is at best a heuristic tool to uncover interesting hypothesis that one could not guess , and buttress those hypothesis by their fidelity with a quantitative phenotypic model for regulation . The computation is like a screen for all solutions to an evolutionary problem given defined rules . To the extent the ingredients of the phenotypic model are plausible and transparent , and the predictions intuitive , they may stimulate experiments .
The main lesson of two decades of quantitative analysis of Drosophila segmentation is that positional information of pair-rule stripes is essentially defined by gap-gene repression ( see e . g . review in [5] ) . Gap genes themselves are positioned by a mixture of cross-repression [9 , 25] and activation provided by maternal gradients . We will build our genetic or phenotypic model for Drosophila by defining an interaction kernel for each gap gene and pair-rule regulatory module . The kernel takes the numerical values of the inputs and outputs the expression . The general functional form is given in the S1 Text , and the specific inputs shown in the next subsection . The evolutionary algorithm that will produce the Anopheles network is allowed to change only the numerical parameters within the kernel functions . Thus the parameters that define the maternal to gap regulation , interactions among the gap genes , and their regulation of the pair rule genes all change . The algorithm does not create new kernels nor add new inputs to existing kernels , but it is important to include from the start all potential regulatory inputs that might play a role during evolution , even if their effect is minor in Drosophila . The output of a kernel is allowed to become 0 signifying its elimination . This conservative choice for the allowed ‘mutations’ , was motivated above , and justified here . Firstly we show that the desired conversion from fly to mosquito can be realized without adding new kernels , and merely modifying existing ones . Binding sites turn over rapidly in modules so parameter evolution in existing kernels should be fast , while creating kernels in the absence of directional selection in anticipation of a future need is more speculative , and arguably slower . Secondly the anterior ( roughly eve stripe 4 and forward ) gap gene pattern in Anopheles and the intermediate species is largely invariant , while several of the pair-rule gene modules control both an anterior and a posterior stripe . Since we will impose that the anterior regulation is invariant , it was most logical to keep the inputs to these two stripe kernels invariant also . Once the allowed mutations are defined , the algorithm proceeds by rounds of mutation-selection . A population of networks is initialized to the Drosophila parameters , each network is mutated and retained if it is more fit than its parent . The most fit half of the population is duplicated and forms the next generation . Details on the code can be found in [26 , 27] , and our code is available upon request . The function ( negative fitness ) that we want to minimize for each network is a sum of terms measuring ( 1 ) deviation of the posterior hb and gt profiles from the Anopheles pattern ( 2 ) deviation of the anterior eve profile from Drosophila . In addition there must be at least 7 eve stripes . From [10] we know hb moves forward in mosquito , while posterior gt is weak and probably plays no role in patterning so we assume it’s absent . Note we constrain the expression profiles , so evolution has to find a way to alter the posterior hb kernel to move its expression forward and match Anopheles . When we include a second pair rule gene , ftz , to define the 14 stripe segment polarity pattern , we insist its stripes alternate with those of eve . Nothing about intermediate species such as Clogmia is assumed . Once the evolutionary path to Anopheles is understood , and with it the regulation , the homology of the 6 Clogmia stripes becomes obvious without any further computation as we explain below . We do not impose that the eve stripes be of equal width , though in a number of instances we checked that local parameter optimization can readily satisfy this constraint , see e . g . S4 Video . Normal Drosophila segmentation is known to be extremely precise , [28 , 29] . However considerable change in eve expression in the blastula is not incompatible with adult viability . An early example was induced by variable bcd dosage [30] . Later examples include loss of parasegments 7 and 11 [31] , and even abdominal segment A5 [32 , 33] , with further details left for the discussion . Some variation in phenotype is essential for evolution . Since one can only claim heuristic value for our evolutionary computations , trying to better define the fitness costs of quantitatively imperfect patterns adds more uncertainty than it resolves and encumbers a simple story . The starting point of our simulations is an idealized Drosophila shown in Fig 1 . S1 Text details our assumptions , we summarize their main features below . There are maternal input gradients , bcd anterior and cad posterior , repressed by bcd . bcd is frozen throughout the simulation . While there is no bcd in mosquito , we assume some other gene such as otd takes its place [34] . In addition we have fixed profiles of tailless ( tll ) and huckebein ( hkb ) in the posterior . Those gradients supply positional information to the gap genes , hb , gt , kni , Kr , which are the only ones we need to follow during the evolution . At the phenotypic level we consider , gap domains look very similar in Drosophila and Anopheles , the main difference being the posterior exchanges between hb and gt . Our description of the kernels defining gap territories incorporates regulatory interactions inferred from genetics , that are presumably conserved in evolution given the observed similarities of the gap patterns ( see details in S1 Text ) . Repression comes from more than the immediately adjacent gap genes , since when these are mutated , expression typically does not extend to the anterior or posterior pole of the embryo . We omit other potential interactions because they do not impact the conserved qualitative gap pattern and would further require detailed molecular data to be fit in a species-specific manner [9 , 35] . The reader will observe that all gap gene expression patterns along the computed pathways from fly to mosquito remain fixed in size , suggesting we are not omitting any essential interactions as gt and hb interchange . For eve we include only stripes 2 to 7 . ( We do not simulate eve stripe 1 because we focus on the posterior regulation , and its regulation is decoupled from the other stripes . ) and thus have four eve modules to consider eve 2 [36] , eve 3+7 , eve 4+6 [14 , 37] and eve 5 [13] . There is good genetic evidence , reinforced by bioinformatic studies [5 , 7] , that their position is largely defined by gap gene repression . We include more than the minimal interactions required to fit the wild type eve and gap gene patterns in the posterior since hb and gt domains interchange as we evolve to mosquito , and mutagenesis experiments in fly suggest stripe regulation by more than the closest gap genes . For instance , in a hb mutant background , neither eve 6 nor eve stripe 7 expand much in the posterior [13] and in a gt mutant , eve 5 stripe only extends posterior to eve 7 stripe [13] . Thus there must be additional repression from the posterior that we assume comes from tll . We allow a uniform activator for stripes 3–7 and 4–6 ( supplied by DSTAT [7 , 37] or Zelda [38 , 39] ) , but in our framework no positional information is given by activators . There are a similar set of ftz modules defined by gap gene repression in Fig 1 . ftz 4 represents a special case in that there is no stripe specific element and it appears that ftz stripe 4 is only expressed as part of the 7 stripe ‘zebra’ element [5] . Thus ftz has partially the character of a secondary pair rule gene that takes input from other primary genes , a fact that will be important in the following .
Simulations begin from the Drosophila network in Fig 1 and target the Anopheles gap pattern as an end point . The number of eve stripes ( including 1 ) must be at least 7 , and there is no restriction on their relative size or position . A typical example of such simulation is provided on Fig 3 , with intermediate steps pictured on the phylogeny in Fig 2A . As the posterior hb domain moves forward it splits eve stripe 7 . The repression from hb that defined the posterior boundaries of eve 6–7 gradually shifts to tll . Stripe 5 transiently fragments into two additional domains , Fig 3B , neither of which emerges as an distinct stripe . But once eve 7 splits in two , the stripe 5 element can disappear while respecting our constraint of at least 7 stripes . After it disappears posterior gt , is superfluous . A variation on this pathway is presented on Fig 4 . This time the evolution of the posterior hb domain anterior , splits eve 6 to create a new eve 8 . Once a new eve stripe appears in the posterior ( Fig 4B ) , posterior gt first disappears so that eve 5 expands posteriorly , fusing with eve 6 ( thus effectively disappearing , ( Fig 4C ) . Thus the eve 5 stripe module is no longer needed and disappears , leading to a final configuration similar to Fig 3D . The evolutionary scenario with creation of a new eve stripe in the posterior and subsequent removal of eve 5 and gt posterior is highly reproducible in our simulations for a variety of conditions that implement the same evolutionary pressures . Thus stripes 4+6 and 3+7 in Drosophila become stripes 4+5 and 3+6 in Anopheles . Furthermore one of these Drosophila modules controls 3 stripes in Anopheles . On Fig 3 , Drosophila eve3+7 gives rise to stripes 3 , 6 , 7 , while on fig 4 , Drosophila eve4+6 gives rise to stripes 4 , 5 , 7 . In both cases , one eve stripe is split by hb to give two stripes in the posterior that are symmetrically positioned around the hb domain . The 8 stripes in Anopheles would be most easily explained if both modules grow another stripe with Drosophila eve 4+6 ( resp . 3+7 ) becoming 4 , 5 , 8 ( resp . 3 , 6 , 7 ) in mosquito . Our simulations also predict that intermediate dipterans must have retained this logic where eve 4+5 ( resp . 3+6 ) are homologous to the eve 4+6 ( resp . 3+7 ) module in Drosophila . Both modules are repressed by kni and hb , so in particular stripes 4–5 and 3–6 should be laid symmetrically with respect to kni in those intermediate dipterans . This is a prediction of our computation , that exploits the known gap gene regulation , but was in no way imposed . Strikingly , eve stripes 4+5 and 3+6 in both Clogmia and Anopheles are indeed laid rather symmetrically with respect to kni contrary to the situation in Drosophila . Furthermore Clogmia has only 6 eve stripes prior to gastrulation and consistent with our model , lack the posterior hb domain that generated the two additional posterior stripes in Anopheles . We could not find examples of viable mutant flies missing 2 consecutive segments ( corresponding to one full pair-rule period ) . This suggests that , even if in some mutants ( e . g . the hopscotch mutant [32] ) , when one eve stripe disappears , the embryo needs to keep some polarity information required for the definition of parasegments . Since eve 5 overlaps and defines A4p and A5a , proper parasegment definition means that the polarity of A4a and A5p must be maintained ( and subsume cells that were in A4p and A5a ) . A natural hypothesis is then to assume another pair rule gene , out of phase with eve , must persist when eve stripe 5 disappears to provide input to the segment polarity system . We chose to add ftz to our model . We recognize that the proximate input to the segment polarity genes is not directly from eve and ftz but we have to insist that the model respect the minimal information logically required for the segment polarity pattern . Thus when an eve stripe disappears , the neighboring ftz stripes merge and only one parasegment disappears . ( We will consider below how the constraints on evolution imposed by the other primary pair-rule genes in Drosophila [5] can be satisfied , if we insist that the relative phase among the pair rules genes is maintained . ) In a first round of simulations where we model ftz as a primary pair-rule gene ( and postulate a pure ftz stripe 4 module delimited by hb on its anterior side and gt posteriorly ) , the evolutionary pathway observed in the previous section dies . There are several reasons for this: first gt controls both eve 5 and the putative ftz 4 , so it is very difficult to have it disappear given this dual role while keeping the ftz/eve alternation that we impose . Second , if a new eve stripe appears in the posterior as before , it has no reason to be coupled properly to a corresponding alternating ftz stripe . Essentially , if ftz is primary , simulations fail to evolve new eve posterior stripes without breaking the alternation of ftz/eve . It is thus interesting in this context that ftz stipe 4 appears only together with the 7 stripe zebra element [5] . Thus if we allow repression of ftz 4-zebra by eve and not gt , it becomes slaved to eve in the posterior and functions as a secondary pair rule gene . In the simulation , Fig 5 , ftz stripes 5 , 6 , 7 that are positioned by gap genes , gradually disappear in favor of the zebra element . The modules that controlled pairs of stripes 1+5 , 2+7 and 3+6 now control only the anterior member and can evolve to interdigitate with the eve stripes . With the posterior ftz stripes controlled by eve repression , the pattern can evolve to the Anopheles configuration as before while preserving eve and ftz alternation throughout , Fig 5 . When eve 5 disappears ftz 4 and ftz 5 merge ( since eve repression is keeping them distinct ) Fig 5B and 5C , thus preserving the eve , ftz alternation . The fact that our evolutionary simulations fail when ftz is purely primary and succeed when ftz is more secondary suggests that it will be the same for other primary pair-rule genes such as h and runt . We nevertheless need to ask how the relative phase of the primary pair rule could be conserved in the evolutionary scenarios presented here . We propose , by means of a quantitative model , that pair-rule regulation in the posterior of the LCA is more dynamic than conventionally assumed in Drosophila . This will imply that there is no homology between the posterior regulation of the pair-rule genes by gap genes , other than for eve itself . Specifically both in Drosophila [40] and Clogmia [24]eve stripes move from posterior to anterior prior to gastrulation . Our idea is that suitable combinations of strong and weak repression among run , h , ftz , and eve can read this phase information and stabilize the pair-rule pattern we observe in Drosophila , without direct gap gene input . The model is related to the pair-rule gene oscillator that patterns the posterior of short germ insects , as previously suggested in [41] . ( However the model is not capable of intrinsic oscillations since eve is driven by gap genes and not by other pair rule genes , though it is easy to envisage how intrinsic pair rule feedback on eve could be gradually replaced by extrinsic gap regulation during the short to long germ band transition . ) Viewed within a single cell , the forward displacement of eve appears as one complete temporal cycle , thus a gene regulatory network derived from a delayed negative feed back oscillator among the pair rule genes can use the same interactions to produce stable phases in space . In certain respects our conjectured LCA resembles Nasonia [42] where the segments posterior to A5 are patterned dynamically as we reconsider in more detail in the Discussion . To implement our model , we control the maternal gradients to move the gap-genes forward and they drag eve along with them . The maternal gradients are adjusted to induce a forward shift of precisely one period in the eve pattern , Fig 6 . Then for both the Drosophila and Anopheles gap gene patterns , the interactions shown in Fig 6A will direct an arbitrary expression pattern for the pair-rule genes other than eve to stably assume the relative phases we know for Drosophila , ( or any other one by adjusting the strengths of repressions , see S3 and S4 Videos ) . For demonstration purposes only , we applied our model to the entire anterior-posterior axis , though we expect as in Fig 5 that the anterior gap gene regulation can persist . More detailed models of the gap gene network reproduce directly the anterior shift that we put in by hand [9 , 35] . However they take as input the dynamic maternal gradients ( in particular cad ) , and further observe that bcd itself is dynamical , which is consistent with what we assumed . A difference is that their models , along with [23] , aim to reproduce precise developmental dynamics , while we have sacrificed this level of detail and prefer to reveal parsimonious phenotypic mechanisms that have a greater claim to validity over the large evolutionary distances we cover . In our simulated evolution from Drosophila to Anopheles , eve 5 always disappeared . Thus an important consistency check is to show how eve 5 can appear when evolving from a LCA as appears in Figs 3 and 5C , to Drosophila . The solution was already suggested in Fig 4C , when a weak eve 2 stripe emerged from the stripe 5 module . Indeed these two stripes share the Kr and gt repressors . In Fig 7 , we indeed see that when the posterior gt domain moves anterior the LCA-eve 2 module develops a second stripe , ( and we imagine a distinct stripe 5 later evolves by drift ) . The tripartite 3 , 6 , 7 stripe loses its last component as hb moves posterior , and a reasonable Drosophila pattern is restored . Our LCA will generate a Clogmia like pattern if we remove posterior hb , illustrated on ? ? . There also is one eve stripe less than our presumptive LCA and Drosophila , because hb is not here to split ancestral eve 3–6 module in two in the posterior . Both these features qualitatively correspond to the observed pre-gastrulation Clogmia pattern , with only 6 stripes ( vs 7 in Drosophila ) .
We have used computational evolution and the observed gap gene patterns in Drosophila and Anopheles to suggest how the gap and pair-rule network evolved between these species and their LCA as well as the relation to Clogmia . Our strongest conclusions are that Anopheles eve stripes 3 , 6 , 7 ( resp . stripes 4 , 5 , 8 ) are derived Drosophila eve modules 3+7 ( resp . 4+6 ) by the elimination of Drosophila eve stripe 5 , and the forward shift and renumbering of stripes 6 , 7 , Fig 2B . Stripes 7 and 8 in Anopheles are the reflection of stripes 6 and 5 respectively in the repositioned hb domain . The Clogmia pattern follows Anopheles , except for the elimination of stripes 7 , 8 which are generated by the posterior hb domain , which is absent in Clogmia . Drosophila eve 5 arises from stripe 2 of the LCA . Our model is formulated entirely within a phenotypic or genetic description of the regulatory network , yet generated surprising , but after the fact , plausible predictions . The homologies between the eve stripe 3+6 and 4+5 modules in Anopheles or Clogmia and Drosophila could have been guessed from their symmetries around the kni domain , but we are not aware of a reference to that effect . But it could not be guessed that the continuous transition from Drosophila to Anopheles could be accomplished merely by adjusting the parameters within the regulatory kernels defined by Drosophila . This is surely the most parsimonious route between fly and mosquito , and perhaps the most rapidly evolved since it only requires mutating binding sites in existing gene regulatory modules which we know to be rapid [19 , 43] . The complex structure of Drosophila regulation , such as duplicate enhancers , only becomes implicated in the evolutionary transition if they were found to persist in intermediate species . We can never preclude more complex scenarios , such as new modules , but their rate of evolution is uncertain in the absence of positive selection . Thus our computation is a useful heuristic tool to show that the desired transition can be accomplished by reparametrizing existing kernels without creating new ones , via evolutionary bricolage [20] . The most immediate tests of our predictions require identifying pair-rule gene regulatory modules in Anopheles and in fact none has been found in that species or Clogmia to our knowledge . The most expeditious route to their discovery , with modern technology ( e . g . [44] ) would be CHIP-seq with antibodies against the gap genes . Putative binding sites could be refined computationally and then clusters of them could be matched numerically against the regulatory regions of relevant genes , [45 , 46] . In Drosophila , computationally defined clusters of binding sites were very successful in reconstructing gap and pair-rule regulation and this approach could in principle be applied to other species . Our first prediction is the existence of eve modules in Clogmia with purely kni hb and tll binding sites and suggestive of their expression as stripes 3+6 and 4+5 , and the analogous prediction of putative 3-stripe modules in Anopheles . A more dramatic confirmation of theory would be observing the expression of these modules in Drosophila . This requires sufficient homology between the gap gene proteins , which is not a given , since computational screens of these other genomes with the Drosophila binding site weight matrices has yielded nothing . However the conservation of enhancer function between Drosophila and Tribolium , in several cases gives one hope [47 , 48] . More speculatively , our model requires a LCA where the posterior pair-rule genes other than eve derive their phasing from the anterior shift of eve observed in Drosophila and Clogmia [24 , 40] . The connection between an anterior shift of the posterior pair-rule genes and a segmentation clock was made in the discussion of a paper that revealed that mechanism in Tribolium , [41] , but is speculative . Our model concerns only long-germ dipterians . Recent work on Nasonia , a long-germ band Hymenoptera , provides a very informative bridge between short and long germ band insects and the state we impute to our LCA . Hymenoptera is an out-group for the order Diptera considered here [42 , 49] . Nasonia gap/eve pattern is qualitatively very similar to Drosophila in the anterior part of the embryo [50 , 51] , precisely until abdominal segment A5 ( corresponding to ftz 5 in fly ) [42] which is the position where some strong variability between species in gap/eve is observed in our simulations . Posterior to this segment , Nasonia pair-rule pattern presents all the characteristic of an insect segmentation clock , with eve on the top of the hierarchy controlling waves of expression of odd [42] . ( Note eve has the segmental period in the posterior , as also seen in the centipede Strigamia [52] . ) Other pair-rule genes necessary to set proper segment polarity appear downstream of this clock system [42] . We chose ftz as the second pair-rule gene in the simulations to define the 14 parasegments since it regulates engrailed and has its zebra regulatory element that allowed the simulation to position the posterior ftz stripes by repression from eve . odd might seem a more logical choice to define the 14 parasegments , since it is part of the posterior oscillator in short germ insects , but the simulation would encounter the same difficulty as found for ftz , namely it is impossible to find paths for posterior hb and gt that are compatible with the known gap gene regulatory kernels in Drosophila and preserve the pair-rule gene alternation . If we combine the phylogenic evidence from Nasonia with our inability to evolve multiple pair-rule genes with purely gap gene regulation , then an alternate conceptual distinction between primary and secondary pair-rule genes naturally arises , along the lines already suggested in [53] using data from Strigamia ( see also data from Glomeris [54] ) . Primary pair-rule genes are those involved in the posterior segmentation clock , the secondary genes take input from the primary and control the segment polarity layer . Delayed negative feedback is a natural way to build an oscillator with a stable period . If the segmentation clock operates by phased sequential repression among the primary pair-rule genes then the same repression could operate in space , anterior to the oscillating growth zone , to fix the relative position of these same genes with the same relative phases . ( A related conversion of a temporal signal to a static one was derived in a prior study on the evolution of Hox patterning during the short to long germ transition [55] . ) This is a prediction that could be tested in Tribolium [56] . We have assumed that in the ancestral short to long germ transition ( or fly to mosquito ) , it is eve that first acquires gap gene input and breaks the negative feedback oscillator , based on circumstantial evidence , but this is not a logical necessity of the model . If we are correct that the LCA used the anterior shift of eve to set the relative phase of the other pair-rule genes , then the posterior regulation of these genes by the gap genes would be recent and derived , and it should not be the basis for classifying primary vs secondary . Thus we would not expect any homology between the posterior gap gene input to the pair-rule genes other than eve in fly and mosquito . This proposal is difficult to test since convergent evolution is a real possibility here , since any module will use the gap genes that are appropriately positioned for its regulation . Most evo-devo studies involve close enough species that there is no question that intermediates are viable . However the LCA of fly and mosquito was more than 200 million years ago [57] and we are proposing an evolutionary chain of events in the blastula and presuming viable adults exist along the way ! The best evidence we can offer , is the hopscotch mutants [32] ( a component of the Jak-Stat pathway ) . A maternal hypomorph rescued by a wildtype male , loses A5 , yet gives rise to fertile flies of both sexes . So in this mutant , no essential part of the anatomy is lost with A5 . The gap gene expression is unaffected , but stripe run 5 is absent , and eve 3/5 are suppressed , eve 5 more so than eve 3 [33] . The embryo tolerates other abdominal segment loss , e . g . , reduced expression of eve 4/6 results in loss of two abdominal segments but viable adults [31] . If we consider the Hox genes as the basic mediators of segment identity then based on expression , abdominal segments 2–7 are identical [58] , but more subtle differences in Hox regulation remain [59] Our modeling differs from earlier work that focused more on the developmental dynamics of gap gene expression in Drosophila , such as [8 , 9] . As noted in [9] , fitting dynamic data will be difficult to scale up for more complex pathways , and these authors did not consider the pair rule genes . Thus it might prove challenging to study significant evolutionary changes with such detailed models . Our coarse-grained description , relying on minimal interaction ( in a spirit similar to [23] ) allows us to model long evolutionary time-scales , moving from microevolution to mesoevolution [60] . Our approach illustrates the interest of phenotypic models for evolutionary systems biology . It gives a quantitative framework to make qualitative predictions ( such as “one stripe appears in this region while one stripe disappears in another region” ) using semi-quantitative phenotypic data directly obtained from experiment ( here , gap gene positioning and constraints on stripe number/alternation ) . The ability to generate novel predictions directly from currently available measurements is thus of interest for a broad swath of biological modeling [21 , 27] . | The last common ancestor of the fruit fly ( Drosophila ) and mosquito ( Anopheles ) lived more than 200 Million years ago . Can we use available data on insects alive today to infer what their ancestor looked like ? In this manuscript , we focus on early embryonic development , when stripes of genetic expression appear and define the location of insect segments ( “segmentation” ) . We use an evolutionary algorithm to reconstruct and predict dynamics of genes controlling stripes in the last common ancestor of fly and mosquito . We predict a new and different combinatorial logic of stripe formation in mosquito compared to fly , which is fully consistent with development of intermediate species such as moth-fly ( Clogmia ) . Our simulations further suggest that the dynamics of gene expression in this last common ancestor were similar to other insects , such as wasps ( Nasonia ) . Our method illustrates how computational methods inspired by machine learning and non-linear physics can be used to infer gene dynamics in species that disappeared millions of years ago . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"invertebrates",
"medicine",
"and",
"health",
"sciences",
"morphogenic",
"segmentation",
"gene",
"regulation",
"animals",
"animal",
"models",
"developmental",
"biology",
"drosophila",
"melanogaster",
"model",
"organisms",
"insect",
"vectors",
"morphogenesis",
"drosophila",
... | 2016 | Predicting Ancestral Segmentation Phenotypes from Drosophila to Anopheles Using In Silico Evolution |
Mms21 , a subunit of the Smc5/6 complex , possesses an E3 ligase activity for the Small Ubiquitin-like MOdifier ( SUMO ) . Here we show that the mms21-CH mutation , which inactivates Mms21 ligase activity , causes increased accumulation of gross chromosomal rearrangements ( GCRs ) selected in the dGCR assay . These dGCRs are formed by non-allelic homologous recombination between divergent DNA sequences mediated by Rad52- , Rrm3- and Pol32-dependent break-induced replication . Combining mms21-CH with sgs1Δ caused a synergistic increase in GCRs rates , indicating the distinct roles of Mms21 and Sgs1 in suppressing GCRs . The mms21-CH mutation also caused increased rates of accumulating uGCRs mediated by breakpoints in unique sequences as revealed by whole genome sequencing . Consistent with the accumulation of endogenous DNA lesions , mms21-CH mutants accumulate increased levels of spontaneous Rad52 and Ddc2 foci and had a hyper-activated DNA damage checkpoint . Together , these findings support that Mms21 prevents the accumulation of spontaneous DNA lesions that cause diverse GCRs .
The Small Ubiquitin-like MOdifier ( SUMO ) regulates many biological processes through its covalent attachment to lysine residues on target proteins via a cascade of an E1-activating enzyme ( Aos1-Uba2 in Saccharomyces cerevisiae ) , an E2-conjugating enzyme Ubc9 , and one of several SUMO E3 ligases [1] . Three mitotic SUMO E3 ligases ( Siz1 , Siz2 and Mms21/Nse2 ) have been identified in S . cerevisiae , and these enzymes control substrate-specific sumoylation in vivo . Siz1 and Siz2 , two paralogs of the PIAS family SUMO E3 ligases [2] , catalyze the bulk of intracellular sumoylation [3 , 4] , while the SUMO E3 ligase Mms21 has fewer known substrates [5 , 6] . This mitotic SUMO pathway is essential for cell viability in S . cerevisiae; individual deletions of AOS1 , UBA2 , or UBC9 , and combined inactivation of all three mitotic SUMO E3 ligases causes lethality [5] . In contrast , sumoylation of proteins by Mms21 is not necessary for viability in the presence of Siz1 and Siz2 in S . cerevisiae nor do mice require the SUMO E3 ligase activity of the mouse Mms21 ortholog NSMCE2 [7] , indicating some redundancy between mitotic E3 ligases . Mms21 is an integral subunit of the Smc5/6 complex and it is essential for cell viability like other subunits in this complex [8] . The Smc5/6 complex belongs to the evolutionarily conserved Structural Maintenance of Chromosomes ( SMC ) family proteins and acts in maintaining chromosome integrity [9] . Loss of the Mms21 SUMO E3 ligase activity does not affect cell viability but causes aberrant increases in homologous recombination ( HR ) intermediates , increased sister chromatid exchange ( SCE ) and accumulations of gross chromosomal rearrangements ( GCRs ) in S . cerevisiae [4 , 10–13] . Consistent with this , mutations in human NSMCE2/MMS21 cause increased SCE [14] and have been recently linked to DNA replication and/or repair defects and primordial dwarfism [15] . How sumoylation by Mms21 acts to suppress the accumulation of HR intermediates and GCRs is not known . These phenotypes could be attributed to a failure in resolving HR intermediates and/or an elevated incidence of DNA lesions that are repaired by HR . These phenotypes , however , are reminiscent of those of cells lacking the Sgs1 helicase [10 , 16] . Sgs1 , the S . cerevisiae ortholog of the human BLM helicase that is deficient in patients with Bloom syndrome , has well-documented roles in resolving HR intermediates as well as participating in resection of DNA double strand breaks ( DSBs ) [17–19] . The similarity between the phenotypes caused by sgs1Δ and mms21 E3 ligase-defective mutations raises the possibility that Mms21 and Sgs1 might function together to regulate or prevent HR [10] . In support of this model , two recent studies showed that sumoylation of Sgs1/BLM by Mms21/NSMCE2 prevents the accumulation of aberrant HR intermediates induced by DNA alkylation damage [20 , 21] . However , the roles of Sgs1 and Mms21 in preventing spontaneous genome rearrangements have not been investigated in sufficient detail , although mutations affecting each cause increased accumulation of GCRs [4 , 22] . In contrast , several lines of evidence suggest that Mms21 and Sgs1 function in separate pathways that act to maintain genome stability . The sgs1 mutations that eliminate DNA damage-induced sumoylation of Sgs1 by Mms21 do not cause appreciable sensitivity to DNA damaging agents [20 , 21] , unlike that seen for mms21 E3 ligase defective mutants and sgs1Δ mutants [5 , 10] . Similarly , combining mutations affecting NSMCE2/MMS21 and BLM/SGS1 caused synthetic growth defects and increased SCE in mouse B cells [7] . We previously demonstrated that Esc2 , a protein containing two SUMO-like domains with an important role in genome maintenance [11 , 23] , functions together with Mms21 in controlling intracellular sumoylation and suppressing GCRs [4] . Mutations affecting both SGS1 and ESC2 cause a synthetic growth defect and elevated gene conversion and joint-molecule formation in S . cerevisiae [24] . Moreover , several studies have suggested that the increased genome instability of mms21 mutants might not be caused by a defect in DNA repair , in contrast to the known repair defects caused by sgs1 mutations [17–19] . For example , the repair of meiotic DNA DSBs occurs with normal kinetics in mms21 mutants [25] . In addition , the increased level of SCE in nsmce2/mms21 mutant mice is not associated with an increase in 53BP1 foci , suggesting a lack of an obvious defect in DNA DSB repair [7] . Together these studies suggest that the genome maintenance functions of the Mms21-Esc2 pathway and Sgs1 might be different . To gain insight into these questions , we performed a detailed study of the defects caused by the mms21-CH mutation , a SUMO E3 ligase-inactive allele of MMS21 that results in C200A and H202A substitutions in the Mms21 SP-RING catalytic domain [4] . Our findings show that a diverse array of genome rearrangements accumulate in mms21-CH mutants , depending on specific DNA repair pathways available and the nature of genomic sequences involved in the formation of the GCRs observed . Collectively , these findings suggest that spontaneous DNA lesions accumulate in the mms21-CH mutant and initiate these genome rearrangements . We further show that Mms21 prevents spontaneous Pol32-dependent break induced replication ( BIR ) event , which is also dependent upon the Rrm3 DNA helicase and a subset of the DNA damage checkpoint , but does not involve resolution of recombination intermediates by Sgs1 and does not involve DNA damage-induced sumoylation of Sgs1 .
We previously showed that the mms21-CH mutation caused a substantial accumulation of GCRs selected in the duplication-mediated GCR ( dGCR ) assay ( also called the yel072w::CAN1/URA3 assay ) [4] . In the dGCR assay , non-allelic HR between divergent homologous sequences on chromosome V and chromosomes IV , X , or XIV resulting in the formation of translocations dominate the GCRs selected in most HR-proficient strains [22 , 26] ( S1 Fig ) . In contrast , the mms21-CH mutation caused only a modest increase in GCR rates in the unique sequence-mediated ( uGCR ) assay ( also called the yel068c::CAN1/URA3 assay ) [4] , which primarily selects for GCRs mediated by terminal deletions healed by de novo telomere additions and various types of micro- and non-homology mediated translocations [27] ( S1 Fig ) . To explore this further , we combined the mms21-CH mutation with mutations affecting individual genes in the RAD52 epistasis group in strains containing the dGCR assay or the uGCR assay . We then performed fluctuation analysis to measure the GCR rates of these single and double mutant strains ( Fig 1 and S1 Table ) . This analysis of the RAD52 epistasis group of genes uncovered three main classes of genetic interactions . Class I mutations included deletions of the RAD51 , RAD52 , RAD54 and RAD55 genes required for HR [28] . In this case , deletion of each gene caused a drastic reduction of the increased dGCR rate caused by an mms21-CH mutation , indicating a requirement for HR in the formation of GCRs selected in the dGCR assay . Two of the Class I mutations , rad51Δ and rad55Δ , did not cause an increased uGCR rate when combined with the mms21-CH mutation , whereas two of the class I mutations , rad52Δ and rad54Δ ( as well as a rad59Δ mutation; see below ) , caused an increased uGCR rate when combined with the mms21-CH mutation; this is consistent with previous observations that some HR pathways suppress GCRs selected in single copy sequence-mediated GCR assays such as the uGCR assay , presumably by promoting sister chromatid HR [22 , 29] . In contrast , deletion of RDH54 , which encodes a Rad54 paralog with a role in meiotic HR [30] , had little effect on the accumulation of GCRs in the mms21-CH mutant . Class II mutations included deletions of RAD59 and CSM2 . Class II mutations partially suppressed the increased GCR rate caused by the mm21-CH mutation in the dGCR assay , but caused an increased GCR rate in the uGCR assay when combined with the mms21-CH mutation . Rad59 is a stimulatory factor for Rad52 and is important for HR involving shorter repeats or when Rad52 is absent [28] . Csm2 is a subunit of the Shu complex [31] , which has been implicated as a regulator of HR , possibly by facilitating the formation of Rad51 filaments [28]; other Shu complex mutations were not tested . Consistent with these accessory roles in HR , deletions of RAD59 and CSM2 in the mms21-CH mutant modestly reduced the rate of accumulating GCRs in the dGCR assay ( Fig 1 , upper panel ) and substantially increased the rate of accumulating GCRs in the uGCR assay in the mms21-CH mutant ( Fig 1 , lower panel ) . Class III mutations included mutations in MRE11 and SAE2 . Class III mutations caused a modest increase in the increased dGCR rate caused by the mms21-CH mutation , but caused a substantial increase in the uGCR rate when combined with the mms21-CH mutation . The Mre11-Rad50-Xrs2 complex , together with Sae2 , performs nucleolytic processing of DNA DSBs , leading to 5’-resection at DSBs and an ordered recruitment of HR proteins [28] . Deletion of MRE11 has been shown to cause substantial increases in the rate of accumulation of GCRs [22] , and deletion of MRE11 in combination with the mms21-CH mutation caused an increase in the rate of accumulating GCRs in both the dGCR and uGCR assays compared to the mms21-CH single mutant ( Fig 1 ) . A mutation inactivating the endonuclease activity of Mre11 , mre11-H125N , alone caused a 30-fold increase and a 122-fold increase in the rate of accumulating GCRs in the dGCR and uGCR assays , respectively ( Fig 1 ) . Interestingly , the mre11-H125N mutation did not appreciably affect the dGCR rate of the mms21-CH mutant , but caused a further increase in the uGCR rate of the mms21-CH mutant , suggesting the involvement of the Mre11 endonuclease activity in suppressing the GCRs selected in the uGCR assay . Sae2 participates in DNA DSB processing by specifically stimulating Mre11 endonuclease activity [32 , 33] . Like the mre11-H125N mutation , deletion of SAE2 only modestly increased the dGCR rate of the mms21-CH mutant , but caused a much larger increase in the uGCR rate of the mms21-CH mutant . Thus , the initial nucleolytic processing by Mre11 endonuclease has a critical role in suppressing the formation of the GCRs selected in the uGCR assay in the mms21-CH mutant . In contrast , deletion of EXO1 , which eliminates a key exonuclease that participates in long-range resection of DNA breaks , had little effect on the rate of accumulating GCRs selected in either the dGCR or uGCR assays in the mms21-CH mutant ( Fig 1 ) . To gain further insight into the effects of the loss of MRE11 and MMS21 function , we investigated the structures of the GCRs selected in the wild-type strain and the mms21-CH , mre11Δ , mre11-H125N , mms21-CH mre11Δ , and mms21-CH mre11-H125N mutant strains . We focused on GCRs selected in the uGCR assay , as the GCRs selected in the dGCR assay are almost exclusively duplication-mediated translocations formed by non-allelic HR between the DSF1-HXT13 segmental duplication on chromosome V and regions of divergent homology on chromosomes IV , X and XIV , consistent with the HR gene dependency observed for GCRs selected in the dGCR assay in the mms21-CH mutant ( S1 Fig ) . We first characterized the GCRs by testing the individual independent GCR-containing isolates for retention of the telomeric hygromycin resistance marker hph located between the telomere and the counter-selectable CAN1/URA3 cassette on the uGCR assay chromosome and by determining the size of the rearranged chromosome V by Pulse Field Gel Electrophoresis ( Fig 2; S2 Table ) . GCRs were divided into three groups: rearranged chromosomes that were larger than the wild-type chromosome V ( group 1 ) and chromosomes that were similar to or slightly shorter than the wild-type chromosome V and either lost ( group 2 ) or retained ( group 3 ) the telomeric hph marker . We classified GCRs in group 2 as de novo telomere addition GCRs , which are formed by the healing of broken chromosomes by the de novo addition of a new telomere [34] . De novo telomere additions are the predominant form of GCRs selected in uGCR assays in strains without telomerase defects [29 , 35 , 36] and are always associated with loss of the hph marker [22] , although it should be noted that rare interstitial deletion GCRs can be associated with deletion of the hph marker . Similarly , we classified GCRs in group 3 as interstitial deletion GCRs , in which the deletion is typically associated with non-homology or microhomology breakpoint junctions when selected in GCR assays containing only unique sequences in the breakpoint region like the uGCR assay used here [37] . Strains containing GCRs falling into group 1 were subjected to whole genome paired end sequencing to determine the structures of the GCRs present ( S3 Table ) . In addition to being able to detect all of the mutations and chromosome modifications introduced into the starting strains during strain construction ( S2 and S3 Figs ) , we were also able to extensively characterize the structures of the GCR-containing chromosomes ( Fig 3 , S4–S9 Figs and S4 Table ) . We observed two distinct types of group 1 GCRs: microhomology-mediated translocations and hairpin-mediated inverted duplications . In microhomology-mediated translocations , the broken end of a broken chromosome V is fused to another broken chromosome such that the broken chromosome V acquires a fragment of the second broken chromosome that is terminated with a telomere ( Fig 3a ) . Copy number analysis indicated that these fusion events duplicated the non-chromosome V target , and junction sequences revealed only short sequences of identity at the translocation junctions . The copy number analysis was also consistent with the presence on an intact copy of the target chromosome , indicating that the microhomology-mediated translocations were non-reciprocal . In hairpin-mediated inverted duplications , the broken end of a broken chromosome V is fused to an inverted copy of itself on the left arm of chromosome V at a position between the CAN1/URA3 cassette and the first centromeric essential gene ( Fig 3b , 3c and 3d ) . The inversion site sequences are consistent with a mechanism in which a broken chromosome V is resected to form a 3’ overhang that then pairs with a short stretch of homologous sequence centromeric to the breakpoint that is processed to yield a hairpin-terminated chromosome followed by replication of the hairpin-terminated chromosome ( S10 and S11 Figs ) . As previously observed [36] , these inverted dicentric duplication chromosomes ( also called isoduplications ) all underwent additional rounds of rearrangement that resolved them to the monocentric translocations observed , although other mechanisms for the hairpin formation and resolution are possible . These secondary rearrangements often , but not always , involved HR between the Ty- or PAU gene-related sequences on chromosome V L and a homology elsewhere in the genome ( Fig 3b and 3c; S12–S14 Figs ) . The secondary rearrangements that initially appeared to involve HR between ura3-52 on chromosome V and YLRCdelta21 on chromosome XII actually proved to target an adjacent full-length Ty element on chromosome XII that was not present in the reference sequence ( S15 Fig ) ; this full-length Ty element has been previously observed by others [38 , 39] . A specific secondary rearrangement between a URA3 fragment in the Ty-inactivated ura3-52 on chromosome V L and the URA3 in the yel068c::CAN1/URA3 cassette first observed in GCRs derived from the tel1Δ uGCR strain was also observed here [36] . An additional type of secondary rearrangement was mediated by microhomologies ( Fig 3d ) ; microhomology-mediated secondary rearrangements were not observed in GCRs selected in tel1Δ mutants [36] . In most cases , the hairpin-mediated inverted duplications underwent a single secondary rearrangement as described above; however , in a small number of cases multiple rounds of secondary rearrangements were observed leading to the formation of monocentric GCRs ( S4 Table ) . GCRs selected in the wild-type uGCR strain were primarily de novo telomere addition GCRs ( Fig 4a; S7 Fig ) , consistent with dominance of de novo telomere addition GCRs among the GCRs selected in the “classical” GCR assay [35 , 40] , which lacks large repetitive sequences in the breakpoint region like the uGCR assay used here . In addition , two interstitial deletions and two hairpin-mediated inverted duplications that were resolved by HR between the ura3-52 allele and the URA3 gene on the terminal chromosome V telomere-containing fragment were recovered . The spectrum of GCRs obtained from the mms21-CH uGCR strain shared this bias towards the formation of de novo telomere addition GCRs , with the other GCRs recovered being translocations involving other chromosomes ( Fig 4a; S5 Fig ) . In contrast , substantially increased numbers of hairpin-mediated inverted duplications and decreased numbers of de novo telomere addition GCRs were selected in the mre11Δ and mre11-H125N single mutant uGCR strains and the mms21-CH mre11Δ and mms21-CH mre11-H125N double mutant uGCR strains ( Fig 4a and 4b; S5–S9 Figs ) . These observations were consistent with role of the Mre11-Rad50-Xrs2 complex in cleaving hairpin structures [33] and the recovery of hairpin-mediated GCRs in strains containing an mre11-H125N mutation [41] . Remarkably , MRE11-deficient strains showed a bias for selection of translocations containing a copy of a long region of chromosome XII R ( Fig 4c; S16 Fig ) , which could reflect either a bias due to increased fragility or accessibility of chromosome XII or due to suppression of mre11-dependent growth defects by duplication of chromosome XII R . We also observed that 8 of the 10 sequenced mms21-CH mre11Δ GCR-containing isolates were disomic for chromosome VIII and 1 of the 10 was disomic for chromosome I ( S2 Table , S17 Fig ) . Taken together , these data are consistent with the idea that the mms21-CH mutation increases the total level of DNA damage without substantially biasing the mechanisms involved in forming GCRs , whereas mre11 defects increase the propensity of damaged DNAs to form hairpin inversions . The dramatic HR-dependent increase in the dGCR rate caused by the mms21-CH mutation ( Fig 1 ) , combined with the fact that the mms21-CH mutation caused at best modest changes in the spectrum of GCRs selected in the uGCR assay ( Fig 4 and S5 Table ) , suggested that the mms21-CH mutation causes an increase in DNA damage that underlies the formation of GCRs without dramatically affecting the DNA repair pathways that act on this DNA damage . Because HR appears to act on this DNA damage to produce GCRs selected in the dGCR assay , we investigated whether BIR or a BIR-related pathway might play a role in the formation of GCRs in mms21-CH dGCR strains . Previous studies of the repair of HO endonuclease-induced DNA DSBs by BIR showed that Pol32 , a subunit of DNA polymerases delta and zeta , is required for BIR [42–44] . However , other studies have found that the pol32Δ mutation only reduced the efficiency of BIR [45] . We previously found that a pol32Δ mutation did not decrease the wild-type dGCR rate nor did the pol32Δ mutation eliminate duplication-mediated GCRs [22] , suggesting that the role of POL32 in promoting BIR may be dependent on the nature of the initiating damage or that the GCRs selected in the dGCR assay are not formed by BIR . Remarkably , we found that deletion of POL32 in the mms21-CH mutant caused a drastic reduction of the dGCR rate by about 15-fold and a relatively modest increase in its uGCR rate ( Fig 5A ) , consistent with an important role of POL32-dependent BIR in forming dGCRs in the mms21-CH mutant . Pif1 has been shown to be required for BIR initiated by HO endonuclease-induced DSBs and is thought to act by promoting DNA synthesis mediated by a migrating D-loop replication intermediate [42 , 43] . Pif1 also dissociates telomerase from single-stranded DNA thereby suppressing GCRs mediated by de novo telomere addition at DSBs [29 , 46] . We found that deleting PIF1 in the mms21-CH mutant caused further increases in both the dGCR and uGCR rates relative to that of the respective single mutants ( Fig 5A ) , which is consistent with the idea that the DNA damage that underlies the formation of GCRs in the mms21-CH mutant is a substrate for de novo telomere additions [34 , 36] . We also screened other DNA helicases for their role in forming GCRs in mms21-CH mutant strains . RRM3 encodes a DNA helicase that travels with DNA replication fork [47] . A recent study showed that Rrm3 participates in the repair of replication-associated DNA breaks [48] , although it is not involved in BIR induced by HO endonuclease . Deletion of RRM3 in the mms21-CH mutant caused a reduction ( 43-fold ) in the dGCR rate without appreciably affecting the uGCR rate compared to that of the respective single mutants ( Fig 5A ) , indicating a requirement of Rrm3 in the formation of duplication-mediated GCRs in the mms21-CH mutant strains . These findings suggest that accumulation of duplication-mediated GCRs in mms21-CH might reflect the formation of replication-associated DNA DSBs that require Rrm3 for BIR-like repair . The DNA helicase Srs2 acts as an anti-recombinase by disrupting the formation of Rad51 filaments and D-loops [49–51] . In addition , the Smc5/6 complex of which Mms21 is a subunit has been shown to control the recombination activity of the Mph1 helicase [12 , 52] . Deletion of SRS2 or MPH1 in the mms21-CH mutant did not appreciably alter the dGCR rate , but caused a drastic increase in the uGCR rate relative to that of the respective single mutants ( Fig 5A ) . This latter result could be explained if Srs2 and Mph1 either suppress the formation of a critical intermediate in the formation of the GCRs selected in the uGCR assay or target the initiating DNA damage to sister chromatid HR to an extent that suppresses GCRs selected in the uGCR assay , but not those selected in the dGCR assay . The Sgs1 helicase has a major role in specifically suppressing dGCRs [16 , 22] , and this has been attributed to its role in preventing crossovers during the resolution of HR intermediates [53] . Interestingly , combining an sgs1Δ with the mms21-CH mutation resulted in synergistic increases in both dGCR and uGCR rates relative to the respective single mutants ( Fig 5A ) , indicating Mms21 and Sgs1 function in distinct pathways to prevent the formation of GCRs . To explore this further , we analyzed the effects of mutating RAD52 , MRE11 and POL32 in the sgs1Δ mutant . A deletion of RAD52 and the mre11-H125N mutation caused similar effects in sgs1Δ and mms21-CH mutants ( comparing Figs 1 and 5 ) . In contrast , deletion of POL32 caused an increase in the dGCR rate of the sgs1Δ mutant whereas deletion of POL32 in the mms21-CH mutant reduced the dGCR rate more than 10-fold ( Fig 5B ) . Thus , the formation of duplication-mediated GCRs in the mms21-CH and sgs1Δ mutants had distinctly different requirements for Pol32 . Because deletion of RRM3 is lethal in an sgs1Δ mutant [54] , we could not compare the role of Rrm3 in the mms21-CH and sgs1Δ mutants . To determine whether the helicase activity or the Top3-binding activity of Sgs1 is involved in the suppressing of dGCRs , we analyzed sgs1-K706A ( helicase-dead ) and sgs1-E12G , H13S ( Top3-binding defective ) mutants [55] . We found that both sgs1 mutations caused approximately the same increase in GCR rates compared to that caused by the sgs1Δ mutation , indicating that the function of Sgs1 in suppressing GCRs requires both its helicase activity and interaction with Top3-Rmi1 ( Fig 5C ) . Recent studies showed that Mms21 specifically catalyzes sumoylation of Sgs1 in response to treatment with DNA alkylating agents [10 , 20 , 21] . We found that the sgs1-3KR mutation that eliminates the sumoylation sites on Sgs1 did not cause a comparable increase in GCR rates to that seen in the sgs1Δ mutant ( Fig 5C ) . Although we cannot exclude the possibility that a low and undetectable level of Sgs1 sumoylation occurs in the sgs1-3KR mutant , this result indicates that the major DNA damage-induced sumoylation of Sgs1 does not have an appreciable role in preventing spontaneous GCRs . Because the DNA lesions accumulated in the mms21-CH mutant are repaired by a variety of DNA repair pathways , especially the HR and more specifically BIR pathway , we reason that inactivating these DNA repair pathways in the mms21-CH mutant could cause a synergistic growth defect . Consistent with this view , we found that mutations of rad52Δ , sgs1Δ and pol32Δ caused a significant slower growth when they are combined with the mms21-CH mutation ( Fig 5D ) . Thus , a failure in properly repairing the spontaneous DNA lesions accumulated in the mms21-CH mutant is detrimental to cell growth . The above findings are consistent with the idea that the mms21-CH mutation causes accumulation of spontaneous DNA lesions that underlie the formation of a diverse range of GCRs . Cells have evolved a signal transduction pathway , the DNA damage checkpoint , to detect endogenous DNA lesions [56] . We therefore examined the Rad53 kinase , which becomes hyperphosphorylated in the presence of such DNA damage and migrates with a slower electrophoretic mobility than non-phosphorylated Rad53 . Treatment by the DNA alkylating agent methyl methane sulfonate ( MMS ) caused a pronounced electrophoretic mobility shift of fully activated Rad53 to slower migrating species ( Fig 6A ) . An increased but sub-stoichiometric amount of Rad53 was found to show slower gel mobility in an mms21-CH mutant that was not treated with MMS compared to untreated wild-type , indicating that Rad53 is partially activated in mms21-CH mutants . Deletion of RAD9 , which encodes an adaptor protein that acts to promote DNA damage-induced activation of Rad53 , reduced the amount of the slower migrating species of Rad53 to an undetectable level in the mms21-CH mutant that was not treated with MMS; note that MMS-induced Rad53 phosphorylation still occurs in a rad9Δ mutant due to the redundant role of Mrc1 in mediating Rad53 activation [57–59] . Ddc2 , together with the Mec1 kinase , is recruited to RPA-coated single stranded DNA at the sites of DNA damage where it can be visualized as sub-nuclear foci [60] . A higher incidence of Ddc2 foci was seen in the untreated mms21-CH mutant compared to untreated wild-type cells ( Fig 6B ) . We also examined the localization of Rad52 , which forms foci at the sites of DNA lesions that undergo DNA repair by HR [60] . Similar to Ddc2 foci , an elevated incidence of Rad52 foci was detected in the mms21-CH mutant compared to wild-type cells ( Fig 6B ) . Together , these results suggest that elevated levels of endogenous DNA lesions occur in the mms21-CH mutant that activate the DNA damage checkpoint and are and processed by Rad52-mediated HR . We next asked whether the DNA damage checkpoint influences the formation of GCRs in the mms21-CH mutant ( Fig 6C and S6 Table ) . The DNA damage checkpoint involves two partially redundant protein kinases , Mec1 and Tel1 . While Mec1 has a major role in controlling the DNA damage response , Tel1 has an important role in telomere length maintenance in wild-type cells and in checkpoint responses in mec1 mutants [61 , 62] . We found that deletion of MEC1 caused a 5-fold reduction in the dGCR rate of the mms21-CH mutant ( Fig 6C , upper panel ) and a substantial increase in the uGCR rate of the mms21-CH mutant ( Fig 6C , lower panel ) . Unlike the mec1Δ mutation , deletion of TEL1 caused little of no change in the dGCR and uGCR rates of the mms21-CH mutant . Like the deletion of MEC1 , deletions of the MEC3 , RAD24 and RAD9 genes involved in the DNA damage checkpoint , caused varying degrees of reduction of the dGCR rate of the mms21-CH mutant with the rad9Δ mutation causing the greatest reduction ( ~ 24-fold ) . In contrast , deletions of the MEC3 , RAD24 and RAD9 genes in the mms21-CH mutant resulted in increases in the uGCR rate , consistent with the known role of DNA damage checkpoint in suppressing GCRs mediated by single-copy sequences [63] . Unlike deletion of RAD9 , deletion of MRC1 caused a modest ( 2-fold ) increase in the dGCR rate of the mms21-CH mutant ( Fig 6C , upper panel ) . Interestingly , deletion of MRC1 caused a drastic increase in the uGCR rate of the mms21-CH mutant ( Fig 6C , lower panel ) . Because Mrc1 also has a role in DNA replication [64] , we next examined the mrc1-AQ mutant , all of whose Mec1 consensus phosphorylation sites are mutated to non-phosphorylatable alanines and is thus unable to mediate Rad53 activation [65] . We found that the mrc1-aq mutation did not appreciably alter the dGCR rate of the mms21-CH mutant although it did cause an increase in the uGCR rate of the mms21-CH mutant , but not to the extent seen with the mrc1Δ mutation . Rad53 , Chk1 , and Dun1 are the downstream effector kinases of the checkpoint pathways . Deletion of RAD53 reduced the dGCR rate of the mms21-CH mutant by about 9-fold ( Fig 6C , upper panel ) , while deletion of CHK1 did not appreciably alter the dGCR rate of the mms21-CH mutant and caused a small increase in the uGCR rate of the mms21-CH mutant . Although deletion of DUN1 did not appreciably alter the dGCR rate of the mms21-CH mutant , it caused a synergistic increase in the uGCR rate of the mms21-CH mutant ( Fig 6C , lower panel ) . These data support the idea that the Mec3/Rad24-Rad9-Mec1-Rad53 pathway plays a role in promoting the GCRs selected in the dGCR assay in the mms21-CH mutant while suppressing the GCRs selected in the uGCR assay in the mms21-CH mutant . Because the mms21-CH mutation appears to cause increased levels of some type of DNA damage , we tested the affect of the mms21-CH mutation on HR between ade2 heteroalleles in a mitotically growing diploid strain [66] . The rate of accumulating Ade+ recombinants in the wild-type strain was 5 . 0 x10-5 [95% confidence interval = 1 . 8 x10-5–8 . 5 x10-5] and the rate in the mms21-CH mutant was 3 . 2 x10-5 [95% confidence interval = 2 . 0 x10-5–1 . 0 x10-4] indicating that there was no change in the rate of HR between ade2 heteroalleles in the mms21-CH mutant . This is unlike the effect of the mms21-CH mutation on the formation of GCRs . General DNA damage such as γ-rays is known to induce HR between heteroalleles [67] . A possible explanation for the difference between the effect of mms21-CH on heteroallelic HR and the formation of GCRs is that the DNA damage that occurs in the mms21-CH mutant is only subjected to one-ended HR such as BIR . This damage , most likely a DSB , would rarely result in HR between heteroalleles as the damage would need to occur between the heteroalleles to yield recombinants , and this is a much smaller target than the region in which a DSB would yield a GCR [22] .
Mutations affecting the Mms21 SUMO E3 ligase cause substantially increased accumulation of DNA damage intermediates and increased accumulation of GCRs [4 , 5 , 10] . Here we show that the Mms21 E3 ligase plays an important role in suppressing the formation of GCRs selected in the dGCR assay , which are typically translocations mediated by non-allelic HR ( Fig 7 ) . The duplication-mediated GCRs formed in Mms21 E3 ligase-null mutants appear to be formed by POL32-dependent BIR-related event in contrast to those formed in wild-type strains , which have little dependence on Pol32 [22] . The increased rate of accumulating GCRs selected in the uGCR assay caused by mms21-CH mutations , which does not reflect the formation of duplication-mediated GCRs , is not accompanied by a change in the spectrum of GCRs relative to the spectrum of GCRs selected in the uGCR assay in wild-type strains . In addition , the mms21-CH mutation causes increased accumulation of spontaneous Ddc2 and Rad52 foci . These observations suggest that the mms21-CH mutation causes increased levels of DNA damage that trigger the DNA damage checkpoint and are processed into GCRs . We cannot rule out the possibility that Mms21 plays roles in some DNA repair pathways; however , the accumulated evidence presented here suggests that Mms21 suppresses genome instability primarily by preventing the formation of initiating DNA damage that potentially occurs during DNA replication . The GCRs selected in the dGCR assay are primarily translocations formed by non-allelic HR between the DSF1-HXT13 region on chromosome V and divergent homologous sequences elsewhere in the genome [22] . The genetic requirements for the formation of the duplication-mediated GCRs that occur at increased rates in mms21-CH mutants are consistent with the idea that these GCRs are formed by non-allelic HR mediated by Pol32-dependent BIR; the increased dGCR rates were greatly reduced when the genes required for HR were deleted , were partially reduced when accessory HR genes were deleted and were greatly reduced when the POL32 gene required for BIR initiated by HO endonuclease-induced DSBs was deleted [44] . The fact that the pol32Δ mutation does not decrease the dGCR rate in wild-type cells [22] may suggest that the DNA lesions that initiate the formation of duplication-mediated GCRs in mms21-CH mutants are more similar to HO endonuclease-induced DSBs or are readily converted to such DSBs than the DNA lesions that underlie duplication-mediated GCRs in wild-type cells . This possibility is also consistent with the constitutive activation of the DNA damage checkpoint in mms21-CH mutants as indicated by increased Rad53 hyperphosphorylation and increased levels of Ddc2 and Rad52 foci in mms21-CH mutants ( Fig 6 ) . The Pif1 DNA helicase has also been shown to be required for BIR initiated from HO endonuclease-induced DSBs [42 , 43]; however , a pif1Δ mutation did not decrease the dGCR rate of mms21-CH mutants , although this predicted effect of the pif1Δ mutation on BIR could be masked by the large increase in the rate of de novo telomere addition GCRs that occurs in pif1Δ strains [34 , 37] . Moreover , the increase in the mms21-CH rates in the uGCR and dGCR assays caused by a pif1Δ mutation is consistent with the possibility that MMS21 suppresses the formation of DNA damage as loss of the Pif1 DNA helicase causes increased GCR rates when combined with many different mutations that lead to increased levels of DNA damage . Together , these data argue that the increased dGCR rate seen in mms21-CH mutants is the result of increased non-allelic HR that is most likely mediated by BIR due to increased levels of DNA lesions that are substrates for BIR . We have not ruled out the possibility that the mms21-CH mutation also alters the activity of some of the HR proteins . We have found that duplication-mediated GCRs that occur at increased rates in mms21-CH mutants depend on both the Rrm3 DNA helicase and the DNA damage checkpoint . Unlike Pif1 , Rrm3 is not known to be required for HO-induced DSB-mediated BIR , and purified Rrm3 is not able to replace Pif1 in the extension of D-loops by DNA polymerase delta in vitro [43] . However , Rrm3 , a homolog of Pif1 [68] , might also play a role in promoting the formation of mms21-CH-induced GCRs mediated by BIR similar to how Pif1 acts [42 , 43] . Recently , it was reported that Rrm3 has an important role in repairing DNA DSBs originating from damaged DNA replication forks [48] . This finding and the requirement of Rrm3 in mediating the formation of duplication-mediated GCRs in mms21-CH mutant reported in this study raises the possibility that the spontaneous DNA legions that accumulate in mms21-CH mutants likely result from defective DNA replication forks that are processed into DNA DSBs . This possibility is also consistent with the synergistic increase in GCR rates caused by combining mms21-CH with mrc1 mutations that cause DNA replication defects . The resulting DNA DSBs are substrates for Pol32-dependent BIR that also involves Rrm3; in contrast , Pif1 may play a more important role than Rrm3 in repairing HO endonuclease-induced DNA DSBs by similar BIR [42 , 43] . We have also identified the DNA damage checkpoint as promoting the formation of duplication-mediated GCRs in mms21-CH mutants . The DNA damage checkpoint has well-documented roles in promoting the homology search during HR [69] which could promote the production of the GCRs selected in the dGCR assay . The DNA damage checkpoint could also act to delay the cell cycle to allow the dGCR events being recovered in our assay . Remarkably , deletion of RAD9 caused a specific reduction of the dGCR rate , which could suggest that GCR-initiating DNA lesions associated with active or stalled replication forks are converted to DSBs that are recognized by the Rad9 branch of the DNA damage checkpoint to facilitate homology search and/or allow time for HR repair . Loss of either Mms21/NSMCE2 or Sgs1/BLM causes increased levels of aberrant HR intermediates , SCE and GCRs [4 , 5 , 10 , 11 , 14] , and sumoylation of Sgs1 by Mms21 prevents the formation of aberrant HR intermediates in response to alkylation damage [20 , 21] . Despite these similarities , the increase in the dGCR and uGCR rates caused by combining the mms21-CH and sgs1Δ mutations argues strongly that these proteins act in different pathways that suppress the formation of GCRs . Consistent with this conclusion , deletion of POL32 had differing effects on dGCR rates in mms21-CH strains compared to deleting SGS1 . Sgs1 is important in resolving HR intermediates [17]; hence , the GCR-based genetic interactions between sgs1Δ and mms21-CH mutations seen here suggest that Mms21 prevents the formation of damage that underlies aberrant HR and that Sgs1 acts to edit these aberrant HR intermediates to prevent non-allelic HR . In this regard , our analysis indicated that it is the DNA helicase activity of Sgs1 that functions in conjunction with Rmi1 and Top3 to suppress GCRs rather than some function of Sgs1 mediated by sumoylation . Thus , despite the known role of Mms21-dependent sumoylation of Sgs1 in repairing exogenously induced DNA damage [20 , 21] , the function of Mms21 in preventing spontaneous genome rearrangements is distinct from that of Sgs1 . The increased accumulation of DSBs or damage that can be converted to DSBs in mms21-CH mutant strains is also consistent with the structures of the GCRs selected in the uGCR assay as determined by whole genome sequencing . The mms21-CH single mutants have increased rates of accumulating de novo telomere addition GCRs and microhomology-mediated translocation GCRs , which reflect different mechanisms of healing broken chromosomes . Interestingly , mutations affecting MRE11 caused an increase in hairpin-mediated inverted duplication GCRs as well as decrease in de novo telomere addition GCRs alone and when combined with an mms21-CH mutation , which is consistent with previous observations [35 , 41] . These results are consistent with increased formation of DSBs in an mms21-CH mutant combined with the inability of mre11 mutants to cleave DNA hairpins generated from these DSBs [33] . The fact that the relative increase in hairpin-mediated GCRs occurs with a relative decrease in de novo telomere addition GCRs in mre11 mutants suggests that hairpin formation is likely to be faster at broken chromosomes than the addition of a new telomere . Due to the relatively small numbers of events classified , there is insufficient evidence to suggest that mre11Δ and mre11-H125N differ significantly in the microhomology-mediated translocations or hairpin-mediated inverted duplications; instead , these events are likely caused by the same defect in nucleolytic processing of DNA lesions in these mre11 mutants . Together , the findings presented here argue that mutations inactivating the Mms21 E3 ligase lead to an accumulation of DNA lesions that are either DNA DSBs or are easily convertible to DNA DSBs , and that these DSBs lead to diverse genome rearrangements , depending on the available DNA repair pathways ( Fig 7 ) . These spontaneous DNA breaks likely result from damaged or defective DNA replication forks and would occur in chromosomal regions that have yet to be fully replicated . In this case , the resulting DSBs would be one-ended which would force these DSBs to be repaired by various DNA repair pathways including BIR with either the sister chromatid or homologous sites elsewhere in the genome . The differences between the mms21-CH and wild-type strains , for example in the POL32-dependence of duplication-mediated GCRs , suggest that much of the damage giving rise to GCRs in wild-type cells may be more complicated than simple DSBs . Considering Mms21 has been shown to sumoylate proteins with essential roles in DNA replication , including the MCM2-7 replicative helicase [4 , 6] , it is tempting to speculate that defects in Mms21-mediated sumoylation of the DNA replisome could cause defective DNA replication , leading to accumulation of DNA DSBs that drive the formation of GCRs . If so , the study of the role of Mms21-dependent sumoylation in regulating DNA replication could provide important insights into how protein sumoylation may prevent genome rearrangements .
Standard S . cerevisiae genetics method was used to introduce mutations . S . cerevisiae strains used in this study are listed in S7 Table . Generation of the sgs1-3KR mutation involved two steps by first deleting the N-terminal region of Sgs1 from -425bp to 2600bp and then repairing it using PCR products containing the sgs1-3KR ( K175R , K621R and K831R ) mutations with a HIS3 marker located at 435bp upstream of the starting codon of Sgs1 to preserve its native promoter . DNA sequencing was used to confirm the integration of sgs1 mutations . Methods used for fluctuation analysis to determine GCR rates have been described previously [70] . Briefly , 14–16 rate measurements were performed using 7–8 independent cultures derived from 2 independently isolated strains and the GCR rate was determined by the method of the median . Fluctuation analysis was also used to determine the rate of HR between ade2 heteroalleles . Diploids were generated from wild-type and mms21-CH derivatives of previously published haploid strains containing different ade2 alleles [66] , one due to an insertion of the I-SceI cut site ( ade2-I ) and another due to a 2bp deletion at the NdeI site ( ade2-n ) . The I-SceI endonuclease was not expressed , allowing spontaneous HR to be measured . Cells were grown from single colonies overnight in YPD until cultures reach an OD600 of 0 . 5 and plated onto CSM complete or CSM minus adenine medium and grown for 3–4 days before counting the resulting colonies . Cells were grown in CSM medium to log phase and examined by live imaging using Olympus BX43 fluorescence microscope with a 60x , 1 . 42 PlanApo N Olympus Oil immersion objective . GFP and mCherry fluorescence were detected using a Chroma FITC filter set and a TxRed filter set respectively and captured with a Qimaging QIClick CCD camera . Images were captured using Meta Morph Advanced 7 . 7 imaging software . Figures were prepared in Adobe Photoshop , keeping processing parameters constant within each experiment . DNA plugs for PFGE were prepared as described [71] . Electrophoresis was performed using a Bio-Rad CHEF-DRII apparatus at 6 V/cm , with a 60 to 120 s switch time for 25 h . The gels were stained with ethidium bromide and imaged . The DNA in the gel was transferred to Hybond-XL membranes by neutral capillary blotting . The DNA was crosslinked to the membrane by UV irradiation in a Stratalinker ( Stratagene ) apparatus at maximum output for 60 seconds . The MCM3 probe was generated by amplifying MCM3 from genomic DNA using the primers 5’-CTGTGCAAGAAATGCCCGAAATG-3’ and 5’-GCCCCGGAGTTGGAATGCTC-3’ followed by random primer labeling of the PCR product with the Biotin DecaLabel DNA Labeling Kit ( Thermo Scientific ) . Probe hybridization was performed at 50°C for 1 hr . Biotin signal was detected using Chemiluminescent Nucleic Acid Detection Module Kit ( Thermo Scientific ) . Multiplexed paired-end libraries were constructed from 2 μg of genomic DNA purified using the Purgene kit ( Qiagen ) . The genomic DNA was sheared using M220 focused-ultrasonicator ( Covaris ) and end-repaired using the End-it DNA End-repair kit ( Epicentre Technologies ) . Common adaptors from the Multiplexing Sample Preparation Oligo Kit ( Illumina ) were then ligated to the genomic DNA fragments , and the fragments were then subjected to 18 cycles of amplification using the Library Amplification Readymix ( KAPA Biosystems ) . The amplified products were fractionated on an agarose gel to select 600 bp fragments , which were subsequently sequenced on an Illumina HiSeq 4000 using the Illumina GAII sequencing procedure for paired-end short read sequencing . Reads from each read pair were mapped separately by bowtie version 2 . 2 . 1 [72] to a reference sequence that contained revision 64 of the S . cerevisiae S288c genome ( http://www . yeastgenome . org ) , hisG from Samonella enterica , and the hphMX4 marker . Sequence data is available from National Center for Biotechnology Information Sequence Read Archive under accession number: SRP106876 . Chromosomal rearrangements were identified after bowtie mapping by version 0 . 6 of the Pyrus suite ( http://www . sourceforge . net/p/pyrus-seq ) [36] . Briefly , after removal of PCR duplicates , read pairs in which both reads uniquely mapped were used to generate the read depth and span depth copy number distributions . The read depth copy number distribution is the number of times each base pair was read in a sample; read depth distributions were the distributions plotted to examine copy number ( S4–S9 Figs ) as this distribution is less distorted than the span depth distribution in regions adjacent to repetitive elements . The span depth copy number distribution is the number of times each base pair in a sample was contained in a read or spanned by a pair of reads; span depth distributions were used to statistically distinguish real rearrangements identified by junction-defining discordant read pairs from discordant read pairs that were noise in the data . Read pair data were then analyzed for junction-defining discordant read pairs that indicated the presence of structural rearrangements relative to the reference genome . Identified rearrangements included junctions produced during strain construction , such as the his3Δ200 deletion ( see S2 and S3 Figs ) , or GCR-related rearrangements ( see S4–S9 Figs ) . Associated junction-sequencing reads , which were reads that did not map to the reference but were in read pairs in which one end was adjacent to discordant reads defining a junction , were used to sequence novel junctions . Most hairpin-generated junctions ( S11 Fig ) could be determined using alignments of junction-sequencing reads . For junctions formed by HR between short repetitive elements ( S12–14 Figs ) and for problematic hairpin-generated junctions ( S11 Fig ) , the junction sequence could be derived by alignment of all reads in read pairs where one read was present in an “anchor” region adjacent to the junction of interest and the other read fell within the junction to be sequenced . Similar strategies involving the alignment of reads paired with reads present in “anchor” regions also were used to sequence de novo telomere addition junctions ( S4–S9 Figs ) and to identify the “YLRWTy1-4” Ty element that was not present in the reference genome ( S15 Fig ) . Protein extracts for Western blot analysis was prepared using a TCA ( trichloroacetic acid ) extraction . To examine Rad53 electrophoretic mobility we used an anti-Rad53 monoclonal antibody ( EL7E1 serum ) from mouse , a gift from Dr . Marco Foiani . | Chromosomal rearrangement is a hallmark of cancer . Saccharomyces cerevisiae Mms21 is an E3 ligase for Small Ubiquitin like MOdifer ( SUMO ) , which has been shown to have a major role in preventing chromosomal rearrangement . Despite extensive studies about the function of Mms21 in regulating the repair of exogenously induced DNA damage , how Mms21 , and its human ortholog NSMCE2 , prevents spontaneous chromosomal rearrangement in unperturbed cells has been unknown . In this study , we provided genetic evidences supporting a novel role of Mms21 in preventing the accumulation of spontaneous DNA breaks , which are likely caused by defective DNA replication , without appreciably affecting how they are repaired . Our findings highlight the central role of faithful DNA replication in preventing spontaneous chromosomal rearrangement , and further suggest that the study of the role of Mms21 dependent sumoylation in DNA replication could yield important insights into how the SUMO pathway prevents chromosomal rearrangement in human disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"chromosome",
"structure",
"and",
"function",
"dna",
"damage",
"telomeres",
"mutation",
"sumoylation",
"dna",
"replication",
"dna",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"sequence",
"alignment",
"bioinformatics",
"proteins",
"chromosome",
"bi... | 2018 | SUMO E3 ligase Mms21 prevents spontaneous DNA damage induced genome rearrangements |
Growing cells are subject to cycles of nutrient depletion and repletion . A shortage of nutrients activates a starvation program that promotes growth in limiting conditions . To examine whether nutrient-deprived cells prepare also for their subsequent recovery , we followed the transcription program activated in budding yeast transferred to low-phosphate media and defined its contribution to cell growth during phosphate limitation and upon recovery . An initial transcription wave was induced by moderate phosphate depletion that did not affect cell growth . A second transcription wave followed when phosphate became growth limiting . The starvation program contributed to growth only in the second , growth-limiting phase . Notably , the early response , activated at moderate depletion , promoted recovery from starvation by increasing phosphate influx upon transfer to rich medium . Our results suggest that cells subject to nutrient depletion prepare not only for growth in the limiting conditions but also for their predicted recovery once nutrients are replenished .
Growing cells require an influx of nutrients from their environment to sustain metabolic activity . Depletion of essential nutrients arrests cell growth until the nutrients are replenished [1] . Prior to this arrest , a starvation program that regulates the expression and activity of multiple genes and proteins is activated . This starvation program increases the level of internal nutrient , e . g . , by expressing high-affinity transport , mobilizing stored nutrients , or scavenging the limiting nutrient from internal or external sources [2] . This starvation program does not eliminate growth arrest but only delays its onset: a growing population will still deplete the available nutrient and arrest , but this arrest will be postponed by a limited number of generations [3 , 4] . Once the depleted nutrient is replenished , cells reenter the cell cycle and begin to proliferate . Return to growth is not immediate but follows a lag time during which cells adjust to the rich medium . Since growth is exponential , minimizing the lag time would provide cells with a significant competitive advantage . Indeed , subjecting cells to continuous cycles of nutrient depletion and replenishment selects for cells with accelerated recovery [5] . We hypothesized that the starvation program activated when nutrients are depleted prepares cells not only for growth in the limiting environment but also for their subsequent recovery once nutrients are replenished . To examine this hypothesis , we focused on the phosphate starvation program in budding yeast , whose molecular basis has been characterized in detail . Cells transferred to a medium containing low phosphate induce dozens of genes . Those genes regulate intracellular phosphate levels by enabling high-affinity phosphate transport [6–8] , phosphate mobilization in and out of vacuolar storage [9 , 10] , and phosphate scavenging from different intra- and extracellular sources ( Fig 1A ) [11–14] . Induction of all phosphate-responsive genes depends on the transcription factor Pho4 . When intracellular phosphate levels are reduced , Pho4 is dephosphorylated and enters the nucleus to activate its target genes [15–19] . We previously suggested that the phosphate starvation program promotes not only growth in phosphate-limiting conditions but also recovery from starvation , based on our analysis of cells that constitutively express PHO84 , the high-affinity phosphate transporter [22] . When transferred to media containing low phosphate levels , the PHO84-constitutive cells induced the starvation program with a delay relative to wild-type cells , and when transferred back to rich media , these cells showed a longer lag time and were outcompeted by wild-type cells . Notably , the impaired recovery was rescued by partially activating Pho4 also in nonstarving cells , suggesting that optimal recovery depends on the proper , early induction of the starvation program , as seen in wild-type cells . Still , the contribution of the phosphate starvation program to cell growth has not been measured directly . In this study , we set to quantify the contribution of the phosphate starvation program to cell growth in limited phosphate and upon recovery from starvation . We reasoned that this contribution may depend on the kinetics by which phosphate is depleted . In particular , gradual depletion of phosphate may provide cells with enough time to prepare for the upcoming limitation , activating the starvation program before arresting cell growth . We therefore considered different kinetics of phosphate depletion and followed , at high temporal resolution , the gene expression and growth rate of wild-type cells and of mutant cells that do not properly activate the starvation program . We find that cells activate the starvation program sequentially: the first activation wave is seen when phosphate is only partially depleted and before cell growth is reduced . This phase may serve as a preparation period , as deleting PHO4 does not affect cell growth at this phase . This initial activation is followed by a second wave of expression , observed at the time when cell growth begins to slow down . At this stage , deletion of PHO4 further reduces growth rate . Notably , we show that the early transcription wave contributes to the recovery from starvation by increasing phosphate influx once nutrients are replenished . We propose that cellular signaling is tuned not only for optimizing instantaneous cell growth but also for preparing cells for predictable future needs .
The phosphate starvation program in budding yeast is activated when intracellular phosphate levels are reduced . The kinetics by which the starvation program is activated therefore depends on the rate by which internal phosphate is depleted . To monitor different kinetics of phosphate depletion , we inoculated cells in media containing different low-phosphate levels , ranging from 0 mM to 0 . 5 mM of inorganic phosphate ( Pi ) . Cells incubated in these media grew for a different number of generations , ranging from approximately 8 generations when incubated to 0 . 5 mM Pi to only approximately 3 . 5 generations when incubated in medium lacking phosphate ( Fig 1B ) . We measured the growth rate of the cells following their transfer to the low-phosphate media and profiled their transcription response at multiple time points following the transfer , as indicated ( Fig 1B and 1C ) . To examine the starvation program , we first focused on the induction of Pho4-target genes ( Fig 1C and S1A and S1B Fig ) . Pho4-targets were defined by comparing gene expression in cells lacking PHO4 to that in cells that express a constitutive strong PHO4 allele ( Materials and methods ) and were consistent with previous reports ( S1A Fig ) [9 , 19] . As expected , the starvation program was induced with different temporal kinetics , depending on the phosphate level in the inoculation medium . Cells transferred to a medium containing 0 . 5 mM Pi , for example , did not activate Pho4-target genes during the approximately 9 initial hours , showing a partial induction only at the 24 . 7-hour time point . By contrast , cells transferred to a medium lacking any phosphate induced Pho4-target gene expression practically immediately upon the transfer ( Fig 1C ) . Previous studies reported that cells transferred to medium lacking phosphate activated the starvation program in 2 sequential waves [19 , 21 , 23] . To examine whether this sequential induction is seen also in our data , we measured the similarity in the expression response at different time points following the transfer , as defined by the Pearson correlation ( Fig 1D and S1C Fig ) . Indeed , 2 waves of transcription response were observed . The temporal delay between the 2 waves varied depending on the incubating conditions , ranging from approximately 2 hours in cells transferred to medium lacking phosphate to approximately 9 . 5 hours in cells transferred to medium containing 0 . 2 mM Pi ( Fig 1C and 1D ) . This differential kinetics reflected the delayed induction of the second transcription wave . By contrast , the first transcription wave was induced practically immediately upon transfer to all media containing low Pi levels < 0 . 5 mM . The 2 waves differed primarily by the strength of gene induction . At the first wave , most ( but not all ) of the Pho4-target genes were moderately induced . At the second wave , the induction of these genes became stronger , and some additional Pho4-targets , not induced in the first wave , were now up-regulated ( Fig 1C ) . Budding yeast activate a characteristic large-scale transcription program termed the environmental stress response ( ESR ) when subjected to a variety of environmental stresses [24] . To examine whether this response is activated also in our data , we considered the group of stress response genes as defined in [20] ( see Materials and methods ) . We noted that this response was not activated immediately upon transfer to low-phosphate media but was observed concomitant with the second wave of Pho4-target gene induction . Also at this time , cells down-regulated the expression of genes coding for ribosomal proteins and ribosome-associated functions ( Fig 1C ) . The synchronization of the stress response with the second wave of Pho4-target induction suggested to us that it is at this point that phosphate becomes growth limiting . To examine this , we monitored cell growth following the transfer . Cells maintained rapid growth rate during the initial stages of incubation , independent of the level of phosphate in the inoculation medium , but began to reduce their growth only at a later stage ( Fig 1B ) . To comonitor growth rate and the induction of Pho4-target genes , we used cells that express a well-established reporter: PHO84 promoter-driven Venus ( PHO84p-Venus ) [25] . PHO84 is a Pho4-target gene that encodes for the high-affinity phosphate transporter . Upon transfer to low-phosphate media , PHO84 expression is induced as part of the first transcription wave and is further up-regulated when the second transcription wave is activated ( cf . Fig 1A ) . Induction of the PHO84p-Venus reporter was seen immediately upon transfer to low-phosphate media ( <0 . 5 mM Pi ) , and its levels continued to increase in time as phosphate was further depleted , with different kinetics depending on the incubation conditions ( Fig 1E ) . Notably , comparing cell growth rate with reporter induction , we observed that cells began to reduce their growth rate when the reporter crossed a particular activation threshold , independent of the initial low-phosphate levels into which the cells were incubated or of the time of incubation ( Fig 1F ) . Further , this activation threshold coincided with the induction of the second wave of Pho4-target gene induction ( S1E Fig ) . We conclude that cells induce the initial transcription wave when phosphate levels are reduced but are not yet limiting for growth . In contrast , the second transcription wave is induced when intracellular phosphate becomes growth limiting . As mentioned above , the group of Pho4-targets induced upon transfer to low-phosphate media includes multiple genes that regulate the availability of internal phosphate , including high-affinity transporters ( PHO84 and PHO89 ) , genes that mobilize phosphate vacuolar storage as PolyP ( PHM1-5 ) , and phosphatases that can scavenge phosphate from other molecules inside or outside the cells ( e . g . , PHO5 , PHM8 , and PHO11 ) . Induction of these genes could therefore promote growth in low-phosphate media . To quantify the contribution of this transcription program to cell growth , we considered 2 mutants that cannot activate the pathway: a strain that lacks the transcription factor Pho4 and a strain that lacks the Pho4 activating protein , Pho81 . We compared the growth rate of the mutant and wild-type cells using a growth-competitive assay . For this assay , we labeled wild-type cells using a fluorescence marker mCherry , driven by the constitutively strong TEF2 promoter ( Fig 2A ) . When mixed with unlabeled cells , the fraction of labeled ( wild-type ) cells in the population was measured using flow cytometry . To monitor the activation of the starvation program , we further engineered the cells to express PHO84p-Venus and monitored its induction using flow cytometry as well ( Fig 2A ) . mCherry and/or Venus reporter expression did not detectably affect growth rate in our experimental conditions ( S2A and S2B Fig ) . Mutant and fluorescently labeled wild-type cells were coincubated in media containing different phosphate levels , and the fraction of wild-type cells in the population was measured periodically following this incubation , as cells grew and further depleted the phosphate ( Fig 2B ) . Initially , the pathway-defective mutant Δpho4 or Δpho81 grew as well as wild-type cells ( Fig 2B and S2D Fig ) . Wild-type cells began to outcompete the mutants only after a delay , which varied depending on the level of phosphate in the incubating media . For example , when incubating the cells in no-Pi or in 0 . 2 mM Pi , the fraction of mutant cells began to decrease only around 2 hours or around 8 . 5 hours after incubation , respectively . The PHO84p-Venus reporter was induced practically immediately upon transfer of wild-type cells to low-phosphate media ( <0 . 5 mM Pi ) and continued to increase with time ( S2C Fig ) , as described above ( Fig 1E ) . Notably , in all conditions , the time at which the frequency of mutant cells in the mixed population began to decrease coincided with the time at which the PHO84p-Venus reporter reached a given activation threshold level , independently of the incubating conditions ( Fig 2C and S2E Fig ) . This time coincided with the second wave of Pho4-target gene induction and the time at which wild-type cells began to reduce their growth rate ( Fig 1F and S1E Fig ) . Therefore , induction of Pho4-target genes promotes growth in low-phosphate media , but only from the onset of the second wave of gene induction , the time at which phosphate becomes limiting for growth . We next examined mutants that express the PHO regulon constitutively ( expressing the PHO4SA12346 allele [19] or with PHO80 deleted ) . Constitutive strong activation of Pho4-target genes reduced growth in rich medium and when phosphate levels were low but not yet growth limiting . By contrast , this constitutive strong activation promoted growth once phosphate became growth limiting ( Fig 3A–3D and S3A–S3D Fig ) . Interestingly , the phenotypic effects of the PHO4SA12346 alleles were fully dependent on the PHM3 gene , which mobilizes phosphate into vacuolar storage [10] , suggesting that the reduced growth rates in both rich and intermediate phosphate conditions are explained by transferring too much phosphate into storage . This excessive storage promotes growth once phosphate becomes growth limiting ( Fig 3D–3G ) . Taken together , we find that the initial induction of Pho4-target genes occurs before phosphate becomes growth limiting and that Pho4-target induction does not contribute to cell growth during this phase . The phosphate starvation program starts contributing to growth only when phosphate becomes growth limiting , a time that is marked by the second wave of Pho4-target gene induction and up-regulation of the stress response . We asked whether , in addition to its role during growth in limited phosphate , the starvation program promotes recovery from starvation once phosphate is replenished . We reasoned that this contribution may depend primarily on the first wave of Pho4-target gene induction , since this wave is activated before phosphate becomes growth limiting and before it is needed for maintaining maximal growth rate . This early induction could prepare cells for upcoming starvation and further contribute to their recovery . We therefore denote the time between the induction of the first and second transcription waves as the “preparation phase . ” Cells that are incubated in media containing intermediate phosphate show a preparation phase that is prolonged relative to cells incubated in medium containing no phosphate ( Fig 1C ) . If preparation promotes recovery , cells with prolonged preparation will recover faster than cells incubated directly in a no-phosphate medium . To examine this , we incubated cells into media containing different levels of phosphate for 21 hours , transferred them back to rich medium , and measured the lag time before growth was recovered . As predicted , the lag time decreased monotonically with increasing levels of phosphate in the incubating media ( Fig 4A ) . In particular , cells incubated directly into medium lacking phosphate showed the longest lag time before resuming growth . To directly examine whether the starvation program promotes recovery , we examined the Δpho4 and Δpho81 mutants that do not activate the pathway . This was done using a competition assay: fluorescently labeled wild-type cells containing the constitutive TEF2p-mCherry marker were mixed with mutant cells , incubated in low-phosphate media for 24 . 7 hours , and then transferred back to rich medium . The fraction of wild-type cells was measured at subsequent times following the transfer to rich medium using a flow cytometer . The gene expression profile of wild-type cells was monitored in parallel to define the kinetics by which cells down-regulate the expression of Pho4-target genes . Replenishing phosphate rapidly repressed Pho4-target genes ( Fig 4B ) . Still , wild-type cells resumed growth faster than pathway-deficient mutants ( Fig 4C and S4A Fig ) . This was observed in cells that showed a prolonged preparation time ( brought to starvation following incubation in media containing an intermediate level of phosphate ) . In fact , in cells incubated in 0 . 2 mM Pi , the starvation program contributed to recovery more than to growth in the low-Pi medium itself ( Fig 2B versus Fig 4C and S2D Fig versus S4A Fig ) . By contrast , the starvation program did not detectably contribute to the recovery of cells that were incubated directly in very low or no-phosphate medium , conditions in which preparation time was limited ( Figs 4C and 1C and S4A Fig ) . Note that when incubated in 0 . 5 mM Pi , the cells arrested before becoming phosphate limited ( Fig 1B and S1D Fig ) , resulting in full dispensability of Pho4 both in the low-phosphate medium and during recovery ( Figs 2B and 4C and S2D and S4A Figs ) . We conclude that the starvation program contributes to cell recovery from phosphate limitation , but only in conditions that introduce a significant delay between the induction of the first and second waves of Pho4-targeted genes . The starvation program could promote recovery from starvation by limiting phosphate toxicity . Indeed , upon recovery , cells are subject to a large , sudden influx of phosphate , which may be toxic . To examine whether phosphate toxicity limits recovery of the pathway defective Δpho4 or Δpho81 cells , we constitutively expressed the high-affinity transporter PHO84 ( PHO84c ) in these backgrounds . We reasoned that this constitutive expression will increase phosphate uptake and toxicity , further delaying recovery from starvation . In contrast to this expectation , constitutive expression of PHO84 did not abrogate but rather compensated for the delayed recovery of Δpho4 and Δpho81 mutants ( Fig 4D and 4E and S4B Fig; see S2F and S2G Fig for growth in low Pi ) . This refutes the hypothesis that pathway activation is needed to prevent toxicity upon phosphate replenishment but suggests that it is required to promote phosphate influx . Indeed , to resume growth , cells must synthesize a large amount of RNA , likely requiring an increased phosphate influx . If phosphate influx is limiting during recovery , cells without the high-affinity phosphate transporter ( Δpho84 cells ) will exit starvation with a delay . To test that , we examined the recovery time of Δpho84 cells ( Fig 4F–4I and S4C and S4D Fig ) . The starvation program in Δpho84 cells was maintained active for at least 10 hours following phosphate replenishment , contrasting its rapid down-regulation in wild-type cells ( Fig 4H–4I and S4D Fig ) and confirming that the high-affinity transporter largely contributes to phosphate influx during recovery . As was expected when phosphate influx limits recovery , the growth of those cells was severely delayed ( Fig 4F–4G and S4C Fig ) . Further , the impaired recovery of Δpho84 cells was rescued by deleting SPL2 ( Fig 4F–4I and S4C and S4D Fig ) . As one of the Pho4-target genes , SPL2 is induced in low-phosphate media and down-regulates the activity of Pho90 ( see S2H–S2J Fig for growth in low Pi ) . Deletion of SPL2 therefore maintains the active status of the low-affinity transporter , leading to a higher phosphate influx , which we suggest explains the cells’ faster recovery . This recovery is still not as fast as wild-type cells , which can utilize both the low- and high-affinity transporters during recovery ( Fig 4F–4I and S4C and S4D Fig ) . Taken together , our results suggest that phosphate influx is limiting during the recovery of cells from phosphate starvation . Pho4-target gene induction may therefore promote recovery by facilitating the increase in internal phosphate upon recovery . Our analysis above mapped the contribution of the starvation program to cell recovery to the first wave of Pho4-target genes , induced before phosphate becomes limiting . Further , we showed that this contribution is pronounced only when the duration of this first wave is sufficiently long . We previously showed that constitutive activation of the high-affinity transporter PHO84 leads to the delayed induction of the first transcription wave and shortens the delay between the 2 waves . Consistent with this first wave being important for recovery , PHO84C cells recover with a delay , and this phenotype is rescued by partial activation of the starvation program using the weak PHO4SA1234 constitutive allele , which may mimic the first induction wave [22] . We recapitulated these results here and confirmed that delayed recovery was apparent when incubating cells in conditions that enable a long preparation phase in wild-type cells ( 0 . 2 mM Pi ) but was less pronounced when preparation was short or absent ( no Pi , Fig 5A and 5B and S5A–S5D Fig ) . Several additional results support the hypothesis that the impaired recovery of the PHO84-constitutive cells is due to an insufficient phosphate influx upon recovery . First , the recovery phenotype was partially rescued by increased phosphate in the recovery media ( 20 mM compared to 7 . 3 mM Pi in synthetic complete ( SC ) medium , Fig 5C ) . It was also rescued by an SPL2 deletion and by constitutive expression of the low-affinity transporter PHO90 ( Fig 5C ) . Further , the PHO84-constitutive cells maintained the starvation program as partially active for a considerable time following phosphate replenishment , indicating low internal phosphate . Notably , Pho4-target genes were in fact repressed rapidly upon phosphate replenishment but were also rapidly reinduced ( Fig 5D ) . As we have shown before , this oscillatory behavior characterizes cells that are transferred from deep to intermediate internal phosphate limitation conditions [21] . This phenotype was again rescued when transferring cells into 20 mM Pi , in Δspl2 deleted cells and in cells that constitutively express PHO90 ( Fig 5D ) . We conclude that the recovery phenotype of PHO84-constitutive cells is due to an insufficient phosphate influx upon recovery . As a complementary approach for examining the deficiency of PHO84-constitutive cells , we subjected these cells to cycles of starvation and recovery , with the intention of selecting compensatory mutations . Sixteen independent lines were followed for approximately 300 generations , corresponding to 10 cycles of phosphate depletion and replenishment ( Fig 6A ) . In 10 cultures , improved recovery was observed , and single-cell colonies isolated from these cultures were sent for whole-genome sequencing . Notably , all strains showing an adaptive phenotype were mutated in the PHO84 gene . Further , 56 of the 60 individually selected cells led to the same substitution ( L74F ) of an amino acid located in the first transmembrane domain ( Fig 6B ) [6] . This mutation , as well as the 2 other amino acid substitutions identified , is found in various PHO84 homologs ( S6A Fig ) . To verify the causality of the identified mutations , we introduced them into a wild-type PHO84 allele and a PHO84-constitutive allele . The impaired recovery of PHO84-constitutive cells was rescued ( Fig 6C–6E and S6E–S6J Fig ) , and the mutation further improved the growth of both the wild-type and the constitutive strains in low-phosphate media , suggesting that it supports higher phosphate influx ( S6B–S6D Fig ) .
Cells respond to the depletion of nutrients by changing the expression and activities of a large number of genes and proteins . Previous studies implicated cellular starvation programs in optimizing cell growth and survival in limiting condition [26–28] . Our results suggest that the starvation programs can also promote the recovery of cells once nutrients are replenished . The phosphate starvation program in budding yeast was investigated extensively , revealing general principles of nutrient homeostasis [12 , 19 , 29–31] . Yet , while the molecular properties of this response are well understood , its contribution to cell growth remained relatively unexplored . Here , we closed this gap by following , at high temporal resolution , the growth of wild-type and mutant cells at different stages of phosphate depletion and recovery . We found that cells activate the starvation response before this response is required for growth . In fact , expression of Pho4-target genes is observed before cells reduce their growth rate or show any signs of stress such as expression of stress-responsive genes or down-regulating genes coding for ribosomal proteins . We propose that this initial response serves as a preparation phase , allowing cells to prepare for the upcoming limitation and for their subsequent recovery . As phosphate levels are further depleted , phosphate becomes limiting for growth , at which stage a second wave of Pho4-target genes is induced , concomitant with the induction of the general stress response . At this point , the starvation program starts contributing to cell growth . It is interesting that induction of the general stress response is delayed and becomes weaker in cells that are deleted of Pho4 and thereby do not activate the starvation program ( S3A–S3C Fig ) , suggesting an inherent connection between this general stress response and the more specific induction of Pho4-target genes . Most notably , we find that the phosphate starvation program also promotes recovery from starvation . As we show , recovery times might be limited by phosphate influx during the recovery , and the starvation program may promote this influx . Further , this role of the starvation program appears to depend on the early wave of Pho4-target genes , induced before phosphate becomes growth limiting . Our results suggest that nutrient homeostasis circuits have evolved not only for promoting growth in the limiting conditions but also for preparing cells for their predictable recovery once nutrients are replenished . Recent studies suggest that this “preparation” or “anticipation” presents a general principle in microbial stress signaling . For example , the ESR is induced by moderate stresses that do not affect growth rate either in the presence or in the absence of the stress response . Thus , rather than contributing to growth in these moderate stresses , the ESR promotes survival in cases of subsequent , more severe stresses [4 , 24 , 32] . Similar anticipatory regulation was observed in other microbial systems , including examples from bacteria and yeast [33–37] . These evolved paradigms strongly suggest that throughout their natural history , yeasts encountered complex and perhaps predictable combinations of stresses so that their signaling circuits evolved to optimize not only the immediate conditions but also probably upcoming needs .
Saccharomyces cerevisiae strains used in this study are listed in S1 Table and were created from the BY4741 background ( MATa his3Δ1 leu2Δ0 ura3Δ0 met15Δ0 ) by standard genetic methods [38] . All of the relevant inserted fragments were validated by PCR followed by sequencing , except for fragments that lead to gene deletion , which were validated only by PCR . Strains fluorescently marked with constitutive cytosolic expression of mCherry were constructed from pFA6a-NAT-TEF2pr-mCherry plasmid . The amplified fragment was inserted into the HO locus by transformation . Strains fluorescently marked with constitutive cytosolic expression of GFP were constructed similarly using the pFA6aNAT-TEF2pr-GFP plasmid . Strains with the starvation response reporter PHO84p-Venus were constructed as previously described [22] . Briefly , kanMX in pBS7 ( Yeast Resource Center ) was replaced with the HIS5 selection gene by BglII and EcoRI digestion from pDH5 . Next , the promotor region of PHO84 ( 1 , 000 bp upstream from the start codon ) was PCR amplified from the genome with primers containing restriction sites of SalI and BamHI and inserted into pBS7 upstream of Venus . The PHO84p-Venus-HIS5 fragment was amplified using the plasmid’s canonical forward and reverse primers of pBS7 ( Yeast Resource Center ) with 40-bp homology upstream and downstream of HIS3 ORF for recombination into the HIS3 locus . To create a deletion of PHO4 ( nos . 5 and 7 , in S1 Table ) , HygromycinB was amplified from the pYM-N14 plasmid [39] , in which the kanMX was replaced with HygromycinB using SacI and BglII . The fragment was amplified with the following primers: AAGAGATGAGCAAAGGAGACAGAACAAGAGTAGCAGAAAGTCCGTACGCTGCAGGTCGAC and CAGTCCGATATGCCCGGAACGTGCTTCCCATTGGTGCACGGGAGCTCGTTAAAGCCTTCG . To generate the deletion of PHO81 in the PHO84p-Venus-HIS5 strain ( no . 8 , in S1 Table ) , kanMX was amplified from pBS7 with the following primers: AATAAATGCTACATAAATGCATGCCCTTAAAACTGTATAACCATGAGATCTGTTTAGCTTGCCTCG and AATATTGATATGTAAAGTTCTGTAATATGTAGTTTTGAAATCTTAATCGATGAATTCGAGCTCG . Strains ( Nos . 6 , 9 , and 10 , in S1 Table ) originated from the yeast-deletion library [40] . To create a deletion of PHO80 in the PHO84p-Venus-HIS5 strain ( no . 15 , in S1 Table ) , the Δpho80::kanMX and flanking regions were amplified from a Δpho80 deletion library strain [40] with the following primers: CTTCGAGAGGCAGATAACC and CCAAATGATGGCCAATTGCT . The amplified fragment was inserted instead of the PHO80 ORF , and cells were selected on YPD +G418 . Strains with partially and fully active PHO4 ( PHO4SA1234 and PHO4SA12346 ) were constructed from previously described plasmids ( EB1264 and EB1265 , respectively ) [19] . The amplified fragments of active PHO4 , with mutations in its phosphorylation sites ( serine to alanine at sites 1 , 2 , 3 , and 4 , and in PHO4SA12346 also proline to alanine at site 6 [19] ) , followed by a fused GFP and a URA3 selection , were inserted instead of the PHO4 ORF . The addition of PHO4SA1234 ( in strains no . 11 and no . 12 , in S1 Table ) was previously described [22] . To create the deletion of PHM3 in PHO4SA12346 cells ( no . 14 , in S1 Table ) , HIS5 was amplified from pDH5 with the following primers: ATTATTACTTAATTATACAGTAAAAAAAACACGCTGTGTATTCGTACGCTGCAGGTCGAC and AAATCGGCCAATAAAAGAGCATAACAAGGCAGGAACAGCTCGTTTAAACTGGATGGCGGC . To create a deletion of PHM3 in PHO84p-Venus-HIS5 strain ( no . 18 , in S1 Table ) , CaURA3 was amplified from pAG61 with the following primers: ATTATTACTTAATTATACAGTAAAAAAAACACGCTGTGTATTGTACAGCTTGCCTCGTCC and AAATCGGCCAATAAAAGAGCATAACAAGGCAGGAACAGCTCTGATTATAATTGGCCAGTC . Strains that constitutively express PHO84 or PHO90 under TDH3 promotor ( PHO84c and PHO90c ) were generated using an amplified PCR product of the TDH3 promoter , which was inserted upstream of PHO84 or PHO90 . KanMX-TDH3pr was amplified from pYM-N14 [39] , and a HygromycinB-TDH3pr fragment was similarly amplified from pYM-N14 in which KanMX was replaced with HygromycinB . To create a deletion of PHO84 ( no . 16 , in S1 Table ) , kanMX was amplified from pBS7 with the following primers: ACCAGGGCACACAACAAACAAAACTCCACGAATACAATCCAAATGAGATCTGTTTAGCTTGCCTCG and TATTTGTTCTAGTTTACAAGTTTTAGTGCATCTTTGAGGCTTTTAATCGATGAATTCGAGCTCG . To create a deletion of SPL2 ( no . 20 , in S1 Table ) , kanMX was amplified from pBS7 with the following primers: GTCACTGCAGCCACGTGCCTAGATCTATTACTATGACTTCCCATGAGATCTGTTTAGCTTGCCTCG and CAGAGGTAGAAGGTATGTATGTGTAACGATTAAGAGATGCAATCAATCGATGAATTCGAGCTCG . The double deletion of PHO84 and SPL2 in the PHO84p-Venus-HIS5 strain ( no . 17 , in S1 Table ) was similarly generated using kanMX and NAT , respectively . Phosphate-limiting media were based on SC medium , prepared from YNB without phosphate ( CYN0804 ) . Phosphate was added in the form of KH2PO4 to reach the specified levels . Fixed potassium levels were maintained by the addition of KCl ( instead of KH2PO4 ) . The media were set to a pH of 5 . WT cells with a starvation response reporter ( Pho84promptor-Venus ) and with or without mCherry were grown overnight in Erlenmeyer flasks in rich Pi medium of 7 . 3 mM Pi to reach logarithmic growth ( OD600 approximately 0 . 4 , NovaSpec Plus Visible Spectrophotometer ) and were then transferred into 5 tubes . The samples were centrifuged , and all supernatant was discarded . Next , for each tube , the relevant Pi medium was added , and the samples were resuspended . The washed samples were then inoculated to the specified low- or rich-Pi medium ( 0 mM , 0 . 06 mM , 0 . 2 mM , 0 . 5 mM , and 7 . 3 mM Pi ) , and the initial OD600 was set to 0 . 05 . Cells were grown in Erlenmeyer flasks at 30°C until Pi was exhausted and they reached the stationary phase ( 26 hours ) . Samples for optical density ( OD600 ) and flow cytometry ( to measure reporter activation levels ) were taken during growth in high Pi ( before wash , t = 0 ) and during growth in the various Pi media approximately every 30 minutes for 12 hours and at a final time point after 26 hours . To calculate the growth rate in the different Pi media , the log2 ( OD600 ) versus time was fitted with the MATLAB function csap ( smoothing parameter of 0 . 4 ) , and the values of the fitted growth curve data were determined at 1-minute intervals . The growth rate was calculated for each measured time point ( between 0 . 5 hours and 11 . 5 hours ) from its slope with 2 surrounding ( ±1 minute ) fitted values . To control for density-dependent effects , the growth rate was normalized by the growth rate of cells transferred to rich phosphate medium: The growth rates in rich medium with the same optical density ( OD ) were subtracted from the cell’s growth rate at each time point in a given Pi medium . The growth rate used for normalization was derived from the fitted growth curve values of cells grown in rich medium ( as above ) . The time point on the fitted growth curve with the closest OD to that of the sample was chosen for normalization . The growth rate of this time point was determined with 2 surrounding ( ±1 minute ) values . Cells were grown as described above for WT growth rate and reporter activation dynamics . Briefly , logarithmically growing cells ( OD600 approximately 0 . 4 ) grown overnight in high Pi levels ( 7 . 3 mM Pi ) were washed in the relevant low-Pi medium . The cells were then inoculated to a specified medium: no Pi , very low Pi ( 0 . 06 mM Pi ) , low Pi ( 0 . 2 mM Pi ) , and intermediate–low Pi ( 0 . 5 mM Pi ) at an initial OD600 of 0 . 05 . The cells were grown at 30°C until Pi was exhausted and they reached the stationary phase ( 24 . 7 hours ) . The following day , the cells were recovered into high Pi levels of 7 . 3 mM or 20 mM as described ( diluted to an OD600 of 0 . 05 ) and grown for 9 hours or 24 . 7 hours ( stationary phase ) . Samples for RNA sequencing were taken prior to entry into low Pi levels ( t = 0 ) and at several time points during growth in low Pi levels until the cells reached the stationary phase at 24 . 7 hours; samples were taken similarly during recovery . The samples were centrifuged for 1 minute at 4 , 000 rpm , the supernatant was discarded , and the pellet was submerged immediately in liquid nitrogen and preserved in −80°C . The breaking of the cell wall was carried out in a deep 96-well plate . Cells were lysed by the addition of 450 μl of lysis buffer ( 1 M sorbitol ( Sigma-Aldrich ) , 100 mM EDTA , and 0 . 45 μl lyticase ( 10 IU/μl ) ) . The plate was incubated for 30 minutes at 30°C and then centrifuged for 10 minutes at 2 , 500 rpm . The supernatant was collected into a new deep 96-well plate . From this point forward , the RNA extraction was carried out using the Nucleospin 96 RNA kit , changing only β-mercaptoethanol with DTT . cDNA was prepared from the RNA extracts , barcoded , and sequenced using the Illumina HiSeq 2500 system , using Truseq SR Cluster Kit v3 -cBot-HS cluster kit and Truseq SBS Kit v3-HS run kit ( 50 cycles ) [41] . Each RNA-seq sample was mapped to the genome of S . cerevisiae ( R64 in SGD ) using bowtie ( parameters: --best –a –m 2 –strata -5 10 ) . Mapped reads were filtered for reads not mapped to rRNA , and the aligned filtered reads were down-sampled to 400 , 000 reads in order to have similar data from all samples . PCR bias was normalized for by using the unique molecular identifier ( UMI ) , scoring each position on the genome by the unique number of UMIs it had out of the 256 possible UMIs [41] . The expression of each gene was determined by summing all of the reads aligned to the 400 base pairs upstream to its 3′ end to 200 base pairs downstream from the 3′ end . For genes with high sequence similarity in the region being summed ( thus having sequences aligning as good to both genes ) , the reads aligned were separated to both genes according to the amount of uniquely mapped sequences . The sum of all gene expression was normalized to be 1 , 000 , 000 , and genes with an expression below the threshold of 10 reads were excluded . All WT and mutant RNA data presented were normalized to logarithmic growing WT cells in 7 . 3 mM Pi ( t = 0 of experiment 1; see S2 Data ) before the cells were distributed between no Pi , 0 . 06 mM , 0 . 2 mM , and 0 . 5 mM Pi and grown for 24 . 7 hours and recovered into 20 mM Pi . Stress and protein synthesis genes were not presented in the figures if ( 1 ) during growth in low Pi , they were below detection levels ( not a number [NaN] ) in the ( normalized ) start time point ( t = 0 , 7 . 3 mM Pi ) or ( 2 ) during recovery from low Pi , they were below detection levels ( NaN ) in the ( normalized ) t = 0 time point of recovery ( the last time point of growth in low Pi prior to recovery ) . Genes were classified as PHO-regulated genes if they met 2 RNA expression criteria . The first required up-regulation in ON cells ( PHO4SA12346 ) versus OFF cells ( Δpho4 ) in at least 15 out of 25 samples . The samples ( 3*8 ) were taken during growth in low Pi of 0 . 06 mM , 0 . 2 mM , and 0 . 5 mM Pi ( time points: 1 , 2 , 3 . 5 , 5 , 6 . 5 , 8 , 9 . 5 , 24 . 7 hours ) , and 1 sample was taken from logarithmically growing cells in high-Pi medium ( prior to the transfer to the different low-Pi media ) . Genes were considered up-regulated if the expression ratio between ON and OFF cells was larger than or equal to 2 or if the OFF cells were below detection levels ( NaN ) and the ON cells were 10 times higher than the defined RNA detection level ( 10 reads ) . The second criterion required up-regulation in WT cells during growth in no Pi medium in at least 6 out of 8 time points ( 1 , 2 , 3 . 5 , 5 , 6 . 5 , 8 , 9 . 5 , and 24 . 7 hours ) . Genes were considered up-regulated if the expression ratio between no Pi and high Pi levels was larger than or equal to 2 . The sample grown in high-Pi medium was taken during logarithmic growth in 7 . 3 mM Pi , prior to transfer into no Pi . Genes that were below detection levels in high Pi were removed from the analysis . Stress response genes ( Stress ) and genes coding for ribosomal proteins ( Protein Synthesis ) were defined in [20] ( see S2 Data for gene names ) . Those transcriptional modules were characterized based on coexpression in over 1 , 000 expression profiles of S . cerevisiae during growth in diverse environmental conditions . The stress module we have used ( 46 genes ) is a subgroup of the ESR [24] . Flow cytometry was done with a BD LSRII system ( BD Biosciences ) . GFP and Venus samples were analyzed with excitation at 488 nm and emission at 525 ± 25 nm . Samples with mCherry were analyzed with excitation at 594 nm and emission at 610 ± 10 nm . A PHO84C strain marked with mCherry and with a reporter of the starvation response ( PHO84p-Venus ) was evolved in the lab to create 16 independent populations . The experimental evolution was performed in 50-ml tubes , with a volume of 10 ml . Stationary cells grown in high-Pi ( 20 mM Pi ) medium were transferred ( 10 μl ) into 16 tubes with low Pi ( 0 . 2 mM Pi ) and grown until they reached the stationary phase ( an OD600 of approximately 8 . 5; cells underwent approximately 10 generations ) . Next , cells were transferred ( 10 μl ) in parallel from each tube to very low-Pi ( 0 . 06 mM Pi ) medium . In order to reach prolonged Pi starvation , cells were grown for at least 2 days , reaching a final OD600 of approximately 4 and undergoing approximately 9 generations . Then , 25 μl of stationary cells from each tube was recovered into a 20 mM Pi medium and grown until stationary phase ( an OD600 of approximately 9 . 5 , cells underwent approximately 10 generations ) . This cycle of fluctuating Pi levels was repeated 10 additional times , for a total of approximately 300 generations of evolution . Before each transfer , OD600 was measured and PHO84p-Venus levels were verified by flow cytometry . Samples were frozen for further analysis throughout the evolution . Whole genome sequencing was performed to identify the adaptive mutations in evolved isolates . To this end , the ancestor and 60 isolates from 10 evolved populations were sequenced . The isolates were taken from the middle ( after approximately 150 generations , 30 isolates ) and the end of a lab-evolution experiment ( after approximately 300 generations , 30 isolates ) . The fitness of the isolates was verified by a competition assay . Only isolates in which the limited recovery of the ancestor ( PHO84C ) cells was rescued were sequenced . All of the isolates after approximately 300 generations were adapted , while after approximately 150 generations , only 30 out of the 56 isolates were adapted . DNA was extracted from 2 ml of saturated overnight culture grown in YPD . The cells were pelleted , resuspended in 10 ml sorbitol 1M at 4°C , and centrifuged for 2 minutes at 4°C at 4 , 000 rpm , and the supernatant was discarded . The cells were then resuspended in 300 μl of lysis buffer ( 50 mM HEPEs pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% TritonX-100 , and 0 . 1% sodium deoxycholate ) and transferred to 2-ml safe-lock Eppendorf tubes with 0 . 5-mm Zirconium Oxide beads . They were then blended in a Bullet Blender 24 ( Next Advance ) for 1 minute at level 8 . The supernatant was transferred to new 1 . 5-ml tubes together with an additional 200 μl of lysis buffer used to wash the beads . Lysates were treated for 60 minutes with RNAse at 37°C and then treated for an additional 2 hours with Proteinase K ( 20 mg/ml ) at 37°C . Cleared lysates were sonicated for 30 minutes ( 30 seconds on , 30 seconds off ) in a Bioruptor plus ( Diagenode ) cooled water bath sonicator , resulting in an average DNA fragment size of approximately 300 bp . Ten ul of the lysate was taken from each sample , and a multiplexed library was prepared for sequencing [42] . The library was sequenced in an Illumina HiSeq 2500 with 100-bp paired-end sequencing . DNA reads from the different isolates were aligned to the yeast genome against the S288C_reference_sequence_R64-1-1_20110203_edited . fa S288C reference genome using bwa ( ver . 0 . 6 . 2 ) [43] . Duplicated reads were removed with picard-tools ( ver . 1 . 73 ) , and the bam files of the samples were merged . Determination of SNP positions was performed using the genome analysis toolkit ( GATK ) Unified Genotyper [44] . Since there was no SNP database available , a database was created from our data . Towards this task , the following GATK ( ver . 2 . 2–16 ) tools were used on the merged bam file: RealignerTargetCreator , IndelRealigner , and UnifiedGenotyper ( -stand_call_conf 50 . 0 -stand_emit_conf 20 . 0 --sample_ploidy 1 --genotype_likelihoods_model BOTH --max_alternate_alleles 1 -dcov 100 ) ; from this first round of variant calling , the variants were filtered using SelectVariants ( -select "QD > 10 . 0 && QUAL>50 && DP>10" ) . The vcf result file was used for the second round of variant identification . In this round , the following GATK tools were used: RealignerTargetCreator , IndelRealigner , BaseRecalibrator , and UnifiedGenotyper ( with the above parameters except for --max_alternate_alleles 4 and –dbsnp ) . SNP annotation was added with the tool snpEff [45] . The amino-acid variety in PHO84 homologs was established with ConSurf [46] . Starting from the PHO84 protein sequence ( SGD ) as input , multiple sequence alignment was built using MAFFT . The homologs were collected from UNIREF90 . CS-BLAST was used for the homolog search algorithm , with search parameters of CSI-BLAST E-value of 0 . 0001 , a maximal ID of 95% , and a minimal ID of 35% , and 3 iterations of CSI-BLAST . CSI-BLAST resulted in 501 sequences , which were used to calculate the residue variety ( in percentage ) for each position in the query sequence ( Pho84 ORF ) . To create strains with the evolved amino-acid substitutions ( nos . 22–25 , in S1 Table ) , each of the 4 base substitutions evolved in the PHO84 ORF during experimental evolution was inserted into the ancestor strain and the WT ( with PHO84p-Venus reporter ) in 2 transformation steps . First , PHO84 ORF was disrupted by CaURA3 insertion , which was lifted from pAG60 [47] with the primers ATGACGAAGGTTTCGGTTGGCAACAAGTTAAGACCATCTCCTGTTTAGCTTGCCTTGTCC and CCAGTAACCAGGTAATGAACCAGCACAAATCAAAATCAGATTCGACACTGGATGGCGGCG . Next , we lifted each of the 4 evolved alleles by PCR with the primers AACATGGCCTTCCACAACC and GTTTATGGTATGCGAAACCG from representative evolved isolates . Each of the 4 amplified fragments was inserted in place of the PHO84 deletion created above . All insertions were verified by PCR and sequencing . | Cells must have sufficient nutrients in order to perform their different functions . This becomes particularly critical when the nutrient composition in their environment changes . To accommodate such changes , cells have evolved specialized adaptation pathways that sense the nutrients available in their surrounding and adapt their gene expression and protein functions accordingly . It is typically assumed that these different starvation pathways have been optimized to enable growth in conditions in which nutrients are limiting . We reasoned , however , that since starvation periods are necessarily followed by nutrient retrieval , the starvation response may further function to optimize the recovery from starvation once nutrients are replenished . In the present study , we show that this is indeed the case for the well-studied phosphate starvation pathway in budding yeast . Using a combination of transcription profiling and high-resolution competition assays , we have characterized , at high temporal resolution , the growth-fitness advantage cells gain from activation of the phosphate starvation program when they enter into starvation and also when they exit starvation when nutrient is replenished . We find that the cells activate the starvation program in 2 temporal waves . The first wave is activated at intermediate depletion that is not yet limiting for growth , while the second wave is activated when phosphate becomes limiting . Of note , we provide evidence that the early response promoted recovery from starvation by increasing phosphate influx . Our results corroborate the notion that cells have evolved not only to optimize their instantaneous growth but also to prepare for future , predicted challenges . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"flow",
"cytometry",
"chemical",
"compounds",
"phosphates",
"cell",
"processes",
"mutation",
"fungi",
"substitution",
"mutation",
"model",
"organisms",
"cell",
"growth",
"experimental",
"organism",
"systems",
"molecular",
"biology",
"techniques",
"research",
"and",
"ana... | 2017 | Dual role of starvation signaling in promoting growth and recovery |
Dengue fever is a ubiquitous arboviral infection in tropical and sub-tropical regions , whose incidence has increased over recent decades . In the absence of a rapid point of care test , the clinical diagnosis of dengue is complex . The World Health Organisation has outlined diagnostic criteria for making the diagnosis of dengue infection , which includes the use of the tourniquet test ( TT ) . To assess the quality of the evidence supporting the use of the TT and perform a diagnostic accuracy meta-analysis comparing the TT to antibody response measured by ELISA . A comprehensive literature search was conducted in the following databases to April , 2016: MEDLINE ( PubMed ) , EMBASE , Cochrane Central Register of Controlled Trials , BIOSIS , Web of Science , SCOPUS . Studies comparing the diagnostic accuracy of the tourniquet test with ELISA for the diagnosis of dengue were included . Two independent authors extracted data using a standardized form . A total of 16 studies with 28 , 739 participants were included in the meta-analysis . Pooled sensitivity for dengue diagnosis by TT was 58% ( 95% Confidence Interval ( CI ) , 43%-71% ) and the specificity was 71% ( 95% CI , 60%-80% ) . In the subgroup analysis sensitivity for non-severe dengue diagnosis was 55% ( 95% CI , 52%-59% ) and the specificity was 63% ( 95% CI , 60%-66% ) , whilst sensitivity for dengue hemorrhagic fever diagnosis was 62% ( 95% CI , 53%-71% ) and the specificity was 60% ( 95% CI , 48%-70% ) . Receiver-operator characteristics demonstrated a test accuracy ( AUC ) of 0 . 70 ( 95% CI , 0 . 66–0 . 74 ) . The tourniquet test is widely used in resource poor settings despite currently available evidence demonstrating only a marginal benefit in making a diagnosis of dengue infection alone . The protocol for this systematic review was registered at PROSPERO: CRD42015020323 .
Dengue is an arboviral infection ubiquitous to tropical and sub-tropical regions , [1–3] where it is transmitted by domesticated day-biting mosquitoes including Aedes aegypti . After an incubation period of 4–10 days ( mean , 7 days ) , illness onset is abrupt ( with headache , fever , myalgia/arthralgia and rash ) and can last up to 14 days[4–8] . Four virus serotypes are in circulation around the world ( DEN-1 , DEN-2 , DEN-3 , DEN-4 ) [9 , 10] , with specific “Asian” genotypes within serotypes DEN-2 and DEN-3 being associated with severe dengue infection particularly in secondary infections [11 , 12] . According to WHO estimates 50–100 million new dengue infections occur annually , resulting in 500 , 000 cases of DHF and 22 , 000 deaths[11 , 12] . It is thought that approximately 2 . 5 billion people , or 40% of the world’s population are at risk of dengue infection , with important factors including warm and humid climate , overcrowding and residence in major urban centers[11–14] . Virus transmission can cause a spectrum of illness from subclinical to severe dengue infection characterized by plasma leakage , haemorrhage and end-organ impairment . Characterisation of specific phenotypes of infection is complex and has recently changed[11–14] . Clinically , dengue fever presents as an acute febrile disease with symptoms of headache , bone or joint and muscular pains , rash and leukopenia . Traditionally a further two stages were described , consisting of dengue haemorrhagic fever ( DHF ) , characterized by high fever , haemorrhagic phenomena , often with hepatomegaly . In severe cases , further signs of circulatory failure may develop culminating in dengue shock syndrome ( DSS ) , which is associated with poor outcomes . More recent consensus guidance[12] recommends distinction of dengue illness into dengue ( with or without warning signs which may precede the development of more severe infection ) and severe dengue ( encompassing the manifestations of severe plasma leakage , severe bleeding or severe end-organ involvement ) [11 , 12] . The clinical diagnosis of dengue is challenging as the symptoms are non-specific and common to many other infections[10–12] , notably malaria and other arboviral infections . To aid diagnosis , specifically during the initial , acute , febrile phase which may last 2–7 days after the development of fever , the WHO recommend the use of the Tourniquet Test ( TT , also known as the Rumpel-Leede or Hess test ) to support diagnostic decision-making [13 , 15–21] . As an inexpensive , quick and easy to perform procedure , use of the TT has become widespread in clinical practice globally . The TT is a marker of capillary fragility and can be undertaken by inflating a blood pressure cuff around the upper arm to the point midway between the individual’s systolic and diastolic blood pressures and leaving it inflated for 5 minutes . The cuff is subsequently released and after two minutes the number of petechiae below antecubital fossa are counted . The test is positive if more than 10 petechiae are present within a square inch of skin on the arm[11 , 12] . The clinical diagnosis of dengue may be confirmed by laboratory testing , which in many settings involves the measurement of an antibody response ( IgM or IgG ) by ELISA[3] , for years considered to be the diagnostic standard[22] . This test is less sensitive in the first 5 days after exposure and frequently relies on testing of paired sera samples . Newer tests available in some centres include reverse-transcriptase PCR ( polymerase chain reaction ) or direct antigen detection ( non-structural protein 1 ) . While these tests are likely to offer an improvement in diagnostic accuracy , the cost and current limitation of not detecting all serotypes limits their application . The evidence to support the recommendation and widespread use of the TT to aid the diagnosis of dengue fever is mixed with variable sensitivity and specificity being reported previously ( 15–21 ) . The aim of this study is to map the evidence , assess the quality of the studies and perform a diagnostic accuracy meta-analysis of the diagnosis of dengue using the TT compared to ELISA .
The protocol for this systematic review was registered at PROSPERO ( International prospective register of systemic reviews , http://www . crd . york . ac . uk/PROSPERO/display_record . asp ? ID=CRD42015020323 ) . We searched Medical Literature databases Analysis and Retrieval System Online ( Medline ) , Excerpta Medical Database ( EMBASE ) , Allied and Complementary Medicine Database ( AMED ) , Global health , Biological Abstracts/Reports , Reviews , Meetings ( BIOSIS ) altogether through OVID . Latin American and Caribbean Health Sciences ( LILACS ) and the Cochrane Library through their website for relevant publications until April 2016 . Additionally , we searched the WHO ICTRP ( International Clinical Trials Registry Platform ) and ClinicalTrials . gov for completed and ongoing studies . The search , performed according to the Cochrane Highly Sensitive Search Strategy , used the following terms: “Rumpel-Leede” OR capillary OR “blood pressure cuff” OR petechiae OR tourniquet OR “Hess” AND dengue . The search was sensitive , we used no study filters and no language or publication restrictions . We checked the reference lists of all primary studies included for additional references . There were no language or publication restrictions on our search . We included cross-sectional and cohort studies that evaluated the diagnostic accuracy of tourniquet test for dengue infection . Both retrospective and prospective studies that consecutively or randomly selected patients were included , together with studies that used delayed verification for gold standard . We included studies looking at patients presenting with fever who were subsequently tested for dengue using both the TT ( index test ) and ELISA detection of antibody response ( reference standard ) . For this review , definitions of dengue were used according to those proposed by the WHO[11 , 12] , as these were the definitions used during the time period from which studies were drawn . For the purposes of this meta-analysis , ‘dengue’ was considered to consist of non-severe ‘dengue fever’ and ‘haemorrhagic dengue fever’ , defined as follows . Dengue fever included fever plus 2 or more symptoms of nausea/vomiting , rash , aches and pains . Dengue hemorrhagic fever ( DHF ) was considered as infection accompanied by haemorrhagic manifestations such as petechiae and mucosal or gastro-intestinal bleeding[11 , 12] . Three comparisons were performed; TT vs . ELISA to diagnose dengue ( i . e . both non-severe dengue fever plus DHF; TT vs . ELISA to diagnose dengue fever and TT vs . ELISA to diagnose DHF . Two review authors ( AJG , HR ) independently assessed all studies identified from the database searches by screening titles and abstracts using the Review Management website Covidence ( http://www . covidence . org ) . We separated potential studies for full-text reading . A third review author ( ET ) resolved any disagreements , and reasons for including and excluding trials were recorded . Two review authors ( AJG , HR ) independently extracted data from the included studies using a standard data extraction form . With this form we extracted information of study design , participant description , index test description , reference test description , dengue classification and total number of participants . A 2x2 table was created for each study comparing both tests . All included studies were assessed for their methodological quality using the quality assessment tool for diagnostic accuracy studies ( QUADAS-2 ) [23] . The tool is composed of 17 items regarding study patient selection , index test , reference standard and flow and timing . For each domain mentioned there are items for risk of bias and applicability . Items were scored as positive ( low risk of bias ) , negative ( high risk of bias ) , or insufficient information ( unclear ) . A description of each assessment was described in the results section . For each study , a 2x2 contingency table was constructed . We calculated sensitivity , specificity and likelihood ratios ( LRs ) . When the primary study had 0 in a cell of the 2x2 table , the value of 1 was added , so calculations could be done[24] , this only happened in one study ( 17 ) . We planned to exclude primary studies reporting two cells with 0 , but this did not occur . The sensitivity , specificity and LRs were pooled from each study and a forest plot was generated with 95% confidence intervals . Due to the variability in diagnostic data , we logit-transformed sensitivity and specificity for each primary study and for the aggregate result , considering variability within-study and between-study . The output results are random effects estimates of the mean sensitivity and specificity with corresponding 95% CI . The weighing considered the inverse of the standard error , so indirectly to the sample size reported in the studies . Inconsistency ( I2 ) was explored as an indicator of statistical heterogeneity[24] . Summary receiver operating characteristic ( ROC ) curves were generated with calculation of area under the curve ( AUC ) as an indicator of test accuracy . To assess for the possibility of publication bias , we constructed funnel plots to visually assess for signs of asymmetry [25] . Statistical analyses were performed with Stata v10 . 0 ( StataCorp LP , Texas , USA ) and with RevMan v5 . 3 ( The Nordic Cochrane Centre , Copenhagen , Denmark ) [26] .
We used the instrument QUADAS-2 , which is composed of four quality categories ( patient selection , reference standard , index test , and flow and timing ) , to critically appraise each included study ( Fig 2 ) . Six studies ( 33% ) were considered to have high risk of bias in patient selection due to inclusion of patient data from a database , raising the possibility of bias from multiple assessors , or selection of patients with pre-existing disease . Two studies ( 17% ) had not adequately described their sampling methods , so were classified as unclear risk . Eight studies ( 50% ) were low risk of bias for patient selection . Considering the Reference standard category ( ELISA ) , all studies were considered low risk of bias . For the Index test category , four studies ( 25% ) had not clearly described the process used to conduct the TT , blind assessors or train assessors . For the flow and timing category , only two studies ( 12 . 5% ) were considered at high risk of bias as the TT was repeated multiple times over a period of several days . Four studies ( 25% ) were considered unclear risk due to lack of information of withdrawals and appropriate sequencing of tests . In this comparison , we included all 16 studies including both non-severe dengue fever and DHF cases . The pooled sensitivity for dengue diagnosis was 0 . 58 ( 95% CI , 0 . 43–0 . 71 ) and the specificity was 0 . 71 ( 95% CI , 0 . 60–0 . 80 ) ( Fig 3 ) . The positive predictive value was 1 . 63 ( 95% CI , 1 . 45–1 . 82 ) . The negative predictive value was 0 . 60 ( 95% CI , 0 . 51–0 . 71 ) . The Diagnostic Odds Ratio was 2 . 88 ( 95% CI , 2 . 17–3 . 83 ) . The area under the curve was 0 . 70 ( 95% CI 0 . 66–0 . 74 ) ( Fig 4A ) . In this comparison , we included six studies . The pooled subgroup analysis sensitivity for dengue fever diagnosis was 0 . 66% ( 95% CI , 0 . 47–0 . 81 ) and the specificity was 0 . 68 ( 95% CI , 0 . 55–0 . 80 ) ( Fig 5 ) . The positive predictive value was 1 . 81 ( 95% CI , 1 . 45–2 . 25 ) . The negative predictive value was 0 . 52 ( 95% CI , 0 . 36–0 . 75 ) . The Diagnostic Odds Ratio was 3 . 80 ( 95% CI , 2 . 40–6 . 00 ) . The area under the curve was 0 . 73 ( 95% CI 0 . 68–0 . 76 ) ( Fig 4B ) . In this comparison , we included seven studies . In the pooled subgroup analysis , sensitivity for dengue haemorrhagic fever diagnosis was 0 . 63 ( 95% CI , 0 . 39–0 . 82 ) and the specificity was 0 . 60 ( 95% CI , 0 . 48–0 . 70 ) . The positive predictive value was 1 . 54 ( 95% CI , 1 . 06–2 . 24 ) ( Fig 6 ) . The negative predictive value was 0 . 59 ( 95% confidence interval , 0 . 37–0 . 86 ) . The Diagnostic Odds Ratio was 2 . 08 ( 95% CI , 1 . 15–6 . 82 ) . The area under the curve was 0 . 66 ( 95% CI , 0 . 62–0 . 70 ) ( Fig 4C ) . None of the included studies reported data comparing TT and ELISA for patients with dengue shock syndrome . We conducted a subgroup analysis for the included studies considering only children and adolescents aged 6 months to 15 years . No analysis with adults were conducted , since all 16 included studies did not explore only adults’ participants , when they analyzed adults they mixed the data with children and adolescents In this subgroup analysis , we included eight studies including both non-severe dengue fever and DHF cases . The pooled sensitivity for dengue diagnosis was 0 . 71 ( 95% CI , 0 . 59–0 . 82 ) and the specificity was 0 . 59 ( 95% CI , 0 . 47–0 . 70 ) ( Fig 7 ) . The positive predictive value was 1 . 66 ( 95% CI , 1 . 45–1 . 91 ) . The negative predictive value was 0 . 52 ( 95% CI , 0 . 43–0 . 64 ) . The Diagnostic Odds Ratio was 3 . 44 ( 95% CI , 2 . 25–5 . 25 ) . The area under the curve was 0 . 69 ( 95% CI , 0 . 65–0 . 73 ) . We conducted the following sensitivity analyses of the Dengue vs ELISA analysis: 1 . In order to analysis the impact of the mix of cut-off points reported by studies ( ≥10 petechiae per one square inch and ≥20 petechiae per one square inch ) we repeated the analysis in just studies using the criteria of ≥20 petechiae per one square inch . 2 . We conducted another sensitivity analysis removing all studies with high risk of selection bias . Thus , we included 12 studies including both non-severe dengue fever and DHF cases . The pooled sensitivity for dengue diagnosis was 0 . 64 ( 95% CI , 0 . 51–0 . 74 ) and the specificity was 0 . 68 ( 95% CI , 0 . 55–0 . 80 ) ( Fig 8 ) . The positive predictive value was 1 . 68 ( 95% CI , 1 . 46–1 . 93 ) . The negative predictive value was 0 . 56 ( 95% CI , 0 . 47–0 . 66 ) . The Diagnostic Odds Ratio was 3 . 37 ( 95% CI , 2 . 33–4 . 86 ) . The area under the curve was 0 . 71 ( 95% CI , 0 . 67–0 . 75 ) . We removed six studies with high risk for selection bias . Thus , 10 studies including both non-severe dengue fever and DHF cases were combined . The pooled sensitivity for dengue diagnosis was 0 . 64 ( 95% CI , 0 . 50–0 . 76 ) and the specificity was 0 . 66 ( 95% CI , 0 . 56–0 . 75 ) ( Fig 9 ) . The positive predictive value was 1 . 74 ( 95% CI , 1 . 52–1 . 98 ) . The negative predictive value was 0 . 57 ( 95% CI , 0 . 48–0 . 69 ) . The Diagnostic Odds Ratio was 3 . 37 ( 95% CI , 2 . 35–4 . 85 ) . The area under the curve was 0 . 70 ( 95% CI , 0 . 66–0 . 74 ) . Funnel plot asymmetry test revealed evidence of publication bias ( Fig 10 ) . The I2 statistics were , as expected in diagnostic meta-analyses , over 95% in all three comparisons made ( Figs 3 , 5 and 6 ) [26] .
The clinical diagnosis of dengue is challenging as disease presentation is almost indistinguishable to many other infections commonly found in the tropics[104] . Current WHO recommendations suggest a combination of clinical history , leukopenia and the tourniquet test result to make a diagnosis if ELISA testing is not available or prior to the availability of results . Given the requirement for paired sera samples in many areas where dengue is endemic to demonstrate an increase in antibody titre , reliance on clinical diagnosis will be still greater . While still widely used , our analyses suggest that data supporting routine use of the tourniquet test is , at best , relatively poor , however it is important to consider that the quality of the evidence is low due to imprecision and inconsistency across the included studies . Furthermore , the data used to underpin current international recommendations likely overestimate its utility . Over reliance on the use of the TT to support a clinical diagnosis of dengue infection may result in misdiagnosis of patients and inaccurate estimates of disease incidence; relatively low sensitivity but higher specificity suggest that disease incidence may be underestimated if the TT is overly relied on . While current recommendations should be re-examined in light of these findings , replacement of the tourniquet test in routine clinical practice will only come once improved point-of-care diagnostics are made more widely available , especially in resource-poor areas . | Dengue is an infectious disease transmitted by mosquitoes in the Tropics . There are 2 . 5 billion people around the world at risk . Dengue presents as an acute febrile illness with symptoms including headache , bone or joint and muscular pains and rash . The objective of this study is to perform a diagnostic accuracy meta-analysis comparing the use of the Tourniquet Test ( TT ) to a laboratory assay standard ( ELISA ) for making a diagnosis of dengue infection . A comprehensive literature search ( to April , 2016 ) was conducted to map and assess the quality of the available evidence , using the following databases: MEDLINE ( PubMed ) , EMBASE , Cochrane Central Register of Controlled Trials , BIOSIS , Web of Science , SCOPUS . We included 16 studies with 28 , 739 participants in the meta-analysis . Pooled sensitivity for dengue diagnosis by TT was 58% ( 95% Confidence Interval ( CI ) , 43%-71% ) and the specificity was 71% ( 95% CI , 60%-80% ) . In the pooled subgroup analysis sensitivity for dengue fever diagnosis was 55% ( 95% CI , 52%-59% ) and the specificity was 63% ( 95% CI , 60%-66% ) . The tourniquet test is widely used in resource poor settings despite currently available evidence demonstrating only a marginal benefit in making a diagnosis of dengue infection alone . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"children",
"medicine",
"and",
"health",
"sciences",
"enzyme-linked",
"immunoassays",
"pathology",
"and",
"laboratory",
"medicine",
"tropical",
"diseases",
"database",
"searching",
"research",
"design",
"age",
"groups",
"mathematics",
"signs",
"and",
"symptoms",
"statis... | 2016 | Tourniquet Test for Dengue Diagnosis: Systematic Review and Meta-analysis of Diagnostic Test Accuracy |
Checkpoint pathways regulate genomic integrity in part by blocking anaphase until all chromosomes have been completely replicated , repaired , and correctly aligned on the spindle . In Saccharomyces cerevisiae , DNA damage and mono-oriented or unattached kinetochores trigger checkpoint pathways that bifurcate to regulate both the metaphase to anaphase transition and mitotic exit . The sensor-associated kinase , Mec1 , phosphorylates two downstream kinases , Chk1 and Rad53 . Activation of Chk1 and Rad53 prevents anaphase and causes inhibition of the mitotic exit network . We have previously shown that the PKA pathway plays a role in blocking securin and Clb2 destruction following DNA damage . Here we show that the Mec1 DNA damage checkpoint regulates phosphorylation of the regulatory ( R ) subunit of PKA following DNA damage and that the phosphorylated R subunit has a role in restraining mitosis following DNA damage . In addition we found that proteins known to regulate PKA in response to nutrients and stress either by phosphorylation of the R subunit or regulating levels of cAMP are required for the role of PKA in the DNA damage checkpoint . Our data indicate that there is cross-talk between the DNA damage checkpoint and the proteins that integrate nutrient and stress signals to regulate PKA .
Progression through mitosis in yeast requires the ordered destruction of two inhibitors: securin or Pds1 and the mitotic or B type cyclin , Clb2 . The inhibitors are ubiquitinated and targeted for destruction by the anaphase promoting complex or cyclosome ( APC/C ) in conjunction with the specificity factors Cdc20 and Hct1/Cdh1 [1]–[3] . Destruction of Pds1 allows the separase Esp1 to cleave the cohesins to allow anaphase [4] , [5] . Exit from mitosis and the establishment of pre-replication complexes requires the inactivation of the mitotic cyclin dependent kinase ( Cdk ) complex , achieved in part by the APC/C-mediated proteolysis of the mitotic cyclins , including Clb2 , and by increased levels of the Cdk inhibitor Sic1 [6]–[9] . During anaphase the separase Esp1 triggers the release of Cdc14 from the nucleolus by modulating the phosphorylated status of its interacting partner Net1 via regulation of the phosphatase PP2ACdc55 [10]–[14] . When Net1 is phosphorylated Cdc14 is released and can exit the nucleolus to dephosphorylate Cdk1 substrates such as Hct1/Cdh1 , Sic1 , Pds1 . Cdc14 release and dephosphorylation of its targets are important for progression through anaphase and for activation of the mitotic exit network ( MEN ) [3] , [7] , [15]–[18] . Accurate transmission of chromosomes to each daughter cell requires that cells block anaphase until all chromosomes have been completely replicated and correctly aligned on the spindle . Similarly , cells that have incurred DNA damage in late S phase or G2 repair the damage before they progress through mitosis . In S . cerevisiae , DNA damage and mono-oriented or unattached kinetochores trigger checkpoint pathways that bifurcate to regulate both the metaphase to anaphase transition and mitotic exit [19]–[23] . In addition , several stress-activated pathways including the cAMP dependent protein kinase ( PKA ) pathway have been identified in S . cerevisiae that inhibit mitosis by targeting proteins involved in mitotic progression [24]–[28] . The sensor-associated kinase , Mec1 is activated in response to DNA damage and together with a signal amplifier , Rad9 , phosphorylates two downstream kinases , Chk1 and Rad53 [29] . Activation of Chk1 prevents anaphase by preventing the destruction of securin [19] , [30]; and activation of Rad53 causes inhibition of the mitotic exit network , helps to prevent securin destruction [31] , [32] , and also prevents segregation of damaged chromosomes by restricting spindle elongation [33] . We showed that PKA had a role in the DNA damage checkpoint . PKA supported mitotic arrest by regulating the phosphorylation of Cdc20 and helped maintain high levels of the mitotic inhibitors , securin ( Pds1 ) and Clb2 . In addition , we showed that phosphorylation of Cdc20 was both Mec1- and PKA-dependent , and that overexpression of PKA catalytic subunits partially rescued the checkpoint defect of mec1-21 cells [34] . In the work described here , we set out to identify the mechanism by which PKA is regulated in response to DNA damage . PKA in its inactive form is a tetramer consisting of two catalytic and two regulatory subunits . In yeast three genes encode the catalytic subunits , TPK1 , TPK2 and TPK3 and one gene encodes the R subunit , BCY1 [35]–[37] . R subunit interaction with cAMP causes the tetramer to disassociate rendering the catalytic subunits active . Thus , activation of PKA is regulated by increasing the intracellular levels of cAMP . At least two other signaling pathways in yeast regulate a transient increase in cAMP levels . The Ras pathway , which can be stimulated by intracellular acidification and the glucose signaling pathway ( reviewed in [38] ) . The glucose kinase , Hxk2 , is required for the transient increase in cAMP levels in both the Ras and glucose signaling pathways [39] . Regulation of PKA can also occur via post-translational modification , protein interactions and sub-cellular localization of the catalytic or R subunits . These mechanisms of regulation have been more thoroughly studied in metazoan cells in which phosphorylation of the regulatory subunit localizes the complex to a specific sub-cellular compartment via interaction with proteins called A protein Kinase Anchoring Proteins ( AKAPs ) . The AKAPs help mediate the specificity of PKA signaling in the cell [40] . In yeast the R subunit is phosphorylated when cells are deprived of glucose and in response to heat shock or addition of calcium to the media [41] , [42] . The Yak1 kinase is required for the phosphorylation of the R subunit when the cells are grown in a non-fermentable carbon source [41] , and Mck1 , a GSK-3 kinase , is required for the phosphorylation of the R subunit in response to heat shock [42] . This phosphorylation is required for re-distribution of the R subunit from the nucleus to both the nucleus and cytoplasm [42] . In addition , Zds1 and Zds2 have been implicated in the regulation of the localization of the R subunit to the cytoplasm in response to the same stresses that lead to R subunit phosphorylation [41] , [42] . Here we show that the checkpoint regulated phosphorylation of the R subunit of PKA has a role in restraining mitosis following DNA damage , suggesting that there is cross-talk between the DNA damage checkpoint and the PKA pathways . In addition we found that proteins that regulate the phosphorylation of the R subunit , and proteins that regulate the levels of intracellular cAMP are required for the role of PKA in the DNA damage checkpoint .
We have previously shown that the PKA pathway plays a role in blocking securin and Clb2 destruction following DNA damage [34] . Our findings also suggested that Mec1 and PKA are in the same signaling pathway and that PKA was likely acting downstream of Mec1 in response to DNA damage . PKA can be regulated by many mechanisms including regulation of cAMP levels , localization of the PKA holoenzyme or catalytic subunits , interactions with other proteins , and phosphorylation of the catalytic and R subunits [38] , [43] . We first set out to determine whether the R subunit was phosphorylated in response to DNA damage , and whether this phosphorylation was dependent on Mec1 . The cdc13-1 allele was used to activate the DNA damage checkpoint that blocks mitotic progression [44] . Growth of cdc13-1 cells at a restrictive temperature results in the inactivation of the telomere binding protein , Cdc13 , which causes single stranded DNA at the telomeres . This single stranded DNA is recognized as DNA damage in G2/M , and the DNA damage checkpoint is activated , blocking anaphase and mitotic exit [44] , [45] . In protein extracts from synchronized cells grown at the restrictive temperature for cdc13-1 , a slower migrating form of the R subunit was detected by western analysis at a time when the DNA damage signal was present ( Figure 1A ) . A similar slower migrating form of the R subunit was detected in protein extracts from cells growing in YP ethanol as previously shown [41] . In order to show that the slower migrating form of the R subunit was due to phosphorylation , the R subunit was isolated by immuno-precipitation from extracts prepared from cells with a DNA damage signal . The immunoprecipitated complexes were treated with phosphatase in the presence or absence of phosphatase inhibitors . Treatment with alkaline phosphatase resulted in a loss of the slower migrating form of the R subunit , while the slower migrating form of the R subunit was maintained when the immunoprecipitated-complexes were treated with both phosphatase and phosphatase inhibitors ( Figure 1B ) . These results indicated that the R subunit was modified by phosphorylation following activation of the DNA damage checkpoint . The R subunit was also phosphorylated in cells grown at 30°C ( in the absence of heat shock ) and treated with the DNA damaging agent , methylmethane sulfonate ( MMS ) ( Figure 1C ) . Pre-anaphase arrest due to activation of the spindle checkpoint by treatment with nocodazole ( in the absence of a DNA damage signal ) did not result in R subunit phosphorylation ( Figure 1C ) . This finding indicated that phosphorylation of the R subunit in cdc13-1 cells was not due to a cell cycle position effect and that activation of the spindle checkpoint does not result in R subunit phosphorylation . The R subunit was not phosphorylated in cdc13-1 cells lacking the upstream checkpoint kinase , Mec1 , ( Figure 1D and Figure S4 ) . This result suggested that Mec1 is required for the phosphorylation of the R subunit in response to DNA damage and supports the hypothesis that Mec1 regulates PKA when there is a DNA damage signal . The GSK3 kinase Mck1 was found to be required for the phosphorylation of the R subunit in response to heat shock [42] . We recapitulated these findings in our lab and found that R subunit phosphorylation in response to heat shock was dependent on Mck1 but not on the DNA damage kinase Mec1 ( data not shown ) . Thus , the requirement for Mec1 for R subunit phosphorylation was specific to a DNA damage signal . We next examined whether Mck1 regulated the phosphorylation of the R subunit in the DNA damage checkpoint . R subunit proteins from cdc13-1 or cdc13-1 mck1Δ cells raised to the non-permissive temperature of 32°C for cdc13-1 were analyzed by Western analysis . No phosphorylated R subunit was detected in cdc13-1 cells lacking MCK1 ( Figure 2A ) . Our data indicate that Mck1 responds to different signals to regulate R subunit phosphorylation and that in the case of DNA damage the signal required the sensor-associated kinase Mec1 . We previously showed that PKA and Chk1 work in parallel pathways to inhibit mitotic progression in the DNA damage checkpoint response [34] . To determine whether Mck1 has a role in the DNA damage checkpoint , we examined whether deletion of MCK1 removed the remaining DNA damage-induced mitotic delay in cells lacking CHK1 . Because PKA pathways have a role in regulating the G1/S transition , we released the cells into the cdc13-1 induced checkpoint from an hydroxyurea ( HU ) arrest ( which synchronizes the cells in late S-phase ) and monitored their progression through mitosis . The number of cells that had failed in the checkpoint-mediated arrest was determined by scoring the number of cells that exhibited a late mitotic phenotype ( separated nuclei ) in the presence of a DNA damage signal . cdc13-1 cells lacking MCK1 or TPK2 were proficient in the checkpoint arrest and remained arrested throughout the experiment ( Figure 2B and data not shown ) , however , cdc13-1 chk1Δ cells lacking MCK1 or TPK2 exhibited more cells with a late mitotic phenotype at earlier time points than the cdc13-1 chk1Δ cells ( Figure 2B ) . These results indicated that Mck1 had a role in the checkpoint-mediated arrest similar to the role of Tpk2 . To determine whether Mck1 was acting in the same pathway as PKA to restrain mitosis following DNA damage , we tested whether deletion of MCK1 would further enhance the checkpoint defect of cdc13-1 chk1Δ tpk2Δ cells . Cells were released into the cell cycle from an α-factor induced G1 arrest . Only cells lacking TPK2 were used in this experiment to eliminate any differences in cell cycle entry due to role of PKA in the G1/S transition . cdc13-1 chk1Δ cells lacking TPK2 failed in the checkpoint with the same kinetics as cdc13-1 chk1Δ cells lacking both TPK2 and MCK1 ( Figure 2C ) , suggesting that Mck1 and Tpk2 were acting in the same pathway following DNA damage . Because Mck1 and Mec1 were required for the phosphorylation of the R subunit following DNA damage , and Mck1 had been previously identified as a high-copy suppressor of the lethality associated with deletion of RAD53 [46] , we wanted to determine whether Mck1 was acting downstream of Mec1 to restrain mitosis following DNA damage . cdc13-1 or cdc13-1 mec1-21 cells containing an empty vector or MCK1 on a multi-copy vector were released from G1 ( α-factor block ) at 32°C and the cells were stained with DAPI to determine the number of cells exhibiting late mitotic phenotype at the indicated time points . Overexpression of Mck1 partially alleviated the checkpoint defect of mec1-21 cells following DNA damage ( Figure 2D ) , suggesting that Mck1 was acting downstream of Mec1 to restrain mitosis following DNA damage . Overexpression of MCK1 did not delay entry into S phase ( appearance of small budded cells ) ( Figure 2E , and Figure S6B ) . Cells with a high copy plasmid encoding MCK1 reached large budded state with an undivided nucleus with the same kinetics as vector controls ( Figure 2F and Figure S6C ) . This was the case whether the cells were released from the G1 block into YPD or minimal media ( Figure S6E–S6H ) . Mck1 has been shown to play a role in restraining the G2/M cell cycle transition through activation of Swe1 , a kinase that negatively regulates Cdk1 [47] . To test whether Mck1 was acting in the Swe1 pathway to restrain entry into mitosis we deleted SWE1 in a chk1Δ mutant and found that SWE1 deletion did not enhance the checkpoint defect of a chk1Δ cell ( Figure 2G ) . These findings suggested that the role of Mck1 following DNA damage was to block mitotic progression , and not to block mitotic entry via Swe1 . Two serine rich clusters on the R subunit were shown to be required for the phosphorylation and cytoplasmic localization of the R subunit in response to heat shock and growth in non-fermentable carbon sources [41] ( Figure 3A ) . However , mutation of cluster I or cluster II serines did not compromise R subunit function in response to carbon source or heat shock responses [41] . To determine whether these sites were phosphorylated in response to DNA damage , protein from cells given a DNA damage signal expressing wild type R subunit or the R subunit with serines in cluster I or cluster II [41] mutated to alanines were analyzed by western blot . The wild type R subunit was phosphorylated under these conditions but the phosphorylation was greatly reduced when the serines in cluster I or II were changed to alanine ( Figure 3A ) . These results suggested that the R subunit is phosphorylated on serine residues located in both clusters I and II . Since Mck1 is both required for the R subunit phosphorylation and has a role in the DNA damage checkpoint similar to PKA , we hypothesized that the role of PKA in the DNA damage checkpoint required phosphorylation of the R subunit on cluster I and cluster II serines . Therefore , phosphorylation defective mutants should have the same phenotype as deletion of a PKA catalytic subunit . cdc13-1 bcy1Δ or cdc13-1 chk1Δ bcy1Δ cells expressing wild type BCY1 or bcy1CI or bcy1CII mutants were released into the cell cycle from an HU-mediated arrest in late S-phase into a DNA damage signal , and the number of cells exhibiting a late mitotic phenotype were counted . Expression of the phosphorylation defective R subunit mutants enhanced the checkpoint defect of a chk1Δ cell to the same extent as deletion of a PKA catalytic subunit ( Figure 3B ) . Furthermore , expression of the phosphorylation defective R subunit mutants did not further enhance the checkpoint defect of cells lacking both CHK1 and MCK1 ( Figure 3C ) . These results suggested that phosphorylation of the serines in cluster I and II play a role in the DNA damage checkpoint and that the role of Mck1 in the DNA damage checkpoint is via regulation of the R subunit by phosphorylation on one or more of the serine residues in clusters I and II . It was previously reported that R subunit undergoes re-localization to the cytoplasm upon heat shock and that this shift in localization was dependent on serine residues in clusters I and II [42] . Based on our observation that the phosphorylation of cluster I and II serines is important for the mitotic delay following DNA damage , we hypothesized that the DNA damage signal would alter the localization of the R subunit . To address this , the localization of GFP-tagged Bcy1 was analyzed by microscopy in WT and cdc13-1 cells . The number of cells with nuclear accumulation of Bcy1-GFP was scored under asynchronous growth conditions , DNA damage , heat shock or by arresting cells in M phase with nocodazole . Upon induction of DNA damage , we observed a shift in the localization of the R subunit from the nucleus into the cytoplasm ( Figure 3D and Figure S2 ) . This re-localization was similar to that observed in cells following heat shock ( Figure S2 ) . As previously reported , asynchronous or nocodazole treated WT cultures had a high percentage of cells with nuclear Bcy1 . These data suggest that the activation of the DNA damage checkpoint , as elicited by a cdc13-1 mutant , promotes the export of the R subunit from the nucleus into the cytoplasm . Two proteins , Zds1 and Zds2 , have been implicated in regulating the cytoplasmic localization of the R subunit in the same conditions in which the R subunit is phosphorylated . Zds1 was required for the cytoplasmic localization of the R subunit in glucose restricting conditions [41] , and Zds1 and its homologue , Zds2 , were required for cytoplasmic localization in response to heat shock and addition of extra-cellular calcium [42] . To test whether Zds1 or Zds2 had a role in the DNA damage checkpoint , we analyzed the rate at which cdc13-1 , or cdc13-1 chk1Δ cells lacking ZDS1 , ZDS2 , or TPK2 failed in the checkpoint mediated arrest by scoring the number of cells exhibiting a late mitotic phenotype . cdc13-1 , cdc13-1 zds1Δ and cdc13-1 zds2Δ cells were proficient in restraining mitosis , deletion of ZDS1 delayed the rate at which chk1Δ cells failed in the checkpoint mediated arrest ( Figure S1A ) . This result was not surprising as cells lacking Zds1 have a delay in G2 and mitosis under normal growth conditions [47] . However cdc13-1 chk1Δ lacking ZDS2 , MCK1 or TPK2 exhibited more cells with a late mitotic phenotype at earlier time points than cdc13-1 chk1Δ ( Figure 4A and Figure S1B ) . The rate at which cdc13-1 chk1Δ lacking ZDS2 , MCK1 or TPK2 failed in the checkpoint was identical , suggesting that Zds2 has a role in the checkpoint similar to the role of Tpk2 and Mck1 . Neither Zds1 nor Zds2 was required for the phosphorylation of the R subunit following DNA damage ( Figure 4B ) , indicating that Zds2 has a role in the checkpoint independent of the phosphorylation of the R subunit . Our genetic experiments support the existence of a signaling complex that includes phosphorylated R subunit , PKA catalytic subunit ( s ) , and possibly Zds2 . To examine whether cAMP is required to activate this complex following DNA damage , proteins involved in the regulation of cAMP signaling were deleted or mutated in cdc13-1 chk1Δ cells . In this way we could gain insight as to whether or not cAMP was required for the role of PKA in the DNA damage checkpoint . A cdc35-1 mutation , which causes inactivation of adenylate cyclase when the cells are grown at the restrictive temperature [48] , [49] , was introduced into cdc13-1 and cdc13-1 chk1Δ cells so that we could determine whether adenylate cyclase had a role in the DNA damage checkpoint . The cells were released from a G1 arrest into the cell cycle . A DNA damage signal was generated and/or adenylate cyclase was inactivated as the cells progressed through S phase by raising the temperature to 35°C . Cells were scored for nuclear division as in previous assays . Due to the timing of Cdc35 inactivation in cdc35-1 cells , only 40% of cdc13-1 cdc35-1 entered S phase as evidenced by budding , consequently only the large-budded fraction was scored for this strain in the later timepoints . All other strains were uniformly large budded at later timepoints . Inactivation of adenylate cyclase enhanced the checkpoint defect of the chk1Δ cell ( Figure 5B ) , suggesting that cAMP is required to activate the PKA complexes that play a role in the DNA damage checkpoint . Hexokinase 2 ( Hxk2 ) is required for transient activation of cAMP signaling upon addition of glucose to cells growing in a non-fermentable carbon source and in cells with intracellular acidification [39] . We show here that deletion of HXK2 exacerbated the checkpoint defect of cdc13-1 chk1Δ cells ( Figure 5A ) . The cdc13-1 chk1Δ hxk2Δ cells failed in the checkpoint with similar kinetics as a cdc13-1 rad9Δ cell ( Figure 5A ) , indicating that the majority of the checkpoint response was gone in cdc13-1 chk1Δ hxk2Δ cells . Deletion of HXK2 also enhanced the checkpoint defect of chk1Δ cells with similar kinetics as inactivation of adenylate cyclase ( Figure 5B ) and did not enhance the checkpoint defect of cdc13-1 chk1Δ tpk2Δ cells ( Figure 5C ) , suggesting that Hxk2 is acting in the same pathway as PKA to help restrain mitosis following DNA damage . In addition , Hxk2 was not required for phosphorylation of the R subunit following DNA damage ( Figure 5D ) . Both Zds1 and Zds2 have been shown to be involved in regulating the cytoplasmic localization of the phosphorylated R subunit under conditions in which the phosphorylation of the R subunit was Mck1 dependent [42] . Thus we examined whether Zds1 , Zds2 and Hxk2 , which did not play a role in R subunit phosphorylation following damage , could regulate PKA by causing the cytoplasmic localization of the R subunit . We also examined whether the cytoplasmic localization following DNA damage was dependent on Mck1 , as it was required for the R subunit phosphorylation in this response . Localization of GFP-tagged Bcy1 was analyzed by microscopy in WT BCY1-GFP cells , cdc13-1 BCY1-GFP , cdc13-1 mck1Δ BCY1-GFP , cdc13-1 hxk2Δ BCY1-GFP , cdc13-1 zds1Δ BCY1-GFP and cdc13-1 zds2Δ BCY1-GFP cells released from a G1 block into the cell cycle at the restrictive temperature for cdc13-1 . As shown before , G2/M cdc13-1 cells had a significant decrease in nuclear enrichment of Bcy1-GFP signal in the nucleus , indicating cytoplasmic re-localization of the R subunit following a DNA damage signal ( Figure 6A , compare nuclear enrichment of Bcy1-GFP in CDC13 vs cdc13-1 cells ) . We found that the re-localization of R subunit in G2/M cells with a DNA damage signal was completely dependent on Mck1 . In fact there was no difference in the cells with damage signal lacking Mck1 ( cdc13-1 mck1Δ BCY1-GFP ) and the cells with no damage signal ( CDC13 BCY1-GFP ) . The re-localization of the R subunit was also dependent on Zds1 , Zds2 and Hxk2 . These results support a model ( Figure 6B ) in which phosphorylation and relocalization of the R subunit as well as cAMP are both required to establish the PKA signal following DNA damage in order to help restrain mitotic progression .
Previous studies showed that growing cells in non-fermentable carbon sources led to phosphorylation of the R subunit [41] , and that addition of glucose to cells growing in a non-fermentable carbon source caused a transient increase of intracellular cAMP [51] . From these data we postulated that activation of PKA in response to DNA damage is carried out in two steps: the phosphorylation of a small population of the R subunit , probably to provide the specificity of the signal , and a requirement for cAMP to activate PKA catalytic activity . We showed that in addition to the serine residues in clusters I and II of the R subunit , adenylate cyclase was also required for the DNA damage checkpoint mediated arrest that could be measured in cells lacking CHK1 . We have identified four proteins that have a role in regulating PKA activity in the DNA damage checkpoint . These proteins were previously implicated in regulation of PKA by 1 ) regulating the phosphorylation of the R subunit ( Mck1 ) , 2 ) regulating the cytoplasmic localization of the R subunit ( Zds2 ) [42] , and 3 ) regulation of cAMP levels ( Hxk2 , Cdc35 ) [39] . We found that all four proteins had a similar role to that of the PKA catalytic subunits in restraining mitosis suggesting that they function in the same pathway . However only Mck1 , but not Zds2 or Hxk2 , was required for the phosphorylation of the R subunit . Mck1 was originally identified and characterized by Shero et al . , as a high copy suppressor of a defect in chromosome segregation caused by a single base pair insertion in CDEIII , one of the three conserved DNA binding domains in the centromere of a marker chromosome fragment [52] . Disruption of Mck1 alone did not cause chromosome segregation defects but the mck1Δ cells were benomyl sensitive . Based on this role of Mck1 , it will be interesting to determine in the future whether Bcy1 phosphorylation after DNA damage plays a role in preventing chromosome segregation defects . Based on the functions previously attributed to these proteins and the evidence presented here we propose that in response to DNA damage the R subunit is phosphorylated in a checkpoint- and Mck1-dependent fashion . The phosphorylated R subunit , which could still be associated with the catalytic subunits , is further regulated by Zds2 , possibly by protein-protein interaction or sub-cellular localization . In the final step of regulation , Hxk2-regulated cAMP levels are required for activation of PKA signaling to help restrain mitosis . Our findings also implicate a possible role for Ras2 in PKA signaling in the DNA damage checkpoint as the cdc35-1 allele encodes a protein with a single amino acid substitution in the Ras-binding domain [49] . In fact , we recently reported that deletion of the IRA1 and IRA2 genes encoding negative regulators of Ras prevents cellular recovery from a cdc13-1-induced DNA damage induced arrest . The ira1Δ ira2Δ recovery defect required the PKA catalytic subunit Tpk2 and the PKA phosphorylation sites on the anaphase promoting complex specificity factor Cdc20 , indicating a link between the recovery defect and PKA regulation of mitosis [53] . Our data support a model in which the R subunit phosphorylation and cAMP signaling are working together to achieve the PKA-mediated restraint of mitosis following DNA damage ( Figure 6B ) . Zds2 may also help provide specificity to PKA signaling . We found that deletion of Zds2 , but not Zds1 , enhanced the checkpoint defect of chk1Δ cells . In fact , deletion of ZDS1 delayed the rate at which chk1Δ cells failed in the checkpoint-mediated arrest ( Figure S1A ) . Cells lacking ZDS1 have a delay in G2 and mitosis under normal growth conditions [47] . Thus , Zds1 may play a role in restraining anaphase that cannot be uncovered due to the fact that lack of Zds1 triggers a G2 delay . Furthermore , it was previously shown that both Zds1 and Zds2 were involved in regulating the cytoplasmic localization of the phosphorylated R subunit under conditions in which the phosphorylation of the R subunit was Mck1 dependent [42] . We show here that the cytoplasmic localization of the R subunit following DNA damage requires both Zds1 and Zds2 . Based on these data we cannot rule out a role for Zds1 in restraining mitosis following DNA damage . The Cdc14 phosphatase plays a key role in reversing Cdk phosphorylation and reducing mitotic cyclin/Cdk activity required for cells to exit mitosis [7] . It was recently shown that Zds1 and Zds2 are required for the timely activation of Cdc14 during mitotic exit . Cdc14 is sequestered in the nucleolus by the inhibitory protein Net1/Cfi1 prior to anaphase . This is achieved by the role of the phosphatase PP2ACdc55 in maintaining Net1/Cfi1 in the de-phosphorylated state . Separase , along with other proteins of the Cdc Fourteen Early Anaphase Release ( FEAR ) pathway , regulate Cdc14 release during anaphase by downregulation of PP2ACdc55 , which results in Net1 phosphorylation . Zds1 and Zds2 interact with PP2ACdc55 and are required for the separase-mediated downregulation of PP2ACdc55 to allow Cdc14 release [54] . One possible role for Zds2 ( and Zds1 ) in restraining mitosis would be that these proteins may act as scaffolds that maintain PP2A in an active state by regulating PKA localization . In this manner PKA could be antagonizing separase in the regulation of PP2A to release Cdc14 . Little is known about how Zds1 and Zds2 regulate R subunit localization , however it is possible that Zds2 is required for the export of the phosphorylated R subunit from the nucleus , but not the PKA catalytic subunit thereby maintaining active catalytic subunits in the nucleus to help restrain mitosis . Alternatively Zds2 could act to retain the holoenzyme in the cytoplasm . Zds1 has been shown to localize to the bud neck and cortex when overexpressed [55] , and complexes at the daughter spindle pole body , bud neck and daughter bud cortex have been shown to play roles in the spatio-temporal regulation of mitotic progression including regulators of MEN [56]–[58] and components of PP2ACdc55 [59] . Therefore , Zds2 ( and Zds1 ) could localize the PKA holoenzyme at multiple sites where it could play a role in regulation of mitotic exit: spindle pole body , daughter cortex or at the bud neck . In yeast increased cAMP due to intracellular acidification , or addition of glucose to cells growing in a non-fermentable carbon source require Hxk2 [39] . Our data show that either deletion of HXK2 or inactivation of adenylate cyclase enhanced the checkpoint defect of chk1Δ cell suggesting that Hxk2 regulates cAMP levels following DNA damage . Since we do not know whether or not cAMP levels increase in response to DNA damage , there are at least two models by which Hxk2 could be regulating cAMP in the checkpoint response . In the first model we propose that a basal level of cAMP , which is maintained by Hxk2 is sufficient for the activation of PKA in the checkpoint response . Alternatively the checkpoint could lead to increased cAMP levels in the cell by regulating Hxk2 and possibly Ras2 ( Figure 6B ) . Our data supports a model in which the DNA damage checkpoint can regulate the PKA pathway to induce specific PKA signaling in order to phosphorylate substrates that act to restrain mitosis . We identified three proteins ( Mck1 , Zds2 , Hxk1 ) that along with adenylate cyclase ( Cdc35 ) and the R subunit ( Bcy1 ) have a novel role in the DNA damage checkpoint via regulation of PKA . Mutation or mis-regulation of PKA subunits has been associated with chromosomal instability in cancer cells [60] . It will be interesting to determine whether PKA also participates in the response to DNA damage in mammals .
Strains used in this study are listed in Table 1 . Yeast strains were generated using standard genetic techniques . For deletion of BCY1 , a DNA fragment was generated by the PCR , which contained the URA3 gene flanked by 50 bp of sequences homologous to the 5′ and 3′-UTRs of BCY1 . The DNA fragment was then transformed into Y300 , resulting in the replacement of BCY1 with URA3 by homologous recombination . Gene disruption was confirmed by PCR . The BCY1:URA3 was introduced into the strains used in these studies by crossing . Deletion of ZDS1 , MCK1 , and HXK2 was carried out by using a construct generated by the PCR which resulted in amplification of the genomic DNA surrounding the gene that had been replaced with KanMX . Genomic DNA from strains from the deletion strain collection ( Open Biosystems ) were used as template for these reactions . The BCY1-GFP strains were generated by crossing clone YIL033C from the GFP-fusion library ( Invitrogen ) into Y300 background five times followed by crossing into cdc13-1 strain in Y300 background . cdc35-1 strains were generated by crossing the cdc35-1 allele from CMY282 [49] into the Y300 strain background before crossing into cdc13-1 strains . Plasmids used in this study are listed in Table 2 . MCK1 including upstream and downstream sequences was amplified using the primers: CTGGATCCTCTTCCCTCTTTCCCAATT , and GCTCTAGATAAACAGCGGATCA AAGG which contained a BamHI and XbaI site respectively . The amplified sequence was ligated into a pRS425 vector that had been cut with BamHI and XbaI . pJS11 was generated by subcloning BCY1 from pXP1 [61] into pRS425 using BamHI and HindIII . pYCJ1 was generated as described in [41] pYCJ2 was generated using a three step PCR method using pJS11 and the primers: Reverse: CTGGCTCGAGCTTGAGCTTGAGCTGCTTGAGGTCTGGAAAATGAC Forward CAAGCTCAAGCTCGAGCCAGAGCGGCTGTTATGTTCAAATCCCCC which generated the serine to alanine mutations described in [41] . The resulting PCR product was used to amplify the region upstream of the mutated sites using pJS11 as the template and the forward primer: CGTCCGACTTTCTTCAGTTC . The PCR product generated in the second PCR was used as the forward primer and the reverse primer: CGTCATACATGAGTCTCTTC were used to amplify the region between BspEI and BsrGI sites containing the nucleotide changes to generate the serine to alanine mutations . The resulting PCR product replaced the fragment generated by digestion of pJS11 with BspEI and BsrGI . Cells were grown in YPD rich medium or SC-Leu medium . When α-factor was added to synchronize cells SC-Leu or YPD at pH 3 . 9 were used . Cells were grown to OD600 = 0 . 5–0 . 8 at 22°C . Cells were synchronized in G1 using α-factor ( 10 µg/ml ) or in late S-phase using 200 mM Hydroxyurea ( HU ) as indicated . Unless indicated otherwise , the temperature was raised to 32°C for 60 min . prior to release . Cells were washed and re-suspended in YPD pH 3 . 9 at 32°C to release the cells into the cell cycle . To stop the cells from undergoing multiple cell cycles , α-factor was added back to the cells when cells released from a G1 block entered S-phase ( re-budded ) or immediately upon release for cells released from a late S-phase arrest . The cells were fixed and permeabilized using 70% ethanol . Cells were re-hydrated in PBS and stained with 0 . 1 mg/ml 4′ , 6-diamidino-2-phenylindole dihydrochloride ( DAPI , Sigma , St . Louis , MO ) . Cells were mounted on glass slides coated with 0 . 1% poly-L-lysine for microscopy . Protein extracts were prepared by trichloro-acetic acid ( TCA ) precipitation as previously described [62] . Proteins were separated on 10% acrylamide/0 . 067% bis-acylamide gels , and transferred to nitrocellulose membranes . Bcy1 was detected by Western analysis using anti-Bcy1 antibody ( Santa Cruz Biotechnology , Santa Cruz , CA ) . The immune complexes were detected by chemiluminescence ( NEN ) . Protein from cdc13-1 cells grown to OD600 = 0 . 5 at 22°C and incubated at 32°C for 120 min . was isolated by TCA precipitation . TCA precipitated protein was re-solubilized by boiling in buffer containing 1% SDS and 10 mM Tris pH 8 . Yeast lysis buffer with no SDS was used to dilute the solubilized protein solution so that the final SDS concentration was 0 . 1% . Bcy1 was immuno-precipitaed using anti-Bcy1 antibody ( Santa Cruz ) and protein-A sepharose beads ( Amersham ) . Bcy1 bound to the beads was treated with alkaline phophatase ( Boehringer Mannheim ) and/or phosphatase inhibitors ( 1 mM sodium orthovanadate , and 1 mM sodium fluoride ) . Bcy1 was released from the beads by boiling in Laemmli sample buffer ( 4% SDS , 20% glycerol , 10% 2-mercaptoethanol , contaning bromophenol blue and 0 . 125 M Tris-HCl pH 6 . 8 ) . Immuno-precipitated proteins were resolved and analyzed as described above . | Previous studies showed that phosphorylation of a subset of regulatory ( R ) subunits of the cAMP-dependent protein kinase ( PKA ) occurred under conditions that down-regulate global PKA activity , including growth on non-fermentable carbon sources . However , the role of the phosphorylation sites has not been elucidated . Addition of glucose to cells growing on a non-fermentable carbon source causes a transient increase of cAMP and PKA activity , which drives cells into S phase . A second peak in cAMP was proposed to restrain mitosis if the daughter cell had not reached an appropriate size . We identified a role for PKA in restraining mitosis following DNA damage . Here we provide evidence of cross-talk between the DNA damage checkpoint and PKA by phosphorylation of the R subunit . The R subunit phosphorylation sites and cAMP are necessary for the role of PKA following DNA damage . We propose that activation of PKA in response to DNA damage occurs in two steps: the phosphorylation of a subset of R subunits , probably to allow localized activation of these complexes , and cAMP to activate PKA . Our work suggests that the checkpoint and nutrient-sensing pathways share a signaling node to restrain mitosis following nutrient-induced rapid transition through the cell cycle and DNA damage . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbiology",
"mitosis",
"model",
"organisms",
"cell",
"division",
"chromosome",
"biology",
"biology",
"molecular",
"biology",
"signal",
"transduction",
"pka",
"signaling",
"cascade",
"cell",
"biology",
"yeast",
"and",
"fungal",
"models",
"molecular",
"cell",
"biolo... | 2011 | Proteins in the Nutrient-Sensing and DNA Damage Checkpoint Pathways Cooperate to Restrain Mitotic Progression following DNA Damage |
Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century . However , sleep scoring cannot currently be performed with brain signals alone , despite the deep neuromodulatory transformations that accompany sleep state changes . Therefore , at heart , the operational distinction between sleep and wake is that of immobility and movement , despite numerous situations in which this one-to-one mapping fails . Here we demonstrate , using local field potential ( LFP ) recordings in freely moving mice , that gamma ( 50–70 Hz ) power in the olfactory bulb ( OB ) allows for clear classification of sleep and wake , thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity . Coupled with hippocampal theta activity , it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone . This method reaches over 90% homology with classical methods based on muscular activity ( electromyography [EMG] ) and video tracking . Moreover , contrary to EMG , OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing . We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time , across individual mice and mouse strains , and under classical drug treatment . Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory , falling asleep is best described as a stochastic and gradual state change . Finally , we demonstrate that OB oscillations also allow us to track other vigilance states . Non-REM ( NREM ) and rapid eye movement ( REM ) sleep can be distinguished with high accuracy based on beta ( 10–15 Hz ) power . More importantly , we show that depth of anaesthesia can be tracked in real time using OB gamma power . Indeed , the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period . Altogether , this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance .
Defining the different states of brain activity and describing how they impact the computations performed by neural networks is an ongoing challenge in neuroscience . In our day-to-day lives , the most dramatic change of state is found at the frontier between sleep and wakefulness , which involves substantial modifications at the level of the brain [1–3] and throughout the whole organism [4 , 5] . Despite these profound transformations , to date , we surprisingly lack an easily measured marker of brain activity that allows unambiguous , moment-to-moment identification of sleep and wake states . Sleep scoring methods in human research are now widely accepted throughout the scientific community [6 , 7] , but there is no clear consensus for rodent studies [8] . Commonly used methods are generally completely manual or rely on manually scored training data to calibrate automatic algorithms ( Table 1 ) , therefore adding the problem of interscorer variability to the already existing issue of variability between laboratories . Moreover , all current sleep scoring methods essentially rely on motor activity to discriminate sleep from wake ( see Table 1 ) . This renders all methods inherently vulnerable to any mismatch between these brain states and the level of motor activity such as during freezing—a commonly used behaviour in mice—or any sleep anomalies causing movement during sleep [9] . The state of the art therefore presents both a conceptual and a technical problem regarding the identification of sleep and wake . Sleep scoring methods are based on the assumption that the information about sleep states is contained in classically recorded signals and that the challenge is to extract the correct marker from them [10] . In order to implement a reliable sleep scoring , the candidate marker of sleep and wake must show a systematic and sustained difference in value throughout each state . Despite multiple studies , such a clear-cut situation has never been found when using brain signals [11] . Therefore , attempts to identify sleep with brain signals rely on more elaborate methods that extract composite features from local field potential ( LFP ) data [12] . However , the first step of dimensionality reduction is highly dependent on the data at hand and often results in mapping data onto axes that vary between animals and sessions , requiring post hoc human labelling procedures [12] . To compensate for the poor quality of the brain-related sleep markers , machine learning has been used in several studies but with no decisive improvement [13–16] . Here we propose a novel brain-related marker allowing us to reliably track transitions from sleep to wakefulness: gamma power ( 50–70 Hz ) measured in the olfactory bulb ( OB ) . This oscillation has been previously shown to vary between sleep states [17] , but we show here that it displays the desired characteristics to continuously identify these states . This oscillation is strongly suppressed during sleep and continuously present during waking . Importantly , the distribution of gamma power follows an easily separable bimodal distribution , the optimal situation for an automatic separation procedure . Coupling this indicator with the classical hippocampal theta/delta power ratio allows us to construct a fully automated sleep scoring algorithm that classifies wake , rapid eye movement ( REM ) sleep , and non-REM ( NREM ) sleep based on brain state alone . We then use these variables to construct a robust 2D phase space that is highly robust across mice—from the same and different strains—and days . This phase space forms the basis of an innovative analytical methodology for the study of fast timescale transitions using kinematic modelling of trajectories . As an application , we provide strong evidence for a deep asymmetry between transitions from sleep to wake and wake to sleep . In particular , the awakening transition displays strongly driven , fast dynamics , whereas the process of falling asleep is more stochastic and slower . Finally , we show that OB LFP activity is a remarkably versatile indicator of global brain states: beta power can be used to discriminate REM and NREM sleep , while gamma power not only tracks changes in vigilance between sleep and wake but also predicts in real time the depth of anaesthesia .
Classical sleep scoring methods differentiate sleep from wake states using electromyography ( EMG ) activity or the animal’s motion recorded using accelerometers or video tracking ( see Table 1 , [11 , 14 , 15 , 18–23] ) . Theta and delta power recorded in the hippocampus ( HPC ) or cortex ( due to the volume conduction of theta oscillations ) can then be used to discriminate REM from NREM sleep . According to classical sleep scoring methods , wake is defined by high EMG activity and irregular HPC activity during quiet wake or theta oscillations during active exploration . Sleep , in contrast , is defined by low EMG power . NREM is discriminated from REM sleep using the theta/delta power ratio in the HPC: during NREM , delta power is strong , whereas highly regular theta oscillations are observed during REM ( Fig 1A ) . Similar results can be obtained when recording electroencephalography ( EEG ) rather than LFP . In order to construct a sleep scoring method that relies on brain signals alone , we screened multiple brain regions to find a good predictor for discriminating between sleep and waking states . We recorded from multiple brain regions in 15 freely moving mice: the OB , the HPC , the prefrontal cortex , and the parietal cortex . Mice were recorded for an average of 6 . 6 ± 0 . 58 h ( minimal recording length: 2 h ) in their home cages in the light period and slept on average 58% of the time . We initially used a classical sleep scoring method based on movement and hippocampal activity to establish a database of recordings from different brain states using 10 mice that were also implanted with an EMG wire in the nuchal muscles ( n = 6 ) or tracked using video ( n = 4 ) . The average spectra over wake , NREM , and REM periods are shown in Fig 1B . In cortical and hippocampal areas , as expected , REM and NREM showed strong differences in the theta and delta band , and wake periods showed less low-frequency power . In cortical and hippocampal areas , no individual frequency band allowed a clear discrimination between sleep and wake . However , we found a strong increase in power in the OB during waking relative to sleep states . This difference was strongest in the low-gamma band centred at 60 Hz , as previously described [17] . OB activity is modulated by the breathing cycle in all vigilance states but displays a sustained gamma band oscillation only in the wake state ( Fig 1A ) . Crucially , gamma power was low in both NREM and REM sleep states and could therefore allow the distinction of wake from REM sleep . Gamma activity is therefore a candidate replacement for muscular activity in sleep scoring . OB gamma power displays strong fluctuations correlated with breathing activity on the scale of around a second [17] . To find the appropriate timescale for tracking the changes in gamma power related to brain state changes , we applied a smoothing window of varying length to the instantaneous gamma power . As the smoothing window increased in length , the distribution of gamma power became more distinctly bimodal and the two underlying distributions clearly separated ( Fig 1C and 1D ) . We found that smoothing windows of 1 s or above produced two normal distributions that overlapped by less than 5% ( Fig 1D bottom ) . This analysis allowed us to establish a set of parameters ( frequency , smoothing window ) for which gamma power in the OB is a promising marker for discriminating between wake and sleep on fine timescales of the order of 1 s without any reliance on muscular activity . A schematic of the sleep scoring algorithm is shown in Fig 2A . All steps are automatic and do not require any supervision by the user . The first step classifies data into sleep and wake periods . Instantaneous smoothed gamma power in the OB shows a bimodal distribution that can be well fit by a sum of two Gaussian functions ( Fig 2B ) ( mean R2 = 0 . 98 ± 0 . 009 ) . The two component distributions correspond to gamma power during sleep and wake periods , respectively . Since the amplitude of these distributions depends on the proportion of time spent in each state , they are normalised to unit area , and the sleep/wake threshold is defined as the intersection of the two corresponding Gaussian curves ( Fig 2Bi ) . Below-threshold values of gamma power are defined as sleep and above-threshold values as wake . The second step classifies sleep data into REM and NREM periods . The theta-to-delta ratio in the HPC during sleep shows a clear peak of low values corresponding to the predominant NREM phase and a flat shoulder of higher values corresponding to the rare REM phase ( Fig 2Bii ) . Fitting a Gaussian distribution to the lower values allows us to automatically place a threshold to discriminate REM from NREM sleep . Each time point is now attributed to one of the three states based on its OB gamma power and HPC theta/delta ratio . Short epochs of any give state lasting less than 3 s are merged with the neighbouring states . An example session is shown in Fig 2C and 2D that illustrates the construction of a 2D phase space for brain states ( Fig 2C ) . This space demonstrates the clear separation of brain states even after the merging and dropping of short epochs . This shows that the continuity hypothesis does not lead to any aberrant classification ( see S1 Movie for data from another mouse ) . We validated the sleep scoring algorithm by comparing it to manual sleep scoring performed using HPC LFPs and EMG activity , the classical golden standard . Two expert scorers independently scored sessions from 4 mice with an average interscorer overlap of 89% ± 3% ( Cohen’s κ: 0 . 81 ) . On average , the OB-based and manual sleep scoring overlapped by 90% ± 2% ( Cohen’s κ: 0 . 83 ) throughout the sessions ( Fig 3A ) . We also performed scoring using an automatic EMG algorithm . Agreement between the OB gamma scoring and the automatic EMG scoring was 93% ( Cohen’s κ: 0 . 85 ) ( Fig 3B ) . Moreover , EMG power and OB gamma power were highly correlated and time locked at transition points ( Fig 3C and 3D ) . Some time points show disagreement between the two variables ( Fig 3C ) ; this shows that by coupling OB gamma activity and EMG measurements , we can hope to clarify the definition of quiet wake and movement during sleep . We next compared how clearly bimodal the distributions of these two variables were to evaluate how well they separated sleep and wake states . Both variables were strongly bimodal , according to Ashman’s D ( Fig 3E , left ) ; however , the error made when fitting their distributions with two Gaussians was higher for EMG power ( Fig 3E , centre and right ) . We found that this higher error was explained by a larger proportion of values in the trough between the two Gaussians for EMG power ( 11% ± 2% for EMG and 4% ± 3% for gamma power ) . This indicates that ambiguous intermediary values between clearly defined sleep and wake are more frequent when using EMG than OB LPF , leading to more potential errors in scoring . This demonstrates that sleep scoring using gamma power in the OB or using EMG , with either automatic or manual methods , gives very similar classification of brain states , confirming that OB gamma power is a good predictor of wake and sleep as classically defined . Moreover , gamma power provides distributions with a clearer separation than EMG power , making it a more reliable predictor . Sleep scoring is often performed on large batches of animals , requiring simple surgeries and a high success rate . It is known that theta rhythm can be easily recorded in the hippocampal area using a single LFP wire . How does the OB gamma power used for sleep scoring depend on the exact placement of the recording site ? To answer this question , we simultaneously recorded activity from multiple depths in the OB covering the outer and inner plexiform layers , the mitral cell layer , and granular cell layer using a 16-site linear probe ( Fig 4A ) . We found that gamma oscillations could be observed at all depths , and sleep scoring performed using electrodes at all depths highly overlapped ( >92% ) with classical , movement-based sleep scoring ( Fig 4B ) . We however observed that the separation between wake and sleep peaks was best in the deeper recording sites , and in particular , the most coherent scoring was found in those sites within the granule cell layer where gamma oscillations are visibly stronger ( Fig 4C and 4D ) . This demonstrates that placement of the LFP wire for reliable scoring does not require great precision during implantation , assuring good scoring for all implanted animals . The granule cell layer , however , appears to be the optimal anatomical region to ensure reliable scoring , since it shows the clearest bimodality of gamma power . The coordinates we recommend aim for the centre of this zone ( anteroposterior [AP] +4 , mediolateral [ML] +0 . 5 , dorsoventral [DV] −1 . 5 ) . We also observed that gamma power recorded in the piriform cortex could be used for reliable scoring ( S4 Fig ) . An optimal sleep scoring technique must provide reproducible results in the same animal throughout time and easily comparable results between animals . In other words , the phase space used to define sleep states must be stable . This phase space was constructed so that the separation between wake and sleep on the one hand and REM and NREM on the other hand used orthogonal axis . This simple space is remarkably consistent among animals and across days , as can be seen by the similar position of the clouds of points representing each state ( Fig 5A ) . We first quantified this similarity in the same animals between days and between light and dark cycles . We used the thresholds defined for one animal on a given light cycle to score test data from the same animal on a subsequent light or dark cycle . The scoring was then compared with that obtained using thresholds determined on the test data . We found that the observed consistency between phase spaces was sufficient to perform highly accurate scoring on the next day’s light cycle ( average over recordings: 97% ± 0 . 5% , Cohen’s κ: 0 . 97 , n = 15 , Fig 5Bi ) and during the following dark cycle ( average over recordings: 96% ± 0 . 9% , Cohen’s κ: 0 . 94 , n = 4 , Fig 5Bii ) using independently defined thresholds . We also found that stable recordings could be obtained for up to 10 wk with maintained thresholds ( S1 Fig ) . Finally , since gamma oscillations in the OB have been linked with information processing and novelty [24] , we exposed 8 mice to a novel environment for 15 min , during which the animals actively explored . On average , only 2% ± 1 . 1% of the time was misclassified as sleep . This demonstrates that any changes in gamma activity linked to behaviour remain well within the bounds of the wake state as previously defined . We next asked whether our method would allow for easy comparison between animals by comparing the phase space used for sleep scoring across individuals . We found that after normalising distributions to the mean NREM values , to account for changes in implantation depth between animals , both OB and HPC distributions were highly reproducible across mice , and the independently determined thresholds had very close values ( Fig 5C ) . Scoring one animal using the thresholds determined for another , we found that scoring was also highly reliable ( average over recordings: 90% ± 2 . 5% , Cohen’s κ: 0 . 85 , n = 15 , Fig 5D ) . We tested whether our novel algorithm was applicable to studies using drug administration by applying it to mice injected with the classical antidepressant fluoxetine that is well known for its REM-suppressing effect . Sleep scoring using this method reproduced the well-known reduction in REM sleep as expected , but more importantly , it agreed with classical EMG-based scoring after drug delivery , and no change in the phase space was observed ( S2 Fig ) . We also verified that the method was applicable in three other mouse strains: Gad2-IRES-Cre knock-in C57BL/6J Rj ( n = 4 ) , C3H/HeNRj ( n = 2 ) , and DBA/2Rj ( n = 3 ) ( S3 Fig ) . Blindly applying the algorithm to these strains produced remarkably similar phase space of brain states and a good overlap with classical EMG methods in all lines . Indeed , despite some large changes in the OB power spectra between strains , they all shared the common feature of a difference between sleep and wake in the 50–70 Hz band , which allowed us to generalise our methodology . For C3H and more particularly for DBA mice , the OB spectrum appears as a smooth 1/f slope; however , subtraction of the spectra from sleep and wake states ( S3B Fig ) clearly reveals the wake-specific 50–70 Hz oscillation that is otherwise obscured by the other oscillation at 30 Hz . It is only by using multiple mice strains recorded during different brain states that we can clearly identify that the apparently continuous OB spectrum in these strains can be decomposed into two different peaks . This demonstrates that brain state–related changes in OB gamma power are quantitatively robust over multiple days and throughout the circadian cycle . Moreover , the phase space thus constructed is highly reproducible between animals . Finally , the method can be applied to studies involving pharmacological manipulation and generalised to other mouse strains . This makes OB gamma power an excellent parameter to use for automatic methods of scoring and a promising tool for comparing sleep in cohorts of animals . A major issue with current approaches to sleep scoring is that EMG activity conflates absence of movement and sleep , which suffers from notable exceptions such as freezing behaviour . Freezing is a widely studied behaviour in paradigms such as fear conditioning . It is defined as a complete absence of all movement except for respiration . This absence of movement is associated with a strong drop in EMG power . Although it has been shown that on average EMG power is lower during sleep than freezing [25] , we investigated whether freezing could be misclassified as sleep using EMG power and whether OB gamma power could resolve this issue . Six mice were therefore fear conditioned by pairing tones with mild foot shocks and , during test sessions , displayed robust freezing to tone presentation ( post conditioned stimulus not associated with shock [CS−] freezing: 25% ± 10%; post conditioned stimulus associated with shock [CS+] freezing: 67% ± 13% ) . The example session shown in Fig 6A illustrates the strong expected drop in EMG power during freezing periods , sometimes below the sleep/wake threshold independently determined during a previous home cage session . Although the EMG power is indeed on average higher during freezing than during the sleep state , certain freezing time points can be misclassified as sleep ( Fig 6B ) . Moreover , EMG shows a similar drop in power at freezing and sleep onset ( Fig 6C ) . In sharp contrast , OB gamma power remains systematically above the sleep/wake threshold ( Fig 6B ) . Gamma power triggered on freezing onset shows that the variable is independent of this abrupt change in movement ( Fig 6C ) . Freezing is a behaviour that dissociates complete immobility from sleep , allowing us to clearly show that OB gamma power is tracking transitions from wake to sleep and not from mobility to immobility . EMG , in contrast , is an unreliable marker for sleep scoring when animals are susceptible to display immobility during wakefulness . Our automated sleep scoring method classifies all data points into periods of wake , NREM , and REM . Thus considered , changes in vigilance are reduced to a sequence of steady states , yet a crucial aspect of sleep concerns the transitions between states . Here we show that these transitions can be studied by visualizing moment-to-moment variations in brain state as a moving point in the previously described phase space ( S1 Movie ) . The trajectories can then be studied using tools from statistical physics . Shifting to this finer timescale , we replaced the previously used threshold with a transition zone , consistent with many experts arguing that there is no single time point corresponding to the onset or offset of sleep [26] . For each point in phase space , the probability of remaining in the current state ( the ‘stay probability’ ) is calculated , revealing highly stable zones far from the thresholds ( Fig 7A , white ) and zones of instability in which the state of the brain is changing close to the thresholds ( Fig 7A , black ) . Two transition zones naturally emerged along the gamma power axis between sleep and wake states and the theta/delta ratio axis between REM and NREM . In the rest of the study , we focus on the sleep/wake transition . The sleep/wake transition zone can be identified by measuring the average stay probability along the gamma power axis ( Fig 7A , lower panel ) , which displays a strong dip between sleep and wake states . We identify this low stay probability region as the transition zone , which is shown between the grey lines in Fig 7A . We propose three measures based on this transition zone to study the dynamics of state change and illustrate that it allows for novel characterisation of the sleep/wake transition . First , we defined ‘true transitions’ as a full crossing of the transition zone . We found that the duration of this crossing depended on the direction . Wake-to-sleep transitions were significantly slower than sleep-to-wake transitions ( Fig 7B ) . Second , we also defined ‘aborted transitions’ during which the brain state enters this transition zone but does not make a full crossing . Aborted wake/wake transitions were more frequent than sleep/sleep transitions ( p = 0 . 005 , n = 15 , paired t test ) . These aborted wake/wake transitions tended to be strongly grouped around the 30 s preceding complete wake/sleep transitions , whereas sleep/sleep transitions were only weakly related in time to sleep/wake transitions ( Fig 7C ) . This indicates that falling asleep is preceded by many aborted transitions contrary to waking up . Finally , we adopted a dynamic approach to analyse these trajectories . Differences in transition durations ( Fig 7B ) from wake to sleep and sleep to wake can be interpreted as differences in speed only if the movement is well described by ‘ballistic’ motion . Ballistic motion indicates that time , speed , and distance are linearly related , as when an apple falls to the ground ( Fig 7H , grey ) . This is not true for all types of motion , particularly those describing stochastic processes such as diffusive motion , as when a grain of pollen diffuses on a bowl of water ( Fig 7H , black ) . To evaluate whether the observed motion is closer to the ballistic or diffusive regime , the mean square displacement as a function of time can be modelled thus: MSD ( t ) =〈 ( x ( t+τ ) −x ( τ ) ) 2〉=2Dtα Theory of Brownian motion indicates that when alpha = 2 , the movement is ballistic , and for alpha = 1 , the movement is diffusive . For alpha < 2 and > 1 , the movement is described as superdiffusive . Transitions with essentially similar dynamics but different speeds are described by the same value of alpha but different values of D . On the other hand , a change in the value of alpha argues for different transition dynamics and thus potentially different underlying mechanisms . Fig 7D–7G compares the same number of transition trajectories from wake to sleep ( top ) and from sleep to wake ( bottom ) . The wake-to-sleep trajectories appear much more convoluted and tile the plane more densely than the sleep-to-wake trajectories , suggesting that their movement is more diffusive . The mean square displacement curves for both transitions types clearly segregate into two groups with a faster rise time for the sleep-to-wake transition , as expected ( Fig 7I ) . We can now ask what the underlying difference between these two curves is by fitting them to the simple equation as defined above ( mean R2 = 0 . 996 , all R2 larger than 0 . 988 ) . Fitting reveals that the motion from sleep to wake is ballistic ( mean alpha = 2 . 1 ) , whereas the motion from wake to sleep is more diffusive ( mean alpha = 1 . 47 ) ( Fig 7J ) . Overall , using the phase space approach , we can clearly demonstrate a strong and novel dichotomy between sleep–wake and wake–sleep transitions . In particular , the awakening transition is fast and ballistic , whereas the process of falling asleep is slower and more stochastic , preceded by multiple failed attempts to transition . Finally , the frequent aborted sleep/sleep transitions that do not precede actual awakenings may be linked to subthreshold arousal phenomena such as the cyclic alternating pattern [27] . When considering previous observations , the most likely explanation is that OB activity can act as an index of vigilance state because it reflects the ongoing neuromodulatory state of the brain , probably because of the numerous projections coming from regions in the brain stem known to regulate sleep and wake ( see Discussion ) . If this reasoning is correct , OB activity should also be different between REM and NREM sleep , given that they show very different neuromodulatory profiles . Accordingly , we observed that beta range activity ( 10–20 Hz ) in the OB was much weaker during REM sleep than NREM sleep ( Fig 8A ) . Activity in this band during sleep clearly segregated into two levels that strongly correlated with HPC theta/delta ratio ( Fig 8B ) . The distribution of OB beta power is similar to that described for the HPC theta/delta ratio: a clear peak of values corresponding to NREM sleep and a broader slab of values for REM sleep . We therefore placed an automatic threshold to separate the two states using the same methodology as for the HPC and applied the rule that beta power below threshold corresponded to REM sleep and above threshold to NREM sleep . We then evaluated the agreement with data scored using HPC LFP . We found that there was a good agreement between the two methods: 97% of NREM periods and 75% of REM periods , as defined by the HPC , were correctly identified using OB scoring ( Fig 8D ) . REM sleep defined by OB scoring corresponded to HPC-defined REM sleep in 83% of cases . We next more closely investigated the periods of REM sleep ( 25% ) identified by the HPC that were misidentified as NREM based on the OB beta power ( Fig 8D ) . We found that in fact some REM bouts were missed entirely when using the OB beta method , accounting for 14% out of the 25% error rate , and they tended to be particularly short ( Fig 8E ) . The finding that OB beta activity can be used to identify REM sleep opens the possibility of sleep scoring using a single wire in the OB to track wake , REM , and NREM sleep , with most errors being restricted to short bouts . This modulation of another OB rhythm by brain state confirms our hypothesis that activity in the brain region is highly sensitive to neuromodulatory changes . Given the remarkable link between OB oscillations and sleep/wake states , we asked whether OB gamma could provide an even more general indicator of vigilance by investigating the impact of anaesthesia . We evaluated the impact of isoflurane and ketamine/xylazine anaesthesia on OB oscillations and linked these changes to two classical measures of anaesthetic depth: response to noxious stimulation and loss of righting reflex ( Fig 9C ) . The 50–70 Hz band that is prominent during wake is suppressed during both sleep and anaesthesia ( Fig 9A and 9B ) . Under anaesthesia , we in fact observed a steady decrease in global power with depth of anaesthesia , which can be fully tracked even after restricting analysis to the 50–70 Hz band . This suggests that measuring gamma power not only provides information about the transition from wake to sleep to anaesthesia but could also be used to monitor depth of anaesthesia . We first imposed different levels of steady-state isoflurane anaesthesia and measured the corresponding activity in the OB . We found that OB gamma power tracks isoflurane concentration increase concomitantly with the loss of righting reflex ( Fig 9D ) . During anaesthesia , we electrically stimulated the eyelids of the mice to provide a reproducible and time-locked measure of their responsivity to noxious stimulation and evaluated their motor responses every 2 min . Predictably , response to stimulation was reduced as isoflurane concentration increased , and the average gamma power tracked this change closely ( Fig 9E ) . These results show that the steady-state value of OB gamma under anaesthesia is a good correlate of two behavioural measures of anaesthetic depth: the righting reflex and response to stimulation . We next asked whether OB gamma power can follow dynamic changes and monitor in real time the depth of anaesthesia . We addressed this question by evaluating whether OB gamma power could predict stimulation responsivity during anaesthesia recovery . Once mice had reached the deepest level of isoflurane anaesthesia , we stopped isoflurane delivery and left them to regain consciousness in their home cage whilst continuing to stimulate at regular intervals so as to track their arousal level ( Fig 9F ) . As can be seen in Fig 9F , as mice began to respond to stimulation and regained motility , the OB gamma power increased with a strikingly similar time course . We found that OB gamma activity during the 3 s prior to eye shock stimulation ( excluding 500 ms prior to stimulation to avoid spectral leakage ) can predict the animal’s future response . First of all , the gamma power in the 3 s before the shock correlated very strongly with the animal’s response ( Fig 9G ) . To evaluate the relevance of this observation , we compared it with one of the main physiological markers of anaesthetic depth , heart rate , and found that the OB gamma was a more reliable correlate of stimulation response . Since the relationships between these variables and stimulation responsivity may follow an unknown nonlinear function , we used the Box-Cox transformation , which explores a smooth range of monotonic functions to find the transformation that yields a maximally linear relationship . This allows a fair comparison of correlation strength between our two variables despite potential nonlinearities in the data . Taking the maximal correlation after transformation , heart rate also correlated well with stimulus reactivity ( Fig 9H ) ; however , OB gamma power systematically showed a stronger correlation ( Fig 9I ) . As can be seen in Fig 9F , reactivity to stimulus is clearly bimodal during recovery , allowing us to place a natural threshold between ‘sedated’ and ‘aroused’ responses . By using receiver operating characteristic ( ROC ) curves , we showed that OB gamma power before stimulation almost perfectly predicts the state of anaesthesia based on the binarised response to noxious stimuli ( Fig 9J and 9K ) . As for the correlation measures , OB gamma was a better predictor than heart rate ( Fig 9J and 9K ) . These results demonstrate that OB activity is as sensitive to levels of vigilance and reactivity during anaesthesia-induced sedation as it is to natural changes during sleep and wake . The OB gamma power can therefore also be used to track depth of anaesthesia with high accuracy and in real time , since it can predict and in fact anticipate the future reaction to stimulation .
Since its invention , EEG has been the tool of choice to attempt to monitor vigilance states using brain activity . This endeavour has been most successful in identifying wake , sleep , and the substages of sleep , and studying their microstructure has become an essential aspect of understanding the brain in health and disease [27] . Therefore , sleep scoring is the essential first step in any study of global brain states . Here we establish for the first time , to our knowledge , a methodology for addressing this issue with brain signals alone . We found that gamma oscillations in the OB allow us to continuously discriminate sleep from wake , substituting muscular activity or body movements , which are required in all the other sleep scoring methods . They can also be used to transpose the evolution of brain states into a phase space in which the fine dynamics of state transitions can be analysed . This methodology also captures the modification of vigilance state induced by anaesthesia . We demonstrated that OB gamma activity can predict and in fact anticipate the response to noxious stimulation and can therefore be used as a real-time tracker of anaesthetic depth . Altogether , this shows that OB activity can be used to track vigilance states in real time in a wide range of situations with high accuracy without having to rely on muscular or other body-related measures . There is a long history of the study of the link between activity in the olfactory system and brain states , which has in particular focused on the correlation of OB oscillations and arousal level . The earliest recordings made in the OB by Adrian identified the fast oscillations under anaesthesia [28] but already noted an ‘awakening reaction’ as anaesthesia gradually lightened [29] . He therefore suggested that the OB oscillations are damped by anaesthetic drugs and increased by waking activity . Later , ‘arousal discharges’ ( 34–48 Hz ) were identified in the cat OB that were potentiated by attention and disappeared during sleep and anaesthesia [30 , 31] . These global changes in OB oscillatory profile depending on level of arousal have been described and modelled as a hierarchy of attractors with increasing potential for fast oscillatory activity ranging from anaesthesia to seizure [32] . More recent studies have confirmed these changes in OB activity related to sleep states [17] and anaesthesia [33 , 34] . The novel sleep scoring method we propose relies on activity recorded in the HPC and the OB only . Implantation of electrodes for recording LFP in these two areas is easy to achieve because they both show robust oscillations in the theta and gamma ranges respectively . After implantation , the method is fully automated and therefore removes the time-consuming steps of manually scoring the data set in its entirety or in part to calibrate semiautomatic algorithms . We have shown that this method for sleep scoring is robust to slight changes in implantation site , across days , and between different animals . This robustness to implantation site may seem surprising given the high spatial heterogeneity of gamma oscillations in the HPC [35] and cortex [36] . However , in the OB , oscillations in the gamma range are highly coherent across large areas , despite fast-varying phase shifts [37] . Finally , the method is applicable to multiple strains of mice and after drug injection . It therefore allows easy comparison between mice and throughout time and should enhance comparability of data sets from different laboratories . Beyond the technical ease of use , this method also provides a promising framework for the study of global brain states . Using activity recorded in the brain and not muscle activity allows us to track sleep/wake activity independently of movement , as we demonstrated with the example of freezing , a period of tonic immobility linked with fear expression . This could provide a heretofore-lacking methodology to study phenomena such as REM without atonia induced in lesion studies [38] . Finally , gamma activity in the OB is a variable with fast dynamics that allows us to study fine timescale transitions not accessible to other , slower sleep-related oscillations such as delta power . The mechanisms that control the transitions between sleep and wake have been extensively studied [39] , but how they relate to changes in more global physiological variables is an open and essential question . The process of falling asleep has been described using behavioural , physiological , and electrophysiological variables as a progressive sleep onset period in humans [26] . After awakening , the prolonged deficits in performing certain tasks , named sleep inertia [40] , has been described in humans and in rats , and mice OFF periods were observed up until 5 min after awakening [41] . All these studies demonstrate that transitions should not be considered as points in time but instead as extended periods justifying our use of transition zones . We advocate constructing phase spaces with electrophysiological variables ( OB gamma and HPC theta/delta ratio ) capable of tracking high-speed changes in the brain state . This phase space remains stable in time and is reproducible between animals , which is a crucial step towards characterising the dynamical evolution of sleep states . Tracking activity within this phase space allows us to model changes in brain state with the rich repertoire of tools taken from statistical physics . The efficiency of this approach is illustrated by its ability to capture the deep asymmetry between falling asleep and waking up [42] . Indeed , falling asleep can be modelled as a slower , diffusive process , whereas waking up corresponds to a faster , ballistic change . This is consistent with the fine evolution of neuronal activity at transition times in brain stem and hypothalamic nuclei responsible for the sleep/wake switch that show faster dynamics upon awakening than falling asleep [43–46] . We also showed that OB activity in the beta range ( 10–20 Hz ) can be used to identify REM and NREM sleep with high reliability . The rare errors of discrimination concern particularly short REM bouts . Given the strength of HPC theta in the rodent allowing for clear identification of REM sleep , we still recommend recording in the HPC whenever possible . However , the use of a single wire in the OB to discriminate sleep and wake ( OB gamma ) and REM and NREM sleep ( OB beta ) could be a useful strategy when using bulky headstages , such as head-mounted microscopes , that may block access to the HPC . Although our main focus is on the ability of OB gamma to discriminate sleep from wake , since this was an important issue in the field of sleep scoring , we also show that it can be used to track anaesthetic depth in real time . Steady-state OB gamma power systematically decreased as we induced deeper levels of anaesthesia by increasing isoflurane concentration and therefore was strongly correlated with two different measures of anaesthetic depth: the loss of righting reflex and the response to eye shock stimulation . Moreover , OB gamma power also closely tracked the changing level of arousal as mice recovered from anaesthesia . It could predict the reaction level to a future stimulation with remarkable accuracy and systematically outperformed another common marker of anaesthesia level , heart rate . These results are particularly striking , since , to date , we lack a clear marker to monitor depth of anaesthesia in surgical settings [47 , 48] , and in particular , the most commonly used indicator , the bispectral index ( BIS ) , fails when ketamine is administered [49] . The similarity of OB activity under isoflurane and ketamine/xylazine anaesthesia therefore makes it a remarkably polyvalent marker to track depth of anaesthesia . Altogether , these observations raise the question as to why , compared with other areas , the OB is so well suited to tracking global changes in brain state . We propose that the OB is ideally situated to produce oscillatory activity that is exquisitely sensitive to brain state , since it receives massive inputs from most of the neuromodulator systems and notably those involved in the control of vigilance: cholinergic [50 , 51] , hypocretinergic/orexinergic [52] , and noradrenergic [53] . Moreover , receptors of these neuromodulatory systems are strongly expressed in the OB [54 , 55] . In turn , the different neuromodulators have been shown to modulate gamma oscillations [34 , 56–58] . More specifically , it is widely agreed upon that the mechanism for gamma generation in the OB relies on the dendrodendritic interactions between mitral/tufted cells and the local granule cells that provide recurrent and lateral inhibition [59–61] . The strength of this inhibition has been shown to be modulated by global brain state via a cholinergic mechanism [62] . This pattern of a macroscopic physiological parameter serving to amplify fluctuations in neuromodulatory tone is reminiscent of a recent method that has been proposed to continuously track vigilance states by recording pupil diameter [63] . It was shown that pupil diameter follows the activity of cholinergic and adrenergic activity in the cortex [64] . Finally , the difference of OB beta power between REM and NREM strongly argues in favour of OB activity providing a general readout of the global neuromodulatory state of the brain . It is possible that OB activity is not just an index of arousal but may actually take an active part in its regulation . In seminal work , Arduini and Moruzzi studied acute cerveau isolé cats and found that stimulation of the olfactory system by blowing air into the nostrils led to arousal , contrary to visual stimulation [65] . These results did not depend on the use of odour stimulations , which suggests that the role of the olfactory system in arousal goes beyond that of the olfactory sensory modality per se . Moreover , perturbation of the olfactory system leads to changes in sleep structure [66] . These observations have led to the proposal that the OB may actively participate in a secondary arousal system [67 , 68] linked to limbic areas that complements the ascending reticular activating system [69] . Even without a direct influence on the regulation of vigilance states , OB gamma activity could also have a functional role in regulating reactions to sensory stimuli during sleep . The functional importance of OB gamma oscillations was first brought to light by the seminal work of W . J . Freeman , in which analysis of the spatial spread of odour-induced gamma amplitude revealed that these patterns changed with learning and were related to the behavioural meaning of the odour [70 , 71] . Since , gamma oscillations in the olfactory system have also been shown to be modified during learning [72] , and their perturbation by pharmacological [73] or genetic [74] tools interfere with odour processing . The importance of gamma oscillations in sensory processing leads us to hypothesise that their suppression during sleep could provide a mechanism for sensory gating , as has been suggested previously [75] . During sleep , cortical responses to sensory stimulation are still observed [76–78] , but they are not accompanied by conscious experience of the stimuli or behavioural reactions . Olfaction is the only sensory system in the mammalian brain that does not travel through the thalamic relay before reaching the cortex . The thalamus is thought to play an important role in information gating during sleep [79] . Therefore , alternative mechanisms must be at play within the olfactory system . A global and continuous shift of the dynamical state of the olfactory system with arousal has been suggested [32] , as well as gating within the piriform cortex [80] . We suggest that direct suppression of local gamma activity in the OB necessary for information processing may also play a role . The question of whether activity in the OB has a functional role in modifying sensory processing during sleep or on sleep regulation itself would require further study . Nevertheless , the results presented here show that recording OB oscillations is an attractive strategy for monitoring a wide range of vigilance states in natural situations independently of motor confounds . Our methodology provides the possibility of tracking levels of vigilance from wakefulness to anaesthesia in real time and of sleep scoring independently of movement and could also provide a potential alternative to HPC theta for discriminating REM from NREM sleep . In conclusion , OB gamma activity clearly opens an unprecedented window onto levels of vigilance .
All behavioural experiments were performed in accordance with the official European guidelines for the care and use of laboratory animals ( 86/609/EEC ) and in accordance with the Policies of the French Committee of Ethics ( Decrees n° 87–848 and n° 2001–464 ) . Animal housing facility of the laboratory where experiments were made is fully accredited by the French Direction of Veterinary Services ( B-75-05–24 , 18 May 2010 ) . Animal surgeries and experimentations were authorised by the French Direction of Veterinary Services for KB ( 14–43 ) . A total of 15 C57Bl6 male mice ( Mus musculus ) , 4 Gad2-IRES-Cre knock-in C57BL/6J Rj , 3 DBA/2Rj , and 2 C3H/HeNRj mice were used in this study . Mice were housed in an animal facility ( 08:00–20:00 light ) , 1 per cage after surgery . At 3–6 mo of age , mice were implanted with electrodes ( tungsten wires ) in the right OB ( AP +4 , ML +0 . 5 , DV −1 . 5 ) and in the right CA1 hippocampal layer ( AP −2 . 2 , ML +2 . 0 , DV −1 . 0 ) . Six of these mice were also implanted with a hooked EMG wire in the right nuchal muscle . Six mice were also implanted in the right prefrontal cortex ( AP +2 . 1 , ML +0 . 5 , DV −0 . 5 ) and parietal cortex ( AP −1 . 7 , ML +1 . 0 , DV −0 . 8 ) . One mouse was recorded with a 16-site linear probe ( 100 μm spacing , Neuronexus Tech , Ann Arbor , MI , United States ) . Four mice were also equipped with ECG wires to monitor heart rate . Two highly flexible coiled wires were sutured above and below the heart and then travelled under the skin to be fixed to the headstage and preamplified with the other recording wires . During recovery from surgery and during all experiments , mice received food and water ad libitum . Recordings began 1 wk after surgery . Signals from all electrodes were recorded using an Intan Technologies amplifier chip ( RHD2216 , sampling rate 20 KHz ) . LFPs were sampled and stored at 1 , 250 Hz . Analyses were performed with custom-made Matlab programs , based on generic code that can be downloaded at http://www . battaglia . nl/computing/ and http://fmatoolbox . sourceforge . net/ . The protocol has been previously described [81] . Habituation and fear conditioning took place in context A consisting of a square transparent plexiglass box in a black environment with a shock grid floor and cleaned with ethanol ( 70% ) before and after each session . Test sessions were performed in context B consisting of cylindrical transparent plexiglass walls with a grey plastic floor placed in a white environment and cleaned with acetic acid ( 1% ) before and after each session . To score freezing behaviour , animals were tracked using a homemade automatic tracking system that calculated the instantaneous position of the animal and the quantity of movement defined as the pixelwise difference between two consecutive frames . The animals were considered to be freezing if the quantity of movement was below a manually set threshold for at least 2 s . On day 1 , mice were submitted to a habituation session in context A , in which they received four presentations of the CS− and of the CS+ ( total CS duration , 30 s; consisting of 50 ms pips at 0 . 9 Hz repeated 27 times , 2 ms rise and fall; pip frequency , 7 . 5 kHz or white noise , 80 dB sound pressure level ) . Discriminative fear conditioning was performed on the same day by pairing the CS+ with a US ( 1 s foot shock , 0 . 6 mA , 8 CS+ US pairings; intertrial intervals , 20–180 s ) . The onset of the US coincided with the offset of the CS+ . The CS− was presented after each CS+ US association but was never reinforced ( 8 CS− presentations; intertrial intervals , 20–180 s ) . On day 2 , conditioned mice were submitted to a test session in context B during which they received 4 and 12 presentations of the CS− and CS+ , respectively . For isoflurane experiments , anaesthesia was initiated by exposing mice to a mixture of 3% isoflurane and oxygen in an induction chamber ( MSS international , IsoTec3 ) . The mouse was then maintained under constant isoflurane in oxygen at the desired concentration with only the nose placed in a small mask . Whenever isoflurane level was changed , recordings or experiments commenced at least 4 min later to allow stabilisation . Throughout the experiment , mice were in contact with a warm pad . For ketamine experiments , mice were injected with a xylazine ( 10 mg/kg ) ketamine ( 100 mg/kg ) mixture at the beginning of the experiment and then recorded continuously whilst in contact with a warm pad . Noxious stimuli were delivered using bilateral eyelid shock with implanted silver wires . This allowed us to evaluate the response levels to a precisely timed and reproducible stimulation , contrary to manual tail pinch , for example . We used 2 V stimulation in all analyses . Righting reflex was measured by removing the mouse quickly from the induction box after 4 min of isoflurane delivery and placing it on its back on a flat surface . If the mouse righted itself ( 4 paws on the tabletop ) within 10 s , it was scored as having retained its righting reflex . After completion of the experiments , mice were deeply anaesthetised with ketamine/xylazine solution ( 10%/1% ) . With the electrodes left in situ , the animals were perfused transcardially with saline ( approximately 50 ml ) , followed by approximately 50 ml of PFA ( 4 g/100 mL ) . Brains were extracted and placed in PFA for postfixation for 24 h , transferred to PBS for at least 48 h , and then cut into 50-μm-thick sections using a freezing microtome and mounted and stained with hard set vectashield mounting medium with DAPI ( Vectorlabs ) . Bimodality was quantified by fitting a mixture of two normal distributions and evaluating either Ashman’s D [82] , D=2|μ1−μ2||σ1+σ2| where D > 2 is required for a clean separation or the overlap of the two distributions . LFP recordings from the OB were filtered in the gamma ( 50–70 Hz ) band and instantaneous amplitude derived from the Hilbert Transform . This time series was then smoothed using a 3 s sliding window ( Fig 1D ) , and the distribution of values could be fit with a mixture of two Gaussian distributions . To maximise the probability of correct classification , the threshold between sleep and wake should be defined as the intersection of these two distributions . This value , however , depends on the amplitude of the two distributions and therefore on the ratio of sleep and wake recorded . To establish a threshold independent of this ratio , the two distributions are normalised to each have area one ( Fig 2Bi , right ) , and the intersection of these distributions is used . Values inferior to this value are classified as sleep and those superior as wake . Periods of sleep and wake shorter than 3 s were merged into the surrounding periods to avoid artificially short epochs . Then , LFP recordings from the HPC restricted to the sleep periods defined above were filtered in the theta ( 5–10 Hz ) and delta ( 2–5 Hz ) bands and instantaneous amplitude derived from the Hilbert transform . The ratio of the theta and delta powers was smoothed using a 2 s sliding window , and the distribution of values was fit by a single normal distribution that accounted for the NREM data points ( low theta/delta ratio ) . The REM/NREM threshold was placed at the value of theta/delta ratio , above which the residuals systematically explained more than 50% of the actual data ( Fig 2Bii ) . Periods of NREM and REM shorter than 3 s were merged into the surrounding periods to avoid artificially short epochs . Automatic EMG scoring was performed in a similar fashion to automatic OB gamma power scoring . EMG data was filtered in the 50–300 Hz band and instantaneous amplitude derived from the Hilbert transform . This time series was then smoothed using a 2 s sliding window , and the distribution of values could be fit with a mixture of two normal distributions . The intersection of these two distributions , once normalised , provided the sleep–wake threshold . The procedure for identifying REM/NREM sleep is identical to that used in the OB-based sleep scoring algorithm as described above . Automatic scoring was performed independently by two experimenters using a homemade Matlab GUI . The scorers were provided with EMG ( raw , filtered in the 50–300 Hz band and smoothed instantaneous amplitude ) and HPC ( raw , low-frequency spectrogram and smoothed instantaneous theta-to-delta ratio ) . Scorers were presented with 3 s windows of data that they had to identify either as NREM , REM , or Wake . When two states were identified in the same window , it was scored as the state occupying more than 50% of the epoch . The percentage agreement between methods is calculated for each state and shown in the relevant figures . Given that average agreement can be potentially misleading , we also used the confusion matrix to calculate Cohen’s κ [83] , defined as the following: K=Po−Pe1−Pe with Po=∑i=13pii , where pii is the probability that both methods classify data as the identical state i ( REM , NREM , wake ) . Pe=∑i=13p1i×p2i where p1i and p2i are the independent probabilities that methods 1 and 2 will classify data as state i . We applied the same criteria as used in [10] to evaluate the quality of the agreement ( Table 2 ) . Step 1: generate stay probability map Step 2: identify transition zones Step 3: identify true and aborted transitions ( example of sleep to wake ) Step 4: diffusion analysis When comparing the strength of two correlations , linearity between the two is assumed , which is not necessarily true of biological data . One option is to use rank statistics such as the Spearman correlation coefficient; however , this method ignores the metric distance between values and therefore can stretch/compress values , depending on local density of points . We therefore preferred to transform data using the Box-Cox transformation . This is in fact a family of transformations that vary continuously depending on the variable λ and covers a large ensemble of transformations . Computing the Pearson correlation coefficient between the transformed variable and the parameter of interest thus allows us to find the value of λ , for which the correlation coefficient is maximal . This allows a fair comparison of the strength of correlation of two variables by taking into account their respective nonlinearities . To quantify the relationship between stimulus reactivity and OB gamma or heart rate , we used the ROC . We first classified stimulus responses into ‘sedated’ ( i . e . , below threshold ) and ‘aroused’ ( i . e . , above threshold ) based on the clearly bimodal distribution of response levels . The ROC analysis quantifies the ability of an ideal observer to predict whether the animal’s response was ‘sedated’ or aroused based purely on the preceding OB gamma power . This analysis presents the advantage over other evaluations of classifier accuracy of being insensitive to class distribution , i . e . , the respective number of ‘sedated’ or ‘aroused’ stimulations , in our case , which can often be strongly skewed depending on the behaviour of the animal . In our case , the observer discriminates the two response levels by placing a threshold ( z ) on the OB gamma power ( γ ) during the 3 s prior to stimulation below which the corresponding stimulation is classified as ‘sedated’ and above which it is classified as ‘aroused’ . The performance of this procedure can be fully determined by two parameters: α ( z ) =P ( γ>Z|sedated ) orfalsepositiverate β ( z ) =P ( γ>Z|aroused ) ortruepositiverate Plotting α and β for increasing values of z yields the ROC curve , and the area under this curve ( ROC value ) represents the probability that an ideal observer can discriminate between the two stimulus response levels based on preceding OB gamma power: it is equal to 0 . 5 if the spectral power carries no information about the behaviour and equals 1 if it is perfectly predictive . | Real-time tracking of vigilance states related to wake , sleep , and anaesthesia has been a goal for over a century . However identification of wakefulness and different sleep states cannot currently be performed routinely with brain signals and instead relies on motor activity . Here we demonstrate that 50–70 Hz electrical oscillations in the olfactory bulb ( OB ) of mice are a reliable indicator for global brain states . Recording this activity with an implanted electrode allows for clear classification of sleep and wake , without the need for motor activity monitoring . We construct a fully automatic sleep scoring algorithm that relies on brain activity alone and is robust throughout time , between animals , and after drug administration . Our method also tracks in real time the depth of anaesthesia both in the steady state under constant anaesthetic and dynamically during the recovery period from anaesthesia . Furthermore , this index predicts responsiveness to noxious stimulation under anaesthesia . Altogether , this methodology opens the avenue for characterisation of vigilance states based on OB recordings . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"medicine",
"and",
"health",
"sciences",
"methods",
"and",
"resources",
"sleep",
"anesthesiology",
"brain",
"electrophysiology",
"brain",
"electrophysiology",
"neuroscience",
"physiological",
"processes",
"clinical",
"medicine",
"pharmaceutics",
"anesthesia",
"gamma",
"spec... | 2018 | Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anaesthesia depth |
Ectomycorrhizal fungi ( EMF ) represent one of the major guilds of symbiotic fungi associated with roots of forest trees , where they function to improve plant nutrition and fitness in exchange for plant carbon . Many groups of EMF exhibit preference or specificity for different plant host genera; a good example is the genus Suillus , which grows in association with the conifer family Pinaceae . We investigated genetics of EMF host-specificity by cross-inoculating basidiospores of five species of Suillus onto ten species of Pinus , and screened them for their ability to form ectomycorrhizae . Several Suillus spp . including S . granulatus , S . spraguei , and S . americanus readily formed ectomycorrhizae ( compatible reaction ) with white pine hosts ( subgenus Strobus ) , but were incompatible with other pine hosts ( subgenus Pinus ) . Metatranscriptomic analysis of inoculated roots reveals that plant and fungus each express unique gene sets during incompatible vs . compatible pairings . The Suillus-Pinus metatranscriptomes utilize highly conserved gene regulatory pathways , including fungal G-protein signaling , secretory pathways , leucine-rich repeat and pathogen resistance proteins that are similar to those associated with host-pathogen interactions in other plant-fungal systems . Metatranscriptomic study of the combined Suillus-Pinus transcriptome has provided new insight into mechanisms of adaptation and coevolution of forest trees with their microbial community , and revealed that genetic regulation of ectomycorrhizal symbiosis utilizes universal gene regulatory pathways used by other types of fungal-plant interactions including pathogenic fungal-host interactions .
Growing evidence has shown that many symbiotic plant-microbial associations including pathogenic as well as mutualistic symbioses are governed by similar genetic interaction mechanisms [1 , 2] . For example , in many groups of pathogenic fungi and oomycetes , coevolution with their plant hosts has resulted in typical 'arms-race' patterns of interactions , in which pathogens evolve batteries of effectors that suppress plant defense responses , while plants evolve modified receptors that sense microbial molecules and reactivate plant defense responses [3] . The molecular functions of several fungal and oomycete effectors involved in host-pathogen recognition have recently been elucidated . For instance , cysteine-rich avirulence genes ( Avr ) have been identified in several fungi including Cladosporium fulvum and Melampsora lini [4 , 5] , while Avr1b was isolated from the oomycete Phytophthora sojae [6] . Studying the functions of these effectors is a challenging task , because of the highly divergent nature of effectors in diverse taxa of pathogenic microbes and the lack of similarity of the sequences of these effectors to other proteins in public databases . Plant defense proteins that perceive microbial effectors include nucleotide-binding leucine-rich repeat ( NB-LRR ) proteins [1 , 7 , 8] and cell membrane receptors ( e . g . phosphatidylinositol 3-P ) [9] . These receptors can be activated by direct binding of effectors or modified by effector-associated proteins , leading to a plant-defense response . Mutualistic plant-fungal interactions , including arbuscular mycorrhizae and ectomycorrhizae , also share similar conserved genetic interaction mechanisms with other symbiotic plant-fungal systems [10–12] . Over 30 plant families are known to form ectomycorrhizal associations with over 80 lineages ( 250 genera ) of fungi [13] . A highly diverse community of EMF form the dominant guild of soil microbes in most of the world's forests [14 , 15] , where they provide their plant hosts with essential resources ( N , P , H2O ) as well as protection from pathogens , in exchange for photosynthetically fixed carbon [16] . Details about molecular interactions between EMF and their plant hosts are emerging . Recent studies have identified differentially expressed genes associated with EMF symbiosis for several EMF-plant interactions including Pisolithus microcarpus with Eucalyptus [17] , Paxillus involutus with Betula [18] , and Laccaria bicolor with different Populus spp . [2] . One of these genes , a small secreted protein ( MiSSP7 ) produced by the ectomycorrhizal basidiomycete Laccaria bicolor , functions as a critical effector for compatible mycorrhizal interaction with Populus . MiSSP7 was shown to be imported into plant nuclei where it suppresses plant host defenses , enabling mycorrhiza formation . Other recent studies also demonstrated that jasmonic acid ( JA ) and related plant defense-activated compounds are produced by Populus in response to signals from their symbiont [19 , 20] . These results suggest a general involvement of JA-mediated and other conserved plant signaling pathways for plant-fungal communication during EMF symbiosis . Similar to the mechanisms of EMF interaction in Laccaria [2] , plant pathogenic fungi ( e . g . M . larici ) can also deliver SSPs to multiple cellular compartments in Populus [21] . These studies demonstrate that EMF are able to modulate plant defense system during symbiosis [2 , 10 , 21] , and suggest that that most plant-microbial associations ( including pathogenic and mutualistic interactions ) may be governed by similar mechanisms . Unlike biotrophic/necrotrophic parasitisms , mutualistic fungal-plant interactions such as EMF must also establish stable long-term relationships with their living host cells , with benefits to both the fungus and its host . Thus , there is considerable potential for an array of distinct elements to regulate the host-specific communications of symbiosis compared to plant-pathogen interactions . Many groups of EMF are known to exhibit preference or specificity for different plant host genera [22 , 23] . A good example of strong host-specificity is the bolete genus Suillus , which grows in association with the conifer family Pinaceae [24 , 25] . Most species of Suillus form ectomycorrhizae with specific Pinaceae host species ( e . g . , white pine , douglas fir , larch ) , suggesting a long history of plant-fungal coevolution in this genus [26–28] . Other examples of EMF with host-specific interactions include Laccaria bicolor , which shows differential host-compatibility with different species of Populus [29] , and Paxillus involutus , which favors Betula as a host over Populus [30] . In order to study the molecular basis for host-specificity between different Pinus and Suillus species , we used pairwise plant-fungal bioassays to identify patterns of compatible and incompatible EMF interactions . Compatible EMF interactions are characterized by morphogenesis of plant and fungal tissues leading to development of modified plant short roots with bifurcated root tips that are sheathed by a hyphal mantle over the root epidermal surface , with hyphal ingrowth into the root cortex to form the Hartig-net [31] . In contrast , incompatible EMF interactions fail to induce root morphogenesis , resulting in little or no mycelial growth , and are morphologically indistinguishable from uninoculated ( non-symbiotic ) roots . The pace of genetic studies of EMF-plant symbiosis has greatly accelerated by expanding numbers of genome sequencing for many EMF [10] . Though study of most EMF is still hindered by a lack of ‘finished’ genomes , we recently developed a procedure that employs RNA-Seq and de-novo assembly and annotation to characterize the metatranscriptome of EMF associated with Pinus taeda from field-collected mycorrhizal root clusters [32] . Here we apply metatranscriptomic profiling to study compatible versus incompatible mycorrhizal interactions from both plant and fungal perspectives . Our studies demonstrate that Suillus and Pinus each exhibit well-differentiated transcriptomic profiles during compatible and incompatible interactions . Comparison of expression patterns in compatible and incompatible pairings helped us to identify gene sets associated with plant-fungal recognition and establishment of EMF symbiosis .
To investigate occurrence of Suillus in natural Pinaceae forests , we first examined patterns of host specificity for Suillus operational taxonomic units ( OTUs ) detected by a recent survey of North American pine forest soils using next generation amplicon sequence analysis of the ribosomal RNA internal transcribed spacer ( ITS ) region [14] . Eleven Suillus OTUs detected by that survey ( out of a total of >10 , 000 fungal OTUs detected across North America ) exhibit distinct host range patterns corresponding with different Pinaceae hosts ( S1 Fig ) : S . glandulosa with Picea glauca; S . hirtellus and S . cothurnatus with Pinus taeda; S . granulatus , S . spraguei ( = S . pictus ) and S . americanus with Pinus strobus; and an unidentified Suillus sp . with Pinus monticola . Several Suillus species were observed to be broadly associated with multiple Pinus species , including Suillus brevipes , which is associated with several Pinus spp . across North America ( S1 Fig ) but was restricted to hosts in the subgenus Pinus ( P . ponderosa , P . contorta , P . banksiana , and P . taeda ) . To study host specificity , a plant bioassay was developed using axenically grown pine seedlings inoculated with Suillus basidiospores to establish Suillus-Pinus mycorrhizae in vitro [31] . Seedlings of ten Pinus species were inoculated in all pairwise combinations with basidiospores of five Suillus species and scored for ectomycorrhiza formation after 8 weeks growth . In Pinus , successful formation of ectomycorrhizae ( compatible interaction ) results in a series of characteristic morphogenetic changes to young root tips that become swollen and bifurcated , and ensheathed by a mycelial mantle which penetrates into the root cortex to form a Hartig net [33] ( Fig 1A and S2 Fig ) . In contrast , incompatible pairings are characterized by little or no colonization of roots by fungal mycelium ( both fungal mantle and Hartig-net absent ) . Basidiospore inoculations of two generalist species , S . hirtellus and S . decipiens , resulted in well-developed ( compatible ) ectomycorrhizae with most Pinus species ( Fig 1B ) , when S . hirtellus had relatively lower rates of colonization on all hosts . Three white pine specialists ( S . granulatus , S . americanus and S . spraguei ) readily formed ectomycorrhizae with white pines ( P . strobus and P . monticola ) , but had lower colonization rates on hard pines ( e . g . P . banksiana ) , and failed to form visible ectomycorrhizae on P . taeda ( incompatible pairing ) ( Fig 1B ) . Variation in mycorrhizal compatibility between different Suillus and Pinus species suggests that genetic differences underlie host recognition and specificity during ectomycorrhizal symbiosis . To test this hypothesis , we compared transcriptomic activities across a panel of compatible and incompatible root tip samples formed by inoculation of three Pinus species ( P . monticola , P . strobus , P . taeda ) with four species of Suillus ( S . americanus , S . granulatus , S . spraguei , and S . decipiens ) . Detailed descriptions of the individual Suillus-Pinus sample pairs , including strains used are listed in S1 Dataset . Transcriptomes from uninoculated pine roots were included as controls ( to confirm that Suillus genes were not expressed by uninoculated roots ) along with pure cultures of each fungal species ( as references for transcriptome assembly ) . Comparative transcriptome profiling was used to identify candidate genes involved in Pinus-Suillus recognition ( Table 1 ) . The computational strategies included a ) de novo transcriptome assembly to identify reads representing genes for different rRNA , Suillus , Pinus , and b ) comparative transcriptomic analysis to identify common ( core ) and unique ( host-specific ) genes involved in symbiosis ( see Materials and Methods , and SI text A1-A4; S3–S5 Figs ) . Unique genes were defined as upregulated genes detected in the RNA contig assembly of one Suillus species , but absent in other species examined . However , whether these genes are truly unique to different Suillus species still need to be determined through whole genome sequencing . Up to 28 million ( M ) high quality reads were recovered from inoculated root tips using RNA-Seq ( approx . 1 mg root tissue per sample , equal to about ten root tips ) ( S1 Dataset ) . Compatible Pinus-Suillus pairs resulted in roughly equal numbers of plant and fungal reads , while incompatible pairs resulted in much lower number of fungal reads compared to the corresponding plant reads ( Fig 2 ) . These differences of Suillus/Pinus reads recovered from compatible and incompatible interactions are also consistent with to the higher proportion of fungal biomass present in compatible versus incompatible mycorrhizal pairings . The Suillus transcriptome generated from de novo assembly of pooled data was used to identify 15M ( 51% of total reads ) and 2M ( 6 . 1% of total reads ) reads from compatible and incompatible reactions , respectively ( Fig 2 and S1 Dataset ) . Approximately 3M ( 11% of total reads ) and 21M ( 66% of total reads ) Pinus transcriptome reads were also recovered from compatible and incompatible pairings , which could be matched to 44% and 69% of publicly available Pinus EST databases ( ~0 . 3M ESTs ) , respectively . In total , 11 , 029 and 5 , 947 Suillus contigs were obtained through de novo assembly from compatible and incompatible root samples respectively ( S1 Dataset ) . We hypothesized that pairings between different Suillus ( and Pinus ) species would share common gene expression patterns during compatible vs . incompatible pairings . Similarly , unique gene sets expressed by individual Suillus/Pinus pairings could also be identified ( Fig 2 ) . Here we defined “common genes” as the core sets of genes that were upregulated ( > 2-fold ) in response to compatible hosts; in contrast , “unique genes” were identified as those were only expressed in individual Suillus spp . in response to specific Pinus host species . To test these hypotheses , we used comparative transcriptomic analysis to identify Suillus and Pinus expressed genes during compatible and incompatible ECM interactions of four Suillus species grown with three different hosts , P . monticola , P . strobus , and P . taeda , ( Fig 3 ) . To compare gene expression patterns between interacting fungal and host genomes , sequencing reads aligned to either Suillus or Pinus contigs were normalized using DESeq package ( ver . 1 . 14 . 0 ) [34] . ( Details were provided in Support Information SI A2 , S5 Fig ) . Gene expression biplots revealed strong differences between compatible and incompatible EMF pairings ( S6A and S6B Fig ) . All of the compatible EMF pairings showed similar expression patterns of Suillus genes , even on different hosts ( e . g . P . strobus and P . monticola ) ( S7 and S8 Figs ) , which suggests that different Suillus species all employ common regulatory pathways across different compatible host species . Significant differences were observed in gene expression between compatible and incompatible reactions ( t-test , p-value < 0 . 01 ) ( Fig 4 ) . On average , 8 , 765 Suillus contigs were upregulated when they grew with compatible hosts , whereas fewer contigs ( 1 , 918 contigs in average ) were upregulated from incompatible pairings ( S1 Dataset ) . Gene expression patterns were analyzed among all individual Suillus-Pinus species pairs to identify common genes involved in both compatible and incompatible interactions ( SI text A2; S5 Fig ) . A majority of Suillus transcripts ( ~3 , 800 contigs ) were similarly regulated in response to different compatible Pinus species . We compared the sequence identities of these genes across all four Suillus species and identified 231 “common genes” that were upregulated during the compatible mycorrhizal interactions ( Fig 3; SI text A3; S1 Dataset ) . In contrast to common genes expressed during compatible interaction , a smaller number of genes ( 261–571 genes ) were found to be upregulated during incompatible interactions in different Suillus species ( Fig 4A ) . BLASTX search against all four Suillus species only identified seven common genes expressed during incompatible mycorrhizal interactions in all species ( S1 Dataset ) . Functional annotations of these seven common genes identified two GHs ( glucoside hydrolase ) , one F-box , one fatty acid desaturase , one signal transduction receptor , and two genes with unknown functions . In contrast to sharing of 231 expressed genes in compatible mycorrhizal interactions , most genes associated with incompatibility were unique to individual Suillus-Pinus species pairs . These included a large number of SSPs , G-proteins , and other genes with little similarity/homology to each other or with other known genes , suggesting that these unique genes for host specificity are highly diverse at the genomic level ( Fig 4A , for detailed analysis strategies see SI text A4 and S5 Fig ) . Unique genes varied among different plant-fungus combinations ( from 68 to 571 genes for an individual pair Fig 4B ) , and were found to represent 14 functional groups ( Fig 4B ) with similar functions but very low sequence similarity to one another ( S1 Dataset ) . Over two thirds of unique genes expressed by Suillus spp . were related to G-protein signaling , such as G-protein coupled receptor ( GPCR ) , GTPase P-loop , Gβ WD40 , and G-protein regulated kinases ( Figs 4B and 5 ) , which suggests a strong involvement of G-protein pathways in host-specific recognition . Other differentially expressed Suillus genes were those related to FAD/AND ( P ) binding , cytochrome P450-related , secretory , catalysis ( proteinase/hydrolysis/reductase/terpene synthesis ) and nucleus-associated genes . Of the 261–571 contigs that were strongly upregulated in response to Suillus-Pinus incompatibility ( Fig 5A ) , functional profiling revealed 22 to 28 contigs for shared functions related to tat signaling pathway for exporting small secreted proteins , GPI anchored proteins , fungal LRR-domain proteins , phosphatase , and pectin lyase ( Figs 4B and 5B ) . Expression of these genes was not detected in most compatible pairings . Fungal small-secreted proteins ( SSPs ) are predicted to be key mycorrhizal effectors for the recognition of EMF by their plant host system . Using domain analysis ( SI text A4 ) , SSPs were defined by several criteria including ( a ) size smaller than 300 amino acid , ( b ) signal peptide predicted at the N-terminal and extracellular localization activity; ( c ) absence of transmembrane domains; ( d ) absence of endoplasmic reticulum retention motifs [12] . 47 Suillus SSP's matching these criteria were upregulated in response to different Pinus hosts ( Fig 6 ) . More SSPs were upregulated during incompatible than compatible interactions . At the sequence level , most SSPs are highly diverse and do not share sequence similarity with other SSPs from currently available databases . Most Suillus SSPs were also observed to be highly diverse in their tertiary structure ( S8A Fig ) . Comparative transcriptional profiling of Pinus genes across the Suillus-root pairs also identified a large number of pine transcripts with similar expression in response to compatible vs . incompatible EMF pairings ( ~18 , 000 contigs; S10 Fig ) . Overall , a smaller number of Pinus genes ( from 253–5452 contigs ) were differentially expressed in response to pairings with different species of Suillus . The largest number of upregulated genes was observed for Pinus-S . spraguei interactions compared to other compatible pairs , suggesting the possibility of a greater Pinus response to S . spraguei . Highly expressed Pinus genes with at least two-fold change ( FDR<0 . 05 ) were further characterized as “pine unique genes” involved in fungal recognition ( expressed by individual Pinus spp . in response to specific species of Suillus ) ( Fig 7 ) . On average , 20 Pinus contigs were identified as unique genes for every Suillus-pair sample . BLASTX annotation identified sets of unique Pinus genes with common function involved in Suillus recognition , including genes for leucine rich ( LRR ) - proteins , UDP-glucosyl transferase , and cytochrome P450 . Inoculation with S . spraguei also upregulated distinct Pinus genes encoding lipoxygenase 2 , suggesting a potential effect on JA pathways for the Pinus-S . spraguei interaction . Two different sets of P . taeda genes were found to be expressed during incompatible response including fungal species-specific ( Fig 7 ) and species-nonspecific genes ( S11 Fig ) . Comparative transcriptomic analysis also captured changes in expression patterns of 460 P . taeda genes associated with incompatibility , but these do not appear to be Suillus species-specific ( S11A Fig ) . A number of Pinus genes known to be associated with defense responses were only weakly or not expressed in compatible pairings and uninoculated roots , including genes involved in plant resistance and water stress response including genes for salicylic acid acquired resistance ( NDR1 ) , ethylene-responsive transcription factor and RNA helicase , leucine rich proteins ( e . g . Cf2 . 1 , receptor kinase ) , thaumatin-like proteins , dehydrin and water deficit induced-LP3 ( S11B Fig ) .
We identified 231 common genes expressed during compatible mycorrhizal interactions ( 9 out of 12 Suillus-Pinus species pairings , Fig 3 ) . In contrast , comparative analysis revealed only 7 common genes expressed during incompatible interactions between all three white-pine specialists when paired with loblolly pine ( P . taeda ) . These findings suggest that different Suillus spp . share a common set of genes involved in compatible but not in incompatible responses . These estimates are likely to be higher , however , since our strategy employing de novo assembly and annotation could not detect less abundantly expressed genes without much deeper sequencing or access to a high quality reference genome . Further mapping of compatible/incompatible gene sets to fully-sequenced reference genomes of Suillus and Pinus is likely to reveal additional shared common genes involved in compatible/incompatible interactions .
To study the distribution of Suillus in natural Pinaceae forest soils , next generation sequencing was conducted to identify fungal operational taxonomic units ( OTUs ) of Suillus from the soils collected in Pinaceae forests across the North America . Technical details and data source to generate S1 Fig can be found in Talbot et al . [15] . For mycorrhizal plant bioassays , seeds of different Pinus species were purchased from Sheffield’s Seed Co . , Inc . ( Locke , NY ) ( see S3 Table for detailed description ) . The seeds were surface sterilized in 10% bleach for 10 min , suspended in sterilized water overnight and stratified at 4°C for different time periods prior to germination . Germinated seedlings were planted in sterilized sand and watered using sterile water . Basidiospores of different Suillus spp . were collected as spore deposits from field-collected fruit bodies by placing pilei overnight on wax paper or aluminum foil . Fruit body collection data are given in SI text A6 . A Suillus-Pinus pairwise bioassay was conducted using basidiospore inoculations with six-week old pine seedlings . Ten Pinus species were crossed with five Suillus species for a total of 50 pairwise combinations ( replicated three times ) . Basidiospores ( 106 spores ) were suspended in sterile water with 0 . 1% Tween-20 , and added to sterilized 400 g of autoclaved sand to fill a four inch pot . Seedlings growing in sterile sand ( without inoculum ) were used as controls for all experiments ( and also to check for airborne growth chamber contamination ) . Seedlings were grown in a growth chamber at 25°C , 80% humidity and fluorescent light at 200 μmol for 12 hours per day . At 180-d post-inoculation , EMF root tips were visualized under a dissection microscope , and percentage of EMF root tips were counted in comparison with bare ( uninoculated ) root tips . Root tips were harvested from the bioassay pots at 90-d post-inoculation . From each plant , 10 root tips were collected using forceps , frozen in liquid N2 and stored in -80°C for RNA extraction . Four species of Suillus ( S . americanus; S . granulatus; S . spraguei ( = S . pictus ) ; S . decipiens ) and three species of Pinus ( P . monticola; P . strobus; P . taeda ) were grown in all 12 pairwise combinations ( each replicated three times ) . Root tips collected from uninoculated Pinus species were also included as controls . The controls included six samples for a total of three species of Pinus that were replications for two different seedlings for each species ( Table 1 ) . Total RNA was extracted using CTAB/chloroform extraction and LiCl precipitation method as described [32] . The mRNA samples for RNA-seq analysis were performed using a TruSeq RNA sample preparation kit ( Illumina , San Diego , CA ) . The cDNA libraries were sequenced on the Illumina HiSeq 2000 ( Illumina , San Diego , CA ) instruments in Duke Center for Genomic and Computational Biology ( GCB ) . Thirteen samples were sequenced using a single lane of Illumina run and generated 38Gb of data . The data generated from four lanes were applied for this study . The raw reads were deposited in the NCBI Short Read Archive ( accession no . SRP057033 ) . We employed a genome-free assembly method to sort reads representing genes for different rRNA , Suillus , Pinus , and other genes ( S3 and S4 Figs and SI Text A1 ) . The computational workflow for sequence assembly ( S3 Fig ) was modified after Liao et al . [32] . First , Suillus sequence references were generated using the sequencing reads generated from Suillus fungal cultures , including S . americanus , S . granulatus , S . spraguei and S . decipiens . Next , de novo assembly was applied using Trinity [34] . The quality of the assembled contigs/unigenes for the four Suillus species are listed in S1 Table . The filtered reads ( ~28 million ) were mapped onto four sets of reference sequences using bowtie with default settings ( http://bowtie-bio . sourceforge . net/index . shtml ) , including references of fungal rRNA , 16S rRNA , contigs generated from Suillus cultures , and EST database of P . taeda . Remaining unmapped reads ( approximately 3-million ) were assembled de novo into contigs using Trinity followed by sorting into fungal and plant reads BlastX . Detailed descriptions of bioinformatics and databases used for three steps are included in SI text A1 . The numbers of reads belonging to Suillus , Pinus , rRNA ( and others ) is shown in S1 Database . Comparative analysis of gene expression was used to evaluate their biological functions . The t-test ( P<0 . 01 ) was used to identify the genes of Suillus in response to their compatible vs . in compatible hosts ( Fig 3 ) . A false discovery rate ( FDR ) of 5% was used to identify highly expressed transcripts with at least 2-fold change for the common and unique genes of Suillus and Pinus ( Figs 4–7 ) . Transcriptome ( EST ) databases for S . americanus ( 19 , 123 contigs ) , S . granulatus ( 15 , 724 contigs ) , S . spraguei ( 18 , 898 contigs ) and S . decipiens ( 16 , 871 contigs ) were assembled de novo from fungal cultures using RNASeq . Besides the transcriptome references generated in our study ( S1 Table ) , the other reference databases used in this study include: Fungal rRNA ( NCBI , UNITE ) ; Bacterial 16S ( Ribosomal Database Project , http://rdp . cme . msu . edu ) ; P . taeda EST database ( NCBI ) . The databases were quality filtered using FASTA within the Galaxy web-based package . Detailed protocols for plant and fungal annotation databases are provided in SI text A2-A4 . | Ectomycorrhizal fungi ( EMF ) comprise the dominant group of symbiotic fungi associated with plant roots in temperate and boreal forests . We examined host-specificity and gene-expression of five EMF Suillus species that exhibited strong patterns of mycorrhizal compatibility/incompatibility with either white pines ( Pinus subg . Strobus ) or hard pines ( subg . Pinus ) . Using RNA-Seq , we identified conserved transcriptomic responses associated with compatible versus incompatible Pinus-Suillus species pairings . Comparative metatranscriptomic analysis of compatible vs . incompatible pairings allowed us to identify unique sets of fungal and plant genes associated with symbiont recognition and specificity . Comparativ transcriptomic study of the Suillus-Pinus system provides insight into the core functions involved in ectomycorrhizal symbiosis , and the mechanisms by which host-symbiont pairs recognize one another . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"fungal",
"genetics",
"symbiosis",
"trees",
"fungi",
"plant",
"science",
"genome",
"analysis",
"plant",
"pathology",
"plants",
"mycology",
"genomics",
"pines",
"gene",
"expression",... | 2016 | Metatranscriptomic Study of Common and Host-Specific Patterns of Gene Expression between Pines and Their Symbiotic Ectomycorrhizal Fungi in the Genus Suillus |
Allantoin is the end product of purine catabolism in all mammals except humans , great apes , and one breed of dog , the Dalmatian . Humans and Dalmatian dogs produce uric acid during purine degradation , which leads to elevated levels of uric acid in blood and urine and can result in significant diseases in both species . The defect in Dalmatians results from inefficient transport of uric acid in both the liver and renal proximal tubules . Hyperuricosuria and hyperuricemia ( huu ) is a simple autosomal recessive trait for which all Dalmatian dogs are homozygous . Therefore , in order to map the locus , an interbreed backcross was used . Linkage mapping localized the huu trait to CFA03 , which excluded the obvious urate transporter 1 gene , SLC22A12 . Positional cloning placed the locus in a minimal interval of 2 . 5 Mb with a LOD score of 17 . 45 . A critical interval of 333 kb containing only four genes was homozygous in all Dalmatians . Sequence and expression analyses of the SLC2A9 gene indicated three possible mutations , a missense mutation ( G616T;C188F ) and two promoter mutations that together appear to reduce the expression levels of one of the isoforms . The missense mutation is associated with hyperuricosuria in the Dalmatian , while the promoter SNPs occur in other unaffected breeds of dog . Verification of the causative nature of these changes was obtained when hyperuricosuric dogs from several other breeds were found to possess the same combination of mutations as found in the Dalmatian . The Dalmatian dog model of hyperuricosuria and hyperuricemia underscores the importance of SLC2A9 for uric acid transport in mammals .
Uric acid is the predominant product of purine metabolism in humans , great apes and one breed of dog , the Dalmatian; all other mammals excrete allantoin . During primate evolution , urate oxidase ( UOX ) , which catalyzes the oxidation of uric acid into allantoin , accumulated several independent nonsense mutations that led to its silencing and resulted in high serum and urine uric acid levels in humans and great apes [1] , [2] . Huu in the Dalmatian results from a different cause [3] , [4] . Uric acid freely circulates in the form of urate , the salt of uric acid , in the plasma where it serves as a free-radical scavenger . Although uric acid has evolved in humans to be the main product of purine metabolism , this change has had some negative effects . High levels of urate predispose humans to gout [5] , [6] . In addition , uric acid levels have been correlated with hypertension , vascular disease and metabolic syndrome although it is unclear whether hyperuricemia is primary or secondary in these cases [7]–[10] . As in humans , all Dalmatian dogs have a defect in urinary metabolism that leads to excretion of uric acid rather than allantoin [11] . As a result , Dalmatians are predisposed to form urinary calculi composed of urate ( Figure 1B ) . Hyperuricosuria in the Dalmatian is relatively easy to identify since Dalmatian urine forms a crystallized precipitate when cooled ( Figure 1C ) . This trait was probably fixed in the breed through selection for a more distinctive spotting pattern [12] , [13] . Dalmatian coat pattern involves mutations in at least three different spotting genes ( Figure 1A ) . Dalmatians have a mutation for extreme white in the MITF gene [14] that leads to an all white coat . A dominant mutation , called T for ticking [15] , is responsible for adding the pigmented spots to the white coat . Based on segregation analysis , the huu locus appears to be closely linked to a modifier of spot size [16] . In mammals that produce uric acid rather than allantoin , the level of uric acid in the blood is controlled by differences in production as well as differences in the amount that is excreted in the urine . In the kidney , uric acid is filtered by the glomerulus and then a portion is reabsorbed in the proximal tubules where it re-enters circulation . There are species-specific differences in the production of uric acid versus allantoin and the relative amounts of reabsorption and secretion in the proximal tubules , making the use of animal models in this area of research challenging [17] . Dogs and humans , unlike many other mammals , undergo bidirectional transport of urate along the nephron , which results in net reabsorption of urate from the glomerular filtrate . In Dalmatians , reabsorption is lost entirely and urate excretion equals or exceeds the glomerular filtration rate [18] . This change in uric acid excretion by the Dalmatian kidney is not secondary to hyperuricemia since non-Dalmatian dogs with artificially raised serum uric acid levels can only clear uric acid at ∼1/3 the rate of the Dalmatian [18] . Free-flow micropuncture experiments were used to demonstrate that in Dalmatian kidneys there is a deficiency of proximal tubular reabsorption of urate [19] . Although findings stated above implicate the kidney in Dalmatian huu , reciprocal liver and kidney transplant experiments between Dalmatian and non-Dalmatian dogs demonstrate that the liver is also important for the phenotype . Kidney transplants between normal dogs and Dalmatians only partially ameliorated the hyperuricosuria phenotype [20] , [21] . However , Dalmatian hepatocyte transplants can correct the phenotype [20] . Therefore , a logical cause for the Dalmatian phenotype is a mutation in urate oxidase , similar to humans . However , Dalmatian liver homogenates are capable of oxidizing uric acid to allantoin and the urate oxidase gene was excluded genetically [3] . The Dalmatian phenotype could also be explained by a generalized defect of urate transport since liver slices were not capable of converting uric acid to allantoin [4] . Dalmatian dog erythrocytes have been shown to transport urate normally , demonstrating that Dalmatians do not have a generalized defect in urate transport [22] . Although Dalmatians have functional urate oxidase activity in their livers , they effectively have a similar phenotype to humans and great apes since they cannot transport urate into the liver for degradation . The Dalmatian phenotype can be summarized as a hepatic and renal urate transport defect which leads to hyperuricosuria and relative ( compared to other dogs ) hyperuricemia . The discovery of various proteins that transport urate has shed some light on the control of serum and urine uric acid levels [23] . In the kidney , urate is transported across the apical membrane of the proximal tubules and then across the basolateral membrane before re-entering circulation . In humans , the transporter that reabsorbs urate across the apical membrane in the proximal tubule is Urat1 , or SLC22A12 [24] , which is expressed exclusively in the kidney . Mutations in SLC22A12 are thought to be the major cause of idiopathic renal hypouricemia in humans [24] , [25] , which is also called “Dalmatian hypouricemia” since people with this disorder spill uric acid into their urine resulting in a phenotype similar to the Dalmatian dogs [26] . In addition , recent work has shown an association of SLC2A9 with serum uric acid levels in several different populations [27] , [28] . Variants in the non-coding region of SLC2A9 are associated with gout and uric acid levels in several human populations [29]–[31] . SLC2A9 has been shown by Xenopus oocyte experiments to transport uric acid [30] . In humans and mice it is expressed in liver and kidney [32] , [33] . In particular , in humans SLC2A9 isoforms have been localized to both the apical and basolateral membrane of the proximal tubules , allowing the possibility that SLC2A9 influences serum uric acid levels by transport in the kidney [34] . All Dalmatians are homozygous for huu , which is inherited as a simple autosomal recessive trait as demonstrated by crosses performed between Dalmatians and other breeds of dogs [12] , [13] . In order to identify the causative gene , an interbreed backcross ( Dalmatian×Pointer ) was developed which introduced the wildtype version of the huu gene into the Dalmatian breed while maintaining the breed characteristics of the Dalmatian . Based on linkage analysis using this cross ( LOD 6 . 55 ) , huu localizes to CFA03 , excluding SLC22A12 as a candidate [35] . Using recombination breakpoints in the interbreed backcross and taking advantage of the homozygosity within the Dalmatian breed for huu , a critical interval containing four candidate genes was defined . Microsatellite markers within this interval gave LOD scores over 17 for linkage to huu . A candidate causal missense mutation ( C188F ) was identified within a highly conserved transmembrane ( TM5 ) of the 12 transmembrane transporter protein , SLC2A9 . The missense mutation is homozygous in all Dalmatians tested ( 247 ) as well as in hyperuricosuric dogs of other breeds .
Linkage analysis using a Dalmatian×Pointer backcross family localized Dalmatian huu to CFA03 . Haplotype analysis defined a 3 . 3 Mb critical interval ( CFA03 72 , 063 , 073–75 , 355 , 028 Mb ) , estimated to contain 24 candidate genes [35] . Additional backcross dogs were genotyped with the microsatellites used for haplotype analysis and with new microsatellites mined from the critical region on CFA03 . The full pedigree of all the dogs used for this analysis is shown in Figure S1 . Urine uric acid/creatinine ratios were used to categorize the dogs' genotypes at huu . LOD scores were determined for a subset of these markers . Two markers within the critical interval defined by recombination breakpoints gave LOD scores over 17 , further confirming the linkage to this region . Recombination events in two dogs narrowed the critical interval to a 2 . 5 Mb region containing 19 candidate genes ( CFA03; 71 , 796 , 048–74 , 348 , 350 Mb ) ( Figure 2 ) . Since Dalmatians are fixed for huu , it was expected that an area of homozygosity around the huu locus would be identified . Blocks of linkage disequilibrium ( LD ) in purebred dogs extend between several megabases in rare breeds with small population sizes to several kilobases in popular breeds with larger population sizes [14] , [36] . Dalmatians have a moderate population size , so the homozygous region surrounding huu should be smaller than 2 . 5 Mb . Microsatellites mined from the canine genome and located ∼100 Kb apart were genotyped in the backcross dogs . Markers that were homozygous in huu/huu individuals and heterozygous in huu/+ individuals were typed in 24 unrelated Dalmatians to verify that the region of homozygosity is not the result of familial linkage disequilibrium . Haplotypes were constructed to rule out the possibility of more than a single ancestral mutation . Regions of homozygosity were found between ms173 and ms9 ( CFA03; 71 , 796 , 048–72 , 363 , 187 ) and ms187 and ms128 ( CFA03; 74 , 183 , 928–74 , 245 , 743 ) . Eighteen SNPs , extending across 2 . 5 Mb , were genotyped in 24 Dalmatians , a wild-type Labrador Retriever and a huu/+ backcross dog . The results excluded the area between ms187 and ms128 and a total of 13 SNPs spanning ∼333 Kb confirmed the homozygous region between ms173 and ms9 in the 24 Dalmatians ( Figure 3 ) . These SNPs are also heterozygous in the huu/+ backcross dog , polymorphic in two unaffected dogs ( ND1 and ND2 ) and the Boxer genome assembly sequence . Four candidate genes were identified in the region of homozygosity , LOC488823 ( similar to mast cell immunoreceptor signal transducer – MIST ) , LOC479092 ( zinc finger protein 518B , KIAA1729 ) , LOC611070 ( similar to WD-repeat protein 1 – WDR1 ) and LOC479093 ( similar to solute carrier family 2 , member 9 protein , isoform 1 – SLC2A9 ) ( Figure 3 ) . RT-PCR established that all four genes were expressed in canine liver and kidney . Since all the candidates were expressed in the appropriate organs , all four genes were sequenced from Dalmatian and non-Dalmatian liver cDNA samples and the untranslated regions ( UTRs ) were determined by 5′ and 3′ RACE ( Genbank EU371511–EU371515 ) . The 5′UTR and exons 1–7 of MIST were outside of the LD region and were not pursued . A single silent mutation was identified in exon 16 of the MIST gene ( T951C ) . Three silent mutations were found in the single exon of the canine KIAA1729 gene ( T932C , C2480T , G3092A ) and a 33 bp insertion/deletion was discovered in the 3′UTR . Both alleles of these three SNPs and the insertion/deletion were seen in unaffected non-Dalmatian dogs . WDR1 was sequenced in liver cDNA and genomic DNA from a Dalmatian and a non-Dalmatian . A single SNP was found in intron 9 that does not affect a splice site . Although SLC2A9 did not have an assigned function related to uric acid metabolism at the time this work was performed , it was considered to be the most promising candidate since it is a transporter protein . A discrepancy in SLC2A9 exon annotation between NCBI and the UC Santa Cruz genome browser was addressed by 5′ RACE . Two SLC2A9 exon 1 variants were found in a Dalmatian and a wild-type Golden Retriever , variant O ( CFA03:72 , 222 , 637–72 , 416 , 753 ) and variant N ( CFA03: 72 , 227 , 605–72 , 416 , 753 ) . The difference between the two variants lies in the first 28 amino acids of the N terminus , similar to known mouse and human variants . Both transcripts were shown to be expressed in canine liver and kidney by RT-PCR . Expression differences were observed for variant O between Dalmatian and non-Dalmatian in both liver and kidney ( equivalent to isoform 2 in human ) but not for variant N ( Figure 4A ) . Expression in the Dalmatian samples was ∼50% of non-Dalmatian levels in both tissues . SLC2A9 expression was further evaluated by RT-PCR using primers that amplify both transcripts in 11 different tissues from an unaffected Beagle . The highest levels of expression were observed in the kidney and liver ( Figure 4B ) . Sequencing of the SLC2A9 gene was performed on canine liver cDNA as well as genomic DNA so that intron/exon boundaries and promoter regions could be evaluated . SLC2A9 coding exons 2–12 , as well as the exon-intron boundaries , were sequenced in a Dalmatian and a Labrador Retriever . Six SNPs were discovered in the SLC2A9 sequence . Two are exonic; exon 5 G563T;Cys188Phe ( nucleotide and amino acid numbering are reported with reference to variant N ) and exon 11 G1303A;Val435Ile ( nucleotide and amino acid numbering are reported with reference to variant N ) , two are located 99 and 101 bp 5′ to the start codon of variant O . SNPs were also identified in introns 1 and 10 . None of the intronic SNPs are within or near conserved splice-site elements . The SLC2A9 exon 11 G1303A SNP is polymorphic in the 24 unrelated Dalmatians tested , consistent with its genomic location outside of the region homozygous in Dalmatians ( SNP17 , Figure 3 ) . The polymorphisms located 5′ to the start codon were tested in a panel of DNA samples from 15 unaffected dogs from various different breeds . Both SNPs are polymorphic in unaffected non-Dalmatian dogs , displaying both the Dalmatian haplotype ( A–C ) and other combinations . These SNPs are always fixed in affected Dalmatian dogs ( SNP9-10 , Figure 3 ) . Primary sequence data from the exon 5 SNP is shown in Figure 5A . The two non-synonymous SNPs found in SLC2A9 coding sequence were tested using the PANTHER program ( http://www . pantherdb . org/tools/ ) , which assigned the Cys188Phe substitution a score of −4 . 047 ( scores range between 0 to −10 , −10 indicating the most deleterious ) with a probability that it is a deleterious substitution of 0 . 74 [37] . The Val435Ile variation was not given a prediction since the substitution is not conserved in other species and unlikely to be deleterious . The SIFT program also scored the Cys188Phe missense mutation as deleterious with a probability of 0 . 01 , where any probability below 0 . 05 is considered deleterious [38] . In order to evaluate the degree of protein conservation in the region flanking the candidate SNP , SLC2A9 protein sequences were compared between chicken , mouse , rat , human , Boxer and Dalmatian ( Figure 4B ) . This region of the protein has a high degree of identity across mammals . The variant O promoter SNPs ( located 99 and 101 base pairs upstream of the initiator methionine ) were analyzed for transcription factor binding site differences and the SNPs are predicted to disrupt binding sites for DeltaE , AML-1a , S8 and Cre-BP in the Dalmatian version . The SLC2A9 exon 5 C188F amino acid substitution was further tested in 247 Dalmatians and 378 non-Dalmatian dogs from 58 different breeds . It was homozygous in all Dalmatians tested and only the wildtype allele was present in the non-Dalmatian dogs . Individual dogs from non-Dalmatian breeds are known to form urate calculi and have been diagnosed with huu [39] , [40] . Two Dogs from two of these breeds , Bulldogs and Black Russian Terriers , that had formed urinary urate calculi were tested for the SLC2A9 exon 5 G563T;Cys188Phe missense mutation ( SNP 15 in Figure 3 ) and the variant O promoter SNPS ( SNP 9 , 10 in Figure 3 ) and found to be homozygous . These four dogs shared the Dalmatian haplotype across the homozygous interval defined during the positional cloning of the huu locus ( Figure 3 ) .
The Dalmatian dog exhibits hyperuricosuria and relative hyperuricemia due to a defect of urate transport in the liver and kidney . Positional cloning of the huu locus using an interbreed backcross , as well as homozygosity within the Dalmatian breed , has identified SLC2A9 as the cause of the change in uric acid handling by this dog breed . Unrelated breeds of dog with hyperuricosuria or urate stone disease share the same haplotype as Dalmatian dogs , providing compelling evidence that this is the gene responsible . This result was somewhat unexpected because SLC2A9 was classified as a member of the large glucose transporter family and did not have an assigned function with respect to urate transport until recently . SLC2A9 is classified as a part of the large glucose transporter family based on amino acid sequence identity of 44% and 38% to Glut5 and Glut1 , respectively [32] . SLC2A9 has been localized to the cell surface in humans [34] . It contains 12 transmembrane domains and , based on homology to other glucose transporters , the central channel is essentially formed by helices 2 , 4 , 5 , 7 , 8 , and 10 [41] . The Cys188Phe amino acid change occurs within a highly conserved residue located within the fifth transmembrane domain of the protein . Although a cysteine to phenylalanine amino acid change is not expected to disrupt the localization of the alpha helix to the transmembrane , this change could disrupt the proper function of the protein by altering the pore . Precedent for single amino acid changes altering substrate specificity has been shown for a number of the SLC2A transporters [42] . Expression differences between Dalmatian and non-Dalmatian samples were observed for one of the isoforms ( O ) of the gene ( Figure 4 ) . SNPs in the promoter region of this variant were identified and were fixed in the Dalmatian breed . These same SNPs were also identified in non-Dalmatians without hyperuricosuria . Therefore , these SNPs alone are not sufficient to confer the phenotype but along with the missense mutation they may contribute to the expression of the phenotype . It remains to be determined if there are variant O transcript differences in the general dog population and if the SNPs identified in this work are responsible for the differences in the level of variant O expression . In humans , the equivalent isoform to variant O is expressed on the apical surface of the proximal tubules [34] . Reduction in the amount of the protein combined with a deficiency in the protein itself may contribute to the decreased conversion to allantoin by the liver as well as the increased excretion of uric acid in these dogs . SLC2A9 tissue expression in dogs is similar to that observed in humans and mice . Expression in humans is highest in kidney , liver and placenta [32] . In humans , isoform 1 has been localized within polarized canine kidney cells to the basolateral membrane of the proximal tubules while isoform 2 is localized to the apical side [34] . The tissue and cellular localization of SLC2A9 is consistent with the Dalmatian huu phenotype and with SLC2A9 having a role in urate transport in both the liver and kidney . Mutations in SLC2A9 will likely have important consequences for a number of different disorders of uric acid homeostasis in people . Significant alterations of SLC2A9 could cause primary renal hypouricemia in people similar to SLC22A12 mutations since genetic heterogeneity exists for this disorder [43]–[45] . Mutations in SLC2A9 could also cause cases of primary gout by significantly altering the serum uric acid concentration . In addition , more subtle changes could alter serum uric acid levels by changing the amount of uric acid excreted in the urine . There is evidence that polymorphisms in SLC22A12 are capable of increasing serum uric acid levels in Japanese populations [46] . A significant association of SLC2A9 to serum uric acid levels was recently reported among Caucasian individuals [28] and among Sardinian and Chianti cohorts [27] . Two other papers were recently published on the importance of the SLC2A9 gene to urate transport in humans . The first demonstrated that human SLC2A9 is a high capacity , low affinity uric acid transporter and that genetic variants are associated with gout; however causative mutations were not determined [30] . The second paper documented a strong association between SLC2A9 and uric acid levels in cohorts of German and Austrian nationalities as well as a correlation between RNA expression levels of this gene and serum uric acid levels [29] . Thus , strong evidence exists that SLC2A9 functions as a urate transporter in humans , and likely in dogs . In addition to the potential influence with respect to human uric acid disorders , the present studies also impact our understanding of the history of this defect in dogs . The story of hyperuricosuria in dogs started in 1916 when Benedict first recognized the similarity of uric acid defects in the Dalmatian dog and people [11] . However , the origin of this defect may have preceded the development of the Dalmatian breed . The present studies show that the same genetic mutation is present in Bulldogs and Black Russian Terriers , breeds that are not known to be closely related to the Dalmatian . It appears that affected individuals from these breeds share the same haplotype as the Dalmatian , indicating that the mutation is identical by descent between these breeds . The mutation must be quite old since it would have to predate breed formation; however , additional evaluation of the extended haplotype in all three breeds is necessary to estimate an actual age . Alternatively , although unlikely , the mutation could have been introduced into these other breeds by crosses between breeds . Although Dalmatians are fixed for hyperuricosuria , this is not true of the other breeds . Therefore , genetic testing and selection in those breeds can eliminate the disease . Within the Dalmatian breed , the possibility exists for correction of this defect by the introduction of unaffected Dalmatian×Pointer backcross dogs into the purebred gene pool . These dogs are currently registered with the United Kennel Club in the United States . The disease allele probably became homozygous in modern Dalmatians through selection for more distinctive spots . However , most low uric acid excreting backcross dogs have acceptably sized spots ( according to the breed standard ) , allowing breeders the unique opportunity to correct a fixed genetic defect while maintaining the breed characteristic that may ultimately be responsible for its fixation . Two independent lines of evidence from different species point to the key role of SLC2A9 in urate transport; the Dalmatian uric acid phenotype itself and genome wide association studies linking SLC2A9 to uric acid levels in people along with direct uric acid transport data . There are many questions to be answered about the role of SLC2A9 in urate homeostasis and its other transport functions . In dogs and other mammals with endogenously low serum uric acid , SLC2A9 may normally play a different transport role than in people where it appears to have an important function in uric acid transport . Since degradation of uric acid to allantoin does not occur in humans and great apes , SLC2A9 may play a different role in transport in the liver as compared to those species that excrete allantoin . The positional cloning of the hyperuricosuria locus in the Dalmatian dog has provided a compelling new avenue of investigation toward a better understanding of urate transport in mammals and successfully completes a story started in 1916 when Benedict first recognized the similarity of the uric acid defect in the Dalmatian dog and people .
DNA samples from backcross dogs were acquired as previously described [35] . The full pedigree of the dogs used in this analysis is shown in Figure S1 . Blood , buccal swab and DNA samples from Dalmatians and non-Dalmatian dogs were obtained from patients of the Veterinary Medical Teaching Hospital at UC Davis , the UC Davis Veterinary Genetics Laboratory , Dr . Gary Johnson at the University of Missouri , Columbia , and from private owners . The use of animals in this research was approved by the University of California , Davis animal care and use committee ( protocol #11962 ) . Urine uric acid and creatinine was measured in 3–7 week old puppies as previously described [13] . LOD scores were calculated as previously described [35] . Primers for microsatellites spaced on average 100 Kb apart in the 3 . 3 Mb candidate region of CFA03 were identified using the May 2005 CanFam2 . 0 sequence assembly on the UCSC genome browser and designed within sequence flanking each repeat using the Primer3 program ( Table S1 ) [47] . Eight SNPs , which are part of the Affymetrix canine SNP array , were chosen because they are polymorphic in the general dog population . Additional SNPs were mined from the canine genome sequence or identified during sequencing of the candidate genes . Primers for SNPs ( Table S2 ) were designed as described above . SNP and microsatellite sequences were PCR amplified and genotyped in the backcross dogs and only informative markers for this family were then genotyped in 24 unrelated purebred Dalmatians . Human mRNA sequences were obtained from GenBank for each of the candidate genes ( MIST NM_052964 . 1 , KIAA1729 NM_053042 , WDR1 NM_005112 . 4 , SLC2A9 AF210317 ) . These sequences were compared to the UCSC Genome Browser annotation of the canine genome using the BLAT function to obtain canine sequence for each gene . The Primer3 program was used to design primers for the canine sequences ( Table S3 ) . PCR reactions , genotyping and sequencing were done as previously described [35] . Sequences were visualized using Chromas2 ( Technelysium , Tewantin , QLD , Australia ) and analyzed with Vector NTI software ( Informax , Frederick , MD , USA ) . TFsearch Version 1 . 3 was used to analyze the Variant O promoter SNPs affect on transcription factor binding sites . 5′ and 3′ RACE were performed for 3 of the 4 candidate genes . RACE primers ( Table S3 ) were designed as described above and RACE products amplified with the SMART RACE cDNA Amplification Kit ( Clontech , Mountain View , CA , USA ) and cloned using the TOPO TA Cloning kit ( pCR2 . 1-TOPO vector ) with One Shot TOP10 Chemically Competent E . coli ( Invitrogen , Carlsbad , CA , USA ) . Products were isolated with the Qiaprep Spin Miniprep kit ( Qiagen , Valencia , CA , USA ) and sequenced as described above . Genbank accession numbers for the transcripts are EU371511–EU371515 . All genomic locations given in the text are based on the May 2005 CanFAm2 . 0 genome assembly and are viewed using the UCSC genome browser . RNA was isolated from kidney and liver samples with the Micro-FastTrack 2 . 0 mRNA isolation kit ( Invitrogen , Carlsbad , CA , USA ) . cDNA was synthesized with the SuperScript III First-Strand Synthesis System for RT-PCR ( Invitrogen , Carlsbad , CA , USA ) . Expression was evaluated for LOC479092 , LOC611070 and both variants of LOC479093 from liver and kidney from Dalmatians and unaffected Newfoundland×Border Collie crosses using primers SLC5′UTREx1VarNF and SLCR1 for variant N and primers IntF-Ex1 and SLCR1 for variant O . The expression of LOC479093 was also evaluated in an array of tissues from an unaffected Beagle ( cerebellum , cerebral cortex , heart , kidney , liver , skeletal muscle , skin , spinal cord , spleen , testis and thymus ) using primers IntF5 and SLCdnaR . RNAs for these tissues were acquired from Zyagen ( San Diego , CA , USA ) and cDNA synthesized as described . GAPDH was amplified ( F primer-5′AAGATTGTCAGCAATGCCTCC3′ , R primer -5′CCAGGAAATGAGCTTGACAAA3′ ) in these tissues to ensure that equivalent amounts of cDNA were added . SLC2A9 genotypes were determined by a restriction fragment length polymorphism ( RFLP ) assay . PCR products were produced as described previously using an unlabeled forward primer , 5′-TGCTTCTCTGAAATTTACCTCCA – 3′ and a fluorescently labeled reverse primer , 5′-6FAM-CGAGAGGATGGTATACGGTGA -3′ ( Applied Biosystems , Foster City , CA ) . Products were then digested with the enzyme HpyCH4V ( New England Biolabs , Ipswitch , MA ) for 1 hour at 37°C . Digestions were analyzed on an ABI 3100 Genetic Analyzer with GeneScan 400HD Rox size standard . A 79 base pair unlabeled product is generated from the 440 bp product by a control cut site . The A allele produces a labeled 361 base pair product and the G allele produces a labeled 106 base pair product . | Animals excrete waste products in their urine . When most mammals metabolize compounds , called purines , they produce allantoin as one waste product in their urine . Humans , great apes , and Dalmatian dogs produce a different breakdown product , uric acid . This leads to high levels of uric acid in the urine and blood . In humans , this can result in diseases such as kidney stones and gout and may cause hypertension . In Dalmatians , high uric acid levels result in bladder stones that often have to be removed surgically . The cause of high uric acid levels in humans and great apes is not the same as in the Dalmatian dog . Here we report the genetic cause of the Dalmatian condition . This change is shared by dogs from unrelated breeds , indicating that it predates the separation of dog breeds . The gene that causes excretion of uric acid in Dalmatians is important for controlling the amount of uric acid in human blood and is therefore important for human diseases . It is not clear why humans and great apes have evolved to excrete uric acid , but it appears that some dogs have developed a different mechanism that leads to the same result: elevations in urine and blood uric acid levels . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology/animal",
"genetics",
"biochemistry",
"genetics",
"and",
"genomics/animal",
"genetics",
"physiology/renal,",
"fluid,",
"and",
"electrolyte",
"physiology",
"physiology/gastroenterology",
"and",
"hepatology"
] | 2008 | Mutations in the SLC2A9 Gene Cause Hyperuricosuria and Hyperuricemia in the Dog |
The current antibody detection tests for the diagnosis of gambiense human African trypanosomiasis ( HAT ) are based on native variant surface glycoproteins ( VSGs ) of Trypanosoma brucei ( T . b . ) gambiense . These native VSGs are difficult to produce , and contain non-specific epitopes that may cause cross-reactions . We aimed to identify mimotopic peptides for epitopes of T . b . gambiense VSGs that , when produced synthetically , can replace the native proteins in antibody detection tests . PhD . -12 and PhD . -C7C phage display peptide libraries were screened with mouse monoclonal antibodies against the predominant VSGs LiTat 1 . 3 and LiTat 1 . 5 of T . b . gambiense . Thirty seven different peptide sequences corresponding to a linear LiTat 1 . 5 VSG epitope and 17 sequences corresponding to a discontinuous LiTat 1 . 3 VSG epitope were identified . Seventeen of 22 synthetic peptides inhibited the binding of their homologous monoclonal to VSG LiTat 1 . 5 or LiTat 1 . 3 . Binding of these monoclonal antibodies to respectively six and three synthetic mimotopic peptides of LiTat 1 . 5 and LiTat 1 . 3 was significantly inhibited by HAT sera ( p<0 . 05 ) . We successfully identified peptides that mimic epitopes on the native trypanosomal VSGs LiTat 1 . 5 and LiTat 1 . 3 . These mimotopes might have potential for the diagnosis of human African trypanosomiasis but require further evaluation and testing with a large panel of HAT positive and negative sera .
Human African trypanosomiasis ( HAT ) , or sleeping sickness , is caused by the protozoan flagellar parasites Trypanosoma brucei ( T . b . ) gambiense and T . b . rhodesiense . The disease is transmitted by tsetse flies ( Glossina spp . ) and therefore only occurs in sub-Saharan Africa . The number of cases is currently estimated between 50 000 and 70 000 [1] . Control of T . b . gambiense HAT is largely based on accurate diagnosis and treatment of the human reservoir [2] . Detection of parasites in blood , lymph node aspirate or cerebrospinal fluid is laborious and insensitive , and therefore only applied on suspected HAT patients . In the absence of reliable antigen detection tests , the screening of the population at risk relies on the detection of antibodies against variant surface glycoproteins ( VSGs ) [2] . These immunogenic VSGs form a dense monolayer of homodimers that completely covers the surface of bloodstream trypanosomes and determines the variable antigen type ( VAT ) of the individual trypanosome [3] . The parasite genome contains hundreds of VSG genes and trypanosomes switch from the expression of one VSG gene to another . This antigenic variation enables the parasite population to survive the host's immune response . Each VSG monomer contains 400–500 amino acids and consists of two domains , a variable N-terminal domain with little primary sequence homology and a relatively conserved C-terminal domain . A glycosylphosphatidylinositol anchor links the C-terminal domain to the cell membrane . All N-terminal domains fold in a similar three-dimensional structure , exposing only a limited subset of , probably discontinuous , epitopes [4]–[7] . The current T . b . gambiense antibody detection tests are based on native VSGs from the VATs LiTat 1 . 3 , LiTat 1 . 5 and LiTat 1 . 6 of T . b . gambiense [8] . These predominant VATs appear early during infection , and induce a strong and specific immune response in most patients [3] . The card agglutination test for trypanosomiasis ( CATT ) [9] , most widely used for mass screening of populations at risk , consists of whole lyophilised trypanosomes of VAT LiTat 1 . 3 and has a sensitivity on whole blood of 87–98% and a specificity around 95% [2] . Although VSG LiTat 1 . 3 is not expressed in all endemic HAT foci [10] , [11] , the low sensitivity of CATT in those foci can be overcome by combining different VATs in one test , as is the case in the LATEX/T . b . gambiense and the ELISA/T . b . gambiense where the combination of VSG LiTat 1 . 3 , 1 . 5 and 1 . 6 is used as antigen [12] , [13] . The use of native VSGs as diagnostic antigens has several disadvantages . Firstly , non-specific epitopes on the native antigens may cause cross-reactions and decrease test specificity . Secondly , VSG production relies on culture of infective T . b . gambiense parasites in laboratory rodents and poses a risk of infection to the staff [14] . These disadvantages might be avoided if native antigens are replaced by synthetic peptides . The production of synthetic peptides is standardised , does not require laboratory animals and is without risk of infection [15] . Peptide phage display is a selection technique based on DNA recombination , resulting in the expression of foreign peptide-variants on the outer surface of phage virions . After an in vitro selection process based on binding affinity , called panning , the selected peptides are characterised by DNA sequencing . Phage display is a powerful tool to identify mimotopes , small peptides that mimic linear , discontinuous and/or non-protein epitopes [16]–[18] . Mimotopes with diagnostic potential have already been identified , e . g . for detection of specific antibodies for Lyme disease [19] , hepatitis C [15] , [20] , typhoid fever [21] , tuberculosis [22] and leishmaniasis [23] . Some mimotopes have been patented to become incorporated in commercially available tests , e . g . for neurocysticercosis [24] . In this study , we aimed to identify mimotopes for epitopes of T . b . gambiense VSG LiTat 1 . 3 and LiTat 1 . 5 that may replace the native proteins in antibody detection tests for sleeping sickness .
Samples from HAT patients and endemic controls were collected within an observational study [13] . All individuals gave their written informed consent before providing serum . Permission for this study was obtained from the national ethical committee of DRC and from the ITM ethical committee , reference number 03 07 1 413 . Monoclonal antibodies ( mAbs ) H12H3 ( IgG3 , anti-VSG LiTat 1 . 5 ) , H13F7 ( IgG3 , anti-VSG LiTat 1 . 3 ) and H18C11 ( IgG1 , anti-VSG LiTat 1 . 3 ) were generated by intraperitoneal infection of Balb/c mice with 106 T . b . gambiense LiTat 1 . 3 and 106 LiTat 1 . 5 cloned parasites . After 2 weeks , splenocytes were isolated and fused with NS0 myeloma cells [25] . Anti-VSG antibody producing hybridomas were identified by enzyme linked immunosorbent assay ( ELISA ) and further propagated . The antibodies were purified from culture supernatant on protein A agarose . The SBA Clonotyping™ system/HRP kit ( Southern Biotech ) was used for mAb isotyping . Anti-VSG mAbs were coated onto anti-mouse IgG functionalised magnetic particles ( MP ) ( 1% w/v , 0 . 35 µm , Estapor/Merck ) at a concentration of 30 mg/g MP and stored in phosphate buffered saline ( PBS , 0 . 01 mol/L phosphate , 0 . 14 mol/L NaCl , pH 7 . 4 ) containing 0 . 1% ( w/v ) bovine serum albumin ( PBS-BSA ) . The coated MP were washed eight times with PBS containing 0 . 25% w/v gelatine and 0 . 1% v/v Tween-20 ( PBSGT ) and resuspended 0 . 25% w/v gelatine in PBS ( PBSG ) . Successful coating of the MP was confirmed by agglutination of VSG coated latex beads ( LATEX/T . b . gambiense ) [12] . Anti-VSG mAb-free MPs were prepared by omitting the coating step . Pannings were performed with the Ph . D . -12 ( 12-mer ) and the Ph . D . -C7C ( cyclic 7-mer ) phage display libraries ( New England Biolabs , NEB ) through two rounds consisting of 1 ) a positive selection with anti-VSG mAbs coated on MP , 2 ) a negative selection with anti-VSG mAb-free MP and 3 ) phage amplification . After these two rounds a third positive selection was performed . For positive selection , 10 µL of the phage display library ( for the 1st positive selection ) or 100 µL of amplified phage ( for the 2nd and 3rd positive selection ) were mixed overnight ( ON ) at 4°C with 1 mg mAb coated-MP in PBSG in a total volume of 1 mL . MP were washed ten times with PBSGT and bound phages were eluted by antigen competition ( A ) followed by acid elution ( P ) . For the antigen competition , the MP were incubated for 1 h with 700 µL PBS containing 0 . 23 mg of corresponding VSG . After collection of the supernatant containing the eluted phages , MP were washed three times with PBSGT . The remaining bound phages were eluted with 600 µL of 0 . 2 mol/L glycine-HCl containing 1 mg/mL BSA ( pH 2 . 2 ) and neutralised with 90 µL of Tris-HCl ( 1 mol/L , pH 9 . 1 ) . The phages eluted from the positive selection were mixed overnight at 4°C with 1 mg of anti-VSG mAb-free MP in a total volume of 1 mL of PBSG . The phages in the supernatant were amplified . Phages were amplified in Escherichia ( E . ) coli ( strain ER2738 , NEB ) in lysogeny broth ( LB ) , supplemented with tetracycline ( 20 mg/mL ) [26] . After 4 . 5 h shaking at 37°C , bacteria were pelleted by centrifugation ( 30 min , 1811 g ) . The phages in the supernatant were precipitated overnight at 4°C with 25% w/v polyethylene glycol-6000 in 2 . 5 mol/L NaCl ( PEG-NaCl ) . Phages were pelleted by centrifugation ( 45 min , 1811 g , 4°C ) and resuspended in 1 mL of PBS . Residual bacteria were pelleted by centrifugation ( 5 min , 15700 g ) and phage precipitation with PEG-NaCl was repeated for 2 to 4 hours at 4°C . After centrifugation ( 20 min , 15700 g , 4°C ) the phage pellet was resuspended in 200 µL of PBS with 0 . 02% w/v NaN3 . After a third positive selection , phages selected through three antigen competition elutions ( AAA ) and three acid elutions ( PPP ) were titered as described below . Phages were titered to obtain well separated , single plaques for analysis . Phages were diluted 103 to 107 in PBS , mixed with an E . coli culture and plated on agar plates containing 1 mL/L IPTG/X-gal ( 1 . 25 g isopropyl β-D-thiogalactoside , 1 g 5-bromo-4-chloro-3-indolyl-β-D-galactoside , 25 mL dimethylformamide ) . After the 3rd positive selection 94 blue clones were picked and each clone was inoculated in 200 µL of LB in a sterile culture plate ( BD Falcon™ Clear 96-well Microtest™ Plate ) . This plate was shaken overnight at 30°C , where after the bacteria were pelleted by 5 min centrifugation at 1312 g . The supernatant was tested in a sandwich ELISA with the homologous mAb . Based on these results , twenty phages per elution method were amplified , tested in a sandwich ELISA with all three mAbs and amplified for DNA extraction . Purification of phage DNA was performed according to the NEB manual [26] . Sequence determination was performed at the VIB Genetic Service Facility of the University of Antwerp with the −96 gIII sequencing primer , 5′-H0CCC TCA TAG TTA GCG TAA CG-3′ ( NEB ) . The obtained sequence chromatograms were read with Chromas 2 . 33 ( Technelysium Pty Ltd ) . Sequence alignment was performed manually and with RELIC software [27] . We searched for discontinuous epitopes with the 3D-Epitope-Explorer ( 3DEX ) [28] and visualised the sequences on the protein model with PyMOL pdb viewer ( PyMOL Molecular Graphics System , Schrödinger , LLC ) . ELISA plates ( Nunc MaxiSorp™ ) were coated with 5 µg/mL anti-VSG mAb in PBS ( 100 µL/well ) and incubated ON at 4°C . Plates were saturated for 1 h at rT with 350 µl PBS-Blotto ( 0 . 01 mol/L phosphate , 0 . 2 mol/L NaCl , 1% w/v skimmed milk powder , 0 . 05% w/v NaN3 ) and washed three times with 0 . 05% v/v Tween-20 in PBS ( PBST ) ( ELx50 , Bio-Tek ELISA washer ) . Wells were incubated for 4 h at rT with 100 µL of phage dilution in PBS-Blotto ( 1/3 for culture plate supernatant or 1/20 for PEG-NaCl purified phage ) . After three washes , horse radish peroxidase ( HRP ) -labelled anti-M13 pVIII mAb ( GE Healthcare ) , diluted 1/2000 in PBST was added to the wells for 1 h at rT ( 100 µl/well ) . After another five washes , wells were incubated for 1 h at rT with 100 µL/well of 2 . 2′-azino-bis- ( 3-ethylbenzthiazoline-6-sulfonic acid ) ( ABTS ) chromogen/substrate solution ( 50 mg tablet/100 mL of ABTS buffer , Roche ) . The plate was shaken for 10 seconds and the optical density ( OD ) was read at 414 nm ( Labsystems Multiskan RC 351 ) . Peptide selection for synthesis ( Eurogentec , Belgium ) was based on strong , reproducible and specific reaction of phage clones with their homologous anti-VSG mAb in ELISA , prediction of peptide hydrophilicity and common motive groups in the sequences . The 12-mer linear peptides were synthesised at >70 or >85% purity . The C-terminus was elongated with a GGGS-CONH2 tail to mimic the GGGS-peptide spacer between the random peptide sequence and the phage protein pIII and to block the negative charge of the carboxyl terminus . The cyclic 7-mer sequences were synthesised at >90% purity . The 7-mer peptides were flanked by two cysteines , and the C-terminus was elongated with GGGS-CONH2 . The biotinylated peptides were synthesised at >85% purity , with an additional lysine-biotin added to the C-terminus . All synthetic peptides were reconstituted in sterile deionised H2O to a concentration of 2 mg/mL . The capacity of the synthetic peptides to mimic the natural VSG epitopes was assessed in an inhibition ELISA . Peptide dilution series of 200 , 67 , 22 , 7 and 0 µg/ml or 67 , 22 , 7 , 2 and 0 µg/ml were prepared in PBS containing 1% BSA ( PBS-BSA ) with mAb H12H3 at 1 µg/mL , and in PBS-Blotto with mAb H13F7 at 11 µg/mL or mAb H18C11 at 0 . 04 µg/mL . The same dilution series were prepared in PBS-BSA or PBS-Blotto but without mAb . These dilutions were rotated ON at 4°C . All samples were tested in duplicate . For mAb H12H3 , ELISA plates were coated ON at 4°C with 100 µL/well of 2 µg/mL VSG LiTat 1 . 5 in PBS and antigen-free wells served as an antigen negative control . The next day , ELISA plates were blocked at rT for 1 h with PBS-BSA and washed three times with PBST . The wells were incubated for 1 h at rT with 100 µl of the dilutions . Plates were washed three times and HRP-labelled goat Fc-specific anti-mouse IgG conjugate ( Jackson ) , diluted 1/500 in PBST was added for 1 h ( 100 µL/well , rT ) . The colour reaction was performed as previously described for the sandwich ELISA . For the mAbs H13F7 and H18C11 the same protocol was followed , using VSG LiTat 1 . 3 , PBS-Blotto 1% and a 1/1000 dilution of the anti-mouse conjugate . The remaining activity ( % RA ) was calculated: first , for each peptide dilution , the average OD measured in the antigen-free control wells was subtracted from the average OD in the corresponding antigen containing wells , thus yielding ODa . Second , the corrected ODc was obtained by subtracting the ODa obtained in the mAb-free wells from the ODa in the corresponding mAb containing wells . Finally , the % remaining activity was calculated as 100×ODc/ODmax , where ODmax was the ODc of wells receiving the peptide-free mAb dilution . Nunc MaxiSorp™ plates were coated with 100 µL/well of 10 µg/mL streptavidin ( NEB ) in carbonate buffer ( CB , 0 . 1 mol/L , pH 9 . 2 ) . Plates were incubated ON at 4°C . After saturation with PBS-BSA ( for mAb H12H3 ) or PBS-Blotto ( for mAb H13F7 ) and three washes with PBST , wells were filled with 100 µL of biotinylated peptide in PBS at a concentration of 5 ( only for peptide 24 , C57 and C59 ) , 1 , 0 . 6 , 0 . 3 and 0 µg/ml . After 1 h at rT , wells were washed three times and incubated for 1 h with 100 µL mAb ( mAb H12H3 at 1 . 07 , 0 . 53 , 0 . 27 µg/mL in PBS-BSA or mAb H13F7 at 0 . 53 and 0 . 27 µg/mL in PBS-Blotto ) . After three washes , HRP-labelled goat anti-mouse IgG ( Fcγ ) conjugate was diluted 1/1000 in PBST and added at 100 µL/well for 1 h . After five washes , plates were incubated for 1 h with 100 µL/well of ABTS . The OD was read at 414 nm and corrected by subtracting the OD obtained in the peptide-free wells from the OD obtained in the corresponding peptide containing wells . The capacity of the synthetic peptides to specifically bind antibodies in serum from HAT patients was assessed in an inhibition ELISA with nine sera from gambiense sleeping sickness patients and ten sera from endemic controls . ELISA plates were coated ON at 4°C with 100 µL/well of 10 µg/mL streptavidin in CB . All samples were tested in duplicate . The following day the plates were saturated with PBS-BSA ( for mAb H12H3 ) or PBS-Blotto ( for mAb H13F7 ) . The dilutions of the biotinylated peptides were made in PBS ( 0 . 01 mol/L phosphate , 0 . 14 mol/L NaCl , pH 6 ) , ranging from 20 µg/mL to 0 . 3 µg/mL , depending on the peptide . After three washes with PBST , the peptide dilutions were added at 100 µL/well and left for 1 h at rT . Plates were tapped dry , sealed and stored frozen at −80°C . Just before use , plates were thawed and washed ( 3× PBST ) . Wells were incubated with 100 µL of a 1/5 human serum dilution in PBS-BSA or PBS-Blotto , depending on the mAb ( 1 h at rT ) . After three washes with PBST , mAb H12H3 ( in PBS-BSA ) or mAb H13F7 ( in PBS-Blotto ) was added at a concentration ranging from 1 to 0 . 25 µg/mL , depending on the peptide ( 100 µl/well , 1 h , rT ) . Plates were washed three times and a 1/1000 dilution in PBST of HRP labelled goat anti-mouse IgG ( Fcγ ) conjugate was added at 100 µL/well ( 1 h , rT ) . Plates were washed five times and incubated for 1 h at rT with 100 µL/well of ABTS . The OD was read at 414 nm and corrected by subtracting the average OD obtained in the antigen-free wells from the average OD obtained in the corresponding antigen containing wells , thus yielding ODc . The percent remaining activity was calculated as 100×ODc/ODmax . P-values were tested with the Wilcoxon rank test ( positive versus negative ) and corrected for multiple comparisons with the Bonferroni method . Based on the results in indirect ELISA with VSG LiTat 1 . 5 and LiTat 1 . 3 ( data not shown ) we selected nine HAT positive ( OD>1 . 5 ) and 10 endemic negative ( OD<0 . 2 ) human serum samples originating from a study on detection of specific antibodies in serum and saliva in the Democratic Republic of Congo ( DRC ) [13] .
Panning of the Ph . D . -12 library yielded phage clones for which median ODs in the sandwich ELISA with the homologous mAb were 1 . 352 ( interquartile range , IQR , 1 . 119–1 . 608 ) for the AAA elution and 2 . 759 ( IQR 2 . 384–2 . 943 ) for the PPP elution . Twenty phage clones eluted with AAA and OD>1 and twenty phage clones eluted with PPP and OD>2 , were selected for amplification , cross reactivity testing and sequencing . All forty amplified phage clones reacted specifically with mAb H12H3 ( median OD 1 . 055 with H12H3 versus 0 . 100 with the heterologous mAb H13F7 and H18C11 ) , therefore excluding that the selected phage clones bound to the conserved Fc part of the mAbs . Phages obtained with the AAA elution expressed six different amino acid ( AA ) sequences ( fig . 1 ) . One sequence could not be read . Phages obtained with the PPP elution expressed nine different sequences . Panning of the Ph . D . -C7C library yielded phage clones for which median ODs in the sandwich ELISA with the homologous mAb were 0 . 713 ( IQR 0 . 485–2 . 338 ) for the AAA elution and 3 . 218 ( IQR 3 . 138–3 . 324 ) for the PPP elution . Twenty phage clones eluted with AAA and OD>2 and twenty phage clones eluted with PPP and OD>3 , were selected for amplification , cross reactivity testing and sequencing . All amplified phage clones reacted specifically with mAb H12H3 ( median OD 3 . 106 with H12H3 versus 0 . 138 with the heterologous mAbs H13F7 and H18C11 ) . Five phage clones corresponding to three sequences did not amplify well ( OD with mAb H12H3<0 . 4 ) and were not withheld for further experiments . With each elution method , phages expressing twelve different amino acid sequences were obtained ( fig . 1 ) . Two of these sequences were found in both the AAA and the PPP elution , bringing the total of different C7C-sequences to twenty-two . Amongst the thirty-seven different sequences obtained through panning with mAb H12H3 , four groups of common motives could be distinguished ( fig . 1 ) . Amino acids with similar structure and characteristics were considered homologous , such as arginine ( R ) and lysine ( K ) ; serine ( S ) and threonine ( T ) ; glutamine ( Q ) and asparagine ( N ) ; alanine ( A ) , valine ( V ) , leucine ( L ) and isoleucine ( I ) ; phenylalanine ( F ) , tyrosine ( Y ) and tryptophan ( W ) ; and aspartic acid ( D ) and glutamic acid ( E ) . Group 1 with common motive SAP ( W/Y ) ( V/A , S or N ) ( L/A or Y/F/W ) ( R/K ) DH ( L/A or Y/F ) ( P or S/T ) L/AxG contained all the Ph . D . -12 sequences , and part of the Ph . D . -C7C sequences . Group 2 , 3 and 4 consisted of Ph . D . -C7C sequences only and had as a common motive AxxxT ( S/T or A ) ( P or L ) ( N/Q ) QWL , AxPVYExHWxxxG , and AxQxPHxxxxG respectively . The sequence CTDFEGMLC did not have more than two AA in common with one of the other sequences and is displayed separately . Homology between peptides and the protein sequence of VSG LiTat 1 . 5 [GenBank HQ662603] was found within AA 268 to 281 of the protein sequence ( maximum 42 . 86% or 6/14 identical AA , fig . 1 ) , in the variable N-terminal domain of VSG LiTat 1 . 5 . Out of the thirty-seven obtained sequences , ten peptides were synthesised . All ten synthetic peptides strongly inhibited the binding of mAb H12H3 to native VSG LiTat 1 . 5 in a dose dependent manner ( <25% remaining activity at a peptide concentration of 67 µg/ml ) ( fig . 1 ) and were resynthesised with a C-terminal lysine-biotin . Peptides selected with mAb H13F7 and H18C11 ( see below ) did not inhibit binding of H12H3 to VSG LiTat 1 . 5 , ( data not shown ) . All biotinylated peptides were recognised by mAb H12H3 in an indirect ELISA . A peptide concentration of >10 µg/mL was necessary to obtain an OD>0 . 5 with peptides C57 and C59 while 5 µg/mL for peptide 24 and concentrations ranging from 0 . 3 to 0 . 6 µg/mL for the other seven peptides , were sufficient to obtain an OD>1 ( data not shown ) . Panning of the Ph . D . -12 library yielded phage clones for which median ODs in the sandwich ELISA with the homologous mAb were 3 . 219 ( IQR 3 . 000–3 . 350 ) for the AAA elution and 0 . 148 ( IQR 0 . 123–2 . 999 ) for the PPP elution . Per elution method , twenty phage clones with OD>2 were selected . All forty amplified clones reacted specifically with mAb H13F7 ( median OD 3 . 022 with H13F7 versus 0 . 113 with the heterologous mAbs H12H3 and H18C11 ) ; this excludes that the selected phage clones bound to the conserved Fc part of the mAbs . Phages obtained with the AAA elution , expressed seven different amino acid sequences . Phages obtained with the PPP elution , expressed three different sequences . One sequence was found in both the AAA and the PPP elution , thus bringing the total of different sequences to nine ( fig . 2 ) . Panning of the Ph . D . -C7C library yielded phage clones for which median ODs in the sandwich ELISA with the homologous mAb were 1 . 481 ( IQR 0 . 739–1 . 869 ) with the AAA eluted phage clones and 0 . 107 ( IQR 0 . 087–0 . 122 ) with the PPP eluted phage clones . None of the PPP eluted clones were withheld . Twenty phage clones of the AAA elution with OD>1 were amplified . All reacted specifically with mAb H13F7 ( median OD 1 . 942 with H13F7 versus 0 . 106 with the heterologous mAbs H12H3 and H18C11 ) . Seven different amino acid sequences were expressed ( fig . 2 ) . Amongst the sixteen different sequences obtained with mAb H13F7 , three groups of common motives could be distinguished ( fig . 2 ) . Group 1 with common motive PPxWINPFPxF contained only 12-mer sequences . Group 2 contained some of the 12-mer and all of the 7-mer sequences and had as common motive PW ( W or L ) PLQ ( W/Y ) ( I/V/L ) F or , with “WPL” in reverse order , Ax ( I/V ) L ( P or S ) WLH ( I/V ) . Peptide 59 and peptide C63 share a common motive , but in reverse order ( F/W ) LPL . Group 3 consisted of two sequences with common motive SPxMLH . Alignment of these sequences with the protein sequence of VSG LiTat 1 . 3 [GenBank AJ304413] , only gave results for group 2 and one sequence of group 3 . These had maximum 28 . 57% identical AA ( 4/14 ) within respectively AA stretch 196 to 210 and AA stretch 338 to 351 of VSG LiTat 1 . 3 ( fig . 2 ) . The motive W ( AA 291 ) P ( AA 290 ) L ( AA 292 ) L ( AA 234 ) T ( AA 230 ) of peptide 64 could be mapped onto the three-dimensional VSG LiTat 1 . 3 protein structure , in the N-terminal domain ( results not shown ) . Out of the sixteen sequences selected with mAb H13F7 , twelve peptides were synthesised . Six synthetic peptides strongly inhibited the binding of the mAb to the native VSG LiTat 1 . 3 ( <50% remaining activity at a peptide concentration of 67 µg/ml ) , and one peptide was a weaker inhibitor ( 76% remaining activity ) ( fig . 2 ) . These seven peptides were resynthesised with a C-terminal lysine-biotin and their reactivity with mAb H13F7 was assessed by indirect ELISA . All biotinylated peptides had an OD>1 with mAb H13F7 at a concentration of 0 . 6 to 0 . 3 µg/mL peptide ( data not shown ) . Panning of the Ph . D . -12 library yielded phage clones for which median ODs in the sandwich ELISA with the homologous mAb were 0 . 124 ( IQR 0 . 109–0 . 185 ) for the AAA elution and 0 . 130 ( IQR 0 . 108–0 . 148 ) for the PPP elution . None of the clones from the Ph . D-C7C-library gave a sufficiently high OD to be amplified . Twenty AAA eluted clones of the Ph . D . -12-library with OD>0 . 5 were amplified . All twenty clones reacted specifically with mAb H18C11 ( median OD 3 . 271 with H18C11 versus 0 . 106 with the heterologous mAbs H12H3 and H13F7 ) . Only one amino acid sequence was expressed: SHSTPYYWKGYI . We could not identify any homology between this peptide and the protein sequence of VSG LiTat 1 . 3 . The corresponding synthetic peptide did not react with mAb H18C11 in indirect ELISA ( data not shown ) and did not inhibit the binding of mAb H18C11 to native VSG LiTat 1 . 3 and was therefore not withheld for further experiments . The diagnostic potential of the biotinylated peptides was assessed in an inhibition ELISA with human sera from nine gambiense HAT patients and ten negative controls . Compared to the HAT negative sera , the HAT positive sera significantly inhibited binding of mAb H12H3 to peptide 23 , C59 and C60 ( p<0 . 05 ) and 21 , 22 , and 28 ( p<0 . 01 ) ( fig . 3 ) . Five of these peptides belong to common motive group 1 , peptide C60 belongs to group 4 ( fig . 1 ) . The HAT positive sera also significantly inhibited binding of mAb H13F7 to peptide 25 ( p<0 . 05 ) and peptides 60 and 61 ( p<0 . 01 ) ( fig . 3 ) . All three peptides belong to common motive group 1 ( fig . 2 ) .
By means of phage display technology we successfully identified peptides that mimic epitopes on the native trypanosomal variant surface glycoproteins LiTat 1 . 5 and LiTat 1 . 3 of T . b . gambiense . These mimotopic peptides were recognised by the monoclonal anti-VSG antibodies H12H3 and H13F7 that were used for panning , and might have potential for the diagnosis of human African trypanosomiasis . Our results indicate that a linear region in the protein sequence of VSG LiTat 1 . 5 was identified . This region is localised in the N-terminal domain of the VSG near the surface of the trypanosome and is therefore a candidate for further testing as a synthetic , linear peptide . Antibodies specific to linear , continuous epitopes on protein antigens typically contact three to four critical amino acids over a six residue segment [16] . The peptide sequences selected with mAb H12H3 had up to six amino acids ( % identity 42 . 86 ) in common with the variable N-terminal domain ( AA 268 to 281 ) of VSG LiTat 1 . 5 . Glycine from the GGGS-spacer , inserted between the peptide sequence and the pIII phage protein , was part of this common motive . It is possible to define the exact residues in the peptide sequences that are essential for binding with the antibody by alanine scanning mutagenesis and recreate the epitope of each monoclonal . A recent example has been the identification of a linear epitope on the VP1 protein of foot-and mouth disease virus by Yang et al . [29] by screening a 12-mer phage display library with a mAb . Contrary to VSG LiTat 1 . 5 , we suspect the epitope of VSG LiTat 1 . 3 to be discontinuous . Alignment of the peptide sequences with the protein sequence of VSG LiTat 1 . 3 located the common motives in different parts of the protein sequence with a maximum % identity of only 28 . 57 . Also , when the VSG was ( partly ) denatured , the OD in ELISA with this mAb dropped . Many protein epitopes are discontinuous and comprise critical binding residues that are distant in the primary sequence but close in the folded native tertiary protein structure . Indeed , WPLLT , the motive of peptide 64 was mapped onto the three-dimensional VSG LiTat 1 . 3 protein structure , near the surface of the trypanosome . As “WPL” or “LPW” is part of the common motive in the peptide sequences of group 2 ( fig . 2 ) it is possible that the discontinuous epitope of mAb H13F7 is localised in this region . All 12-mer peptides that strongly inhibited the binding of the mAbs to the VSG contain one or more proline residues . Proline limits the flexibility of the peptide and may therefore favour the forming of the mAb-peptide complex [30] . The 7-mer peptides are already constrained by two flanking cysteines , which may account for the fact that the sequence of four of the best cyclic inhibitors contains no proline . Cortese [31] reports that many mAbs fail to select specific peptides . Due to the limitation of library complexity it is often impossible to isolate peptides of high affinity and there is no general rule applicable as to what type of library suits a certain application [18] . In our study mAb H18C11 selected specific peptides , but only with the 12-mer library . Additionally , all phage clones selected with this mAb expressed the same peptide sequence . It is possible that one phage clone overgrew other , higher-affinity phage clones , during amplification . The peptide selected with mAb H18C11 and some of the synthetic peptides selected with mAb H12H3 and mAb H13F7 failed to react with the corresponding mAb in indirect ELISA . It has been described before that phage-born peptides can lose their ability to bind the target molecule when synthesised chemically [17] . Furthermore the conformation of peptides in binding assays may differ from the presentation on the phage . Although direct coating was only successful for some peptides ( data not shown ) , we successfully demonstrated the capacity of several of these peptides in solution to inhibit the binding of their mAb to the corresponding VSG . Based on these results we selected some peptides for resynthesis and biotinylation . The biotinylated peptides were bound to streptavidin , which was coated onto the ELISA plate , this improved the peptide presentation to such an extent that all of the biotinylated peptides were able to bind their corresponding mAb . Although the aim of our study was to identify mimotopes for antibody detection in human serum , we opted to perform the panning with mouse monoclonal antibodies . Indeed , sera from sleeping sickness patients contain an important fraction of trypanosome unrelated antibodies as a consequence of polyclonal B cell stimulation [32] , [33] . Therefore , the risk to select mimotopes unrelated to sleeping sickness by applying human sera for the panning is considerable , unless only the trypanosome specific antibody fraction of these sera is used . Moreover , it was demonstrated that mAbs identified peptide mimotopes similar to those selected with pooled sera of typhus patients [21] . Also , mAb H13F7 was able to cause lysis of trypanosomes of VAT LiTat 1 . 3 in the immuno-trypanolysis test , which demonstrates that this mAb recognises VSG epitopes , exposed on living bloodstream trypanosomes , similar to those recognised by human sleeping sickness sera [3] . Finally , we chose to perform the screening with mAbs that bind to different epitopes on the VSGs to increase the chance that the selected mimotopes would bind different antibodies in the polyclonal patient sera . Human sleeping sickness sera inhibited the binding of anti-LiTat 1 . 5 mAb H12H3 to peptides 21 , 22 , 23 , 28 , C59 and C60 and the binding of anti-LiTat 1 . 3 mAb H13F7 to peptides 25 , 60 and 61 , auguring for their value as diagnostic antigens . Not all peptides were equally well recognised by human sera . It is possible that some peptides react weakly with positive sera in spite of their specificity for immunodominant regions , which can be explained if these peptides mimic only part of the structure of the corresponding region on the antigen [20] . This may be the reason why the mimotopes corresponding to the linear region in the protein sequence of VSG LiTat 1 . 5 that interacts with the mAb seem to be more easily recognised by human antibodies than the mimotopes of the discontinuous LiTat 1 . 3 epitope , where a correct conformational presentation is crucial . The mouse and human immune system may as well react with different vigour to certain epitopes or recognise different principal epitopes . The fraction of antibodies in HAT sera that bind the same epitope as the mAbs may therefore be relatively small . By using an inhibition ELISA , low serum dilutions could be applied , maximising the reaction with the peptides , but avoiding the non-specific reactions often observed with HAT sera at low dilutions . Unexpectedly , with several peptides , addition of human serum in the inhibition ELISA resulted in higher ODs than those obtained with the serum-free control wells . This observation might be explained if human serum contains a substance that changes the conformation of a peptide in a way that facilitates binding to the mAb . With this study , we aimed at delivering the proof of principle that mimotopes for VSGs can be selected from a peptide phage display library making use of mAbs . However , the study has some limitations . Firstly , by making use of only three mAbs , it is likely that other diagnostic mimotopes have been missed . Secondly , no affinity measurements e . g . via surface plasmon resonance , have been performed . Considering the polyclonal character of antibodies in patients' sera and the inherent differences in antibody response between individual patients , we opted to assess only the diagnostic potential of the selected peptides by means of ELISA . In conclusion , we successfully demonstrated that polyclonal antibodies in human sleeping sickness sera recognise mimotopes of VSG LiTat 1 . 3 and 1 . 5 , indicating diagnostic potential of peptides selected with monoclonal antibodies . Still , replacement of the native T . b . gambiense LiTat 1 . 3 and LiTat 1 . 5 VSGs in the currently existing diagnostic formats might not be straightforward . It should be preceded by confirming the diagnostic potential of the selected peptides , or variations and combinations thereof , on larger panels of HAT positive and negative sera , and , if needed , peptide sequence optimisation . | The control of human African trypanosomiasis or sleeping sickness , a deadly disease in sub-Saharan Africa , mainly depends on a correct diagnosis and treatment . The aim of our study was to identify mimotopic peptides ( mimotopes ) that may replace the native proteins in antibody detection tests for sleeping sickness and hereby improve the diagnostic sensitivity and specificity . We selected peptide expressing phages from the PhD . -12 and PhD . -C7C phage display libraries with mouse monoclonal antibodies specific to variant surface glycoprotein ( VSG ) LiTat 1 . 3 or LiTat 1 . 5 of Trypanosoma brucei gambiense . The peptide coding genes of the selected phages were sequenced and the corresponding peptides were synthesised . Several of the synthetic peptides were confirmed as mimotopes for VSG LiTat 1 . 3 or LiTat 1 . 5 since they were able to inhibit the binding of their homologous monoclonal to the corresponding VSG . These peptides were biotinylated and their diagnostic potential was assessed with human sera . We successfully demonstrated that human sleeping sickness sera recognise some of the mimotopes of VSG LiTat 1 . 3 and LiTat 1 . 5 , indicating the diagnostic potential of such peptides . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"humoral",
"immunity",
"sequencing",
"medicine",
"clinical",
"laboratory",
"sciences",
"protein",
"interactions",
"immunology",
"microbiology",
"parastic",
"protozoans",
"glycoproteins",
"sequence",
"analysis",
"synthetic",
"peptide",
"biology",
"proteomics",
"biochemistry",
... | 2011 | Identification of Peptide Mimotopes of Trypanosoma brucei gambiense Variant Surface Glycoproteins |
Despite many advances in AIDS research , a cure for HIV infection remains elusive . Here , we performed autologous hematopoietic stem cell transplantation ( HSCT ) in three Simian/Human Immunodeficiency Virus ( SHIV ) -infected , antiretroviral therapy ( ART ) -treated rhesus macaques ( RMs ) using HSCs collected prior to infection and compared them to three SHIV-infected , ART-treated , untransplanted control animals to assess the effect of conditioning and autologous HSCT on viral persistence . As expected , ART drastically reduced virus replication , below 100 SHIV-RNA copies per ml of plasma in all animals . After several weeks on ART , experimental RMs received myeloablative total body irradiation ( 1080 cGy ) , which resulted in the depletion of 94–99% of circulating CD4+ T-cells , and low to undetectable SHIV-DNA levels in peripheral blood mononuclear cells . Following HSC infusion and successful engraftment , ART was interrupted ( 40–75 days post-transplant ) . Despite the observed dramatic reduction of the peripheral blood viral reservoir , rapid rebound of plasma viremia was observed in two out of three transplanted RMs . In the third transplanted animal , plasma SHIV-RNA and SHIV DNA in bulk PBMCs remained undetectable at week two post-ART interruption . No further time-points could be assessed as this animal was euthanized for clinical reasons; however , SHIV-DNA could be detected in this animal at necropsy in sorted circulating CD4+ T-cells , spleen and lymph nodes but not in the gastro-intestinal tract or tonsils . Furthermore , SIV DNA levels post-ART interruption were equivalent in several tissues in transplanted and control animals . While persistence of virus reservoir was observed despite myeloablation and HSCT in the setting of short term ART , this experiment demonstrates that autologous HSCT can be successfully performed in SIV-infected ART-treated RMs offering a new experimental in vivo platform to test innovative interventions aimed at curing HIV infection in humans .
The introduction of antiretroviral therapy ( ART ) has dramatically reduced the morbidity and mortality associated with HIV infection and AIDS . However , currently available ART requires life long treatment with significant potential side effects and a cost that places an inordinate burden on public health systems . While reduction of HIV viral loads below detectable limits is often achieved in ART-treated individuals , a treatment that can eradicate or functionally cure HIV infection remains elusive . Many studies indicate that the key obstacle to cure HIV infection is the presence of a persistent reservoir of latently infected cells that are not eliminated by ART [1] , [2] . Thus , interruption of ART consistently results in a rebound of viremia to pre-treatment levels [3] , [4] . Several biological aspects of this virus reservoir , including its exact cellular and anatomic origin as well as the mechanisms responsible for its establishment and persistence under ART remain poorly understood . This limited knowledge represents a fundamental barrier to a cure for HIV infection , and novel therapeutic strategies aimed at eliminating the reservoir will likely not be developed until we overcome this barrier . In 2009 it was reported that an HIV-infected individual with acute myelogenous leukemia treated with myeloablative chemotherapy and allogeneic hematopoietic stem cell transplant ( HSCT ) from a Δ32ccr5 homozygous donor had remained without detectable HIV replication in the absence of ART for 1 . 8 years [5] , [6] . This first demonstration of a functional cure in this patient was confirmed in 2013 in a follow-up study showing no signs of recrudescent HIV replication and waning of HIV-specific immune responses five years after interruption of ART [7] . More recently , two HIV-infected individuals have been described with prolonged ( i . e . , 3–8 months ) suppression of viremia in absence of ART following allogeneic HSCT from donors homozygous for the wild type ccr5 allele [8] , [9] . Similar to the “Berlin patient” described above , these two transplant recipients were themselves Δ32ccr5 heterozygotes . The factors involved in the lack of detectable virus replication after ART interruption in HIV-infected individuals undergoing HSCT are complex , and may include ( i ) the myeloablative regimen involving various combinations of chemotherapy , immunosuppression , and total body irradiation ( TBI ) ; ( ii ) the deficiency of CCR5 in the transplanted donor cells ( in the first case ) ; and ( iii ) a graft versus host effect that may target cells that are latently infected with HIV ( i . e . , graft versus reservoir effect ) . Assessing the relative contribution of these factors will likely provide useful information to define the clinical potential of HSCT as a cure for HIV infection . SIV infection of non-human primates , such as rhesus macaques ( RMs ) has been used for over two decades as an in vivo model for studies of HIV pathogenesis , prevention , and treatment [10] . SIV-infected RMs show remarkable similarities to HIV-infected individuals in terms of mechanisms and markers of disease progression , and current ART regimens can fully suppress virus replication in these animals [11]–[14] , thus making this model suitable for probing HIV eradication strategies . In this study , we conducted a controlled test of the contribution of pre-transplant myeloablative irradiation to clearance of the viral reservoir in a cohort of RMs infected with a chimeric simian-human immunodeficiency virus ( SHIV ) and treated with ART . To the best of our knowledge , this is the first time HSCT has been utilized in RMs to investigate viral persistence . The procedure was successfully performed after SHIV infection and ART-induced control of virus replication using HSCs collected prior to infection . While these recipients showed undetectable plasma viremia and low to absent SHIV-DNA in PBMCs after HSCT , interruption of ART resulted in a rapid rebound of virus replication in two out of three animals . The one transplanted RM who maintained undetectable viremia and SHIV-DNA PCR in PBMCs after ART interruption showed low but detectable levels of SHIV-DNA in sorted circulating CD4+ T-cells , spleen and lymph nodes but not in the gastro-intestinal tract or tonsils . Collectively , these results indicate that the massive reset of the lympho-hematopoietic compartment that follows TBI-induced myeloablation was not sufficient to eliminate the total-body virus reservoir in SHIV-infected RMs in the setting of short term ART . However , this study provides a critical foundation upon which to test other potential contributors to a transplant-mediated cure of HIV .
Six RMs were included in this study . All six RMs were males with an average age of 4 . 2 years ( Table 1 ) . Figure 1 shows an overview of the experimental design . Three rhesus macaques ( T1 , T2 , T3 ) were treated with G-CSF for CD34+ stem cell mobilization followed by HSC collection by leukopheresis and cryopreservation of the collected cells . The six RMs were infected i . v with 104 TCID50 RT-SHIVTC . Starting at week four post-infection all six RMs were initiated on ART . The ART regimen consisted of two nucleotide/side reverse transcriptase inhibitors ( PMPA/tenofovir and FTC/emtricitabine ) , one non-nucleoside reverse transcriptase inhibitor ( efavirenz ) and one integrase inhibitor ( raltegravir ) . After five to eight weeks on ART , RMs T1-T3 received myeloablative TBI as pre-transplant conditioning . The leukopheresis products collected before infection were infused within 24 hours following the last dose of TBI . Recipients were given a total of 7 . 3×108+/−1 . 3×108 total nucleated cells ( TNC ) /kg which corresponded to 2 . 9×106+/−1 . 1×106 CD34+ cells/kg . After successful engraftment of donor cells ( five to eleven weeks post-transplant ) , ART was interrupted in RMs T1-T3 as well as in the control RMs . As shown in Figure 2A , following experimental infection with RT-SHIVTC the six RMs experienced a rapid , exponential increase in virus replication that peaked at week two post infection ( 105–107 SHIV-RNA copies/ml plasma ) . ART initiated at week four after infection drastically reduced plasma viral load to less than 100 copies of SHIV-RNA per ml of plasma . Consistent with prior studies of SIV/SHIV infection in RMs , the absolute number of peripheral CD4+ T-cells was decreased following infection and partially restored on ART ( Figure 2B ) . The myeloablative TBI resulted in a drastic reduction of the absolute count of blood cells including neutrophils , monocytes , lymphocytes and CD4+ T-cells ( Figure 3A ) . The nadir was observed at day eleven post-TBI for neutrophils ( 41 . 6–78 . 2 neutrophils/µl ) , day seven post-TBI for monocytes ( 4 . 4–14 . 8 monocytes/µl ) , and between day one and day five post-TBI for lymphocytes and CD4+ T-cells ( 54–60 lymphocytes/µl and 6 . 7–45 . 5 CD4+ T-cells/µl ) . Of note , between 94 . 2 and 99 . 2% of circulating CD4+ T-cells were eliminated by the TBI ( Figure 3A ) . Engraftment was demonstrated by increasing neutrophil and platelet counts unsupported by transfusion . Neutrophil engraftment was defined as an absolute neutrophil count ( ANC ) exceeding 500 cells/µl for three consecutive days . The first of these three consecutive days was considered the day of engraftment . As shown in Figure 3B , neutrophil engraftment was successfully achieved between day sixteen and day eighteen post-HSC infusion in the three transplanted animals . During HSCT , the three transplanted animals received platelet and whole blood transfusions for thrombocytopenia prior to platelet engraftment , as well as several antimicrobial prophylactic interventions ( Figure S1 ) . Platelet engraftment was defined as a blood platelet count exceeding 20 , 000 cells/µl in absence of transfusion support for seven consecutive days . According to this definition , platelet recovery was achieved at 42 , 22 and 33 days post-transplant for T1 , T2 and T3 , respectively ( Figure 3C ) . Following transplantation and engraftment , we observed a rapid increase in the absolute leukocyte count and a slower reconstitution of the circulating CD4+ T-cells ( Figure 4A and B ) . The peripheral reconstitution of CD4+ T-cells appeared to involve peripheral T-cell expansion as evidenced by the increased proportion of circulating CD4+ T-cells expressing the proliferation antigen Ki-67 ( Figure S2A ) . In addition , HLA-DR and CCR5 were increased on CD4+ T-cells following HSCT ( Figure S2B , C ) . Further immunophenotypic analyses revealed a significant increase in the proportion of memory CD4+ T-cells ( including memory stem cells , central memory , and effector memory ) following transplantation ( p = 0 . 03 , Figure S3 ) , similar to previous reports of both autologous and allogeneic HSCT [6] , [15] . These results are consistent with CD4+ T-cells recovery occurring primarily through the homeostatic proliferation of memory CD4+ T-cells post-transplant . A few blips of transient low-level viremia were observed in the plasma of the three transplanted animals immediately after TBI and HSC infusion and while still on ART ( Figure 5A ) . The origin of these transient increases in viral load is not clear , but it may represent release of virus from pre-existing reservoirs in the setting of events of CD4+ T-cell activation during conditioning and the peri-transplant period . With the exception of these transient episodes of viremia , the plasma viral load remained undetectable in all six animals on ART ( Figure 5A ) . Of note , the ART regimen alone reduced the level of SHIV-DNA in PBMCs ( i . e . , the peripheral viral reservoir ) by 1 . 0–1 . 5 log in the three control RMs ( Figure 5B ) . In the transplanted animals , the reduction in cell-associated viral DNA was more pronounced , with two RMs showing levels of SHIV-DNA in PBMCs below the limit of detection and one RM ( T1 ) close to this level ( as low as 130 copies/million PBMC , Figure 5B ) . The normalization of the cell-associated SHIV-DNA level to the CD4+ T-cells counts suggest a decrease in the frequency of infection of these cells post-transplant ( Figure 5B ) . ART was interrupted after stem cell engraftment ( between 78 and 128 days post-initiation , Table 1 ) . As expected , a rapid viral rebound was observed in the plasma of the three control animals as early as one week post ART cessation ( Figure 5A ) . Two out of the three transplanted animals also experienced a rapid plasma viral rebound post ART interruption . The remaining transplanted animal ( T2 ) maintained an undetectable plasma viral load at two weeks post ART interruption ( Figure 5A ) . Unfortunately , further time-points were not analyzed in this animal as he was euthanized due to progressive renal failure . As shown in Figure 5B , ART interruption led to an increase of the SHIV-DNA levels in the PBMCs of the two transplanted RMs who also experienced a plasma viral rebound . This rebound in PBMC SHIV-DNA was observed at the first assessment post-ART interruption in both animals ( day 28 for T1 and day 15 for T3 ) . Of note , no SHIV-DNA was detected in the PBMCs of RM T2 who also maintained undetectable plasma viral load at two weeks after ART interruption . However , further analyses of this animal revealed low but detectable levels of SHIV-DNA in sorted peripheral CD4+ T-cells obtained at the same time-point ( i . e . , two weeks after ART interruption at necropsy ) ( Figure 5C ) . Several tissues were collected at necropsy including ileum , jejunum , colon , rectum , superficial and mesenteric lymph nodes as well as tonsils . SHIV-DNA levels in cell suspensions obtained from these tissues were quantified by PCR . As shown in Figure 6 , low levels of SHIV-DNA were detected in the spleen and lymph nodes of the transplanted RM who maintained an undetectable peripheral viral load post ART interruption ( T2 ) but not in the tonsils or gut compartments . Of note , we were able to detect SHIV-DNA in the gut and tonsils of the other two transplanted RMs ( T1 and T3 ) who exhibited a rapid rebound of viremia after ART interruption .
The apparent cure of HIV infection in the “Berlin patient” [5]–[7] has energized efforts to understand the mechanisms of virus persistence despite ART-mediated suppression of virus replication . The factors thought to be involved in the favorable outcome of the Berlin patient following HSCT include ( i ) the myeloablative conditioning regimen; ( ii ) the donor's homozygosity for Δ32ccr5; and ( iii ) the graft versus host effect . In this test-of-concept study of autologous HSCT in SHIV-infected RMs we interrogated the relative contribution of a myeloablative conditioning regimen in eliminating the persistent reservoir of latently infected cells . To the best of our knowledge this is the first time that a study of similar design has been conducted . The key findings of this study are the following: ( i ) autologous HSCT using apheresis products collected prior to infection is feasible in SHIV-infected RMs; ( ii ) as expected , the myeloablative TBI used for conditioning induced a massive reset of the lympho-hematopoietic compartment , consequently resulting in the depletion of 94 . 2–99 . 2% of circulating CD4+ T-cells; ( iii ) animals receiving autologous HSCT under ART exhibited a prompt and pronounced decline in the peripheral blood viral reservoir ( with undetectable SHIV-DNA in PBMCs in two out of three RMs ) and maintained undetectable SHIV-RNA viremia with the exception of a few minor blips; ( iv ) two of the three transplanted RMs showed a very rapid rebound of viremia after ART interruption; and ( v ) the third transplanted RM , who was sacrificed for clinical reasons at day fourteen post ART interruption , had no detectable virus in plasma , PBMCs , tonsils , and GI tract , low but detectable levels of SHIV-DNA in sorted peripheral CD4+ T-cells and lymph nodes , and moderate levels of SHIV-DNA in the spleen . Due to many logistical challenges of this experiment we chose to conduct the study in a temporally compressed fashion , with 37–53 days of ART before autologous HSCT , and interruption of ART after hematopoietic reconstitution , rather than prolonged continuation of therapy . This study was therefore designed to determine the impact of myeloablative irradiation on the viral reservoir , rather than the impact of prolonged viral suppression in conjunction with myeloablation . It is therefore possible that a similarly designed study , in which ART is maintained for a significantly longer period both before and after autologous HSCT , would have a different outcome , possibly demonstrating a more dramatic effect of autologous HSCT on the persistent reservoir of latently infected cells . Moreover , we cannot rule our the possibility that the level of virus suppression achieved by the short-term ART regimen in this experiment might not be as complete as what is observed in HIV-infected individuals on long-term ART . In this model of SHIV-infected RM , 5 to 7 weeks on ART pre-transplant may have been insufficient to fully suppress viral replication and the transient low-level viremia observed immediately post-transplant could be attributed to an insufficient period of ART pre-transplant . However , similar viral blips were observed in one patient who received allogeneic stem cell transplant after many years on combined ART [8] . Although the origin of these transient blips is unknown , it may represent release of the virus from latently infected cells in the setting of cell activation during conditioning and the peri-transplant period . In keeping with this hypothesis , it should be noted that in our study the post-transplant period was characterized by an expansion of CD4+ T-cells expressing CCR5 as well as proliferation and activation markers . Together with the observed increased proportion of memory CD4+ T-cells post-transplant , these results suggest that the CD4+ T-cell compartment recovered primarily through homeostatic proliferation of memory CD4+ T-cells . The myeloablative TBI used for conditioning resulted in the depletion of 94 . 2–99 . 2% of circulating CD4+ T-cells . Unfortunately , due to the clinical challenges of this innovative experiment , no tissue biopsies could be obtained immediately post-transplant to evaluate the TBI-induced CD4+ T-cell depletion in tissues . However , this study shows that myeloablative TBI and autologous HSCT did not prevent a rebound of viremia post-ART interruption in two out of three RMs despite relatively early ART initiation ( day 28 post-infection ) . Moreover , while the SHIV-DNA level in PBMCs was undetectable or close to undetectable post autologous HSCT , it rapidly rebounded after ART interruption to levels that were similar or higher than those observed in the control animals at the same time-point . While in the third animal ( T2 ) there was no sign of virus present in the plasma , PBMCs , and various tissues at the time of necropsy , this RM had to be sacrificed due to kidney failure at day fourteen after ART interruption making the interpretation of these data somewhat difficult . Of note , this study was not designed to identify the cellular and anatomic sources of the rapid plasma viral rebound observed in two transplanted RMs following ART interruption . Determining the relative contribution of tissue CD4+ T-cells , macrophages , and potentially other sources represents an important area for future investigation , amenable for interrogation with this model . We acknowledge a number of limitations in our study including the small number of animals and the foreshortened time line involved . However , the demonstrated feasibility of this test-of-concept study in a non-human primate model of AIDS virus infection is per se an important result given the extreme complexity of the experimental protocol . The RMs included in this study underwent a series of procedures that have been only rarely , if ever , used in the same animal , including stem cell mobilization and harvesting by apheresis , RT-SHIV infection , daily four-drug ART administration , total body irradiation , re-infusion of HSCs , repeated platelet transfusions , and receipt of several antimicrobial prophylaxes . The feasibility of HSCT in SIV- or SHIV-infected RMs suggests , in our view , that further studies using this model in conjunction with longer term ART as well as additional interventions aimed at purging both the peripheral blood and lymphoid tissue-based viral reservoirs will provide critical information for the requirements to cure HIV infection in humans . With respect to our understanding of the mechanisms responsible for “curing” HIV infection in the Berlin patient , our study supports the hypothesis that myeloablative TBI can cause a significant decrease in the viral reservoir in circulating PBMCs , even though it was not sufficient to eliminate all reservoirs . While the conditioning regimen in the Berlin patient also included antithymocyte globulin and chemotherapy , the use of a Δ32ccr5 homozygous donor and/or the presence of graft versus host disease likely played a significant role in that clinical context . The importance of graft versus host disease that effectively results in a “graft versus reservoir” effect is also emphasized by the recent observation of two HIV-infected patients in which a prolonged ( i . e . , 3–8 months ) period of undetectable viremia in absence of ART was observed after allogeneic HSCT from donors with wild-type ccr5 alleles [9] , although these patients did eventually develop rebound of viremia [16] . Future studies of allogeneic HSCT in SIV- or SHIV-infected RMs in the presence or absence of gene therapy interventions to knock out ccr5 would be very informative in this regard , and may elucidate the mechanism of the sustained cure seen in the Berlin patient but not the above mentioned recipients of donor cells wild type for ccr5 . In conclusion , we have conducted the first test-of-concept study of myeloablative irradiation and autologous HSCT in ART-treated SHIV-infected RMs . This experiment demonstrated that autologous HSCT is a feasible intervention that can lead to a marked reduction of the virus reservoir in the peripheral blood , and can be used as an experimental in vivo platform to test innovative interventions aimed at curing HIV infection in humans .
This study was conducted in strict accordance with USDA regulations and the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , and were approved by the Emory University Institutional Animal Care and Use Committee ( Protocol # YER-20000373-061714 ) . SIV-infected animals were housed in standard non-human primate cages , received standard primate feed as well as fresh fruit and enrichment daily , and had continual access to water . Cages also contained additional sources of animal enrichment including objects such as perching and other manipulanda . Animal welfare was monitored daily . Appropriate procedures were performed to ensure that potential distress , pain , or discomfort was alleviated . The sedatives Ketamine ( 10 mg/kg ) or Telazol ( 4 mg/kg ) were used for blood draws and biopsies . Euthanasia of RMs , using Pentobarbital ( 100 mg/kg ) under anesthesia , was performed only when deemed clinically necessary by veterinary medical staff and according to IACUC endpoint guidelines . Six Indian RMs ( Macaca mulatta ) , with exclusion of Mamu B*08 and B*17 positive animals , were included in this study . All animals were housed at the Yerkes National Primate Research Center ( Atlanta , GA ) and treated in accordance with Emory University and Yerkes National Primate Research Center Institutional Animal Care and Use Committee regulations . Autologous HSCs were harvested at two separate time points in each animal using our previously described apheresis procedure [17] . Animals were prepared for leukopheresis with epoeitin alfa ( nine doses of 150 mg/kg , Amgen ) , given in the two months prior to leukopheresis to increase red cell mass and thus increase the safety of the leukopheresis procedure and filgastrim ( G-CSF , 50 mg/kg intramuscularly daily to a maximum of 300 mg , Amgen ) for six days prior to leukopheresis to mobilize HSCs as previously described [18] . The leukopheresis was analyzed for cell content and then cryopreserved in 10% DMSO using standard clinical techniques . Both apheresis units were infused into the transplant recipient within 24 hours of the completion of TBI . The leukopheresis products were analyzed by flow cytometry prior to cryopreservation for the total nucleated cell dose , the CD34+ cell dose , CD3+ T-cell dose , CD4+ T-cell dose , CD8+ T-cell dose , and the CD20+ B-cell dose using the following antibodies; CD3 ( clone SP34-2 ) , CD34 ( clone 563 ) , CD45 ( clone D058-1283 ) , CD8 ( clone RPA-T8 ) from BD Biosciences; CD20 ( clone 2h7 ) , CD4 ( clone OKT4 ) from eBioscience . The RMs were intravenously ( i . v . ) infected with 10 , 000 50% tissue culture infective doses ( TCID50 ) of RT-SHIVTC . The virus stock was provided by Dr . Tom North ( Emory University ) and prepared as previously described [19] , [20] . The RT-SHIV used for this study had the T-to-C substitution at position 8 of the SIV tRNA primer binding site which is necessary for high replication of RT-SHIV in vivo [21] . Efavirenz was provided by Bristol-Myers Squib , raltegravir was provided by Merck , and emtricitabine ( FTC ) and tenofovir ( PMPA ) were provided by Gilead Sciences . Efavirenz was fed at 200 mg per day by mixing the contents of a 200 mg capsule into food . Raltegravir was fed at 100 mg twice daily by mixing the drug into food . Stock solutions of FTC were prepared in phosphate-buffered saline ( PBS , pH 7 . 4 ) . PMPA was suspended in distilled water , with NaOH added to a final pH of 7 . 0 . FTC and PMPA stocks were filter sterilized and stored at 4°C . These drugs were administered subcutaneously , at a dose of 30 mg/kg of body weight once daily . Drug dosages were adjusted weekly according to body weight . The pre-transplant preparative regimen consisted of myeloablative TBI to a total dose of 10 . 8 Gy , given in three divided fractions of 3 . 6 Gy each ( at a rate of 7 . 5 cGy/minute ) using a Varian Clinac 23 EX ( Varian ) . Irradiation took place on days −2 , −1 , and 0 ( the day of transplant ) , with the final dose of irradiation given just prior to infusion of the first of two leukopheresis products . Animals were treated with the following empiric antimicrobial agents in the peri-transplant period , as previously described [17] , [22] . ( Figure S1 ) : ( 1 ) Polymixin B ( 1 , 000 , 000 units orally daily , Ben Venue Laboratories , Inc ) and neomycin sulfate ( 500 mg orally daily , Teva Pharmaceuticals ) . Dosing of both agents was begun on day −7 and continued until neutrophil engraftment ( Absolute neutrophil count >500 cells/µl for three consecutive days ) . ( 2 ) Enrofloxacin ( 7 mg/kg intramuscularly daily , Bayer Healthcare ) starting on day −1 and continuing until neutrophil engraftment . ( 3 ) Fluconazole ( 5 mg/kg orally daily , Pfizer ) starting on day −1 and continuing until neutrophil engraftment . ( 4 ) Cidofovir ( 5 mg/kg i . v . , Gilead ) starting on day +6 and continuing once weekly as clinically tolerated , to prevent CMV reactivation . Cidofovir was given to transplant recipients 1 and 2 . However , because we observed significant increases in serum creatinine in these recipients , the third transplant recipient was treated with oral valganciclovir ( 60 mg twice daily , Genentech ) , which was begun after neutrophil engraftment was observed . Transplanted animals received both platelet rich plasma and whole blood ( irradiated at 2200 rad prior to transfusion ) to treat thrombocytopenia ( platelet count <50×106/ml ) or anemia ( hemoglobin <10 g/dl ) or with the development of clinically significant bleeding . Blood product support adhered to ABO antigen matching principles . EDTA-anticoagulated blood samples were collected regularly and used for a complete blood count , routine chemical analysis and immunostaining , with plasma separated by centrifugation within 1 h of phlebotomy . PBMCs were prepared by density gradient centrifugation . CD4+ T-cells were negatively selected from frozen PBMCs using magnetically labeled microbeads and subsequent column purification according to the manufacturer's protocol ( Miltenyi Biotec ) . Tissue samples including ileum , jejunum , colon , tonsils and mesenteric and superficial lymph nodes were collected post-mortem . After two washes in RPMI and removal of connective and fat tissues , gut tissues were cut in small pieces and lymph nodes and tonsils were grinded using a 70-µm cell strainer . Gut cells were isolated by digestion with collagenase and DNase I for 2 h at 37°C and then passed through a 70-µm cell strainer . The cell suspensions obtained were washed and immediately used for immunostaining , cryopreserved or lysed in RLT+ buffer and stored at −80°C until use . Plasma viral quantification was performed as described previously [23] . DNA was extracted from PBMCs , sorted peripheral CD4+ T-cells , and tissue cell suspensions using the Blood DNA Mini Kit ( QIAGEN ) . Quantification of SIVmac gag DNA was performed as previously described on the extracted cell-associated DNA by quantitative PCR using the 5′ nuclease ( TaqMan ) assay with an ABI7500 system ( PerkinElmer Life Sciences ) . The sequence of the forward primer for SIVmac gag was 5′-GCAGAGGAGGAAATTACCCAGTAC-3′; the reverse primer sequence was 5′-CAATTTTACCCAGGCATTTAATGTT-3′; and the probe sequence was 5′-6 FAM-TGTCCACCTGCCATTAAGCCCGA-TAMRA-3′ . For cell number quantification , quantitative PCR was performed simultaneously for monkey albumin gene copy number . All PCR were performed in duplicate with 10 , 000 cell equivalent per reaction with a limit of detection of 1 copy per reaction . Multicolor flow cytometric analysis was performed on whole blood or frozen PBMCs using predetermined optimal concentrations of the following fluorescently conjugated mAbs: CD3-PacBlue or -APC-Cy7 ( clone SP34-2 ) , CD95-PE-Cy5 ( clone DX2 ) , Ki-67-AF700 ( clone B56 ) , HLA-DR-PerCP-Cy5 . 5 ( clone G46-6 ) , CCR7-PE-Cy7 ( clone 3D12 ) , CCR5-PE or -APC ( clone 3A9 ) , CD45RA-FITC ( clone L48 ) , Biotin-CD122 ( clone Mik-β3 ) from BD Biosciences; CD8-BV711 ( clone RPA-T8 ) , CD4-APC-Cy7 or -BV650 ( clone OKT4 ) , Streptavidin-PE from Biolegend , and CD28-ECD ( clone CD28-2 ) from Beckman-Coulter . Flow cytometric acquisition and analysis of samples was performed on at least 100 , 000 events on an LSRII flow cytometer driven by the FACSDiva software package ( BD Biosciences ) . Analyses of the acquired data were performed using FlowJo Version 10 . 0 . 4 software ( TreeStar ) . For the comparison of SHIV-DNA in sorted CD4+ T-cells in transplanted and control RMs the nonparametric Mann-Whitney U test was used . For the comparison of the proportion of memory CD4+ T-cells before and after transplant , a Wilcoxon matched-pairs signed rank test was used . Statistical significance was set at p<0 . 5 . All analyses were performed using GraphPad Prism v4 . 0 . | While antiretroviral therapy ( ART ) can reduce HIV replication , it does not eradicate the virus from an infected individual . Replication-competent viruses persist on ART and our incomplete understanding of these viral reservoirs greatly complicates the generation of a cure for HIV . In this study we performed , for the first time , hematopoietic stem cell transplant ( HSCT ) in the established model of SIV infection of rhesus macaques ( RM ) . The HSC originating from the bone marrow were collected before SIV infection . After SIV infection , RM were treated with ART for several weeks to reduce viral replication before performing a total body irradiation and a transplant with their own , pre-infection , stem cells . The irradiation eliminated 94–99% of the circulating CD4+ T-cells , the main cell target of HIV/SIV infection . A successful engraftment of the HSC was observed and blood viral reservoirs were drastically reduced . However , when ART was interrupted , a rapid rebound of plasma viremia was observed in two out of three transplanted RM indicating that the massive reset of the hematopoietic compartment was not sufficient to eliminate the total-body virus reservoir in the setting of short term ART . This model of HSCT in SIV-infected RM provides a new platform to investigate HIV eradication strategies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"animal",
"models",
"of",
"infection",
"antiviral",
"therapeutics",
"viral",
"persistence",
"and",
"latency",
"virology",
"biology",
"and",
"life",
"sciences",
"microbiology"
] | 2014 | Persistence of Virus Reservoirs in ART-Treated SHIV-Infected Rhesus Macaques after Autologous Hematopoietic Stem Cell Transplant |
Leptospirosis is a bacterial zoonosis of major concern on tropical islands . Human populations on western Indian Ocean islands are strongly affected by the disease although each archipelago shows contrasting epidemiology . For instance , Mayotte , part of the Comoros Archipelago , differs from the other neighbouring islands by a high diversity of Leptospira species infecting humans that includes Leptospira mayottensis , a species thought to be unique to this island . Using bacterial culture , molecular detection and typing , the present study explored the wild and domestic local mammalian fauna for renal carriage of leptospires and addressed the genetic relationships of the infecting strains with local isolates obtained from acute human cases and with Leptospira strains hosted by mammal species endemic to nearby Madagascar . Tenrec ( Tenrec ecaudatus , Family Tenrecidae ) , a terrestrial mammal introduced from Madagascar , is identified as a reservoir of L . mayottensis . All isolated L . mayottensis sequence types form a monophyletic clade that includes Leptospira strains infecting humans and tenrecs on Mayotte , as well as two other Malagasy endemic tenrecid species of the genus Microgale . The lower diversity of L . mayottensis in tenrecs from Mayotte , compared to that occurring in Madagascar , suggests that L . mayottensis has indeed a Malagasy origin . This study also showed that introduced rats ( Rattus rattus ) and dogs are probably the main reservoirs of Leptospira borgpetersenii and Leptospira kirschneri , both bacteria being prevalent in local clinical cases . Data emphasize the epidemiological link between the two neighbouring islands and the role of introduced small mammals in shaping the local epidemiology of leptospirosis .
Leptospirosis is a bacterial zoonosis caused by pathogenic spirochetes of the genus Leptospira . Human infection is common in tropical countries where warm and humid conditions favour the survival of Leptospira spp . in water and soil . Furthermore , human populations in these countries often live in rural areas or in impoverished urban zones with inadequate sanitation and can be at higher risk of exposure to the infection via the contaminated environment . A wide variety of mammal species can be infected by Leptospira spp . but not all act as reservoirs . Animal reservoirs support the chronic colonization of their renal tubules by a biofilm of leptospires and thus , release over prolonged periods the bacteria into the environment via their urine . Rodents and particularly rats , especially Rattus spp . , are considered as the principal reservoir of these pathogenic bacteria and the main source of human infection , a role likely favoured by the worldwide distribution and commensal behaviour of some invasive species in this genus . Two methods are currently used to identify leptospires: a phenotypic classification based on the microscopic agglutination test ( MAT ) that recognizes over 25 serogroups and 300 serovars [1] and a more recently introduced genetic classification based on bacterial DNA sequences that allows the identification of 20 Leptospira spp . , nine of which are pathogenic [2] . Genotyping through Multiple Locus Sequence Typing ( MLST ) based on a selected set of housekeeping genes [3] has become a method of choice to identify Leptospira at the species and infra-species levels . Indeed , molecular typing allows deciphering the molecular epidemiology of the disease , sharing data among laboratories investigating distant geographic regions and analyzing the evolution of host-parasite relationships using robust molecular characters [4] . The incidence of human leptospirosis has been reported to be highest on tropical islands and this observation holds true for the southwestern Indian Ocean ( SWIO ) region [5] . Poorly documented on Mauritius and Madagascar , the incidence of the disease in the Seychelles ranks first worldwide among surveyed countries [5] , while on Reunion Island , a French overseas department , the rate of human leptospirosis has been reported to be nearly 20 times higher than in continental France [6] . Mayotte , the most southern island of the Comoros Archipelago is a French overseas department of about 375 km² , located in the northern entrance of the Mozambique Channel about 300 km off the northwestern coast of Madagascar . Human leptospirosis is highly prevalent on the island with an annual incidence in 2013 estimated at 35 per 100 , 000 [6] . Most interestingly , Leptospira strains isolated from patients , hereafter referred to clinical isolates , showed a much larger species diversity on Mayotte than on Reunion Island: 16 different sequence types ( STs ) were identified by MLST on Mayotte [7] , while only three on Reunion Island [8] . Further , pathogenic leptospires causing acute human infections on Mayotte were identified as Leptospira interrogans , Leptospira borgpetersenii , Leptospira kirschneri and members of a previously undefined phylogroup that has recently been named as Leptospira mayottensis [7 , 9] . By contrast , only L . interrogans and L . borgpetersenii have been reported on Reunion Island [8] . The high diversity of leptospires on Mayotte , unique so far within the SWIO region , likely reflects local eco-epidemiologic specificities of animal reservoirs . With the exception of bats , no native small mammals occur on Mayotte [7] . Although one study has previously investigated animal reservoirs on Mayotte [7] , it targeted Rattus rattus , the only rat species known on the island , for kidney carriage of pathogenic leptospires , whereas other species of the local wild and domestic fauna were screened by MAT , a serological test that provides evidence of previous infections but does not assess the actual carrier state of the investigated animals . Through the investigation of the local wild and domestic fauna , the present study aimed at identifying animal reservoirs of leptospires on Mayotte , with a particular focus on L . mayottensis . This leptospiral species has been recently detected in wild small mammals endemic to neighbouring Madagascar [4] and , hence , it can be hypothesized that the geographic proximity and various socio-economic exchanges between these two islands could have facilitated the introduction of this pathogenic bacteria to Mayotte .
All animal procedures carried out in this study were performed in accordance with the European Union legislation for the protection of animals used for scientific purposes ( Directive 2010/63/EU ) . The ethical terms of the research protocol were approved by the CYROI Institutional Animal Care and Use Committee ( Comité d’Ethique du CYROI n° 114 , IACUC certified by the French Ministry of Higher Education and Research ) under accreditation 03387 ( LeptOI ) and 03584 ( BatMan ) . Mammals were trapped on Mayotte during three field sessions carried out from July 2012 to December 2014 . Rattus rattus and Tenrec ecaudatus were captured using live traps baited with grilled coconut at eight and five localities , respectively . Sampling sites were selected in order to maximize the species diversity and geographic distribution of mammals in our sample . Trapping localities included a variety of different ecological settings across the island ( see S1 Fig ) . In a local laboratory , R . rattus were sacrificed by cervical dislocation and T . ecaudatus by percussive blow to the head . For each individual , tissue was quickly collected from kidney , lung and spleen , pooled together and immediately stored in dry vials at -80°C . Insectivorous bats , Chaerephon sp . ( Family Molossidae ) , were captured at dusk or at night using mist nets placed either in the vicinity of synanthropic roost sites or across flyways , while frugivorous bats , Pteropus seychellensis comorensis ( Family Pteropodidae ) , were captured at night using mist nets set around fruiting trees . Captured bats were individually placed in clean cloth bags and brought back to a local laboratory . In most cases , animals spontaneously urinated after their removal from holding bags allowing the collection of urine . Bats were subsequently released at dusk at the initial capture site . Dogs ( Canis lupus familiaris ) were only manipulated and sampled by a local veterinary doctor; kidneys were obtained from animals that were euthanized for medical purposes , when possible urine collected from these same animals and immediately kept in dry vials at -80°C . In addition , urine samples were obtained from non-euthanized dogs through aseptic urethral catheterization . Kidneys from zebus ( Bos indica ) were purchased at the slaughterhouse of Mayotte ( Kaweni ) or obtained during traditional butchering . All samples were sent in liquid nitrogen to Reunion Island for analyses . In addition to the sampling and storage at -80°C of organs and urine mentioned above , a few urine droplets and/or a small piece of freshly sampled kidney ( crushed under sterile conditions ) were used individually to inoculate three distinct culture media: ( i ) Ellinghausen-McCullough-Johnson-Harris ( EMJH ) liquid medium ( Difco , Detroit , MI , USA ) supplemented with Albumin Fatty Acid Supplement ( AFAS; Royal Tropical Institute , Amsterdam , Netherlands ) [10] ( ii ) EMJH liquid medium supplemented with AFAS , rabbit serum and fetal calf serum ( 1% each ) ; and ( iii ) semisolid Fletcher medium ( Difco , Detroit , MI , USA ) supplemented with rabbit serum ( 8% ) . All media were supplemented with 5-fluorouracil ( 5-FU ) at a final concentration of 200 μg . mL-1 . Cultures were incubated at 28°C , visually checked for the presence of leptospires using a dark field microscope once a week for four months and positive cultures were further sub-cultured in fresh EMJH liquid medium deprived of 5-FU . DNA was extracted from 1 mL of each positive culture using the EZ1 Biorobot with Qiagen EZ1 DNA Tissue kits ( Qiagen , Les Ulis , France ) . Approximately 1 mm³ of pooled organs ( kidney , lung and spleen ) from each R . rattus and T . ecaudatus specimen and only kidney from dogs and zebus were dissected on sterile ice and further processed as previously described [11] . Thirty microliters of urine from bats and dogs were combined with 120 μL of Dulbecco’s modified medium ( GIBCO , Grand Island , NY , USA ) and 50 μL of ATL buffer ( Qiagen , Les Ulis , France ) . Subsequently , total nucleic acids were extracted from urine or homogenized tissues by using an EZ1 extraction robot and the EZ1 Virus Mini Kit version 2 . 0 . A reverse transcription step was performed on total nucleic acids with GoScript Reverse Transcriptase ( Promega , Madison , WI , USA ) to obtain cDNA . Detection of a portion of the 16S rRNA gene of pathogenic Leptospira spp . was then carried out on 5 μL of cDNA using a probe-specific real-time Polymerase Chain Reaction system ( RT-PCR ) [12] . Leptospira spp . in samples testing positive by RT-PCR and/or culture were genotyped using a previously described MLST scheme encompassing six genes: secY , adk , rrs2 , icdA , lipL32 and lipL41 [13] , and recently optimized to improve the amplification of SWIO lineages [4] . RT-PCR is more sensitive than any MLST PCR ( likely because of the very short length of RT-PCR amplicon ) , and some samples that were positive by the former detection system were MLST negative . In an attempt to further characterize Leptospira spp . from these particular samples , we used an alternative primer set ( LA-LB ) targeting a shorter piece of rrs2 [14] . The amplification of each marker was realized with GoTaq Hot Start Green Master Mix 2X ( Promega , Madison , WI , USA ) and further sequenced on both strands by direct Sanger sequencing ( Genoscreen , Lille , France ) using the same amplification primer sets . All sequences were deposited on GenBank under the following accession numbers: KT338823-KT338942 , KT725237-KT725243 and KX427207-KX427227 . Accessible sequences from clinical Leptospira isolates from Mayotte were included in the study [15] . Since icdA sequences were not provided for these clinical isolates , we used five out of the six markers of the MLST scheme in our analyses . In addition , we added bacterial sequences obtained from endemic terrestrial small mammals from Madagascar , Microgale cowani , Microgale dobsoni , Microgale majori , Microgale longicaudata and Microgale principula ( Family Tenrecidae , Subfamily Oryzorictinae ) . The complete MLST of these Leptospira strains were sequenced in a previous study [4] and from cultures realized during field missions carried out in the Central Highlands of Madagascar , Réserve Spéciale d’Ambohitantely , in March and October 2014 [16] . Phylogenetic analyses were performed on each gene separately and subsequently on concatenated sequences using the best model of sequence evolution determined by jModelTest v . 0 . 1 . 1[17] for each dataset . Bayesian analyses were performed with MrBayes 3 . 1 . 2 [18] and consisted of two independent runs of four incrementally heated Metropolis Coupled Markov Chain Monte Carlo ( MCMCMC ) starting from a random tree . MCMCMC was run for 10 , 000 , 000 generations with trees and associated model parameters sampled every 100 generations . The convergence level of each phylogeny was validated by an average standard deviation of split frequencies inferior to 0 . 05 . The initial 10% of trees from each run were discarded as burn-in and the consensus phylogeny along with posterior probabilities were obtained from the remaining trees . Bayesian trees were visualized and rooted to midpoint with FigTree v . 1 . 3 . 1 ( Andrew Rambaut , Institute of Evolutionary Biology , University of Edinburgh , 2006 to 2009; http://tree . bio . ed . ac . uk/ ) . Genetic diversity was compared between the identified clades in the multilocus phylogeny by estimating the nucleotide diversity ( π ) within each clade from the concatenated sequences using DNASP v . 5 . 10 . 01[19] .
Pathogenic Leptospira spp . were detected by RT-PCR in 27 . 0% of T . ecaudatus , 15 . 9% of R . rattus , 13 . 2% of dogs , 10 . 0% of P . seychellensis comorensis and 5 . 6% of zebus . All urine samples from Chaerephon sp . tested negative by RT-PCR . Bacterial cultures were attempted using 117 freshly sampled kidney and 94 urine samples . Overall , leptospires culture was successful for samples testing positive through RT-PCR only . Among the 81 R . rattus tested by culture inoculated with kidney , 14 were positive by RT-PCR , of which eight allowed positive cultures ( 57 . 1% ) . Similarly , eight of the 10 RT-PCR-positive samples ( 80 . 0% ) from T . ecaudatus allowed isolation of leptospires by culture . Two of them ( MDI295 and MDI321 ) yielded positive cultures from both urine and kidney samples . Lastly , no isolate grew in urine cultures from RT-PCR positive P . seychellensis comorensis samples . No culture was attempted when sampling dogs or zebus . We were able to PCR amplify all six loci of the MLST scheme using DNA extracted from all 16 Leptospira cultures , obtained from eight R . rattus and eight T . ecaudatus samples . Interestingly , for T . ecaudatus , we failed to amplify the lipL41 gene with the conventional primers from Ahmed et al . [13] , and used alternative primers designed by Dietrich et al . [4] . As expected , PCR targeting MLST loci were more arduous when using DNA extracted from tissue and urine of PCR-positive samples for which culture had failed . For instance , no successful PCR targeting MLST loci was recorded for the two RT-PCR positive T . ecaudatus failing to yield positive culture . Using the MLST scheme , only rrs2 locus was amplified from four PCR-positive R . rattus , while two loci ( secY and rrs2 ) were amplified from two other R . rattus . Similarly , only rrs2 was amplified from six out of the seven RT-PCR-positive dog samples and from one out of the two RT-PCR-positive P . seychellensis comorensis samples . Lastly , no sequence could be obtained from the single zebu sample testing positive by RT-PCR . In addition , for seven PCR-positive R . rattus samples failing to produce any data using the MLST scheme , we successfully amplified and sequenced the rrs2 gene using an alternative primer set ( LA-LB ) . Altogether , we could identify leptospiral diversity at the species level for 21 R . rattus , eight T . ecaudatus , six dogs and one frugivorous bat from Mayotte ( see Table 1 ) . Rattus rattus-borne Leptospira spp . appeared genetically diverse as L . borgpetersenii , L . kirschneri and L . interrogans were identified for thirteen , 5 and 3 samples , respectively . The sequencing of rrs2 locus in dog samples identified the infecting Leptospira spp . as L . borgpetersenii ( n = 3 ) and L . kirschneri ( n = 3 ) . We also identified one of the two PCR-positive frugivorous bats as infected by L . kirschneri . Most importantly , all Leptospira strains isolated and/or genotyped from T . ecaudatus samples were identified as L . mayottensis . We constructed a concatenated phylogeny ( 2215 bp ) by using bacterial sequences from the 16 animal Leptospira isolates from Mayotte of the present study , 17 previously described clinical isolates from Mayotte [20] and nine Leptospira strains infecting Malagasy endemic Tenrecidae ( six previously published [4] and three obtained from three distinct animals sampled in Réserve Spéciale d’Ambohitantely ) . The phylogeny presented on Fig 1 shows that all bacterial sequences obtained from T . ecaudatus clustered into the monophyletic L . mayottensis clade that includes two clinical L . mayottensis isolates ( str . 200901116 and str . 200901122 ) . This clade also contains Leptospira sequence types ( STs ) obtained from Microgale cowani and M . dobsoni , these latter species being tenrecids endemic to Madagascar . Out of the eight sequences obtained from T . ecaudatus in Mayotte , seven were identical to each others and similar to clinical L . mayottensis str . 200901122 ( 100% of pairwise identity based on 2215 bp ) and the last one ( MDI272 ) was closely related to the clinical L . mayottensis str . 200901116 ( 99 . 70% of pairwise identity based on 2215 bp ) . Of note , the couple of kidney/urine cultures obtained from two T . ecaudatus ( MDI295 and MDI321 ) yielded identical sequence . Leptospira mayottensis infecting Malagasy Microgale spp . displayed a 27-fold higher nucleotide diversity ( π = 0 . 00298 ) compared to that infecting T . ecaudatus from Mayotte in four different sampling sites ( π = 0 . 00011 , S1 Fig ) . The phylogeny confirms R . rattus as carriers of L . interrogans and L . borgpetersenii . For L . interrogans , the same ST is shared by one R . rattus ( MDI219 ) and the L . interrogans clinical isolate str . 200901482 ( 100% of pairwise identity based on 2215 bp ) . For L . borgpetersenii , one clade of this bacterial species included seven sequences obtained from R . rattus that were almost identical ( 99 . 99% pairwise identity , 2215 bp ) to five previously described clinical L . borgpetersenii isolates . Noteworthy , sequences embedded in this clade were clearly distinct from L . borgpetersenii sequences obtained from Malagasy tenrecids . The nucleotide diversity of L . borgpetersenii from Malagasy tenrecids ( π = 0 . 00181 ) was higher than that found in R . rattus from Mayotte ( π = 0 , one single ST ) . Although full MLST was not successful for bats , dogs and for some of the R . rattus , we still used rrs2 sequences alone in order to disentangle the role of these animals in human leptospirosis on Mayotte . For instance , the single leptospiral rrs2 ( obtained using MLST scheme primers ) sequence obtained from a frugivorous bat ( P . seychellensis comorensis ) was genetically related to clinical L . kirschneri isolates ( 98 . 70% pairwise identity , based on 452 bp ) although nucleotide divergence at this locus together with PCR failure on all other loci suggested that L . kirschneri detected in frugivorous bats was actually divergent from clinical samples ( see S2 Fig ) . Sequences obtained from dogs using the same primers revealed perfect identity with L . borgpetersenii ( n = 3 ) and L . kirschneri ( n = 2 ) clinical isolates from Mayotte . Lastly , as these rrs2 primers failed to produce amplification on some rat samples , we used an alternative primer set ( LA-LB ) that allowed amplification of a shorter rrs2 sequence , revealing five identical L . kirschneri sequences that , importantly , diverged from clinical isolates ( 3 mismatches out of 245 bp ) , thus in favour of a predominant role of dogs in L . kirschneri transmission to humans .
Recent reports of human leptospirosis in the SWIO have stressed specificities singularizing Mayotte from the other islands of the region . Leptospira spp . infecting humans on Mayotte are diverse and belong to four bacterial species of which one , Leptospira mayottensis , was recently elevated to the rank of new species [9] . This situation clearly contrasts from that occurring on Reunion Island where Leptospira interrogans and Leptospira borgpetersenii are the only two species reported in human cases [8 , 21] . Our screening of Mayotte fauna allowed identifying 36 Leptospira strains as L . interrogans , L . borgpetersenii , L . mayottensis and L . kirschneri . We revealed either perfect or nearly perfect identity between ( i ) the single L . interrogans ST obtained from patients and Rattus rattus ( 100% identity ) , ( ii ) the prevailing L . borgpetersenii ST obtained from patients and R . rattus ( 100% identity ) , and ( iii ) both L . mayottensis STs obtained from patients and Tenrec ecaudatus ( 100% identity and 99 . 7% identity ) . The sequencing of rrs2 locus suggests that L . kirschneri reported in clinical cases originates from dogs although this phylogeny , based on the single rrs2 locus , known to display low polymorphism [22] , is clearly weaker than the phylogeny based on full MLST . Finally , although a previous study reported a seroprevalence of 10% in Pteropus seychellensis comorensis in Mayotte [7] , here all urine samples from bats tested negative for Leptospira sp . Although we cannot exclude that we missed positive samples due to our limited number of samples and the dynamic nature of leptospires excretion in bats over time [23] , the single L . kirschneri rrs2 sequence obtained from a P . seychellensis comorensis specimen was clearly distinct from the lineages involved in human disease , suggesting that local bat species do not appear to play a significant epidemiological role in the transmission of leptospirosis . Considering bats being the only indigenous mammal species occurring on Mayotte , and dogs , R . rattus and T . ecaudatus the more widely represented mammal species in the environment [7] , these results highlight the role of introduced wild and domestic small mammal species in the epidemiology of leptospirosis on Mayotte , and are specifically discussed hereafter . Firstly and most importantly , our results suggest that T . ecaudatus is a main reservoir of L . mayottensis as this species was detected only in this host ( infection rate: 27 . 0% ) and not in the 289 R . rattus investigated herein . Although a previous study reported the detection of L . mayottensis in two out of 20 positive R . rattus [7] , our results on a much larger sampling suggests that this species is not a significant reservoir of L . mayottensis . Moreover , all Leptospira strains infecting T . ecaudatus on Mayotte were strictly typed as L . mayottensis , although a larger sample size would be needed to confirm that L . mayottensis is the only Leptospira species infecting T . ecaudatus . Secondly , the genetic analyses suggest that L . mayottensis likely originates from neighbouring Madagascar . Indeed , all L . mayottensis sequences cluster into a monophyletic well-supported clade including sequences obtained from three terrestrial wild small mammal species , namely T . ecaudatus , Microgale dobsoni and Microgale cowani , all belonging to the highly diversified Tenrecidae family representing an adaptive radiation of at least 32 endemic species , which colonized Madagascar some 25–30 million years ago [24] . This introduction scenario is further supported by the narrow genetic diversity of L . mayottensis in T . ecaudatus from Mayotte , based on positive samples from four different geographical sites . However , additional sampling of T . ecaudatus in Madagascar is necessary to confirm this hypothesis . Thirdly , R . rattus is a major reservoir of pathogenic leptospires on Mayotte as also reported worldwide . Unexpectedly , the screening of a large number of rat samples ( n = 289 ) from different localities ( n = 8 ) shows that R . rattus on Mayotte is only marginally infected by L . interrogans . This feature contrasts with many investigations reported worldwide , including on the neighbouring islands of Madagascar and Reunion Island where rats are only infected with L . interrogans [8 , 25] . This peculiarity of Mayotte supports a massive contamination of the environment on this island by a diversity of Leptospira species , notably L . borgpetersenii detected in 62 . 0% of analysed R . rattus . Noteworthy , all L . borgpetersenii and L . interrogans obtained from R . rattus , showed perfect identity with human isolates based on the multilocus analysis . Although we identified L . kirschneri in five R . rattus , sequence analyses ( rrs2 ) are rather supportive of a predominant role of dogs as reservoirs of L . kirschneri lineages of local medical importance . However , the absence of culture did not allow full genotyping of dog-hosted leptospires and their implication in human transmission needs further investigations . Fourthly , L . borgpetersenii STs isolated from acute human cases on Mayotte group with two clearly distinct clades . The first lineage can be traced to R . rattus and is interestingly also closely related to a clade previously described as associated with endemic Malagasy Microgale species [4] , indicating a possible host shift from endemic ( Tenrecidae ) to introduced ( rats ) mammals . The second L . borgpetersenii lineage , represented by two human isolates ( str . 200901118 and 201001688 ) , appears related to L . borgpetersenii lineages reported worldwide , e . g . Denmark , China , and Slovakia [4] , but not in any wild and domestic animals sampled herein . This cosmopolite lineage may have been introduced through a reservoir , which is yet to be identified . Finally , the remarkably high success rate to grow in culture L . mayottensis from T . ecaudatus ( 8/10 , 80 . 0% ) and L . borgpetersenii from R . rattus ( 7/13 , 53 . 8% ) indicates some specific biological peculiarities of these strains on Mayotte . Indeed , using the same protocol , we could grow in culture on Madagascar only three strains of L . mayottensis ( obtained from kidney extracts from M . dobsoni , included on Fig 1 ) , while other Leptospira species were found in terrestrial Malagasy wild animals [4 , 25] . Hence , this property to readily grow on liquid culture medium may reflect some adaptive advantage to survive and/or multiply in the environment . Interestingly , L . mayottensis was reported to grow over a wide range of temperature including 11°C and 37°C in contrast to other pathogenic strains [9] . Islands are considered as exceptionally prone to the successful introduction and subsequent invasive nature of exotic animals and plants [26] and have been studied in detail because of adaptive features of species in insular contexts . Our data document that a pathogen most certainly originally endemic to Madagascar [4] has been introduced to the small island of Mayotte where it expanded to become a significant emerging human pathogen . From an evolutionary perspective , our study highlights that beside macro-organism diversity , the associated micro-biodiversity , including endemic microbial lineages , deserves to be included in studies of invasive biology in the context of island biogeography . For instance , although we cannot exclude that other animal species may excrete L . mayottensis on Mayotte Island , the absence of L . mayottensis in other animal species together with the monophyly of L . mayottensis clade , composed of lineages strictly infeoded to tenrecids , support that this new Leptospira species has actually coevolved during the Malagasy radiation of tenrecs , as recently proposed for bat-borne Leptospira lineages in Madagascar [22] . | Islands are exceptionally prone to species introduction , including pathogens with detrimental public health consequences for which the invasive alien species could act as reservoirs . Our study shows how the local non-native mammal fauna of Mayotte Island is associated with the introduction and epidemiology of human leptospirosis , a zoonosis strongly affecting tropical islands . Data presented herein identify Tenrec ecaudatus ( Family Tenrecidae ) , an omnivorous species introduced from neighbouring Madagascar , as a reservoir of the recently named Leptospira mayottensis . Further , we suggest a Malagasy origin of L . mayottensis , which occurs naturally within the Tenrecidae radiation endemic to Madagascar . Finally , we provide evidence that dogs and rats are reservoirs for other Leptospira lineages of medical importance on Mayotte . Altogether , our study highlights the impact of species introduction on human health and further suggests that the biogeography of microorganisms in insular ecosystems , including pathogenic endemic lineages , should be considered from evolutionary and medical perspectives . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"reverse",
"transcriptase-polymerase",
"chain",
"reaction",
"medicine",
"and",
"health",
"sciences",
"leptospira",
"body",
"fluids",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"vertebrates",
"animals",
"mammals",
"dogs",
"urine",
"molecular... | 2016 | Identification of Tenrec ecaudatus, a Wild Mammal Introduced to Mayotte Island, as a Reservoir of the Newly Identified Human Pathogenic Leptospira mayottensis |
Visceral leishmaniasis ( VL ) , caused by protozoa of the Leishmania donovani complex , is a widespread parasitic disease of great public health importance; without effective chemotherapy symptomatic VL is usually fatal . Distinction of asymptomatic carriage from progressive disease and the prediction of relapse following treatment are hampered by the lack of prognostic biomarkers for use at point of care . All IgG subclass and IgG isotype antibody levels were determined using unpaired serum samples from Indian and Sudanese patients with differing clinical status of VL , which included pre-treatment active VL , post-treatment cured , post-treatment relapsed , and post kala-azar dermal leishmaniasis ( PKDL ) , as well as seropositive ( DAT and/or rK39 ) endemic healthy controls ( EHCs ) and seronegative EHCs . L . donovani antigen-specific IgG1 levels were significantly elevated in relapsed versus cured VL patients ( p<0 . 0001 ) . Using paired Indian VL sera , consistent with the known IgG1 half-life , IgG1 levels had not decreased significantly at day 30 after the start of treatment ( p = 0 . 8304 ) , but were dramatically decreased by 6 months compared to day 0 ( p = 0 . 0032 ) or day 15 ( p<0 . 0001 ) after start of treatment . Similarly , Sudanese sera taken soon after treatment did not show a significant change in the IgG1 levels ( p = 0 . 3939 ) . Two prototype lateral flow immunochromatographic rapid diagnostic tests ( RDTs ) were developed to detect IgG1 levels following VL treatment: more than 80% of the relapsed VL patients were IgG1 positive; at least 80% of the cured VL patients were IgG1 negative ( p<0 . 0001 ) . Six months after treatment of active VL , elevated levels of specific IgG1 were associated with treatment failure and relapse , whereas no IgG1 or low levels were detected in cured VL patients . A lateral flow RDT was successfully developed to detect anti-Leishmania IgG1 as a potential biomarker of post-chemotherapeutic relapse .
The leishmaniases are widespread neglected infectious diseases of major public health importance , caused by protozoan parasites of the Leishmania ( Leishmania ) and Leishmania ( Viannia ) subgenera . There are two principal symptomatic clinical presentations of leishmaniasis: a ) visceral ( kala-azar , VL ) caused by the Leishmania donovani complex , with circa 400 , 000 cases per year [1] , which without appropriate chemotherapy is usually fatal , and b ) cutaneous ( CL ) caused by diverse Leishmania species , some of which may give rise to diffuse cutaneous leishmaniasis ( DCL ) or metastatic mucocutaneous disease ( MCL ) , the latter with devastating destruction of the nasopharynx [2] . The effective clinical management , chemotherapy and control of transmission of VL are largely dependent on early and unequivocal diagnosis . Given that many VL patients live below the poverty threshold in remote areas poorly serviced by the health system , the diagnostic tools should be ASSURED ( Affordable , Sensitive , Specific , User Friendly , Rapid , Equipment free and Deliverable where needed ) [3] . The most sensitive and specific method to detect the causative agent of VL is microscopic examination of ( invasive ) spleen aspirates; bone marrow and lymph node aspirates provide similar high specificity but lesser sensitivity . More user-friendly point-of-care ( POC ) diagnostics have been developed based on antibody detection against rK39 and these provide high diagnostic accuracy in suspected first-time episodes of VL when combined with a clinical case definition [4]–[6] . However , these rapid diagnostic tests based on antibody detection are unable by themselves to distinguish asymptomatic carriers from those who will progress to acute fatal disease , and following chemotherapy they remain positive for many months precluding the detection of any relapse . To resolve the limitations of current diagnostic tools higher resolution , non-invasive , rapid and affordable POC tests are thus needed that define clinical status and indicate prognosis . Current serological tests for VL include the enzyme linked immunosorbent assay ( ELISA ) with either crude or purified Leishmania promastigote antigens , the direct agglutination test ( DAT ) or indirect immunofluorescence test ( IFAT ) [7] , [8] . Each of these tests has disadvantages: the ELISA requires laboratory facilities and technical training; the DAT test has limited commercial access and involves several hours or overnight incubation before reading the results , and the IFAT uses a costly fluorescence microscope . The recombinant repetitive antigen rK39 , a product of the Leishmania kinesin-like gene , which was first cloned from Brazilian Leishmania infantum ( synonym L . chagasi ) [9] has been adapted to an immunochromatographic , lateral flow format and applied extensively as a rapid diagnostic test ( RDT ) for VL . However , in multicentre trials rK39 had much higher sensitivity in South Asia than in East Africa [10] , [11] . This could be due to the geographical diversity in the 6 . 5 repeats within the rK39 antigen sequence [12] and/or regional differences in anti-Leishmania IgG titres between the different human populations [13] . A modified recombinant derivative of rK39 , designated rK28 , which incorporated the first two rK39 repeats of a Sudanese kinesin flanked by HASPB1 and HAPB2 repeats , improved serological sensitivity for East African VL patients [14] . The ELISA , DAT , IFAT , rK39 and rK28 serological tests all rely on the detection of anti-L . donovani complex IgG . In human VL the IgG is produced secondarily , after IgM . IgG is divided into IgG1 , IgG2 , IgG3 , and IgG4 subclasses , according to their ranked relative abundance in normal serum [15] . In general , human IgG1 and IgG3 are generated in response to protein antigens , whilst IgG2 and IgG4 are instead predominantly produced in response to polysaccharide antigens [15] . Previous studies on the humoral responses during active VL and after treatment , and on post kala-azar dermal leishmaniasis ( PKDL ) , have been performed using samples from multiple endemic regions and a variety of serological tests [16]–[31] . Several of these studies , almost all of which used ELISAs , assessed anti-L . donovani complex titres of IgG , its subclasses and other Ig isotypes . In terms of IgG subclasses , increased IgG1 titres were identified in active disease ( VL or PKDL ) compared to healthy controls , and reduced IgG1 titres were reported after successful VL treatment . This published work suggested that the dynamics between proportionate IgG subclass responses and the clinical status of patients warranted further investigation to search for prognostic VL biomarkers . A recent WHO report [8] designated the development of new diagnostics to determine cure as a research priority for VL , and there is an additional need for biomarkers to distinguish asymptomatic non-progressors from early progressive VL . Here we explore the potential of differential IgG subclass profiles to provide a biomarker for patients who relapsed after chemotherapy and therefore require urgent follow-up and life-saving additional or alternative chemotherapy . We show that the dynamics and levels of specific IgG1 responses can be indicative of relapse , and can be assessed using a simple , lateral flow immunochromatographic RDT format .
In India , research on comparative serology and the collection of all serum or plasma samples was approved by the Ethics Committee of the Banaras Hindu University , Varanasi . Similarly , in Sudan research and collection of serum samples was approved by the Ethical Research Committee , Faculty of Medicine , University of Khartoum and the National Health Research Ethics Committee , Federal Ministry of Health . Written informed consent was obtained from all adult subjects included in the study or from the parents or guardians of individuals less than 18 years of age . This research was also approved , as part of the NIDIAG project , by the London School of Hygiene and Tropical Medicine Ethics Committee . Indian plasma samples were collected after 2007 from active VL , cured , relapsed , PKDL and asymptomatic groups from the endemic region of Muzaffarpur , Bihar state , north-eastern India , and control subjects from a region where VL is not endemic . In India active cases of VL were diagnosed parasitologically by microscopy of splenic aspirates . Sudanese serum samples were collected in 2011 and 2013 , from active VL , treated , relapsed , PKDL , and endemic controls in the Gedaref region in eastern Sudan . All patients were HIV negative . Active cases of VL were diagnosed by a combination of microscopy of bone marrow or lymph node aspirates in conjunction with serological assays . We have previously observed that serum and plasma derived from the same sample show no difference in titre in ELISA against L . donovani lysate ( unpublished observations ) . L . donovani strain MHOM/IN/80/DD8 isolated from India , and MHOM/SD/97/LEM3458 isolated from Sudan , were cultured in αMEM ( Sigma , UK ) supplemented as described [32] . Mid-to-late log phase promastigote cultures were washed three times in phosphate-buffered saline ( PBS ) , followed by three cycles of flash-freezing in liquid nitrogen and thawing in a 26°C water bath . Subsequently , these cells were subjected to three 30 seconds , 12-micron , sonication cycles on ice at 30 second intervals using a Soniprep 150 sonicator ( MSE , UK ) . Sonicates were centrifuged at 12000× g for 1 minute at 4°C , and the supernatants were used as antigen . Protein concentrations in these lysates were determined using the BCA Protein Assay kit ( Fisher Scientific , Loughborough , UK ) . Lysates of L . donovani DD8 ( for Indian samples ) or LEM3458 ( for Sudanese samples ) , diluted to 2 µg/ml in 35 mM NaHCO3/15 mM NaCO3 buffer ( pH 9 . 6 ) , were added at 100 µl/well to Immulon 4HBX ELISA plates ( VWR , Lutterworth , UK ) and incubated overnight at 4°C . After washing the plates three times using PBS containing 0 . 05% ( vol/vol ) Tween 20 ( Sigma , Gillingham , UK ) ( PBST ) , they were blocked using 200 µl/well PBST containing 2% skimmed milk powder ( Premier International Foods , Spalding , UK ) ( PBSTM ) at 37°C for 2 hr . Following three PBST washes , the human sera were added at 1∶400 dilution for the Indian sera or 1∶100 dilution for the Sudanese sera , in PBSTM , and the plates were incubated at 37°C for 1 hr . The different sample concentrations were used due to the significantly lower anti-Leishmania IgG response generated by the Sudanese VL patients , as recently described [13] . Following six PBST washes , 100 µl of horseradish peroxidase-conjugated subclass-specific mouse monoclonal antibodies specific for human IgG1 ( ab99774 ) , IgG2 ( ab99784 ) , IgG3 ( ab99829 ) or IgG4 ( ab99817 ) ( Abcam , UK ) , diluted 1∶5000 for the Indian sera or 1∶1000 for the Sudanese sera , in PBSTM , were added and plates were incubated at 37°C for 1 hr . Human IgG isotype responses were determined using 100 µl/well of a 1∶5000 dilution for the Indian sera or 1∶2500 dilution for the Sudanese sera , of horseradish peroxidase–conjugated donkey anti-human IgG secondary antibody ( 709-035-149: Jackson ImmunoResearch , West Grove , USA ) with incubation at 37°C for 1 hr . Following six PBST washes , 50 mM phosphate/citrate buffer ( pH 5 . 0 ) containing 2 mM o-phenylenediamine HCl and 0 . 007% ( vol/vol ) H2O2 ( Sigma , UK ) was added at 100 µl/well and incubated in the dark at room temperature for 15 minutes . The substrate reactions were then stopped by the addition of 2M H2SO4 ( 50 µl/well ) and the ELISA plates were read at 490 nm ( Spectra Max 190 , Molecular Devices , Sunnyvale , USA , or MRX II , Dynex Technologies , Chantilly , USA ) . These assays were performed on duplicate plates , simultaneously . ELISAs cut-offs were determined by the mean values plus three standard deviations obtained from the seronegative endemic healthy control serum samples . Statistical analysis on ELISA data ( 2-tailed unpaired t-test for unpaired samples , and paired t-test for paired/sequential samples , with 95% confidence interval in both cases ) was performed using GraphPad Prism version 4 . 02 for Windows ( GraphPad Software , San Diego , California , USA ) . The prototype lateral flow immunochromatographic tests consisted of a cassette with a nitrocellulose membrane , a sample pad , a conjugate pad and an absorbent pad , backed with a plastic strip . The nitrocellulose membranes were sensitized with crude L . donovani ( strain LEM3458 ) antigen lysate produced as described above and anti-human IgG1-specific antibody labelled with colloidal gold was dried on the conjugate pad . This strip was housed in a plastic cassette with two windows: the application well and the test/reading window . Two prototypes of this assay were developed and tested . For prototype 1 , 2 µl of serum and 2 µl of buffer were added to the nitrocellulose strip and then 90 µl of buffer was added to the application well . In prototype 2 , the application of 2 µl of serum on the strip was followed by the addition of 75 µl of buffer in the application well . The human IgG that migrated over the nitrocellulose membrane reacted with the immobilized target antigens . The anti-human IgG1-specific conjugated MAbs rehydrated by the buffer recognised the antigen-bound human IgG1 in the sample , thereby resulting in a red-purple coloured band . A control line ensured that the human IgG and conjugated anti-human IgG1 migration had occurred successfully . The prototype tests were used with sets of post-chemotherapeutic sera from Indian patients considered to have relapsed VL ( n = 30 for prototype 1 , of which 22 were also used with prototype 2 ) or cured VL ( n = 21 for prototype 1 , of which 5 were also used with prototype 2 ) . Fisher's exact 2-tailed test was used for testing the difference between proportions .
Figure 1A and Table S1 show the results of the initial pilot study of IgG subclass in unpaired Indian VL patients with different clinical status . There was marked elevation of IgG1 levels in active ( untreated ) VL patients; IgG1 levels were dramatically reduced in patients who were treated and considered to be cured by clinical and parasitological assessments . Thus , 90 . 0% ( 18/20 ) pre-treatment active VL patients were serologically positive , above the cut-off value , whereas only 33 . 3% ( 7/22 ) patients considered to be cured remained positive , all of whom had low IgG1 titres ( p<0 . 0001 , cured VL versus active or relapsed VL; Figure 1A ) . However , the IgG1 levels in the patients who were unsuccessfully treated were high and at levels comparable to the group of patients with active VL prior to treatment ( p = 0 . 485; Figure 1A; Table S1 ) . This trend for IgG1 levels was partially mirrored by IgG3 . The IgG3 levels were however lower in the active VL patients ( IgG1: 90 . 0% positive: IgG3: 75 . 0% positive ) and the relapsed VL patients ( IgG1: 84 . 2% positive: IgG3: 52 . 6% positive ) patients and 47 . 4% ( 9/19 ) of these relapsed VL patients were IgG3 negative ( Figure 1A; Table S1 ) . In comparison IgG2 profiles were very weak ( active VL: 5 . 0% positive; relapsed VL: 15 . 8% positive; cured VL: 0 . 0% positive ) and IgG4 levels were negative or at the cut off boundary , except for one cured patient and one in the other disease group ( a meningitis patient ) ( Figure 1A ) . Based on the initial study showing that elevated IgG levels were a potential biomarker of Indian VL treatment failure and relapse , a larger number of unpaired samples from Indian VL patients were analysed , together with those from a post kala-azar dermal leishmaniasis ( PKDL ) group , and from a group of patients who were VL asymptomatic but all of whom had positive DAT and/or rK39 serology . Since this second larger cohort was investigated after the initial study ( Trial 1 ) , the results are presented separately in Figure 1B , but were also incorporated into Table S1 . In Trial 2 similar results were obtained to those in Trial 1 , with 67 . 4% , 71 . 4% and 3 . 6% of the active VL , relapsed VL and cured VL patients , respectively , being IgG1 positive ( Figure 1B , Table S1 ) . A lower percentage of the cured VL patients were IgG1 positive in Trial 2 compared to Trial 1 ( Trial 1: 33 . 3% positive; Trial 2: 3 . 6% positive ) , although the absolute readings for Trial 1 cured were low and adjacent to cut-off borderline . IgG1 detection showed a much greater sensitivity and specificity than IgG3 . Thus amongst the four IgG subclasses IgG1 was indicated for potentially identifying VL Indian patients with relapse versus cure after chemotherapy . Comparisons of Sudanese VL IgG subclass responses were performed with unpaired sera from active VL , treated VL and PKDL patients , as well as DAT-positive and DAT–negative endemic healthy controls ( Table S1 ) , at 1∶100 dilution to allow for the overall lower IgG titres of Sudanese VL sera compared to Indian sera [13] . A slightly lower percentage of the Sudanese active VL patients ( 57 . 4% ) had positive specific IgG1 levels compared to the Indian patients ( Trial 1: 90 . 0%; Trial 2: 67 . 4% ) , but the treated Sudanese VL patients had low positive IgG1 levels ( 4 . 6% ) similar to the cured VL patients in the Indian Trial 2 ( 3 . 6% ) . Thus the IgG1 levels in Sudanese active VL and Sudanese treated VL were significantly different ( p<0 . 0001; Figure 1C , Table S1 ) . IgG1 levels were elevated in Indian PKDL compared to cured patients . Positivity rate was higher in Indian PKDL ( Trial 2 , 45 . 8% ) than in Sudanese PKDL ( 4 . 3% ) , ( Figures 1B and 1C , Table S1 ) , although overall antibody levels in Sudanese VL were also lower , as we have reported previously [13] . Having determined that specific IgG1 levels were elevated in single serum samples from cases of VL treatment failure but were decreased in cured VL patients , we compared , for single individuals , paired serum samples obtained prior to or at start of treatment and again at subsequent particular times . As shown in Table 2 , for Indian VL paired samples from three Indian VL groups were analysed: group 1: day 0 and 30 , group 2: day 0 and 180 and group 3: day 15 and 180 . L . donovani antigen-specific IgG1 levels were not significantly reduced at 30 days after start of treatment ( group 1: p = 0 . 8304; Figure 2A ) but were significantly decreased at approximately 180 days after the start of therapy ( group 2: p = 0 . 0032; group 3: p<0 . 0001; Figures 2B and 2C , respectively ) . In contrast , in Indian PKDL patients , a significant decrease in specific IgG1 levels was not observed using the sequential ( day 0 to 30 , 60 , 180 or 360 ) samples ( Table 2: groups 4–7 ) . Assay of IgG1 levels with paired samples from treated Sudanese VL patients ( day 0 and 11 , 17 or 30 ) showed that IgG1 subclass responses do not fall precipitously immediately after start of treatment ( Figure 2D ) , as with Indian VL patients , which also showed no rapid fall in IgG1 level shortly after start of treatment ( Figure 2A ) . However , with one exception , in which titre increased ( patient 16 , Figure 2D ) all Sudanese VL patients showed a non-significant trend for titre to decrease slightly . Two prototype immunochromatographic ( lateral flow ) RDTs were designed ( see Methods ) and tested to determine whether they could be used to discriminate between relapsed and cured VL patients by the detection of L . donovani antigen-specific IgG1 levels . The results for some of these patients are shown in Figure 3 . As summarised in Table 3 , most of the serum samples provided clear positive IgG1 results for the relapsed VL patients . Thus with prototype 1 , 25 of 30 ( 83 . 3% ) relapsed VL patients were positive but only 4 of 21 ( 19 . 0% ) cured VL patients . Similarly , with prototype 2 , 19 of 23 ( 82 . 6% ) relapsed VL patients were positive but only 1 of 5 ( 20% ) cured patients ( p<0 . 0001 for the prototypes 1 and 2 cumulative results , Fisher's exact 2-tailed test ) . None of these 5 cured patients tested with either prototype 1 or 2 gave strong positive IgG1 signals . One of patients with malaria and no diagnosed VL had detectable IgG1 . Thus , these prototype L . donovani antigen-specific IgG1 lateral flow assays could clearly discriminate between most of the relapsed and cured VL patients .
Diagnosis of VL is not straightforward: clinical symptoms may overlap with other infectious diseases associated with fever syndrome; parasitological methods of diagnosis are invasive and have limited sensitivities . The currently recommended rK39-based rapid diagnostic tests have their limitations as they cannot distinguish systematically and reliably between the different clinical phases of VL . A large Indian/Nepalese population study reported an association between higher DAT and/or rK39 titres and risk of progression from asymptomatic to symptomatic VL [38] . Recent papers have studied asymptomatic ( seropositive ) populations in Bangladesh [39] and elsewhere [40]–[42] . In Bangladesh when assessing a patient cohort at 24 months follow-up for VL disease development , discrepancies were found between the molecular and serological tests [39] . In one long-term follow-up of 55 rK39 seropositive asymptomatic cases in India 69% developed VL and 31% remained asymptomatic [43] . However , the proportion of L . donovani complex seropositive asymptomatic individuals that progresses to symptomatic VL can be minor , and varies between endemic regions , for example between 1: 2 . 4 in Sudan [44] , 4∶1 in Kenya [45] , 8∶1 in Brazil [46] , 4∶1 in Bangladesh [47] , 8 . 9∶1 in India and Nepal [48] , and 50∶1 in Spain [49] . There is no rapid diagnostic test that determines which asymptomatic carriers will progress to active VL . Nor is there a rapid diagnostic test that is a biomarker of treatment failure and relapse as opposed to cure after chemotherapy . Thus development of such point of care tests has been identified by WHO as a research priority [8] . We are by no means the first to investigate the dynamics of antibody response in the different clinical phases and evolution of VL . The persistence of detectable anti-Leishmania IgG years after treatment , mainly using DAT and rK39 , has been reported from India [33] , [34] , Brazil [35] , and Sudan [36] , [37] . We are not the first to mention that IgG subclass profiles may be associated with clinical status . The evaluation of IgG subclasses has been applied in other parasitic infections including echinococcosis [50]–[52] , toxoplasmosis [53] , [54] , and malaria [55]–[57] . Table 4 summarises previous published studies on IgG subclasses and clinical status of VL . Here , using comparative plate ELISAs and in the context of previous literature , we have specifically examined the capacity of anti-L . donovani IgG subclass antibodies to act as a biomarker of therapeutic failure and relapse as opposed to cure . Furthermore , and most importantly , we have shown that the biomarker can be adapted to a lateral flow rapid diagnostic test suitable for use at point of care . We analysed the IgG subclass profiles of patients in India with active VL and treated VL in comparison with asymptomatic seropositives , PKDL cases , other infectious diseases and endemic healthy controls . Future work could include a wider range of other diseases , including fungal infections . In a pilot study , we saw a remarkable decline in IgG1 levels in samples from unpaired Indian patients who were treated six months previously and were considered to be cured , so much so that the IgG1 titres for almost all of the individual patients fell below the ELISA cut off value for seropositivity ( Figure 1A ) . IgG1 is produced in response to protein antigens and its decline with cure is thus presumably due to disappearance of the antigenic stimulus . A similar profile was to some extent also seen with IgG3 , although IgG3 was not consistently raised above the cut-off in active VL . IgG2 levels were low across all groups ( Figure 1 ) . The results were similar for Sudan , in that there was a significantly lower level of IgG1 in non-recently treated patients , in almost all cases to below ELISA cut-off , compared to pre-treatment patients ( p<0 . 0001; Figure 1C ) , although overall IgG and subclass titres were much lower for Sudanese active VL than Indian , as reported previously [13] . Other authors have referred to high IgG1 levels in active VL compared to healthy controls , and a decrease in IgG1 following successful therapy , as assessed by ELISA [17] , [21] , [23] , [29] or in one case by flow cytometry [30] . To explore the timing of the decline in IgG1 levels following successful chemotherapy of Indian VL we compared IgG1 ELISA titres prior to treatment , shortly after the start of treatment or approximately 180 days later , using paired Indian samples ( groups 1–3 , Table 2 ) . At day 30 after start of treatment ( group 1 , Table 2 ) decline in IgG1 was minimal and not significant ( p = 0 . 8304; Figure 2A ) , which is not surprising as the half life of human IgG1 is estimated to be around 21 days [58] . The slow decline in IgG1 shortly after treatment was confirmed with paired sera from treated Sudanese patients who had active VL ( p = 0 . 3939; Table 2 , Figure 2D ) . We have not performed a western blot analysis with sera taken at different time points after treatment to determine whether the decrease in IgG1 titres relates to response to particular L . donovani antigens . However , one published study comparing western blot profiles using subclass specific conjugates and sera taken before and after treatment reported a general decline in band recognition and not the selective disappearance of bands [26] . Here we have used antigen derived from cultured promastigotes . However , in human VL the stage of the Leishmania life cycle is the amastigote , and given access to sufficient quantity of amastigote antigens it would be worthwhile to repeat such a comparative western blot study with amastigotes and subclass specific conjugates . In this way it might be possible to identify and subsequently isolate a specific amastigote antigen ( s ) applicable to determination of cure . Recently decreases in IgG1 and IgG3 after cure [59] or post-active disease scarring [60] have also been reported for Brazilian cutaneous leishmaniasis ( CL ) , and higher levels of these IgG subclasses in Turkish patients with active CL compared to endemic controls [61] . IgG subclass responses have been reported for experimental murine models of Leishmania infection and for canine infections with L . infantum , based on FcγR binding [62] . For canine leishmaniasis there are conflicting interpretations of IgG subclass profiles , ( reviewed in [63] ) , reportedly due to confusion in subclass nomenclature of the commercial polyclonals used [63] , [64] . A recent study has proposed improvement of comparative studies by categorising canine IgG subclasses against function of their human analogues [65] . The detection of VL relapse following unsuccessful chemotherapy is of special importance because without effective treatment symptomatic VL is considered to be almost invariably fatal . Thus , if relapse is not recognized and followed up with repeated or alternative treatment , patients , who are often relatively isolated in rural endemic regions , will succumb to the disease . We were therefore interested to examine the IgG subclass profiles in unsuccessful treatment and relapse . Notably , IgG1 levels were raised in patients who failed to respond to chemotherapy and were considered to have relapsed 6 months after start of treatment . As in active VL the majority of relapsed patients had IgG1 titres clearly above the ELISA cut-off ( Figures 1A and 1B ) . Such elevation of IgG1 in patients not responding to treatment has been mentioned rarely in the literature [21] , [23] . Accordingly , to provide an RDT for point of care application we devised two lateral flow prototypes , with which we assessed IgG1 seropositivity in treatment failure and relapse . Although we had already demonstrated that IgG1 levels were drastically reduced in cure , 6 months after treatment , for comparison we included a set of serum samples from such cured patients . Strikingly , the majority of patients considered to have relapsed were strongly positive in the IgG1 specific RDT , whereas none of the cured patients were strongly positive and the vast majority were entirely negative ( Table 3 , Figure 3 ) . Further validation is indicated with a one year longitudinal study ( since some patients may relapse between 6 months and one year after treatment [66] ) and a larger cohort of patients , ideally with baseline application of the RDT prior to treatment , since despite successful treatment , extent of decline of IgG1 might be influenced by the pre-treatment titre . All relapse patients might be screened/rescreened for HIV infection to assess whether relapse may be associated with immunocompromised status . Thus , with optimisation and standardisation of reagents , and preferably with higher discriminative sensitivity , this RDT may provide an important and life-saving epidemiological tool to detect relapse of VL . We have shown the potential of IgG1 to be a simple indicator of VL clinical status in terms of relapse . Symptomatic VL is treated at an acute phase of infection , which may favour the decline of IgG1 seropositivity in cure; it remains to be seen whether such markers are equally applicable to the treatment of prolonged chronic infections . As PKDL is a long-established chronic infection , this may explain why IgG1 does not decline with time after chemotherapy . A recent study has proposed that a high seropositivity in asymptomatic people may be associated with greater risk of progression to symptomatic VL [38] . Further work could investigate whether high anti-L . donovani IgG1 titre in asymptomatic patients is a potential biomarker for progression to active VL . Seroepidemiological comparisons with more detailed and sophisticated technological analysis of patient profiles is clearly a promising approach to defining precise , robust and widely applicable biomarkers . Based on analysis and interpretation of our results , in conjunction with review of the relevant literature , we conclude that: Further investigation of seroepidemiological indicators is clearly justified to find even better alternative biomarkers of clinical status , in particular to identify asymptomatic progressors to VL , as well as to distinguish cure from treatment failure and relapse . | Visceral leishmaniasis ( VL ) is a systemic disease with highest prevalence in South Asia , East Africa , and Brazil . VL is caused by protozoan ( unicellular ) parasites of the Leishmania donovani complex , transmitted to humans when an infected sandfly takes a bloodmeal . Within the human host , the parasites replicate within cells , particularly of bone marrow and spleen . Without effective treatment , symptomatic VL is usually fatal . As outlined in a recent World Health Organisation report , the development of new diagnostic tools to test for successful cure after chemotherapy is a research priority . In this work we investigated the association of clinical status of VL patients ( active pre-treatment , and those deemed cured or relapsed post-treatment ) with subclasses of the IgG antibody response made to L . donovani infection . We show that high levels of subclass IgG1 are found in pre-treatment and relapsed patients , but are very much lower in patients deemed to be cured . We further show that the decrease in IgG1 is detectable in patients 6 months after successful treatment , and that this detection method can be adapted to a rapid diagnostic test format requiring minimal technical expertise . Thus we believe that IgG1 levels are potentially a biomarker of post-chemotherapeutic monitoring . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"kala-azar",
"biochemistry",
"immune",
"system",
"proteins",
"veterinary",
"diseases",
"zoonoses",
"medicine",
"and",
"health",
"sciences",
"antibody",
"isotypes",
"proteins",
"leishmaniasis",
"protozoan",
"infections",
"biology",
"and",
"life",
"sciences",
"immunology",
... | 2014 | IgG1 as a Potential Biomarker of Post-chemotherapeutic Relapse in Visceral Leishmaniasis, and Adaptation to a Rapid Diagnostic Test |
The information processing capacity of the human mind is limited , as is evidenced by the so-called “attentional-blink” deficit: When two targets ( T1 and T2 ) embedded in a rapid stream of events are presented in close temporal proximity , the second target is often not seen . This deficit is believed to result from competition between the two targets for limited attentional resources . Here we show , using performance in an attentional-blink task and scalp-recorded brain potentials , that meditation , or mental training , affects the distribution of limited brain resources . Three months of intensive mental training resulted in a smaller attentional blink and reduced brain-resource allocation to the first target , as reflected by a smaller T1-elicited P3b , a brain-potential index of resource allocation . Furthermore , those individuals that showed the largest decrease in brain-resource allocation to T1 generally showed the greatest reduction in attentional-blink size . These observations provide novel support for the view that the ability to accurately identify T2 depends upon the efficient deployment of resources to T1 . The results also demonstrate that mental training can result in increased control over the distribution of limited brain resources . Our study supports the idea that plasticity in brain and mental function exists throughout life and illustrates the usefulness of systematic mental training in the study of the human mind .
A major limitation in human information processing arises from the time required to consciously identify and consolidate a visual stimulus in short-term memory [1] . This process can take more than a half-second before it is free for a second stimulus , as is revealed by the attentional-blink paradigm: If the second target stimulus ( T2 ) of two target stimuli is presented within 500 ms of the first one ( T1 ) in a rapid sequence of distracters , it is often not detected [2 , 3] . This deficit in the ability to process two temporally close , meaningful stimuli is believed to result from competition between the two targets for limited attentional resources [4]: When many attentional resources are devoted to the processing of T1 , too few may be available for T2 , rendering its representation vulnerable to distracter interference . Yet , the attentional blink does not reflect a general , immutable bottleneck , because most individuals are able to identify T2 on at least a portion of trials ( e . g . , [5] ) . This suggests that some control ( which need not be voluntary ) over the allocation of attentional resources is possible . The current study examined whether intensive meditation can affect the distribution of limited attentional resources , as measured by performance in an attentional-blink task and scalp-recorded brain potentials . A major ingredient of meditation is mental training of attention . Such mental training is thought to produce lasting changes in brain and cognitive function , significantly affecting the way stimuli are processed and perceived . In line with this view , recent studies have reported cognitive and neural differences in attentional processing between expert meditators and novices [6 , 7] . However , to corroborate the idea that mental processes are flexible skills that can be trained through meditation , longitudinal data examining such changes over time within the same individuals are required . Previous research in non-practitioners has shown that the adult human brain is capable of plastic change in response to environmental stimulation ( e . g . , [8 , 9] ) and that intensive training of an external task , such as a computer game , can improve attention skills , as reflected by enhanced performance on new cognitive tasks [10] . Yet , it is unclear at present whether purely mental training of certain attentional skills can benefit performance on novel tasks , which do not require meditation , but do call upon the trained skills . Here we present a longitudinal study investigating effects of 3 mo of intensive Vipassana meditation on the distribution of limited attentional resources . In this common style of meditation , one starts by focusing or stabilizing concentration on an object such as the breath . Then one broadens one's focus , cultivating a non-reactive form of sensory awareness or “bare” attention . This form of attention is non-reactive in the sense that , ideally one does not become caught up in judgments and affective responses about sensory or mental stimuli . On the basis of previous findings in expert meditators [6 , 7] , we hypothesized that 3 mo of intensive Vipassana meditation training would produce significant changes in attentional processing . More specifically , as this style of meditation cultivates non-reactive awareness , we predicted that after 3 mo of intensive practice , ( 1 ) attention would be captured less by T1 , resulting in a smaller attentional blink for T2; and ( 2 ) this reduction in T1 capture would be reflected in a smaller T1-elicited P3b , a brain-potential index of resource allocation [11] . Data were collected from 17 participants at the beginning and end of a 3-mo meditation retreat during which they meditated for 10–12 h per day ( practitioner group ) . Control data were collected from 23 participants interested in learning about meditation ( novice group ) , who received a 1-h meditation class and were asked to meditate for 20 min daily for 1 w prior to each session . In each session , participants performed an attentional-blink task in which they had to identify two targets ( both numbers ) embedded in a rapid stream of distracter letters ( Figure 1 ) . T2 could follow T1 after either a short or a long interval , so that T2 could occur within ( 336 ms post T1 ) or outside ( 672 ms post T1 ) the attentional-blink time window . Participants were not engaged in formal meditation during task performance .
Our first prediction was that intensive mental training would reduce attentional capture by T1 , as reflected by a smaller attentional blink for T2 . The behavioral results showed that significantly more practitioners ( 17 out of 17 ) than novices ( 16 out of 23 ) showed higher T2 detection rates at the second session ( time 2 ) when T2 followed T1 within the time window of the attentional blink ( Figure 2A; Fisher exact test: p = 0 . 029 ) . In line with previous reports ( e . g . , [5] ) , large variability between individuals of both groups was observed in attentional-blink size at the initial session ( time 1 ) . Three practitioners and one novice performed at , or near chance level at both time points ( i . e . , T2 accuracy around 14% ) . Regardless of whether or not these participants were included in the analysis , a significant reduction in attentional-blink magnitude over time was observed for the practitioner group compared to the novice group , as reflected by a three-way interaction between Interval , Group , and Session ( Figure 2B; at-chance participants included: F ( 1 , 38 ) = 4 . 5 , p = 0 . 040; at-chance participants excluded: F ( 1 , 34 ) = 4 . 3 , p = 0 . 045 ) . All two-way interaction terms and main effects shown in Figure 2B were significant ( Table 1 ) . Post hoc analyses confirmed that only the practitioner group showed a significantly smaller attentional blink at time 2 ( interaction Interval by Time; Practitioners: F ( 1 , 16 ) = 16 . 1 , p = 0 . 001; Novices: F ( 1 , 22 ) = 1 . 12 , p > 0 . 05 ) . In addition , the mental training-related improvement in T2 accuracy was selective to the time window of the attentional blink ( interaction Group by Time; short-interval trials: F ( 1 , 38 ) = 7 . 4 , p = 0 . 010; long-interval trials: F ( 1 , 38 ) = 0 . 7 , p = 0 . 41 ) . It is possible that there was more room for improvement in T2 accuracy in those participants with lower T2 accuracy scores at time 1 and , therefore , that inter-individual differences in task performance at time 1 critically influenced the observed reduction in attentional-blink size over time in the practitioners . However , this possibility was excluded by the results of an analysis of covariance ( ANCOVA ) in which we controlled for T2 accuracy at time 1 by entering this measure as a covariate in the analysis . The difference in T2 accuracy at time 2 versus time 1 was used as the dependent variable and Group was used as a fixed factor . Importantly , the reduction in attentional-blink size over time was still significantly greater in the practitioner versus control group ( F ( 1 , 37 ) = 5 . 1 , p = 0 . 029 ) , arguing against the possibility of differential room for improvement over time between groups . One interesting observation was that although only the practitioners showed a smaller attentional blink at time 2 , T2 accuracy generally improved slightly over time: both groups had significantly higher T2 accuracy scores at time 2 in both short-interval trials ( novices: t = 2 . 9 , p = 0 . 008; practitioners: t = 6 . 4 , p < 0 . 001 ) and long-interval trials ( novices: t = 2 . 8 , p = 0 . 010; practitioners: t = 2 . 9 , p = 0 . 011 ) . T1 accuracy also generally improved slightly over time ( F ( 1 , 38 ) = 8 . 1 , p = 0 . 007 ) . It is possible that with practice , the target stimuli became easier to detect as a result of perceptual learning [12] or that participants became better at predicting the timing of the target stimuli in the stream . Note , however , that such general practice effects cannot explain the observed selective improvement in T2 accuracy in short-interval trials in the practitioner group . T1 accuracy was not significantly different between groups and , as mentioned above , improved slightly over time , indicating that improved T2 detection did not impair T1 detection . At time 1 , the practitioners and novices accurately identified T1 on 78% and 79% of the short-interval trials , respectively , and on 88% and 88% of the long-interval trials , respectively . At time 2 , the practitioners and novices accurately identified T1 on 83% and 82% of the short-interval trials , respectively , and on 91% and 91% of the long-interval trials , respectively . The design also included T2-absent trials in which only T1 was presented and T2 was replaced by a blank ( see Materials and Methods ) . Average correct report of T2 absence showed no main effect of Session or any significant interactions , including Group , and will not be discussed further . The behavioral results showed that intensive mental training was associated with a significant reduction in attentional-blink size . We predicted that this reduction in attentional-blink size would be associated with a reduction in brain-resource allocation to T1 , as reflected by a smaller T1-elicited P3b . The change in T1-elicited P3b over time was assessed in the 350–650-ms window for short-interval trials in which both targets were correctly identified ( i . e . , no-blink trials ) and for trials in which only T1 was correctly identified ( i . e . , blink trials ) . For each sample and channel , voltage values in blink and no-blink trials were submitted to a repeated-measures analysis of variance ( ANOVA ) with T2 accuracy ( blink or no-blink ) and Session ( time1 or time2 ) as within-subject factors and Group ( practitioners or novices ) as a between-subjects factor . Ten practitioners and 12 controls had enough artifact-free blink and no-blink electroencephalogram ( EEG ) trials ( n > 15 ) at both time points to be included in this analysis . In line with the overall group behavioral findings , the reduction in blink size was significantly larger for the practitioner subgroup than for the novice subgroup ( Interval × Group × Session interaction: F ( 1 , 20 ) = 5 . 4 , p = 0 . 030; T2 accuracy in short-interval trials for the practitioners: 80% [time 2] vs . 61% [time 1] , and the novices: 69% [time 2] vs . 60% [time1] ) . The average number of trials included in the event-related potential analysis was Novices: 73 ( time 1 ) and 99 ( time 2 ) no-blink trials , and 45 ( time 1 ) and 40 ( time 2 ) blink trials; Practitioners: 81 ( time 1 ) and 121 ( time 2 ) no-blink trials , and 51 ( time 1 ) and 28 ( time 2 ) blink trials . In line with our prediction , intensive mental training was associated with a reduction in T1-elicited P3b amplitude over time in no-blink versus blink trials ( Figure 3A ) . Significant Group × T2 accuracy × Session interaction effects were observed for the early phase of the P3b between 394–450 ms ( largest at electrode Pz: F ( 1 , 20 ) : 4 . 4–9 . 7 , p-values < 0 . 05 ) and for its later phase between 488–551 ms post T1-onset ( largest at CP3: F ( 1 , 20 ) : 7 . 8–27 . 5 , p-values < 0 . 05 ) . Additional analyses revealed that both effects reflected a selective reduction across sessions of T1-elicited P3b amplitude in no-blink versus blink trials in practitioners ( Figure 3B; early phase at Pz: Fmax ( 1 , 9 ) = 8 . 7 , p = 0 . 016; late phase at CP3: Fmax ( 1 , 9 ) = 40 . 4 , p = 0 . 0001 ) . No such reduction in P3b amplitude was observed for novices ( early phase: all F-values for F ( 1 , 11 ) < 1; late phase: all F-values for F ( 1 , 11 ) < 4 . 6 ) . As mentioned above , the attentional blink is thought to result from suboptimal sharing of limited attentional resources: When many resources are devoted to T1 processing , T2 is more likely to be missed [4 , 13] . We hypothesized , therefore , that the magnitude of decrease in T1-elicited P3b amplitude would be predictive of the magnitude of decrease in attentional-blink size . Indeed , correlation analyses revealed that those individuals—practitioner or novice—that showed the largest decrease in brain-resource allocation to T1 over time generally showed the greatest improvement in detecting T2: a reliable , negative cross-subject correlation was observed between the increase over time in T2 accuracy and the corresponding change in T1-elicited P3b amplitude on no-blink trials for both the early phase ( Figure 4; r = −0 . 68 , p = 0 . 001; data from Pz ) and the late phase ( r = −0 . 46 , p = 0 . 030; data from CP3 ) of the P3b . Note , however , that as predicted , only the practitioners could exploit this resource-sharing mechanism: the P3b amplitude reduction to T1 at the time of the second recording was present only for this group and not for the novices ( see above; see Figure 3A ) . No significant Group × T2 accuracy × Session interaction effects were observed outside of the T1-elicited P3b window , including the time window of the P3b elicited by T2 ( all p-values > 0 . 05 ) . Previous brain-potential studies have reported that when T2 is not seen , the P3b to T2 is largely or completely suppressed [14 , 15] . Replicating these prior findings , we found greater positivity in no-blink versus blink trials in the time window of the T2-elicited P3b ( 800–1 , 000 ms ) , as reflected by significant main effects of T2 accuracy at several dorsal posterior electrode sites , including Pz ( Practitioners: F ( 1 , 9 ) : 5 . 1–9 . 9; Novices: F ( 1 , 11 ) : 5 . 9–36 . 5; all p-values < 0 . 05 ) . As effects of intensive mental training on scalp-recorded brain activity appeared specific to no-blink trials ( see Figure 3B ) , we ran another analysis that included all participants that had enough no-blink trials ( n > 15 ) at both time points ( 14 practitioners and 20 novices ) . This repeated-measures ANOVA using the within-subjects factor Session ( time1 or time2 ) and the between-subjects factor Group ( novices or practitioners ) replicated the above described reductions in T1-elicited P3b amplitude over time in the practitioner group ( see Figure 5A ) , as reflected by significant Session × Group interaction effects ( early phase[316–445 ms] at Pz: F ( 1 , 33 ) : 4 . 5–26 . 6 , p-values < 0 . 05; late phase [477–563 ms] at CP3: F ( 1 , 33 ) : 4 . 5–9 . 9 , p-values < 0 . 05 ) . Importantly , the correlation between the reduction in T1-elicited P3b amplitude and the increase in T2 accuracy over time also remained significant using this larger sample of participants ( early phase: r = −0 . 42 , p = 0 . 014; late phase: r = −0 . 36 , p = 0 . 036 ) , illustrating the robustness of this finding . It is important to note that the scalp-recorded brain responses to T1 and T2 overlapped in time . Previous brain-potential studies have shown that while early visual processing of T2 is not affected by T2 visibility , T2 processing in no-blink and blink trials may differ as early as 170 ms post T2 [16] . It is therefore possible that the reduction in posterior positivity during the later part of the T1-evoked P3b observed in short-interval trials ( i . e . , 488–551 ms post-T1 or 152–215 ms post-T2 ) was due to differential T2 processing rather than differential T1 processing . To examine this possibility , we ran two additional analyses , focusing on trials in which the T1-elicited P3b was not confounded by neural activity associated with T2 . The first analysis focused on long-interval T2-present no-blink trials , in which T2 followed T1 after 672 ms , i . e . , after the T1-evoked P3b occurred . The second analysis focused on short-interval T2-absent trials in which no T2 was presented . If for these trial types we observed a similar reduction in scalp-recorded brain activity in the time window of the T1-elicited P3b as we did for short-interval T2-present trials , this would strengthen the conclusion that meditation training changed the neural processing of T1 . All participants had enough trials to be included in the two additional analyses . The average number of trials included in the long-interval T2-present analysis was Novices: 45 ( time 1 ) and 52 ( time 2 ) trials; and Practitioners: 48 ( time 1 ) and 53 ( time 2 ) trials . The average number of trials included in the T2-absent analysis was Novices: 46 ( time 1 ) and 49 ( time 2 ) trials; and Practitioners: 46 ( time 1 ) and 45 ( time 2 ) trials . Of central importance , in both long-interval T2-present trials ( Figure 5B ) and in T2-absent trials ( Figure 5C ) , we observed a reduction in T1-elicited P3b amplitude ( around 400 ms ) for the practitioners only ( long-interval T2-present trials at Pz [370–413 ms]: F ( 1 , 16 ) : 4 . 7–8 . 3 , p-values < 0 . 05; T2-absent trials at Pz [398–410 ms]: F ( 1 , 16 ) : 4 . 57–5 . 31 , p-values < 0 . 05 ) . This reduction in early T1-elicited P3b was highly similar both in time course and scalp topography to the reduction in early T1-elicited P3b that we observed for short-interval T2-present no-blink trials , albeit less pronounced , in particular for T2-absent trials ( Figure 5D ) . Similar effects were not observed for the novices ( all p-values > 0 . 05 ) . For long-interval T2-present trials , the selective reduction of T1-elicited P3b amplitude for the practitioners was expressed in a significant interaction between Group and Session ( 367 and 402 ms; largest at electrode P3: F ( 1 , 37 ) : 4 . 2–6 . 6 , p-values < 0 . 05 ) . For T2-absent trials , the interaction did not reach significance ( p-values > 0 . 05; p ( min ) = 0 . 12 ) ( but see next paragraph ) . Interestingly , in contrast to short-interval T2-present trials , no mental training-related reduction in late T1-elicited P3b was observed in long-interval T2-present trials or in T2-absent trials . This indicates that intensive mental training may have reduced T1 capture , as well as affected early T2-related processes . The short-interval T2-present trial analysis showed that the effects of intensive mental training on scalp-recorded brain potentials were selective to no-blink trials: only in no-blink trials , but not in blink trials , a reduction in brain activity in the T1-elicited P3b time window was observed ( Figure 3B ) . The long-interval T2-present trial analysis and the T2-absent trial analysis may not have been as sensitive to detecting mental training-related effects as the short-interval T2-present trial analysis , because both these trial types included a mixture of “blink” and “no-blink” trials . As to long-interval T2-present trials: the longer interval between the two targets conceivably allowed for T2 detection even in those trials in which many resources were devoted to T1 processing and that would have resulted in an attentional blink had the T1–T2 interval been short . Long-interval no-blink T2-present trials thus include both “blink” and “no-blink” trials . A similar argument can be made for T2-absent trials: when a participant indicated that s/he did not see a second target in a T2-absent trial , this could have been because s/he just did not see a second target ( a “blink” trial ) or because s/he was absolutely certain that no T2 had been present in the stream ( a “no-blink” trial ) . T2-absent trials can thus also be said to include a mixture of “blink” and “no-blink” trials . As the short-interval T2-present trial analysis showed that mental training-related effects were specific to no-blink trials , the additional long-interval T2-present and T2-absent trial analyses may therefore not have been as sensitive to detecting mental training-related changes over time . This argument receives critical support from an extra analysis examining whether the decrease in brain activity in the time window of the T1-elicited P3b in short-interval T2-present trials was smaller when averaging across no-blink and blink trials . This analysis showed that the mean decrease in T1-elicited P3b amplitude over time was significantly smaller when averaging across blink and no-blink trials compared to when averaging across no-blink trials alone: 1 . 24 versus 1 . 70 μV ( p = 0 . 015; early phase P3b as measured at Pz ) and 1 . 05 versus 1 . 60 μV ( p = 0 . 008; late phase P3b ) . These findings indicate that the observed decreases in T1-elicited P3b in long-interval T2-present trials and in T2-absent trials likely were less pronounced , because both trial types include a mixture of blink and no-blink trials .
This study examined whether intensive mental training can affect one of the major capacity limits of information processing in the brain: the brain's limited ability to process two temporally close meaningful items . Using performance in an attentional-blink task and scalp-recorded brain potentials , we found , as predicted , that 3 mo of intensive mental training resulted in a smaller attentional blink and reduced brain-resource allocation to the first target , as reflected by a smaller T1-elicited P3b . Of central importance , those individuals that showed the largest decrease in brain-resource allocation to T1 generally showed the greatest reduction in attentional-blink size . These novel observations indicate that the ability to accurately identify T2 depends upon the efficient deployment of resources to T1 and provide direct support for the view that the attentional blink results from suboptimal resource sharing [5 , 13 , 15 , 16] . Importantly , they demonstrate that through mental training , increased control over the distribution of limited brain resources may be possible . Because participants did not engage in formal meditation during task performance , the observed reduction in T1 capture after 3 mo of intensive meditation suggests that purely mental training of certain attention skills can influence performance on a novel task that calls upon those skills . Green and Bavelier [10] reported that intensive action video-game playing can improve attention skills , as reflected by enhanced performance on new cognitive tasks , including the attentional-blink task . Here , we show that improvements in performance of a novel , external task may also be achieved by pure mental training . As such , our findings extend previous research showing that the adult human brain is capable of plastic change in response to environmental stimulation ( e . g . , [8 , 9 , 10] ) . Note that it is unlikely that motivational differences between groups can explain our findings , because previous work has shown that motivating participants to do well on an attentional-blink task by paying them according to their performance does not affect the magnitude of the attentional blink [17] . In addition , the current findings corroborate previous findings in expert meditators [6 , 7] by showing longitudinally , within subjects , that attention processes are flexible skills , which can be enhanced through mental training . The observed reduction in T1 capture after 3 mo of intensive Vipassana meditation training confirms first-person reports that this style of meditation affects attentional processes and can significantly affect the way stimuli are processed and perceived . Future longitudinal studies are needed to examine how long effects of mental training on attention may persist and whether even shorter-term training may demonstrably benefit various attentional skills . Although they differ in the specific mechanisms , cognitive accounts of the attentional blink have generally held that there is a capacity-limited stage in stimulus processing and that competition between different stimuli for limited processing resources underlies the attentional-blink deficit ( e . g . , [18–21] ) . In line with this idea , several recent brain-potential studies have shown that the ability to accurately identify T2 is related to the latency and/or amplitude of the T1-elicited P3b [5 , 13 , 16 , 22] . A delayed or larger T1-evoked P3b was observed in trials in which T2 was missed versus seen [16] and in individuals exhibiting a relatively large attentional blink [5 , 13] . In addition , the amplitude of the T1-evoked P3b has recently been shown to be dependent on T1 probability and T1 cue validity [22] . Together , these findings indicate that variability in the duration or difficulty of the T1 task ( as indexed by T1-elicited P3b amplitude ) may affect the severity of the competition between T1 and T2 . In the current study , the magnitude of decrease in T1-elicited P3b amplitude over time in no-blink trials predicted the magnitude of decrease in attentional-blink size over time . This observation provides important additional support for the idea that the ability to accurately identify two temporally close , meaningful items depends upon the efficient deployment of resources to the first item [4 , 13] . Beyond this , the current study indicates that through mental training , people may gain some control ( which need not be voluntary ) over the amount of attentional resources devoted to the processing of the first item . Vipassana meditation allegedly reduces ongoing mental noise in the brain , enabling the practitioner to remain in the present moment . Three months of intensive training in this style of meditation may therefore have decreased mental capture by any stimulus , i . e . , distracters and targets alike [5] , resulting in reduced distracter interference . Although we cannot fully exclude the possibility that reduced distracter interference may have contributed to our findings , mental training-related effects were not observed outside of the time window of the T1-elicited P3b , including the time window of the T2-elicited P3b . This observation supports the idea that intensive mental training selectively reduced brain-resource allocation to T1 . Intensive mental training may , however , have affected relatively early T2-related processes; In trials in which the T1-elicited P3b was not confounded by neural activity associated with T2 ( i . e . , long-interval trials and T2-absent trials ) , a mental training-related reduction in posterior positivity was only observed for the early phase of the T1-elicited P3b ( around 400 ms post-T1 ) , but—in contrast to short-interval T2-present trials—not for its later phase ( around 500 ms post-T1 ) . The timing of this later effect ( i . e . , 152–215 ms post-T2 ) concurs with effects observed in a recent brain-potential study [16] . This study elegantly showed that brain events occurring as early as 170 ms post-T2 were affected by the conscious perception of T2 . Three months of intensive mental training may therefore not only have reduced T1 capture , but may have also influenced relatively early T2-elicited processes . The current findings allow us to speculate on candidate brain structures that intensive Vipassana meditation training may affect . Previous neuroimaging studies have implicated a network of frontal , parietal , and temporal brain areas in the generation of the scalp-recorded P3b [23] . Activation of a similar network of brain areas has been associated with conscious target processing in the attentional-blink task [24] . Three months of intensive mental training may thus have affected the recruitment of this distributed neural network . In summary , the results presented here are consistent with the idea that the ability to accurately identify T2 depends upon the efficient processing of T1 . They furthermore demonstrate that , through mental training , increased control over the allocation of limited processing resources may be possible . Our study corroborates the idea that plasticity in brain and mental function exists throughout life , and illustrates the usefulness of systematic mental training in the study of the human mind .
Seventeen practitioners ( seven male; median age , 41 y , range 22–64 y; median education , 18 y ) were recruited prior to the start of a 3-mo meditation retreat at the Insight Meditation Society in Barre , Massachusetts . Twenty-three matched controls ( nine male; median age , 41 y , range 20–62 y; median education , 17 y ) with no prior meditation experience were recruited via advertisements in local newspapers directed at individuals interested in learning about meditation . The participants had no history of mental or neurological illness , and gave informed consent to participate . Participants were trained in Vipassana meditation , which cultivates concentration and “bare” attention ( see Introduction ) . Through this style of training , one allegedly is able to be more finely attentive to experience from moment to moment and gain insight into one's habits and assumptions about identity and emotions . Practitioners were also trained in “metta , ” a loving kindness and compassion meditation . The practitioners self-selected for the meditation group and all had prior experience with meditation . Practitioners differed greatly in the style ( s ) of meditation previously practiced ( e . g . , Vipassana , open presence , mantra , or yoga meditation ) , in the traditions of the learned meditation ( e . g . , Zen , Theravada , or Tibetan ) , and in the amount of prior meditation experience . No relationship was observed between prior meditation experience ( i . e . , number of days in a retreat prior to our study ) and attentional-blink task performance at time 1 . Prior meditation experience also did not interact with the meditation intervention , as there was no significant relationship between prior meditation experience and the change over time in attentional-blink task performance . The absence of an association between the amount of prior meditation training and our study results may be due to the fact that there was great variation across the practitioners in the styles and traditions of the previously learned meditation . Longitudinal research examining and comparing the effects of different styles of meditation on brain and mental function and the duration of such effects is needed . Stimuli were presented in black on a gray ( 40 cd/m2 ) background at the center of a computer screen . Each trial started with a 1 , 780-ms fixation cross ( 0 . 5° × 0 . 5° ) , followed by a rapid serial stream of 15 or 19 letters ( 0 . 8° × 0 . 8° ) ( Figure 1 ) . Each letter was randomly drawn ( without replacement ) from the alphabet ( except B , I , O , Q , and S ) and presented for 50 ms , followed by a 34-ms blank . On each trial , one or two letters were replaced with a number , randomly drawn ( without replacement ) from the set 2–9 . When only one letter was replaced by a number , a second letter was replaced with a blank screen ( T2-absent trials ) . The temporal distance between the first ( T1 ) and second ( T2 ) number ( or the blank screen ) could be short ( 336 ms ) or long ( 672 ms ) . T2 and the blank screen were presented at temporal position 3–5 from the end of the stream . To prevent the saliency of T2-absent trials , each distracter could be replaced by an empty screen with a 20% probability , except those surrounding T1 and T2 and the last distracter in the sequence ( cf . [25] ) . Participants were informed that there could be one or two numbers in the letter stream , and , 1 , 000 ms after the stream ended , were asked to report these numbers by typing the numbers in order on a keyboard . Participants were instructed to guess T2 if they thought that T2 had been presented , but were not entirely sure about its identity . If they were absolutely sure that no T2 was presented , they entered zero for this number . A new trial began 200 ms after the second response . After a short practice block , participants performed four blocks of 102 trials each , consisting of 192 short-interval/T2-present trials , 72 long-interval/T2-present trials , 72 short-interval/T2-absent trials and 72 long-interval/T2-absent trials , all intermixed within blocks . In each session , after practicing the task first for 34 trials , participants performed four blocks of 102 trials of the attentional-blink task while their EEG was recorded . EEG was recorded at 512 Hz from 64 Ag–AgCl electrodes using the Active-Two system ( BioSemi , http://www . biosemi . com ) . Additional electrodes recorded the right and left mastoid process and the electrooculogram . T1 and T2 accuracy data were submitted to separate repeated-measures ANOVAs with Interval ( short or long ) and Session ( time 1 or time 2 ) as within-subject factors , and Group ( novices or practitioners ) as a between-subject factor . T2 accuracy was based only on those trials in which T1 was correctly reported . EEGLAB [26] and Matlab ( Mathworks , http://www . mathworks . com ) were used for off-line EEG data processing . Due to technical problems , the EEG data of two novices could not be analyzed . Data of the remaining participants were high-pass filtered ( 1 Hz ) , re-referenced to the average of both mastoids , and cleaned of large movement-related artifacts . Independent component analysis was then used to remove ocular artifacts [27] . For each condition of interest , trials were epoched in synchrony with T1 onset , and baseline-corrected ( 200 ms preceding T1 ) . Trials with remaining artifacts ( exceeding ±80 μV ) were removed . The remaining trials were low-pass filtered ( 20 Hz ) and averaged . The change in T1-elicited P3b over time was assessed in the 350–650-ms window . A significance criterion of p < 0 . 05 for at least 16 consecutive samples ( 31 ms ) on at least four adjacent electrodes was used . | Meditation includes the mental training of attention , which involves the selection of goal-relevant information from the array of inputs that bombard our sensory systems . One of the major limitations of the attentional system concerns the ability to process two temporally close , task-relevant stimuli . When the second of two target stimuli is presented within a half second of the first one in a rapid sequence of events , it is often not detected . This so-called “attentional-blink” deficit is thought to result from competition between stimuli for limited attentional resources . We measured the effects of intense meditation on performance and scalp-recorded brain potentials in an attentional-blink task . We found that three months of intensive meditation reduced brain-resource allocation to the first target , enabling practitioners to more often detect the second target with no compromise in their ability to detect the first target . These findings demonstrate that meditative training can improve performance on a novel task that requires the trained attentional abilities . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience",
"homo",
"(human)"
] | 2007 | Mental Training Affects Distribution of Limited Brain Resources |
All Yersinia species target and bind to phagocytic cells , but uptake and destruction of bacteria are prevented by injection of anti-phagocytic Yop proteins into the host cell . Here we provide evidence that CD8+ T cells , which canonically eliminate intracellular pathogens , are important for restricting Yersinia , even though bacteria are primarily found in an extracellular locale during the course of disease . In a model of infection with attenuated Y . pseudotuberculosis , mice deficient for CD8+ T cells were more susceptible to infection than immunocompetent mice . Although exposure to attenuated Y . pseudotuberculosis generated TH1-type antibody responses and conferred protection against challenge with fully virulent bacteria , depletion of CD8+ T cells during challenge severely compromised protective immunity . Strikingly , mice lacking the T cell effector molecule perforin also succumbed to Y . pseudotuberculosis infection . Given that the function of perforin is to kill antigen-presenting cells , we reasoned that cell death marks bacteria-associated host cells for internalization by neighboring phagocytes , thus allowing ingestion and clearance of the attached bacteria . Supportive of this model , cytolytic T cell killing of Y . pseudotuberculosis–associated host cells results in engulfment by neighboring phagocytes of both bacteria and target cells , bypassing anti-phagocytosis . Our findings are consistent with a novel function for cell-mediated immune responses protecting against extracellular pathogens like Yersinia: perforin and CD8+ T cells are critical for hosts to overcome the anti-phagocytic action of Yops .
Three Yersinia species cause disease in humans: Y . pestis , Y . pseudotuberculosis , and Y . enterocolitica [1] . Plague results from Y . pestis , introduced via fleabite or contaminated aerosols , and manifests in bubonic , septicemia , and pneumonic forms [2] . Oral ingestion of Y . pseudotuberculosis or Y . enterocolitica from contaminated food or water causes yersiniosis , which is typified by gastroenteritis and mesenteric lymphadenitis , with occasional arthritic sequelae [3] . Despite differing infection routes and disease manifestations , all Yersinia species demonstrate a tropism for lymphoid tissue and an ability to disseminate from initial infection sites to colonize systemic organ sites , where unchecked bacterial replication causes fatal disease [4] , [5] . Yersinia express a battery of virulence factors to cause disease in susceptible human and animal hosts . Enteric Yersinia express several adhesins in order to bind to host cells and penetrate the intestinal epithelium [6]–[12] . Y . pestis expresses a different subset of adhesins to mediate host cell binding and facilitate dissemination [13]–[16] . Adhesion of Yersinia to host cells is required for delivery of Yersinia outer proteins ( Yops ) into the host cell cytoplasm via a specialized secretion machine called the type III secretion system ( T3SS ) [17] . Yersinia express six Yops; YopE , H , J ( also called P ) , M , O , and T [18] , which perturb eukaryotic cell signaling pathways , resulting in inhibition of phagocytosis , alteration of cytokine production [18] , and direct intoxication of phagocytes during animal infections [19] . The importance of Yop-induced inhibition of phagocytosis is supported by two lines of evidence: 1 ) Yop-deficient Yersinia species fail to cause disease [20]–[23] , and 2 ) histological studies demonstrate that Yersinia localize to the extracellular space of infected tissues [24]–[26] . This anti-phagocytosis phenotype can be easily reproduced during Yersinia association with tissue culture cells [27] . Delivery of Yops requires intimate attachment , as bacteria lacking defined adhesion factors are defective for Yop translocation [28] . Thus , the combined action of adhesins and Yops synergize to localize Yersinia in a discrete niche as extracellularly attached bacteria [29] . T cells survey for pathogens by virtue of T cell receptor recognition of antigen bound to major histocompatibility complex ( MHC ) on the surface of infected host cells . CD4+ T cells recognize exogenous antigen from vacuolar pathogens that is presented by MHC II . CD8+ T cells respond to cytoplasmic antigens , with processed peptide complexed with MHC I [30] . A requirement for cell-mediated immune responses against Yersinia has been dismissed , given that Yersinia is an extracellular pathogen and that immune serum is sufficient to protect mice against virulent challenge [31] , [32] . The observed intracellular localization of Yop proteins [33] , however , has suggested that T cells may respond to Yop-derived antigens . Moreover , there is evidence showing that CD4+ and CD8+ T cells are required for protection against Y . pestis and Y . enterocolitica in animal models of infection [34]–[36] . The mechanism of T cell restriction of Yersinia is unknown . Studies have suggested that IFNγ secretion by T cells is important [37] , presumably by activating antimicrobial functions in phagocytes [38] . This suggestion explains the function of Yersinia-specific CD4+ T cells , but may only partially account for the mechanism of CD8+ T cell restriction of the bacteria . CD8+ , or cytolytic T cells ( CTLs ) , can eliminate pathogen-harboring cells by inducing death of target cells , which removes a niche for intracellular pathogen replication [39] . The possibility that CTL-induced host cell death could interfere with disease by extracellular pathogens has not been addressed . We hypothesized that the intimate association of Y . pseudotuberculosis with host cells may allow recognition by CD8+ T cells and result in restriction of disease . To test this hypothesis , a model of murine infection with live attenuated Y . pseudotuberculosis was used to determine the disease susceptibility of mice defective in specific arms of cell-mediated immunity . CD8+ T cells and perforin-dependent cytolysis were critical for protecting against Y . pseudotuberculosis . CD8+ T cell recognition of target cells bypasses anti-phagocytosis by Y . pseudotuberculosis , which is consistent with the model that restriction results from CTLs marking host cells and attached bacteria for phagocytic removal .
As naïve mice rapidly succumb to disease caused by virulent Y . pseudotuberculosis [4] , [5] , we instead used the attenuated ksgA− strain [40] to investigate how CD8+ T cells protect against Y . pseudotuberculosis . The absence of KsgA results in a lowered replication rate in culture relative to wild-type bacteria [41] due to the loss of dimethylation of 16S rRNA [42] . To confirm that Y . pseudotuberculosis ksgA− attenuates virulence in animal hosts , C57BL/6 mice were orally inoculated with 5×108 colony-forming units ( CFU ) of the ksgA− strain and bacterial burden was followed in target organs over time . Mice inoculated with the mutant showed detectable levels of bacteria in all organs examined for the first ten days post-inoculation , after which the levels dropped below the limit of detection ( Figure 1 ) , and most animals survived ( data not shown ) . The small intestine and Peyer's patches were colonized at higher levels than the mesenteric lymph nodes , spleen or liver . In contrast , C57BL/6 mice orally inoculated at the same dose with the virulent parental strain supported increasingly greater levels of bacteria until eventually succumbing to disease eight days post-inoculation [43] . The ksgA− strain was attenuated following a non-intestinal route of inoculation , as well . After intravenous delivery , ksgA− bacteria colonized the spleen and liver at lower levels than the parental strain at day 5 post-inoculation and at levels approaching a known avirulent strain lacking the ability to translocate Yops into cells ( yopB− [44] ) ( Figure S1 ) . Prior exposure to attenuated pathogens can generate protective immunity against subsequent challenge with fully virulent organisms [45]–[47] . Previous oral inoculation with Y . pseudotuberculosis ksgA− bacteria conferred protection against the virulent YPIII pIB1 comparable to a published vaccine strain that overproduces Dam methylase [48] ( 91% versus 100% survival , Table 1 ) , whereas all naïve mice succumbed to challenge ( 0% survival ) . Moreover , orally immunized mice were protected against challenge with intravenously administered virulent bacteria ( 100% ) , indicating that the specificity of the response was not restricted to antigens expressed by bacteria at the initial immunization site . Immunization with ksgA− bacteria , which is derived from the YPIII pIB1 background [40] , also protected against oral challenge with strain IP2666 , another virulent Y . pseudotuberculosis strain [49] capable of causing fatal disease in naïve mice ( data not shown ) . These results demonstrate that exposure to attenuated Y . pseudotuberculosis ksgA− bacteria generates immune responses sufficient to protect against virulent disease . Correlates of protective immunity include the presence of antigen-specific antibodies and T cell responses following antigenic exposure . The total antibody response to Y . pseudotuberculosis was significantly higher in sera from ksgA−-immunized mice than from naïve mice , with a 28 fold greater median value for the half-maximal antibody titer in immune animals ( Figure 2A , P = 0 . 0012 ) . Antibody isotyping revealed that ksgA−-immune mice possessed anti-Yersinia IgA antibodies , as would be predicted from a mucosal immunization regimen ( Figure 2B ) , as well as robust IgG2a and detectable IgG1 responses ( data not shown ) . Y . pseudotuberculosis-immune mice displayed a significantly more IgG2a than IgG1 isotypic antibodies ( Figure 2C , median ratios 5 . 9 and 0 . 4 , respectively , P = 0 . 0004 ) , indicative of a TH1-type T cell response [50] . Correspondingly , CD4+ and CD8+ T cells were activated at day seven post-inoculation with the ksgA− mutant , as determined by an increase in CD69+ lymphocytes in mesenteric lymph nodes as compared to naïve animals , although activated lymphocytes were not detected in the ksgA−-colonized Peyer's patches at this time point ( Figure 2D , E ) . The increase in the percentage of activated lymphocytes was due to an increase in the number of activated cells relative to the total number of lymphocytes . The number of CD8+ lymphocytes was similar between animals regardless of the presence of bacteria ( data not shown ) , but the number of activated CD8+ lymphocytes was higher in animals harboring bacteria . Taken together , these results indicate that both humoral and cell-mediated immune responses are stimulated in mice exposed to attenuated Y . pseudotuberculosis . The observation that exposure to attenuated Y . pseudotuberculosis activates CD8+ T cells was intriguing , given that CD8+ T cells are thought to target intracellular pathogens , while Yersinia species are generally regarded to be extracellular pathogens . To investigate if CD8+ T cells restrict Y . pseudotuberculosis colonization , mice deficient for β2-microglobulin ( B2m−/− ) , which lack MHC Class I and thus lack CD8+ T cells [51] , [52] , were inoculated with the ksgA− mutant . At eight days post-inoculation , the ksgA− mutant colonized the Peyer's patches ( Figure 3A ) and small intestine ( data not shown ) of C57BL/6 and B2m−/− mice at similar levels , indicating that Y . pseudotuberculosis replicates in the gastrointestinal tract independently of β2-microglobulin-dependent mechanisms . However , the mesenteric lymph nodes , spleen and liver of B2m−/− mice were colonized at significantly higher levels with attenuated bacteria as compared to C57BL/6 mice , demonstrating that B2m−/− mice are highly susceptible to the ksgA− mutant ( Figure 3B , C , D ) . In addition to lacking cell-mediated immune functions , B2m−/− mice have an additional phenotype of iron overload , similar to human hemochromatosis [53] . Given that hemochromatosis is a risk factor for extraintestinal infection by enteric Yersinia in humans [54] , we evaluated the colonization of Y . pseudotuberculosis in B2m−/− mice following parenteral inoculation . This allows the infection route to bypass the intestine and avoid hemochromatosis-dependent enhancement of extraintestinal dissemination . At nine to ten days post-inoculation , intravenously delivered ksgA− mutant bacteria colonized the spleen and liver of B2m−/− knockout animals at significantly higher levels than observed in C57BL/6 mice ( Figure 3E , F ) , indicating that the enhanced susceptibility of the knockout mice to Y . pseudotuberculosis cannot entirely be attributed to increased extraintestinal dissemination . Therefore CD8+ T cells , or cells and/or functions requiring β2-microglobulin , are necessary to eliminate attenuated Y . pseudotuberculosis . To more specifically isolate the role of CD8+ T cells in controlling colonization with attenuated Y . pseudotuberculosis , C57BL/6 mice were depleted of CD8+ T cells [55] ( Materials and Methods ) and inoculated with ksgA− bacteria . Bacteria were delivered directly to the systemic organs by intravenous injection , to ensure that the mutant was introduced into organ sites that have a high likelihood of depletion . This was effective in reducing the scatter found in CFU isolated from systemic tissue sites after oral inoculation , presumably resulting from bottlenecking associated with extraintestinal dissemination [43] . At 14 days post-inoculation , mice depleted of CD8+ T cells displayed a 1 . 2 and 1 . 4 log increase in CFU in the spleen and liver , respectively ( Figure 4A , B ) , as compared to mock-depleted mice , although differences were only statistically significant in the liver ( P = 0 . 008 ) . More striking was the frequency of colonization: while some of the mock-depleted animals showed no colonization ( 33% , 5 of 15 mice ) , all of the CD8-depleted mice contained ksgA− bacteria in either the liver or spleen ( 100% , 12 of 12 mice ) . CD8−/− mice [56] also demonstrated increased susceptibility to systemic colonization with ksgA− bacteria at day 14 post-inoculation ( median value of 104 versus 102 CFU/gram tissue in livers of CD8−/− as compared to C57BL/6 mice , P = 0 . 02 ) . Although these experiments addressed the requirement for CD8+ T cells during colonization with attenuated Y . pseudotuberculosis in naïve animals , that is , those not previously exposed to the bacterium , the observed phenotype of increased colonization in CD8-deficient mice was detected only at 14 days post-inoculation , at a time when adaptive immune mechanism are likely involved in protection . To demonstrate that the requirement for CD8+ T cells is associated with enhanced adaptive immunity to Y . pseudotuberculosis , and to show that CD8+ T cell responses are effective against fully virulent bacteria , mice orally immunized with the ksgA− mutant were depleted of CD8+ T cells prior to and following intravenous challenge with wild-type Y . pseudotuberculosis . 100% of depleted mice succumbed to virulent bacteria within one week post-inoculation , whereas all mock-depleted immune mice survived challenge ( Figure 4C ) ; the two survival curves were significantly different ( P = 0 . 003 ) . These results demonstrate that effective protective immunity to Y . pseudotuberculosis requires the presence of CD8+ T cells . CD8+ T cells mediate protective immune responses via secreting cytokines or by directly killing target cells and removing a niche for pathogen intracellular replication . Cytolytic T cells ( CTLs ) can kill target cells via localized secretion of perforin , which inserts into target cell membranes and facilitates entry of other secreted molecules involved in target cell killing such as granzymes and granulysins , interrupting growth of intracellular pathogens in target cells [57] . To address the requirement for perforin in protecting against Y . pseudotuberculosis replication , perforin-deficient ( PKO ) [58] and -sufficient mice were intravenously inoculated with the ksgA− mutant and bacterial colonization in systemic organ sites was examined . PKO mice were highly susceptible to growth of attenuated Y . pseudotuberculosis , with increased bacterial burden as compared to C57BL/6 mice in both spleen ( 1 . 4 log CFU median increase , P = 0 . 0006 ) and liver ( 1 . 5 log CFU median increase , P = 0 . 0003 ) at day 14–15 post-inoculation ( Figure 5A , B ) . These results demonstrate that perforin function is required to eliminate attenuated Y . pseudotuberculosis , and suggest that CD8+ T cells limit bacterial replication by perforin-dependent mechanisms . To determine if perforin plays a role in protective immune responses to Y . pseudotuberculosis , we repeated immunizations of perforin-deficient animals and C57BL/6 mice , extending the time post-inoculation beyond 14 days . Perforin deficiency impaired the immune response even to the attenuated strain , as 3 of the 9 knockout animals succumbed to immunizing strain by 3 weeks post-inoculation , as compared to 1 of 10 control animals . Those animals surviving ksgA− immunization were then subjected to challenge with virulent Y . pseudotuberculosis . Mice intravenously immunized with 102 CFU ksgA− 60 days prior were challenged via the same route with 103 CFU YPIII pIB1 , a dose that is virulent for naïve C57BL/6 animals ( data not shown ) . At seven days post-challenge , perforin-deficient Y . pseudotuberculosis-immune mice demonstrated higher bacterial burden in spleens and livers than control immune C57BL/6 mice ( Figure 5C , D ) ; knockout animals with detectable colonization had significantly higher median CFU values: in the spleen , 5 . 67 versus 4 . 39 for perforin-deficient versus C57BL/6 ( p-value = 0 . 02 ) ; in the liver , 3 . 93 versus 3 . 08 for perforin-deficient versus C57BL/6 mice , p-value = 0 . 01 ) . Thus , perforin-deficient animals immunized with Y . pseudotuberculosis were more susceptible to challenge with fully virulent bacteria than control C57BL/6 mice . The increased susceptibility of perforin-deficient mice both to primary infection with attenuated Y . pseudotuberculosis and challenge infection with virulent bacteria indicates that perforin is required during the initial and memory phases of the immune response . During primary exposure to attenuated Y . pseudotuberculosis , the enhanced susceptibility in perforin-deficient mice as compared to mice lacking CD8+ T cells could result from the absence of perforin in other cell types such as natural killer ( NK ) and NK T cells [39] . Even so , there is no established model for how any of these cells could clear extracellular pathogens in a perforin-dependent fashion . Cell-mediated immune responses are thought to eliminate intracellular pathogens . While members of the Yersinia genus are generally considered to be extracellular pathogens , Y . pestis and Y . pseudotuberculosis have been shown to replicate inside cultured macrophages if the bacteria express low levels of Yops [59] , [60] . This raises the possibility that a reservoir of intracellular bacteria may seed the extracellular bacterial population , allowing Yersinia-specific CTLs to restrict Yersinia colonization by targeting phagocytes with intracellular bacteria . This model predicts that mice with impaired CTL function have a larger fraction of intracellular Yersinia than do wild type mice . To test this hypothesis , we determined the levels of intracellular Y . pseudotuberculosis in mice lacking perforin , β2-microglobulin , or CD8+ T cells , using a previously described “ex vivo” gentamicin protection assay ( [61] see Materials and Methods ) . Mice were intravenously inoculated with Y . pseudotuberculosis , and at the indicated time points post-inoculation , spleen cell suspensions were treated with gentamicin ( Figure 6 ) . As a positive control for intracellular bacteria , animals were also inoculated with S . typhimurium [61] . Spleen cell suspensions from Y . pseudotuberculosis-inoculated C57BL/6 harbored lower numbers of gentamicin-protected bacteria at 3–4 days post-inoculation as compared to animals inoculated with S . typhimurium 5 days prior ( Figure 6A ) . The phoP− YPIII pIB1 strain displayed reduced numbers of intracellular bacteria in spleen suspensions ( Figure 6A ) and in cultured macrophages ( Figure S2 ) as compared to a phoP+ strain ( IP2666 ) , confirming prior observations of the importance of PhoP for intracellular survival replication [62] , [63] . Using the gentamicin protection assay , we then examined the yields of intracellular bacteria in PKO , B2m−/− and control C57BL/6 mice at 10–14 days post-inoculation with the YPIII ksgA− strain , when bacterial burdens were highest in the spleen ( Figures 3 , 5 ) . The ksgA− mutant displayed very low yields of gentamicin protection in spleen cell suspensions from C57BL/6 mice ( Figure 6B ) . The protection efficiencies of bacteria isolated from spleens of animals lacking either β2-microglobulin or perforin were similar to those obtained from control C57BL/6 cell suspensions ( Figure 6B ) . Moreover , when ksgA− immunized mice were depleted of CD8+ T cells during challenge with wild type Y . pseudotuberculosis , the fraction of gentamicin-protected bacteria in spleen cells was no higher than in mock-depleted mice or age-matched naïve mice at day 4 post-challenge ( Figure 6C ) . These observations indicate that mice lacking β2 microglobulin , perforin , or CD8+ T cells do not show increased susceptibility to Y . pseudotuberculosis as a result of an increased burden of intracellular bacteria , but are consistent with a mechanism of cell-mediated immune restriction against Y . pseudotuberculosis that functions to eliminate extracellular bacteria . We next considered the possibility that the predominant locale for Yersinia , extracellular but attached to host cells , allows cytolytic CD8+ T cells ( CTLs ) to directly inhibit bacterial replication , by instructing antigen-presenting cells ( APCs ) to internalize or kill surface-bound Y . pseudotuberculosis . To test this possibility , we established a cell culture system that recapitulates CD8+ T cell cytolysis of APCs harboring Y . pseudotuberculosis . We used a surrogate CTL antigen , as natural bacterial antigens recognized by CD8+ T cells from Yersinia-immune mice have not been identified . Primary macrophages , a target of Yersinia in vivo [19] , were used as APCs and incubated with CrpA63–71 peptide , which is recognized by CTLs derived from Chlamydia-immune mice [64] . Peptide-presenting macrophages were effectively lysed by a CrpA63–71-specific CTL line , regardless of the presence of bacteria ( Figure 7A ) , indicating that Y . pseudotuberculosis did not impair CTL killing of APCs . In contrast , bacteria-associated macrophages lacking peptide showed high viability following T cell exposure , similar to the macrophages exposed to media only . The degree of CTL-induced macrophage death was comparable to that observed with a standard antigen-presenting cell line ( thymic carcinoma cell line EL4 , Figure 7A ) , confirming that macrophages are targeted equally as well as conventional APCs . These results demonstrate that antigen-specific CTLs kill cultured APCs harboring surface-attached Y . pseudotuberculosis . We then examined the consequence of CD8+ T cell targeting upon Y . pseudotuberculosis internalization by the target host cell . APCs were incubated with bacteria , pulsed with peptide and exposed to CTLs , and bacterial internalization by the APCs was assessed using an immunofluorescence protection assay on fixed cells ( see Methods ) . Control incubations of APCs with wild-type Y . pseudotuberculosis or mutant bacteria deficient for translocation of anti-phagocytosis proteins ( T3SS+ or T3SS− , respectively ) demonstrated that wild-type bacteria localize as extracellularly-attached bacteria ( Figure 7B , panels 1–5 , pink bacteria ) , while T3SS− bacteria were found intracellularly ( Figure 7B , panels 6–10 , blue bacteria ) , as has been previously observed [65] . CTL exposure to APCs with associated Y . pseudotuberculosis failed to alter the localization of wild-type bacteria , regardless of whether peptide was added ( Figure 7B , panels 11–15 , no peptide; 16–20 , plus peptide , Figure 7C ) . Altering the CTL exposure time or the CTL-to-APC ratio did not impact bacterial localization , nor did CTL targeting of APCs cause any decrease in viability of surface-attached bacteria ( data not shown ) . Therefore , CTL killing of Y . pseudotuberculosis-associated APCs is insufficient to directly influence bacterial uptake or viability in this cell culture system . We next tested if CTLs could eliminate Y . pseudotuberculosis by targeting APCs and attached bacteria for phagocytosis by activated macrophages . We first tested whether recognition of dead cells by activated phagocytes could be readily demonstrated . Media-treated ( viable ) GFP+ cells resisted engulfment , whereas gliotoxin-treated ( apoptotic ) GFP+ cells were efficiently internalized by GFP− IFNγ-activated macrophages ( Figure 8A , panels 1–3 media , panels 4–6 gliotoxin; Figure 8B ) . Thus , this assay could be used to analyze the role of CTLs in promoting engulfment by activated phagocytes . To this end , GFP+ APCs with attached extracellular bacteria were exposed to CTLs , incubated with nonfluorescent activated macrophages , and uptake of apoptotic corpses and bacteria was quantified . Internalization of bacteria and GFP+ APCs by the activated macrophages required CTL targeting of the APCs . In the absence of CTLs , Y . pseudotuberculosis exposure was insufficient to mark GFP+ APCs for engulfment by neighboring activated macrophages ( Figure 8A , panels 7–9 , Figure 8B ) . Similarly , APCs exposed to both bacteria and CTLs in the absence of peptide resisted engulfment ( Figure 8A , panels 10–12 ) , as only 9 . 2+1 . 8% of GFP+ APCs harboring bacteria were engulfed by activated macrophages if there was no peptide present ( Figure 8B ) . However , GFP+ APCs harboring Y . pseudotuberculosis were phagocytosed at a high frequency , with 55 . 6±3 . 2% of cells internalized , if they were challenged with peptide and targeted by CTLs prior to exposure to the activated macrophages ( Figure 8A , panels 13–15; Figure 8B ) . As CTL targeting enhanced engulfment of GFP+ APCs , we then determined if the bacteria associated with APCs were altered for localization , using the previously described antibody protection assay . CTL targeting of GFP+ APCs significantly enhanced bacterial internalization by activated macrophages ( Figure 8A , panels 13–15 , Figure 8C , third column ) , as compared to the absence of targeting ( Figure 8A , panels 10–12; Figure 8C , second column ) . Phagocytosis of APC-associated Y . pseudotuberculosis after CTL targeting ( Figure 8C , third column ) was comparable to that observed with bacteria lacking the TTSS ( Figure 8D , second column ) . Eliminating the possibility that bystander phagocytosis was limited to those APCs already harboring intracellular bacteria , the non-phagocytosed APCs displayed the same proportions of intracellular bacteria regardless of CTL targeting ( Figure S3 ) . Thus , CTL killing of Y . pseudotuberculosis-associated APCs defeats the anti-phagocytic activities of Yops , and allows bystander phagocytes to co-engulf bacteria associated with dead host cells .
We show here that CD8+ T cells and perforin are involved in protection against Yersinia pseudotuberculosis . We also present evidence that CD8+ T cells target antigen-presenting cells with attached bacteria for bystander phagocytosis . These findings are significant because CD8+ T cells and perforin targeting of APCs are thought to clear intracellular pathogens , while Yersinia species are generally considered to be extracellular [18] . Yersinia express proteins that facilitate tight binding to phagocytes , then inject anti-phagocytic Yops to maintain an extracellular , but attached , locale . We propose a model for how CTLs counteract bacteria in this niche: CD8+ T cells kill Yersinia-associated host cells , thus allowing apoptotic cells to be ingested by neighboring host cells and attached bacteria to be co-engulfed ( Figure 8D ) . This uptake could be a death knell for the bacteria , as it essentially trumps the anti-phagocytic Yops . The observation that CD8+ T cells were activated by attenuated Y . pseudotuberculosis bacteria ( Figure 2 ) suggested that these host cells might protect against Yersinia . Indeed , mice devoid of CD8+ T cells due to the absence of β2-microglobulin ( Figure 3 ) were more susceptible to colonization by the attenuated ksgA− mutant , showing increased bacterial burden in systemic organs regardless of inoculation route . Interestingly , the B2m−/− mice did not display increased bacterial colonization in the Peyer's patches . Given our prior observations that the bacterial populations responsible for seeding systemic organs are derived from those bacteria present in both the intestinal lumen and the Peyer's patches [43] , the increased bacterial burden in the systemic organs of B2m−/− mice suggests that the bottleneck of lumen dissemination has been widened in these mice , such that bacterial populations present in systemic organs should be more representative of those in the lumen , rather than the Peyer's patches . With regards to β2-microglobulin-dependent immune responses , the enhanced susceptibility of B2m−/− mice to diverse pathogens such as Trypanosoma cruzi , Listeria monocytogenes , Chlamydia trachomatis , Mycobacterium tuberculosis and Klebsiella pneumoniae [66]–[70] has been used to demonstrate a requirement for MHC I-restricted T cells in protection against infection . However , at least for M . tuberculosis infection , the defect of B2m−/− mice may be due to iron overload or hemochromatosis [71] , as β2-microglobulin associates with the MHC I family member HFE , which is involved in iron homeostasis [72] . Given that hemochromatosis is a risk factor for disseminated infection by enteric Yersinia in humans [54] , the increased susceptibility of B2m−/− mice to oral inoculation with attenuated Y . pseudotuberculosis ( Figure 3 ) may only partially result from the absence of CD8+ T cells . We did observe that knockout animals inoculated via a parental , i . e . non-oral , route also showed increased bacterial burden in systemic tissues , indicating that the enhanced susceptibility of the knockout mice cannot solely be explained by enhanced intestinal dissemination . It is also possible that the lack of natural killer T cells in B2m−/− mice [73] may contribute to the increased susceptibility to attenuated Y . pseudotuberculosis in this model . To more precisely examine the requirement for CD8+ T cells in protecting against Y . pseudotuberculosis , we tested additional mouse models of CD8 deficiency . The failure of CD8-depleted naïve mice to resist colonization with attenuated Y . pseudotuberculosis or CD8-depleted immune mice to survive virulent challenge ( Figure 4 ) demonstrates that CD8+ cells are required for protective anti-Y . pseudotuberculosis immune responses . Similar observations have been made for Y . pestis , where CD8+ T cells help protect Y . pestis-immune mice from challenge [35] , [36] . The mechanisms used by T cells to protect hosts against challenge with Yersinia are undefined , as tools to probe Yersinia-specific T cell responses are lacking . Yop proteins are obvious candidates for natural antigens recognized by T cells , by virtue of being directly translocated by the bacteria into the host cell cytoplasm , a feature that has been exploited to deliver Yop protein fusions into host cells to stimulate antigen-specific CD4+ and CD8+ T cells [74] , [75] . Additionally , T cells specific to Yops have been isolated from Y . pseudotuberculosis-infected rats [76] or Yop-immunized mice [77] . This indicates that Yops can be processed into MHC I-binding peptides , and that virulence factors comprise a subset of natural antigens . Regardless of antigenic specificity , CTLs likely use multiple mechanisms to eliminate Yersinia . For instance , IFNγ and other CTL-secreted cytokines activate anti-microbial function in phagocytes , allowing them to ingest nearby bacteria [38] . T cell-secreted IFNγ does contribute to protection against Y . pestis [78] and Y . enterocolitica [34] . However , cytokine-mediated bystander action cannot be the sole explanation for restriction of Y . pseudotuberculosis , as cytolytic T cells ( or other perforin-expressing killer cells ) are clearly involved in protection ( Figure 5 ) . PKO mice are known to be defective for clearing intracellular pathogens such as lymphocytic choriomeningitis virus [79] , L . monocytogenes ( [80] , M . tuberculosis [81] and Leishmania amazonensis [82] but our observation that perforin-dependent mechanisms limit Y . pseudotuberculosis is unique , given the bacteria's preferred extracellular locale . We considered the possibility that CD8+ T cells and perforin function to restrict Yersinia in a conventional manner , that is , to eliminate host cells harboring intracellular bacteria . Y . pseudotuberculosis and Y . pestis have been shown to replicate inside cultured macrophages if the bacteria are deficient or repressed for Yop expression [59] , [60] . The importance of intracellular replication for virulence in animal hosts is unclear , and has never been demonstrated . One expectation of this model is that the absence of CTLs results in an increased likelihood of bacteria being found in an intracellular locale . However , we failed to observe an increase in intracellular bacteria in the absence of CTLs or CTL function ( Figure 6B , C ) , suggesting that CD8+ T cells are not functioning in a conventional manner to limit Yersinia replication . On the other hand , this does not eliminate the possibility that during the priming phase of immune responses , antigen-presenting cells harboring intracellular bacteria stimulate Yersinia-specific CD8+ T cell responses . As our experiments are directed toward asking how immunity is controlled by CD8+ cells at times significantly past the priming stage , we cannot rule out the possibility that intracellular bacteria are critical for triggering CD8+ T cell responses in a naïve animal . In an immune animal , another alternative model posits that macrophages harboring intracellular bacteria are recognized and killed by T cells in a perforin-dependent fashion , and this leads to local inflammation that activates bystander phagocytes to clear extracellular bacteria . Arguing against this possibility is the observation that apoptotic cells are rapidly removed after in vivo CTL targeting [83] to prevent the pathogenic inflammation that results when apoptotic cells proceed to secondary necrosis [84] . Given these observations , and evidence showing that Yersinia localizes predominantly extracellularly ( Figure 6 ) [24]–[26] , [61] , we favor a model whereby CTLs and perforin function to protect against extracellular Yersinia . How would killing of host cells with attached extracellular Y . pseudotuberculosis lead to clearance of bacteria ? At least two hypotheses exist . Firstly , perforin targeting of infected host cells may directly eliminate bacteria , either by killing bacteria on the host cell surface or by inducing uptake of attached bacteria . This model is consistent with observations that perforin insertion in host cells induces a wounded membrane response , in which lysosomes exocytose and release lysosomal contents on the cell surface [85] . The T3SS can also induce membrane wounding , which leads to increased uptake of Y . pseudotuberculosis lacking Yops [86] . Despite these observations , we found no evidence that CTL targeting of host cells with TTSS+ surface-bound Y . pseudotuberculosis altered the extracellular localization or viability of the bacteria , even at low CTL-to-host cell ratios or short incubation times to lower the effective dose of perforin ( Figure 7 , data not shown ) . A second hypothesis is that T cell-induced cytolysis of Y . pseudotuberculosis-associated host cells could indirectly eliminate the attached bacteria . In this model , cytolysis marks the target cell for removal by neighboring phagocytes , allowing cell-associated bacteria to be phagocytosed along with the dead cell . This outcome is similar to what is presumed to happen to the intracellular pathogen L . monocytogenes after CTL targeting . T cell cytolysis releases the intracellular bacteria from infected APCs , allowing bystander phagocytes to clear released bacteria and thus prevent the bacteria from spreading to infect nearby cells [87] . While we did not observe that CTL targeting caused Y . pseudotuberculosis to be released from cells ( data not shown ) , we did observe that bacteria associated with CTL-targeted cells were engulfed by bystander phagocytes ( Figure 8A , C ) and presumably destroyed , although limitations of the cell culture model prevented this analysis . Thus , in this model , the propensity of Y . pseudotuberculosis to intimately associate with host cells also renders Yersinia susceptible to phagocytic removal after CTLs kill the bacteria-associated host cells ( Figure 8D ) . Other extracellular pathogens are also restricted by CD8+ T cells . Uropathogenic Escherichia coli ( UPEC ) , a causative agent of urinary tract infections ( UTIs ) , binds tightly to bladder epithelial cells and lives in part extracellularly [88] . It was recently shown that in a mouse UTI model , UPEC colonization of the bladder stimulates CD8+ T cell responses , which contribute to clearance of the pathogen [89] , albeit by an undescribed mechanism . It is tempting to speculate that the mechanism described here , in which extracellularly attached bacteria are phagocytosed as bystanders to APC elimination , may be a common strategy to restrict this class of pathogens . In addition to CTL killing , it is likely that other mechanisms of host cell death restrict Yersinia replication . Interestingly , the YopJ/P protein expressed by Yersinia can induce apoptotic death in macrophages and dendritic cells [90]–[93] . YopJ/P either de-ubiquitinates or acetylates members of the mitogen-activated protein kinase ( MAPK ) and nuclear factor kappa B ( NFκB ) signaling pathways , resulting in suppression of inflammatory cytokine production and induction of cell death in infected phagocytes [94] . Although Yersinia lacking YopJ/P are minimally affected for virulence [95]–[98] , recent work demonstrates that Y . pseudotuberculosis and Y . pestis strains that hypersecrete YopJ or express the more cytotoxic YopP variant cause more death in cultured cells and have lowered virulence in the mouse model [99] , [100] . As enhanced apoptosis is thus restrictive for Yersinia replication , host cell death may represent an important aspect of the anti-Yersinia immune response . There is precedent for host cell death being protective against bacterial infection . S . typhimurium kills phagocytes by pyroptosis , a caspase-1-dependent death mechanism [101] . Caspase-1-deficient mice are more susceptible to S . typhimurium colonization than are wild-type mice [102] , demonstrating that caspase-1-dependent death , or other caspase-1-dependent functions , are required to protect hosts from Salmonella . Furthermore , in a mouse model of Streptococcus pneumoniae lung colonization , inhibition of macrophage apoptosis was shown to increase bacterial burden in the lung and bloodstream , indicating that macrophage cell death mediates bacterial clearance [103] . Our work provides further evidence that eukaryotic cell death , in this case induced by host functions such as perforin , may serve to limit bacterial infection and reduce disease . The relative importance of the combined contributions of host- and pathogen-induced cell death upon Yersinia infection remains to be determined .
B6 . 129-B2mtm1Jae ( β2-microglobulin-deficient ) and age-matched C57BL/6 mice were obtained from Taconic Farms ( Germantown , NY ) , while C57BL/6-Prf1tm1Sdz/J ( perforin-deficient ) , B6 . 129S2-Cd8atm1Mak/J ( CD8−/− ) mice and all other C57BL/6 mice were obtained from The Jackson Laboratory ( Bar Harbor , ME ) . C57BL/6-Tg ( CAG-EGFP ) 1Osb/J ( transgenic EGFP+ ) mice were a kind gift from the laboratory of Dr . Diana Bianchi , Tufts University Medical Center . 8–10 week-old female mice were used for all experiments and were allowed to acclimatize for 5–7 days prior to use . Mice were housed in sterile specific-pathogen-free conditions . For depletion studies , mice were injected intraperitoneally with 200 µg monoclonal antibodies against murine CD8 in 200 µL phosphate-buffered saline ( PBS ) , at days 3 and 1 prior to infection , then days 1 , 4 , 7 , and 10 post-infection; control mice received PBS only [55] . Antibodies against murine CD8 ( Ly-2 . 2 , clone 2 . 43 , American Type Culture Collection ) were purified from hybridoma supernatants ( Dr . Douglas Jefferson , Tufts University Medical Center ) and determined to be endotoxin-free ( data not shown ) . The CD8 deficiency status of the knockout or depleted mice was confirmed by fluorescence-activated cell sorting ( FACS ) analysis of spleen cells stained with mouse antibodies against CD8 ( see Flow Cytometry below ) ; depleted mice routinely displayed a 97–98% reduction in splenic CD8+ T cells , while knockout mice contained no detectable CD8+ cells ( data not shown ) . Two serotype III Y . pseudotuberculosis strain backgrounds were used: YPIII pIB1 [43] and IP2666 [60] , [61]; YPIII pIB1 has a mutation in the phoP gene , while IP2666 encodes a functional allele [62] . YPIII pIB1− and IP2666c , both of which lack the Yersinia virulence plasmid , have been described previously [61] . The ksgA− strain , which harbors a transposon in the kasugamycin resistance gene [40] and the damOP strain , which harbors a kanamycin resistance gene in the dam gene and a plasmid constitutively overexpressing Escherchia coli Dam methylase [48] were derived from YPIII pIB1 . The damOP strain was a kind gift from Dr . Michael Mahan , University of California Santa Barbara , and Salmonella enterica serovar Typhimurium strain SL1344 was kindly provided by Dr . Brad Cookson , University of Washington . For mouse inoculations , Y . pseudotuberculosis strains were grown overnight in L broth at 26°C with aeration , pelleted and resuspended in PBS to the desired concentration . Mice were deprived of food for 18–20 hours and inoculated by oral gavage ( feeding needle no . 7920; Popper & Sons , Inc . , New Hyde Park , NY ) , first with 100 µL 5% sterile sodium bicarbonate , to buffer stomach contents , then with 200 µL of 5×108 colony-forming units ( CFU ) of ksgA− or YPIII pIB1 damOP for immunization studies , or 5×109 CFU of virulent YPIII pIB1 for challenge experiments . Alternatively , mice were inoculated intravenously via the lateral tail vein ( 30½ gauge needle , Becton Dickinson & Co . , Franklin Lakes NJ ) , with 200 µL of 102 CFU ksgA− or 103–104 CFU virulent YPIII pIB1 , IP266 or 102 SL1344 . For survival experiments , animals were sacrificed upon displaying signs of morbidity ( hunched , scruffy fur , lethargy ) and scored as non-surviving . To determine CFU levels in organs , mice were sacrificed by cervical dislocation at the indicated time points , tissues harvested , placed in pre-weighed tubes containing sterile PBS , and weighed to determine tissue weight . Tissues were mechanically homogenized using a tissue homogenizer ( Omni , Marietta GA ) and 100 µL of dilutions of tissue homogenate were plated on L agar plates containing 1 µg/mL of irgasan [104] . After 48 hours incubation at room temperature , CFU were enumerated and normalized to the gram weight of each tissue . As each animal may have an organ weight different from the other animals , the CFU values at or below the limit of detection ( 10 CFU per mL of organ homogenate ) can be different between individual animals . To generate a Y . pseudotuberculosis total antigen preparation for indirect sandwich ELISA [55] , YPIII pIB1 bacteria were grown with aeration in either high calcium medium at 26°C ( Luria broth , 18–20 hours at 26°C ) to inhibit Yop production or low calcium medium at 37°C ( 2× YT broth with 20 mM sodium oxalate and 20 mM MgCl2 , 1 . 5 hours at 26°C , 1 . 5 hours at 37°C ) to induce Yop production , then pelleted , resuspended in PBS and sonicated on ice ( Sonic Dismembrator , Fisher Scientific , Waltham MA ) every other 30 seconds for 5 minutes . Suspensions were cleared of unbroken cells by low-speed centrifugation ( Eppendorf , Westbury NY ) , and concentration determined by bicinchoninic acid assay ( Pierce , Rockford IL ) . Samples prepared from bacteria grown in high or low calcium conditions were pooled to constitute a total Y . pseudotuberculosis antigen preparation and used to coat 96 well flat-bottom plates ( Corning Costar , Lowell MA ) at a concentration of 10 µg/mL . Standards for isotype ELISAs included purified mouse IgA , IgG1 or IgG2a ( BD Biosciences Pharmingen ) , which were coated on plates at 1 µg/mL . After overnight incubation of antigen and standards at 4°C , plates were blocked with 10% fetal calf serum , and mouse serum samples were applied in serial dilution . Sera were prepared by centrifugation ( Microtainer Serum Separator Tube , Becton Dickinson & Co . , Franklin Lakes NJ ) of mouse blood obtained by tail vein incision or cardiac puncture . Bound antibodies were probed with alkaline phosphatase ( AP ) -conjugated goat antisera against “total” ( IgD+IgM+IgG+IgA ) and IgA , IgG1 , and IgG2a isotype mouse antibodies ( SouthernBiotech , Birmingham AL ) , and bound AP detected with BluePhos substrate solution ( KPL , Gaithersburg MD ) at an optical density ( OD ) of 595 nm . Single cell suspensions of Peyer's patches and mesenteric lymph nodes were prepared by gently dissociating the tissue through 70 µm nylon mesh ( Falcon , BD Biosciences Discovery Labware , San Jose CA ) . After removal of red blood cells by PharmLyse , approximately 106 cells were stained in the presence of Fc block ( clone 2 . 4G2 ) with antibodies to murine CD4 ( L3T4 , clone RM4-4 , FITC conjugated ) , CD8 ( Ly-2 , clone 53-6 . 7 , PE conjugated ) and CD69 ( Very Early Activation Antigen , clone H1 . 2F3 , PE-Cy7 conjugated ) . Stained cells were fixed with 2% paraformaldehyde ( Electron Microscopy Services , Hatfield PA ) , and data acquired on a FACSCalibur ( BD Biosciences Immunocytometry Systems , San Diego CA ) . All flow cytometry reagents and antibodies were purchased from BD Biosciences Pharmingen ( San Jose CA ) . Single cell suspensions of spleens from animals exposed to bacteria were prepared by gently dissociating the tissue through 70 µm nylon mesh ( Falcon , BD Biosciences Discovery Labware , San Jose CA ) . The samples were assayed for gentamicin-protected bacteria using two similar protocols . The first protocol was the same as previously described [61]: samples were divided in two 0 . 5-ml aliquots: one aliquot was treated with 100 µg/ml gentamicin while the other aliquot was left untreated . After 2 hour at 37°C in 5% CO2 , the cells were washed three times with PBS , lysed with 100 µl of 1% Triton-X-100 for 5 min followed by the addition of 900 µl PBS , then lysates diluted and plated for colon-forming units to determine the number of total and gentamicin-protected bacteria . The second protocol is similar to the first protocol , except aliquots for CFU determination were removed from the same sample prior to and then after 1 hour gentamicin treatment . The two protocols yielded comparable results with regards to levels of % gentamicin-protected bacteria ( value equal to the number of gentamicin-protected CFU divided by the total number of CFU multiplied by 100 ) . Primary cultures of bone marrow-derived macrophages were established from femurs and tibias of C57BL/6 and EGFP+ mice . Bone marrow cells were grown for 6 days in high-glucose DME medium , supplemented with 20% heat-inactivated fetal calf serum , 10% NIH 3T3-CSF or 30% L-cell conditioned cell supernatant , 2 mM L-glutamine , 1 mM sodium pyruvate , and 55 µM β-mercaptoethanol ( Invitrogen , Carlsbad CA ) . Macrophages were cultured in 60×15 mm or 150×25 mm petri dishes ( Nunc LabTek Fisher Scientific , Rochester NY ) prior to reseeding in tissue culture dishes for individual assays ( below ) . Where indicated , macrophages were activated with 1 ng/mL recombinant mouse IFNγ ( R&D Systems , Minneapolis MN ) for 24 hours or treated with 5 µM gliotoxin ( Calbiochem , San Diego CA ) for 4–6 hours . The CrpA63–71-specific T cell line was derived from Chlamydia trachomatis-immune C57BL/6 mice ( W . P . Loomis and M . N . Starnbach , unpublished ) and was maintained in RPMI 1640 medium supplemented with 10% heat-inactivated fetal calf serum ( Hyclone , Logan UT ) , 5% rat T-STIM with ConA ( IL-2 supplement , BD Biosciences Discovery Labware , San Jose , CA ) , 50 mM methyl-α-D-mannopyranoside ( Calbiochem , San Diego , CA ) , 2 mM L-glutamine , 50 µM β-mercaptoethanol , 100 u/mL penicillin and 100 µg/mL streptomycin ( Invitrogen , Carlsbad CA ) . Every 7 days , T cells were restimulated with irradiated syngeneic splenocytes and irradiated EL4 thymoma cells stably transfected with crpA . Non-transfected EL4 thymoma cells , used as control antigen-presenting cells , were maintained in RPMI 1640 media supplemented exactly as for CrpA-specific CD8+ T cells except for the absence of IL2 supplement; 600 µg/mL geneticin ( Invitrogen , Carlsbad CA ) was used for selection of EL4-crpA transfectants . The protocol is similar to that described previously [60] . C57BL/6 bone-marrow derived macrophages ( day 6 post-harvest/derivation ) were seeded onto glass coverslips ( Fisherbrand , Fisher Scientific , Waltham MA ) in 24 well plates at 2×105 per well and allowed to adhere overnight . Bacteria were grown with aeration in Luria broth for 18–20 hours at 26°C , and PBS-washed samples added to macrophage-containing wells at an estimated multiplicity of infection of 25∶1 . Bacteria were spun onto macrophages at 1000 rpm , then co-cultures incubated at 37°C/5% CO2 for 40 minutes . After washing twice in PBS , cells were overlaid with media containing 100 µg/mL gentamicin ( t = 0 ) and incubated for 1 hour . After washing twice in PBS , cells were overlaid with media containing 5 µg/mL gentamicin ( t = 1 ) and incubated further . At the indicated timepoints , cells were washed twice with PBS and lysed with 0 . 2 mL 1% Triton X-100 . Following the addition of 0 . 8 mL PBS , samples were serially diluted and plated to determine CFU . The % gentamicin bacteria results from the number of gentamicin-protected CFU divided by the total number of CFU multiplied by 100 . Bone-marrow derived macrophages were seeded to 96 well plates at 104 cells per well , allowed to adhere overnight , pulsed with or without 100 nM CrpA63–71 peptide ( Biosynthesis Incorporated , Lewisville TX ) for 1 hour , and then excess peptide was washed away . The thymoma EL4 cells were pulsed with peptide in suspension , then seeded similarly as macrophages . CrpA63–71-specific CD8+ T cells were harvested at five days post-restimulation and added to antigen-presenting cells at the indicated effector∶target cell ratios , co-cultured for 4 hours , after which the plates were centrifuged , supernatant removed and assayed for the presence of the cytoplasmic enzyme lactate dehydrogenase using the Cytotox 96 assay kit ( Promega , Madison WI ) according to the manufacturers' recommendations . % Cytotoxicity was calculated as follows: 100×[ ( experimental release – effector T cell spontaneous release – target cell spontaneous release ) / ( target cell maximum release – target cell spontaneous release ) ] . CTL targeting used two experimental procedures . In the first protocol , C57BL/6 bone-marrow derived macrophages were seeded onto glass coverslips ( Fisherbrand , Fisher Scientific , Waltham MA ) in 24 well plates at 105 per well , allowed to adhere overnight , and then incubated with media containing Y . pseudotuberculosis ( grown in low-calcium medium to induce Yop expression , diluted to give a multiplicity of infection of 10∶1 ) and 100 nM CrpA63–71 peptide . After 1 hour , macrophages were washed three times with PBS to remove unbound bacteria and peptide and CrpA63–71-specific CD8+ T cells ( harvested at five days post-restimulation ) added at an effector∶target cell ratio of 2∶1 . After 15 minutes , the cells were fixed with 4% paraformaldehyde and processed for immunofluorescence microscopy ( below ) . In the second protocol , GFP+ bone marrow derived macrophages , plated in 60×15 mm Petri dishes at approximately 5×106 cells per dish , were challenged with Y . pseudotuberculosis at an MOI of 10∶1 and pulsed with 100 nM CrpA63–71 peptide for 1 hour , then washed three times with PBS . Flow cytometry-sorted CrpA-specific CTLs were added to the infected antigen-presenting cells at an effector∶target cell ratio of 2∶1 and co-cultures incubated for 1 hour . All cells were then harvested and processed for phagocytosis assays ( below ) . Viable CTLs were sorted away from dead irradiated feeder cells based on forward scatter and side scatter differences ( Tufts University Department of Pathology Flow Cytometry Core ) before being added to infected antigen-presenting cells; sorted CTLs were as effective as unsorted CTLs at targeting antigen-presenting cells for LDH release ( data not shown ) . C57BL/6 bone-marrow derived macrophages were seeded onto glass coverslips ( Fisherbrand , Fisher Scientific , Waltham MA ) in 24 well plates at 0 . 5×105 per well in medium containing 1 ng/mL IFNγ and allowed to adhere overnight . Substrate cells were then added to IFNγ-activated macrophages at a ratio of 1∶1 substrate cell∶activated phagocyte and incubated together for 1–1 . 5 hours , after which time the cells were fixed with 4% paraformaldehyde and processed for immunofluorescence microscopy ( below ) . Coverslips with fixed stained cells were mounted with Fluoroguard Antifade Reagent ( Bio-Rad , Hercules , CA ) and individual cells inspected using a Plan-NeoFluar 100×/1 . 3 Ph3 objective on a Zeiss Axioskop microscope ( Carl Zeiss , Thornwood , NY ) or using a Plan-Fluor 100×/1 . 3 Ph3 objective on a Nikon Eclipse TE300 inverted microscope ( Nikon , Tokyo , Japan ) . Images were captured using a Hamamatsu Orca II camera ( Hamamatsu Photonics , Hamamatsu City , Japan ) . For each condition , 3 coverslips and 100 GFP+ substrate cells per coverslip were inspected and GFP+ cells scored for ingestion by non-GFP IFNγ-activated macrophages . % ingestion was calculated as 100× ( ingested GFP+ cells/total GFP+ cells ) . Immunofluorescence-based bacterial uptake and replication assays were performed similarly to those described previously [105] , [106] . After fixation , cells were blocked with 4% goat serum and probed with rabbit polyclonal antibodies against Y . pseudotuberculosis serogroup O3 followed by goat anti-rabbit IgG conjugated to Alexa 594 ( for uptake assays ) or Cascade Blue ( for intracellular replication assays ) ( Invitrogen Molecular Probes , Eugene OR ) . The cells were then permeabilized by immersion in ice-cold methanol for 10 seconds , blocked once more and reprobed with anti-Y . pseudotuberculosis antibodies followed by goat anti-rabbit IgG conjugated to Cascade Blue ( for uptake assays ) or FITC ( for intracellular replication assays ) ( Invitrogen Molecular Probes , Eugene OR ) . Where indicated , for uptake assays , macrophages were stained with rat antibodies against murine CD11b ( Integrin αM chain , clone M1/70 , BD Biosciences Pharmingen , San Jose CA ) followed by goat anti-rat IgG conjugated to FITC ( Zymed , San Francisco CA ) . For both uptake and intracellular replication assays , 3 coverslips and 100 infected host cells per coverslip were inspected by fluorescence microscopy as described above . To determine uptake , cell-associated bacteria scored for localization and % intracellular bacteria was calculated as 100× ( intracellular bacteria/total bacteria ) . To determine intracellular replication , macrophages were scored for the number of intracellular bacteria per phagosome , and the % replicative phagosomes calculated as 100× ( phagosomes with 10+ bacteria/total bacteria-containing phagosomes ) . FlowJo ( Tree Star , Ashland OR ) was used to analyze flow cytometry data . Prism ( GraphPad Software , La Jolla CA ) was used for graphing and statistical analysis . To calculate the half-maximal antibody titer of total anti-Yersinia antibodies , OD595 and log-transformed serial dilution values were fit to a sigmoidal dose-response curve . Survival curves were estimated using the Kaplan Meier method and significance calculated using the log-rank test . The nonparametric Mann-Whitney U test and unpaired Student's t test were used to determine statistical differences between groups of data from animal and tissue culture experiments , respectively . | Pathogenic Yersinia are bacteria that cause diverse diseases such as gastroenteritis and plague . Yersinia binds to specialized immune cells called macrophages , which attempt to engulf and destroy the bacteria . The bacteria resist destruction by injecting proteins called Yops into macrophages , which stops the engulfment process . Yersinia thus survives as attached but extracellular bacteria to cause disease . Yersinia disease can be prevented by immunization . In this study , we identified one mechanism of protective immunity—that host cells called CD8+ T lymphocytes are important to restrict Yersinia infection . This observation is unusual because CD8+ T cells generally protect against intracellular pathogens: T cells destroy the host cell harboring the pathogen , thus preventing the pathogen's replication . We present data consistent with the model that CD8+ T cells can also restrict extracellular bacteria by showing that T cells target host cells with extracellularly attached Yersinia , thus allowing the host cells and associated bacteria to be engulfed and removed by neighboring macrophages . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"infectious",
"diseases/bacterial",
"infections",
"immunology/immunity",
"to",
"infections"
] | 2009 | CD8+ T Cells Restrict Yersinia pseudotuberculosis Infection: Bypass of Anti-Phagocytosis by Targeting Antigen-Presenting Cells |
The microtubule-based motor dynein generates pulling forces for centrosome centration and mitotic spindle positioning in animal cells . How the essential dynein activator dynactin regulates these functions of the motor is incompletely understood . Here , we dissect the role of dynactin's microtubule binding activity , located in the p150 CAP-Gly domain and an adjacent basic patch , in the C . elegans zygote . Analysis of p150 mutants engineered by genome editing suggests that microtubule tip tracking of dynein-dynactin is dispensable for targeting the motor to the cell cortex and for generating robust cortical pulling forces . Instead , mutations in p150's CAP-Gly domain inhibit cytoplasmic pulling forces responsible for centration of centrosomes and attached pronuclei . The centration defects are mimicked by mutations of α-tubulin's C-terminal tyrosine , and both p150 CAP-Gly and tubulin tyrosine mutants decrease the frequency of early endosome transport from the cell periphery towards centrosomes during centration . Our results suggest that p150 GAP-Gly domain binding to tyrosinated microtubules promotes initiation of dynein-mediated organelle transport in the dividing one-cell embryo , and that this function of p150 is critical for generating cytoplasmic pulling forces for centrosome centration .
Cytoplasmic dynein 1 ( dynein ) is the major microtubule ( MT ) minus-end directed motor in animals and transports various cargo from the cell periphery to the cell interior . The motor also moves and positions intracellular structures such as nuclei and centrosomes by pulling on the MTs to which they are connected . To generate pulling force , dynein is either attached to anchor proteins fixed at the cell cortex ( cortical pulling ) [1–4] , or dynein is anchored on organelles in the cytoplasm ( cytoplasmic pulling ) [5 , 6] . In the latter instance , dynein generates MT length-dependent pulling forces by working against viscous drag as it transports organelles along MTs toward centrosomes . Dynactin is an essential multi-subunit activator of dynein that forms a tripartite complex with the motor and cargo-specific adaptors proteins [7–11] , but how dynactin supports the diverse functions of dynein remains incompletely understood . Dynactin is built around a short actin-like Arp1 filament and has its own MT binding activity , which resides at the end of a long projection formed by the largest subunit p150 [12] . p150 has a tandem arrangement of MT binding regions consisting of an N-terminal cytoskeleton-associated protein glycine-rich ( CAP-Gly ) domain and an adjacent patch rich in basic residues [13 , 14] . The CAP-Gly domain binds to MTs and to the MT plus-end tracking proteins ( +TIPs ) CLIP-170 and end-binding ( EB ) protein . In animal cells , +TIP binding of dynactin recruits dynein to growing MT ends [9 , 15–18] . The p150 CAP-Gly domain recognizes the C-terminal EEY/F motif present in α-tubulin and EB/CLIP-170 [19–22] . The C-terminal tyrosine of α-tubulin can be removed and re-ligated in a tyrosination-detyrosination cycle and is proposed to regulate the interactions with molecular motors and other MT binding proteins [23 , 24] . Tubulin tyrosination is required in mouse fibroblasts to localize CAP-Gly proteins , including p150 , to MT plus ends [25] , and recent work in vitro demonstrated that the interaction between p150's CAP-Gly domain and tyrosinated MTs enhances the initiation of processive dynein motility [26] . The functional significance of MT binding by p150 in animals is best understood in neurons . Single point mutations in the CAP-Gly domain cause the ALS-like motor neuron degenerative disease HMN7B and a form of parkinsonism known as Perry syndrome [27–29] . Cellular and in vivo studies addressing the underlying molecular defects revealed that p150 CAP-Gly domain-dependent binding of dynactin to dynamic MTs in the distal axon enhances the recruitment of dynein , which in turn facilitates efficient initiation of retrograde transport [30–32] . While the critical role of p150's CAP-Gly domain in neuronal trafficking is firmly established , little is known about how MT binding by dynactin regulates dynein functions in other cellular contexts . A study in D . melanogaster S2 cells reported multipolar spindles with a p150Glued construct lacking the CAP-Gly domain , suggesting a role in organizing MT arrays [33] . In budding yeast , introduction of the motor neuron disease mutation into p150Nip100 inhibited the initial movement of the spindle and nucleus into the bud neck during mitosis [34] , suggesting that dynactin binding to MTs helps dynein generate pulling forces under load . In budding and fission yeast , dynein is off-loaded to cortical anchors via MTs for subsequent force production , and in budding yeast this requires MT tip tracking of dynein [35–39] . Whether MT tip tracking of dynein plays a role in delivering the motor to the cortex in animal cells remains to be determined . MT binding of dynactin is significantly enhanced by electrostatic interactions between the p150 basic patch and the acidic tails of tubulins [14 , 40 , 41] . In the filamentous fungus A . nidulans , deletion of the basic patch in p150NUDM diminishes the accumulation of dynactin and dynein at MT tips and partially impairs nuclear migration and early endosome distribution [42] . Interestingly , humans express tissue-specific splice isoforms of p150 that lack the basic patch [43 , 44] , but the implications for dynactin function are unclear . In the C . elegans one-cell embryo , dynein and dynactin are essential for centrosome separation , migration of the maternal and paternal pronucleus , centration and rotation of the two pronuclei and the associated centrosomes ( the nucleus-centrosome complex , NCC ) , assembly and asymmetric positioning of the mitotic spindle , chromosome congression , and transversal spindle oscillations in anaphase [45–49] . Here , we use a set of p150dnc-1 and α-tubulin mutants constructed by genome editing to define the role of dynactin's MT binding activity in this system . Our results uncover a functional link between the efficient initiation of dynein-mediated organelle transport , which requires dynactin binding to tyrosinated MTs , and the cytoplasmic pulling forces responsible for centration of centrosomes .
To investigate whether dynactin's MT binding activity contributes to dynein function in the early C . elegans embryo , we first asked whether dynactin is present at MT plus ends at this developmental stage . Live confocal imaging in the central plane of metaphase one-cell embryos co-expressing endogenous GFP::p50DNC-2 and transgene-encoded EBP-2::mKate2 revealed that dynactin travelled on growing MT tips from mitotic spindle poles to the cell cortex ( Fig 1A , S1 Movie ) . Imaging of the cortical plane allowed end-on visualization of MT tips as they arrived at the cortex ( Fig 1B and 1C ) , which facilitated quantification of dynactin levels at plus ends . Measurements of fluorescence intensity revealed the expected positive correlation between GFP::p50DNC-2 and EBP-2::mKate2 levels , but also showed that there is considerable variation in the amount of GFP::p50DNC-2 at MT plus ends ( S1A Fig ) . Cortical residency times for EBP-2::mKate2 and GFP::p50DNC-2 were nearly identical ( 1 . 67 ± 0 . 03 s and 1 . 50 ± 0 . 05 s , respectively ) and agreed with previously published measurements for cortical residency times of GFP::EBP-2 ( S1C and S1D Fig ) [50] . We also generated a dynein heavy chaindhc-1::gfp knock-in allele to assess the localization of endogenous dynein . DHC-1::GFP was readily detectable on growing MT plus ends in early embryos ( S1B Fig , S2A Fig , S5A Fig , S2 Movie ) , although the signal appeared weaker than that of GFP::p50DNC-2 . We conclude that a pool of dynein-dynactin tracks with growing MT plus ends in the early C . elegans embryo . We used the quantitative cortical imaging assay to determine which +TIPs were required for MT tip targeting of dynactin and dynein . RNAi-mediated depletion of the three EB paralogs revealed that EBP-2 is required for GFP::p50DNC-2 targeting to MT tips , while EBP-1 and EBP-3 are dispensable ( Fig 1B and 1D , S2C Fig ) . In mammalian cells , CLIP-170 acts as an essential linker between EB and dynactin [51–53] . To assess whether the CLIP-170-like protein CLIP-1 recruits dynactin to MT tips in C . elegans , we generated a null allele of clip-1 in the gfp::p50dnc-2 background ( S2D Fig ) . This revealed that CLIP-1 is dispensable for MT tip localization of GFP::p50DNC-2 ( Fig 1C and 1D ) , suggesting that dynactin is directly recruited by EBP-2 . Next , we depleted dynein intermediate chainDYCI-1 and the dynein co-factor LIS-1 . In both cases , GFP::p50DNC-2 levels at MT tips decreased substantially ( Fig 1C and 1D ) . Conversely , depletion of p150DNC-1 showed that DHC-1::GFP targeting to MT tips was dependent on dynactin ( S2A and S2B Fig ) . We conclude that in the C . elegans early embryo , dynein and dynactin are interdependent for targeting to growing MT plus ends and require EBP-2 and LIS-1 , but not EBP-1 , EBP-3 , or the CLIP-170 homolog CLIP-1 ( Fig 1E ) . Having established that dynein and dynactin require the EB homolog EBP-2 for targeting to MT tips , we next examined the role of the dynactin subunit p150DNC-1 , whose N-terminal CAP-Gly domain ( residues 1–69 ) mediates binding to EB and MTs ( Fig 2A ) . In addition , p150DNC-1 contains a ~200-residue basic-serine rich region between the CAP-Gly domain and the first coiled-coil ( CC1A ) , which has been proposed to regulate p150DNC-1 association with MTs [54 , 55] ( Fig 2A ) . The highest density of basic residues is found between residues 140–169 ( 30% K or R , pI = 12 . 02 ) . This region is encoded by exon 4 and part of exon 5 , which are subject to alternative splicing ( Fig 2B , S3A Fig ) . This is similar to human p150 , which contains an alternatively-spliced basic patch of 28 residues ( 43% K or R , pI = 12 . 7 ) adjacent to the CAP-Gly domain [43] . We detected four splice isoforms of p150dnc-1 by reverse transcription PCR of RNA isolated from adult animals ( S3B Fig ) : full-length p150dnc-1 including exons 4 and 5 , p150dnc-1 without exon 4 ( Δexon 4 ) , p150dnc-1 without exon 5 ( Δexon 5 ) , and p150dnc-1 lacking exons 4 and 5 ( Δexon 4–5 ) . To define the function of individual splice isoforms , we edited the p150dnc-1 locus to generate animals in which p150dnc-1 expression was restricted to one of the four isoforms ( Fig 2B , S3A Fig ) . Reverse transcription PCR confirmed that animals expressed single p150dnc-1 isoforms corresponding to full length , Δexon 4 , Δexon 5 , or Δexon 4–5 ( S3B Fig ) . All mutant animals were homozygous viable and fertile ( S3C Fig ) , demonstrating that none of the p150DNC-1 isoforms is essential . Despite differences in predicted molecular weight of only a few kDa ( S3A Fig ) , single isoforms expressed in mutant animals were distinguishable by size on immunoblots with an antibody raised against a C-terminal region of p150DNC-1 ( Fig 2C ) . Side-by-side comparison of isoform mutants and wild-type animals on the same immunoblot revealed that neither full-length p150DNC-1 nor p150DNC-1 ( Δexon 4–5 ) is prevalent in wild-type adults ( Fig 2C ) . Instead , immunoblotting , together with reverse transcription PCR data ( S3B and S3D Fig ) , suggested that p150DNC-1 ( Δexon 4 ) is the predominant isoform . Humans express the neuron-specific splice variant p135 , which lacks the entire N-terminal MT binding region [56] . In C . elegans hermaphrodite adults , 302 out of 959 somatic cells are neurons , yet we did not find evidence for a p135dnc-1 isoform at the mRNA level ( S3D Fig ) , nor did our p150DNC-1 antibody detect any protein below ~150 kDa in wild-type animals ( Fig 2C ) . We also generated a p150dnc-1::3xflag knock-in allele , and immunoblotting with antibody against 3xFLAG similarly failed to detect a p135 isoform ( S3E Fig ) . We speculated that specifically suppressing the expression of p150DNC-1 isoforms might facilitate the detection of p135 and engineered a null allele of p150dnc-1 by inserting a stop codon in exon 1 immediately following the start codon ( S3A Fig ) . The null mutation did not affect splicing of p150dnc-1 mRNA ( S3B Fig ) and therefore should permit expression of p135 from an alternative start codon , as is the case in humans [56] . However , immunoblotting produced no evidence of p135 expression in the absence of p150DNC-1 isoforms ( Fig 2C ) . We conclude that C . elegans does not express significant amounts of a p135 isoform . Next , we used cortical imaging of GFP::p50DNC-2 in one-cell embryos to determine the effect of p150DNC-1 isoforms on dynactin recruitment to MT tips . Full-length p150DNC-1 and p150DNC-1 ( Δexon 4 ) fully supported dynactin targeting to MT tips , and dynactin levels were even slightly increased ( 108 ± 4% of controls ) for the Δexon 4 isoform ( Fig 2D ) . By contrast , expression of p150DNC-1 ( Δexon 5 ) or p150DNC-1 ( Δexon 4–5 ) decreased dynactin levels at MT tips to 73 ± 4% and 86 ± 4% of controls , respectively ( Fig 2D ) . Thus , surprisingly , the similarly basic regions encoded by exon 4 ( 27% K/R; pI = 11 . 2 ) and exon 5 ( 19% K/R; pI = 12 ) make differential contributions to dynactin targeting . We conclude that splice isoforms of p150DNC-1 regulate dynactin levels at MT tips . Our analysis of p150DNC-1 isoforms suggested that the basic region had a relatively minor role in targeting dynactin to MT tips . To examine the role of the CAP-Gly domain , we used genome editing to separately introduce three point mutations into p150DNC-1 that compromise CAP-Gly domain function and cause neurodegenerative disease in humans ( Fig 2E ) : G33S corresponds to human G59S , which causes motor neuropathy 7B [27]; G45R corresponds to human G71R , which causes Perry Syndrome [28]; and F26L corresponds to human F52L , which was recently identified in a patient with Perry Syndrome-like symptoms [29] . The F26L and G45R mutants could be propagated as homozygotes with high embryonic viability ( 99 ± 1% and 90 ± 2% , respectively ) , whereas the G33S mutant was lethal in the F2 generation ( 1 ± 1% embryonic viability ) ( S4A Fig ) . Immunoblotting of homozygous F1 adults showed that G33S animals had decreased levels of p150DNC-1 , indicating that the mutation destabilized the protein ( Fig 2F and 2G ) . By contrast , total levels of p150DNC-1 were not affected in the F26L or G45R mutant . Central plane imaging in one-cell embryos expressing GFP::p50DNC-2 showed that dynactin containing the F26L or G45R mutation was present on the mitotic spindle and prometaphase kinetochores but displaced from MT tips ( Fig 2H , S3 Movie ) . Cortical imaging after introduction of the EBP-2::mKate2 marker revealed that GFP::p50DNC-2 levels at MT tips were reduced to 34 ± 4% and 27 ± 4% of controls in the F26L and G45R mutant , respectively ( Fig 2I and 2J , S4 Movie , S5 Movie ) . Deletion of the basic patch encoded by exons 4 and 5 in the G45R mutant ( G45R + Δexon 4–5 ) further reduced GFP::p50DNC-2 levels at MT tips to 15 ± 4% ( Fig 2J ) but had no additive effect on embryonic viability ( 90 ± 2% ) ( S4A Fig ) . Additional quantifications showed that in both the F26L and G45R mutant , GFP::p50DNC-2 still targeted to the nuclear envelope and kinetochores , while GFP::p50DNC-2 levels were reduced on spindle MTs ( S4B Fig ) . We also introduced the mutations into animals expressing DHC-1::GFP , which confirmed that dynein levels were decreased at MT tips and on spindle MTs ( S5B–S5D Fig ) . We conclude that point mutations in the p150DNC-1 CAP-Gly domain that cause human neurodegenerative disease reduce dynein-dynactin levels on MTs and greatly diminish the ability of dynein-dynactin to track with MT tips . Next , we asked whether the p150dnc-1 mutants affected dynein-dynactin function in the one-cell embryo . We crossed the mutants with animals co-expressing GFP::histone H2B and GFP::γ-tubulin , which allowed precise tracking of pronuclei and centrosomes , respectively ( Fig 3A ) . None of the mutants exhibited defects in centrosome separation , and pronuclear migration along the anterior-posterior axis proceeded with normal kinetics until pronuclear meeting , which occurred at the correct position in the posterior half of the embryo ( Fig 3A and 3B , S6A Fig ) . However , subsequent centration of the nucleus-centrosome complex ( NCC ) slowed substantially in p150dnc-1 F26L , G45R , and G45R + Δexon 4–5 mutants , and NCC rotation was defective ( Fig 3A–3D , S6 Movie ) . NCC centration was not significantly perturbed in the isoform mutants ( S6A and S6B Fig ) , but the Δexon 4–5 mutant exhibited defects in NCC rotation ( Fig 3D , S6C Fig ) . In all mutants , spindle orientation recovered during prometaphase , so that the spindle axis was largely aligned with the anterior-posterior axis of the embryo at the time of anaphase onset ( Fig 3D , S6C Fig ) . In controls , the mitotic spindle was displaced from the embryo center toward the posterior in preparation for asymmetric division ( Fig 3C ) . By contrast , spindle assembly in p150dnc-1 CAP-Gly mutants already occurred in the posterior half of the embryo , and the spindle had to be moved only slightly to the posterior to be correctly positioned . In controls , the regular and vigorous oscillations of spindle rocking began at anaphase onset and lasted for approximately 100 s ( Fig 3E , S7 Movie ) . By contrast , spindle rocking in p150dnc-1 CAP-Gly mutants was irregular and significantly dampened . In addition to defects in NCC centration/rotation and spindle rocking , we observed a slight but consistent delay in chromosome congression in p150dnc-1 CAP-Gly mutants , indicating problems with the interactions between chromosomes and spindle MTs ( S7A and S7B Fig , S6 Movie , S7 Movie ) . This did not result in obvious chromosome mis-segregation in the first embryonic division ( S7A Fig ) . However , when the spindle assembly checkpoint ( SAC ) was inactivated by RNA-mediated depletion of Mad1MDF-1 , embryonic viability decreased by 28% and 22% in the G45R and F26L mutant , respectively , whereas Mad1mdf-1 ( RNAi ) in controls decreased embryonic viability by just 6% ( S7C Fig ) . This suggests that SAC signaling is required during embryogenesis to prevent chromosome segregation errors when the p150DNC-1 CAP-Gly domain is compromised . We conclude that mutations in the p150DNC-1 CAP-Gly domain perturb a specific subset of dynein-dynactin functions in the one-cell embryo . Anaphase spindle rocking requires cortical dynein pulling on astral MTs [2 , 57] . Since spindle rocking was affected in p150dnc-1 CAP-Gly mutants , we sought to assess the extent of phenotypic overlap between p150dnc-1 CAP-Gly mutants and inhibition of dynein-dependent cortical pulling . We tracked centrosomes and pronuclei after co-depleting GPR-1 and GPR-2 , which are required for cortical anchoring of dynein-dynactin [2 , 57] . In contrast to p150dnc-1 CAP-Gly mutants , gpr-1/2 ( RNAi ) delayed the initial separation of centrosomes and the onset of pronuclear migration ( Fig 4A–4C ) . Pronuclear migration and NCC centration subsequently occurred at a slightly faster rate than in controls , so that the NCC achieved near-normal centration by nuclear envelope breakdown ( NEBD ) ( Fig 4A and 4C ) . These results are consistent with slowed centrosome separation and faster centering reported after co-depletion of GOA-1 and GPA-16 , the Gα proteins acting upstream of GPR-1/2 [58 , 59] . Thus , the kinetics of pronuclear migration and NCC centration differ between gpr-1/2 ( RNAi ) and p150dnc-1 CAP-Gly mutants . NCC rotation , by contrast , was affected in both perturbations ( Fig 4D ) . Importantly , gpr-1/2 ( RNAi ) in the p150dnc-1 ( G45R ) mutant enhanced the rotation defect of gpr-1/2 ( RNAi ) on its own , arguing that GPR-1/2 and the p150DNC-1 CAP-Gly domain contribute to NCC rotation through parallel pathways . After NEBD , depletion of GPR-1/2 prevented posterior displacement of the spindle and the lack of cortical pulling was especially evident in the track of the posterior centrosome ( Fig 4C ) . In addition , the mitotic spindle was shorter than controls during metaphase and failed to elongate properly in anaphase ( Fig 4B ) . By contrast , posterior centrosome movement towards the cortex in p150dnc-1 CAP-Gly mutants was indistinguishable from controls ( Fig 3C , Fig 4C ) , and spindle length was normal throughout metaphase and anaphase ( Fig 4B ) . These results argue that , although spindle rocking is compromised in p150dnc-1 CAP-Gly mutants , cortical dynein is still able to generate robust pulling forces on astral MTs . We also tracked centrosomes after depletion of EBP-2 , which , just like p150dnc-1 CAP-Gly mutants , delocalized dynein-dynactin from MT tips ( Fig 1B and 1D ) . Strikingly , posterior spindle displacement was exaggerated in ebp-2 ( RNAi ) embryos compared with controls ( Fig 4C ) . These results suggest that cortical pulling forces used for asymmetric spindle positioning can be generated in the absence of MT tip-localized dynein-dynactin . The robust dynein-dependent cortical pulling observed in one-cell embryos depleted of EBP-2 and in embryos of p150dnc-1 CAP-Gly mutants implied that the motor was able to target to the cortex under these conditions . To test this directly , we measured the intensity of the DHC-1::GFP signal in line scans drawn across the cortex in one-cell embryos at anaphase . This revealed a cortically enriched pool of DHC-1::GFP that was dependent on GPR-1/2 , as expected ( Fig 4E ) . Cortical dynein in the one-cell embryo was unaffected in the p150dnc-1 ( G45R ) mutant and after ebp-2 ( RNAi ) . We also imaged the 4-cell embryo , in which dynactin and dynein become prominently enriched at the EMS-P2 cell border prior to EMS and P2 spindle rotation [60 , 61] . Quantification of GFP::p50DNC-2 and DHC-1::GFP levels at the EMS-P2 cell border revealed that cortical levels of dynein-dynactin were unchanged in the p150dnc-1 ( G45R ) mutant and after ebp-2 ( RNAi ) ( Fig 4F ) . We conclude that MT tip tracking of dynein-dynactin is dispensable for cortical targeting of the motor in the early embryo . CAP-Gly domains bind the C-terminal EEY/F motif of α-tubulin , and the tyrosine residue is critical for the interaction ( Fig 2A ) [22] . We therefore asked whether decreased affinity of dynactin for tyrosinated MTs could be contributing to the defects observed in p150dnc-1 CAP-Gly mutants . Of the 9 α-tubulin isoforms in C . elegans , TBA-1 and TBA-2 are the major α-tubulin isotypes expressed during early embryogenesis [62] . We mutated the C-terminal tyrosine of TBA-1 and TBA-2 to alanine and obtained animals homozygous for either mutation alone ( YA ) or both mutations combined ( YA/YA ) ( Fig 5A ) . Immunoblotting of adult animals with the monoclonal antibody YL1/2 , which is specific for tyrosinated tubulin , revealed that levels of tubulin tyrosination were decreased in tba-1 ( YA ) and tba-2 ( YA ) single mutants , with tba-2 ( YA ) having a more pronounced effect ( Fig 5B ) . Combining the two mutations dramatically decreased total levels of tubulin tyrosination . Importantly , immunoblotting with an antibody insensitive to tubulin tyrosination confirmed that total α-tubulin levels were not affected in the three mutants ( Fig 5B ) . We then used immunofluorescence to directly assess tyrosinated tubulin levels in the early embryo . In controls , the mitotic spindle of the one-cell embryo was prominently stained with the antibody against tyrosinated tubulin ( Fig 5C ) . By contrast , the tubulin tyrosination signal was undetectable in the tba-1/2 ( YA/YA ) double mutant , despite normal spindle assembly . Thus , we were able to generate animals without detectable tubulin tyrosination in early embryos . We next addressed the functional significance of tubulin tyrosination in the one-cell embryo . Strikingly , we found that the tba-1/2 ( YA/YA ) mutant exhibited NCC centration/rotation defects reminiscent of those observed in p150dnc-1 CAP-Gly mutants ( Fig 5D–5G , S8 Movie ) . Combining the tba-1/2 ( YA/YA ) mutant with the p150dnc-1 ( G45R ) mutant did not significantly exacerbate the centration/rotation defects of the either mutant on its own , indicating that both mutants act in the same pathway ( Fig 5D–5G , S8 Movie ) . Interestingly , in contrast to p150dnc-1 CAP-Gly mutants , anaphase spindle rocking was not affected in the tba-1/2 ( YA/YA ) mutant ( Fig 5H ) . We also examined the effect of the tba-1/2 ( YA/YA ) mutant on GFP::p50DNC-2 localization and found that dynactin levels at MT tips were identical to controls ( Fig 5I ) . Thus , in contrast to mouse fibroblasts [25] , tubulin tyrosination in the C . elegans embryo is not required to target dynactin to MT tips . Dynein-mediated transport of small organelles along MTs towards centrosomes is proposed to generate the cytoplasmic pulling forces for centration ( the centrosome-organelle mutual pulling model ) [5 , 63 , 64] . To ask whether the centration defects in our mutants correlate with defects in MT minus end-directed organelle transport , we monitored the movement of early endosomes , labelled with mCherry::RAB-5 , from pronuclear meeting until NEBD . Time-lapse sequences recorded in a focal plane that included the NCC were used for semi-automated tracking of early endosomes that moved from the cell periphery towards centrosomes ( Fig 6A ) . In control embryos , we counted 16 . 3 ± 3 . 5 tracks/min during the ~6 min centration interval ( Fig 6B ) . This was reduced to 0 . 8 ± 0 . 6 tracks/min in embryos depleted of p150DNC-1 by RNAi , confirming that dynactin is required for early endosome movement directed towards centrosomes . The p150dnc-1 ( G45R + Δexon 4–5 ) mutant also strongly reduced the number of observed tracks to 5 . 3 ± 1 . 1 tracks/min ( Fig 6B , S9 Movie ) . The tba-1/2 ( YA/YA ) mutant had a less severe effect but still substantially reduced the number of tracks to 10 . 6 ± 2 . 2 per min . We also determined the maximal velocity in each track ( determining the mean speed was complicated by frequent pausing of particles ) and the total track displacement . This revealed only minor differences between controls and either mutant ( Fig 6B ) . We conclude that p150dnc-1 CAP-Gly and α-tubulin tyrosine mutants reduce the frequency with which early endosomes move towards centrosomes during the centration phase . These results are consistent with the idea that dynactin binding to tyrosinated MTs enhances the efficiency of transport initiation by dynein , as recently documented in vitro [26] . In the distal axon of neuronal cells , EB-dependent recruitment of dynactin to dynamic MT plus ends is proposed to ensure efficient initiation of retrograde transport by dynein [32] . To test whether EBP-2 plays a role in the initiation of centrosome-directed organelle transport in the one-cell C . elegans embryo , we tracked early endosomes after ebp-2 ( RNAi ) . The number of early endosome tracks was reduced from 16 . 3 ± 3 . 5 to 6 . 9 ± 1 . 7 per minute after EBP-2 depletion ( Fig 6B ) . Strikingly , ebp-2 ( RNAi ) in the tba-1/2 ( YA/YA ) mutant further reduced the number of early endosome tracks to 2 . 7 ± 1 . 0 per minute ( Fig 6B ) and enhanced the NCC centration defect compared to the individual perturbations ( Fig 6C , S8 Movie ) . This suggests that EBP-2 is able to promote the initiation of dynein-mediated transport from MT tips even in the absence of tubulin tyrosination , consistent with the observation that dynactin targeting to MT tips is unaffected in the tubulin tyrosine mutant ( Fig 5I ) . We conclude that EBP-2 and tubulin tyrosination independently contribute to the initiation of dynein-mediated organelle transport and NCC centration .
Dynactin's MT binding activity is crucial in neurons , as illustrated by single point mutations that compromise the function of the p150 CAP-Gly domain and cause neurodegenerative disease [30 , 31 , 65 , 66] . Here , we introduced these CAP-Gly mutations into C . elegans p150DNC-1 to investigate how dynactin's interaction with MTs and +TIPs contributes to dynein function in early embryogenesis . Together with the analysis of engineered p150dnc-1 splice isoform and tubulin tyrosine mutants , our work provides insight into the regulation and function of MT tip tracking by dynein-dynactin in animals and uncovers a link between dynactin's role in initiating dynein-mediated transport of small organelles and the generation of cytoplasmic pulling forces . Dynein accumulates at and tracks with growing MT plus ends in species ranging from fungi to mammals , but requirements for MT tip tracking differ . In the C . elegans early embryo , MT tip recruitment of dynein-dynactin shares similarity with the pathway in budding yeast ( dynactin depends on dynein and LIS-1 ) and mammalian cells/filamentous fungi ( dynein depends on dynactin ) . Surprisingly , similar to what was reported for the fungus U . maydis [65] , accumulation of dynein-dynactin at MT tips does not require a CLIP-170-like protein in C . elegans . Instead , dynactin is likely directly recruited by EBP-2 , one of the three EB homologs . Work in mouse fibroblasts knocked out for tubulin tyrosine ligase showed that decreased tyrosinated tubulin levels displaced CLIP-170 and p150 from MT tips [25] . By contrast , we show that MT tip targeting of C . elegans dynactin is independent of tubulin tyrosination , possibly because there is no requirement for a CLIP-170 homolog . Overall , our analysis of dynein-dynactin targeting to MT tips in C . elegans highlights the evolutionary plasticity of +TIP networks . Our characterization of engineered p150dnc-1 mutants establishes the functional hierarchy among p150DNC-1's tandem arrangement of MT binding regions: the CAP-Gly domain clearly provides the main activity , while the adjacent basic region plays an auxiliary role . Together with previous work in human cells [43] , our results support the idea that alternative splicing of p150's basic region constitutes a conserved mechanism in animals for fine-tuning dynactin's affinity for MTs . In budding yeast , dynein must first be targeted to MT tips prior to associating with cortical anchors [35 , 67] . Our results indicate that this pathway may not be used in C . elegans , as cortical accumulation of dynein-dynactin in the early embryo was unaffected in the p150dnc-1 ( G45R ) mutant and after depletion of EBP-2 , which displaced the majority of dynactin and dynein from MT tips . In agreement with normal cortical targeting of the motor , dynein-dependent cortical pulling forces remained robust in p150dnc-1 CAP-Gly mutants , although defects in spindle rocking indicate that the p150DNC-1 CAP-Gly domain does contribute to proper cortical force generation in anaphase . Importantly , depletion of EBP-2 even appeared to enhance cortical pulling during posterior spindle displacement . Thus , our results argue that dynein is recruited by its cortical anchors directly from the cytoplasm , and that dynein-dependent cortical pulling is therefore mechanistically uncoupled from prior MT tip tracking of the motor ( Fig 6D ) . Surprisingly , even the p150dnc-1 ( G45R + Δexon 4–5 ) mutant , which shows the most severe reduction in dynactin levels at MT tips ( 15 ± 4% of controls ) , is viable and fertile , suggesting that MT tip tracking of dynein-dynactin is by and large dispensable for development . If not delivery of dynein to the cell cortex via MTs , what is the purpose of dynactin's MT binding activity ? We found that p150dnc-1 CAP-Gly mutants have defects in the centration and rotation of the NCC , which consists of the two centrosomes and the associated female and male pronucleus . Experimental work and biophysical modelling support the idea that centration forces in the one-cell embryo are generated by dynein-mediated cytoplasmic pulling [5 , 63 , 64] , although a centration/rotation model based on cortical pulling has also been proposed [68] . In the cytoplasmic pulling model , dynein works against viscous drag as it transports small organelles ( e . g . endosomes , lysosomes , yolk granules ) along MTs towards centrosomes , which generates pulling forces on MTs that move the NCC . Prior work showed that movements of early endosomes and centrosomes are correlated , and RNAi-mediated depletion of adaptor proteins that tether dynein to early endosomes and lysosomes inhibited centration , indicating that there is a functional link between organelle transport and cytoplasmic pulling forces [5] . In agreement with this idea , the p150dnc-1 ( G45R + Δexon 4–5 ) mutant not only inhibited centration but also significantly decreased the number of early endosomes that displayed directed movement toward centrosomes . This effect on early endosome transport is consistent with the p150 CAP-Gly domain's role in initiating dynein-mediated transport , which is well-established in the context of retrograde axonal transport in neurons [30–32] . Compromising the efficiency with which organelle transport is initiated is predicted to decrease cytoplasmic pulling forces , because the magnitude of the net pulling force acting on centrosomes is proportional to the number of organelles travelling along MTs . The frequency of centrosome-directed early endosome movement was also decreased in the tba-1/2 ( YA/YA ) mutant , which severely reduced the levels of tubulin tyrosination in the early embryo . This fits well with recent work in vitro demonstrating that the interaction between the p150 CAP-Gly domain and tyrosinated MTs enhances the efficiency with which processive motility of dynein-dynactin is initiated [26] . Furthermore , a recent study in neurons provided evidence that initiation of retrograde transport in the distal axon is regulated by tubulin tyrosination [69] . Interestingly , depletion of EBP-2 , both on its own and in the tba-1/2 ( YA/YA ) mutant , also decreased early endosome transport . This suggests that EBP-2 promotes dynein-mediated transport initiation from MT tips , presumably through its interaction with the p150DNC-1 CAP-Gly domain , and that it can do so even in the absence of tyrosinated tubulin . In agreement with this idea , we observed that dynactin was still recruited to MT tips in the tubulin tyrosine mutant . Importantly , in addition to lowering the frequency of early endosome transport , the tba-1/2 ( YA/YA ) mutant also affected centration of the NCC , and depletion of EBP-2 in the tba-1/2 ( YA/YA ) mutant exacerbated the centration defect , as predicted by the centrosome-organelle mutual pulling model ( Fig 6D ) . Why do the p150dnc-1 CAP-Gly and tubulin tyrosine mutants affect centration of the NCC , but not pronuclear migration until pronuclear meeting ? One plausible explanation is that during pronuclear migration the male and female pronuclei , which are large ( ~10 μm diameter ) and equal in size , assist each other's movement as dyneins anchored on the female pronucleus walk along MTs nucleated by the centrosomes attached to the male pronucleus [70] . By contrast , during centration , the two pronuclei must be moved in the same direction , which might render cytoplasmic pulling forces more sensitive to changes in centrosome-directed transport of small ( ~1 μm diameter ) organelles . Finally , our data suggest that MT binding by dynactin contributes to chromosome congression . The effect is unlikely an indirect consequence of the delay in spindle orientation along the A-P axis , as chromosome congression problems were not observed after gpr-1/2 ( RNAi ) , which also causes spindle orientation defects . Likewise , normal chromosome congression after ebp-2 ( RNAi ) suggests that the defect in p150dnc-1 CAP-Gly mutants is not due to delocalization of dynactin from MT tips . Therefore , it is likely that the contribution to chromosome congression comes from the p150DNC-1 CAP-Gly domain pool at kinetochores , where it could aid in the capture of MTs . The decrease in embryonic viability in p150dnc-1 CAP-Gly mutants after inhibition of the SAC indicates that the chromosome congression defects persist in later embryonic divisions . In summary , our work demonstrates that dynactin's MT binding activity is functionally relevant in the context of embryonic cell division . Unlike previous work that addressed p150 CAP-Gly domain function in D . melanogaster S2 cells [33] , we do not observe defects in bipolar spindle formation in p150dnc-1 CAP-Gly mutants . Instead , the most striking consequence of inhibiting p150DNC-1 CAP-Gly function or tubulin tyrosination is defective centrosome centration , which we propose is a consequence of defective initiation of dynein-mediated organelle transport , in agreement with the centrosome-organelle mutual pulling model [5] . The transport initiation function of p150's CAP-Gly domain is likely generally relevant in circumstances where positioning of subcellular structures depends on dynein-mediated cytoplasmic pulling , for example the centration of sperm asters in the large eggs of amphibians and sea urchins [6 , 71–73] .
Worm strains used in this study are listed in S1 Table . Worms were maintained at 16 , 20 or 25°C on standard NGM plates seeded with OP50 bacteria . A Mos1 transposon-based strategy ( MosSCI ) was used to generate strains stably expressing EBP-2::mKate2 and mKate2::EBP-1 [74] . Transgenes were cloned into pCFJ151 for insertion on chromosome II ( ttTi5605 locus ) , and transgene integration was confirmed by PCR . The following alleles were generated by marker-free CRISPR-Cas9-based genome editing , as described previously [75 , 76]: gfp::p50dnc-2 , dynein heavy chaindhc-1::gfp , p150dnc-1 ( F26L ) , p150dnc-1 ( G33S ) , p150dnc-1 ( G45R ) , p150dnc-1 ( exon 4-5-6 fusion ) , p150dnc-1 ( Δexon 4 + exon 5–6 fusion ) , p150dnc-1 ( Δexon 5 + exon 3–4 fusion ) , p150dnc-1 ( Δexon 4–5 ) , p150dnc-1 null , α-tubulintba-1 ( Y454A ) , α-tubulintba-2 ( Y448A ) , CLIP-170clip-1 null , and p150dnc-1::3xflag . Genomic sequences targeted by sgRNAs are listed in S2 Table . The modifications were confirmed by sequencing and strains were outcrossed 6 times with the wild-type N2 strain . Other fluorescent markers were subsequently introduced by mating . The p150dnc-1 ( G33S ) allele and the p150dnc-1 null allele were maintained using the GFP-marked genetic balancer nT1 [qIs51] . Homozygous F1 progeny from balanced heterozygous mothers were identified by the absence of GFP fluorescence . None of the homozygous F1 p150dnc-1 null progeny reached adulthood , and homozygous F2 p150dnc-1 ( G33S ) progeny died during embryogenesis . For production of double-stranded RNA ( dsRNA ) , oligos with tails containing T3 and T7 promoters were used to amplify regions from N2 genomic DNA or cDNA . Primers used for dsRNA production are listed in S3 Table . PCR reactions were cleaned ( NucleoSpin Clean-up , Macherey-Nagel ) and used as templates for T3 and T7 transcription reactions ( MEGAscript , Invitrogen ) . Transcription reactions were cleaned ( NucleoSpin RNA Clean-up , Macherey-Nagel ) and complementary single-stranded RNAs were annealed in soaking buffer ( 3x soaking buffer is 32 . 7 mM Na2HPO4 , 16 . 5 mM KH2PO4 , 6 . 3 mM NaCl , 14 . 1 mM NH4Cl ) . dsRNAs were delivered by injecting L4 hermaphrodites , and animals were processed for live imaging after incubation at 20°C for 24 h or 48 h for partial and penetrant depletions , respectively . An affinity-purified rabbit polyclonal antibody against the N-terminal region of dynein intermediate chainDYCI-1 ( residues 1–177 ) was generated as described previously [77] . In brief , a GST fusion was expressed in E . coli , purified , and injected into rabbits . Serum was affinity purified on a HiTrap N-hydroxysuccinimide column ( GE Healthcare ) against covalently coupled DYCI-11−177 . Antibodies against p150DNC-1 ( GC2 ) and p50DNC-2 ( GC5 ) were described previously [78] . For immunofluorescence of C . elegans embryos , 10–12 adult worms were dissected into 3 μL of M9 buffer ( 86 mM NaCl , 42 mM Na2HPO4 , 22 mM KH2PO4 , 1 mM MgSO4 ) on a poly-L-lysine-coated slide . A 13 mm2 round coverslip was placed on the 3 μl drop , and slides were plunged into liquid nitrogen . After rapid removal of the coverslip ( "freeze-cracking" ) , embryos were fixed in −20°C methanol for 20 min . Embryos were re-hydrated for 2 x 5 min in PBS ( 137 mM NaCl , 2 . 7 mM KCl , 8 . 1 mM Na2HPO4 , and 1 . 47 mM KH2PO4 ) , blocked with AbDil ( PBS with 2% BSA , 0 . 1% Triton X-100 ) in a humid chamber at room temperature for 30 minutes , and incubated with primary antibodies [mouse monoclonal anti-α-tubulin DM1A ( 1:1000 ) and rat monoclonal anti-tyrosinated α-tubulin YL1/2 ( 1:500 ) ] for 2 h at room temperature . After washing for 4 x 5 min in PBS , embryos were incubated with secondary antibodies conjugated with fluorescent dyes [Alexa Fluor 488 goat anti-rat IgG ( 1:1000 ) and Alexa Fluor 568 goat anti-mouse IgG ( 1:1000 ) ; Life Technologies—Molecular Probes] for 1h at room temperature . Embryos were washed for 4 x 5 min in PBS and mounted in Prolong Gold with DAPI stain ( Invitrogen ) . Images were recorded on an inverted Zeiss Axio Observer microscope at 1 x 1 binning with a 100x NA 1 . 46 Plan-Apochromat objective and an Orca Flash 4 . 0 camera ( Hamamatsu ) . Image files were imported into Fiji for further processing . For each condition , 100 worms were collected into 1 mL M9 buffer and washed 3 x with M9 buffer and once with M9 / 0 . 05% Triton X-100 . To 100 μL of worm suspension , 33 μL 4x SDS-PAGE sample buffer [250 mM Tris-HCl , pH 6 . 8 , 30% ( v/v ) glycerol , 8% ( w/v ) SDS , 200 mM DTT and 0 . 04% ( w/v ) bromophenol blue] and ~20 μL of glass beads were added . Samples were incubated for 3 min at 95°C and vortexed for 2 x 5 min . After centrifugation at 20000 x g for 1 min at room temperature , supernatants were collected . Proteins were resolved by 7 . 5% or 10% SDS-PAGE and transferred to 0 . 2 μm nitrocellulose membranes ( Hybond ECL , Amersham Pharmacia Biotech ) . Membranes were rinsed 3 x with TBS ( 50 mM Tris-HCl , pH 7 . 6 , 145 mM NaCl ) , blocked with 5% non-fat dry milk in TBST ( TBS / 0 . 1% Tween 20 ) and probed at 4°C overnight with the following primary antibodies: mouse monoclonal anti-FLAG M2 ( Sigma , 1:1000 ) , mouse monoclonal anti-α-tubulin B512 ( Sigma , 1:5000 ) , rat monoclonal anti-tyrosinated α-tubulin YL1/2 ( Bio-Rad Laboratories , 1:5000 ) , rabbit polyclonal anti-DYCI-1 ( GC1 , 1:1000 ) , rabbit polyclonal anti-DNC-1 ( GC2 , 1:1000 ) , and rabbit polyclonal anti-DNC-2 ( GC5 , 1:5000 ) . Membranes were washed 5 x with TBST , incubated with goat secondary antibodies coupled to HRP ( JacksonImmunoResearch , 1:5000 ) for 1 hour at room temperature , and washed again 3 x with TBST . Proteins were detected by chemiluminescence using Pierce ECL Western Blotting Substrate ( Thermo Scientific ) and X-ray film ( Fuji ) . Total RNA was isolated from adult hermaphrodites using the TRIzol Plus RNA Purification Kit ( Invitrogen ) . After 3 washes with M9 , pelleted worms were homogenized in 200 μL of TRIzol reagent with a pellet pestle homogenizer and incubated at room temperature for 5 min . After addition of 40 μL chloroform , samples were shaken vigorously by hand , incubated at room temperature for 3 min , and centrifuged at 12000 x g for 15 min at 4°C . The upper phase containing the RNA was transferred to an RNase-free tube and an equal volume of 70% ethanol was added . Further RNA purification steps were performed according to the manufacturer's instructions . Purified RNA was treated with DNase I ( Thermo Scientific ) , and cDNA was synthesized with the iScript Select cDNA Synthesis Kit ( Bio-Rad Laboratories ) . The following oligos were used for the PCR reactions in S3B Fig: forward oligo on p150dnc-1 exon 3 ( GAATGTCACCTGCTGCTT ) ; forward oligo on p150dnc-1 exon 4 ( AAAGCGGTCTACAACTCC ) ; reverse oligo on p150dnc-1 exon 5 ( GATTGCGATAAGTTGGAGA ) ; reverse oligo on p150dnc-1 exon 6 ( AGTAGTCGTGGACGCTTT ) . For the SL1 PCR shown in S3D Fig , the following oligos were used: forward oligo on SL1 ( GGTTTAATTACCCAAGTTTGA ) ; reverse oligo on p150dnc-1 exon 6 ( TCCAGTATCATCAATCTTCTT ) . Embryonic viability tests were performed at 20°C . L4 hermaphrodites were grown on NGM plates with OP50 bacteria for 40 h at 20°C , then singled-out to mating plates ( NGM plates with a small amount of OP50 bacteria ) . After 8 h , mothers were removed and the number of hatched and unhatched embryos on each plate was determined 16 h later . Gravid hermaphrodite worms were dissected in a watch glass filled with Egg Salts medium ( 118mM KCl , 3 . 4 mM MgCl2 , 3 . 4 mM CaCl2 , 5 mM HEPES , pH 7 . 4 ) , and embryos were mounted onto a fresh 2% agarose pad . Imaging was performed in rooms kept at 20°C . Embryos co-expressing GFP::histone H2B and GFP::γ-tubulin were imaged on an Axio Observer microscope ( Zeiss ) equipped with an Orca Flash 4 . 0 camera ( Hamamatsu ) , a Colibri . 2 light source , and controlled by ZEN software ( Zeiss ) . Embryos expressing GFP::p50DNC-2 , dynein heavy chainDHC-1::GFP , EBP-2::mKate2 , and mCherry::RAB-5 were imaged on a Nikon Eclipse Ti microscope coupled to an Andor Revolution XD spinning disk confocal system composed of an iXon Ultra 897 CCD camera ( Andor Technology ) , a solid-state laser combiner ( ALC-UVP 350i , Andor Technology ) , and a CSU-X1 confocal scanner ( Yokogawa Electric Corporation ) , controlled by Andor IQ3 software ( Andor Technology ) . All imaging was performed in one-cell embryos unless otherwise indicated . Image analysis was performed using Fiji software ( Image J version 2 . 0 . 0-rc-56/1 . 51h ) . Values in figures and text are reported as mean ± SEM with a 95% confidence interval . Statistical analyses was performed with GraphPad Prism 7 . 0 software . The type of statistical analysis performed is indicated in the figure legends . Differences were considered significant at P values below 0 . 05 . | Animal cells rely on molecular motor proteins to distribute intracellular components and organize their cytoplasmic content . The motor cytoplasmic dynein 1 ( dynein ) uses microtubule filaments as tracks to transport cargo from the cell periphery to the cell center , where the microtubule minus ends are embedded at the centrosome . Conversely , when dynein is anchored at the cell cortex or on organelles in the cytoplasm , the motor can pull on microtubules to position centrosomes within the cell . The intracellular location of centrosomes determines cell geometry and cell fate , and studying the underlying mechanisms will help us understand polarized cell behaviors such as cell migration or neurite outgrowth , and how cleavage plane orientation is established during cell division . Here , we show in C . elegans embryos that dynactin , an essential dynein regulator , uses its microtubule binding activity to help dynein pull on microtubules for centrosome positioning during the first mitotic division . Our results with engineered dynactin and tubulin mutants suggest that microtubule binding by dynactin increases the efficiency with which dynein can initiate the transport of small organelles towards centrosomes . More organelles moving along microtubules through the viscous cytoplasm means that correspondingly larger pulling forces act on centrosomes . Thus , our work provides evidence for a novel functional link between dynactin's role in initiating transport of dynein cargo and the generation of cytoplasmic pulling forces critical for the positioning of centrosomes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"centrosomes",
"vesicles",
"caenorhabditis",
"animals",
"animal",
"models",
"developmental",
"biology",
"caenorhabditis",
"elegans",
"dyneins",
"molecular",
"motors",
"model",
"organisms",
"mathematics",
"statistics",
"(mathematics)",
"experimental",
"organis... | 2017 | Dynactin binding to tyrosinated microtubules promotes centrosome centration in C. elegans by enhancing dynein-mediated organelle transport |
In recent years , two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations . However , the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators . Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past , but it is an open question how far performance can be improved . Here , we report the results of the spikefinder challenge , launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing . We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods . Interestingly , the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models , yet provide highly correlated estimates of the neural activity . The competition shows that benchmark challenges can drive algorithmic developments in neuroscience .
Two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations [1 , 2] . Indeed , the latest advances in scanning technologies make it now possible to record neural activity from hundreds or even thousands of cells simultaneously [3–5] . However , the resulting fluorescence signal is only an indirect measurement of the underlying spiking activity , as it reflects the comparatively slow cellular dynamics of cellular calcium and the fluorescent calcium indicators [6–8] . Thus , to relate large-scale population recordings to the spiking activity of neural circuits we fundamentally require techniques to infer spike rates from the fluorescent traces . Over the past decade , a number of algorithms for solving this problem have been proposed . Many of them assume a forward generative model of the calcium signal and attempt to invert it to infer spike rates . Examples of this approach include deconvolution techniques [9 , 10] , template-matching [4 , 11] and approximate Bayesian inference [6 , 12 , 13] . Such forward models incorporate a priori assumptions about how the measured signal is generated , e . g . about the shape of the calcium fluorescence signal induced by a single spike and the statistics of the noise . In contrast , comparatively few groups have attempted to solve the problem through supervised learning [14 , 15] , where a machine learning algorithm is trained to infer the spike rate from calcium signal using simultaneously recorded spike and calcium data for training . Despite this progress , it is still an open question whether current algorithms already achieve the best possible performance for the task , or whether the observed performance can still be improved upon by algorithmic developments . To answer this question , we organized the spikefinder challenge . This challenge aimed at two goals: ( 1 ) provide a standardized framework to evaluate existing spike inference algorithms on identical data and ( 2 ) catalyze the development of new spike inference algorithms through crowd-sourcing . Such challenges have been used successfully in machine learning , computer vision or physics to drive algorithmic developments [16 , 17] . We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods [15] . Interestingly , the top-performing algorithms are based on a range of principles from deep neural networks to generative models , yet provide highly correlated estimates of the neural activity .
For the spikefinder challenge , we used five benchmark data sets consisting in total of 92 recordings from 73 neurons , acquired in the primary visual cortex and the retina of mice ( see Table 1 ) . In brief , data sets I , II and IV were collected with OGB-1 as a calcium dye , while data sets III and V were collected with the genetically encoded indicator GCamp6s . Similarly , there were differences in scanning method and scan rate between the data sets: For example , data set I was recorded using 3D AOD scanners at very high scan rates [3] , while data set II was recorded using conventional galvo-scanners at fairly low speed . For all data sets , calcium imaging had been performed simultaneously with electrophysiological recordings allowing to evaluate the performance of spike rate inference algorithms on ground truth data [15] . Importantly , all data was acquired at a zoom factor typically used during population imaging experiments , ensuring that all benchmark results reflect performance under the typical use-case conditions . For the challenge , we split the data into a training and a test set , making sure that all recordings from a single neuron were either assigned to the training or the test set . For the training data , we made both the calcium and the spike traces publicly available , but kept the spike traces secret for the test data . Additionally , the publicly available data sets provided by the GENIE project [18] were available as training data . This allowed participants to adjust their models on the training data set , while avoiding overfitting to the specific benchmark data set providing a realistic estimate of the generalization performance . Participants could upload predictions for the spike rate generated by their algorithm on a dedicated website ( see Methods ) and see their performance on the training set during the competition phase . Results on the test set were not accessible to the participants during the competition . The primary evaluation measure for the competition was the Pearson correlation coefficient between the true spike trace and the prediction sampled at 25 Hz ( equivalent to 40 ms time bins ) as previously described [15] . We obtained 37 submissions , from which we selected all algorithms performing better than the spike-triggered-mixture model algorithm ( STM ) , which had previously been shown to outperform other published algorithms on this data [15] . In addition , if there were multiple submissions from the same group , we used the one with the highest correlation on the test set . This resulted in a total of ten algorithms that we studied in greater detail and that are included in this paper . Notebooks and code showing how to run the individual algorithms are available at https://github . com/berenslab/spikefinder_analysis ( see Table 2 ) . While seven of these algorithms were designed specifically for the purpose of the challenge , three were heavily based on methods published previously ( see Table 2 for overview ) . Interestingly , these submissions include algorithms based on very different principles: some of the algorithms built on the classical generative models of spike-induced calcium dynamics [6] , while others relied on purely data-driven training of deep neural networks or pursued hybrid strategies . Algorithms based on generative models of the calcium fluorescence include the MLspike algorithm by Team 1 [12] , which performs efficient Bayesian inference in a biophysical model of measured fluorescence including a drifting baseline and nonlinear calcium to fluorescence conversion ( for a detailed description of each algorithm , see S1 Text ) . Within the same group of algorithms , Team 6 took a decidedly different approach , approximating the calcium fluorescence by an autoregressive process and finding the spike trains by solving a non-negative sparse optimization problem [13 , 19] . A similar approach was taken by Team 7 , who used L0-deconvolution in a linear model of calcium fluorescence with exponential calcium filters . In contrast , many other algorithms took a purely data-driven approach [15] and trained different variants of deep neural networks to learn the relationship between measured spike and calcium traces . For example , the algorithm by Team 2 used a straightforward network architecture with eight convolutional layers with consecutively smaller convolutional filters and one intermediate recurrent LSTM layer . The filters learned in the first layer provide a rich basis set for different spike-calcium relationships ( see S1 Text ) . Similarly , the algorithm by Team 5 used fairly standard components , consisting of convolutional and max-pooling layers . In contrast , the algorithms proposed by Teams 3 , 4 , and 8 combined more involved elements such as residual blocks [20] or inception cells [21] . The key features of the different DNN-based approaches are summarized in Table 3 . The best algorithm increased the average correlation on the test set from 0 . 36 by 0 . 08 to 0 . 44 compared to the STM ( Fig 1A and 1B; Table 4 ) . This corresponds to an increase of more than 40% in variance explained for the best algorithms , similar to the improvement seen between the STM algorithm and f-oopsi ( see Table 4 and ref . [15] ) . For all algorithms , performance varied substantially between data sets with the best results observed on data set I . Interestingly , performance gains were typically larger on GCaMP6 than on OGB-1 data sets ( Fig 1B ) . Surprisingly , the top group of six algorithms performed equally well , despite using very different methodologies . Indeed , when we computed a repeated measures ANOVA , we were not able to distinguish the first six algorithms during post-hoc testing ( Fig 1C ) . In addition , we evaluated to what extent the algorithms overfitted the training data . For example , it is possible that algorithms extracted peculiarities of the training data that did not transfer to the test data , resulting in artificially high correlation coefficients on the training data . We found that most algorithms showed similar performance for both the training and the test set , with evidence for overfitting in some of the DNN-based algorithms ( Fig 1D ) . To explore the generality of our findings , we additionally analyzed the performance of the algorithms at different temporal resolutions and using different evaluation measures . To this end , we computed the average correlation coefficient between the inferred and the true spike rates for bins of 40 , 83 , 167 and 333 ms , respectively ( Fig 2 ) . As expected , the average correlation increased with increasing bin width ( e . g . for algorithm by team 1: 0 . 44 to 0 . 73 ) . Interestingly , the rank of the algorithms was consistent across bin widths . In addition , we evaluated the performance of the algorithm using the AUC and information gain ( Fig 3 , Table 4 , see Methods ) . The AUC measures the accuracy with which the presence of spiking in a given bin is detected , neglecting differences in the number of spikes . The information gain provides a model-based estimate of the amount of information about the spike rate extracted from the calcium trace [15] . The ranking of the algorithms was broadly consistent with the ranking based on correlation , despite minor differences . As the algorithms in the top group used a range of algorithmic strategies , we wondered whether they also made different predictions , e . g . , each capturing certain aspects of the spike-calcium relationship but not others . However , the predictions of the different algorithms were typically very similar with an average pairwise correlation coefficient among the first six algorithm of 0 . 82± . 04 ( mean ± SD , Fig 4 ) . Also , averaging the top six predictions in an ensembling approach did not yield substantially better performance ( c ¯ = 0 . 4436 compared to c ¯ = 0 . 4382 for Team 1 ) . This indicates that despite their different algorithmic strategies , all algorithms captured similar aspects of the spike-fluorescence relationship .
Interestingly , algorithms based on very different approaches yielded very similar performance . For example , algorithms based on generative models such as those by Team 1 and 6 perform on par with—in principle—more flexible deep learning-based approaches . Each algorithm comes with their own advantages and disadvantages regarding speed , interpretability , and incorporation of prior knowledge . For example , training the DNN-based models can be computationally quite costly and their efficient use may require specialized hardware such as GPUs . In practice , when a trained algorithm is applied to infer spike rates , we found all DNN-based method comparably efficient with a run time of less than a second per recording . With supervised methods , care has to be taken when using complex models to avoid overfitting the training set , as this could lead to false confidence about the prediction performance on new data . In fact , we observed quite heavy overfitting for two of the DNN-based approaches ( Fig 1D ) . Nevertheless , supervised spike inference algorithms have been shown to generalize well to new data sets for which no data had been used during training [15] , indicating that adapting supervised algorithms to new settings like indicators with different dynamics should be reasonably straightforward . In contrast , the algorithms based on generative models may be less easily adapted to novel settings as indicator dynamics , saturation or adaption effects and noise properties need to first be accurately assessed—simply swapping the measured calcium transient from isolated spikes may not be sufficient . In addition , inference in such models can be more time consuming as shown by the performance of the MLspike algorithm with an average of 15 seconds per recording . Hybrid approaches such as pursued here by Team 9 or more recently by [22] may offer a way towards combining the respective strengths of both approaches . The spikefinder challenge raises the question of what the actual performance bound of an ideal decoder is . Model simulations can help to answer these questions [8 , 12] , but their interpretation is limited by the accuracy of the model regarding indicator dynamics , noise structure , and other experimental factors [15] . For example , in vitro recordings zooming in on individual neurons will have a different maximal performance than recordings in awake , behaving animals . Of course , the achievable upper bound on performance always depends on the desired temporal resolution ( Fig 2 ) and experimental factors . For example , cells in data set I recorded at very high sampling rates using 3D AOD scanning yielded on average much higher correlation than neurons recorded using the same indicator in the same area with much lower scan rate ( Fig 1A ) . It remains to be seen whether new and larger data sets of simultaneously recorded and imaged neurons will yield further improvements and distinguish more clearly between different algorithmic strategies . It will also be interesting to see whether new indicators will allow for more precise spike rate inference . We also considered the AUC and information gain as alternatives to our primary evaluation measure , the correlation coefficient . While the latter is easy to interpret and more sensitive than the AUC , it is still invariant under global scaling of the predicted spike rate [15] . Although information gain as a model based measured is considered a canonical model comparison criterion for probabilistic predictions [15 , 23] , it can be more difficult to interpret than correlation coefficients or AUC . In general , all three measures yielded similar estimates of the ranking of the algorithms , with the AUC resolving the present differences least . In fact , different metrics can in principle lead to different conclusions about which algorithm is optimal since the metric contains part of the task specification [24] . Metrics for spike rate inference are a matter of current debate in the literature—see for example refs . [5 , 25] for recent proposals . In addition to improving on the state-of-the-art , competitions such as the spikefinder challenge can boost standardization of algorithms , something that has been lacking from neuroscience analysis [26] . For example , several of the processing choices made for this challenge triggered a debate among the submitting teams as to their utility and practicality . For example , we resampled all data to 100 Hz for ease of comparison , which induced problems for some of the submitted algorithms through the properties of the used filter . In addition , most participating teams found it necessary to introduce means of adapting the model parameters to the specific data set . These differences may have been introduced through different preprocessing procedures in the labs that contributed data and even between different scanning methods and speeds within the same lab ( 3D AOD vs . galvo scanning vs . resonant scanning ) . Even greater care should be taken to avoid such confounds in future competitions on this topic . In particular , a future challenge should explicitly address the potential of each algorithm to easily adapt to a data set not previously seen as part of the training set , testing for the transfer learning capabilities of each algorithm . It would also be interesting to explicitly evaluate algorithms for different recording conditions ( e . g . in-vitro vs . awake ) , as the difference in recording conditions could even make different algorithmic strategies optimal . Finally , the challenge was performed on traces extracted from the raw imaging data by averaging all the pixels within manually placed regions-of-interest ( ROIs ) . It is thus possible that the extracted signals contain contamination from the neuropil or were suboptimally placed , a problem tackled by methods that combine ROI placement and calcium-trace extraction in a single algorithm [27 , 28] . However , at least for data with simultaneous imaging and electrophysiological recordings , it is not clear how methods integrating ROI placement and spike rate extraction should be evaluated and compared to regular data , since the recording electrode is always present in the picture , adding a confound to automated ROI extraction through the different image statistics . We believe that quantitative benchmarks are an essential ingredient for progress in the field , providing a reference point for future developments and a common standard with regards to how new algorithms should be evaluated . We strongly believe that many fields of computational neuroscience can benefit from community-based challenges to assess where the field stands and how it should move forward . As for the problem of spike rate inference from two-photon imaging , the spikefinder challenge should not be considered the last word in this matter: More comprehensive data sets and new functional indicators may require organizing another round of community-based development , further pushing the boundaries of what is attainable . Which algorithm to choose ? The answer to that depends on a lot of factors , including performance , desired programming language , envisioned run time and not the least the simplicity of the method—certainly , an algorithm consisting of ten simple lines of code like that by team 10 is more intuitive than a highly nonlinear DNN . The algorithms submitted as part of this challenge offer a range of options regarding these criteria and will provide a solid basis to further advance the field .
The challenge was based on data sets collected for a previous benchmarking effort [15] and the publicly available cai-1 data set from crcns . org [18] . Details about the recording region , scan method , indicators , scan rate and cell numbers are summarized in Table 1 and described in detail in Theis et al . ( 2016 ) . All data was resampled to 100 Hz independent of the original sampling rate . Upon request during the challenge , we made the data available at the native sampling rate . For the challenge , we split the available data into training and test sets ( see Table 1 ) . The training set contained both calcium and spike data , while for the test set , only calcium data was available during the challenge period . We made sure that multiple recordings from individual neurons contained in some data sets were either assigned to the training or the test set . The GENIE datasets were only used as training data , since they are completely publicly available and consist of recordings from individual zoomed-in cells . The data and instructions were available on a dedicated website , based on an open-source web framework ( https://github . com/codeneuro/spikefinder ) . There was a discussion board linked from the website to allow for questions and discussion among participants . Each team could make multiple submissions , but during the challenge period , only results on the training set were shown . The challenge ran from 30/11/2016 to 04/05/2017 . The submitted algorithms are described in detail in the Appendix . For comparison , we used publicly available implementations of the STM algorithm [15] and fast-oopsi [9] . STM parameters were optimized on the entire training set . The evaluation of the submissions was done in Python using Jupyter notebooks . All evaluation functions and notebooks are available at https://github . com/berenslab/spikefinder_analysis . We used the correlation coefficient c between the inferred and the real traces resampled to 25 Hz ( 40 ms time bins ) as primary quality measure . To make the observed increase in correlation more interpretable , we converted it to variance explained r2 and report the improvement in performance as the average increase in variance explained compared to the STM algorithm: 100 · ( < c a l g o 2 c S T M 2 > - 1 ) % Here , <> denotes an average over cells , omitting the dependence of c on cells for clarity . For completeness , we also computed the area under the ROC curve ( AUC ) and the information gain as in ref . [15] . We used the roc_curve function from scikit-learn [29] to compute the AUC for classifying whether or not a spike was present in a given bin . Assuming Poisson statistics , independence of spike counts in different bins , an average firing rate λ and a predicted firing rate of λt at time t , the expected information gain ( in bits per bin ) can be estimated as I g = 1 T ∑ t k t log 2 λ t λ + λ - 1 T ∑ t λ t Since the different algorithms were not necessarily optimized for this model , we transformed the predicted firing rate λt using a piecewise linear monotonically increasing function f optimized to maximize the information gain across all cells [15] . We used the R package afex to compute a repeated measures ANOVA on the correlation coefficients with within-subject factor algorithm and cells as subjects . Pairwise comparisons between algorithms were performed using the lsmeans package with Holm-Bonferroni correction for 66 tests . | Two-photon calcium imaging is one of the major tools to study the activity of large populations of neurons in the brain . In this technique , a fluorescent calcium indicator changes its brightness when a neuron fires an action potential due to an associated increase in intracellular calcium . However , while a number of algorithms have been proposed for estimating spike rates from the measured signal , the problem is far from solved . We organized a public competition using a data set for which ground truth data was available . Participants were given a training set to develop new algorithms , and the performance of the algorithms was evaluated on a hidden test set . Here we report on the results of this competition and discuss the progress made towards better algorithms to infer spiking activity from imaging data . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"machine",
"learning",
"algorithms",
"action",
"potentials",
"medicine",
"and",
"health",
"sciences",
"neural",
"networks",
"applied",
"mathematics",
"membrane",
"potential",
"electrophysiology",
"neuroscience",
"simulation",
"and",
"modeling",
"algorithms",
"mathematics",
... | 2018 | Community-based benchmarking improves spike rate inference from two-photon calcium imaging data |
For many bacteria with sequenced genomes , we do not understand how they synthesize some amino acids . This makes it challenging to reconstruct their metabolism , and has led to speculation that bacteria might be cross-feeding amino acids . We studied heterotrophic bacteria from 10 different genera that grow without added amino acids even though an automated tool predicts that the bacteria have gaps in their amino acid synthesis pathways . Across these bacteria , there were 11 gaps in their amino acid biosynthesis pathways that we could not fill using current knowledge . Using genome-wide mutant fitness data , we identified novel enzymes that fill 9 of the 11 gaps and hence explain the biosynthesis of methionine , threonine , serine , or histidine by bacteria from six genera . We also found that the sulfate-reducing bacterium Desulfovibrio vulgaris synthesizes homocysteine ( which is a precursor to methionine ) by using DUF39 , NIL/ferredoxin , and COG2122 proteins , and that homoserine is not an intermediate in this pathway . Our results suggest that most free-living bacteria can likely make all 20 amino acids and illustrate how high-throughput genetics can uncover previously-unknown amino acid biosynthesis genes .
Although it has been known for decades how the model bacterium Escherichia coli synthesizes all 20 of the standard amino acids , novel pathways for amino acid biosynthesis continue to be discovered in other bacteria and in archaea . For example , unlike E . coli , most bacteria use tRNA-dependent pathways for the biosynthesis of some of these amino acids [1] . More recent discoveries include a novel pathway for methionine biosynthesis in methanogenic archaea and sulfate-reducing bacteria [2–4] and a novel route by which Pelagibacter ubique , which is perhaps the most abundant bacterium on earth , synthesizes glycine from a waste product of photosynthetic organisms [5] . Our incomplete knowledge of these pathways makes it difficult to accurately predict a bacterium’s minimal growth requirements from its genome sequence . Besides saving experimental effort , such predictions would make it possible to understand the ecological role of bacteria that are difficult to cultivate . For example , Mee and colleagues [6] and D’Souza and colleagues [7] both used comparative genomics to predict that most bacteria cannot synthesize all 20 of the standard amino acids . Both groups suggested that most free-living bacteria are reliant on cross-feeding of amino acids that are released by other bacteria . However , neither group tested this experimentally by measuring bacterial growth requirements . In contrast , we recently studied 24 heterotrophic bacteria from 15 different genera [8] and found that all but one of these bacteria grew in defined minimal media with no amino acids present . If we used the automated method that Mee and colleagues relied on [9] and excluded Escherichia coli , then we had predictions for bacteria from 12 genera that grow in defined media . All of these bacteria were incorrectly predicted to be auxotrophic for multiple amino acids . In other words , there were many putative gaps in the biosynthetic pathways—enzymatic steps that are required for biosynthesis and were missing from the genome annotation—but these gaps were misleading . To understand why , we manually examined the predictions for bacteria from 10 different genera . Although the automated method identified a total of 173 gaps , we argue that just 11 of these represent genuine gaps in our biological knowledge . Another gap relates to a recently-discovered and poorly-understood pathway for the biosynthesis of homocysteine ( which is a precursor of methionine ) in sulfate-reducing bacteria and archaea [2–4] . To identify the genes that encode these reactions , we used genome-wide mutant fitness assays , in which a pool of ~40 , 000–500 , 000 randomly-barcoded transposon mutants are grown together and DNA sequencing is used to measure how each mutant’s abundance changes during growth ( RB-TnSeq ) [8 , 10] . Given the genetic data , we looked for genes that were important for fitness in minimal media , but not in rich media , and whose mutants were rescued by the addition of a specific amino acid . Of the 11 genuine gaps , we identified genes to fill nine of them; these novel enzymes explain the biosynthesis of four different amino acids by bacteria from six different genera . And we found that homocysteine synthesis in Desulfovibrio vulgaris Miyazaki F required DUF39 , NIL/ferredoxin , and COG2122 proteins , as expected from studies of this pathway in other organisms [2 , 3 , 11] . Our genetic data imply that , in contrast to all other known pathways , homoserine is not an intermediate in homocysteine synthesis in D . vulgaris .
Mee and colleagues [6] relied on predicted phenotypes from the Integrated Microbial Genomes web site ( https://img . jgi . doe . gov/; [9] ) , while D’Souza and colleagues [7] made their own predictions . We focus our analysis on the IMG predictions because they are publicly available , but the predictions by D’Souza and colleagues have similar issues ( see S1 Text ) . Among 24 heterotrophic bacteria that grow in defined media without added amino acids and for which we have mutant fitness data , we selected one representative of each genus whose genome is present in IMG . We also excluded the traditional model bacterium E . coli . This left us with 12 bacteria , and for each bacterium , IMG incorrectly predicted auxotrophy for at least two amino acids . We examined 10 of these bacteria in more detail . On average , IMG predicts that these bacteria are prototrophic for 9 . 6 amino acids , auxotrophic for 6 . 2 amino acids , and makes no prediction either way for 4 . 2 amino acids ( Fig 1 ) . To verify that these bacteria grow in the absence of any externally provided amino acids , we performed multi-transfer growth experiments for seven of them ( S2 Text ) . We also tested the vitamin requirements of these seven bacteria ( S2 Text ) . Six of the bacteria ( Burkholderia phytofirmans PsJN , Desulfovibrio vulgaris Miyazaki F , Herbaspirillum seropedicae SmR1 , Marinobacter adhaerens HP15 , Phaeobacter inhibens BS107 , and Pseudomonas stutzeri RCH2 ) did not require the addition of any amino acids or vitamins for growth . Sinorhizobium meliloti 1021 did not grow without added vitamins , which is consistent with a previous report that it requires biotin ( but not amino acids ) for growth [12] . For each of the 104 cases in which IMG failed to predict that the bacterium could synthesize the amino acid , we used the IMG website to identify the gaps—the reactions that were necessary for biosynthesis of the amino acid but no gene was predicted ( in IMG ) to encode them . In many cases , a pathway had more than one gap ( more than one reaction that is required to make the amino acid was not associated with a gene ) . Overall , there were a total of 173 gaps in amino acid biosynthesis among the 10 bacteria . To identify candidate genes for the gaps , we began with three standard annotation resources: TIGRFam [13] , KEGG [14] , and SEED/RAST [15] . If a protein was annotated with the putatively missing enzymatic capability by at least two out of three of these resources , then we considered it to be a clear candidate . We found that 140 of the 173 gaps ( 81% ) had clear candidates ( see overview in Table 1 ) . Five of the clear candidates are fused to other biosynthetic enzymes , and these fusions might cause the genes to be missed when searching for bidirectional best hits [9] . In some other cases , the presence of a potential ortholog is noted on the IMG website , but the gene’s annotation was not deemed high-confidence enough to predict that the pathway is present . These ambiguous cases are labeled as “auxotroph” on the IMG website ( as of August 2017 ) and were so considered in the analyses of Mee and colleagues [6] . To test if these 140 clear candidates were actually involved in amino acid biosynthesis , we examined genome-wide mutant fitness data for each of the 10 bacteria across dozens of growth conditions [8] . In general , a gene that is involved in the biosynthesis of amino acids should be important for fitness in most defined minimal media experiments but not during growth in media that contains yeast extract ( which contains all of the standard amino acids ) or casamino acids ( which contains all of the standard amino acids except tryptophan ) . For nine of the gaps , more than one clear candidate was identified in the genome and no strong phenotype was found in the mutant fitness data ( S1 Table ) , which may indicate genetic redundancy . For the remaining 131 steps , we classified 61 genes as auxotrophs because they were important for fitness in most defined media conditions . Some examples are shown in Fig 2 . Note that we define gene fitness in a condition as the log2 change in the abundance of mutants in that gene after growth from an optical density of 0 . 02 to saturation ( usually 4–8 generations ) [10] . Genes that are not important for fitness will have fitness values near zero , and fitness values of under -2 indicate a strong defect in growth . As shown in Fig 2 , these auxotrophs had strong fitness defects if amino acids are absent and had little phenotype ( fitness near zero ) if amino acids were added . Furthermore , in some cases , the gene is expected to be required for the synthesis of just one amino acid , and we measured gene fitness with that amino acid as the sole source of carbon or nitrogen . In these cases , the amino acid rescues the auxotroph , as expected . For example , the bottom right panel in Fig 2 shows that mutants in hisF from Sphingomonas koreensis ( Ga0059261_1048 ) were rescued when L-histidine was present . Overall , the fitness data shows that these 61 gaps were filled correctly . Another 54 of the clear candidates were identified as putatively essential for growth in rich media with added amino acids because few transposon mutants in those genes were recovered [8] . Amino acid biosynthesis genes may be essential in rich media because the bacterium cannot take up the amino acid or because the biosynthetic pathway overlaps with another essential process . For example , in E . coli , dapABDEF are required for both lysine synthesis and peptidoglycan synthesis and are essential for growth in standard rich media such as LB [8 , 16] . In the 10 bacteria under consideration , the clear candidates are much more likely than other proteins to be essential ( 41% versus 9%; [8] ) , which suggests that most of the essential candidates are truly involved in amino acid biosynthesis . Among the remaining clear candidate genes , we had insufficient coverage to quantify mutant fitness [10] for nine ( non-essential ) genes . ( Some genes have few insertions because they are short , or because of random factors in library generation; also , if mutants in a gene grow slowly in rich media , then it may be difficult to study them with a pooled approach . ) Another five of the clear candidates may be genetically redundant with other genes ( S1 Table ) . There were just two clear candidates for which the lack of an auxotrophic phenotype was surprising ( Psest_1986 and DvMF_1902 ) , and we identified a potential explanation for Psest_1986 ( S3 Text ) . Overall , we confirmed 115 of 140 of the clear candidate genes ( 82% ) as being either auxotrophic or essential . Of the remaining 33 gaps , two gaps were due to an error in the genome sequence or in the identification of a protein-coding gene . First , a missing step in Azospirillum brasilense Sp245 is due to a sequencing error that created a frameshift in histidinol dehydrogenase . Nucleotides 1 , 148 , 979 to 1 , 150 , 284 of the main chromosome ( NC_016594 . 1; [17] ) are very similar ( over 80% amino acid identity ) to hisD ( AZL_d03600 ) from Azospirillum sp . B510 , but the reading frame is interrupted by a frameshift . This region lies between AZOBR_RS19500 and _RS19510 and is not currently annotated with any genes . Transposon mutants in this region have reduced fitness in defined media ( data of [8] ) , which suggested that this region of the genome is functional . We sequenced the region on both strands using Sanger sequencing and both reads identified a single nucleotide insertion error at nucleotide 1 , 149 , 827 of the published sequence . Once this error is corrected , there is an open reading frame for the complete hisD gene ( it aligns over its full length and without gaps to AZL_d03600 ) . Second , in the original annotation of A . brasilense , which is used in IMG , there is a pseudogene that is annotated as two parts of shikimate kinase ( AZOBR_40120 , AZOBR_40121 ) . ( Shikimate kinase is required for the biosynthesis of aromatic amino acids . ) In the updated annotation for the same genome sequence in NCBI’s RefSeq , there is instead a protein-coding gene ( AZOBR_RS03225 ) that is annotated as shikimate kinase . The newly predicted protein does not contain any frameshifts ( the error was purely due to gene calling ) . In a mutant library with 105 , 000 different transposon insertions in A . brasilense , there are no insertions within the 636 nucleotides of AZOBR_RS03225 [8] . This suggests that AZOBR_RS03225 is a genuine and essential protein . Of the remaining 31 gaps , 18 were transaminase reactions , which may be nonspecific [18] . For example , IMG lists aromatic amino-acid transaminase as the necessary gene for the final step in the biosynthesis of phenylalanine and tyrosine , but E . coli contains multiple transaminases with overlapping substrate specificities that can perform these steps . E . coli tyrB and aspC contribute to the synthesis of tyrosine and phenylalanine; ilvE contributes to phenylalanine synthesis; and all three of these genes contribute to the synthesis of other amino acids as well , namely aspartate , isoleucine , and valine [19] . IMG predicted that aromatic amino-acid transaminase is missing in eight of the ten bacteria we examined , but all eight of these bacteria contain multiple amino acid transaminase genes . IMG also lists N-succinyldiaminopimelate aminotransferase as required for the synthesis of lysine via succinylated intermediates . In E . coli , this activity is provided by both argD and serC , which also catalyze transamination reactions in the biosynthesis of arginine and serine [18] ( also reviewed in EcoCyc , [20] ) . Similarly , in Azospirillum brasilense Sp245 and in Phaeobacter inhibens BS107 , a putative ornithine transaminase ( argD; AZOBR_RS19025 or PGA1_c24230 ) may provide the missing N-succinyldiaminopimelate aminotransferase activity . This gene is essential in both organisms [8] , which is consistent with our proposal because this step is also required for peptidoglycan synthesis . IMG also failed to predict that two of the bacteria could synthesize alanine because of a missing alanine aminotransferase . E . coli encodes many different transaminases that can form alanine , and many of these also carry out other transamination reactions [21] . For example , one of the major alanine transaminases in E . coli is AvtA , which is also believed to use valine as a substrate [22] . Because many amino acid transaminases have multiple physiological substrates and because their specificity is currently difficult to annotate , the absence of a transaminase should not be used to predict auxotrophy . After removing the transaminase reactions , 13 gaps remained , but two of these gaps had already been filled by experimental studies . First , in Shewanella oneidensis MR-1 , SO3749 is the missing acetylornithine deacetylase for arginine synthesis [23] . Second , the sulfate-reducing bacterium Desulfovibrio vulgaris Miyazaki F contains recently-discovered genes for the biosynthesis of homocysteine , which is a precursor to methionine ( DUF39 , NIL/ferredoxin , and COG2122; [2 , 3 , 11] ) . Unfortunately , the information from these studies has not made its way into the annotation databases . Of the 173 gaps in amino acid biosynthesis from the automated tool , just 11 represented genuine gaps in biological knowledge . For 9 of these 11 gaps , we provide genetic evidence for the genes that provide the missing enzymatic capabilities . In addition , our genetic data for Desulfovibrio vulgaris Miyazaki F provides insights into the recently-discovered pathway for methionine synthesis . We will first describe methionine synthesis in D . vulgaris in more detail and then each of the 9 gaps that we filled using high-throughput genetics data . We recently found that a DUF39 protein ( DUF is short for domain of unknown function ) is required for homocysteine formation in Desulfovibrio alaskensis [3] . A genetic study in the methanogen Methanosarcina acetivorans [2] also found that a DUF39 protein ( MA1821 ) is involved in homocysteine synthesis , along with a protein containing NIL and ferredoxin domains ( MA1822 ) . ( The NIL domain is named after a conserved subsequence and the PFam curators suggest that it might be a substrate binding domain . ) Biochemical studies of cell extracts suggest that in methanogens , homocysteine is formed by reductive sulfur transfer to aspartate semialdehyde , and this process requires the DUF39 and/or the NIL/ferredoxin proteins [4] . In contrast , in the well-characterized pathways for methionine synthesis , aspartate semialdehyde is converted to homocysteine via multiple steps: aspartate semialdehyde is first reduced to homoserine by homoserine dehydrogenase; then the alcohol group is activated by acetylation , succinylation , or phosphorylation; and finally the sulfide is transferred to form homocysteine . The molecular functions of the DUF39 and NIL/ferredoxin proteins are not known , but a conserved cysteine in the DUF39 protein MA1821 is modified to a persulfide in vivo , which suggests that it is involved in the transfer of a sulfur atom to aspartate semialdehyde [24] . Rauch and colleagues [24] also propose that DUF39 might produce a thioaldehyde intermediate which could be reduced by the NIL/ferredoxin protein to yield homocysteine . The genome of Desulfovibrio vulgaris Miyazaki encodes DUF39 and NIL/ferredoxin proteins and does not appear to encode any of the well-characterized pathways for methionine biosynthesis , so we expected that it would synthesize methionine by the recently discovered pathway . Indeed , we found that the DUF39 protein ( DvMF_1464 ) and the NIL/ferredoxin protein ( DvMF_0262 ) were important for growth in minimal media and that mutants in these genes were rescued by added methionine ( Fig 3 ) . As far as we know , this is the first experimental evidence that the NIL/ferredoxin protein is required for methionine synthesis in Desulfovibrio . We also found that homoserine dehydrogenase ( DvMF_1412 ) , which is required for homoserine synthesis , was important for fitness in minimal media ( Fig 3 ) . This was expected because homoserine is an intermediate in the biosynthesis of threonine as well as in the standard pathway of homocysteine biosynthesis . Furthermore , supplementing the medium with threonine alone was sufficient to rescue the homoserine dehydrogenase mutants ( Fig 3 ) , which implies that homoserine dehydrogenase is not required for methionine synthesis . This confirms that homoserine is not an intermediate in the DUF39 pathway for homocysteine synthesis , as previously suggested based on a biochemical study of cell extracts from methanogens [4] . Comparative genomics analyses had also suggested that COG2122 might be involved in the this pathway [2 , 3] . ( COG is short for clusters of orthologous groups . ) COG2122 is distantly related to the flavin transferase ApbE [25 , 26] , but the homology does not seem to extend to the flavin binding region of ApbE [11] . Rauch and colleagues [24] instead suggested that COG2122 might be involved in the persulfide modifications of DUF39 and also of O-phosphoseryl-tRNA-cysteinyl-tRNA synthase ( SepCysS ) , which is required for cysteine synthesis in M . acetivorans ( but is not present in D . vulgaris ) . We found that the COG2122 protein in D . vulgaris ( DvMF_0044 ) was important for growth in defined media and that mutants were rescued by added methionine ( Fig 3 ) . We also found that , across a variety of growth conditions , the fitness of the COG2122 protein was virtually identical to that of the DUF39 protein ( r = 0 . 98 across 170 fitness experiments ) . This strongly suggests that the COG2122 protein is also required for homocysteine formation . In contrast , Rauch and colleagues [11] found that the orthologous protein from Methanosarcina acetivorans ( MA1715 ) was important for growth in defined media at low sulfide concentrations , but that mutants could be rescued by either higher sulfide concentrations or by added cysteine or homocysteine . Because we grew D . vulgaris with sulfate as the electron acceptor ( which is reduced to sulfide ) , and because we added 1 mM sulfide to the media as a reductant , it seemed implausible that COG2122 would be important in D . vulgaris because of low sulfide concentrations . Nevertheless , we performed a fitness experiment with added D , L-cysteine . We found that in D . vulgaris , mutants of all three homocysteine biosynthesis genes were partially rescued by added cysteine ( Fig 3; mean fitness = -1 . 0 ) . This suggests that D . vulgaris might have a minor alternate route to homocysteine , perhaps via a putative cystathionine β-lyase ( DvMF_1822 ) . In any case , our data confirm that COG2122 is involved in the conversion of aspartate semi-aldehyde and sulfide to homocysteine; its apparent dispensability for homocysteine formation in M . acetivorans under some conditions might be due to genetic redundancy . In summary , we identified three genes in D . vulgaris that are required for homocysteine synthesis , as expected from previous studies of another species of Desulfovibrio and of methanogens . We provided genetic evidence that homoserine is not an intermediate in this pathway and that COG2122 is required . IMG predicts that D . vulgaris is a threonine auxotroph because of a missing homoserine kinase . We identified mutants in three genes as being rescued by added threonine: threonine synthase ( DvMF_1945 ) , homoserine dehydrogenase ( DvMF_1412 ) , and DvMF_0971 , which was originally annotated as a shikimate kinase ( Fig 3 ) . This observation suggests that DvMF_0971 might instead be a homoserine kinase , as both reactions involve the phosphorylation of an alcohol group . Indeed , the genome encodes another shikimate kinase ( DvMF_1410 ) which appears to be essential , as are other genes in the chorismate synthesis pathway ( DvMF_1750 , DvMF_0373 , DvMF_0962 , DvMF_1748 , and DvMF_1408 ) . Furthermore , DvMF_1410 is similar to the shikimate kinase II from E . coli ( 47% identical ) , while DvMF_0971 is more distantly related ( 27% identical ) . To test our prediction that DvMF_0971 is a homoserine kinase , we cloned it and transformed it into a thrB- strain of E . coli from the Keio deletion collection [27] . This strain of E . coli does not grow in minimal media due to a lack of homoserine kinase activity , and its growth was rescued by the expression of DvMF_0971 . Thus , DvMF_0971 is the homoserine kinase of D . vulgaris . The IMG website makes no prediction as to whether Phaeobacter inhibens DSM 17395 ( formerly P . gallaeciensis; also known as strain BS107 ) can synthesize methionine because it was unable to identify a gene for methionine synthase . We propose that P . inhibens contains a vitamin B12-dependent methionine synthase that is split into three genes ( Fig 4A ) . For comparison , in E . coli , the vitamin B12-dependent methionine synthase ( MetH ) contains a methyltransferase domain ( PF02574 ) , a pterin binding domain ( PF00809 ) , a vitamin B12 binding cap ( PF02607 ) , and a vitamin B12 binding domain ( PF02310 ) . In P . inhibens , PGA1_c13370 has the methyltransferase domain , PGA1_c16040 has the pterin-binding domain , and MtbC ( PGA1_c13350 ) was originally annotated as "putative dimethylamine corrinoid protein" and contains the vitamin B12-binding cap and vitamin B12-binding domains . Thole and colleagues [28] previously suggested that two of these proteins might be involved in methionine synthesis . We found that all three of these genes were important for growth in defined media , and their mutants were rescued by the addition of methionine or of casamino acids ( Fig 4B ) . Furthermore , these genes had similar phenotypes across 270 diverse fitness experiments ( all r > 0 . 7 , Pearson correlations ) . This confirms that they work together to provide the missing methionine synthase activity . The activity of vitamin B12-dependent methionine synthase also requires the “reactivation” of vitamin B12 to reduce co ( II ) balamin , which can form as a side reaction of this enzyme , to co ( I ) balamin . In E . coli , the reactivation of B12 is provided by yet another domain ( PF02965 ) at the C terminus of the MetH protein , but in other bacteria this can be a separate protein . However no member of PF02965 was found in P . inhibens or in related bacteria such as Dinoroseobacter shibae . Thole and colleagues [28] proposed that PGA1_c13360 , which contains a radical SAM domain , might be involved in B12 activation , but we found that this gene was not important for growth in minimal media ( all fitness values were within -0 . 5 to +0 . 5 ) . Instead , we identified two other genes that had correlated fitness with the other methionine synthase genes and are likely to be involved in B12 reactivation: a protein with ferredoxin and DUF4445 domains ( PGA1_c15200 ) and a DUF1638 protein ( PGA1_c13340 ) . As shown in Fig 4B , mutants in these genes are rescued by added methionine . The DUF4445 protein is distantly related to RamA , which uses ATP to drive the reductive activation of corrinoids in methanogens [30] . Indeed , Ferguson and colleagues [30] predicted that bacterial homologs of RamA would be involved in vitamin B12 reactivation , and we previously proposed that in Desulfovibrio alaskensis , a RamA-like protein ( Dde_2711 ) would be involved in B12 reactivation because it is cofit with MetH ( r = 0 . 90; [3] ) . We also found evidence that DUF4445 is involved in the reactivation of B12 in D . vulgaris , which encodes a B12-dependent methionine synthase ( DvMF_0476 ) that lacks the standard B12 activation domain . This methionine synthase has a very similar fitness pattern as DvMF_1398 , which contains two DUF4445 domains ( r = 0 . 92 across 170 experiments; also see Fig 3 ) . We infer that DUF4445 proteins perform the reactivation of vitamin B12 in diverse bacteria . We do not have a specific proposal for the function of the DUF1638 protein . Although it is adjacent to the gene that encodes the vitamin B12-binding cap and vitamin B12-binding domains , the DUF1638 protein is downstream and at the end of the operon , so its phenotype is unlikely to be due to polar effects . We thought that DUF1638 could be involved in the synthesis of vitamin B12 rather than in B12 reactivation per se , but unlike the DUF1638 protein , the genes in the vitamin B12 synthesis pathway have few insertions and are probably essential in P . inhibens . ( The CobIGJMKFLHBNSTQDPV proteins are all essential , as is one of two CobO-like proteins . Vitamin B12 synthesis may be essential , even when methionine is provided , because of a vitamin B12-dependent ribonucleotide reductase . ) Across the α-Proteobacteria , the presence or absence of DUF1638 is nearly identical to that of the RamA-like protein ( Fig 4C ) , which is consistent with a close functional relationship . One unexplained aspect of this distribution is that some of the bacteria with the RamA-like and DUF1638 proteins also encode MetH with a standard reactivation domain ( i . e . , Sinorhizobium meliloti 1021 ) . Overall , we identified five genes that are involved in methionine synthesis in P . inhibens: three pieces of methionine synthase and two proteins for the reactivation of vitamin B12 , including a RamA-like protein that is also involved in vitamin B12 reactivation in Desulfovibrio . In Burkholderia phytofirmans and in Herbaspirillum seropedicae , we were unable to find the standard D-phosphoglycerate dehydrogenase ( serA ) , which catalyzes the first step in serine biosynthesis . We propose that another oxidase provides this missing activity: BPHYT_RS03150 in B . phytofirmans or HSERO_RS19500 in H . seropedicae . These two proteins are very similar ( 76% amino acid identity ) and both were originally annotated as “FAD-linked oxidase . ” They contain an N-terminal DUF3683 domain , FAD-binding and FAD oxidase domains , a 4Fe-4S dicluster domain , two cysteine-rich CCG domains ( which are often associated with redox proteins ) , and a C-terminal DUF3400 domain . In B . phytofirmans , this oxidase was important for fitness in most defined media , but not in rich media ( LB ) , or when casamino acids were added , or when L-serine was the nitrogen source ( Fig 5A ) . If we supplemented our standard glucose/ammonia minimal media with L-serine , then mutants in this gene were partially rescued , as were mutants in the gene for the next step in serine synthesis ( phosphoserine transaminase or serC; Fig 5B ) . B . phytofirmans may preferentially uptake ammonia instead of serine , which could explain why the two serine synthesis genes are only partially rescued by added serine if ammonia is present . The gene for the final step in serine synthesis ( phosphoserine phosphatase or serC , BPHYT_RS09200 ) may be important for fitness even in rich media , as mutant strains were at low abundance in our pool of mutants . In H . seropedicae , this oxidase appears to be essential in rich media with amino acids . Mutants in the phosphoserine transaminase ( HSERO_RS18435 ) and the phosphoserine phosphatase ( HSERO_RS03150 ) were also at low abundance in our mutant pool , so the poor viability of mutants in HSERO_RS19500 is consistent with a role in serine synthesis . To look for other candidates for this step , we collected fitness data for H . seropedicae in minimal media with and without added L-serine , but we did not identify any genes whose mutants were rescued by added L-serine . ( Averaging across two replicate experiments , there were no genes with fitness under -2 in minimal glucose media and fitness above -1 in minimal glucose media that was supplemented with 1 mM L-serine . ) We predict that in both bacteria , the phosphoglycerate dehydrogenase activity is provided by the FAD-linked oxidase that has additional DUF3683 , CCG , and DUF3400 domains . However , neither BPHYT_RS03150 nor HSERO_RS19500 complemented the growth deficiency of a serA- strain of E . coli from the Keio collection in minimal media [27] . The FAD-linked oxidase might require another cofactor or protein for activity , or it might have some other unexpected role in serine synthesis . Both organisms contain genes for both of the other steps in serine synthesis ( the phosphoserine transaminase serC and the phosphoserine phosphatase serB ) , and these genes are either essential or their mutants are auxotrophic , so we do not expect the FAD-linked oxidase to be involved in these other steps . We propose that in four of the 10 bacteria , genes that were originally annotated as phosphoserine phosphatases provide the missing histidinol-phosphate phosphatase activities . Phosphoserine and histidinol phosphate are both of the form R-C ( NH3+ ) -CH2OPO32- , so this is biochemically plausible . These genes are: BPHYT_RS03625 from Burkholderia phytofirmans; Psest_3864 from Pseudomonas stutzeri RCH2; HP15_461 from Marinobacter adhaerens HP15; and HSERO_RS03150 from Herbaspirillum seropedicae SmR1 . In three of the four bacteria , this gene was important for fitness in minimal media but not in minimal media that was supplemented with histidine ( Fig 6A , 6B and 6C ) . In H . seropedicae , mutants in HSERO_RS03150 are at low abundance in our pools , so we do not have fitness data for it . The genes in H . seropedicae whose mutants were rescued by added histidine are all annotated as performing other steps in histidine biosynthesis ( Fig 6D ) , so our data does not suggest another candidate for this step . The four putative histidinol-phosphate phosphatases are all similar to each other ( ≥45% amino acid identity ) , so the results from the various bacteria corroborate each other . We also note that each of these bacteria contain another gene that is annotated as phosphoserine phosphatase ( BPHYT_RS09200 , Psest_0489 , HP15_2518 , and HSERO_RS15175 ) . We believe that these genes are correctly annotated , but we only have fitness data for one of them . We found that Psest_0489 from P . stutzeri is important for fitness in most but not all defined media conditions . It is possible that another enzyme in this organism also has phosphoserine phosphatase activity: Psest_2327 is 89% identical to ThrH from P . aeruginosa , which has this activity [31] . If so , this would explain why Psest_0489 is not important for fitness in some defined media conditions with no serine added . The mutant fitness data for D . vulgaris did not identify a candidate for phosphoribosyl-ATP diphosphatase , which is required for histidine biosynthesis . By sequence analysis , we identified DvMF_3078 as a candidate , but we did not have fitness data for this gene . DvMF_3078 is related to MazG ( nucleotide pyrophosphatase ) , which performs a similar reaction . ( In both reactions , a nucleotide 5’-triphosphate is converted to a nucleotide 5’-monophosphate . ) The ortholog of DvMF_3078 in D . alaskensis G20 ( Dde_2453 ) is important for fitness in minimal media and its fitness pattern is most similar to that of hisI ( r = 0 . 96; data of [3 , 32] ) . These observations suggested that DvMF_3078 encodes the missing phosphoribosyl-ATP diphosphatase . To test this hypothesis , we studied a transposon mutant of the orthologous gene from D . vulgaris Hildenborough ( DVU1186 ) . We found that a transposon mutant of DVU1186 ( strain GZ8414 ) grew little if at all in minimal media and that its growth was rescued by the addition of 0 . 1 mM L-histidine ( Fig 7 ) . As a control , we also tested a transposon mutant in DVU2938 ( a DUF39 protein which is involved in methionine synthesis ) and found that it was unable to grow in defined media even if histidine was added ( Fig 7 ) . Thus , in the genus Desulfovibrio , a MazG family protein is required for histidine biosynthesis and is probably the missing gene for phosphoribosyl-ATP diphosphatase . It is interesting to note that the MazG family is distantly related to HisE , which provides the phosphoribosyl-ATP diphosphatase activity in most bacteria [33] . Two of the 11 genuine gaps remain unfilled . First , we did not identify the phosphoserine phosphatase in D . vulgaris . We thought that this activity might be provided by DvMF_0940 , which is annotated as histidinol-phosphate phosphatase and could be bifunctional . We also thought that a putative phosphatase ( DvMF_1903 ) that lies downstream of the phosphoglycerate dehydrogenase ( which is the previous step in serine synthesis ) was a plausible candidate . Unfortunately , we do not have fitness data for either of these genes . We cloned each of these proteins into an E . coli serB- strain from the Keio collection , which lacks phosphoserine phosphatase , and neither of the proteins from D . vulgaris were able to rescue its growth in minimal media . As another test , we constructed a deletion of DVU0338 from D . vulgaris Hildenborough , which is similar to the putative phosphatase DvMF_1903 ( 77% amino acid identity ) . We found that the DVU0338- strain could grow in defined media . We suspect that some other protein provides the phosphoserine phosphatase activity in D . vulgaris . Second , the fitness data did not identify a candidate for homoserine kinase in Phaeobacter inhibens . We thought that the shikimate kinase ( PGA1_c14090 ) , which is essential , might be bifunctional and act on homoserine as well . However , when we cloned this protein into an E . coli thrB- strain from the Keio collection , which lacks homoserine kinase , it did not rescue growth in minimal media .
Although amino acid biosynthesis is well understood in model organisms such as Escherichia coli , our results imply that there are many steps that remain unknown , even in the relatively well-studied Proteobacteria . Once genome-wide fitness data from more diverse bacteria is available , we hope to explain many more mysteries . For example , at least one more pathway of homocysteine synthesis remains to be discovered: thermophilic autotrophs from several divisions of prokaryotes ( i . e . , Aquifex aeolicus , Pyrolobus fumarii 1A , and Acidimicrobium ferrooxidans DSM 10331 ) contain neither the traditional nor the DUF39 pathways of homocysteine biosynthesis , nor the protein thiocarboxylate pathway [34] . It also appears that novel homoserine kinases remain be discovered , as some bacteria that grow in minimal media encode threonine synthase but not any of the known homoserine kinases ( i . e . , Bacteroides thetaiotaomicron VPI-5482 [35] and Dehalococcoides ethenogenes 915 [36] ) . Given our limited knowledge , it may be premature to try to predict whether bacteria can synthesize amino acids from their genome sequences . We did identify some recurring types of gaps that should not be used to predict auxotrophy . If we exclude the gaps from IMG that had clear candidates from other annotation resources or were due to errors in gene models , then there were 31 gaps , of which 18 were transaminases , 5 were phosphatases , and 3 were kinases . We cannot determine if this information would lead to better predictions of auxotrophies , as we only studied prototrophic bacteria . If the growth requirements were known for large numbers of bacteria along with their genome sequences , it should become possible to make useful predictions . It would also help if there were a complete database of proteins that have already been experimentally characterized , as this could eliminate the “known” gaps . A related issue is that variant pathways are often not represented in pathway databases [37] . For example , as of November 2017 , the DUF39 and the protein thiocarboxylate pathways of methionine synthesis are absent from MetaCyc and KEGG , and the proteins that are known to be involved in these pathways are not annotated correctly in UniProt . Finally , because there are so many proteins whose functions cannot be predicted accurately , we need new approaches to obtain large-scale data about proteins’ functions . The heterotrophic bacteria that we studied are not a random sample of all heterotrophic bacteria . Nevertheless , of the heterotrophic bacteria that we studied previously , 23 of 24 grew in minimal media [8] , and we selected these on the basis of their genetic tractability rather than their growth requirements . Furthermore , as far as we know , these bacteria were isolated and propagated in media that was supplemented with yeast extract , such as LB , R2A , or marine broth . We do not see why these media would select for prototrophic bacteria . So we predict that most free-living bacteria can synthesize all 20 amino acids , even though we do not know how . By free living , we mean bacteria that live in water , soil , or sediment as opposed to living primarily within or on a larger organism ( i . e . , parasites , pathogens , endosymbionts , or dedicated commensal bacteria ) . Our impression is that auxotrophies are widespread in dedicated pathogens and in endosymbionts , while they are uncommon in plant-associated commensal bacteria . We are not sure about bacteria that are commensal with animals: it would not be surprising if bacteria that evolve in protein-rich environments would become auxotrophic . On the other hand , some animal-associated bacteria ( for example B . thetaiotaomicron , which is abundant in the human colon ) , can make all of the amino acids . Also , although lactic acid bacteria are adapted to high nutrient levels , some of them are prototrophic for amino acids [38 , 39] , and those that are auxotrophic have lost the capability to make amino acids recently [40] . Lactic acid bacteria from dairy products are more likely to be auxotrophic , which may reflect their evolution in particularly protein-rich environments [40] . We also note that even free-living bacteria with reduced genomes can synthesize all 20 amino acids . For example , consider the abundant ocean bacterium Pelagibacter ubique , which has a streamlined genome and has just 1 , 354 protein-coding genes [41] . P . ubique has unusual nutritional requirements for reduced sulfur compounds and for glycolate , but given these compounds , it can make all 20 amino acids [5] . These compounds are released by photosynthetic organisms in the ocean , so it appears that P . ubique synthesizes all 20 amino acids in nature . A limitation of our argument is that all of the bacteria that we studied belong to the cultured minority and all are Proteobacteria . Because isolations are usually performed with media that contain peptides or amino acids , we expect that as-yet uncultured bacteria are about as likely to be auxotrophs as cultured bacteria . Similarly , because the Proteobacteria are the best-studied phylum of bacteria , we expect that there are even more gaps in the amino acid biosynthesis pathways of other bacteria . Anecdotally , we know of many gaps in amino acid biosynthesis in non-Proteobacteria . We have already mentioned gaps in Aquifex , Acidimicrobium , Dehalococcoides , and Bacteroides , and S1 Text discusses erroneous predictions of auxotrophy in Clostridium and Trichodesmium . Also , some of the amino acid biosynthesis genes that we studied in Proteobacteria have likely orthologs in other phyla of bacteria . First , Dehaloccoides ethenogenes 915 grows in minimal media and appears to use the DUF39 pathway of homocysteine synthesis ( encoded by DET0921:DET0919 ) . Second , Thermodesulfatator indicus CIR29812 DSM 15286 grows in minimal media [42] and encodes a positional ortholog ( TheinDRAFT_1819 ) of the phosphoribosyl-ATP diphosphatase DVU1186 , and its genome does not contain any other obvious candidate gene for this activity . Under the black queen hypothesis , dependencies between organisms can be selected for if the capability is “leaky” and benefits other organisms nearby [43] . For example , an organism that degrades a toxic compound will also reduce the concentration of that compound that is experienced by its neighbors . It has been suggested that this mechanism could favor the loss of amino acid synthesis genes [7] , but we argue that amino acid synthesis is not so leaky . Even if small amounts of amino acid or protein leak out of nearby cells , it seems questionable that this would provide adequate amino acids for growth , given that about half of the dry weight of bacteria is protein ( BioNumbers 101955; [44] ) . And although some mutant strains of E . coli will secrete amino acids in sufficient quantities to maintain the growth of auxotrophic strains [45] , we do not know of any evidence that this is occurs in nature , except for endosymbionts . Although we are skeptical about the idea that bacteria cross-feed each other amino acids , there is some evidence for the cross feeding of vitamins [46] . Because bacteria need vitamins at far lower concentrations than they need amino acids , it seems more plausible that the black queen mechanism could apply to vitamins . Alternatively , because vitamins are present at low concentrations , they might be preferentially recycled from lysed cells rather than broken down for energy . If a subset of bacteria synthesize vitamins rather than taking them up and release them when they die , then many other bacteria would not need to synthesize vitamins . ( Even if vitamins are available , some bacteria might be selected to synthesize them if they require relatively high amounts of the vitamin for their metabolism or if vitamin receptors are targeted by phage . ) Nevertheless , of the seven bacteria we tested , six grew without added vitamins . Most free-living bacteria may not require exogenous vitamins for growth either .
We began with 24 heterotrophic bacteria from 15 genera that we had collected large-scale mutant fitness data for [8] . We had previously found that 23 of these bacteria grew in defined media without added amino acids [8] . Since that study was conducted , we also generated a mutant library in Herbaspirillum seropedicae SmR1 ( see below ) , which is a plant-associated ( endophytic ) and nitrogen-fixing bacterium . Although all of these bacteria have been sequenced , not all of them are available in the IMG website , so not all of them have auxotrophy predictions . Also , we arbitrarily selected one representative of each genus . This left us with 13 bacteria . One of these is Escherichia coli , which is a traditional model organism and , not surprisingly , IMG correctly predicts that it can make all 20 amino acids . Also , we did not do a detailed analysis for Dinoroseobacter shibae DFL-12 or Dechlorosoma suillum PS ( also known as Azospira suillum PS ) . These had 5 and 6 spurious auxotrophies , respectively , which is similar to numbers for the other bacteria that we did a detailed analysis of . Predictions of amino acid synthesis capabilities were taken from the IMG web site ( https://img . jgi . doe . gov/ ) on May 20 , 2016 . Except for H . seropedicae , mutant libraries were described in [8] . The original sources of the strains are given in Table 2 along with the standard minimal media that we used for each organism and the standard carbon source that we used . These media all contain ammonium chloride as the standard nitrogen source , but this was omitted for some nitrogen source experiments . The minimal media also contain inorganic salts , buffer , and either Wolfe’s vitamins or Thauer’s vitamins . Media components are given in the supplementary material of [8] and are available for each experiment in the Fitness Browser ( http://fit . genomics . lbl . gov/ ) . We also studied individual transposon mutants of DVU1186 and DVU2938 from D . vulgaris Hildenborough ( ATCC 29579 ) . These were obtained by using barcoded variants of the mini-Tn5 transposon delivery vector pRL27 [47] , which were delivered by conjugation . Transformants were selected on agar plates ( 1 . 5 g/L ) with the antibiotic G418 ( 400 μg/ml ) and a rich lactate-sulfate medium . Individual colonies were picked into 96 well plates and characterized by arbitrary PCR and Sanger sequencing [47] . We also studied a deletion mutant of DVU0338 from D . vulgaris Hildenborough ( strain JW9475 ) . This was constructed in a upp- background , with JW710 as the parent strain [48] . All bacteria were cultured aerobically with shaking , except that D . vulgaris Miyazaki ( which is strictly anaerobic ) was grown without shaking in 18 x 150 mm hungate tubes with a butyl rubber stopper and an aluminum crimp seal ( Chemglass Life Sciences , Vineland , NJ ) with a culture volume of 10 mL and a headspace of about 15 mL . Media for D . vulgaris Miyazaki was prepared in a Coy anaerobic chamber with an atmosphere of about 2% H2 , 5% CO2 , and 93% N2 . Wild type and mutant strains of D . vulgaris Hildenborough were grown in a similar way as D . vulgaris Miyazaki , but with these differences: the culture volume was only 5 mL; media was degassed for 5 min with 100% nitrogen prior to being autoclaved; and 1 . 4 mM thioglycolate was used as the reductant in the medium ( instead of 1 mM sulfide ) . Complementation assays were performed using deletion strains of thrB , serA , or serB from the Keio collection [27] . Genes of interest were cloned under the control of the bla promoter from pUC19 into the vector pBBR1-MCS5 [49] . After sequence verification , we introduced the complementation plasmids into the corresponding knockout strains by transformation . The growth of these strains was tested on M9 minimal media agar plates . The mutant library of H . seropedicae will be described in more detail elsewhere . It was generated using an E . coli conjugation donor that contains the plasmid pKMW7 ( which delivers a Tn5 transposon with 20-nucleotide random barcodes ) and is an auxotroph for diaminopimelate [10] . The transposon insertion sites were amplified as described previously [10] and sequenced using Illumina HiSeq2500 in rapid run mode . Each read links a 20 nucleotide barcode to a location in the genome . We identified insertions ( supported by at least two reads ) at 82 , 441 different locations in the 5 . 5 MB genome ( NC_014323 ) . We associated 98 , 021 different barcodes with insertions in the genome ( with at least 10 reads for each of these barcodes ) and estimated fitness values for 3 , 878 of the 4 , 243 non-essential proteins . We identified essential proteins in H . seropedicae as described previously for the other bacteria [8] . This approach was validated previously [8 , 50] . Briefly , we limited the analysis to protein-coding genes that were long enough such that the absence of a transposon insertion in the central 10–90% of the gene would be surprising . Protein-coding genes that were long enough ( at least 400 nt for H . seropedicae ) were considered essential if the density of insertion locations ( which was normalized by GC content ) and the total reads ( summed across all insertions ) divided by the gene’s length were both less than 20% of the typical protein’s value . Using these thresholds , we identified 472 essential proteins ( S2 Table ) . These proteins might not be entirely essential but they should be required for good growth in LB . We also manually classified three steps in biosynthetic pathways as being essential . These involved genes that were not considered in the automated analysis but lack any insertions . In A . brasilense , shikimate kinase ( AZOBR_RS03225 ) was originally annotated as a pseudogene , so it was not considered during the automated analysis , but it appears to be essential . Also in A . brasilense , aspartyl/glutamyl-tRNA amidotransferase contains three subunits , of which two were automatically identified as essential and one ( AZOBR_RS20640 ) was too short for the automated approach but had no insertions . We classified this step as being essential . Similarly , we classified this step as essential in P . stutzeri despite the short length of Psest_3328 , which also has no insertions . Most of the mutant fitness assays that we analyzed for this study were described previously [8 , 10] . The compendium of mutant fitness assays for H . seropedicae will be described elsewhere: it includes growth in minimal media with 26 different carbon sources; growth in minimal media with 1 alternative nitrogen source; and growth in rich media with 12 different inhibitory compounds added . For this study , we conducted additional mutant fitness assays with amino acids as additional nutrients . These assays were performed and analyzed as described previously [8] . Briefly , a pool of transposon mutants is recovered from the freezer in rich media and grown until it reaches log phase . It is then inoculated at OD600 = 0 . 02 into 5 mL of media in a glass tube and allowed to reach saturation . Gene fitness values are computed by comparing the sample after growth to the sample before growth ( i . e . , at the time of transfer ) via genomic DNA extraction , PCR amplification of barcodes , and sequencing on Illumina HiSeq . For each fitness experiment , metrics of internal consistency and biological consistency were computed and experiments with low quality scores were discarded , as described previously [10] . Some of the samples were sequenced with a staggered “BarSeq2” primer rather than the primer we used previously . The staggered primer contains 2–5 random nucleotides just downstream of the Illumina adapter . This increases the diversity of the sequence and allows BarSeq to be conducted with the HiSeq 4000 . For the mutant libraries that we published previously [8 , 10] , we used the same strains to estimate gene fitness , so that gene fitness values would match for the previously-published results . We amplified the hisD region from A . brasilense by PCR using the primers TCTCCCAGGAGGAGGTGGAC and ATCGCCTTCACGCTGTCCGCATCG . The same primers were used for Sanger sequencing . To assign genes to TIGRFams [13] , we used HMMer 3 . 1b1 44 [51] and the trusted score cutoff for each family in TIGRFam 15 . 0 . TIGRFam assigns enzyme commision numbers to some of its families . To assign genes to enzyme commission numbers via KEGG [14] , we downloaded the last public release of KEGG ( from 2011 ) and we searched for a best hit with over 30% identity and above 80% coverage using RapSearch v2 . 22 [52] . If the best hit was assigned an enzyme commission number by KEGG , then we transferred that annotation to the gene . To assign genes to enzyme commision numbers via SEED and RAST [15] , we used the SEED server , based on code from http://servers . nmpdr . org/sapling/server . cgi ? code=server_paper_example6 . pl . These results were viewed using the Fitness Browser ( http://fit . genomics . lbl . gov/ ) . To compute the phylogenetic profile of DUF1638 and the RamA-like protein , we used MicrobesOnline [29] . For DUF1638 , we used the presence or absence of PF07796 . For the RamA-like protein , we used the presence of an ortholog of the RamA-like protein ( VIMSS 5050244 ) . However in Roseobacter sp . SK209-2-6 , the RamA-like protein is split into two proteins ( RSK20926_19262 and RSK20926_19267 ) , and we manually classified the RamA-like protein as present in this bacterium . Code for analyzing fitness data and for the Fitness Browser is available at https://bitbucket . org/berkeleylab/feba . | For a few bacteria , it is well known how they can make all 20 of the standard amino acids ( the building blocks of proteins ) . For many other bacteria , their genome sequence implies that there are gaps in these biosynthetic pathways , so that the bacteria cannot make all of the amino acids and would need to take up some of them from their environment instead . But many bacteria can grow in minimal media ( without any amino acids ) despite these apparent gaps . We studied 10 bacteria with predicted gaps in amino acid biosynthesis that nevertheless grow in minimal media . Most of these gaps were spurious , but 11 of the gaps were genuine and could not be explained by current knowledge . Using high-throughput genetics , we systematically identified genes that were required for growth in minimal media and identified the biosynthetic genes that fill 9 of the 11 gaps . We hope that this approach can be applied to many more bacteria and will eventually allow us to accurately predict the nutritional requirements of a bacterium from its genome sequence . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"b",
"vitamins",
"chemical",
"compounds",
"enzymes",
"enzymology",
"organic",
"compounds",
"phosphatases",
"serine",
"genetic",
"elements",
"basic",
"amino",
"acids",
"amino",
"acids",
"cobalamins",
"genomics",
"proteins",
"chemistry",
"vitamins",
"methionine",
"sulfur"... | 2018 | Filling gaps in bacterial amino acid biosynthesis pathways with high-throughput genetics |
The intrinsic oncotropism and oncosuppressive activities of rodent protoparvoviruses ( PVs ) are opening new prospects for cancer virotherapy . Virus propagation , cytolytic activity , and spread are tightly connected to activation of the PDK1 signaling cascade , which delays stress-induced cell death and sustains functioning of the parvoviral protein NS1 through PKC ( η ) -driven modifications . Here we reveal a new PV-induced intracellular loop-back mechanism whereby PKCη/Rdx phosphorylates mouse PDK1:S138 and activates it independently of PI3-kinase signaling . The corresponding human PDK1phosphoS135 appears as a hallmark of highly aggressive brain tumors and may contribute to the very effective targeting of human gliomas by H-1PV . Strikingly , although H-1PV does not trigger PDK1 activation in normal human cells , such cells show enhanced viral DNA amplification and NS1-induced death upon expression of a constitutively active PDK1 mimicking PDK1phosphoS135 . This modification thus appears as a marker of human glioma malignant progression and sensitivity to H-1PV-induced tumor cell killing .
Protoparvoviruses ( PVs ) are non-enveloped icosahedral particles 24 nm in diameter , with a 5 . 1 kb linear single-stranded DNA genome encoding two capsid ( VP ) and several nonstructural ( NS ) proteins . Many rodent PVs , including H-1PV , were initially discovered as opportunistic infectants of human-cancer-derived cell lines [1] and are now widely recognized for their intrinsic oncotropism and oncolytic activity . This , together with their non-association with human disease , has led to a first phase I/IIa clinical trial of wild-type replication-competent H-1PV in glioma patients [2] . NS1 , the major protoparvoviral regulatory protein , is required for multiple steps in the virus life cycle , ranging from viral DNA amplification and trans-regulation of viral and cellular transcription to the egress and spread of progeny particles [3] . Because it interferes with multiple cellular pathways , NS1 appears as the main cytotoxic agent responsible for the oncolytic activity of PVs [4 , 5] . NS1 functioning is tightly regulated by phosphorylation , catalyzed by two kinases: PKCλ and the short-lived PKCη [6 , 7] , both of which require activation by the phosphoinositide-dependent kinase 1 ( PDK1 ) . To ensure virus propagation and spread , the PV minute virus of mice ( MVM ) has evolved a mechanism for stimulating PDK1 and the downstream kinase PKCη in permissive host cells . This activation of PDK1 is associated with its PV-induced trans-phosphorylation by ( an ) unidentified kinase ( s ) [8] . The PI3K ( phosphoinositide 3-kinase ) /PDK1/protein kinase B ( PKB/Akt ) signaling cascade regulates pathways involved in the translational control of protein synthesis and in cell metabolism , differentiation , death , and survival [9] . Accordingly , human cancers frequently display somatic mutations affecting PI3K /PDK1/PKB signaling . The master kinase PDK1 has multiple downstream targets besides PKB/Akt1 , including SGK , S6K1 , RSK , PKN , and the PV-regulating PKCs [10] . PDK1 exerts constitutive basal activity , but it is strongly upregulated by PI ( 3 , 4 , 5 ) P3 , produced by PI3K , [11 , 12] , itself controlled by growth factor receptor signaling [13] . Additional upregulations include src-family-kinase-driven tyrosine phosphorylations , which act in cooperation with the chaperone Hsp90 to stabilize active PDK1 [14] . The PKC protein kinase family comprises three groups ( atypical , novel , and classical or a , n , and c PKCs ) having different regulatory domains and cofactor requirements [15] . These proteins are involved in regulating processes as diverse as cell metabolism , polarity , differentiation , proliferation , motility , survival , and death . In keeping with their functions and their stimulation by phorbol esters , the n and c PKCs have been implicated in cancer progression , but a negative influence on tumorigenesis has also been evidenced . This is best-studied of the widely expressed nPKCs PKCε and PKCδ , which respectively promote cell survival and death [16] . To perform their multiple and very distinct functions , PKCs are tightly regulated . Besides cofactor binding , a series of phosphorylation events , driven by PDK1 and other kinases , trigger conformational changes controlling the activity of PKCs and their interactions with potential substrates [17 , 18] . For example , nPKCη must first be phosphorylated by PKCλ at its PDK1-docking site before getting activated by PDK1 [8] . In contrast , aPKCλ requires no such priming because , instead of the serine present in nPKCη , it has a glutamic acid creating a constitutively active PDK1-docking site [17] . PKC down regulation occurs through dephosphorylation followed by ubiquitin-associated degradation [17 , 18] . Additional regulation can be achieved through interaction with adaptor proteins [19] . Upon activation , PKCs undergo a switch from affinity for scaffold structures to association with membranes , mediated by acid lipids and diacylglycerols . ERM-family proteins ( ezrin [Ez] , radixin [Rdx] , moesin [Moe] ) act as intermediates between F-actin and membranes [20] . This adaptor function and the involvement of ERM proteins in the same processes as PKCs [21] suggest that the former may act as PKC-regulating auxiliary proteins . This is substantiated by a recent analysis of parvovirus-host cell interactions [22] , which demonstrated strong colocalization of PKCη with the ERM-family protein radixin upon PV infection , accompanied by PKCη activation and modulation of PKCη-induced phosphorylation of viral proteins . The present study aimed to characterize the mechanism underlying activation of PDK1signaling in PV-infected permissive cells ( A9 mouse fibroblasts ) . Our work has led to the discovery of a PV-induced loop-back mechanism where PKCη , in a complex with radixin , phosphorylates PDK1 at S138 . Investigation of the corresponding mechanism ( PKCη/Rdx-induced phosphorylation of PDK1 at S135 ) in human cells and glioma samples has led us to propose PDK1phosphoS135 as a marker of both tumor progression and responsiveness to parvovirus treatment and this pathway , as a potential new target for cancer therapy .
MVM-infected A9 cells display changes in PDK1/PKC/PKB signaling that are essential to promoting a productive infection . This is illustrated by the results presented in Fig . 1 , which confirm and extend previous findings . Firstly , MVM infection triggers activation of PDK1 and of the downstream kinases PKCη [8] and PKB/Akt1 ( Fig . 1A ) . This is accompanied by relocation of active PDK1 and PKCη from plasma membrane ruffles to the nuclear periphery [8] , where they both co-localize with the ERM-family cellular auxiliary protein radixin ( Rdx ) ( [22] and Fig . 1B ) , involved in MVM propagation and spreading and known to modulate PKCη-driven phosphorylation of NS1 . Surprisingly , activated PKCη was detected after the onset of viral protein synthesis but before activation of PDK1 ( Fig . 1A ) . These observations led us to hypothesize the existence of a loop-back mechanism where Rdx acting as an adaptor protein controls the subcellular localization , activity , and/or substrate specificity of PKCη so as to activate PDK1 . To test this hypothesis , we first examined whether Rdx or other ERM-family proteins might interact physically with PKCη and modulate its activity . A9 cells and derivatives expressing Myc-tagged PKCη ( MycPKCη ) , either alone or in the presence of a Flag-tagged ERM variant , were infected with MVM and harvested 24 h post-infection . Complexes containing Flag-tagged ERM were recovered by immunoprecipitation with anti-Flag and tested for the presence of MycPKCη by western blotting with anti-Myc . As shown in Fig . 2A ( left panel ) , MycPKCη was pulled down with both active RdxE ( RdxT564E ) and , to a minor extent , inactive RdxA ( RdxT564A ) . No MycPKCη was detected in the absence of recombinant Flag-ERM or in the presence of Flag-Ez or Flag-Moe . The specificity of the interaction was confirmed with the reverse co-immunoprecipitation assay with αMyc ( Fig . 2A right panel ) . While immunoprecipitation with MycPKCη was able to capture significant amounts of endogenous Rdx , only minor quantities were detected in absence of Myc-tagged proteins or MycCKIIα . PKCη thus appears to bind specifically to Rdx in MVM-infected A9 cells . We next tested how this binding might affect the properties of PKCη . First , MVM-infected A9 cells and derivatives expressing dominant-negative RdxA were harvested 24 and 48 hours post-infection and autophosphorylation of endogenous PKCη at T655 was measured by western blotting with an antibody against PKCη:phosphoT655 ( Fig . 2B ) . A cell line expressing dominant-negative PKCη ( ηTA: PKCηT512A ) served as control . Both the control cells and the RdxA-expressing cells showed a strongly reduced level of PKCη:phosphoT655 , indicating that the Rdx-PKCη interaction controls the activity of PKCη . Next , to see if Rdx binding to PKCη might influence the substrate specificity of the kinase , we performed in vitro phosphorylation assays followed by tryptic phosphopeptide profiling . For this , a purified non-phosphorylated recombinant peptide , either PDK1N446 ( aa 1–446 ) or NS1C ( aa 545–672 ) used as control , was incubated with PKCη and γ32P-ATP in the presence or absence of purified functionally active Rdx ( Fig . 2C ) . Whichever fragment was used , some 32P-labeled peptides appeared only when Rdx was included in the reaction . Taken together , these results suggest that Rdx acts as an adaptor to control PKCη activity and substrate specificity and further support our hypothesis that in the perinuclear area , a PKCη/Rdx complex mediates PDK1 phosphorylation and upregulation . To further test our hypothesis , we measured the activity and phosphorylation of ( recombinant ) PDK1 in MVM-infected A9 cells where either PKCη , another candidate protein kinase , or an ERM-family protein was inactivated by expression of a dominant-negative mutant ( Fig . 3A ) . As measured by metabolic 32P-labeling , the steady-state level of ( Myc ) PDK1 phosphorylation was found to be markedly reduced in cells expressing either dnPKCηT512A or dnRdxdl[P] , as compared to mock- , dnCKIIα- , dnEz- , and dnMoe-expressing cells ( Fig . 3A top panel ) . Endogenous PDK1 activity showed similar modulation ( S1 Fig . ) , suggesting that PKCη/Rdx controls PDK1 activity in MVM-infected A9 cells . To distinguish PKCη/Rdx-mediated phosphorylation from autophosphorylation , tryptic phosphopeptide analyses were performed ( Fig . 3A bottom panel ) . The wild-type pattern consists of six phosphopeptides ( a-f ) , four of which ( a-d ) are dependent on PDK1 kinase activity ( i . e . absent when catalytically inactive PDK1:S244 is used ) , while other kinases appear to be responsible for the other two ( e , f ) [8] . Although the overall phosphorylation of MycPDK1 was markedly reduced upon inactivation of PKCη or Rdx , a faint , incomplete autophosphorylation pattern ( peptides a-d ) remained visible allowing the identification/localization of the individual spots . Interestingly , besides the pronounced reduction of autophosphorylation ( a-d ) , no labeling of peptide “e” could be detected after expression of PKCηT512A or Rdxdl[P] . Considering that the appearance of this phosphopeptide is independent of the intrinsic catalytic activity of PDK1 ( i . e . present in PDK:S244A [8] and S265A [Fig . 3B bottom panel] ) and since no differences in accumulation of MycPDK1 occurs under these conditions ( Fig . 3A top panel ) , this observation strongly suggests that trans-phosphorylation of PDK1on peptide “e” is mediated by PKCη/Rdx and controls the overall activity of PDK1 . As inhibition of CKIIα or of the ERM-family protein ezrin or moesin did not impede PDK1 trans-phosphorylation ( peptide “e” ) or autophosphorylation ( peptides a-d ) , PKCη and Rdx appear as specific regulators of PDK1 . To identify the site ( s ) of PKCη/Rdx-driven trans-phopshorylation in PDK1 , candidate target phosphorylation sites were mutated from serine/threonine to inert alanine , and A9 cell lines expressing the Myc-tagged PDK1 variants were generated . As shown in Fig . 3B top panel , some mutations [S108A , T131A , T325A , S411A , T516A , T525A , deletion of the whole PH-domain ( dlPH ) ] had little to no effect on PDK1 phosphorylation or activity , while others [S138A , S237A , S261A , S265A] strongly reduced PDK1 activity ( PDKphosphoS244 ) and steady-state phosphorylation ( P-MycPDK1 ) . As MycPDK1:T185A was hardly detectable by western blotting , this modification probably affects the stability of the polypeptide . As shown in Fig . 3B bottom panel and in agreement with the results in the top panel , kinase-active mutants yielded clearly detectable auto-phosphorylated peptides ( a-d ) and trans-phosphorylated peptides ( e , f ) , whereas mutants S138A , T185A , S237A , and S265A gave rise to very faint , incomplete patterns . With mutants S138A and S237A , no peptide “e” was observed ( dotted circles Fig . 3B bottom panel ) . This suggests that residues S138 and S237 could be ( direct or indirect ) targets of the PKCη/Rdx complex , and that , in the absence of phosphorylation at these positions , PDK1 loses its kinase activity . Although no effects on intrinsic enzyme activity were observed after dlPH deletion or T516A substitution , these mutations abolished phosphorylation of peptide “f” ( dotted circles ) , suggesting that T516 may be another trans-phosphorylation site in PDK1 . Interestingly , substitution of glutamic acid for this residue renders PDK1 constitutively active as regards PKB activation [23] , and phosphorylation at this site is specifically triggered upon MVM infection [8] . To identify PDK1 phosphorylation sites directly targeted by the PKCη/Rdx complex , we performed in vitro tryptic phosphopeptide analyses ( Fig . 3C ) . In agreement with Fig . 2C , a single PDK1 phosphopeptide was specifically induced in the presence of Rdx ( arrow vs . dotted circle ) . This peptide was not visible upon mutation of S138 to alanine , while it was visible in all the other mutants . Together with the above evidence , this result indicates that PKCη/Rdx phosphorylates PDK1 at residue S138 , thereby activating the kinase . Constitutive activation of the PDK1/PKB signaling cascade is a hallmark of highly invasive cancers , and viruses exploit it to extend the lifespan of infected cells under stress [9 , 24] . This led us to investigate whether the PV-inducible PKCη/Rdx-mediated phosphorylation of PDK1 at S138 ( in mouse ) or S135 ( in human ) might be a cancer pathway leading to constitutive PDK1 activation . Several human cancer cell lines were analyzed for PDK1phosphoS135 and compared with normal diploid fibroblasts . As shown in Fig . 4A , PDK1phosphoS135 was detected ( along with Rdx and PKCη ) in the Kaposi sarcoma ( KS ) cell line and all six glioma ( NCH ) cell lines , but not in normal diploid MRC-5 cells . In most tumor cell lines , phosphorylation of PKB at T308 was also observed , suggesting PDK1:S135 phosphorylation mobilizes an intracellular PI3K-independent survival pathway in cancer cells . It should be noted , however , that MCF-7 mammary carcinoma cells , although proficient in PKB:T308 phosphorylation , showed no significant PDK1phosphoS135 signal . Normally , PDK1 is a master kinase regulating cell metabolism and survival , in conjunction with PKB . Both kinases are tightly controlled by the availability of cofactor PIP3 , produced by PI3K , which in turn is regulated by growth-factor-dependent receptor tyrosine kinases . Stress signaling counteracts this pathway , causing cell death . We hypothesized that PKCη/Rdx-driven phosphorylation of PDK1 might activate the kinase independently of PIP3 by altering its conformation . If so , interrupting this loop-back mechanism should impair cell metabolism and cause cell death . We tested this in several cell lines , independently of any PV infection , by transducing them with a rAAV vector overexpressing a mutant form of PDK1 , PKCη , or Rdx . The cell lines used were A9 ( where we have characterized this pathway after MVM infection ) , the PDK1phosphoS135-positive , H-1PV-permissive human glioblastoma-derived cancer cell lines NCH149 and NCH82 ( Figs . 4A and S2 ) ( known to display enhanced PKB/Akt1 activity and for resistance to apoptosis inducers ( [25] , and the PDK1phosphoS135-negative , H-1PV-resistant BJ-1 ( foreskin ) and MRC-5 ( embryonic lung ) normal human diploid fibroblasts ( Figs . 4A and S2 ) , which are fairly insensitive to PDK1 silencing [26] . Metabolic activity ( Mitotracker staining ) and death by apoptosis/necrosis ( nuclear fragmentation/PI-staining ) were measured by immunofluorescence staining 48 h post rAAV treatment ( S3A Fig . ) . As summarized in Figs . 4B and S3B , knockdown of endogenous PDK1 , PKCη , or Rdx significantly ( p<0 , 01 ) reduced the metabolic activity of A9 , NCH149 , and NCH82 cells , causing a large proportion ( p<0 , 01 ) of the cells to die . No effect was seen with the control vector or in the presence of constitutively active caPKCη or caRdx . Normal human cells showed only minor fluctuations in metabolic activity and no apparent cell death . These data support our hypothesis that PKCη/Rdx-mediated phosphorylation of PDK1 at S135/S138 controls cell metabolic activity and viability of cancer cells . To further address this issue , we generated constitutively active PDK1 variants mimicking PKCη/Rdx-driven phosphorylation by replacing candidate serine/threonine residues with aspartic or glutamic acid residues . To test the activity of these mutants in A9 cells , we measured PDK1:S244 autophosphorylation and PKB:T308 trans-phosphorylation by immunofluorescence microscopy after transfection with plasmid constructs . Strong signals were obtained with PDK1:S138E and PDK1:S237D , suggesting that both mutants have enhanced activity ( S4 Fig . ) . These and other candidates were then transduced by rAAV vectors into A9 , NCH149 , NCH82 , BJ-1 , and MRC-5 cells and the impact of their expression on cell metabolic activity and survival was evaluated in the presence and absence of the PI3K inhibitor wortmannin ( Figs . 5 and S5 ) . A9 , BJ-1 , and MRC-5 cells treated with this drug showed dramatically reduced cell metabolic activity and massive death when mock-treated or transduced with any rAAV except rAAV:PDK1:S138E and , for A9 cells rAAV:PDK1S237D as well ( p<0 , 01 ) . The cancer cell lines NCH149 and NCH82 resisted wortmannin treatment whether transduced or not . These cells should indeed be at least partly independent of growth factor signaling since , unlike normal cells , they produce PDK1phosphoS135 through PKCη/Rdx-mediated phosphorylation ( Fig . 4 ) . A9 cells , although capable of activating PDK1 by PKCη/Rdx-mediated phosphorylation , appear to require acceleration of this loop-back mechanism ( e . g . through PV stimulation ) , in agreement with the low level of PDK1phosphoS138 detected in non-infected A9 cells [[8]; Fig . 4] . Altogether , these results strongly suggest that PKCη/Rdx-induced phosphorylation at PDK1:S135 enables cells to remain viable in the absence of growth factor signaling through PI3K . Parvovirus propagation depends strongly on active PDK1 and PKCs . In natural hosts , accordingly , this signaling cascade is stimulated after the onset of viral protein synthesis . This led us to hypothesize that PV propagation in illegitimate hosts might depend on constitutively active PDK1/PKC/PKB signaling , since some PV-permissive human cancer cells ( e . g . NCH149 ) display , irrespectively of infection , significantly higher levels of PDK1phosphoS241 and PKBphosphoT308 than normal cells , together with PKCη/Rdx-induced phosphorylation of PDK1:S135 ( Fig . 4 ) . We thus wondered if activating PDK1/PKC signaling artificially might sensitize H-1PV-resistant , PDK1phosphoS135-negative normal human cells to H-1PV . To test this we used MRC-5 and BJ-1 cells , which , although unable to support NS1-dependent viral DNA amplification ( S2A Fig . ) , do permit H-1PV entry and the early steps leading to NS1 synthesis ( Figs . 6A and S2B ) . Accordingly , the proportion of cells having initiated PV replication , as determined by NS1 immunostaining , was only slightly higher 24 h post-infection among caPDK1:S138E-treated vs . untreated MRC-5 and BJ-1 cells ( Fig . 6B ) . To activate PDK1/PKC signaling in MRC-5 cells , genes encoding constitutively active PDK1 , PKCη , and Rdx variants were transduced into these cells on rAAV vectors and overexpressed prior to infection with H-1PV ( Fig . 6C ) . The reasons for including caPKCη and caRdx in addition to caPDK1 are that PKCη , poorly expressed in MRC-5 cells , is required to activate the viral-DNA-amplification function of NS1 [6] and that Rdx and PKCη act together to activate PDK1 . Under these conditions , overexpression of caPDK:S138E was found to stimulate viral DNA amplification significantly ( Fig . 6C ) . Furthermore , as shown in Fig . 6D , treatment with caPDK:S138E strongly sensitized both MRC-5 and BJ-1 cells to H-1PV-induced cell killing , causing a 5- to 10-fold increase in the proportion of dead cells after PV infection ( p<0 , 01 ) . In agreement with their action upstream from PDK1 , individually overexpressed caPKCη ( and for MRC-5 caRdx as well ) had a significant ( p<0 . 02 ) , but less pronounced PV-sensitizing effect on normal human fibroblasts , too . The PDK1/PKC signaling cascade thus appears important for PV oncotropism , since its forced activation in normal human cells confers some degree of permissiveness for PV replication and cytotoxicity . As aberrant activation of PDK1 is thought to contribute to cancer progression and tumor cell invasiveness [9 , 27] and as PDK1phosphoS135 is detected in tumor cell lines , we performed immunofluorescence staining to detect this modification on cryosections of tumors resected from patients suffering from glioblastoma multiforme , a highly invasive brain cancer . As shown in Figs . 7A , 7B , and S6 , about 70% of the examined tumor samples ( n = 36 ) tested positive for PDK1phosphoS135 , while cultured normal human astrocytes , normal muscle tissues and a sample of safety margin of healthy looking tissue of tumor #56 ( BrainRg4 ) were negative . These results were confirmed by western blotting , as illustrated in Fig . 7C for specimen #43: PDK1phosphoS135 was found in the brain tumor sample together with PKCη and Rdx , but not in muscle and “normal” brain tissue . These results suggest that intracellular PKCη/Rdx-driven activation of PDK1 through phosphorylation at residue S135 takes place in a large proportion of human gliomas , where it may contribute to cancer progression .
Prolonging host cell survival under stress seems to be a common strategy used by viruses , to ensure sufficient progeny particle production and spread [24 , 28] , and by cancer cells , for rapid proliferation and dissemination from the primary tissue [26 , 27] . It can be achieved through activation of the PDK/PKB signaling cascade . Here we reveal a new mechanism of PDK1/PKC/PKB upregulation , induced by PVs in permissive cells . After initial activation of PKCη by PDK1 , PVs accelerate a PDK1-stimulating loop-back mechanism that depends on PKCη but not on growth factor signaling . As illustrated in Fig . 8 ( left panel ) , this is achieved by enabling the ERM-family protein radixin to form with PKCη a complex mediating PDK1 phosphorylation at residue S135 ( of human PDK1 ) or S138 ( of mouse PDK1 ) . Besides supporting activation of the short-lived PKCη necessary for NS1 replicative functions and cytotoxicity , this stimulation of PDK1 activity contributes to signaling through other downstream kinases , including PKB . Possibly in conjunction with other PV-triggered processes , this loop-back activation promoting cell metabolism and survival appears to extend the window of cell competence for sustaining PV replication by preventing premature death of infected cells . As both PVs and cancer cells depend on activated PDK1 signaling and as host cells can overcome limitations to PV replication through malignant transformation [29] , we suspected that the newly discovered mechanism might be active in human cancer cells and contribute to the natural oncotropism of PVs . We accordingly found PKCη/Rdx-mediated activation of PDK1/PKB signaling to occur in several PV-permissive human tumor cell lines , and this should be relevant to both tumor cell physiology and PV oncoselectivity ( Fig . 8 , compare the left and right panels ) . Abnormal activation of the growth-factor-stimulated PI3K/PDK1/PKB signaling cascade is a common feature in cancer [9] . PDK1 is a crucial component thereof , controlling numerous downstream protein kinases , including PKB/Akt , SGK , S6K , RSK , and PKC isoforms . Thus , despite its universal role in cell homeostasis , accumulating evidence points to PDK1 as a valid target for cancer therapy . For example , PDK1 downregulation can inhibit migration and experimental metastasis of human breast cancer cells [30] . PDK1 depends strongly on extracellular stimuli . It is typically upregulated by phosphatidylinositols , whose production is driven by PI3K under the control of growth-factor-triggered signaling through receptor tyrosine kinases . Uncontrolled growth and dissemination of tumor cells require mechanisms activating this master kinase independently of the extracellular environment provided by the surrounding tissue . As previous studies show , this can be achieved through post-translational modifications triggering conformational changes [23 , 27] . The results presented here suggest a similar mode of PDK1 activation though PKCη/Rdx-driven phosphorylation of mouse PDK1:S138 ( human PDK1:S135 ) . The demonstration of PDK1phosphoS135 in some highly invasive cancers ( e . g . malignant gliomas ) leads us to advocate including PDK1phosphoS135 in the panel of tumor markers , as tumor cells acquiring this intracellular activation mechanism are likely to gain a significant growth advantage under conditions of restricted external stimuli . We here identify PKCη as a major driver of PDK1 phosphorylation . Although somatic alterations of PKC genes seem rare in tumor cells , this kinase family has been implicated in cancer progression . PKC accumulation and activation have been found to correlate with both acquired resistance and poor prognosis in a number of cancers [16] . This has been attributed to the anti-apoptotic effects of PKCs and their ability to promote proliferation , anchorage-independent growth , and metastasis . Yet PKC-targeting cancer therapies have been hampered by the by the versatility of PKCs and the low isoform specificity of the inhibitors used , and the involvement of individual PKCs in cancer progression remains controversial . PKCη is thus reported to activate PKB/Akt1 ( and thereby promote cell survival ) in some cancers [31] , but to down regulate it in others [32] . This discrepancy may be due to interaction of PKCη with different adaptor proteins . Here we identify the ERM-family protein radixin as an accessory protein controlling the activity and substrate specificity of PKCη . It is noteworthy that ERM proteins , like PKCs , have been implicated in cancer development , promoting growth and migration [21] . Because they interact with and regulate PKCs , they are likely to modulate the impact of these kinases on cancer development . Our present finding that the PKCη/Rdx complex acts as an intracellular regulator of PDK1 activity through S135 phosphorylation supports the view that ERM proteins can play a role in promoting carcinogenesis . Altogether , our results suggest that PKCη/Rdx complex formation and concomitant PDK1phosphorylation represent a crucial step in both PV propagation and cancer progression . In MVM-infected A9 cells this event coincides with virus-induced translocation of the three proteins to a distinct microcompartment of the cell , i . e the perinuclear area . These data lead us to propose that the direct interaction of Rdx with PKCη modulates the substrate specificity of this kinase , enabling PKCη to target PDK1 and phosphorylate it at S138 . Rdx compoexing with PKCη may depend on the previously reported Rdx-binding to the NS1“targeting”-domain ( aa 278–379 ) , although the ensuing Rdx phosphorylation by the NS1/CKIIα-complex does not appear to be required for PKCη/Rdx driven phosphorylation of PDK1 [22] . Upon interaction with NS1 , Rdx may be translocated to the perinuclear area , where it was shown to be involved , together with NS1 , in the formation and trafficking of exocytic vesicles [33] . Interestingly , MVM-infected A9 cells are characterized by the accumulation of cytoskeletal structures ( e . g . tropomyosin filaments ) around the nuclear lamina [34–36] . It is tempting to speculate that these perinuclear structures serve as scaffolds bringing together NS1 , Rdx , PKCη , and PDK1 , and organizing the sequential interactions of these proteins . The complexity of these interactions with multiple different cellular factors might account for the incapability of rodent PVs to induce this signaling in normal human cells . H-1PV is currently being validated as a therapeutic agent in a phaseI/IIa clinical trial inpatients suffering from glioblastoma multiforme [2] . In vitro , these highly aggressive cancers prove extremely sensitive to H-1PV-induced killing , even when they have acquired resistance to common death inducers such as cisplatin or TRAIL [25] . The present study provides a first molecular clue to ( H-1 ) PV oncoselectivity: on the one hand , growth-factor- and PI3K-independent activation of PDK1/ ( PKB ) signaling may contribute to cancer progression in an unfavorable environment; on the other hand , the newly identified PKCη/Rdx-driven loop-back activation of PDK1 induced by PVs in cancer cells , but not in normal cells , appears to favor PV amplification and cytotoxicity and to counteract virus-induced stress responses . We thus propose PDK1phosphoS135 as a potential marker of both cancer progression and tumor sensitivity to parvovirotherapy .
Written informed consent was obtained from all patients . The study was approved by the local Ethics committee of the Medical Faculty of the University of Heidelberg , Germany ( 74–2000 ) . Site-directed mutagenesis was performed by single or chimeric PCR [6] . PCRs were performed with an N-terminal primer ( consisting of a unique restriction site generating blunt ends , followed by the Flag-tag or Myc-tag sequence plus 40 nts of effector-protein-coding sequence ) and a C-terminal reverse primer ( consisting of a unique XbaI or NotI site plus >30 nts from the coding sequence of interest ) . PCR fragments were cloned into pCR2 . 1 vectors ( Invitrogen ) and verified by sequencing . Stable transfectants were generated with pP38-X and the selection plasmid pSV2neo or pTK-hyg at the molar ratio 25:1 [6] . Colonies were pooled after growth under selection and frozen stocks prepared . Experiments were performed in the absence of the drugs . Transfectants were kept in culture for less than 25 passages . Cells were grown on spot slides ( Roth ) , mock-treated , rAAV-transfected , and/or PV-infected , and further incubated for the appropriate time . If applicable , wortmannin ( 0 . 5 μM ) was added for 4 h at 37°C . Cell metabolism was measured by incubating live cells with Mitotracker ( 200 nM ) for 30 min at 37°C . Necrosis was determined by incubating live cells with propidium iodide ( 1 μg/ml ) for 30 min at 37°C . Cultures were fixed with 3% paraformaldehyde and permeabilized with 0 . 2% Triton X-100 . Specimens were pre-adsorbed , incubated with primary antibodies , and stained with Alexa Fluor 488 , 564 , or 647-conjugated anti-species antibodies . DAPI ( 10 μg/ml ) was added to the secondary antibody solutions . Analyses were performed with a Leica DMIRBE microscope and Powerscan software or with an Olympus Fluoview FV1000 confocal microscope for visualization of individual slices of a stack and quantified with ImageJ software [38] . Cell extracts were prepared by incubating cell pellets for 30 min on ice in extraction buffer containing 20 mM Hepes-KOH , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 0 . 2% NP-40 and clarified by centrifugation . Supernatants were pre-adsorbed with FCS and protein G Sepharose for 2 h at room temperature before obtaining MycPKCη immunoprecipitates with anti-Myc overnight at 4°C . After washes in CoIp buffer , samples were analyzed by western blotting with rabbit anti-Myc . Cell extracts were produced by incubating cell pellets in extraction buffer containing 20 mM Hepes-KOH pH 7 . 5 , 300 mM NaCl , 1 mM EDTA , 0 . 2% NP-40 on ice , and clarified by centrifugation . Tumor samples were processed for 40 sec in Lysing Matrix D ( MP chemicals ) with a Precellys24 homogenizer and clarified by centrifugation . Proteins were analyzed by SDS-PAGE , blotted onto nitrocellulose membranes , and identified with appropriate primary antibodies in 10% dry milk/PBS or 2% casein ( phosphospecific antibodies ) , stained with horseradish-peroxidase-conjugated secondary antibodies for 1 h , and detected by chemiluminescence ( Amersham ) [33] . Metabolic labeling and tryptic phosphopeptide analyses were performed as described [39] . Cultures were infected with MVM ( 30 pfu/cell ) , incubated for 24 h , and labeled for 4 h in medium containing 0 . 1 nCi/cell of [32P] orthophosphate ( MP biochemicals ) . Labeled proteins were isolated by immunoprecipitation , purified by SDS-PAGE , and blotted onto PVDF membranes . 32P-labeled proteins were revealed by autoradiography , excised , and the membrane-bound proteins were digested with 50 units of trypsin . Two-dimensional tryptic phosphopeptide analysis was performed on thin-layer cellulose plates ( Merck ) by electrophoresis in pH 1 . 9 buffer followed by chromatography in phosphochromatography buffer . In vitro kinase reactions were performed with recombinant PKCη ( supplemented or not with HisRdx ) together with 100 ng bacterially expressed NS1C or wild-type or mutant PDK1N446 as substrate . Assays were performed for 40 min at 37°C with γ[32P]ATP ( 30 μCi ) in 50 μl labeling buffer , in the presence of TPA and PS . Reaction products were purified by SDS-PAGE and further processed for tryptic phosphopeptide analysis [39] . | The H-1 protoparvovirus ( H-1PV ) is the first replication-competent member of the Parvoviridae family to undergo a phase I/IIa clinical trial in patients suffering from glioblastoma multiforme . Although the intrinsic oncotropism and oncolytic activity of protoparvoviruses are well known , the underlying molecular mechanisms remain elusive . Here we identify a PV-induced intracellular loop-back mechanism that promotes PV replication and cytotoxicity through PI3-kinase-independent stimulation of PDK1 and of the PKC and PKB/Akt1 downstream kinases . This mechanism involves PKCη/Rdx-mediated phosphorylation of PDK1 ( at S138 in mouse or S135 in human ) . Interestingly , this phosphorylation appears as a hallmark of highly aggressive brain tumors . Although H-1PV does not promote it in normal human cells , experimentally administered activated PDK1 variants were able to sensitize these cells to virus infection . These data lead us to propose PDK1phosphoS135 as a new candidate marker for monitoring tumor progression and responsiveness to oncolytic parvovirotherapy , particularly in the case of highly aggressive brain tumors . Furthermore , the sensitivity of PDK1phosphoS135-positive cell lines to inhibitors of PKCη/Rdx argues for considering this complex as a potential target for anticancer drug development . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] | [] | 2015 | PKCη/Rdx-driven Phosphorylation of PDK1: A Novel Mechanism Promoting Cancer Cell Survival and Permissiveness for Parvovirus-induced Lysis |
The activation of interferon ( IFN ) -regulatory factor-3 ( IRF3 ) , characterized by phosphorylation and nuclear translocation of the latent transcription factor , is central to initiating innate antiviral responses . Whereas much has been learned about the upstream pathways and signaling mechanisms leading to IRF3 activation , how activated IRF3 operates in the nucleus to control transcription of IFNs remains obscure . Here we identify EAP30 ( a . k . a , SNF8/VPS22 ) , an endosomal sorting complex required for transport ( ESCRT ) -II subunit , as an essential factor controlling IRF3-dependent antiviral defense . Depletion of EAP30 , but not other ESCRT-II subunits , compromised IRF3-dependent induction of type I and III IFNs , IFN-stimulated genes ( ISGs ) and chemokines by double-stranded RNA or viruses . EAP30 , however , was dispensable for the induction of inflammatory mediators of strict NF-κB target . Significantly , knockdown of EAP30 also impaired the establishment of an antiviral state against vesicular stomatitis virus and hepatitis C virus , which are of distinct viral families . Mechanistically , EAP30 was not required for IRF3 activation but rather acted at a downstream step . Specifically , a fraction of EAP30 localized within the nucleus , where it formed a complex with IRF3 and its transcriptional co-activator , CREB-binding protein ( CBP ) , in a virus-inducible manner . These interactions promoted IRF3 binding to target gene promoters such as IFN-β , IFN-λ1 and ISG56 . Together , our data describe an unappreciated role for EAP30 in IRF3-dependent innate antiviral response in the nucleus .
The early induction of interferons ( IFNs ) and proinflammatory cytokines and chemokines is a hallmark of host innate immune responses to viral infections [1] . This process starts with the detection of viral components , most notably nucleic acids , by immune sentinel molecules termed pattern recognition receptors ( PRRs ) . Of these , the membrane-bound Toll-like receptors ( TLRs ) and the cytosolic retinoic-acid-inducible gene I ( RIG-I ) -like receptors ( RLRs , RIG-I and MDA5 ) are the most characterized [2] . During viral infection , TLR3 located in endolysosomal compartments responds to the presence of double-stranded ( ds ) RNA [3] , which is usually produced as viruses replicate their genomes . TLR3 signals through Toll-interleukin-1 receptor domain-containing adaptor inducing IFN-β ( TRIF ) leading to the activation of the classical IκB kinase ( IKK ) complex and IKK-related kinases , TANK-binding kinase 1 ( TBK1 ) and IKKε . These kinases further activate and result in the nuclear translocation of nuclear factor-kappa B ( NF-κB ) and IFN-regulatory factors ( IRFs ) , transcriptional factors that cooperatively initiate production of type I & III IFNs and inflammatory chemokines such as regulated on activation normal T cell expressed and secreted ( RANTES ) , IFN-γ-inducible protein 10 ( IP-10 ) , etc [4–6] . In the cytoplasm , viral dsRNAs or RNAs bearing 5’-triphosphates /diphosphates are detected by the RLRs , which signal through the mitochondrial antiviral signaling protein ( MAVS , a . k . a . , IPS-1 , VISA and Cardif ) adaptor . Engagement of the RLR-MAVS pathway also leads to activation of IKK and IKK-related kinases and subsequently , NF-κB and IRFs activation [7–9] . IFNs , once induced through the RLR and/or TLR3 pathways , act in an autocrine/paracrine fashion to upregulate the expression of hundreds of IFN-stimulated genes ( ISGs ) that collectively establish an antiviral state , halting viral replication and spread [1] . Within the IRF family of transcription factors , IRF3 is crucial for the initial induction of type I IFN response in a majority of cell types [10] . This protein is constitutively expressed as a latent form that predominantly resides in the cytoplasm . Following virus infection , engagement of the RLR or TLR3 pathway culminates in the phosphorylation of specific serine residues in the C-terminal part of IRF3 by TBK1 or IKKε [4 , 6] . This post-translational modification relieves IRF3 from its auto-inhibitory conformation , enabling the transcription factor to dimerize and subsequently translocate to the nucleus . Therein , activated IRF3 associates with its transcriptional coactivators , CREB-binding protein ( CBP/p300 ) , assembling an “enhanceosome” that stimulates transcription from the IFN-β promoter [4 , 11–13] . In addition , the promoters of type III IFNs [14 , 15] and a subset of ISGs , e . g . , ISG56 , ISG15 , ZAP and OASL [16 , 17] , and those of the chemokines RANTES and IP-10 [18 , 19] , are also transcriptionally controlled by IRF3 . In contrast to the rich information available concerning the signaling pathways and mechanisms leading to IRF3 phosphorylation in the cytoplasm , current knowledge on how activated IRF3 operates in the nucleus to induce antiviral gene expression falls short . The endosomal sorting complex required for transport ( ESCRT ) machinery participates in numerous cellular processes including multi-vesicular body biogenesis , cellular division , and viral budding , etc . The ESCRT machinery comprises a pathway of five distinct complexes , ESCRTs -0 , -I , -II , -III and Vps4 , with each containing multiple subunit proteins [20] . Recently , specific ESCRT proteins/complexes have been implicated in the life cycle of several enveloped viruses , including human immunodeficiency type 1 ( HIV-1 ) , hepatitis B virus ( HBV ) , and hepatitis C virus ( HCV ) , primarily in viral envelopment/budding process [21–26] . Of particular interest , the ESCRT-II complex , a Y-shaped heterotetramer consisting of two copies of EAP20 ( ELL-associated protein of 20 kDa , a . k . a . , VPS25 ) and one subunit each of EAP30 ( a . k . a . , Vps22 or SNF8 ) and EAP45 ( a . k . a . , Vps36 ) [23 , 27] , plays an essential part in HBV RNA trafficking and genome encapsidation [25] . The EAP30 subunit also has been reported to regulate HIV-1 genomic RNA trafficking in the cytoplasm , viral gene expression and production [22] . Together , these recent data highlighted a role for ESCRT-II in viral RNA trafficking in the cytoplasm , prompting us to determine whether this complex or its component ( s ) is involved in innate immune sensing of viruses , which has not been investigated previously . As described below , our study uncovered an unappreciated role of EAP30 in IRF3-dependent antiviral responses . Surprisingly , we found that EAP30 acts in the nucleus to execute this novel function , by facilitating the binding of activated IRF3 to CBP and its target gene promoters .
To determine whether the ESCRT-II complex proteins are involved in innate immune response to viral infections , we studied the impact of depleting each subunit , i . e . , EAP20 , EAP30 and EAP45 , on induction of innate immune genes following stimulation by poly ( I:C ) or infection with Sendai virus ( SeV ) , well-characterized inducers for TLR3 and RIG-I signaling pathways , respectively [4 , 9 , 28] . We first used PH5CH8 cells , which were derived from non-neoplastic hepatocytes transformed with SV40 large T antigen . This cell line harbors intact TLR3 and RIG-I/MDA5 antiviral signaling pathways similar to primary human hepatocytes [28 , 29] . We transfected the cells with a synthetic siRNA duplex specifically targeting each human ESCRT-II subunit or a non-targeting , negative control siRNA , then stimulated the cells by poly ( I:C ) or SeV prior to quantitative reverse-transcription PCR ( qPCR ) analysis of RANTES and IP-10 mRNA expression . Both chemokines are known to be highly induced early after viral infections , and their transcription is under coordinate control of IRF3 and NF-kB [18 , 19 , 29] . qPCR data demonstrated that the ESCRT-II siRNAs were highly effective , curtailing expression of their cognate targets by ~90% , with or without poly ( I:C ) or SeV stimulation ( S1A Fig ) . We note that expression of all three ESCRT-II subunits was unaltered by poly ( I:C ) or SeV stimulation . Immunoblot analyses showed that these siRNAs also efficiently inhibited the expression of their cognate target proteins ( S1B Fig ) . Compared to cells receiving control siRNA , cells transfected with EAP30 or EAP45 siRNA were significantly impaired for poly ( I:C ) - or SeV-induced RANTES and IP-10 mRNA expression ( Fig 1A ) . No such effect , however , was observed in EAP20 siRNA-transfected cells . We confirmed by ELISA that SeV-induced RANTES production in culture supernatants was significantly lower in EAP30 knockdown cells than in cells transfected with control siRNA ( S2 Fig ) . We examined SeV RNA levels in siRNA-transfected cells and demonstrated impaired induction of chemokines in EAP30- or EAP45-depleted cells was not due to reduced viral replication ( S3 Fig ) . To determine whether the observation holds true in a different virus infection setting and in other cell types , we investigated the effects of ESCRT-II knockdown on chemokines production in HCV-infected Huh7 . 5-TLR3 cells . These hepatoma cells are stably reconstituted for TLR3 expression and signaling but are defective for RIG-I [30]; they respond to HCV infection by upregulating RANTES and IP-10 expression through the TLR3 pathway [29] . Efficient , siRNA-mediated knockdown of EAP20 , EAP30 , and EAP45 in Huh7 . 5-TLR3 cells was confirmed by qPCR ( S4 Fig ) . As shown in Fig 1B , poly ( I:C ) - or HCV-induced transcription of RANTES and IP-10 mRNAs was significantly decreased in EAP30 knockdown cells . In contrast , silencing EAP20 or EAP45 did not exhibit an inhibitory effect . HCV RNA levels in cells transfected with control , EAP20 or EAP45 siRNAs were comparable , while significantly higher in EAP30 knockdown cells ( S5 Fig ) , suggesting that the decrease in chemokine induction following EAP30 knockdown was not attributed to reduced viral replication or pathogen-associated molecular pattern ( PAMP ) expression . Taken together , these data suggest that EAP30 is implicated in innate immune responses to viral infections triggered through either TLR3 or RIG-I while EAP45 contributes particularly to signaling through the RIG-I pathway . Apart from inflammatory cytokine/chemokine production , a salient feature of the innate antiviral responses is the early induction of type I IFNs . We determined how depletion of each ESCRT-II subunit influenced activation of the IFN-β promoter via the TLR3 and RIG-I/MDA5 pathways ( illustrated in S6A Fig ) . Following overexpression of individual signaling proteins downstream of the TLR3 pathway including TRIF , TBK1 , IKKε , and IRF3-5D ( a constitutively active , phospho-mimetic IRF3 mutant ) , upregulation of the IFN-β promoter was clearly observed in control siRNA transfected cells , as was in cells transfected with EAP20 or EAP45 siRNA . In contrast , knockdown of EAP30 strongly inhibited IFN-β promoter activation following ectopic expression of any of the 4 signaling molecules ( Fig 2A ) . When we examined activation of the IFN-β promoter via the RIG-I/MDA5 pathway by overexpressing RIG-I , MDA5 , or the downstream adaptor , MAVS , we observed strong inhibitory effects after we silenced expression of EAP30 or EAP45 , but not that of EAP20 , as compared with control siRNA ( Fig 2B ) . Immunoblot analyses confirmed efficient expression of MAVS and two downstream signaling components ( IKKε and IRF3-5D ) that are shared by TLR3 and RLR pathways and proximal to IFN-β induction in cells receiving EAP30 or EAP45 siRNA , as compared with control- or EAP20 siRNA-transfected cells ( S6B and S6C Fig ) . Collectively , these results indicate that EAP30 is an essential contributor to TLR3 and RIG-I/MDA5 signaling pathways leading to IFN-β expression and acts downstream of the IRF3 kinases and the IRF3 phosphorylation step , whereas EAP45 is specifically involved in IFN-β activation via the RIG-I/MDA5-MAVS pathway . These data also are in line with the chemokine production results following dsRNA and viral stimuli ( Fig 1 ) . In subsequent investigations , we focused on dissecting the role of EAP30 . To determine the mechanism by which EAP30 contributes to the IFN response , we performed luciferase reporter assay examining the effects of EAP30 knockdown on viral activation of the IFN-β promoter and the IRF3-responsive PRDIII-I and NF-κB-dependent PRDII elements within the promoter . Interestingly , depletion of EAP30 significantly reduced SeV-induced activation of the IFN-β promoter and the IRF3-dependent PRDIII-I element , but left activation of the NF-κB-dependent PRDII motif unaffected ( Fig 3A ) . This result is consistent with our earlier observation that EAP30 acts downstream of IRF3 phosphorylation ( Fig 2A ) . The specific inhibition on IRF3-dependent promoter was not due to a decrease in IRF3 expression , as EAP30 depletion had no effect on the native IRF3 promoter ( Fig 3B ) , which is constitutively active and whose activity does not change after viral infections [31] . Moreover , the levels of endogenous IRF3 transcript ( S7 Fig ) and protein ( see below , Fig 4B and 4D ) were not altered by EAP30 knockdown . To further confirm that EAP30 is dispensable for viral activation of NF-κB-dependent gene expression , we quantified transcript for IL-6 , IL-8 , and MIP-1β . SeV strongly upregulated the expression of all 3 strict NF-kB target genes , without a discernable difference between control and EAP30 siRNA-transfected cells ( Fig 3C ) . Examining the expression of 7 additional genes ( TRAF1 , IL32 , RELB , P100 , BIRC3 , COX2 and SGPP2 ) whose expression is predominantly controlled by NF-kB [32–36] led to the same conclusion ( S8 Fig ) . Under the same experimental conditions , EAP30 knockdown led to profound reduction in virus-induced IFN-β mRNA abundance ( Fig 3D ) . In addition , the upregulation of IFN-λ1 ( a . k . a . , IL29 ) and IFN-λ2/3 ( a . k . a . , IL28A/B ) mRNAs , which is also critically dependent on IRF3 , was impaired by 80% and 93% , respectively , following EAP30 silencing ( Fig 3D ) . Quantification of IFN antiviral activity in culture supernatants by a bioassay based on VSV plaque reduction revealed that SeV-induced IFN production was diminished in EAP30 knockdown cells , in comparison to cells transfected with control siRNA ( Fig 3E ) . Q-PCR analysis showed that knockdown of EAP30 resembled the effect of IRF3 silencing in that it substantially curtailed the induction of 4 well-characterized ISGs , OASL , PKR , MX1 , and ISG15 , by poly ( I:C ) ( Fig 4A ) or SeV ( Fig 4C ) . The same could be said regarding the induction of ISG56 protein ( Fig 4B and 4D ) . Immunoblotting data also demonstrated that EAP30 siRNA did not affect IRF3 protein expression , as compared to control siRNA ( Fig 4B and 4D , compare lane 5 vs lane 3 ) . Moreover , depletion of EAP30 significantly impaired SeV-induced upregulation of five additional ISGs , i . e . , OAS1 , ISG20 , RSAD2 ( a . k . a . , viperin ) , IRF7 and IRF1 ( S9 Fig ) , further ruling out a gene-specific effect of EAP30 on ISG induction . Altogether , these results suggest that EAP30 is specifically required for IRF3-dependent innate immune response , including activation of type I and III IFN production and induction of ISGs . To corroborate these findings in a different cell type , we developed HEK293 cells with stable knockdown of EAP30 ( referred to as 293-shEAP30 cells ) by lentiviral-mediated transfer of a shRNA construct specifically targeting EAP30 . Cells transduced with a non-targeting , scrambled control shRNA ( referred to as 293-shCon cells ) served as a negative control . As shown in S10 Fig , there was significant reduction in EAP30 protein abundance in 293-shEAP30 cells as compared with 293-shCon cells . This was correlated with diminished viral induction of ISG15 , IFIT3 and MDA5 proteins . Viral upregulation of these 3 ISGs is controlled by IRF3 [16 , 37] , similar to that of ISG56 . In stark contrast , the upregulation of P100 and TRAF1 proteins by SeV infection was comparable between 293-shEAP30 and 293-shCon cells ( S10 Fig ) , again suggesting that EAP30 is dispensable for viral activation of the NF-κB arm of host responses . Of note , we did not observe any difference in cell viability or proliferation rate between 293-shCon cells and 293-shEAP30 cells ( S11 Fig ) . Thus , the impaired ISG induction following EAP30 knockdown cannot be attributed to a general effect on cellular physiology . Although a small subset of ISGs such as ISG56 , ISG15 , and OASL , etc , can be transcriptionally upregulated by activated IRF3 directly , majority of the ~300 known ISGs is regulated by the paracrine/autocrine action of IFNs through the IFN receptors . Thus , we next investigated whether EAP30 has a role in IFN signaling through the Jak-Stat pathway . To this end we examined ISG induction following IFN-α stimulation ( Fig 4E ) . In control siRNA transfected cells , IFN-α upregulated transcription of OASL , PKR , MX1 , and ISG15 to various degrees . This effect was not inhibited by EAP30 or IRF3 siRNA . Likewise , we found IFN-α was similarly effective in upregulating the protein levels of ISG15 and IFIT3 between 293-shCon and 293-shEAP30 cells ( S12A Fig ) . Consistent with the ISG protein data , pretreatment of IFN-α acted comparably in 293-shCon and 293-shEAP30 cells in protecting cells from subsequent challenge by VSV-Luc ( S12B Fig ) , a recombinant vesicular stomatitis virus ( VSV ) encoding luciferase that serves as a readout of viral replication . In aggregate , we conclude that EAP30’s contribution to innate immune responses is prior to the induction of IFNs and that EAP30 is not involved in downstream IFN signaling , once IFNs are produced . Having shown that EAP30 is specifically required for IRF3-dependent innate antiviral gene expression , we sought to directly assess the biological function of this protein in antiviral defense . We transfected PH5CH8 cells with control or EAP30 siRNA , then mock-treated or stimulated cells with poly ( I:C ) , followed by infecting them with VSV-Luc . In the absence of poly ( I:C ) pretreatment , VSV-Luc replicated robustly in the cells as indicated by high levels of luciferase activity . Compared to control siRNA transfected cells , cells with EAP30 knockdown supported 4 . 6-fold higher level of VSV replication ( Fig 5A , compare filled bar with empty bar in the “no pIC” group ) . Prior poly ( I:C ) stimulation induced an antiviral state , reducing VSV-Luc replication by 42-fold in control siRNA-transfected cells ( compare the two empty bars between “pIC” and “no pIC” groups ) . EAP30 knockdown severely compromised poly ( I:C ) -induced antiviral state , enabling 38 . 5-fold higher viral replication than in control siRNA-transfected , poly ( I:C ) -stimulated cells ( compare filled bar with empty bar in the “pIC” group ) . Of note , the protective effect of poly ( I:C ) , as gauged by the fold-reduction in VSV-encoded luciferase activity , was attenuated by ~88% in EAP30 knockdown cells ( ~5-fold , compare the two filled bars between “pIC” and “no pIC” groups ) compared with that in control siRNA-transfected cells ( 42-fold ) . These data provide direct evidence that endogenous EAP30 is essential for the establishment of an antiviral state in the cell . Confirming that the observation is not a cell-specific or virus-specific phenomenon , we found that knockdown of EAP30 also resulted in significant weakening of antiviral defense against HCV infection in hepatoma cells with and without poly ( I:C ) pre-treatment , enabling higher levels of intracellular HCV RNA replication than in cells transfected with control siRNA ( Fig 5B ) . We next investigated whether overexpression of EAP30 enhances cellular antiviral defense . Compared with empty control vector-transfected cells , PH5CH8 cells transiently overexpressing EAP30 supported similar levels of VSV-Luc replication , and poly ( I:C ) -mediated antiviral effect was also comparable ( Fig 5C ) . Similar results were obtained comparing HCV RNA replication in HCV-infected hepatoma cells ectopically expressing control vector or EAP30 ( Fig 5D ) . We conclude from these experiments that while EAP30 is required for establishment of an antiviral state in the cells , overexpression of EAP30 per se is not sufficient for activating or augmenting antiviral defense . Although our earlier gene reporter data generated using the phospho-mimetic IRF3-5D ( Fig 2A ) had suggested that EAP30’s contribution to IFN activation is downstream of IRF3 , they did not reveal directly whether EAP30 depletion affects virus-induced activation of IRF3 . To clarify , we determined the status of SeV-induced phosphorylation , dimerization and nuclear translocation of IRF3 in cells transfected with control or EAP30 siRNA . Immunoblotting data demonstrated that the abundance of phosphor-IRF3 induced by SeV infection was comparable in PH5CH8 cells with and without EAP30 knockdown ( Fig 6A , compare lanes 4 vs 2 ) . Likewise , SeV-induced IRF3 dimerization was not disturbed by EAP30 depletion ( Fig 6B , compare lanes 4 vs 2 ) . In addition , cytoplasmic ( CE ) and nuclear ( NE ) fraction assay revealed that knockdown of EAP30 did not influence the accumulation of IRF3 in the nucleus of SeV-infected cells ( Fig 6C , compare lanes 8 vs 6 ) . Interestingly , a fraction of EAP30 was found to localize in the nucleus , and the nuclear abundance of EAP30 was not different before and after SeV infection ( compare lanes 6 vs 5 and 8 vs 7 , respectively ) . This observation was confirmed by confocal fluorescence microscopy , which revealed that in PH5CH8 cells endogenous EAP30 was indeed distributed to both cytoplasm and nucleus , prior to and after SeV infection ( S13 Fig ) . IRF3 immunofluorescence staining data demonstrated that SeV-induced IRF3 nuclear translocation occurred in ~95% and ~98% of PH5CH8 cells with or without EAP30 knockdown ( S14 Fig ) , again suggesting IRF3 nuclear translocation is EAP30-independent . Collectively , these data are in keeping with the IRF3-5D overexpression data ( Fig 2A ) , and lend further support to the notion that EAP30 regulates a step downstream of IRF3 activation , likely after phosphorylated IRF3 has moved into the nucleus . To determine the exact role of EAP30 in IRF3-dependent antiviral response , we conducted comparative studies in293-shEAP30 cells and293-shCon cells . Immunoblot analysis revealed profound loss of EAP30 protein in 293-shEAP30 cells as compared with 293-shCon cells ( Fig 6D , compare lanes 3 and 4 vs 1 and 2 ) , concomitant with diminished ISG56 induction by SeV ( compare lanes 4 vs 2 ) . This was observed despite that virus-induced IRF3 phosphorylation was intact in EAP30 knockdown cells ( compare lanes 4 vs 2 ) . These data thus confirmed in an additional cell line that EAP30 is not required for IRF3 phosphorylation . In addition to permitting IRF3 dimerization and translocation to the nucleus , virus-induced phosphorylation of IRF3 C-terminal serine residues accommodates binding of the transcription factor to its co-activators , such as CBP , thereby forming an enhanceosome that activates IFN-β promoter transcription [11–13] . To evaluate whether EAP30 is required for IRF3 binding to CBP , we conducted Co-IP experiments in 293-shCon and 293-shEAP30 cells , mock-infected or infected with SeV for 8 h . We found that SeV infection induced a complex formation between endogenous IRF3 and CBP in HEK293-shCon control cells . However , this was diminished in 293-shEAP30 cells ( Fig 6E ) . To characterize the functional consequence of impaired IRF3-CBP complex formation as a result of EAP30 depletion , we further performed ChIP assay quantifying the IRF3 binding to three well-known IRF3 target gene promoters , IFN-β , IFNL1 , and IFIT1 ( a . k . a . , ISG56 ) . We found that SeV infection triggered significant IRF3 binding to IFN-β , IFNL1 , and IFIT1 promoters in 293-shCon cells , but was barely able to do so in 293-shEAP30 cells ( Fig 6F ) . These results imply that EAP30 is required for IRF3 binding to CBP and subsequently its target gene promoters , explaining why EAP30 knockdown was associated with impaired viral induction of type I and III IFNs and ISGs despite intact IRF3 activation . Since our earlier data ( Fig 3 and S8 Fig ) had shown that EAP30 knockdown had no demonstrable effect on viral induction of NF-κB-dependent and IRF3-independent genes , we also performed ChIP assay to evaluate SeV-induced NF-κB binding in 293-shEAP30 cells in comparison with 293-shCon cells ( S15 Fig ) . The data showed that the increased p65 binding to NF-κB motifs within IL8 , CXCL1 and IL32 promoters following SeV infection was not impaired by EAP30 knockdown . To determine how EAP30 regulates the IRF3-CBP complex formation and binding of IRF3 to target promoters , we tested the hypothesis that EAP30 acted as an interaction partner facilitating IRF3 binding to CBP and the target promoters in the nucleus . Co-IP experiments were performed using nuclear extracts of HEK293FT cells to evaluate the potential interactions between EAP30 and CBP , EAP30 and IRF3 , prior to and after SeV infection ( Fig 7 ) . It was found that EAP30 weakly interacted with CBP ( lane 8 ) and IRF3 ( lane 14 ) in uninfected cells , and that both associations were notably stronger after SeV infection ( compare lanes 11 vs 8 and lanes 17 vs 14 , respectively ) . In contrast , EAP20 did not form a complex with either CBP ( lanes 9 and 12 ) or IRF3 ( lane 15 and 18 ) , regardless of SeV infection status , suggesting that the EAP30 associations with CBP and IRF3 were both specific . Moreover , immunoblotting of CE and NE fractions of the cell lysates demonstrated that indeed EAP30 not only localized to the cytoplasm but also was present in the nuclear fraction , and that the abundance of nuclear EAP30 protein did not obviously change following SeV infection ( S16 Fig ) . These data are in agreement with our earlier CE/NE fractionation data generated from PH5CH8 cells ( Fig 6C ) . Both datasets thus support a role of EAP30 in the nucleus . Activation of the IFN-β promoter occurs after activated IRF3 translocates to nucleus and associates with the CBP/p300 co-transcriptional factors . This transcriptional complex further binds to IFN-β promoter to drive up type I IFN transcription [38] . Since our earlier data showed that ectopic expression of EAP30 alone was insufficient in conferring antiviral activity ( Fig 5C and 5D ) , we speculated that coordinate action of EAP30 , IRF3 and CBP is required for activating antiviral responses . This hypothesis is mostly plausible for the functional significance of the interactions of EAP30 with IRF3 and CBP ( Fig 7 ) . To test this , we set up co-expression experiments in HEK293 cells while keeping the transfected plasmid DNA amount constant , followed by challenging cells with VSV-Luc to evaluate the antiviral protection ( Fig 8A ) . The results showed that overexpression of EAP30 , CBP , or IRF3 alone was not sufficient to confer antiviral activity as none inhibited replication of VSV-Luc compared to vector control . Co-expression of CBP and IRF3 reduced VSV-Luc replication by 50% , while co-expression of EAP30 with IRF3 or CBP induced negligible antiviral effect . Interestingly , when EAP30 was expressed together with CBP and IRF3 , VSV-Luc replication was curtailed by ~75% . In contrast , we did not observe a synergistic effect when we replaced the EAP30 vector with an EAP20 construct in the triple transfection group . Immunoblotting data confirmed the successful expression of CBP , IRF3 , EAP30 , and EAP20 in each transfection conditions ( Fig 8B ) . The antiviral effects were accompanied with expression of IRF3-dependent antiviral genes , IFN-β ( Fig 8C ) , OASL ( Fig 8D ) , and IFN-λ1 ( Fig 8E ) , and the extent of antiviral gene upregulation was correlated with the observed antiviral effects . Specifically , substantial upregulation of the three representative IRF3 target genes was seen in cells co-transfected with IRF3 and CBP . Adding EAP30 , but not EAP20 , to the transfection cocktail enhanced the antiviral gene expression further ( Fig 8C–8E ) , resulting in the most potent antiviral effect ( Fig 8A , the last column from the left ) . Collectively , these data demonstrate that EAP30 synergizes with IRF3 and CBP to activate the IFN antiviral response , suggesting that EAP30 is required for optimal induction of IRF3-dependent innate antiviral defense .
In this study , we have uncovered a novel role for the ESCRT-II subunit EAP30 in IRF3-dependent innate immune responses to viral infections . We found that depletion of EAP30 dampens the induction of type I and type III IFNs , ISGs and chemokines via TLR3 and RLR signaling pathways , impairing the establishment of an antiviral state . Importantly , this previously unappreciated effect of EAP30 was observed in multiple cell types , including non-neoplastic hepatocytes PH5CH8 , hepatoma Huh7-TLR3 and Huh7 . 5-TLR3 cells , and HEK293 cells . It also held true in distinct viral infection settings , i . e . , following infection by SeV , HCV or VSV , or stimulation by poly ( I:C ) , a viral dsRNA surrogate . Interestingly , unlike its conventional role in the ESCRT pathway and its emerging role in viral budding , EAP30 was found to operate in the nucleus rather than cytoplasm to fulfill its function in facilitating IRF3-mediated antiviral defense . Specifically , our data demonstrate that EAP30 forms a complex with IRF3 and its transcriptional co-activator , CBP ( Fig 7 ) , and these interactions are pivotal for controlling transcription of antiviral genes , as depletion of EAP30 resulted in profound reduction in virus-induced IRF3 binding to CBP ( Fig 6E ) and to target gene promoters such as IFN-β , IFN-λ1 and ISG56 ( Fig 6F ) . Lending further support to this notion , overexpression of EAP30 together with IRF3 and CBP activated expression of antiviral genes and protected cells against VSV challenge ( Fig 8 ) . Taken together , our data suggest that EAP30 is an essential factor that bridges the collaborative action of IRF3 and CBP in the nucleus in inducing innate antiviral responses . Several lines of evidence suggest that EAP30 acts independent of the ESCRT-II complex to facilitate IRF3-mediated innate immune responses . First , knockdown of EAP20 , another component of the ESCRT-II complex , had no demonstrable effect on induction of IRF3 target genes ( Figs 1 and 2 ) . Second , EAP20 was not associated with IRF3 or CBP , regardless of viral infection status ( Fig 7 ) . Third , overexpression of EAP20 failed to act in synergy with ectopically co-expressed IRF3 and CBP to drive up IFN antiviral responses , as opposed to the effect of EAP30 ( Fig 8 ) . Fourth , while EAP30 knockdown inhibited activation of the IFN-β promoter downstream of both TLR3 and RLR pathways ( Fig 2 ) , depletion of the third ESCRT-II component , EAP45 , undermined the RLR pathway ( Fig 2B ) but left TLR3 signaling intact ( Fig 2A ) . Consistent with this , our data showed that EAP45 acts downstream of RIG-I , MDA5 , and MAVS but had no effect on IFN activation by the IRF3 kinases , TBK1 and IKKε , or by IRF3-5D , a constitutively active IRF3 mutant mimicking phosphorylated IRF3 ( Fig 2 ) . Preliminary experiments indicated that EAP45 is required for activation of NF-κB downstream of the RLR-MAVS pathway ( S17 Fig ) . Further studies are needed to understand the precise role of EAP45 in RLR signaling . The activation of IRF3 , characterized by its C-terminal phosphorylation , dimerization and subsequent nuclear translocation , has been extensively studied . A plethora of information is available concerning the upstream PRRs , adaptors , kinases and other regulators that control IRF3 activation . In contrast , relatively little is known about the molecular mechanisms by which activated IRF3 operates in the nucleus to induce transcription of IFNs and ISGs of direct IRF3 target . Our data have clearly shown that EAP30 regulates a step after IRF3 activation , as knockdown of EAP30 had no appreciable effect on virus-induced phosphorylation ( Fig 6A and 6D ) , dimerization ( Fig 6B ) , or nuclear translocation ( Fig 6C ) of IRF3 , while inhibiting IFN induction by IRF3-5D ( Fig 2A ) . The lack of an effect on activation of NF-κB-dependent promoter ( Fig 3A ) and upregulation of NF-κB-dependent and IRF3-independent genes ( Fig 3C and S8 Fig ) is also consistent with the fact that bifurcation of the two signaling branches occurs prior to the kinases that activate these transcription factors . The data that a fraction of EAP30 localizes in the nucleus ( Fig 6C ) and is required for virus-induced association of IRF3 and CBP ( Fig 6E ) and IRF3 binding to its target promoters ( Fig 6F ) strongly argue nucleus as the location where EAP30 exerts its effect on IRF3-mediated innate immune responses . Of note , a role for ESCRT-II proteins in regulating nuclear processes is not unexpected . It has been reported that ESCRT-II plays role in gene transcription in the nucleus by associating with the elongation factor for RNA Polymerase II ( ELL ) and increasing the catalytic rate of transcription elongation by Pol II [39–41] . It should be noted , however , the novel role of EAP30 in IRF3-dependent innate immune responses revealed in our study is by no means a non-specific effect on gene transcription . As noted above , EAP30 depletion did not impact viral induction of genes solely controlled by NF-κB ( Fig 3C and S8 Fig ) . Knockdown of EAP30 had no demonstrable effect on ISG expression and establishment of an antiviral state induced by IFN-α through the Jak-Stat signaling , either ( Fig 4E and S12 Fig ) . What is the exact role of EAP30 in regulating IRF3-mediated innate immune responses ? We found overexpression of EAP30 per se did not augment the antiviral phenotypes ( Figs 5C and 5D and 8 ) , which may be explained by the data that the endogenous EAP30 was already abundantly expressed . Alternatively , additional factors/processes may be involved . Our data demonstrate EAP30 forms a complex with IRF3 and CBP in the nucleus ( Fig 7 ) and is required for virus-induced association of IRF3 and CBP and IRF3 binding to target promoters ( Fig 6E and 6F ) , suggesting a model in which EAP30 may bridge the IRF3-CBP interactions , or perhaps helps maintain certain conformations of IRF3 and/or CBP , thereby facilitating the formation of so-called “enhanceosome” that assembles on and activates IFN-β and IFN-λ promoters . Supporting this hypothesis , we found that EAP30 bound both IRF3 and CBP in nuclear extracts and the associations were notably stronger after SeV infection ( Fig 7 ) . In addition , when EAP30 was ectopically co-expressed with IRF3 and CBP , IFN antiviral responses ensued and VSV replication was inhibited ( Fig 8 ) , supporting the notion that EAP30 acts in synergy with IRF3 and CBP . It should be noted that CBP is known to bind to a variety of transcriptional factors including the STATs and NF-κB [42] . Further studies will be needed to determine why EAP30 was required for IRF3-dependent gene transcription but dispensable for expression of genes downstream of NF-κB or Jak-STAT signaling . At present , we do not know the precise mechanism that confers EAP30 the ability to synergize the action of IRF3 and CBP on activating the IFN response . Possible scenarios include that EAP30 , IRF3 , and CBP may assemble in different manners and possibly recruit additional factors to the promoter after virus infection . Clearly , overexpression of EAP30 alone was not sufficient to activate IFN production ( Fig 8 ) , and the distribution of EAP30 in the nucleus vs cytoplasm did not exhibit an obvious change before and after SeV infection ( Fig 6C , S13 and S16 Figs ) . It will be interesting to determine in future studies whether EAP30 undergoes post-translational modifications and/or conformational changes after viral infection that alters its ability to interact with IRF3 and CBP . In summary , this is the first study that demonstrates the ESCRT-II subunit EAP30 is involved in IRF3-dependent innate immune responses by facilitating the IRF3 binding to CBP and its target gene promoters . Our data provide novel insights into nuclear processes that regulate IRF3-mediated antiviral gene expression . Conceivably , exploiting or enhancing EAP30-mediated signaling may lead to new antiviral strategies for combating infectious diseases . Conversely , targeting EAP30 for inhibition may open new avenues of treating autoimmune diseases associated with aberrant IFN responses .
PH5CH8 non-neoplastic hepatocytes ( provided by Nobuyuki Kato , Okayama University , Japan ) [43] , human hepatoma Huh7 . 5-TLR3 and Huh7-TLR3 cells that were stably reconstituted for the expression of human TLR3 ( developed in this laboratory ) [29 , 30] , and human embryo kidney HEK293 cells ( obtained from American Type Culture Collection ) , were maintained as described previously [28 , 29 , 44] . HCV ( JFH1 strain ) was produced by transfecting Huh7 . 5 cells with in vitro-transcribed JFH1 RNA and infectious HCV titers were determined as described [29 , 45] . VSV-GFP was a gift from Sean Whelan ( Harvard University ) . Sendai virus ( SeV , Cantell strain ) was obtained from Charles River Laboratories . Poly ( I:C ) was purchased from Sigma ( Sigma-Alrich , St . Louis , MO ) . For stimulation of cells , poly ( I:C ) was added directly into culture medium at a final concentration of 10–20 μg/ml to engage the TLR3 pathway or complexed with Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) ( at 1:2 ratio ) before adding to the cells to elicit RLR signaling . For HCV and SeV infections , cells were infected with JFH1 virus ( MOI = 0 . 1 ) and with 160 hemagglutinin units ( HAU ) /ml of SeV , respectively . A human CCL5/RANTES DuoSet ELISA kit ( R & D systems ) was used to measure RANTES production in cell culture supernatants . Production of biologically active human IFNs in cell culture supernatants was determined by a microplaque reduction assay using VSV on Vero cells , as described previously [46] . Plasmid vectors encoding human TRIF , RIG-I , and MAVS have been described previously [28 , 44 , 47 , 48] . The following plasmids were provided by their indicated contributors: pcDNA3-FlagTBK1 and pcDNA3-FlagIKKε ( Kate Fitzgerald ) [4]; pEFBos-RIG-I-N ( encoding the N-terminal 229 amino acids of RIG-I ) and pEFBos-FlagMDA5 ( Takashi Fujita ) [9]; pGFP-IRF3-5D ( Rongtuan Lin ) [13] . pcDNA3β-FLAG-CBP-HA ( Addgene , Cambridge , MA , plasmid #32908 ) [49] . The reporter plasmids pIFN-β-Luc [13] , PRDII-Luc [50] , ( PRDIII-I ) 4-Luc [51] , and pIRF3 ( -779 ) -Luc containing human IRF3 promoter upstream of firefly luciferase reporter gene [31] were kind gifts from Rongtuan Lin , Michael Gale , Christina Ehrhardt , and Paula Pitha-Rowe , respectively . pRL-TK ( Promega , Madison , WI ) was used to normalize transfection efficiencies . Plasmid DNAs were transfected into cells using Lipofectamine 2000 as per the manufacturer's instructions . To generate expression constructs for human EAP20 and EAP30 , full-length cDNA encoding EAP20 or EAP30 was amplified from PH5CH8 cells by RT-PCR with the following gene-specific primers: EAP20-EcoRI-ClaI-F ( 5’- ctctaGAATTCATCGATATGGCGATGAGTTTCGAGTGG -3’ ) and EAP20-KpnI-NheI-R ( 5’- taaGCTAGCGGTACCCTAGAAGAACTTGACGCCTC-3’ ) for the EAP20 construct; EAP30-EcoRI-F ( 5’-ttGAATTCATGCACCGCCGCGGGGTGGGAG-3’ ) and EAP30-KpnI-R ( 5’-aaGGTACCTCAGGGGAGGGCTTCTCTGGC-3’ ) for the EAP30 plasmid . The cDNA fragment of the EAP20 or EAP30 gene was cloned between the EcoRI and Kpn I restriction sites of the pCAGGS-HA mammalian expression vector ( a gift from Shaobo Xiao ) . The target gene sequences in the pCAGGS-HA-EAP30 and pCAGGS-HA-EAP20 recombinant plasmids were confirmed by DNA sequencing . To knock down the expression of individual ESCRT-II subunits , siRNA duplexes targeting human EAP20 , EAP30 , and EAP45 were synthesized by GE Dharmacon ( Fisher Scientific , Pittsburgh , PA ) with sequences as follows: siEAP30 , CUUGCAGAGGCCAAGUAUA ( UU ) [22]; siEAP20 , GUCGAUCCAGAUUGUAUUA ( UU ) and siEAP45 , GGAAUAUUGCAGGUGCCUU ( UU ) [25] . Cells ( 2–4 x105 cells per well in 6-well plates ) were transfected with 200 μM siRNA by Lipofectamine 2000 ( Invitrogen ) for 48 h followed by indicated stimuli . In each experiment , the efficiency of siRNA silencing was evaluated by qPCR . To stably knock down EAP30 in HEK293 cells , pLKO . 1-shSNF8#27 carrying a human EAP30 shRNA with the target sequence of ATCTTGACTGACATCCTGGGC ( GE Dharmacon , TRCN0000015727 ) was used for lentivirus packaging and transduction of cells . Following selection in medium containing 2 μg/ml of puromycin , survived cells were pooled and their phenotypes were characterized by immunoblotting . Total cellular RNA was extracted using TRIzol ( Invitrogen ) and subsequently used for synthesis of cDNA . The abundance of RANTES , IP-10 , IFN-β , IFN-λ1 , IFN-λ2/3 , OASL , Mx1 , PKR , ISG15 , IL-6 , IL-8 , MIP-1β and 28S ( or β-actin , internal controls ) transcripts , and HCV RNA levels were analyzed by qPCR using gene-specific primers ( S1 Table ) and GoTaq qPCR Master Mix ( Promega ) on an iCycler IQ5 real-time PCR system ( Bio-Rad , Hercules , CA ) . For PCR detection , after enzyme activation of 2 minutes at 95°C , 40 cycles of 15 seconds at 95°C and 1 minute at 60°C were performed . Cq values were obtained by using the formula 2-ΔCq . The relative abundance of each target gene was determined by normalization to endogenous 28S or β-actin mRNA . Fold change in expression of each target gene in response to the indicated stimulus was calculated by comparing with mock-stimulated cells . The promoter activities of IFN-β , PRDII , PRDIII-I , and IRF3 ( -779 ) were determined by cotransfecting cells with pIFN-β-Luc , pPRDII-Luc , pPRDIII-I-Luc , or pIRF3 ( -779 ) -Luc , respectively , together with the pRL-TK plasmid that expresses Renilla luciferase as an internal control , as described previously [29 , 47] . Briefly , cells ( 4 x104 cells per well in 48-well plates ) were cotransfected with the indicated promoter-reporter plasmid ( 80 ng ) and pRL-TK ( 20 ng ) , with/without addition of 100–200 ng of an expression vector encoding a gene of interest . Twenty-four hours later , cells were mock-treated or infected with SeV for 8 h , then lysed and assayed for firefly luciferase and Renilla luciferase activities . Data are expressed as mean relative luciferase activity plus standard deviation for one representative experiment carried out in triplicate of at least three independent experiments . Immediately before harvesting , cells were washed with cold phosphate-buffered saline ( PBS ) . Whole cell lysates were prepared using a modified radioimmunoprecipitation assay ( RIPA ) buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 1% NP-40 , 0 . 1% SDS , 5 mM EDTA , 0 . 5% sodium deoxycholate ) supplemented with protease inhibitor cocktails ( Sigma-Alrich ) . Whole cell extracts were quantified and subjected to electrophoresis and immunoblot analysis as previous described [28 , 48] . The following monoclonal ( mAb ) or polyclonal ( pAb ) primary antibodies were used: mouse anti-FLAG M2 mAb at 1:1 , 000 ( Stratagene , La Jolla , CA ) ; anti-actin mAb at 1:10 , 000 ( Sigma-Alrich ) ; anti-ISG15 mAb at 1:1000 , anti-IFIT3 mAb at 1:2000 , anti-TRAF1 mAb at 1:1000 , anti-p100/p52 mAb at 1:1000 , anti-IRF3 mAb at 1:2 , 000 , anti-CBP mAb at 1:200 , and anti-EAP30 mAb at 1:1 , 000 ( Santa Cruz Biotechnology , Dallas , TX ) ; and anti-HA ( 12CA5 , hybridoma supernatant ) at 1:100; rabbit anti-IRF3 pAb at 1:10 , 000 ( a gift from Michael David , University of California-San Diego ) ; rabbit anti-phospho-Ser396-IRF3 at 1:1 , 000 ( Cell signaling Technology , Danvers , MA ) ; rabbit anti-CBP pAb at 1:200 ( Santa Cruz ) ; rabbit anti-ISG56 pAb at 1:500 [30]; goat anti-MDA5 pAb at 1:200 ( Abcam ) ; rabbit anti-SeV pAb at 1:10 , 000 ( a gift from Ilkka Julkunen , National Public Health Institute , Helsinki ) ; rabbit anti-SNF8 ( EAP30 ) pAb at 1:300 ( Proteintech , Chicago , IL ) . The secondary antibodies were peroxidase-conjugated goat anti-rabbit or anti-mouse IgGs ( Southern Biotech , Birmingham , AL ) . Protein bands were visualized using Immobilon Western Chemiluminescent HRP substrate ( Milipore , Billerica , MA ) , followed by exposure to X-ray film . Cells ( 2–4 x106 ) cultured in 10-cm dish were transfected and stimulated as indicated . At time of harvesting , cells were lysed in a buffer containing 50 mM HEPES ( pH 7 . 4 ) , 1 . 5 mM EDTA , 150 mM NaCl , 10% glycerol , 10 mM NaF , 1 mM Na3VO4 , 0 . 5 mM dithiothreitol ( DTT ) , 1% Triton X-100 , and protease inhibitor cocktails , and clarified by centrifugation . Six hundred micrograms of each sample were subjected to IP . Samples were pre-cleared with protein A/G PLUS agarose ( Santa Cruz ) and control IgG for 1–2 hours at 4°C and then supernatant were incubated for overnight with anti-CBP antibodies ( 1:50 ) or rabbit anti-IRF3 pAb ( 1:500 ) at 4°C before incubation with protein A/G PLUS agarose for additional 4 hours . Protein-antibody immune complexes were washed with PBS containing 0 . 5% NP-40 and dissolved in SDS loading buffer and subsequently subjected to immunoblot analysis as described above . Cells ( 4 x104 ) grown in 8-well chamber slides were transfected with indicated siRNA for 48 h prior to mock infection or infection with SeV for additional 16 h . Cells were fixed with 3 . 7% formaldehyde , permeabilized in 0 . 2% Triton X-100 , and subsequently immunostained with rabbit anti-IRF3 pAb at 1:400 or mouse anti-EAP30 mAb ( 1:100 ) followed by Alexa Fluor 488-conjugated anti-rabbit secondary antibody ( Invitrogen ) or Texas Red-conjugated anti-mouse secondary antibody ( Southern Biotech ) at 1:200 . After counterstaining the nuclei with 4' , 6-diamidino-2-phenylindole ( DAPI ) , slides were mounted and examined by fluorescence microscopy . Twenty micrograms of each protein sample were fractionated on 7 . 5% native acrylamide gel as described previously [52] . After electrophoresis , proteins were transferred to nitrocellulose membrane and analyzed by immunoblotting . IRF3 dimer and monomer were detected by rabbit anti-IRF3 pAb . Cytoplasmic and nuclear extracts were prepared from cells following indicated stimulus . Briefly , cells grown in 10-cm dish were washed with cold PBS and scraped off in 1 ml of PBS . The cells were pelleted at 3 , 000 rpm for 30 seconds at 4°C . To extract cytoplasmic proteins , a low salt buffer was added ( 50 mM HEPES pH 7 . 9 , 10 mM KCl , 1 mM EDTA pH 8 . 0 , 1 mM EGTA pH 8 . 0 , 1 mM Na3VO4 , 1mM Na4P2O7 , 20 mM NaF , 1 mM DTT , 0 . 5 mM PMSF , and protease inhibitor cocktails ) and cells were left on ice for 10 min before adding 1% NP-40 to the suspension . After centrifugation at 6 , 000 rpm for 30 seconds , the supernatant was collected and kept as cytoplasmic extract ( CE ) . To further extract nuclear proteins , pellets were washed in 1 . 0 M sucrose in low salt buffer and centrifuged at 12 , 000 rpm for 10 min at 4°C . The pellets were then lysed in a high salt buffer ( 50 mM HEPES pH 7 . 9 , 400 mM KCl , 1 mM EDTA pH 8 . 0 , 1 mM EGTA pH 8 . 0 , 20 mM NaF , 1 mM Na3VO3 , 1mM Na2P2O7 , 10% glycerol , 1 mM DTT , 0 . 5 mM PMSF , and protease inhibitor cocktails ) and vortexed at 4°C for 20 min . The nuclear extract ( NE ) was collected from supernatant after 10 min of centrifugation . HEK293-shEAP30 and HEK293-shCon cells ( 1 . 5x107 cells cultured in 15-cm dish ) mock-infected or infected with SeV were cross-linked with 1% formaldehyde at room temperature for 10 min . The reaction was then stopped by adding 0 . 125 M glycine . ChIP assays were performed using the ChIP-ITTM Express kit ( Active Motif , Carlsbad , CA ) according to the manufacturer’s instructions . In brief , the cells were harvested and suspended in a hypotonic lysis buffer ( 10 mM Tris-HCl , pH 7 . 5 , 10 mM KCl , 2mM MgCl2 , 2 . 5 mM sodium pyrophosphate , 1 mM beta-glycerolphosphate , 0 . 5% NP-40 , protease inhibitor cocktails ) to prepare the nuclei . After centrifugation , nuclei were resuspended in a nuclear lysis buffer ( 50 mM Tris-HCl , pH 8 . 1 , 10 mM EDTA , 1% SDS , protease inhibitor cocktails ) and lysates were sonicated to generate DNA fragments with an average length of ~300 bp . After removal of cell debris by centrifugation , supernatants were pre-cleared with magnetic beads and control IgG for 1 h at 4°C . Following removal of the beads , lysates were incubated for overnight with 2 μg of rabbit IgG control , rabbit anti-IRF3 pAb , or rabbit anti-p65 plus the beads . The immune complexes were collected , washed four times , and subsequently eluted in elution buffer containing 1% SDS and 0 . 05 M NaHCO3 . Crosslinks were then broken in 250 mM NaCl by incubation at 65°C for 2 . 5 h . The resulting samples were treated with 10 mM EDTA , 40mM Tris-HCl , pH 6 . 5 and 200 μg/ml of proteinase K at 37°C for 1 h and DNA was purified with the MiniElute PCR purification kit ( Qiagen , Hilden , Germany ) . The immunoprecipitated DNAs and input DNA were analyzed by qPCR for IRF3-binding motifs in IFNB , IFNL1 , and IFIT1 promoters , or for NF-κB-binding motifs in IL8 , CXCL1 , and IL32 promoters , using GoTaq Q-PCR mixture ( Promega ) as described above . The sense and antisense primers for amplifying the putative IRF3-binding and NF-κB-binding sequences in each promoter are listed in S2 Table . Data analysis was performed using GraphPad Prism v6 . 0g software ( Graphpad ) . Statistical differences were determined using a Two-tailed Student t test . Data are expressed as mean ± standard deviations ( SD ) of data from at least three sample replicates . Differences with a P-value of < 0 . 05 are considered statistically significant . | The induction of IFN antiviral response is a hallmark of immediate host immune responses to viral infections . In a majority of cell types , eliciting this intrinsic defense mechanism depends on activation of IRF3 , characterized by C-terminal phosphorylation , dimerization and subsequent nuclear translocation of the latent transcription factor . While the molecular pathways and signaling events leading to IRF3 activation have been extensively studied , how activated IRF3 operates in the nucleus to trigger antiviral gene expression remains murky . In this study , we reveal that EAP30 , an endosomal sorting complex required for transport ( ESCRT ) -II subunit , is a novel , pivotal factor controlling IRF3-dependent antiviral defense . We demonstrate that a fraction of EAP30 resides in the nucleus , where it forms a complex with IRF3 and its transcriptional co-activator , CBP , in response to viral insults . These interactions are essential for IRF3 binding to its target gene promoters and subsequent transcription of antiviral genes including those encoding type I and III IFNs and IFN-stimulated genes . Our data unveil a previously unappreciated role for EAP30 in facilitating IRF3-mediated antiviral response in the nucleus and raise the possibility that this novel function of EAP30 may be targeted for developing antiviral interventions to combat infectious diseases . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"phosphorylation",
"medicine",
"and",
"health",
"sciences",
"vesicular",
"stomatitis",
"virus",
"pathology",
"and",
"laboratory",
"medicine",
"molecular",
"probe",
"techniques",
"gene",
"regulation",
"pathogens",
"immunology",
"immunoblotting",
"microbiology",
"immune",
"... | 2017 | Pivotal role for the ESCRT-II complex subunit EAP30/SNF8 in IRF3-dependent innate antiviral defense |
Leprosy is a persistent infectious disease caused by Mycobacterium leprae that still affects over 200 , 000 new patients annually . The host genetic background is an important risk factor for leprosy susceptibility and the PARK2 gene is a replicated leprosy susceptibility candidate gene . The protein product of PARK2 , Parkin , is an E3 ubiquitin ligase that is involved in the development of various forms of Parkinsonism . The human macrophage is both a natural host cell of M . leprae as well as a primary mediator of natural immune defenses , in part by secreting important pro-inflammatory cytokines and chemokines . Here , we report that down-regulation of Parkin in THP-1 macrophages , human monocyte-derived macrophages and human Schwann cells resulted in a consistent and specific decrease in interleukin-6 ( IL-6 ) and monocyte chemoattractant protein 1 ( MCP-1/CCL2 ) production in response to mycobacteria or LPS . Interestingly , production of IL-6 at 6 hours by THP-1 cells stimulated with live M . leprae and M . bovis BCG was dependent on pretreatment with 1 , 25-dihydroxyvitamin D3 ( VD ) . Parkin knockdown in VD-treated cells blocked IL-6 induction by mycobacteria . However , IκB-α phosphorylation and levels of IκB-ξ , a nuclear protein required for IL-6 expression , were not affected by Parkin silencing . Phosphorylation of MAPK ERK1/2 and p38 was unaffected by Parkin silencing while JNK activation was promoted but did not explain the altered cytokine production . In a final set of experiments we found that genetic risk factors of leprosy located in the PARK2 promoter region were significantly correlated with M . leprae sonicate triggered CCL2 and IL6 transcript levels in whole blood assays . These results associated genetically controlled changes in the production of MCP-1/CCL2 and IL-6 with known leprosy susceptibility factors .
Leprosy , also called Hansen's disease , is a persistent infectious disease caused by Mycobacterium leprae . While leprosy can be transmitted by armadillos in the Southern United States [1] humans are the most important reservoir for M . leprae and the more common route is human-human transmission . Leprosy can lead to the destruction of peripheral nerves and subsequent extreme deformities of the skin and peripheral limbs . Despite effective chemotherapy and active case finding , the global number of new leprosy cases is estimated at over 200 , 000 annually [2] . Furthermore , microbiologically cured patients require follow-up to prevent nerve damage from sequellae of the disease . Due to the extreme social stigma and the long term follow-up , leprosy presents a large emotional and financial burden for affected communities and health care systems . There is strong evidence for an important role of host genetics on leprosy susceptibility . For example , disease concordance is significantly higher among monozygotic as compared to dizygotic twins [3] , and a number of complex segregation analyses have provided evidence for the presence of major genes in leprosy susceptibility [4] . A recent genome-wide association study detected and replicated susceptibility factors in CCDC12 , C13orf31 , NOD2 , TNFSF15 , RIP2K , and the HLA-DR/DQ locus and revealed a striking overlap with Crohn's disease susceptibility factors [5] , [6] . Likewise , a number of candidate gene association studies have implicated additional genes in leprosy susceptibility [7] . Employing a genome-wide linkage based strategy , two major susceptibility loci were identified on chromosome 6 in Vietnamese families [8] . The locus on the short arm of the chromosome led to the identification of MHC class I and class III leprosy susceptibility factors [9] , [10] while genetic variants in the regulatory region of PARK2/PACRG on chromosome 6q25 were uncovered as a common risk factor for leprosy [8] . The role of PARK2 as susceptibility gene in infectious diseases was further supported by the subsequent identification of PARK2 promoter variants as risk factors for typhoid fever [11] . The identification of PARK2 as leprosy susceptibility gene was unexpected and the role of the PARK2 encoded protein , Parkin , in leprosy pathogenesis has remained unknown . Parkin is an E3 ubiquitin ligase which is malfunctioning in autosomal recessive juvenile parkinsonism ( AR-JP ) [12]–[14] . Parkin has been given multiple roles related to neuronal survival in the context of Parkinsonism [15]–[22] . However , none of these functions explains the identification of PARK2 as a genetic susceptibility factor for leprosy or typhoid fever , diseases characterized by the infection of macrophages with intracellular bacteria . Parkin/PACRG have been shown to be important for the autophagic elimination of aggregated proteins [21] , [23] , [24] . Interestingly , a role in autophagy is also found with immune regulators such as NOD2 and other genes associated with both Crohn's disease and mycobacterial infections ( reviewed in [25] ) . As first step towards the elucidation of Parkin in leprosy pathogenesis , we studied the impact of Parkin on measures of innate immunity in human macrophages and Schwann cells which are the main host cells of M . leprae in humans . We report here that abrogation of PARK2 in macrophages and Schwann cells affects their ability to produce IL-6 and MCP-1 , two key pro-inflammatory cytokines . Moreover , we demonstrate significant correlation of IL6 and CCL2 transcript levels in a whole blood assay with specific variants of PARK2 previously identified as leprosy risk factors .
For the gene expression experiments , 62 unrelated Vietnamese Kinh individuals were recruited at the Dermato-Venereology ( DV ) Hospital in Ho Chi Minh City . Of those , 56 were leprosy patients while six had no history of leprosy disease . Since leprosy shows a strong gender bias , 43 subjects were males and 19 were females . Healthy Caucasian volunteers were enrolled for derivation of monocyte-derived macrophages . The study was conducted according to the principles expressed in the declaration of Helsinki . Signed informed consent was obtained from every person participating in the study . The study and subject enrolment were approved by the Ethical and Scientific Committee of the Dermato-Venerology Hospital and the Health Services Peoples' Committee , Ho Chi Minh City , Viet Nam , and the Research Ethics Board at the McGill University Health Centre , Montreal , QC , Canada . RPMI-1640 , GlutaMAX , penicillin , streptomycin , fetal bovine serum ( FBS ) , TRIzol , and AlexaFluor 488 anti-mouse were purchased from Invitrogen ( Carlsbad , CA ) . Middlebrook 7H9 and ADC enrichment were purchased from BD Biosciences ( Mississauga , ON , Canada ) . Phorbol 12-myristate 13-acetate ( PMA ) , 4′ , 6-diamidino-2-phenylindole ( DAPI ) , 1 , 25-dihydroxyvitamin D3 ( VD ) , anti-Parkin monoclonal antibody ( clone PRK8 ) and monoclonal anti-beta actin were purchased from Sigma-Aldrich ( St-Louis , MO ) . Antibodies against IκBζ , phospho-IκBα ( S32 ) , phospho-ERK1/2 , phospho-JNK , phospho-p38 and total JNK were purchased from New England Biolabs ( Pickering , ON ) . PI3Kγ inhibitor AS605240 was from Santa Cruz Biotechnology ( Santa Cruz , CA ) . HRP-conjugated goat anti-mouse and anti-rabbit antibodies were from Pierce Biotechnology ( Rockford , IL ) . Recombinant human MCP-1 was purchased from R&D Systems ( Minneapolis , MN ) . THP-1 promonocytic cells were obtained from the American Type Culture Collection ( Manassass , VA ) and cultured in RPMI supplemented with 10% fetal bovine serum , GlutaMAX and penicillin-streptomycin antibiotic cocktail ( Invitrogen ) . Primary human Schwann cells were purchased from Sciencell ( San Diego , CA ) and cultured in SCM supplemented with 5% serum and growth supplements ( Sciencell ) . For stimulation and cytokine determination , Schwann cell medium was replaced with RPMI+10% FBS . THP-1 cells were differentiated into macrophages by incubating with 200 nM PMA for 24 h , then leaving the adherent cells to recover and differentiate for another 24 hr in medium without PMA . For some experiments , 10 nM VD was added to culture medium during and after PMA treatment . For human monocyte-derived macrophages , venous blood was collected from healthy volunteers . The PBMC fraction was isolated by Ficoll-Hypaque ( Sigma ) according to the manufacturer's instructions , diluted in RPMI medium and incubated for 1 hour onto gelatin-coated culture dishes . Adherent monocytes were trypsinized and transferred to a culture flask containing RPMI-10% FBS supplemented with 15% of L929 conditioned medium as a source of M-CSF . After 5 days , adherent , differentiated macrophages were collected and used for experiments . The purity was consistently >95% as determined by the expression of the macrophage mannose receptor . All cells were cultured in a humidified incubator at 37°C and 5% CO2 . When M . leprae bacteria were added , cells were cultured under the same conditions except that antibiotics were omitted and the temperature was set to 33°C . Viable Mycobacterium leprae were obtained from the National Hansen's Disease Programs Laboratory at Louisiana State University ( Baton Rouge , LA ) . The Thai-53 isolate of M . leprae was maintained in the footpads of athymic nu/nu mice and harvested as described previously [26] . Extraneous mouse tissue was removed by incubating the bacterial suspension in 0 . 1N NaOH for 3 min followed by extensive washing in RPMI 1640 ( Gibco ) +10% FCS ( HyClone ) . Bacterial viability was determined by radiorespirometry , which measures the oxidation of 14C-palmitic acid to 14CO2 [27] and vital staining which measures cell wall integrity [28] . All M . leprae preparations underwent quality control testing for microbial contamination . Freshly harvested bacilli were stored at 4°C . Mice used in the propagation of M . leprae were housed in accordance with standards established in the PHS Guide to the Care and Use of Laboratory Animals ( 8th Edition ) under a protocol approved by the IACUC of the National Hansen's Disease Program , Baton Rouge , La . ( Assurance # A3032-1 ) . M . leprae whole cell sonicate was generated with support from the NIH/NIAID Leprosy Contract N01-AI-25469 at Colorado State University . Inactivated ( irradiated ) armadillo-derived M . leprae whole cells were probe sonicated with a Sanyo sonicator to >95% breakage to produce whole cell sonicate . Mycobacterium bovis BCG ( Pasteur ) and M . tuberculosis H37Ra were gifts of Dr . Marcel Behr ( McGill University ) . They were cultured in Middlebrook 7H9 medium containing ADC supplement and used fresh to make single-cell suspensions . STEALTH siRNA duplexes directed against Parkin and scrambled control duplexes were obtained from Invitrogen . siRNA sequences ( sense , RNA ) were as follows: Scrambled 5′-GGACUACAUGAUUCGACGUCAACUG-3′; Parkin_A 5′-GGAAACAUCAGUAGCUUUGCACCUG-3′; Parkin_B 5′-UUGCUUAGACUGUUUCCACUUAUAC-3′ . Parkin A and B correspond respectively to positions 758–782 and 875–899 of human PARK2 mRNA ( NCBI accession BC022014 . 2 ) . Cells were transfected with a Microporator device ( NanoEnTek , Seoul , South Korea ) following the manufacturer's instructions . Briefly , adherent cells were detached by trypsin digestion , rinsed , resuspended with a final concentration of 10 nM siRNA and electroporated with a single 20 ms pulse set at 1700 volts . Mortality due to electroporation was minimal , typically less than 10% . Cells were then returned to normal culture conditions for 48 hours , after which they were stimulated and assayed . We collected 20 ml of whole blood from each individual by venipuncture . Blood samples were split in two aliquots and each aliquot was mixed 1∶2 with RPMI medium containing L-glutamine ( 300 mg/L ) and HEPES ( 10 mM ) . One aliquot was stimulated with M . leprae sonicate at a concentration of 20 µg/ml , which approximately corresponds to an MOI of 50 M . leprae per white blood cell . The second aliquot was left untreated . Each aliquot , the stimulated one and the control , was divided into four 50 ml polystyrene tubes to facilitate better leukocytes adhesion and aeration of blood . Tubes were incubated for 30 hrs at 37°C , 5% CO2 . Total RNA from blood samples was extracted employing a modified protocol of the LeukoLOCK RNA extraction kit ( Ambion , CA , USA ) . Briefly , blood aliquots were filtered by gravity through LeukoLOCK filters to isolate leukocytes . Collected cells were rinsed to eliminate red blood cells and lysed directly on the LeukoLOCK filters . Extraction of total RNA was performed according to the manufacturer's instructions . Isolated RNAs were kept under ethanol and ammonium acetate at −80°C . Prior to further experiments , all samples were cleaned with the RNeasy kit ( Qiagen , Germany ) . The qualitative and quantitative analysis of RNA samples was done using Bioanalyzer 2100 ( Agilent , USA ) . The QuantiTect Reverse Transcription kit ( Qiagen , Germany ) was utilized for reverse transcription of RNA samples . In brief , 500 ng of total RNA were treated with gDNA wipeout reagent to remove genomic DNA contamination and further transcribed following the manufacturer's instructions . Real-Time PCR was performed using the Rotor-Gene RG-3000 system ( Corbett Research/Qiagen , Germany ) . The final volume of the PCR mix was 20 µl , with 16 ng of cDNA , 10 µl of Maxima Probe/ROX qPCR Master Mix ( Fermentas , Lithuania ) , and 1 µl of IL-6 or MCP-1 TaqMan Gene Expression Assay probe mix ( Applied Biosystems , USA ) . The HPRT gene probe was used as reference house-keeping gene . A comparative ΔΔCt method [29] was used to determine the regulation of the gene expression in response to M . leprae sonicate . Genomic DNA was obtained from all subjects enrolled in the study . We then obtained the genotypes for 31 SNPs that span approximately 320 kb of genomic DNA in the promoter region , exon 1 and intron 1 of PARK2 . Based on the tag SNP information available from the International HapMap project database ( www . hapmap . org/ ) for the Chinese population , which we know to strongly resemble the Vietnamese situation , these SNPs represent over 80% of the common genetic information of the target region . These SNPs were genotyped on the high-throughput SEQUENOM MassARRAY platform , which uses the iPLEX assay to incorporate mass-modified terminal nucleotides in the SBE step , which are then detected by MALDI-TOF MS [30] or the ultra-high throughput Illumina platform . This platform uses the GoldenGate assay followed by a bead-based technology to resolve individual SNP genotypes [31] . Macrophages were seeded into 24-well plates at a density of 5×105 cells per well and Schwann cells at a density of 5×104 cells per well . Cells were treated with stimuli for 6–24 hours; the supernatants were collected , cleared by centrifugation and assayed using Milliplex MAP 42-plex human cytokine kit on a Milliplex Analyzer 3 . 1 Luminex 200 machine ( Millipore , Chicago , IL ) according to the manufacturer's instructions . Alternatively , supernatants were analyzed using custom Q-Plex chemiluminescent multiplex ELISA arrays measuring human IL-1β , IL-6 , IL-8 , IL-10 , MCP-1 and TNF ( Quansys Biosciences , Logan , UT ) . Array images were acquired and analyzed using the Quansys Q-View imager and software . THP-1 macrophages were cultured in serum-free medium overnight then treated with 100 ng/ml LPS for various times and lysed in denaturing buffer ( 8 M urea , 1% SDS , 40 mM Tris , pH 8 . 0 ) . Twenty micrograms of protein were loaded onto a 12% polyacrylamide gel and transferred to a nitrocellulose membrane . The membrane was blocked with 1% casein in TBS and stained with antibodies overnight at 4°C then with HRP-conjugated goat anti-rabbit or anti-mouse for one hour . The signal was revealed with Immobilion Western substrate ( Millipore , Billerica , MA ) . Equal loading was verified with an antibody against β-actin following reprobing of the membrane . TransAM transcription factor assay kits for NF-κB and AP-1 families were obtained from Active Motif ( Carlsbad , CA ) . The assays were used to measure the amount of nuclear protein that could bind to κB and AP-1 consensus oligonucleotides immobilized on a solid substrate . The bound nuclear proteins were probed with anti-p65 for NF-κB and anti-phospho-c-Jun for AP-1 . In brief , macrophages were seeded into 6-well plates at 2×106 cells per well and treated for 2 hours . Nuclear extracts were prepared according to the instructions given in the manual and 5 µg of nuclear proteins were used for the binding assays . Positive control extracts for NF-κB and AP-1 assays ( 5 µg of Raji or TPA-treated K562 extracts , respectively ) were used to normalize the values . The expression levels of IL6 and CCL2 in M . leprae sonicate stimulated and non-stimulated whole blood assays for all 62 subjects were used as quantitative traits in an eQTL analysis . Association between expression levels of CCL2 and IL6 and SNP alleles in PARK2/PACRG was tested by means of the Likelihood Ratio Test as implemented in the GENMOD procedure of the SAS software version 9 . 2 ( SAS Institute , Cary , NC , USA ) and assuming a dominant genetic model since this is the model providing best evidence of association with leprosy [32] . Due to the known strong impact of gender on leprosy risk , we adjusted the analysis for sex of subjects .
Parkin is expressed in many cells of the immune system , including macrophages and T-cells . We used siRNA-mediated PARK2 gene knockdown to study the function of Parkin in macrophages . siRNA duplexes were delivered by high-efficiency electroporation into differentiated cells of the human acute monocytic leukemia cell line THP-1 . Real-time quantitative PCR and indirect immunofluorescence analysis of Parkin showed that THP-1 cells transfected with Parkin-specific siRNAs expressed substantially less Parkin than THP-1 control cells transfected with a scrambled siRNA control ( Figure 1A ) , with knockdown efficiencies ranging from ∼80% to 90% as determined by qPCR ( data not shown ) . To determine a possible role of Parkin in the production of soluble immune mediators by macrophages , THP-1 cells were differentiated into macrophages , transfected with siRNA ( either Parkin A or scrambled control ) and stimulated with live M . tuberculosis H37Ra or viable M . leprae ( ML ) for six hours ( Figure 1B ) . A multiplex quantitation of 42 soluble immune mediators using the Milliplex system was performed on the culture supernatants to identify cytokines modulated by Parkin knockdown . While M . tuberculosis H37Ra induced a robust cytokine response , ML had a much lower impact . Out of the 42 soluble factors , a total of 12 cytokines/chemokines could be detected either at ex vivo production levels or induced by at least one stimulus ( Figure 1B ) . When expressed as the ratio of cytokine concentrations secreted by Parkin knocked-down cells over controls , we observed that IL-6 , induced by H37Ra , and MCP-1 either at constitutive production levels or induced are both diminished approximately four-fold by Parkin knockdown . By contrast , all other cytokines are modulated less than two-fold . We could not detect baseline IL-6 or induced by ML under these conditions . In a second round of experiments , we sought confirmationof these initial observations by screening with a more limited number of cytokines and a different assay system , the Q-Plex multiplex ELISA . We also considered it important to test a 24 hour time point . One limitation was that H37Ra induced visible toxicity in macrophages at six hours which resulted in significant cell death at 24 hours ( data not shown ) precluding its use as a reliable stimulant for kinetics studies . We instead opted for Mycobacterium bovis – Bacille Calmette Guerin ( BCG ) which was nontoxic under the same experimental conditions . Since it was possible that BCG would not induce the same magnitude and range of response as H37Ra , we also included LPS , a well-known macrophage stimulant , as a positive control . Six cytokines/chemokines were measured: IL-1β , IL-6 , IL-8 ( CXCL8 ) , IL-10 , MCP-1 ( CCL2 ) and TNF ( Figure 1C , D ) . Similar to the first round , a pattern emerged with IL-6 and MCP-1 being specifically repressed by Parkin silencing . At 6 hours , IL-6 was only affected employing LPS as stimulant since BCG and M . leprae induced little IL-6 secretion ( Figure 1C ) . At 24 hours , both LPS and BCG induced secretion of IL-6which was repressed by Parkin knockdown for both stimulants ( Figure 1D ) . All IL-8 values at 24 hours , even in the un-induced state , were above the highest standard and are thus not represented in the figure . The overall Parkin effect was less pronounced at 24 hours , possibly reflecting a time-dependent plateau effect for cytokine production ( Figure 1C , D ) . In order to verify and extend the previous observations , a third round of experiments was carried out focusing on IL-6 and MCP-1 and adding a second distinct siRNA for Parkin knockdown , as well as cells of different types . Replication of the results with a second siRNA duplex was done to rule out off-target effects . While we had obtained experimental evidence for an effect of Parkin on IL-6 production in response to H37Ra and LPS , we could not detect a similar effect for BCG and M . leprae at 6 hours , presumably due to the low and delayed responsiveness of THP-1 macrophages to these mycobacteria . It had been proposed that priming of macrophages with the active form of vitamin D ( 1–25-hydroxyvitamin D3 , VD ) may increase the responsiveness of macrophages to microbial ligands [33] possibly due to the fact that VD can induce the expression of NOD2 , an important sensor of intracellular bacteria [34] . We therefore primed THP-1 macrophages with 10 nM VD for the duration of the experiment and stimulated the cells with LPS , BCG or M . leprae and measured IL-6 and MCP-1 levels by ELISA ( Figure 2A–B , top right panel ) . VD treatment strongly promoted IL-6 production by THP-1 macrophages in response to the mycobacteria ( Figure 2A ) and allowed the determination of the effect of Parkin knockdown on ML-induced IL-6 . Parkin silencing with Parkin-A siRNA reduced ML-triggered IL-6 production to 48% of control ( p<0 . 05 ) and to 35% ( p<0 . 01 ) with Parkin B . However , VD treatment had no effect on the induction of MCP-1 , which was still near baseline ( Figure 2B , top right ) . VD treatment did not prime human monocyte-derived macrophages to produce IL-6 in response to BCG or ML . These cells were not very sensitive to LPS either with only a fivefold increase in IL-6 ( mostly repressed by Parkin knockdown ) and no increase in MCP-1 production ( Figure 2A–B , bottom left panel ) . Nonetheless , VD-treated MDMs spontaneously secreted substantial amounts of both cytokines which was significantly inhibited by Parkin knockdown ( p<0 . 01 ) . Schwann cells are the primary host cells of M . leprae in human leprosy patients and express Parkin at levels similar to macrophages [8] . We transfected primary human Schwann cell cultures with siRNA and stimulated them for 6 hours as with macrophages . Both IL-6 and MCP-1 could be detected in the supernatants of Schwann cell cultures and were not affected by the presence of VD ( data not shown ) . LPS induced a 5-fold induction of IL-6 and an approximate two-fold increase in the production of MCP-1 over baseline production levels . Neither BCG nor M . leprae stimulation resulted in a significant induction of either cytokines over resting levels . Parkin knockdown led to a general decrease of IL-6 and MCP-1 production , both at resting levels ( inclusive the presence of BCG and ML ) or induced by LPS ( Figure 2A–B , bottom right panel ) . To resolve the apparent selectivity of Parkin in cytokine modulation , we measured IκB-α phosphorylation , a measure of canonical NF-κB activation , as well as the levels of the NF-κB-inducible IκB-ζ , a nuclear factor that associates with and activates a subset of NF-κB-dependent promoters upstream of genes such as Il-6 , Il-12b , Csf2 and HBD2 [35] , [36] . Western blotting of THP-1 lysates following Parkin knockdown and LPS stimulation did not reveal differences in phosphorylation of IκB-α or induction of IκB-ζ protein , strongly arguing against a role of these factors in the selectivity of Parkin immunomodulation ( Figure 3A ) . Moreover , a transcription factor ELISA measuring NF-κB DNA-binding complexes in LPS-stimulated THP-1 nuclear extracts did not reveal a consistent effect of Parkin on transcriptional activity ( Figure 3B ) . Triggering of the MAPK cascade by TLR leads to the assembly of the AP-1 complex on cytokine/chemokine promoters and is essential for expression of most pro-inflammatory mediators . We measured the phosphorylation of the three major MAPK: ERK1/2 , JNK and p38 , in cells with normal or depleted Parkin levels and treated with LPS for 0–60 minutes ( Figure 3C ) . ERK1/2 and p38 phosphorylation were not affected by Parkin . JNK phosphorylation was higher in knocked-down cells , which agrees with previous reports of Parkin suppressing JNK activity [37] , [38] . To test a possible contribution of JNK to the abrogation of LPS-induced IL-6 production , we pretreated cells with a well-known JNK inhibitor , SP600125 . Inactivation of JNK strongly suppressed IL-6 induction by LPS but failed to abolish the effect of Parkin silencing ( Figure 3D ) . In addition , stronger JNK activation in the absence of Parkin did not translate into stronger AP-1 DNA binding , which was unaffected by PARK2 knockdown ( Figure 3B ) . While the above experiments provided strong evidence for a role of Parkin in MCP-1 and IL-6 production , they did not address the effect of different PARK2 alleles on the production of these cytokines . Polymorphisms in the PARK2 promoter region are strong risk factors for leprosy in the Vietnamese and the Brazilian populations [8] . To investigate a possible effect of genetic polymorphisms in the PARK2 promoter region on CCL2 and IL6 transcript levels , we stimulated whole blood from 62 Vietnamese subjects with M . leprae sonicate and extracted total RNA for gene expression analysis . Whole M . leprae had low stimulation potential in the cellular assays . Hence , we opted for M . leprae sonicate as stimulant since it was reported to elicit a more robust immune response , presumably by exposing otherwise inaccessible antigens [39] , [40] . We determined the gene expression levels before and after stimulation with M . leprae sonicate for both CCL2 and IL6 ( Figure 4 ) . In non-stimulated blood cultures both CCL2 and IL6 could be readily detected with IL6 showing a slightly higher mean of transcript levels . Stimulation with M . leprae sonicate resulted in a strong upregulation of both genes . The extent of up-regulation and fold-induction of both genes were very similar ( Figure 4 ) . Ex vivo CCL2 and IL6 transcript levels as well as the M . leprae sonicate triggered increase in CCL2 and IL6 transcripts were then correlated with a panel of SNPs that span the promoter region , exon 1 and part of intron 1 of the PARK2 5′ region . The selected SNPs capture more than 80% of the common genetic variation ( allele frequency>5% ) in the target region . Based on the existing linkage disequilibrium in the samples used for the analysis three groups of moderately correlated SNPs ( r2>0 . 5 SNP bins ) can be distinguished ( Table 1 ) . We detected significant evidence for association between SNPs of one bin and baseline levels of CCL2 ( ΔCt ) as well as the increase of CCL2 and IL6 transcripts ( ΔΔCt ) following stimulation with sonicate ( Table 1 ) . Significant evidence for association was observed under a dominant effect of the major allele . The most consistent and strongest evidence for association was observed for a group of 3 SNPs ( rs6915128 , rs10806768 , rs1333955 ) located approximately 60 kbp upstream of PARK2 ( Table 1 ) . Interestingly , these were the same SNPs that had shown replicated evidence for association of their major allele with leprosy in both Vietnamese and Indian leprosy patients [32] . We then asked if the major SNP allele was correlated with increased or decreased CCL2 and IL6 transcript levels . As can be seen from the regression coefficients in Table 1 , in resting non-stimulated whole blood cultures the absence of the major alleles leads to a significant decrease in CCL2 transcripts . The same trend is seen for IL6 but fails to reach significance . Conversely , in M . leprae sonicate triggered cultures increase in both CCL2 and IL6 transcripts is significantly correlated with absence of the major allele . The most parsimonious explanation for this effect is that sonicate stimulation led to a plateau of both transcripts which was largely independent of the SNP genotypes . This resulted in a smaller increase in those cultures that already had a higher baseline transcript level . However , the most interesting aspect of these experiments was that the same alleles in the same SNPs impacted both CCL2 and IL6 transcript levels and susceptibility to leprosy .
Our results showed that Parkin participates in the modulation of IL-6 and MCP-1 production , two key mediators of innate immunity . PARK2 knockdown in THP-1 macrophages , and Schwann cells consistently repressed LPS-induced IL-6 and basal MCP-1 levels . This repression displayed a degree of specificity as modulation of other cytokines or chemokines tested in our study was less pronounced . Nevertheless , we cannot rule out a significant effect of Parkin on other immune mediators that were either not included in our screen or not secreted by our cellular model . The preferential impact on certain immune mediators argued against a non-specific effect of Parkin on the general state of cellular physiology . Conversely , these results supported a Parkin-dependent modulation of specific pathways of immune responsiveness to microbial antigens . We hypothesized that the link between Parkin immune modulation and pathogen stimulation might be provided by Toll-like receptors ( TLRs ) . Modulation of IL-6 and MCP-1 secretion via the TLR signaling cascade is a reasonable hypothesis since TLR recognition of M . leprae antigens is a well-established step in leprosy pathogenesis . In addition , genetic polymorphisms in TLR1 [41]–[43] , TLR2 [44] , and TLR4 [45] are associated with leprosy and/or leprosy reactions . We evaluated the ability of Parkin-silenced cells to phosphorylate IκB-α , a critical step in the TLR pathway leading to activation of NF-κB , and found no difference of the IκB-α phosphorylation state . Parkin levels did not affect the induction of nuclear IκB-ζ , a positive regulator with some selectivity for the IL6 promoter [35] that is partially under the control of NF-κB itself [36] . We also investigated the MAPK pathway which leads to the activation of the AP-1 transcriptional factor complex and found higher phosphorylation of c-Jun N-terminal kinase ( JNK ) after LPS treatment in Parkin-silenced cells , while ERK1/2 and p38 phosphorylation were unaffected . This agrees with previous reports of Parkin inhibiting JNK through mono-ubiquitination of Hsp70 [37] , [38] . The role of JNK in modulating cytokine production by human macrophages is controversial . Some studies reported an inhibition of LPS-induced IL-12 expression by JNK in THP-1 cells [46] , [47] , while other studies showed the opposite [48] . However , we did not attempt to investigate this question in more detail since the impact of Parkin on IL-6 production was not related to its inhibition of JNK activation . Finally , a transcription factor ELISA of NF-κB and AP-1 complexes did not reveal any consistent effect of Parkin on LPS-induced DNA binding . While we failed to identify the mechanism by which Parkin modulates IL-6 and MCP-1 production , the results of our experiments argue against a general role of Parkin in the canonical TLR signaling cascade and are consistent with the more specific effect that Parkin exerts on IL-6 and MCP-1 production . In our experiments , the strongest inducer of cytokines/chemokines was LPS and the impact of Parkin on host cell responsiveness was most easily detectable in response to this stimulus in all cell types tested . Consistent with previous reports M . leprae was a poor inducer of host responses [49] , [50] , [51] . Interestingly , the use of VD as a priming agent significantly boosted the ability of THP-1 cells to respond to mycobacteria with secretion of IL-6 . Our results were similar to studies in human monocytes [49] . In these cells , BCG triggered production of IL-6 and MCP-1 while M . leprae triggered production of MCP-1 [49] . Why THP-1 cells would respond less to M . leprae is presently not known but it is possible that the dose of M . leprae used in our experiment ( MOI = 50 ) was too low to promote MCP-1 production . Nevertheless , baseline production levels of MCP-1 and stimulus triggered production of IL-6 by Schwann and THP-1 cells were all diminished by Parkin knockdown . Given that both Schwann cells and THP-1 are sensitive to TLR2 ligands [52] , [53] it appears that the immunogenic potential of BCG and M . leprae is dampened in intact bacteria . Our functional studies identified PARK2 as mediator of IL-6 and MCP-1 production by macrophages . While this observation is exciting it did not address the impact of genetic PARK2 polymorphisms on IL-6 and MCP-1 production . One efficient means to probe for a possible link between genetic variation and mechanistic phenotypes are so-called expression quantitative loci ( eQTL ) studies . We established a dense map of genetic markers of the 5′ region of the PARK2 gene . These genetic markers were then correlated with IL6 and CCL2 transcript levels in whole blood assays in the presence and absence of high concentrations of M . leprae sonicate . We opted for high sonicate concentrations since in the functional studies whole M . leprae had relatively low potential to induce secretion of cytokines . A common set of three SNPs upstream of the PARK2 promoter was significantly associated with induced IL6 and CCL2 and non-triggered ex-vivo transcript levels of CCL2 . The same set of polymorphisms has recently been found to be key leprosy susceptibility factors in both Vietnamese and Indian leprosy patients [32] . The latter observation is important since pattern of linkage disequilibrium between Indians and Vietnamese differ substantially in the studied PARK2 region suggesting a direct link between these SNPs and leprosy . Genome-wide eQTL studies have previously clearly shown that genetic variation at points far from the QTL , including different chromosomes ( trans eQTL ) , can be significantly correlated with QTL expression levels [54] . While eQTL studies can tell us nothing about the mechanisms that cause the observed correlations , they do hold valuable information about which genes are likely to belong to the same host response pathway . What is the possible functional relevance of Parkin modulating IL-6 and MCP-1 expression ? IL-6 is a pluripotent cytokine and a key mediator of inflammation . IL-6 inhibits TGF-β dependent Treg differentiation and induces differentiation of naïve T-cells to the IL-17-producing TH17 lymphocyte subset [55] . IL-17 is part of the anti-tuberculosis immune response via its role in organizing granulomas [56] and by inducing the TH17 generation of cathelicidin [57] , a potent antimicrobial peptide [58] . Interestingly , a low TH17 response in tuberculosis patients correlates with decreased IL-6 receptor expression on CD4+ T cells [59] . Besides its role in the anti-mycobacterial immune response , IL-6 signaling is important for nerve regeneration and myelination following injury [60] , cellular processes specifically disrupted by M . leprae [61] , [62] . A conditional knockout of a major IL-6 family signal transducer ( gp130 ) leads to Schwann cell degradation and peripheral nerve demyelination [63] , which is consistent with reports of IL-6 promoting myelination in cultured Schwann cells [64] , [65] . Just as IL-6 , MCP-1 is a major immune regulator of granuloma formation in response to mycobacterial infections . In the mouse model of tuberculosis , Ccl2 knock-out mice are significantly more susceptible to M . tuberculosis than their wild type littermates . In human genetic studies , evidence is accumulating for a role of MCP-1 in tuberculosis susceptibility possibly via the regulation of IL-12 levels [66] , [67] . Less is known about MCP-1 in leprosy susceptibility . However , monocytes isolated from leprosy patients showed a reduced ability to produce MCP-1 in response to BCG and M . leprae as compared to healthy donors [51] . This observation is fully consistent with our results of ( i ) Parkin being a critical modulator of MCP-1 levels and ( ii ) the observed correlation of CCL2 transcript levels with genetic markers in the PARK2 promoter region that are confirmed leprosy susceptibility factors . Taken together , the present results provide an example of how genetic risk factors of leprosy may impact on leprosy pathogenesis by modulating the production of IL-6 and MCP-1 , two key host response mediators . | Leprosy is an infectious disease with a strong host genetic component . The identification of host genetic lesions predisposing to disease is a powerful approach for mapping key junctions in the host pathogen interplay . Genetic variants located in the promoter region of the PARK2 gene are replicated leprosy susceptibility factors . To better understand a possible contribution of PARK2 to host effector mechanisms in leprosy patients , we developed a cellular model to test the contribution of the PARK2 encoded parkin protein to host responses to mycobacterial antigens . We observed that parkin was a mediator of IL-6 production in response to mycobacterial antigen in both THP-1 macrophages and human Schwann cells while human monocyte-derived macrophages needed to be pre-activated with VitD to show the same impact . Parkin also impacted on the constitutive production of MCP-1 . The regulatory activity of parkin on cytokine production was found to be independent of the canonical TLR-NFκB signalling pathway . We also tested association of IL6 and CCL2 gene expression levels in whole blood assays with PARK2 polymorphisms . For both cytokines , we found significant associations with those PARK2 variants that were established leprosy susceptibility factors . Hence , our results show that genetic PARK2 variants that are correlated with leprosy susceptibility are also correlated with production of these cytokines following stimulation with M . leprae sonicate . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"neglected",
"tropical",
"diseases",
"leprosy"
] | 2013 | PARK2 Mediates Interleukin 6 and Monocyte Chemoattractant Protein 1 Production by Human Macrophages |
Establishment of left-right ( LR ) asymmetry occurs after gastrulation commences and utilizes a conserved cascade of events . In the mouse , LR symmetry is broken at a midline structure , the node , and involves signal relay to the lateral plate , where it results in asymmetric organ morphogenesis . How information transmits from the node to the distantly situated lateral plate remains unclear . Noting that embryos lacking Sox17 exhibit defects in both gut endoderm formation and LR patterning , we investigated a potential connection between these two processes . We observed an endoderm-specific absence of the critical gap junction component , Connexin43 ( Cx43 ) , in Sox17 mutants . Iontophoretic dye injection experiments revealed planar gap junction coupling across the gut endoderm in wild-type but not Sox17 mutant embryos . They also revealed uncoupling of left and right sides of the gut endoderm in an isolated domain of gap junction intercellular communication at the midline , which in principle could function as a barrier to communication between the left and right sides of the embryo . The role for gap junction communication in LR patterning was confirmed by pharmacological inhibition , which molecularly recapitulated the mutant phenotype . Collectively , our data demonstrate that Cx43-mediated communication across gap junctions within the gut endoderm serves as a mechanism for information relay between node and lateral plate in a process that is critical for the establishment of LR asymmetry in mice .
The elaboration of the major axes of the embryo ( anterior-posterior , dorsal-ventral , and left-right ) occurs at the time of gastrulation , the morphogenetic process that forms the primary germ layers ( ectoderm , mesoderm , and definitive endoderm ) . The molecular definition of a left-right ( LR ) axis precedes the establishment of an overt asymmetry in that dimension , a common feature across bilateral animals . Despite some species-specific differences , the sequence of key events and core components involved in the establishment of LR asymmetry appears conserved across vertebrates [1] , [2] . An initial LR symmetry-breaking event occurs in a specialized organ located at the embryonic midline , this being the node in the mouse . Around embryonic day ( E ) 7 . 75 , equivalent to the early head-fold ( EHF ) stage , cilia protruding from the posterior-apical surface of node cells begin rotating , thereby generating directional fluid flow within the node microenvironment [3] . Whether through interpretation of mechanical flow forces [4] , [5] or asymmetric distribution of signals [3] , [6] , nodal flow is believed to trigger a wave of Ca2+ on the left side of the node [4] , as well as left-biased asymmetric perinodal expression of Nodal [7] . By E8 . 5 , equivalent to the ∼4 somite stage , an asymmetric readout of this symmetry-breaking event is the activation of the Nodal/Lefty2/Pitx2 genetic cascade within the left lateral plate mesoderm ( LPM ) [8] . Activation of these factors is required for asymmetric organogenesis [8] . The embryonic midline is thought to act as a “midline barrier” in keeping signals confined to their respective sides , both by virtue of its morphological structure and by expressing specific factors [2] . An unresolved question in the establishment of LR asymmetry in the mouse embryo is the mechanism of communication between the midline site of symmetry-breaking and the lateral plate tissues initiating asymmetric morphogenesis . By virtue of their location , cells that lie between the node and lateral plate are likely to provide the medium for signal relay . The gut endoderm and paraxial mesoderm are attractive candidate tissues for mediating signal transmission since they lie adjacent to both the node and lateral plate mesoderm . Moreover , perinodal asymmetric events , including calcium release and Nodal expression , occur in endodermal cells lining the node [4] , [7] , [9] . Using live imaging and genetic labeling , we previously noted that the gut endoderm of the mouse embryo forms by widespread intercalation of epiblast-derived definitive endoderm ( DE ) cells into the overlying visceral endoderm ( VE ) epithelium [10] . At gastrulation , DE progenitors intercalate into the distally positioned embryonic VE epithelium , also referred to as the emVE [11] , [12] , which constitutes the surface cell layer of the embryo . This multifocal intercalation of DE cells leads to the widespread dispersal and dilution of emVE cells [13] . As a result , the emergent gut endoderm tissue is comprised of cells of two distinct origins: DE and emVE . Furthermore , we noted that in addition to their scattered distribution within the gut endoderm , residual emVE cells exhibited a second stereotypic distribution in that they were absent from , but congregate around , the node and midline [10] , [14] , [15] . Even though the precise cellular dynamics orchestrating emVE displacement at the node and midline remain unknown , these data suggest that lateral dispersal in the future gut endoderm and midline displacement in the future notochord may be regulated independently . The Sry-related HMG box transcription factor Sox17 is a key conserved factor involved in endoderm formation in vertebrates [16] . Mice lacking Sox17 have a depletion of DE cells , possess an abnormal gut tube , and die around E10 . 5 [17] . Interestingly , Sox17 mutant mouse embryos also display gross morphological features commonly observed in mutants of LR asymmetry establishment , including a failure to turn from a lordotic to a fetal position , an open body wall , and cardiac defects [17]–[19] . We therefore reasoned that a detailed analysis of the Sox17 mutant might provide further insight into the gut endoderm defects and whether gut endoderm morphogenesis and LR patterning are coupled . Here we report that Sox17 mutant embryos exhibit a failure in emVE dispersal as well as defects in LR patterning . We noted that widespread emVE and DE cell intercalation in the prospective gut endoderm was severely affected in mutants . By contrast , emVE displacement at the midline was not . This suggested that lateral dispersal in the future gut endoderm and midline displacement in the future notochord are likely to be distinct morphogenetic processes , the former of which requires Sox17 . One mode of communication across epithelia is through gap junctions . We identified Connexin43 ( Cx43 ) as the predominant gap junctional constituent expressed in the endoderm at a time soon after widespread emVE and DE cell intercalation is complete , when node to LPM signal relay likely occurs . In Sox17 mutants , we noted that Cx43 is absent in the gut endoderm . We demonstrated gap junctional coupling on both the left and right sides of the gut endoderm of wild-type embryos , but not in Sox17 mutants . We also observed that gap junction coupling in the mesoderm was isolated from the endoderm . Since Cx43 localization within the mesoderm was comparable in wild-type and mutant embryos , we concluded that LR signals must be propagated across the endoderm epithelium . Our studies also revealed an absence of gap junctional coupling across cells at the midline in wild-type embryos , thereby providing the first functional visualization of a midline barrier in the mouse . Collectively our observations identify the gut endoderm as a key tissue of communication between node and LPM during the establishment of LR asymmetry in the mouse . We demonstrate that Cx43-mediated gap junction coupling across the endoderm is necessary for the correct temporal and spatial propagation of asymmetric signal ( s ) from the node to the LPM .
Sox17 mutant embryos exhibit a dysmorphic heart , have an open ventral body wall , and fail to turn ( Figure S1A–S1D′ ) [17]–[19] . Since these features are characteristic of mutants with defects in LR patterning [8] , they prompted us to determine whether LR asymmetry is established in Sox17 mutants . To do so , we analyzed the expression of components of the core circuitry controlling the establishment of LR asymmetry in mice: genes encoding the TGFβ family proteins Nodal and Lefty2 , and the homeodomain protein Pitx2 . In E8 . 5 wild-type embryos , Nodal was expressed around the node and along the left LPM ( Figure 1A and 1B ) . In stage-matched Sox17 mutants , Nodal was present around the node , but absent ( three out of eight embryos analyzed ) or reduced and restricted posteriorly ( five out of eight embryos analyzed ) in the left LPM ( Figure 1A′–1B″ ) . Sox17 mutants also exhibited ectopic patchy domains of Nodal expression on both the left and right sides . Notably , these ectopic patches of gene expression were not in cells of the mesoderm layer; instead , they were located superficially and were confined to the endoderm layer of the embryo , as analyzed below . These observations were confirmed using a NodalLacZ knock-in reporter allele [20] , where lacZ activity was detected around the node and along the left LPM in NodalLacZ/+ embryos ( Figure 1C and 1D ) . By contrast , in NodalLacZ/+;Sox17−/− embryos , the reporter was present around the node but absent from the left LPM ( Figure 1C′ and 1D′ ) . Furthermore , ectopic lacZ expressing cells were observed on both sides of the midline . Next , we analyzed expression of Lefty1/2 , which in wild-type embryos was present in the midline and left LPM ( Figure 1E and 1F ) . In Sox17 mutants , Lefty1/2 was either absent from the left LPM ( four out of nine embryos analyzed ) or severely reduced and truncated anteriorly and posteriorly ( five out of nine embryos analyzed ) ( Figure 1E′–1F″ ) . As with Nodal , ectopic patches of Lefty1/2 expression were observed on both sides of the midline . Lefty1/2 was detectable in the midline in a subset of the Sox17 mutant embryos analyzed , but this midline domain was truncated anteriorly as compared to wild-type stage-matched embryos ( Figure S1E and S1E′ ) . In wild-type embryos Pitx2 was present in the cephalic region and in the left LPM ( Figure 1G and 1H ) . In Sox17 mutants , Pitx2 was present in the cephalic region , but was absent from the left LPM ( Figure 1G′ and 1H′ ) . Patches of ectopic Pitx2 expression were also observed bilaterally . Sections through mutant embryos revealed that the ectopic patches of gene expression comprised cells that were located on the embryo's surface , while in wild-type embryos gene expression was only detected in cells residing in a deeper location within the embryo , within the mesoderm ( Figure S1F–S1H′ ) . Sox17 mutant embryos at the early somite ( ESom ) stage ( E8 . 25 ) , prior to the onset of LPM expression , already exhibited ectopic expression of Nodal , Lefty1/2 , and Pitx2 ( Figure S1I–S1N′ ) . Thus ectopic gene expression preceded LPM gene expression , and so could be due to a failure in the downregulation of an earlier domain of expression . Analysis of Nodal in early head-fold ( EHF ) stage ( E7 . 75 ) Sox17 mutants revealed widespread ectopic patchy expression in the gut endoderm epithelium on the surface of the embryo ( Figure S1O–S1Q′ ) . These data demonstrate that Sox17 is required for left LPM expression of the Nodal/Lefty/Pitx2 genetic cascade operating in the establishment of LR patterning in mice , and that absence of Sox17 results in patchy ectopic expression of these genes within the gut endoderm ( see summarizing cartoon in Figure 1I and Table S1 ) . Having confirmed that Sox17 mutants failed to establish LR asymmetry , we investigated whether this phenotype was linked to the endoderm defects that were previously reported [17] . To better understand the progression of gut endoderm morphogenesis in Sox17 mutants , we used the Afp::GFP reporter [21] . The Afp::GFP transgene permits visualization of VE cells ( GFP-positive ) , which can be discriminated from DE cells ( GFP-negative ) within the gut endoderm on the embryo's surface [10] . Since the VE encapsulates the epiblast and extraembryonic ectoderm prior to gastrulation , Afp::GFP embryos exhibit homogenous widespread GFP fluorescence across the entire surface of the conceptus . By the EHF stage , the cellular movements driving gut endoderm morphogenesis during gastrulation are near complete . Extraembryonic VE ( exVE ) GFP-positive cells positioned proximally overlying the extraembryonic ectoderm remain homogenous in the region that will form the visceral yolk sac . By contrast , GFP-positive emVE descendents located distally overlying the epiblast become dispersed and are scattered between DE cells on the embryo's surface . We analyzed EHF stage wild-type and Sox17 heterozygous embryos that were also hemizygous for the Afp::GFP reporter and confirmed that the emVE had fully dispersed , as evident by a distally positioned scattered GFP-positive population of cells in the gut endoderm ( Figure 2A–2D and unpublished data ) . By contrast in Sox17 mutant embryos , the distally positioned emVE appeared as a uniform GFP-positive sheet on the ventral surface of the embryo ( Figure 2A′–2D′ ) . The only regions in which GFP-negative cells could be identified were around the anterior intestinal portal , the site of foregut invagination , and around the node and midline . Overall , these observations suggested that in Sox17 mutants there was a failure to disperse the emVE within the future mid- and hindgut and that the displacement of emVE at the midline was generally unaffected . To confirm that the Afp::GFP reporter was functioning as a short-term cell lineage label due to perdurance of GFP ( as we noted previously , [10] , [14] , [15] ) and exclude the possibility that GFP was being ectopically expressed by DE cells within the gut endoderm of Sox17 mutants , we determined the localization GFP transcripts in Sox17+/+; Afp::GFPTg/+ , as well as Sox17−/−;Afp::GFPTg/+ embryos . In head-fold ( HF ) stage ( E7 . 75–8 . 0 ) wild-type Afp::GFP embryos , GFP was transcribed only in the exVE ( Figure S2A ) . In Sox17 mutants , GFP was only expressed in the exVE ( Figure S2A′ ) , indicating that no ectopic expression occurred in cells of the gut endoderm overlying the epiblast . Additionally , the pan-VE marker Apoc2 was undetectable in the gut endoderm of both wild-type and Sox17 mutants ( Figure S2B–S2D′ ) . These data suggest that in Sox17 mutants , as in wild-type embryos [10] , emVE cells change their state and downregulate expression of VE markers coincident with the intercalation of epiblast-derived DE . The GFP-positive cells that colonized the gut endoderm area of Sox17 mutants were therefore emVE-derived . These cells had undergone an initial step in endoderm morphogenesis , namely cell identity reprogramming , by downregulating emVE markers , and had become poised for integration into the gut tube . To determine if the failure to disperse the emVE persisted , we analyzed Sox17 mutants at later embryonic stages . At the ∼4 somite stage ( E8 . 5 ) , wild-type embryos and Sox17 mutants were indistinguishable by gross morphology ( Figure 2E and 2E′ ) . However , high-resolution examination of the localization of GFP-positive cells in embryos hemizygous for the Afp::GFP transgene revealed an aberrant distribution suggesting severe gut endoderm defects in Sox17 mutants ( Figure 2F–2L′ ) . We simultaneously analyzed the distribution of FoxA2 , a marker of gut endoderm as well as node and midline cells . Anteriorly , in wild-type embryos the region in the vicinity of the anterior intestinal portal contained sparsely scattered emVE cells ( Figure 2F–2H ) . By contrast in Sox17 mutants , a greater density of emVE cells was observed ( Figure 2F′–2H′ ) . Posteriorly , in the prospective hindgut region of wild-type embryos , emVE cells were dispersed , while in the midline emVE cells were displaced and congregated around the node and midline ( Figure 2I–2K ) . By contrast , in Sox17 mutants the epithelium on the ventral surface of the embryo was homogenously GFP-positive and so almost entirely emVE-derived . This suggested a failure in emVE cell dispersal and DE intercalation ( Figure 2I′–2K′ ) . However , the node and midline regions appeared as largely GFP-negative areas . This suggested that the morphogenetic movements of gut endoderm morphogenesis ( involving emVE dispersal ) , and midline formation ( which we propose involves emVE displacement ) , can be genetically uncoupled and are likely to be distinct . Notably , closer scrutiny of the midline structures indicated that in a subset of Sox17 mutant embryos , emergent node areas did contain some emVE-derived cells ( Figure 2L–2L′ ) . This indicates that even though gut endoderm morphogenesis and midline formation are likely to be regulated by different mechanisms , the two processes are dependent on each other for correct execution . Collectively , these observations demonstrate that defective gut endoderm morphogenesis in Sox17 mutants occurs due to a failure to disperse the emVE . Moreover , our results reveal that the morphogenetic events that drive gut endoderm and midline morphogenesis are distinct . To better understand the role of Sox17 during gut endoderm and midline morphogenesis , we determined the localization of Sox17 at the time when these tissues form . Maximum intensity projections of confocal z-stacks of Sox17 immunofluorescence on wild-type embryos revealed that Sox17 protein was always nuclear-localized with a high signal-to-noise ratio . At the late bud ( LB ) stage ( E7 . 5 ) , when emVE cells were dispersed within the gut endoderm of wild-type embryos , and node and midline structures were emerging and displacing emVE cells [2] , Sox17 was detected in both emVE and DE cells within the gut endoderm ( Figure 3A–3E , and Movie S1 ) . Anterior views of LB stage embryos revealed the region around the midline comprised mainly of emVE cells displaying low levels of Sox17 ( Figure 3F and 3G , and Movie S2 ) . Some GFP-negative patches were present , likely corresponding to first cohorts of anterior primitive streak ( APS ) -derived node and midline cells having reached the embryo's surface . These cell cohorts were devoid of Sox17 . Posterior views of LB stage embryos revealed uniform levels of Sox17 in all gut endoderm cells , including cells overlying the primitive streak ( PS ) representing the posterior visceral endoderm ( PVE ) domain ( Figure 3H and 3I ) . By the EHF stage when the node and midline had emerged ( E7 . 75 ) , Sox17 was present in cells of the gut endoderm , but absent from cells of both the node and the midline ( Figure 3J–3M , and Movie S2 ) . At the late head-fold ( LHF ) stage ( E8 . 0 ) , when the anterior intestinal portal begins its invagination , Sox17 was present throughout the gut endoderm except in regions of the foregut where it had become downregulated , while continuing to be undetectable in cells of the node and midline ( Figure 3N–3Q ) . These data indicate that Sox17 localized to cells of the gut endoderm irrespective of their origin and that it was absent from cells of the node and midline . This suggested that the primary site of action of Sox17 was within the gut endoderm . The observation that Sox17 was absent from node and midline in wild-type embryos suggested that Sox17 is not involved in node morphogenesis per se . Thus , the inability of emVE cells to completely clear the node area in Sox17 mutants was likely a secondary effect resulting from the failure of the emVE to disperse . Since the node is the key symmetry-breaking organ , we further investigated the cells of the node and their behavior in Sox17 mutants . This would reveal if any emVE cells failing to clear the vicinity of the node might be responsible for the LR asymmetry phenotype . We therefore determined if nodal cells contained cilia , if these cilia were motile , and if they could generate nodal flow . Furthermore we determined if asymmetric perinodal gene expression was induced around the node of Sox17 mutants . We analyzed the distribution of Arl13B , a small GTPase that localizes to cilia [22] , in wild-type and Sox17 mutant embryos carrying the Afp::GFP VE-reporter transgene at the EHF stage ( E7 . 75 ) , when nodes are fully formed [22] . In wild-type embryos , robust Arl13B-positive puncta were detected in cells within the node ( Figure 4A–4C ) . We interpreted these puncta as representing the elongated cilia present on the apical surface of cells of the node . In Sox17 mutant nodes , Arl13B-positive puncta were also present , except on emVE cells that had remained in the node region ( Figure 4A′–4C′ ) . Analysis of confocal z-stacks revealed an absence of robust Arl13B-positive puncta in cells that remained submerged and thus covered by emVE cells ( unpublished data ) . Scanning electron microscopy ( SEM ) permitted the high-resolution visualization of nodal cilia in wild-type embryos ( Figure 4D–4G ) . In the Sox17 mutant , cilia with normal morphology protruded from the posterior-apical surface of node cells , except from the larger cells that resembled emVE ( Figure 4D′–4G′ ) . Quantitation revealed that the average length of nodal cilia at the EHF stage ( E7 . 75 ) was comparable in wild-type and mutant embryos ( Figure 4H ) . Having established that cilia were present on the nodes of mutants , we investigated their motility through high-speed , high-contrast DIC imaging . At the LHF stage ( E8 . 0 ) , nodal cilia of wild-type embryos and Sox17 mutants moved in a comparable clockwise motion ( Movie S3 ) . To determine if these motile cilia generated directional nodal flow , we visualized the movement of fluorescent latex beads placed on the apical surface of nodes [23] . Beads invariably migrated leftward in the nodes of both wild-type and mutant embryos ( Movie S4 ) . Nodal flow results in left-biased perinodal gene expression asymmetries necessary to establish LR asymmetry within the LPM [7] . To determine if Sox17 mutants generated perinodal asymmetries in gene expression , we determined the expression of Nodal , Cerl2 , and Gdf1 , three genes asymmetrically expressed around the node at the ∼2–4 somite stage . Nodal expression [7] was slightly higher on the left side of the node , in wild-type and Sox17 mutants ( Figure 4I and 4I′ ) . In both wild-type and mutant embryos , Cerl2 expression [24] was elevated on the right side of the node ( Figure 4J and 4J′ ) , while Gdf1 expression [25] was elevated on the left side ( Figure 4K and 4K′ ) . We digitally quantified in situ hybridization signal intensities and confirmed that similar ratios of perinodal asymmetries were generated in wild-type and mutant embryos ( Figure 4L–4N ) . These findings demonstrate that , even though in some Sox17 mutant embryos emVE cells failed to completely clear the area of the node , the node that formed was comparable to wild-type in shape , possessed nodal cilia with normal morphology and motility , generating nodal flow , and was able to induce asymmetric perinodal gene expression . Having determined that Sox17 mutants exhibited asymmetric perinodal gene expression , we reasoned that an event downstream of the node must cause the laterality defect observed in the LPM . This implicated the gut endoderm or paraxial mesoderm , the two tissues positioned between the node and LPM , as potentially responsible for signal relay between these two distant sites . Taking into consideration that the asymmetries in the node region occurred in perinodal cells of endodermal origin [4] , [7] , [9] , and since the Sox17 was specifically expressed by gut endoderm cells , and mutants appeared to exhibit defects specific to the gut endoderm , we favored the gut endoderm as the tissue involved . Defects within the gut endoderm might result in perturbed communication across the epithelium lying between node and lateral plate , and consequently result in a failure in the establishment of LR asymmetry . Gap junction communication has been implicated in signal relay between the site of symmetry-breaking and tissues of asymmetric morphogenesis in several organisms , including frog , chick , and rabbit [26] . Our previous studies in the mouse demonstrated that cell-cell junctions dynamically disassemble and reassemble during emVE dispersal and concomitant DE cell intercalation takes place during gut endoderm morphogenesis [10] . We therefore investigated the presence and distribution of gap junctions within the gut endoderm of wild-type embryos and Sox17 mutants . Since connexins are core gap junction components , we determined which connexins were expressed in the mouse embryo around the time of LR asymmetry establishment . Expression profiling of embryonic regions of EHF stage ( E7 . 75 ) wild-type embryos revealed that of the 19 characterized connexin genes , all of which were present on the array , only two were expressed above background levels ( Figure S3A–S3C ) . The most abundant was Gja1 , the gene encoding Connexin43 ( Cx43 ) . We investigated the localization of Cx43 in wild-type embryos and Sox17 mutants carrying the Afp::GFP VE-reporter transgene . Cx43 immunofluorescent localization was generally observed as puncta located at cell-cell interfaces . We interpret these puncta as representing gap junction complexes . Up until the onset of emVE dispersal , the distribution of Cx43 was comparable in wild-type and Sox17 mutant embryos . Notably , Cx43 puncta were observed in all tissue layers: throughout the visceral endoderm , in the extraembryonic ectoderm , as well as within the epiblast and mesoderm ( Figure S4A–S4L′ and unpublished data ) . By the EHF stage ( E7 . 75 ) , when the emVE had dispersed , Cx43 puncta were detected within exVE and gut endoderm of wild-type embryos ( Figure 5A–5H ) . However , in Sox17 mutants , while Cx43 puncta were detected in exVE ( Figure 5A′–5D′ ) , they were absent in the undispersed emVE ( Figure 5E′–5H′ ) . To determine if this absence of Cx43 localization persisted , we analyzed embryos at later embryonic stages . At the ESom stage ( E8 . 25 ) , posterior views of wild-type embryos showed Cx43 puncta in the node , midline , mesoderm , yolk sac , and all gut endoderm cells , regardless of their origin ( emVE or DE ) ( Figure 5I–5P and Movie S5 ) . In Sox17 mutants , Cx43 puncta were present in the node , midline , and yolk sac , but notably were absent from the gut endoderm epithelium ( Figure 5I′–5P′ and Movie S5 ) . Collectively , these observations reveal that puncta comprising Cx43 , the major gap junction component expressed in embryos of these stages , were not present amongst cells of the gut endoderm in Sox17 mutants , suggesting a specific absence of gap junctions in this tissue . These observations prompted us to examine gap junction communication between cells of the gut endoderm in wild-type and Sox17 mutant embryos . To assay for gap junctional coupling , we performed single-cell iontophoretic dye injections into living embryos ( Figure 6A and 6B ) . Two dyes , Neurobiotin and Alexa568 , were co-injected into individual endoderm cells on the embryo's surface . While the high molecular weight Alexa dye remained confined within the injected cell , the lower molecular weight tracer Neurobiotin propagated intercellularly by coupling specifically through gap junctions [27] , [28] . Injections into gut endoderm cells of wild-type Afp::GFP VE-reporter expressing embryos resulted in Neurobiotin dye propagation across several cell diameters within the gut endoderm epithelium ( Figure 6C–6I ) . Neurobiotin propagation occurred regardless of whether an emVE or DE cell was injected . Moreover , the propagation of Neurobiotin occurred irrespective of whether cells situated on the right or left side of the embryo were injected ( Figure 6J–6L ) . Interestingly , even though we observed Cx43 puncta in the node and midline of wild-type embryos , Neurobiotin never propagated from the gut endoderm to cells of the node and midline , and so never crossed the midline between the left to the right side of gut endoderm ( Figure 6J–6L and Movie S6 ) . The Neurobiotin signal also did not couple from the injected endoderm to adjacent mesoderm cells ( Figure 6H–6I ) . Thus , gap junction communication within the gut endoderm was uncoupled from other germ layers and was isolated between left and right sides of the gut endoderm by a midline barrier in wild-type embryos . We then performed dye tracer experiments on Sox17 mutants . When individual gut endoderm cells in Sox17 mutants were injected , they always retained both the Neurobiotin as well as the Alexa dye ( Figure 6M–6Q ) . We consistently failed to detect dye coupling irrespective of the side of the embryo being injected ( Figure 6R–6V and Movie S7 ) . This suggested that in Sox17 mutants , gap junction cell-cell coupling did not occur across emVE cells , likely due to the absence of Cx43 and resulting lack of functional gap junctions . As an independent test of whether gap junction communication was required for the establishment of LR patterning in mice , we investigated whether its pharmacological inhibition might repress signal transfer from the embryonic midline ( the node ) where symmetry is broken , to the lateral plate , where it is affected , and in doing so affect expression of LR asymmetry markers in the LPM . Embryos from the EHF-LHF stage ( E7 . 75–E8 . 0 ) were cultured until the ∼4 somite stage in the presence of gap junction inhibitors ( Figure 7A ) . To ensure that inhibitors did not interfere with node morphogenesis , we selected only embryos in which the node had emerged to the surface of the embryo . To ensure that inhibitors were acting in the temporal window during which node to lateral plate signal relay must occur and were effective before LPM gene activation , we selected only embryos in which somites had not yet formed . Two inhibitors were used: Mefloquine hydrochloride , which selectively blocks Cx36 and Cx50 [29] , and 18 alpha-Glycyrrhetinic acid , a general gap junction blocker [30] . After overnight culture in the presence or absence of specific inhibitors , Lefty1/2 expression was assayed . Embryos cultured in the absence of inhibitors exhibited wild-type Lefty1/2 expression ( Figure 7B and 7C ) . By contrast , the majority of embryos cultured in 18 alpha-Glycyrrhetinic acid exhibited no Lefty1/2 signal ( 10/14 ) ( Figure 7D and 7E ) . Only a small subset of embryos exhibited weak Lefty1/2 signal limited to posterior regions of the left LPM ( 3/14 ) ( Figure 7F and 7G ) . Embryos cultured in Mefloquine hydrochloride exhibited wild-type Lefty1/2 expression ( Figure 7H and 7I ) . Embryos cultured in the presence of either inhibitor show normal cilia movement in the node and are able to create perinodal asymmetries ( unpublished data ) . These observations indicated that blocking gap junction function at the time when LR asymmetry is established prevented correct LR patterning , and so molecularly recapitulated the mutant phenotype ( Table S1 ) .
Analysis of the Nodal/Lefty/Pitx2 cascade of asymmetrically expressed genes indicated that LR asymmetry was not correctly established in Sox17 mutants . Pitx2 , the gene downstream in the pathway , was never detected in the left LPM of Sox17 mutants . Interestingly , our data revealed some variability in the expression of asymmetry genes higher up in the cascade in embryos lacking Sox17 . While many Sox17 mutants exhibited no expression of Nodal and Lefty1/2 in the LPM , others exhibited reduced and regionally restricted expression of these genes within the left LPM . In wild-type embryos , expression of Nodal in the LPM starts in a small region at the level of the node and subsequently expands anteriorly and posteriorly within the LPM [7] . The observation of reduced Nodal and Lefty1/2 expression in the LPM of some Sox17 mutant embryos revealed that the pathway had been activated , suggesting some signal was being relayed from the node to the LPM . This raised the possibility that minimal emVE cell dispersal , observed in some mutants , may have been sufficient for gap junction communication . Dye coupling tracer experiments argued against the possibility of gap junction coupling in the Sox17 mutant , because Neurobiotin was never observed to propagate between cells within the gut endoderm of Sox17 mutants . Nonetheless , since it was not technically feasible to inject every cell within the gut endoderm epithelium of a single embryo , we cannot rule out the possibility that occasional gap junctional coupling may have occurred in a minor population of endoderm cells in some Sox17 mutants . Alternatively , another connexin may partially compensate for the absence of Cx43 in Sox17 mutants . Our expression profiling did identify Cx31 , another connexin gene expressed at EHF stages , albeit at low levels ( Figure S3 ) . An upregulation of compensatory connexin ( s ) within the gut endoderm of Sox17 mutants might permit some communication between node and LPM , allowing sufficient signal transmission to occur for minimal activation of the pathway in the left LPM . The levels of signal might not be robust enough to fully activate the cascade , preventing it from spreading within the LPM . Indeed , when wild-type embryos were cultured in the presence of inhibitors of all connexins , we observed a complete failure to activate LPM gene expression . Our analysis of LR asymmetry markers also revealed the presence of ectopic patches of expression on both sides of the midline of Sox17 mutants , with ectopically expressing cells situated on the embryo's surface . Notably , we never observed such ectopic expression in wild-type embryos that had been cultured in the presence of gap junction inhibitors , which we added at the HF stages , suggesting they originated from a defect preceding the emergence of the node and subsequent events of LR asymmetry establishment . In support of this , expression analysis in EHF stage Sox17 mutants already revealed ectopic expression of Nodal in the endoderm . In PS stage wild-type embryos , Nodal is expressed in the entire emVE and then becomes downregulated [11] . This downregulation might not occur correctly in Sox17 mutants , possibly as a consequence of the emVE not being dispersed . The ectopic Nodal patches are subsequently likely to induce Lefty2 and Pitx2 . Even though some of the features have been documented [31] , the precise cellular behaviors that drive node and midline morphogenesis , and in particular regarding the emergence of cells onto the embryo's surface , remain to be elucidated . It has been proposed that groups of node precursor cells emerge from the APS and insert into the emVE epithelium at the distal tip of the conceptus [31] . Once on the embryo's surface , cohorts of node cells gradually coalesce to form a single node pit [2] , thereby providing a mechanism for collectively displacing emVE cells from the midline . The absence of Sox17 from the node and midline suggested that this transcription factor is not directly involved in node and midline formation . Accordingly , the node and midline structures emerged onto the embryo's surface in Sox17 mutants . This suggested that morphogenesis of the gut endoderm and node/midline are distinct processes and that they are uncoupled in the absence of Sox17 . We therefore interpret that failure to clear all emVE cells from the node region in a subset of mutant embryos as a secondary defect resulting from a failure in emVE dispersal . A completely emVE-derived gut endoderm might confine emVE cells to the midline , thereby hampering emergence of node progenitors onto the embryo's surface . Notably , the presence of residual emVE cells within the node of mutants did not disrupt leftward nodal flow and subsequent induction of asymmetric perinodal gene expression . The observation that gut endoderm of Sox17 mutants lacked Cx43 puncta focused our attention on gap junctions as the potential cause for the failure in LR asymmetry establishment . We previously demonstrated that dynamic and widespread rearrangements of cell-cell junctions occur during the intercalation of DE cells into the overlying emVE layer during gut endoderm morphogenesis [10] . Prior to the onset of gastrulation , Cx43 puncta were detected at cell-cell interfaces within the emVE of Sox17 mutants . Since in Sox17 mutants the emVE did not become dispersed and would not need to be rearranged to accommodate intercalation of DE cells , we expected the Cx43 puncta to be maintained in the mutant endoderm . However , Sox17 mutants displayed no Cx43 puncta within the endoderm between the no bud ( OB ) and EHF stages . LR defects have not been described in mutants lacking Cx43 . The original study characterizing a Cx43 knockout mouse strain reported that mutants survive to term , dying at birth from heart defects [32] . Moreover , subsequent work revealed genetic background-related differences in the phenotypes resulting from conditional Cx43 ablations [33] . It remains to be determined whether Cx43 mutant animals exhibit features representing a failure of LR patterning , including situs inversus or heterotaxia . Even though cells comprising the gut endoderm of Sox17 mutants were almost exclusively of emVE origin , our findings argued that they complete an initial step in gut endoderm morphogenesis by downregulating markers of VE identity , as do emVE cells in wild-type embryos prior to their dispersal [10] . Thus in the absence of DE cells within the gut endoderm epithelium of Sox17 mutants , a new wave of gap junction formation may have failed to occur . Further studies will be required to determine whether genes encoding gap junction components are direct targets of Sox17 or whether a failure in epithelial remodeling affects Cx43 localization . In either case , we reasoned that absence of Cx43 puncta within the gut endoderm caused a failure in gap junction coupling across this tissue in Sox17 mutants . Our findings revealed that gap junction coupling occurs between gut endoderm cells and that this mode of communication propagates signals in both the left and right sides of the embryo . Gap junction coupling occurred across several cell diameters extending from a perinodal location to the lateral plate . Since coupling occurred between DE and emVE cells , cells of two distinct origins intercalate to form a congruent epithelium comprising the gut endoderm . Gap junction communication in the gut endoderm is planar and isolated from surrounding tissues . The observation that the gut endoderm was capable of gap junction communication on both the left and the right sides suggested that it acts as a passive medium for relay of asymmetric LR information . This is in accord with the fact that in certain mutants having defects in LR asymmetry , expression of genes in the LPM can be right-sided or bilateral [8] . If the node region , as a result of perturbed nodal flow , dictates to send asymmetry signals to the right or in both directions , the unbiased gut endoderm obediently propagates the signal to the right or to both sides . Furthermore , by revealing a lack of gap junctional coupling across the midline , our experiments also provide the first functional visualization of a midline barrier in the mouse . Importantly , the fact that some Sox17 mutants exhibited limited expression of Nodal and Lefty1/2 in the left LPM suggests that the LPM is responsive to LR patterning signals . Thus , we conclude that the LR asymmetry defect in Sox17 mutants lies in perturbed LR signal propagation . Notably , these observations have also been noted by Saijoh and colleagues , who have gone on to demonstrate that the left LPM of Sox17 mutants is responsive to LR patterning signals from Nodal-source cells ( Y . Saijoh et al . , unpublished work , personal communication ) . The identity of the transmitted signal ( s ) is unknown: the signal that relays information must be produced in the vicinity of the node , transferred across the gut endoderm , which when reaching the interface with LPM must trigger the Nodal/Lefty/Pitx2 cascade . One molecule shown to travel through gap junctions is calcium [34]–[36] . LR asymmetric localization of calcium has been reported in fish [37] , chick [38] , and mice [4] , [39] . The two-cilia model of left-right asymmetry establishment in the mouse proposes that non-motile mechano-sensory perinodal cilia detect flow and elicit an asymmetric influx of Ca2+ ions through activity of PKD , a calcium-permeable ion channel [4] , [5] , [40] . Interestingly , mutants that fail to induce asymmetric calcium levels do not express LR asymmetry genes in the LPM [4] , [40] . Another candidate is serotonin [41] . Serotonin has been demonstrated to cross gap junctions [42] , and disruption of serotonergic signaling leads to aberrant LR patterning in frog and chick [43] , [44] . Additional candidates likely exist , and further work will be required to identify the factors that travel through gap junctions across the gut endoderm to mediate LR asymmetry establishment in mice .
Mouse strains used were: Sox17cKO/cKO [45] , Sox2::Cre [46] , NodalLacZ/+ [20] , Afp::GFP [21] , and wild-type ICR ( Taconic ) . The Sox17cKO strain was used to generate the null allele by crossing to the Sox2::Cre strain . Work on mice was subject to approval by , and carried out in accordance with guidelines from , the MSKCC IACUC . Embryos were dissected in DMEM-F12 ( Gibco ) /5% FCS ( Lonza ) and staged according to Downs and Davies [47] . For ex utero cultures EHF-LHF stage embryos were roller cultured in 50% rat serum/50% DMEM-F12 [48] , a gaseous mixture of 5% O2; 5% CO2 , and 90% N2 [49] . Inhibitors used were: 18 alpha-Glycyrrhetinic acid ( 1∶1 , 000 , Sigma ) and Mefloquine hydrochloride ( 1∶5 , 000 , Sigma ) . Post-culture embryos were processed for in situ hybridization . For in situ hybridization ( ISH ) , embryos were fixed in 4% PFA/PBS overnight at 4°C , dehydrated , and stored at −20°C . ISH was performed using antisense riboprobes [50] and standard protocols [51] . β-galactosidase staining was performed according to standard protocols [51] . For vibrating microtome sections of stained embryos , samples were placed in 30% sucrose/PBS overnight at 4°C , transferred into 0 . 4% gelatin/14% BSA/18% sucrose/10% glutaraldehyde , and sectioned at 16–20 µm ( VT1000S , Leica ) . Immunofluorescent ( IF ) staining was carried out as previously described in [10] . Antibodies used were: Arl13B ( 1∶300 , gift of K . Anderson , MSKCC ) , Connexin43 ( 1∶300 , Santa Cruz ) , FoxA2 ( 1∶1 , 000 , Abcam ) , and Sox17 ( 1∶1 , 000 , R&D Systems ) . Secondary Alexa-Fluor conjugated antibodies ( Invitrogen/Molecular probes ) were used at 1∶1 , 000 . DNA was visualized with Hoechst 33342 ( 5 µg/mL , Invitrogen ) . For cryosectioning , fixed embryos were taken through a sucrose gradient , embedded in O . C . T . ( Tissue-Tek ) , and sectioned at 12 µm ( CM3050S , Leica ) . Widefield images were collected with Axiocam MRc , MRm , or HSm CCD cameras ( Zeiss ) on a Leica MZ165FC . Laser scanning confocal images were acquired on a LSM510 META ( Zeiss ) as previously described [10] , [15] . Fluorescence was excited with: 405 nm diode laser ( Hoechst ) , 488 nm Argon laser ( GFP ) , 543 nm HeNe laser ( Alexa-543/555/568 ) , and 633 nm HeNe laser ( Alexa-633/647 ) . Images were acquired using Plan-Apo 20×/NA0 . 75 and Fluar 5×/NA0 . 25 objectives . Optical sections ranged between 0 . 2 and 2 µm . Data were processed with AIM software ( Zeiss ) and assembled in Photoshop CS4 ( Adobe ) . 3-D reconstructions of confocal z-stacks are depicted as maximum intensity projections ( MIPs ) . ISH quantitations were performed with the Gel Analyzer tool ( ImageJ , NIH ) . For scanning electron microscopy ( SEM ) , embryos were prepared as previously described [39] and imaged with a Field Emission Supra 25 ( Zeiss ) . Fluorescent latex beads ( 0 . 5 µm diameter , Sigma ) were placed over the node of embryos positioned ventral side up in 1% agarose submerged in culture medium . Beads were time-lapse imaged and tracked/annotated using the Manual Tracking plug-in ( ImageJ , NIH ) . The high speed imaging system used ( Zeiss HRm CCD camera , mounted on a Leica M165FC microscope and operated with Zeiss Axiovision software ) did not , however , permit analysis of the relative speeds of beads being imaged . EHF stage embryos were bisected along the extraembryonic-embryonic junction and the embryonic portion placed in TRIzol ( Invitrogen ) ( N = 3 ) . Triplicates were hybridized to Mouse-6 Illumina arrays ( Illumina Inc ) , and data were analyzed with Partek Genomics Suit ( Partek Inc ) . These data are MIAME compliant and have been deposited in NCBI's Gene Expression Omnibus ( GEO ) [52] , where it is publicly accessible through the accession number GSE33353 . LHF/ESom stage embryos were placed ventral side up and held in place with a metal mesh in artificial cerebral spinal fluid containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 1 . 25 KH2PO4 , 1 MgCl2 , 2 CaCl2 , 25 NaHCO3 , 1 . 3 ascorbate , 2 . 4 pyruvate , and 25 glucose ( gassed with 95% O2 and 5% CO2 ) at room temperature . An Olympus BX51WI equipped with epifluorescence illumination , a CCD camera , and two water immersion objective lenses ( UMPlanFI 10×/0 . 30W and LUMPlanFI/IR 60×/0 . 90W , Olympus ) was used to visualize and target recording electrodes to cells . Glass recording electrodes ( 10–15 MΩ resistance ) were filled with ( in mM , pH 7 . 25 ) : 130 potassium gluconate , 16 KCl , 2 MgCl2 , 0 . 2 EGTA , 10 HEPES , 4 Na2ATP , 0 . 4 Na3GTP , 0 . 2% Alexa-568 ( Invitrogen ) , and 0 . 5% Neurobiotin ( Vector Laboratories ) . After obtaining a whole-cell patch recording , Alexa-568 and Neurobiotin were iontophoretically ejected through the recording electrode using anodal current repeated on ( 350 ms ) and off ( 250 ms ) for 420 s . Immediately after , embryos were placed in DMEM-F12 ( Gibco ) /5% FCS ( Lonza ) and live imaged . | Superficially , humans , like other vertebrates , are bilaterally symmetrical . Nonetheless , the internal configuration of visceral organs reveals a stereotypical asymmetry . For example , human hearts are generally located on the left and the liver on the right side within the body cavity . How this left-right asymmetry is established is an area of interest , for both intrinsic biological significance and its medical application . In the mouse , the initial event that breaks left-right symmetry occurs at the node , a specialized organ located in the midline of the developing embryo . Somehow this initial asymmetry leads to a cascade of events that results in the activation of a genetic circuit on the left side of the embryo , which then leads to asymmetric organ formation . Here we show that the laterality information that is generated at the node is transferred to the lateral extremity of the embryo across the gut endoderm , which is the precursor tissue of the respiratory and digestive tracts and associated organs such as lungs , liver , and pancreas . Sox17 mutant mouse embryos exhibit defects in gut endoderm formation and fail to establish left-right asymmetry . Analysis of the mutants reveals that gap junction coupling across the gut endoderm is the mechanism of left-right information relay from the midline site of symmetry breaking to the site of asymmetric organogenesis in mice . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"histology",
"genetics",
"biology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] | 2012 | Role of the Gut Endoderm in Relaying Left-Right Patterning in Mice |
Vibrio cholerae is an aquatic gram-negative microbe responsible for cholera , a pandemic disease causing life-threatening diarrheal outbreaks in populations with limited access to health care . Like most pathogenic bacteria , V . cholerae secretes virulence factors to assist colonization of human hosts , several of which bind carbohydrate receptors found on cell-surfaces . Understanding how pathogenic virulence proteins specifically target host cells is important for the development of treatment strategies to fight bacterial infections . Vibrio cholerae cytolysin ( VCC ) is a secreted pore-forming toxin with a carboxy-terminal β-prism domain that targets complex N-glycans found on mammalian cell-surface proteins . To investigate glycan selectivity , we studied the VCC β-prism domain and two additional β-prism domains found within the V . cholerae biofilm matrix protein RbmC . We show that the two RbmC β-prism domains target a similar repertoire of complex N-glycan receptors as VCC and find through binding and modeling studies that a branched pentasaccharide core ( GlcNAc2-Man3 ) represents the likely footprint interacting with these domains . To understand the structural basis of V . cholerae β-prism selectivity , we solved high-resolution crystal structures of fragments of the pentasaccharide core bound to one RbmC β-prism domain and conducted mutagenesis experiments on the VCC toxin . Our results highlight a common strategy for cell-targeting utilized by both toxin and biofilm matrix proteins in Vibrio cholerae and provide a structural framework for understanding the specificity for individual receptors . Our results suggest that a common strategy for disrupting carbohydrate interactions could affect multiple virulence factors produced by V . cholerae , as well as similar β-prism domains found in other vibrio pathogens .
The recognition of carbohydrate receptors on host cell-surfaces is an important strategy for achieving the selectivity and potency of virulence factors including adhesion molecules , toxins , and biofilm proteins [1–3] . Often mediated by a canonical set of lectin domains with conserved folds , these proteins may broadly recognize terminal sugars on the end of long glycan chains , or specific polysaccharide motifs with complex branched stereochemistry [4] . Understanding the structural mechanism for glycan specificity by lectin domains is important in determining how effector proteins recognize specific host cells and for developing drugs against pathogenic proteins [5–7] . Vibrio cholerae is a pernicious human pathogen that secretes factors that utilize carbohydrate receptors , most notably the classical cholera toxin ( CT ) , which binds to GM1 gangliosides on the intestinal epithelium [8] . V . cholerae also secretes a pore-forming toxin called Vibrio cholerae cytolysin ( VCC ) , which helps defend the bacteria from the host immune system according to mouse models [9 , 10] . VCC recognizes complex N-glycans commonly found on animal cells [11] through a carboxy-terminal domain with a type I β-prism fold ( Fig 1A ) ; deletion of this domain results in a more than 99 . 9% loss in cytolytic activity [11] . Even though complex N-glycans are the preferred target of VCC , the exact footprint recognized by the VCC β-prism domain and the structural mechanism for this interaction are currently unknown . β-prism folds in V . cholerae are not unique to VCC: three additional β-prism domains exist in two biofilm matrix proteins called RbmC ( rugosity and biofilm structure modulator C ) [12] and Bap1 ( biofilm associated protein 1 ) [13] ( Fig 1B ) . The formation of biofilms by V . cholerae is an important survival strategy [14–16] that facilitates bacterial attachment to surfaces , helps protect against environmental insults [17] , and is also implicated in human transmission of the disease [18 , 19] . The biofilm itself is primarily composed of secreted molecules including an exopolysaccharide [17] called VPS ( Vibrio polysaccharide ) assembled and exported by two clusters of VPS-related genes ( vps I and vps II ) [20] , several matrix proteins produced by the rbm gene cluster , and a mixture of nucleic acids and small biomolecules [20] . RbmC and Bap1 are related multidomain proteins with overlapping functions [12 , 21] , involved in the surface attachment of biofilms and encapsulation of growing cell clusters [21–23] . Simultaneous deletion of RbmC and Bap1 leads to colonies deficient in biofilm formation , however , either gene is sufficient to restore function [21] . Interestingly , mass spectral analysis of isolated biofilm material shows that RbmC , Bap1 , and VCC are all retained in the biofilm matrix and may therefore display affinity for a subset of related ligands [21] . To better understand the structural specificity of β-prism-carbohydrate interactions in V . cholerae biofilm proteins and toxins , we cloned and expressed the two β-prism domains from RbmC and determined their glycan specificity by screening against a chip-based mammalian glycan library [24 , 25] . Using isothermal titration calorimetry ( ITC ) and a fluorescence-based binding assay , we determined the binding affinity of the vibrio β-prisms to multiple fragments of target N-glycans to determine the unique footprint recognized by these domains . We crystallized and solved the structure of one β-prism domain from RbmC bound to two fragments of the glycan core . Finally , we show that VCC and both RbmC β-prism domains can target glycans present on rabbit blood cells , a model system that contains complex N-glycans on the cell surface . Our results illustrate a common structural mechanism by which vibrio toxin and biofilm β-prism domains target glycans on host cell-surfaces , facilitating cell lysis in one case and cell attachment in another . Our structures provide a model for how Vibrio cholerae targets the invariant core of cell-surface receptors commonly found on vertebrate cells while allowing for heterogeneity in the rest of the glycan , a model that may apply to β-prism domains found elsewhere in nature .
A protein BLAST [26] search ( blastp ) was conducted using the C-terminal Vibrio cholerae cytolysin β-prism domain ( residues 587 to 716 from PDB ID 1XEZ in Vibrio cholerae strain N16961 ) , which identified three additional β-prism domains in two open reading frames ( Fig 1B ) , belonging to rbmc ( NCBI gene ID = 2614150 ) and bap1 ( NCBI gene ID = 2613517 ) , which encode biofilm matrix proteins [12 , 27] . For simplicity , we refer to the two β-prism domains of RbmC as RbmC1 and RbmC2 ( Fig 1B ) . A sequence alignment of the four V . cholerae β-prism domains ( Fig 1C ) indicates that the degree of sequence identity between VCC and the other three domains is 36 . 5% , 40 . 5% , and 33 . 3% , for RbmC1 , RbmC2 , and Bap1 , respectively . Attempts at expressing full-length RbmC and Bap1 proteins in E . coli were unsuccessful , however expression of the two β-prism domains from RbmC was achieved via generation of a fusion construct with bacterially optimized GFPUV . Unfortunately , expression of the Bap1 β-prism domain in a soluble form was not possible , even as a GFPUV-fusion construct . To determine the glycan specificity of RbmC1 and RbmC2 , we labeled the purified proteins with a fluorescent tag and subjected them to glycan screening by the Consortium for Functional Glycomics ( CFG ) against the mammalian glycan chip v . 5 . 2 , which contains 609 mammalian glycans arrayed via amino linkers on an N-hydroxysuccinimide-activated glass slide ( http://www . functionalglycomics . org/ ) . Screening results indicated a similar pattern of glycan recognition between RbmC1 , RbmC2 , and VCC [11] , with top hits containing a similar pattern of biantennary complex N-glycans ( Fig 2A and 2B ) . The glycans identified in the screen typically contained an NGA2-type conserved heptasaccharide motif ( GlcNAcβ1-2Manα1-6 ( GlcNAcβ1-2Manα1–3 ) Manβ1-4GlcNAcβ1-4GlcNAc ) , although binding to some glycans missing one or both antennae ( truncated at the mannotriose core , see Fig 2 ) was also observed for all three proteins . The top four previously determined VCC hits [11] all contained a complete heptasaccharide NGA2 core , although there was one single-antenna complex N-glycan within the top 10 glycans identified . As was observed for VCC , binding to high-mannose type glycans ( which contain highly-branched mannose chains past the mannotriose core ) was not observed . The glycan chip results are not necessarily quantitative , meaning the fluorescence signal should not be interpreted as being directly proportional to the binding affinity since the glycan density on the chip may vary . For this reason , further binding experiments were required to determine the affinity of the target proteins for individual glycan fragments . Our results show that the VCC toxin and RbmC β-prisms target a similar repertoire of carbohydrate moieties on cell surfaces , suggesting a common strategy of cell-surface recognition . While complex N-linked glycans are found in all higher eukaryotes including plants and animals [28] , later processing steps in the Golgi vary across different organisms yielding differently modified repertoires of cell-surface glycans [29 , 30] . Complex N-glycans are abundantly expressed on differentiated epithelial cells in the gastrointestinal tract [31] and are also present on mucin proteins [32] . While the processing of N-glycans on surface proteins yield a heterogeneous mixture of branching and modification characteristics , the core structures found in the screen are likely present in glycans found on the intestinal epithelium . Owing to the design of the glycan chip , our results indicate that the β-prism domains of VCC and RbmC are capable of targeting mammalian cell-surface glycans . While it is possible that the β-prism domains of RbmC might also bind to the exopolysaccharide abundant in V . cholerae biofilms ( VPS ) , it is reasonable to posit that binding of this tetrasaccharide repeat is preferentially coordinated by any of the additional domains found in RbmC , or by other biofilm matrix proteins , and that the primary role of the RbmC β-prism domains is to target host cell glycans . This hypothesis is supported by the strong affinity ( low nanomolar ) of VCC and RbmC β-prisms for N-glycans and absence of hits that resemble the VPS tetrasaccharide repeat [33] . Additionally , another biofilm matrix protein , RbmA , has been implicated in VPS binding in the V . cholerae biofilm [34] . To better understand of the binding footprint for complex N-glycan recognition by vibrio β-prism domains , we measured the binding affinity of the VCC and RbmC β-prisms to complex N-glycan fragments using a top-down approach ( Fig 3 and S1 Fig ) . Due to the variable availability of different glycan fragments , both isothermal titration calorimetry ( ITC ) and intrinsic tryptophan fluorescence spectroscopy were utilized in characterizing the glycan footprint of the vibrio β-prism domains . For glycan fragments that could not feasibly be obtained in the quantities required for ITC measurements , the intrinsic tryptophan fluorescence of purified β-prism domains was used to monitor ligand association . The footprinting results indicate that the VCC β-prism binds most tightly to the NGA2 heptasaccharide core , with a measured binding affinity of 0 . 2 μM . Glycan fragments smaller than NGA2 bound less tightly , including a 228 . 6 μM affinity for the mannotriose branch point and 2 . 7 mM for the methyl-α-mannose monosaccharide ( as reported previously in [11] ) . We were unable to detect binding for the N-acetyl-D-lactosamine ( LacNAc ) disaccharide , which is present in one arm of a typical complex N-glycan . Glycans with arms longer than NGA2 , including the NA2 and A2 glycans ( which respectively extend galactose and sialic acid modifications onto the terminal ends of the biantennary arms ) , did not display tighter binding affinities . To investigate whether the vibrio β-prism domains are also capable of binding a mammalian-derived glycoprotein , we measured the binding affinity of VCC to bovine asialofetuin . While asialofetuin is a good indicator of general binding to complex N-glycans , analysis of these binding phenomena are overestimated by ITC because the glycoprotein contains three glycosylation sites occupied by heterogeneous mixtures of bi- and tri-antennary glycans [35] . VCC bound this protein as previously reported [36] with an affinity in the low micromolar range . Binding studies were repeated for several of the glycan fragments with the RbmC1 and RbmC2 β-prism domains . In contrast to the 2 . 7 mM methyl-α-mannose affinity with VCC , RbmC1 and RbmC2 displayed tighter affinities of 178 . 8 μM and 502 . 6 μM , respectively ( Fig 3 ) . We were also unable to observe binding of RbmC1 or RbmC2 to the LacNAc disaccharide , while RbmC2 did bind N-acetylglucosaminyl-β-1 , 2-mannose ( GlcNAc-Man ) with an affinity of 158 . 8 μM . The mannotriose core ( 1 , 3-α-1 , 6-α-D-mannotriose ) also bound to the RbmC1 and RbmC2 β-prism domains with affinities of 0 . 6 μM and 4 . 1μM , respectively . Binding of NGA2 to RbmC2 exhibited one of the highest affinities measured for any β-prism domain to a glycan fragment , with an affinity of 1 . 1 nM . A pentasaccharide fragment of NGA2 missing the two anchoring GlcNAc sugars provided a similar affinity of 1 . 5 nM , which is still much tighter than binding to mannotriose ( 4 . 1 μM ) . This suggests that at least for RbmC2 , it is the pentasaccharide core that is the likely footprint targeted by the β-prism domain . RbmC1 and RbmC2 also bound tightly to asialofetuin , with apparent affinities of 15 . 9 nM and 18 . 4 nM , respectively . If we assume that the three β-prism domains employ a similar footprint for glycan recognition ( as supported by the glycan chip data ) , our results indicate that the binding footprint is likely centered around the mannotriose core . At least for VCC , glycans larger than NGA2 do not bind any tighter , likely because the extended chains do not strongly interact with the β-prism . Similarly , for RbmC2 the pentasaccharide and NGA2 glycans had similar affinities ( but better than mannotriose ) and asialofetuin did not bind any better than the pentasaccharide . It is possible that the minimal recognition motif is comprised of a tetrasaccharide made up of mannotriose plus a single N-acetylglucosamine , however due to the difficulty in obtaining asymmetrically branched glycans , this interaction could not be tested . High-mannose type glycans ( or oligomannose , containing only mannose past the mannotriose core ) are present on the mammalian glycan chip , but did not elicit strong binding by any of the vibrio β-prism domains tested . In general , the RbmC β-prism domains bound more tightly to glycan fragments than the VCC toxin domain by a factor of approximately 50-100-fold . To gain insight into to the mechanism of glycan recognition by vibrio β-prism domains , we performed crystallization trials in the presence of N-glycan core fragments . Although we were unable to obtain well-diffracting crystals of any β-prism domain with a bound pentasaccharide fragment , crystals were obtained for RbmC2 bound to mannotriose and N-acetylglucosaminyl-β-1 , 2-mannose ( GlcNAc-Man ) . X-ray data were collected to 1 . 5 Å resolution and the structure solved by molecular replacement using the VCC β-prism lectin domain ( Table 1 ) . The electron density map displayed unambiguous density for the mannotriose ligand in a single orientation ( Fig 4A ) . Calculations using PISA [37] indicated 384 Å2 of buried surface area between the ligand and the protein . As previously observed in VCC and other β-prism lectins , a conserved aspartic acid residue ( D617 in VCC , D853 in RbmC2 ) mediates hydrogen-bonding interactions with hydroxyl groups on one of the mannose rings ( Fig 4B ) . In VCC , mutation of this residue to alanine results in a 50-fold loss in hemolytic activity of the toxin . In the RbmC2 structure , D853 interacts with the α1 , 6-linked mannose , whose O4 and O6 hydroxyl groups make putative hydrogen-bonding interactions with the D853 side-chain . This position is highly conserved across β-prism domains found throughout nature and is likewise found in all four of the vibrio β-prism domains expressed by V . cholerae ( Fig 1C ) . Analysis of bonding interactions within a distance less than 3 . 35 Å indicated a total of ten direct hydrogen bonds between RbmC2 and the mannotriose ligand ( Figs 4B , S2 and S3 ) , with eight of these interactions targeting the α1 , 6-linked mannose . Of the eight hydrogen bonds with the α1 , 6-linked mannose , six are interactions with the peptide backbone , and two are with the oxygens of the D853 side-chain . The position occupied by the α1 , 6-linked mannose is the same as that observed in the structure of the VCC β-prism in complex with the monosaccharide , methyl-α-mannose [11] . The α1 , 6-linked mannose also makes the most extensive surface contacts with RbmC2 , with 165 Å2 of buried surface area . The central and α1 , 3-linked mannose rings each make a single hydrogen bond to the protein , both to side-chain atoms . Not surprisingly , the two more “loosely”-bound sugars had higher average crystallographic B-factors ( 3 . 5 Å2 , 7 . 4 Å2 , and 14 . 0 Å2 for the α1 , 6-linked , central , and α1 , 3-linked mannose sugars , respectively ) . Binding of RbmC2 to these saccharides also resulted in progressively less buried surface area at 121 Å2 and 98 Å2 for the central and α1 , 3-linked mannose , respectively . The high resolution of the structure confirmed that the central mannose best refined as the β-anomer , as expected [30] . Also of note is a centrally located water molecule ( B-factor = 4 . 2 Å2 ) coordinated between ligand and protein groups ( Figs 4B and S3 ) , including W948 and N971 . The latter residue is on a flexible loop that adopts a different conformation in the apo structure , suggesting that this particular water molecule is only present when the glycan is bound . RbmC2 was crystallized in the presence of N-acetylglucosaminyl-β-1 , 2-mannose ( GlcNAc-Man ) , and the structure solved to 1 . 8 Å resolution . Density for the GlcNAc-Man ligand was observed in the same binding pocket identified in the RbmC2/mannotriose and VCC β-prism/methyl-α-mannose complexes . While occupancy refinement of the mannotriose sugar suggested near full occupancy , the N-acetylglucosaminyl-β-1 , 2-mannose ligand density suggested a mixture of the carbohydrate and glycerol ( overlapping with the C4 , C5 , and C6 carbons of the mannose sugar ) . Alternate configurations for the two ligands were built into the density and their relative occupancies were refined , yielding a ratio of 65% GlcNAc-Man to 35% glycerol ( presumably from the cryoprotectant ) . The mannose moiety occupies the same location as the α1 , 6-linked mannose in the mannotriose structure ( Fig 4C ) , making a similar constellation of hydrogen-bonding interactions . Coordination of the disaccharide by RbmC2 resulted in a total of 338 Å2 of buried surface area with a contribution of 160 Å2 by the mannose moiety and 179 Å2 by the N-acetylglucosamine . Interestingly , the centrally located water molecule observed in the mannotriose structure was also present , even though the fourth coordination site ( previously made by the central mannose moiety ) is missing in this structure ( Figs 4D and S4 ) . In the apo structure ( with three protein molecules in the asymmetric unit ) , glycerol molecules from the cryo-protection solution are found in the glycan binding pocket . In two copies of the asymmetric unit , a water is present bound to W948 as in the mannotriose and GlcNAc-Man structures . In the third copy , a hydroxide group from a glycerol molecule replaces this position . As previously stated , the loop containing the N871 residue is unstructured in two of three copies of the asymmetric unit and the ordered copy is in a different conformation than in the ligand-occupied structures . This suggests that binding of the ligand may lead to the ordering of this loop and that the water molecule coordinated by N871 and W948 may help mediate this interaction . The N-acetylglucosamine moiety of the GlcNAc-Man structure was observed projecting away from the mannotriose binding cleft . Fewer hydrogen-bonding contacts are made between RbmC2 and this sugar , perhaps contributing to the higher B-factors and less well-defined electron density for this group . In fact , only a single hydrogen bond was observed with the GlcNAc group: between the acetyl O7 atom and the guanidinium group of R876 . This residue is not conserved among the various vibrio β-prism domains , but is instead replaced by an asparagine in the RbmC1 sequence ( and presumably structure ) , while in the VCC β-prism structure this location is occupied by a serine , which is also present in the Bap1 β-prism sequence . The only other observed contact between RbmC2 and the GlcNAc moiety is a van der Waals contact between the sugar ring and the side-chain of V874 . The distance between the CG2 carbon of this valine and the C4 carbon of GlcNAc is 3 . 6 Å . This residue is not conserved across the other three β-prism domains , suggesting that interactions with this sugar may vary between the different vibrio β-prism domains . RbmC2 apo crystals were obtained with space group C2221 , with three copies in the asymmetric unit . In general , RbmC2 only displayed slight differences between the apo , mannotriose , and GlcNAc-Man structures . The all-atom RMSD between the two ligand-bound structures is 0 . 21 Å2 and approximately 0 . 34 Å2 between liganded and apo ( compared to 1 . 2 Å2 for RbmC2 vs . VCC β-prism ) . Comparing glycan-bound structures to the three copies of RbmC2 in the apo structure asymmetric unit revealed differences in a loop ( with the sequence PVQGT ) , which is not present in the other vibrio lectin domains ( Figs 1C and S5 ) . Threonine 870 is the only amino acid within this loop that directly contacts the ligand forming a hydrogen bond between the peptide backbone and O3 of the α1 , 6-linked mannose group . Aside from the rearrangement of this loop , only subtle side-chain rearrangements of F850 , W896 , and W948 are observed between the apo and mannotriose-bound , and GlcNAc-Man-bound structures . Taking all of the observed protein-ligand interactions into account , the structural basis for the increased binding affinity of the RbmC2 domain over VCC is likely due to substitutions of several key residues that interact with the bound ligand . The F850 and W896 residues that participate in van der Waals and ring-stacking interactions with the mannotriose ligand are conserved in RbmC1 and RbmC2 , but replaced by an alanine ( A614 ) and tyrosine ( Y654 ) , respectively , in VCC . The increased surface area available for ring-stacking due to the substitution of tryptophan in RbmC1 and RbmC2 for the tyrosine in VCC may account for some of the increased affinity of the RbmC domains for glycans . Furthermore , the side-chain hydroxyl group of Y894 that forms a hydrogen bond with the central mannose group in the RbmC2/mannotriose structure is conserved in RbmC1 , but replaced with a hydroxyl-lacking phenylalanine in VCC . The remaining residues contacting the ligand through side-chain or backbone interactions are either conserved in all three domains , or between VCC and one of the two RbmC β-prism domains . To gain a better understanding of the functional importance of complex N-glycan interactions , we made mutations to residues that line the ligand binding pocket . Because we do not have a functional assay for RbmC biofilm activity , mutations were made in VCC , where the lytic activity against mammalian cells ( rabbit erythrocytes in our model system ) can be monitored . As an enteric pathogen , Vibrio cholerae is unlikely to encounter erythrocytes during an infection . However , we demonstrated previously that VCC lyses rabbit blood cells as well as human T-cells , monocytes , and neutrophils [36] , the latter of which is a likely target [10] . Furthermore , rabbit erythrocytes are highly decorated with biantennary complex N-glycans [39] making them a good model for targeting studies . The change in activity of the mutant is reported as the ratio of HD50 values ( the concentration that elicits 50% cell lysis , Fig 5A ) between the mutant and wild-type toxin ( which is typically around 100 pM [36] ) . We classified the effects of individual mutations ( Fig 5B ) into those that elicited a 0-10-fold loss ( 10–100% WT activity ) , 10-100-fold loss ( 1–10% WT activity ) , and >100-fold loss in activity ( <1% WT activity ) . To put these data into context , we built a composite ligand model in which we extended the mannotriose core outwards , including the next two β1 , 2-linked N-acetylglucosamine moieties seen in complex N-glycans ( Fig 5C ) . We used the GlcNAc-Man structure to place the GlcNAc moiety connected to the α1 , 6-linked mannose , and modeled in the second GlcNAc moiety , followed by energy-minimization . D617K , a mutation targeting the residue that forms two hydrogen bonds with the α1 , 6-linked mannose group in both VCC and RbmC2 ( S2 Fig ) , elicited a 178-fold loss in activity . A previous study showed that D617K and D617A had similar deleterious effects on VCC activity [11] . Not expected was that mutation of L707 to alanine , a residue that forms the floor of the binding pocket and is conserved between three of the four vibrio β-prism domains ( S2 Fig ) , led to a 505-fold loss in activity . Mutations resulting in 10 to 100-fold activity loss included three aromatic residues that contact the ligand in RbmC2: F652 , Y654 , and W706 ( VCC numbering ) . W706 adopts two distinct rotamer conformations across several crystal structures of VCC [11] and could potentially form a stacking interaction with the N-acetylglucosamine residue attached to the α1 , 6-linked mannose ( yellow in Fig 5C ) . To see how disrupting the N-acetylglucosamine residue that exits the opposite side of the binding pocket ( attached to the α1 , 3-linked mannose ) might affect binding , we mutated G613 ( a glycine in all three lectin domains investigated here ) to a lysine residue to sterically block the glycan exit channel . Lysine was selected as it is a bulky side-chain that is not tightly constrained in rotamer positions with a positive charge to maintain protein solubility . This mutation led to a 47-fold loss in activity suggesting that this side of the binding channel must remain unoccluded to allow the glycan arm to exit the pocket ( Fig 5C ) . While mutation of glycine residues to any amino acid can cause issues with allowed backbone Ramachandran angles , the two glycine mutants could still be expressed in a soluble form and retained some hemolytic activity , suggesting that the mutations did not lead to a substantial misfolding of the domain . Several mutations only displayed modest effects on the activity of the VCC toxin . N630 and G631 line the bottom lip of the binding pocket in a similar position to the attachment point of the PVQGT loop in RbmC2 . The mutants N630A and G631K led to 1 . 7-fold and 4 . 9-fold losses in activity , respectively , indicating that these loop side-chains do not interact substantially with bound glycans in VCC . Likewise , Q658A had virtually no effect ( 1 . 2-fold loss ) on VCC activity . This residue is at a position just outside W706 designating the outer boundary of the glycan footprint extending from the α1 , 3-linked mannose . Together , these results support a model where the bound glycan projects antennae outward through both sides of the binding pocket ( Fig 5C ) . Such a bound conformation would tolerate both heterogeneity in the N-glycan branches , as well as core fucosylation of the first N-acetylglucosamine that attaches the glycan to cell-surface proteins ( a common modification on glycosylated proteins [30] ) . To determine whether the RbmC1 and RbmC2 β-prism domains can recognize mammalian cells with complex N-glycans on their surface , we used GFPUV fusion proteins to label rabbit whole blood cells and imaged with fluorescence microscopy . Attempts to label cells with a GFPUV-VCC were unsuccessful , possibly due to the lower affinity of VCC for complex N-glycans . To ensure that our imaging assay reflected specific binding to these cells , we made the analogous point mutation in RbmC1 ( D539A ) and RbmC2 ( D853A ) to the D617 mutation in VCC that greatly diminishes hemolytic activity against rabbit cells . ITC experiments against bovine asialofetuin confirmed that the wild-type fusion was active ( S6 Fig ) and that the mutant fusion exhibited greatly diminished binding . ITC experiments also identified a lack of binding of RbmC2 D853A to mannotriose . Both constructs expressed at similar levels , appeared identical on an SDS-PAGE gel , and exhibited monodisperse behavior on a size-exclusion chromatography column suggesting that the mutation did not disrupt the structure of the β-prism domain . To further confirm that mutation of the key sugar binding aspartic acid did not disrupt the folding or stability of the protein , we subjected RbmC2 WT and RbmC2 D853A β-prism domains to a thermal melt monitored via circular dichroism ( CD ) . CD spectra for RbmC2 WT and RbmC2 D853A before melting ( at 20°C ) exhibited similar profiles ( S7A Fig ) and estimated secondary structure composition ( calculated with β-structure selection ( BeStSel ) [40] , S7B Fig ) , also consistent with the RbmC2 crystal structure . RbmC2 WT and RbmC2 D853A maintained mostly β-strand secondary structure through 96°C and fitting with BeStSel showed an increase in parallel and left-twisted antiparallel β-sheets , suggesting that unfolding likely led to the formation of amyloid structures ( S7B Fig ) . Although RbmC2 D853A displayed decreased CD signal beyond 54°C ( perhaps due to lower solubility caused by the D853A mutation or a different high-temperature state ) , our results suggest that mutation of the aspartic acid residue did not destabilize the RbmC2 domain at temperatures below 50°C ( S7C Fig ) , confirming validity of the blood binding assays and ITC , which were performed at room temperature . Due to the low solubility of the RbmC1 domain following cleavage from the GFPUV fusion protein , we did not perform a CD melt on the RbmC1 mutant . Both RbmC1 WT and RbmC1 D539A fusion proteins expressed and purified in a soluble form and led to monodisperse peaks on a sizing column suggesting that like RbmC2 , they adopt a folded structure at room temperature . Fluorescence images showed binding of both the RbmC1 and RbmC2 wild-type fusions to the rabbit blood cells ( Fig 6A and 6B ) , while binding of the aspartic acid substitution mutants was not detected . RbmC1 and RbmC2 clearly recognized and bound tightly to mammalian blood cells known to display complex N-glycans in a specific manner ( dependent on interactions with D539/D853 ) , suggesting that RbmC1 and RbmC2 domains have the ability to target mammalian cell-surface glycans with high-affinity .
Our results indicate that the β-prism domains of VCC and RbmC share a strong preference for complex N-glycans and are likely directed against similar targets , whether in the environment or in the host . Similar to results seen with VCC [11] , screening against the glycan library failed to show binding of RbmC1 and RbmC2 to high-mannose type glycans , suggesting that vibrio β-prism domains prefer glycans highly represented on mammalian cell surfaces [29 , 41] . Our protein-glycan complex structures illustrate the basis for this selectivity . The vibrio β-prism domain sugar-binding pocket is able to accommodate branching of carbohydrate moieties extending from the α1 , 3-linked mannose and a select subset of carbohydrate extensions from the α1 , 6-linked mannose of the mannotriose core . In complex N-glycans , the GlcNAc moiety attaches to the O2-position of the core α1 , 6-linked mannose [30] , a configuration tolerated by the vibrio β-prism binding pocket . In branched high-mannose glycans , α1 , 3- and α1 , 6- linkages to the α1 , 6-linked mannose of the mannotriose core are utilized , both of which are sterically incompatible with the vibrio β-prism domain glycan binding pocket . Complex N-glycans with more than two antennae on the α1 , 6-linked mannose residue are also unlikely to bind , because these additional branches extend with β1 , 4- and β1 , 6-linkages , and both positions are also sterically blocked in the sugar binding pocket . Furthermore , Vibrio cholerae VPS consists of modified gulose , glucose and galactose monomers linked in α- and β1 , 4-linkages [33] . This linear polymer conformation is distinct from the bent shape made by α1 , 6 and α1 , 3-linkages in the mannotriose core and lacks the O4 position necessary for crucial hydrogen-bonding interactions with the key aspartic acid position in the β-prism domain . Therefore , we expect that VPS is sterically unlikely to bind to VCC and RbmC β-prism domains . We were unable to express the β-prism domain from the biofilm matrix protein Bap1 and therefore cannot comment on whether Bap1 has a similar preference for complex N-glycans as VCC and RbmC . While Bap1 maintains the key aspartate residue ( D348 ) and ring-stacking tryptophan ( W387 ) that anchor the mannotriose ligand in RbmC2 ( S2 Fig ) , the core tryptophan and leucine residues ( W706/L707 in VCC , W948/L949 in RbmC2 ) are missing in Bap1 and instead replaced by a seven-residue insertion . RbmC and Bap1 have similar , but not identical roles in biofilm formation [21 , 22] and could thus target a different assortment of glycan receptors . This may involve the attachment of growing colonies to a host cell surface , or the absorption of clusters of cells already surrounded by biofilms to host surfaces in the gut . To the best of our knowledge , the majority of experiments involving biofilm formation using V . cholerae strains are performed on glass coverslips , glass liquid culture tubes , polystyrene plates or Teflon surfaces , all of which are unlikely to contain N-glycan anchor points . Under these conditions , we would not expect β-prism domains to be utilized in making adhesive contacts . Future experiments investigating biofilms grown on cultured cells or tissue surfaces may uncover variations in how the biofilm matrix forms , particularly when these additional adhesive interactions are part of the complex system . Our structural and functional results suggest a model for N-glycan recognition by vibrio β-prism domains . Rather than targeting motifs on antennae termini that may vary heterogeneously across different cell types , they selectively bind to the invariant core . This arrangement is also less likely to be affected by further modifications , like core fucosylation of the N-acetylglucosamine stem [42] . Some glycans have an additional bisecting N-acetylglucosamine residue projecting out of the central mannose in the mannotriose core with a β1 , 4-linkage [42] . The orientation in which this core is bound in RbmC2 leaves little space for this modification , although binding could occur if W948 flips to a different rotamer . Plant complex N-glycans often contain an additional β1 , 2-xylose modification on the central mannose of the mannotriose core [28] . This position is relatively unhindered in our structures ( Fig 4B ) , raising the possibility that vibrio biofilms could attach to plant surfaces , although these glycans were not represented on the mammalian glycan chip . By accommodating these and other modifications , the β-prism lectin may help toxins and biofilm proteins target a wider range of glycan structures in the environment and in hosts . Because the core structure described in this study is shared in all complex N-glycans , it would also be nearly impossible for a host to evolve resistance without substantial truncation of cell-surface glycans . Our results also indicate that the affinity for N-glycans varies among the different vibrio β-prism domains . We determined the affinity for mannotriose and heptasaccharide glycan cores to be at least 100-fold stronger for RbmC β-prism domains than for the VCC β-prism domain . This 100-fold difference theoretically requires approximately 2 . 7 kcal/mol of binding energy , which can be explained by the presence of one or two additional hydrogen bonds or <150 Å2 of newly buried surface energy . The increased binding energy required for the 100-fold difference observed between RbmC and VCC β-prism domains can be accounted for by the additional hydrogen bonding interaction of Y894 ( Y575 in RbmC1 , F652 in VCC ) , the additional surface area provided by W896 ( W577 in RbmC1 , Y654 in VCC ) , or the additional hydrogen bond facilitated by T870 in RbmC2 . VCC lyses cells at picomolar concentrations , so comparing these affinities , we expect RbmC to bind very tightly to cell surfaces containing N-glycan groups , particularly due to avidity effects from having two β-prism domains . Bap1 , with only one β-prism domain , may bind less strongly . A mutant form of RbmC missing the C-terminal β-prism domain ( RbmC2 ) is still able to rescue biofilm formation by a ΔrbmC/Δbap1 double mutant [21] , likely due to the redundant nature of the β-prism domain . Both Bap1 , with a single β-prism domain , and full-length RbmC are also able to rescue biofilm formation in a double knockout [12] . Our results demonstrate a common mechanism by which vibrio biofilm and toxin proteins utilize complex N-glycans to attach to and attack host cell membranes . While the role of biofilm attachment to complex N-glycans in the disease cycle has not yet been explored , it is possible that clusters of biofilm-encapsulated bacteria could strongly attach to the human intestinal epithelium following ingestion helping to seed new colonies . Or , newly established colonies in the gut could utilize epithelial surfaces as biofilm-dependent anchors . Interestingly , biofilm formation is negatively regulated by quorum-sensing pathways [43 , 44] , suggesting that it may be adventitious to form biofilms during early stages of colonization , but not so when the pathogen prepares to leave the host . At the same time , VCC could help defend against immune cells [10 , 45] or cause localized inflammation by attacking the epithelial surface directly before quorum sensing eventually leads to the down-regulation of VCC [46] and activation of classical cholera toxin and other important virulence factors [47] . A better understanding of the role fulfilled by these glycan-interactions is important for devising interventions to block intoxication and biofilm formation by bacterial pathogens and may provide additional methods to target specific cell types . For example , cancer cells often display modified glycan structures on their cell membranes [48] , a marker that might be exploited by agents specifically targeted against these glycans . These results also suggest further experiments aimed at studying biofilm formation on cellular substrates , where carbohydrate adhesion interactions may further modify the growing biofilm matrix .
Glycans used in this paper: methyl α-D-mannopyranoside ( Sigma , M6882 ) , N-acetyl-D-lactosamine ( Carbosynth , OA08244 ) , N-acetylglucosaminyl-β-1 , 2-mannose ( Dextra , M292 ) , 1 , 3-α-1 , 6-α-D-mannotriose ( Carbosynth , OM05762 or Dextra , M336 ) , N-linked core pentasaccharide ( Dextra , M592 ) , NGA2 N-linked heptasaccharide ( Prozyme , GKC-004300 ) , NA2 N-linked polysaccharide ( Prozyme , GKC-024300 ) , A2 N-linked polysaccharide ( Prozyme , GKC-224300 ) , asialofetuin ( Sigma , A4781 ) . NEB5α E . coli ( C2987I ) , Shuffle T7 E . coli ( C3026J ) , and T7 Express E . coli ( C2566I ) . All strains were obtained from New England Biolabs , Ipswich , MA . Full-length RbmC and Bap1 were cloned by PCR from Vibrio cholerae El Tor strain N16961 genomic DNA into the pET28b vector ( Novagen , Inc . ) . Individual β-prism lectin domains ( denoted RbmC1 and RbmC2 , from residues S505 to T640 and S823 to Y957 , respectively ) were cloned into the pNGFP-BC vector to form GFPUV fusion proteins [49] , confirmed by DNA sequencing , and transformed into T7 Express E . coli for expression . The full-length VCC gene ( hlyA , from Vibrio cholerae O1 El Tor strain 8731 ) [50] , the VCC β-prism lectin domain cloned into the pET-28b vector [11] , and the RbmC2 β-prism domain in pNGFP-BC were subjected to site-directed mutagenesis using a previously described procedure [51] . Briefly , complementary primers containing the desired mutation were used to amplify the entire gene-containing plasmid by PCR and the resulting DNA was digested by DpnI to remove parental DNA . The reaction was then transformed into NEB5α-cells . The resulting clones were miniprepped and sequenced to confirm the introduction of the site-directed mutation . For expression of the RbmC1 and RbmC2 β-prism domains , LB broth supplemented with 100 mg/L ampicillin was inoculated with overnight cultures ( 1:60 dilution , v/v ) , grown at 37°C to an O . D . of 0 . 6 , induced with 1 mM IPTG , and incubated at 30°C for 4 hours . Cells were pelleted by centrifugation at 3500 x g in a Sorvall LYNX 6000 centrifuge ( F9-6x1000 LEX rotor ) and lysed by passing three times through an Emulsiflex-C5 high-pressure homogenizer ( Avestin , Inc . ) . The lysate was cleared at 40 , 000 x g for 30 minutes at 4°C ( F20-12x50 LEX rotor ) and the resulting supernatant loaded onto a 10 ml TOYOPEARL AF-Chelate-650M nickel column ( Tosoh Corporation ) equilibrated in TBS buffer ( 20 mM Tris pH 7 . 6 , 150 mM NaCl ) . The column was washed with 10 column volumes of TBS buffer containing 40 mM imidazole and the protein eluted in TBS buffer containing 250 mM imidazole . To remove the GFP tag , the fusion proteins were incubated with 1:100 ( wt/wt ) human α-thrombin ( Haematologic Technologies ) for 4 hours at room temperature or 1:500 ( wt/wt ) trypsin ( Sigma Aldrich ) for 1 hour at room temperature and the reaction stopped with 20 mM EDTA and 1 mM AEBSF . Wild-type RbmC β-prism domains were separated from the polyhistidine-tagged GFP fusion partner by passing the over a Sepharose S6 10/300 size-exclusion column ( GE Healthcare ) in TBS buffer . RbmC2 D853A was separated from cleaved GFP by passage over a Superdex 200 Increase 10/300 column ( GE Healthcare ) in TBS buffer . VCC full-length and VCC β-prism lectin domain constructs were expressed and purified similarly , with the following modifications . VCC full-length protein was expressed in Shuffle T7 E . coli cells ( New England Biolabs ) for 4 hours at 30°C and the VCC β-prism lectin domain was expressed for 8 hours at 37°C in T7 Express E . coli cells . Both VCC proteins were purified over a 5-ml HisTrap Ni-NTA column ( GE Healthcare ) followed by a Superdex 200 10/300 size-exclusion column ( GE Healthcare ) run in TBS buffer . Purified RbmC1 and RbmC2 GFPUV fusions were fluorescently labeled by primary amine chemistry using an AlexaFluor 488 succinimidyl ester reagent ( Thermo Fisher Scientific ) . Proteins were concentrated to 5 . 5 or 3 . 4 mg/ml and diluted into 100 mM sodium bicarbonate buffer , pH 9 . 0 . While stirring , 0 . 15 mg of the dye ( resuspended in 0 . 15 ml DMSO ) was added to 1 . 1 ml of protein and incubated for 1 hour at room temperature . Unreacted dye was bound by adding 0 . 1 ml 1 . 5 M Tris pH 8 . 5 and removed from labeled proteins by running over a Superose 6 10/300 size exclusion column equilibrated in TBS buffer with 1mM EDTA and 1mM sodium azide . Labeled proteins were sent to Core H of the CFG for analysis against the mammalian glycan screen v . 5 . 2 . In brief , glycan chips were incubated with 180 μg/ml of labeled lectin for 1 hour , washed three times to remove non-specific binding , and dried under nitrogen before imaging using a Perkin Elmer ProScanArray XL4000 scanner . The data are reported as the average response units of six replicates after removing the highest and the lowest data points . The entire dataset is freely available through the CFG website ( www . functionalglycomics . org ) . VCC β-prism lectin domain was buffer exchanged into PBS ( 20 mM sodium phosphate pH 7 . 4 , 150 mM NaCl ) by running over a Superdex 200 10/300 size exclusion column ( GE Healthcare ) . Fluorescence assays were performed using a Fluoromax-2 spectrophotometer ( Horiba Jobin-Yvon Inc . ) . Data were collected in 6 mm x 6 mm quartz cuvettes ( Starna Cells , Inc . ) continuously stirred throughout the span of the experiment . The intrinsic protein fluorescence was monitored by exciting protein samples at 295 nm with a band-pass of 4 x 4 nm . Upon addition of glycans ( dissolved in PBS buffer ) , the increase in intrinsic fluorescence was measured and the fractional increase calculated and normalized to the highest point . All data were acquired in triplicate and the results plotted in Origin v . 8 . 0 ( OriginLab Corporation ) and fit using the RandoA function . Purified VCC was activated by proteolytic cleavage using α-chymotrypsin ( 1:350 wt/wt ) for 30 minutes at room temperature and serially diluted . Activated VCC dilutions were added to wells in a 96-well clear bottom plate containing defibrinated rabbit whole blood diluted to an absorbance at 595 nm of 1 . 0 in blood dilution buffer ( 20 mM sodium phosphate pH 7 . 4 , 150 mM NaCl , 1mg/ml BSA ) . The absorbance was monitored at 595 nm every 15 seconds at room temperature in an iMark 96-well plate reader ( Bio-rad Laboratories , Inc . ) . Raw absorbance data were converted into % lysis and HD50 values calculated as described previously [36] using KaleidaGraph v . 4 . 1 . 3 ( Synergy Software ) . Purified RbmC1-GFPUV fusion , RbmC2-GFPUV fusion , and VCC β-prism domains were dialyzed against TBS buffer overnight using a 3-kDa cutoff membrane . ITC data were collected using a MicroCal VP-ITC calorimeter at 25°C . Carbohydrates were dissolved in TBS and titrated ( 5 mM for mannotriose , N-acetyl-D-lactosamine , and N-acetylglucosaminyl-β-1 , 2-mannose; 120 μM for asialofetuin ) into 1 . 44 ml of dialyzed protein ( 100 μM for VCC β-prism lectin domain , 48 μM for RbmC β-prism lectin domains ) . Blank subtracted ITC raw data were processed using NITPIC v 1 . 2 . 0 [52] , analyzed with SEDPHAT v . 12 . 1b [53] and processed with GUSSI v . 1 . 0 . 8 ( downloaded from http://biophysics . swmed . edu/MBR/software . html ) . Purified RbmC2 lectin domain in TBS was concentrated to ~5 mg/ml using a 3 kDa cutoff Vivaspin concentrator ( GE Healthcare Life Sciences ) . Crystals were grown using vapor diffusion in 24-well tissue culture plates by mixing 1:1 ( v/v ) protein and precipitant solution ( 0 . 1 M sodium acetate pH 4 . 6 , 2M ammonium sulfate for all crystals ) and suspending drops on siliconized cover slips over a 0 . 5 ml reservoir solution . For crystallization with ligands , a 1:10 molar excess of sugar was added to the RbmC2 protein solution . Apo and mannotriose X-ray diffraction data were collected on crystals ( cryoprotected in mother liquor supplemented with 20% glycerol and flash-cooled in liquid nitrogen ) using an Oxford Xcalibur Nova X-ray generator with an Onyx CCD detector ( Oxford Diffraction ) and indexed using CrysAliasPro ( Rigaku Corporation ) and Aimless [54] . GlcNAc-Man X-ray data were collected on a Rigaku HighFlux HomeLab system with a Raxis IV++ detector and processed using iMOSFLM [55] . Molecular replacement using the VCC β-prism lectin domain ( PDB ID 1XEZ ) as a search model was carried out by Phaser [56] . Multiple rounds of XYZ coordinate and individual B-factor refinement with phenix . refine [57] were interspersed with model building/rebuilding via Coot [58] using |2Fo|—|Fc| and |Fo|—|Fc| electron density maps . The progress of refinement was monitored by following the Rwork/Rfree ratio ( Rfree consisting of 5–10% of reflections ) . Waters were selected and refined using the automated water picking feature of phenix . refine and ligands built into the electron density maps following the first round of rebuilding and refinement . Simulated annealing OMIT maps were constructed by removing the ligand from the final refined structure file , performing three macro-cycles of refinement with simulated annealing , and calculating a |Fo|—|Fc| map with phenix . maps . Occupancy refinement was carried out using phenix . refine for the GlcNAc-Man structure , which appeared to contain a mixture of the sugar and glycerol in the binding pocket . The two ligands were defined as alternate conformers and refined to a combined occupancy of 1 . 0 ( final occupancies = 0 . 65 GlcNAc-Man , 0 . 35 glycerol ) . To construct the RbmC2/pentasaccharide model , superimposed mannotriose and GlcNAc ( β1–2 ) Man structures ( which share an overlapping mannose ) were used as a template to build the Man3GlcNAc core and a second GlcNAc residue built in from scratch . Five macro-cycles of geometry minimization and regularization were carried out using the phenix . geometry_minimization feature of Phenix [59] . Buried surface area calculations were performed using PISA [37] as implemented in Coot [58] . Contacts were determined using LigPlot+ v . 1 . 4 . 5 [60] with hydrogen bonds filtered using a cutoff of 3 . 35 Å . Model quality statistics were calculated using the MolProbity server accessed at http://molprobity . biochem . duke . edu . Trypsin-cleaved RbmC2 WT and RbmC2 D853A domains were separated from the GFPUV fusion protein on Superose 6 and Sephadex 200 10/300 Increase size exclusion columns , respectively , in TBS . Samples were concentrated using a Vivaspin 5 kDa-cutoff centrifugal concentrator to a concentration of 340 μM and diluted to a final concentration of 12 . 5 μM with 10 mM sodium phosphate buffer pH 7 . 4 . Samples were loaded into 0 . 2 cm quartz cuvettes ( Cole-Parmer , Staffordshire , UK ) . CD data were collected on a Jasco J-810 spectrometer ( Jasco Inc . , Easton , MD ) . Thermal denaturation was monitored using the change in molar extinction at 222 nm while changing the temperature in 2 degree increments from 20°C to 96°C . Tm values were calculated from the mid-point of sigmoidal fits to the temperature data . Secondary structure composition was estimated using the β-structure selection server ( BeStSel , http://bestsel . elte . hu ) using CD data from 200–250 nm [40] . Defibrinated whole rabbit blood ( Remel , ThermoFisher Scientific ) was washed once with PBS buffer and resuspended cells incubated with a 0 . 5 and 2 . 5 μM concentrations of purified RbmC2-GFPUV for 5 minutes on ice , or a 2 . 5 μM concentration of RbmC1-GFPUV . As a control for non-specific binding , a point mutation shown to significantly reduce glycan binding was used as a control ( D853A in RbmC2 and D539A in RbmC1 ) . Cells were centrifuged at 500 x g in a microcentrifuge tube for 5 minutes to pellet cells , the supernatant removed , and the cells gently resuspended in 25 μL of PBS buffer . Bright-field and fluorescence microscopy images were acquired using a DeltaVision RT imaging system ( Applied Precision ) adapted to an Olympus ( IX71 ) microscope . Images were acquired with a fixed exposure time ( 3 s for brightfield and 4 s for fluorescence ) so that samples could be compared on the same intensity scales . Z-stack sections of 0 . 5 μm were collected using a 60X or 100X objective and images put on identical scales and superimposed using the Fiji distribution of Image J [61] . For GFP fluorescence images , excitation and emission filter wavelengths were 490 and 528 nm , respectively . | Bacterial pathogens secrete multiple virulence factors to aid in infection including adhesion molecules , effector proteins , enzymes , toxins and biofilm proteins . To increase the potency and specificity of these molecules , many factors contain binding sites for host cell-surface receptors . This study involves two such factors from the human pathogen Vibrio cholerae: a toxin that forms cytotoxic pores in the cell-membranes of target cells ( most likely immune cells like neutrophils ) and biofilm matrix proteins that help form a protective sheath around growing bacterial colonies . We show that both factors utilize similar carbohydrate receptors to recognize cell surfaces . Uncovering the structural basis for how host cells are targeted is important in understanding how V . cholerae and similar organisms cause disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"biofilms",
"bacteriology",
"medicine",
"and",
"health",
"sciences",
"toxins",
"crystal",
"structure",
"pathology",
"and",
"laboratory",
"medicine",
"chemical",
"compounds",
"pathogens",
"vibrio",
"condensed",
"matter",
"physics",
"microbiology",
"carbohydrates",
"organic... | 2018 | Structural basis of mammalian glycan targeting by Vibrio cholerae cytolysin and biofilm proteins |
Meiotic recombination , which is necessary to ensure that homologous chromosomes segregate properly , begins with the induction of meiotic DNA double-strand breaks ( DSBs ) and ends with the repair of a subset of those breaks into crossovers . Here we investigate the roles of two paralogous genes , CG12200 and CG31053 , which we have named Narya and Nenya , respectively , due to their relationship with a structurally similar protein named Vilya . We find that narya recently evolved from nenya by a gene duplication event , and we show that these two RING finger domain-containing proteins are functionally redundant with respect to a critical role in DSB formation . Narya colocalizes with Vilya foci , which are known to define recombination nodules , or sites of crossover formation . A separation-of-function allele of narya retains the capacity for DSB formation but cannot mature those DSBs into crossovers . We further provide data on the physical interaction of Narya , Nenya and Vilya , as assayed by the yeast two-hybrid system . Together these data support the view that all three RING finger domain-containing proteins function in the formation of meiotic DNA DSBs and in the process of crossing over .
Homologous recombination is an essential feature of meiosis and is required to ensure proper chromosome segregation . Although several core aspects of meiosis are highly conserved , many of the proteins and structures that mediate meiosis have features that are unique to each model organism . This is most apparent when comparing the process of meiotic recombination in Drosophila to other model organisms . Meiotic recombination begins with the induction of programmed DNA double-strand breaks ( DSBs ) . In Drosophila ( as well as Caenorhabditis elegans ) this event occurs in the context of full-length synaptonemal complex ( SC ) [1 , 2 , 3] . Therefore , in flies , synapsis is not dependent on DSB formation , as it is in other model organisms like budding yeast , plants and mammals [4 , 5 , 6 , 7 , 8] . DSBs are catalyzed by the evolutionarily conserved protein Spo11 ( MEI-W68 in Drosophila [9] ) , the homolog of subunit A of TopoVI DNA topoisomerase [10 , 11] . Although nine other DSB accessory proteins ( Mre11 , Rad50 , Xrs2 , Ski8 , Rec102 , Rec104 , Rec114 , Mei4 and Mer2 ) have been identified in budding yeast ( reviewed in [12 , 13] ) , only three proteins have been demonstrated to be required for DSB formation in Drosophila ( MEI-P22 , Trem , and Vilya ) [14 , 15 , 16] . MEI-P22 has sequence homology to the transducer domain found within the B subunit of TopoVI DNA topoisomerase [17] , and therefore may interact directly with MEI-W68 as a complex . Trem is a C2H2 zinc finger domain protein with no known homologs in other model systems [15] . Vilya , a RING finger domain-containing protein , has homology to Zip3-like family members found in several organisms [16] . However , none of the members in other systems appear to affect the formation of DSBs themselves [18 , 19 , 20 , 21 , 22 , 23 , 24 , 25] . Once DSBs are made , they must be repaired into either crossovers or noncrossovers . This is a multistep process utilizing enzymes and proteins that stabilize crossover intermediates and further promote crossover maturation . Early-acting pro-crossover proteins in most organisms ( yeast , plants , nematodes and mammals ) include the heterodimer of Msh4 and Msh5 ( reviewed in [26] ) . The Msh4/5 complex is required for stabilizing crossover intermediates and promoting repair through the crossover pathway . Drosophila lacks this complex and instead is thought to use the MEI-MCM complex ( REC , MEI-217 and MEI-218 ) for this function [27 , 28 , 29] . In addition to the lack of conservation in early pro-crossover proteins , Drosophila also seems to lack the homologs of late pro-crossover proteins that are required for crossover maturation [29] . Instead of the endonuclease MutLγ ( Mlh1 and Mlh3 ) that is used to resolve crossovers in most organisms , Drosophila appears to use an endonuclease complex consisting of MEI-9 , Ercc1 , Mus312 and Hdm ( reviewed in [29 , 30] ) . Although many of the yeast proteins necessary to create DSBs and determine their fate as crossovers or noncrossovers are not conserved in flies , we recently identified a protein named Vilya , which is required for DSB formation and localizes to the recombination nodule ( RN ) , a protein structure assembled only at sites of crossing over [16] . Vilya appears to be homologous to the Zip3-like protein family that is involved in crossover fate by stabilizing crossover intermediates and aiding crossover maturation . In fact , Vilya appears to link DSB formation and crossover formation in Drosophila . Zip3-like proteins fall into two subgroups: the Zip3-RNF212 group and the HEI10 group , with all members of both groups sharing conserved structural properties ( reviewed in [31] ) . Most of these Zip3-like proteins appear to have dynamic localization patterns that involve either a redistribution of the protein from the SC to sites of recombination intermediates and/or an increase in their concentration at these sites as recombination intermediates are processed into crossovers . Studies in multiple organisms argue Zip3-like proteins act as post-translational regulators at sites of crossing over either through sumoylation or ubiquitination or both [20 , 22 , 32 , 33 , 34 , 35 , 36 , 37] . Recently , a study in C . elegans identified three paralogs of a previously known member of this group , ZHP-3 , which were shown to function in two separate heterodimeric complexes [25] . These complexes are thought to form a signaling network that mediates crossover assurance and crossover interference by functioning both to stabilize crossover intermediates ( ZHP3/4 ) [25 , 38] and to promote crossover maturation ( ZHP1/2 ) [25] , similar to the roles found in mammalian RNF212 and HEI10 , respectively . Here we report on the identification of two paralogs , narya ( CG12200 ) and nenya ( CG31053 ) , that encode proteins that are both structurally and functionally related to Vilya and have homology to the Zip3-like family . In D . melanogaster , narya likely arose from a gene duplication of nenya less than 40 million years ago , and the two show genetic redundancy and are required for meiotic DSB formation . Using the CRISPR-Cas9 system to tag the endogenous copy of narya , we find that Narya localizes to DSBs and colocalizes with Vilya throughout pachytene . As we previously showed Vilya to be a component of the RN , this would suggest that Narya ( and likely Nenya as well ) are also RN components . In addition , as is true for Vilya , the localization of Narya to discrete foci within the SC is dependent on DSB formation , and in the absence of DSBs , Narya localizes uniformly along the SC . Finally , we report the identification of a separation-of-function allele of narya ( naryaG4 ) that links Narya directly to crossover maturation . Therefore , Narya , and most likely Nenya , appear to be the second and third examples after Vilya of proteins linking DSB formation with DSB fate , and likely Narya is the second protein to make up the RN in Drosophila .
Because many organisms have multiple Zip3-like proteins that play a role in meiosis , we conducted a genome-wide search for Zip3-related genes in Drosophila melanogaster . We identified two genes ( CG12200 and CG31053 ) that appeared to encode good Zip3-like candidates . CG12200 ( FBgn0031018 ) is located on the X chromosome at map position 18C7 in the last ( 6th ) intron of CG32533 . CG32533 is a gene with unknown function that is predicted to be a helicase . CG31053 ( FBgn0051053 ) is located on the 3rd chromosome at map position 98B6 in the first intron of minotaur ( CG5508 ) , a conserved member of the glycerol-3-phosphate O-acyltransferase ( GPAT ) family . Both CG12200 and CG31053 are predicted to encode proteins that have similar structural properties to Zip3-like family members ( including Vilya in Drosophila [16] ) , such as an N-terminal C3HC4 RING finger domain and an internal predicted coiled-coil domain ( S1A Fig ) . Therefore , we named these genes narya ( CG12200 ) and nenya ( CG31053 ) to complete the Three Rings of Power given by the Elves of Eregion [39] . We used the protein sequences of Narya and Nenya to identify homologous proteins in other model organisms to determine if we could identify either Zip3-like family members or proteins outside of this family that had known roles specifically in meiosis or meiotic recombination . In addition to showing protein homology to Zip3 in budding yeast , Narya , Nenya and Vilya showed homology to all four Zip3-like family members in C . elegans ( ZHP-1 , ZHP-2 , ZHP-3 and ZHP-4 ) and to RNF212 and RNF212B in several mammalian species . ( RNF212B is a protein known to affect the recombination rate in both cattle and sheep [40 , 41] . ) All three of the D . melanogaster RING proteins ( Narya , Nenya and Vilya ) cluster with the Zip3-RNF212 subgroup ( S1B Fig ) [16] . We then investigated the conservation of vilya , narya , and nenya in the 12 fully sequenced genomes from the Drosophila Genomes Consortium . Using a tBLASTn search , we identified the most likely homolog in each of the 12 Drosophila genomes and determined if the gene locations maintained synteny . While we found evidence of vilya and nenya homologs across the Drosophila genus , we could not identify homologs of narya outside the melanogaster subgroup ( Fig 1A ) . Maximum-likelihood phylogenetic analyses suggest that narya arose as a gene duplication event of nenya less than 40 Mya , prior to the separation of the melanogaster subgroup ( Fig 1B ) . Within D . melanogaster , narya and nenya nucleotide sequences are 69 . 1% identical to each other , while Narya and Nenya protein sequences share only 49 . 1% identity and 66% similarity ( Fig 1C ) . However , despite their high level of divergence , narya and nenya are evolving at a similar rate ( S1 Table ) . Given that narya and nenya are homologous to many of the Zip3 family members , we assessed whether these two genes had roles during meiosis . We had previously created several mutations in narya using TALEN-based mutagenesis where we specifically targeted the RING finger domain [42] . Since RING finger domains are known to mediate protein-protein interactions and are required for mediating E3 ligase activity , we speculated that mutations in this domain would abolish narya function . One such mutation resulted from an indel ( insertion of 3 nucleotides/deletion of 13 nucleotides during repair ) causing a frameshift at amino acid 42 that eventually truncates the protein to 115 amino acids . This truncated allele , known as naryaJJ6 , also lacks the last two conserved cysteines in the RING finger domain and therefore is likely nonfunctional ( see Fig 1C ) . Using FLP/FRT-mediated recombination with two piggyBac transposons that each flanked the nenya gene [43] , we created a chromosomal deletion of nenya ( nenyadel ) . Because nenya is located within the intron of minotaur , a gene known to be required for silencing the piRNA pathway in oocytes [44] , we also created an RNAi construct specific for nenya to assay its function in the absence of potential effects created by disrupting the minotaur gene . We used the GAL4-UAS system under the control of the nanos ( nos ) promoter ( Pnos-GAL4::VP16 ) to induce expression of the nenya RNAi hairpin transgene ( hereafter referred to as nenyaRNAi ) . The nosGAL4::VP16-UAS system results in high levels of expression in the germline throughout most stages of oogenesis , including the germarium where meiosis begins [45 , 46 , 47] . qPCR analysis indicated that the nenya transcript levels were reduced by at least 50% in whole ovaries when pValium22-nenyaRNAi was driven within the germline ( S2 Fig ) . We tested each of these mutant alleles , individually and in combination with each other , for effects on meiotic chromosome segregation ( Table 1 ) . There was at most a weak effect on meiotic nondisjunction compared to controls for the homozygous mutants when tested individually . naryaJJ6 showed low ( 2 . 2% ) , but statistically significant , levels of X chromosome nondisjunction when compared to the control ( 0 . 0% ) , while the nenya mutant ( nenyaRNAi ) repeatedly showed wild-type chromosome segregation ( 0 . 3% X chromosome nondisjunction ) . In addition , there was no significant effect on meiotic segregation when there was only one copy of wild-type narya in the complete absence of nenya ( 0 . 5% X chromosome nondisjunction ) , suggesting that narya is not haploinsufficient as has been reported for members of this group in other species [22 , 48 , 49] . In contrast , double mutants ( either naryaJJ6; nenyadel or naryaJJ6; nenyaRNAi ) showed high levels of X chromosome nondisjunction ( 49 . 0% and 32 . 4% , respectively ) , indicating that these genes have redundant functions . Supporting this proposal , we were able to rescue the nondisjunction phenotype in the naryaJJ6; nenyadel double mutant with expression of a narya:gfp transgene in the germline ( 0 . 0% X chromosome nondisjunction ) ( Table 1 ) . In vilya mutants , the increase in meiotic nondisjunction is a result of failed initiation of the meiotic recombination process . To determine if the meiotic nondisjunction we observe in narya nenya double mutants occurs through a similar mechanism , we assayed the presence of DSBs formed in the pro-oocytes during pachytene ( Fig 2 ) . To do this , we used a phospho-specific antibody against the histone 2A variant ( γH2AV ) . Phosphorylation of H2AV is an evolutionarily conserved rapid response that occurs at DSB sites [51 , 52 , 53] . We found that in the absence of only nenya , DSBs are formed at wild-type levels in early pachytene pro-oocytes ( mean 10 . 2 DSBs , SD ± 0 . 90 compared to 10 . 8 DSBs in the same meiotic stage in a wild-type background [54] ) , consistent with the normal levels of chromosome disjunction ( Fig 2A and 2B and S3 Fig ) . However , in the naryaJJ6; nenyaRNAi double mutant , meiotic recombination failed to initiate in early pachytene cysts , with few , if any , DSBs detected ( mean 1 . 1 DSBs , SD ± 0 . 78 ) ( Fig 2A and 2B and S3 Fig ) . Similar results were obtained when analyzing γH2AV foci number in the naryaJJ6; nenyadel double mutant ( average 0 . 74 DSBs , SD ± 0 . 97 in 27 pro-oocytes ) , indicating that the level of RNAi knockdown for nenya transcript ( less than 50% of wild-type nenya transcript levels within the whole ovary ) was sufficient to mimic the genomic nenya deletion with regard to DSB formation function ( see Materials and Methods ) . We also failed to detect crossovers when assaying crossover formation using genetic markers along the X chromosome ( Fig 2C ) . The failure to detect meiotically induced DSBs using the γH2AV antibody is not due to a general defect in modifying the histones at the DSB sites , as we can detect γH2AV foci during the endoreduplication cycle ( S3 Fig ) . In addition , these effects on DSB formation are unlikely to be caused by defects in synaptonemal complex formation or in the selection of the oocyte by early-mid pachytene , as these processes appeared to be normal in the absence of narya and nenya ( S3 Fig ) . In the narya TALEN-based mutagenesis described above , we also created a second allele ( naryaG4 ) that deletes five amino acids , including the last cysteine in the RING finger domain , one amino acid prior to it , and the three amino acids that follow it ( see Fig 1C ) . The reading frame in naryaG4 is maintained after the deletion , thus this mutant likely expresses a form of the protein that is missing key residues to form the RING finger domain . We assayed whether naryaG4 , which lacks part of the RING finger domain , was able to facilitate DSB formation in the absence of nenya . We found that DSBs were formed in the naryaG4; nenyaRNAi double mutant ( mean 7 . 6 DSBs , SD ± 2 . 83 ) , unlike in the naryaJJ6; nenyaRNAi double mutant , indicating that DSB formation is not fully dependent on an intact RING finger domain of Narya ( Fig 2B ) . In the naryaG4; nenyaRNAi double mutant , DSBs were induced at ~70% of the level observed for nenyaRNAi alone ( Fig 2B ) , which led us to reason that we would see a decrease in the amount of nondisjunction ( see Table 1 ) compared to the naryaJJ6; nenyaRNAi double mutant that failed to form DSBs . Therefore , we assayed for both the level of nondisjunction and the presence of crossing over on the X chromosome in naryaG4; nenyaRNAi double mutant females and compared that to the nenyaRNAi mutant and the double naryaJJ6; nenyaRNAi mutant ( Fig 2C ) . As expected , due to the severe reduction in DSBs in naryaJJ6; nenyaRNAi females , we failed to recover any recombinant X chromosomes in their progeny ( map distance of 0 . 0 cM , E0 frequency of 1 . 0 ) . These females also showed high levels of X nondisjunction ( 32 . 4% ) compared to nenyaRNAi alone , which makes wild-type levels of DSBs and disjoins homologous chromosomes properly ( map distance 39 . 9 cM , E0 frequency of 0 . 346 , 0 . 3% X nondisjunction ) . We found that while the naryaG4; nenyaRNAi mutant was able to form DSBs ( see Fig 2B ) , those DSBs were not converted into crossovers ( map distance of 0 . 2 cM , E0 frequency of 0 . 996 ) , and females maintained high levels of X chromosome nondisjunction ( 39 . 5% ) seen in the naryaJJ6; nenyaRNAi mutant ( Fig 2C ) . Although the frequency of X chromosome nondisjunction in the naryaG4; nenyaRNAi females was greater than what was observed in the DSB-deficient naryaJJ6; nenyaRNAi females , this difference is statistically not significant with the number of progeny scored , and both are consistent with published data for mutants that fail to form crossovers due to the absence of either DSBs or homologous chromosome synapsis ( S2 Table ) [55] . The failure to form crossovers was not due to a global defect in DSB repair , as we did not detect any delay in removal of the γH2AV mark at mid-pachytene ( S4A Fig ) . We also failed to detect any defect in karyosome structure , such as a fragmented karyosome , that is typical of DNA repair mutants ( S4B Fig ) [56] . In addition , the fertility did not decrease from that of the naryaJJ6; nenyaRNAi double mutant ( each double mutant combination yielded ~19 progeny per female in the recombination assay ) . These data suggest that the naryaG4 allele is a separation-of-function mutant that maintains the ability to form DSBs , albeit at reduced numbers , but causes a deficiency in the ability to repair those DSBs into crossovers . This also predicts a direct function of Narya in the formation of crossovers , in addition to its separable role in DSB formation . Since the presence of either narya or nenya is required for DSB formation , and Narya is functionally redundant with Nenya , we next asked whether Narya localized to sites of DSBs . We analyzed the localization of Narya during pachytene by creating a green fluorescent protein ( GFP ) -tagged version of narya at the genomic locus using CRISPR/Cas9 technology . We tested the naryaGFPcrispr alone and in combination with both nenya alleles to determine if the naryaGFPcrispr allele was completely functional . Females that were homozygous for naryaGFPcrispr in the absence of nenya ( either nenyadel or nenyaRNAi ) showed little to no meiotic chromosome segregation errors ( S3 Table ) , indicating that naryaGFPcrispr is fully functional . Immunofluorescence studies on whole ovaries showed that naryaGFPcrispr is highly expressed at the same stage in which DSBs are induced , as both a haze ( detected in undeconvolved images ) and faint staining along the SC with predominant numerous foci that decrease in number as the cysts progress through pachytene ( S5 Fig and see below ) . Further analysis indicated that NaryaGFP foci colocalized with γH2AV foci , the histone modification created at the DSB site ( Fig 3 ) . These results are similar to our observation that Vilya also localizes to DSBs [16] . However , although Vilya , when overexpressed , colocalizes to ~60% of the γH2AV foci , NaryaGFP colocalized with γH2AV foci 93% of the time when expressed at the endogenous level ( S6 Fig ) . In the 10 nuclei analyzed in early pachytene , the average number of DSBs was 13 . 1 and the average number of NaryaGFP foci was 10 . 6 . In addition , since NaryaGFP is expressed from its endogenous promotor , we could determine that NaryaGFP also localized to the DSBs that are induced in the nurse cells within the 16-cell interconnected cyst . The number of NaryaGFP foci in the oocyte nuclei decreased as the cyst moved from early pachytene stage into early-mid pachytene ( Region 2B ) ( see Fig 4 ) , where the average number of NaryaGFP foci was 5 . 4 in the 12 nuclei analyzed . The number of Narya foci is similar to the number of VilyaHA foci ( 4 . 8 foci ) previously found at this stage [16] , both of which are consistent with the number of crossovers formed per female meiosis . Since NaryaGFP associated with DSB sites and the number of NaryaGFP foci decreased as pachytene progressed , we reasoned that these NaryaGFP foci might colocalize with Vilya foci . Therefore , we analyzed the localization of NaryaGFP in ovaries expressing vilyaHA in the germline using the nos-GAL4/UAS system ( Fig 4 ) . We found that VilyaHA and NaryaGFP colocalized in SC-positive cells and remained colocalized as both types of foci decreased in number from early pachytene to early-mid pachytene ( Region 2A to Region 2B ) . Examination of single-gallery z-slices throughout an early pachytene nucleus shows the faint localization of NaryaGFP to the SC and the association of NaryaGFP foci with VilyaHA foci ( S7 Fig ) . The maintenance of colocalization in early-mid pachytene ( Region 2B , see Fig 4 ) , a stage where VilyaHA localizes to RNs by immuno-EM [16] , demonstrates that Narya is a component of the RN . Previous studies using high-resolution imaging followed by straightening of each of the chromosome arms have shown that at early-mid pachytene , the localization of VilyaHA foci are consistent with both the number and position of crossovers , with each stretch of euchromatic SC between homologous chromosome arms primarily containing one VilyaHA focus [16] . Taken together these results suggest that Vilya and Narya localize to the majority of the DSBs in early pachytene , and as the cyst progresses to early-mid pachytene , both proteins are maintained and concentrated at DSB sites destined to become crossovers . In addition , as we saw in earlier studies with Vilya , at late pachytene ( Stage 5 ) when γH2AV foci are no longer present , there is a change in the localization of NaryaGFP from the discrete foci found at early pachytene to threads of staining exclusively along the SC , where it colocalizes with VilyaHA ( Fig 4 , see Discussion ) . Based on the number and localization of the NaryaGFP foci at sites of DSBs and the fact that the number of these foci decreased as DSBs were repaired into crossovers , we asked what effect DSB formation ( Fig 5A ) and/or lack of DSB repair ( Fig 5B ) had on the localization and number of NaryaGFP foci . As is also true for Vilya , NaryaGFP fails to form discrete foci and instead localizes along the SC when DSBs are absent ( either in the absence of mei-W68 or in the absence of vilya ) . However , in the absence of DSB repair , as in an okra ( DmRAD54 ) mutant , NaryaGFP foci form , and the foci number in early-mid pachytene is similar to when DSB repair is normal . These results indicate that Narya displays two types of staining patterns depending on the presence or absence of DSBs . First , in the presence of DSBs , Narya forms discrete foci at DSB sites . Moreover , if there is a failure to repair those DSBs , there is not an increase in number of Narya foci at early-mid pachytene . We interpret this data to mean there is not an increase in the number of designated crossover sites in the absence of DSB repair . Second , in the absence of DSBs , either because the DSBs are undergoing normal repair or fail to form , Narya displays thread-like SC staining . Since Narya and Vilya colocalize at sites of DSBs and crossovers , and Narya and Nenya are functionally redundant , we wanted to determine if Nenya can physically associate with Narya and/or Vilya . Due to the lack of a functional nenya epitope-tagged transgene or antibodies to any of the RING finger domain proteins , we used the yeast two-hybrid system to help us understand the associations and/or interactions between these three proteins . We cloned narya , nenya and vilya into yeast two-hybrid vectors and tested their ability to interact with each other in all pairwise combinations . In addition , we tested for the ability of each protein to interact with itself ( Fig 6 ) . We found that Narya , Nenya and Vilya interact with each other ( Fig 6A ) as well as with themselves ( Fig 6B ) . Previous studies showed that both the RING finger domain of Vilya as well as its C-terminal region are required for its interaction with the DSB accessory protein MEI-P22 [16] , so we further investigated the interaction of Vilya with Narya and Nenya by testing whether C-terminal and RING finger domain mutants of Vilya could still bind to Narya and Nenya . Neither Vilya’s RING finger domain nor it’s C-terminal region were required for its interaction with either Narya or Nenya ( S8 Fig ) , indicating that Vilya likely interacts with Narya and Nenya through the middle region of the Vilya protein , perhaps assisted by the coiled-coil domain . Additionally , although Vilya interacts with MEI-P22 as well as Narya and Nenya , neither Narya nor Nenya were able to interact with MEI-P22 by yeast two-hybrid ( S9 Fig ) . Finally , since Narya interacted with both Nenya and Vilya , we then tested the ability of NaryaG4 to interact with each of these proteins ( Fig 6C ) . We found that Nenya-NaryaG4 and Vilya-NaryaG4 binding were substantially reduced compared to binding with wild-type Narya protein . We also found that NaryaG4 was unable to interact with itself . This inability of NaryaG4 to strongly interact with Nenya , Vilya or itself is not due to the lack of expression of NaryaG4 ( S10 Fig ) . When considered with the results above showing that Narya and Vilya colocalize , these yeast two-hybrid data indicate that all three proteins likely function as part of the RN .
The finding of three Zip3 family members in Drosophila , Narya , Nenya and Vilya , is consistent with studies in other organisms that show that the presence of multiple Zip3 homologs within an organism is not uncommon [25 , 48 , 57] . These proteins share common structural features such as a RING finger domain near the N-terminus , and in those organisms that form SC , a predicted coiled-coil domain within the middle third of the protein ( reviewed in [31] and [23] ) . The presence of a RING finger domain suggests that these proteins play roles in either the ubiquitination or sumoylation pathway as E3 ligases [58] . Indeed , members of this family have been shown to be required for sumoylation ( e . g . , Zip3 [33 , 37] , RNF212 [59] ) as well as ubiquitination ( e . g . , HEI10 , mammals [36 , 59] ) or are speculated to be a sumoylation/ubiquitination switch ( e . g . , HEI10 , Sordaria [20] ) necessary to stabilize and/or promote crossover formation . However , the mechanism ( s ) by which the Drosophila homologs act is currently unknown . Studies in a number of organisms have shown that Zip3-like proteins function as pro-crossover factors during meiosis and localize along the SC as linear arrays of foci and/or as discrete foci at crossover sites ( reviewed in [31] ) . We provide evidence that at least two of the RING finger domain-containing proteins in Drosophila , Narya ( this study ) and Vilya [16] , also localize in this manner . Using an overexpression construct , we previously showed that Vilya localizes along the central region of the SC and at sites of DSBs . Eventually Vilya becomes concentrated at crossover sites , as immuno-EM studies demonstrated that Vilya localizes at RNs . In this study , we analyze the localization pattern of Narya using a CRISPR/Cas9-engineered epitope-tagged version of narya at the genomic locus , which eliminates many of the caveats of using an overexpression system . Although very faint Narya SC staining could be seen when analyzing naryaGFPcrispr , the predominant staining was discrete foci that localized to the majority of DSBs early in pachytene , and those foci decreased in number as pachytene ( and DSB repair ) progressed ( see Fig 4 ) . Vilya has also been shown to localize to a subset of DSBs during early pachytene [16] . The discrete Narya foci observed in both early and early-mid pachytene colocalized with Vilya , indicating that these two proteins are found together within the SC at DSBs as they form and are repaired . These findings indicate that Narya is also found at crossover sites and is a component of the RN . The identification of two Drosophila Zip3-like proteins at sites of maturing crossovers is consistent with studies of all other homologs in that they also localize at or associate with proteins known to be at crossover sites [19 , 20 , 22 , 25 , 32 , 37 , 38 , 48 , 60] . The similarities in localization of both Narya and Vilya to other Zip3 family members predict these proteins may play a role in crossover control . However , our previous studies and those described here indicate that , in a fashion that is so far unique to Drosophila , Narya , Nenya and Vilya first function prior to DSB fate determination; which is to say that they are essential for meiotic DSB formation . Our data demonstrate that narya and nenya encode functionally redundant proteins that are necessary for the induction of meiotic DSBs during early pachytene . Only in the absence of both gene products is there an increase in meiotic nondisjunction resulting from the lack of recombination due to the failed induction of DSBs . This severe reduction of DSB induction is not seen in mutants that affect the formation of the SC . Mutants that fail to form SC ( c ( 3 ) G ) or that form fragmented SC ( c ( 2 ) M ) do not eliminate DSBs but reduce their numbers in the pro-oocytes [61] . Therefore , we propose that Narya and Nenya play a direct role in the formation of DSBs . In addition , the absence of vilya , or other genes required for DSB formation ( e . g . , mei-W68 or mei-P22 ) , results in identical meiotic phenotypes [9 , 14 , 16 , 61] . However , because we are basing the lack of DSBs on the absence of γH2AV signal , we cannot rule out the possibility that narya and nenya , and possibly vilya , instead allow the very rapid repair of DNA lesions thereby reducing the number and/or amount of γH2AV signals , as has recently been found for RNF212 in female mouse oocytes [62] . Previous studies demonstrated that Vilya interacts with MEI-P22 , the potential partner of DmSpo11 , which is known to be required for DSB formation . In addition to the colocalization of Narya and Vilya throughout pachytene , yeast two-hybrid studies show that Narya , Nenya and Vilya all interact with each other . The direct interaction of Narya or Nenya with Vilya does not appear to require a functional N-terminal RING finger domain of Vilya , which was necessary for its interaction with MEI-P22 , or its C-terminal region that is known to be required for DSB formation . This may indicate that it is the middle third of Vilya that is necessary for its interaction with Narya and Nenya . As the middle region of Vilya contains the predicted coiled-coil domain , a domain that can mediate protein-protein interactions , it is highly possible that these proteins interact through their coiled-coil domains . However , the observation that the RING finger domain mutant , NaryaG4 , failed to interact with itself , Nenya and Vilya in the yeast two-hybrid assay , may indicate that the coiled-coil domains are not sufficient for interaction and that the RING finger domain may also be required for protein-protein interactions . We should note that the mutations in the RING finger domain of Vilya used in this analysis differed from the mutation in Narya . The Vilya mutations were single amino acid substitutions , whereas the mutation in Narya resulted in a five amino acid deletion . It is possible that the deletion in Narya alters the protein structure , thus disrupting the ability of the coiled-coil domain to interact with other proteins . Many proteins that localize to the SC contain coiled-coil domains , and our studies here show that while Narya primarily localizes to discrete foci , SC localization is observed at low levels in a naryaGFPcrispr background in early pachytene and Narya is exclusively found along the SC in late pachytene ( Fig 4 ) . The SC localization at early pachytene could be due to the propensity of coiled-coil proteins to localize to the SC , or this localization may be required for wild-type levels of DSBs , as most meiotic mutants that fail to assemble SC only induce DSBs at a reduced level [61] . In addition , we show that in the absence of DSB formation , discrete Narya foci fail to form and instead Narya localizes along the SC in a similar staining pattern to that of the SC protein C ( 3 ) G . The Narya SC localization occurs in the absence of either mei-W68 or vilya , indicating that although Narya and Vilya colocalize and may interact directly , Narya’s localization to the SC is not dependent on Vilya . The exclusive localization of Narya to the SC during late pachytene in the presence of wild-type DSB repair was similar to the distribution of Vilya in the same genetic backgrounds . In this study , however , we were able to assess the localization of Narya at endogenous levels throughout pachytene , and therefore we are confident that there is a change in the localization pattern from foci in early-mid pachytene to linear staining along the SC in late pachytene . Currently , we do not understand the function of this redistribution . It is possible that Narya , and perhaps Vilya , play a role in the disassembly of SC that occurs post DSB repair . Based on the relationship of Narya to other Zip3 homologs and its localization and association with Vilya , which is found at RNs , it seems likely that Narya might have a role in processing DSBs into crossovers . However , the fact that narya and nenya are also required for DSB formation makes it difficult to analyze the role of either in crossover formation . An analogous problem arose when studying mutations that affected the function of vilya [16] . In that case , we reasoned that if Vilya could be recruited to exogenous DSBs from its localization along the SC when DSBs were absent , it would provide strong evidence that Vilya had a role in crossover formation . Using X-rays to produce exogenous DSBs , that is precisely what we found . In the absence of mei-W68 ( Dm Spo11 ) , but following X-irradiation , Vilya , which in this background is found exclusively along the SC , formed discrete foci at a subset of exogenous DSBs . Here we provide direct evidence that Narya plays an essential role in the formation of crossovers . We obtained an in-frame deletion within the RING finger domain of narya ( naryaG4 ) and analyzed its role in DSB formation and crossing over in the absence of nenya . Unlike the null allele of narya ( naryaJJ6 ) , naryaG4 retained its ability to function in DSB formation ( Figs 2 and S3 ) . There was a slight decrease in the mean DSB number , and a wider range of DSBs in the nuclei assayed , but a significant number of DSBs ( average of 70% ) were formed . Surprisingly though , none of the DSBs that were formed were able to be converted into crossovers . The DSBs in naryaG4; nenyaRNAi oocytes were most likely repaired as noncrossovers , given that we did not see either a karyosome fragmentation defect associated with the lack of DSB repair or a more severe fertility defect from the naryaJJ6; nenyaRNAi females . The presence of DSB repair combined with the lack of crossovers resulted in high levels of nondisjunctional progeny . In summary , our data demonstrate that in Drosophila , members of the Zip3 family are required to both form DSBs and repair those DSBs into crossovers , and flies use a mechanism to ensure these processes are directly linked . Future studies will need to be done to determine the precise function of Narya and whether it acts to stabilize crossover intermediates and/or in the maturation of crossovers . Based on sequence comparison , narya appears to have duplicated from nenya less than 40 million years ago , after the split of D . ananassae from the melanogaster subgroup . Both genes have been maintained in all the sequenced species of the melanogaster subgroup . We provide evidence that narya and nenya encode proteins that are functionally redundant with regard to their role in the early steps of meiosis . The preservation of both genes and their functional redundancy is surprising since genetic redundancy in Drosophila is not prevalent [63 , 64] . In fact , studies have shown that the vast majority of meiotic genes are not duplicated [65] . In addition to the duplication of nenya found in the melanogaster subgroup , we also found evidence of a gene duplication of vilya in D . ananassae . However , unlike the vilya homolog in D . ananassae that maintains synteny , the duplicated gene is intronless , likely caused by a retrotransposition event . Retrotransposed duplicates do not bring upstream and downstream regulatory regions with them and are often pseudogenized , as they are less likely to be expressed or maintained [66] . It is not obvious why the melanogaster subgroup has maintained two meiotic genes with the same function . As we presently lack any alleles that allow for visualization of Nenya , we can only speculate that Nenya is behaving exactly as Narya . However , we cannot rule out that the functional redundancy of these two genes is due to their roles in DSB formation , and that Narya may be more important than Nenya at the RN in the formation of crossovers . While we failed to detect any meiotic chromosome nondisjunction in the absence of nenya , we consistently observed low levels of chromosome segregation errors in the absence of narya ( X chromosome nondisjunction levels ranging from 2–4% , see Table 1 ) . We know based on their sequence alignment that the C-terminal region shows the least conservation . Perhaps future studies will determine whether this domain could be important for independent functions of the two proteins . Our studies here have shown that Narya’s RING finger domain is critical for crossing over but not for its role in DSB formation; it will be interesting to dissect these same domains in Nenya . Taken together , these studies identify two functionally redundant genes , narya and nenya , that are required for the induction of meiotic DSBs . Both of these genes encode proteins that are structurally and functionally similar to the Drosophila protein Vilya , and all three show similarities to a family of proteins found in many organisms that are required to process meiotic crossover events . We show here that in addition to its role in DSB formation , Narya is required for crossover formation . While Drosophila may lack a subset of both DSB accessory and pro-crossover homologs present in the majority of model systems , flies have clearly found a way to utilize the proteins they do have for both processes .
Drosophila strains were maintained on standard food at 24°C . Descriptions of genetic markers and chromosomes can be found at http://www . flybase . org/ . Stocks used in this study include Pnos-GAL4::VP16 [45] , PUASp-vilya3XHA [16] , vilya826 [16] , mei-W684572 [67] , naryaJJ6 [42] , Pnos-GAL4::VP16 naryaJJ6 ( this study ) , naryaG4 [42] , Pnos-GAL4::VP16 naryaG4 ( this study ) , nenyadel ( this study ) , okraAA cn bw/CyO and okraRU cn bw/CyO [56] . vilya refers to the genotype vilya826 , mei-W68 refers to the genotype mei-W684572 , and okra refers to the genotype okraRU/okraAA . The rescue construct ( below ) and all alleles of narya generated in this manuscript were made using the Canton-S stock or the Canton-S narya sequence . The Canton-S narya sequence differs from the narya sequence on FlyBase at 10 bases . Nine of these base changes encode for the same amino acid . One of the base changes result in an amino acid change from alanine at position 69 in FlyBase to glutamic acid in the Canton-S stock . naryaJJ6 and naryaG4 were generated using TALEN mutagenesis as described in [42] . naryaJJ6 deletes 16 bases , adds 3 and makes a nonsense allele after the lysine , and naryaG4 removes 15 bases , causing the deletion of 5 amino acids ( deletion CGQVL ) , but maintains the frame of the gene . nenyadel was generated by FLP/FRT recombination with two piggyBacs ( PBac{WH}CG5508[f01088] and PBac{WH}CG5508[f04927] ) that both reside in the intron of CG5508 , which also contains CG31053 ( nenya ) . Coding sequences obtained from FlyBase for D . melanogaster vilya , nenya and narya were used as BLAST queries in order to retrieve homologous sequences for additional Drosophila species . The tBLASTn option was used with the expect threshold set to 0 . 05 . Retrieved genes were then examined for shared synteny with D . melanogaster . For narya and nenya in particular , this was done by determining whether they were found within the introns of the homologs of D . melanogaster minotaur or CG32533 , respectively . Originally , the narya homolog in D . sechellia could not be definitively determined due to the poor coverage in the area , although partial narya sequence could be found in the first intron of the CG32533 homolog . With the recent release of a new D . sechellia genome [68] , full sequence of a syntenic narya homolog was identified . Nucleotide sequences for identified homologs were aligned using the PRANK+F algorithm [69] . Maximum-likelihood trees were inferred using IQ-TREE [70] , with the best-fit model selected by ModelFinder [71] . To infer the relative evolutionary rates of narya and nenya , Tajima’s relative rate tests [72] were performed using MEGA7 [73] on the PRANK-aligned nucleotide sequences . A naryaGFP knock-in was generated using CRISPR/Cas9 technology . Using the CRISPR Optimal Target Finder ( http://tools . flycrispr . molbio . wisc . edu/targetFinder/ ) , two genomic regions were selected for making the gRNAs [CCTTCCACTTGACCCAGTGCCGG and AGATCTTCTCCGCGTTGACTGGG ( the PAM sequences are underlined ) ] and were cloned into the pU6-BbsI-chiRNA vector ( gift from Melissa Harrison , Kate O’Connor-Giles and Jill Wildonger; Addgene plasmid #45946 ) [74] by the protocol outlined at http://flycrispr . molbio . wisc . edu/protocols/gRNA using oligos ( IDT ) 5’-CTTCGCCTTCCACTTGACCCAGTGC-3’ and the complement 5’-AAACGCACTGGGTCAAGTGGAAGGC-3’ and 5’-CTTCGAGATCTTCTCCGCGTTGACT-3’ and its 5’-AAACAGTCAACGCGGAGAAGATCTC-3’ , respectively . Plasmid DNA was isolated using a Qiagen Midi Prep Kit . The homologous recombination repair template containing the narya gene with a 3’ GFP epitope tag with 1 , 000 bases of genomic sequence both up- and downstream of the narya gene was generated in the pBS-KS+ vector ( Clontech ) by the following method . Using the Canton-S stock as the genomic DNA source ( gift from Dana Carroll ) , we first cloned in the region 5’ to the narya gene and the majority of the narya gene using primers 5’-[Phos]GTGGCGCATCGTTGTCAGTC-3’ and internal gene primer 5’-[Phos]CAGAAGGCATATCCGACGGC-3’ using the EcoRV site in pBS that was previously digested and dephosphorylated . The insertion of this fragment was sequenced for directionality so that the 3’ end of the narya gene was positioned closest to the XbaI site in the pBS vector . The pBS vector containing this piece of the genome was digested with StuI ( which cuts only within the narya gene ) and XbaI ( which cuts within the pBS vector ) . A StuI/XbaI fragment that contained the end of the narya gene at the internal StuI site through a 3’ in-frame GFP tag was amplified from pUASP-attB-naryaGFP ( below ) using primers 5’-GTATGCGGCCGGATGTTTCGAGTGCA-3’ and 5’-GCGCTCTAGATTACTTGTACAGCTC-3’ and then digesting with StuI and XbaI and used to clone into the vector . The 1 , 000 bases of genomic region 3’ to the narya gene was then cloned into this vector using primers 5’-GCCGTCTAGATCACTCCAATTACTTG-3’ and 5’-GTACTCTAGACTGCGATCCTCGACAG-3’ and cloned into the XbaI site in the above vector . The insertion of this fragment was sequenced for directionality . Following the creation of the homologous repair template , which consisted of 1 , 000 bases upstream of narya , the narya gene with cloned GFP tagged at the 3’ end of the gene and 1 , 000 bases downstream of narya , the two PAM sequences in the narya gene were mutated using the Quik Change II XL Site-Directed Mutagenesis Kit ( Agilent Technology ) . The base changed in the PAM sequence is in bold above . In both cases , the codon remains unchanged . 250 ng of each gRNA plasmid and 500 ng of the homologous repair template plasmid were injected ( BestGene ) into y w; nosCas9 ( on II at attP40 ) ( gift to BestGene from Shu Kondo ) . Potential CRISPR/Cas9 hits were screened with primers 5’-GTTGCAGCAGCTGGAGCAGA-3’ and 5’-GGTGAGTGCTCCCCAGATTG-3’ , which amplify a region spanning the GFP insertion on the homologous repair template , allowing for PCR fragment size to visualize a repair off the homologous template . Once a CRISPR/Cas9 insertion was identified , the entire homologous region used in repair was sequenced . In this case , only one G0 fly had the correct insertion and was used for further analysis . pUASp-attB [75] naryaGFP was made by cloning the CDS of CG12200 minus stop codon with primers 5’-GTATGCGGCCGCATGTTTCGAGTGCATTGCA-3’ and 5’-GTATGCGGCCGCCAAGACGAAAGCCTTTAGTG-3’ into a NotI digested pUASp-attB vector that previously had cloned in a venus ( GFP ) tag at NotI and XbaI . The CDS was sequenced for directionality . An RNAi hairpin for nenya was identified using http://www . flyrnai . org/cgi-bin/RNAi_find_primers . pl . The sequence identified ( GGACATAGATTGCCTTGAAGA ) ( underlined below ) had no predicted off-targets and only shares five bases with narya . The hairpin was cloned using the oligos ( IDT ) 5’-CTAGCAGTGGACATAGATTGCCTTGAAGATAGTTATATTCAAGCATATCTTCAAGGCAATCTATGTCCGCG-3’ and 5’-AATTCGCGGACATAGATTGCCTTGAAGATATGCTTGAATATAACTATCTTCAAGGCAATCTATGTCCACTG-3’ into the pValium22 vector ( gift from Jian-Quan Ni and Norbert Perrimon ) , https://fgr . hms . harvard . edu/trip-plasmid-vector-sets . qPCR determined that the level of nenya knockdown , when expressed in the female germline using the nos-GAL4::VP16 driver , was greater than 50% of nos-GAL4/ +; naryaRNAi/ + or Canton-S ( wild-type ) nenya transcript levels . While the nenya transcript levels are higher than what might be expected given the phenotype , this observation may be explained by the process in which the cDNA was synthesized . Since random hexamer primers were used to amplify cDNA from total RNA , we cannot rule out that the remaining levels of nenya transcript in the presence of RNAi knockdown are not from amplified , unspliced RNA from minotaur in which nenya resides . It is also possible that the remaining levels of nenya transcript are from expression of nenya in the somatic cells of the ovary , since the knockdown was specific to the germline . As well , based on published data of nanos RNA and protein , there are varying levels of expression in egg chambers of different stages within the ovariole [46] . The narya RNAi hairpin ( GCAAGATCTCCAAGTTCCAAG ) , which had no predicted off-targets and differed from nenya sequence at three bases , was used as a non-specific RNAi control . Two qPCR nenya primer pairs were used to determine the relative level of transcript present in nos-GAL4/+; nenyaRNAi/+ ovaries compared to Canton-S control ovaries . Total RNA from ovaries was isolated using the Promega Maxwell RSC Simply RNA Tissue Kit using standard protocol except for increasing the amount of DNase to 10 μL per sample . cDNA was synthesized from total RNA using the Invitrogen SuperScript III First-Strand Synthesis System for RT-PCR using random hexamers . Using the CAS qPCR Setup Robot to prepare the plates , each genotype was run in triplicate using Quanta Biosciences PerfeCTa SYBR Green FastMix ROX reagent . The nenya primer set was 5’-ACGTCGAGCCAACGTTGATC-3’ and 5’-TCGATCGGAATCGCTCGCAG-3’ , and the control transcript primer set used was 5’-TGGACAGGTCATCACCATCGGAAA-3’ and 5’-TTGTAGGTGGTCTCGTGAATGCCA-3’ for ACT42A ( FBgn0000043 ) . The frequencies of meiotic nondisjunction and meiotic recombination on the X chromosome were measured by crossing single virgin females of the listed genotypes to y sc cv v f·car / BsY males . This cross allows for the recognition of nondisjunctional offspring from the mother as Bs females ( diplo-X exceptions ) and B+ males ( nullo-X exceptions ) . Normal segregation results in B+ females and Bs males . Nondisjunction frequency is calculated as the sum of exceptional progeny X 2 ( to correct for the inviability of triplo-X and nullo-X exceptional progeny ) divided by the sum of all progeny classes ( viable plus inviable; denoted as adjusted total progeny scored ) . For X recombination analysis , only the female progeny ( denoted as n ) were analyzed for the intervals sc-cv and cv-f . y and v markers were unable to be scored due to the presence of y+ and v+ in the PUASp-nenyaRNAi transgene inserted at attP40 . Yeast transformation , mating and two-hybrid assays were done according to The Matchmaker Gold Yeast Two-Hybrid System User Manual ( Clontech ) . AH109 yeast were used in place of Y2Hgold . The AH109 genotype is as follows: MATa , trp1-901 , leu2-3 , 112 , ura3-52 , his3-200 , gal4Δ , gal80Δ , LYS2 : : GAL1UAS-GAL1TATA-HIS3 , GAL2UAS-GAL2TATA-ADE2 , URA3 : : MEL1UAS-MEL1 TATA-lacZ . Y187 genotype is as follows: MATα , ura3-52 , his3-200 , ade2-101 , trp1-901 , leu2-3 , 112 , gal4Δ , met– , gal80Δ , URA3 : : GAL1UAS-GAL1TATA-lacZ . cDNAs were cloned into either the pGADT7 or the pGBKT7 prey and bait vectors using restriction sites within the vector and contained within the PCR primers . The CDS for narya and nenya were obtained from Canton-S , as these genes do not contain introns . Western blot analysis from yeast haploid cells was performed as described in [16] . Germarium preparation for whole-mount immunofluorescence was performed as described in [16] . Primary antibodies used included affinity-purified rabbit anti-Corolla ( animal 210 ) ( 1:2000 ) [76] , mouse anti-C ( 3 ) G 1A8-1G2 ( 1:500 ) [77] , anti-Cona ( animal 20 ) ( 1:500 ) [78] , high-affinity rat anti-HA ( clone 3F10 , Roche ) ( 1:100 ) , rabbit anti-histone H2AvD pS137 ( 1:500 ) ( Rockland Inc . ) , mouse anti-γH2AV ( 1:1000 ) ( Iowa Hybridoma Bank ) [54] , monoclonal mouse anti-GFP ( 1:500 ) ( clone 3E6 , Thermo Fisher Scientific ) and rabbit anti-GFP ( 1:500 ) ( AB6556 , AbCam Inc . ) . Secondary goat anti-mouse , rabbit or rat Alexa-488 , Alexa-555 and Alexa-647 IgG H&L chain conjugated antibodies were all used at 1:500 ( Molecular Probes , Life Technologies , NY ) . Images were acquired using a DeltaVision system ( GE Healthcare ) supplied with a 1x70 inverted microscope with a high-resolution CCD camera . Images were deconvolved using SoftWoRx v . 6 . 1 or 7 . 0 . 0 ( Applied Precision/GE Healthcare ) software . Image analysis was performed using either SoftWoRx v . 6 . 1 or Imaris software 8 . 3 . 1 ( Bitplane , Zurich , Switzerland ) . Brightness and contrast were adjusted minimally to visualize signals during figure preparation . | Errors in chromosome segregation during meiosis are the leading cause of miscarriages and can result in genetic abnormalities like Down syndrome or Turner syndrome . For chromosomes to segregate faithfully , they must recombine with their homolog during the early steps of meiosis . An essential component of the process of meiotic recombination is creating the lesions ( double-strand breaks , DSBs ) that are required to form a crossover with the homologous chromosome . Crossovers are required to ensure chromosomes segregate properly at the first meiotic division . In this study we have identified two genes , narya and nenya , that are essential in DSB formation . We found that narya arose from a duplication of nenya , and these two genes are functionally redundant . In addition to its role in DSB formation , narya also plays a role in processing DSBs into crossovers . Strengthening our knowledge about the mechanism by which Narya both creates DSBs and processes them into crossovers will lead to a better understanding of the process of meiotic chromosome segregation not only in flies but many other organisms , as these genes have homologs in yeast , worms , plants , mice and humans . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"invertebrates",
"meiosis",
"yeast",
"two-hybrid",
"assays",
"rna",
"interference",
"cell",
"cycle",
"and",
"cell",
"division",
"cell",
"processes",
"animals",
"animal",
"models",
"germ",
"cells",
"oocytes",
"drosophila",
"melanogaster",
"model",
"organisms",
"protein... | 2019 | Narya, a RING finger domain-containing protein, is required for meiotic DNA double-strand break formation and crossover maturation in Drosophila melanogaster |
MicroRNAs ( miRNAs ) , single-stranded non-coding RNAs , influence myriad biological processes that can contribute to cancer . Although tumor-suppressive and oncogenic functions have been characterized for some miRNAs , the majority of microRNAs have not been investigated for their ability to promote and modulate tumorigenesis . Here , we established that the miR-191/425 cluster is transcriptionally dependent on the host gene , DALRD3 , and that the hormone 17β-estradiol ( estrogen or E2 ) controls expression of both miR-191/425 and DALRD3 . MiR-191/425 locus characterization revealed that the recruitment of estrogen receptor α ( ERα ) to the regulatory region of the miR-191/425-DALRD3 unit resulted in the accumulation of miR-191 and miR-425 and subsequent decrease in DALRD3 expression levels . We demonstrated that miR-191 protects ERα positive breast cancer cells from hormone starvation-induced apoptosis through the suppression of tumor-suppressor EGR1 . Furthermore , enforced expression of the miR-191/425 cluster in aggressive breast cancer cells altered global gene expression profiles and enabled us to identify important tumor promoting genes , including SATB1 , CCND2 , and FSCN1 , as targets of miR-191 and miR-425 . Finally , in vitro and in vivo experiments demonstrated that miR-191 and miR-425 reduced proliferation , impaired tumorigenesis and metastasis , and increased expression of epithelial markers in aggressive breast cancer cells . Our data provide compelling evidence for the transcriptional regulation of the miR-191/425 cluster and for its context-specific biological determinants in breast cancers . Importantly , we demonstrated that the miR-191/425 cluster , by reducing the expression of an extensive network of genes , has a fundamental impact on cancer initiation and progression of breast cancer cells .
MicroRNAs ( miRNAs ) are a class of evolutionarily conserved regulatory RNAs that pleiotropically suppress gene expression at post-transcriptional level [1] . MiRNAs control the expression of 10–30% of the human transcriptome and are crucial regulators of both physiologic and pathologic processes [2]–[4] . In cancer , the spectrum of miRNAs expressed in neoplastic cells differs dramatically from that found in normal cells and it is now well established that miRNAs play fundamental roles in essentially all aspects of tumor biology [5] , [6] . In breast cancer , divergent miRNA expression between normal and neoplastic tissues has been demonstrated , as well as differential miRNA expression among the molecular subtypes of breast cancer , including luminal A , luminal B , Her2+ and basal-like [7] , [8] . MiRNAs have been shown to play an important role in breast cancer initiation and progression . For example , overexpression of miR-21 in breast carcinomas has been shown to target important tumor-suppressor genes such as PTEN , PDCD4 , and TPM1 , and was associated with advanced clinical stage , lymph node metastasis , and poor patient prognosis [9] , [10] . MiR-10a was reported to be overexpressed in about 50% of metastatic breast cancer and transcriptionally activated by the pro-metastatic transcription factor TWIST1 [11] . Reduced expression of miR-126 and miR-335 in the majority of primary breast tumors from relapsed patients was reported , and simultaneous loss of miR-126 and miR-335 expression was associated with poor distal metastasis-free survival [12] . Oncogene regulation by miRNAs has also been reported , including tyrosine kinase receptors HER-2 and HER-3 by miR-125b and miR-205 , respectively [13] , [14] , and the miR-200 family , known to reduce cell migration and invasiveness by targeting ZEB transcription factor members , was suppressed in metastatic breast cancer [15] , [16] . miRNA regulation by estrogen receptor-alpha ( ERα ) , the most important prognostic and therapeutic indicator in breast cancer , has recently been described by us and others [17]–[20] . Specifically , the majority of miRNAs upregulated by ERα are key components of a negative feedback loop that restrict E2 action and thus play a tumor suppressive role . In this regard , ERα-activation of let-7 family members limits the expression of oncogenes , such as Ras and c-Myc , and promotes differentiation of cancer cells [18]; ERα-mediated activation of the miR-17/92 cluster functions as a tumor suppressing mechanism in breast cancer through the downregulation of cyclin D1 and AIB1 by the miR-17/20/106 family and the direct suppression of ERα mediated by miR-18 and miR-19 [19] . We and others have described a double-negative feedback loop involving E2-suppressed microRNAs that target ERα , specifically miR-206 and miR-221&222 , resulting in upregulation of ERα expression and low miRNA level in luminal A-type breast cancers [17] , [21] . Recent works from our group have shown that miR-191 is highly induced in several human solid tumors including colon , lung , pancreas , prostate , and stomach cancer [22] , as well as acute lymphocytic leukemia ( ALL ) -associated hematopoietic malignancies [23] . We have also reported a strong positive correlation between miR-191 expression and ERα levels in breast tumors [7] , suggesting an oncogenic function for this miR . A role for miR-191 in tumorigenesis is further strengthened by several findings , including that miR-191 is induced by a dioxin family carcinogen , the miR is hypomethylated and overexpressed in liver cancer [24] , [25] , and miR-191 inhibition decreases cell proliferation and tumor growth of hepatocellular carcinoma cells [24] . Furthermore , miR-191 overexpression promotes cell growth and suppresses apoptosis of gastric cancer cells [26] . However , in ovarian and thyroid follicular cancer , miR-191 represses MDM4 or CDK6 expression , respectively , thereby delaying cancer progression and tumor-related death [27] , [28] . These contradictory findings indicate that the precise role for miR-191 in human neoplasia may be tumor-type specific and not well understood . In this current study , we report a positive association between ERα expression and miR-191 and miR-425 , two intronic miRNAs hosted by the putative protein coding gene DALR anticodon binding domain containing 3 ( DALRD3 ) , and further show direct control of the miR-191/425/DALRD3 transcriptional unit by the E2/ERα axis . We evaluated that the estrogen dependent activation of miR-191/425 induces proliferation in part by targeting the estrogen modulated tumor-suppressor gene , EGR1 . We also demonstrated that , when constitutively expressed in highly aggressive ERα negative breast cancer cells , the miR-191/425 cluster reprograms gene expression to impair tumorigenicity and metastatic potential through the suppression of several different oncogenic proteins .
MiR-191 and miR-425 are highly conserved miRNAs found on human chromosome 3 within the first intron of DALRD3 ( Figure S1 ) . Given their genomic organization and proximity , we hypothesized that miR-191 and miR-425 are co-transcribed and transcriptionally dependent on the host gene DALRD3 . We examined expression of mature miR-191 , miR-425 , and DALRD3 mRNA in 20 different normal human tissues using qRT-PCR ( Figure S2A ) . Both miRNAs were detected in all tissues and , their levels of expression were highly correlated , as shown by scatter plot analyses , ( R2 = 0 . 7351; p<0 . 001 ) ( Figure S2B ) . However , only a partial correlation was observed between the host gene DALRD3 and miR-191 ( R2 = 0 . 4058; p<0 . 001 ) or miR-425 ( R2 = 0 . 2101; p<0 . 001 ) ( Figure S2B ) , suggesting the existence of DALRD3-independent mechanism of miR-191/425 expression/accumulation in some tissues . Based on the previous association between miR-191 and ERα and the miR-191 and miR-425 co-expression results ( Figure S2A ) , it was of interest to examine ERα positive breast tumors for the expression of miR-191 and miR-425 . qRT-PCR analysis of 44 human breast cancer specimens with different ERα status revealed that miR-191 and miR-425 expression was higher ( p-value<0 . 01 ) in ERα positive than ERα negative tumors ( Figure 1A ) . DALRD3 mRNA also showed a significant positive correlation with the ERα status ( Figure 1A and Figure S3A ) . Next , to further verify the positive association between ERα levels and miR-191/425 expression , miRNA in-situ hybridization was performed on an independent set of 132 human breast cancer specimens . As anticipated , the majority of ERα positive breast tumors were also miR-191 ( 80% ) and miR-425 ( 87% ) positive , while only 23% and 15% of ERα negative specimens expressed miR-191 and miR-425 , respectively ( Figure 1B and Figure S4A ) . Furthermore , co-labeling of miR-191 and miR-425 by miRNA in situ-hybridization on the same ERα positive breast specimens showed co-localization of the two microRNAs in the majority of breast tumor cells ( Figure S4B ) . Finally , a set of 16 different breast cancer cells , clustered by ERα , progesterone receptor ( PR ) and HER2 expression was also analyzed for the expression of miR-191 , miR-425 and the host gene DALRD3 . Expression of both miR-191 and miR-425 was higher in the ERα positive cell lines , with the exception of MDA-MB-453 ( a non-aggressive ERα negative/androgen receptor positive breast cancer cell line but with a gene expression profile that overlaps with ERα positive breast cancer cells [29] ) ( Figure 1C , 1D ) . DALRD3 expression correlated with the expression levels of the mature miRNAs ( R2 = 0 . 725 for miR-191 , p<0 . 01; R2 = 0 . 63 for miR-425 , p<0 . 01 ) ( Figure 1C , 1D and Figure S2C ) . Moreover , we assessed the expression levels of the two different alternative splicing variants of DALRD3 and confirmed that the two variants are both transcribed and their expression levels are higher in the ERα positive than ERα negative breast cancer cells ( Figure S3B ) . Taken together , these data revealed for the first time that miR-191 and miR-425 are co-transcribed and preferentially expressed in ERα positive breast cancer cells and tumors . Recently , various microarray approaches have been used to identify E2-induced miRNA expression in hormone-dependent breast cancer cells [17]–[20] . However , based on the lack of consensus on E2-regulated changes in miRNA expression [30] , we investigated global changes in endogenous miRNA expression after E2 stimulation of breast cancer cells using the multiplexed Taqman microRNAs assay , a highly sensitive technology that allowed us to detect changes in 754 miRNAs ( “miRNome” ) with the same sensitivity of a Taqman realtime PCR . ERα positive MCF7 cells were hormone starved for 6 days and then exposed to 10 nM of E2 for 6 h . The miRNome was determined at 2 , 4 , 6 days of hormone deprivation and 6 h after E2 stimulation ( Figure 2A and Table S1 ) . After 6 days of E2 deprivation , downregulation of 146 and upregulation of 25 mature miRNAs , organized in 69 different miRNA genes , were observed ( fold change 1 . 2 , p-value<0 . 05 ) ( Figure 2A ) . Of these 69 miRNA genes , 43 genes ( 85 mature miRNAs ) were modulated after 6 h of E2 stimulation ( Figure 2A ) . The miR-191/425 cluster showed a progressive downregulation during the 6 days of hormone deprivation ( p-value<0 . 05; fold change miR-191: 2 d: 0 . 82; 4 d: 0 . 63; 6 d: 0 . 45; miR-425: 2 d: 0 . 81; 4 d: 0 . 78; 6 d: 0 . 35 ) followed by a significant induction by 6 h of E2 stimulation ( p-value<0 . 05; fold change miR-191: 1 . 36; miR-425: 1 . 12 ) ( Table S1 ) . We assessed the reliability of the treatment by using qRT-PCR to evaluate the expression levels of the E2-regulated genes , TFF1/pS2 and miR-17 after 3 , 6 , 24 , 48 and 72 h of E2 stimulation [19] ( Figure S5A ) . Both genes showed a strong and stable induction over time after E2 treatment . Next , we performed qRT-PCR on miR-191 and miR-425 and both miRNA levels increased after E2 stimulation although with a different kinetic of induction compared to miR-17 ( Figure 2B ) . Specifically , after 72 h of E2 treatment , we detected a 2- to 3 . 5-fold induction of miR-191 and -425 compared to untreated cells and the presence of a block in their induction at 24 h after E2 treatment ( Figure 2B ) . Next , we assessed expression levels of the primary precursor of miR-191 and miR-425; the induction profile was similar to the mature miRNAs ( Figure S5B ) . Despite the positive correlation between miR-191/425 and the host gene DALRD3 in breast cancer cells ( Figure 1C , 1D ) , the expression level of the total DALRD3 mRNA was decreased of 35% after 72 h of E2 treatment compared to untreated cells ( p-value = 0 . 053 ) ( Figure 2B ) . qRT-PCR for the two different alternative splicing variants of DALRD3 also showed a repression of both variants after estrogen stimulation ( Figure S5C ) . Moreover , total DALRD3 mRNA and both variants were also highly upregulated in hormone-deprived MCF7 cells ( Figure S5D ) . To further confirm the ability of E2 to modulate miR-191/425 , MCF7 were treated with fulvestrant , an ERα antagonist that induces ERα protein degradation ( Figure S6A ) . We observed a consistent reduction in miR-191/425 levels and a constant increase in DALRD3 levels after fulvestrant treatment ( Figure S6B , S6C ) . TFF1/pS2 expression was downregulated by hormone deprivation or fulvestrant treatment ( Figure S5D; Figure S6C ) . Collectively , the data showed that miR-191/425 levels are positively regulated by ERα , and the increased levels of miR-191 and miR-425 after estrogen stimulation are associated with a reduction in the accumulation of the host gene DALRD3 . Next , we addressed the direct involvement of ERα in the regulation of miR191/425 cluster by performing chromatin immunoprecipitation ( ChIP ) experiments across nine different regions spanning miR-191/425 cluster and covering a region of 4200 bp ( Figure 2C ) . MCF7 cells were E2 starved for 6 days ( 0 h ) and then treated with E2 ( 10 nM ) for 3 h , 6 h and 24 h . Enrichment of ERα after E2 treatment was identified at region 3 and 8 ( Figure 2C ) . Region 3 showed a specific enrichment of ERα that reached the highest levels after 3–6 h of treatment and started to decrease at 24 h . Although ERα was also detected at region 8 after 3 h and 24 h of E2 treatment , this enrichment was considered to be non-specific since it was also detected for the ERα negative MDA-MB-436 cells ( Figure 2C ) . We also examined the localization of the non-phosphorylated RNA polymerase II large subunit ( polII ) and the acetylation status of the histone H3 ( AcH3 ) after E2 treatment ( Figure 2C ) . Immunoprecipitation against polII showed the presence of two different areas of enrichment: region 3 , with an E2-dependent recruitment of polII that decreased over time , and region 7–9 which showed a progressive reduction in polII recruitment during E2 treatment ( Figure 2C ) . AcH3 ChIP showed a specific enrichment at region 1 , 2 and 8 with a significant increase in H3 acetylation after 6 h of E2 treatment only for region 2 ( Figure 2C ) . Taken together , these experiments show that ERα is recruited to the miR-191/425 genomic locus , in response to the estrogen stimulation . Because of the presence of two sites of enrichment of polII and the presence of two CpG islands located at the 5′end of the two isoforms of DALRD3 ( Figure 2C ) , we hypothesized the existence of two promoter regions: one responsible for the transcription of the longest isoform of DALRD3 , which includes miR-191 and -425 and a second responsible only for the transcription of the short isoform of DALRD3 . Computer-assisted analysis ( Figure S7A ) identified two distinct predicted regions as possible candidates for promoters regulating miR-191/425/DALRD3 gene transcription: 3900 bp ( prom1 ) a marginal predicted region , located upstream of the long isoform of DALRD3 and also involved in the production of miR-191/425; 6500 bp ( prom2 ) a highly likely predicted region , associated only to the transcription of the short isoform of DALRD3 mRNA ( Figure S7A ) . To test the transcriptional activity of these two elements , both putative promoters ( Figure 2D ) were cloned individually in the promoter-less pGL3basic luciferase vector , and their expression was examined in HEK293 cells . Both vectors showed an increase in the luciferase activity , and as expected , the highly likely predicted region prom2 showed the strongest basal luciferase activity ( Figure 2D ) . Next , we assessed the E2 responsiveness of the two identified promoter regions . We first tested the luciferase activity of both plasmids in five breast cancer cell lines with different ERα expression levels ( Figure S7B ) . Both promoter elements showed higher levels of activity in the three ERα positive cell lines ( MCF7 , T47D , BT-474 ) compared to the ERα negative cells ( BT-459 , MDA-MB-436 ) . Treatment with E2 for 6 h induced a 3-fold increase in luciferase activity for the prom1 element ( Figure 2E ) ; in contrast , luciferase activity for the prom2 region was repressed by E2 treatment ( Figure 2E ) . Furthermore , silencing of ERα by siRNA reduced luciferase activity of the prom1 reporter vector by approximately 50% specifically in ERα positive cells , but no effect on prom2 activity was detected ( Figure S7C ) . Taken together , these experiments showed that ( 1 ) ERα directly regulated miR-191/425 cluster expression and ( 2 ) verified the existence of two promoter elements involved in the transcription of the two DALRD3 isoforms , allowing a differential accumulation of miR-191/425 and DALRD3 upon E2 stimulation . To identify the functional role of the E2 mediated-induction of miR-191 and miR-425 in ERα positive breast cancer cells , both miRNAs were knocked down in estrogen dependent MCF7 cells in normal culture condition . A 33% reduction in cell proliferation rate was observed compared to a control oligonucleotide ( Figure 3A ) . Indeed , enforced expression of miR-191/-425 in hormone deprived MCF7 cells , with low levels of endogenous miR-191/425 ( Table S1 ) , induced a 70% increase in cell proliferation ( Figure 3A ) . To shed more light in the proliferative effects of miR-191/425 in ERα positive breast cancer cells , flow cytometric analyses of transiently-transfected cells were performed and revealed an increased number of cells in G1 and fewer cells in G2/M following knockdown of either miR-191 or miR-425 compared to control cells ( Figure 3B and Figure S8A ) . Moreover , enforced expression of miR-191/425 in hormone deprived MCF7 cells protects cells from hormone starvation induced apoptosis ( Figure 3C ) . We next evaluated the in vivo effect of miR191/425 knockdown on tumor growth . Specifically , miR-191/425 were transiently inhibited in ERα positive MCF7 cells for 48 h and tumor growth was assessed after subcutaneous transplantation of the transfected MCF7 cells in nude mouse . A 50% reduction in tumor growth was observed ( Figure 3D ) in miR-191/425 knocked-down cells compared to control cells . Same results were also obtained after xenotrasplantation of miR-191/425 knocked-down ERα positive ZR-75-1 cells ( Figure S8B ) . To uncover the molecular players involved in the proliferative response of ERα positive breast cancer cells controlled by the E2 mediated activation of miR-191/425 , published transcriptomic data set of E2 induced ERα positive MCF7 and ZR-75-1 cells were compared with the predicted miR-191/425 target genes [31] , [32] . Specifically , the target genes of miR-191 and miR-425 obtained from the prediction program Targetscan v5 . 2 ( Table S2 ) were compared with the pool of E2 downregulated genes . 43 and 23 miR-191 targets and 199 and 116 miR-425 targets were found in the E2 repressed gene lists of MCF7 and ZR-75-1 , respectively ( Figure S9A ) . Only 5 and 18 targets for miR-191 and miR-425 were repressed by estrogen in both cell lines respectively ( Figure S9A ) . We focus our attention on the early growth response 1 ( EGR1 ) , a member of the early growth response ( EGR ) transcription factor family that has been implicated in breast cancer progression and antiestrogen resistance [33]–[35] . First , the expression levels of EGR1 were assessed after E2 stimulation in MCF7 cells . EGR1 expression showed a 50% induction after 30 minutes from the stimulation ( Figure S9A ) followed by a continuous repression ( Figure S9B ) . To verify that miR-191 regulates the expression of EGR1 , knockdown of miR-191 was performed in MCF7 cells and western blot analyses confirmed the upmodulation of EGR1 and its direct transcriptional target CDKN1A ( p21 ) ( Figure 3E ) [34] . Next , to assess that miR-191 directly controls EGR1 in cells , a luciferase reporter assay was performed with a luciferase expressing plasmid containing the conserved miR-191 predicted binding site for EGR1 cloned after the luciferase reporter gene ( Figure 3F ) . Co-transfection of miR-191 with the reporter plasmid significantly suppressed ( p-value<0 . 01 ) the luciferase activity of the reporter , relative to transfection of the control oligonucleotide ( Figure 3F ) . Disruption of the predicted binding site reduced the inhibitory activity of miR-191 overexpression on the luciferase activity ( Figure 3F ) . To study in more depth the interaction miR-191/EGR1 , hormone deprived MCF7 cells were transfected with miR-191 inhibitor and control oligonucleotide and 48 h later treated with estradiol . Western blot analyses showed that miR-191 inhibition prevents EGR1 degradation at 6 h and 24 h after E2 treatment compared to control cells ( Figure 3G ) . qRT-PCR showed that EGR1 mRNA is also under the control of miR-191 but only in the early phase of E2 induction ( Figure 3H ) . As expected , induction of p21 transcript was confirmed by qRT-PCR specifically in miR-191 knocked-down ( Figure 3H ) . MiR-191 inhibition was also confirmed by qRT-PCR ( Figure S9C ) . Taken together , these results highlight the proliferative effects of E2-induced miR-191/425 cluster in ERα positive breast cancer cells that are in part related to the miR-191 repression of the tumor-suppressor gene EGR1 . Approximately 75% of diagnosed breast tumors express ERα , and this ERα-positive status is associated with a better prognosis and response to hormonal treatment [36] . Several studies suggested that a fraction of ER-negative tumors arise from ER-positive precursors [37] . Moreover , restoration of functional ERα expression in ERα-negative human breast cancer cells can block their proliferation and aggressiveness , supporting the notion that ERα confers a less aggressive phenotype of breast cancer [38] , . To determine if miR-191/425 cluster as a part of the ERα signaling can partially mediate the anti-proliferative effect that ERα showed in the aggressive breast cancer cells , a genome-wide expression analysis in aggressive MDA-MB-231 cells , which express low levels of miR-191/425 , was performed 72 h after enforcing expression of both miR-191 and miR-425 and control oligonucleotide ( Figure 4A ) . Unsupervised clustering analyses showed significant deregulation of gene expression by miR-191/425 , with 753 upregulated and 1105 downmodulated genes ( by>1 . 5 fold; p-value 0 . 001 ) ( Table S2 ) . Functional profiling of these genes defined that the greatest proportion of them is associated with cell adhesion , adherens junction followed by phosphatidylinositol signaling ( Figure 4B ) . We used qRT-PCR to validate the modulation of over 20 genes identified in the microarray analyses or to their related molecular pathways in two different breast cancer cell lines ( Figure 4C ) . Expression of many genes involved in promoting growth and metastasis of breast cancer cells was found to be downmodulated by miR-191/425 cluster: CCND1 , CCND2 , E2F1 , CSDA and API5 , regulatory proteins of the cell cycle progression and apoptosis [40]–[42]; FSCN1 , TNC , VEGFA , CDC42 and SOX4 , which have roles in angiogenesis and migration , and are involved in filopodia/invadopodia formation [43]–[48]; the protooncogene MYC , which initiates the transcription of a large set of genes involved in cell growth by stimulating metabolism and protein synthesis [49]; and SATB1 , which reprograms gene expression to enhance aggressive histomorphological features and invasive capabilities [50] . We also found that miR-191/425 cluster represses cell-structure and adhesion genes typical of invasive breast cancer cells such as fibronectin , an ECM adhesive glycoprotein , and vimentin , the intermediate filament protein of mesenchymal cells , which together provide cellular integrity and resistance against stress [51] . Finally , miR-191/425 cluster upregulates zonula occludens-1 ( ZO-1 ) , a component of the tight junction barrier in epithelial and endothelial cells [52]; E-cadherin ( CDH1 ) , an important marker of epithelial tumor progression; and β-catenin ( CTNN1 ) a component of wnt pathway that drives progression in various cancers [53] . To confirm that targets of the mir-191/425 cluster showed an enrichment signature in this dataset , we assessed the cumulative density function ( cdf ) plot comparing the expression changes of mir-191 and miR-425 targets based on TargetScan v5 . 1 gene list [54] . We found that the mir-191/425 targets set ( targets ) was more repressed than the control set of genes ( control ) matched for 3′UTR length , dinucleotide composition , and expression level ( Figure 5A ) . Stronger repression was observed for the conserved miR-191/425 cluster targets ( conserved targets ) , suggesting further enrichment of genuine targets in this set ( Figure 5A ) . These observations supported the utility of this expression data for the discovery of novel miRNA targets based on miR-associated genes . Because the expression levels of target mRNAs tend to correlate negatively with the expression levels of their specific miRNAs [55] , we next focused on the miR-191/425 downregulated genes . First , the target prediction program TargetScanv5 . 1 was used to search for predicted target genes of miR-191 and miR-425 in the pool of downregulated genes in miR-191/425-expressing MDA-MB-231 cells ( Table S2 ) . This list of genes was further compared with the list of target genes downregulated exclusively by the expression of miR-191 or miR-425 ( Figure S10 and Table S2 ) . A total of 37 and 346 downregulated targets were obtained for miR-191 and miR-425 , respectively ( Figure 5B ) . Among these large set of genes , we selected 12 genes ( SATB1 , CCND2 , CTDSP2 , SOX4 , LRRC8A , SLC16A2 , CSDA for miR-191 and FSCN1 , TNC , SIAH2 , CCND1 , CSDA for miR-425 ) predicted to have at least one potential binding site for miR-191 and/or mir-425 in their 3′UTRs . Based on their reduction in miR-191/425-expressing cells ( Figure 4C and Table S2 ) , we tested whether these genes are direct targets of miR-191 and miR-425 constructing reporter plasmids containing the miRNA binding site in the 3′UTR of these genes downstream of a luciferase reporter gene ( Figure S11A ) . Co-transfection experiments showed that the introduction of either miR-191 or miR-425 markedly suppressed the expression of a luciferase containing the 3′UTR of these downregulated genes ( Figure 5C ) but did not affect the luciferase activity of the 3′UTR-CCND1 plasmid , indicating that CCND1 is not a direct target of miR-425 ( data not show ) . Mutations that disrupt base paring with miR-191 and miR-425 rescued the luciferase expression for all the target genes , further confirming that these genes are direct targets of miR-191 and miR-425 ( Figure S11B ) . We next focused our attention exclusively on SATB1 , CCND2 and FSCN1 as mediators of miR-191 and miR-425 effects , respectively , because of their strong repression obtained after miRNA expression and their reported tumorigenic function in breast cancer [46] , [50] , [56] , [57] . Western blot analyses on MDA-MB-231 expressing either miR-191 or miR-425 showed a strong suppression of SATB1 only after enforced miR-191 expression ( Figure 5D ) . Because of SATB1 repression , we also detected marked repression of fibronectin and to lesser extent of vimentin ( Figure 5D ) . Further , we also observed a ∼2 fold increase of the β-catenin protein ( Figure 5D ) and its sequestration at the cytoplasmic membrane due to the increased expression of e-cadherin ( Figure 5D , 5E ) . Indeed , miR-191 over-expressing cells also showed a specific repression of CCND2 as well as CDK6 ( Figure 5D ) , a previously demonstrated miR-191 target [28] . Furthermore , we observed a decrease in the levels of CCND1 , E2F1 and a strong upmodulation of CDKN1A ( p21 ) for both miR-191 and miR-425 ( Figure 5D ) . In contrast , miR-425 over-expression specifically reduced expression of FSCN1 , TNC and CDC42 ( Figure 5D ) . Pathway analyses also revealed a repression of the PI3K-AKT pathway in miR-191/425 over-expressing cells . Western blot analyses against pERK1/2 , pAKT and its direct targets pGSK3β confirmed the inhibition of PI3K-AKT signaling and highlighted that miR-191 is primarily responsible for the inhibition ( Figure 5D ) . Moreover , we performed silencing of SATB1 , CCND2 and FSCN1 in order to evaluate the specific contribution of each target to modulated miR-191/425 pathways . We found that only SATB1 knockdown , as well as miR-191 over-expression , were responsible for the up-modulation of β-catenin , whereas both CCND2 and FSCN1 silencing decreased β-catenin expression ( Figure 5F ) . Finally , we found that SATB1 and CCND2 silencing controlled AKT pathway activation ( Figure 5F ) . Taken together , these data indicate that miR-191/425 modify a number of genes that play critical roles in controlling the progression of highly invasive breast cancer . Next , we assessed the in vitro biological effect of miR-191/425 on aggressive breast cancer cells . First , enforced expression of miR-191 or miR-425 in MDA-MB-231 and MDA-MB-436 cells induced an approximately 50% reduction in cell proliferation ( Figure 6A and Figure S12A ) . Lentivirally-infected cells over-expressing either miR-191 or miR-425 were generated ( Figure S12B ) , and cell proliferation was assessed using a ( 2D ) colony formation assay ( Figure 6B and Figure S12C ) . Cells over-expressing miR-191 not only showed a reduced number of colonies compared to control but also developed smaller colonies than control ( Figure 6B and Figure S12D ) ; in contrast , miR-425-expressing cells exhibited mainly a reduction in the number of colonies ( Figure 6B and Figure S12D ) . Further , we tested the abilities of lentivirally-infected MDA-MB-231 cells to form colonies in soft agar . Compared to control cells , cells over-expressing either miR-191 or miR-425 formed significantly fewer colonies , indicating a decrease in anchorage-independent growth ( Figure S12E ) . We then performed proliferation assays with cells cultured in three dimensions ( 3D ) within Matrigel , and we observed that over-expression of either miR-191 or miR-425 impaired the formation of large filopodia/invadopodia-like structures at the periphery of the aggregates like in the control cells , thus resulting in the appearance of tightly adherent aggregates ( Figure 6C ) . These results demonstrated that gain of cell adhesion and reduced migration are related to the degree of miR-191 and miR-425 expression in aggressive breast cancer cells . To more accurately quantify the anti-proliferative properties of miR-191/425 in aggressive breast cancer cells , flow cytometric analyses of transiently-transfected cells revealed fewer cells in S phase and an increased number of cells in G1 following over-expression of either miR-191 or miR-425 compared to scrambled transfected cells ( Figure 6D and Figure S12F ) . To gain additional insight regarding the numbers of cells arrested in G1 , we treated the cells with the microtubule-destabilizing agent nocodazole , which traps cycling cells in M phase . Cell populations with enforced miR-191 or miR-425 expression were characterized by significantly increased numbers of cells remaining in G1 ( Figure S12G ) , confirming that both miRNAs caused cell-cycle arrest . We next evaluated the in vivo effect of miR191/425 over-expression on tumor growth . First , we tested if over-expression of either miR-191 or miR-425 inhibits tumor growth of highly aggressive MDA-MB-231 cells . Lenti-miR-191 and lenti-miR-425 infected MDA-MB-231 were subcutaneously injected into the right flank of athymic nude mice and the tumor growth was monitored compared to control lenti-GFP infected and parental MDA-MB-231 cells . Tumors in the parental and GFP control groups were large , poorly differentiated , heavily necrotic and highly vascularized that formed within only 22 days post-implantation ( 5 out of 5 mice per group ) . In contrast , all five mice injected with either miR-191- or miR-425-infected cells exhibited greatly reduced tumor growth ( Figure 6E ) . Interestingly , miR-191 and miR-425 over-expressing tumors were strictly non-invasive , as shown by their circumscribed profiles and confinement within dense fibrotic capsules ( Figure 6F ) , in stark contrast to the spindle-like morphology of the parental and control tumors along with islands of cancer cells invading the fat pad and the muscle ( Figure 6F and Figure S13A ) . Hence , ectopic expression of miR-191 and miR-425 in MDA-MB-231 cells impaired tumor growth and invasion in the surrounding tissue . To determine whether miR-191 and miR-425 expression in the primary tumors affects cell proliferation , we performed immunohistochemistry for the proliferation marker Ki-67 . We found that the total number of Ki-67 positive cells in the tumors over-expressing miR-191 or miR-425 were significantly lower relative to the number observed in the control tumors ( lenti-GFP control cells: 97 . 3%; lenti-miR-191: 81%; lenti-miR-425: 89%; p-value<0 . 05 ) ( Figure 6F ) . High expression of miR-191 and miR-425 in the tumor cells was confirmed by qRT-PCR ( Figure S13B ) . qRT-PCR revealed that miR-191 induced a reduction of mesenchymal ( fibronectin ) and acquisition of epithelial ( e-cadherin and β-catenin ) markers while miR-425 only a specific increase in e-cadherin ( Figure S13C ) . Reduction of SATB1 , CCND2 by miR-191 and FSCN1 by miR-425 over-expressing tumors was confirmed by western blot analyses ( Figure 6G; and Figure S13D ) . Based on these numerous observations , we concluded that the impaired tumor growth of miR-191- or miR-425-over-expressing cells was a consequence of the reduced cell proliferation . We then assessed the effects of miR-191/425 over-expression on migration and metastasis by using in vitro and in vivo experimental approaches . First , we evaluated the rate of cell migration by using the Boyden Chamber assay and found that miR-191- and miR-425-transfected cells migrated more slowly than control MDA-MB-231 cells ( miR-191: p-value<0 . 05 , ∼3-fold; miR-425: p-value<0 . 05 , ∼6-fold ) ( Figure 7A ) . Further , we performed wound-healing assays on lenti-miR-191 , lenti-miR-425 cells and GFP control ( Figure 7B ) . By 16 hour post wounding , parental cells and GFP control cells migrated into the wound , resulting in 90% and 70% closure , respectively . In contrast , wound closure was significantly less in miR-191 and highly impaired in miR-425 ( miR-191: 60% closed; miR-425: 25% closed ) ( Figure 7B ) . Migration and wound healing experiments were also performed using MDA-MB-436 cells , and the results were essentially similar ( Figure S14A and S14B ) . Finally , we tested the differential migratory abilities of miR-191 or 425-over-expressing cells by using an in vivo metastasis assay . Control lenti-GFP , lenti-miR-191 , lenti-miR-425 infected-cells ( 2×10∧6 cells ) were injected into the lateral tail vein of 6-week-old NOD-SCID mice , and their survival was evaluated in circulation , extravasation to and growth in lungs . After 8 weeks , histological analyses revealed that the number of micrometastasis was markedly reduced in the lungs of mice injected with miR-191 or miR-425 cells compared to the control tumor cells ( Figure 7C ) . Of note , we also observed pneumonitis only in mice injected with the control GFP cells ( Figure 7C ) . Collectively , all these data support the idea that sustained miR-191 and miR-425 activity impairs local invasion and metastatic colonization of breast cancer cells .
Defining the role of the differentially regulated miRNAs in breast cancer could lead to the development of new diagnostic tools and therapeutic approaches . In the present study , we provide new evidence for the role of miR-191 and miR-425 in breast cancer . We demonstrate that expression of miR-191 and miR-425 occurs as a part of the same transcriptional unit and strongly correlates with cellular ERα status . Moreover , we show that ERα directly regulates the expression of miR-191 and miR-425 . Finally , our functional studies demonstrate that miR-191/425 cluster exerts a dual role in breast cancer cells depending on their ERα status: in ERα positive cells miR-191/425 work as oncogenes by inducing proliferation in part through the suppression of EGR1 during the E2 stimulation; in ERα negative cells , they impair tumor growth and invasiveness conferring a more epithelial phenotype to highly aggressive breast cancer cells . We have demonstrated that miR-191 and miR-425 are co-expressed ( Figure S4B ) and , at least in part , transcriptionally dependent from the host gene DALRD3 in normal human tissues ( Figure S2A , S2B ) . The identification of two distinct promoter regions responsible for the production of the two DALRD3 isoforms may allow the independent production of DALRD3 from the miRNAs and thus explain the partial correlation between miR-191/425 and DALRD3 found in some of the human tissues . Furthermore , the existence of the dual promoter for DALRD3 may contribute to “fine-tuning” of the estrogen-dependent regulation of miR-191/425 and DALRD3 gene transcription . We demonstrate that while E2/ERα signaling induces an increase in miR-191/425 expression ERα activation has a negative effect on the expression of the host gene DALRD3 ( Figure 2B , Figure S5C and S5D , and Figure S6 ) . qRT-PCR of the two different alternative splicing variants of DALRD3 showed that both variants are preferentially expressed in ERα positive cells and both reduced during E2 stimulation ( Figure S3B and Figure S5C ) . These results highlight that E2 stimulation of the miR-191/425/DALRD3 transcriptional unit is essentially related to the production of miR-191 and miR-425 . The reduction of the host gene isoform 1 may be explained with the mechanism proposed by Gromak et al . which showed that the cleavage of an intron can affect alternative splicing if it occurs between an alternatively spliced exon and its intronic regulatory elements [58] . Moreover , it has been demonstrated that ERα directly interacts with Drosha to modulate the processing of E2-regulated microRNAs [59] . In this scenario , we can hypothesize that the recruitment of ERα at the upstream promoter ( Figure 2C ) might improve the assembly of the Microprocessor complex at miR-191/425 locus and increase the cleavage of the intron for the production of the miRs , impairing the processing of the pre-mRNA . We further show that the increase of miR-191 and miR-425 upon E2 stimulation is associated with gradual reduction of polII accumulation on the downstream promoter ( Figure 2C ) . Interestingly , this negative effect on DALRD3 promoter 2 is independent by ERα ( silencing of ERα does not modify the downstream promoter activity ) , but is still related to E2 treatment , based on the strong reduction of promoter activity after E2 treatment ( Figure 2E and Figure S6C ) . Both genomic and non-genomic estrogen actions may contribute to the regulation of miR-191/425-DALRD3 transcriptional unit [60] , [61]: E2 treatment induces recruitment of ERα at the upstream promoter to improve only the accumulation of miR-191/425 ( i . e . , genomic regulation/processing activity ) , while estrogen-mediated effects , transmitted via enzymatic pathways or ion channels , induces repression of the downstream promoter ( non-genomic regulation ) . Next , we focused on the functional role of miR-191 and miR-425 in ERα signaling . Inhibition of miR-191 and miR-425 strikingly impairs cell proliferation and tumor formation in ERα positive cells ( Figure 3A , 3D ) . Moreover , miR-19/425 overexpression in hormone deprived ERα positive cells , which have low levels of endogenous miR-191/425 , reduces cell cycle arrest and apoptosis ( Figure 3C ) . In silico analyses , based on the endonucleolytical activity of microRNAs , identify Early Growth Response 1 ( EGR1 ) as a miR-191 target ( Figure 3E , 3F and Figure S9A ) . EGR1 is involved in the regulation of cell growth and differentiation in response to signals , such as mitogens , growth factors , and stress stimuli [62] , [63] . In most human tumors , such as breast cancer , fibrosarcoma , and glioblastoma , EGR1 is described to be a tumor suppressor gene [64]–[66] . In fact , re-expression of EGR1 in human tumor cells inhibits neoplastic transformation [63] . EGR1 represents also an important upstream gatekeeper of the p53 tumor suppressor pathway and many p53 downstream target genes , such as CDKN1A ( p21 ) , are dependent on EGR1 status . We demonstrate that during E2 stimulation , after an initial increase , the levels of EGR1 are repressed ( Figure 3G and Figure S9B ) . Inhibition of miR-191 blocks the suppression of EGR1 and induces high levels of CDKN1A ( p21 ) ( Figure 3G , 3H ) explaining at least in part the anti-proliferative activity of miR-191/425 cluster knockdown . However , the tumor-suppressive role of EGR1 seems to be tissue specific , because several studies implicated a tumor growth-promoting role of EGR1 in prostate cancer progression [67]–[69] . The loss of ERα expression causes tumor growth that is no longer under estrogen control , which leads to greater cancer aggressiveness and the failure of endocrine therapy . Therefore , restoration of ERα protein expression or signaling in ERα negative breast cancer cells represents an important key event to promote apoptosis and differentiation of aggressive breast cancer . Since miR-191 and miR-425 are players of the ERα signaling , we also inquire their role in ERα negative breast cancer . To this aim , we overexpressed both miRs in ERα negative cells and showed that miR-191 and miR-425 markedly alters the transcriptome of aggressive breast cancer cells , resulting in impaired tumor growth and metastasis ( Figure 4 and Figure 5 ) . Mechanistically , the effects of miR-191 and miR-425 on tumor growth and invasion require , at least in part , the suppression of SATB1 , CCND2 and FSCN1 . Specifically , miR-191-mediated SATB1 repression is associated with gain of epithelial markers ( e . g . , such as e-cadherin ) , and loss of mesenchymal markers ( e . g . , fibronectin and vimentin ) ( Figure 5C and Figure 6D ) . The increase of e-cadherin levels , mediated by miR-191/425 , results in greater cell-cell adhesion , reduced detachment of cells , and cytoplasmic localization of β-catenin ( Figure 5E ) . Mounting evidence indicates multiple reciprocal interactions of e-cadherin and cytoplasmic β-catenin with EMT-inducing transcriptional repressors to destabilize an invasive mesenchymal phenotype of epithelial tumor cells . Moreover , SATB1 and CCND2 repression by miR-191 are related to the suppression of the PI3K/AKT pathway and the corresponding reduced cell proliferation and tumor growth . We have also identified FSCN1 , which is responsible for the reduced invasiveness and partial reversion to an epithelial morphology , as a target of miR-425 ( Figure 6C ) . All together our experiments demonstrate a duality in the biological role of miR-191/425 cluster in breast cancer: estrogen dependent-high levels of miR-191/425 induce proliferation in ERalpha positive cells by suppressing a strong tumor-suppressor gene , such as EGR1; low levels of miR-191/425 cluster are essential for the high expression of important modulators , such as SATB1 , CCND2 and FSCN1 , which confer a proliferative advantage to aggressive breast cancer cells .
Human breast cancer cell lines MCF10A , MCF10F , MCF7 , T47D , BT474 , BT483 , ZR-75-1 , MDA-MB361 , HBL-100 , SKBr3 , MDA-MB-468 , MDA-MB-453 , BT549 , MDA-MB-436 , MDA-MB-231 as well as the Human Embryonic Kidney cell line HEK293 , were purchased from the American Type Culture Collection ( ATCC ) and grown in accordance with ATCC recommendations . ERα , progesterone receptor ( PGR ) and HER2 status were confirmed for all cell lines by Western blot analyses . All transfections were carried out with Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) according to the manufacturer's instructions . For hormone depletion experiments , MCF7 cells were grown to 70% confluency in phenol red–free DMEM supplemented with 5% charcoal–dextran-stripped FBS for 6 days and collected every two days with the relative normal growth control . For estradiol ( E2 ) treatments ( Sigma Aldrich ) , MCF7 cells were hormone starved for 6 days and then treated with E2 ( 10 nM ) at the indicated times . For Fulvestrant treatments , MCF7 cells were treated daily with fulvestrant ( Sigma Aldrich ) ( 100 nM ) and collected at the reported time points . The 44 breast tumor tissue samples were provided from the Department of Pathology , The Ohio State University . All human tissues were obtained according to a protocol approved by the Ohio State Institutional Review Board . Quantitative real-time PCR ( qRT-PCR ) was performed with the TaqMan PCR Kit ( Applied Biosystems , Foster City , CA ) , followed by the detection with the Applied Biosystems 7900HT Sequence Detection System ( P/N: 4329002 , Applied Biosystems ) . PCR was carried out in 10 µL of reaction buffer containing 0 . 67 µL RT product , 1 µL TaqMan Universal PCR Master Mix ( P/N: 4324018 , Applied Biosystems ) , 0 . 2 mM TaqMan probe , 1 . 5 mM forward primer , and 0 . 7 mM reverse primer . The reaction mixture was incubated in a 96-well plate at 95°C for 10 minutes , followed by 40 cycles of denaturation ( 95°C for 15 seconds ) and extension ( 60°C for 1 minute ) . All reactions were performed in triplicate . Simultaneous quantification of small endogenous nucleolar RNA U44/U48 was used as a reference for TaqMan assay data normalization . For quantification of DALRD3 , trefoil factor 1 ( TFF1/pS2 ) , pri-miR-191 , pri-miR-425 , VEGFA , FSCN1 , EGR1 , TNC , CDC42 , SATB1 , SOX4 , CCND1 , VIM , CCND2 , E2F1 , SIAH2 , API5 , FIBR , CSDA , MYC , CTNN1 and CDH1 mRNAs , the appropriate TaqMan probes were purchased from Applied Biosystems . The TaqMan Array Human MicroRNA Card ( Applied Biosystem ) Set v3 . 0 is a two-card set containing a total of 384 TaqMan MicroRNA Assays per card that enables accurate quantification of 754 human miRNAs . Included on each array are three TaqMan MicroRNA Assays as endogenous controls to aid in data normalization and one TaqMan MicroRNA Assay not related to human as a negative control . The hybridized Human Genome U133A 2 . 0 Array ( Affymetrix ) was scanned and analyzed with the Affymetrix Microarray Analysis Suite version 5 . 0 . The average density of hybridization signals from three independent samples was used for data analysis , and genes with signal density less than 300 pixels were omitted from the analysis . P values were calculated with two-sided t-tests with unequal variance assumptions . To correct for multiple hypothesis testing , the false discovery rate was calculated . Differentially expressed genes were selected using both a false discovery rate of less than 0 . 01 and a fold-change greater than 1 . 5 or less than −1 . 5 . A tree cluster was generated by hierarchical cluster analysis to classify the miR-transfected cells; for this analysis , we used average linkage metrics and centered Pearson correlation ( Cluster 3 . 0 ) . Java Treeview 1 . 1 ( http://sourceforge . net/projects/jtreeview/ ) was used for tree visualization . The associations between gene modulations by two miRNAs were examined using a two-sided Fisher exact test . The association between modulations by any two miRNAs was statistically significant if P was less than . 001 . The online program Pathway-Express ( http://vortex . cs . wayne . edu/Projects . html ) was used to explore the most biologically relevant pathways affected by a list of input genes . Specific biological pathways were defined by the Kyoto Encyclopedia of Genes and Genomes database ( Kanehisa Laboratories , Kyoto , Japan ) ( http://www . genome . jp/kegg/pathway . html ) . Pathways were considered statistically significant if the corrected gamma P was less than 0 . 01 . For cell-cycle analysis , MDA-MB-231 and MDA-MB-436 cells were plated in 6 cm dishes , transfected as indicated in the figures , trypsinized , washed in PBS , and fixed with ice-cold 70% ethanol while vortexing . Cells were rehydrated in PBS and stained 30 min at RT with propidium iodide ( 50 mg/ml PI , 0 . 5 mg/ml RNase in PBS ) prior to flow-cytometric analysis . Lenti-GFP , lenti-191 and lenti-425 infected-cells were also analyzed by flow cytometry after 12 h treatment with nocodazole . All mouse experiments were conducted following protocols approved by the institutional animal care and use committee at the Ohio State University . Parental MDA-MB-231 , lenti-GFP , lenti-191 and lenti-425 infected-cells ( 5×106 ) were injected subcutaneously into the right flank of 6-week-old athymic nude mice . Tumor size was assessed twice per week using a digital caliper . Tumor volumes were determined by measuring the length ( l ) and the width ( w ) of the tumor and calculating the volume ( V = lw2/2 ) . Statistical significance between the control and treated mice was evaluated using Student's t test . We sacrifiedthe mice 35 days after injection and tumors were excided and processed for histology and for RNA and protein extractions . 4 µm sections of tumor tissues were stained with hematoxylin/eosin and with Ki-67 by immunohistochemistry . For MCF7 and ZR-75-1 xenografts , estradiol pellets ( Innovative Research ) were implanted in nude mouse and after two weeks mice were injected subcutaneously with one 10 cm plate of anti-miR191/425 transfected MCF7 or ZR-75-1 cells . Mouse experiments were conducted after approval by the institutional animal care and use committee at Ohio State University . Transwell insert chambers with an 8-µm porous membrane ( Greiner Bio One ) were used for the assay . Cells were washed three times with PBS and added to the top chamber in serum-free medium . The bottom chamber was filled with medium containing 10% FBS . Cells were incubated for 24 h at 37°C in a 5% CO2 humidified incubator . To quantify migrating cells , cells in the top chamber were removed by using a cotton-tipped swab , and the migrated cells were fixed in PBS , 25% glutaraldehyde and stained with crystal violet stain , visualized under a phase-contrast microscope and photographed . Crystal-violet–stained cells were then solubilized in acetic acid and methanol ( 1∶1 ) , and absorbance was measured at 595 nm . For the scratch assay , parental MDA-MB-231 cells , lenti-GFP , lenti-miR191 and lenti-miR425 infected-cells were plated in culture dishes and after 24 h the confluent monolayer was scratched . Images were acquired directly after scratching ( 0 h ) and after 5 h , 9 h and 16 h . For quantification of migration distance Image J software was used . The distance covered was calculated by converting pixel to millimeters . In situ hybridization ( ISH ) was carried out on deparaffinized human breast tissues using previously published protocol ( Nuovo GJ , 2009 ) , which includes a digestion in pepsin ( 1 . 3 mg/ml ) for 30 minutes . The probes contained the dispersed locked nucleic acid ( LNA ) modified bases with digoxigenin conjugated to the 5′ end . The probe cocktail and tissue miRNA were co-denatured at 60°C for 5 minutes , followed by hybridization at 37°C overnight and a stringency wash in 0 . 2× SSC and 2% bovine serum albumin at 4°C for 10 minutes . The probe-target complex was seen due to the action of alkaline phosphatase on the chromogen nitroblue tetrazolium and bromochloroindolyl phosphate ( NBT/BCIP ) . Negative controls included the use of a probe that should yield a negative result in such tissues ( scrambled miRNA ) . Total RNA isolation was performed with Trizol ( Invitrogen , Carlsbad , CA ) according to the manufacturer's instructions . For , acrylamide northern blotting 10 µg aliquots of total RNA were resolved on a 15% denaturing polyacrylamide gel ( Bio-Rad , Hercules , CA ) and were electrophoretically transferred to BrightStar blotting membrane ( Ambion Inc , Austin , TX ) . The oligonucleotide encoding the complementary sequence of the mature miRNA annotated in the miRNA Registry ( release 14: September 2009 ) was end-labeled with [γ32 P]-ATP by T4 polynucleotide kinase ( USB , Cleveland , OH ) . RNA-blotted membrane was prehybridized in Ultrahyb Oligo solution ( Ambion Inc ) and subsequently hybridized in the same solution containing probe at a concentration of 106 cpm/mL at 37°C overnight . The membrane was washed at high stringency in the solution containing 2× standard saline citrate and 1% sodium dodecyl sulfate at 37°C . Northern hybridization signals were captured and converted to digital images with the Typhoon Scanner ( GE Healthcare Biosciences , Piscataway , NJ ) . Chromatin immunoprecipitation ( ChIP ) assays were performed with the ChIP assay kit ( Upstate Biotechnology , Lake Placid , NY ) with minor modifications . Briefly , MCF7 and MDA-MB-436 cells were hormone starved for 6 days and then treated with E2 ( 10 nM ) for 3 h , 6 h and 24 h . The cross-linking was performed with 1% formaldehyde at 37°C for 10 minutes . Cells were then rinsed with ice-cold PBS and resuspended in 0 . 4 mL of lysis buffer containing 1% sodium dodecyl sulfate , 10 mM EDTA , 50 mM Tris–HCl , pH 8 . 1 , 1× protease inhibitor cocktail ( Roche Molecular Biochemicals ) , and sonicated . A 30 µL aliquot of the preparation was treated to reverse the cross-linking , deproteinized with proteinase K , extracted with phenol–chloroform , and the DNA concentration determined by Nanodrop 2000c ( Thermo Scientific , Wilmington , DE ) measurements . An aliquot of chromatin preparation containing 25 µg DNA was used per ChIP . The primary antibodies used for immunoprecipitation were rabbit polyclonal ERα ( Bethyl Laboratories [Montgomery , TX] A300-498A ) , rabbit IgG control ( Zymed , Carlsbad , CA ) , rabbit polyclonal acetyl-H3 ( Upstate Biotechnology ) , rabbit polyclonal polIII ( Upstate Biotechnology ) . ChIP-enriched DNA was subjected to SYBR green qPCR ( Applied Biosystems ) . Primer sequences are listed in the Primer Table . Results were expressed as relative enrichment according to the following formula: 2−[ ( ctChIP−ctinput ) − ( ctIgG−ctinput ) ] , where ctChIP , ctIgG , and ctinput indicate the cycle threshold for the specific antibody , IgG control , and input ( 5% of the total amount of immunoprecipitated material ) , respectively . For miR-191 and -425 promoter prediction , a 9200 base pair ( bp ) DNA genomic region spanning miR-191 and-425 was used as input for the online software Promoter 2 . 0 ( http://www . cbs . dtu . dk/services/promoter/ ) . To generate SATB1 , CCN2 , CTDSP2 , SOX4 , LRCC8A , SLC16A2 , EGR1 , CSDA , FSCN1 , TNC , SIAH2 and CSDA luciferase reporter constructs , the 3′UTRs were amplified by polymerase chain reaction ( PCR ) and cloned downstream of the luciferase-coding sequence in the pGL3-control vector at the XbaI restriction site ( Promega ) . Mutations were introduced into the miRNA-binding sites by using the QuikChange Mutagenesis Kit ( Stratagene , La Jolla , CA ) . To map the miR-191-425 promoter , prom1 or prom2 genomic region ( see schematic representation of miR-191/425-DARLD3 transcription unit , Figure 3 , C ) were amplified by PCR and cloned at the NheI and XhoI sites of the pGL3-basic vector ( Promega ) . All constructs were sequenced to verify integrity . To confirm that SATB1 , CCN2 , CTDSP2 , SOX4 , LRCC8A , SLC16A2 , EGR1 , CSDA , FSCN1 , TNC , SIAH2 , CSDA harbor responsive seed regions ( complementary sequences ) so that miR-191 and/or miR-425 can bind to their 3′UTRs , 250 ng of pGL3 reporter vector carrying the miR-191 or miR-425 binding site ( see plasmid construct , Figure S9A ) , 25 ng of the phRL-SV40 control vector ( Promega ) , and 100 nM miRNA precursors or scrambled sequence miRNA control ( Ambion , Inc , Austin , TX ) were cotransfected into HEK293 cells in 24-well plates . To map the miR-191 and miR-425 promoter , 250 ng of pGL3 reporter vector carrying prom1 or prom2 genomic region ( see schematic representation of miR-191/425-DARLD3 transcription unit , Figure 3 , C ) and 25 ng of the phRL-SV40 control vector were cotransfected into HEK293 cells in 24-well plates . To asses estrogen responsiveness of the two promoter regions , same experiment was carried out in 5 breast cancer cell lines with different ERalpha status , in MCF7 cells after E2 ( 10 nM ) treatment and in MCF7 after ERalpha silencing . Firefly luciferase activity was measured with a Dual Luciferase Assay Kit ( Promega ) 24 hours after transfection and normalized with a Renilla luciferase reference plasmid . Reporter assays were carried out in quadruplicate . Statistical significance was analyzed by the unpaired Student t test . All cell lysates were prepared by using RadioImmuno Precipitation Assay Buffer ( Pierce , Rockford , IL ) . Fifty micrograms of cell lysates was separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and then electroblotted onto a polyvinylidene fluoride membrane ( Hybond P; Amersham Biosciences , Piscataway , NJ ) . All primary antibodies used for western blot analyses are reported in Supplemental Materials and Methods ( available online ) . Detection was performed with horseradish peroxidase–conjugated secondary antibodies ( specific to rabbit and mouse ) and enhanced chemiluminescence ( Pierce ) . Nuclear/Cytoplasmic differential protein extraction was performed by using the NE-PER Nuclear and Cytoplasmic extraction kit ( Pierce ) according to the manufacturer's instructions . MDA-MB-231 cells were stably infected with the Human pre-microRNA Expression Construct Lenti-miR expression plasmid containing the full-length miR-191 or miR-425 and the GFP gene under the control of two different promoters ( System Biosciences ) . An empty vector was used as control . Pre-miRs expression and control constructs were packaged with pPACKH1 Lentivector Packaging Plasmid mix ( System Biosciences ) in a 293TN packaging cell line . Viruses were concentrated using PEGit Virus Precipitation Solution , and titers were analyzed using the UltraRapid Lentiviral Titer Kit ( System Biosciences ) . Infected cells were selected by FACS analysis ( FACScalibur; BD Bioscience ) . Infection efficiency >90% was verified by fluorescent microscopy and confirmed by real-time PCR for miRs expression . MDA-MB-231 cells , previously transfected with miR-191 or miR-425 precursors for 72 h , were plated ( 3000 per well ) in 96-well plates and grown for 96 hours after transfection ( final miRNA concentration of 100 nM ) in normal culture conditions . MCF7 in normal culture conditions ( +E2 ) transfected with anti-miR-191/425 and CTR oligonucleotide or in hormon deprivation conditions ( −E2 ) transfected with miR-191/425 and CTR oligonucleotide were plated in 96-well plates and grown for 96 hours after transfection . Cell proliferation was documented every 24 hours for 4 days using a 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide assay kit ( Promega , Madison , WI ) , and absorbance at 490 nm was evaluated by a SpectraMax 190 microplate reader ( Molecular Devices , Sunnyvale , CA ) . | MicroRNAs are small noncoding RNAs that act as posttranscriptional repressors of gene expression . A pivotal role for miRNAs in all the molecular processes driving initiation and progression of various malignancies , including breast cancer , has been described . Divergent miRNA expression between normal and neoplastic breast tissues has been demonstrated , as well as differential miRNA expression among the molecular subtypes of breast cancer . Over half of all breast cancers overexpress ERα , and several studies have shown that miRNA expression is controlled by ERα . We assessed the global change in microRNA expression after estrogen starvation and stimulation in breast cancer cells and identified that miR-191/425 and the host gene DALRD3 are positively associated to ERα-positive tumors . We demonstrated that ERα regulates the miR-191/425 cluster and verified the existence of a transcriptional network that allows a dual effect of estrogen on miR-191/425 and their host gene . We show that estrogen induction of miR-191/425 supports in vitro and in vivo the estrogen-dependent proliferation of ERα positive breast cancer cells . On the contrary , miR-191/425 cluster reprograms gene expression to impair tumorigenicity and metastatic potential of highly aggressive ERα negative breast cancer cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"oncology",
"medicine",
"cancer",
"genetics",
"genetics",
"biology",
"basic",
"cancer",
"research",
"genetics",
"and",
"genomics"
] | 2013 | Estrogen Mediated-Activation of miR-191/425 Cluster Modulates Tumorigenicity of Breast Cancer Cells Depending on Estrogen Receptor Status |
Shiga toxin ( Stx ) is the main virulence factor of enterohemorrhagic Escherichia coli , which are non-invasive strains that can lead to hemolytic uremic syndrome ( HUS ) , associated with renal failure and death . Although bacteremia does not occur , bacterial virulence factors gain access to the circulation and are thereafter presumed to cause target organ damage . Stx was previously shown to circulate bound to blood cells but the mechanism by which it would potentially transfer to target organ cells has not been elucidated . Here we show that blood cell-derived microvesicles , shed during HUS , contain Stx and are found within patient renal cortical cells . The finding was reproduced in mice infected with Stx-producing Escherichia coli exhibiting Stx-containing blood cell-derived microvesicles in the circulation that reached the kidney where they were transferred into glomerular and peritubular capillary endothelial cells and further through their basement membranes followed by podocytes and tubular epithelial cells , respectively . In vitro studies demonstrated that blood cell-derived microvesicles containing Stx undergo endocytosis in glomerular endothelial cells leading to cell death secondary to inhibited protein synthesis . This study demonstrates a novel virulence mechanism whereby bacterial toxin is transferred within host blood cell-derived microvesicles in which it may evade the host immune system .
Shiga toxin ( Stx ) is the major virulence factor of enterohemorrhagic Escherichia coli ( EHEC ) . EHEC are non-invasive bacteria [1] causing gastrointestinal infection presenting with diarrhea , hemorrhagic colitis and in severe cases leading to hemolytic uremic syndrome ( HUS ) characterized by thrombocytopenia , microangiopathic hemolytic anemia and acute renal failure . The renal cortical lesions affect both glomeruli and tubuli . In glomeruli the lesion is termed thrombotic microangiopathy presenting with glomerular capillary endothelial cell damage and formation of microthrombi [2] . In tubuli extensive apoptosis has been described [3] . The tubular damage can be reproduced in mouse models after infection with EHEC [4–6] or intraperitoneal injection of Stx2 and lipopolysaccharide ( LPS ) [7] . Mice orally infected with EHEC develop systemic and neurological symptoms 7–8 days after inoculation [8] with extensive intestinal and renal pathology , the latter with fibrinogen deposition in glomeruli , as well as marked apoptosis of both tubular and glomerular cells [3 , 6 , 8 , 9] . Laboratory investigation demonstrated fragmented red blood cells , thrombocytopenia and elevated creatinine [5 , 8] . Thus EHEC-infected mice exhibit clinical and pathological findings that mimic certain aspects of human infection and HUS . Using isogenic strains of E . coli O157:H7 these findings were most specifically attributed to the strain’s production of Stx [8] . In order for cells to be affected by Stx , the toxin needs to first bind to its receptor , globotriaosylceramide ( Gb3 ) [10] via its B-binding subunits , followed by endocytosis of the holotoxin . Intracellularly toxin is transported to the endoplasmic reticulum [11] where the A-subunit binds to ribosomes and cleaves an adenine base from 28S rRNA of the 60S ribosomal subunit [12] , thus inhibiting protein synthesis . The presence of a glycolipid receptor capable of binding Stx has been considered essential for predicting which cells the toxin will affect [13–16] . However , human intestinal cells may be damaged by Stx even in the absence of the toxin receptor [17] and murine glomeruli , lacking the Gb3 receptor , develop toxin-related injury in vivo [18–20] . These findings suggest that Stx may also mediate cytotoxicity to target organ cells in a Gb3 receptor-independent manner . The means by which Stx affects target organ cells has not been clarified . Negligible amounts of free toxin are present in the circulation during HUS [21] . The toxin circulates preferentially in cell-bound form , mainly bound to platelets , neutrophils and monocytes [22 , 23] . In order to affect renal cells the toxin would first have to be released from blood cells possibly due to higher affinity for renal endothelial cells [24 , 25] . A prerequisite for this to occur would be that the toxin remains on the cell membrane and does not undergo receptor-mediated endocytosis . Evidence has , however , shown that the toxin does undergo endocytosis in platelets [26] . Furthermore , stimulation of blood cells with Stx leads to the release of platelet and leukocyte-derived microvesicles [22 , 27] with surface-bound tissue factor [22] as well as C3 and C9 deposition [27] , contributing to a pro-thrombotic state . Microvesicles are small ( <1 μm ) , pro-inflammatory vesicles shed by host cells during activation and apoptosis . They contain surface markers of their parent cells [28 , 29] . Microvesicles mediate cell-to-cell communication by transferring cell surface receptors [30 , 31] , chemokines [32] , mRNAs [33] and microRNAs [34] from the cell of origin to target cells . They circulate in elevated levels during EHEC-associated HUS [22 , 27 , 35] . In this study we investigated the possibility that blood cell-derived microvesicles contain Stx that is thus transferred into target organ cells and if Stx within microvesicles retains cytotoxic potential . We found Stx within blood cell-derived microvesicles in the circulation of patients with HUS and of mice infected with E . coli O157:H7 , and within human and murine renal tissue . In vitro studies showed that human blood cell-derived microvesicles containing Stx underwent endocytosis in human glomerular endothelial cells where microvesicles released the toxin and lead to cell death by inhibition of protein synthesis . Bacterial toxin can thus be transferred within host cell-derived microvesicles and evade the host response .
High levels of platelet and leukocyte-derived microvesicles were detected in plasma from patients with HUS ( n = 13 , Patients 1–13 in S1 Table , supporting information ) by flow cytometry ( Table 1 ) . Most of the microvesicles were of platelet origin . Similarly , red blood cell ( RBC ) -derived microvesicles were detected in plasma from patients with HUS ( n = 6 , Patients 6–11 ) . Significantly higher levels of circulating microvesicles ( derived from platelets and leukocytes ) , and microvesicles containing Stx2 ( derived from platelets , leukocytes and RBCs ) , were detected in plasma during the acute phase of HUS compared to after recovery ( Table 1 ) . Levels of platelet and leukocyte microvesicles at recovery were similar to those found in controls . Microvesicles levels were slightly elevated in patients with hemorrhagic colitis ( n = 5 , Patients 15–19 ) and two of these patients exhibited Stx2 in microvesicles from platelets and leukocytes . No Stx2 was detected in microvesicles from the controls ( n = 10 ) or patients with acute renal failure ( n = 2 ) , as expected . In the absence of membrane permeabilization with saponin no Stx2 was detected on the surface of microvesicles . A renal cortical biopsy from a patient with E . coli O157:H7-induced HUS ( Patient 14 ) was examined by immune-electron microscopy labeled for Stx2 . Thirty cellular profiles were examined in glomerular and tubular regions . Numerous platelet- and leukocyte-derived microvesicles labeled for Stx2 were demonstrated adjacent to and within endothelial cells ( Fig . 1A , B ) . Altogether , 0–10 platelet- or leukocyte-derived microvesicles containing Stx2 were demonstrated per cellular profile . No specific binding of control antibodies was observed in the tissue in general , and specifically on or within microvesicles . The findings in HUS patients were further studied in EHEC-inoculated mice . BALB/c mice ( n = 10 ) were infected with the Stx2-producing E . coli O157:H7 strain 86–24 . Blood was drawn from two mice each day between days 2–6 after inoculation , before any symptoms developed , and plasma levels of circulating microvesicles were measured by flow cytometry and compared to control mice ( n = 2 , samples taken on day 6 ) . Plasma taken between days 2–5 showed considerably higher levels of circulating platelet- and leukocyte-derived microvesicles ( Fig . 2 ) compared to the controls . Stx2 was detected in microvesicles released from platelets ( Fig . 2A ) , neutrophils ( Fig . 2B ) and monocytes ( Fig . 2C ) at all time points ( not assayed for RBCs ) . No Stx2 was detected in microvesicles from control mice . In a separate experiment mice were inoculated with the Stx2-producing E . coli O157:H7 strain 86–24 ( n = 5 ) and the isogenic non-Stx producing E . coli O157:H7 strain 87–23 ( n = 4 ) and sacrificed 3 days after inoculation . There was no statistical difference between the strains regarding the total number of platelet-derived microvesicles but only microvesicles from mice infected with the Stx2-producing strain contained Stx , as expected ( Fig . 2D ) . Similarly , mice injected intraperitoneally with Stx2 ( n = 9 ) also exhibited an increase in Stx2 within platelet-derived microvesicles on days 2–4 post-injection ( Fig . 2E ) , albeit at lower concentrations than in EHEC-infected mice . Electron microscopy of kidneys from mice infected with E . coli O157:H7 showed extensive glomerular endothelial ( Fig . 3A ) and tubular epithelial ( Fig . 3B ) damage on Day 6 post-inoculation , in comparison to controls ( Fig . 3C , D ) . Kidneys from infected and control mice were examined for the presence of Stx2-containing platelet- and leukocyte-derived microvesicles ( ≤1 μm ) on days 2–6 after inoculation . On days 3–6 post-inoculation Stx2-containing platelet- and leukocyte-derived microvesicles were observed on ( Fig . 3E-H ) and within ( Fig . 3I ) glomerular endothelial cells as well as within endothelial cells in peritubular capillaries ( Fig . 3J ) . Furthermore , Stx2 containing blood cell-derived microvesicles were identified within the glomerular ( Fig . 3K ) and tubular basement membranes ( Fig . 3L ) and within podocytes ( Fig . 3K ) and tubular epithelial cells ( Fig . 3M , N ) . At all localizations Stx2 was identified within microvesicles as well as in free form ( Fig . 3N ) . Quantification of Stx2 containing platelet- and leukocyte-derived microvesicles was carried out in 50 cell profiles in the glomerular and peritubular capillary endothelium as well as in the tubular epithelium in infected and control mice as presented in Table 2 . The results indicate that most Stx2-containing microvesicles were of platelet origin and localized to the glomerular and peritubular capillary endothelium in the infected mice . In the non-infected mice minimal background signal was observed ( 0–3 gold particles ) . Control antibodies bound minimally and unspecifically . Microvesicles containing Stx2 were detected in whole blood stimulated with Stx2 by flow cytometry . Stx2 induced a significant increase in the release of microvesicles compared with the phosphate-buffered saline ( PBS ) -treated samples ( Fig . 4A ) . Stx2 was detected in microvesicles released from platelets , monocytes and neutrophils . Most microvesicles were of platelet origin . Similarly , purified RBCs stimulated with Stx2 released microvesicles in which Stx2 was detected . No Stx2 was detected within microvesicles from the PBS-treated samples , or on the surface of microvesicles . Transfer of Stx2 to glomerular endothelial cells by blood cell-derived microvesicles was investigated by incubation of conditionally immortalized glomerular endothelial cells ( CiGEnC ) with Stx2-containing microvesicles and visualization by electron microscopy ( n = 2 ) . Results showed that platelet- and leukocyte-derived Stx2-containing microvesicles bound to ( Fig . 4B , C ) and fused with CiGEnC after 1h ( Fig . 4D ) and were demonstrated within the cell cytoplasm ( Fig . 4E ) or in early endosomes ( Fig . 4F ) after 3h . At 12h the membranes of the early endosomes were disrupted and free Stx2 was visualized in the cytoplasm ( Fig . 4G ) . After 24h Stx2 was bound to ribosomes in the cytoplasm ( Fig . 4H ) . No specific binding of the control antibodies was detected . The cytotoxic effect of Stx2-containing microvesicles was examined by incubation of CiGEnC with microvesicles isolated from Stx2-treated whole blood . These microvesicles induced significantly more cell death compared to microvesicles from untreated blood ( P<0 . 001 , Fig . 5A ) . After 36h incubation the number of viable cells was reduced to a median of 30% ( range 24–53% , n = 5 ) in the samples incubated with microvesicles from the Stx2-treated blood samples whereas a median of 75% ( range 63–87% ) of the cells treated with microvesicles from the unstimulated blood samples were viable in comparison to the untreated cells ( defined as 100% viability ) . Cells treated with microvesicles containing the enzymatically inactive Stx2 mutant showed a slight reduction in viability ( median 85% , range 80–88% ) . CiGEnC incubated with Stx2-treated washed samples ( purified Stx2 exposed to washing steps similar to microvesicles before exposure to the cells , but without microvesicles ) exhibited 62% ( range 57–73% ) viability while treatment of the cells with pure Stx2 ( without washing steps ) reduced viability to 46% ( range 40–51% ) ( Fig . 5A ) . To determine if the cytotoxic effect of microvesicles containing Stx2 was associated with inhibited protein synthesis cultured CiGEnC were incubated with Stx2-containing microvesicles and [35S]-methionine incorporation was measured . Protein synthesis was reduced to 9% ( median , range 8–32% , n = 3 , Fig . 5B ) in CiGEnC treated with Stx2-containing microvesicles compared to untreated cells . Incubation of cells with Stx2-containing media exposed to washing steps similar to the microvesicles reduced protein synthesis to 44–47% ( n = 3 , median 45% ) , respectively , and exposure of cells to purified Stx2 inhibited protein synthesis to 6–15% , ( n = 3 , median 10% ) . Cells treated with microvesicles from unstimulated samples showed a slightly increased protein synthesis ( median 110% , range 93–124% , n = 3 ) .
In this study we showed that Stx2 , after binding to blood cells , was released from these cells within microvesicles and that these microvesicles could thereafter bind to renal glomerular and peritubular capillary endothelial cells and undergo endocytosis . Inside renal cells Stx was released from microvesicles and exerted a cytotoxic effect equivalent to purified toxin . Not all toxin was released from microvesicles , as certain Stx2-containing microvesicles were transferred from cell to cell , even via the glomerular or tubular basement membranes , thus reaching podocytes and tubular epithelial cells . This mechanism of Stx endocytosis would be independent of the toxin Gb3 receptor . The transfer of virulence factors within host blood cell-derived microvesicles is a novel mechanism of bacterial pathogenesis . Several mechanisms of microvesicle-mediated communication with cells have been described . Microvesicles can bind to cells by expressing specific receptors , by fusion with the cell membrane , by endocytosis and by release of mediators that bind to the cells [36] . Ultramorphological results presented here indicate that Stx2-containing microvesicles underwent endocytosis as they were labeled with blood cell markers within glomerular and peritubular capillary endothelial cells . In vitro experiments using glomerular endothelial cells indicated that microvesicles were either taken up by early endosomes or underwent fusion with the cell membrane . Blood cell-derived microvesicles taken up in endosomes were not destroyed within endothelial cells and could thus transit intact between cells and through basement membranes until the contents of the microvesicle were released intracellularly . The necessity of the Gb3 or another glycolipid receptor for Stx internalization and the induction of cellular injury has been described [37] , and in mice Gb3-deficiency conferred resistance to the effects of Stx administered intravenously [38] . However , it is , as yet , unclear whether Stx binds to and damages human intestinal epithelial cells or gains access to the systemic circulation by other pathways . The toxin is capable of damaging human intestinal cells that lack the receptor [39] and Stx was found within non-Gb3 expressing intestinal cells of a patient infected with EHEC [40] . Furthermore , Stx B subunit was shown to undergo endocytosis by macropinocytosis in a clathrin-independent manner [40] . Another Gb3-independent mechanism of toxin binding was exhibited on neutrophils ( lacking the Gb3 and Gb4 receptor ) which bound Stx via its A enzymatic subunit [41 , 42] . Here we demonstrated an alternative Gb3-independent process of toxin uptake mediated by the endocytosis of toxin-containing microvesicles derived from blood cells . The latter process requires the presence of Gb3 in order for the initial binding of Shiga toxin to platelets [43] , monocytes [44] and red blood cells [45] to occur . After inducing hemorrhagic colitis [46] Stx will come in contact with blood cells . The toxin will thus bind to blood cells , including platelets [26 , 47] , leukocytes [23 , 44 , 48–50] and possibly RBCs , leading to blood cell activation and membrane blebbing [22] . Shiga toxin may be internalized within platelets [26] , macrophages [51] and possibly within other blood cells . The internalized toxin could thus be released within microvesicles . An alternative speculative explanation for the presence of Shiga toxin within microvesicles is the loss of lipid asymmetry in the membrane lipid bilayer typical for microvesicles by which substances bound to lipid receptors on the outer Gb3 or an alternative toxin receptor of a microvesicle may potentially flip to the inside while remaining membrane-bound [52] . Free toxin is minimal in the bloodstream of infected patients [21 , 53 , 54] and thus most of the toxin is cell-bound or internalized . We propose , based on the results presented here , that the release of toxin-containing microvesicles from these blood cells will enable the transfer of toxin to its target cells . This seems more likely than the transfer of toxin from one cell to another after binding to its receptor and endocytosis . Toxin within microvesicles will , in this manner , evade host immune recognition and then be taken up by renal cells and induce cell damage . An interesting finding was that Stx-containing microvesicles were present in both glomerular and peritubular capillary endothelial cells simultaneously . This would indicate that circulating microvesicles affect both cell types thus explaining why intense damage to both glomeruli and tubuli occurs during Stx-mediated HUS [3 , 55] . This degree of damage to both cell types was also noted in the murine model of infection where glomerular endothelial cell damage would be achieved in a Gb3-independent manner as murine glomerular endothelial cells do not express Gb3 [7] . Similar glomerular findings were noted in murine models of Stx injection [18–20] , which could also be explained by an indirect effect of toxin on tubular epithelial cells ( which express Gb3 [7] ) extending to ischemic damage of the entire nephron . However , our results show that toxin within microvesicles is internalized in non-Gb3 expressing cells and we therefore suggest that the toxin affects cells that do not express the specific glycolipid receptor by this mechanism . Toxin-containing microvesicles did not appear to be targeted for lysosomal degradation and could thus proceed to affect cell viability after release of contents . Once within a cell free StxA1 will be translocated in a retrograde fashion to the large ribosomal subunit where it inhibits protein synthesis and ultimately leads to cell death [12] . A certain amount of toxin was demonstrated in free form within the cell cytoplasm , enabling cellular damage to occur , while other toxin-containing microvesicles passed through cells without total release of contents . It is thus unclear if microvesicles undergo partial release of their contents while passing within a cell or if total release of microvesicle contents occurs and which mechanisms regulate the process of microvesicle uptake , release of contents and/or transfer to a neighboring cell . An interesting finding was that mice injected with Stx2 also exhibited the presence of platelet-derived microvesicles containing Stx , although at lower concentrations than mice inoculated with EHEC . EHEC infection will have a more profound effect in mice , as persistent colonization will allow continuous release of toxin , as well as other bacterial virulence factors , followed by a more severe host response [5 , 8 , 9] . This would explain why EHEC infection promotes the release of higher numbers of toxin-containing microvesicles . Nevertheless , the murine model using purified toxin and in vitro experiments demonstrate that toxin may bind to blood cells and be released within microvesicles even in the absence of other bacterial or host systemic as well as intestinal factors involved in EHEC infection . Microvesicles have been well-characterized with regard to their ability to induce thrombosis by expressing phosphatidylserine capable of activating coagulation factors [56] and by expression of tissue factor [22 , 57 , 58] . In Stx-mediated HUS they also possess complement deposits indicating that complement activation occurred on the parent cells and reflecting the inflammatory and thrombogenic process occurring during HUS [27] . Here we defined a new mechanism of virulence in this non-invasive bacterial infection showing that microvesicles transfer bacterial toxin to target cells . Thus blood cell derived-microvesicles may play an important role in the development of HUS . Future studies will address the therapeutic option of interference with microvesicle release during EHEC infection . In addition to the damaging effects of microvesicles presented here , microvesicle release may also be a beneficial mechanism by which cells rid themselves of unwanted substances ( foreign as well as host-derived ) . The effects of blocking microvesicle release , particularly from blood cells , will require further study .
Blood samples were available from 9 boys and 7 girls , aged 1–10 years ( median 4 years , patients 1–12 and 15–18 in S1 Table , supporting information ) , treated for EHEC infection at the Department of Pediatrics , Skåne University Hospital , Lund and Malmö . Samples were obtained within three days of admission while all children still had diarrhea and all but four had HUS . Blood samples were also available from two adults with EHEC infection , one with and one without HUS , treated at the Department of Infectious Diseases , Skåne University Hospital ( Patients 13 and 19 ) . HUS was defined as hemolytic anemia ( hemoglobin levels <100 g/L ) , thrombocytopenia ( platelet counts <140 x 109/L ) and acute renal failure . Patients 7–12 were previously described [27 , 59] . Samples were also available from four pediatric controls , three girls and one boy aged 6–15 years ( median 9 years ) , seen at the outpatient clinic for unrelated conditions and from two patients with acute renal failure . These patients were treated for acute myeloid leukemia ( a 13-year-old boy ) or for acute renal failure associated with sepsis ( an adult male at the Department of Nephrology ) . Blood was , in addition , obtained from 15 healthy adult volunteers ( 9 women , 6 men ) not using any medications . A renal cortical biopsy was available from a 13-year-old boy with EHEC-associated HUS ( Patient 14 ) . The biopsy was previously described [59] and performed 2 days after the development of HUS . Tissue was prepared for paraffin-embedding according to hospital routines and used in electron microscopy . Blood was drawn by venipuncture into vacutainer tubes ( Becton Dickinson , Franklin Lakes , NJ ) containing 0 . 5 mL of 0 . 129 M sodium citrate ( Becton Dickinson ) through an intravenous cannula or a butterfly needle ( Terumo Medical products Hangzhou CO , Hangzhou , China ) with low tourniquet . The first tube following venipuncture was discarded . Within 60 minutes of blood collection , blood cells were removed by centrifugation ( 2600×g , 15 min , 20°C ) followed by a second centrifugation step ( 9900×g , 5 min , 20°C ) to obtain platelet-free-plasma ( PFP ) . PFP was carefully removed without disturbing the buffy coat , divided in 200 μL aliquots and stored at—80°C until analyzed for the presence of microvesicles . BALB/c mice were bred in the animal facilities of the Department of Microbiology , Immunology and Glycobiology , Lund University . Both female and male mice were used at 9–14 weeks of age . Studies from our group and others have shown that BALB/c mice develop symptoms after EHEC infection [60 , 61] and Stx injection [62] . The Stx2-producing E . coli O157:H7 strain 86–24 and the isogenic non-Stx producing E . coli O157:H7 strain 87–23 ( kindly provided by A . D . O'Brien , Uniformed Services University of the Health Sciences , Bethseda , MD ) were previously characterized [9] . Streptomycin-resistant derivatives of these strains were isolated as previously described [8] . Bacteria were grown overnight at 37°C in Luria-Bertani broth supplemented with 50 μg/mL Streptomycin sulfate ( Sigma-Aldrich , St . Louis , MO ) , harvested by centrifugation , washed in sterile phosphate buffered saline ( PBS , pH 7 . 4 , Medicago AB , Uppsala , Sweden ) and re-suspended in 20% ( w/v ) sucrose and 10% ( w/v ) NaHCO3 in sterile water at a concentration of 109 colony forming units ( CFU ) /mL . The bacterial concentration was confirmed by plating serial dilutions of the bacterial suspension on Luria-Bertani agar plates . Each mouse was infected orally with 108 CFU in 100 μl solution . The E . coli O157:H7 infection protocol has been previously described [6] . Fecal samples were collected on day one , three and five after inoculation to confirm colonization . In this infection model symptoms usually develop on day 7–8 after inoculation . Mice were anesthetized with isoflurane ( Forene , Abbott , Wiesbaden , Germany ) , blood was collected from vena saphena and by heart puncture , and the mice were then sacrificed by cervical dislocation at day 2 , 3 , 4 , 5 or 6 after inoculation . For analysis of microvesicles and blood urea nitrogen ( BUN , QuantiChrom urea assay kit , Bioassay Systems , Hayward , CA ) , blood was collected into citrated syringes and treated with sterile-filtered paraformaldehyde ( PFA , Histolab , Gothenburg , Sweden ) at a final concentration of 2% to enable transfer of the sample . For analysis of platelet counts blood was collected in microvettes with EDTA ( Sarstedt , Nümbrecht , Germany ) and fixed in 0 . 5% PFA . Platelet counts were assayed using a Sysmex Kx-21N system according to the manufacturer’s instructions ( Sysmex America , Mundelein , IL ) . Platelet counts and BUN are presented in S1 Fig . Kidneys were fixed overnight in 2 . 5% glutaraldehyde in 0 . 15 M sodium-cacodylate buffer ( pH 7 . 9 ) . Purified Stx2 ( obtained from C Thorpe , Phoenix Lab , Tufts Medical Center , Boston , MA ) was diluted in PBS and injected intraperitoneally at a dose 285 pg/g weight as previously described [8] . In this model symptoms usually develop on day 3–5 after injection . At day 1 , 2 , 3 or 4 blood was collected from vena saphena and by heart puncture after isoflurane anesthesia and the animals were then sacrificed by cervical dislocation . Blood samples for analysis of microvesicles were treated as described above . Murine microvesicles was purified from whole blood fixed in 2% PFA by centrifugation for 15 min at 1500g at 20°C to remove the pellet of blood cells . The platelet-poor-plasma supernatant was collected and further centrifuged at 13000g for 3 min to obtain platelet-free-plasma in the supernatant . The sample was diluted in sterile-filtered PBS and further centrifuged for 45 min at 24000g at 15°C to obtain a precipitate containing a microvesicle-enriched suspension . Identification of blood cell-derived microvesicles in murine plasma was carried out as per S2 Table ( in supporting information ) . Using flow cytometry microvesicles were identified by incubation of the enriched suspension with a mixture of rat anti-mouse CD41:APC ( 1:40 , detects platelets ) , rat anti-mouse CD45R/B220:PerCP-Cy5 . 5 ( 1:300 , detects monocytes , B-cells , NK-cells and T-cells ) and rat anti-mouse Ly-6G:PE ( 1:300 , detects granulocytes and monocytes ) or isotype controls IgG1:APC , IgG2a:PE or IgG2a:PerCP-Cy5 . 5 ( all from BD Biosciences , San Diego , CA ) for 20 min at rt . Events staining positively for both CD45R/B220 and Ly-6G were considered to represent microvesicles released from monocytes while events staining for Ly-6G alone were considered to represent microvesicles released from neutrophils . Stx2 was detected by incubation with polyclonal rabbit anti-Shiga toxin ( Stx ) 2 B-subunit ( 1:200 , BEI Resources , Manassas , VA , diluted in 0 . 1% saponin ( Sigma-Aldrich ) to enable intravesicular staining of Stx2 ) . Rabbit IgG ( eBioscience , San Diego , CA ) was used as the negative control and swine anti-rabbit:FITC F ( ab´ ) 2 ( 1:300 , Dako , Glostrup , Denmark ) as the secondary antibody . Renal tissue sections from the HUS patient were embedded and subject to antigen retrieval with metaperiodate[63] . Grids were blocked with 5% ( v/v ) goat serum diluted in 0 . 2% bovine serum albumin ( pH 7 . 6 , Aurion , Wageningen , Netherlands ) for 15 min followed by incubation with polyclonal rabbit anti-Stx2 B-subunit ( 1:80 ) and mouse anti-human CD42b ( 1:80 , to detect platelets ) or mouse anti-human CD45 ( 1:100 , to detect leukocytes , both from BioLegend , San Diego , CA ) overnight at 4°C . Samples were then incubated with gold-conjugated goat anti-rabbit IgG:5nm ( 1:10 ) or goat anti-mouse IgG:10nm ( 1:20 , both from BBI , Cardiff , UK ) for 1h at rt followed by fixation in 2% glutaraldehyde and post-stained with uranyl acetate and lead citrate . Rabbit IgG or mouse IgG ( BioLegend ) were used as negative controls . Similarly , renal tissues from mice were fixed and sectioned as described above and incubated with rabbit anti-Stx2 ( 1:80 ) , rat anti-mouse CD41 ( 1:100 , detects platelets ) or rat anti-mouse CD18 ( 1:100 , detects leukocytes , both from eBioscience ) and gold-conjugated reagents as described above . Rabbit IgG , rat IgG1 or rat IgG2b ( all from eBioscience ) was used as negative controls . Sections were examined with a transmission electron microscope ( CM100 Twin , Philips , Eindhoven , Holland ) operated at a 60 kV accelerating voltage . The images were recorded with a side-mounted Olympus Veleta camera ( Olympus , Münster , Germany ) . Whole blood diluted 1:2 in LPS-free DMEM ( Invitrogen , Paisley , UK ) containing Gly-Pro-Arg-Asp ( GPRP , 10 μM , Sigma-Aldrich ) was incubated with purified Stx2 ( 200 ng/mL , a gift from T . G . Obrig , Department of Microbiology and Immunology , University of Maryland , Baltimore or obtained from C . Thorpe as above ) or the catalytically inactive Stx2-mutant ( mutated in the enzymatic A subunit active site [64] 200 ng/mL , from C . Thorpe ) for 1h . The LPS content of the Stx2 preparation ( from T . G . Obrig ) was assayed by the limulus amebocyte assay ( Coatex , Gothenburg Sweden ) and found to be less than 50 pg/ml ( the detection limit ) and the preparations from C Thorpe were assayed by Endochrome 140 ( Charles River , L’Arbresle Cedex , France ) and found to be 25 ng/mL ( 300 endotoxin units/mL ) . For electron microscopy experiments whole blood was first incubated with Stx2 for 1h followed by incubation with a calcium ionophore ( A23187 , 10 μM , Sigma-Aldrich ) for 30 min to increase the total numbers of microvesicles . Red blood cells ( RBCs ) were isolated immediately after blood collection by centrifugation ( 830g , 5 min , 20°C ) , and washed three times in PBS . The RBCs ( 4 . 5x108/mL ) were diluted 1:2 in RBC-donor specific citrated plasma and LPS-free RPMI1640 ( Invitrogen ) and incubated with Stx2 for 40 min at 37°C under gentle shaking . All samples were analyzed for the release of microvesicles containing Stx2 by flow cytometry , as described below . Microvesicles were isolated from patient or control platelet-free-plasma or from in vitro stimulation experiments ( whole blood and RBC stimulation ) as previously described [27] with the following modifications . Aliquots of platelet-free-plasma ( 200 μL ) were thawed in a 37°C water bath for 5 min and centrifuged at 20800g for 10 min at 7°C . 150 μL of the supernatant were discarded and the remaining 50 μL were washed twice ( for flow cytometry ) or five times ( for the cytotoxicity assay and electron microscopy ) in sterile Hank’s Balanced Salt Solution without Ca2+ and Mg2+ ( HBSS , PAA Laboratories GMBH , Pasching , Austria ) and centrifuged as above . For flow cytometry assay microvesicles were fixed in 1% PFA for 30 min . To reduce background all buffers , cell media and PFA were filtered through a 0 . 2 μm pore-sized filter ( Pall Corporation , Ann Arbor , MI ) . Subpopulations of blood cell-derived microvesicles were determined by incubation with mouse anti-human CD42b:APC ( 1:20 ) , mouse anti-human CD38:PerCP-Cy5 . 5 ( 1:100 , detects monocytes ) , mouse anti-human CD66:PE ( 1:40 , neutrophils ) or mouse anti-human CD235a:PE ( 1:800 , RBCs ) for 30 min in the dark . Mouse IgG1:APC , mouse IgG1:PerCP-Cy5 . 5 , mouse IgG1:PE or mouse IgG2b:PE were used as iso-type controls ( all from BD Biosciences ) . Microvesicles from patients or in vitro experiments were fixed in 1% PFA and incubated with polyclonal rabbit anti-Stx2 B-subunit ( 1:200 , used in whole blood experiments ) or monoclonal mouse anti-Stx2 IgG1 ( 11E10 , 200 ng/mL , a gift from T . G . Obrig , used in RBC experiments ) for 30 min . The choice of anti-Stx used depended on the species origin of the microvesicles . Rabbit IgG ( eBioscience ) or mouse IgG1k ( Dako ) were used as negative controls and swine anti-rabbit:FITC F ( ab´ ) 2 ( 1:300 , Dako ) or goat anti-mouse:FITC ( 1:1000 , Dako ) as the secondary antibodies . All antibodies were diluted in 0 . 1% sterile-filtered saponin/PBS for detection of toxin within microvesicles . To detect Stx2 on the surface of the microvesicles certain experiments were carried out without saponin . Samples were analyzed using a FACSCantoTMII flow cytometer with FACSDiva software version 6 . 0 ( BD Immunocytometry Systems , San Jose , CA ) or a CyFlow Cube 8 flow cytometer ( Partec , Görlitz , Germany ) with FCS Express 4 Flow Research Edition software version 4 . 07 . 0003 ( De Novo Software , Glendale , CA ) . Microvesicles were defined by size and positive fluorescence using cell-specific antibodies . Both forward- ( FSC ) and side scatter ( SSC ) signals were recorded with logarithmic gain and flow rate was set to low . The microvesicle gate was generated using 0 . 5 , 0 . 9 and 3 . 0 μm beads ( Megamix beads , BioCytex , Marseille , France ) to determine upper limits in both FSC ( 280 ) and SSC ( 340 ) signals and the lower limits were placed above the background level of the machine and/or buffer which was determined by running 0 . 2 μm-filtered HBSS . Single-stained controls were used to check fluorescence compensation settings and fluorescence-minus-one ( FMO ) was used to define events with fluorescence above background levels to set up positive regions . A microvesicle was defined as an event positive for a specific cell marker and ≤1 μm in size . Microvesicles were quantified as previously described [27] . Conditionally immortalized glomerular cells ( CiGEnC ) were obtained from Dr . Simon Satchell ( Academic Renal Unit , University of Bristol , UK ) , and cultured as described [65] . CiGEnC were used at passage 26–36 . Cells were grown in endothelial growth medium 2—microvascular ( EGM2-MV ) supplemented with 5% fetal bovine serum and growth factors ( all from Lonza , Walkersville , MD ) as well as 100 U/mL penicillin and 100 μg/mL streptomycin ( PAA Laboratories Gmbh ) . Cells were grown to 80–90% confluence at 33°C in 5% CO2 and then allowed to differentiate for at least five days at 37°C . CiGEnC were cultured in T25 culture flasks ( TPP AG , Trasadingen , Switzerland ) and grown to 90% confluence . The cells were washed with Ca2+/Mg2+ free HBSS and incubated with Stx2-containing microvesicles isolated from 10 mL whole blood/DMEM . Microvesicles were diluted 1:10 in the cell culture media described above and incubated with the cells for 1 , 3 , 12 or 24 hours , respectively . Cells were washed with Ca2+ /Mg2+ free HBSS and detached from the culture flasks using 1x TrypLETM Select ( Life Technology , Carlsbad , CA ) , washed and fixed in 2 . 5% glutaraldehyde in 0 . 15 M sodium-cacodylate buffer ( pH 7 . 9 ) for 24h . Samples were then dehydrated and prepared for electron microscopy . After overnight polymerization , ultra-thin sections were cut and incubated with polyclonal rabbit anti-Stx2 B-subunit ( 1:80 ) and mouse anti-human CD42b ( 1:80 ) or mouse anti-human CD45 ( 1:100 ) . To detect early endosomes or ribosomes samples were incubated with rabbit anti-Rab5 antibody ( 1:50 , Abcam , Cambridge UK ) or mouse anti-rRNA Antibody ( 1:100 , Thermo Fisher Scientific Inc . , USA ) , respectively . Samples were then incubated with gold-conjugated goat anti-rabbit IgG:5nm to label Stx or goat anti-mouse IgG:10nm to label platelet or leukocyte microvesicle membranes and goat anti-rabbit IgG:20nm or goat anti-mouse IgG:20nm to label early endosomes or ribosomes , respectively ( BBI ) . Rabbit IgG or mouse IgG ( BioLegend ) were used as negative controls . CiGEnC were plated in 96-well plates ( Nunc , Roskilde , Denmark ) at a density of 1x104 cells/well and incubated as described above . The cells were washed in Ca2+/Mg2+-free HBSS and pre-incubated with cycloheximide ( 300 μg/mL , Sigma-Aldrich ) in order to enhance protein synthesis-inhibition induced by Shiga toxin , as previously described [66] , diluted in EGM2-MV for 3h , washed with HBSS and incubated with microvesicles ( 2x105/well , diluted in EGM2-MV , the quantity was determined by flow cytometry ) . Microvesicles were isolated from Stx2- or Stx2 mutant-treated , or untreated whole blood for 36h . All microvesicle samples were washed five times in HBSS . Control samples were treated with Stx2 under two conditions . In the one Stx2 ( 200 ng/mL , diluted in EGM2-MV ) was exposed to washing steps similar to microvesicles before exposure to the cells and in the other cells were exposed to pure Stx2 ( 200 ng/mL , final concentration diluted in EGM2-MV ) without washing steps . Cell viability was determined by crystal violet staining [67] and absorbance measured at 570nm . The effect on cell viability was calculated as the difference in absorbance percentage in the presence or absence of microvesicles . All experiments were performed in triplicate and repeated five times . Protein synthesis was determined by assaying the incorporation of [35S]-methionine into newly synthesized proteins . CiGEnC were grown to 80–90% confluence in 24-well plates ( Becton Dickinson , Lincoln Park , NJ ) as described above . The cells were washed in Ca2+ /Mg2+ free HBSS and incubated with cycloheximide for 3 h . Subsequently , the cells were washed and incubated with microvesicles ( 1 x 106/well , diluted in EGM2-MV ) isolated from Stx2-stimulated or unstimulated whole blood , for 36 h , after which the cells were pulsed with [35S] methionine ( 50 μCi/mL , PerkinElmer , Waltham , MA ) , diluted in methionine-free DMEM ( Life Technology ) for 2 h . After incubation , the cells were washed with Ca2+ /Mg2+-free HBSS , detached with TrypLe and lysed in a radioimmune precipitation assay ( RIPA ) buffer ( 0 . 15 M NaCl , 30mM HEPES , pH 7 . 3 , 1% Triton-X ( v/v ) , 1% sodium deoxycholate ( w/v ) , and 1% SDS ( w/v ) ) at-20°C for at least one h . Proteins were precipitated with 55% trichloroacetic acid ( TCA , Fisher Scientific , Leicestershire , UK ) , washed with acetone and resuspended in 0 . 1 M Tric-HCl , pH 8 . The proteins were added to liquid scintillation fluid and counted in a liquid scintillation counter . All experiments were done in duplicate and repeated three times ( large amounts of blood ( >100 ml/analysis ) were required for the microvesicle isolation resulting in three experiments and precluding statistical analysis ) . Inhibition of protein synthesis was defined as a decrease in the ability of the cells to incorporate [35S]-methionine divided by total protein . Differences between patients and controls or differences between stimulated and unstimulated samples were assayed by the non-parametrical Mann-Whitney test . All data were analyzed using GraphPadPrism 6 . 0 ( GraphPad Software , Inc . , La Jolla , CA ) . P values of ≤0 . 05 were considered significant . Samples from patients , pediatric controls and healthy donors were taken with the informed written consent of the subjects or their parents . All children had consent given by a parent/guardian as well as their own consent , if over 15 years of age . All adult participants provided their own written informed consent . The study was performed with the approval of the Ethics committee of the Medical Faculty , Lund University ( permit #323-2006 ) . All animal experiments were approved by the Laboratory Animal Ethics Committee of Lund University ( permit #M391-12 ) in accordance to guidelines of the Swedish National Board of Agriculture and the EU directive for the protection of animals used for scientific purposes . | Shiga toxin-producing enterohemorrhagic Escherichia coli are non-invasive bacteria that , after ingestion , cause disease by systemic release of toxins and other virulence factors . These infections cause high morbidity , including hemolytic uremic syndrome with severe anemia , low platelet counts , renal failure , and mortality . The most common clinical isolate is E . coli O157:H7 . In 2011 an E . coli O104:H4 strain caused a large outbreak in Europe with high mortality . After Shiga toxin damages intestinal cells it comes in contact with blood cells and thus gains access to the circulation . In this study we have shown that the toxin is released into circulating host blood cell-derived microvesicles , in which it retains its toxicity but evades the host immune response . Our results suggest that these microvesicles can enter target organ cells in the kidney and transfer toxin into these cells as well as between cells . Such a mechanism of virulence has not been previously described in bacterial infection . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | A Novel Mechanism of Bacterial Toxin Transfer within Host Blood Cell-Derived Microvesicles |
The structural proteins of DNA viruses are generally encoded by late genes , whose expression relies on recruitment of the host transcriptional machinery only after the onset of viral genome replication . β and γ-herpesviruses encode a unique six-member viral pre-initiation complex ( vPIC ) for this purpose , although how the vPIC directs specific activation of late genes remains largely unknown . The specificity underlying late transcription is particularly notable given that late gene promoters are unusually small , with a modified TATA-box being the only recognizable element . Here , we explored the basis for this specificity using an integrative approach to evaluate vPIC-dependent gene expression combined with promoter occupancy during Kaposi’s sarcoma-associated herpesvirus ( KSHV ) infection . This approach distinguished the direct and indirect targets of the vPIC , ultimately revealing a novel promoter motif critical for KSHV vPIC binding . Additionally , we found that the KSHV vPIC component ORF24 is required for efficient viral DNA replication and identified a ORF24 binding element in the origin of replication that is necessary for late gene promoter activation . Together , these results identify an elusive element that contributes to vPIC specificity and suggest novel links between KSHV DNA replication and late transcription .
A conserved feature of double-stranded ( ds ) DNA viruses is that they possess a set of genes that become strongly activated only following viral DNA replication [1] . These “late genes” generally encode factors involved in viral assembly that are essential for virion production . The mechanisms underlying their transcriptional regulation are often non-canonical or distinct from those involved in activating either cellular genes or early viral genes . For example , baculoviruses encode a multisubunit , α-amanitin-resistant DNA-directed RNA polymerase dedicated specifically to the activation of compact late promoters [2] . Similarly , bacteriophage T7 encodes its own late gene specific RNA polymerase that recognizes unique promoter elements to transcribe late genes [3 , 4] . Adenoviruses regulate late gene expression using a complex combination of viral and cellular factors; beyond promoter activation , these also regulate transcription termination and contribute to differential posttranscriptional processing of late viral mRNA [5] . Several studies suggest that the expression of late genes is also uniquely regulated in β- and γ-herpesviruses , which include human cytomegalovirus ( HCMV ) , Epstein-Barr virus ( EBV ) , and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) [6] . Unlike other eukaryotic dsDNA viruses , β and γ herpesviruses do not engage cellular TATA binding protein ( TBP ) during late gene pre-initiation complex formation [7] . Their core late gene promoters are unusually small ( 12–15 base pairs ( bp ) ) , with the only recognizable conserved element being a modified TATA box ( TATT ) ~30 bp upstream of the transcription start site ( TSS ) [8–10] . This is in contrast to eukaryotic and most early herpesviral promoters , which have multiple promoter elements that are important for promoter recognition and activation by RNA polymerase II ( RNA pol II ) [11] . Another notable feature is that activation of these minimal promoters requires a virally-encoded six-protein complex called the viral Pre-Initiation Complex ( vPIC ) that coordinates mammalian RNA pol II [12 , 13] . The vPIC is presumed to be uniquely adapted to activate minimal promoters , yet the mechanisms underlying its activity and specificity remain largely unknown . The core component of the KSHV vPIC is ORF24 ( BcRF1 in EBV and UL87 in HCMV ) , a viral TBP mimic that displays putative structural similarity to cellular TBP [7 , 14 , 15] . Unlike cellular TBP , the viral TBP mimics have expanded functionality in that they bind RNA pol II in addition to promoter DNA , making them unique among transcription factors that function in eukaryotic cells . Beyond ORF24 , the other essential components of the KSHV vPIC are ORFs 18 , 30 , 31 , 34 , and 66 , but little is known about how each of these factors contributes to vPIC activity [7 , 16–18] . ORF34 interacts with multiple components of the vPIC , and is thus thought to function as a scaffolding factor [13] . The ORF18 homolog in HCMV ( pUL79 ) has been shown to function as an elongation factor , although whether it plays a similar role in KSHV and EBV is unclear [19] . Despite the paucity of functional information on the individual vPIC components , protein-protein interaction mapping has provided insight into the overall complex organization [13 , 18] . Furthermore , point mutants that disrupt individual inter-subunit vPIC interactions abrogate late gene transcription , highlighting the fact that vPIC complex integrity is critical for function [13 , 20 , 21] . The mechanistic basis for promoter selectivity has also been challenging to grasp in the context of the minimal , largely nondescript nature of late gene promoters . While a modified TATA box with a TATT sequence is enriched in several late promoters of KSHV , MHV68 , and EBV [8–10 , 22] , the ORF24 TBP mimic shows partial binding to a mutant promoter in which the TATT sequence is modified to resemble a canonical TATA box [8] . However , ORF24 does not bind promoters that inherently have a canonical TATA box [8] , which suggests that other DNA elements surrounding the TATT sequence in late gene promoters contribute to ORF24 binding or vPIC assembly on the DNA . One possible reason that additional DNA elements involved in vPIC promoter recognition have not been identified is that we do not yet know the subset of viral promoters under the direct influence of the vPIC . Techniques such as RNA Sequencing ( RNA Seq ) and microarrays have helped define the set of genes that are expressed with late kinetics and in a manner dependent on viral DNA replication [23–26] . Furthermore , viral mutagenesis and knockdown studies have revealed viral genes whose expression is impaired in the absence of vPIC components [17 , 18 , 22 , 27 , 28] . However , these assays cannot distinguish direct vPIC targets from indirect targets , such as genes regulated by factors that are themselves dependent on the vPIC . Inclusion of such indirect targets in the pool of vPIC-regulated promoters could confound the identification of conserved elements important for direct vPIC regulation . Here , we integrated RNA Seq with chromatin immunoprecipitation high-throughput sequencing ( ChIP Seq ) for vPIC components during KSHV infection to distinguish genes whose expression is directly versus indirectly controlled by the vPIC . In doing so we made four key findings that provide insight into late gene promoter activation in KSHV . First , we defined the set of direct vPIC promoter targets within the KSHV genome and showed that other promoters are indeed nonproductively bound or only indirectly regulated by the vPIC . Second , by restricting our analysis to these direct targets , we revealed a novel promoter motif that contributes significantly to vPIC binding and thus provides insight into features governing vPIC specificity . Notably , this motif did not emerge when the indirect vPIC targets were included in the analysis . Third , we found that the KSHV TBP mimic ORF24 is required for efficient viral DNA replication . This was unexpected given that the other studied vPIC components have been shown to be dispensable for this process . Finally , we identified a binding site for ORF24-34 at the lytic origin of replication that is essential for activating late gene expression , providing a new connection between viral DNA replication and late gene transcription .
Three leucine residues in the N-terminal domain of KSHV ORF24 ( amino acids 73–75 ) are essential for its interaction with RNA Pol II and its ability to promote late gene transcription [7] . Given that most viral proteins are multifunctional , we engineered the ORF24RLLLG->RAAAG mutation ( ORF24RAAAG ) into KSHV BAC16 to precisely evaluate the impact of the transcriptional function of ORF24 during lytic infection . We also generated the corresponding mutant rescue ( ORF24RAAAG-MR ) to ensure that any observed phenotypes were not the effect of secondary mutations in the BAC . Identical banding patterns from RsrII digestion of WT and mutant BAC16 DNAs verified that there were no gross rearrangements of the viral genome during mutagenesis ( S1 Fig-lanes1-3 ) . We established stable cell lines latently infected with WT , ORF24RAAAG , and ORF24RAAAG-MR KSHV in the iSLK background , which contains a doxycycline-inducible version of the KSHV lytic transactivator protein ORF50 ( RTA ) to enable lytic reactivation [29] . During lytic reactivation , these three viruses expressed similar levels of the early protein ORF59 , while the ORF24RAAAG mutant was selectively unable to express the K8 . 1 late protein or produce infectious virions ( Fig 1A and 1B ) . These findings are consistent with previous complementation data [7] , and confirm the importance of the ORF24-Pol II interaction for late gene expression . Surprisingly , we also observed ~6-fold defect in DNA replication in the ORF24RAAAG mutant ( Fig 1C ) , which was unexpected given that previous studies with mutations in ORF24 or other components of the vPIC did not show this defect [7 , 13 , 16–18] . This defect was rescued in the ORF24RAAAG-MR cell line , indicating that it was not due to secondary mutations in the BAC ( Fig 1C ) . However , to independently confirm this observation , we engineered two additional ORF24 viral mutants: the point mutant ORF24R328A , which abrogates its essential interaction with the ORF34 component of the vPIC [13] , and a stop mutant ( ORF24Stop ) that has a premature stop codon in the ORF24 coding sequence . Inclusion of the ORF24Stop virus allowed us to evaluate whether the point mutants were functioning in a dominant negative manner , for example through nonproductive DNA binding . Finally , we also N-terminally tagged endogenous WT ORF24 with HA ( HA-ORF24 ) to assess whether any alterations to this locus impacted viral DNA replication . Notably , we observed a similar ~5-6-fold defect in DNA replication in the ORF24RAAAG , ORF24R328A , and ORF24Stop viruses , which was restored to WT levels in the ORF24RAAAG and ORF24R328A MR viruses . Moreover , there was no DNA replication defect in the HA-ORF24 virus ( Fig 1C ) , demonstrating that the defect in DNA replication is not a consequence of manipulating the ORF24 locus but is specific to disrupting its role in transcription . To more comprehensively analyze how the ORF24-Pol II interaction impacts gene expression during infection , we deep sequenced RNA from three replicates each of iSLK cells infected with the ORF24RAAAG or the ORF24RAAAG-MR virus . Samples were sequenced at 24 and 48 h post lytic reactivation with doxycycline and sodium butyrate , as these times captured predominantly early ( 24 h ) versus both early and late ( 48 h ) viral gene expression ( S2 Fig ) . Given that overlapping transcripts are pervasive in the KSHV genome [22 , 30] , we calculated the primary transcript counts as described in Bruce et al [30] to accurately quantify transcript levels . Differential expression analysis of genes in infected cells containing WT ORF24 ( ORF24RAAAG MR ) confirmed that the majority of viral genes are upregulated between 24 and 48 h post reactivation ( Fig 2A ) , and those that are most strongly upregulated are predominantly classified as late genes ( shown in blue ) . Conversely , there was a generalized reduction in transcript levels at the 48 h time point in cells containing KSHV ORF24RAAAG compared to MR , with the most strongly downregulated genes being those classified as late genes ( Fig 2B ) . Combining the above ORF24RAAAG mutant and MR induction data into a heatmap confirmed that in general , genes with later expression kinetics tended to be more severely impacted by the ORF24RAAAG mutation compared to genes with early expression kinetics ( Fig 2C ) . ( The fold change data was scaled to allow for easy visualization ) . However , not all genes fit this model . Some genes categorized as early based on their induction pattern showed a moderate dependence on the ORF24-Pol II interaction ( e . g . ORFs 9 , 40 , 34 , 41 , 16 and 37 ) . Similarly , a subset of genes that show late kinetics were less dependent on the ORF24-Pol II interaction ( e . g . ORFs 67A , 62 , K4 . 2A , 64 , K7 , 54 , 67 , 56 , 66 , K4 . 2 and vIRFs-2 , 3 and 4 ) . Classification as early or late and dependence on the ORF24-Pol II interaction was determined using mean log2 fold change . Together , these data suggest that not all kinetic late genes are regulated by the vPIC and there may be subset of early genes that are direct or indirect targets of the vPIC . We then looked for motifs enriched in the promoters of genes most strongly downregulated in the ORF24RAAAG mutant using Multiple EM for Motif Elicitation ( MEME ) [31] , a program that discovers un-gapped motifs that are enriched in a given set of sequences . We defined the promoter sequence as 100 bp upstream from the transcription start site ( TSS ) or 300 bp upstream from the translation start site when the TSS was unknown . Analysis of promoter sequences from 14 of the most strongly down-regulated genes ( log2 fold change greater than one standard deviation from the mean ) showed that TATTWAA is the only motif that is enriched in this set of sequences ( Fig 2D ) . We next monitored for the occurrence of this motif across all promoters in the viral genome using Find Individual Motif Occurrences ( FIMO ) [32] , which scans a set of sequences for matches to motifs provided by the user . 23 of 83 viral promoters were found to have the TATTWAA motif with a high score , of which 16 promoters had the motif ~30 bp upstream from the TSS ( Table 1 ) , consistent with the location of the canonical TATA box in eukaryotic promoters . Of the remaining seven promoter sequences , the TSS of six of the promoters is not known ( shown in red in Table 1 ) ; hence , the exact position of the TATTWAA motif from the TSS cannot be estimated . ORF25 is the only exception , where the TATTWAA sequence is not present ~30 bp upstream from the TSS , although it does have a canonical TATA box ( TATAAAA ) at that position . Notably , a subset of the genes containing the TATTWAA motif showed less than average repression by the ORF24RAAAG mutation ( ORF55 , ORF64 , ORF66 , ORF59 , K4 . 1 , ORF4 , ORF8 , ORF58 , ORF26 and ORF62 ) . One explanation for this observation is that these genes could be regulated by cellular transcription factors and other cis elements which are absent in the genes that are downregulated in the ORF24RAAAG mutant . The data also suggest that the motif may not be sufficient to mark regulation by the vPIC , which is consistent with a similar observation in MHV68 [8] . Furthermore , several genes ( ORF28 , ORF30 , K15 , ORF31 and ORF75 ) were strongly affected by the mutation ( log2 fold change greater than one standard deviation from the mean ) but lack the TATTWAA motif . The number of genes in this set increases to 26 if we broaden the criteria to include all the genes that show greater than average repression in the presence of the mutation . These genes could either be down regulated by an indirect mechanism in the absence of the ORF24-Pol II interaction or could be bound by the vPIC in a manner independent of the TATT motif . Our RNA Seq experiments identified KSHV genes whose expression is influenced by the transcription regulatory function of ORF24 . However , RNA Seq does not distinguish genes that are directly bound by the vPIC from those that are indirect transcriptional targets ( e . g . controlled by other factors whose expression is induced by ORF24 ) . We therefore sought to identify promoters bound by the KSHV vPIC complex using ChIP Seq . Previous studies suggest that ORF34 acts as a scaffolding factor that brings together all components of the vPIC [13] including ORF24 , which binds both RNA Pol II and TATT-containing DNA . We reasoned that ORF34 promoter occupancy would therefore be a good proxy for vPIC assembly , and additionally would avoid potential pitfalls associated with any non-productive or independent interactions between ORF24 and DNA . To facilitate ChIP Seq , we added an N-terminal HA tag to endogenous ORF34 in KSHV BAC16 ( HA-ORF34 ) ( S3A Fig ) , and generated a stable , doxycycline-inducible iSLK cell line containing this tagged virus as previously described . HA-ORF34 produced WT levels of infectious virions in a supernatant transfer assay ( S3B Fig ) , indicating that the tag does not disrupt ORF34 activity . The integrity of the BAC DNA was verified by RsrII digestion ( S1 Fig-Lane7 ) . We performed the ChIP Seq experiment in duplicate at 48 h post reactivation to ensure active late gene expression . Reactivated iSLK cells with untagged BAC16 served as a negative control for the immunoprecipitation . The individual ChIP peaks for the two replicates as well as the respective inputs and controls are shown in S3C Fig and S1 File . The ChIP Seq data showed that HA-ORF34 bound several locations across the viral genome ( Fig 3A ) but was not detectably associated with the host genome . A peak calling algorithm ( HOMER ) [33] identified 25 common peaks in the viral genome across two replicates that were significantly enriched in HA-ORF34 relative to untagged WT or input DNA ( S1 File and S3C Fig ) . Notably , 20 of the 25 peaks were found upstream of ORFs , with ~80% of the peaks centered less than 200 bp from the closest TSS ( S1 File ) . Given the link between DNA replication and late transcription in herpesviruses , it was particularly interesting that two of the most prominent peaks are upstream of the two minimal lytic origins of replication ( Ori ) in the KSHV genome ( Fig 3A ) . One of the peaks could not be uniquely assigned to a single ORF and was not considered further . Of the remaining 19 ORFs associated with peaks , 13 are involved in packaging , capsid assembly ( or form part of capsid itself ) , 4 are involved in immune evasion and 2 in DNA replication ( Table 2 ) . While several of them have also been previously classified as late genes ( 12/20 ) , it is notable that 6 of them have been classified as early genes , again suggesting that not all targets of the vPIC are late genes . To confirm that the observed binding pattern was representative of vPIC assembly , we independently validated several prominent ChIP Seq peaks by ChIP-qPCR from cells infected with KSHV containing HA-tagged ORF24 ( HA-ORF24 ) . Similar to HA-ORF34 , the HA-ORF24 virus produced WT levels of infectious virions ( S3B Fig ) . Indeed , the HA-ORF24 ChIP-qPCR data were consistent with the HA-ORF34 ChIP-Seq data ( Fig 3B ) , with peaks showing higher percent input values compared to control regions ( ORF64 and ORF37 promoters ) . A MEME analysis of the promoters associated with the peaks again showed that the only motif enriched in these promoters is TATTWAARV ( Fig 3C ) . 15 of the 19 promoters have the TATTWAA motif and in a majority of the sequences , it is present ~30 bp from the TSS ( Table 2 ) . Of the four exceptions , two of the promoters ( ORF53 and ORF6 ) have a TATATAA motif with an A at position 4 in place of the T . Indeed , mORF53 has previously shown to be a target of the vPIC in MHV68 despite the deviation from consensus [8] . Two key points can be drawn from the ChIP seq data . First , several genes whose promoters contain a TATTWAA motif do not show ORF34 binding ( e . g . ORFs 17 , 32 , 22 , 55 , 58 , 64 and 66 ) . Second , while most promoters bound by ORF34 have a TATTWAA motif , there are some exceptions ( ORF45 , ORF68 , ORF6 and ORF53 ) . These observations indicate that while the TATTWAA motif is enriched in promoters regulated by the vPIC , it is not sufficient to ensure binding of this specialized transcription complex . Combining the RNA Seq and ChIP Seq data , we defined the direct targets of the vPIC as genes that are both downregulated in the absence of the ORF24-Pol II interaction and show ORF34 binding in the promoter region . By this measure , a total of 13 genes are direct targets of the vPIC ( Fig 4A and Table 2 –gray rows ) . Of the 13 direct targets , 11 code for structural proteins or proteins involved in viral egress while two are tegument proteins involved in immune evasion . Genes whose expression was impaired in the KSHV ORF24RAAAG mutant but lacked ORF34 binding at their promoters are likely to be indirect targets of the vPIC ( Fig 4A ) . Finally , it is interesting to note that not all genes that have a ChIP peak are down regulated in the RNA Seq data ( e . g . ORFs 4 , 6 , K4 . 1 , 59 , 62 and 68 ) . These could be false positive ChIP peaks and/or have alternate mechanisms of regulation that ensure their transcription even in the absence of the vPIC . A defining feature of characterized late gene promoters is that they are minimalistic , with no conserved motifs beyond the TATTWAA signature recognized by ORF24 . Yet , our data indicate that the TATTWAA motif is insufficient to confer activation by the late gene transcription complex . We hypothesized that the inclusion of potentially indirect vPIC targets into prior motif searches may have hindered identification of other regulatory sequences within these promoters . Indeed , repeating the MEME analysis on the 13 direct targets revealed that an additional 5 bp motif ( RVNYS ) downstream of the TATTWAA motif was enriched in these promoter sequences ( Fig 4B ) . To explore the contribution of the 5 bp region downstream of the TATTWAA sequence for regulation by the vPIC , we used a plasmid-based promoter activation assay in which expression of a luciferase reporter was driven by either the K8 . 1 late or ORF57 early promoter ( Fig 5A ) . The KSHV left lytic origin of replication ( ori-lyt ) was included on the plasmids , as data from MHV68 , KSHV and EBV indicate that the presence of ori-lyt in cis significantly boosts late gene promoter activation during infection [8 , 10 , 34] . Each plasmid was transfected together with a plasmid constitutively expressing renilla luciferase ( to normalize for transfection efficiency ) into lytically reactivated KSHV-positive iSLK cells and the luciferase signal was measured 48 h post transfection and reactivation . As expected , the presence of ori-lyt enhanced K8 . 1 promoter-driven luciferase expression by 15–20-fold ( Fig 5B ) . Furthermore , unlike the consistently robust ORF57 promoter activity , K8 . 1 promoter activity on the plasmid was completely repressed in the presence of DNA replication inhibitor PAA ( Fig 5C ) or in cells lytically infected with the ORF24RAAAG mutant ( Fig 5D ) . Taken together , these data confirm that the plasmid assay accurately recapitulates late gene promoter activity . Notably , mutating the 5 bp RVNYS motif in the K8 . 1 promoter decreased transcription by > 50% ( Fig 5E ) . This decrease was comparable to mutating the T at position four to an A in the TATTWAA sequence , which has been shown to be important ( although not sufficient ) for recognition and binding by ORF24 [10] . Replacement of the 5 bp RVNYS motif with either synonymous ( -S ) or nonsynonymous ( -NS ) bases yielded a similar decrease in promoter activity ( Fig 5E-K8 . 1GGGAC->TACGA-S , K8 . 1GGGAC->ACTCG-NS ) , suggesting a role for the overall architecture of this motif rather than the primary sequence . In support of this hypothesis , a preliminary analysis of four structural features of the expanded motif in the WT and mutant predict that the average roll of the DNA in the expanded motif is lower in the mutant promoter compared to the WT promoter ( S4 Fig ) . Combining the TATT->TATA mutation with the mutation of the 5 bp motif completely abolished transcription , indicating that these elements are absolutely essential for promoter activation ( Fig 5E ) . Furthermore , with the exception of GG89TT-NS , systematic single and double base substitutions in the RVNYS motif were well tolerated , arguing that this region may not be contributing to base specific contacts with the vPIC ( Fig 5F ) . In summary , while the expanded RVNYS motif appears important for transcriptional activation , we hypothesize it may contribute to vPIC specificity by impacting the local topology of the promoter DNA rather than through base specific contacts . The RVNYS motif could either contribute to vPIC binding to its target promoters , as is the case for the TATTWAA sequence , or it could modulate downstream events involved in transcription initiation at vPIC bound promoters . To distinguish these possibilities , we used ChIP to measure HA-ORF34 occupancy on the luciferase plasmids containing WT or mutant K8 . 1 promoter in lytically reactivated HA-ORF34 iSLK cells . We engineered qPCR primers that enabled distinction of the K8 . 1 promoter on the plasmid DNA from the endogenous viral locus ( Fig 6A ) . Notably , both the K8 . 1TATT->TATA and K8 . 1GGGAC->ACTCG mutations similarly impaired HA-ORF34 binding to the plasmid promoter DNA ( Fig 6B- left panel ) . The ChIP signal was specific for HA-ORF34 , as we observed only a background signal when the assay was performed in the WT iSLK cells containing an untagged ORF34 locus . Furthermore , there was comparable vPIC assembly at the genomic K8 . 1 locus in cells transfected with WT or the mutant plasmids , confirming similar immunoprecipitation efficiencies across samples ( Fig 6B—right panel ) . Together , these data indicate that the expanded promoter motif is important for vPIC assembly on the promoter DNA . Given that late gene transcription requires the presence of the viral lytic origin of replication ( Fig 5B ) , binding of ORF34 and ORF24 at the KSHV Ori was particularly notable ( see Fig 3A and 3B ) . The binding site mapped to a region of the Ori just outside of the core minimal region that is required for viral DNA replication ( Fig 7A ) . To examine the role of vPIC Ori binding on K8 . 1 promoter activation , we engineered a mutant version of the K8 . 1 promoter-driven luciferase plasmid lacking the ORF24-34 binding element ( BE ) . A deletion encompassing this region has been shown to only modestly reduce ( ~30% ) viral DNA replication [35 , 36] . However , we observed a marked reduction in K8 . 1 promoter activation in the absence of the ORF24-34 Ori binding element ( Fig 7B ) . The element by itself was insufficient to activate transcription in the absence of core minimal Ori , in agreement with the requirement for DNA replication to license late gene activation . Ori binding by ORF24 and ORF34 could impact late gene transcription either by enabling vPIC promoter binding or by facilitating a downstream stage of transcription . To distinguish between these possibilities , we used our transient ChIP assay to compare HA-ORF34 binding to the K8 . 1 promoter on the plasmid containing either the WT Ori or the Ori lacking the ORF24-34 binding element . Removing the binding element within the Ori resulted in near complete loss of HA-ORF34 binding to the K8 . 1 promoter on the plasmid ( Fig 7C ) , indicating that ORF24-34 occupancy at the lytic origin of replication is essential for vPIC assembly at the late gene promoter .
Late gene expression is uniquely regulated in KSHV by a set of viral proteins collectively known as the vPIC . In this study , we generated a comprehensive list of direct targets of the vPIC by combining transcriptome and genome-wide binding data of the complex . Among the direct targets , we identified an expanded promoter recognition motif that contributes to stable binding of the vPIC on the promoter . This is particularly useful to understanding alternate mechanisms of transcription initiation by RNA pol II because of the unique ability of the vPIC to directly recruit RNA Pol II to highly compact promoters . We also identified a vPIC binding element in the lytic origin of replication that licenses late gene activation at a distal promoter , suggesting a novel mechanism may underlie the link between DNA replication and late gene transcription . Several key points emerge from independent analysis of the RNA Seq and ChIP Seq data . First , the previously identified TATTWAA motif is insufficient to mark binding and regulation by the vPIC , as demonstrated by the multiple ORFs that have the motif in their promoter ( 23/83 ORFs ) but are not repressed in the ORF24RAAAG mutant ( 10/23 ORFs ) or bound by ORF34 ( 7/23 ORFs ) . Second , the TATTWAA motif is not a pre-requisite for regulation or binding by the vPIC . For example , although ORF53 expression is dependent on the ORF24-Pol II interaction and its promoter is bound by ORF34 , its promoter does not have the conserved TATTWAA sequence but a TATATAA sequence in its place . Third , several genes that exhibit late kinetics escape repression in the mutant , indicating that not all late genes are direct or indirect targets of vPIC . It is notable that several of the kinetic late genes that escape repression are involved in immune regulation , consistent with a similar observation in EBV [28] . In particular , EBV BPLF1 is a late gene whose expression is independent of the vPIC , and we observe that its homolog ORF64 also shows a similar pattern in KSHV . Finally , while there are several early genes that are indirect targets of the vPIC , only 1 of 13 direct targets exhibited early kinetics ( ORF45 ) . It is notable that ORF45 is a tegument protein , which might explain the requirement for its continued regulation at later times of infection . Our integrative analysis revealed that a distinct 5 bp motif is integral to vPIC promoter recognition . Rather than engaging in base specific contacts ( as is the case for the TATTWAA sequence ) [7] , we instead hypothesize that the importance of this motif lies in its shape or structure . In this regard , a preliminary analysis of basic structural properties of the expanded motif suggest that the average roll ( representing the rotational flexibility ) of the DNA in the expanded motif is lower in the 5 bp mutant promoter compared to WT K8 . 1 promoter . Structure-based recognition and a contribution of base pairs flanking the core binding site to binding affinity is a common feature among transcription factors , although the specific structural properties that influence binding differ for various classes of factors [37] . A previous study of the bZIP family of transcription factors identified a correlation between the roll of DNA sequences that flank the core recognition element and binding affinity [37] . Consistent with that , we observed a defect in the assembly of the vPIC when the expanded motif was mutated , suggesting a stabilizing role for the flanking sequence . The location of the expanded motif is reminiscent of the downstream recognition element of the general transcription factor TFIIB ( BREd ) [38] . Interestingly , the BREd element also shows low sequence conservation and does not require an active promoter sequence to conform with the consensus sequence at all positions [38] , as seen with the expanded motif . Interaction of TFIIB with the BREd element is thought to stabilize the TFIIB-TBP interaction on the promoter DNA , leading to activation or repression of the promoter in a context-dependent manner . While there is some evidence to suggest that TFIIB is present at late gene promoters [7] , whether the interaction between the vPIC and the expanded motif is mediated by TFIIB or any other factors remains to be elucidated . Notably , we found that KSHV DNA replication was reduced in the absence of functional ORF24 , including with an ORF24 null mutant or ORF24 point mutants that fail to bind either RNA Pol II or the vPIC component ORF34 . This was surprising , given that deletion or mutation of several other vPIC components has been shown not to affect DNA replication [7 , 13 , 17 , 18] . Furthermore , we previously failed to observe a replication defect in an ORF24 null virus [7] . We hypothesize that the discrepancy for the ORF24 null mutant relates to the methods used to establish the infected cell lines . In our earlier study , the viral BAC DNA was directly transfected into iSLK cells , resulting in only a ~5-7-fold increase in genome copy number upon reactivation . It is likely that the majority of viral DNA was present in a non-reactivatable state , providing a level of background that significantly reduced the dynamic range for DNA replication measurements . Here , the iSLK cells were established through co-culture with reactivated 293T cells harboring the BAC DNA , i . e . through infection rather than transfection . This reproducibly yields ~50-70-fold increases in genome copy number upon lytic reactivation , more closely representing actual infection . That said , the role of ORF24 in viral DNA replication may be unique to KSHV , as this link has not been observed upon depletion of its homologs in EBV and MHV68 [28 , 39] . One possibility for how KSHV ORF24 might contribute to DNA replication is through its indirect transcriptional targets . We observed that components of the helicase/primase complex ( ORFs 40 , 41 ) , which are involved in DNA replication , show more than average repression in the absence of functional ORF24 ( Fig 2 ) . Importantly , these genes were not repressed in previous studies using stop mutants of the vPIC components ORF18 , ORF30 and ORF31 [17 , 18] , raising the possibility that ORF24 has additional gene regulatory functions outside the context of the vPIC . For example , our observed binding of ORF24 and ORF34 at the lytic origins of replication could facilitate DNA replication . Consistent with this hypothesis , two previous studies that defined the boundaries of the lytic origin of replication observed a modest decrease in replicated DNA upon deletion of the ORF24-34 binding site identified here [35 , 36] . Interestingly , we also observed that binding of ORF24-34 at the origin is critical for origin dependent late gene expression , as deletion of the ORF24-34 binding element at the origin prevented vPIC assembly and transcription at the distal K8 . 1 late promoter . We note that although the binding element overlaps the promoter of K4 . 1/K4 . 2A , we observe only average or less than average reduction in these transcript levels in the mutant compared to the mutant rescue . We hypothesize that alternate mechanisms of maintaining steady-state K4 . 1/K4 . 2A RNA levels are adopted in the absence of a functional vPIC and binding of ORF24-34 at the origin is likely related to the link between DNA replication and late gene activation . The proximity of the ORF24-34 binding element to the other origin elements also raises the possibility of vPIC recruitment by other origin binding proteins . Exploring the mechanism underlying this link between origin binding and late gene activation will be an exciting area for future study .
HEK293T cells ( ATCC CRL-3216 ) were maintained in DMEM ( Invitrogen ) + 10% Fetal Bovine Serum ( FBS ) . iSLK BAC16 cells [29] ( kindly provided by Jae Jung’s lab ) were maintained in DMEM + 10% FBS and 0 . 5 mg/mL hygromycin B . HEK293T cells stably expressing ORF24-3xFlag were maintained in DMEM ( Invitrogen ) + 10% Fetal Bovine Serum ( FBS ) and 325 ug/ml of zeocin . iSLK-puro cells were maintained in DMEM + 10% FBS . A 100 bp fragment of the K8 . 1 promoter or ORF57 promoter was generated by PCR from the BAC16 genome and cloned into the KpnI and HindIII sites of the pGL4 . 16 reporter plasmid using Infusion cloning ( Clontech ) . The left origin of replication was similarly generated by PCR from the BAC16 genome and cloned into the NotI and BstXI sites of the K8 . 1Pr-pGL4 . 16 plasmid . The promoter mutants were generated by site-directed mutagenesis using KAPA HiFi DNA polymerase using the primers shown in S1 Table . To make the ORF24-3xFlag pJLM1 plasmid for lentiviral transduction , the ORF24-3xFlag fragment was generated by PCR using ORF24-3xFlag-pCDNA4 as the template and cloned in to pLJM1 plasmid digested with AgeI and EcoRI . All primers used for cloning and mutagenesis are listed in S1 Table . The ORF24RAAAG , ORF24R328A and ORF24Stop mutants , the corresponding mutant rescues ( MR ) for ORF24RAAAG , ORF24R328A and N terminal HA tagged ORF24 and ORF34 were engineered using the scarless Red recombination system in BAC16 GS1783 E . coli [40] as described previously . The modified BACs were purified using the NucleoBond BAC 100 kit ( Clontech ) . To establish iSLK cell lines latently infected with the modified virus , HEK293T cells were transfected with 5–10 ug of the modified BAC16 using linear polyethylenimine ( PEI , MW ~25 , 000 ) at a 1:3 DNA:PEI ratio . The following day , the 293T cells were mixed 1:1 with iSLK-puro cells ( which contain inducible RTA but lack KSHV ) and treated with 30 nM 12-O-Tetradecanoylphorbol-13-acetate ( TPA ) and 300 nM sodium butyrate for 4 days to induce lytic replication . Cells were then grown in selection media containing 300 ug/ml hygromycin B , 1 ug/ml puromycin and 250 ug/ml G418 . The hygromycin concentration was gradually increased to 500 ug/ml and then 1 mg/ ml until all the HEK293Ts cells died . To generate the ORF24RAAAG , ORF24R328A , and ORF24Stop ( which are defective in infectious virion production ) , HEK293T cells that stably express ORF24-3xFlag were used for the initial transfection and co-culture . For supernatant transfer assays to assess virion production , BAC16-containing iSLK cells were plated at a density of 1x106 cells in 10 cm dishes and treated with 1 ug/mL doxycycline and 1 mM sodium butyrate the following day . 72 h post induction , the supernatant was filtered through a 0 . 45 uM syringe filter and 2 ml of the supernatant was mixed with 1x106 freshly trypsinized HEK293T cells in a 6 well plate and centrifuged at 1 , 500xg for 2 h at 37°C . The following day , cells were trypsinized , fixed in 4% paraformaldehyde and the percentage of cells expressing GFP was determined by flow cytometry ( BD Csampler , BD Biosciences ) . To measure viral genome replication , iSLK-BAC16 cells were reactivated for 72 h as described above . The cells were then scraped into the media and the media + cells were digested with proteinase K ( 80μg/mL ) ( Promega ) in 5x proteinase K digestion buffer ( 50mM Tris-HCl pH 7 . 4 , 500mM NaCl , 5mM EDTA , 2 . 5% SDS ) overnight at 55°C . The gDNA was isolated using Zymo Quick gDNA Miniprep Kit according to the manufacturer’s instructions . Quantitative PCR ( qPCR ) was performed on the isolated DNA using iTaq Universal SYBR Green Supermix on a QuantStudio3 Real-Time PCR machine . DNA levels were quantified using relative standard curves with primers specific for KSHV ORF59 promoter and human CPSF6 promoter ( S1 Table ) . The relative genome numbers were normalized to CPSF6 to account for loading differences and to uninduced samples to account for differences in starting genome copy number . To look at viral transcript levels , total RNA was isolated from reactivated iSLK cells at the indicated time points using the Zymo Direct-Zol RNA kit following the manufacturer’s protocol . The purified RNA was treated with DNase and cDNAs were synthesized using AMV reverse transcriptase ( Promega ) . The cDNAs were directly used for qPCR analysis using the iTaq Universal SYBR Green Supermix . The qPCR signal for each ORF was normalized to 18s rRNA . Cells were lysed in 1x lysis buffer ( 50mM Tris-HCl pH 7 . 5 , 150mM NaCl , 0 . 5% NP-40 , cOmplete EDTA-free Protease Inhibitors [Roche] ) by rotating for 30 min at 4°C . The lysate was clarified by centrifugation at 21 , 000 x g for 15 min at 4°C . The supernatant was quantified by Bradford assay , and equivalent amounts of each sample were resolved by SDS-PAGE and western blotted with rabbit polyclonal antibodies against vinculin ( Abcam , 1:1000 ) , ORF59 , or K8 . 1 . The ORF59 and K8 . 1 antibodies were generated by injecting rabbits with purified MBP-ORF59 or MBP-K8 . 1 ( gifts from Denise Whitby [41] ) ( Pocono Rabbit Farm and Laboratory ) . Total RNA was isolated from reactivated iSLK cells at indicated time points using Zymo Direct-Zol RNA kit following the manufacturer’s protocol . The samples were ribo-depleted and stranded libraries were prepped by the QB3 Berkeley core sequencing facility . Multiplex sequencing was performed using the Illumina HiSeq 4000 to generate 100 bp paired end reads . The quality of the raw reads was checked using FastQC [42] and adapters were trimmed using TrimGalore [43] using default parameters . The trimmed reads were aligned to both the viral ( NC_009333 . 1 ) and host genomes ( hg19 ) using STAR aligner ( ver2 . 5 . 3a ) [44] . The aligned reads were counted using htseq-counts [45] and differential expression was assessed using DESeq2 [46] . To compensate for the problem of quantitative assessment of transcripts due to the overlapping transcripts in the KSHV genome , the primary counts of the transcripts were calculated as recommended in Bruce et al [30] with the following small modifications . Length correction was not applied to allow for differential expression analysis by DESeq2 . Transcripts for which the counts became negative in all 3 replicates when calculating primary counts were removed before running DESeq2 . Bar plots showing Log2 Fold Change were generated using R . The heatmap was generated using the heatmap . 2 package in R . The fold change values were scaled by column before plotting to ease visualization of the data . Raw and processed data files are available from the GEO database ( accession number: GSE126602 ) . ChIP was performed on 3*15-cm plates of iSLK-cells ( WT KSHV , KSHVHA-ORF34 and KSHVORF24-HA ) reactivated for 48 h with 1 ug/ml dox and 1 mM sodium butyrate . Cells were crosslinked in 2% formaldehyde for 15 min at room temperature , quenched in 0 . 125 M glycine , and washed twice with ice-cold PBS . Crosslinked cell pellets were mixed with 1 ml ice-cold ChIP lysis buffer ( 5mM PIPES pH 8 . 0 , 85mM KCl , 0 . 5% NP-40 ) and incubated on ice for 10 min , whereupon the lysate was dounce homogenized to release nuclei and spun at 1700 g for 5 min at 4°C . Nuclei were then resuspended in 800 μl of nuclei lysis buffer ( 50mM Tris-HCl pH 8 . 0 , 0 . 3% SDS , 10mM EDTA ) and rotated for 10 min at 4°C followed by sonication using a QSonica Ultrasonicator with a cup horn set to 25 amps for 20 min total ( 5 min on , 5 min off ) . Chromatin was spun at 16 , 000 x g for 10 min at 4°C and the pellet was discarded . The chromatin was precleared with protein A + protein G beads blocked with 5 ug/ml glycogen , 50 ug/ml BSA , 100 ug/ml Ecoli tRNA in 1ml dilution buffer ( 1 . 1% Triton-X-100 , 1 . 2mM EDTA , 16 . 7mM Tris-HCl pH 8 , 167mM NaCl ) for 2 hours . 350 μl of chromatin was diluted in ChIP dilution buffer ( 16 . 7 mM Tris-HCl pH 8 . 0 , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 167 mM NaCl ) to 500 μl and incubated with 10 μg anti-HA antibody ( Cell Signaling C29F4 ) overnight , whereupon samples were rotated with 20 μl pre-blocked protein A + G beads ( Thermofisher ) for 2 h at 4°C . Beads were washed with low salt immune complex ( 20 mM Tris pH 8 . 0 , 1% Triton-x-100 , 2 mM EDTA , 150 mM NaCl , 0 . 1% SDS ) , high salt immune complex ( 20 mM Tris pH 8 . 0 , 1% Triton-x-100 , 2 mM EDTA , 500 mM NaCl , 0 . 1% SDS ) , lithium chloride immune complex ( 10mM Tris pH 8 . 0 , 0 . 25 M LiCl , 1% NP-40 , 1% Deoxycholic acid , 1 mM EDTA ) , and Tris-EDTA for 10 min each at 4°C with rotation . DNA was eluted from the beads using 100 μl of elution buffer ( 150 mM NaCl , 50 μg/ml Proteinase K ) and incubated at 50°C for 2 h , then 65°C overnight . DNA was purified using the Zymo PCR purification kit . For ChIP-qPCR , purified DNA was quantified by qPCR using iTaq Universal SYBR Mastermix ( BioRad ) and the indicated primers ( S1 Table ) . Each sample was normalized to its own input . For ChIP-Seq , the concentration of the eluted DNA was measured using Qubit . 10–15 ng of eluted DNA was used to generate libraries using the Accel-NGS 2S Plus DNA Library Kit ( Swift Biosciences ) . The libraries were sequenced on a Illumina HiSeq 4000 sequencer at the QB3 Berkeley core facility to generate 100 bp single end reads . The quality of the raw reads was checked using FastQC [42] and adapters were trimmed using TrimGalore [43] using default parameters . The trimmed reads were aligned to both the viral and host genomes ( hg19 ) using Bowtie2 aligner using default parameters . The aligned reads were filtered for uniquely mapped reads using SAMTools . The filtered reads were converted to tdf format using IGV Tools and visualized on Integrated Genome Viewer [47 , 48] . We used HOMER [33] with the following minor adjustments to default parameters to call peaks for the immunoprecipitated and input samples from cells that have HA tagged ORF34 and untagged ORF34 ( WT ) as a control . The fold over input and local fold change were set to 1 . 3 as recommended for smaller and denser viral genomes ( personal communication with Chris Benner , UCSD ) . The peak size and fragment length were also set at 200 and 150 respectively . From the resulting peaks , only the ones that were present in both conditions ( HA-ORF34 vs HA-Input , HA-ORF34 vs WT ) and both replicates were retained for further analysis . The common peaks identified were verified visually on the Integrated Genome Viewer and were manually annotated . To identify the direct targets , transcripts that had a Log2 fold change lower than the mean Log2 fold change in the ORF24RAAAG mutant vs MR were considered to be down regulated in the mutant . Although ORF8 and ORF26 were not included in the differential expression analysis , they were considered to be downregulated because of negative values for their levels in the mutant and not in the mutant rescue on calculation of primary transcript levels . Raw and processed data files are available from the GEO database ( accession number: GSE126601 ) . We defined the promoter sequence as 100 base pairs ( bp ) upstream from the transcription start site ( TSS ) or 300 bp upstream from the translation start site when the TSS was unknown . The TSS data was obtained from Table 1 in [23] and a custom python script was used to extract the sequences . These were then run through MEME [31] allowing for multiple occurrences of a motif in a sequence and only searching the given strand to identify enriched motifs . The identified motif was then used to search for similar motifs across all 83 promoter sequences from the viral genome , again only allowing for searching the given strand . 5*105 iSLK-BAC16 ( WT or ORF24RAAAG ) cells were plated in a 6 well plate 24 h before transfection and reactivation . The cells were reactivated with 1 μg/ml doxycycline and 1 mM Sodium Butyrate and simultaneously transfected with 0 . 95 μg luciferase reporter plasmid and 0 . 05 μg renilla plasmid using polyjet transfection reagent . 48 h post transfection and reactivation , the cells were rinsed twice with room temperature PBS , lysed by rocking for 15 min at room temperature in 300 μl of Passive Lysis Buffer ( Promega ) , and clarified by centrifuging at 14 , 000 g for 1 min . 20 μl of the clarified lysate was added in triplicate to a white chimney well microplate ( Greiner bio-one ) to measure luminescence on a Tecan M1000 using a Dual Luciferase Assay Kit ( Promega ) . The firefly luminescence was normalized to the internal Renilla luciferase control . The induced sample was then normalized to the uninduced ( but transfected ) control to look at the fold induction during reactivation . 3 . 5x106 iSLK-BAC16 were plated in a 15 cm dish 24 h before transfection and reactivation . The cells were reactivated with 1 μg/ml doxycycline and 1 mM Sodium Butyrate and simultaneously transfected with 8 μg of reporter plasmid using polyjet transfection reagent . 48 h post reactivation and transfection , cells were crosslinked and lysed as mentioned above . Nuclei were sonicated using a Covaris S220 ( Covaris ) for 5 min with the recommended sonication settings ( Power: 140 W , Bursts per cycle: 200 , Duty cycle: 5% ) . The chromatin was pre-cleared as mentioned above and the concentration measured using a qubit . 25 μg of pre-cleared chromatin was used in the IP and the following steps were same as mentioned in the ChIP protocol above . | Gene expression in DNA viruses often occurs in temporal waves , with expression of essential structural proteins occurring late in infection , after viral genome replication has begun . Strategies underlying expression of these viral late genes are often sophisticated; for example , the β- and γ-herpesviruses encode a six-component viral complex that directs late gene transcription , largely by unknown mechanisms . Here , we evaluated how this complex specifically recognizes late promoters during infection with the oncogenic human γ-herpesvirus Kaposi’s sarcoma-associated herpesvirus ( KSHV ) . We found that one of the components of the late transcription complex was required for robust viral DNA replication and binds at the origin of replication , suggesting new links between KSHV replication and transcription . Combined measurements of late gene expression and promoter occupancy then revealed which KHSV genes are directly controlled by the late gene transcription complex , leading to identification of a key new regulatory element in KSHV late promoters . Together , these data help explain how the late gene transcription complex is able to bind seemingly minimal promoters with high specificity , ensuring robust expression of viral factors necessary for assembly of progeny virions . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"and",
"health",
"sciences",
"luciferase",
"pathology",
"and",
"laboratory",
"medicine",
"enzymes",
"pathogens",
"gene",
"regulation",
"microbiology",
"enzymology",
"dna",
"transcription",
"viruses",
"dna",
"replication",
"dna",
"viruses",
"sequence",
"motif... | 2019 | An integrative approach identifies direct targets of the late viral transcription complex and an expanded promoter recognition motif in Kaposi’s sarcoma-associated herpesvirus |
We have investigated the molecular-level structure of the Vaccinia virion in situ by protein-protein chemical crosslinking , identifying 4609 unique-mass crosslink ions at an effective FDR of 0 . 33% , covering 2534 unique pairs of crosslinked protein positions , 625 of which were inter-protein . The data were statistically non-random and rational in the context of known structures , and showed biological rationality . Crosslink density strongly tracked the individual proteolytic maturation products of p4a and p4b , the two major virion structural proteins , and supported the prediction of transmembrane domains within membrane proteins . A clear sub-network of four virion structural proteins provided structural insights into the virion core wall , and proteins VP8 and A12 formed a strongly-detected crosslinked pair with an apparent structural role . A strongly-detected sub-network of membrane proteins A17 , H3 , A27 and A26 represented an apparent interface of the early-forming virion envelope with structures added later during virion morphogenesis . Protein H3 seemed to be the central hub not only for this sub-network but also for an ‘attachment protein’ sub-network comprising membrane proteins H3 , ATI , CAHH ( D8 ) , A26 , A27 and G9 . Crosslinking data lent support to a number of known interactions and interactions within known complexes . Evidence is provided for the membrane targeting of genome telomeres . In covering several orders of magnitude in protein abundance , this study may have come close to the bottom of the protein-protein crosslinkome of an intact organism , namely a complex animal virus .
The virion of Vaccinia , the prototypical poxvirus , is one of the largest among the animal viruses . While its ultrastructural characterization is the beneficiary of 60+ years of electron microscopic examination [1–3] and references therein , attempts to better understand its molecular and atomic architecture have fallen foul of various properties of the Vaccinia virion such as asymmetry , polymorphic character , tendency to aggregate , and the general incompatibility of enveloped viruses with X-ray crystallography . Electron microscopy ( EM ) and atomic force microscopy ( AFM ) studies have established clear ultrastructural compartments of the mature virion ( MV ) [4] including a central , genome-containing ‘core’ that also houses a number of virus-encoded enzymes of mRNA transcription and modification , a proteinaceous wall surrounding the core , a pair of ‘lateral body’ structures flanking the core wall , a single lipid bilayer envelope , and an outer protein-rich coat that appears late during maturation . The virion contains between 58 and 73 distinct gene products [5] . Some of these have been localized at low resolution on the basis of immunogold EM [6–10] , while the compartmental locale of others can be inferred from clearly identifiable transmembrane ( TM ) domains and other bioinformatics signatures , known function and/or the conditions required for the extraction from the virion . Proteins and visible structures localizing to outer compartments of the virion ( outside of the core ) have been identified via their fractionation in vivo during virus entry [10 , 11] or under pseudo-entry conditions recreated by the gentle , controlled treatment of virions with nonionic detergent or nonionic detergent+disulfide reductant [9 , 12–15] . A number of core enzymes , including the virus-encoded multisubunit DNA-dependent RNA polymerase ( RPO ) , heterodimeric virion capping enzyme ( CA ) , early transcription factor ( ETF ) , poly ( A ) polymerase ( PAP ) , two protein kinases , at least two proteases and two glutaredoxins have been released from the virion under more harsh conditions ( 0 . 2% ionic detergent ( sarkosyl ) and high salt [16] ) , retaining solubility , integrity and activity after detergent removal [4] . By contrast , a number of structural proteins of the virion core remain insoluble during virion extraction even in ionic detergent . Aside from these compartmentalization approaches , little is known of the virion’s internal organization at the molecular level . Certainly , the heteromultimeric status of the above core enzymes has long been known [4] , and the homomultimeric status of yet other virion proteins has been revealed by X-ray crystallography ( eg . proteins H1 [17 , 18] and A27 [19] ) . Some binary protein-protein interactions have been successfully recapitulated and identified in a yeast two-hybrid system [20] . Other proteins , and fragments thereof , have been co-immunoprecipitated from cell extracts , pulled-out as tagged complexes [2] or inferred by genetic and directed mutational studies . However , larger macromolecular and ultrastructural assemblies clearly dissociate under the conditions required for full virion disruption . For example , the presence , within the virion core , of a ‘transcriptosome’ assembly was inferred in studies down-regulating the Vaccinia RNA polymerase subunit RAP94 . Under non-permissive conditions , virions were morphologically mature but showed low infectivity [21] . Albeit the virus genome was packaged in normal amounts as were ETF and the structural proteins , low or undetectable amounts of RPO , CA , PAP large subunit , and proteins NTP1 , RNA helicase and topoisomerase were packaged suggesting the coordinated packaging of the latter components . Such a ‘transcriptosome’ complex may correspond to the formation , within the core , of a genome-containing tubular ultrastructure [22] that can be resolved by EM under sample preparation conditions that include high pressure freezing [23] . However , no such ultrastructure or any subassembly thereof has been isolated biochemically: Capping enzyme can form a binary complex with RPO in vitro [24] , but the soluble fraction from a sarkosyl virion core lysate , for example , even under gentle gradient sedimentation conditions , has yielded no higher order assemblies beyond the sedimentation of RPO as a discrete entity and the partial co-sedimentation of RPO with viral capping enzyme and NTP1 [25] . Other enzymes , including those apparently co-packaged with RAP94 ( above ) sedimented separately , towards the top of the gradient , suggesting an irreversible disruption of interactions within the transcriptosome upon core rupture . To our knowledge , no comprehensive transcriptosome , or other packaged superstructure has been ( re ) assembled biochemically as a positive correlate to the subtractive approaches of genetics . Here , we have taken an approach to the molecular structure of the Vaccinia virion that is neither destructive , reconstructive nor exclusively applicable to binary complexes , namely protein-protein crosslinking mass spectrometry ( XL-MS ) . We address the virion in its natural state in situ , with the potential to interrogate multivalent protein complexes . Technical challenges in this approach were not inconsiderable: At the outset of the current study , higher profile XL-MS studies in the literature had focused upon stoichiometric or near-stoichiometric isolated protein complexes , containing around ten or fewer polypeptides , with known crystal structures . Examples of these would include the 26S proteasome [26] , multi-ringed TRiC/CCT chaperonin [27 , 28] , the RNA polymerase II pre-initiation complex [29–31] , RNA polymerase I [32] and RNA polymerase III [33] . By contrast , the Vaccinia virion likely contains a variety of protein complexes covering an abundance dynamic range of ~5000 [34] or greater , only a minority of which have yielded X-ray crystallographic structures . Our XL-MS results with Vaccinia are described below .
Virions ( intact or activated for mRNA transcription ) were incubated with bifunctional chemical crosslinkers to impose inter-protein distance restraints . Crosslinked virus was then dissolved and trypsinized to peptides , followed by peptide-level nanoLC-MS/MS and bioinformatics to identify crosslinked peptides . For disuccinimidyl suberate ( DSS ) , the crosslinker used in the majority of experiments , the restraint comprised a lysine Nζ-Nζ distance of ≤ 10–11 . 4 Å with corresponding Cα-Cα distances of ≤ 32 Å ( give or take molecular dynamics considerations ) . Crosslinkable lysines thereby sweep a sphere of Cα-Cα distances up to ~6 nm , or ~2% of the diameter of a Vaccinia virion for proteins not forming extended , repeating arrays . Due to the low intrinsic ionizability of crosslinked peptide pairs and the potential for low saturation crosslinking within/between low abundance proteins in the virion , a strategy of variation [5] ( Table 1 ) was implemented to maximize opportunities for the detection of crosslink ( XL ) ions ( Fig 1 ) . This was combined with a total of six distinct XL search engines , used in parallel ( Fig 1 and Materials & methods ) . After data thresholding and filtering , a unique meta-score ( ‘DFscore’ , or detection frequency score ) was introduced as a guide to the extent of internal confirmation within the dataset . The resulting XL dataset yielded a total of 4609 confidently-identified unique-mass ions , each corresponding to a crosslinked peptide pair ( S1A and S1B Table ) . Of these , 1486 ( 32 . 2% ) had a DFscore > 1 . The highest DFscore for any ion was 178 , and the four top-scoring ions each corresponded to p4a intra-protein XL , of which the two highest scoring were light/heavy versions of the same ion and the third represented a small shift in XL position for one of the two crosslinked peptides ( S1A and S1B Table ) ) . 3725 of the 4609 unique-mass ions represented intra-protein XL while 884 were inter-protein , consistent with the known tendency for XL to fall within rather than between proteins . 273 of the 884 inter-protein XL ions had a DFscore > 1 among which the highest DFscore was 83 ( p4a-position 876 crosslinked to p4b-position 563 ) . By merging ( a ) distinct charge states for a crosslinked peptide , ( b ) identical crosslinked accessions/positions detected within distinct peptide species , ( c ) light/heavy isotopic forms of the crosslinker and ( d ) crosslinked peptides with secondary modifications , the 4609 unique XL ion masses collapsed down to 2534 unique pairs of residues within the proteome . 625 of these were inter-protein and , of these , 157 ( 25 . 1% ) had a DFscore > 1 with the highest DFscore for an inter-protein accession/position pair being 475 ( for the p4a-876/p4b-563 XL mentioned above ) . This accession/position pair was represented by 43 distinct m/z crosslinked peptide ions . S1C Table shows all crosslinked protein pairs in the dataset . S1 Fig shows crosslinking partners among all proteins considered to be packaged in the virion [5] for which XL were detected , and Table A in S1 Text reconciles the proteome of S1 Fig with the contents of the XL search database . Orthogonal approaches to the validation of in situ–detected protein-protein interactions all seemed less direct than XL-MS itself ( involving virion disruption , recapitulation of interactions in vitro , and/or the expression of virus proteins in heterologous systems ) . We therefore sought to validate the XL dataset via inference criteria , asking four basic questions as follows: All six XL search engines employed a target-decoy approach [35] ( Table 2 ) and primary score thresholding comprised false discovery rate ( FDR ) or its surrogate , q-value ( Materials & methods ) . For four of the six engines we took the unprecedented step of also applying a second threshold , via the score-type that is native to the engine itself ( Table 2 ) . A small fraction of the ions discarded solely on the basis of threshold 2 were then rescued according to the criteria described in Materials & Methods . With a primary threshold alone , namely 5% FDR , around 230 of our 4609 unique-mass ions would have arisen from our decoy database . Via our dual thresholding/rescue approach ( see the “Data Assembly” section of “Materials & methods” ) , only 15 of the 4609 ions involved a decoy accession , representing an effective FDR of just 0 . 33%—an exceptionally low number . We regard our low effective FDR as a bona fide validation step , and an indication of low technical noise in the dataset . All 15 decoy hits had a DFscore of 1 with one exception , whose DFscore was 2 . Non-randomness was evaluated on the basis of several criteria: Inter-protein vs . intra-protein XL: For a database of 86 proteins , random partner selection would result in a 1/86 ( 1 . 12% ) chance of both tryptic peptides in a crosslinked pair arising from the same protein , assuming an equal number of tryptic peptides from each protein in the database . Experimentally , however , far more opportunities exist for efficient crosslinking within a protein than between proteins . Of the 1742 unique accession/position pairs in the dataset , 1294 ( 74 . 3% ) were intra-protein , conforming to the experimental expectation rather than the random selection of peptides during bioinformatics . Protein abundance: During MS data acquisition , ions were prioritized for sequencing on the basis of intensity ( high-to-low ) leading to an expectation of XL detection at a higher frequency for relatively abundant proteins . Consistent with this , the dataset was dominated by XL between the abundant virion structural proteins p4a and p4b ( S1C Table ) . This provided a clear validation of data on the basis of known protein abundance . Non-random lysine occupancy per protein: If search engines were picking lysine XL sites randomly , then the proportion of lysines occupied with XL would be expected to be fairly constant from protein-to-protein . However , lysine occupancy on a per protein basis covered a broad range , from 32 . 5% to 100% ( Fig 2a ) . Search engines were therefore not simply picking sites from the database randomly . Some proteins were clearly more ‘detectably crosslinkable’ than others for reasons that presumably included protein abundance , solvent accessibility and lysine basicity for reaction with succinimide-based crosslinkers . Non-random ‘hotspotting’ of lysine XL sites within a protein: Individual XL sites within a protein may vary in exposure , reactivity or flexibility or the number of reactive partners within crosslinking range , resulting in the appearance of crosslinking ‘hotspots’ [36] . The crosslinkability of some protein N-termini in particular ( S1 Fig ) likely arises from their exposure and flexibility , combined with a pKa [37] that promotes chemical reactivity . Consistent with this , individual lysines in our dataset showed substantial variation in predisposition towards XL ‘hotspotting’ ( Fig 2b ) . F17 residue K74 , for example , provided a particularly concentrated crosslinking hotspot , appearing in a total of 45 distinct accession/position pairs ( Fig 2b ) among 15 protein partners ( S1 Fig ) . By contrast , many other positions in various accessions appeared just once ( Fig 2b , S1 Fig ) . Non-random coverage of inter-protein XL space: Our 86-protein search database provided a theoretical space of 3655 potential protein-protein pairs from which the XL dataset contained just 449 . Despite the depth of analysis ( 4609 XL ions ) , this 12 . 3% coverage of theoretical inter-protein crosslinking space suggested a level of specificity . At the time of writing , partial or complete X-ray crystallographic structures covered the crosslinked portions of 12 proteins in our XL dataset , with an additional two crystallographic structures from other orthopoxviruses ( Table B in S1 Text ) . All possible lysine-lysine through-space ( Euclidian ) and solvent-accessible surface ( SAS ) distances within all of these structures [38] were binned , and the resulting two histograms were found to be centered at ~43 and ~54 Å , respectively ( Fig 3 ) . By contrast , the Euclidian/SAS distance histograms for all experimental XL found within the 14 proteins was centered at 14 . 9 and 13 . 5 Å respectively , with 103 or 114 ( SAS/Euclidian ) out of the 136 experimental XL distances being structurally rational ( ≤ 32 Å , Cα to Cα distance ) . Based on the Kolmogorov-Smirnov test , the probability that the “All lys-lys” and “experimental XL” distance histograms ( Fig 3 ) were sampled from a single population was < 10−4 , providing 99 . 99% statistical confidence that the crosslinking dataset was structurally rational . Further assessment of the XL dataset was largely biological , namely , whether the identities of crosslinked protein pairs were consistent with known protein functions . For this analysis , accessions with strong functional annotations were collected into groups ( Table 3 ) . Interactions within any group were considered ‘biologically rational’ , while the pairing of a membrane-group protein with a transcriptosome-group protein was designated ‘biologically non-rational’ since these two groups of proteins are considered , based on controlled degradation studies [9 , 16] , the most likely among the various groups to occupy distinct virion compartments—separated by the core wall . All other protein-protein pairings were disregarded for the purposes of biological validation as being relatively uninterpretable . Membrane-group proteins showed a moderate , yet unmistakable global positive predilection for other membrane-group proteins as crosslinking partners , and a mild antipathy , globally , for transcriptosome proteins ( Fig 4a ) . Transcriptosome proteins , as a class , showed a mild but unmistakable predilection for other transcriptosome proteins as crosslinking partners and a mild antipathy for the membrane class ( Fig 4b ) . While not absolute , the trends shown in Fig 4 were consistent with accepted compartmentalization models for virion proteins , with the likely location of the transcriptosome within the virion core enclosed by a core wall , and virion TM proteins likely occupying a two-dimensional membrane compartment surrounding the core wall . This provided a suggestion of biological rationality within the XL dataset . Among the top 28 crosslinked protein pairs by DFscore , 12 were ‘rational’ and only 2 were ‘non rational’ ( S1C Table ) . The top 28 protein pairs contained 1205 of the 1849 total XL ions represented in S1C Table , and the top 12 “Y” protein pairs represent 92% of all XL ions in S1C Table associated with a “Y” ( ie . that were biologically ‘rational’ ) .
We have investigated the molecular structure of the Vaccinia virion , a highly non-stoichiometric protein assembly , via XL-MS . Analysis of protein-protein interactions in the virion in situ avoided the need for their preservation during virion extraction with reagents such as deoxycholate , an ionic detergent used for the release of virion core enzymes [16] . There was no requirement to rebuild virus protein complexes de novo , avoiding a need for the correct folding of challenging or insoluble structural proteins in vitro and/or in a heterologous system . Finally , multivalent/higher order complexes could be addressed that were not accessible via binary assays such as Y2H [20] . As in any XL-MS study , challenges included: The availability and appropriate spacing of crosslinkable sites at protein interfaces; good occupancy of crosslinking sites and robust reaction of both ends of the crosslinker; efficient laboratory digestion of crosslinked proteins ( given the tendency of trypsin recognition sites , for example , to become derivatized ) ; the detection of crosslinked peptide pairs against a large excess of non-crosslinked peptides in the same digest; rarity of inter-protein XL ( the most informative kind ) with respect to other kinds ( intra-protein , intra-peptide , and single-ended XL ) ; the tendency of large ( more than double-size ) crosslinked peptide pairs to ionize less efficiently during MS; inefficient fragmentation and combinatorial complexity of fragment ion mass spectra when simultaneously fragmenting peptide pairs , and the challenge of distinguishing true intra-molecular XL from those that may cross homomultimer interfaces . For Vaccinia as a target , the above issues were compounded by: Unknown permeability of the virion core to crosslinker; a protein abundance dynamic range in Vaccinia MV of 5000-fold [34] or more; a paucity of existing high resolution protein structures for validation , and the possibility of molecular heterogeneity arising from mixed viroforms in MV preparations and/or mixed proteoforms within a single particle . Addressing the above challenges ( most particularly the abundance range and sensitivity issues ) we adopted a “strategy of experimental variation” , as explored initially in our analysis of the MV phosphoproteome [117] . For XL-MS this strategy involved a ‘multithreaded’ workflow ( Fig 1 ) in which experimental steps were matrixed combinatorially ( Table 1 ) . In this way , individual XL were placed in a variety of ionic contexts for MS detection , and key interfaces were painted as clusters of alternative XL between closely spaced crosslinking sites . This was combined with the use of diverse XL search engines for the identification of crosslinked peptides , and the use of isotopically coded crosslinkers where available . Our 86-protein search database comprised the maximum set of viral proteins considered likely to be packaged [5] . For all but two of these proteins XL were detected , the exceptions being proteins A14 and I2 . These two short proteins ( 90 aa , 73aa respectively ) possess relatively few sites for crosslinking and trypsin cleavage ( 3 lys/2 arg; 4 lys/0 arg , respectively ) . Due largely to the absence of strong corroborating data for our XL-MS dataset such as comprehensive atomic-resolution three dimensional structures , validation relied largely on statistics and trends . The effective FDR of 0 . 33% for the final dataset as a whole ( “Results” ) , suggested a remarkably low level of bioinformatics noise . Consistent with this , non-target databases from uncorrelated proteomes , namely all human proteins or the non-packaged subset of Vaccinia proteins yielded very weak results in preliminary searches . Alongside the detection of clear crosslinkome sub-networks ( “Results” ) were many single-detect inter-protein XL ( DFscore = 1 , S1 Fig ) . Notwithstanding the excellent bioinformatic signature for the dataset as a whole ( above ) , it was difficult to ascertain to what extent the single-detect XL were real ( from , for example , low abundance proteins , low abundance viroforms , inefficient XL , or poorly ionizing peptides ) , or represented biochemical noise ( eg . virion dissociation pathways during virus preparation or specific experiments ) . On the one hand , evidence that single-detect XL were true positives included the tendency of single-detect crosslinking patterns within a protein sub-network to conform to patterns of XL with higher DFscore . For example , among the 22 inter-protein XL shown in Fig 5a , 18 were multi-detects vs . 8 single-detects , all contributing to the same overall crosslinking pattern . On the other hand , high DFscoring XL showed a higher ratio of biologically rational:non-rational XL than did single-detect XL , lending greater confidence to former . For example , among the 37 inter-protein XL in the dataset with DFscore > 5 ( Table E in S1 Text ) , the number that were considered biologically rational exceeded the number designated non-rational by a factor of 9 . 5 while , among the single-detect XL from the same table , rational exceeded non-rational XL with a factor of only 1 . 5 . Transcriptosome proteins , albeit presumably packaged in relatively low abundance , nonetheless showed a number of strongly detected inter-protein XL ( Tables F , G in S1 Text ) . Some of these , including some of the most strongly detected inter-protein XL in the dataset ( S1C Table; Table F in S1 Text , orange ) , were between transcriptosome and membrane proteins ( Table F in S1 Text ) , including ectodomains of the latter . These XL were considered biologically “non-rational” ( above ) since the transcriptosome is located within the virion core while the TM proteins surround it according to conventional models . They were strongly supported by their DFscores , were not filterable by raising score thresholds , and their DFscores did not drop when switching between singly- and dually-thresholded filtering ( Materials & methods ) . We were therefore unable to falsify a hypothesis that contacts can occur between transcriptosome components and the ectodomains of membrane proteins , the significance of which is unclear . Possibilities for these resilient , yet ‘non-rational’ XL may include that: ( a ) the core wall is not a fundamental barrier to crosslinking ( it is porous ) —indeed the 7 nm inside-diameter pores that have been imaged in the core wall [9 , 118] may be sufficiently large for the majority of Vaccinia polypeptides to pass through entirely if they are globular and approximately spherical [119] , ( b ) TM and transcriptosome proteins are both implanted in the barrier ( from opposite sites ) –a situation , on the transcription side , observed in the cores of turreted Reoviruses [120 , 121] , ( c ) TM proteins are located in more than one compartment , ( d ) MV preparations contain developmental viroforms from a time prior to the full emergence of the core wall , ( e ) they are cryptically artefactual . The dataset contained evidence for viroforms/proteoforms from proteolytic maturation . Peptides crossing known [2] sites of viral AG| specific proteolytic processing in proteins A17 , VP8 , G7 , p4b and p4a ( site2 ) can be found in tryptic digests of purified MV [5] . These peptides represent pre-cleaved proteoforms . Such peptides were also found in the current study , within crosslinked pairs , from proteins A17 , VP8 , A12 and G7 ( Table H in S1 Text ) . XL connecting the N-terminal amino group of pre-cleaved A17 with the C-terminal region of the same protein ( “Results” ) may be an example of the same phenomenon . In some cases the crosslinker directly spanned an AG| processing site . Apparently , then , MV harvested from Hela cells late in infection followed by 2x sucrose gradient-purification were accompanied by immature viroforms that are detectable by highly sensitive MS . Among crosslinked peptides could be found no trace , however , of a characteristic and abundant marker of IV , namely the external scaffold protein D13 when using XL search databases that included this protein . Apparently , in MV , in which the external scaffold , along with fragment 2 of protein p4a ( Fig 5b ) are close to or below the detection limit , unprocessed forms of proteins A17 , VP8 and A12 are still readily detectable . If , speculatively , MV preparations contain trace viroforms that appear morphologically mature ( having already escaped the external scaffold and perhaps received tail-anchored and SFE proteins ) , but which still lack a fully formed interior and/or an impermeable core wall , then this may account for some of the more counter-intuitive XL detected here . Alternatively , some XL may represent structures that appear only transiently in the virion maturation pathway . Another possibility may be that MV particles , albeit fully mature , retain unprocessed proteoforms by design . Within an A17 homodimer , for example , one subunit might be processed and the other not . Evidence for multiple viroforms/proteoforms also arose from interactions between p4a , p4b and TM proteins: p4a fragment 3 was found to be within crosslinking range of seven distinct membrane proteins ( S1 Fig , Fig 6 ) and also within crosslinking range of the C-terminal region of p4b , while none of the seven membrane proteins were apparently within crosslinking range of p4b Fig 6 , S2A and S2B Fig ) . Moreover , a crosslinking ‘hotspot’ in p4a fragment 3 ( K736 ) interacted with three membrane proteins as well as p4b ( S1 Fig ) , in the absence of any detectable crosslinking between the latter . While steric factors may allow p4a , p4b and membrane proteins to triangulate in a way that leaves all membrane proteins out of range of p4b , it seems also possible that membrane proteins and p4b may interact with alternate proteoforms of p4a . This could result from distinct and segregated p4a complexes within individual MV , or distinct viroforms in the virus preparation ( e . g . the rearrangement of p4a fragment 3 during maturation ) . In conclusion: Here , we have covered the crosslinkome of a relatively small whole organism in depth , detecting inter-protein XL for all but two of the 86 proteins that represent the maximal virion proteome . Strategies were developed to detect XL in a proteome covering a wide abundance dynamic range and with minimal pre-existing crystallographic information , allowing the reconstruction of several key virion protein complexes . The challenge of synthesizing the data into an extended understanding of the internal molecular architecture requires some knowledge of intra-particle protein stoichiometry .
Vaccinia virus was purified by sucrose or tartrate gradient as described [5] and protein quantitated using BCA ( ThermoFisher Inc . ) , determining concentrations to be between 1 and 3 . 5 mg ml-1 . DSS-H12 , DSS-D12 , DSG-H6 , and DSG-D6 were obtained from Creative Molecules Inc . BS3-H4 , BS3-D4 , BS ( PEG ) 5 , BS ( PEG ) 9 , Zeba Spin Desalting Column ( 7K MWCO ) , and Lys-N were obtained from Thermo Scientific . DSS , bis ( sulfosuccinimidyl ) suberate ( BS3 ) , and disuccinimidyl glutarate ( DSG ) were used as 1:1 mixtures of DSS-H12/DSS-D12 ( ‘DSS-H12/D12’ ) , BS3-H4/BS3-D4 ( ‘BS3-H4/D4’ ) , and DSG-H6/DSG-D6 ( ‘DSG-H6/D6’ ) respectively . Trypsin , dimethyl sulfoxide ( DMSO ) , Benzonase , iodoacetamide , n-LS , adipic acid dihydrazide ( ADH ) , 4- ( 4 , 6-dimethoxy-1 , 3 , 5-triazin-2-yl ) -4-methylmorpholinium chloride ( DMTMM ) , 1-[bis ( dimethylamino ) methylene]-1H-1 , 2 , 3-triazolo[4 , 5-b]pyridinium 3-oxid hexafluorophosphate ( HATU ) , and cyanogen bromide ( CNBr ) were from Sigma-Aldrich . GluC , AspN , LysC , LysN and ArgC were from Promega . AspN was from Roche Diagnostics . C18 and SCX filters were obtained from 3M . N , N-Diisopropylethylamine ( DIPEA ) was from Alpha Aesar . Centrifugal concentrators ( Vivacon , 10kDa MWCO ) were from Sartorius Stedim Biotech . Prior to crosslinking , virus was washed 5x with phosphate buffered saline ( PBS ) , pH 7 . 4 , by centrifugation and resuspension . For some experiments , washed virus pellets were then resuspended in 10 μL of 0 . 1 M triethylammonium bicarbonate ( TEAB , pH 8 . 5 ) and supplemented with an equal volume of 2x ‘pre-treatment’ buffer comprising either 0 . 1 M TEAB , 0 . 1% NP40 ( pH 8 . 5 ) , or 0 . 1 M TEAB , 0 . 1% NP40 , 80 mM TCEP ( pH 8 . 5 ) , followed by 2 min incubation . The method of ref . [122] ( ‘xQuest crosslink method’ ) was used with some modifications . Pre-treated virus suspension ( above ) , or intact virus suspended in 0 . 1 M TEAB ( pH 8 . 5 ) , was supplemented with 1/10 volume of 10x crosslinking buffer ( 0 . 2 M 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( HEPES ) , KOH , pH 8 . 2 ) . Crosslinker , dissolved freshly in DMSO , was then added at a final concentration of 7 . 5 mM . Following 30–60 min incubation at 37°C , samples were quenched by adding 1 M ammonium bicarbonate ( AmBic ) to a final concentration of 50 mM followed by 30 min incubation at 37°C . ADH with either HATU/DIPEA or DMTMM: Pre-treated virus suspension ( above ) was supplemented with 10x ADH-XL buffer ( 0 . 2 M HEPES-NaOH , pH 7 . 2 ) to 1x ADH-XL buffer ( final ) then supplemented with ADH and HATU ( dissolved separately in 1x ADH-XL buffer ) to final concentrations of 6 mM and 9 . 2 mM respectively . 100% DIPEA was then added to a final concentration of 46 mM . After 120 min incubation at room temperature with continuous shaking , crosslinked virus was exchanged into 50 mM AmBic using a spin desalting column ( Zeba , ThermoFisher , Inc . ) following the manufacturer’s instructions . In some experiments , HATU/DIPEA were replaced with DMTMM , using concentrations of ADH and DMTMM described [123] . ADH/EDC/NHS or EDC/NHS alone: Pre-treated virus suspension ( above ) was supplemented with 10x ADH-XL buffer ( 0 . 2 M HEPES-NaOH , pH 7 . 2 ) to 1x ADH-XL buffer ( final ) then supplemented with ADH ( dissolved separately in 1x ADH-XL buffer ) to a final concentration of 6 mM . N- ( 3-dimethylaminopropyl ) -N′-ethylcarbodiimide hydrochloride ( EDC ) and N-hydroxysuccinimide ( NHS ) , dissolved separately in 1x XL buffer were then added at final concentrations of 8 mM and 10 mM , respectively . After 120 min incubation at room temperature , free crosslinker was removed by spin desalting into 50 mM AmBic ( above ) . ADH with EDC or EDC alone: Pre-treated virus suspension ( above ) was supplemented with 10x MES buffer ( 0 . 1 M 2- ( N-morpholino ) ethanesulfonic acid ( MES ) , 20 mM NaCl , pH 4 . 7 ) to 1x MES buffer ( final ) , then supplemented with ADH and EDC ( dissolved separately in 1x MES buffer ) to final concentrations of 6 mM and 2 mM , respectively . After incubation for 120 min at room temperature , free crosslinker was removed by spin desalting into 50 mM AmBic ( above ) . For some experiments ( EDC-alone crosslinking ) ADH was omitted . Crosslinked virus samples in 50 mM AmBic were disaggregated by supplementing with 0 . 5 M TCEP , 1 M TEAB and solid urea or guanidine , then diluting to achieve a final formulation of 8 M urea , 0 . 1 M TEAB , 10 mM TCEP , pH 8 . 5 ( urea buffer ) or 6 M GuHCl , 0 . 1 M TEAB , 10 mM TCEP , pH 8 . 5 ( guanidine buffer ) . In some experiments , crosslinked virus suspension in 50 mM AmBic was instead supplemented with an equal volume of 2x detergent solution to achieve 0 . 5% sodium deoxycholate ( SDOC ) , 12 mM n-laurosarcosine ( n-LS ) , 5 mM TCEP , 50 mM TEAB , pH 8 . 5 ( final ) . After 30 min incubation at 37°C , some samples were alkylated with iodoacetamide at either 5 mM ( if supplemented with urea or GuHCl ) , followed by 30 min incubation in the dark ) or 10 mM ( if supplemented with detergents ) , followed by 15 min incubation in the dark . Some samples were then incubated with Benzonase ( 250 units ) for 60 min . All samples were then diluted with 50 mM AmBic for cleavage , according to the manufacturer’s recommendation for tolerable denaturant ( below ) . Cleavage employed the following reagents/reagent combinations: Trypsin , ArgC , GluC , AspN , LysN , LysC , or Trypsin+GluC , Trypsin+AspN , ArgC+AspN , ArgC+GluC , AspN+GluC or CNBr+Trypsin . For digestions containing GluC , samples were diluted to a final urea concentration of 0 . 5 M . For digestion with LysN , samples were diluted to a final urea concentration of either 1 M or 5 M . For all other proteases , samples were diluted to final denaturant concentrations of either 0 . 6 M GuHCl , 1 M urea , or 0 . 1% SDOC/2 . 4 mM n-LS/1 mM TCEP . With the exception of DigDeApr experiments ( below ) , a protease:substrate ratio of 1:50 or 1:100 was used . With the exception of LysC , which was used for 72 hr at room temperature , all protease digestions were overnight at 37°C . For CNBr+Trypsin digestion , quenched amine-amine crosslinking samples ( above ) were supplemented with 100% formic acid ( FA ) to 70% ( final ) followed by the addition of one crystal ( ~20–100 molar excess ) of CNBr and overnight incubation at room temperature in the dark . After evaporation to dryness under vacuum , samples were redissolved in urea buffer ( above ) , followed by 30 min incubation at 37°C in the dark . Samples were then diluted to 1 M urea with 50 mM TEAB ( pH 8 . 5 ) , and trypsin added to an estimated enzyme-to-substrate ratio of 1:100 followed by overnight incubation at 37°C . A fresh equivalent of trypsin ( same amount ) was then added , followed by a further 4 hrs digestion . Undigested material was precipitated by centrifugation at 14 , 000 g for 2 min followed by resuspension in 70% FA and re-digestion with CNBr and trypsin following the same method . This was done following ref . [124] with modifications . Briefly , samples were digested with either trypsin alone ( enzyme:substrate ratio of 1:2500 ) or Trypsin+AspN , Trypsin+GluC or AspN+GluC ( 1:1:2500 ) . After overnight incubation at 37 °C , samples were centrifuged into a centrifugal concentrator ( 10kDa MWCO , Vivacon ) at 2500 x g . After collection of flow through , the filter was washed by centrifugation at 2500 g with 8 M urea , 0 . 1 M TEAB pH 8 . 5 then with 2 M urea , 0 . 1 M TEAB pH 8 . 5 ( Wash buffer ) for 2 min at 2500 x g . Flow through and wash-throughs were combined . Using a new collection vial , urea buffer was added to the filter which was then inverted and spun for 2 min at 2500 x g . The process was repeated and the combined urea buffer washes were brought to 1 M urea with 0 . 1 M TEAB then treated again with the same protease combination at an enzyme:substrate ratio of 1:100 ( for GluC digestion , samples were diluted to 0 . 5 M urea , 100 mM TEAB ) overnight at 37 °C . All cleaved samples were acidified with FA to 2% FA final then desalted as described [125] using stacked C18-SCX filters . After washing the filters , peptides were transluted from the C18 to the SCX phase using 80% CH3CN/0 . 1% FA ( translution buffer ) . Peptides were eluted with 5% NH4OH/80% CH3CN ( Buffer X ) or with six steps of 20% CH3CN/0 . 5% FA containing ammonium acetate in the range 160–800 mM followed by a final step of Buffer X . Elutions were dried under vacuum then re-dissolved in 0 . 1% FA in water for MS . nanoLC-MS/MS was performed using an LTQ Velos Pro Orbitrap mass spectrometer with Easy-nLC 1000 ( ThermoFisher ) . 2 microL injections were followed by a segmented LC gradient ( solvent A = 0 . 1% FA in water , solvent B = 0 . 1% FA in CH3CN ) , progressing from 0 to 10% B over 10 min then to 35% B over 230 min . Some runs used a straight gradient of 0–35% B over 135 min . Precursor spectra were acquired in FT mode at a resolution of 100 , 000 ( centroid ) in the range 200–2000 Th . For isotopic pairs with 12 Da mass split ( DSS crosslinker ) , the top 3 most intense ions were selected for HCD activation ( above a precursor signal threshold of 150 ) on the basis of isotopic pairs with m/z spacing of either 4 . 02524 , 3 . 01893 or 2 . 41515 ( representing +3 to +5 charge-states ) , and intensity ratio better than 2:1 . For a 6 Da mass split ( DSG crosslinker ) , m/z deltas for isotopic pair selection were 2 . 01456 , 1 . 51092 or 1 . 20874 . For a 4 Da mass split ( BS3 crosslinker ) , m/z deltas were 1 . 34156 or 1 . 00616 . Both isotopic partners were fragmented . HCD activation used a normalized collision energy ( NCE ) of 45 , activation time of 0 . 1 mSec and an isolation width of 2 m/z . MS2 spectra were acquired in FT mode with a resolution of 7500 ( centroid ) . The dynamic exclusion list size was 500 , exclusion duration was 60 sec , repeat duration was 30 sec and the repeat count was 2 , with early expiration enabled . Charge state screening was enabled , with rejection of 1+ and 2+ and unassigned charge states . Data acquired for xQuest were activated in IT-CID mode instead of HCD . Here , NCE was 35 , activation Q = 0 . 25 and activation time was 10 mSec . For non-isotopic crosslinkers , the 10 most intense ions in each precursor spectrum were subjected to HCD fragmentation , as above , if above a minimum signal threshold of 250 ( or 2000 in some early experiments ) . Protein names used throughout this report follow entry names in the UniProtKB Vaccinia WR reference proteome minus the species identifier suffix . They are comprehensively cross-referenced to other naming schemes in Table S1 of ref . [5] . Instrument raw files were converted to mgf , mzXML or mzML using MSConvert by ProteoWizard . Using the resulting data , XL were identified using the following XL search engines: Protein Prospector [126] , pLINK [127] , xQuest ( in combination with xProphet ) [122] , Kojak [128] ( in combination with ‘Percolator’ [129–131] ) , ECL [132] and ECL2 [133] , as follows: Protein Prospector: Instrument raw data files were converted to mgf format then uploaded to Protein Prospector via the UCSF online server using parameters outlined in Table I in S1 Text . Non-standard , PEGylated bis ( sulfosuccinimidyl ) suberatecrosslinkers ( BSPEG5 and BSPEG9 ) were imputed as user defined parameters . The results file from each run was generated using the program’s Search Compare function . Results were sorted by ascending expectation value and “Report type” was set to “crosslinked peptides” . ‘SD-E’ = ScoreDiff–log10 ( Exp2 ) ( [126] , Robert Chalkley , Personal communication ) where ScoreDiff is the difference in score between the top- and second-ranked peptide 1 in the search output for a crosslinked pair , Exp2 is the score for peptide 2 . pLink: pLink was downloaded from pFind Studio . A parameter file was configured for each experiment and a folder created , containing mgf files pertaining to that experiment along with the search DB . The ‘pLINK . ini’ configuration file was modified for each experiment to include the path to the mgf and search DB and search parameters ( Table I in S1 Text ) . The enzyme . ini and xlink . ini files were modified for any non-standard cleavage specificities/combinations and crosslinkers , respectively . Results files for loop linked and mono linked peptides were generate using “non-interexport” and “drawpsm” , respectively . pLink was run through the flow . exe application . xQuest: The xQuest VMware package was installed on a Windows PC . Directories were created following instructions provided with xQuest . Search parameters are given in Table I in S1 Text . Files “Xmm . def” and “xquest . def” were modified for the relevant crosslinker isotopic mass , shift and ion charge states . A text file was created containing the mzML file name and parameter files for xProphet . xQuest , then xProphet , were run from the command line . Results were viewed on the xQuest webserver then downloaded . Values reported by xProphet as "FDR" may be Percolator-derived q-values . Kojak: Kojak and Percolator [134] were installed and run in Linux from the command line . Folders were created for mzML formatted data and search results . The program’s configuration file was modified to contain all relevant crosslinkers and the paths to individual data files . Parameters are outlined in Table I in S1 Text . Digestion specificity rules were based on the parameters provided . ECL/ECL2: ECL and ECL2 were installed on a Java-capable Windows PC and run from the command line . The program’s parameter file was modified to contain search parameters given in Table I in S1 Text . Crosslinker masses were entered manually . Percolator: For Kojak and ECL , FDR was converted to a q-value using the program ‘Percolator’ [134] , run from the Linux command line . For Kojak , Percolator input comprised “inter” , “intra” , and “loop” search output files . q-value can be regarded as the expected proportion of false positives among all features as or more extreme than the observed one [132 , 135] or , alternatively , the minimal FDR threshold at which a given peptide-spectral match is accepted [130 , 131] . Data assembly: Using in-house code , XL search engine/Percolator/xProphet outputs corresponding to various nanoLC-MS/MS runs in various experiments were parsed in their native formats , accepting individual XL to a single unified dataset according to dual score thresholds for each program including in-house-calculated FDR for Protein Prospector ( see above and Table 2 ) . The resulting dataset was then sorted by ascending exp_Mr . Groups ( blocks ) of masses matching to within 25 ppm were annealed , then each block that contained multiple accession/PeptideSeq/ProteinPos was sorted and divided into distinct sub-blocks of ions that were tagged with a common ‘ambig code’ ( representing sub-blocks having functionally isomeric mass but had been assigned , by XL search engines , distinct apparent identities ) . The resulting ‘mature blocks’ each represented a unique combination of exp_Mr and accession/PeptideSeq/ProteinPos . This dataset was reformatted/collapsed into a matrix with one row per mature block , and one column for each nanoLC-MS/MS run in the project . The matrix was filled with XL search engine identifiers to indicate all engines identifying a specific mature block member in a specific nanoLC-MS/MS run and the number of times identified . Each row was assigned a DFscore as the sum of search engine identifiers/times identified by that engine that had been assigned to the row . Groups of mature blocks sharing a common ambig code were likelihood-scored against one another as follows: If they all represented intra-protein XL or all represented inter-protein XL , then the ambigscore assigned to those mature blocks was a simple proportion of its DFscore/∑ ( DFscores for all blocks sharing a common ambig code ) . If they were a mixture of intra-protein and inter-protein , then intra-protein mature-block ( s ) were scored 1 . 0 and inter-protein mature-block ( s ) 0 ( assuming the intra-protein XL to be correct by default ) . If the ambiguity was simply in choice between multiple lysines within an otherwise identical peptide , both choices were scored 1 . 0 since both reflect the same approximate position within the same protein partners . Finally , for every specific position in a specific protein represented by multiple rows in the matrix: If the intra-protein XL were discovered by multiple engines and the inter-protein XL were discovered by one only , the latter were annotated as “filterable” . The resulting annotated matrix was written to an Excel worksheet then copied to a second sheet which was re-sorted by protein position then accession . A ‘discard matrix’ ( comparable in structure to the above , ‘passing’ matrix ) was generated representing all XL ions in the above assembly that passed threshold1 but were rejected after failing threshold2 . Each row of the ‘discard’ matrix was annotated with: ( a ) DFscore; ( b ) whether the XL ( Accession1/Accession2/ProteinPos1/ProteinPos2 ) was also present in the passing matrix ( above; this criterion being denoted ‘also’ in the following discussion ) and ( c ) ‘biological rationality’ ( ‘BR’ ) based on six groups of functionally-related virion proteins ( Table 3 ) , annotating”Y” , if the two crosslinked proteins were in same BR group , and “N” if one was from the ‘membrane’ group and the other from the ‘transcription’ group . Networks and sub-networks for individual accessions or groups of accessions were rendered using CrosslinkViewer [136] . Using in-house code , rows in the passing matrix ( above ) were picked provided either one or both crosslinked proteins did not match accessions within a user-definable excluded-accession group . ‘Filterable’ rows of the matrix ( above ) were excluded . The list of picked rows was supplemented with those from the ‘discard’ matrix ( above ) if DFscore > 1 or ‘also’ = “Y” or BR = “Y” . Rows with common Accession1/Accession2/ProteinPos1/ProteinPos2 were then collapsed summing DFscores , and the resulting dataset reformatted for input to CrosslinkViewer . The resulting DFscores were rendered . If 100% of matrix rows for a given Accession1/Accession2/ProteinPos1/ProteinPos2 had been flagged as ambig ( above ) , then the XL was flagged to be rendered with a broken line . Protein monolinks were ignored in all data operations . Domain prediction: TM regions were predicted using program TMHMM [68 , 95] . Domain boundaries were predicted using DomPred [137 , 138] . Output traces show endpoint density profiles for PSI-BLAST alignments generated between a query sequence and a database in which all sequence fragments had been removed . ROC analysis of the global crosslinking dataset: Each of the 81 proteins in the dataset for which crosslinked partner proteins were found , was flagged according to membership of one of two ‘biological rationality’ groups in Table 3 ( ‘Membrane’ and ‘Transcription’ ) , and total number of distinct crosslinking partner proteins was printed alongside . In each of two replicates of this listing was printed the # of partners belonging specifically to one of the two groups and the list was sorted ( descending ) according to proportion of total partners belonging to the specific group . After incrementing four number series at each row in the list that contained either: a membrane protein , not ( a membrane protein ) , a transcription protein and not ( a transcription protein ) , then proportionating each series to a scale from 0 to 1 , ROC curves were drawn based on the proportionated values . In ROC space , points above and below the line of no-discrimination ( diagonal ) represent positive ( better than random ) and negative correlation , respectively such that a curve representing perfect positive correlation would ascend vertically from ( x , y ) = ( 0 , 0 ) to ( 0 , 1 ) then travel horizontally to ( 1 , 1 ) . Perfect negative correlation would yield the converse curve: ( 0 , 0 ) to ( 1 , 0 ) to ( 1 , 1 ) . Inter-protein XL partitioning analysis: For each XL ion in the global XL dataset ( each row of S1A Table ) representing an inter-protein XL , DFscores from each experiment ( column ) in which the ion was detected were binned according to whether sample pre-treatment included or excluded NP40 or TCEP ( -NP40 , +NP40 , -TCEP or +TCEP ) . The resulting tetra-bin DFscore values for individual ions were accumulated on a per-accession basis , according to the accession on each side of the crosslink . The accumulated four DFscore values for each accession were then converted to a proportion of the summed DFscore across the four bins ( ‘POSD’ ) for that accession , and the resulting POSD values were finally normalized to the mean POSD per accession for each of the four pre-treatment conditions . See legend to S3 Fig for further details . Distance analysis of the form shown in ref . [29] ) was generated using ‘TopoLink’ [38] installed on a computer cluster and run from the command line , in combination with 14 relevant pdb files ( Table B in S1 Text ) . Before calculating Euclidean and SAS distances for each experimental XL , “inputfile . inp” was modified to include the crosslinker type , maximum linker distance and reactive residues . All lys-lys Euclidean and SAS distances were also calculated within the 14 structures , setting maximum linker distance to 100 Å . Each of the resulting four distance datasets , in spreadsheet format , was binned for display as a histogram . The mean and standard deviation ( SD ) from each “all lys-lys distances” histogram informed a normal ( Gaussian ) curve overlay . For each “experimental XL distances” histogram , Ln ( mean ) and ln ( SD ) informed a log-normal curve overlay . “Experimental XL distances” and “all lys-lys distances” datasets were compared to one another via the Kolmogorov Smirnov test , run using the Excel plugin XLSTAT ( https://www . xlstat . com/en/ ) . | Vaccinia is one of the most complex virions among the animal viruses , containing 70+ distinct gene products . Although virion ultrastructure has been apparent , at least in outline by electron microscopy since the year 1961 or earlier , its molecular architecture is largely unknown: Vaccinia is resistant to classical structural approaches requiring virus crystallization and moderately resistant to cryoEM . Molecular approaches requiring the maintenance of protein assemblies during virion deconstruction , reconstruction of protein complexes in heterologous or in vitro systems , or internalization of bulky reagents such as antibodies or gold particles may have been already pursued close to exhaustion . Here , protein interfaces within and around the intact virion were identified by virus incubation with bifunctional chemical crosslinkers in situ followed by proteolysis and peptide-level mass spectrometry . This minimally invasive approach revealed the molecular arrangements of structural and membrane protein complexes within the virus , confirming and extending several aspects of virus biology . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"&",
"methods"
] | [
"chemical",
"bonding",
"urea",
"protein",
"interactions",
"chemical",
"compounds",
"enzymes",
"microbiology",
"enzymology",
"viral",
"structure",
"organic",
"compounds",
"tartrates",
"membrane",
"proteins",
"serine",
"proteases",
"cellular",
"structures",
"and",
"organell... | 2019 | The Vaccinia virion: Filling the gap between atomic and ultrastructure |
Cells live in changing , dynamic environments . To understand cellular decision-making , we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks . Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented , or encoded , in the output of a signaling system over time . We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error , which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest , and mechanistic error , which occurs because biochemical reactions comprising the signaling mechanism are stochastic . Trade-offs between these two errors can determine the system's fidelity . By developing mathematical approaches to derive dynamics conditional on input trajectories we can show , for example , that increased biochemical noise ( mechanistic error ) can improve fidelity and that both negative and positive feedback degrade fidelity , for standard models of genetic autoregulation . For a group of cells , the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial . We can also predict the dynamic signal for which a given system has highest fidelity and , conversely , how to modify the network design to maximize fidelity for a given dynamic signal . Our approach is general , has applications to both systems and synthetic biology , and will help underpin studies of cellular behavior in natural , dynamic environments .
Cells are continuously challenged by extra- and intracellular fluctuations , or ‘noise’ , [1]–[3] . We are only starting to unravel how fluctuating inputs and dynamic interactions with other stochastic , intracellular systems affect the behavior of biomolecular networks [4]–[9] . Such knowledge is , however , essential for studying the fidelity of signal transduction [10] , [11] and therefore for understanding and controlling cellular decision-making [12] . Indeed , successful synthetic biology requires quantitative predictions of the effects of fluctuations at the single-cell level , both in static and dynamic environments [13] . Furthermore , sophisticated responses to signals that change over time are needed for therapeutics that involve targeted delivery of molecules by microbes [14] , [15] or the reprogramming of immune cells [16] . Here we begin to address these challenges by developing a general framework for analysing the fidelity with which dynamic signals are represented by , or ‘encoded’ in , the output of noisy biomolecular networks .
For cellular signaling to be effective , it should maintain sufficient fidelity . We wish to quantify the extent to which the current output of an intracellular biochemical network , , can represent a particular feature of a fluctuating input ( Fig . 1 ) . This signal of interest , , is generally a function of the history of the input , denoted . By its history , we mean the value of the input at time and at all previous times . The signal could be , for example , the level of the input at time or a time average of the input over a time window in the most recent past . The output of the signaling network , , is able to perfectly represent the signal if can be inferred exactly from at all times , . The system then has zero fidelity error . However , for a stochastic biochemical mechanism , a given value of will map to multiple possible values of the output , . We will assume that the conditional mean , , is an invertible function of : it takes different values for any two values of . It is then a perfect representation of . The output will , however , usually be different from and have a fidelity error , defined as the difference between and . The notation is read as conditioned on , or given , the value of the variable at time . We use , as for example in , to denote averaging over all random variables except those given in the conditioning . Therefore is itself a random variable: it is a function of the random variable ( we give a summary of the properties of conditional expectations in the SI ) . Many response functions , , in biochemistry and physiology ( for example , Hill functions ) satisfy the requirement of invertibility or can be made to do so by defining appropriately—for example , when a response exactly saturates for all input values above a threshold , those values can be grouped to form a single input state . Furthermore , we know from the properties of conditional expectations that is closer to in terms of mean squared fidelity error than to any other representation ( function ) of ( SI ) . The difference between the conditional expectations and , for example , is important . The former , , is the average value of the output at time given a particular history of the input . It will often coincide with the deterministic ( macroscopic ) solution when the same input trajectory is applied to the network . The output shows random variation around this average , , for identical realisations of the trajectory of . By contrast , is the average value of given that the trajectory of up to time ends at the value . By the properties of conditional expectations , this is also the average value of over all trajectories ending in the value : that is , . These mathematical definitions are illustrated diagrammatically in Fig . 2 . We distinguish between two types of error that reduce fidelity between and . We can decompose the output into the sum of the faithfully transformed or transmitted signal , , the dynamical error , and the mechanistic error: ( 5 ) for all times . Eq . 5 is an orthogonal decomposition of the random variable —each pair of random variables on the right-hand side has zero correlation ( Methods ) . The variance of therefore satisfies ( 6 ) where the magnitude of the fidelity error is given by , which is because of the orthogonality . This magnitude of the fidelity error is also equal to the expected conditional variance of the output , . We note that we can generalize this decomposition , and thus extend our approach , for example , to study different components of the mechanistic error ( Methods ) . To compare signaling by different biochemical mechanisms , we normalize by the square root of its variance , writing , and define the fidelity as a signal-to-noise ratio: ( 7 ) for some signal of interest , . Eq . 7 is dimensionless and a montonically decreasing function of . Indeed , we have shown that the maximal mutual information between and across all possible signal distributions is bounded below by a decreasing function of ( and so an increasing function of our fidelity ) , for a suitable choice of distribution of the signal and when is an invertible function of [7] . Comparing biochemical systems using the fidelity measure is equivalent to comparison based on the magnitude of the fidelity error , , where and the error is measured in units of the standard deviation of the output . Eq . 7 is maximized when is minimized . One minus the magnitude of the fidelity error is the fraction of the variance in the output that is generated by the signal of interest . In information theoretic approaches , normalizing the output by its standard deviation is also important , because the normalization allows determination of the number of ‘unique’ levels of output that can be distinguished from one other despite the stochasticity of the output , as least for Gaussian fluctuations [18] . When and have a bivariate Gaussian distribution , the instantaneous mutual information , , is monotonically related to the fidelity and exactly equal to [7] , where denotes the correlation coefficient . Also in this Gaussian case , is equal to the minimum mean squared error ( normalised by ) between and the linear , optimal estimate , . ( This is the optimal ‘filter’ when only the current output is available , although typically a filter such as the Wiener filter would employ the entire history of up to time . ) Gaussian models of this sort for biochemical signalling motifs were considered in [19] , with instantaneous mutual information expressed in terms of a signal-to-noise ratio equivalent ( for their models ) to the fidelity of Eq . 7 . Such Gaussian models ( if taken literally , rather than used to provide a lower bound on the information capacity [19] ) would imply that the input-output relation , , is linear and that does not depend on ( by the properties of the multivariate normal distribution ) . Our approach requires neither assumption . Whenever is a linear function of , that is for constants and , we consider to be the gain for the signal of interest [19] . The fidelity then depends on the ratio of the squared gain to the fidelity error and is given by . Methods of analysis of stochastic systems with dynamic inputs are still being developed . We argue that deriving expectations of network components conditional upon the histories of stochastic inputs is a powerful approach . We have developed three methods to determine components of Eqs . 5 and 6 ( SI ) : We note that our methods require that the inputs can be modeled as exogenous processes that are unaffected by interactions with the biochemistry of the signaling network ( a distinction emphasised in [20] ) . By an exogenous process we mean one whose future trajectory is independent , given its own history , of the history of the biochemical system . This model for an input is reasonable , for example , when the input is the level of a regulatory molecule , such as a transcription factor , that has relatively few binding sites in the cell . Transcriptional regulation is a primary means by which cells alter gene expression in response to signals [21] . We now provide an exact , in-depth analysis of a two-stage model of gene expression [22] where the fluctuating input , , is the rate ( or propensity ) of transcription and the signal of interest , , equals the current value of the input , . For example , may be proportional to the extracellular level of a nutrient or the cytosolic level of a hormone regulating a nuclear hormone receptor . The cellular response should account for not only the current biological state of but also future fluctuations . If we consider an input that is a Markov process , future fluctuations depend solely on the current value , and the cell would need only to ‘track’ the current state as effectively as possible and then use the representation in protein levels to control downstream effectors . These ideas are related to those underlying predictive information [23] , [24] . Our analysis requires only the stationary mean and variance of the input and that has exponentially declining ‘memory’ ( SI ) . Consequently , the autocorrelation function of is a single exponential with autocorrelation time ( the lifetime of fluctuations in ) . Examples include a birth-death process or a two-state Markov chain . We can generalize using , for example , weighted sums of exponentials to flexibly model the autocorrelation function of . Solving the ‘conditional’ master equation with a time-varying rate of transcription , we find that the conditionally expected protein level is a double weighted ‘sum’ of past levels of the signal ( SI ) : ( 9 ) ( where for simplicity the equation is stated for the case of zero initial mRNA and protein ) . We denote the rate of translation per molecule of mRNA by , the rate of mRNA degradation per molecule by , and the rate of degradation of protein per molecule by . The most recent history of the input exerts the greatest impact on the current expected output , with the memory of protein levels for the history of the input determined by the lifetimes of mRNA and protein molecules . Eq . 9 gives the signal of interest , ( a function of the history of the fluctuating transcription rate ) , that gene expression transmits with the highest fidelity to protein levels ( see Eq . 8 ) . Notice that the current value of the input , , cannot be recovered exactly from , which is therefore not a perfect representation of . We find , by contrast , that is an invertible , linear function of : ( 10 ) when the dynamics reach stationarity , and that the stationary unconditional mean is ( SI ) . Notice that does not converge for large to the average ‘steady-state’ solution for a static , but depends on . The discrepancy between Eqs . 9 and 10 results in dynamical error with non-zero magnitude ( Fig . 3B ) . Using our solutions for the conditional moments , we can calculate the variance components of Eq . 6 ( SI ) . For the faithfully transformed signal , when , we have ( 11 ) where is the ratio of the lifetime of mRNA to the lifetime of fluctuations in , and is the ratio of the lifetime of protein to the lifetime of fluctuations in . The magnitude of the dynamical error is in this case proportional to Eq . 11 ( 12 ) and the magnitude of the mechanistic error satisfies ( 13 ) When the autocorrelation time of becomes large ( and tending to zero ) , the dynamical error therefore vanishes ( Eq . 12 ) . In this limit , the output effectively experiences a constant input during the time ‘remembered’ by the system . To gain intuition about the the effect of relative lifetimes on the fidelity of signaling , we first suppose the mechanistic error is small relative to . Eq . 7 then becomes simply if protein lifetime is large relative to mRNA lifetime , ( as expected for many genes in budding yeast [25] ) . The fidelity thus improves as the protein lifetime decreases relative to the lifetime of fluctuations in , and the output is able to follow more short-lived fluctuations in the signal . This observation is only true , however , for negligible mechanistic error . It is the aggregate behavior of dynamical and mechanistic errors as a fraction of the total variance of the output that determines signaling fidelity , Eq . 7 . Effective network designs must sometimes balance trade-offs between the two types of error . Our framework naturally adapts to the scenario of controlling a network output to approach a desired ‘target’ response when , for example , the cell's environment changes . Combined with model search procedures for synthetic design [32] , it is a promising approach to the design of synthetic biomolecular networks . If the target response is given by , which is a function of the input history , then to guide the design process , we can decompose the error analogously to Eq . 5 and find an equivalent to Eq . 6 , a dissection of the network performance into orthogonal components ( SI ) .
Cells use the information conveyed by signaling networks to regulate their behavior and make decisions . Not all features of the input trajectory will , however , be relevant for a particular decision , and we define the fidelity between the output of the network and a signal of interest , , which is a function of the input trajectory . Information encoded in upstream fluctuations must eventually either be lost or encoded in current levels of cellular constituents . We have therefore focused on the fidelity with which is represented by the current output , . Using an orthogonal decomposition of the network's output into the faithfully transformed signal and error terms , we are able to identify two sources of error – dynamical and mechanistic . We assume the transformed signal , , to be an invertible function of . The aggregate behavior of the two types of error determines the signaling fidelity , and ignoring either may cause erroneous conclusions . We interpret as the current cellular estimate or ‘readout’ of the faithfully transformed signal . The magnitude of the fidelity error relative to the variance in , Eq . 7 , is a dimensionless measure of the quality of that estimate since . Furthermore , we have shown that is related to the mutual information between the input and output [7] . To apply our approach experimentally , we can use microfluidic technology to expose cells to the same controlled but time-varying input in the medium [33] , and a fluorescent reporter to monitor the network output , . This reporter could measure , for example , a level of gene expression or the extent of translocation of a transcription factor . The transformed signal , , and its variance ( for a given probability distribution of the input process ) can then be estimated with sufficient amounts of data by monitoring in each cell and in the microfluidic medium . We can determine the mechanistic error by measuring the average squared difference between the output of one cell and that of another — because the outputs of two cells are conjugate given the history of the input [7] –and hence determine the dynamical error by applying Eq . 6 . Our analysis is complementary to one based on information theory and the entire distribution of input and output [7] . Without making strong assumptions about the network and the input , calculation of mutual information is challenging for dynamic inputs . Previous work has considered either the mutual information between entire input and output trajectories with a Gaussian joint distribution of input and output [19] , [34] , or the ‘instantaneous’ mutual information between input and output at time [19] ( applicable in principle to non-Gaussian settings ) . Our approach , however , depends only on conditional moments and avoids the need to fully specify the distribution of the input process , which is often poorly characterized . The environments in which cells live are inherently dynamic and noisy . Here we have developed mathematical techniques to quantify how cells interpret and respond to fluctuating signals given their stochastic biochemistry . Our approach is general and will help underpin studies of cellular behavior in natural , dynamic environments .
Define , the transformed signal with zero mean . Then the signal and error components of Eq . 5 are pairwise uncorrelated: ( 14 ) Eq . 5 is a special case of the following general decomposition for any random variable ( with finite expectation ) , here denoted . Consider a filtration , or increasing sequence of conditioning ‘information sets’ , , where and . Let for , and let . Then the decomposition ( 15 ) satisfies for all since the sequence is a martingale difference sequence with respect to the filtration ( SI ) . Therefore , . | Cells do not live in constant conditions , but in environments that change over time . To adapt to their surroundings , cells must therefore sense fluctuating concentrations and ‘interpret’ the state of their environment to see whether , for example , a change in the pattern of gene expression is needed . This task is achieved via the noisy computations of biomolecular networks . But what levels of signaling fidelity can be achieved and how are dynamic signals encoded in the network's outputs ? Here we present a general technique for analyzing such questions . We identify two sources of signaling error: dynamic error , which occurs when the network responds to features of the input other than the signal of interest; and mechanistic error , which arises because of the inevitable stochasticity of biochemical reactions . We show analytically that increased biochemical noise can sometimes improve fidelity and that , for genetic autoregulation , feedback can be deleterious . Our approach also allows us to predict the dynamic signal for which a given signaling network has highest fidelity and to design networks to maximize fidelity for a given signal . We thus propose a new way to analyze the flow of information in signaling networks , particularly for the dynamic environments expected in nature . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"complex",
"systems",
"systems",
"biology",
"mathematics",
"applied",
"mathematics",
"biology",
"genetics",
"and",
"genomics"
] | 2013 | The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks |
Changes in histone acetylation occur during oocyte development and maturation , but the role of specific histone deacetylases in these processes is poorly defined . We report here that mice harboring Hdac1−/+/Hdac2−/− or Hdac2−/− oocytes are infertile or sub-fertile , respectively . Depleting maternal HDAC2 results in hyperacetylation of H4K16 as determined by immunocytochemistry—normal deacetylation of other lysine residues of histone H3 or H4 is observed—and defective chromosome condensation and segregation during oocyte maturation occurs in a sub-population of oocytes . The resulting increased incidence of aneuploidy likely accounts for the observed sub-fertility of mice harboring Hdac2−/− oocytes . The infertility of mice harboring Hdac1−/+/Hdac2−/−oocytes is attributed to failure of those few eggs that properly mature to metaphase II to initiate DNA replication following fertilization . The increased amount of acetylated H4K16 likely impairs kinetochore function in oocytes lacking HDAC2 because kinetochores in mutant oocytes are less able to form cold-stable microtubule attachments and less CENP-A is located at the centromere . These results implicate HDAC2 as the major HDAC that regulates global histone acetylation during oocyte development and , furthermore , suggest HDAC2 is largely responsible for the deacetylation of H4K16 during maturation . In addition , the results provide additional support that histone deacetylation that occurs during oocyte maturation is critical for proper chromosome segregation .
Post-translational modifications of histones , e . g . , phosphorylation , methylation , ubiquitination , and acetylation , are critically involved in a number of cellular processes that range from regulating gene expression to repair of DNA damage [1]–[3] . Lysine acetylation of histones is controlled by histone acetyl transferases ( HATs ) and histone deacetylases ( HDACs ) . In mammals , eighteen HDACs have been identified and grouped into four classes [4] . Class I enzymes HDAC1 and HDAC2 are highly homologous and ubiquitously expressed in different tissues [5] . HDAC1 and HDAC2 lack a DNA binding domain , as do all histone deacetylases , and execute their function by interacting with transcription factors as either homo- or heterodimers , or being part of multi-component repressor complexes [5] . Loss-of-function studies in mice have generated important insights regarding the function of HDAC1 and HDAC2 in regulating cell proliferation , apoptosis , and differentiation . One common theme of several tissue specific HDAC1/2 knockout studies is redundancy and compensation [6]–[9] . Nevertheless , results of other studies support the notion that HDAC1 and HDAC2 have distinct functions in some cells and tissues [10]–[13] . Taken together , these results indicate that the physiological functions of HDAC1 and HDAC2 are complicated and diversified in different tissues or cell types . Recently , we demonstrated compensatory functions of HDAC1 and HDAC2 during mouse oocyte development in which Hdac1 and Hdac2 were specifically deleted in oocytes [14]; deletion of both genes in oocytes results in infertility due to failure of follicle development beyond the secondary follicle stage with ensuing oocyte apoptosis attributed to hyperaceytlation of TRP53 . We also noted in that study that deleting Hdac1−/− in oocytes has no effect on fertility , as also observed for mice harboring Hdac1−/−/Hdac2−/+ oocytes . In contrast , mice in which Hdac2−/− was only deleted in oocytes are sub-fertile despite increased amounts of HDAC1 protein whereas their Hdac1−/+/Hdac2−/− counterparts are infertile [14] . Taken together , these results suggest a more prominent role for HDAC2 than HDAC1 in oocyte development , whereas HDAC1 plays a more prominent role in preimplantation development [13] . The molecular basis for the sub-fertility of mice harboring Hdac2−/− oocytes or infertility of mice harboring Hdac1−/+/Hdac2−/− , however , was not examined . We report here a characterization of the infertility and sub-fertilty phenotypes observed in mice harboring Hdac1−/+/Hdac2−/− or Hdac2−/− oocytes , respectively . We find that full-grown or nearly full-grown oocytes can be obtained from mice containing Hdac2−/− or Hdac1−/+/Hdac2−/− oocytes , respectively , albeit highly reduced numbers of Hdac1−/+/Hdac2−/− oocytes are recovered . Oocytes derived from Hdac1−/+/Hdac2−/− mice are not capable of supporting development due to defects beyond failure to develop past the secondary follicle stage that include abnormal spindle assembly or inability to initiate DNA replication following maturation and insemination . Similar to double mutant Hdac1:2−/− oocytes , Hdac1−/+/Hdac2−/− oocytes obtained from secondary follicles display reduced levels of transcription and decreased amounts of histone H3K4me1-3 . A subset of Hdac1−/+/Hdac2−/− and Hdac2−/− oocytes not only fails to undergo the maturation-associated deacetylation of histone H4K16 but also fails to segregate chromosomes correctly . This failure is likely a consequence of compromised kinetochore function in mutant oocytes manifested by a decreased ability to form cold-stable microtubule-kinetochore interactions and a reduced amount of CENP-A localized to centromeres . Last , although zygotes derived from Hdac2−/− eggs replicate their DNA , zygotes derived from Hdac1−/+/Hdac2−/− eggs do not .
We previously found that ovarian weight in 6-week-old-mice was reduced by ∼60% in Hdac1−/+/Hdac2−/− mice and by ∼70% in Hdac1:2−/− mice compared to wild-type ( WT ) mice [14] . The size of ovaries obtained from 6-week old Hdac1−/+/Hdac2−/− mice was intermediate of WT and double mutant mice ( Figure 1A and Fig . 2 in [14] ) . In addition , although antral follicles were found in ovaries from Hdac1−/+/Hdac2−/− mice , their number appeared decreased when compared to WT ovaries ( Figure 1A ) ; no antral follicles were found in ovaries from Hdac1:2−/− mice [14] . Consistent with fewer antral follicles in Hdac1−/+/Hdac2−/− mice significantly fewer oocytes were obtained ( Figure 1B ) and fewer MII eggs were recovered following ovulation ( Figure 1C ) . The diameter of the Hdac1−/+/Hdac2−/− oocytes was about 90% that of full-grown wild-type oocytes ( 77 . 4±0 . 8 µm , wild-type; 69 . 9±1 . 1 µm mutant , mean ± SEM , p<0 . 0001 ) . Oocyte development is accompanied by a change in chromatin configuration , the so-called non-surrounded nucleolus ( NSN ) to surrounded nucleolus ( SN ) configuration , which is associated with acquisition of developmental competence [15] . This transition was modestly inhibited in mutant oocytes; whereas in WT oocytes about 75% of the oocytes are in the SN configuration , we found that 46% and 51% were in the SN configuration for Hdac1−/+/Hdac2−/− and Hdac2−/− oocytes , respectively . Histological analysis of ovarian sections was conducted using ovaries from mice 18-days of age to capture the full spectrum of follicle development; the size of mutant ovaries was smaller than ovaries present in wild-type mice ( 4 . 25±0 . 12 mg vs 2 . 46±0 . 18 , n = 5 , p = 0 . 005 ) . The analysis revealed that primary , secondary , and antral follicles were observed in Hdac1−/+/Hdac2−/− ovaries ( Figure 1D ) . Although there were no differences in the number of primordial and primary follicles , mutant ovaries had more secondary follicles but fewer antral follicles than WT ovaries ( Figure 1E ) . Thus , development of secondary follicles to antral follicles in mutant mice appeared either delayed or inhibited , with the decrease in the number of antral follicles being similar to the increase in the number of secondary follicles . These results demonstrated that in Hdac1−/+/Hdac2−/− mice oocyte development was mainly blocked at the secondary follicle stage , and one allele of Hdac1 was not sufficient to overcome this block , supporting our previous conclusion that HDAC2 is the major HDAC in oocyte development [14] . Last , we observed no overt sign of oocyte degeneration in Hdac1−/+/Hdac2−/− mice , which is in striking contrast to the increased incidence of oocyte degeneration in Hdac1:2−/− oocytes [14] . Our previous study characterizing the phenotype of Hdac1:2−/− oocytes was performed with 12-day-old mice [14] . Accordingly , we conducted studies regarding transcription and histone modifications using Hdac1−/+/Hdac2−/− oocytes collected on day-12 post-partum to make valid comparisons . Hdac2 transcripts were reduced by >95% in oocytes obtained from 12-day-old Hdac1−/+/Hdac2−/− mice , whereas Hdac1 mRNA level only decreased ∼15% ( Figure 2A ) , which was reflected by a dramatic decrease in the nuclear staining of HDAC2 and only modest decrease ( <15% ) in HDAC1 nuclear staining ( Figure 2B ) . The small decrease in Hdac1 mRNA likely reflects a compensatory increase in Hdac1 expression in face of loss of Hdac2 [14] . Similar to double mutant oocytes [14] , there was an increase in acetylation of both histone H3 and H4 on specific lysine residues ( Figure 2C and 2E ) . Transcription was reduced by ∼20% , as assayed by EU incorporation , in Hdac1−/+/Hdac2−/− oocytes when compared to WT oocytes ( Figure 2D and 2E ) , being intermediate when compared to Hdac1:2−/− oocytes in which transcription is reduced by 40% [14] . Hdac2−/− oocytes did not exhibit any significant decrease in the extent of EU incorporation relative to WT ( 53±4 relative units vs 48±2 relative units , respectively; p>0 . 20 . The data are expressed as mean ± SEM , and the number of oocytes examined was 16 and 24 , respectively ) . We also observed a significant decrease in the intensity of H3K4me1&2 ( ∼20% ) and H3K4me3 ( ∼40% ) nuclear staining in Hdac1−/+/Hdac2−/− oocytes ( Figure 2D and 2E ) ; active promoters are marked in general by trimethylated H3K4 ( H3K4me3 ) , whereas dimethylated H3K4 ( H3K4me2 ) is often found in the coding region [16] , [17] . A decrease in H3K4me1 of ∼20% , and a decrease in H3K4me2 and H3K4me3 of ∼35% and 60% , respectively were observed in Hdac1:2−/− oocytes ( Figure 3 and Fig . 6D in [14] ) . By using antibodies that specifically recognize CTD S2 phosphorylation , which is a marker for RNA polymerase II engaged in transcription [18] , a significant reduction ( ∼20% ) was observed in CTD S2 phosphorylation in Hdac1−/+/Hdac2−/− growing oocytes ( Figure 2D and 2E ) . This decrease was consistent with the 20% decrease in global transcription . We previously observed a 40-fold increase in the amount of Kdm5b transcript in Hdac1:2−/− oocytes and no change in Hdac1−/− oocytes [14]; KDM5B is apparently the only histone lysine demethylase that can demethylate H3K4me3 , H3K4me2 and H3K4me1 [19] . Kdm5b transcripts were also increased , as determined by qRT-PCR , by 18-fold in Hdac1−/+/Hdac2−/− oocytes and 3-fold in Hdac2−/− oocytes . The extent of up-regulation of Kdm5b transcripts was related to the extent of loss of Hdac1 and Hdac2 , with a corresponding decrease in histone H3K4me1-3 ( Figure 3B ) . These results suggest that KDM5B plays a significant role in establishing the steady-state amount of methylated histone H3K4 . In contrast to the dramatic increase in TRP53K379 acetylation that occurs in Hdac1:2−/− oocytes [14] and results in increased TRP53 activity [20] , [21] , TRP53 was not hyperacetylated in Hdac1−/+/Hdac2−/− oocytes ( Figure 2D and 2E ) , consistent with apoptosis not being observed in oocytes from these mice . The absence of TRP53 acetylation suggests that only one allele of Hdac1 or Hdac2 is sufficient to prevent increased TRP53 activity in growing oocytes . Although most Hdac1−/+/Hdac2−/− oocytes were arrested within secondary follicles ( Figure 1D and 1E ) , a small number of nearly full-grown oocytes could be recovered from Hdac1−/+/Hdac2−/− ovaries ( Figure 1B ) . A single mutant of Hdac1 or Hdac2 has no apparent effect on oocyte maturation and subsequent preimplantation development [14] and Figures S1 and S2 ) . We next asked if depletion of HDAC2 combined with a reduction of HDAC1 has any effect on oocyte maturation by using Hdac−/+/Hdac2−/− oocytes . We first checked Hdac1 and Hdac2 expression in full-grown Hdac1−/+/Hdac2−/− oocytes . qRT-PCR revealed that Hdac2 mRNA was reduced by >98% in Hdac1+/−/Hdac2−/− full-grown oocytes ( Figure 4A ) . Hdac1 mRNA levels , however , showed no difference between Hdac1−/+/Hdac2−/− and WT oocytes ( Figure 4A ) , which suggests an ∼2-fold compensatory increase in Hdac1 expression in Hdac1−/+/Hdac2−/− oocytes . Consistent with these results , the amount of HDAC1 protein exhibited a mild increase in Hdac1−/+/Hdac2−/− oocytes when compared to WT oocytes ( Figure 4B ) , whereas there was a significant increase of both Hdac1 mRNA and HDAC1 protein in Hdac2−/− oocytes ( Figure 4A and 4B ) . Because the amount of HDAC1 present in Hdac1−/+/Hdac2−/− and Hdac2−/− oocytes was similar to or more than that in WT oocytes , Hdac1−/+/Hdac2−/− and Hdac2−/− oocytes provide a system to assess the role of maternal HDAC2 in oocyte maturation and subsequent fertilization and preimplantation development . Genome-wide histone deacetylation mediated by HDACs at several lysine residues occurs during oocyte maturation in mouse [22] , [23] , [24] and pig [25] . The identity of the responsible HDACs , however , is poorly defined in mouse . Accordingly , we first assessed the effect of deleting maternal HDAC2 on histone acetylation during oocyte maturation . Remarkably , immunostaining revealed that although the amount of HDAC1 associated with chromosomes was unchanged in mutant oocytes , the acetylation state of histone H4K16 was affected , being increased ∼2 . 3-fold in Hdac2−/− and ∼4-fold in Hdac1−/+/Hdac2−/− MII eggs ( Figure 4C and 4D ) ; the maturation-associated deacetylation of histone H4K5 , H4K8 , H4K12 , H3K4 , H3K9 , and H3K14 appeared to occur normally in these cells ( Figure S3 ) . It was unlikely that an increase in MYST1 , an H4K16-specific histone acetyltransferase [26] and largely responsible for H4K16 acetylation [27] , accounted for the observed increase in acetylated H4K16 following maturation because there was no obvious change in the amount of MYST1 protein in Hdac1−/+/Hdac2−/− or Hdac2−/− oocytes ( Figure S4A ) . The amount of acetylated H416 was increased in growing Hdac1−/+/Hdac2−/− oocytes , suggesting nuclear HDAC2 can deacetylate H4K16 ( Figure 2C and 2D ) . Nuclear HDAC2 in full-grown oocytes , however , appeared unable to deacetylate H4K16 because similar amounts of acetylated H4K16 were observed in full-grown WT and Hdac1−/+/Hdac2−/− oocytes ( Figure S4B ) . The increased acetylation of H4K16 following maturation of mutant oocytes was presumably due to loss of HDAC2 activity because Hdac2−/− oocytes injected with an Hdac2 cRNA , but not an Egfp cRNA , exhibited the maturation-associated decrease in acetylated H4K16 to a similar degree as observed in wild-type oocytes ( Figure 5 ) . Note that HDAC2 was expressed to similar levels in mutant oocytes when compared to wild-type oocytes , minimizing the likelihood that deacetylation of H4K16 was due to off-targeting effects . In addition , chromosomes appeared less condensed in Hdac1−/+/Hdac2−/− and Hdac2−/− MII eggs; whereas only 2 . 4% of WT eggs ( 4 out of 163 ) had chromosomes that appeared not fully condensed , this incidence increased to 30 . 8% ( 28 out of 91 ) and 44 . 9% ( 40 out of 89 ) in Hdac2−/− and Hdac1−/+/Hdac2−/− MII eggs , respectively . The effect of deleting Hdac1 and Hdac2 on the acetylation state of H4K16 and chromosome condensation next led us to examine spindle formation and chromosome alignment in Hdac1−/+/Hdac2−/− and Hdac2−/− eggs following oocyte maturation . Analysis of meiotic spindle configurations in MII eggs revealed a significant increase in the proportion of eggs with abnormal spindles and misaligned chromosome in Hdac2−/− and Hdac1−/+/Hdac2−/− eggs when compared to WT eggs ( Table 1 and Figure 6 ) . As anticipated the incidence of aneuploidy was increased in Hdac1−/+/Hdac2−/− and Hdac2−/− eggs ( Table 2 and Figure S5 ) . Absence of HDAC2 protein was likely the proximate cause for the observed increased incidence of abnormal spindles and misaligned chromosomes because over-expressing HDAC2 in Hdac2−/− oocytes restored , in large part , the WT phenotype ( Figure 7 ) . In somatic cells , histone hyperacetylation can interfere with kinetochore assembly [28] and in budding yeast , which have 125 bp “point” centromeres ( which contrasts to 0 . 1–5 Mb bp “regional” centromeres in fission yeast and humans ) , hypoacetylation of H4K16 is critical to maintain kinetochore function [29] . Hyperacetylation of H4K16 in oocyte lacking HDAC2 , therefore , could also compromise kinetochore function and lead to the increased incidence of misaligned chromosomes on the spindle . Such appears to be the case . Most spindle microtubules depolymerize at low temperature , except for kinetochore microtubules , which are preferentially stabilized [30] , i . e . , compromised kinetochore function leads to a decrease in the number of cold-stable microtubules . Finding that the incidence of kinetochores , as detected by CREST staining , not associated with cold-stable microtubules was significantly increased in mutant oocytes strongly implies that kinetochore function is compromised ( Figure 8 ) . This conclusion is further buttressed by observing less CENP-A staining at centromeres in mutant oocytes ( Figure 9 ) . The incidence of aneuploidy in Hdac2−/− eggs could account for the observed sub-fertility in mutant female mice , but would not account for the infertility in Hdac1−/+/Hdac2−/− mice . Accordingly we ascertained whether Hdac1−/+/Hdac2−/− eggs could be fertilized , and if so assessed their ability to develop . Hdac1−/+/Hdac2−/− eggs were readily fertilized as evidenced by formation of a male and female pronucleus; only small fraction ( ∼15% ) of these eggs failed to be fertilized . When Hdac1−/+/Hdac2−/− female mice were mated to wild-type males , no blastocysts were recovered following development in vivo . Performing a similar mating in which 1-cell embryos were recovered and then permitted to develop in vitro demonstrated that most Hdac1−/+/Hdac2−/− embryos arrested at the 1-cell ( 53% ) and 2-cell ( ∼20% ) stages , and a higher incidence of fragmentation was observed ( Figure 10A ) . The high incidence of failure of Hdac1−/+/Hdac2−/− 1-cell embryos to cleave to the 2-cell stage led us to examine whether failure to undergo DNA replication was the cause . Whereas BrdU was readily incorporated by zygotes obtained from WT and Hdac2−/− eggs , all zygotes obtained from Hdac1−/+/Hdac2−/− eggs failed to incorporate BrdU ( Figure 10B ) . The 2-cell embryos observed following insemination of Hdac1−/+/Hdac2−/− eggs , therefore , were likely the products of pseudo-cleavage . Last , BrdU incorporation by zygotes obtained from Hdac2−/− eggs suggests that the up-regulation of HDAC1 in Hdac2−/− oocytes compensates for loss of HDAC2 . Deleting both Hdac1 and Hdac2 in dividing cells results in a cell cycle block in G1 phase through induction of expression of the cyclin-dependent kinase inhibitors P21 and P57 [9] . Due to the similar phenotype observed in zygotes derived from Hdac1−/+/Hdac2−/− eggs , we asked whether a similar situation occurred . Although there was an increased abundance of p57 and p21 transcripts in these zygotes compared to WT , there was no obvious increase in the amount of P57 or P21 protein ( Figure S6A and S6B ) . DNA replication in 1-cell embryos appears to require recruitment of two maternal mRNAs during maturation , namely , CDC6 and ORC6L [31] , [32] . Cdc6 and Orc6l mRNAs were effectively recruited during maturation as evidenced by strong fluorescence signal observed in the pronuclei ( Figure S6C ) ; little or no CDC6 or ORCL protein is present in oocytes [31] , [32] . This finding minimizes the likelihood that insufficient amounts of CDC6 and ORC6L protein were the cause for failure of fertilized Hdac1−/+/Hdac2−/− eggs to initiate DNA replication .
We previously demonstrated that specifically deleting in oocytes both Hdac1 and Hdac2 genes led to developmental failure beyond the secondary follicle stage , the likely consequence of a 40% decrease in transcription , a massive perturbation in the transcriptome , and TRP53 hyperacetylation leading to apoptosis . The results reported here extend our understanding of HDAC1 and HDAC2 functions during oocyte development by identifying processes they control during oocyte maturation and following fertilization . The ability of Hdac1−/+/Hdac2−/− or Hdac2−/− oocytes to reach the nearly full-grown or full-grown stage within preovulatory antral follicles is likely a consequence that they fail to undergo apoptosis because TRP53 is not hyperacetylated . Nevertheless , only a small fraction of Hdac1−/+/Hdac2−/− oocytes develop beyond the secondary follicle stage , whereas development beyond the secondary follicle stage is quite robust in Hdac2−/− oocytes . This difference may reflect that global transcription is reduced by 20% in Hdac1−/+/Hdac2−/− oocytes but unaffected in Hdac2−/− oocytes . The perturbation in transcription in Hdac1−/+/Hdac2−/− oocytes could impact the communication that exists between oocytes and the surrounding granulosa/cumulus cells and is essential for both oocyte growth and follicle cell proliferation [33] . Although not determined here , the transcriptome of Hdac1−/+/Hdac2−/− oocytes is likely dramatically perturbed as it is in double knock out oocytes [14] . That such an alteration could compromise follicle cell function , is supported by our finding that whereas ovarian weight increases ∼2-fold following eCG priming of 21-day-old WT mice , ovaries from mutant mice fail to respond ( data not shown ) . We also find that deleting Hdac2 results in an increased incidence of aneuploidy that is associated with hyperacetylation of H4K16 following oocyte maturation , a finding implicating HDAC2 in chromosome condensation and segregation . Last , depletion of HDAC2 combined with reduction of HDAC1 in Hdac1−/+/Hdac2−/− oocytes results in a subpopulation that can mature to MII , but following egg activation , fail to replicate their DNA . There is no apparent effect on fertility of Hdac1−/−/Hdac2−/+ mice [14] , whereas Hdac1−/+/Hdac2−/− mice are infertile , their infertility attributed to a combination of defects in oocyte development , maturation , and embryo development . These results provide further support that HDAC2 plays a more prominent role during oocyte development , whereas HDAC1 , which is zygotically expressed , is the major HDAC regulating preimplantation development [13] . HDAC1 function during oocyte maturation and following fertilization remains less defined . The amount of HDAC1 protein in Hdac1−/− full-grown oocytes is ∼55% that of WT oocytes ( Figure S1B ) , confounding analysis of HDAC1 function because such mice are fully fertile [14] . The highest levels of Hdac1 expression are in the primordial and primary follicle stages [14] , [34] , which may explain why the Zp3-Cre mediated strategy did not effectively deplete HDAC1; the Zp3 promoter becomes active during the primordial to primary follicle transition [35] . Oocyte growth is accompanied by a transition from the so-called non-surrounded nucleolus ( NSN ) configuration in which condensed chromatin does not surround the nucleolus to surrounded nucleolus ( SN ) configuration that is characterized by highly condensed chromatin around the nucleolus [36] , [37] . During this transition , which is uncoupled for the onset of transcriptional quiescence [22] , [38] , [39] , there is an increase in both histone acetylation and methylation [40] . These increases that occur during oocyte growth are not affected in Hdac2−/− oocytes ( Figure S7A , S7B ) , i . e . , full-grown mutant SN oocytes display the increase whereas mutant NSN oocytes do not . Hdac1−/+/Hdac2−/− oocytes that develop beyond the secondary follicle stage and are capable of undergoing meiotic maturation display two phenotypes . One phenotype is a failure to condense fully chromosomes ( see below for further discussion of HDAC2 in chromosome dynamics ) . For those oocytes that mature to MII , the phenotype is a failure to undergo DNA replication following insemination . Whether the failure to replicate DNA is directly linked to altered histone acetylation or a consequence of perturbed transcription during oocyte development/acetylation of non-histone proteins is unknown . In general , however , there is a positive relationship between histone acetylation and DNA replication ( [41] and references therein ) , possibly a consequence of increased chromatin accessibility that facilitates pre-replicative complex assembly . Last , the observed early developmental arrest is similar to several maternal-effect mutants ( e . g . , Npm2 , Stella , Zar1 , Hsf1 , MLL2 , and Mater ) [38] , [39] , [42]–[45] , which arrest primarily at the 1-2-cell stage . No DNA replication defects , however , were reported for these mutants , suggesting a different mechanism for developmental arrest . Mouse oocyte maturation is accompanied by a global decrease in acetylated H3 and H4 histones [22]–[24] , but the responsible HDACs have yet to be identified . Histone H3 and H4 deacetylation are not observed during M-phase for preimplantation embryos , except for deacetylation of H4K5 [23] . A similar situation is observed in NIH 3T3 cells , i . e . , only H4K5 is deacetylated during M phase [23] . HDACs are clearly active in full-grown GV-intact oocytes because treatment with TSA , an HDAC inhibitor , results in increased histone acetylation [22] . Likewise , histone hyperacetylation is observed in growing oocytes in which Hdac1 and Hdac2 have been specifically deleted [14] . The amount of acetylated histone in GV-intact oocytes , therefore , represents a steady-state level reflecting HDAC and HAT activities . HDAC activity presumably outstrips HAT activity following maturation , thus accounting for the global decrease in histone acetylation . For example , treating MII eggs with TSA does not lead to an increase in histone acetylation , suggesting that HATs are inactive ( or cannot access their histone substrates ) . In contrast , HDACs remain functional ( or have access to their histone substrates ) because oocytes matured in the presence of TSA fail to exhibit histone deacetylation but do so shortly following transfer to TSA-free medium [23] . HDAC2 is clearly implicated in the maturation-associated deacetylation of H4K16 because an increase in H4K16 is observed in Hdac1−/+/Hdac2−/− and Hdac2−/− MII eggs , but not in Hdac1−/−/Hdac2−/+ oocytes ( Figure S7C ) . The apparent greater increase in acetylated H4K16 in Hdac1−/+/Hdac2−/− MII eggs when compared to Hdac2−/− MII eggs ( Figure 4C ) also suggests a role for HDAC1 in controlling the acetylation state of H4K16 , albeit less than that of HDAC2 . It should be noted that HDAC2 is not associated with chromosomes in MII eggs [13] , [14] and the amount of chromosome-associated HDAC1 is similar in Hdac1−/+/Hdac2−/− and Hdac2−/− MII eggs ( Figure 4C ) . Because the total amount of HDAC1 is greater in Hdac2−/− oocytes than in Hdac1−/+/Hdac2−/− oocytes , HDAC1 not associated with chromosomes may be responsible for the decreased amount of acetylated H4K16 in Hdac2−/− eggs when compared to Hdac1−/+/Hdac2−/− eggs . To date , only class III HDACs ( sirtuins ) have been shown to deacetylate specifically histone H4K16 [46] . Taken together , our results suggest that HDAC2 is the HDAC largely responsible for the maturation-associated deacetylation of H4K16 , i . e . , Class I HDACs are involved . This conclusion contrasts with the conclusion drawn from a study using porcine oocytes that Class I HDACs are not involved and was based on the inability of valproic acid , an inhibitor of Class I HDACs , to inhibit the maturation-associated decrease in histone acetylation [47] . That study , however , only assayed for the acetylation state of histone H4K8 and H3K14 , and not H4K16 , which could reconcile the different conclusions . Last , other acetylated lysine residues in H3 and H4 are deacetylated during maturation in both Hdac1−/+/Hdac2−/− or Hdac2−/− oocytes . The identity of the responsible HDACs remains to be determined . A striking finding reported here is the increase in H4K16 acetylation in virtually all oocytes lacking HDAC2 following oocyte maturation and the associated failure of chromosomes to condense fully and align on the metaphase plate properly in a subpopulation; these failures presumably underlie the observed increased incidence in aneuploidy . That not all mutant oocytes show abnormal chromosome condensation and chromosome alignment despite an increase in H4K16 acetylation in all mutant oocytes is consistent with the finding that when global histone hyperacetylation is induced by treatment with TSA , a substantial fraction [but not all] of the maturing oocytes become euploid ( ∼40% ) [48] . Thus , even when the maturation-associated deacetylation of histones is inhibited and histone acetylation is increased , oocytes/eggs apparently have robust mechanisms to ensure proper chromosome segregation . This ability is consistent with the centrality of oocytes/eggs in reproduction , i . e . , robust mechanisms to segregate properly chromosomes are an outcome of strong selective pressures to maintain reproductive fitness . The lower incidence of aneuploidy exhibited in eggs lacking HDAC2 , when compared to TSA-treated oocytes , is consistent with mutant oocytes undergoing normal histone deacetylation , except for H4K16 . Acetylation of H4K16 inhibits formation of the higher order 30 nm chromatin structure , and loss of H4K16 shows defects equivalent to the loss of the H4 tails [49] . The increased acetylation of H4K16 in oocytes lacking HDAC2 is likely contributes to the failure of chromosomes to condense fully during oocyte maturation , despite deacetylation of other acetylated lysines . In addition , the increased incidence in mutant oocytes of kinetochores not able to interact with microtubules to form cold-stable microtubules is a functional assay confirming that kinetochore function is indeed compromised . The basis for compromised kinetochore function may reside in histone hyperacetylation interfering with proper kinetochore assembly [50] that leads to the observed decrease in CENP-A staining in mutant oocytes . Although an age-dependent loss of cohesion can account for the majority of aneuploidies associated with increased maternal age [51] , compromised histone deacetylation during maturation may be another source . For example , following maturation of mouse oocytes obtained from old mice , normal deacetylation of H4K8 and H4K12 do not occur , whereas H4K16 and H3K14 are properly deacetylated [48] . Likewise , less deacetylation of histone H4K12 occurs following maturation of oocytes obtained from older women when compared to younger women , and moreover , residual acetylation is correlated with misaligned chromosomes [52] . Thus , compromised histone deacetylation following oocyte maturation in general may result in an increased incidence of aneuploidy by leading to misaligned chromosomes on the meiotic spindle . In summary , the results reported here provide further evidence for a critical role of HDAC2 in oocyte development and provide explanations for the infertility observed in Hdac1−/+/Hdac2−/− and sub-fertility in Hdac2−/− female mice . In particular , the results suggest that HDAC2 is largely responsible for deacetylation of H4K16 that occurs during oocyte maturation and that such deacetylation is critical for proper chromosome segregation .
Details for generating mutant mouse lines , histological analysis of ovaries , oocytes and embryos collection , RNA extraction and real time RT-PCR , immunostaining and immunoblot analysis are described in [14] . Hdac1 or Hdac2 mutants , in which the gene has only been deleted in oocytes , are referred to as Hdac1−/− or Hdac2−/− , respectively . Hdac1-Hdac2 mutants ( double mutant ) are referred to as Hdac1:2−/− . Hdac1 heterozygotes-Hdac2 null oocytes are referred to as Hdac1−/+/Hdac2−/− and Hdac1 null-Hdac2 heterozygote oocytes are referred to as Hdac1−/−/Hdac2−/+ . To obtain 1-cell embryos for BrdU incorporation assays , Hdac1−/+/Hdac2−/− female mice were superovulated with the injection of 5 IU of PMSG , followed 48 hours later by 5 IU of human chorionic gonadotropin ( hCG ) . The mice were then mated with B6D2F1/J males ( Jackson Laboratory , Bar Harbor , ME ) and 1-cell embryos collected 24 h post hCG administration . Cumulus cells were removed by a brief hyaluronidase treatment ( 3 mg/ml ) . All animal experiments were approved by the institutional animal use and care committee and were consistent with the National Institutes of Health ( NIH ) guidelines . Mouse Hdac2 cDNA was cloned in a PIVT plasmid using standard recombinant DNA techniques . To prepare cRNAs , plasmids were linearized , and capped mRNAs were generated by in vitro transcription using T7 mMESSAGE mMachine ( Ambion ) according to the manufacturer's instructions . Following in vitro transcription , cRNA was polyadenylated using the PolyA Tailing Kit ( Ambion ) . Synthesized cRNA was then purified using an MEGAclear Kit ( Ambion ) , redissolved in RNase-free water , and stored at −80°C . Full-grown oocytes were collected from mice of different genotypes and cultured in CZB [53] medium containing 0 . 2 mM IBMX in an atmosphere of 5% CO2 in air at 37°C . Microinjection of oocytes was performed as previously described [54] . Injections were done in 10-µl drops of modified Whitten's medium [55] containing 15 mM HEPES , pH 7 . 2 , 7 mM Na2HCO3 , 10 µg/ml gentamicin and 0 . 01% PVA containing 2 . 5 µM milrinone . Approximately 10 pl of EGfp or Hdac2 cRNA was injected into the cytoplasm of GV oocytes using a PLI-100 Pico-Injector ( Harvard Apparatus , Holliston , MA ) on the stage of a Nikon TE2000 microscope equipped with Hoffman optics and Narishige micromanipulators . Following microinjection , oocytes were cultured in CZB plus IBMX for 24 h , and then the injected oocytes were matured by washing and culturing them in IBMX-free CZB medium for 18 h . 5-Ethynyl uridine ( 5EU ) incorporation assays to detect transcription were performed as previously described [56] . Meiotically incompetent growing oocytes ( or embryos ) were cultured in the presence of 1 mM 5EU in CZB medium for oocytes or KSOM+AA for embryos [57] for 1 h and then fixed in 2% paraformaldehyde in PBS for 20 min at room temperature . 5EU incorporation into RNA was detected using Click-iT RNA Alexa Fluor 488 HCS Assay ( Invitrogen ) according to the manufacturer's protocol . Fluorescence was detected on a Leica TCS SP laser-scanning confocal microscope . The intensity of fluorescence was quantified using Image J software ( National institutes of Health ) as previously described [58] . Assays were conducted as previously described [32] except the 1-cell embryos were cultured for 5–8 h rather than 30 min in KSOM medium supplemented with 10 µM BrdU ( Sigma ) . Monastrol treatment , immunocytochemical detection of kinetochores and chromosome counting were performed as previously described [51] . Images were collected with a spinning disk confocal miroscope at 0 . 4 µm intervals to span the entire region of the spindle , using a 100×1 . 4 NA oil immersion objective . To obtain a chromosome count for each egg , serial confocal sections were analyzed to determine the total number of kinetochores . Wild-type , Hdac2−/− and Hdac1−/+/Hdac2−/− full-grown oocytes were matured in CZB medium for 7 h to MI and then transferred to in MEM medium and placed on ice for 8 min before fixing in 2% paraformaldehyde for 20 min . Immunocytochemistry was performed with CREST autoimmune serum and anti-TUBB antibody to label kinetochores and microtubules , respectively . Images were collected with a spinning disk confocal miroscope at 0 . 4 µm intervals to span the entire region of the spindle , using a 100×1 . 4 NA oil immersion objective as described before [51] . TUNEL ( TdT-mediated dUTP nick end labeling ) assays were performed with an In Situ Cell Death Detection Kit ( Roche Diagnostics , Basel , Switzerland ) according to the manufacturer's instructions . The following antibodies were used for immunofluorescence and/or immunoblotting blotting: anti-CDC6 rabbit polyclonal antibody ( 11640-1AP; Proteintech; IF , 1∶200 ) , anti-P21 mouse monoclonal antibody ( 556430 , BD Pharmingen; IF , 1∶100 ) , anti-P57 rabbit monoclonal antibody ( 2372-1; Epitomics; IF , 1∶200 ) , anti-ORC6L rat monoclonal antibody ( 4737; Cell signaling; IF , 1∶100 ) , and anti-CENP-A rabbit monoclonal antibody ( 2048; Cell signaling; IF , 1∶200 ) . All the other antibodies used in this paper have been described previously [14] . Experiments were performed at least three times and the values are presented as mean ± SEM . All proportional data were subjected to an arcsine transformation before statistical analysis . Statistics were calculated with Microsoft Excel software . A P-value of <0 . 05 was considered to be statistically significant . | Oocyte development is becoming of increasing interest not only in the broad research community but also within the general public due , in part , to the ever increasing demand for and use of assisted reproductive technologies ( ART ) to treat human infertility , and because the oocyte-to-embryo transition encompasses a natural reprogramming of gene expression , a process central to forming iPS cells . Dramatic changes in chromatin structure and gene expression occur during oocyte development , but the role of such changes in generating oocytes that are capable of maturing , being fertilized , and giving rise to offspring is very poorly understood . Histone deacetylases ( HDACs ) are critically involved in modulating chromatin structure . Here , we describe the effect of specifically deleting the gene encoding Hdac2 in mouse oocytes and find the fertility of female mice harboring such oocytes is compromised . Although such mutant oocytes can grow they fail to mature properly to become an egg . The primary defect is that histone H4 acetylated on lysine 16 fails to become deacetylated as the oocyte matures to become an egg , with the consequence that the ability of chromosomes to interact with spindle microtubules is compromised , which in turn leads to improper chromosome segregation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"genetics",
"biology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] | 2013 | Histone Deacetylase 2 (HDAC2) Regulates Chromosome Segregation and Kinetochore Function via H4K16 Deacetylation during Oocyte Maturation in Mouse |
Asparagine synthetase ( ASNS ) and CTP synthase ( CTPS ) are two metabolic enzymes crucial for glutamine homeostasis . A genome-wide screening in Saccharomyces cerevisiae reveal that both ASNS and CTPS form filamentous structures termed cytoophidia . Although CTPS cytoophidia were well documented in recent years , the filamentation of ASNS is less studied . Using the budding yeast as a model system , here we confirm that two ASNS proteins , Asn1 and Asn2 , are capable of forming cytoophidia in diauxic and stationary phases . We find that glucose deprivation induces ASNS filament formation . Although ASNS and CTPS form distinct cytoophidia with different lengths , both structures locate adjacently to each other in most cells . Moreover , we demonstrate that the Asn1 cytoophidia colocalize with the Asn2 cytoophidia , while Asn2 filament assembly is largely dependent on Asn1 . In addition , we are able to alter Asn1 filamentation by mutagenizing key sites on the dimer interface . Finally , we show that ASN1D330V promotes filamentation . The ASN1D330V mutation impedes cell growth in an ASN2 knockout background , while growing normally in an ASN2 wild-type background . Together , this study reveals a connection between ASNS and CTPS cytoophidia and the differential filament-forming capability between two ASNS paralogs .
Intracellular compartmentation is crucial for the function of a cell . In 2010 , three studies reported that the metabolic enzyme CTP synthase ( CTPS ) , forms filamentous compartments , termed cytoophidia , in fruit flies , bacteria and budding yeast cells [1–3] . Subsequent studies revealed that the CTPS cytoophidium also exists in fission yeast , human and Arabidopsis cells [4–7] . CTPS can form cytoophidia not only in the cytoplasm but also in the nucleus of eukaryotic cells [8–10] . A genome-wide screening identified that at least 23 proteins , including CTPS and asparagine synthetase ( ASNS ) , can form filaments in budding yeast [9] . Both CTPS and ASNS are glutamine-utilizing enzymes . While CTPS converts the nucleotide UTP into CTP , the enzyme ASNS catalyzes the conversion of L-aspartate into L-asparagine . Both enzymes have a significant impact on glutamine homeostasis [11 , 12] . In Saccharomyces cerevisiae , there are two ASNS genes , ASN1 and ASN2 , and two CTPS genes , URA7 and URA8 [13 , 14] . Genetic studies have demonstrated that asparagine auxotrophy in yeast results from a combination of ASN1 and ASN2 mutations , while neither ASN1 nor ASN2 mutation can individually lead to total auxotrophy [15] . Double ASN1 and ASN2 mutants have no effect on cell cycle progression in S . cerevisiae , while ASNS mutation lead to G1 phase arrest in hamster [13 , 16] . ASNS knockdown significantly deregulated the expression of CDK4 , CDK6 and Cyclin D1 and suppressed the growth of melanoma cells and epidermoid carcinoma cells [17] . To better understand filamentation of metabolic enzymes , here we use ASNS and CTPS in S . cerevisiae as examples . We are particularly interested in the following important questions: 1 ) Is ASNS filamentation sensitive to glucose deprivation ? 2 ) Is ASNS present in the same cytoophidium as CTPS ? 3 ) Does the Asn1 cytoophidium behave identically to the Asn2 cytoophidium ? 4 ) What are the consequences when the ASNS filamentation is disturbed ?
ASNS has been discovered to form cytoophidia in the cytoplasm and nucleus in vivo [9] . Using GFP-tagged strains , we confirmed that both Asn1 and Asn2 could form cytoophidia in the cytoplasm and nucleus ( Fig 1A and 1B ) . Furthermore , the GFP tagged Asn1 or Asn2 cells were cultured in different growth phases to test the cytoophidium abundance . Previous studies defined the different growth phases of budding yeast [18 , 19] . We collected a population of cells cultured for 6 hours , 24 hours and 7 days . These three durations represent the exponential , diauxic and stationary phases , respectively ( Fig 1A–1C ) . In the exponential phase , neither Asn1 nor Asn2 formed detectable cytoophidia under light microscopy . In contrast , we could detect Asn1 and Asn2 cytoophidia in the diauxic phase . The percentage of cells containing cytoophidia was 35 . 97% ( Asn1 ) and 18 . 84% ( Asn2 ) , respectively . In the stationary phase , 80 . 69% of cells showed Asn1 cytoophidia and 90 . 82% cells contained Asn2 cytoophidia . After transitioning from the exponential phase to the stationary phase , it was clear that the abundances of both Asn1 and Asn2 cytoophidia were greatly increased ( Fig 1C ) . The alignment of the Asn1 and Asn2 proteins shows 88% identity ( Fig 1D ) . These results imply that the formation of cytoophidia correlates with the growth phases and the cytoophidium responds to the change of culture condition . These data confirm our previous study in which we screened 4159 GFP-tagged proteins in budding yeast [9] . CTPS has been found to form filaments due to the deprivation of carbon source [3] . Among five filament-forming enzymes ( Gcd2-GFP , Glt1-GFP , Psa1-GFP , Sui2-GFP and Ura7-GFP ) , only Ura7 reacted to carbon deprivation . To test whether carbon deprivation regulation is a CTP synthase-exclusive process or a more general process , we conducted the glucose deprivation experiment in Asn1-GFP and Asn2-GFP cells . Results indicate that ASNS based filaments show similar behavior to CTP synthase-based filaments under carbon source starvation . Distilled water treatment induced filament formation in both Asn1-GFP and Asn2-GFP strains and YP media induced filaments in the Asn1-GFP strain ( Fig 2A and 2B ) . The add-in of glucose showed a severe inhibition effect to those starvation-induced filaments in both strains . Asn1 and Asn2 filament positive cells percentages were merely around 10% , far less than the percentage of Asn1 and Asn2 filaments when the strains reach the stationary phase ( Fig 2A ) . At the diauxic shift , when cells begin to utilize other carbon sources rather than glucose , the add-in of glucose pulls cells back to glucose metabolism . Results showed that the add-in of glucose severely reduced filaments in both Asn1-GFP and Asn2-GFP strains ( Fig 2C ) . ASNS is responsible for the biosynthesis of aspargine , while CTPS catalyzes the rate-limiting reaction for the de novo synthesis of the pyrimidine nucleotide CTP . In budding yeast , both ASNS and CTPS use glutamine as the substrate and both enzymes have the ability to form cytoophidia . To study the relationship between CTPS and ASNS , we co-expressed GFP-tagged Ura7 ( CTPS ) and mCherry-tagged Asn1 ( ASNS ) in the same strain . We classified the spatial relationship of the Ura7 cytoophidium and the Asn1 cytoophidium into the following four categories . 1 ) Adjacent ( The Ura7 cytoophidium localizes adjacently to the Asn1 cytoophidium , either head-to-head or side-by-side ) . 2 ) Separated ( The Ura7 cytoophidium and the Asn1 cytoophidium localize separately with each other ) . 3 ) Ura7 only ( Only Ura7-GFP is detectable ) . 4 ) Asn1 only ( Only Asn1-mCherry is detectable ) ( Fig 3 ) . Among 791 fluorescence positive cells growing at the stationary phase , 84% fell into the “Adjacent” category , while only 9% cells showed separated distributions of Asn1 and Ura7 ( Fig 3C ) . Moreover , very few cells showed only one type of cytoophidia ( 5% Ura7 only and 2% Asn1 only , respectively ) . CTPS and ASNS can form cytoophidia both in the cytoplasm and in the nucleus . Nuclear cytoophidia are much smaller and more difficult to be detected than cytoplasmic cytoophidia . The data in Fig 3 were mostly focusing on the cytoplasmic cytoophidia . To determine the relationship of nuclear cytoophidia between Ura7 and Asn1 , we captured many cells under high resolution . In most cases , one nucleus contains one Ura7 positive cytoophidium and one Asn1 positive cytoophidium . The Asn1 nuclear cytoophidium seemed always localizing adjacently to the Ura7 nuclear cytoophidium , no matter whether the Asn1 cytoplasmic cytoophidium is adjacent to or separated from the Ura7 cytoplasmic cytoophidium in the same cell ( Fig 4 ) . The intimate spatial relationship both in the cytoplasm and in the nucleus suggest a functional coordination between ASNS and CTPS cytoophidia . Further studies are required for understanding the coordinated filamentation between these two metabolic enzymes . Disrupting either ASN1 or ASN2 alone has no effect on growth rate but simultaneous disruption of ASN1 and ASN2 leads to an asparagine auxotroph mutant [15] . Since both Asn1 and Asn2 formed cytoophidia and they had high identity , we were curious about the subcellular localization of Asn1 and Asn2 cytoophidia . To this end , we tagged Asn1 with GFP and tagged Asn2 with mCherry to obtain a double-labelling strain . Confocal images of double-labelling cells showed that Asn1 cytoophidia colocalize with Asn2 cytoophidia both in the cytoplasm and in the nucleus ( Fig 5A ) . Super-resolution images that were obtained under stimulated emission depletion ( STED ) microscopy confirmed the colocalization of Asn1 and Asn2 cytoophidia ( Fig 5B ) . Next , we wanted to know whether filamentation of Asn1 and Asn2 is independent with each other . Our strategy was to knockout one ASNS and then tag the other ASNS protein with GFP . We generated the following four strains: 1 ) Asn1-GFP ( A1G ) , 2 ) Asn2-GFP ( A2G ) , 3 ) Asn1-GFP Asn2-knockout ( A1G A2KO ) , and 4 ) Asn2-GFP Asn1-knockout ( A2G A1KO ) . We checked the morphology and measured the abundance of the ASNS cytoophidia . ASNS cytoophidia were detectable in most A1G , A2G and A1G A2KO cells , but only very few A2G A1KO cells showed ASNS cytoophidia ( Fig 6A ) . Whilst only 9 . 85% of the A2G A1KO cells showed the Asn2 cytoophidium , more than 80% of the A2G cells contain the Asn2 cytoophidium ( Fig 6B ) . These results make us believe that Asn1 has a great impact on the filamentation of Asn2 . In the A1G and A1G A2 KO cells , the abundances of ASNS cytoophidia reached 87 . 12% and 92 . 01% , respectively ( Fig 6B ) . In addition , we compared the ASNS protein level and growth rate among these four strains . ASNS protein levels showed no significant difference among all four strains ( Fig 6C ) . To study whether the ASNS knockout has an effect on cell growth , we did spot assay experiments to test the cell growth rate . Our results showed that neither ASN1 knockout nor ASN2 knockout alone affected cell growth rate ( Fig 6D ) . The biosynthesis of asparagine from aspartic acid occurs in three steps: activation of aspartate , glutamine hydrolysis , and synthesis of a beta-aspartyl-AMP intermediate and its subsequent reaction with ammonia [20] . A study of E . coli ASNS B ( ASNB ) showed that ASNS consists of two distinct domains: an N-terminal domain that mediates the hydrolysis of glutamine to glutamate and a C-terminal domain that is involved in the activation of aspartate by ATP [20] . The dimeric interface of E . coli ASNB is formed by both the N- and C-terminal region of each monomer , which includes an α-helix-turn-β-sheet structure ( A13 –S40 ) of the N-terminal and an α-helix-turn-α-helix structure ( V301 –M339 ) of the C-terminal domain , with both ligand-binding sites being distant from the dimer interface . Several salt-bridge pairs enhance the binding between two dimers , such as R17:D306 , R25:D310 , R28:D30 , E48:R334 in ASNB . In addition , R25 and R334 could potentially form a cation-π interaction with Y313 and W34 respectively . The homology model of Asn1 shared the same dimer interface and conserved salt-bridge interactions with the structure of E . coli ASNB , including K25:D330 , R28:D330 , E48:R354 , as in Asn1 ( Fig 7 ) . K25 and R354 also formed cation-π interactions with Y333 and W34 , respectively . We chose D330V , R354E and E48K for the following research ( Fig 7B and 7C ) , aiming to disrupt or decrease the dimerization thus filament formation . We utilized a site-directed mutagenesis strategy to target the specific site on Asn1 dimerization interface . To avoid the complementary effect of Asn1 and Asn2 , we knocked out Asn2 in order to study the Asn1 cytoophidium only . We first generated three GFP tagged Asn1 mutant strains ( D330V , R354E and E48K ) . Then we knocked out ASN2 in each of these three ASN1 mutant strains . Eventually we obtained the following eight strains: ASN1WT-GFP ASN2 WT , ASN1D330V-GFP ASN2 WT , ASN1R354E-GFP ASN2 WT , ASN1E48K-GFP ASN2 WT , ASN1WT-GFP ASN2 KO , ASN1D330V-GFP ASN2 KO , ASN1R354E-GFP ASN2 KO and ASN1E48K-GFP ASN2 KO . Similar to ASN1WT , both ASN1E48K and ASN1R354E showed one large cytoplasmic cytoophidium and one small nuclear cytoophidium in ASN2 knockout background ( Fig 8A , 8B and 8C ) . A typical cytoplasmic cytoophidium is straight without any branches or slightly curved . However , the Asn1 cytoophidium in ASN1D330V-GFP ASN2 KO cells displayed irregular morphology with a variety of shapes , many of which contain multiple branches ( Fig 8D–8H ) . In ASN2 wild type background , ASN1WT , ASN1R354E and ASN1E48K present filament structure ( Fig 8I–8K ) . Whereas the morphology change of ASN1D330V cytoophidium in ASN1D330V-GFP ASN2 WT cells is less dramatic ( Fig 8L ) . Furthermore , we checked the percentage of cells containing cytoophidia and the Asn1 protein level in the stationary phase . In the ASN1WT-GFP ASN2 KO strain , the percentage of cells with cytoophidia reached 22 . 72% . When we cultured mutant strains in the same medium , 60 . 91% of ASN1D330V-GFP ASN2 KO cells showed obvious Asn1 cytoophidia in the stationary phase , while only 3 . 65% and 8 . 81% of ASN1R354E-GFP ASN2 KO cells and ASN1E48K-GFP ASN2 KO cells , respectively , contained Asn1 cytoophidia ( Fig 9A ) . The protein level of Asn1 in ASN1D330V-GFP ASN2 KO cells was upregulated significantly comparing with the WT control and the other two mutants ( R354E and E48K ) ( Fig 9B and 9C ) . In ASN1-GFP cells , we have already shown that there are no visible Asn1 cytoophidia in the exponential phase ( Fig 1B ) . To investigate whether the Asn1D330V had a similar behavior to Asn1WT , we collected ASN1WT-GFP ASN2 KO cells and ASN1D330V-GFP ASN2 KO cells after a 6-hour culture . In the ASN1WT-GFP ASN2 KO cells , the Asn1 cytoophidium was still undetectable in the exponential phase . To our surprise , 21 . 58% of ASN1D330V-GFP ASN2 KO cells showed visible Asn1 cytoophidia ( Fig 9D ) . These results suggest that D330V promotes the assembly of Asn1 cytoophidia . To determine if the assembly of ASNS cytoophidia is sensitive to nutrient availability , we cultured both ASN1WT-GFP ASN2 KO cells and ASN1D330V-GFP ASN2 KO cells both for 10 days and then switched them to fresh media for 15 minutes . The percentage of cells containing cytoophidia dropped from 23 . 76% to 2 . 63% in the ASN1WT-GFP ASN2 KO strain , while decreasing from 82 . 71% to 18 . 21% in the ASN1D330V-GFP ASN2 KO strain ( Fig 9E ) . In conclusion , both Asn1WT and Asn1D330V cytoophidia are sensitive to culture conditions . We used spot assays to compare the growth rates of various strains . In a normal ASN2 background , the growth rate of ASN1D330V-GFP cells was similar to that of ASN1WT-GFP and of the other two mutants , ASN1R354E-GFP and ASN1E48K-GFP ( Fig 10A ) . Both ASN1R354E-GFP and ASN1E48K-GFP cells grew normally in an ASN2 KO background . However , the growth of ASN1D330V-GFP cells slowed down when ASN2 was knockout ( Fig 10B ) . Next , we used the alpha mating factor to arrest the ASN1WT-GFP ASN2 KO cells and ASN1D330V-GFP ASN2 KO cells in G1 phase , and subsequently released those cells at the same time . We collected those groups at 0 min , 30 min , 60 min and 90 min after alpha factor release and analyzed the cell cycle by FACS ( Fig 10C ) . At 30 min after release , there were two distinct peaks formed by ASN1WT-GFP ASN2 KO cells and more than a half of cells successfully progressed from the G1 to G2 phase . However , ASN1D330V-GFP ASN2 KO cells delayed the transition from G1 to G2 phase . At 60 min after the release , the proportion of ASN1WT-GFP ASN2 KO cells in G1 phase was obviously lower than that of ASN1D330V-GFP ASN2 KO cells . These results suggest that the ASN1D330V mutation impairs cell growth in the absence of ASN2 .
The two CTPS proteins , Ura7 and Ura8 , colocalize with each other in S . cerevisiae [3] . Like CTPS proteins , the two ASNS proteins , Asn1 and Asn2 , are capable of assembling into cytoophidia in the cytoplasm and the nucleus . Looking into the relationship between CTPS cytoophidia and ASNS cytoophidia , we find the following interesting features . 1 ) ASNS cytoophidia do not colocalize with CTPS cytoophidia , suggesting ASNS and CTPS form different structures . 2 ) Both CTPS and ASNS use glutamine as a substrate in budding yeast . 3 ) Both CTPS and ASNS form cytoophidia not only in the cytoplasm , but also in the nucleus . 4 ) The number of ASNS cytoophida , similar to that of CTPS cytoophidia , is one in the cytoplasm and one in the nucleus in many cells . 5 ) In most budding yeast cells that we analyzed , the ASNS cytoophidia localize adjacently to the CTPS cytoophidia . These two types of structures can touch with each other head-to-head or side-by-side . 6 ) The close spatial relationship between ASNS and CTPS does not limited to the cytoplasm . In the nucleus , we often observe that the ASNS filament sits next to the CTPS filament . 7 ) The length of the ASNS cytoplasmic cytoophidium is shorter than that of the CTPS cytoplasmic cytoophidium . 8 ) The length of the ASNS nuclear cytoophidium is shorter than that of the CTPS nuclear cytoophidium . 9 ) Nuclear cytoophidia are much smaller than cytoplasmic cytoophidia , whether the composition is CTPS or ASNS . 10 ) Both CTPS and ASNS cytoophidia are very sensitive to nutrient changes and growth phases . Further studies are required to understand the dynamic interaction and functional connection , if any , between ASNS and CTPS cytoophidia . Previous studies have shown that CTPS cytoophidia have interfilament interaction with inosine monophosphate dehydrogenase ( IMPDH ) cytoophidia [21 , 22] , suggesting that the interaction between two cytoophidia may be a phenomenon more general than we have appreciated . Asn1 and Asn2 have identical distribution pattern , raising the possibility that Asn1 and Asn2 have the same filament-forming capability . To our surprise , our data indicate that Asn1 and Asn2 behave distinctly in the absence of its partner . Removing Asn2 does not prevent Asn1 to form cytoophidia in most cells . However , Asn2 alone can rarely form filaments in the absence of Asn1 . The discrepancy between Asn1 and Asn2 provides an interesting angle to study the mechanism of the filamentation of metabolic enzymes in general . It would be interested to test if differences in transcription , translation , stability or half-life between Asn1 and Asn2 influence their filamentation outcomes . Three mutations in the dimerization interface of Asn1 were designed aiming to disrupt the filament formation . We observe no obviously morphological changes of cytoophidia in ASN1R354E-GFP ASN2 KO and ASN1E48K-GFP ASN2 KO cells , although these two mutant strains show less cells containing cytoophidia than the wild-type strain . The third mutation , ASN1D330V , results in branched cytoophidia , which are hardly seen in the wild-type strain . We also find that ASN1D330V promotes cytoophidia formation , which reminds us the famous E6V mutation in beta hemoglobin leads to the polymerization of hemoglobin tetramers . Here the reason for Asn1 filamentation may be similar . A new protein-protein interface may be induced by the D330V mutation , either through a stacking or bridging and stacking mechanism [23] . The Asn1 protein level in ASN1D330V-GFP ASN2 KO cells is higher than that in ASN1WT-GFP ASN2 KO , ASN1R354E-GFP ASN2 KO and ASN1E48K-GFP ASN2 KO cells . However , the growth rate of ASN1D330V-GFP ASN2 KO cells are much slower than the other three strains . Currently we are unable to tell whether branched cytoophidia in ASN1D330V-GFP ASN2 KO cells contribute to high protein level and/or slow grow rate . Ultrastructural analysis of cytoophidia containing wild-type and mutant Asn1 proteins will help us gain insights into the molecular mechanism of ASNS cytoophidium assembly . CTPS cytoophidia was first reported by three studies in 2010 [1–3] . Eight years later , the physiological role of CTPS cytoophidia remains elusive . There were some lines of evidence support that forming filaments can sequester or promote the enzymatic activity of CTPS in various organisms [24–28] . Does the ASNS cytoophidium have similar functions of the CTPS cytoophidium ? Since there are quite a few metabolic enzymes showing the filament-forming capability , we speculate that cytoophidia have more general functions in the cell [21 , 22] . The close proximity between ASNS and CTPS cytoophidia raises the possibility that both enzymes function coordinately in maintaining metabolic homeostasis in the cytoplasm and nucleus . In summary , we use budding yeast as a model system to study the filamentation of two glutamine-utilizing enzymes CTPS and ASNS . We have detected the close relationship between CTPS and ASNS cytoophidia both in the cytoplasm and in the nucleus . Moreover , we have demonstrated that two ASNS proteins exhibit differential filament-forming capability even their distribution seems identical in the cytoplasm and nucleus . Finally , we identify a mutation , ASN1D330V , which leads to branched cytoophidia , increased protein level and delayed growth . Our results provide new opportunities to study how filamentation and compartmentation impact glutamine metabolism .
The yeast strains used in this study are derived from BY4741 [29] . All the S . cerevisiae strains used are listed in S1 Table . Yeast cells were cultured at 30 °C on YPD rich media ( 1% Bacto yeast extract , 2% peptone , 2% glucose ) or SC medium with appropriate supplements depending on strains . In glucose deprivation assay , exponential phase cells ( 12 hours culturing ) were centrifuged and added into previous media , YP media ( 1% yeast extract , 2% peptone ) , distilled water , and 2% glucose solution . After 1 hour 32°C culturing , cells were fixed and pictures were taken by confocal microscope . In glucose recovery assay , 2% glucose was added into culturing system . After 1 hour 32°C culturing , cells were fixed and pictures were taken by confocal microscope . Fluorescent protein tagging in the genome was done by transforming yeast strains with a PCR product that encoded a selective marker gene and 5’ and 3’ 40-bp flanking sequences homologous to the target gene . pFA6a-mCherry ( S65T ) -kanMX6 plasmid was updated by pFA6a-GFP ( S65T ) -kanMX6 [30] . pFA6a-GFP ( S65T ) -His3MX6 and pFA6a-mCherry ( S65T ) -KanMX6 were utilized in GFP tagging and mCherry tagging , respectively . The yeast strain BY4741 and plasmids were gifts from Jinqiu Zhou ( SIBS , Shanghai , China ) . PCR products harboring the target gene sequence were used to transform the BY4741 haploid wild type strain . After transformation and homologous recombination , transformants were selected based on the selective marker . Correct integration results in a C-terminal in-frame GFP fusion , whose expression is driven by the endogenous promoter . Primers used in GFP or mCherry tagging of ASN1 and ASN2 are listed in S2 Table . Sequences in upper case indicate homology to the genome of S . cerevisiae and those in lower case indicate homology to the PCR cassette employed . The ASN1 and ASN2 disruption cassettes were constructed respectively in pRS306 and pRS303 plasmids . For each gene disruption , the 5’ untranslated region and 3’ region were sub-cloned into the corresponding vector , cut using specific restriction enzymes , and then transferred into yeast to replace the given gene with a selective marker by homologous recombination [31] . The disruption cassettes were both linearized with EcoR I . Gene disruption or genomic tagging was confirmed by PCR . Primers used in ASN1 or ASN2 deletion are listed in S3 Table . Yeast cells were spun down for 1 min at 4000 rpm/min . 100 μl of 4% paraformaldehyde were added to each tube to fix the cells for 10 min at room temperature , and then the cells were washed once with PBS . A few microliters of cell suspension were mixed with agarose gel ( 1 . 2% low melting temperature agarose and 100 μM N-propyl gallate in YPD ) to avoid movement of the cells during imaging . Images were acquired under 63x objectives on a Zeiss LSM 710 inverted fluorescence confocal microscope . For the co-localization experiment , super-resolution stimulated emission depletion ( STED ) microscopy was used . The abundance of filaments in cells was quantified by capturing images in at least four different areas containing a minimum of 100 cells each . The homology model of Asn1 of S . cerevisiae and its dimer were built based on the structure of E . coli ASNB [20] ( PDB: 1CT9 ) , by using the MODELLER software [32] . The sequence identity between Asn1 and ASNB is 47% . Residues on the dimer interfaces are highly conserved . This method is applied to alter single nucleotides in DNA that has been previously cloned in a plasmid . The plasmid is then transferred back into yeast cells to generate a strain that carries the mutated version of the gene of interest on the plasmid but lacks the corresponding wild type locus in the genome in order to assess the function of the mutant allele . Insert fragments ( including about 1 kb upstream of the promoter element and 0 . 5 kb downstream of the 3′-untranslated region of GFP-tagged target gene ) were cloned into pRS315 . Subsequently , this vector was transformed into an ASN1 deletion strain . This strain was regarded as the control strain . The vector was also used as the template for point mutant vectors ( E48K , D330V and R354E ) . DpnI was used to eliminate the template and then point mutant vectors were transformed into the ASN1 deletion strain . Oligonucleotide primers used for site-directed mutagenesis are listed in S4 Table . We used Western blots to analyze the protein level of ASNS . Yeast cells were collected at different time points and run on an 8% SDS-PAGE gel before transfer to a PVDF membrane ( Bio-Rad ) . Following incubation in blocking buffer ( TBST containing 5% nonfat dry milk ( Bio-Rad ) ) for 1 h , the membrane was hybridized in blocking buffer containing primary antibody overnight at 4 °C . The membrane was then washed and incubated with a horseradish peroxidase-conjugated secondary antibody for 1 h at room temperature . Detailed information about the antibodies used , with concentration and source , can be found in S5 Table . Enhanced ECL was used for signal detection . The image signal was collected by an Amersham Imager 600 ( GE Healthcare Life Science ) . Data were processed and analyzed by Image J software . All quantifications were done by normalizing protein levels to actin . Yeast cells were grown overnight and diluted roughly to an optical density ( O . D . ) of 1 . 0 . Four or five ten-fold dilutions were spotted onto YPD agar plates . A multichannel pipette was used to transfer 10 μl cells to 90 μl culture medium . After mixing well , 8 μl of each dilution were spotted onto the plates . Plates were incubated at 30 °C , examined regularly , and photographed after 48 hours incubation . | Asparagine synthetase ( ASNS ) is an essential enzyme for biosynthesis of asparagine . We have recently shown that ASNS , similar to CTP synthase ( CTPS ) , can assemble into snake-shaped structures termed cytoophidia . In this study , we reveal that the ASNS cytoophidium stays close with the CTPS cytoophidium in most cells . Two ASNS proteins , Asn1 and Asn2 , localize in the same structure . The Asn1 protein is important for the formation of the Asn2 filaments . Mutant cells with branching Asn1 cytoophidia grow slower than wild-type cells . Our findings provide a better understanding of the ASNS cytoophidium as well as its relationship with the CTPS cytoophidium . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"chemical",
"compounds",
"enzymes",
"enzymology",
"carbohydrates",
"organic",
"compounds",
"glucose",
"asparagine",
"fungi",
"model",
"organisms",
"materials",
"science",
"experimental",
"organism",
"systems",
"enzyme",
"metabolism",
"acidic",
"amino",
"acids",
"amino",
... | 2018 | Filamentation of asparagine synthetase in Saccharomyces cerevisiae |
Brucellosis is a worldwide recognized bacterial zoonotic disease . There is currently no information on bovine brucellosis sero-prevalence in South Sudan regardless of the economic , social and public health impact on populations . Therefore , for the first time in 33 years , we report the sero-prevalence of brucellosis in cattle and their herders . Furthermore , we characterize the drivers associated with the disease at the human-animal interface in Bahr el Ghazal region , South Sudan . A total of 893 and 87 animal and human sera respectively were examined between December 2015 and May 2016 . Rose Bengal Plate Test ( RBPT ) and Competitive Enzyme Linked Immuno Sorbent Assay ( c-ELISA ) were used in parallel to detect anti-Brucella antibodies . Questionnaires were administered to collect relevant metadata used for the association analysis in R version 3 . 2 . 3 . Odds Ratio ( OR ) and Confidence Intervals ( CI ) were determined . Overall bovine brucellosis prevalence was 31% ( 95%CI = 28 . 0–34 . 2 ) , with the highest 63% ( 95%CI = 53–70 ) and lowest 10% ( 95%CI = 4 . 5–20 . 1 ) prevalence estimates in Wau and Gogrial states respectively . The bovine sero-prevalence was approximately equally distributed among the male 30 . 4% ( 26 . 9–34 . 2 ) and the females 32 . 5% ( 26 . 8–38 . 7 ) . Poor body condition ( OR = 0 . 22; 95%CI = 0 . 07–0 . 54 ) and larger herd sizes ( OR = 0 . 05; 95%CI = 0 . 008–0 . 173 ) were protective factors for brucellosis , while the opposite was true for the second ( OR = 1 . 70; 95%CI = 1 . 08–2 . 67 ) and third ( OR = 2 . 5; 95%CI = 1 . 46–4 . 47 ) lactation stage . The overall brucellosis sero-prevalence in herders was estimated at 33 . 3% ( 23 . 9–44 . 3 ) . We report a high prevalence of anti-Brucella antibodies in cattle and their herders in Bahr el Ghazal , indicating an enzootic status in the cattle population being an important source of infection for humans . This represents a genuine public health challenge . Therefore , there is need to raise awareness and build capacity and infrastructure in this fragile state to underwrite future public health strategies for brucellosis .
Brucellosis is described as a highly contagious zoonotic disease , and a cause of significant reproductive losses in livestock [1] . Brucellosis is common in Low and Middle Income Countries ( LMICs ) , characterized by poor hygiene , consumption of raw animal products ( like milk and meat ) , and lack of public health education programs[2 , 3] . Animal brucellosis causes direct socio-economic effects in communities dependent on animal production as their livelihood . Losses in animals are attributed to loss of offspring due to abortion , stillbirth and infertility . Indirect losses are due to reduction in milk yields and humans suffering from the disease . Bovine brucellosis is caused mainly by Brucella abortus , although Brucella melitensis may spillover from the small ruminant reservoir and infect the cattle too . On the other hand , Brucella suis infects pigs , as well as humans [4] . In LMICs , the prevalence of animal and human brucellosis is generally unknown due to a myriad of challenges with diagnostics , reporting and weak to non-existent surveillance systems , especially in malaria endemic areas [5 , 6] . In Africa , brucellosis is an enzootic disease in livestock [7 , 8] . In South Sudan , brucellosis prevalence is unknown due to the lack of awareness among communities about the disease , but more importantly due to weakened animal and public health systems as a result of political and civil instability . As a consequence , the disease remains largely neglected with little attention given to prevention and control in livestock and humans . Livestock , especially cattle , is the main source of livelihood for communities in Bahr el Ghazal region [9 , 10] , which inherently increases their risk to zoonotic diseases [11] . Animal ownership has for long been documented as the main risk for exposure to Brucella spp infection through direct contact with infected animal material and consumption of raw milk and infected meat[2] . Therefore , documenting the risk profile at the human-cattle interface in such settings is central to developing control strategies in such a setting . Here , we estimate the sero-prevalence of brucellosis in cattle and their herders , as well as characterize the drivers associated with the disease at the human-animal interface in cattle camps of Bahr el Ghazal region , South Sudan .
This was a cross-sectional study of herders and their cattle conducted between December 2015 and May 2016 in the Greater Bahr el Ghazal region . We collected quantitative data and metadata ( socio-demographic data and animal attributes ) using a structured questionnaire . Greater Bahr el Ghazal region is situated in the Northwestern part of South Sudan . The region consists of ten states , Aweil , Aweil East , Gogrial , Lol , Tonj , Twic , Wau , Gok , Eastern Lakes , and Western Lakes . The region consists of vast land with iron plateau and swamps feeding 12 million heads of cattle which represents 50% of the National herd [12 , 13] . It is predominantly inhabited by the Dinka ethnic group , who are cattle herders; other ethnic groups like Balanda and Kerash mostly practice agro-pastoralism [2] . This mixture of land use allows for complex human-animal interactions usually compounded by the high population density [13] . It is these complex dynamics that our study was aiming to unravel with respect to brucellosis . A map of our study area is shown in Fig 1 . The study population consisted of cattle and herders from cattle camps in Bahr el Ghazal region . The cattle belonged to herders in the areas of Aweil , Gogrial , Tonj , and Wau states . Majority of cattle is owned by pastoralists who migrate throughout the dry season looking for pastures in small groups of families or in large groups of villages [12] . In effect , the herds in each grouping can be owned by more than one family , but usually it is a herd per family . The herdsmen who tend to the animals are usually relatives of the owners , but in rare circumstances , families can employ herdsmen to manage their cattle herds . The sample size for cattle was estimated using a bovine brucellosis prevalence ( 6 . 5% ) previously reported in Greater Bahr el Ghazal [13] . We assumed a brucellosis test sensitivity and specificity of 85% , and 90% respectively [14] , and a precision 0 . 05 with 95% confidence intervals . We would then be expected to sample 346 cattle , however , 893 samples were collected using a systematic random sampling procedure , which allowed us to improve the precision of the sero-prevalence estimates [15] . Since herds are owned and maintained in cattle camps , large numbers of cattle tend to be clustered in fewer cattle camps . In this regard , we conveniently screened 87 herders from 37 cattle camps in two of the four states ( Tonj and Aweil ) . Note that the screening was done with consent from the herders , as described in the ethical consideration section . The four states where the study was conducted were purposively selected basing on the safety of the area at the time of the study . Between one and three cattle camps were selected per state , for each cattle camp; lists of herders with their respective herds were obtained from the veterinary office . Usually , a camp contained up to 100 herds each with on average 150 animals . We randomly selected thirty-percent of the herds in each camp . This was done by dividing the number of herds on the camp-herd list by thirty , and the quotient was used as the interval for selecting the herds from the camp-herd list . For the selected herds , we then sought permission from the herd owner and only if granted , would we ask for the number of animals in their herds . This number would then be divided by 10 , and the quotient was used as an interval for selecting individual animals lined up in a kraal . If the herd owner rejected our request , the herd was dropped and we continued with same frequency as before , in order to get a replacement herd . A total of 893 animals were selected from 37 cattle camps from four the states . It is noteworthy that we selected a minimum of 20 animals per herd using this strategy . We also collected blood samples from the herders who were in direct contact with animals and were willing to participate in this study according to the informed consent . However , it is important to note that some of the herders were below eighteen years; in this case we sought permission from their parents and guardian . Information about animal body condition status , history of abortion and presence of hygroma as indicators of brucellosis were captured on data sheet during blood collection by observation and interviewing animal’s owners . In addition , information on herd size , lactating stage and age of the animal was collected . Age was determined using the dentition method of ageing cattle . From all the participants , a questionnaire was administered , and we collected information about the individual’s occupation , age , sex , marital status and education level . Ten ( 10 ) ml of blood were collected from the jugular vein of the selected animal by a veterinary research assistant; on the other hand , 5 mL of blood was collected from the cephalic vein of each herder by a registered nurse . All blood samples were then kept at room temperature ( 25°C-30°C ) , and tilted at an angle of 45° for 6–8 hours to allow for clotting . The sera were aliquoted into new set of labeled Eppendorf tubes , stored on ice packs and transported to Wau Teaching Hospital Laboratory , where they were kept in a deep freezer at -80°C . The samples were then transported to the Central Diagnostic Laboratory at Makerere University , College of Veterinary Medicine and Biosecurity , Kampala-Uganda by air after completion of data collection . Here , serological tests that included , Rose Bengal Plate Test ( RBPT ) , and Competitive Enzyme Linked Immuno sorbent Assay ( c-ELISA ) were done within five days of delivery as described below . Statistical analysis was done in SPSS and R , version 24 and 3 . 2 . 3 respectively . For descriptive statistics , that is to say , proportions and percentage of the positive against the number tested were estimated; while Chi-square was employed in assessing the relationship between various factors and test outcome ( test positivity ) . A positive Brucella sample was defined as sample that was positive on RBPT and confirmed by c-ELISA , while a negative sample was defined as a sample that was negative on RBPT , as well as c-ELISA . A logistic regression model was developed to identify factors associated with bovine brucellosis in cattle . The model developed by adding variables in a forward selection process adjusting for confounding , starting with variables that had the lowest p-value from the univariable analysis . Only variables that had a p value <0 . 25 were included in the model , these were added and removed to see if they still retained their level of statistical significance ( p < 0 . 05 ) , and checked for potential confounding effects as well . The least complex model was chosen based on the lowest Akaike information criterion ( AIC ) . Standard post estimation statistics were also done . This study involved an administration of questionnaires to the herders , as well as blood sampling from cattle . Therefore , the study protocol ( SBLS/REC/15/133 ) was assessed and approved by the Ethical Review Committee of the College of Veterinary Medicine , Animal Resources and Biosecurity ( COVAB ) , Makerere University , Uganda; reference number SBLS . NA . 2015 ( S1 ) . We also obtained permission to collect human and animal samples from Ministry of Health ( MOH ) ( S2 ) , and Ministry of Agriculture , Animal Industry and Fisheries ( MAAIF ) —RSS/MLFI/DVS/J/15/7 ( S3 ) , South Sudan . Furthermore , we sought consent from the participants in this study . Their decision to participate was arrived at , after we explained the objectives and the potential benefits of this work to them as individuals and their communities at large . For individuals who were below 18 years of age , we sought permission from their parents or guardians . All this was done in local ethnic language of the group , to which the individual belonged . All this was in addition to the assurance of anonymity as required by the ethical approval obtained . We also obtained import and export permits for biological sample transportation from Ministries of Agriculture of Uganda and South Sudan ( S4& S5 ) .
A total of 893 serum samples were examined in the study; 138 , 70 , 198 , and 487 from Wau , Gogrial , Tonj and Aweil , respectively . We sampled 644 female and 249 male cattle with an estimate median age of cattle being seven years ( Table 1 ) . Our sero-prevalence estimates are based on c-ELISA , but we estimate a 98% ( 95% CI = 97–100 ) kappa agreement between the two tests ( Supplementary R code ) . The overall estimate of bovine brucellosis sero-prevalence was 31 . 0% ( 95%CI = 28 . 0–34 . 2 ) , which varied by states , the highest and lowest recorded in Wau 63% ( 95%CI = 53–70 ) and Gogrial 10% ( 95%CI = 4 . 5–20 . 1 ) respectively . We observe that the sero-prevalence increased with age; 26% ( 95%CI = 21 . 8–32 . 4 ) among the young , and 44 . 2% ( 95%CI = 37 . 3–51 . 4 ) among the old . The same trend was generally true for herd size ( Table 1 ) . There was indication of an association between bovine brucellosis sero-prevalence and age , herd size , body condition status , lactation stage and presence of hygroma and abortion history based on the univariable analysis ( Table 1 ) . Indeed , after taking into account the variation due to all chosen factors , we still observe that herd size , lactation stage and body condition status are still significantly in association with brucellosis sero-prevalence in this area . We observe that other than Lactation stage , the rest of the herd factor seems to be protective for brucellosis sero-prevalence ( Table 2 ) . A total of 87 cattle herders were recruited in the study from Aweil ( n = 40 ) and Tonj ( n = 47 ) states . Our sample size contained more males , majority of who were between 16–60 years of age and were illiterate . Overall , we estimate the sero-prevalence of zoonotic brucellosis was estimated to be 33 . 3% ( 23 . 9–44 . 3 ) . The sero-prevalence was comparable between states and occupational activities , but appears to increase by age ( Table 3 ) .
Some of the limitations were; security instability in the region due to the civil war and cattle raiding , and the refusal by most of the herders to participate in the study alleging that sampling from cattle would affect their productivity . Moreover , cattle camps were distributed in vast area and difficult to reach . We report a high sero-prevalence of brucellosis in cattle in Bahr el Ghazal indicating an enzootic status . Individual animal and herd management factors are linked to the prevalence in animals . This represents a genuine public health challenge underpinned here by the high sero-prevalence estimates in herders . Control of brucellosis in livestock through approved strategies such as vaccinations either with S19 or RB51 reduces the likelihood of a transmission event from animal-animal . This over time can have the result of lower incidence of brucellosis , thus decreasing the spill over into human populations . Furthermore , there is therefore need to build capacity and infrastructure in veterinary delivery services in this fragile state to underwrite future veterinary public health strategies for controlling brucellosis in livestock and mitigating transmission to humans . | Enzootic brucellosis is a bacterial infectious disease , which represents millions of dollars in production losses in livestock , as well as Disability- Adjusted Life Years ( DALYS ) associated with health and treatment in the human populations for Low and Middle Income Countries ( LMICs ) . Despite these unequivocally known challenges , there has not been any reporting on this disease in livestock and humans in the recent past . The limitations the disease puts on livestock health and production , coupled with the zoonotic form , which is primarily driven by risky food consumption habits , and interaction with livestock in an enzootic setting with uncontrolled animal movement , means that this disease is not only of veterinary public health , but of trans-boundary importance as well . | [
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] | [
"livestock",
"medicine",
"and",
"health",
"sciences",
"ruminants",
"tropical",
"diseases",
"geographical",
"locations",
"vertebrates",
"south",
"sudan",
"animals",
"brucellosis",
"mammals",
"bacterial",
"diseases",
"neglected",
"tropical",
"diseases",
"africa",
"veterinar... | 2018 | The sero-prevalence of brucellosis in cattle and their herders in Bahr el Ghazal region, South Sudan |
Accumulated experimental observations demonstrate that protein stability is often preserved upon conservative point mutation . In contrast , less is known about the effects of large sequence or structure changes on the stability of a particular fold . Almost completely unknown is the degree to which stability of different regions of a protein is generally preserved throughout evolution . In this work , these questions are addressed through thermodynamic analysis of a large representative sample of protein fold space based on remote , yet accepted , homology . More than 3 , 000 proteins were computationally analyzed using the structural-thermodynamic algorithm COREX/BEST . Estimated position-specific stability ( i . e . , local Gibbs free energy of folding ) and its component enthalpy and entropy were quantitatively compared between all proteins in the sample according to all-vs . -all pairwise structural alignment . It was discovered that the local stabilities of homologous pairs were significantly more correlated than those of non-homologous pairs , indicating that local stability was indeed generally conserved throughout evolution . However , the position-specific enthalpy and entropy underlying stability were less correlated , suggesting that the overall regional stability of a protein was more important than the thermodynamic mechanism utilized to achieve that stability . Finally , two different types of statistically exceptional evolutionary structure-thermodynamic relationships were noted . First , many homologous proteins contained regions of similar thermodynamics despite localized structure change , suggesting a thermodynamic mechanism enabling evolutionary fold change . Second , some homologous proteins with extremely similar structures nonetheless exhibited different local stabilities , a phenomenon previously observed experimentally in this laboratory . These two observations , in conjunction with the principal conclusion that homologous proteins generally conserved local stability , may provide guidance for a future thermodynamically informed classification of protein homology .
Protein structure and function are ultimately determined by thermodynamics . For example , Anfinsen's seminal work [1] demonstrated that the native state of a protein exists at a minimum in Gibbs free energy of stability under physiological conditions . Binding and catalysis are also governed by free energy: the sign and magnitude of the free energy change of each functional reaction controls the reaction's direction and equilibrium extent , respectively [2] , [3] . Gibbs free energy ( ΔG ) results from the summed , often opposing , contributions of enthalpy ( ΔH ) and entropy ( TΔS ) : ΔG = ΔH−TΔS . Generally , in the case of proteins , changes in free energy are small as compared to the underlying enthalpic or entropic changes [4] . Reactions can be dominated by either enthalpy or entropy , but it is most often the case that a sometimes delicate balance between enthalpy and entropy controls protein structure and function . Unfortunately for the goal of thermodynamic characterization of protein folds , each of these quantities can be challenging to accurately predict . While enthalpy can be rationalized in terms of information derived from atomic coordinates ( i . e . from the number and types of bonds seen in the structure ) [5] , entropy is harder to estimate , frequently requiring knowledge not apparent from a single structure , such as information about the conformational degeneracy of the protein [6]–[8] . Equally as challenging is the task of developing a robust analysis that reports the position-specific ( i . e . local ) stability within the protein , rather than reporting either: 1 ) the energetic contribution of a residue ( which would be highly sequence-dependent ) or 2 ) the stability of a protein as a whole ( i . e . global stability ) . Due in part to the inherent difficulty of accurately computing global and local enthalpy , entropy , and free energy , all protein structure classification strategies of which we are aware do not incorporate thermodynamic information . It is our hypothesis that this theoretical omission limits the complete understanding of protein fold space . There may also be practical consequences to such an omission . For example , it is possible that thermodynamic information , as a protein observable independent of sequence or structure[9] , could improve computational tools for sequence alignment , fold recognition [10] , or homology detection , thereby clarifying discrepancies in existing classification schemes that are based on only sequence and structure . Thermodynamic information may also yield new understanding , not available from current schemes , about evolutionary sequence , structure , and functional relationships [11] . One particularly important and as yet unanswered question is the degree to which protein stability and its components ( i . e . enthalpy and entropy ) are conserved during fold evolution: does the concept of “thermodynamic homology” meaningfully exist beyond conservative point mutations ? As a step towards integration of thermodynamic information into existing protein classification schemes , the local ( or position-specific ) free energy of stability ( ΔG ) , enthalpy ( ΔH ) , and entropy ( TΔS ) are here computed for a large representative database of protein domains using the previously described COREX/BEST algorithm [12]–[14] . Importantly , the diverse proteins studied have accepted evolutionary relationships [15] and are expertly curated [16] such that any homologs are remote ( i . e . “twilight zone” [17] pairwise sequence identity or less on average ) . Thus , by experimental design , trivial comparisons between the thermodynamics of closely related proteins are explicitly excluded from this analysis . The central aim of this work is to assess the degree of thermodynamic conservation among remotely homologous protein domains . Three findings relating thermodynamics to protein sequence and structure are reported . First , in accordance with previous work [18] , it is confirmed that homologous proteins exhibit correlated thermodynamic information . Second , enthalpy and entropy are less correlated than stability , suggesting that homologous sequence differences result in enthalpic and entropic changes that largely balance to preserve the local stability of an evolved protein as compared to an ancestral one . Third , based on manual inspection of structural and thermodynamic alignments of homologous and non-homologous pairs of proteins , an organizational framework is postulated to guide the future integration of COREX/BEST thermodynamic information into theories of protein fold evolution .
Structural coordinates for all protein domains of length less than or equal to 150 residues were obtained from the ASTRAL 1 . 69 database [16] of 40% maximum sequence identity representatives . Those domains defined as SCOP [15] class “e” ( membrane protein domains ) were discarded , as the COREX/BEST algorithm was parameterized for globular proteins and thus was not expected to accurately estimate the thermodynamic characteristics of membrane proteins . To focus on single domains , those included in SCOP class “f” ( multidomain proteins ) were also discarded . Coordinate files were preprocessed and standardized to minimize run-time errors during subsequent analysis; these minor edits included modification of selenomethionine residues to methionine , removal of multiple atom occupancies other than “A” , removal of multiple NMR models other than “1” , and modification of non-standard amino acids to alanine . In total , 3 , 688 domains from 666 unique SCOP families , 463 SCOP superfamilies , and 292 SCOP folds were represented within the five SCOP classes: all-α , all-β , α+β , α/β and small proteins . These statistics demonstrated a reasonable and diverse sampling of single domain protein structure space , yet included thousands of homologous protein pairs ( as defined by SCOP ) at less than approximately “twilight-zone” ( i . e . <25% ) sequence identity . The COREX/BEST algorithm [12]–[14] constructs a protein conformational ensemble using its high-resolution structure as a template . COREX/BEST requires as input the three-dimensional structural coordinates of a protein and employs a sliding window to generate a large number of conformational microstates varying from fully folded to fully unfolded . Output is a thermodynamic ( i . e . energetic ) model of the protein's native state ensemble . The algorithm has been tested by both retrospective validation and blind prediction [12] , [14] , [19]–[23] , and thus has been demonstrated to reasonably represent the ensemble . For this work , a COREX/BEST analysis was performed on each member of the preprocessed ASTRAL database described above using standard parameters: window size , 12; minimum window size , 4; temperature T , 25 . 0°C; and entropy weighting , W , 0 . 5 . The strength of COREX/BEST is the ability to capture local , also known as “position-specific” , thermodynamic quantities . The important distinguishing feature of these position-specific quantities is that they reflect the ensemble-averaged thermodynamic contributions of many residues in the three-dimensional neighborhood of one residue , rather than reflecting the independent contribution of only that particular residue [24] . Thus , local thermodynamic quantities , although reported at individual residue positions , greatly depend on the rest of the protein , in the sense that surrounding residues may influence the probability of a particular residue being folded , making it more likely , for example , for blocks of folded residues to be found together . In other words , this ensemble-based formalism separates the energetic contribution of the residue from the position itself . It is possible , and preferable , for these quantities to be obtained from experiment , for example local stability as measured by NMR-detected hydrogen exchange[25] or local enthalpy as measured by the temperature dependence of local stability [26]–[28] . Indeed , comparisons with such experiments have shown that COREX/BEST thermodynamic quantities plausibly reproduce the measured values [12] , [14] . However , large scale studies such as the present one are currently difficult , if not impossible , to execute experimentally . Computation of position-specific thermodynamic quantities from a COREX/BEST ensemble has been described in detail [12] , [24] , [29] . Briefly , for each partially folded microstate i of the ensemble , a Gibbs free energy of global stability ΔGi is computed from a previously validated and calibrated energy function composed of solvent-exposed surface area and conformational entropy terms [12] . From these stabilities , the probability Pi of each microstate i can be estimated by ( 1 ) In Equation ( 1 ) , Ki = exp ( −ΔGi/RT ) is the statistical weight of each microstate , R is the gas constant and Q is the partition function for the system . Given the probabilities of each microstate , a so-called “residue stability constant” , κf , j , can be defined for every residue j of the protein [12]: ( 2 ) In Equation ( 2 ) , the numerator is the summed probability of states in the ensemble in which a particular residue j is in a folded conformation and the denominator is the corresponding sum for states in which residue j is in an unfolded conformation . The residue stability constant directly gives the local thermodynamic stability ΔG at each residue position j , equivalent to the difference in energy between the Boltzmann-weighted subensembles of states in which residue j is folded ( f ) and unfolded ( nf ) [24] , [29]: ( 3 ) Similarly , local enthalpy ( ΔH ) and entropy ( TΔS ) were computed as a function of residue position j in each protein from the COREX/BEST ensembles as differences between the folded and unfolded subensembles for each respective thermodynamic descriptor [24]: ( 4 ) ( 5 ) In Equations ( 4 ) – ( 5 ) , subscript “ap” refers to energetic contributions arising from apolar solvent accessible surface area , “pol” refers to contributions from polar surface area , and “conf” refers to conformational entropy . The specific values of T and W are given above . Note that the total entropy of the calculation , Equation ( 5 ) , reflects contributions from both solvent and conformational terms , while the enthalpy , Equation ( 4 ) , reflects contributions from only solvent . Thus , this statistical thermodynamic treatment can distinguish between the two main classes of entropy . Under the native state conditions simulated in this work , the total entropy appears largely dominated by solvent contributions ( Text S1 , Figure S1 . ) . At least two different strategies could be envisioned to compare local thermodynamic quantities of two proteins: direct alignment of thermodynamic quantities or alignment of quantities according to residue equivalencies obtained from another source . Although the former strategy is under development [18] , [30] , for expediency we chose here to implement the latter strategy by aligning thermodynamic quantities according to structure alignment . Pairwise structure alignment was performed for the proteins in the dataset in an all-vs . -all manner using the DALI-Lite package [31] with default parameters . More than 6 million nonredundant pairwise comparisons were attempted; approximately 95% of these comparisons were successful and were retained for further analysis . Given two sets of N equivalenced thermodynamic descriptors , a Pearson correlation coefficient r [32] was computed using the equation: ( 6 ) where , x and y represent sets , one set from each protein , of thermodynamic descriptors ( ΔG , ΔH , or TΔS from Equations ( 3 ) – ( 5 ) , the corresponding correlation coefficients are denoted rΔG , rΔH , rTΔS , respectively , in the text ) . The horizontal bar indicates an average . A perfect positive correspondence was given by r = +1 , no correspondence by r = 0 , and a perfect negative correspondence by r = −1 . Structural alignments of less than an arbitrary length cutoff of 20 residues were ignored , to reduce artifactual correlations due to the sensitivity of the Pearson r to outlier data points . Thermodynamic descriptors of the first or last four residues in every protein were also ignored , due to end effects in the COREX/BEST calculation caused by the minimum window size . The Spearman rank-order correlation method [32] , perhaps less widely used but more statistically rigorous than the Pearson r , was implemented as an additional test of the robustness of the results . It was observed in essentially all pairwise thermodynamic comparisons , regardless of homology , that the Spearman and Pearson r values were highly correlated ( Pearson r = 0 . 92 , Pearson p<10−6 , Spearman r = 0 . 92 , Spearman p<10−6 , 9 , 241 , 311 points , data not shown ) , with significant individual Spearman p-values of p<0 . 05 occurring at Pearson r values of approximately |r|>0 . 25 . As this threshold value of significance represented more than 45% of all 9 , 241 , 311 data points , it was decided to report the data in terms of the more widely used Pearson r . However , it is emphasized that the qualitative results and conclusions drawn were unchanged whether the Pearson or Spearman methods were used . A relatively small , but not necessarily exhaustive , number ( <50 ) of homologous protein comparisons involving conformational changes ( data not shown ) were discovered through manual inspection and discarded , since the conformational change usually dominated the thermodynamics . Although biologically interesting and deserving of future investigation , these changes were not the principal objects of the present study . Mode estimations for probability distributions of correlation coefficients and other quantities were computed using the method of Bickel and Fruewirth [33] . The results reported below were additionally filtered to only include relatively well-determined X-ray crystallographic structures ( resolution of ≤2 . 5 Å ) . However , all conclusions were unchanged when NMR structures and structures with resolution >2 . 5 Å were also included ( data not shown ) . The statistical significance of individual structural and thermodynamic alignments was assessed through construction of two simple null models . In Null Model 1 , the probability of chance occurrence at a particular level of structural or thermodynamic similarity was empirically estimated from the frequency of observed length-matched DALI-alignments at or above the particular similarity level . In this model , separate background distributions were used for homologs and non-homologs . In Null Model 2 , the probability of chance occurrence at a particular level of structural or thermodynamic similarity was estimated from the frequency of observed length-matched gapless alignments between randomly selected pairs of non-homologous protein fragments . In this model , a minimum alpha-carbon RMSD structure superposition [34]–[36] of the fragment pair as well as the Pearson r-value between thermodynamic descriptors was computed . 30 , 000 pairs of fragments were chosen for each gapless alignment length L , where 10≤L≤100 . In effect , the two null models occupied extremes of background distributions: Null Model 1 accounted for the interdependence of thermodynamic and structural similarity , while Null Model 2 weakened this interdependence . In both models , p-values were conservatively estimated , rounding up to the next lesser power of 10 .
Figure 1 illustrates the methods used to compare position-specific thermodynamic descriptors of homologous ( and non-homologous ) protein pairs . A structural superposition of two homologous SH2-family domains , human Xlp protein SAP and mouse Eat2 , is displayed in Figure 1A . The equivalenced residue pairings from this structure superposition were employed in Figure 1B to align the thermodynamic descriptors ( e . g . local stability , ΔG ) of the two proteins . A Pearson correlation of the aligned thermodynamic descriptors ( Figure 1C ) quantified the similarity between the two sets of descriptors . Analogous correlations were performed using the enthalpic ( ΔH ) and entropic ( TΔS ) values ( data not shown ) . This process was repeated for all non-redundant pairwise comparisons in the structure and sequence diverse protein set , as described in Materials and Methods . Because every protein in the set held a known position in the SCOP hierarchy , many comparisons could be sub-classified into either homologous ( identical SCOP family ) or likely non-homologous ( different SCOP class ) relationships . A clear pattern emerged when the correlations were tabulated for these two subsets: regardless of the thermodynamic descriptor used ( i . e . , ΔG , ΔH , TΔS ) , homologous proteins exhibited significantly more highly correlated descriptors than did non-homologous proteins ( Figure 2 ) . The general absence of sequence similarity between protein pairs suggested the importance of the structural context of the position ( as opposed to the identity of the amino acid at that position ) in determining the energetics at each position . In quantitative terms , the mode of the homologous pairs' distribution of stability correlations was 0 . 61 , as compared to 0 . 29 for the non-homologous pairs ( Figure 2A and Table 1 ) . Similarly , the modes for the enthalpy correlation distributions were 0 . 39 and 0 . 06 for homologs and non-homologs , respectively ( Figure 2B ) . Modes for the entropy distributions were 0 . 50 and 0 . 19 for homologs and non-homologs , respectively ( Figure 2C ) . Closer inspection of the correlation distributions suggested a second pattern: within homologous proteins , enthalpy and entropy generally did not exhibit correlations as great as those for stability ( modes of 0 . 39 , 0 . 50 , and 0 . 61 respectively , Table 1; differences between these homolog distributions were all highly significant , exhibiting p<10−6 as assessed by chi-square tests with 19 d . o . f ) . This trend was more fully revealed by plotting individual enthalpy and entropy correlations as a function of the stability correlation for the same homologous protein pair ( Figure 3A ) . Examination of selected thermodynamic descriptor alignments demonstrated that the source of the differences in correlation coefficients was due to greater variation in position-specific enthalpy and entropy values as compared to the variation in stability values ( Figure 3B ) . In particular , continuous regions of approximately 10 – 20 residues appeared to encompass much of the variation ( Figure 3B , boxes ) . Within these variable regions , changes in enthalpy between the two proteins appeared to be somewhat balanced by changes in entropy such that the overall difference in stability was minimized ( Figure 3B , boxes , discussed in detail below ) . A clear “gradient” was observed relating structural similarity to thermodynamic correlation: as structural similarity and likelihood of homology decreased , thermodynamic similarity also decreased ( Table 1 ) . In other words , proteins of similar structure exhibited similar thermodynamic stability . Such an overall gradient was not surprising , given that it would be expected that in the limit of two identical structures , two identical COREX/BEST ensembles , and thus identical thermodynamics , would result . However , the correlation distributions of Figure 2 showed a non-negligible degree of overlap between homologs and non-homologs . For example , approximately 10 percent of non-homologous pairs exhibited stability correlation coefficients larger than the homolog mode of 0 . 61 , and the same percentage of homologous pairs even exhibited zero or negative correlation . There are at least two explanations for the significant overlap between the distribution of correlations for homologous and non-homologous proteins . The first is that the overlap is real and reflects actual differences between structural and thermodynamic representations of proteins . The second is that the cases of high correlation between non-homologs are a statistical artifact stemming from an enrichment of poorly described data in certain sequence stretches . To address this issue , we adopted a two-fold strategy designed to probe both for biases in the thermodynamics of the different positions associated with the correlations , as well as biases in the amino acid compositions in those positions . First , in an effort to ensure that the overlap regions were not enriched with residue positions that occupied a particular region of thermodynamic parameter space , we performed principal components analysis ( PCA ) on the thermodynamic parameter space of the sequence segments that had the highest frequency of occurrence ( top 10% ) in the overlap regions and compared the eigenvalues to those obtained for the overall dataset , as well as for the datasets corresponding to the regions of no overlap[9] . The results ( Text S1 , Figure S2 ) revealed no bias in the overlap region , indicating that the high correlations were not driven by sequences enriched in a certain type of energetic environment . To further investigate possible sampling bias as a source of the overlap in the distributions , we investigated the thermodynamic information content of those sequence segments that most frequently aligned with non-homologous proteins . Previously , propensities of amino acids in different thermodynamic environments were used as the basis for a fold recognition algorithm , demonstrating that the thermodynamic architecture outlined in this study represented a general framework within which to understand protein organization [10] , [24] , [29] . Among several noteworthy results from those studies was the ability to match all helical ( or all beta ) sequences to their folds ( as described by a thermodynamic signature ) using propensity information derived exclusively from all beta ( or all helical ) proteins[29] , a result that demonstrated the universality of the thermodynamic representation of proteins as well as its independence from structural descriptors . To ensure that frequently paired non-homologous sequences ( i . e . those sequence stretches that most frequently paired with non-homologs ) contained the same thermodynamic information as the overall set , we performed fold recognition experiments using thermodynamic propensities derived exclusively from those sequences . The comparable fold recognition success ( Text S1 , Figure S3 ) clearly demonstrated that the thermodynamic information content was identical across the distribution of sequences . In short , the similarity in both the range of thermodynamic parameter space occupied , as well as the distribution of amino acids within this parameter space between sequences that frequently correlate with non-homologs and those that do not , suggested that the overlap regions in the distributions shown in Figure 2 are not statistical artifacts . Instead , the results may provide insight into the relationship between structure , energy , and the evolution of this diverse library of folds . This point is discussed in more detail below . As expected , inspection of the proteins contained in the overlap regions in Figure 2 revealed interesting exceptions to the overall structural-thermodynamic gradient , exceptions that required a more nuanced interpretation of the gradient . More generally , these exceptions suggested an organizational framework for the integration of thermodynamic information into existing fold classification schemes ( as described below ) . The exceptions could be broadly ordered into at least three distinct classes: 1 ) non-homologous proteins that contained regions of coincident structural and thermodynamic similarity , 2 ) homologous proteins containing regions of thermodynamic similarity and structural dissimilarity , and 3 ) homologous proteins containing regions of structural similarity and thermodynamic dissimilarity . To facilitate quantitative description of these exceptional cases , two empirical probability models of thermodynamic similarity were constructed to assess how often these cases might be expected due to chance , as described in Materials and Methods and displayed in Figure 4 . These models could be regarded as occupying extremes in structural and thermodynamic similarity space and consequently resulted in different probability estimates . The first model ( Null Model 1 ) accounted for the interdependence of structural and thermodynamic similarity at each alignment length . P-values for homologs and non-homologs were determined separately at each length by comparing the specific combination of structural and thermodynamic similarities with the frequency of obtaining such a combination across all comparisons . The density of points is summarized in Figure 4A for different alignment lengths . We note that the comparisons in Null Model 1 are DALI-aligned structures and thus represent comparisons between sequence stretches that have been selected for high structural similarity . To determine the probability of obtaining a particular thermodynamic correlation across any sequence comparison in the database , a second null model ( Null Model 2 ) was adopted . According to Null Model 2 , length-matched gapless alignments of randomly paired protein fragments were examined , a step taken to reduce the interdependence of structural and thermodynamic similarity . The Null Model 2 exhibited an inverse dependence of structural and thermodynamic similarity on length , in particular revealing that alignments of less than 20 residues had a substantial probability of high positive or negative thermodynamic correlation ( Figure 4B ) . Because the background distribution of Null Model 2 covered a larger amount of structural/thermodynamic similarity space , p-values estimated from Null Model 2 were generally more significant , as compared to Null Model 1 . Projections of these two-dimensional null model distributions into the single dimension of thermodynamic stability similarity , for alignments of approximately 70 residues in length , are displayed in Figure 4C . As Figure 4C reveals , the probability density of stability correlation coefficients for random alignments of approximately 70 residue stretches ( Null Model 2 ) is centered on zero , with approximately 80% of the comparisons falling below correlations of 0 . 5 . As expected , the probability density functions of structurally aligned sequences for both non-homologs and homologs are shifted to higher correlations , with the shift for homologs being more dramatic . The significance of this result is discussed in more detail below . For now we simply note that these distributions can be used to identify statistically significant exceptions to homologous structural and thermodynamic similarity and to investigate the possible biological and evolutionary relevance of such examples . Several examples of non-homologous proteins that nonetheless exhibited correlated position-specific stability are displayed in Figure 5 . These examples were representative of approximately 10% of non-homologs with high thermodynamic correlation ( defined as those above the homolog mode stability correlation value of 0 . 61 , about 10% of the total non-homologs ) , in that they contained structurally and thermodynamically similar regions within otherwise dissimilar proteins . Some specific types repeatedly observed were β-α-β units ( Figure 5A ) , non-local β-hairpins forming a sheet ( Figure 5B ) , antiparallel helices ( Figure 5C ) , and amphipathic single helices ( Figure 5D ) . Additional statistically significant exceptions to the structural-thermodynamic gradient , involving homologous proteins , are displayed in Figures 6 and 7 . Figure 6 shows three instances of homologous pairs exhibiting conserved local stability despite secondary structure variation . This phenomenon has been previously identified as a possible thermodynamic mechanism for evolutionary fold change[9] , and the examples seen here , occurring in a variety of secondary structural contexts , suggest its generality . However , a novel hypothesis is that these regions of thermodynamically conserved structure change possibly coincide with regions of functional importance; this hypothesis is illustrated with several examples . Figure 6A shows the structure superposition and aligned stability profiles of two immunoglobulin C1-set domains . Highlighted are two boxed regions where stability is conserved despite sequence and structure variation; one region contains functional residues involved in binding of the murine cytomegalovirus m144 protein , alpha 3 domain to the β2m subunit [37] . Figure 6B highlights a strand to helix conversion between aspartate and glutamate racemases , located in a region known to mediate the different dimerization modes of the two enzymes [38] , [39] . Similarly , Figure 6C highlights a region of structure change important for dimerization in each of two biotin carboxylase C-terminal domain-like proteins . In contrast , Figure 7 shows three statistically significant examples of homologous protein pairs whose native state structures were quite similar ( RMSD ≈1 Å ) and yet exhibited low or modest thermodynamic stability correlations ( rΔG≤0 . 5 ) . One similar example of thermodynamic dissimilarity in the context of high structural similarity has recently been experimentally confirmed using point mutations of Escherichia coli adenylate kinase[40] . As suggested by the relatively small area of negative correlations between homologs in Figures 2A–C , structure similarity in the absence of thermodynamic similarity did not occur very often between homologous proteins in the database ( only 8% of homologous pairs with an RMSD <2 . 5 Å exhibited a negative correlation coefficient ) . Despite its relatively low frequency of occurrence , this class of exceptions to the structural-thermodynamic gradient also may have functional relevance , as illustrated by several examples . Displayed in Figure 7A are the superposition and aligned stability profiles of two extremely structurally similar thioredoxins from E . coli and human , with an RMSD of 1 . 2 Å over 122 CA atoms . However , the stability profiles are only weakly correlated ( r = 0 . 45 ) , largely due to stability differences in the middle half of the proteins' alignment . The region of largest difference ( approximately alignment positions 60 – 80 ) encompasses the conserved Cys 73 residue , not found in the E . coli protein , which facilitates a unique and functionally important dimer form of human thioredoxin [41] . Figure 7B shows the comparison between two MurCD N-terminal domains from Haemophilus influenzae and the thermophile Thermotoga maritima; the low correlation between stability profiles clearly results from the greater predicted stability of the thermophile . Similarly , the stabilized N-terminal region of the zeta-class GST N-terminal domain shown in Figure 7C reduces the correlation with its delta-class homolog's stability profile . The predicted increase in stability is possibly related to the region's unique active site residues and associated novel functionality noted for the zeta-class [42] .
Position-specific thermodynamic attributes of proteins , such as local stability , enthalpy , and entropy , are preserved to a large degree in remote ( i . e . , twilight-zone sequence identity and below ) homologs . One implication of this result is that thermodynamics reinforces structure and sequence similarity , suggesting that thermodynamic attributes are likewise evolutionarily conserved properties . Upon closer inspection , however , several important features of the current analysis emerge regarding the relationship between the conservation of structure and energy . As noted above , Figure 4C reveals the shifting of the probability density function for non-homologs and homologs when comparisons are made with DALI-aligned structures , relative to random alignments . The shift observed for the non-homologs relative to the random sequence comparison is expected . In anecdotal terms , this result indicates that a particular stretch of structural elements ( e . g . , a helix-loop-helix ) will have more similar energetics than two stretches of randomly selected structure . Perhaps surprisingly , the energetic correlation for homologs is improved over the non-homologs for a given sequence length ( even though homologs with substantial sequence similarity were specifically not included in the analysis ) . This latter result is important because the difference between the improvement between homologs and nonhomologs provides a quantitative measure of the impact of the “structural context” of the specific sequences being compared . This is noteworthy because it undermines the notion that thermodynamic identity is defined by the RMSD of the structural units being compared . To the contrary , the results suggest a great deal of energetic heterogeneity for a particular structural motif . In other words , not all helix-loop-helix motifs of a given length and structural similarity , for example , will be thermodynamically equal . In fact , over the entire database , the results not only reveal significant instances of energetic heterogeneity for a specific structural motif , but more importantly , energetic similarity between different structures . It is our hypothesis , which we are currently testing , that it is precisely this context dependence of the energies of structural elements that determines how different folds can evolve from parental folds and why minimal sequence changes can dramatically change a protein fold[43]–[45] . Another implication of the conservation of local stability in remotely homologous proteins suggests that some aspect of protein behavior vital to the robustness of the organism is contingent on maintaining the regional stability . There are at least two possible reasons for such conservation . First , it is possible that a specific balance of regional stability within a protein may bias ( or preclude ) certain folding pathways , thus rendering the stability hierarchy in the protein critical to maintaining folding fidelity [46] , [47] . Second , and perhaps more prevalent , is that the locally unfolded state plays an important functional role . Indeed , locally unfolded states have been shown to be functionally important in numerous native state ensembles , mediating catalysis [48] , [49] , allostery [50] , [51] , and signaling transduction [22] , [52] . Intriguingly , exceptions to the trend of thermodynamic conservation exist , just as they are already known to exist for structure or sequence ( i . e . homologous sequences are able to adopt unrecognizably different structures [44] , [53] and homologous structures can result from unrecognizably different sequences [54] , [55] , respectively ) . As suggested by the examples given in Figures 6 and 7 , these exceptions to thermodynamic conservation may be evolutionarily or functionally important , despite their low frequency of occurrence . One interesting type of exception found here is that position-specific enthalpy and entropy are less conserved than stability . This observation suggests that in regions where this phenomenon occurs , the overall stability is more important than the thermodynamic mechanism utilized to achieve that stability . It is tempting to speculate that amino acid mutation driven changes in local entropy and enthalpy balance in conservation of local stability , as seen in Figure 3B . However , such “enthalpy-entropy compensation” , long reported in proteins as well as other chemical systems , has a controversial history [56]–[59] , with the apparent compensation being due ( in many cases ) to errors in enthalpy and entropy that are effectively amplified when the free energy is determined from the difference between them . Thus , although it is possible that such balance is somehow a mathematical artifact [60] , there is currently no evidence for such an artifact in the current analysis . Other types of exceptions , in the form of stability differences , may arise from differences of structure , organism temperature , or functionality . It is also possible that thermodynamic similarities between putative non-homologs , now treated as “exceptions” ( i . e . thermodynamic analogy ) , may reveal heretofore unknown evolutionary relationships . We propose in this regard that thermodynamics can mediate mechanisms for evolutionary fold change[9] . In other words , local native state structure change between two homologous proteins is possible without major disruption of local stability and , possibly , enthalpy or entropy . Conversely , functional or temperature adaptation can be achieved by changing the thermodynamics of excited state conformational fluctuations without disrupting the ground ( native ) state structure[40] . These two complementary processes may be thought of as ways to affect function by “sculpting” ( i . e . , changing the size , shape , and energetic properties of ) a protein's native state ensemble . Future experimental work will be directed at ways to intelligently employ these processes in protein design and engineering . Finally , we note that considerable debate has emerged regarding whether protein fold space is continuous or discontinuous [61]–[63] , with a major limiting factor in its resolution being the absence of a metric that can quantitatively compare different structures within a unified framework . One potential benefit of the unique thermodynamic representation of protein fold space used here is that it provides a quantitative connection between protein stability and fold specificity , in effect providing a vehicle for directly addressing this question . Indeed , this discovery of conserved position-specific thermodynamics not only furthers our understanding of the role of energetics in protein structure , function , and evolution , but also suggests an organizational framework for a possible thermodynamically-informed classification of protein homology . | Protein structure and function are fundamentally determined by thermodynamics . However , for technical as well as historical reasons , current evolutionary classification schemes and bioinformatics tools do not fully utilize thermodynamic information to describe or analyze proteins . In this work , we address this deficiency by computationally estimating the position-specific thermodynamic quantities of stability ( ΔG ) , enthalpy ( ΔH ) , and entropy ( TΔS ) for a large and diverse representative sample of protein structures . The sample was drawn from an expertly curated database , such that accepted evolutionary relationships existed for all protein pairs . Importantly , trivial relationships between pairs highly similar in amino acid sequence were explicitly excluded . We found that all position-specific thermodynamic quantities ΔG , ΔH , and TΔS were more similar between proteins that were evolutionarily related ( i . e . , homologous ) , and were less similar between proteins that were not evolutionarily related ( i . e . , non-homologous ) , with stability being particularly similar between homologous proteins . However , interesting statistically significant exceptions to these trends were observed , exceptions that could indicate novel processes of functional adaptation or evolutionary fold change , mediated by thermodynamics , for the proteins involved . Taken together , these results expand our understanding of the role of thermodynamics in protein evolution and suggest an organizational framework for a future thermodynamically-informed classification of protein homology . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biophysics",
"evolutionary",
"biology/bioinformatics",
"computational",
"biology"
] | 2010 | Investigating Homology between Proteins using Energetic Profiles |
Mechanical unfolding of a single domain of loop-truncated superoxide dismutase protein has been simulated via force spectroscopy techniques with both all-atom ( AA ) models and several coarse-grained models having different levels of resolution: A Gō model containing all heavy atoms in the protein ( HA-Gō ) , the associative memory , water mediated , structure and energy model ( AWSEM ) which has 3 interaction sites per amino acid , and a Gō model containing only one interaction site per amino acid at the Cα position ( Cα-Gō ) . To systematically compare results across models , the scales of time , energy , and force had to be suitably renormalized in each model . Surprisingly , the HA-Gō model gives the softest protein , exhibiting much smaller force peaks than all other models after the above renormalization . Clustering to render a structural taxonomy as the protein unfolds showed that the AA , HA-Gō , and Cα-Gō models exhibit a single pathway for early unfolding , which eventually bifurcates repeatedly to multiple branches only after the protein is about half-unfolded . The AWSEM model shows a single dominant unfolding pathway over the whole range of unfolding , in contrast to all other models . TM alignment , clustering analysis , and native contact maps show that the AWSEM pathway has however the most structural similarity to the AA model at high nativeness , but the least structural similarity to the AA model at low nativeness . In comparison to the AA model , the sequence of native contact breakage is best predicted by the HA-Gō model . All models consistently predict a similar unfolding mechanism for early force-induced unfolding events , but diverge in their predictions for late stage unfolding events when the protein is more significantly disordered .
No other scientific discipline has been so challenged to match the standard of physics-based simplicity as molecular and cell biology , perhaps in parts due to the inherent complexity of the systems under study and to our incomplete knowledge of the structure and function of the living cell . In narrowing this gap , minimal models of proteins have been developed as a step towards the goal of finding an “irreducible element” that still captures at least some of the essential physics and can thus reproduce and predict experimental measurements [1 , 2] . In this regard , minimal models have enjoyed success in testing , refining , and validating the conceptual foundations of the energy landscape theory of protein folding [3–7] as well as forced unfolding mechanisms [8] . A minimal model attempts to capture the essential dynamical behavior of a protein , while upholding the notion of simplicity along with its concommitant computational efficiency . In practice this involves coarse-grained ( CG ) representations of a protein with fewer degrees of freedom than the atomic level of description , simpler , phenomenological interaction potentials , and classical rather than quantum dynamics . Various semi-quantitative comparisons between CG models and experiments have been made [9–11] . At present however , systematic tests comparing the accuracy of coarse-grained models with fully atomistic models are still in need . Fully-atomistic models of proteins have their own shortcomings , including the inability of current atomistic force-fields to fold some proteins such as ubiquitin , a problem which has however been addressed recently and at least partially resolved [12] . However , all-atom models have now been successful in folding small proteins [13 , 14] , elucidating the binding properties of small-molecule drugs [15] , and characterizing complex molecular processes such as ribosomal translation [16] . Steered molecular dynamics ( SMD ) simulations can provide an in silico realization of experimental force microscopy studies [17–19] , where a force can be applied to a single protein– by optical tweezers for example– to unfold it [9 , 10 , 20] . Such computational studies can reveal details of the conformations of proteins during forced unfolding at atomic resolution . Force-extension curves obtained from atomic force microscopy ( AFM ) or optical trap assays generally display a saw-tooth pattern , where each partial unfolding event corresponds to a sudden drop in resistive force [9 , 10 , 20–22] . Our objective in this paper is to evaluate several CG models in SMD simulations by comparing the unfolding mechanisms predicted by each model to those predicted by a reference all-atom simulation under the same conditions . To this end , we construct scaling procedures such that the time , energy , and force scales can be meaningfully compared , and we develop several different metrics that each provide a different viewpoint of the unfolding dynamics . It has been shown that the dynamics of small , globular proteins is well-depicted by all-atom force fields with CHARMM22* with explicit TIP3P water molecules as solvent [14] . Atomistic simulations with explicit solvent , however , are limited in length and time scales of order 100nm and a few μs , unless specialized hardware is used [23] . Simulating the complete unfolding process of a full protein in explicit solvent is currently unfeasible if one wishes to simulate the unfolding mechanism with the same pulling rates as in experiments , and obtain comparable statistics . Thus , to simulate and sample large systems , coarse-grained models are required , because the energy function can be evaluated rapidly and the resulting molecular dynamics does not require a short time step . Various aspects of the protein dynamics and folded structures are successfully captured by structure-based Gō-like models [1 , 24–27] , in which the protein is biased towards its native folded state by native interactions . An interesting question is whether structure-based models can accurately capture the dynamics and the intermediate conformations of partially unfolded proteins during the mechanical unfolding process [9 , 10] . Here we consider three Gō-like models at different levels of resolution: the Associative memory , Water mediated , Structure and Energy Model ( AWSEM-Gō ) [27]; a heavy-atom Gō model [25] that considers all atoms except hydrogen; and a one bead per residue Cα-based Gō model [24] . Several previous studies have compared CG models to all-atom simulations and experiments [9 , 10] . Nevertheless , none of these studies have taken into account that effective time and energy scales must be normalized for meaningful comparison . There is some disagreement whether or not the unfolding pathways predicted by structure based models agree with all-atom simulations or experimental observations [9 , 10] . The authors of ref . [10] propose that the unfolding pathway from both CG models of titin I27 domain protein and all-atom implicit solvent simulations are not consistent with the experimental results even at low pulling speeds . On the other hand , CG pulling simulations of T4 lysozyme in Ref . [9] qualitatively agree with the experimental findings [28–30] . Sun et al . [31] have compared structure-based Gō models and experiments using force-clamp simulations; these comparisons show general agreement but often fail when sequence details are important in determining the weights of folding intermediates . In this paper , we study the forced unfolding process of a monomer of a loop-truncated variant of superoxide dismutase ( SOD1 ) . SOD1 was the first protein discovered in which mutations had an autosomal-dominant causal relationship to amyotrophic lateral sclerosis ( ALS ) [32 , 33] , an invariably fatal motor neuron degenerative disease characterized by progressive loss of motor neurons [34] , with a lifetime risk by age 70 of about 1/1000 [35] . The loop-truncated variant of SOD1 has loops IV ( residues 49–81 ) and VII ( residues 124–139 ) replaced with short Gly-Ala-Gly tripeptide linkers; here we denote this variant simply as tSOD1 [36 , 37] . tSOD1 consists of a β-barrel tertiary fold containing 8 β-strands and 110 residues . While full-length SOD1 readily forms a homodimer , tSOD1 is obligately monomeric . Moreover , the disulfide bond between C57 and C146 is no longer formed due to the truncation of loop IV and removal of the putative C57 . In experimental protein constructs , the remaining cysteines are mutated ( C6A/C111S/C146S ) to avoid intermolecular crosslinking; we employ the same construct here . In what follows , we first present the details of each model and the simulation set-up . We next describe the normalization of time and energy across models , by calibrating the pulling-rate , temperature , and force in the CG models with respect to the all-atom model . Then we discuss the force-extension curves we obtained , the evolution of structure as the protein is unfolded , and the predictions of the unfolding pathways provided by each model . We finally conclude and briefly discuss the implications of our results .
The aim of this study is to simulate the pulling process of the loop truncated SOD1 protein [37] , and compare the results of an all-atom model with several coarse-grained ( CG ) models . The experimental structure of the tSOD1 monomer can be found as chain A of PDB ID 4BCZ . Force spectroscopy simulations were carried out by tethering both termini with a harmonic potential . The last residue ( C-terminus ) is then moved along the vector from C- to N-terminus with constant velocity of 1 m/s . The stiffness of the spring that imparts the pulling force on the protein was set to 1000 kJ/ ( mol · nm2 ) . Experimental pulling speeds in atomic force microscopy ( AFM ) vary widely between 10−8-10−2 m/s [38–40] , while typical speeds in atomistic simulations are significantly faster , also varying widely between 1–1000 m/s [9 , 10 , 22 , 41] . Simulating and sampling the unfolding mechanism of a full protein in explicit solvent with the same pulling rate as in experiments is currently not feasible . The faster pulling rates in simulations may preempt slow dynamical transitions on the unfolding pathway that would otherwise occur at slower rates . A systematic study of the dependence of the unfolding mechanism on pulling rate for the present system is an interesting topic for future research . Four different types of force fields and protein models were considered: an all-atom ( AA ) simulation in explicit solvent , a heavy atom Gō model ( HA-Gō ) [25] , the Associative memory , Water mediated , Structure and Energy Model ( AWSEM-Gō ) [27] , and a Cα-Gō model [24] in order of decreasing resolution . In the HA-Gō model [25] , all heavy atoms are present . The AWSEM-Gō [27] model is an associative memory Hamiltonian model with a three-bead representation per amino acid . In the Cα-Gō [24] model , each amino acid is represented by only one bead [11 , 24] . Note that in Gō models , only native interactions are attractive , while non-native interactions are purely repulsive . Further description of the Gō model including interaction potentials is given in the specific models sections below . Fig 1 shows a representation of four amino acids in each of the models . Pulling simulations were repeated 20 times for each model , with the same initial structure but different random seeds . To compare the mechanical unfolding pathway of the protein in the all-atom and coarse-grained models , we computed the number of native contacts of all configurations during the pulling simulations . The definition of a native contact is the same throughout this paper . We calculated the native contacts for pairwise distances of all the moieties i and j in each model for any protein structure ( these may be heavy atoms , or coarse-grained residues ) . The fraction of the native contacts Q for conformation X , Q ( X ) , is defined as Q ( X ) = 1 | S | ∑ ( i , j ) ∈ S 1 1 + exp [ β 0 ( r i j ( X ) - λ r i j 0 ) ] , ( 3 ) where rij ( X ) is the distance between moieties i and j in conformation X , r i j 0 is the distance between the corresponding moieties i to j in the native state conformation , S is the set of all pairs of native contacts ( i , j ) belonging to the native structure . Amino acids having a native contact must be separated by four or more residues in the primary sequence and r i j 0 < r c u t ( rcut is a model-dependent cutoff distance given in Table 1 ) in the native state [58] , β0 is a smoothing parameter and the factor λ takes into account the fluctuations of the contacts . As a result of adjusting rcut , different models exhibit approximately the same native contact map , and a scatter plot of the number of native contacts present during the pulling trajectory for the AA model vs the CG model exhibits a slope of unity ( y ≈ x ) , ( see Supporting Information S1 Fig ) . Table 1 summarizes the values of β0 , λ , and rcut for each model . For the all-atom and HA-Gō models , the same set of parameters were used as the models share the same structure . The number of contacts for pairs of residues in the Cα-Gō model were weighted with respect to the number of contacts between the same pair in the native state of the protein in the all-atom model , i . e . a given pair of residues could have more than one contact between them , in proportion to how many of their heavy atoms were in contact . All new contacts that are formed during the simulations between moieties i and j are considered non-native contacts if the distance between i , j in the PDB structure is larger than rcut , see Table 1 for values of rcut in each model . To count the total number of non-native contacts in configuration X , we introduce a smooth function that interpolates between 1 and 0 as distance between i and j is increased , with a characteristic length scale R0 given by the mean of the distances between native pairs in the PDB structure: R 0 = 〈 r i j 0 〉 . The smoothing parameter β0 and the factor λ are the same as for native contacts ( see Table 1 ) . The number of non-native contacts in configuration X is then: N n n ( X ) = ∑ ( i , j ) ′ 1 1 + exp [ β 0 ( r i j ( X ) - λ R 0 ) ] , ( 4 ) R0 = 0 . 24 , 0 . 46 , 0 . 91 nm for the AA & HA-Gō , AWSEM , and Cα-Gō model , respectively . We use Q in our analysis of all models as a convenient order parameter on which to project the unfolding mechanism , independent of its accuracy as a kinetic reaction coordinate . In what follows , we will also look at other quantities describing unfolding , such as β sheet dissociation , and structural alignment of remaining parts of the native fold . The interpretation of “time” and “energy” in a CG model must be carefully considered . The energy landscape of CG models is generally smoother , due to softer interaction potentials , reduced degrees of freedom , and lack of explicit solvent molecules . A smoother potential energy surface leads to faster dynamics in comparison to all-atom forcefields . Therefore , the meaning of time in CG models is not the same as in all-atom explicit simulations . When comparing time , velocity , energy , and forces in CG models and all-atom force fields , we should interpret the results with respect to an “effective” energy and time in the system .
Fig 3 depicts representative snapshots of a pulling simulation in the all-atom model . The N- and C-termini are shown in red and blue spheres , respectively , and the structure of the protein is color-coded based on the residue index in the primary sequence . The reported values are the change in separation distance δx = xi − x0 , where x0 and xi are the initial and instantaneous separation distance between tether points respectively ( see Fig 3b ) . As we strain the protein , destabilized contacts between residues break , and regions of secondary structure in the protein are disrupted and dissociate . β-strands lose their native contacts , and locally unfold . The residues in the dissociated regions are then free to form turns or coil structures . In the all-atom simulations , the dissociation of the C-terminus at δx = 4 . 2 nm is the first unfolding event , see Fig 3b . In the unfolding trajectory shown in Fig 3c , we observe the dissociation of part of the N-terminus ( β1-strand ) at δx = 9 . 2 nm . In the snapshot shown in Fig 3f , the β5 and β6 sheets unravel last . At δx = 30 nm , the protein loses all its native contacts and forms a coiled chain . In force spectroscopy simulations , the force ramps up until multiple contacts break , releasing the applied load . We observe multiple force drops ( corresponding to multiple unfolding events ) in the force extension curves . Fig 4 shows a force extension curve for one run of the AA ( black line ) , AWSEM ( red line ) , HA-Gō ( blue line ) , and Cα model ( cyan line ) . Contact maps of the protein averaged over the four runs in Fig 5 are depicted in Fig 6 . In this work , native contacts are defined from the initial PDB structure . The upper triangle shows all native contacts at Q = 0 . 8 , Q = 0 . 5 , and Q = 0 . 1 , respectively from left to right . The bottom triangle shows all non-native contacts , i . e . all new contacts that are formed during the course of the simulation . Since some of these residue pairs may also posess native contacts , they will appear in both maps . It is clear from the figure that native contacts induce the formation of many nearby contacts in the contact map when thermal fluctuations are taken into account . Native contacts between residues k and l are color coded by the thermal average number of contacts divided by the total number of contacts in the PDB structure , 〈Qkl ( Q ) 〉 . Non-native contacts do not have a particular reference structure to normalize with respect to . We thus color code the non-native contact between residues k and l by the frequency of occurrence of any non-native contacts between those residues in the ensemble of structures at Q , i . e . the fraction of conformations at Q that have at least one non-native contact between residues k and l . Here “at Q” means within the bin Q − δQ , Q + δQ , where δQ = 0 . 01 . At Q = 0 . 8 , the native contact maps are approximately the same for all the models ( see first column , Q = 0 . 8 ) . As the protein unfolds from Q = 0 . 8 to Q = 0 . 5 ( second column ) , the C-terminal domain unfolds completely in all the models . The contact maps predict the same general unfolding events until Q ≈ 0 . 5 . As the protein unfolds further to Q = 0 . 1 ( third column ) , the unfolding processes begin to take different pathways across models . For the HA-Gō and Cα-models , the largest folded domain is located at residues 50–70 , while for the AWSEM model the folded domain lies in residues 10–30 . The remaining structured domain in the AA model is larger but only partially folded , consisting of residues 10–60 . We wish to emphasize that Fig 6 is not intended to illustrate the dominant unfolding mechanism for each model , but is simply an analysis of a subset of the unfolding trajectories , chosen only because they were distinct . A further analysis of the dominant unfolding mechanism will be discussed in the subsequent text and corresponding figures . Fig 6 shows that , for all models having more than one interaction site per amino acid , non-native contacts consist largely of what one might call “near-native” contacts . For example in the AA and HA-Gō models , pairs of amino acids have several native contacts between their constituent atoms , however some atom pairs exist between these same amino acids that are not in contact in the native PDB structure . “Near-native” contacts would involve these particular atom pairs , and the non-native contact map , which does not have any native interactions by construction , appears quite similar to the native contact map as a result . The presence of native interactions increases the likelihood of proximal non-native interactions . On the other hand , the Cα-Gō model has only one interaction site per amino acid and so cannot exhibit near-native contacts . The non-native contact map is thus sparser than the other models , and involves distinct amino acid pairs . The short-range contacts reminiscent of α-helical structure that are observed at Q = 0 . 1 in the Cα-Gō model are a consequence of the lenient cutoff used for contacts between Cα residues– the other models would show these non-native contacts as well , but because they have more degrees of freedom their cutoff distance for non-native interactions are shorter . Non-native interactions between amino acid pairs wherein one amino acid has been shifted in primary sequence by one , i . e . from amino acids ( m , n ) to ( m ± 1 , n ) or ( m , n ± 1 ) , can be induced by the shear forces between β-strands in the present assay , so that strands may slide over each other or reptate . Similar reptation has been observed in unbiased folding simulations of a β-hairpin [65] . Here , such “off-native” contacts are relatively common for all models that have more than one interaction site per amino acid; the relative numbers of amino acid pairs that partake in off-native contacts compared to the number of amino acid pairs partaking in native contacts , at the values of Q in Fig 6 , are given in Table 4 . To determine the sequence of the unfolding residues , we monitored the number of native contacts for each residue during the pulling simulations . Fig 7A plots the average fraction Qk ( Q ) of a given residue k as a function of total Q for all models . To calculate Qk ( Q ) , we normalize the number of contacts at Q by the number of contacts that residue k possesses in the native structure where Q = 1 . Red color corresponds to Qk ( Q ) = 1 and white indicates Qk ( Q ) = 0 , i . e . the residue has lost all its native contacts . The color scheme in Fig 7B represents the sequence ( in terms of the global order parameter Q ) by which residues lose more than 50% of their contacts during unfolding . The most persistent residues are colored dark blue , and the residues that are broken first in sequence are colored white . From Fig 7 , it is clear that all models predict as first event the dissociation of the C-terminus , residues 100–110 ( β8 ) . Then , in the AA , HA-Gō , and Cα-Gō model , the N-terminus detaches . The average unfolding pathways predicted by the AA model are very similar to the HA-Gō model , where residues in the N- and C-terminus dissociate first , and the contacts of residues 50–74 are broken last . In contrast , the sequence of unfolding in the AWSEM model starts from β8 and β7 , and the last domains to rip off are β3 and β2 . In summary , the similarity between unfolding events depicted in Fig 7B may be quantified by computing the correlation coefficient between the degree of remaining structure for individual β strands ( the similarity of the darkness of the bands for each model in Fig 7B ) . This gives the following correlation coefficients: between AA and HA-Gō: 0 . 94 , between AA and Cα-Gō: 0 . 86 , and between AA and AWSEM: 0 . 62 . In order to determine whether there exists a well defined unfolding pathway of the tSOD1 protein , and if so , to compare it across models , we used the template modeling score ( TM-score ) [66] to compare the similarity between the protein structures of different pulling trajectories at the same Q . The TM-score for the alignment of two structures is defined as [66]: TM=1L∑N11+ ( did ) 2 , d=1 . 24 ( N−15 ) 3−1 . 8 , ( 8 ) where N is the number of residue pairs , di is the distance between identical residues i in two structures , and L is the number of residues in the reference structure . The TM-score lies between 0 and 1; a TM-score of one indicates that the two protein structures are perfectly matched . Usually , two structures with TM-score higher than 0 . 5 are considered to have the same folded conformations , while uncorrelated protein structures have a TM-score of less than 0 . 2 [66] . Measuring the TM-alignment , as well as clustering of structures by TM-score , was performed by using Maxcluster ( http://www . sbg . bio . ic . ac . uk/maxcluster ) [67] . TM-scores of an all-against-all structure comparison of folded segment of protein structures obtained from each run for Q = 0 . 8 , Q = 0 . 4 , and Q = 0 . 2 are shown in Fig 8 . The color code quantifies the TM-score of pairs of structures at the same value of Q , obtained from all pairs of trajectories: red color indicates perfectly matched structures , and white represents a TM-score of zero . For comparing the conformations , we only considered Cα-atoms in the backbone for the folded region of the protein . This folded region at each Q-value was defined as a contiguous sequence of n residues with residue index i ≤ j ≤ i + n , where 〈Qi ( Q ) 〉 > 0 . 5 and 〈Qi+n ( Q ) 〉 > 0 . 5 . The average here corresponds to the ensemble of states of all trajectories . If there is an unfolded region with more than 10 residues in between i and i + n , then the largest contiguous sequence of residues with 〈Qi ( Q ) 〉 > 0 . 5 was considered . In Fig 8 , TM-scores for Q = 0 . 8 ( see left column in Fig 8 ) are high for all four models , which indicates that at the beginning of the unfolding process , the backbone of the protein is very similar in the unfolding trajectories . The Cα-model and the HA-Gō model exhibit slightly larger deviations between trajectories at this value of Q . As the protein unfolds further , at Q = 0 . 4 ( second column in Fig 8 ) , the TM-scores drop to lower values . In the AA model , the average TM-score of one trajectory ( run 20 ) is 0 . 33 , while other runs have higher TM-scores . For the HA-Gō model , values of the TM-score range between 0 . 3–0 . 6 . In the Cα-Gō model , the TM-scores range between 0 . 5–0 . 76 . At the same Q = 0 . 4 , the TM-scores in the AWSEM model are still much higher and vary between 0 . 6–0 . 94 , which indicates the presence of one dominant pathway . It is clear from the large number of trajectories with high TM-scores that the AWSEM model exhibits a much stronger pathway behavior than the other models , which begin to balkanize into clusters of residual structure . This can also be clearly seen by plotting the mean TM-score between all M ( M − 1 ) /2 trajectories ( M = 20 here ) as a function of Q , for all four models , see Fig 9 . At Q = 0 . 2 , the TM-scores for AA , HA-Gō , and Cα-Gō models have reached about 0 . 2 , which is comparable to the TM-score of a random coil ensemble . This indicates a highly diverse residual structure between trajectories . The length of the residual folded structures at Q = 0 . 2 is only about 24 , 38 , 21 , and 27 for AA , HA-Gō , AWSEM , and Cα-Gō models . Thus , the AA , HA-Gō , and Cα-Gō models predict multiple unfolding pathways for lower values of Q . On the other hand , the AWSEM model still has a fairly high TM-score; indicating that it predicts only one main unfolding pathway . Two structures that are nearly folded at Q ≈ 1 are obliged to have a high TM score , while two structures at low Q are not so obliged . We thus also plot in Fig 9 a reference curve to compare the structural overlap . We construct this curve by taking a window containing a given number of residues ( e . g . 50 ) , and slide this window along all possible locations of the primary sequence ( 1–50 , 2–51 , etc . ) , to obtain a set of partial native structures , one structure for each window position . This process is repeated for all window sequence lengths . The native contacts Q are calculated for all of the structures , binned , and TM-aligned . This gives a randomized set of partially unfolded structures , which nevertheless lack thermal fluctuations and strain distortions , and so would tend to have larger TM-alignments when they overlap . Interestingly , this curve lies roughly between the AWSEM model and all other models , consistent with the strong pathway-like unfolding mechanism of the AWSEM model . In order to more clearly render the unfolding pathways predicted by each model , we clustered the protein conformations based on the TM-scores during the unfolding at several different Q-values , see Figs 10 and 11 . The structures shown are centroids of the corresponding clusters that emerge from the clustering analysis . A TM-score cut-off of 0 . 6 is used to define when configurations no longer belong to a given cluster . The coloring is based on the residue index , where the C-terminus of the structured protein is in red and the N-terminus is colored blue . The thickness of the lines is proportional to the fraction of total trajectories in each cluster . As can be seen in Figs 10 and 11 , each model predicts a dominant unfolding route , which is shown with a thick black line . All models predict one unique unfolding pathway until Q ≈ 0 . 44 . Along this pathway , β strand 8 at the C-terminus loses structure first , however subsequent events differ between models . As the structure continues to unfold from Q ≈ 0 . 44 to Q ≈ 0 . 2 , we observe multiple unfolding pathways in all models but the AWSEM model; see Fig 10 panel a ) AA , b ) HA-Gō , and Fig 11b ) Cα-Gō models . The protein structures from different pulling simulations in the above 3 models are distributed in multiple diverse conformations . For the AA , HA , and Cα-Gō models , β strand 1 on the N-terminus generally dissociates after β strand 8 at the C-terminus . In 3 out of 20 trajectories of the AA model however , β strands 1 and 2 were the last to unfold . This mechanism with β strands 1 and 2 unfolding last is the pathway observed in the AWSEM model . Generally , the last unfolding events involve breakage of contacts in β strands 5 and 6 in the AA model . The sequence of unfolding events along the main forced unfolding pathway in the AA model is β strand 8 , then β1 and 7 , β2 , then β3 and 4 , β6 , and then finally β5 . In the HA-Gō model , the sequence of unfolding of events is β8 and β1 , then β2 , β7 , then β3 and 4 , then β6 and finally β5 is the last domain to unfold , which is similar to the AA model . In the Cα-Gō , the first unfolding event is also dissociation of C-terminal β strand 8 , then β1 , β strands 2 and 7 , then β3 , β4 , β6 , and finally β5 . In contrast to the above three models , the AWSEM model ( Fig 11c ) predicts only one unfolding pathway . In this pathway , the unfolding of the protein starts from the C-terminal β strand 8 , then β7 , β4 , the C-terminal portion constituting roughly half of β strand 3 , the N-terminal portion constituting roughly half of β strand 1 , β strands 5 and 6 and the remainder of β strand 3 , the remainder of β1 , and β2 . Strands 1 and 2 were the last to dissociate in all the 20 trajectories . We also compare the main pathway of unfolding of the AA-model with other models by calculating the TM-score between the AA model and the three CG models . For comparison across different models , TM-score was calculated using the program TM-align [68] . The conformations of the most populated cluster at Q in the AA model was compared to the corresponding conformations in the other models at the same value of Q . In order to compare CG with AA models , the TM-alignment only includes the Cα atoms in the backbone of the folded segment of the protein as described above . The TM-score versus Q , for pairs of two models , AA with HA-Gō ( black line ) , AA with AWSEM ( red line ) , and AA with Cα-Gō ( blue line ) , is depicted in Fig 12a . A TM-score with a value of > 0 . 5 for a pair of proteins means that the structures are similar [68] . The observed high TM-scores between AA and all CG models for Q > 0 . 45 indicate that all CG models predict unfolding pathways similar to the AA model by this metric . Interestingly , in the range of Q between 0 . 45 and 1 , the AWSEM model shows the best agreement with the AA model , and the HA-Gō model shows the least agreement . As the protein is unfolded below Q ≤ 0 . 44 , the TM-score shows a more sensitive dependence upon models . At Q less than about 0 . 25 , the TM-scores have reached small values that would be expected for the alignment of random dissimilar structures . We conclude therefore that all models predict similar unfolding pathways until the protein is about half unfolded , at which point the mechanisms begin to diverge from the AA model . The AWSEM model does not predict multiple pathways as the other models do , but the dominant pathway observed for the AWSEM model is structurally as similar to the AA model as any of the other CG models . None of the CG models can completely capture the unfolding mechanism at the lower values of Q for the AA model . The above conclusion is recapitulated by analyzing the corresponding alignment between models using the more conventional metric of RMSD . Comparing the folded core of the AA model in the most populated cluster , as defined in Section “Residue Contacts” , to the same region in the CG models ( most populated cluster , same sequence length as in the AA model ) yields a plot of RMSD vs . Q , as shown in Fig 12b . By this metric , the AWSEM model again shows the best structural alignment ( lowest RMSD ) until Q ≈ 0 . 3 , while the HA-Gō model shows the worst structural alignment . In this paper we explored the limits of validity of several structural-based coarse-grained ( CG ) models by comparing the unfolding mechanisms of a truncated variant of superoxide dismutase , when the protein is subjected to force-induced unfolding . An all-atom ( AA ) , explicit-solvent model is used as the benchmark standard to which the other models are compared . A more desirable comparison would be with experimental data , however no experimental data exists for this particular system , and moreover the data that does exist for other systems does not have the atomic resolution that we have measured and compared with here . Unfortunately then such a comparison is not possible at present . One may entertain the possibility that one of the coarse-grained models could agree better with experiments than the all-atom model– at this time however , such comparisons are purely speculative and without any definitive precedent . To facilitate the present comparison between coarse-grained models and all-atom simulations , the models were normalized in terms of time , energy and force scales . We analyzed in detail several different metrics of the unfolding process: force-extension curves , evolution of contact maps , sequence of unfolding via loss of contacts involving a particular residue , and backbone alignment quantified by TM-score and RMSD . We found that the force-induced unfolding mechanisms of all CG models differ to varying degrees from that in the AA model . Both HA and Cα-Gō models do capture most aspects of the sequence of unfolding events . Comparing the all-atom model with a heavy-atom Gō model gives some clues as to the combined importance of both energetic heterogeneity of native contacts , and non-native interactions , in modulating the unfolding mechanism . The varying strength of native interactions can alter the free energy barriers to unfolding , possibly increasing them in special cases when polymer entropy cost is compensated by stronger interactions , but generally decreasing the folding/unfolding barrier [69–75] . The HA-Gō model does capture some effects of energetic heterogeneity by counting multiple contacts between amino acids involving large side-chains , but otherwise is an uncontrolled approximation that may return erroneous conclusions , particularly when electrostatic effects and solvation are important [76] . The HA-Gō model also captures entropic heterogeneity due to the variable backbone polymer length between residues participating in native contacts [77] . Unless they are strong enough to result in long-lived off-pathway intermediates , non-native interactions also generally decrease folding/unfolding barriers , and they can modulate unfolding mechanisms [70 , 71 , 78–80] , or modify the diffusion coefficient along the folding reaction coordinate [81–86] . The HA-Gō model was the softest model examined , after suitable normalization was performed to equate the unfolding free energy across models . This is not obvious , given that it was not the most coarse-grained model that we had investigated . The Cα model closely follows as the next softest model . The AWSEM model differed from all other models insofar as all folding trajectories follow a single unfolding pathway that does not branch out in the final stages , as one approaches the unfolded state . This pathway is part of the ensemble of paths observed in the AA model , however it is not the dominant pathway . On the other hand , the backbone structure predicted by AWSEM agrees best with the AA model while the protein is still mostly folded . These findings substantiate that a combination of metrics is required to obtain a full picture of the unfolding dynamics . No single coarse-grained model studied here agreed best with all of those metrics simultaneously . It is perhaps surprising that the Cα-Gō model , as the simplest model , does not perform substantially worse than the more detailed models . This finding may not be generically true however: A force peak specifically due to non-native interactions was observed in AA forced-unfolding simulations of DDFLN4 , a predominantly β-sheet protein [87] , which recapitulates experimental observations [88] but was not observed in structure-based Gō models . In this study , we assumed that the melting temperature of the AA model was equivalent to the experimental melting temperature , because of the difficulty in effective sampling for AA models of large proteins . This was used to normalize the temperature scales for the various coarse-grained models to their corresponding melting temperatures . We have found that the unfolding mechanism of the AA model is not particularly sensitive to variations in temperature of ±10K . In the future however , it would be worthwhile to attempt to surmount this difficulty using a combination of biased sampling techniques and non-equilibrium relations to reconstruct the free energy landscape [89 , 90] An interesting future study will be to apply the tools developed here to full length SOD1 , which includes a long loop of 35 amino acids between β-strands 4 and 5 , and another long loop of 22 amino acids between β-strands 7 and 8 . There is nothing necessarily absolute about the force-induced unfolding mechanism found here , which may differ from the unfolding mechanism in either thermal or chemical denaturation . Even within the context of force-induced unfolding , the mechanism may be linkage dependent [91 , 92] , and may depend on the magnitude of the applied force [41 , 93] . | Although experimentalists can now unfold single proteins in the lab by pulling them apart and measuring the force and extension , a clear idea of how the protein changes shape and loses structure during this process is currently missing . Molecular dynamics simulations can offer insight as to what is actually happening structurally when you pull a protein apart . However , typical simulations of processes that happen nearly instantaneously in the lab take weeks to perform , when every atom must be accounted for . Researchers have thus resorted to much faster “coarse-grained models” , where the system is simplified by removing select atoms and the remaining interactions rescaled , but the accuracy of such simulations are known to suffer as a result . How accurate or inaccurate are the current coarse-grained models in capturing the unfolding mechanisms of proteins ? Our findings upon investigating this question suggest that , while coarse-grained models successfully capture early unfolding events of nearly-folded proteins , they suffer when trying to describe the late stages of unfolding in mostly-disordered proteins . By showing how coarse-grained models may fail to capture the accuracy of their more sophisticated but cumbersome counterparts , we can shed light on how to improve their reliability , increase their speed , and enhance their relevance in capturing biologically-relevant phenomena . | [
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] | [
"enzymes",
"condensed",
"matter",
"physics",
"enzymology",
"dismutases",
"simulation",
"and",
"modeling",
"protein",
"structure",
"prediction",
"protein",
"structure",
"research",
"and",
"analysis",
"methods",
"relaxation",
"(physics)",
"proteins",
"melting",
"phase",
"... | 2016 | As Simple As Possible, but Not Simpler: Exploring the Fidelity of Coarse-Grained Protein Models for Simulated Force Spectroscopy |
Chlamydia trachomatis attachment to cells induces the secretion of the elementary body–associated protein TARP ( Translocated Actin Recruiting Protein ) . TARP crosses the plasma membrane where it is immediately phosphorylated at tyrosine residues by unknown host kinases . The Rac GTPase is also activated , resulting in WAVE2 and Arp2/3-dependent recruitment of actin to the sites of chlamydia attachment . We show that TARP participates directly in chlamydial invasion activating the Rac-dependent signaling cascade to recruit actin . TARP functions by binding two distinct Rac guanine nucleotide exchange factors ( GEFs ) , Sos1 and Vav2 , in a phosphotyrosine-dependent manner . The tyrosine phosphorylation profile of the sequence YEPISTENIYESI within TARP , as well as the transient activation of the phosphatidylinositol 3-kinase ( PI3-K ) , appears to determine which GEF is utilized to activate Rac . The first and second tyrosine residues , when phosphorylated , are utilized by the Sos1/Abi1/Eps8 and Vav2 , respectively , with the latter requiring the lipid phosphatidylinositol 3 , 4 , 5-triphosphate . Depletion of these critical signaling molecules by siRNA resulted in inhibition of chlamydial invasion to varying degrees , owing to a possible functional redundancy of the two pathways . Collectively , these data implicate TARP in signaling to the actin cytoskeleton remodeling machinery , demonstrating a mechanism by which C . trachomatis invades non-phagocytic cells .
Chlamydiae are obligate intracellular bacterial pathogens that are responsible for a number of human diseases [1] . Different serological variants of Chlamydia trachomatis are primarily pathogens of humans . Serological variants ( serovar ) A , B , Ba , and C are the etiologic agents of trachoma , the leading cause of preventable blindness worldwide . Serovars D to K are associated with sexually transmitted diseases , and serovars L1 , L2 , and L3 cause lymphogranuloma venereum , a more invasive sexually transmitted disease . Chlamydophila psittaci is a zoonotic agent that causes a pneumonia-like respiratory disease in humans . Chlamydophila pneumoniae is a causative agent of community-acquired pneumonia , and has recently been associated with cardiovascular diseases . The genii Chlamydia and Chlamydophila share several biological properties , including a biphasic developmental cycle that includes cell types adapted for extracellular survival ( elementary bodies or EBs ) or intracellular multiplication ( reticulate bodies or RBs ) [2] . Intracellular development occurs within a protective vacuole called an inclusion , which is nonfusogenic with endocytic vesicles , but is instead interactive with an exocytic pathway that delivers sphingomyelin and cholesterol from the Golgi apparatus to the inclusion [3] . Because of the obligate intracellular nature of chlamydiae , access to the inside of the cell is paramount to survival . To this end , Chlamydiae have evolved to efficiently invade non-phagocytic cells through a process that has been described as “parasite-specified phagocytosis” [4] . Because entry into host cells is a critical step in the chlamydial developmental cycle , this stage of infection is an especially attractive chemotherapeutic target for inhibition . Thus , considerable efforts have been put forth to understand the molecular mechanism of chlamydial invasion . Several chlamydial ligands and host receptors have been proposed , although there has been little consensus as to which of the number of chlamydial ligands and host receptors are of primary importance . It is likely that many of these ligand-receptor interactions function in infection of different cell and tissue types conferring an advantage during infection [5] . Chlamydial invasion is initiated by the electrostatic and reversible interaction of EBs mediated through host heparan sulfate-like proteoglycans , followed by an irreversible host-dependent step that leads to internalization of EBs [6]–[8] . While the identity of the host factors in this secondary irreversible step has yet to be identified , the characterization of the molecular mechanism of the post-attachment stages of chlamydial infection is beginning to be defined [7] , [9]–[12] . Upon attachment of EBs , there is a demonstrable rapid recruitment of actin at the sites of attachment , leading to the the formation of pedestal-like structures underneath attached EBs [9] , [11] . This recruitment of actin is transient and eventually leads to the uptake of EBs into membrane-bound vesicles that are devoid of known early endosomal markers [13] . Recently , a chlamydial protein associated with the uptake of EBs was found to be translocated by a type III secretion system into the host cell at the site of entry [14] . Once in the cytosol , the protein quickly becomes tyrosine phosphorylated by host kinases . This protein , termed TARP for translocated actin-recruiting protein , is likely to be involved in chlamydial invasion in that it is able to interact with both filamentous and monomeric actin [15] . Interestingly , live cell imaging studies demonstrated tyrosine phosphorylation preceding actin recruitment , leading to the hypothesis that TARP plays a key role in initiating a signal transduction cascade that leads to the activation of the cellular actin remodeling machinery [14] . A striking feature of the TARP protein is the presence of several tyrosine-rich tandem repeats of approximately 50 amino acids in length . The number of repeats differs , with C . trachomatis urogenital isolate , serovar D , containing three repeat units and an LGV strain with almost six full repeat units [16] . Some isolates lack these repeats , and are also able to recruit and remodel actin to facilitate their invasion [11] . This is consistent with recent functional studies of TARP that concluded a C-terminal domain located downstream of the tandem repeat region contributes to actin recruitment and nucleation [15] . Here we report of an alternate mechanism of actin remodeling by TARP that involves the repeat domain . Our data show that phosphorylation of critical residues in this region initiates a signal transduction cascade by interacting with guanine nucleotide exchange factors , Sos1 and Vav2 . Mutations in the relevant tyrosine residues resulted in the loss of the ability of TARP to interact with these proteins , preventing recruitment of Rac and actin , and reduced invasion .
Upon interaction of the EBs with epithelial cells , the TARP tyrosine residues that are phosphorylated are within the context of the phosphorylation sites for members of the Src-family of kinases and recognition sites of various src-homology 2 domain ( SH2 ) -containing adaptor proteins ( Figure 1a ) . The presence of these sequences raises the possibility that TARP may recruit signaling molecules that recruit and remodel actin . To directly test this hypothesis , a mammalian expression vector with an insert that encodes for a fusion protein containing the N-terminal extracellular domain of CD4 ( amino acids 1–372 ) and one phosphodomain unit , with wild type or mutant sequences was synthesized , and co-transfected into Cos-7 cells along with either GFP-actin or GFP-Rac1 . The second repeated ( amino acids 174–222 ) unit has the sequence DAAADYEPISTTENIYESIDDSSTSDPENTSGGAAALNSLRGSSYSNYD , with the relevant tyrosines underlined ( Figure 1a ) . These tyrosine residues were targeted because they are in the context of the recognition motifs for various Src kinase family and SH2 domain-containing adapter proteins [17] . Interestingly , these features of the tyrosines in the TARP phosphodomain are shared by the critical tyrosine in the Tir protein of enteropathogenic E . coli [18] . Expression of the fusion proteins was allowed to continue for 24 h post-transfection . The cells were incubated for 2 h with 4 . 5 uM beads coated with anti-CD4 antibody molecules to induce aggregation of plasma membrane-localized CD4-1xR fusion proteins . The highly localized tyrosine phosphorylation [14] and subsequent formation of actin-rich structures ( i . e . pedestal and microvilli ) directly underneath the attaching EB particle [9] are indicative of a signaling process that is restricted to the area directly underneath the invading EB particle . Indeed , the re-initiation of the formation of these actin-rich structures after disruption by the actin-destabilizing agent cytochalasin D was preferentially localized to the sites of chlamydia attachment ( Figure S1 ) . The induced aggregation provided defined spaces in which to monitor the recruitment of GFP-actin and/or GFP-Rac1 . Doubly transfected cells were decorated with the beads and appeared green under fluorescence microscopy . For those transfected with CD4-1xR ( WT ) , a ring-like localization of GFP-actin could easily be seen surrounding the beads . CD4-1xR ( Y1F2 ) and ( F1Y2 ) , in which the tyrosines were mutated to phenylalanines , also demonstrated recruitment of GFP-actin and GFP-Rac1 , while the double mutant CD4-1xR ( F1F2 ) failed to show the same recruitment ( Figure 1a ) . The differences in the ability to recruit actin and Rac1 was not due to differences in expression level as all constructs were expressed equally well ( Figure 1b ) . The tyrosine residues in the CD4-1xR fusion protein that were actually phosphorylated in vivo were determined to be Tyr179 or Tyr189 , using a numbering system that starts with the Asp174 residue of the second repeat of the serovar L2 TARP homolog . Figure 1b shows that the mutation of the two tyrosines to phenylalanines ( F1F2 ) eliminated any reactivity with the 4G10 anti-phosphotyrosine antibody . Thus Tyr179 and Tyr189 , but not Tyr218 and Tyr221 were phosphorylated . The doublet observed in the lane marked WT was likely due to the singly and doubly phosphorylated forms . Thus , one unit of the phosphodomain of TARP is functional , and that recruitment of actin and Rac1 appeared to require at least one tyrosine to be phosphorylated . Because one copy of the repeated unit is apparently sufficient to recruit actin and Rac1 in our cell culture assay , and the dependence of these activities on the phosphorylation of Tyr179 and Tyr189 , it is hypothesized that the domain may act as a signaling platform to which host signaling molecules are recruited . To directly test this possibility , biotinylated oligopeptides with the sequence DAAADYEPISTTENIYESIDDSSTSDPENTSGGAAALNSLRGSSYSNYD were custom synthesized either as non-phosphorylated tyrosines ( WT ) , individually phosphorylated tyrosines ( pY1 and pY2 ) , or in which phenylalanines have been substituted for the tyrosines ( F1F2 ) . The biotinylated oligopeptides were incubated with lysates from HeLa cells and the presence of molecules known to participate with Rac1 in signal transduction pathways , specifically the Rac guanine nucleotide exchange factors Vav2 and Sos1 were monitored by Western blotting . The specificity of Sos1 towards Rac1 is conferred by the Abi1 and Eps8 proteins [19] . Sos1 exclusively bound to the phosphorylated pY1 peptide , as shown in Figure 2a , while Abi1 bound equally well to pY1 and pY2 oligopeptides . Vav2 bound equally well to pY1 and pY2 . Pulldown samples from the WT and F1F2 oligopeptides showed background or undetectable levels of the all the proteins monitored , indicative of the requirement for phosphorylated tyrosine residues . Interestingly , Rac was only detected in the pY1 fraction , coinciding with the presence of Sos1 , Abi1 , and Eps8 in the same fraction . While Vav2 was also present in the pY1 , it is unlikely that Rac is binding to this protein , because Rac was not pulled down by pY2 despite the presence of Vav2 . However , it has been reported previously that Rac association with Vav2 could be enhanced by the addition of PI 3 , 4 , 5-P3 [20]–[22] . Therefore , a water-soluble analog of this phospholipid was added to the lysates , and the pulldown fraction was assayed for the presence of Rac ( Figure 2b ) . When lysates were pre-treated with the PI 3 , 4 , 5-P3 analog , the Rac GTPase protein could be detected from the pulldown fractions from both pY1 and pY2 . Thus , it appears that interaction of Rac with the phosphodomain of TARP via Vav2 required PI 3 , 4 , 5-P3 . A series of lysates was prepared from HeLa cells depleted of Sos1 , Abi1 , and Eps8 , and the co-precipitation of these signaling molecules was performed to determine which proteins are required for the interaction of the complex with the pY1 oligopeptide ( Figure 2c ) . The depletion of the Abi1 protein markedly affected the levels of Eps8 and Sos1 coprecipitated by the pY1 oligopeptide , while neither the depletion of Eps8 nor Sos1 significantly affected the level of coprecipitated Abi1 protein . However , depletion of Eps8 negatively affected the ability of Sos1 to be coprecipitated . Sos1 depletion also had a negative effect on Eps8 pulldown . Taken together , the depletion and pulldown data indicate that Abi1 binding to TARP , which is likely to be indirect due to the lack of any SH2 domain , mediated the coprecipitation of Eps8 and Sos1 in a complex . Interestingly , Sos1 and Eps8 may stabilize each other's association with the complex as depletion of Sos1 decreased the coprecipitated levels of the Eps8 and vice versa . Because the presence of PI 3 , 4 , 5-P3 appears to be necessary for optimum GEF activity of Vav2 towards Rac , the localized synthesis of this phospholipid at the site of chlamydial entry was investigated , using the probe BTK-PH-GFP , where the pleckstrin homology ( PH ) domain of Bruton's tyrosine kinase ( BTK ) was fused to GFP . This domain has been demonstrated to be specific for PI 3 , 4 , 5-P3 [23]–[27] . Cells expressing this probe were infected by CMTPX-labeled C . trachomatis serovar L2 , and monitored using live microscopy ( Figure S2 ) . As shown in Figure 3 , localized bursts of EGFP-BTK-PH recruitment could be observed . Both the recruitment and disappearance of the fluorescent probe were rapid and transient . To test if the recruitment of BTK-PH-GFP was due to PI 3 , 4 , 5-P3 synthesis , transfected cells pre-treated with 100 nM wortmannin were monitored by live cell microscopy . As shown in Figure 3a and Figure S3 , BTK-PH-GFP recruitment at the site of entry was not observed . Thus , BTK-PH-GFP localization at the site of entry was likely associated with PI 3 , 4 , 5-P3 synthesis . This localized PI 3 , 4 , 5-P3 synthesis would be expected to participate in the activation of Rac1 by the Vav2 GEF . That not all EBs localized with the BTK-PH-EGFP reporter could be attributed to the presence of a relatively large number of non-infectious EB particles , which is common in purified EB preparations , or that some simply do not utilize the PI3-kinase pathway of chlamydia entry . The localized burst of PI 3 , 4 , 5-P3 synthesis implies the presence of the PI3-kinase at the site of chlamydia entry . Figure 3b shows that the p85 subunit of the Class I PI3-kinase interacted with the pY1 oligopeptide , but not with pY2 or the non-phosphorylated WT and F1F2 oligopeptides . In conjunction with the localized PI 3 , 4 , 5-P3 synthesis , the interaction of the p85 subunit implies its localization at the site of chlamydia entry . Sos1 , Eps8 , and Abi1 have been shown previously to act as a Rac-specific GEF , and Vav2 required binding of PI 3 , 4 , 5-P3 to the Dbl homology domain for optimal Rac binding and activation [19]–[22] , [28] . We sought to determine if the Sos1 , Eps8 , and Abi1 complex and Vav2 proteins precipitated by the pY1 oligopeptide could act as a Rac GEF . Post-nuclear supernatants were prepared from HeLa cells that were depleted of Sos1 or Vav2 proteins by small interfering RNA ( siRNA ) . As a control , lysates were prepared from HeLa cells transfected with scrambled siRNA . The levels of Sos1 and Vav2 from the respective lysates were markedly reduced . A representative Western blot of an siRNA depletion experiment is shown in Figure S4 . The different precipitates were evaluated for GEF activity towards purified His-tagged Rac1 or His-tagged Cdc42 ( Table 1 ) . His-Rac1 and His-Cdc42 molecules were mixed with the different precipitates along with 32P-α-GTP . An additional control in which the His-tagged Rac1 was omitted from the reaction was included . Total HeLa cell lysate showed the highest GEF activity towards the His-tagged Rac1 and Cdc42 . When no biotinylated oligopeptide was present , the amount of 32P radioactivity was relatively low and comparable to samples in which the His-Rac has been omitted . Pulldown samples from the WT oligopeptide showed an approximately 4-fold increase relative to the samples with no oligopeptide , possibly representing the non-specific binding of proteins to the oligopeptides . However , using His-Rac1 as the target , the supernatant incubated with the pulldown samples from the pY1 oligopeptide displayed significantly elevated levels of retained 32P label relative to the pulldown from the WT oligopeptide for all lysates examined . An exception was the Sos1-depleted lysate , indicating the requirement for the Sos1 GEF . The pY1 precipitate from the PI 3 , 4 , 5-P3 -supplemented lysate showed an increase in GEF activity for both His-Rac1 and His-Cdc42 ( 31038 cpm vs . 51169 ( Rac1 ) and 2821 cpm vs . 28660 cpm ( Cdc42 ) ) . The corresponding increases could be attributed to the presence of Vav2 , which is a GEF for Rac , Cdc42 , and RhoA . Indeed , for the exchange reaction using His-Rac1 as the target , Vav2 depletion by siRNA returned the 32P level to that of the untreated cell lysate , even in the presence of PI 3 , 4 , 5-P3 . While a similar reaction was not performed using Cdc42 as the target , the significantly lower level of GEF activity in the absence of PI 3 , 4 , 5-P3 indicate that Vav2 may be involved as well . From the data , it appears that withdrawal of PI 3 , 4 , 5-P3 was functionally similar to the depletion of Vav2 , as this GEF appears to be inefficient in catalyzing the exchange reaction in Rac1 and Cdc42 in the absence of PI 3 , 4 , 5-P3 . Note that the remaining GEF activity found in the Vav2-depleted lysates was likely due to the presence of Sos1 , because its depletion resulted in the further 10-fold decrease in retained 32P label . The pY1 oligopeptide contained Sos1 and Vav2 exchange activities . The pulldowns with pY2 yielded relatively high numbers with the PI 3 , 4 , 5-P3 -treated ( 31700 cpm ) or untreated ( 7634 cpm ) lysates . The former indicated the presence of a PI 3 , 4 , 5-P3 -dependent Vav2 activity , which was confirmed by the loss of retained label ( 870 cpm ) when Vav2 was depleted even in the presence of PI 3 , 4 , 5-P3 . Similar to pY1 , the exchange reaction using Cdc42 as the target was stimulated by the addition of PI 3 , 4 , 5-P3 , indicating the involvement of Vav2 . That Vav2 depletion resulted in values that are similar to the background levels suggests that Vav2 is the only GEF towards Rac1 and Cdc42 in the pY2 pulldown . Interestingly , there was still a relatively high level of GEF activity in the pY2 pulldown from untreated lysates ( 7634 cpm ) compared to background ( 722 cpm ) . This result was reproducible , and the significance of this observation is unclear . As expected , the oligopeptides in which the tyrosine residues have been replaced by phenylalanine failed to yield values that are statistically different from the negative controls . Taken together , the lysates pulled down by the phosphorylated peptides contained GEF activity towards His-Rac ( pY1 ) and Cdc42 ( pY1 and pY2 ) , and that this GEF activity was due to the presence of Sos1 and Vav2 , with the latter requiring the addition of PI 3 , 4 , 5-P3 . The involvement of Cdc42 in the in vitro GEF reaction poses an interesting question as this GTPase has been shown not to be required in C . trachomatis invasion [10] . Phalloidin and 4G10 antibodies also colocalize at the sites of entry , where TARP molecules are predicted to be translocated across the host plasma membrane [9] , [14] . Interaction of Sos1 , Eps8 , Abi1 , and Vav2 with TARP would result in the localization of these molecules at the sites of chlamydial entry . Indeed , colocalizations of Sos1 , Eps8 , Abi1 , and Vav2 with invading EBs were observed by antibody staining and indirect immunofluorescence ( Figure 4 ) . All four proteins were present as distinct puncta . We observed that 30% of EBs colocalized with Sos1 , 41% with Abi1 , 30% with Eps8 , and 24% with Vav2 . The significance of these values is unclear as they could be skewed by the quality of EB preparations and the transient nature of the localization of the signaling molecules . This transient localization of the signaling molecules to the sites of entry may have prevented the visualization of some of these recruitment events in fixed cells . Another possibility is the utilization of alternate mechanisms for some of the invading EBs . Abi1 and Eps8 adaptor proteins , when in a complex with Sos1 can function as a Rac-specific guanine nucleotide exchange factor [19] , [28] . Vav2 is also a well-characterized Rac GEF [20] , [21] . That these proteins are found to co-precipitate with Rac and the repeated domain of TARP underscores their potential importance in chlamydial invasion . The roles of Sos1 , Eps8 , Abi1 , and Vav2 in chlamydial entry were investigated in HeLa cells depleted individually of each protein . At 48 h post-transfection Sos1 , Eps8 , Abi1 , and Vav2 proteins were reduced to minimal levels ( Figure S4 ) with the knockdowns resulting in a decrease in invasion efficiency of approximately 40% , 60% , 10% , and 80% respectively ( Figure 5 ) . This suggests that Vav2 makes a very limited contribution to chlamydial invasion or that the presence of the Sos1 pathway compensates for the loss of Vav2 . To explore the possible cooperation between the two GEF activities in chlamydial invasion , Sos1-depleted cells were also treated with wortmannin , which is implicated in the Vav2-dependent activation of Rac . Figure 5 shows that the addition of wortmannin had no effect on mock-transfected cells , which would be expected if the PI3-kinase-independent GEF ( Sos1/Eps8/Abi1 ) is truly functionally redundant to Vav2 with respect to chlamydial invasion . However , wortmannin treatment in conjunction with the depletion of Sos1 protein led to a significant decrease in chlamydial invasion efficiency . For comparison , cells treated with the actin filament destabilizing drug cytochalasin D were found to be the least able to support chlamydial invasion
Intracellular pathogens have evolved a number of different mechanisms to subvert the actin cytoskeleton and facilitate their uptake by the host cell [29]–[31] . Manipulation of the actin cytoskeleton by pathogens typically involves the modulation of the activities of host cellular proteins that participate in the complex dynamics of actin recruitment and remodeling . Some pathogens , like Salmonella and Shigella achieve this through secretion of soluble Type III effectors , while others , like Listeria directly bind host cell receptors whose signaling constitutes a cascade that regulates actin cytoskeletal remodeling [31] . Here we report the identification of the relevant GEFs that activate Rac during C . trachomatis invasion . Both the Sos1/Eps8/Abi1 multiprotein complex and Vav2 were found to associate with the phosphodomain of TARP in a phosphorylation-dependent manner . Optimal Rac activation by Vav2 in vitro also required the presence of the phospholipids PI 3 , 4 , 5-P3 , which is generated at the site of chlamydial attachment by virtue of the interaction of the p85 subunit of PI3-kinase with the TARP phosphodomain . Localization to the sites of chlamydial entry and their functional involvement in chlamydial invasion demonstrate their importance in chlamydial invasion of non-phagocytic cells . The multiple phosphotyrosine residues in the repeat region of C . trachomatis TARP are likely recognized by a number of signaling molecules containing Src homology 2 ( SH2 ) domains , and thus act as a scaffolding to which the signaling proteins relevant to actin remodeling are recruited . The binding of the Vav2 GEF and the p85 subunit of PI3-kinase is likely due to their respective SH2 domains , which recognize phosphorylated tyrosines . There is , however , a level of specificity to this interaction as not all of the SH2 domain-containing proteins examined bound to the phosphorylated oligopeptides . For example , the adaptor protein Grb-2 failed to bind to either pY1 or pY2 oligopeptides at levels above background . Furthermore , depletion of Grb-2 by siRNA did not affect chlamydial invasion ( data not shown ) . It has been previously shown that invasion of HeLa cells by C . trachomatis serovar L2 is Rac-dependent , and that WAVE2 and Abi1 are downstream effectors that activate the Arp2/3 complex [9] , [10] , [32] . Given this requirement for Rac activation during chlamydial invasion , the identification of the relevant Rac GEFs defines a mechanistic pathway of chlamydia invasion at the molecular level . A model of the proposed signaling pathway is shown in Figure 6 . Scita et . al . have previously reported the specificity of the Sos1 protein towards Rac is conferred by the Eps8 and Abi1 proteins [33] . Their report demonstrated that , while Sos1 by itself could activate both Rac and Ras , the addition of Eps8 and Abi1 shifted the specificity of Sos1 towards Rac . We confirmed that the fraction that contained Sos1/Abi1/Eps8 complex has at least a GEF activity towards Rac1 . These proteins , indeed function as a complex as depletion of one of the components affected the efficiency of co-precipitation of the others . Based on this set of pulldown experiments ( Figure 2c ) , we propose that Abi1 mediates the indirect interaction of Sos1 and Eps8 with the pY1 oligopeptide , while Sos1 and Eps8 require each other for their respective interaction with Abi1 . The presence of a low level of Eps8 in pY2 in Figure 2a may be due to its interaction with Abi1 , and its low level relative to that observed for pY1 was likely due to the absence of Sos1 in the pY2 fraction . We have previously shown that expression of the dominant negative mutant of the Rac GTPase inhibited chlamydial invasion , and this inhibition correlated with the loss of actin localization at the sites of entry . The depletion of guanine nucleotide exchange factors that activate Rac , in essence is functionally equivalent to the dominant negative Rac mutant . Thus , it is likely that inhibition of the guanine nucleotide exchange factor activity towards Rac would result in the similar loss of actin localization at the sites of chlamydial invasion . Vav2 demonstrated GEF activity towards both Rac1 and Cdc42 . Our results are consistent with previous data that characterized Vav2 specificity [34] . However , the previously reported exclusive dependence of the C . trachomatis serovar L2 invasion on Rac1 seems to contradict our data [10] , [32] . One explanation could be the lack of a sustained signal to or by Cdc42 during chlamydial invasion . Three observations appear to support this hypothesis . First , our previous data that demonstrated the lack of any detectable activation of Cdc42 could be interpreted as short-lived activation that was undetected during the time intervals chosen for the experiment [10] . Second , the synthesis of the Vav2 co-factor PI 3 , 4 , 5-P3 at the site of entry is extremely short-lived , resulting in the transient interaction of Vav2 with its downstream GTPase effectors . Thirdly , there is a precedent for a difference in the duration of signals transduced by GTPase protein . An excellent example is the difference in Rac and Ras signaling , in which Ras signaling , mediated by the Sos1/Grb-2 complex during growth factor stimulation , was short-lived relative to signaling by Rac , which was mediated by Sos1/Abi1/Eps8 , under similar conditions of growth factor stimulation [35] . Thus , it is quite possible that the transient production of PI 3 , 4 , 5-P3 may explain the apparent lack of Cdc42 involvement in chlamydial invasion . On the other hand , Rac1 could be activated by two distinct pathways; one of which is independent of PI 3 , 4 , 5-P3 . This Rac activation pathway would be expected to predominate during conditions of high PI 3 , 4 , 5-P3 turnover . The presence of a PI 3 , 4 , 5-P3 -independent pathway of Rac1 activation would be expected to confer a wortmannin-insensitive invasion mechanism . Indeed , entry of serovar L2 is insensitive to treatment with 100 nM wortmannin . During inhibition of PI 3 , 4 , 5-P3 synthesis , the Sos1/Abi1/Eps8 pathway of Rac activation remains functional , with the host cell still supporting chlamydial uptake . We observed highly localized and transient bursts of synthesis of PI 3 , 4 , 5-P3 at the sites of chlamydial entry . This implies the recruitment of the regulatory subunit ( p85 ) of PI3-kinase and its binding partner , the catalytic ( p110 ) subunit . Indeed , the p85 subunit was found to bind preferentially the pY1 oligopeptide . In conjunction with the observed interaction of Vav2 with the pY2 oligopeptide , these two components may bind independently to two distinct binding sites , but cooperate to activate the Vav2-dependent pathway . That the PI3-kinase p85 subunit displayed the same preferential binding to pY1 , as the Sos1/Abi1/Eps8 complex is suggestive of a competition or a hierarchical control that possibly determines which pathway is utilized for chlamydia uptake . The reproducible decrease in invasion efficiency during depletion of the Sos1 protein indicate the preferential utilization of this pathway compared to the Vav2 pathway . In addition , because TARP homologs within the C . trachomatis species have at least two phosphodomains , it is intriguing to speculate that the domains cooperate with each other to recruit signaling molecules , and bring into proximity components to stabilize the signaling complex and/or transduce the desired signal . However , the presence of multiple potential binding sites adds another level of complexity to this signaling process in the regulation of this signaling pathway . Chlamydophila species that possess TARP homologs , but do not contain the repeated phosphodomain clearly demonstrate that alternative mechanisms of actin remodeling and recruitment exist . An attractive scenario is the requirement for an additional bacterial factor that interacts with a conserved region of TARP and substitute for the tyrosine phosphorylation during recruitment of signaling molecules . The enterohemorrhagic E . coli ( EHEC ) EspFu/TccP protein directly binds to the GTPase binding domain of N-WASP to induce pedestal formation independent of Tir tyrosine phosphorylation and Nck recruitment [36] . Whether a similar mechanism is at work in other species of chlamydia is certainly a topic that warrants further investigations . Equally important is how the actin nucleating function of the conserved C-terminal domain of TARP fit with the model that requires signaling to the Arp2/3 complex to activate its actin-nucleating activity . It is unlikely that the Arp2/3-independent actin nucleating activity of the C-terminal domain of TARP is sufficient for chlamydia-induced actin recruitment and invasion . It has been reported that the invasion of C . trachomatis serovar L2 depends on Arp2/3 . A model that we favor is a cooperative one , in which the activation of the Arp2/3 complex by the Rac-dependent signaling pathway initiates actin nucleation forming short actin filaments . These nascent filaments are then bound by the WH2 motifs within the C-terminal domain of TARP , nucleating spontaneous actin polymerization . This model fits a number of critical observations – a ) C . trachomatis invasion is Arp2/3-dependent; b ) actin polymerization can be mediated by a minimal TARP domain that contains the WH2 motifs in an Arp2/3-independent manner; and c ) the C-terminal domain has a higher affinity for F-actin . How these two mechanisms cooperate is under investigation . In summary , this report is the first to directly implicate the Chlamydia trachomatis Type III effector TARP in invasion by identifying the host signaling molecules that link TARP to the actin remodeling machinery . The potential for TARP to be differentially phosphorylated at the two tyrosine residues described in this report and the presence of multiple phosphodomains together imply the presence of a control mechanism that fine tunes the function of TARP in chlamydia invasion . This modulation may be at the level of phosphorylation , binding of the signaling complexes , their respective stability , or the ability of the signaling pathway to cooperate with the nucleating function of the C-terminal domain of TARP . Clearly , many questions still need to be answered to gain a full understanding of the invasion process of this medically important obligate intracellular pathogen .
C . trachomatis serovars L2 ( LGV-434 ) were grown in and harvested from HeLa 229 cells as previously described [37] . EBs used for infections were purified by Renografin ( E . R . Squibb and Sons , Princeton , NJ ) density gradient centrifugation . Fluorescent CMTPX-labeled EBs were prepared as described previously [38] , with slight modifications [10] . Antiphosphotyrosine monoclonal antibody ( MAb ) clone 4G10 and FITC-conjugated 4G10 were purchased from Upstate USA ( Waltham , MA ) ; anti-Sos1 monoclonal , anti-Abi1 monoclonal , anti-p85 , and anti-Eps8 polyclonal antibodies were from and Abcam; anti-Vav2 rabbit polyclonal antibody was from Zymed ( South San Francisco , CA ) ; and anti-Rac1 monoclonal antibody was purchased from Cytoskeleton . Secondary antibodies for immunoblotting were horseradish peroxidase-conjugated anti-mouse or anti-rabbit ( Cell Signaling Technology , Inc . , Beverly , MA ) . GFP-actin ( from Dr . S . Grieshaber , University of Florida ) , GFP-BTK-PH ( from Dr . J . Celli , LICP , NIAID ) and GFP-Rac ( from Dr . M . Way , Cancer Research UK , London , UK ) were described previously [39] , [40] . siRNAs against human Sos1#16747 , #16656 , and #16561 ) , Eps8 ( #107412 , #107411 , and #14635 ) , Abi1 ( #137945 , #137944 , #137946 ) , and Vav2 ( #13196 , #214755 , #138982 ) were purchased from Ambion . Synthesis of pEGFP-C1 L2 TARP was performed as follows . The full length L2 TARP gene was amplified using KpnI linker-containing FWD primer 5′-ATGGTACCATGACGAATTCTATATCAGGTG-3′ and the BamH1 linker-containing reverse primer 5′-ATGGATCCTGTTCTCCTTTGCTTTTTA-3′ with the PCR product digested with KpnI ( New England Biolabs ) and BamH1 ( New England Biolabs ) for an overnight ligation into the pEGFP-C1 vector ( Clontech ) , which was pre-digested with the same restriction enzymes and dephosphorylated using calf intestinal alkaline phosphatase ( New England Biolabs ) . Ligation was performed at 15°C for 16 h . The N-terminal domain of CD4 ( amino acids 1–372 ) was subcloned from pCMV-Sport XL5-CD4 purchased from Origene by PCR using the primers 5′-CACCATGAACCGGGGAGTCCCTTTT-3′ and 5′-AAGCTTCTTCTACGGATCGGGTTACTT-3′ into the pcDNA3 . 1 TOPO vector ( Stratagene , Carlsbad , CA ) . The resulting vector was digested with Kpn1 to accommodate the PCR fragment containing one repeated unit ( amino acids 174–222 ) of the TARP phosphodomain region . This PCR fragment was generated by the amplification of the pEGFP-C1 L2 TARP using the primers 5′-ATGGTACCCTTCAGAAAGCTCAGAAACTA-3′ and 5′-ATGGTACCGTAGGAGGAGCCTCTTAGA-3′ containing Kpn1 linker sequences . The orientation of the insert relative to the the CD4 reading frame was determine by colony PCR using the primers CD4 forward primer 5′-CACCATGAACCGGGGAGTCCCTTTT-3′ and 5′-CTTAGTCATCAATACTCTCATAAATATTTTCAGTAGTGCTTATCG-3′ , which annealed to an internal region of the 1xR sequence . HeLa 229 or Cos7 cells were seeded on 12-mm glass coverslips in 24-well plates to obtain a monolayer of approximately 50% confluence . Transfections of plasmid constructs were performed using FuGene ( Roche , Indianapolis , IN ) according to the manufacturer's instructions . The transfection mixture was prepared as follows . The FuGene reagent ( 3 µl was diluted into 97 µl Optimem ( Invitrogen ) serum-free media , and to which DNA ( 1 . 0 µg ) and added . After a 20-minute incubation at room temperature , the complexes were added to 1 well of a 24-well plate containing 100 µl of Optimem . The transfection cocktail was incubated at 37°C . After 4 hours , the transfection medium was removed and antibiotic-free RPMI media with 10% fetal bovine serum was added . Expression from the transfection vectors was allowed to proceed for 24 hours at 37°C . siRNA transfection with the transfection reagent Ribojuice was performed per the manufacturer's instructions . Briefly , 5 nM siRNA was incubated with 4 ul ( for a well in a 24-well plate ) or 100 ul ( for a 100 mm dish ) of Ribojuice in OPTI-MEM and incubated at room temperature for 30 min . HeLa cells at 80% confluency were washed once with 1× HBSS and incubated in 100 ul or 1 ml OPTI-MEM . The siRNA transfection solution was added to the cells and incubated for 4 h . The transfection medium was removed and replaced with complete RPMI ( 10% FBS , L-glutamine , and gentamicin ) . At 48 h post-transfection , the levels of the proteins of interest were evaluated by Western blot . Briefly , post-nuclear supernatants were prepared as described previously [32] , and divided into five aliquots of 0 . 75 ml each . Each aliquot was incubated with 10 µM of one type of oligonucleotide ( Sigma-Genosys ) for 1 h at 4°C with rocking , followed by an additional hour of incubation with 20 µl of streptavidin-coated paramagnetic beads ( Dynal ) . The beads were removed from suspension with a magnet ( Dynal ) , and washed three times with cold RIPA buffer . The precipitated fractions were suspended in 150 µl of Laemmli buffer and boiled prior to gel electrophoresis . Proteins were separated on a 10 . 5–14% continuous gradient sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) gels ( Bio-Rad , Hercules , CA ) and transferred to a 0 . 45-µm Trans-blot nitrocellulose membrane ( Bio-Rad ) . Immunoblots were developed using Super Signal West Femto chemiluminescence reagent ( Pierce Biotechnology , Rockford , IL ) per the manufacturer's instructions . The oligopeptide pulldown fractions described above were subjected to an in vitro GDP-GTP exchange assay [41] . 25 µCi of [α-32P]GTP ( 3000 Ci/mmol , Amersham ) and 83 pmol of cold GDP ( 10-fold excess over labeled ) , and 1 µg of Rac-His or Cdc42-His ( Cytoskeleton ) were added to 200 µl of exchange buffer ( 50 mM HEPES , pH 7 . 5 , 1 mM MgCl2 , 1 mM Dithiothreitol , 100 mM KCl , and 0 . 1 mg/ml bovine serum albumin ) . 10 µl of this solution was added to the oligopeptide pulldown fraction . The reaction was incubated at room temperature for 30 min , and terminated by the addition of 1 ml of the stop buffer ( 50 mM HEPES , pH 7 . 5 , 5 mM MgCl2 , 1 mM DTT , 10 ug/ml BSA , and 0 . 1 mM GTP ) and immediate incubation on ice . The beads containing the oligopeptide and associated proteins were pelleted ( 30 sec at 15 , 000 rpm ) , and the supernatant were loaded onto nitrocellulose filters using a vacuum manifold ( Bio-Rad ) . The filters were washed three times with ice cold PBS with 5 mM MgCl2 . Radioactivity retained on the filters was counted by scintillation . Cells grown on coverslips were fixed in freshly prepared 4% paraformaldehyde in PBS for at least 1 h at 4°C . If required , the fixed cells were permeabilized with 0 . 1% Triton X-100 in PBS for 2 min at RT . The permeabilization buffer was removed and the cells rinsed three times with 1× PBS . Primary antibodies were diluted to their respective working concentrations ( α-Sos1 1∶250 ( Abcam ) , α-Abi1 1∶1000 ( Abcam ) , α-Eps8 1∶250 ( Abcam ) , α-Vav2 1∶500 ( Zymed ) , α-L2 EB , 1∶1000 from Ted Hackstadt , RML , NIAID ) , added to the fixed cells , and incubated at 4°C overnight . Anti-rabbit or anti-mouse IgG secondary antibodies used were conjugated to either Alexa 488 or Alexa 594 ( Invitrogen ) . Coverslips were mounted using Mowiol on glass slides , and samples visualized using the Olympus Fluoview 500 Laser Scanning Microscope . Images were processed using Adobe Photoshop Creative Suite . Cells grown on Delta T culture dish ( 0 . 17 mm thick ) ( Bioptechs ) overnight and transfected with a GFP-BTK-PH expression construct . At 18 h post-transfection , the cells were infected with CMTPX-labeled C . trachomatis LGV serovar L2 EBs , and observed at 5 s interval using an UltraView Live Cell Imaging system fitted with a Bioptechs Delta T4 objective heater . Images were assembled into a time lapse Quicktime movie using NIH ImageJ ( Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://rsb . info . nih . gov/ij/ , 1997–2007 . ) Assay for invasion was performed as described previously [7] . The experiments were performed three independent times , in triplicate each time . The Student's t-test was used to determine statistical significance . | The human pathogen Chlamydia trachomatis is the causative agent of the most prevalent bacterial sexually transmitted disease in industrialized nations , and of the preventable blinding condition trachoma in developing countries . Survival and replication of chlamydial species occur exclusively inside a host cell , and thus , gaining access to the protective intracellular niche is an absolute requirement . This report describes how the chlamydia protein TARP , which is secreted at the base of the bacteria and across the host membrane , acts as a scaffold to which host signaling proteins bind . This assembly of the complex of signaling proteins , which include Sos1 , Abi1 , Eps8 , and Vav2 results in the remodeling of the host cytoskeleton to facilitate engulfment of the infecting chlamydia . We conclude that these proteins have a role in chlamydia based on a number of observations including their interaction with the TARP protein , their ability to switch on known signaling participants in chlamydia invasion , their localization at the site of chlamydia entry , and the inhibition of chlamydia invasion in their absence . Altogether , the data functionally link TARP with signaling pathways that function in chlamydial invasion , demonstrating the direct involvement of TARP in the invasion of host cells by C . trachomatis . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"cell",
"biology/cell",
"signaling"
] | 2008 | Chlamydial Entry Involves TARP Binding of Guanine Nucleotide Exchange Factors |
After endocytic uptake , influenza viruses transit early endosomal compartments and eventually reach late endosomes . There , the viral glycoprotein hemagglutinin ( HA ) triggers fusion between endosomal and viral membrane , a critical step that leads to release of the viral segmented genome destined to reach the cell nucleus . Endosomal maturation is a complex process involving acidification of the endosomal lumen as well as endosome motility along microtubules . While the pH drop is clearly critical for the conformational change and membrane fusion activity of HA , the effect of intracellular transport dynamics on the progress of infection remains largely unclear . In this study , we developed a comprehensive mathematical model accounting for the first steps of influenza virus infection . We calibrated our model with experimental data and challenged its predictions using recombinant viruses with altered pH sensitivity of HA . We identified the time point of virus-endosome fusion and thereby the diffusion distance of the released viral genome to the nucleus as a critical bottleneck for efficient virus infection . Further , we concluded and supported experimentally that the viral RNA is subjected to cytosolic degradation strongly limiting the probability of a successful genome import into the nucleus .
Seasonal influenza epidemics and periodical pandemics remain a constant threat to the human population . Influenza A virus ( IAV ) infection is a multi-step process that critically depends on the viral spike protein hemagglutinin ( HA ) , which mediates host cell adhesion by binding to sialic acid-containing receptors within the host cell plasma membrane ( Fig 1A ) . Viruses are subsequently internalized into endosomes , which undergo a complex maturation process involving acidification and centripetal transport along microtubules [1] . Endosomal acidification is highly critical since virus uncoating and genome release depend on membrane fusion mediated by a pH-dependent conformational change of the HA protein . However , although microtubule-associated transport of IAV has been shown before [2–4] , the relevance of this directed transport for virus infection still remains largely unclear . The genome of IAV consists of eight individual genome segments coding for a total of 11 viral proteins [5] . The negative-sense single-stranded RNA is packaged together with the viral nucleoprotein ( NP ) and the polymerase complex ( PA , PB1 and PB2 ) forming rod-like ribonucleoprotein complexes ( vRNP ) . After membrane fusion , released vRNPs travel to the nuclear membrane , most likely by passive diffusion [6] . Since endosomal transport and acidification are concurrent the distance they need to overcome depends on the time and location of membrane fusion and thereby also on the pH-dependent conformational change of HA ( i . e . HA’s pH sensitivity ) . Whether the eight different vRNPs stay in one complex or rather dissociate after being released is still under debate [7 , 8] . In any case , the released vRNPs bind to importin-α by means of their nuclear localization signals and get shuttled across the nuclear membrane through nuclear pores [7 , 9 , 10] . To establish a successful infection allowing the production of progeny viruses , it is essential that all eight genome segments are transported into the host nucleus where genome replication takes place . Therefore , the early infection phase may represent a bottleneck for the infection that could potentially contribute to host cell specificity of the virus . Indeed , it was suggested that adaptation to a different host does not only require binding to specific receptors but also modification of the pH sensitivity of HA , possibly to adapt to variations in the endosomal pH [11–13] . Here , we address the question whether an altered pH sensitivity of HA modulates the residence time of diffusing vRNPs in the cytoplasm and thereby influences viral infectivity . Mathematical models have been used before to foster a better understanding of IAV replication on the population [14 , 15] as well as on the single-cell level [16–19] . However , none of those seem to be adequate for the analysis and comparison of different IAV strains with varying pH sensitivity . We developed a model accounting for the first critical phase of IAV infection using a combination of ordinary differential equations ( ODE ) and a spatial modeling approach . The model parameters were calibrated using experimental data from infection studies covering the individual steps of the infection process ( Fig 1A ) . Our model enabled us to ( 1 ) predict differences in the infection efficiency ( i . e . delivered number of vRNPs in the nucleus ) of two IAV strains with altered pH sensitivity and to ( 2 ) simulate the diffusion of vRNPs as complex or individual particles utilizing a spatial stochastic model accounting for the specific cell geometry as well as the stochastic nature of the underlying processes . Our simulations predicted that the pH sensitivity of HA critically controls the time of fusion and thereby the location of genome release . We further concluded that a dissociation of released vRNP segments is highly unlikely to result in an efficient infection and that the viral RNA ( vRNA ) is subjected to degradation during cytosolic transport . This in turn might affect the number of intact vRNPs arriving in the nucleus and thus the infection efficiency of a specific virus . Using two HA variants with differing pH sensitivities , we complemented our simulations with experimental evidence validating the major model predictions . Taken together , we propose that the pH dependence of influenza virus fusion resulting in the release of the viral RNP complex plays a determinative role for the initial phase of virus infection . Our modeling data further suggest that diffusive transport and cytosolic stability of vRNPs represent limiting factors for efficient infection .
To understand how a varying pH sensitivity can affect infection efficiency , we need to understand the dynamics of the entry process after virus binding . To this end , we developed a dynamic ODE model that accounts for the early steps of the infection cycle and parameterized it using experimental time course data . The model consists of a set of four coupled ODEs describing ( 1 ) the temporal behavior of the amount of surface bound virions , ( 2 ) the quantity of endocytosed viruses , and ( 3 ) the concentration of released vRNPs in the cytoplasm as well as ( 4 ) in the nucleus . It describes the processes of endocytosis , endosome maturation , endosomal fusion and nuclear import of vRNPs . Our experimental data led us to include cytosolic vRNP diffusion and degradation into the model . The reaction network is depicted in Fig 1B , and the corresponding equations are introduced in the Materials and Methods section . To calibrate our model , we measured the corresponding steps of virus infection—virus-endosome fusion , endosomal pH development , and nuclear NP accumulation—between 0 and 40 minutes post infection ( p . i . ) ( Fig 2 ) . Intracellular fusion was measured by infecting MDCK cells using viruses labeled with R18 at self-quenched concentration . Individual viruses were detected using confocal microscopy and fusion events were counted using automated image processing . R18 dequenching is a commonly used method to detect IAV fusion in vitro [20] and a significant increase in R18 intensity was expected upon virus-endosome fusion ( S1 Fig ) . Indeed , we observed that the intensity of internalized single viruses showed a strong increase between 10 and 20 min p . i . reaching a plateau after about 25 min ( S2 Fig ) . Notably , we could reproduce the virus-endosome fusion kinetics using a FRET-based fluorescence readout as described previously [21] . This trend correlated with the accumulation of viral NP inside the nucleus , which started to increase slightly delayed after 15–20 min ( Fig 2 ) as detected by anti-NP immunostaining . In order to identify pH-dependent effects on IAV infection , it was crucial to know the actual endosomal pH dynamics . In the model , we defined the endosomal pH as an independent variable pHend , which served as an input to the coupled reaction system . pHend followed an exponential decay , that we fitted to experimental data , with a decay parameter kATPase that corresponds to the endosomal V-ATPase activity . To monitor the endosomal pH kinetics and calibrate kATPase , we used a double-labeled dextran as a pH-sensitive endosomal probe as described in Materials and Methods . Briefly , after a short starvation period , cells were loaded with dextran for the indicated time points , washed and immediately analyzed by flow cytometry . We observed an exponential decrease of the endosomal pH over the observed time period ( Fig 2 ) . HA’s membrane fusion activity is characterized by a narrow pH regime of switching from zero to complete fusion [22] . To accommodate for this behavior , in our model , endosomal fusion follows a Hill kinetics [23] with the threshold parameter kH+ and the Hill coefficient h . We could determine these parameters by measuring the viral fusion kinetics in vitro using R18 fluorescence dequenching ( FDQ ) in the pH range from 5 . 0 to 7 . 4 . We observed a steep increase of viral fusion with decreasing pH ( S3 Fig ) and could use our model to determine the fusion pH threshold value kpH ( i . e . the pH value where FDQ is half-maximal ) of the IAV X-31 strain to be at pH 5 . 6 from these data sets . The combination of results from in vitro virus fusion with the endosomal fusion time course data allowed us to fit the parameters of the model with high confidence ( see Table 1 and S4 Fig ) further enabling us to introduce perturbations into the model . During model development , it became evident that introduction of vRNP diffusion delay and degradation is necessary to fit all measured data at once . In order to predict the system’s behavior for a higher value of kpH resulting in earlier virus-endosome fusion , we initially did not include vRNP diffusion and degradation into the model . In that case an unchanged steady state of accumulated vRNPs in the nucleus vRNPnuc was predicted . Hence , the pH sensitivity had no measurable effect on virus infection in that model . To experimentally determine the effect of an altered pH sensitivity of HA on viral infectivity , we constructed a recombinant virus based on influenza virus A/WSN/33 ( H1N1 ) with the H1-subtype HA replaced by H3 HA of A/X-31 ( H3N2 ) yielding the so-called WSN H3 wild type virus ( WSN H3 wt ) [24] as well as a WSN H3 mutant virus carrying the destabilizing double mutation T212E-N216R in the HA protein ( WSN H3 mut ) [25] . The fusion pH threshold of WSN wild type and mutant viruses was assessed in vitro as described above for A/X-31 . Our experiments showed that , similar to the A/X-31 strain , a pH threshold of 5 . 6 triggers fusion of the WSN H3 wild type strain confirming that fusion mainly depends on the pH sensitivity of HA ( S3 Fig ) . In contrast , for the WSN H3 mutant , this threshold was shifted to pH 5 . 8 due to the destabilizing double mutation in the HA protein ( Fig 3 ) . We integrated these data into the dynamic model and again could not detect an effect on nuclear vRNP accumulation suggesting—at first glance—no effect of HA’s pH sensitivity on virus infection . However , from measuring the infection efficiency of recombinant WSN viruses in MDCK cells under identical conditions ( MOI of 0 . 2 ) , we found that the amount of expressed viral NP 20h p . i . was 40–50% lower for the destabilized WSN H3 mutant virus than for the wild type ( Fig 3B ) . Both , measuring the infection efficiency 5h p . i . as well as growth curves for both virus strains over 72h , could confirm the attenuation of WSN H3 mut ( S6 Fig ) . If we considered a correlation between accumulated vRNPs in the nucleus and expressed NP protein 20h p . i . , these results clearly contradicted our model prediction . Therefore , we extended our model to reproduce the infection efficiency observed in the experiment . Based on our experimental observations of virus-endosome fusion taking place some μm away from the nucleus ( S7 Fig ) , we introduced a spatial component that accounts for the diffusion of released vRNPs through the cytosol . We constructed a 3D reaction-diffusion model that allowed us to relate fusion distance to delay and efficiency of vRNP delivery into nucleus and to compare the different IAV strains quantitatively . The simulation settings were adjusted to biologically relevant values ( see Materials and Methods section ) . For the assumed cell geometry as well as the position of the nucleus , please see S8 and S9 Figs . Due to the small number of vRNP particles ( 1 to about 80 ) in the cell and a binary outcome ( infection/no infection ) for each cell , we could not assume a mean-field approach to capture the full behavior of the system and thus decided to utilize a stochastic simulation environment . To explain the experimental result of reduced infection efficiency , we established the hypothesis that free vRNPs are subjected to degradation while diffusing through the cytosol [26] . We further allowed two forms of vRNP diffusion: ( 1 ) vRNPs are released from the virus as a complex consisting of all eight genome segments diffusing together . ( 2 ) The released complex dissociates and the vRNP segments diffuse individually with a higher diffusion coefficient than the larger complexes . The infection is only counted as successful if a full set of eight different vRNPs has reached the nuclear membrane where they bind to importins to be shuttled into the nucleus . In our spatial model , three parameters were critical for the connection between infection efficiency and pH sensitivity: ( 1 ) vRNP degradation , ( 2 ) vRNP release distance from the nucleus and ( 3 ) vRNP dissociation . Our computational approach made it possible to comprehensively sample a large sub-space of combinations of different dissociation rates of vRNP complexes ( kdiss ) and degradation rates of individual or complexed vRNPs ( kdeg ) . Fig 4B shows the results of such two-dimensional parameter scans . The x- and y-axes indicate the parameter values of kdiss and kdeg . Each color-coded bin corresponds to the average number of successful infections after 40 min in a cell population of 1000 cells . The populations were all simulated with the corresponding parameters on the axes . The initial condition for the simulation is one complete vRNP package starting to diffuse at distance dnuc from the nucleus ( S10 Fig ) . Interestingly , these parameter scans suggested that , given the measured diffusion rates , already small degradation rates would be sufficient to degrade the whole viral genome if virus-endosome fusion and thus vRNP release happens too far away from the nucleus . Furthermore , although single particles have a much higher mean displacement due to their smaller size , our simulations clearly showed that the dissociation of complexes lowered the probability of a complete genome in the nucleus . The larger the dissociation constant in the model was set , the lower the percentage of complete genomes . This effect also increased with larger fusion distances from the nucleus , possibly because of an increased probability that the vRNPs were degraded before reaching the nucleus . Our observations at the cell population level ( Fig 3B ) revealed that the infection efficiency of an IAV strain with a higher pH threshold was reduced as compared to the reference strain ( WSN H3 wt ) . By adding a degradation term to the freely diffusing vRNP particles , we could establish a link between the pH sensitivity of HA and the viral infection efficiency: the larger the distance between the location of fusion and the nucleus , the higher the probability that the vRNPs are degraded and thus the lower the number of functional vRNPs arriving in the host cell’s nucleus . To test whether vRNPs are subjected to degradation while diffusing in the cytoplasm , we measured the amount of cytosolic vRNA ( HA segment ) by RT-qPCR while blocking the nuclear import using 40 and 100 μM importazole , respectively [27 , 28] ( for experimental details see Materials and Methods ) . The resulting data are shown in Fig 2 . As also reported by Kublun et al . [28] , we could observe a higher inhibitory effect on nuclear import using 40 μM importazole than using the higher concentration of 100 μM which might be due to cell damage or the activation of other import pathways for the higher dose of the drug . In any case , for both importazole concentrations the results indicate a fast drop of cytosolic ( including intact viral particles ) vRNA in the first 10 min p . i . . We also compared the total ( nuclear + cytosolic ) vRNP concentration with and without importazole 60 min p . i . showing that with importazole the vRNP levels are significantly lower ( for control experiments without importazole see S11 Fig ) . By including these data into the fit of our ODE model , we could identify the model parameters accounting for vRNA degradation ( kdeg ) and diffusion time ( kτ ) as well as their confidence intervals ( S4 Fig , Table 1 ) . With these parameters we could use the spatial model to further characterize the impact of fusion distance on infection . Combining the results of the ODE and the 3D diffusion model enabled us to determine the mean fusion distance of vRNP particles in our simulations . Based on the calibration data for the ODE model , we could estimate an optimal delay time between membrane fusion and vRNP arrival at the nucleus τ of 4 minutes for IAV X-31 ( Fig 4A , upper panel ) . Using the spatial model simulations , we could then translate this value into a distance of fusion from the nucleus of ≈ 3 μm for the complex and ≈ 5 μm for individually diffusing vRNPs ( Fig 4A , lower panel ) . In the simulations , the estimated diffusion delay parameter kτ for IAV X-31 represents the delay between the half maximum times of fusion and import into the nucleus which we denote as τeff ( Fig 5 ) . It is reasonable to assume that the vRNPs of WSN H3 wt and H3 mut have the same diffusion coefficient and import rate ( kimp ) as the one fitted for IAV X-31 . Furthermore , for the higher pH threshold of WSN H3 mut , an increase of the vRNP concentration in the nucleus can at best happen as early as for the wild type because we assume active transport to be faster than diffusion . Using our model , we can now combine endosome acidification data and HA pH sensitivity measurements to predict the dynamics of the mutant infection . Simulations showed the mutant fusing about three minutes earlier than the WSN H3 wt ( Δτeff ≈ 3 min ) which corresponds to a fusion distance of ≈ 6 μm for the complex . If we simulated the model for the mutant with a prolonged delay τmut ≈ τwt + Δτeff we could predict a strong reduction in the amount of vRNPs reaching the nucleus , which could be a possible explanation for the reduced infection efficiency observed for the mutant . Next , we used the estimated degradation rate ( kdeg = 0 . 19 min−1 ) in the stochastic model to estimate upper limits for fusion distances from the nucleus for a successful virus infection by simulating the diffusion of cytosolic complexed vRNPs from different distances . We found that for a fusion distance greater than dnuc = 10 μm from the nucleus , on average only 0 . 4 complete sets of viral genomes would arrive in the nucleus when running the simulation with one complete set . The number of complete sets quickly reached zero with larger distances . Simultaneously , we could scan through the parameter kdiss to obtain a parameter landscape of successful infection ( Fig 4B ) . Again , the dissociation of vRNPs affected transport to and import into the nucleus and proved to be detrimental for successful infection: for kdiss > 0 . 1 nearly no vRNPs would reach the nucleus even for small distances .
Viral escape from cellular endosomes is an essential step of the IAV replication cycle . To allow uncoating and vRNP release , IAV utilizes the complex maturation program of the endocytic system . Among other changes , it involves two inherently connected processes: endosomal motility and lumenal acidification [1] . Endosomal motility is dominated by the association with and movement along microtubules . This transport is mediated by two motor protein families , kinesins and dyneins , which operate in opposite directions . Although endosomes typically associate with both motors , which can lead to a bidirectional movement , they perform a net translocation towards the microtubule organizing center ( MTOC ) in the nuclear periphery [1] . Indeed , live-cell tracking of fluorescently labeled IAV showed that the particles are transported towards the perinuclear region of the cell [2 , 4] . During the directed transport inside endosomal vesicles , the virus finally reaches late endosomal compartments ( pH 5 . 0-6 . 0 ) where the acid-induced conformational change of HA triggers membrane fusion and vRNP release into the cytosol . Due to the connection between lumenal acidification and endosome movement towards the nucleus , timing of fusion affects the location of the fusion event inside the cell . Several studies suggested that the pH sensitivity of HA needs to be adapted to the endosomal properties of the respective host cell in order to allow for efficient virus replication [11–13] . However , so far it remained largely unclear how HA’s pH sensitivity affects time and location of fusion and as a consequence , the infection efficiency of the virus . Here , we present a mathematical model that quantitatively describes IAV cell entry . We combined cell-specific parameters like geometry , endosomal uptake and acidification with virus-specific determinants such as the pH sensitivity of HA and were able to estimate the distance of endosomal fusion from the nucleus for different HA variants . Furthermore , we showed that vRNA is subjected to cytosolic degradation and thereby demonstrated the importance of the exact timing of fusion for the efficiency of infection in a given host cell . Measuring the intracellular fusion kinetics of IAV X-31 , we found a mean fusion time of 10 min in MDCK cells which is in good agreement with Lakadamyali et al . [2] who reported a mean fusion time of 8 min in CHO cells . Our result also correlates well with the study of Sakai et al . [21] , who used the same intracellular fusion assay , although in a different cell line . As reported previously [2 , 4] , fusion events were detected close to the nucleus , which we could demonstrate to be on average located in the center of the cell ( S7 and S8 Figs ) . Using our combined stochastic and spatial model , we could predict that IAV X-31 and a recombinant WSN virus carrying the H3 HA of IAV X-31 fuse at an estimated distance of 3 μm from the nucleus in MDCK cells , whereas a more pH sensitive mutant ( kpH = 5 . 8 compared to 5 . 6 for the wild type ) fuses earlier at a distance of 6 μm from the nucleus . We proposed that the released vRNPs are subjected to degradation , thereby strongly attenuating the infection as well as the replication efficiency of the WSN H3 mutant virus as observed experimentally ( Fig 3 and S6 Fig ) . Indeed , we could show that vRNAs , complexed in diffusing vRNPs after being released from the virus , are degraded within a few minutes in the cytosol ( degradation rate of 0 . 19 min−1 ) . Adding this limiting factor to our model allowed us to predict that the earlier release of the viral genome , i . e . in larger distance to the nucleus ( due to a higher pH sensitivity of HA ) , results in a lower level of nuclear vRNPs and thus , in a lower infection efficiency . It was shown previously that IAV propagation over multiple passages can lead to accumulation of defective interfering RNAs , which give rise to defective interfering particles ( DIP ) that interfere with the replication of non-defective viruses [29 , 30] . Since the presence of DIPs and in particular deletions in HA and NP could affect our results , we performed a segment-specific PCR . We included the PB2 segment since DI RNAs predominantly originate from the large polymerase genes ( segments 1–3 ) [31] . In addition to all three full length segments , for PB2 we found a smaller PCR product indicating the presence of DIPs in our virus sample ( S12 Fig ) . However , for both the HA as well as the NP segment , we could not detect a significant amplification of smaller PCR products , thereby ensuring the unperturbed detection of HA vRNA as well as incoming NP protein during our virus-entry measurements . With our model , we were also able to challenge existing hypotheses on the different strategies of how vRNP segments reach the nucleus ( i . e . as a complex or as individual vRNPs ) . Hence , we simulated various degrees of dissociation of vRNP segments after fusion and compared the effect on infection efficiency . Our model suggests that diffusion of complexed vRNPs is more favorable for infection than dissociation of vRNPs in the cytosol . Transport as a complex might assure the arrival of all eight vRNPs in the nucleus simultaneously and thus be much more efficient and reliable than gating single vRNPs one-by-one . Our model thus supports the tight association of released vRNP segments during cytosolic diffusion as it was also recently suggested from single-molecule fluorescence in situ hybridization ( smFISH ) data [8] . Our mathematical model integrates cell-specific as well as virus-specific parameters . Therefore , it is useful to predict the infection of a given virus in a specific host cell . Mathematical models of virus entry have been presented for Semliki Forest virus in BHK-21 cells and of the baculovirus-insect cell system [32 , 33] . These viruses—similar to influenza—require endocytic uptake and , after release of the viral genome by membrane merger , transport to a specific compartment for replication . Our model extends the current understanding of the role of the fusion protein’s pH sensitivity on viral entry dynamics and the impact of vRNA degradation as well as vRNP dissociation for the success of infection . These newly determined parameters might also be relevant for other models of viral entry . Still , the complex nature of a given host cell and the possible presence of other yet unknown factors of the cellular immune response may lead to deviations from our model predictions . For example , the activation of signaling pathways upon viral binding can interfere with the uptake of the virus what would considerably influence the dynamics of viral entry . The impact of such factors on infection can vary greatly between different viruses and cell lines and thus is difficult to predict . Furthermore , in this study we only focused on the early steps of IAV infection , which limits its predictive power for viral replication . To improve predictions for this case the model could be combined with existing models that describe the whole infection cycle [34] , which might be very useful for several applications e . g . vaccine production [16] . Taken together , we describe the first critical phase of IAV infection using mathematical and 3D diffusion modeling , which accounts for the pH sensitivity of HA as well as cell-specific parameters such as endosomal acidification , cell geometry and the degradation rate of vRNPs , a newly identified host factor of the cellular immune system . Using recombinant viruses with differing HA pH sensitivities we could validate our model experimentally and at the same time provide evidence that the interplay between pH-dependent fusion of HA and degradation of viral RNA by the cellular immune system represents a bottleneck for IAV entry determining the success of viral infection . | Influenza A virus carries its segmented genome inside a lipid envelope . Since genome replication occurs inside the nucleus , the main goal of virus infection is to deliver all genome segments through the cytoplasm into the nucleus . After endocytic uptake , influenza viruses transit early endosomal compartments and eventually reach late endosomes . Within a complex maturation process , the endosomal lumen acidifies while the vesicles are transported trough the cytosol . If and how these early processes affect virus infection remained mostly speculative . To reach a better understanding and to quantify the physical interplay between membrane fusion , genome diffusion and infection , we developed a mathematical model that comprises all initial steps of virus infection until genome delivery . We calibrated our model using experimental data and challenged its predictions using recombinant viruses to introduce perturbations . Our results provide a theoretical framework to understand the importance of the endosomal virus passage before membrane fusion and genome release . We further unraveled RNA degradation as a previously unknown limiting factor for virus infection . Our work will help to make predictions and evaluate newly occurring virus strains , regarding their infection efficiency in a given host cell , by simply considering their pH sensitivity . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"vesicles",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"signaling",
"networks",
"orthomyxoviruses",
"viruses",
"wireless",
"sensor",
"networks",
"membrane",
"fusion",
"rna",
"... | 2016 | Viral RNA Degradation and Diffusion Act as a Bottleneck for the Influenza A Virus Infection Efficiency |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.